code stringlengths 2k 1.04M | repo_path stringlengths 5 517 | parsed_code stringlengths 0 1.04M | quality_prob float64 0.02 0.95 | learning_prob float64 0.02 0.93 |
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import ctypes, ctypes.util
from OpenGL.platform import baseplatform, ctypesloader
class EGLPlatform( baseplatform.BasePlatform ):
"""EGL platform for opengl-es only platforms"""
@baseplatform.lazy_property
def GLES1(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLESv1_CM', # ick
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLES2(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLESv2',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLES3(self):
# implementers guide says to use the same name for the DLL
return self.GLES2
@baseplatform.lazy_property
def GL(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GL',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return self.GLES2 or self.GLES1
@baseplatform.lazy_property
def GLU(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLU',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLUT( self ):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'glut',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def OpenGL(self): return self.GL
@baseplatform.lazy_property
def EGL(self):
# TODO: the raspberry pi crashes on trying to load EGL module
# because the EGL library requires a structure from GLES2 without
# linking to that library... Github issue is here:
# https://github.com/raspberrypi/firmware/issues/110
import os
if os.path.exists('/proc/cpuinfo'):
info = open('/proc/cpuinfo').read()
if 'BCM2708' in info or 'BCM2709' in info:
assert self.GLES2
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'EGL',
mode=ctypes.RTLD_GLOBAL
)
except OSError as err:
raise ImportError("Unable to load EGL library", *err.args)
@baseplatform.lazy_property
def getExtensionProcedure( self ):
eglGetProcAddress = self.EGL.eglGetProcAddress
eglGetProcAddress.restype = ctypes.c_void_p
return eglGetProcAddress
@baseplatform.lazy_property
def GLE( self ):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'gle',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
DEFAULT_FUNCTION_TYPE = staticmethod( ctypes.CFUNCTYPE )
@baseplatform.lazy_property
def GetCurrentContext( self ):
return self.EGL.eglGetCurrentContext | env/Lib/site-packages/OpenGL/platform/egl.py | import ctypes, ctypes.util
from OpenGL.platform import baseplatform, ctypesloader
class EGLPlatform( baseplatform.BasePlatform ):
"""EGL platform for opengl-es only platforms"""
@baseplatform.lazy_property
def GLES1(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLESv1_CM', # ick
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLES2(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLESv2',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLES3(self):
# implementers guide says to use the same name for the DLL
return self.GLES2
@baseplatform.lazy_property
def GL(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GL',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return self.GLES2 or self.GLES1
@baseplatform.lazy_property
def GLU(self):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'GLU',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def GLUT( self ):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'glut',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
@baseplatform.lazy_property
def OpenGL(self): return self.GL
@baseplatform.lazy_property
def EGL(self):
# TODO: the raspberry pi crashes on trying to load EGL module
# because the EGL library requires a structure from GLES2 without
# linking to that library... Github issue is here:
# https://github.com/raspberrypi/firmware/issues/110
import os
if os.path.exists('/proc/cpuinfo'):
info = open('/proc/cpuinfo').read()
if 'BCM2708' in info or 'BCM2709' in info:
assert self.GLES2
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'EGL',
mode=ctypes.RTLD_GLOBAL
)
except OSError as err:
raise ImportError("Unable to load EGL library", *err.args)
@baseplatform.lazy_property
def getExtensionProcedure( self ):
eglGetProcAddress = self.EGL.eglGetProcAddress
eglGetProcAddress.restype = ctypes.c_void_p
return eglGetProcAddress
@baseplatform.lazy_property
def GLE( self ):
try:
return ctypesloader.loadLibrary(
ctypes.cdll,
'gle',
mode=ctypes.RTLD_GLOBAL
)
except OSError:
return None
DEFAULT_FUNCTION_TYPE = staticmethod( ctypes.CFUNCTYPE )
@baseplatform.lazy_property
def GetCurrentContext( self ):
return self.EGL.eglGetCurrentContext | 0.166947 | 0.087408 |
from typing import Dict, Optional, Tuple, Union
import torch
from torch.distributions import Bernoulli, Beta, Uniform
from kornia.augmentation.random_generator._2d.probability import random_prob_generator
from kornia.augmentation.random_generator.base import RandomGeneratorBase
from kornia.augmentation.utils import (
_adapted_beta,
_adapted_rsampling,
_adapted_sampling,
_adapted_uniform,
_common_param_check,
_joint_range_check,
)
from kornia.geometry.bbox import bbox_generator
from kornia.utils.helpers import _extract_device_dtype
class CutmixGenerator(RandomGeneratorBase):
r"""Generate cutmix indexes and lambdas for a batch of inputs.
Args:
p (float): probability of applying cutmix.
num_mix (int): number of images to mix with. Default is 1.
beta (torch.Tensor, optional): hyperparameter for generating cut size from beta distribution.
If None, it will be set to 1.
cut_size (torch.Tensor, optional): controlling the minimum and maximum cut ratio from [0, 1].
If None, it will be set to [0, 1], which means no restriction.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (num_mix, B).
- crop_src (torch.Tensor): element-wise probabilities with a shape of (num_mix, B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes. By default,
the parameters will be generated on CPU in float32. This can be changed by calling
``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.
"""
def __init__(
self,
cut_size: Optional[Union[torch.Tensor, Tuple[float, float]]] = None,
beta: Optional[Union[torch.Tensor, float]] = None,
num_mix: int = 1,
p: float = 1.0,
) -> None:
super().__init__()
self.cut_size = cut_size
self.beta = beta
self.num_mix = num_mix
self.p = p
if not (num_mix >= 1 and isinstance(num_mix, (int,))):
raise AssertionError(f"`num_mix` must be an integer greater than 1. Got {num_mix}.")
def __repr__(self) -> str:
repr = f"cut_size={self.cut_size}, beta={self.beta}, num_mix={self.num_mix}"
return repr
def make_samplers(self, device: torch.device, dtype: torch.dtype) -> None:
if self.beta is None:
self._beta = torch.tensor(1.0, device=device, dtype=dtype)
else:
self._beta = torch.as_tensor(self.beta, device=device, dtype=dtype)
if self.cut_size is None:
self._cut_size = torch.tensor([0.0, 1.0], device=device, dtype=dtype)
else:
self._cut_size = torch.as_tensor(self.cut_size, device=device, dtype=dtype)
_joint_range_check(self._cut_size, 'cut_size', bounds=(0, 1))
self.beta_sampler = Beta(self._beta, self._beta)
self.prob_sampler = Bernoulli(torch.tensor(float(self.p), device=device, dtype=dtype))
self.rand_sampler = Uniform(
torch.tensor(0.0, device=device, dtype=dtype),
torch.tensor(1.0, device=device, dtype=dtype),
validate_args=False,
)
def forward(self, batch_shape: torch.Size, same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
if not (type(height) is int and height > 0 and type(width) is int and width > 0):
raise AssertionError(f"'height' and 'width' must be integers. Got {height}, {width}.")
_device, _dtype = _extract_device_dtype([self.beta, self.cut_size])
_common_param_check(batch_size, same_on_batch)
if batch_size == 0:
return dict(
mix_pairs=torch.zeros([0, 3], device=_device, dtype=torch.long),
crop_src=torch.zeros([0, 4, 2], device=_device, dtype=torch.long),
)
with torch.no_grad():
batch_probs: torch.Tensor = _adapted_sampling(
(batch_size * self.num_mix,), self.prob_sampler, same_on_batch
)
mix_pairs: torch.Tensor = torch.rand(self.num_mix, batch_size, device=_device, dtype=_dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_rsampling((batch_size * self.num_mix,), self.beta_sampler, same_on_batch)
# Note: torch.clamp does not accept tensor, cutmix_betas.clamp(cut_size[0], cut_size[1]) throws:
# Argument 1 to "clamp" of "_TensorBase" has incompatible type "Tensor"; expected "float"
cutmix_betas = torch.min(torch.max(cutmix_betas, self._cut_size[0]), self._cut_size[1])
cutmix_rate = torch.sqrt(1.0 - cutmix_betas) * batch_probs
cut_height = (cutmix_rate * height).floor().to(device=_device, dtype=_dtype)
cut_width = (cutmix_rate * width).floor().to(device=_device, dtype=_dtype)
_gen_shape = (1,)
if same_on_batch:
_gen_shape = (cut_height.size(0),)
cut_height = cut_height[0]
cut_width = cut_width[0]
# Reserve at least 1 pixel for cropping.
x_start: torch.Tensor = _adapted_rsampling(_gen_shape, self.rand_sampler, same_on_batch) * (
width - cut_width - 1
)
y_start: torch.Tensor = _adapted_rsampling(_gen_shape, self.rand_sampler, same_on_batch) * (
height - cut_height - 1
)
x_start = x_start.floor().to(device=_device, dtype=_dtype)
y_start = y_start.floor().to(device=_device, dtype=_dtype)
crop_src = bbox_generator(x_start.squeeze(), y_start.squeeze(), cut_width, cut_height)
# (B * num_mix, 4, 2) => (num_mix, batch_size, 4, 2)
crop_src = crop_src.view(self.num_mix, batch_size, 4, 2)
return dict(
mix_pairs=mix_pairs.to(device=_device, dtype=torch.long),
crop_src=crop_src.floor().to(device=_device, dtype=_dtype),
)
def random_cutmix_generator(
batch_size: int,
width: int,
height: int,
p: float = 0.5,
num_mix: int = 1,
beta: Optional[torch.Tensor] = None,
cut_size: Optional[torch.Tensor] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate cutmix indexes and lambdas for a batch of inputs.
Args:
batch_size (int): the number of images. If batchsize == 1, the output will be as same as the input.
width (int): image width.
height (int): image height.
p (float): probability of applying cutmix.
num_mix (int): number of images to mix with. Default is 1.
beta (torch.Tensor, optional): hyperparameter for generating cut size from beta distribution.
If None, it will be set to 1.
cut_size (torch.Tensor, optional): controlling the minimum and maximum cut ratio from [0, 1].
If None, it will be set to [0, 1], which means no restriction.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (num_mix, B).
- crop_src (torch.Tensor): element-wise probabilities with a shape of (num_mix, B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Examples:
>>> rng = torch.manual_seed(0)
>>> random_cutmix_generator(3, 224, 224, p=0.5, num_mix=2)
{'mix_pairs': tensor([[2, 0, 1],
[1, 2, 0]]), 'crop_src': tensor([[[[ 35., 25.],
[208., 25.],
[208., 198.],
[ 35., 198.]],
<BLANKLINE>
[[156., 137.],
[155., 137.],
[155., 136.],
[156., 136.]],
<BLANKLINE>
[[ 3., 12.],
[210., 12.],
[210., 219.],
[ 3., 219.]]],
<BLANKLINE>
<BLANKLINE>
[[[ 83., 125.],
[177., 125.],
[177., 219.],
[ 83., 219.]],
<BLANKLINE>
[[ 54., 8.],
[205., 8.],
[205., 159.],
[ 54., 159.]],
<BLANKLINE>
[[ 97., 70.],
[ 96., 70.],
[ 96., 69.],
[ 97., 69.]]]])}
"""
_device, _dtype = _extract_device_dtype([beta, cut_size])
beta = torch.as_tensor(1.0 if beta is None else beta, device=device, dtype=dtype)
cut_size = torch.as_tensor([0.0, 1.0] if cut_size is None else cut_size, device=device, dtype=dtype)
if not (num_mix >= 1 and isinstance(num_mix, (int,))):
raise AssertionError(f"`num_mix` must be an integer greater than 1. Got {num_mix}.")
if not (type(height) is int and height > 0 and type(width) is int and width > 0):
raise AssertionError(f"'height' and 'width' must be integers. Got {height}, {width}.")
_joint_range_check(cut_size, 'cut_size', bounds=(0, 1))
_common_param_check(batch_size, same_on_batch)
if batch_size == 0:
return dict(
mix_pairs=torch.zeros([0, 3], device=_device, dtype=torch.long),
crop_src=torch.zeros([0, 4, 2], device=_device, dtype=torch.long),
)
batch_probs: torch.Tensor = random_prob_generator(
batch_size * num_mix, p, same_on_batch, device=device, dtype=dtype
)
mix_pairs: torch.Tensor = torch.rand(num_mix, batch_size, device=device, dtype=dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_beta((batch_size * num_mix,), beta, beta, same_on_batch=same_on_batch)
# Note: torch.clamp does not accept tensor, cutmix_betas.clamp(cut_size[0], cut_size[1]) throws:
# Argument 1 to "clamp" of "_TensorBase" has incompatible type "Tensor"; expected "float"
cutmix_betas = torch.min(torch.max(cutmix_betas, cut_size[0]), cut_size[1])
cutmix_rate = torch.sqrt(1.0 - cutmix_betas) * batch_probs
cut_height = (cutmix_rate * height).floor().to(device=device, dtype=_dtype)
cut_width = (cutmix_rate * width).floor().to(device=device, dtype=_dtype)
_gen_shape = (1,)
if same_on_batch:
_gen_shape = (cut_height.size(0),)
cut_height = cut_height[0]
cut_width = cut_width[0]
# Reserve at least 1 pixel for cropping.
x_start = (
_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_width, device=device, dtype=dtype),
(width - cut_width - 1).to(device=device, dtype=dtype),
same_on_batch,
)
.floor()
.to(device=device, dtype=_dtype)
)
y_start = (
_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_height, device=device, dtype=dtype),
(height - cut_height - 1).to(device=device, dtype=dtype),
same_on_batch,
)
.floor()
.to(device=device, dtype=_dtype)
)
crop_src = bbox_generator(x_start.squeeze(), y_start.squeeze(), cut_width, cut_height)
# (B * num_mix, 4, 2) => (num_mix, batch_size, 4, 2)
crop_src = crop_src.view(num_mix, batch_size, 4, 2)
return dict(
mix_pairs=mix_pairs.to(device=_device, dtype=torch.long),
crop_src=crop_src.floor().to(device=_device, dtype=_dtype),
) | kornia/augmentation/random_generator/_2d/cutmix.py | from typing import Dict, Optional, Tuple, Union
import torch
from torch.distributions import Bernoulli, Beta, Uniform
from kornia.augmentation.random_generator._2d.probability import random_prob_generator
from kornia.augmentation.random_generator.base import RandomGeneratorBase
from kornia.augmentation.utils import (
_adapted_beta,
_adapted_rsampling,
_adapted_sampling,
_adapted_uniform,
_common_param_check,
_joint_range_check,
)
from kornia.geometry.bbox import bbox_generator
from kornia.utils.helpers import _extract_device_dtype
class CutmixGenerator(RandomGeneratorBase):
r"""Generate cutmix indexes and lambdas for a batch of inputs.
Args:
p (float): probability of applying cutmix.
num_mix (int): number of images to mix with. Default is 1.
beta (torch.Tensor, optional): hyperparameter for generating cut size from beta distribution.
If None, it will be set to 1.
cut_size (torch.Tensor, optional): controlling the minimum and maximum cut ratio from [0, 1].
If None, it will be set to [0, 1], which means no restriction.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (num_mix, B).
- crop_src (torch.Tensor): element-wise probabilities with a shape of (num_mix, B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes. By default,
the parameters will be generated on CPU in float32. This can be changed by calling
``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.
"""
def __init__(
self,
cut_size: Optional[Union[torch.Tensor, Tuple[float, float]]] = None,
beta: Optional[Union[torch.Tensor, float]] = None,
num_mix: int = 1,
p: float = 1.0,
) -> None:
super().__init__()
self.cut_size = cut_size
self.beta = beta
self.num_mix = num_mix
self.p = p
if not (num_mix >= 1 and isinstance(num_mix, (int,))):
raise AssertionError(f"`num_mix` must be an integer greater than 1. Got {num_mix}.")
def __repr__(self) -> str:
repr = f"cut_size={self.cut_size}, beta={self.beta}, num_mix={self.num_mix}"
return repr
def make_samplers(self, device: torch.device, dtype: torch.dtype) -> None:
if self.beta is None:
self._beta = torch.tensor(1.0, device=device, dtype=dtype)
else:
self._beta = torch.as_tensor(self.beta, device=device, dtype=dtype)
if self.cut_size is None:
self._cut_size = torch.tensor([0.0, 1.0], device=device, dtype=dtype)
else:
self._cut_size = torch.as_tensor(self.cut_size, device=device, dtype=dtype)
_joint_range_check(self._cut_size, 'cut_size', bounds=(0, 1))
self.beta_sampler = Beta(self._beta, self._beta)
self.prob_sampler = Bernoulli(torch.tensor(float(self.p), device=device, dtype=dtype))
self.rand_sampler = Uniform(
torch.tensor(0.0, device=device, dtype=dtype),
torch.tensor(1.0, device=device, dtype=dtype),
validate_args=False,
)
def forward(self, batch_shape: torch.Size, same_on_batch: bool = False) -> Dict[str, torch.Tensor]:
batch_size = batch_shape[0]
height = batch_shape[-2]
width = batch_shape[-1]
if not (type(height) is int and height > 0 and type(width) is int and width > 0):
raise AssertionError(f"'height' and 'width' must be integers. Got {height}, {width}.")
_device, _dtype = _extract_device_dtype([self.beta, self.cut_size])
_common_param_check(batch_size, same_on_batch)
if batch_size == 0:
return dict(
mix_pairs=torch.zeros([0, 3], device=_device, dtype=torch.long),
crop_src=torch.zeros([0, 4, 2], device=_device, dtype=torch.long),
)
with torch.no_grad():
batch_probs: torch.Tensor = _adapted_sampling(
(batch_size * self.num_mix,), self.prob_sampler, same_on_batch
)
mix_pairs: torch.Tensor = torch.rand(self.num_mix, batch_size, device=_device, dtype=_dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_rsampling((batch_size * self.num_mix,), self.beta_sampler, same_on_batch)
# Note: torch.clamp does not accept tensor, cutmix_betas.clamp(cut_size[0], cut_size[1]) throws:
# Argument 1 to "clamp" of "_TensorBase" has incompatible type "Tensor"; expected "float"
cutmix_betas = torch.min(torch.max(cutmix_betas, self._cut_size[0]), self._cut_size[1])
cutmix_rate = torch.sqrt(1.0 - cutmix_betas) * batch_probs
cut_height = (cutmix_rate * height).floor().to(device=_device, dtype=_dtype)
cut_width = (cutmix_rate * width).floor().to(device=_device, dtype=_dtype)
_gen_shape = (1,)
if same_on_batch:
_gen_shape = (cut_height.size(0),)
cut_height = cut_height[0]
cut_width = cut_width[0]
# Reserve at least 1 pixel for cropping.
x_start: torch.Tensor = _adapted_rsampling(_gen_shape, self.rand_sampler, same_on_batch) * (
width - cut_width - 1
)
y_start: torch.Tensor = _adapted_rsampling(_gen_shape, self.rand_sampler, same_on_batch) * (
height - cut_height - 1
)
x_start = x_start.floor().to(device=_device, dtype=_dtype)
y_start = y_start.floor().to(device=_device, dtype=_dtype)
crop_src = bbox_generator(x_start.squeeze(), y_start.squeeze(), cut_width, cut_height)
# (B * num_mix, 4, 2) => (num_mix, batch_size, 4, 2)
crop_src = crop_src.view(self.num_mix, batch_size, 4, 2)
return dict(
mix_pairs=mix_pairs.to(device=_device, dtype=torch.long),
crop_src=crop_src.floor().to(device=_device, dtype=_dtype),
)
def random_cutmix_generator(
batch_size: int,
width: int,
height: int,
p: float = 0.5,
num_mix: int = 1,
beta: Optional[torch.Tensor] = None,
cut_size: Optional[torch.Tensor] = None,
same_on_batch: bool = False,
device: torch.device = torch.device('cpu'),
dtype: torch.dtype = torch.float32,
) -> Dict[str, torch.Tensor]:
r"""Generate cutmix indexes and lambdas for a batch of inputs.
Args:
batch_size (int): the number of images. If batchsize == 1, the output will be as same as the input.
width (int): image width.
height (int): image height.
p (float): probability of applying cutmix.
num_mix (int): number of images to mix with. Default is 1.
beta (torch.Tensor, optional): hyperparameter for generating cut size from beta distribution.
If None, it will be set to 1.
cut_size (torch.Tensor, optional): controlling the minimum and maximum cut ratio from [0, 1].
If None, it will be set to [0, 1], which means no restriction.
same_on_batch (bool): apply the same transformation across the batch. Default: False.
device (torch.device): the device on which the random numbers will be generated. Default: cpu.
dtype (torch.dtype): the data type of the generated random numbers. Default: float32.
Returns:
params Dict[str, torch.Tensor]: parameters to be passed for transformation.
- mix_pairs (torch.Tensor): element-wise probabilities with a shape of (num_mix, B).
- crop_src (torch.Tensor): element-wise probabilities with a shape of (num_mix, B, 4, 2).
Note:
The generated random numbers are not reproducible across different devices and dtypes.
Examples:
>>> rng = torch.manual_seed(0)
>>> random_cutmix_generator(3, 224, 224, p=0.5, num_mix=2)
{'mix_pairs': tensor([[2, 0, 1],
[1, 2, 0]]), 'crop_src': tensor([[[[ 35., 25.],
[208., 25.],
[208., 198.],
[ 35., 198.]],
<BLANKLINE>
[[156., 137.],
[155., 137.],
[155., 136.],
[156., 136.]],
<BLANKLINE>
[[ 3., 12.],
[210., 12.],
[210., 219.],
[ 3., 219.]]],
<BLANKLINE>
<BLANKLINE>
[[[ 83., 125.],
[177., 125.],
[177., 219.],
[ 83., 219.]],
<BLANKLINE>
[[ 54., 8.],
[205., 8.],
[205., 159.],
[ 54., 159.]],
<BLANKLINE>
[[ 97., 70.],
[ 96., 70.],
[ 96., 69.],
[ 97., 69.]]]])}
"""
_device, _dtype = _extract_device_dtype([beta, cut_size])
beta = torch.as_tensor(1.0 if beta is None else beta, device=device, dtype=dtype)
cut_size = torch.as_tensor([0.0, 1.0] if cut_size is None else cut_size, device=device, dtype=dtype)
if not (num_mix >= 1 and isinstance(num_mix, (int,))):
raise AssertionError(f"`num_mix` must be an integer greater than 1. Got {num_mix}.")
if not (type(height) is int and height > 0 and type(width) is int and width > 0):
raise AssertionError(f"'height' and 'width' must be integers. Got {height}, {width}.")
_joint_range_check(cut_size, 'cut_size', bounds=(0, 1))
_common_param_check(batch_size, same_on_batch)
if batch_size == 0:
return dict(
mix_pairs=torch.zeros([0, 3], device=_device, dtype=torch.long),
crop_src=torch.zeros([0, 4, 2], device=_device, dtype=torch.long),
)
batch_probs: torch.Tensor = random_prob_generator(
batch_size * num_mix, p, same_on_batch, device=device, dtype=dtype
)
mix_pairs: torch.Tensor = torch.rand(num_mix, batch_size, device=device, dtype=dtype).argsort(dim=1)
cutmix_betas: torch.Tensor = _adapted_beta((batch_size * num_mix,), beta, beta, same_on_batch=same_on_batch)
# Note: torch.clamp does not accept tensor, cutmix_betas.clamp(cut_size[0], cut_size[1]) throws:
# Argument 1 to "clamp" of "_TensorBase" has incompatible type "Tensor"; expected "float"
cutmix_betas = torch.min(torch.max(cutmix_betas, cut_size[0]), cut_size[1])
cutmix_rate = torch.sqrt(1.0 - cutmix_betas) * batch_probs
cut_height = (cutmix_rate * height).floor().to(device=device, dtype=_dtype)
cut_width = (cutmix_rate * width).floor().to(device=device, dtype=_dtype)
_gen_shape = (1,)
if same_on_batch:
_gen_shape = (cut_height.size(0),)
cut_height = cut_height[0]
cut_width = cut_width[0]
# Reserve at least 1 pixel for cropping.
x_start = (
_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_width, device=device, dtype=dtype),
(width - cut_width - 1).to(device=device, dtype=dtype),
same_on_batch,
)
.floor()
.to(device=device, dtype=_dtype)
)
y_start = (
_adapted_uniform(
_gen_shape,
torch.zeros_like(cut_height, device=device, dtype=dtype),
(height - cut_height - 1).to(device=device, dtype=dtype),
same_on_batch,
)
.floor()
.to(device=device, dtype=_dtype)
)
crop_src = bbox_generator(x_start.squeeze(), y_start.squeeze(), cut_width, cut_height)
# (B * num_mix, 4, 2) => (num_mix, batch_size, 4, 2)
crop_src = crop_src.view(num_mix, batch_size, 4, 2)
return dict(
mix_pairs=mix_pairs.to(device=_device, dtype=torch.long),
crop_src=crop_src.floor().to(device=_device, dtype=_dtype),
) | 0.971293 | 0.534916 |
import numpy as np
__all__ = ['MolFiniteDifference']
class MolFiniteDifference:
"""Helper to calculate derivatives of some value as a function of Cartesian displacements in a molecule."""
weights_der1 = {'3': np.array([-1 / 2, 0, 1 / 2]),
'5': np.array([1 / 12, -2 / 3, 0, 2 / 3, -1 / 12])
}
weights_der2 = {'3': np.array([1, -2, 1]),
'5': np.array([-1 / 12, 4 / 3, -5 / 2, 4 / 3, -1 / 12])
}
@staticmethod
def displace_molecule(eq_geom, atm_cd, dx, num_disps):
"""
Displace atm along cd
:param eq_geom: Geometry from which you will be displaced
:param atm_cd: a tuple that has a paricular atom of interest to displace in a particular dimension (x,y,or,z)
:param dx: The amount each geometry will be replaced
:param num_disps: int of how many displacements to do, will take the form of 3 or 5 for harmonic analysis.
:return: Displaced coordinates in a 3D array (n,m,3). If displaced in two directions, then still (n,m,3)
"""
in_either_direction = num_disps // 2
dx_ordering = np.arange(-in_either_direction, in_either_direction + 1)
dx_ordering = dx_ordering * dx
if len(atm_cd) == 2:
"""Displace one atom in each direction"""
atm = atm_cd[0] # atom of interest
cd = atm_cd[1] # x,y, or z
displaced_cds = np.zeros(np.concatenate(([len(dx_ordering)], np.shape(eq_geom))))
for disp_num, disp in enumerate(dx_ordering):
dx_atms = np.zeros(eq_geom.shape)
dx_atms[atm, cd] += disp
displaced_cds[disp_num] = eq_geom + dx_atms
elif len(atm_cd) == 4:
"""Displace two atoms in each direction. For 2D (mixed) derivatives"""
atm1 = atm_cd[0]
cd1 = atm_cd[1]
atm2 = atm_cd[2]
cd2 = atm_cd[3]
displaced_cds = np.zeros(np.concatenate(([len(dx_ordering) ** 2], np.shape(eq_geom))))
ct = 0
for disp_num, disp in enumerate(dx_ordering):
for disp_num_2, disp2 in enumerate(dx_ordering):
dx_atms = np.zeros(eq_geom.shape)
dx_atms[atm1, cd1] += disp
dx_atms[atm2, cd2] += disp2
displaced_cds[ct] = eq_geom + dx_atms
ct += 1
return displaced_cds
@classmethod
def differentiate(cls, values, dx, num_points, der):
if der == 1: # First derivative, one dimension
wts = cls.weights_der1[str(num_points)]
wts = wts / dx
diff = np.dot(wts, values)
elif der == 11: # Mixed first derivative
wts = cls.weights_der1[str(num_points)]
wts = wts / dx
diff = np.dot(wts, np.dot(wts, values))
elif der == 2: # Second derivative, one dimension
wts = cls.weights_der2[str(num_points)]
wts = wts / dx ** 2 # since it's one dimensional
diff = np.dot(wts, values)
return diff | pyvibdmc/simulation_utilities/initial_conditioner/finite_difference.py | import numpy as np
__all__ = ['MolFiniteDifference']
class MolFiniteDifference:
"""Helper to calculate derivatives of some value as a function of Cartesian displacements in a molecule."""
weights_der1 = {'3': np.array([-1 / 2, 0, 1 / 2]),
'5': np.array([1 / 12, -2 / 3, 0, 2 / 3, -1 / 12])
}
weights_der2 = {'3': np.array([1, -2, 1]),
'5': np.array([-1 / 12, 4 / 3, -5 / 2, 4 / 3, -1 / 12])
}
@staticmethod
def displace_molecule(eq_geom, atm_cd, dx, num_disps):
"""
Displace atm along cd
:param eq_geom: Geometry from which you will be displaced
:param atm_cd: a tuple that has a paricular atom of interest to displace in a particular dimension (x,y,or,z)
:param dx: The amount each geometry will be replaced
:param num_disps: int of how many displacements to do, will take the form of 3 or 5 for harmonic analysis.
:return: Displaced coordinates in a 3D array (n,m,3). If displaced in two directions, then still (n,m,3)
"""
in_either_direction = num_disps // 2
dx_ordering = np.arange(-in_either_direction, in_either_direction + 1)
dx_ordering = dx_ordering * dx
if len(atm_cd) == 2:
"""Displace one atom in each direction"""
atm = atm_cd[0] # atom of interest
cd = atm_cd[1] # x,y, or z
displaced_cds = np.zeros(np.concatenate(([len(dx_ordering)], np.shape(eq_geom))))
for disp_num, disp in enumerate(dx_ordering):
dx_atms = np.zeros(eq_geom.shape)
dx_atms[atm, cd] += disp
displaced_cds[disp_num] = eq_geom + dx_atms
elif len(atm_cd) == 4:
"""Displace two atoms in each direction. For 2D (mixed) derivatives"""
atm1 = atm_cd[0]
cd1 = atm_cd[1]
atm2 = atm_cd[2]
cd2 = atm_cd[3]
displaced_cds = np.zeros(np.concatenate(([len(dx_ordering) ** 2], np.shape(eq_geom))))
ct = 0
for disp_num, disp in enumerate(dx_ordering):
for disp_num_2, disp2 in enumerate(dx_ordering):
dx_atms = np.zeros(eq_geom.shape)
dx_atms[atm1, cd1] += disp
dx_atms[atm2, cd2] += disp2
displaced_cds[ct] = eq_geom + dx_atms
ct += 1
return displaced_cds
@classmethod
def differentiate(cls, values, dx, num_points, der):
if der == 1: # First derivative, one dimension
wts = cls.weights_der1[str(num_points)]
wts = wts / dx
diff = np.dot(wts, values)
elif der == 11: # Mixed first derivative
wts = cls.weights_der1[str(num_points)]
wts = wts / dx
diff = np.dot(wts, np.dot(wts, values))
elif der == 2: # Second derivative, one dimension
wts = cls.weights_der2[str(num_points)]
wts = wts / dx ** 2 # since it's one dimensional
diff = np.dot(wts, values)
return diff | 0.879742 | 0.76882 |
load("//tools:defaults.bzl", "rollup_bundle", "ts_library")
load("@npm_bazel_karma//:index.bzl", "karma_web_test_suite")
def karma_test_prepare(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point):
ts_library(
name = name + "_env",
testonly = True,
srcs = env_srcs,
deps = env_deps,
)
rollup_bundle(
name = name + "_env_rollup",
testonly = True,
entry_point = env_entry_point,
deps = [
":" + name + "_env",
"@npm//rollup-plugin-commonjs",
"@npm//rollup-plugin-node-resolve",
],
)
native.genrule(
name = name + "_env_trim_map",
testonly = True,
srcs = [
":" + name + "_env_rollup.umd",
],
outs = [
name + "_env_rollup_trim_map.js",
],
cmd = " && ".join([
"cp $(@D)/" + name + "_env_rollup.umd.js $@",
]),
)
ts_library(
name = name + "_test",
testonly = True,
srcs = test_srcs,
deps = test_deps,
)
rollup_bundle(
name = name + "_rollup",
testonly = True,
entry_point = test_entry_point,
config_file = "//packages/zone.js:rollup-es5.config.js",
deps = [
":" + name + "_test",
"@npm//rollup-plugin-commonjs",
"@npm//rollup-plugin-node-resolve",
],
)
native.genrule(
name = name + "_trim_map",
testonly = True,
srcs = [
":" + name + "_rollup.umd",
],
outs = [
name + "_rollup_trim_map.js",
],
cmd = " && ".join([
"cp $(@D)/" + name + "_rollup.umd.js $@",
]),
)
def karma_test(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point, bootstraps, ci):
first = True
for subname in bootstraps:
bootstrap = bootstraps[subname]
firstFlag = first
if first:
first = False
karma_test_prepare(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point)
_karma_test_required_dist_files = [
"//packages/zone.js/dist:task-tracking-dist-dev-test",
"//packages/zone.js/dist:wtf-dist-dev-test",
"//packages/zone.js/dist:webapis-notification-dist-dev-test",
"//packages/zone.js/dist:webapis-media-query-dist-dev-test",
"//packages/zone.js/dist:zone-patch-canvas-dist-dev-test",
"//packages/zone.js/dist:zone-patch-fetch-dist-dev-test",
"//packages/zone.js/dist:zone-patch-resize-observer-dist-dev-test",
"//packages/zone.js/dist:zone-patch-user-media-dist-dev-test",
":" + name + "_trim_map",
]
karma_web_test_suite(
name = subname + "_karma_jasmine_test",
srcs = [
"fake_entry.js",
],
bootstrap = [
":" + name + "_env_trim_map",
] + bootstrap +
_karma_test_required_dist_files,
browsers = ["//tools/browsers:chromium"],
static_files = [
":assets/sample.json",
":assets/worker.js",
":assets/import.html",
],
tags = ["zone_karma_test"],
runtime_deps = [
"@npm//karma-browserstack-launcher",
],
)
if ci and firstFlag:
karma_web_test_suite(
name = "karma_jasmine_test_ci",
srcs = [
"fake_entry.js",
],
bootstrap = [
":saucelabs.js",
":" + name + "_env_trim_map",
"//packages/zone.js/dist:zone-testing-bundle-dist-test",
] + _karma_test_required_dist_files,
browsers = ["//tools/browsers:chromium"],
config_file = "//:karma-js.conf.js",
configuration_env_vars = ["KARMA_WEB_TEST_MODE"],
data = [
"//:browser-providers.conf.js",
"//tools:jasmine-seed-generator.js",
],
static_files = [
":assets/sample.json",
":assets/worker.js",
":assets/import.html",
],
tags = ["zone_karma_test"],
# Visible to //:saucelabs_unit_tests_poc target
visibility = ["//:__pkg__"],
runtime_deps = [
"@npm//karma-browserstack-launcher",
],
) | packages/zone.js/test/karma_test.bzl | load("//tools:defaults.bzl", "rollup_bundle", "ts_library")
load("@npm_bazel_karma//:index.bzl", "karma_web_test_suite")
def karma_test_prepare(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point):
ts_library(
name = name + "_env",
testonly = True,
srcs = env_srcs,
deps = env_deps,
)
rollup_bundle(
name = name + "_env_rollup",
testonly = True,
entry_point = env_entry_point,
deps = [
":" + name + "_env",
"@npm//rollup-plugin-commonjs",
"@npm//rollup-plugin-node-resolve",
],
)
native.genrule(
name = name + "_env_trim_map",
testonly = True,
srcs = [
":" + name + "_env_rollup.umd",
],
outs = [
name + "_env_rollup_trim_map.js",
],
cmd = " && ".join([
"cp $(@D)/" + name + "_env_rollup.umd.js $@",
]),
)
ts_library(
name = name + "_test",
testonly = True,
srcs = test_srcs,
deps = test_deps,
)
rollup_bundle(
name = name + "_rollup",
testonly = True,
entry_point = test_entry_point,
config_file = "//packages/zone.js:rollup-es5.config.js",
deps = [
":" + name + "_test",
"@npm//rollup-plugin-commonjs",
"@npm//rollup-plugin-node-resolve",
],
)
native.genrule(
name = name + "_trim_map",
testonly = True,
srcs = [
":" + name + "_rollup.umd",
],
outs = [
name + "_rollup_trim_map.js",
],
cmd = " && ".join([
"cp $(@D)/" + name + "_rollup.umd.js $@",
]),
)
def karma_test(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point, bootstraps, ci):
first = True
for subname in bootstraps:
bootstrap = bootstraps[subname]
firstFlag = first
if first:
first = False
karma_test_prepare(name, env_srcs, env_deps, env_entry_point, test_srcs, test_deps, test_entry_point)
_karma_test_required_dist_files = [
"//packages/zone.js/dist:task-tracking-dist-dev-test",
"//packages/zone.js/dist:wtf-dist-dev-test",
"//packages/zone.js/dist:webapis-notification-dist-dev-test",
"//packages/zone.js/dist:webapis-media-query-dist-dev-test",
"//packages/zone.js/dist:zone-patch-canvas-dist-dev-test",
"//packages/zone.js/dist:zone-patch-fetch-dist-dev-test",
"//packages/zone.js/dist:zone-patch-resize-observer-dist-dev-test",
"//packages/zone.js/dist:zone-patch-user-media-dist-dev-test",
":" + name + "_trim_map",
]
karma_web_test_suite(
name = subname + "_karma_jasmine_test",
srcs = [
"fake_entry.js",
],
bootstrap = [
":" + name + "_env_trim_map",
] + bootstrap +
_karma_test_required_dist_files,
browsers = ["//tools/browsers:chromium"],
static_files = [
":assets/sample.json",
":assets/worker.js",
":assets/import.html",
],
tags = ["zone_karma_test"],
runtime_deps = [
"@npm//karma-browserstack-launcher",
],
)
if ci and firstFlag:
karma_web_test_suite(
name = "karma_jasmine_test_ci",
srcs = [
"fake_entry.js",
],
bootstrap = [
":saucelabs.js",
":" + name + "_env_trim_map",
"//packages/zone.js/dist:zone-testing-bundle-dist-test",
] + _karma_test_required_dist_files,
browsers = ["//tools/browsers:chromium"],
config_file = "//:karma-js.conf.js",
configuration_env_vars = ["KARMA_WEB_TEST_MODE"],
data = [
"//:browser-providers.conf.js",
"//tools:jasmine-seed-generator.js",
],
static_files = [
":assets/sample.json",
":assets/worker.js",
":assets/import.html",
],
tags = ["zone_karma_test"],
# Visible to //:saucelabs_unit_tests_poc target
visibility = ["//:__pkg__"],
runtime_deps = [
"@npm//karma-browserstack-launcher",
],
) | 0.251096 | 0.19619 |
import unittest
from pygorithm.math import (
lcm,
lcm_using_gcd,
sieve_of_eratosthenes,
factorial,
conversion,
matrix_operations,
)
class TestLCM(unittest.TestCase):
def test_lcm(self):
self.assertEqual(lcm.lcm([3, 12, 16]), 48)
def test_lcm_using_gcd(self):
self.assertEqual(lcm_using_gcd.lcm_using_gcd([3, 12, 16]), 48)
class TestSieveOfEratosthenes(unittest.TestCase):
def test_sieve_of_eratosthenes(self):
self.assertEqual(
sieve_of_eratosthenes.sieve_of_eratosthenes(11), [2, 3, 5, 7, 11]
)
class TestFactorial(unittest.TestCase):
def test_factorial(self):
self.assertEqual(factorial.factorial(10), 3628800)
class TestConversion(unittest.TestCase):
def test_dec_to_bin(self):
self.assertEqual(conversion.decimal_to_binary(2), "10")
def test_bin_to_dec(self):
self.assertEqual(conversion.binary_to_decimal("1010"), 10)
def test_dec_to_hex(self):
self.assertEqual(conversion.decimal_to_hex(30), "1E")
def test_hex_to_dex(self):
self.assertEqual(conversion.hex_to_decimal("1E"), 30)
class TestMatrixOperations(unittest.TestCase):
def test_matrix_addition(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1], [6, 7, 3], [4, 5, 9]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(matrix.add(), [[17, 15, 4], [10, 12, 9], [11, 13, 18]])
def test_matrix_subtraction(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1], [6, 7, 3], [4, 5, 9]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(matrix.subtract(), [[7, -1, 2], [-2, -2, 3], [3, 3, 0]])
def test_matrix_multiplication(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1, 2], [6, 7, 3, 0], [4, 5, 9, 1]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(
matrix.multiply(),
[[114, 160, 60, 27], [74, 97, 73, 14], [119, 157, 112, 23]],
)
def test_matrix_transpose(self):
X = [[12, 7], [4, 5], [3, 8]]
matrix = matrix_operations.Matrix(X)
self.assertEqual(matrix.transpose(), [[12, 4, 3], [7, 5, 8]])
def test_matrix_rotate(self):
X = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]
matrix = matrix_operations.Matrix(X)
self.assertEqual(
matrix.rotate(),
[[5, 1, 2, 3], [9, 10, 6, 4], [13, 11, 7, 8], [14, 15, 16, 12]],
)
def test_matrix_unique_paths(self):
matrix = matrix_operations.Matrix()
self.assertEqual(matrix.count_unique_paths(3, 3), 6)
def test_matrix_exceptions(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8], [6, 7], [4, 5]]
matrix = matrix_operations.Matrix(X, Y)
# test exception
self.assertRaises(Exception, matrix.add)
self.assertRaises(Exception, matrix.subtract)
if __name__ == "__main__":
unittest.main() | WEEKS/CD_Sata-Structures/_RESOURCES/pygorithm/tests/test_math.py | import unittest
from pygorithm.math import (
lcm,
lcm_using_gcd,
sieve_of_eratosthenes,
factorial,
conversion,
matrix_operations,
)
class TestLCM(unittest.TestCase):
def test_lcm(self):
self.assertEqual(lcm.lcm([3, 12, 16]), 48)
def test_lcm_using_gcd(self):
self.assertEqual(lcm_using_gcd.lcm_using_gcd([3, 12, 16]), 48)
class TestSieveOfEratosthenes(unittest.TestCase):
def test_sieve_of_eratosthenes(self):
self.assertEqual(
sieve_of_eratosthenes.sieve_of_eratosthenes(11), [2, 3, 5, 7, 11]
)
class TestFactorial(unittest.TestCase):
def test_factorial(self):
self.assertEqual(factorial.factorial(10), 3628800)
class TestConversion(unittest.TestCase):
def test_dec_to_bin(self):
self.assertEqual(conversion.decimal_to_binary(2), "10")
def test_bin_to_dec(self):
self.assertEqual(conversion.binary_to_decimal("1010"), 10)
def test_dec_to_hex(self):
self.assertEqual(conversion.decimal_to_hex(30), "1E")
def test_hex_to_dex(self):
self.assertEqual(conversion.hex_to_decimal("1E"), 30)
class TestMatrixOperations(unittest.TestCase):
def test_matrix_addition(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1], [6, 7, 3], [4, 5, 9]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(matrix.add(), [[17, 15, 4], [10, 12, 9], [11, 13, 18]])
def test_matrix_subtraction(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1], [6, 7, 3], [4, 5, 9]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(matrix.subtract(), [[7, -1, 2], [-2, -2, 3], [3, 3, 0]])
def test_matrix_multiplication(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8, 1, 2], [6, 7, 3, 0], [4, 5, 9, 1]]
matrix = matrix_operations.Matrix(X, Y)
self.assertEqual(
matrix.multiply(),
[[114, 160, 60, 27], [74, 97, 73, 14], [119, 157, 112, 23]],
)
def test_matrix_transpose(self):
X = [[12, 7], [4, 5], [3, 8]]
matrix = matrix_operations.Matrix(X)
self.assertEqual(matrix.transpose(), [[12, 4, 3], [7, 5, 8]])
def test_matrix_rotate(self):
X = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]
matrix = matrix_operations.Matrix(X)
self.assertEqual(
matrix.rotate(),
[[5, 1, 2, 3], [9, 10, 6, 4], [13, 11, 7, 8], [14, 15, 16, 12]],
)
def test_matrix_unique_paths(self):
matrix = matrix_operations.Matrix()
self.assertEqual(matrix.count_unique_paths(3, 3), 6)
def test_matrix_exceptions(self):
X = [[12, 7, 3], [4, 5, 6], [7, 8, 9]]
Y = [[5, 8], [6, 7], [4, 5]]
matrix = matrix_operations.Matrix(X, Y)
# test exception
self.assertRaises(Exception, matrix.add)
self.assertRaises(Exception, matrix.subtract)
if __name__ == "__main__":
unittest.main() | 0.58522 | 0.660487 |
import copy
import json
import click
from maxrect import get_maximal_rectangle, rect2poly, get_intersection
@click.command('max-rect')
@click.argument('polygon', default='-', required=False, nargs=1)
@click.option('--compare', is_flag=True, help='Leaves the original polygon in the feature collection')
@click.pass_context
def maxrect(ctx, polygon, compare):
"""
Get an approximately maximal area, axis-aligned rectangle
for a polygon represented by GeoJSON.
"""
if polygon == '-':
src = click.open_file('-')
if not src.isatty():
data = src.read()
else:
click.echo(ctx.get_usage())
ctx.exit(1)
else:
with open(polygon, 'r') as src:
data = src.read()
geojson = json.loads(data)
if geojson.get('type') == 'Feature':
geojson = {
'type': 'FeatureCollection',
'features': [geojson]
}
features = geojson.get('features')
n_features = len(features)
if compare:
features = features + copy.deepcopy(features)
for feature in features[:n_features]:
coordinates = get_maximal_rectangle(feature['geometry']['coordinates'][0])
feature['geometry']['coordinates'] = [rect2poly(*coordinates)]
geojson['features'] = features
click.echo(json.dumps(geojson))
@click.command('poly-intersect')
@click.argument('input_geoms', required=True, nargs=-1)
@click.pass_context
def polyinter(ctx, input_geoms):
"""Find an intersection polygon given multiple geometry inputs"""
coords = []
for ig in input_geoms:
with open(ig, 'r') as src:
data = src.read()
geojson = json.loads(data)
if geojson.get('type') == 'Feature':
geojson = {
'type': 'FeatureCollection',
'features': [geojson]
}
features = geojson.get('features')
n_features = len(features)
for feature in features[:n_features]:
c = feature['geometry']['coordinates'][0]
coords.append(c)
inter_geojson, _ = get_intersection(coords)
geojson = {
'type': 'FeatureCollection',
'features': [inter_geojson]
}
click.echo(json.dumps(geojson)) | maxrect/scripts/cli.py | import copy
import json
import click
from maxrect import get_maximal_rectangle, rect2poly, get_intersection
@click.command('max-rect')
@click.argument('polygon', default='-', required=False, nargs=1)
@click.option('--compare', is_flag=True, help='Leaves the original polygon in the feature collection')
@click.pass_context
def maxrect(ctx, polygon, compare):
"""
Get an approximately maximal area, axis-aligned rectangle
for a polygon represented by GeoJSON.
"""
if polygon == '-':
src = click.open_file('-')
if not src.isatty():
data = src.read()
else:
click.echo(ctx.get_usage())
ctx.exit(1)
else:
with open(polygon, 'r') as src:
data = src.read()
geojson = json.loads(data)
if geojson.get('type') == 'Feature':
geojson = {
'type': 'FeatureCollection',
'features': [geojson]
}
features = geojson.get('features')
n_features = len(features)
if compare:
features = features + copy.deepcopy(features)
for feature in features[:n_features]:
coordinates = get_maximal_rectangle(feature['geometry']['coordinates'][0])
feature['geometry']['coordinates'] = [rect2poly(*coordinates)]
geojson['features'] = features
click.echo(json.dumps(geojson))
@click.command('poly-intersect')
@click.argument('input_geoms', required=True, nargs=-1)
@click.pass_context
def polyinter(ctx, input_geoms):
"""Find an intersection polygon given multiple geometry inputs"""
coords = []
for ig in input_geoms:
with open(ig, 'r') as src:
data = src.read()
geojson = json.loads(data)
if geojson.get('type') == 'Feature':
geojson = {
'type': 'FeatureCollection',
'features': [geojson]
}
features = geojson.get('features')
n_features = len(features)
for feature in features[:n_features]:
c = feature['geometry']['coordinates'][0]
coords.append(c)
inter_geojson, _ = get_intersection(coords)
geojson = {
'type': 'FeatureCollection',
'features': [inter_geojson]
}
click.echo(json.dumps(geojson)) | 0.372163 | 0.304688 |
from ostorlab.runtimes.local.models import models
def testModels_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, db_engine_path):
"""Test when database does not exists, scan is populated in a newly created database."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
models.Scan.create(title='test', asset='Asset')
assert models.Database().session.query(models.Scan).count() == 1
assert models.Database().session.query(models.Scan).all()[0].title == 'test'
models.Database().drop_db_tables()
def testScanUpdate_always_updatesExistingScan(mocker, db_engine_path):
"""Test Agent save implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
models.Scan.create('test')
database = models.Database()
database.session.commit()
assert database.session.query(models.Scan).count() == 1
scan = database.session.query(models.Scan).first()
scan.title = 'test2'
database.session.commit()
assert database.session.query(models.Scan).count() == 1
scan = database.session.query(models.Scan).first()
assert scan.title == 'test2'
def testModelsVulnerability_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, db_engine_path):
"""Test Vulnerability Model implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
create_scan_db = models.Scan.create('test')
init_count = models.Database().session.query(models.Vulnerability).count()
models.Vulnerability.create(title='MyVuln', short_description= 'Xss', description= 'Javascript Vuln',
recommendation= 'Sanitize data', technical_detail= 'a=$input', risk_rating= 'HIGH',
cvss_v3_vector= '5:6:7', dna= '121312', scan_id=create_scan_db.id)
assert models.Database().session.query(models.Vulnerability).count() == init_count + 1
assert models.Database().session.query(models.Vulnerability).all()[0].title == 'MyVuln'
assert models.Database().session.query(models.Vulnerability).all()[0].scan_id == create_scan_db.id
def testModelsScanStatus_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, tmpdir, db_engine_path):
"""Test Scan Status Model implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
create_scan_db = models.Scan.create('test')
init_count = models.Database().session.query(models.ScanStatus).count()
models.ScanStatus.create(key='status', value='in_progress', scan_id=create_scan_db.id)
assert models.Database().session.query(models.ScanStatus).count() == init_count + 1
assert models.Database().session.query(models.ScanStatus).all()[-1].key == 'status'
assert models.Database().session.query(models.ScanStatus).all()[-1].value == 'in_progress'
assert models.Database().session.query(models.ScanStatus).all()[-1].scan_id == create_scan_db.id | tests/runtimes/local/models/models_test.py | from ostorlab.runtimes.local.models import models
def testModels_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, db_engine_path):
"""Test when database does not exists, scan is populated in a newly created database."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
models.Scan.create(title='test', asset='Asset')
assert models.Database().session.query(models.Scan).count() == 1
assert models.Database().session.query(models.Scan).all()[0].title == 'test'
models.Database().drop_db_tables()
def testScanUpdate_always_updatesExistingScan(mocker, db_engine_path):
"""Test Agent save implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
models.Scan.create('test')
database = models.Database()
database.session.commit()
assert database.session.query(models.Scan).count() == 1
scan = database.session.query(models.Scan).first()
scan.title = 'test2'
database.session.commit()
assert database.session.query(models.Scan).count() == 1
scan = database.session.query(models.Scan).first()
assert scan.title == 'test2'
def testModelsVulnerability_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, db_engine_path):
"""Test Vulnerability Model implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
create_scan_db = models.Scan.create('test')
init_count = models.Database().session.query(models.Vulnerability).count()
models.Vulnerability.create(title='MyVuln', short_description= 'Xss', description= 'Javascript Vuln',
recommendation= 'Sanitize data', technical_detail= 'a=$input', risk_rating= 'HIGH',
cvss_v3_vector= '5:6:7', dna= '121312', scan_id=create_scan_db.id)
assert models.Database().session.query(models.Vulnerability).count() == init_count + 1
assert models.Database().session.query(models.Vulnerability).all()[0].title == 'MyVuln'
assert models.Database().session.query(models.Vulnerability).all()[0].scan_id == create_scan_db.id
def testModelsScanStatus_whenDatabaseDoesNotExist_DatabaseAndScanCreated(mocker, tmpdir, db_engine_path):
"""Test Scan Status Model implementation."""
mocker.patch.object(models, 'ENGINE_URL', db_engine_path)
models.Database().create_db_tables()
create_scan_db = models.Scan.create('test')
init_count = models.Database().session.query(models.ScanStatus).count()
models.ScanStatus.create(key='status', value='in_progress', scan_id=create_scan_db.id)
assert models.Database().session.query(models.ScanStatus).count() == init_count + 1
assert models.Database().session.query(models.ScanStatus).all()[-1].key == 'status'
assert models.Database().session.query(models.ScanStatus).all()[-1].value == 'in_progress'
assert models.Database().session.query(models.ScanStatus).all()[-1].scan_id == create_scan_db.id | 0.704465 | 0.421254 |
import numpy as np
from pyNastran.converters.stl.stl import read_stl
from scipy.spatial import cKDTree
import scipy.interpolate
def projected_barycentric_coord(p, q, u, v):
r"""
points p, q
vector u, v
3
*v
/ \ <----p
q*----*u
1 2
u = p2 - p1
v = p3 - p1
"""
n = np.cross(u, v)
one_over_4_area_squared = 1.0 / (n @ n)
w = p - q
b[2] = (np.cross(u, w) @ n) * one_over_4_area_squared
b[1] = (np.cross(w, v) @ n) * one_over_4_area_squared
b[0] = 1.0 - b[1] - b[2]
return b
def project_points_onto_stl(stl, points):
"""
Parameters
----------
nodes : (n, 3) ndarray floats
The nodes on the surface.
elements : (n, 3) ndarray ints
The elements on the surface.
"""
nodes = stl.nodes
elements = stl.elements
if not hasattr(stl, 'centroids'):
n1 = elements[:, 0]
n2 = elements[:, 1]
n3 = elements[:, 2]
p1 = nodes[n1, :]
p2 = nodes[n2, :]
p3 = nodes[n3, :]
centroids = (p1 + p2 + p3) / 3.
stl.centroids = centroids
tree = cKDTree(centroids, leafsize=16, compact_nodes=True,
copy_data=False, balanced_tree=True)
stl.tree = tree
#tree = scipy.spatial.KDTree(data, leafsize=10)
#tree.query_ball_point(x, r, p=2., eps=0)
#tree.query_ball_tree(other, r, p=2., eps=0)
#tree.query_pairs(r, p=2., eps=0)
#tree.sparse_distance_matrix(other, max_distance, p=2.)
tree = stl.tree
#d : array of floats
#The distances to the nearest neighbors.
#If x has shape tuple+(self.m,), then d has shape tuple+(k,).
#Missing neighbors are indicated with infinite distances.
#i : ndarray of ints
#The locations of the neighbors in self.data.
#If `x` has shape tuple+(self.m,), then `i` has shape tuple+(k,).
#Missing neighbors are indicated with self.n.
dist, i = tree.query(points, k=1, eps=0, p=2,
distance_upper_bound=np.inf, n_jobs=1)
# distance from centroid to point, such that we get the element id directly
print('dist =', dist)
print('i =', i)
n1 = elements[i, 0]
n2 = elements[i, 1]
n3 = elements[i, 2]
p1 = nodes[n1, :]
p2 = nodes[n2, :]
p3 = nodes[n3, :]
u = p2 - p1
v = p3 - p1
#w = points_rotated - p1
n = np.cross(u, v)
#n2 = 1 / n**2
#gamma_a = (np.cross(u, w) @ n) / n2
#gamma_b = (np.cross(u, w) @ n) / n2
try:
nmag = np.linalg.norm(n, axis=1)
except ValueError:
print('n.shape =', n.shape)
raise
#area = nmag / 2.
assert nmag.size == n1.size, 'nmag.size=%s n1.size=%s' % (nmag.size, n1.size)
print('n1 =', n1)
print('n2 =', n2)
print('n3 =', n3)
p = points
a = p1
b = p2
c = p3
# http://math.stackexchange.com/questions/544946/determine-if-projection-of-3d-point-onto-plane-is-within-a-triangle
pc = p - c
alpha = np.linalg.norm(np.cross(p - b, pc)) / nmag
beta = np.linalg.norm(np.cross(pc, p - a)) / nmag
gamma = 1 - alpha - beta
#print('alpha =', alpha)
#print('beta =', beta)
#print('gamma =', gamma)
#print('a*p =', alpha[:, np.newaxis] * p1)
p_prime = alpha[:, np.newaxis] * p1 + beta[:, np.newaxis] * p2 + gamma[:, np.newaxis] * p3
#print('p_prime =\n', p_prime)
#tree.query_ball_point(x, r, p=2., eps=0)
#tree.query_ball_tree(other, r, p=2., eps=0)
#tree.query_pairs(r, p=2., eps=0)
#tree.sparse_distance_matrix(other, max_distance, p=2.)
return p_prime
def project_line_onto_stl(stl, pa, pb, npoints=11):
"""top down projection"""
normal = np.array([0., 0., -1.], dtype='float32')
#max_z = nodes[:, 2].max()
#min_z = nodes[:, 2].min()
# TODO: rotate if want a new normal
#dz = max_z - min_z
#dzi = dz / 20.
#points_rotated = points
#out_points = project_points_onto_stl(stl, points)
# TODO: rotate if want a new normal
p = np.linspace(0., 1., num=npoints, endpoint=True)
p21 = pb - pa
ratio = p21 / np.linalg.norm(p21)
print('p =', p)
print('ratio =', ratio)
points = pa + p[:, np.newaxis] * ratio
print('points =', points)
out_points = project_points_onto_stl(stl, points)
return out_points
def project_curve_onto_stl(stl, points, npoints=11):
"""top down projection"""
normal = np.array([0., 0., -1.], dtype='float32')
#max_z = nodes[:, 2].max()
#min_z = nodes[:, 2].min()
# TODO: rotate if want a new normal
#dz = max_z - min_z
#dzi = dz / 20.
#points_rotated = points
#out_points = project_points_onto_stl(stl, points)
# TODO: rotate if want a new normal
# create interpolation curve from points
p2 = points[1:, :]
p1 = points[:-1, :]
dx = np.linalg.norm(p2 - p1, axis=1)
assert dx.size == p1.shape[0]
t = dx.sum()
pa = points[0, :]
dp = points - pa
dx2 = np.linalg.norm(dp, axis=1)
t = dx2.sum()
# http://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.interpolate.interp1d.html
func = scipy.interpolate.interp1d(t, dx2, kind='cubic', axis=-1,
copy=True,
bounds_error=None,
fill_value=np.nan,
assume_sorted=False) # cubic spline
p = np.linspace(0., t, num=npoints, endpoint=True)
t2 = func(p)
dx = func(t2) + pa
#p21 = pb - pa
#ratio = p21 / np.linalg.norm(p21)
#print('p =', p)
#print('ratio =', ratio)
points = pa + dx
print('points =', points)
out_points = project_points_onto_stl(stl, points)
return out_points
def main():
import os
import pyNastran
PKG_PATH = pyNastran.__path__[0]
stl_filename = os.path.join(PKG_PATH, 'converters', 'stl', 'sphere.stl')
stl = read_stl(stl_filename)
#XYZ Global = (2.0035907914418716, 1.3287668328026303, 2.873731014735773)
#NodeID = 142; xyz=(1.88823, 1.5, 2.94889)
#lineNo=2110 annotate_cell_picker()
#XYZ Global = (1.9419959964242275, 1.141259948469464, 2.869267723165781)
#NodeID = 141; xyz=(1.93018, 1.02165, 2.85504)
#lineNo=2110 annotate_cell_picker()
#XYZ Global = (2.1320656653448338, 1.4367816967143772, 2.83778333777658)
#NodeID = 137; xyz=(2.25, 1.5, 2.79904)
# nids = [142, 137, 141]
# 2.0035907914418716, 1.3287668328026303, 2.873731014735773
points = np.array([
[2.0035907914418716, 1.3287668328026303, 2.873731014735773],
[2.25, 1.5, 2.79904],
[2.25, 1.5, 2.79903],
], dtype='float32')
pa = points[0, :]
pb = points[1, :]
out_points = project_points_onto_stl(stl, points)
out_points2 = project_line_onto_stl(stl, pa, pb, npoints=11)
#out_points3 = project_curve_onto_stl(stl, points, npoints=11)
def build():
from pyNastran.bdf.bdf import BDF
model = BDF(debug=False)
xyz1 = [0., 0., 0.]
xyz2 = [0., 1., 0.]
xyz3 = [1., 1., 0.]
xyz4 = [1., 0., 0.]
model.add_grid(1, xyz=xyz1)
model.add_grid(2, xyz=xyz2)
model.add_grid(3, xyz=xyz3)
model.add_grid(4, xyz=xyz4)
model.add_cquad4(eid=1, pid=1000, nids=[1, 2, 3, 4])
model.add_pshell(pid=100, mid1=1000, t=0.1)
model.add_mat1(mid=1000, E=1e7, G=None, nu=0.3)
if __name__ == '__main__':
build()
main() | pyNastran/converters/stl/dev/stl_mesh.py | import numpy as np
from pyNastran.converters.stl.stl import read_stl
from scipy.spatial import cKDTree
import scipy.interpolate
def projected_barycentric_coord(p, q, u, v):
r"""
points p, q
vector u, v
3
*v
/ \ <----p
q*----*u
1 2
u = p2 - p1
v = p3 - p1
"""
n = np.cross(u, v)
one_over_4_area_squared = 1.0 / (n @ n)
w = p - q
b[2] = (np.cross(u, w) @ n) * one_over_4_area_squared
b[1] = (np.cross(w, v) @ n) * one_over_4_area_squared
b[0] = 1.0 - b[1] - b[2]
return b
def project_points_onto_stl(stl, points):
"""
Parameters
----------
nodes : (n, 3) ndarray floats
The nodes on the surface.
elements : (n, 3) ndarray ints
The elements on the surface.
"""
nodes = stl.nodes
elements = stl.elements
if not hasattr(stl, 'centroids'):
n1 = elements[:, 0]
n2 = elements[:, 1]
n3 = elements[:, 2]
p1 = nodes[n1, :]
p2 = nodes[n2, :]
p3 = nodes[n3, :]
centroids = (p1 + p2 + p3) / 3.
stl.centroids = centroids
tree = cKDTree(centroids, leafsize=16, compact_nodes=True,
copy_data=False, balanced_tree=True)
stl.tree = tree
#tree = scipy.spatial.KDTree(data, leafsize=10)
#tree.query_ball_point(x, r, p=2., eps=0)
#tree.query_ball_tree(other, r, p=2., eps=0)
#tree.query_pairs(r, p=2., eps=0)
#tree.sparse_distance_matrix(other, max_distance, p=2.)
tree = stl.tree
#d : array of floats
#The distances to the nearest neighbors.
#If x has shape tuple+(self.m,), then d has shape tuple+(k,).
#Missing neighbors are indicated with infinite distances.
#i : ndarray of ints
#The locations of the neighbors in self.data.
#If `x` has shape tuple+(self.m,), then `i` has shape tuple+(k,).
#Missing neighbors are indicated with self.n.
dist, i = tree.query(points, k=1, eps=0, p=2,
distance_upper_bound=np.inf, n_jobs=1)
# distance from centroid to point, such that we get the element id directly
print('dist =', dist)
print('i =', i)
n1 = elements[i, 0]
n2 = elements[i, 1]
n3 = elements[i, 2]
p1 = nodes[n1, :]
p2 = nodes[n2, :]
p3 = nodes[n3, :]
u = p2 - p1
v = p3 - p1
#w = points_rotated - p1
n = np.cross(u, v)
#n2 = 1 / n**2
#gamma_a = (np.cross(u, w) @ n) / n2
#gamma_b = (np.cross(u, w) @ n) / n2
try:
nmag = np.linalg.norm(n, axis=1)
except ValueError:
print('n.shape =', n.shape)
raise
#area = nmag / 2.
assert nmag.size == n1.size, 'nmag.size=%s n1.size=%s' % (nmag.size, n1.size)
print('n1 =', n1)
print('n2 =', n2)
print('n3 =', n3)
p = points
a = p1
b = p2
c = p3
# http://math.stackexchange.com/questions/544946/determine-if-projection-of-3d-point-onto-plane-is-within-a-triangle
pc = p - c
alpha = np.linalg.norm(np.cross(p - b, pc)) / nmag
beta = np.linalg.norm(np.cross(pc, p - a)) / nmag
gamma = 1 - alpha - beta
#print('alpha =', alpha)
#print('beta =', beta)
#print('gamma =', gamma)
#print('a*p =', alpha[:, np.newaxis] * p1)
p_prime = alpha[:, np.newaxis] * p1 + beta[:, np.newaxis] * p2 + gamma[:, np.newaxis] * p3
#print('p_prime =\n', p_prime)
#tree.query_ball_point(x, r, p=2., eps=0)
#tree.query_ball_tree(other, r, p=2., eps=0)
#tree.query_pairs(r, p=2., eps=0)
#tree.sparse_distance_matrix(other, max_distance, p=2.)
return p_prime
def project_line_onto_stl(stl, pa, pb, npoints=11):
"""top down projection"""
normal = np.array([0., 0., -1.], dtype='float32')
#max_z = nodes[:, 2].max()
#min_z = nodes[:, 2].min()
# TODO: rotate if want a new normal
#dz = max_z - min_z
#dzi = dz / 20.
#points_rotated = points
#out_points = project_points_onto_stl(stl, points)
# TODO: rotate if want a new normal
p = np.linspace(0., 1., num=npoints, endpoint=True)
p21 = pb - pa
ratio = p21 / np.linalg.norm(p21)
print('p =', p)
print('ratio =', ratio)
points = pa + p[:, np.newaxis] * ratio
print('points =', points)
out_points = project_points_onto_stl(stl, points)
return out_points
def project_curve_onto_stl(stl, points, npoints=11):
"""top down projection"""
normal = np.array([0., 0., -1.], dtype='float32')
#max_z = nodes[:, 2].max()
#min_z = nodes[:, 2].min()
# TODO: rotate if want a new normal
#dz = max_z - min_z
#dzi = dz / 20.
#points_rotated = points
#out_points = project_points_onto_stl(stl, points)
# TODO: rotate if want a new normal
# create interpolation curve from points
p2 = points[1:, :]
p1 = points[:-1, :]
dx = np.linalg.norm(p2 - p1, axis=1)
assert dx.size == p1.shape[0]
t = dx.sum()
pa = points[0, :]
dp = points - pa
dx2 = np.linalg.norm(dp, axis=1)
t = dx2.sum()
# http://docs.scipy.org/doc/scipy-0.17.0/reference/generated/scipy.interpolate.interp1d.html
func = scipy.interpolate.interp1d(t, dx2, kind='cubic', axis=-1,
copy=True,
bounds_error=None,
fill_value=np.nan,
assume_sorted=False) # cubic spline
p = np.linspace(0., t, num=npoints, endpoint=True)
t2 = func(p)
dx = func(t2) + pa
#p21 = pb - pa
#ratio = p21 / np.linalg.norm(p21)
#print('p =', p)
#print('ratio =', ratio)
points = pa + dx
print('points =', points)
out_points = project_points_onto_stl(stl, points)
return out_points
def main():
import os
import pyNastran
PKG_PATH = pyNastran.__path__[0]
stl_filename = os.path.join(PKG_PATH, 'converters', 'stl', 'sphere.stl')
stl = read_stl(stl_filename)
#XYZ Global = (2.0035907914418716, 1.3287668328026303, 2.873731014735773)
#NodeID = 142; xyz=(1.88823, 1.5, 2.94889)
#lineNo=2110 annotate_cell_picker()
#XYZ Global = (1.9419959964242275, 1.141259948469464, 2.869267723165781)
#NodeID = 141; xyz=(1.93018, 1.02165, 2.85504)
#lineNo=2110 annotate_cell_picker()
#XYZ Global = (2.1320656653448338, 1.4367816967143772, 2.83778333777658)
#NodeID = 137; xyz=(2.25, 1.5, 2.79904)
# nids = [142, 137, 141]
# 2.0035907914418716, 1.3287668328026303, 2.873731014735773
points = np.array([
[2.0035907914418716, 1.3287668328026303, 2.873731014735773],
[2.25, 1.5, 2.79904],
[2.25, 1.5, 2.79903],
], dtype='float32')
pa = points[0, :]
pb = points[1, :]
out_points = project_points_onto_stl(stl, points)
out_points2 = project_line_onto_stl(stl, pa, pb, npoints=11)
#out_points3 = project_curve_onto_stl(stl, points, npoints=11)
def build():
from pyNastran.bdf.bdf import BDF
model = BDF(debug=False)
xyz1 = [0., 0., 0.]
xyz2 = [0., 1., 0.]
xyz3 = [1., 1., 0.]
xyz4 = [1., 0., 0.]
model.add_grid(1, xyz=xyz1)
model.add_grid(2, xyz=xyz2)
model.add_grid(3, xyz=xyz3)
model.add_grid(4, xyz=xyz4)
model.add_cquad4(eid=1, pid=1000, nids=[1, 2, 3, 4])
model.add_pshell(pid=100, mid1=1000, t=0.1)
model.add_mat1(mid=1000, E=1e7, G=None, nu=0.3)
if __name__ == '__main__':
build()
main() | 0.611614 | 0.49823 |
from torch.testing._internal.common_utils import (
TestCase, run_tests,
)
from datetime import timedelta, datetime
import tempfile
import time
from torch.monitor import (
Aggregation,
Event,
log_event,
register_event_handler,
unregister_event_handler,
Stat,
TensorboardEventHandler,
)
class TestMonitor(TestCase):
def test_interval_stat(self) -> None:
events = []
def handler(event):
events.append(event)
handle = register_event_handler(handler)
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(milliseconds=1),
)
self.assertEqual(s.name, "asdf")
s.add(2)
for _ in range(100):
# NOTE: different platforms sleep may be inaccurate so we loop
# instead (i.e. win)
time.sleep(1 / 1000) # ms
s.add(3)
if len(events) >= 1:
break
self.assertGreaterEqual(len(events), 1)
unregister_event_handler(handle)
def test_fixed_count_stat(self) -> None:
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(hours=100),
3,
)
s.add(1)
s.add(2)
name = s.name
self.assertEqual(name, "asdf")
self.assertEqual(s.count, 2)
s.add(3)
self.assertEqual(s.count, 0)
self.assertEqual(s.get(), {Aggregation.SUM: 6.0, Aggregation.COUNT: 3})
def test_log_event(self) -> None:
e = Event(
name="torch.monitor.TestEvent",
timestamp=datetime.now(),
data={
"str": "a string",
"float": 1234.0,
"int": 1234,
},
)
self.assertEqual(e.name, "torch.monitor.TestEvent")
self.assertIsNotNone(e.timestamp)
self.assertIsNotNone(e.data)
log_event(e)
def test_event_handler(self) -> None:
events = []
def handler(event: Event) -> None:
events.append(event)
handle = register_event_handler(handler)
e = Event(
name="torch.monitor.TestEvent",
timestamp=datetime.now(),
data={},
)
log_event(e)
self.assertEqual(len(events), 1)
self.assertEqual(events[0], e)
log_event(e)
self.assertEqual(len(events), 2)
unregister_event_handler(handle)
log_event(e)
self.assertEqual(len(events), 2)
class TestMonitorTensorboard(TestCase):
def setUp(self):
global SummaryWriter, event_multiplexer
try:
from torch.utils.tensorboard import SummaryWriter
from tensorboard.backend.event_processing import (
plugin_event_multiplexer as event_multiplexer,
)
except ImportError:
return self.skipTest("Skip the test since TensorBoard is not installed")
self.temp_dirs = []
def create_summary_writer(self):
temp_dir = tempfile.TemporaryDirectory() # noqa: P201
self.temp_dirs.append(temp_dir)
return SummaryWriter(temp_dir.name)
def tearDown(self):
# Remove directories created by SummaryWriter
for temp_dir in self.temp_dirs:
temp_dir.cleanup()
def test_event_handler(self):
with self.create_summary_writer() as w:
handle = register_event_handler(TensorboardEventHandler(w))
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(hours=1),
5,
)
for i in range(10):
s.add(i)
self.assertEqual(s.count, 0)
unregister_event_handler(handle)
mul = event_multiplexer.EventMultiplexer()
mul.AddRunsFromDirectory(self.temp_dirs[-1].name)
mul.Reload()
scalar_dict = mul.PluginRunToTagToContent("scalars")
raw_result = {
tag: mul.Tensors(run, tag)
for run, run_dict in scalar_dict.items()
for tag in run_dict
}
scalars = {
tag: [e.tensor_proto.float_val[0] for e in events] for tag, events in raw_result.items()
}
self.assertEqual(scalars, {
"asdf.sum": [10],
"asdf.count": [5],
})
if __name__ == '__main__':
run_tests() | test/test_monitor.py |
from torch.testing._internal.common_utils import (
TestCase, run_tests,
)
from datetime import timedelta, datetime
import tempfile
import time
from torch.monitor import (
Aggregation,
Event,
log_event,
register_event_handler,
unregister_event_handler,
Stat,
TensorboardEventHandler,
)
class TestMonitor(TestCase):
def test_interval_stat(self) -> None:
events = []
def handler(event):
events.append(event)
handle = register_event_handler(handler)
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(milliseconds=1),
)
self.assertEqual(s.name, "asdf")
s.add(2)
for _ in range(100):
# NOTE: different platforms sleep may be inaccurate so we loop
# instead (i.e. win)
time.sleep(1 / 1000) # ms
s.add(3)
if len(events) >= 1:
break
self.assertGreaterEqual(len(events), 1)
unregister_event_handler(handle)
def test_fixed_count_stat(self) -> None:
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(hours=100),
3,
)
s.add(1)
s.add(2)
name = s.name
self.assertEqual(name, "asdf")
self.assertEqual(s.count, 2)
s.add(3)
self.assertEqual(s.count, 0)
self.assertEqual(s.get(), {Aggregation.SUM: 6.0, Aggregation.COUNT: 3})
def test_log_event(self) -> None:
e = Event(
name="torch.monitor.TestEvent",
timestamp=datetime.now(),
data={
"str": "a string",
"float": 1234.0,
"int": 1234,
},
)
self.assertEqual(e.name, "torch.monitor.TestEvent")
self.assertIsNotNone(e.timestamp)
self.assertIsNotNone(e.data)
log_event(e)
def test_event_handler(self) -> None:
events = []
def handler(event: Event) -> None:
events.append(event)
handle = register_event_handler(handler)
e = Event(
name="torch.monitor.TestEvent",
timestamp=datetime.now(),
data={},
)
log_event(e)
self.assertEqual(len(events), 1)
self.assertEqual(events[0], e)
log_event(e)
self.assertEqual(len(events), 2)
unregister_event_handler(handle)
log_event(e)
self.assertEqual(len(events), 2)
class TestMonitorTensorboard(TestCase):
def setUp(self):
global SummaryWriter, event_multiplexer
try:
from torch.utils.tensorboard import SummaryWriter
from tensorboard.backend.event_processing import (
plugin_event_multiplexer as event_multiplexer,
)
except ImportError:
return self.skipTest("Skip the test since TensorBoard is not installed")
self.temp_dirs = []
def create_summary_writer(self):
temp_dir = tempfile.TemporaryDirectory() # noqa: P201
self.temp_dirs.append(temp_dir)
return SummaryWriter(temp_dir.name)
def tearDown(self):
# Remove directories created by SummaryWriter
for temp_dir in self.temp_dirs:
temp_dir.cleanup()
def test_event_handler(self):
with self.create_summary_writer() as w:
handle = register_event_handler(TensorboardEventHandler(w))
s = Stat(
"asdf",
(Aggregation.SUM, Aggregation.COUNT),
timedelta(hours=1),
5,
)
for i in range(10):
s.add(i)
self.assertEqual(s.count, 0)
unregister_event_handler(handle)
mul = event_multiplexer.EventMultiplexer()
mul.AddRunsFromDirectory(self.temp_dirs[-1].name)
mul.Reload()
scalar_dict = mul.PluginRunToTagToContent("scalars")
raw_result = {
tag: mul.Tensors(run, tag)
for run, run_dict in scalar_dict.items()
for tag in run_dict
}
scalars = {
tag: [e.tensor_proto.float_val[0] for e in events] for tag, events in raw_result.items()
}
self.assertEqual(scalars, {
"asdf.sum": [10],
"asdf.count": [5],
})
if __name__ == '__main__':
run_tests() | 0.698124 | 0.454714 |
import sys
import tensorflow as tf
sys.path.append("..")
from model_utils import bi_rnn_encoder
class Content_Enc_Model(object):
"""
content encoder model (pretrained as a character recognizer), input format-3
"""
def __init__(self,
init_lr = 0.001,
batch_size=1024,
max_seq_len=100,
class_num=3755,
rnn_size=256,
rnn_layers=1,
fc_hidden_num = 256,
input_keep_prob = 1.0,
output_keep_prob = 1.0,
state_keep_prob = 1.0,
fc_keep_prob = 1.0,
is_training=True,
reuse=tf.AUTO_REUSE):
self.init_lr = init_lr
self.batch_size = batch_size
self.max_seq_len = max_seq_len
self.class_num = class_num
self.rnn_size = rnn_size
self.rnn_layers = rnn_layers
self.fc_hidden_num = fc_hidden_num
self.input_keep_prob = input_keep_prob
self.output_keep_prob = output_keep_prob
self.state_keep_prob = state_keep_prob
self.fc_keep_prob = fc_keep_prob
self.is_training = is_training
self.build_model(reuse=reuse)
def build_model(self, reuse=tf.AUTO_REUSE):
"""Define model architecture."""
self.input_data = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.max_seq_len, 3])
self.seq_lens = tf.placeholder(dtype=tf.int32, shape=[self.batch_size])
self.labels = tf.placeholder(dtype=tf.int32, shape=[self.batch_size])
# encode
all_h, self.last_h = bi_rnn_encoder(
self.input_data,
self.seq_lens,
layers=self.rnn_layers,
rnn_size=self.rnn_size,
cell_fn = tf.contrib.rnn.LSTMCell,
# cell_fn = tf.contrib.rnn.IndyLSTMCell,
input_keep_prob = self.input_keep_prob,
output_keep_prob = self.output_keep_prob,
state_keep_prob = self.state_keep_prob,
scope="Content_Encoder",
reuse=reuse)
with tf.variable_scope("classification", reuse=reuse):
fc_hidden = tf.layers.Dense(self.fc_hidden_num, name='fc_hidden')
fc_output = tf.layers.Dense(self.class_num, name='fc_output')
cls_logits = fc_output(tf.layers.dropout(
fc_hidden(self.last_h),
rate=1.0 - self.fc_keep_prob))
onehot_labels = tf.one_hot(self.labels, self.class_num)
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
labels=onehot_labels, logits=cls_logits))
self.acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(cls_logits, axis=1, output_type=tf.int32),
self.labels), dtype=tf.float32))
if self.is_training:
with tf.variable_scope("optimizer", reuse=reuse):
self.lr = tf.Variable(self.init_lr, trainable=False)
optimizer = tf.train.AdamOptimizer(self.lr)
gvs = optimizer.compute_gradients(self.loss)
g = 1.0 # grad_clip
capped_gvs = [(tf.clip_by_value(grad, -g, g), var) for grad, var in gvs if grad is not None]
self.train_op = optimizer.apply_gradients(capped_gvs)
with tf.name_scope("summary"):
loss_summ = tf.summary.scalar("loss", self.loss)
acc_summ = tf.summary.scalar("accuracy", self.acc)
self.summ = tf.summary.merge([loss_summ, acc_summ])
else:
assert self.input_keep_prob == 1.0
assert self.output_keep_prob == 1.0
assert self.state_keep_prob == 1.0
assert self.fc_keep_prob == 1.0 | pretrain_content_enc/content_enc_model.py | import sys
import tensorflow as tf
sys.path.append("..")
from model_utils import bi_rnn_encoder
class Content_Enc_Model(object):
"""
content encoder model (pretrained as a character recognizer), input format-3
"""
def __init__(self,
init_lr = 0.001,
batch_size=1024,
max_seq_len=100,
class_num=3755,
rnn_size=256,
rnn_layers=1,
fc_hidden_num = 256,
input_keep_prob = 1.0,
output_keep_prob = 1.0,
state_keep_prob = 1.0,
fc_keep_prob = 1.0,
is_training=True,
reuse=tf.AUTO_REUSE):
self.init_lr = init_lr
self.batch_size = batch_size
self.max_seq_len = max_seq_len
self.class_num = class_num
self.rnn_size = rnn_size
self.rnn_layers = rnn_layers
self.fc_hidden_num = fc_hidden_num
self.input_keep_prob = input_keep_prob
self.output_keep_prob = output_keep_prob
self.state_keep_prob = state_keep_prob
self.fc_keep_prob = fc_keep_prob
self.is_training = is_training
self.build_model(reuse=reuse)
def build_model(self, reuse=tf.AUTO_REUSE):
"""Define model architecture."""
self.input_data = tf.placeholder(dtype=tf.float32, shape=[self.batch_size, self.max_seq_len, 3])
self.seq_lens = tf.placeholder(dtype=tf.int32, shape=[self.batch_size])
self.labels = tf.placeholder(dtype=tf.int32, shape=[self.batch_size])
# encode
all_h, self.last_h = bi_rnn_encoder(
self.input_data,
self.seq_lens,
layers=self.rnn_layers,
rnn_size=self.rnn_size,
cell_fn = tf.contrib.rnn.LSTMCell,
# cell_fn = tf.contrib.rnn.IndyLSTMCell,
input_keep_prob = self.input_keep_prob,
output_keep_prob = self.output_keep_prob,
state_keep_prob = self.state_keep_prob,
scope="Content_Encoder",
reuse=reuse)
with tf.variable_scope("classification", reuse=reuse):
fc_hidden = tf.layers.Dense(self.fc_hidden_num, name='fc_hidden')
fc_output = tf.layers.Dense(self.class_num, name='fc_output')
cls_logits = fc_output(tf.layers.dropout(
fc_hidden(self.last_h),
rate=1.0 - self.fc_keep_prob))
onehot_labels = tf.one_hot(self.labels, self.class_num)
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
labels=onehot_labels, logits=cls_logits))
self.acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(cls_logits, axis=1, output_type=tf.int32),
self.labels), dtype=tf.float32))
if self.is_training:
with tf.variable_scope("optimizer", reuse=reuse):
self.lr = tf.Variable(self.init_lr, trainable=False)
optimizer = tf.train.AdamOptimizer(self.lr)
gvs = optimizer.compute_gradients(self.loss)
g = 1.0 # grad_clip
capped_gvs = [(tf.clip_by_value(grad, -g, g), var) for grad, var in gvs if grad is not None]
self.train_op = optimizer.apply_gradients(capped_gvs)
with tf.name_scope("summary"):
loss_summ = tf.summary.scalar("loss", self.loss)
acc_summ = tf.summary.scalar("accuracy", self.acc)
self.summ = tf.summary.merge([loss_summ, acc_summ])
else:
assert self.input_keep_prob == 1.0
assert self.output_keep_prob == 1.0
assert self.state_keep_prob == 1.0
assert self.fc_keep_prob == 1.0 | 0.682679 | 0.206014 |
import psycopg2
import metautils
from dbsettings import settings
metautils.setsettings(settings)
print '\nMarking all Bonn Google data as rejected (needs to be changed if Google searches are ever resumed!'
cur = metautils.getDBCursor(settings, dictCursor = True)
cur.execute('update data set accepted = %s where city = %s and source = %s', (False,'bonn','g'))
metautils.dbCommit()
print '\nResetting open...'
cur = metautils.getDBCursor(settings, dictCursor = True)
cur.execute('select url, licenseshort from data')
for ores in cur.fetchall():
if ores['licenseshort'].strip() == '':
license = 'nicht bekannt'
open = None
else:
open = metautils.isopen(ores['licenseshort'].strip())
license = ores['licenseshort'].strip()
cur.execute('update data set licenseshort = %s, open = %s where url = %s', (license, open, ores['url']))
metautils.dbCommit()
print 'Finding cities with data...'
cities = metautils.getCitiesWithData()
print cities
print '\nRemoving search machine data that has been found with own crawler...'
for city in cities:
cur = metautils.getDBCursor(settings, dictCursor = True)
#Get all Google and Bing data to see if the files have also been found by crawling
cur.execute('SELECT source, url FROM data WHERE city LIKE %s AND (source = %s OR source = %s) AND accepted = %s', (city,'b','g', True))
gbres = cur.fetchall()
cur.execute('SELECT filelist FROM data WHERE city LIKE %s AND source = %s OR source = %s AND array_length(filelist,1)>0', (city,'c','d'))
allfiles = [t[0].split('/')[-1].lower() for t in [f for f in [res['filelist'] for res in cur.fetchall()]]]
for result in gbres:
if result['url'].split('/')[-1].lower() in allfiles:
print 'Excluding ' + result['url'] + ' from results (source: ' + result['source'] + ').'
cur.execute('UPDATE data SET accepted = %s, checked = %s WHERE url LIKE %s', (False, True, result['url']))
metautils.dbCommit()
print '\nRemoving cities with no data that are not part of the original database...'
cur = metautils.getDBCursor(settings)
cur.execute('DELETE FROM cities WHERE city_shortname IN (SELECT cities.city_shortname FROM cities LEFT JOIN data ON data.city = cities.city_shortname WHERE data.city IS NULL AND cities.city_type IS NULL)')
metautils.dbCommit()
print '\nRemoving search machine and crawl data that is from the data catalog...'
cur = metautils.getDBCursor(settings, dictCursor = True)
#Get all portals
cur.execute('SELECT city_shortname, open_data_portal, odp_alias FROM cities WHERE catalog_read = %s', (True,))
citieswithportals = cur.fetchall()
for result in citieswithportals:
cur = metautils.getDBCursor(settings, dictCursor = True)
city = result['city_shortname']
print city
cur.execute('SELECT url FROM data WHERE city LIKE %s AND source = %s OR source = %s OR source = %s AND accepted = %s', (city,'b','g','c',True))
citydata = cur.fetchall()
#Now that we've done that, test all c/b/g results to see if they contain a data portal url or alias, and exclude accordingly
completeportals = []
if result['odp_alias'] != None:
completeportals.extend(result['odp_alias'])
completeportals.append(result['open_data_portal'])
else:
print result['open_data_portal']
completeportals = (result['open_data_portal'],)
print completeportals
completeportalssimplified = []
for portal in completeportals:
if 'http://' in portal:
completeportalssimplified.append(portal[7:len(portal)].strip())
print 'Excluding ' + portal[7:len(portal)].strip() + ' from ' + city
else:
completeportalssimplified.append(portal.strip())
print 'Excluding ' + portal.strip() + ' from ' + city
for entry in citydata:
if any(x in entry['url'] for x in completeportalssimplified):
print 'Excluding ' + entry['url'] + ' from ' + city + ' results'
cur.execute('UPDATE data SET accepted = %s, checked = %s WHERE url LIKE %s', (False, True, entry['url']))
metautils.dbCommit()
print '\nChecking if all cities with data have coordinates...'
metautils.updateCitiesWithLatLong()
print '\nTransferring old categories...'
metautils.convertOldCategories()
print '\nRemoving unknown categories...'
metautils.removeUnknownCategories() | utils/consistentDb.py | import psycopg2
import metautils
from dbsettings import settings
metautils.setsettings(settings)
print '\nMarking all Bonn Google data as rejected (needs to be changed if Google searches are ever resumed!'
cur = metautils.getDBCursor(settings, dictCursor = True)
cur.execute('update data set accepted = %s where city = %s and source = %s', (False,'bonn','g'))
metautils.dbCommit()
print '\nResetting open...'
cur = metautils.getDBCursor(settings, dictCursor = True)
cur.execute('select url, licenseshort from data')
for ores in cur.fetchall():
if ores['licenseshort'].strip() == '':
license = 'nicht bekannt'
open = None
else:
open = metautils.isopen(ores['licenseshort'].strip())
license = ores['licenseshort'].strip()
cur.execute('update data set licenseshort = %s, open = %s where url = %s', (license, open, ores['url']))
metautils.dbCommit()
print 'Finding cities with data...'
cities = metautils.getCitiesWithData()
print cities
print '\nRemoving search machine data that has been found with own crawler...'
for city in cities:
cur = metautils.getDBCursor(settings, dictCursor = True)
#Get all Google and Bing data to see if the files have also been found by crawling
cur.execute('SELECT source, url FROM data WHERE city LIKE %s AND (source = %s OR source = %s) AND accepted = %s', (city,'b','g', True))
gbres = cur.fetchall()
cur.execute('SELECT filelist FROM data WHERE city LIKE %s AND source = %s OR source = %s AND array_length(filelist,1)>0', (city,'c','d'))
allfiles = [t[0].split('/')[-1].lower() for t in [f for f in [res['filelist'] for res in cur.fetchall()]]]
for result in gbres:
if result['url'].split('/')[-1].lower() in allfiles:
print 'Excluding ' + result['url'] + ' from results (source: ' + result['source'] + ').'
cur.execute('UPDATE data SET accepted = %s, checked = %s WHERE url LIKE %s', (False, True, result['url']))
metautils.dbCommit()
print '\nRemoving cities with no data that are not part of the original database...'
cur = metautils.getDBCursor(settings)
cur.execute('DELETE FROM cities WHERE city_shortname IN (SELECT cities.city_shortname FROM cities LEFT JOIN data ON data.city = cities.city_shortname WHERE data.city IS NULL AND cities.city_type IS NULL)')
metautils.dbCommit()
print '\nRemoving search machine and crawl data that is from the data catalog...'
cur = metautils.getDBCursor(settings, dictCursor = True)
#Get all portals
cur.execute('SELECT city_shortname, open_data_portal, odp_alias FROM cities WHERE catalog_read = %s', (True,))
citieswithportals = cur.fetchall()
for result in citieswithportals:
cur = metautils.getDBCursor(settings, dictCursor = True)
city = result['city_shortname']
print city
cur.execute('SELECT url FROM data WHERE city LIKE %s AND source = %s OR source = %s OR source = %s AND accepted = %s', (city,'b','g','c',True))
citydata = cur.fetchall()
#Now that we've done that, test all c/b/g results to see if they contain a data portal url or alias, and exclude accordingly
completeportals = []
if result['odp_alias'] != None:
completeportals.extend(result['odp_alias'])
completeportals.append(result['open_data_portal'])
else:
print result['open_data_portal']
completeportals = (result['open_data_portal'],)
print completeportals
completeportalssimplified = []
for portal in completeportals:
if 'http://' in portal:
completeportalssimplified.append(portal[7:len(portal)].strip())
print 'Excluding ' + portal[7:len(portal)].strip() + ' from ' + city
else:
completeportalssimplified.append(portal.strip())
print 'Excluding ' + portal.strip() + ' from ' + city
for entry in citydata:
if any(x in entry['url'] for x in completeportalssimplified):
print 'Excluding ' + entry['url'] + ' from ' + city + ' results'
cur.execute('UPDATE data SET accepted = %s, checked = %s WHERE url LIKE %s', (False, True, entry['url']))
metautils.dbCommit()
print '\nChecking if all cities with data have coordinates...'
metautils.updateCitiesWithLatLong()
print '\nTransferring old categories...'
metautils.convertOldCategories()
print '\nRemoving unknown categories...'
metautils.removeUnknownCategories() | 0.128006 | 0.122392 |
import random
from rest_framework import serializers
from .payments import flutter_wave
from django.contrib.auth.models import update_last_login
from .models import Wallet, TransactionHistory
from django.contrib.auth import get_user_model, authenticate
from rest_framework_jwt.settings import api_settings
from django.shortcuts import get_object_or_404
from decimal import Decimal
User = get_user_model()
JWT_PAYLOAD_HANDLER = api_settings.JWT_PAYLOAD_HANDLER
JWT_ENCODE_HANDLER = api_settings.JWT_ENCODE_HANDLER
class SignUpSerializer(serializers.ModelSerializer):
password = serializers.CharField(max_length=128, min_length=8, write_only=True)
wallet = serializers.CharField(read_only=True)
class Meta:
model = User
fields = ('email', 'password', 'wallet',)
def create_no(self):
new_num = str(random.randint(7500000001, 7599999999))
try:
existing_num = Wallet.objects.get(account_no=new_num)
return self.create_no()
except:
return new_num
def create(self, validated_data):
user = User.objects.create_user(**validated_data)
wallet = Wallet.objects.create(owner=user, account_no=self.create_no())
return {'email': user.email, 'wallet': wallet }
class UserLoginSerializer(serializers.Serializer):
email = serializers.CharField(max_length=255)
password = serializers.CharField(max_length=128, write_only=True)
token = serializers.CharField(max_length=255, read_only=True)
def validate(self, data):
email = data.get("email", None)
password = data.get("password", None)
user = authenticate(email=email, password=password)
if user is None:
raise serializers.ValidationError(
'A user with this email and password is not found.'
)
try:
payload = JWT_PAYLOAD_HANDLER(user)
jwt_token = JWT_ENCODE_HANDLER(payload)
update_last_login(None, user)
except User.DoesNotExist:
raise serializers.ValidationError(
'User with given email and password does not exists'
)
return {
'email':user.email,
'token': jwt_token
}
class P2PTransferSerializer(serializers.Serializer):
recipient = serializers.EmailField(write_only=True)
amount = serializers.DecimalField(max_digits=10, decimal_places=2, write_only=True)
detail = serializers.CharField(write_only=True)
message = serializers.CharField(read_only=True)
status = serializers.CharField(read_only=True)
balance = serializers.DecimalField(max_digits=10, decimal_places=2, read_only=True)
def transfer(self, sender, recipient, amount, detail):
sending_wallet = Wallet.objects.get(owner=sender)
get_user = get_object_or_404(User, email=recipient)
recipient_wallet = Wallet.objects.get(owner = get_user)
if recipient_wallet == sending_wallet:
return {
'status' : 'error',
'message' : 'You cannot transfer money to your self',
}
if amount > sending_wallet.balance:
return {
'status' : 'error',
'message' : 'You do not have enough balance to perform this transaction',
}
if amount <= 0:
return {
'status' : 'error',
'message' : 'Enter a Valid Amount',
}
sending_wallet.balance -= amount
recipient_wallet.balance += amount
sending_wallet.save()
recipient_wallet.save()
TransactionHistory.objects.create(sender=sending_wallet, trans_type=TransactionHistory.DEBIT, amount=amount, recipient=recipient_wallet, details=detail )
TransactionHistory.objects.create(sender=sending_wallet, trans_type=TransactionHistory.CREDIT, amount=amount, recipient=recipient_wallet, details=detail )
return {
'status' : 'success',
'message' : 'Transfer successful',
'balance' : sending_wallet.balance
}
def validate(self, data):
recipient = data.get("recipient", None)
amount = data.get("amount", None)
detail = data.get("detail", None)
sender = self.context['request'].user
transfer = self.transfer(sender, recipient, amount, detail)
if transfer['status'] == 'success':
return {
'status' : transfer['status'],
'message' : transfer['message'],
'balance' : transfer['balance']
}
else:
return {
'status' : transfer['status'],
'message' : transfer['message']
}
class FundWalletSerializer(serializers.Serializer):
amount = serializers.DecimalField(max_digits=10, decimal_places=2, write_only=True)
message = serializers.CharField(read_only=True)
status = serializers.CharField(read_only=True)
balance = serializers.DecimalField(max_digits=10, decimal_places=2,read_only=True)
def fund_wallet(self, amount, recipient):
recipient_wallet = Wallet.objects.get(owner=recipient)
recipient_wallet.balance += amount
recipient_wallet.save()
TransactionHistory.objects.create(sender=recipient_wallet, trans_type=TransactionHistory.FUND_WALLET, amount=amount, recipient=recipient_wallet, details='Fund Wallet')
return {
'status' : 'success',
'message' : 'Wallet Funded',
'balance' : recipient_wallet.balance
}
def validate(self, data):
amount = data.get("amount", None)
recipient= self.context['request'].user
payment = flutter_wave(amount, recipient.email)
if payment["status"] == "error":
return {
'status': payment["status"],
'message': payment["error"]["errMsg"]
}
if payment["status"] == "success":
amount = Decimal(payment["success"]["amount"])
fund_wallet = self.fund_wallet(amount, recipient)
return {
'status' : 'success',
'message' : fund_wallet['message'],
'balance' : fund_wallet['balance']
}
class TransactionHistorySerializer(serializers.ModelSerializer):
sender = serializers.SlugRelatedField(slug_field='account_no', read_only=True)
recipient = serializers.SlugRelatedField(slug_field='account_no', read_only=True)
class Meta:
model = TransactionHistory
fields = ('__all__') | Wallets/serializers.py | import random
from rest_framework import serializers
from .payments import flutter_wave
from django.contrib.auth.models import update_last_login
from .models import Wallet, TransactionHistory
from django.contrib.auth import get_user_model, authenticate
from rest_framework_jwt.settings import api_settings
from django.shortcuts import get_object_or_404
from decimal import Decimal
User = get_user_model()
JWT_PAYLOAD_HANDLER = api_settings.JWT_PAYLOAD_HANDLER
JWT_ENCODE_HANDLER = api_settings.JWT_ENCODE_HANDLER
class SignUpSerializer(serializers.ModelSerializer):
password = serializers.CharField(max_length=128, min_length=8, write_only=True)
wallet = serializers.CharField(read_only=True)
class Meta:
model = User
fields = ('email', 'password', 'wallet',)
def create_no(self):
new_num = str(random.randint(7500000001, 7599999999))
try:
existing_num = Wallet.objects.get(account_no=new_num)
return self.create_no()
except:
return new_num
def create(self, validated_data):
user = User.objects.create_user(**validated_data)
wallet = Wallet.objects.create(owner=user, account_no=self.create_no())
return {'email': user.email, 'wallet': wallet }
class UserLoginSerializer(serializers.Serializer):
email = serializers.CharField(max_length=255)
password = serializers.CharField(max_length=128, write_only=True)
token = serializers.CharField(max_length=255, read_only=True)
def validate(self, data):
email = data.get("email", None)
password = data.get("password", None)
user = authenticate(email=email, password=password)
if user is None:
raise serializers.ValidationError(
'A user with this email and password is not found.'
)
try:
payload = JWT_PAYLOAD_HANDLER(user)
jwt_token = JWT_ENCODE_HANDLER(payload)
update_last_login(None, user)
except User.DoesNotExist:
raise serializers.ValidationError(
'User with given email and password does not exists'
)
return {
'email':user.email,
'token': jwt_token
}
class P2PTransferSerializer(serializers.Serializer):
recipient = serializers.EmailField(write_only=True)
amount = serializers.DecimalField(max_digits=10, decimal_places=2, write_only=True)
detail = serializers.CharField(write_only=True)
message = serializers.CharField(read_only=True)
status = serializers.CharField(read_only=True)
balance = serializers.DecimalField(max_digits=10, decimal_places=2, read_only=True)
def transfer(self, sender, recipient, amount, detail):
sending_wallet = Wallet.objects.get(owner=sender)
get_user = get_object_or_404(User, email=recipient)
recipient_wallet = Wallet.objects.get(owner = get_user)
if recipient_wallet == sending_wallet:
return {
'status' : 'error',
'message' : 'You cannot transfer money to your self',
}
if amount > sending_wallet.balance:
return {
'status' : 'error',
'message' : 'You do not have enough balance to perform this transaction',
}
if amount <= 0:
return {
'status' : 'error',
'message' : 'Enter a Valid Amount',
}
sending_wallet.balance -= amount
recipient_wallet.balance += amount
sending_wallet.save()
recipient_wallet.save()
TransactionHistory.objects.create(sender=sending_wallet, trans_type=TransactionHistory.DEBIT, amount=amount, recipient=recipient_wallet, details=detail )
TransactionHistory.objects.create(sender=sending_wallet, trans_type=TransactionHistory.CREDIT, amount=amount, recipient=recipient_wallet, details=detail )
return {
'status' : 'success',
'message' : 'Transfer successful',
'balance' : sending_wallet.balance
}
def validate(self, data):
recipient = data.get("recipient", None)
amount = data.get("amount", None)
detail = data.get("detail", None)
sender = self.context['request'].user
transfer = self.transfer(sender, recipient, amount, detail)
if transfer['status'] == 'success':
return {
'status' : transfer['status'],
'message' : transfer['message'],
'balance' : transfer['balance']
}
else:
return {
'status' : transfer['status'],
'message' : transfer['message']
}
class FundWalletSerializer(serializers.Serializer):
amount = serializers.DecimalField(max_digits=10, decimal_places=2, write_only=True)
message = serializers.CharField(read_only=True)
status = serializers.CharField(read_only=True)
balance = serializers.DecimalField(max_digits=10, decimal_places=2,read_only=True)
def fund_wallet(self, amount, recipient):
recipient_wallet = Wallet.objects.get(owner=recipient)
recipient_wallet.balance += amount
recipient_wallet.save()
TransactionHistory.objects.create(sender=recipient_wallet, trans_type=TransactionHistory.FUND_WALLET, amount=amount, recipient=recipient_wallet, details='Fund Wallet')
return {
'status' : 'success',
'message' : 'Wallet Funded',
'balance' : recipient_wallet.balance
}
def validate(self, data):
amount = data.get("amount", None)
recipient= self.context['request'].user
payment = flutter_wave(amount, recipient.email)
if payment["status"] == "error":
return {
'status': payment["status"],
'message': payment["error"]["errMsg"]
}
if payment["status"] == "success":
amount = Decimal(payment["success"]["amount"])
fund_wallet = self.fund_wallet(amount, recipient)
return {
'status' : 'success',
'message' : fund_wallet['message'],
'balance' : fund_wallet['balance']
}
class TransactionHistorySerializer(serializers.ModelSerializer):
sender = serializers.SlugRelatedField(slug_field='account_no', read_only=True)
recipient = serializers.SlugRelatedField(slug_field='account_no', read_only=True)
class Meta:
model = TransactionHistory
fields = ('__all__') | 0.357231 | 0.190366 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class RCAN(nn.Module):
"""
RCAN:
Image Super-Resolution Using Very Deep Residual Channel Attention Networks,
<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>
"""
def __init__(self, upscale_factor, input_channels, channel_mask, output_channels, opt):
super().__init__()
assert(upscale_factor==4)
self.upscale_factor = upscale_factor
# TODO: read parameters from the config
self.upsample = 'pixelShuffle' # nearest, bilinear or pixelShuffle, as in the paper
self.num_blocks_g = 10 # G: number of outer RG blocks, as in the paper
self.num_blocks_b = 20 # B: number of innter RCAB blocks per RG block, as in the paper
self.num_channels = 64 # C: number of channels in the convolutions
self.channel_downscaling = 16 # r: channel downscaling for the CA blocks
self.reduced_channels = self.num_channels // self.channel_downscaling
self.channel_mask = channel_mask
self._build_net(input_channels, output_channels)
class ChannelAttentionBlock(nn.Module):
def __init__(self, rcan):
super().__init__()
#self.downscaling = nn.Conv1d(rcan.num_channels, rcan.reduced_channels, 1)
#self.upscaling = nn.Conv1d(rcan.reduced_channels, rcan.num_channels, 1)
self.downscaling = nn.Linear(rcan.num_channels, rcan.reduced_channels)
self.upscaling = nn.Linear(rcan.reduced_channels, rcan.num_channels)
def forward(self, x):
b,c,w,h = x.shape
z = torch.mean(x.view(b,c,w*h), dim=2)
s = self.downscaling(z)
s = F.leaky_relu(s)
s = self.upscaling(s)
s = F.sigmoid(s)
s = s.view(b, c, 1, 1)
return x * s
def _ca(self): # Channel Attention (CA) block
return RCAN.ChannelAttentionBlock(self)
class ResidualChannelAttentionBlock(nn.Module):
def __init__(self, rcan):
super().__init__()
self.pre = nn.Sequential(
nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1),
nn.LeakyReLU(inplace=True),
nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
)
self.ca = rcan._ca()
def forward(self, x):
return x + self.ca(self.pre(x))
def _rcab(self): # Residual Channel Attention (RCAB) block
return RCAN.ResidualChannelAttentionBlock(self)
class ResGroup(nn.Module):
def __init__(self, rcan):
super().__init__()
self.blocks = nn.ModuleList([rcan._rcab() for i in range(rcan.num_blocks_b)])
self.post = nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
def forward(self, x):
f = x
for block in self.blocks:
f = block(f)
f = self.post(f)
return f + x
def _resgroup(self): # Residual group of B RCABs with short skip connections
return RCAN.ResGroup(self)
class RIR(nn.Module):
def __init__(self, rcan):
super().__init__()
self.blocks = nn.ModuleList([rcan._resgroup() for i in range(rcan.num_blocks_g)])
self.post = nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
def forward(self, x):
f = x
for block in self.blocks:
f = block(f)
f = self.post(f)
return f + x
def _rir(self): # residual in residual blocks wih long skip connection
return RCAN.RIR(self)
def _upsample(self, factor=None, in_channels=None):
if factor is None:
factor = self.upscale_factor
if in_channels is None:
in_channels = self.num_channels
if self.upsample == 'nearest':
return nn.UpsamplingNearest2d(scale_factor=factor), in_channels
elif self.upsample == 'bilinear':
return nn.UpsamplingBilinear2d(scale_factor=factor), in_channels
elif self.upsample == 'pixelShuffle':
return nn.PixelShuffle(factor), in_channels // (factor**2)
else:
raise ValueError('Unknown upsample mode %s'%self.upsample)
def _build_net(self, input_channels, output_channels):
upsample, upsample_channels = self._upsample()
self.net = nn.ModuleDict({
'pre':nn.Conv2d(input_channels, self.num_channels, 3, padding=1),
'rir':self._rir(),
'up':upsample,
'post':nn.Conv2d(upsample_channels, output_channels, 3, padding=1)
})
def forward(self, inputs):
x = self.net['pre'](inputs)
x = self.net['rir'](x)
x = self.net['up'](x)
outputs = self.net['post'](x)
residual = outputs - F.interpolate(inputs[:,self.channel_mask,:,:],
size=(outputs.shape[2], outputs.shape[3]),
mode='bilinear')
outputs = torch.clamp(outputs, 0, 1)
return outputs, residual | SuperresolutionNetwork/models/rcan.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class RCAN(nn.Module):
"""
RCAN:
Image Super-Resolution Using Very Deep Residual Channel Attention Networks,
<NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>
"""
def __init__(self, upscale_factor, input_channels, channel_mask, output_channels, opt):
super().__init__()
assert(upscale_factor==4)
self.upscale_factor = upscale_factor
# TODO: read parameters from the config
self.upsample = 'pixelShuffle' # nearest, bilinear or pixelShuffle, as in the paper
self.num_blocks_g = 10 # G: number of outer RG blocks, as in the paper
self.num_blocks_b = 20 # B: number of innter RCAB blocks per RG block, as in the paper
self.num_channels = 64 # C: number of channels in the convolutions
self.channel_downscaling = 16 # r: channel downscaling for the CA blocks
self.reduced_channels = self.num_channels // self.channel_downscaling
self.channel_mask = channel_mask
self._build_net(input_channels, output_channels)
class ChannelAttentionBlock(nn.Module):
def __init__(self, rcan):
super().__init__()
#self.downscaling = nn.Conv1d(rcan.num_channels, rcan.reduced_channels, 1)
#self.upscaling = nn.Conv1d(rcan.reduced_channels, rcan.num_channels, 1)
self.downscaling = nn.Linear(rcan.num_channels, rcan.reduced_channels)
self.upscaling = nn.Linear(rcan.reduced_channels, rcan.num_channels)
def forward(self, x):
b,c,w,h = x.shape
z = torch.mean(x.view(b,c,w*h), dim=2)
s = self.downscaling(z)
s = F.leaky_relu(s)
s = self.upscaling(s)
s = F.sigmoid(s)
s = s.view(b, c, 1, 1)
return x * s
def _ca(self): # Channel Attention (CA) block
return RCAN.ChannelAttentionBlock(self)
class ResidualChannelAttentionBlock(nn.Module):
def __init__(self, rcan):
super().__init__()
self.pre = nn.Sequential(
nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1),
nn.LeakyReLU(inplace=True),
nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
)
self.ca = rcan._ca()
def forward(self, x):
return x + self.ca(self.pre(x))
def _rcab(self): # Residual Channel Attention (RCAB) block
return RCAN.ResidualChannelAttentionBlock(self)
class ResGroup(nn.Module):
def __init__(self, rcan):
super().__init__()
self.blocks = nn.ModuleList([rcan._rcab() for i in range(rcan.num_blocks_b)])
self.post = nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
def forward(self, x):
f = x
for block in self.blocks:
f = block(f)
f = self.post(f)
return f + x
def _resgroup(self): # Residual group of B RCABs with short skip connections
return RCAN.ResGroup(self)
class RIR(nn.Module):
def __init__(self, rcan):
super().__init__()
self.blocks = nn.ModuleList([rcan._resgroup() for i in range(rcan.num_blocks_g)])
self.post = nn.Conv2d(rcan.num_channels, rcan.num_channels, 3, padding=1)
def forward(self, x):
f = x
for block in self.blocks:
f = block(f)
f = self.post(f)
return f + x
def _rir(self): # residual in residual blocks wih long skip connection
return RCAN.RIR(self)
def _upsample(self, factor=None, in_channels=None):
if factor is None:
factor = self.upscale_factor
if in_channels is None:
in_channels = self.num_channels
if self.upsample == 'nearest':
return nn.UpsamplingNearest2d(scale_factor=factor), in_channels
elif self.upsample == 'bilinear':
return nn.UpsamplingBilinear2d(scale_factor=factor), in_channels
elif self.upsample == 'pixelShuffle':
return nn.PixelShuffle(factor), in_channels // (factor**2)
else:
raise ValueError('Unknown upsample mode %s'%self.upsample)
def _build_net(self, input_channels, output_channels):
upsample, upsample_channels = self._upsample()
self.net = nn.ModuleDict({
'pre':nn.Conv2d(input_channels, self.num_channels, 3, padding=1),
'rir':self._rir(),
'up':upsample,
'post':nn.Conv2d(upsample_channels, output_channels, 3, padding=1)
})
def forward(self, inputs):
x = self.net['pre'](inputs)
x = self.net['rir'](x)
x = self.net['up'](x)
outputs = self.net['post'](x)
residual = outputs - F.interpolate(inputs[:,self.channel_mask,:,:],
size=(outputs.shape[2], outputs.shape[3]),
mode='bilinear')
outputs = torch.clamp(outputs, 0, 1)
return outputs, residual | 0.885322 | 0.497376 |
# pylint: disable=broad-except, bad-continuation, no-member
import pathlib
import json
import os
import tempfile
import zipfile
from integrationhelper import Validate, Logger
from aiogithubapi import AIOGitHubException
from .manifest import HacsManifest
from ..helpers.misc import get_repository_name
from ..hacsbase import Hacs
from ..hacsbase.exceptions import HacsException
from ..hacsbase.backup import Backup
from ..handler.download import async_download_file, async_save_file
from ..helpers.misc import version_left_higher_then_right
from ..helpers.install import install_repository, version_to_install
RERPOSITORY_CLASSES = {}
def register_repository_class(cls):
"""Register class."""
RERPOSITORY_CLASSES[cls.category] = cls
return cls
class RepositoryVersions:
"""Versions."""
available = None
available_commit = None
installed = None
installed_commit = None
class RepositoryStatus:
"""Repository status."""
hide = False
installed = False
last_updated = None
new = True
selected_tag = None
show_beta = False
track = True
updated_info = False
first_install = True
class RepositoryInformation:
"""RepositoryInformation."""
additional_info = None
authors = []
category = None
default_branch = None
description = ""
state = None
full_name = None
file_name = None
javascript_type = None
homeassistant_version = None
last_updated = None
uid = None
stars = 0
info = None
name = None
topics = []
class RepositoryReleases:
"""RepositoyReleases."""
last_release = None
last_release_object = None
last_release_object_downloads = None
published_tags = []
objects = []
releases = False
class RepositoryPath:
"""RepositoryPath."""
local = None
remote = None
class RepositoryContent:
"""RepositoryContent."""
path = None
files = []
objects = []
single = False
class HacsRepository(Hacs):
"""HacsRepository."""
def __init__(self):
"""Set up HacsRepository."""
self.data = {}
self.content = RepositoryContent()
self.content.path = RepositoryPath()
self.information = RepositoryInformation()
self.repository_object = None
self.status = RepositoryStatus()
self.state = None
self.manifest = {}
self.repository_manifest = HacsManifest.from_dict({})
self.validate = Validate()
self.releases = RepositoryReleases()
self.versions = RepositoryVersions()
self.pending_restart = False
self.logger = None
self.tree = []
self.treefiles = []
self.ref = None
@property
def pending_upgrade(self):
"""Return pending upgrade."""
if self.status.installed:
if self.status.selected_tag is not None:
if self.status.selected_tag == self.information.default_branch:
if self.versions.installed_commit != self.versions.available_commit:
return True
return False
if self.display_installed_version != self.display_available_version:
return True
return False
@property
def config_flow(self):
"""Return bool if integration has config_flow."""
if self.manifest:
if self.information.full_name == "hacs/integration":
return False
return self.manifest.get("config_flow", False)
return False
@property
def custom(self):
"""Return flag if the repository is custom."""
if self.information.full_name.split("/")[0] in [
"custom-components",
"custom-cards",
]:
return False
if self.information.full_name in self.common.default:
return False
if self.information.full_name == "hacs/integration":
return False
return True
@property
def can_install(self):
"""Return bool if repository can be installed."""
target = None
if self.information.homeassistant_version is not None:
target = self.information.homeassistant_version
if self.repository_manifest is not None:
if self.repository_manifest.homeassistant is not None:
target = self.repository_manifest.homeassistant
if target is not None:
if self.releases.releases:
if not version_left_higher_then_right(self.system.ha_version, target):
return False
return True
@property
def display_name(self):
"""Return display name."""
return get_repository_name(
self.repository_manifest,
self.information.name,
self.information.category,
self.manifest,
)
@property
def display_status(self):
"""Return display_status."""
if self.status.new:
status = "new"
elif self.pending_restart:
status = "pending-restart"
elif self.pending_upgrade:
status = "pending-upgrade"
elif self.status.installed:
status = "installed"
else:
status = "default"
return status
@property
def display_status_description(self):
"""Return display_status_description."""
description = {
"default": "Not installed.",
"pending-restart": "Restart pending.",
"pending-upgrade": "Upgrade pending.",
"installed": "No action required.",
"new": "This is a newly added repository.",
}
return description[self.display_status]
@property
def display_installed_version(self):
"""Return display_authors"""
if self.versions.installed is not None:
installed = self.versions.installed
else:
if self.versions.installed_commit is not None:
installed = self.versions.installed_commit
else:
installed = ""
return installed
@property
def display_available_version(self):
"""Return display_authors"""
if self.versions.available is not None:
available = self.versions.available
else:
if self.versions.available_commit is not None:
available = self.versions.available_commit
else:
available = ""
return available
@property
def display_version_or_commit(self):
"""Does the repositoriy use releases or commits?"""
if self.releases.releases:
version_or_commit = "version"
else:
version_or_commit = "commit"
return version_or_commit
@property
def main_action(self):
"""Return the main action."""
actions = {
"new": "INSTALL",
"default": "INSTALL",
"installed": "REINSTALL",
"pending-restart": "REINSTALL",
"pending-upgrade": "UPGRADE",
}
return actions[self.display_status]
async def common_validate(self):
"""Common validation steps of the repository."""
# Attach helpers
self.validate.errors = []
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
if self.ref is None:
self.ref = version_to_install(self)
# Step 1: Make sure the repository exist.
self.logger.debug("Checking repository.")
try:
self.repository_object = await self.github.get_repo(
self.information.full_name
)
self.data = self.repository_object.attributes
except Exception as exception: # Gotta Catch 'Em All
if not self.system.status.startup:
self.logger.error(exception)
self.validate.errors.append("Repository does not exist.")
return
if not self.tree:
self.tree = await self.repository_object.get_tree(self.ref)
self.treefiles = []
for treefile in self.tree:
self.treefiles.append(treefile.full_path)
# Step 2: Make sure the repository is not archived.
if self.repository_object.archived:
self.validate.errors.append("Repository is archived.")
return
# Step 3: Make sure the repository is not in the blacklist.
if self.information.full_name in self.common.blacklist:
self.validate.errors.append("Repository is in the blacklist.")
return
# Step 4: default branch
self.information.default_branch = self.repository_object.default_branch
# Step 5: Get releases.
await self.get_releases()
# Step 6: Get the content of hacs.json
await self.get_repository_manifest_content()
# Set repository name
self.information.name = self.information.full_name.split("/")[1]
async def common_registration(self):
"""Common registration steps of the repository."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
# Attach repository
if self.repository_object is None:
self.repository_object = await self.github.get_repo(
self.information.full_name
)
# Set id
self.information.uid = str(self.repository_object.id)
# Set topics
self.information.topics = self.repository_object.topics
# Set stargazers_count
self.information.stars = self.repository_object.attributes.get(
"stargazers_count", 0
)
# Set description
if self.repository_object.description:
self.information.description = self.repository_object.description
async def common_update(self):
"""Common information update steps of the repository."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.debug("Getting repository information")
# Set ref
if self.ref is None:
self.ref = version_to_install(self)
# Attach repository
self.repository_object = await self.github.get_repo(self.information.full_name)
# Update tree
self.tree = await self.repository_object.get_tree(self.ref)
self.treefiles = []
for treefile in self.tree:
self.treefiles.append(treefile.full_path)
# Update description
if self.repository_object.description:
self.information.description = self.repository_object.description
# Set stargazers_count
self.information.stars = self.repository_object.attributes.get(
"stargazers_count", 0
)
# Update default branch
self.information.default_branch = self.repository_object.default_branch
# Update last updaeted
self.information.last_updated = self.repository_object.attributes.get(
"pushed_at", 0
)
# Update topics
self.information.topics = self.repository_object.topics
# Update last available commit
await self.repository_object.set_last_commit()
self.versions.available_commit = self.repository_object.last_commit
# Get the content of hacs.json
await self.get_repository_manifest_content()
# Update "info.md"
await self.get_info_md_content()
# Update releases
await self.get_releases()
async def install(self):
"""Common installation steps of the repository."""
await install_repository(self)
async def download_zip(self, validate):
"""Download ZIP archive from repository release."""
try:
contents = False
for release in self.releases.objects:
self.logger.info(f"ref: {self.ref} --- tag: {release.tag_name}")
if release.tag_name == self.ref.split("/")[1]:
contents = release.assets
if not contents:
return validate
for content in contents or []:
filecontent = await async_download_file(self.hass, content.download_url)
if filecontent is None:
validate.errors.append(f"[{content.name}] was not downloaded.")
continue
result = await async_save_file(
f"{tempfile.gettempdir()}/{self.repository_manifest.filename}",
filecontent,
)
with zipfile.ZipFile(
f"{tempfile.gettempdir()}/{self.repository_manifest.filename}", "r"
) as zip_file:
zip_file.extractall(self.content.path.local)
if result:
self.logger.info(f"download of {content.name} complete")
continue
validate.errors.append(f"[{content.name}] was not downloaded.")
except Exception:
validate.errors.append(f"Download was not complete.")
return validate
async def download_content(self, validate, directory_path, local_directory, ref):
"""Download the content of a directory."""
from custom_components.hacs.helpers.download import download_content
validate = await download_content(self, validate, local_directory)
return validate
async def get_repository_manifest_content(self):
"""Get the content of the hacs.json file."""
if self.ref is None:
self.ref = version_to_install(self)
try:
manifest = await self.repository_object.get_contents("hacs.json", self.ref)
self.repository_manifest = HacsManifest.from_dict(
json.loads(manifest.content)
)
except (AIOGitHubException, Exception): # Gotta Catch 'Em All
pass
async def get_info_md_content(self):
"""Get the content of info.md"""
from ..handler.template import render_template
if self.ref is None:
self.ref = version_to_install(self)
info = None
info_files = ["info", "info.md"]
if self.repository_manifest is not None:
if self.repository_manifest.render_readme:
info_files = ["readme", "readme.md"]
try:
root = await self.repository_object.get_contents("", self.ref)
for file in root:
if file.name.lower() in info_files:
info = await self.repository_object.get_contents(
file.name, self.ref
)
break
if info is None:
self.information.additional_info = ""
else:
info = info.content.replace("<svg", "<disabled").replace(
"</svg", "</disabled"
)
self.information.additional_info = render_template(info, self)
except (AIOGitHubException, Exception):
self.information.additional_info = ""
async def get_releases(self):
"""Get repository releases."""
if self.status.show_beta:
self.releases.objects = await self.repository_object.get_releases(
prerelease=True, returnlimit=self.configuration.release_limit
)
else:
self.releases.objects = await self.repository_object.get_releases(
prerelease=False, returnlimit=self.configuration.release_limit
)
if not self.releases.objects:
return
self.releases.releases = True
self.releases.published_tags = []
for release in self.releases.objects:
self.releases.published_tags.append(release.tag_name)
self.releases.last_release_object = self.releases.objects[0]
if self.status.selected_tag is not None:
if self.status.selected_tag != self.information.default_branch:
for release in self.releases.objects:
if release.tag_name == self.status.selected_tag:
self.releases.last_release_object = release
break
if self.releases.last_release_object.assets:
self.releases.last_release_object_downloads = self.releases.last_release_object.assets[
0
].attributes.get(
"download_count"
)
self.versions.available = self.releases.objects[0].tag_name
def remove(self):
"""Run remove tasks."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.info("Starting removal")
if self.information.uid in self.common.installed:
self.common.installed.remove(self.information.uid)
for repository in self.repositories:
if repository.information.uid == self.information.uid:
self.repositories.remove(repository)
async def uninstall(self):
"""Run uninstall tasks."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.info("Uninstalling")
await self.remove_local_directory()
self.status.installed = False
if self.information.category == "integration":
if self.config_flow:
await self.reload_custom_components()
else:
self.pending_restart = True
elif self.information.category == "theme":
try:
await self.hass.services.async_call("frontend", "reload_themes", {})
except Exception: # pylint: disable=broad-except
pass
if self.information.full_name in self.common.installed:
self.common.installed.remove(self.information.full_name)
self.versions.installed = None
self.versions.installed_commit = None
self.hass.bus.async_fire(
"hacs/repository",
{
"id": 1337,
"action": "uninstall",
"repository": self.information.full_name,
},
)
async def remove_local_directory(self):
"""Check the local directory."""
import shutil
from asyncio import sleep
try:
if self.information.category == "python_script":
local_path = "{}/{}.py".format(
self.content.path.local, self.information.name
)
elif self.information.category == "theme":
local_path = "{}/{}.yaml".format(
self.content.path.local, self.information.name
)
else:
local_path = self.content.path.local
if os.path.exists(local_path):
self.logger.debug(f"Removing {local_path}")
if self.information.category in ["python_script", "theme"]:
os.remove(local_path)
else:
shutil.rmtree(local_path)
while os.path.exists(local_path):
await sleep(1)
except Exception as exception:
self.logger.debug(f"Removing {local_path} failed with {exception}")
return | config/custom_components/hacs/repositories/repository.py | # pylint: disable=broad-except, bad-continuation, no-member
import pathlib
import json
import os
import tempfile
import zipfile
from integrationhelper import Validate, Logger
from aiogithubapi import AIOGitHubException
from .manifest import HacsManifest
from ..helpers.misc import get_repository_name
from ..hacsbase import Hacs
from ..hacsbase.exceptions import HacsException
from ..hacsbase.backup import Backup
from ..handler.download import async_download_file, async_save_file
from ..helpers.misc import version_left_higher_then_right
from ..helpers.install import install_repository, version_to_install
RERPOSITORY_CLASSES = {}
def register_repository_class(cls):
"""Register class."""
RERPOSITORY_CLASSES[cls.category] = cls
return cls
class RepositoryVersions:
"""Versions."""
available = None
available_commit = None
installed = None
installed_commit = None
class RepositoryStatus:
"""Repository status."""
hide = False
installed = False
last_updated = None
new = True
selected_tag = None
show_beta = False
track = True
updated_info = False
first_install = True
class RepositoryInformation:
"""RepositoryInformation."""
additional_info = None
authors = []
category = None
default_branch = None
description = ""
state = None
full_name = None
file_name = None
javascript_type = None
homeassistant_version = None
last_updated = None
uid = None
stars = 0
info = None
name = None
topics = []
class RepositoryReleases:
"""RepositoyReleases."""
last_release = None
last_release_object = None
last_release_object_downloads = None
published_tags = []
objects = []
releases = False
class RepositoryPath:
"""RepositoryPath."""
local = None
remote = None
class RepositoryContent:
"""RepositoryContent."""
path = None
files = []
objects = []
single = False
class HacsRepository(Hacs):
"""HacsRepository."""
def __init__(self):
"""Set up HacsRepository."""
self.data = {}
self.content = RepositoryContent()
self.content.path = RepositoryPath()
self.information = RepositoryInformation()
self.repository_object = None
self.status = RepositoryStatus()
self.state = None
self.manifest = {}
self.repository_manifest = HacsManifest.from_dict({})
self.validate = Validate()
self.releases = RepositoryReleases()
self.versions = RepositoryVersions()
self.pending_restart = False
self.logger = None
self.tree = []
self.treefiles = []
self.ref = None
@property
def pending_upgrade(self):
"""Return pending upgrade."""
if self.status.installed:
if self.status.selected_tag is not None:
if self.status.selected_tag == self.information.default_branch:
if self.versions.installed_commit != self.versions.available_commit:
return True
return False
if self.display_installed_version != self.display_available_version:
return True
return False
@property
def config_flow(self):
"""Return bool if integration has config_flow."""
if self.manifest:
if self.information.full_name == "hacs/integration":
return False
return self.manifest.get("config_flow", False)
return False
@property
def custom(self):
"""Return flag if the repository is custom."""
if self.information.full_name.split("/")[0] in [
"custom-components",
"custom-cards",
]:
return False
if self.information.full_name in self.common.default:
return False
if self.information.full_name == "hacs/integration":
return False
return True
@property
def can_install(self):
"""Return bool if repository can be installed."""
target = None
if self.information.homeassistant_version is not None:
target = self.information.homeassistant_version
if self.repository_manifest is not None:
if self.repository_manifest.homeassistant is not None:
target = self.repository_manifest.homeassistant
if target is not None:
if self.releases.releases:
if not version_left_higher_then_right(self.system.ha_version, target):
return False
return True
@property
def display_name(self):
"""Return display name."""
return get_repository_name(
self.repository_manifest,
self.information.name,
self.information.category,
self.manifest,
)
@property
def display_status(self):
"""Return display_status."""
if self.status.new:
status = "new"
elif self.pending_restart:
status = "pending-restart"
elif self.pending_upgrade:
status = "pending-upgrade"
elif self.status.installed:
status = "installed"
else:
status = "default"
return status
@property
def display_status_description(self):
"""Return display_status_description."""
description = {
"default": "Not installed.",
"pending-restart": "Restart pending.",
"pending-upgrade": "Upgrade pending.",
"installed": "No action required.",
"new": "This is a newly added repository.",
}
return description[self.display_status]
@property
def display_installed_version(self):
"""Return display_authors"""
if self.versions.installed is not None:
installed = self.versions.installed
else:
if self.versions.installed_commit is not None:
installed = self.versions.installed_commit
else:
installed = ""
return installed
@property
def display_available_version(self):
"""Return display_authors"""
if self.versions.available is not None:
available = self.versions.available
else:
if self.versions.available_commit is not None:
available = self.versions.available_commit
else:
available = ""
return available
@property
def display_version_or_commit(self):
"""Does the repositoriy use releases or commits?"""
if self.releases.releases:
version_or_commit = "version"
else:
version_or_commit = "commit"
return version_or_commit
@property
def main_action(self):
"""Return the main action."""
actions = {
"new": "INSTALL",
"default": "INSTALL",
"installed": "REINSTALL",
"pending-restart": "REINSTALL",
"pending-upgrade": "UPGRADE",
}
return actions[self.display_status]
async def common_validate(self):
"""Common validation steps of the repository."""
# Attach helpers
self.validate.errors = []
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
if self.ref is None:
self.ref = version_to_install(self)
# Step 1: Make sure the repository exist.
self.logger.debug("Checking repository.")
try:
self.repository_object = await self.github.get_repo(
self.information.full_name
)
self.data = self.repository_object.attributes
except Exception as exception: # Gotta Catch 'Em All
if not self.system.status.startup:
self.logger.error(exception)
self.validate.errors.append("Repository does not exist.")
return
if not self.tree:
self.tree = await self.repository_object.get_tree(self.ref)
self.treefiles = []
for treefile in self.tree:
self.treefiles.append(treefile.full_path)
# Step 2: Make sure the repository is not archived.
if self.repository_object.archived:
self.validate.errors.append("Repository is archived.")
return
# Step 3: Make sure the repository is not in the blacklist.
if self.information.full_name in self.common.blacklist:
self.validate.errors.append("Repository is in the blacklist.")
return
# Step 4: default branch
self.information.default_branch = self.repository_object.default_branch
# Step 5: Get releases.
await self.get_releases()
# Step 6: Get the content of hacs.json
await self.get_repository_manifest_content()
# Set repository name
self.information.name = self.information.full_name.split("/")[1]
async def common_registration(self):
"""Common registration steps of the repository."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
# Attach repository
if self.repository_object is None:
self.repository_object = await self.github.get_repo(
self.information.full_name
)
# Set id
self.information.uid = str(self.repository_object.id)
# Set topics
self.information.topics = self.repository_object.topics
# Set stargazers_count
self.information.stars = self.repository_object.attributes.get(
"stargazers_count", 0
)
# Set description
if self.repository_object.description:
self.information.description = self.repository_object.description
async def common_update(self):
"""Common information update steps of the repository."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.debug("Getting repository information")
# Set ref
if self.ref is None:
self.ref = version_to_install(self)
# Attach repository
self.repository_object = await self.github.get_repo(self.information.full_name)
# Update tree
self.tree = await self.repository_object.get_tree(self.ref)
self.treefiles = []
for treefile in self.tree:
self.treefiles.append(treefile.full_path)
# Update description
if self.repository_object.description:
self.information.description = self.repository_object.description
# Set stargazers_count
self.information.stars = self.repository_object.attributes.get(
"stargazers_count", 0
)
# Update default branch
self.information.default_branch = self.repository_object.default_branch
# Update last updaeted
self.information.last_updated = self.repository_object.attributes.get(
"pushed_at", 0
)
# Update topics
self.information.topics = self.repository_object.topics
# Update last available commit
await self.repository_object.set_last_commit()
self.versions.available_commit = self.repository_object.last_commit
# Get the content of hacs.json
await self.get_repository_manifest_content()
# Update "info.md"
await self.get_info_md_content()
# Update releases
await self.get_releases()
async def install(self):
"""Common installation steps of the repository."""
await install_repository(self)
async def download_zip(self, validate):
"""Download ZIP archive from repository release."""
try:
contents = False
for release in self.releases.objects:
self.logger.info(f"ref: {self.ref} --- tag: {release.tag_name}")
if release.tag_name == self.ref.split("/")[1]:
contents = release.assets
if not contents:
return validate
for content in contents or []:
filecontent = await async_download_file(self.hass, content.download_url)
if filecontent is None:
validate.errors.append(f"[{content.name}] was not downloaded.")
continue
result = await async_save_file(
f"{tempfile.gettempdir()}/{self.repository_manifest.filename}",
filecontent,
)
with zipfile.ZipFile(
f"{tempfile.gettempdir()}/{self.repository_manifest.filename}", "r"
) as zip_file:
zip_file.extractall(self.content.path.local)
if result:
self.logger.info(f"download of {content.name} complete")
continue
validate.errors.append(f"[{content.name}] was not downloaded.")
except Exception:
validate.errors.append(f"Download was not complete.")
return validate
async def download_content(self, validate, directory_path, local_directory, ref):
"""Download the content of a directory."""
from custom_components.hacs.helpers.download import download_content
validate = await download_content(self, validate, local_directory)
return validate
async def get_repository_manifest_content(self):
"""Get the content of the hacs.json file."""
if self.ref is None:
self.ref = version_to_install(self)
try:
manifest = await self.repository_object.get_contents("hacs.json", self.ref)
self.repository_manifest = HacsManifest.from_dict(
json.loads(manifest.content)
)
except (AIOGitHubException, Exception): # Gotta Catch 'Em All
pass
async def get_info_md_content(self):
"""Get the content of info.md"""
from ..handler.template import render_template
if self.ref is None:
self.ref = version_to_install(self)
info = None
info_files = ["info", "info.md"]
if self.repository_manifest is not None:
if self.repository_manifest.render_readme:
info_files = ["readme", "readme.md"]
try:
root = await self.repository_object.get_contents("", self.ref)
for file in root:
if file.name.lower() in info_files:
info = await self.repository_object.get_contents(
file.name, self.ref
)
break
if info is None:
self.information.additional_info = ""
else:
info = info.content.replace("<svg", "<disabled").replace(
"</svg", "</disabled"
)
self.information.additional_info = render_template(info, self)
except (AIOGitHubException, Exception):
self.information.additional_info = ""
async def get_releases(self):
"""Get repository releases."""
if self.status.show_beta:
self.releases.objects = await self.repository_object.get_releases(
prerelease=True, returnlimit=self.configuration.release_limit
)
else:
self.releases.objects = await self.repository_object.get_releases(
prerelease=False, returnlimit=self.configuration.release_limit
)
if not self.releases.objects:
return
self.releases.releases = True
self.releases.published_tags = []
for release in self.releases.objects:
self.releases.published_tags.append(release.tag_name)
self.releases.last_release_object = self.releases.objects[0]
if self.status.selected_tag is not None:
if self.status.selected_tag != self.information.default_branch:
for release in self.releases.objects:
if release.tag_name == self.status.selected_tag:
self.releases.last_release_object = release
break
if self.releases.last_release_object.assets:
self.releases.last_release_object_downloads = self.releases.last_release_object.assets[
0
].attributes.get(
"download_count"
)
self.versions.available = self.releases.objects[0].tag_name
def remove(self):
"""Run remove tasks."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.info("Starting removal")
if self.information.uid in self.common.installed:
self.common.installed.remove(self.information.uid)
for repository in self.repositories:
if repository.information.uid == self.information.uid:
self.repositories.remove(repository)
async def uninstall(self):
"""Run uninstall tasks."""
# Attach logger
if self.logger is None:
self.logger = Logger(
f"hacs.repository.{self.information.category}.{self.information.full_name}"
)
self.logger.info("Uninstalling")
await self.remove_local_directory()
self.status.installed = False
if self.information.category == "integration":
if self.config_flow:
await self.reload_custom_components()
else:
self.pending_restart = True
elif self.information.category == "theme":
try:
await self.hass.services.async_call("frontend", "reload_themes", {})
except Exception: # pylint: disable=broad-except
pass
if self.information.full_name in self.common.installed:
self.common.installed.remove(self.information.full_name)
self.versions.installed = None
self.versions.installed_commit = None
self.hass.bus.async_fire(
"hacs/repository",
{
"id": 1337,
"action": "uninstall",
"repository": self.information.full_name,
},
)
async def remove_local_directory(self):
"""Check the local directory."""
import shutil
from asyncio import sleep
try:
if self.information.category == "python_script":
local_path = "{}/{}.py".format(
self.content.path.local, self.information.name
)
elif self.information.category == "theme":
local_path = "{}/{}.yaml".format(
self.content.path.local, self.information.name
)
else:
local_path = self.content.path.local
if os.path.exists(local_path):
self.logger.debug(f"Removing {local_path}")
if self.information.category in ["python_script", "theme"]:
os.remove(local_path)
else:
shutil.rmtree(local_path)
while os.path.exists(local_path):
await sleep(1)
except Exception as exception:
self.logger.debug(f"Removing {local_path} failed with {exception}")
return | 0.615319 | 0.091058 |
from datetime import timedelta
from .securitas_direct_new_api.dataTypes import (
AirQuality,
Sentinel,
Service,
)
from homeassistant.components.sensor import SensorDeviceClass, SensorEntity
from homeassistant.const import PERCENTAGE, TEMP_CELSIUS
from . import CONF_ALARM, HUB as hub
SCAN_INTERVAL = timedelta(minutes=30)
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Securitas platform."""
sensors = []
if int(hub.config.get(CONF_ALARM, 1)):
for item in hub.sentinel_services:
sentinel_data: Sentinel = hub.session.get_sentinel_data(
item.installation, item
)
sensors.append(SentinelTemperature(sentinel_data, item))
sensors.append(SentinelHumidity(sentinel_data, item))
air_quality: AirQuality = hub.session.get_air_quality_data(
item.installation, item
)
sensors.append(SentinelAirQuality(air_quality, sentinel_data, item))
add_entities(sensors)
class SentinelTemperature(SensorEntity):
"""Sentinel temperature sensor."""
def __init__(self, sentinel: Sentinel, service: Service) -> None:
"""Init the component."""
self._update_sensor_data(sentinel)
self._attr_unique_id = sentinel.alias + "_temperature_" + str(service.id)
self._attr_name = "Temperature " + sentinel.alias.lower().capitalize()
self._sentinel: Sentinel = sentinel
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
sentinel_data: Sentinel = hub.session.get_sentinel_data(
self._service.installation, self._service
)
self._update_sensor_data(sentinel_data)
def _update_sensor_data(self, sentinel: Sentinel):
self._attr_device_class = SensorDeviceClass.TEMPERATURE
self._attr_native_value = sentinel.temperature
self._attr_native_unit_of_measurement = TEMP_CELSIUS
class SentinelHumidity(SensorEntity):
"""Sentinel Humidity sensor."""
def __init__(self, sentinel: Sentinel, service: Service) -> None:
"""Init the component."""
self._update_sensor_data(sentinel)
self._attr_unique_id = sentinel.alias + "_humidity_" + str(service.id)
self._attr_name = "Humidity " + sentinel.alias.lower().capitalize()
self._sentinel: Sentinel = sentinel
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
sentinel_data: Sentinel = hub.session.get_sentinel_data(
self._service.installation, self._service
)
self._update_sensor_data(sentinel_data)
def _update_sensor_data(self, sentinel: Sentinel):
self._attr_device_class = SensorDeviceClass.HUMIDITY
self._attr_native_value = sentinel.humidity
self._attr_native_unit_of_measurement = PERCENTAGE
class SentinelAirQuality(SensorEntity):
"""Sentinel Humidity sensor."""
def __init__(
self, air_quality: AirQuality, sentinel: Sentinel, service: Service
) -> None:
"""Init the component."""
self._update_sensor_data(air_quality)
self._attr_unique_id = sentinel.alias + "airquality_" + str(service.id)
self._attr_name = "Air Quality " + sentinel.alias.lower().capitalize()
self._air_quality: AirQuality = air_quality
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
air_quality: Sentinel = hub.session.get_air_quality_data(
self._service.installation, self._service
)
self._update_sensor_data(air_quality)
def _update_sensor_data(self, air_quality: AirQuality):
self._attr_device_class = SensorDeviceClass.AQI
self._attr_native_value = air_quality.value | custom_components/securitas/sensor.py | from datetime import timedelta
from .securitas_direct_new_api.dataTypes import (
AirQuality,
Sentinel,
Service,
)
from homeassistant.components.sensor import SensorDeviceClass, SensorEntity
from homeassistant.const import PERCENTAGE, TEMP_CELSIUS
from . import CONF_ALARM, HUB as hub
SCAN_INTERVAL = timedelta(minutes=30)
def setup_platform(hass, config, add_entities, discovery_info=None):
"""Set up the Securitas platform."""
sensors = []
if int(hub.config.get(CONF_ALARM, 1)):
for item in hub.sentinel_services:
sentinel_data: Sentinel = hub.session.get_sentinel_data(
item.installation, item
)
sensors.append(SentinelTemperature(sentinel_data, item))
sensors.append(SentinelHumidity(sentinel_data, item))
air_quality: AirQuality = hub.session.get_air_quality_data(
item.installation, item
)
sensors.append(SentinelAirQuality(air_quality, sentinel_data, item))
add_entities(sensors)
class SentinelTemperature(SensorEntity):
"""Sentinel temperature sensor."""
def __init__(self, sentinel: Sentinel, service: Service) -> None:
"""Init the component."""
self._update_sensor_data(sentinel)
self._attr_unique_id = sentinel.alias + "_temperature_" + str(service.id)
self._attr_name = "Temperature " + sentinel.alias.lower().capitalize()
self._sentinel: Sentinel = sentinel
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
sentinel_data: Sentinel = hub.session.get_sentinel_data(
self._service.installation, self._service
)
self._update_sensor_data(sentinel_data)
def _update_sensor_data(self, sentinel: Sentinel):
self._attr_device_class = SensorDeviceClass.TEMPERATURE
self._attr_native_value = sentinel.temperature
self._attr_native_unit_of_measurement = TEMP_CELSIUS
class SentinelHumidity(SensorEntity):
"""Sentinel Humidity sensor."""
def __init__(self, sentinel: Sentinel, service: Service) -> None:
"""Init the component."""
self._update_sensor_data(sentinel)
self._attr_unique_id = sentinel.alias + "_humidity_" + str(service.id)
self._attr_name = "Humidity " + sentinel.alias.lower().capitalize()
self._sentinel: Sentinel = sentinel
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
sentinel_data: Sentinel = hub.session.get_sentinel_data(
self._service.installation, self._service
)
self._update_sensor_data(sentinel_data)
def _update_sensor_data(self, sentinel: Sentinel):
self._attr_device_class = SensorDeviceClass.HUMIDITY
self._attr_native_value = sentinel.humidity
self._attr_native_unit_of_measurement = PERCENTAGE
class SentinelAirQuality(SensorEntity):
"""Sentinel Humidity sensor."""
def __init__(
self, air_quality: AirQuality, sentinel: Sentinel, service: Service
) -> None:
"""Init the component."""
self._update_sensor_data(air_quality)
self._attr_unique_id = sentinel.alias + "airquality_" + str(service.id)
self._attr_name = "Air Quality " + sentinel.alias.lower().capitalize()
self._air_quality: AirQuality = air_quality
self._service: Service = service
def update(self):
"""Update the status of the alarm based on the configuration."""
air_quality: Sentinel = hub.session.get_air_quality_data(
self._service.installation, self._service
)
self._update_sensor_data(air_quality)
def _update_sensor_data(self, air_quality: AirQuality):
self._attr_device_class = SensorDeviceClass.AQI
self._attr_native_value = air_quality.value | 0.866232 | 0.189427 |
"""Tests for tensorflow.kernels.edit_distance_op."""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
def ConstantOf(x):
x = np.asarray(x)
# Convert to int64 if it's not a string or unicode
if x.dtype.char not in "SU":
x = np.asarray(x, dtype=np.int64)
return constant_op.constant(x)
class EditDistanceTest(test.TestCase):
def _testEditDistanceST(self,
hypothesis_st,
truth_st,
normalize,
expected_output,
expected_shape,
expected_err_re=None):
edit_distance = array_ops.edit_distance(
hypothesis=hypothesis_st, truth=truth_st, normalize=normalize)
if expected_err_re is None:
self.assertEqual(edit_distance.get_shape(), expected_shape)
output = self.evaluate(edit_distance)
self.assertAllClose(output, expected_output)
else:
with self.assertRaisesOpError(expected_err_re):
self.evaluate(edit_distance)
def _testEditDistance(self,
hypothesis,
truth,
normalize,
expected_output,
expected_err_re=None):
# Shape inference figures out the shape from the shape variables
# Explicit tuple() needed since zip returns an iterator in Python 3.
expected_shape = [
max(h, t) for h, t in tuple(zip(hypothesis[2], truth[2]))[:-1]
]
# SparseTensorValue inputs.
with ops.Graph().as_default() as g, self.session(g):
# hypothesis and truth are (index, value, shape) tuples
self._testEditDistanceST(
hypothesis_st=sparse_tensor.SparseTensorValue(
*[ConstantOf(x) for x in hypothesis]),
truth_st=sparse_tensor.SparseTensorValue(
*[ConstantOf(x) for x in truth]),
normalize=normalize,
expected_output=expected_output,
expected_shape=expected_shape,
expected_err_re=expected_err_re)
# SparseTensor inputs.
with ops.Graph().as_default() as g, self.session(g):
# hypothesis and truth are (index, value, shape) tuples
self._testEditDistanceST(
hypothesis_st=sparse_tensor.SparseTensor(
*[ConstantOf(x) for x in hypothesis]),
truth_st=sparse_tensor.SparseTensor(*[ConstantOf(x) for x in truth]),
normalize=normalize,
expected_output=expected_output,
expected_shape=expected_shape,
expected_err_re=expected_err_re)
def testEditDistanceNormalized(self):
hypothesis_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
hypothesis_values = [0, 1, 1, -1]
hypothesis_shape = [2, 2]
truth_indices = [[0, 0], [1, 0], [1, 1]]
truth_values = [0, 1, 1]
truth_shape = [2, 2]
expected_output = [1.0, 0.5]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceUnnormalized(self):
hypothesis_indices = [[0, 0], [1, 0], [1, 1]]
hypothesis_values = [10, 10, 11]
hypothesis_shape = [2, 2]
truth_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
truth_values = [1, 2, 1, -1]
truth_shape = [2, 3]
expected_output = [2.0, 2.0]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_output)
def testEditDistanceProperDistance(self):
# In this case, the values are individual characters stored in the
# SparseTensor (type DT_STRING)
hypothesis_indices = ([[0, i] for i, _ in enumerate("algorithm")] +
[[1, i] for i, _ in enumerate("altruistic")])
hypothesis_values = [x for x in "algorithm"] + [x for x in "altruistic"]
hypothesis_shape = [2, 11]
truth_indices = ([[0, i] for i, _ in enumerate("altruistic")] +
[[1, i] for i, _ in enumerate("algorithm")])
truth_values = [x for x in "altruistic"] + [x for x in "algorithm"]
truth_shape = [2, 11]
expected_unnormalized = [6.0, 6.0]
expected_normalized = [6.0 / len("altruistic"), 6.0 / len("algorithm")]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_unnormalized)
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_normalized)
def testEditDistance3D(self):
hypothesis_indices = [[0, 0, 0], [1, 0, 0]]
hypothesis_values = [0, 1]
hypothesis_shape = [2, 1, 1]
truth_indices = [[0, 1, 0], [1, 0, 0], [1, 1, 0]]
truth_values = [0, 1, 1]
truth_shape = [2, 2, 1]
expected_output = [
[np.inf, 1.0], # (0,0): no truth, (0,1): no hypothesis
[0.0, 1.0]
] # (1,0): match, (1,1): no hypothesis
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthHypothesis(self):
hypothesis_indices = np.empty((0, 2), dtype=np.int64)
hypothesis_values = []
hypothesis_shape = [1, 0]
truth_indices = [[0, 0]]
truth_values = [0]
truth_shape = [1, 1]
expected_output = [1.0]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthTruth(self):
hypothesis_indices = [[0, 0]]
hypothesis_values = [0]
hypothesis_shape = [1, 1]
truth_indices = np.empty((0, 2), dtype=np.int64)
truth_values = []
truth_shape = [1, 0]
expected_output = [np.inf] # Normalized, loss is 1/0 = inf
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthHypothesisAndTruth(self):
hypothesis_indices = np.empty((0, 2), dtype=np.int64)
hypothesis_values = []
hypothesis_shape = [1, 0]
truth_indices = np.empty((0, 2), dtype=np.int64)
truth_values = []
truth_shape = [1, 0]
expected_output = [0] # Normalized is 0 because of exact match
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceBadIndices(self):
hypothesis_indices = np.full((3, 3), -1250999896764, dtype=np.int64)
hypothesis_values = np.zeros(3, dtype=np.int64)
hypothesis_shape = np.zeros(3, dtype=np.int64)
truth_indices = np.full((3, 3), -1250999896764, dtype=np.int64)
truth_values = np.full([3], 2, dtype=np.int64)
truth_shape = np.full([3], 2, dtype=np.int64)
expected_output = [] # dummy; ignored
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_output,
expected_err_re=(r"inner product -\d+ which would require writing "
"to outside of the buffer for the output tensor|"
r"Dimension -\d+ must be >= 0"))
if __name__ == "__main__":
test.main() | tensorflow/python/kernel_tests/array_ops/edit_distance_op_test.py | """Tests for tensorflow.kernels.edit_distance_op."""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test
def ConstantOf(x):
x = np.asarray(x)
# Convert to int64 if it's not a string or unicode
if x.dtype.char not in "SU":
x = np.asarray(x, dtype=np.int64)
return constant_op.constant(x)
class EditDistanceTest(test.TestCase):
def _testEditDistanceST(self,
hypothesis_st,
truth_st,
normalize,
expected_output,
expected_shape,
expected_err_re=None):
edit_distance = array_ops.edit_distance(
hypothesis=hypothesis_st, truth=truth_st, normalize=normalize)
if expected_err_re is None:
self.assertEqual(edit_distance.get_shape(), expected_shape)
output = self.evaluate(edit_distance)
self.assertAllClose(output, expected_output)
else:
with self.assertRaisesOpError(expected_err_re):
self.evaluate(edit_distance)
def _testEditDistance(self,
hypothesis,
truth,
normalize,
expected_output,
expected_err_re=None):
# Shape inference figures out the shape from the shape variables
# Explicit tuple() needed since zip returns an iterator in Python 3.
expected_shape = [
max(h, t) for h, t in tuple(zip(hypothesis[2], truth[2]))[:-1]
]
# SparseTensorValue inputs.
with ops.Graph().as_default() as g, self.session(g):
# hypothesis and truth are (index, value, shape) tuples
self._testEditDistanceST(
hypothesis_st=sparse_tensor.SparseTensorValue(
*[ConstantOf(x) for x in hypothesis]),
truth_st=sparse_tensor.SparseTensorValue(
*[ConstantOf(x) for x in truth]),
normalize=normalize,
expected_output=expected_output,
expected_shape=expected_shape,
expected_err_re=expected_err_re)
# SparseTensor inputs.
with ops.Graph().as_default() as g, self.session(g):
# hypothesis and truth are (index, value, shape) tuples
self._testEditDistanceST(
hypothesis_st=sparse_tensor.SparseTensor(
*[ConstantOf(x) for x in hypothesis]),
truth_st=sparse_tensor.SparseTensor(*[ConstantOf(x) for x in truth]),
normalize=normalize,
expected_output=expected_output,
expected_shape=expected_shape,
expected_err_re=expected_err_re)
def testEditDistanceNormalized(self):
hypothesis_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
hypothesis_values = [0, 1, 1, -1]
hypothesis_shape = [2, 2]
truth_indices = [[0, 0], [1, 0], [1, 1]]
truth_values = [0, 1, 1]
truth_shape = [2, 2]
expected_output = [1.0, 0.5]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceUnnormalized(self):
hypothesis_indices = [[0, 0], [1, 0], [1, 1]]
hypothesis_values = [10, 10, 11]
hypothesis_shape = [2, 2]
truth_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
truth_values = [1, 2, 1, -1]
truth_shape = [2, 3]
expected_output = [2.0, 2.0]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_output)
def testEditDistanceProperDistance(self):
# In this case, the values are individual characters stored in the
# SparseTensor (type DT_STRING)
hypothesis_indices = ([[0, i] for i, _ in enumerate("algorithm")] +
[[1, i] for i, _ in enumerate("altruistic")])
hypothesis_values = [x for x in "algorithm"] + [x for x in "altruistic"]
hypothesis_shape = [2, 11]
truth_indices = ([[0, i] for i, _ in enumerate("altruistic")] +
[[1, i] for i, _ in enumerate("algorithm")])
truth_values = [x for x in "altruistic"] + [x for x in "algorithm"]
truth_shape = [2, 11]
expected_unnormalized = [6.0, 6.0]
expected_normalized = [6.0 / len("altruistic"), 6.0 / len("algorithm")]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_unnormalized)
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_normalized)
def testEditDistance3D(self):
hypothesis_indices = [[0, 0, 0], [1, 0, 0]]
hypothesis_values = [0, 1]
hypothesis_shape = [2, 1, 1]
truth_indices = [[0, 1, 0], [1, 0, 0], [1, 1, 0]]
truth_values = [0, 1, 1]
truth_shape = [2, 2, 1]
expected_output = [
[np.inf, 1.0], # (0,0): no truth, (0,1): no hypothesis
[0.0, 1.0]
] # (1,0): match, (1,1): no hypothesis
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthHypothesis(self):
hypothesis_indices = np.empty((0, 2), dtype=np.int64)
hypothesis_values = []
hypothesis_shape = [1, 0]
truth_indices = [[0, 0]]
truth_values = [0]
truth_shape = [1, 1]
expected_output = [1.0]
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthTruth(self):
hypothesis_indices = [[0, 0]]
hypothesis_values = [0]
hypothesis_shape = [1, 1]
truth_indices = np.empty((0, 2), dtype=np.int64)
truth_values = []
truth_shape = [1, 0]
expected_output = [np.inf] # Normalized, loss is 1/0 = inf
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceZeroLengthHypothesisAndTruth(self):
hypothesis_indices = np.empty((0, 2), dtype=np.int64)
hypothesis_values = []
hypothesis_shape = [1, 0]
truth_indices = np.empty((0, 2), dtype=np.int64)
truth_values = []
truth_shape = [1, 0]
expected_output = [0] # Normalized is 0 because of exact match
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=True,
expected_output=expected_output)
def testEditDistanceBadIndices(self):
hypothesis_indices = np.full((3, 3), -1250999896764, dtype=np.int64)
hypothesis_values = np.zeros(3, dtype=np.int64)
hypothesis_shape = np.zeros(3, dtype=np.int64)
truth_indices = np.full((3, 3), -1250999896764, dtype=np.int64)
truth_values = np.full([3], 2, dtype=np.int64)
truth_shape = np.full([3], 2, dtype=np.int64)
expected_output = [] # dummy; ignored
self._testEditDistance(
hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
truth=(truth_indices, truth_values, truth_shape),
normalize=False,
expected_output=expected_output,
expected_err_re=(r"inner product -\d+ which would require writing "
"to outside of the buffer for the output tensor|"
r"Dimension -\d+ must be >= 0"))
if __name__ == "__main__":
test.main() | 0.859914 | 0.736211 |
def test_map():
# apply an operation to each member of a sequence
items = [1, 2, 3, 4, 5]
squared = []
for x in items:
squared.append(x ** 2)
assert squared == [1, 4, 9, 16, 25]
# -------------------------------------------------------------------------
# OR:
items = [1, 2, 3, 4, 5]
def sqr(x):
return x ** 2
squared = list(map(sqr, items)) # map() returns a map object
assert squared == [1, 4, 9, 16, 25]
# -------------------------------------------------------------------------
# how about:
items = [1, 2, 3, 4, 5]
squared = list(map((lambda x: x ** 2), items))
assert squared == [1, 4, 9, 16, 25]
# so generally, map(a_function, a_sequence)
# BUT: does a_sequence have to be "values"???
# -------------------------------------------------------------------------
# cool stuff:
def square(x):
return x ** 2
def cube(x):
return x ** 3
funcs = [square, cube]
output = []
for r in range(5):
value = list(map(lambda x: x(r), funcs))
output.append(value)
assert output == [[0, 0], [1, 1], [4, 8], [9, 27], [16, 64]]
# note: map can be faster than the manually coded equivalent for loop...
# -------------------------------------------------------------------------
# using multiple sequence parameters in parallel
assert pow(2, 10) == 1024
assert pow(3, 11) == 177147
assert pow(4, 12) == 16777216
assert list(map(pow, [2, 3, 4], [10, 11, 12])) == [1024, 177147, 16777216]
# -------------------------------------------------------------------------
from operator import add
x = [1, 2, 3]
y = [4, 5, 6]
assert list(map(add, x, y)) == [5, 7, 9]
# -------------------------------------------------------------------------
"""
The map call is similar to the list comprehension expression.
But map applies a function call to each item instead of an arbitrary expression.
Because of this limitation, it is somewhat less general tool.
In some cases, however, map may be faster to run than a list comprehension
such as when mapping a built-in function. And map requires less coding.
"""
# An identity function takes a single argument and returns it unchanged:
x = 1
def func(x):
return x
assert func(x) == x
assert lambda x: x == x
# -------------------------------------------------------------------------
m = [1, 2, 3]
n = [1, 4, 9]
new_tuples = list(map(lambda x, y: (x, y), m, n))
assert new_tuples == [(1, 1), (2, 4), (3, 9)]
# but better:
assert list(zip(m, n)) == [(1, 1), (2, 4), (3, 9)]
# -------------------------------------------------------------------------
m = [1, 2, 3]
n = [1, 4, 9, 10]
assert list(zip(m, n)) == [(1, 1), (2, 4), (3, 9)] # !!!!
# perhaps use
from itertools import zip_longest
assert list(zip_longest(m, n)) == [(1, 1), (2, 4), (3, 9), (None, 10)]
# -------------------------------------------------------------------------
def test_reduce():
"""
reduce is in functools It is more complex. It accepts an iterator to process,
but it's not an iterator itself. It returns a single result.
Coded longhand:
"""
l = [1, 2, 3, 4]
result = l[0]
for x in l[1:]:
result = result * x
assert result == 24
result = l[0]
for x in l[1:]:
result = result / x
assert result == 0.041666666666666664
# -------------------------------------------------------------------------
# use reduce
from functools import reduce
assert reduce((lambda x, y: x * y), [1, 2, 3, 4]) == 24
assert reduce((lambda x, y: x / y), [1, 2, 3, 4]) == 0.041666666666666664
# -------------------------------------------------------------------------
# strings too
l = ["I ", "passed ", "the ", "Python ", "certificate"]
assert reduce((lambda x, y: x + y), l) == "I passed the Python certificate"
def test_filter():
assert list(range(-5, 5)) == [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
assert list(filter((lambda x: x < 0), range(-5, 5))) == [
-5,
-4,
-3,
-2,
-1,
]
# Items in the sequence or iterable for which the function returns a true,
# the result are added to the result list. Built in and fast
# -------------------------------------------------------------------------
a = [1, 2, 3, 5, 7, 9]
b = [2, 3, 5, 6, 7, 8]
assert list(filter(lambda x: x in a, b)) == [2, 3, 5, 7]
# -------------------------------------------------------------------------
# but perhaps a comprehension instead
a = [1, 2, 3, 5, 7, 9]
b = [2, 3, 5, 6, 7, 8]
assert [x for x in a if x in b] == [2, 3, 5, 7]
# ------------------------------------------------------------------------- | app/language_features/map_red_filt/ex.py |
def test_map():
# apply an operation to each member of a sequence
items = [1, 2, 3, 4, 5]
squared = []
for x in items:
squared.append(x ** 2)
assert squared == [1, 4, 9, 16, 25]
# -------------------------------------------------------------------------
# OR:
items = [1, 2, 3, 4, 5]
def sqr(x):
return x ** 2
squared = list(map(sqr, items)) # map() returns a map object
assert squared == [1, 4, 9, 16, 25]
# -------------------------------------------------------------------------
# how about:
items = [1, 2, 3, 4, 5]
squared = list(map((lambda x: x ** 2), items))
assert squared == [1, 4, 9, 16, 25]
# so generally, map(a_function, a_sequence)
# BUT: does a_sequence have to be "values"???
# -------------------------------------------------------------------------
# cool stuff:
def square(x):
return x ** 2
def cube(x):
return x ** 3
funcs = [square, cube]
output = []
for r in range(5):
value = list(map(lambda x: x(r), funcs))
output.append(value)
assert output == [[0, 0], [1, 1], [4, 8], [9, 27], [16, 64]]
# note: map can be faster than the manually coded equivalent for loop...
# -------------------------------------------------------------------------
# using multiple sequence parameters in parallel
assert pow(2, 10) == 1024
assert pow(3, 11) == 177147
assert pow(4, 12) == 16777216
assert list(map(pow, [2, 3, 4], [10, 11, 12])) == [1024, 177147, 16777216]
# -------------------------------------------------------------------------
from operator import add
x = [1, 2, 3]
y = [4, 5, 6]
assert list(map(add, x, y)) == [5, 7, 9]
# -------------------------------------------------------------------------
"""
The map call is similar to the list comprehension expression.
But map applies a function call to each item instead of an arbitrary expression.
Because of this limitation, it is somewhat less general tool.
In some cases, however, map may be faster to run than a list comprehension
such as when mapping a built-in function. And map requires less coding.
"""
# An identity function takes a single argument and returns it unchanged:
x = 1
def func(x):
return x
assert func(x) == x
assert lambda x: x == x
# -------------------------------------------------------------------------
m = [1, 2, 3]
n = [1, 4, 9]
new_tuples = list(map(lambda x, y: (x, y), m, n))
assert new_tuples == [(1, 1), (2, 4), (3, 9)]
# but better:
assert list(zip(m, n)) == [(1, 1), (2, 4), (3, 9)]
# -------------------------------------------------------------------------
m = [1, 2, 3]
n = [1, 4, 9, 10]
assert list(zip(m, n)) == [(1, 1), (2, 4), (3, 9)] # !!!!
# perhaps use
from itertools import zip_longest
assert list(zip_longest(m, n)) == [(1, 1), (2, 4), (3, 9), (None, 10)]
# -------------------------------------------------------------------------
def test_reduce():
"""
reduce is in functools It is more complex. It accepts an iterator to process,
but it's not an iterator itself. It returns a single result.
Coded longhand:
"""
l = [1, 2, 3, 4]
result = l[0]
for x in l[1:]:
result = result * x
assert result == 24
result = l[0]
for x in l[1:]:
result = result / x
assert result == 0.041666666666666664
# -------------------------------------------------------------------------
# use reduce
from functools import reduce
assert reduce((lambda x, y: x * y), [1, 2, 3, 4]) == 24
assert reduce((lambda x, y: x / y), [1, 2, 3, 4]) == 0.041666666666666664
# -------------------------------------------------------------------------
# strings too
l = ["I ", "passed ", "the ", "Python ", "certificate"]
assert reduce((lambda x, y: x + y), l) == "I passed the Python certificate"
def test_filter():
assert list(range(-5, 5)) == [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
assert list(filter((lambda x: x < 0), range(-5, 5))) == [
-5,
-4,
-3,
-2,
-1,
]
# Items in the sequence or iterable for which the function returns a true,
# the result are added to the result list. Built in and fast
# -------------------------------------------------------------------------
a = [1, 2, 3, 5, 7, 9]
b = [2, 3, 5, 6, 7, 8]
assert list(filter(lambda x: x in a, b)) == [2, 3, 5, 7]
# -------------------------------------------------------------------------
# but perhaps a comprehension instead
a = [1, 2, 3, 5, 7, 9]
b = [2, 3, 5, 6, 7, 8]
assert [x for x in a if x in b] == [2, 3, 5, 7]
# ------------------------------------------------------------------------- | 0.820793 | 0.938011 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .blocks import *
__all__ = [
"XceptionNet",
]
class XceptionNet(nn.Module):
def __init__(
self,
image_channels: int,
num_classes: int,
):
super().__init__()
# Entry Flow
self.conv1 = ConvBlock(image_channels, 32, 3, 2)
self.conv2 = ConvBlock(32, 64, 3)
# Entry Flow Xception Blocks
self.xception_block1 = XceptionBlock(
inp=64,
outp=128,
reps=2,
s=2,
start_act=False,
grow_first=True,
)
self.xception_block2 = XceptionBlock(
inp=128,
outp=256,
reps=2,
s=2,
start_act=True,
grow_first=True,
)
self.xception_block3 = XceptionBlock(
inp=256,
outp=728,
reps=2,
s=2,
start_act=True,
grow_first=True,
)
self.middle_flow_xception_blocks = nn.Sequential(
*[
XceptionBlock(
inp=728,
outp=728,
reps=3,
s=1,
start_act=True,
grow_first=True,
)
for _ in range(8)
]
)
self.xception_block4 = XceptionBlock(
inp=728,
outp=1024,
reps=2,
s=2,
start_act=True,
grow_first=False,
)
self.conv3 = SeparableConv(1024, 1536)
self.conv4 = SeparableConv(1536, 2048)
self.pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = Classifier(2048, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Entry flow
x = self.conv1(x)
x = self.conv2(x)
x = self.xception_block1(x)
x = self.xception_block2(x)
x = self.xception_block3(x)
# Middle flow
x = self.middle_flow_xception_blocks(x)
# Exit flow
x = self.xception_block4(x)
x = self.conv3(x)
x = self.conv4(x)
# Classification Flow
x = self.pooling(x)
x = self.classifier(x)
return x | models/Xception/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .blocks import *
__all__ = [
"XceptionNet",
]
class XceptionNet(nn.Module):
def __init__(
self,
image_channels: int,
num_classes: int,
):
super().__init__()
# Entry Flow
self.conv1 = ConvBlock(image_channels, 32, 3, 2)
self.conv2 = ConvBlock(32, 64, 3)
# Entry Flow Xception Blocks
self.xception_block1 = XceptionBlock(
inp=64,
outp=128,
reps=2,
s=2,
start_act=False,
grow_first=True,
)
self.xception_block2 = XceptionBlock(
inp=128,
outp=256,
reps=2,
s=2,
start_act=True,
grow_first=True,
)
self.xception_block3 = XceptionBlock(
inp=256,
outp=728,
reps=2,
s=2,
start_act=True,
grow_first=True,
)
self.middle_flow_xception_blocks = nn.Sequential(
*[
XceptionBlock(
inp=728,
outp=728,
reps=3,
s=1,
start_act=True,
grow_first=True,
)
for _ in range(8)
]
)
self.xception_block4 = XceptionBlock(
inp=728,
outp=1024,
reps=2,
s=2,
start_act=True,
grow_first=False,
)
self.conv3 = SeparableConv(1024, 1536)
self.conv4 = SeparableConv(1536, 2048)
self.pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = Classifier(2048, num_classes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# Entry flow
x = self.conv1(x)
x = self.conv2(x)
x = self.xception_block1(x)
x = self.xception_block2(x)
x = self.xception_block3(x)
# Middle flow
x = self.middle_flow_xception_blocks(x)
# Exit flow
x = self.xception_block4(x)
x = self.conv3(x)
x = self.conv4(x)
# Classification Flow
x = self.pooling(x)
x = self.classifier(x)
return x | 0.931463 | 0.343163 |
import autodisc as ad
import goalrepresent as gr
def get_system_config():
system_config = ad.systems.Lenia.default_config()
system_config.version = "pytorch_fft"
system_config.use_gpu = True
return system_config
def get_system_parameters():
system_parameters = ad.systems.Lenia.default_system_parameters()
system_parameters.size_y = 256
system_parameters.size_x = 256
return system_parameters
def get_model_config(model_name):
model_class = eval("gr.models.{}Model".format(model_name))
model_config = model_class.default_config()
if 'network' in model_config:
if "ProgressiveTree" in model_name:
## network
model_config.node_classname = "VAE"
model_config.node.network.name = "Burgess"
model_config.node.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
model_config.node.create_connections = {"lf": True,"gf": False, "gfi":True, "lfi": True, "recon": True, }
model_config.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
## device
model_config.node.device.use_gpu = True
else:
## network
model_config.network.name = "Burgess"
model_config.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
## initialization
model_config.network.initialization.name = "kaiming_uniform"
model_config.network.initialization.parameters = {}
## loss
model_config.loss.name = "VAE"
model_config.loss.parameters = {"reconstruction_dist": "bernoulli"}
## optimizer
model_config.optimizer.name = "Adam"
model_config.optimizer.parameters = {"lr": 1e-3, "weight_decay": 1e-5 }
# device
model_config.device.use_gpu = True
## logging
model_config.logging.record_valid_images_every = 100
model_config.logging.record_embeddings_every = 400
## checkpoint
model_config.checkpoint.save_model_every = 1
## evaluation
model_config.evaluation.save_results_every = 5000
return model_config
def get_explorer_config():
explorer_config = ad.explorers.ProgressiveExplorer.default_config()
explorer_config.seed = 9
explorer_config.num_of_random_initialization = 1000
explorer_config.run_parameters = []
# Parameter 1: init state
parameter = ad.Config()
parameter.name = 'init_state'
parameter.type = 'cppn_evolution'
parameter.init = ad.cppn.TwoDMatrixCCPNNEATEvolution.default_config()
parameter.init.neat_config_file = 'neat_config.cfg'
parameter.init.n_generations = 1
parameter.init.best_genome_of_last_generation = True
parameter.mutate = ad.cppn.TwoDMatrixCCPNNEATEvolution.default_config()
parameter.mutate.neat_config_file = 'neat_config.cfg'
parameter.mutate.n_generations = 2
parameter.mutate.best_genome_of_last_generation = True
explorer_config.run_parameters.append(parameter)
# Parameter 2: R
parameter = ad.Config()
parameter.name = 'R'
parameter.type = 'sampling'
parameter.init = ('discrete', 2, 20)
parameter.mutate = {'type': 'discrete', 'distribution': 'gauss', 'sigma': 0.5, 'min': 2, 'max': 20}
explorer_config.run_parameters.append(parameter)
# Parameter 3: T
parameter = ad.Config()
parameter.name = 'T'
parameter.type = 'sampling'
parameter.init = ('discrete', 1, 20)
parameter.mutate = {'type': 'discrete', 'distribution': 'gauss', 'sigma': 0.5, 'min': 1, 'max': 20}
explorer_config.run_parameters.append(parameter)
# Parameter 4: b
parameter = ad.Config()
parameter.name = 'b'
parameter.type = 'sampling'
parameter.init = ('function', ad.helper.sampling.sample_vector, (('discrete', 1, 3), (0, 1)))
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.1, 'min': 0, 'max': 1}
explorer_config.run_parameters.append(parameter)
# Parameter 5: m
parameter = ad.Config()
parameter.name = 'm'
parameter.type = 'sampling'
parameter.init = ('continuous', 0, 1)
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.1, 'min': 0, 'max': 1}
explorer_config.run_parameters.append(parameter)
# Parameter 6: s
parameter = ad.Config()
parameter.name = 's'
parameter.type = 'sampling'
parameter.init = ('continuous', 0.001, 0.3)
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.05, 'min': 0.001, 'max': 0.3}
explorer_config.run_parameters.append(parameter)
# visual representation
explorer_config.visual_representation = gr.representations.SingleModelRepresentation.default_config()
explorer_config.visual_representation.seed = 9
explorer_config.visual_representation.training.output_folder = "./training"
explorer_config.visual_representation.model.name = "ProgressiveTree"
explorer_config.visual_representation.model.config = get_model_config(explorer_config.visual_representation.model.name)
# goal space selection
explorer_config.goal_space_selection.type = 'adaptive'
if explorer_config.goal_space_selection.type in ['probability_distribution']:
explorer_config.goal_space_selection.distribution = None
elif explorer_config.goal_space_selection.type in ['adaptive']:
explorer_config.goal_space_selection.measure = ad.Config()
explorer_config.goal_space_selection.measure.type = 'score_per_goalspace'
explorer_config.goal_space_selection.measure.n_steps = None
if None is not None and None is not None:
raise ValueError('Only explorer_config.goal_space_selection.measure.n_bins_per_dimension or explorer_config.goal_space_selection.measure.n_bins can be defined!')
if None is not None:
explorer_config.goal_space_selection.measure.diversity = ad.Config()
explorer_config.goal_space_selection.measure.diversity.type = 'NBinDiversityNBinPerDim'
explorer_config.goal_space_selection.measure.diversity.n_bins_per_dimension = None
elif None is not None:
explorer_config.goal_space_selection.measure.diversity = ad.Config()
explorer_config.goal_space_selection.measure.diversity.type = 'NBinDiversityNBins'
explorer_config.goal_space_selection.measure.diversity.n_bins = None
# add constraint to the diversity measure
explorer_config.goal_space_selection.measure.update_constraints = dict( active = 'after_split', filter = 'statistics.classifier_animal')
explorer_config.goal_space_selection.selection_algo = ad.Config()
explorer_config.goal_space_selection.selection_algo.type = 'softmax'
if explorer_config.goal_space_selection.selection_algo.type in ['epsilon_greedy']:
explorer_config.goal_space_selection.selection_algo.epsilon = None
elif explorer_config.goal_space_selection.selection_algo.type in ['softmax']:
explorer_config.goal_space_selection.selection_algo.beta = 5
elif explorer_config.goal_space_selection.selection_algo.type in ['epsilon_softmax']:
explorer_config.goal_space_selection.selection_algo.epsilon = None
explorer_config.goal_space_selection.selection_algo.beta = 5
# goal selection
explorer_config.goal_selection.type = 'random'
explorer_config.goal_selection.sampling_from_reached_boundaries = ad.Config()
explorer_config.goal_selection.sampling_from_reached_boundaries.margin_min = 0
explorer_config.goal_selection.sampling_from_reached_boundaries.margin_max = 0
# progressive growing parameters
explorer_config.progressive_growing.split_trigger = ad.Config(dict(active = True, fitness_key= 'recon', type= 'plateau', parameters= dict(epsilon= 20, n_steps_average= 50), conditions= dict(min_init_n_epochs = 2000, n_min_points = 500, n_max_splits= 10, n_epochs_min_between_splits= 200), save_model_before_after= True))
explorer_config.progressive_growing.split_trigger.boundary_config = {"z_fitness": "recon_loss", "algo": "cluster.KMeans"}
# progressive training parameters
explorer_config.progressive_training.dataset_constraints = [dict( active = True, filter = ('statistics.is_dead', '==', False))]
explorer_config.progressive_training.dataset_augment = True
explorer_config.progressive_training.n_runs_between_stages = 100
explorer_config.progressive_training.n_epochs_per_stage = 100
explorer_config.progressive_training.train_batch_size = 128
explorer_config.progressive_training.importance_sampling_new_vs_old = 0.3
explorer_config.progressive_training.alternated_backward = {"active": True, "ratio_epochs": {"connections": 2, "core": 8}}
# how are the source policies for a mutation are selected
explorer_config.source_policy_selection.type = 'optimal'
explorer_config.source_policy_selection.constraints = []
return explorer_config
def get_number_of_explorations():
return 5000 | experiments/IMGEP-HOLMES (SLP)/repetition_000009/experiment_config.py | import autodisc as ad
import goalrepresent as gr
def get_system_config():
system_config = ad.systems.Lenia.default_config()
system_config.version = "pytorch_fft"
system_config.use_gpu = True
return system_config
def get_system_parameters():
system_parameters = ad.systems.Lenia.default_system_parameters()
system_parameters.size_y = 256
system_parameters.size_x = 256
return system_parameters
def get_model_config(model_name):
model_class = eval("gr.models.{}Model".format(model_name))
model_config = model_class.default_config()
if 'network' in model_config:
if "ProgressiveTree" in model_name:
## network
model_config.node_classname = "VAE"
model_config.node.network.name = "Burgess"
model_config.node.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
model_config.node.create_connections = {"lf": True,"gf": False, "gfi":True, "lfi": True, "recon": True, }
model_config.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
## device
model_config.node.device.use_gpu = True
else:
## network
model_config.network.name = "Burgess"
model_config.network.parameters = {"n_channels": 1, "input_size": (256,256), "n_latents": 16, "n_conv_layers": 6, "hidden_channels": 16, "hidden_dim": 64, "encoder_conditional_type": "gaussian", "feature_layer": 2}
## initialization
model_config.network.initialization.name = "kaiming_uniform"
model_config.network.initialization.parameters = {}
## loss
model_config.loss.name = "VAE"
model_config.loss.parameters = {"reconstruction_dist": "bernoulli"}
## optimizer
model_config.optimizer.name = "Adam"
model_config.optimizer.parameters = {"lr": 1e-3, "weight_decay": 1e-5 }
# device
model_config.device.use_gpu = True
## logging
model_config.logging.record_valid_images_every = 100
model_config.logging.record_embeddings_every = 400
## checkpoint
model_config.checkpoint.save_model_every = 1
## evaluation
model_config.evaluation.save_results_every = 5000
return model_config
def get_explorer_config():
explorer_config = ad.explorers.ProgressiveExplorer.default_config()
explorer_config.seed = 9
explorer_config.num_of_random_initialization = 1000
explorer_config.run_parameters = []
# Parameter 1: init state
parameter = ad.Config()
parameter.name = 'init_state'
parameter.type = 'cppn_evolution'
parameter.init = ad.cppn.TwoDMatrixCCPNNEATEvolution.default_config()
parameter.init.neat_config_file = 'neat_config.cfg'
parameter.init.n_generations = 1
parameter.init.best_genome_of_last_generation = True
parameter.mutate = ad.cppn.TwoDMatrixCCPNNEATEvolution.default_config()
parameter.mutate.neat_config_file = 'neat_config.cfg'
parameter.mutate.n_generations = 2
parameter.mutate.best_genome_of_last_generation = True
explorer_config.run_parameters.append(parameter)
# Parameter 2: R
parameter = ad.Config()
parameter.name = 'R'
parameter.type = 'sampling'
parameter.init = ('discrete', 2, 20)
parameter.mutate = {'type': 'discrete', 'distribution': 'gauss', 'sigma': 0.5, 'min': 2, 'max': 20}
explorer_config.run_parameters.append(parameter)
# Parameter 3: T
parameter = ad.Config()
parameter.name = 'T'
parameter.type = 'sampling'
parameter.init = ('discrete', 1, 20)
parameter.mutate = {'type': 'discrete', 'distribution': 'gauss', 'sigma': 0.5, 'min': 1, 'max': 20}
explorer_config.run_parameters.append(parameter)
# Parameter 4: b
parameter = ad.Config()
parameter.name = 'b'
parameter.type = 'sampling'
parameter.init = ('function', ad.helper.sampling.sample_vector, (('discrete', 1, 3), (0, 1)))
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.1, 'min': 0, 'max': 1}
explorer_config.run_parameters.append(parameter)
# Parameter 5: m
parameter = ad.Config()
parameter.name = 'm'
parameter.type = 'sampling'
parameter.init = ('continuous', 0, 1)
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.1, 'min': 0, 'max': 1}
explorer_config.run_parameters.append(parameter)
# Parameter 6: s
parameter = ad.Config()
parameter.name = 's'
parameter.type = 'sampling'
parameter.init = ('continuous', 0.001, 0.3)
parameter.mutate = {'type': 'continuous', 'distribution': 'gauss', 'sigma': 0.05, 'min': 0.001, 'max': 0.3}
explorer_config.run_parameters.append(parameter)
# visual representation
explorer_config.visual_representation = gr.representations.SingleModelRepresentation.default_config()
explorer_config.visual_representation.seed = 9
explorer_config.visual_representation.training.output_folder = "./training"
explorer_config.visual_representation.model.name = "ProgressiveTree"
explorer_config.visual_representation.model.config = get_model_config(explorer_config.visual_representation.model.name)
# goal space selection
explorer_config.goal_space_selection.type = 'adaptive'
if explorer_config.goal_space_selection.type in ['probability_distribution']:
explorer_config.goal_space_selection.distribution = None
elif explorer_config.goal_space_selection.type in ['adaptive']:
explorer_config.goal_space_selection.measure = ad.Config()
explorer_config.goal_space_selection.measure.type = 'score_per_goalspace'
explorer_config.goal_space_selection.measure.n_steps = None
if None is not None and None is not None:
raise ValueError('Only explorer_config.goal_space_selection.measure.n_bins_per_dimension or explorer_config.goal_space_selection.measure.n_bins can be defined!')
if None is not None:
explorer_config.goal_space_selection.measure.diversity = ad.Config()
explorer_config.goal_space_selection.measure.diversity.type = 'NBinDiversityNBinPerDim'
explorer_config.goal_space_selection.measure.diversity.n_bins_per_dimension = None
elif None is not None:
explorer_config.goal_space_selection.measure.diversity = ad.Config()
explorer_config.goal_space_selection.measure.diversity.type = 'NBinDiversityNBins'
explorer_config.goal_space_selection.measure.diversity.n_bins = None
# add constraint to the diversity measure
explorer_config.goal_space_selection.measure.update_constraints = dict( active = 'after_split', filter = 'statistics.classifier_animal')
explorer_config.goal_space_selection.selection_algo = ad.Config()
explorer_config.goal_space_selection.selection_algo.type = 'softmax'
if explorer_config.goal_space_selection.selection_algo.type in ['epsilon_greedy']:
explorer_config.goal_space_selection.selection_algo.epsilon = None
elif explorer_config.goal_space_selection.selection_algo.type in ['softmax']:
explorer_config.goal_space_selection.selection_algo.beta = 5
elif explorer_config.goal_space_selection.selection_algo.type in ['epsilon_softmax']:
explorer_config.goal_space_selection.selection_algo.epsilon = None
explorer_config.goal_space_selection.selection_algo.beta = 5
# goal selection
explorer_config.goal_selection.type = 'random'
explorer_config.goal_selection.sampling_from_reached_boundaries = ad.Config()
explorer_config.goal_selection.sampling_from_reached_boundaries.margin_min = 0
explorer_config.goal_selection.sampling_from_reached_boundaries.margin_max = 0
# progressive growing parameters
explorer_config.progressive_growing.split_trigger = ad.Config(dict(active = True, fitness_key= 'recon', type= 'plateau', parameters= dict(epsilon= 20, n_steps_average= 50), conditions= dict(min_init_n_epochs = 2000, n_min_points = 500, n_max_splits= 10, n_epochs_min_between_splits= 200), save_model_before_after= True))
explorer_config.progressive_growing.split_trigger.boundary_config = {"z_fitness": "recon_loss", "algo": "cluster.KMeans"}
# progressive training parameters
explorer_config.progressive_training.dataset_constraints = [dict( active = True, filter = ('statistics.is_dead', '==', False))]
explorer_config.progressive_training.dataset_augment = True
explorer_config.progressive_training.n_runs_between_stages = 100
explorer_config.progressive_training.n_epochs_per_stage = 100
explorer_config.progressive_training.train_batch_size = 128
explorer_config.progressive_training.importance_sampling_new_vs_old = 0.3
explorer_config.progressive_training.alternated_backward = {"active": True, "ratio_epochs": {"connections": 2, "core": 8}}
# how are the source policies for a mutation are selected
explorer_config.source_policy_selection.type = 'optimal'
explorer_config.source_policy_selection.constraints = []
return explorer_config
def get_number_of_explorations():
return 5000 | 0.481941 | 0.216726 |
import pytest
from django.contrib import auth
from django.urls import reverse
from webdev.users.models import User
from pytest_django.asserts import assertContains
# Registro
@pytest.fixture
def resposta_registro(client):
resp = client.get(reverse('registro'))
return resp
def test_registro_status_code(resposta_registro):
assert resposta_registro.status_code == 200
def test_formulario_de_registro_presente(resposta_registro):
assertContains(resposta_registro, '<form')
def test_btn_salvar_presente(resposta_registro):
assertContains(resposta_registro, '<button type="submit"')
@pytest.fixture
def usuario(db):
return User.objects.create(
email='<EMAIL>',
password='<PASSWORD>',
)
# Seja um anfitrião
@pytest.fixture
def resposta_ser_anfitriao(client):
return client.get(reverse('ser_anfitriao'))
def test_ser_anfitriao_status_code(resposta_ser_anfitriao):
assert resposta_ser_anfitriao.status_code == 200
def test_formulario_ser_anfitriao(resposta_ser_anfitriao):
assertContains(resposta_ser_anfitriao, f'<form action="{reverse("ser_anfitriao")}"')
def test_input_email_presente(resposta_ser_anfitriao):
# Apesar do usuário ser logado com seu endereço de email, a classe LoginView do django chama esse input de username
assertContains(resposta_ser_anfitriao, '<input type="text" name="email"')
def test_btn_enviar_email_ser_anfitriao(resposta_ser_anfitriao):
assertContains(resposta_ser_anfitriao, '<button type="submit"')
# Login
@pytest.fixture
def resposta_login(client):
resp = client.get(reverse('login'))
return resp
def test_login_status_code(resposta_login):
assert resposta_login.status_code == 200
def test_formulario_de_login_presente(resposta_login):
assertContains(resposta_login, '<form')
def test_input_username_presente(resposta_login):
# Apesar do usuário ser logado com seu endereço de email, a classe LoginView do django chama esse input de username
assertContains(resposta_login, '<input type="text" name="username"')
def test_btn_enviar_presente(resposta_login):
assertContains(resposta_login, '<button type="submit"')
# Logout
@pytest.fixture
def resposta_logout(client, usuario):
client.login(
email='<EMAIL>',
password='<PASSWORD>',
)
resp = client.get(reverse('logout'))
return resp
def test_logout_status_code(resposta_logout):
assert resposta_logout.status_code == 302
def test_usuario_nao_autenticado(resposta_logout):
assert auth.get_user(resposta_logout.client).is_anonymous
# Botões presente
@pytest.fixture
def resposta_home_logado(client, usuario):
client.force_login(usuario)
resp = client.get('/')
return resp
def test_btn_logout_presente(resposta_home_logado):
assertContains(resposta_home_logado, f'<a href="{reverse("logout")}"')
@pytest.fixture
def resposta_home_anonimo(client):
return client.get('/')
def test_btn_login_presente(resposta_home_anonimo):
assertContains(resposta_home_anonimo, f'<a href="{reverse("login")}"') | webdev/users/tests/test_users_get.py | import pytest
from django.contrib import auth
from django.urls import reverse
from webdev.users.models import User
from pytest_django.asserts import assertContains
# Registro
@pytest.fixture
def resposta_registro(client):
resp = client.get(reverse('registro'))
return resp
def test_registro_status_code(resposta_registro):
assert resposta_registro.status_code == 200
def test_formulario_de_registro_presente(resposta_registro):
assertContains(resposta_registro, '<form')
def test_btn_salvar_presente(resposta_registro):
assertContains(resposta_registro, '<button type="submit"')
@pytest.fixture
def usuario(db):
return User.objects.create(
email='<EMAIL>',
password='<PASSWORD>',
)
# Seja um anfitrião
@pytest.fixture
def resposta_ser_anfitriao(client):
return client.get(reverse('ser_anfitriao'))
def test_ser_anfitriao_status_code(resposta_ser_anfitriao):
assert resposta_ser_anfitriao.status_code == 200
def test_formulario_ser_anfitriao(resposta_ser_anfitriao):
assertContains(resposta_ser_anfitriao, f'<form action="{reverse("ser_anfitriao")}"')
def test_input_email_presente(resposta_ser_anfitriao):
# Apesar do usuário ser logado com seu endereço de email, a classe LoginView do django chama esse input de username
assertContains(resposta_ser_anfitriao, '<input type="text" name="email"')
def test_btn_enviar_email_ser_anfitriao(resposta_ser_anfitriao):
assertContains(resposta_ser_anfitriao, '<button type="submit"')
# Login
@pytest.fixture
def resposta_login(client):
resp = client.get(reverse('login'))
return resp
def test_login_status_code(resposta_login):
assert resposta_login.status_code == 200
def test_formulario_de_login_presente(resposta_login):
assertContains(resposta_login, '<form')
def test_input_username_presente(resposta_login):
# Apesar do usuário ser logado com seu endereço de email, a classe LoginView do django chama esse input de username
assertContains(resposta_login, '<input type="text" name="username"')
def test_btn_enviar_presente(resposta_login):
assertContains(resposta_login, '<button type="submit"')
# Logout
@pytest.fixture
def resposta_logout(client, usuario):
client.login(
email='<EMAIL>',
password='<PASSWORD>',
)
resp = client.get(reverse('logout'))
return resp
def test_logout_status_code(resposta_logout):
assert resposta_logout.status_code == 302
def test_usuario_nao_autenticado(resposta_logout):
assert auth.get_user(resposta_logout.client).is_anonymous
# Botões presente
@pytest.fixture
def resposta_home_logado(client, usuario):
client.force_login(usuario)
resp = client.get('/')
return resp
def test_btn_logout_presente(resposta_home_logado):
assertContains(resposta_home_logado, f'<a href="{reverse("logout")}"')
@pytest.fixture
def resposta_home_anonimo(client):
return client.get('/')
def test_btn_login_presente(resposta_home_anonimo):
assertContains(resposta_home_anonimo, f'<a href="{reverse("login")}"') | 0.31785 | 0.315551 |
from __future__ import annotations
from ssl import create_default_context
from typing import TYPE_CHECKING
from urllib.parse import urlparse
from weakref import WeakValueDictionary
from anyio import BrokenResourceError, EndOfStream, Event, Lock, connect_tcp, sleep
from anyio.streams.tls import TLSStream
from certifi import where
from h2.config import H2Configuration
from h2.connection import H2Connection
from ...utils import exponential_backoff
from ...http.base import BaseHTTPClient, ConnectionState
if TYPE_CHECKING:
from ssl import SSLContext
from types import TracebackType
from typing_extensions import Self
def create_ssl_context() -> SSLContext:
context = create_default_context(
cafile=where(),
)
context.set_alpn_protocols(["h2"])
return context
class Bucket:
lock: Lock
def __init__(self, lock: Lock) -> None:
self.lock = lock
self.deferred = False
async def __aenter__(self) -> Lock:
await self.lock.acquire()
return self
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
if self.deferred:
return
await self.release()
def defer(self) -> None:
if not self.deferred:
self.deferred = True
async def release(self) -> None:
self.lock.release()
class BucketManager:
buckets: dict[str, Bucket]
global_limiter: Event
def __init__(self: Self) -> None:
self.global_limiter = None
self.buckets = WeakValueDictionary()
def get(self, key: str) -> Bucket:
try:
bucket = self.buckets[key]
except KeyError:
bucket = self.buckets[key] = Bucket(Lock())
return bucket
async def clear_global(self: Self) -> None:
self.global_limiter.set()
def is_global(self: Self) -> bool:
if self.global_limiter is None:
return False
return self.global_limiter.is_set()
async def wait_global(self: Self) -> None:
await self.global_limiter.wait()
async def set_global(self: Self) -> None:
self.global_limiter = Event()
class HTTPClient(BaseHTTPClient):
socket: TLSStream
state_lock: Lock
bucket_manager: BucketManager
connect_lock: Lock
read_lock: Lock
def __init__(
self: Self,
max_reconnect_retries: int = 3,
max_request_retries: int = 3,
default_headers: list[tuple[bytes, bytes]] | None = None,
) -> None:
BaseHTTPClient.__init__(
self,
max_reconnect_retries=max_reconnect_retries,
max_request_retries=max_request_retries,
default_headers=default_headers,
)
self.state_lock = Lock()
self.bucket_manager = BucketManager()
self.connect_lock = Lock()
self.read_lock = Lock()
@property
def port(self: Self) -> int:
return 443
async def connect(self: Self, url: str) -> Self:
if self.connection_initialized:
return
self.url = urlparse(url)
self.server_name = self.url.netloc.encode("ascii")
server_name = self.url.netloc
self.connection = None
retries = self.max_reconnect_retries
back_off = exponential_backoff(2, 0)
ssl_context = create_ssl_context()
async with self.connect_lock:
while True:
try:
self.socket = await connect_tcp(
server_name,
self.port,
tls=True,
ssl_context=ssl_context,
)
except (OSError, TimeoutError, BrokenResourceError):
if retries == 0:
raise
retries -= 1
back_off = exponential_backoff(2, back_off)
await sleep(back_off)
else:
self.connection_initialized = True
break
assert isinstance(self.socket, TLSStream)
self.connection = H2Connection(
config=H2Configuration(validate_inbound_headers=False)
)
self.connection.initiate_connection()
await self._stream_send(self.connection.data_to_send())
return self
async def _stream_recv(self: Self, max_bytes: int) -> bytes:
try:
read = await self.socket.receive(max_bytes)
except EndOfStream:
read = b""
return read
async def _stream_send(self: Self, data: bytes) -> None:
await self.socket.send(data)
async def aclose(self: Self) -> None:
if self.connection_initialized:
self.connection.close_connection()
await self._stream_send(self.connection.data_to_send())
self.state = ConnectionState.CLOSED | discpyth/backends/_anyio/http.py | from __future__ import annotations
from ssl import create_default_context
from typing import TYPE_CHECKING
from urllib.parse import urlparse
from weakref import WeakValueDictionary
from anyio import BrokenResourceError, EndOfStream, Event, Lock, connect_tcp, sleep
from anyio.streams.tls import TLSStream
from certifi import where
from h2.config import H2Configuration
from h2.connection import H2Connection
from ...utils import exponential_backoff
from ...http.base import BaseHTTPClient, ConnectionState
if TYPE_CHECKING:
from ssl import SSLContext
from types import TracebackType
from typing_extensions import Self
def create_ssl_context() -> SSLContext:
context = create_default_context(
cafile=where(),
)
context.set_alpn_protocols(["h2"])
return context
class Bucket:
lock: Lock
def __init__(self, lock: Lock) -> None:
self.lock = lock
self.deferred = False
async def __aenter__(self) -> Lock:
await self.lock.acquire()
return self
async def __aexit__(
self,
exc_type: type[BaseException] | None,
exc_val: BaseException | None,
exc_tb: TracebackType | None,
) -> None:
if self.deferred:
return
await self.release()
def defer(self) -> None:
if not self.deferred:
self.deferred = True
async def release(self) -> None:
self.lock.release()
class BucketManager:
buckets: dict[str, Bucket]
global_limiter: Event
def __init__(self: Self) -> None:
self.global_limiter = None
self.buckets = WeakValueDictionary()
def get(self, key: str) -> Bucket:
try:
bucket = self.buckets[key]
except KeyError:
bucket = self.buckets[key] = Bucket(Lock())
return bucket
async def clear_global(self: Self) -> None:
self.global_limiter.set()
def is_global(self: Self) -> bool:
if self.global_limiter is None:
return False
return self.global_limiter.is_set()
async def wait_global(self: Self) -> None:
await self.global_limiter.wait()
async def set_global(self: Self) -> None:
self.global_limiter = Event()
class HTTPClient(BaseHTTPClient):
socket: TLSStream
state_lock: Lock
bucket_manager: BucketManager
connect_lock: Lock
read_lock: Lock
def __init__(
self: Self,
max_reconnect_retries: int = 3,
max_request_retries: int = 3,
default_headers: list[tuple[bytes, bytes]] | None = None,
) -> None:
BaseHTTPClient.__init__(
self,
max_reconnect_retries=max_reconnect_retries,
max_request_retries=max_request_retries,
default_headers=default_headers,
)
self.state_lock = Lock()
self.bucket_manager = BucketManager()
self.connect_lock = Lock()
self.read_lock = Lock()
@property
def port(self: Self) -> int:
return 443
async def connect(self: Self, url: str) -> Self:
if self.connection_initialized:
return
self.url = urlparse(url)
self.server_name = self.url.netloc.encode("ascii")
server_name = self.url.netloc
self.connection = None
retries = self.max_reconnect_retries
back_off = exponential_backoff(2, 0)
ssl_context = create_ssl_context()
async with self.connect_lock:
while True:
try:
self.socket = await connect_tcp(
server_name,
self.port,
tls=True,
ssl_context=ssl_context,
)
except (OSError, TimeoutError, BrokenResourceError):
if retries == 0:
raise
retries -= 1
back_off = exponential_backoff(2, back_off)
await sleep(back_off)
else:
self.connection_initialized = True
break
assert isinstance(self.socket, TLSStream)
self.connection = H2Connection(
config=H2Configuration(validate_inbound_headers=False)
)
self.connection.initiate_connection()
await self._stream_send(self.connection.data_to_send())
return self
async def _stream_recv(self: Self, max_bytes: int) -> bytes:
try:
read = await self.socket.receive(max_bytes)
except EndOfStream:
read = b""
return read
async def _stream_send(self: Self, data: bytes) -> None:
await self.socket.send(data)
async def aclose(self: Self) -> None:
if self.connection_initialized:
self.connection.close_connection()
await self._stream_send(self.connection.data_to_send())
self.state = ConnectionState.CLOSED | 0.812682 | 0.084682 |
from object_library import all_lorentz, Lorentz
from function_library import complexconjugate, re, im, csc, sec, acsc, asec, cot
try:
import form_factors as ForFac
except ImportError:
pass
UUS1 = Lorentz(name = 'UUS1',
spins = [ -1, -1, 1 ],
structure = '1')
UUV1 = Lorentz(name = 'UUV1',
spins = [ -1, -1, 3 ],
structure = 'P(3,2) + P(3,3)')
SSS1 = Lorentz(name = 'SSS1',
spins = [ 1, 1, 1 ],
structure = '1')
FFS1 = Lorentz(name = 'FFS1',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1)')
FFS2 = Lorentz(name = 'FFS2',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1) - ProjP(2,1)')
FFS3 = Lorentz(name = 'FFS3',
spins = [ 2, 2, 1 ],
structure = 'ProjP(2,1)')
FFS4 = Lorentz(name = 'FFS4',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1) + ProjP(2,1)')
FFV1 = Lorentz(name = 'FFV1',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,1)')
FFV2 = Lorentz(name = 'FFV2',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1)')
FFV3 = Lorentz(name = 'FFV3',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) - 2*Gamma(3,2,-1)*ProjP(-1,1)')
FFV4 = Lorentz(name = 'FFV4',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) + 2*Gamma(3,2,-1)*ProjP(-1,1)')
FFV5 = Lorentz(name = 'FFV5',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) + 4*Gamma(3,2,-1)*ProjP(-1,1)')
VSS1 = Lorentz(name = 'VSS1',
spins = [ 3, 1, 1 ],
structure = 'P(1,2) - P(1,3)')
VVS1 = Lorentz(name = 'VVS1',
spins = [ 3, 3, 1 ],
structure = 'Metric(1,2)')
VVV1 = Lorentz(name = 'VVV1',
spins = [ 3, 3, 3 ],
structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(2,3)*Metric(1,3) + P(1,2)*Metric(2,3) - P(1,3)*Metric(2,3)')
SSSS1 = Lorentz(name = 'SSSS1',
spins = [ 1, 1, 1, 1 ],
structure = '1')
FFFF1 = Lorentz(name = 'FFFF1',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-3,1)*ProjM(-2,3)')
FFFF2 = Lorentz(name = 'FFFF2',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,1)*ProjM(-2,3)')
FFFF3 = Lorentz(name = 'FFFF3',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,3)*ProjM(-2,1)')
FFFF4 = Lorentz(name = 'FFFF4',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,3)*ProjP(-3,1)')
FFFF5 = Lorentz(name = 'FFFF5',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,3)*ProjP(-3,1)')
FFFF6 = Lorentz(name = 'FFFF6',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,1)*ProjP(-2,3)')
FFFF7 = Lorentz(name = 'FFFF7',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,1)*ProjP(-3,3)')
FFFF8 = Lorentz(name = 'FFFF8',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,1)*ProjP(-3,3)')
FFFF9 = Lorentz(name = 'FFFF9',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,3)*ProjP(-2,1)')
VVSS1 = Lorentz(name = 'VVSS1',
spins = [ 3, 3, 1, 1 ],
structure = 'Metric(1,2)')
VVVV1 = Lorentz(name = 'VVVV1',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - Metric(1,3)*Metric(2,4)')
VVVV2 = Lorentz(name = 'VVVV2',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) + Metric(1,3)*Metric(2,4) - 2*Metric(1,2)*Metric(3,4)')
VVVV3 = Lorentz(name = 'VVVV3',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - Metric(1,2)*Metric(3,4)')
VVVV4 = Lorentz(name = 'VVVV4',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,3)*Metric(2,4) - Metric(1,2)*Metric(3,4)')
VVVV5 = Lorentz(name = 'VVVV5',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - (Metric(1,3)*Metric(2,4))/2. - (Metric(1,2)*Metric(3,4))/2.') | examples/example4_systematics_DY/data/mg_processes/DY_sys/bin/internal/ufomodel/lorentz.py |
from object_library import all_lorentz, Lorentz
from function_library import complexconjugate, re, im, csc, sec, acsc, asec, cot
try:
import form_factors as ForFac
except ImportError:
pass
UUS1 = Lorentz(name = 'UUS1',
spins = [ -1, -1, 1 ],
structure = '1')
UUV1 = Lorentz(name = 'UUV1',
spins = [ -1, -1, 3 ],
structure = 'P(3,2) + P(3,3)')
SSS1 = Lorentz(name = 'SSS1',
spins = [ 1, 1, 1 ],
structure = '1')
FFS1 = Lorentz(name = 'FFS1',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1)')
FFS2 = Lorentz(name = 'FFS2',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1) - ProjP(2,1)')
FFS3 = Lorentz(name = 'FFS3',
spins = [ 2, 2, 1 ],
structure = 'ProjP(2,1)')
FFS4 = Lorentz(name = 'FFS4',
spins = [ 2, 2, 1 ],
structure = 'ProjM(2,1) + ProjP(2,1)')
FFV1 = Lorentz(name = 'FFV1',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,1)')
FFV2 = Lorentz(name = 'FFV2',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1)')
FFV3 = Lorentz(name = 'FFV3',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) - 2*Gamma(3,2,-1)*ProjP(-1,1)')
FFV4 = Lorentz(name = 'FFV4',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) + 2*Gamma(3,2,-1)*ProjP(-1,1)')
FFV5 = Lorentz(name = 'FFV5',
spins = [ 2, 2, 3 ],
structure = 'Gamma(3,2,-1)*ProjM(-1,1) + 4*Gamma(3,2,-1)*ProjP(-1,1)')
VSS1 = Lorentz(name = 'VSS1',
spins = [ 3, 1, 1 ],
structure = 'P(1,2) - P(1,3)')
VVS1 = Lorentz(name = 'VVS1',
spins = [ 3, 3, 1 ],
structure = 'Metric(1,2)')
VVV1 = Lorentz(name = 'VVV1',
spins = [ 3, 3, 3 ],
structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(2,3)*Metric(1,3) + P(1,2)*Metric(2,3) - P(1,3)*Metric(2,3)')
SSSS1 = Lorentz(name = 'SSSS1',
spins = [ 1, 1, 1, 1 ],
structure = '1')
FFFF1 = Lorentz(name = 'FFFF1',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-3,1)*ProjM(-2,3)')
FFFF2 = Lorentz(name = 'FFFF2',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,1)*ProjM(-2,3)')
FFFF3 = Lorentz(name = 'FFFF3',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,3)*ProjM(-2,1)')
FFFF4 = Lorentz(name = 'FFFF4',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,3)*ProjP(-3,1)')
FFFF5 = Lorentz(name = 'FFFF5',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,3)*ProjP(-3,1)')
FFFF6 = Lorentz(name = 'FFFF6',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,1)*ProjP(-2,3)')
FFFF7 = Lorentz(name = 'FFFF7',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,1)*ProjP(-3,3)')
FFFF8 = Lorentz(name = 'FFFF8',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,1)*ProjP(-3,3)')
FFFF9 = Lorentz(name = 'FFFF9',
spins = [ 2, 2, 2, 2 ],
structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,3)*ProjP(-2,1)')
VVSS1 = Lorentz(name = 'VVSS1',
spins = [ 3, 3, 1, 1 ],
structure = 'Metric(1,2)')
VVVV1 = Lorentz(name = 'VVVV1',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - Metric(1,3)*Metric(2,4)')
VVVV2 = Lorentz(name = 'VVVV2',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) + Metric(1,3)*Metric(2,4) - 2*Metric(1,2)*Metric(3,4)')
VVVV3 = Lorentz(name = 'VVVV3',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - Metric(1,2)*Metric(3,4)')
VVVV4 = Lorentz(name = 'VVVV4',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,3)*Metric(2,4) - Metric(1,2)*Metric(3,4)')
VVVV5 = Lorentz(name = 'VVVV5',
spins = [ 3, 3, 3, 3 ],
structure = 'Metric(1,4)*Metric(2,3) - (Metric(1,3)*Metric(2,4))/2. - (Metric(1,2)*Metric(3,4))/2.') | 0.397588 | 0.462534 |
import os
import sys
import imp
import os.path
import linecache
import re
from . import callbacks, conf, log, registry
installDir = os.path.dirname(sys.modules[__name__].__file__)
_pluginsDir = os.path.join(installDir, 'plugins')
class Deprecated(ImportError):
pass
def loadPluginModule(name, ignoreDeprecation=False):
"""Loads (and returns) the module for the plugin with the given name."""
files = []
pluginDirs = conf.supybot.directories.plugins()[:]
pluginDirs.append(_pluginsDir)
for dir in pluginDirs:
try:
files.extend(os.listdir(dir))
except EnvironmentError: # OSError, IOError superclass.
log.warning('Invalid plugin directory: %s; removing.', dir)
conf.supybot.directories.plugins().remove(dir)
if name not in files:
matched_names = filter(lambda x: re.search(r'(?i)^%s$' % (name,), x),
files)
if len(matched_names) == 1:
name = matched_names[0]
else:
raise ImportError, name
moduleInfo = imp.find_module(name, pluginDirs)
try:
module = imp.load_module(name, *moduleInfo)
except:
sys.modules.pop(name, None)
raise
if 'deprecated' in module.__dict__ and module.deprecated:
if ignoreDeprecation:
log.warning('Deprecated plugin loaded: %s', name)
else:
raise Deprecated, format('Attempted to load deprecated plugin %s',
name)
if module.__name__ in sys.modules:
sys.modules[module.__name__] = module
linecache.checkcache()
return module
def loadPluginClass(irc, module, register=None):
"""Loads the plugin Class from the given module into the given Irc."""
try:
cb = module.Class(irc)
except TypeError, e:
s = str(e)
if '2 given' in s and '__init__' in s:
raise callbacks.Error, \
'In our switch from CVS to Darcs (after 0.80.1), we ' \
'changed the __init__ for callbacks.Privmsg* to also ' \
'accept an irc argument. This plugin (%s) is overriding ' \
'its __init__ method and needs to update its prototype ' \
'to be \'def __init__(self, irc):\' as well as passing ' \
'that irc object on to any calls to the plugin\'s ' \
'parent\'s __init__.' % module.__name__
else:
raise
except AttributeError, e:
if 'Class' in str(e):
raise callbacks.Error, \
'This plugin module doesn\'t have a "Class" ' \
'attribute to specify which plugin should be ' \
'instantiated. If you didn\'t write this ' \
'plugin, but received it with Supybot, file ' \
'a bug with us about this error.'
else:
raise
cb.classModule = module
plugin = cb.name()
public = True
if hasattr(cb, 'public'):
public = cb.public
conf.registerPlugin(plugin, register, public)
assert not irc.getCallback(plugin), \
'There is already a %r plugin registered.' % plugin
try:
renames = []#XXX registerRename(plugin)()
if renames:
for command in renames:
v = registerRename(plugin, command)
newName = v()
assert newName
renameCommand(cb, command, newName)
else:
conf.supybot.commands.renames.unregister(plugin)
except registry.NonExistentRegistryEntry, e:
pass # The plugin isn't there.
irc.addCallback(cb)
return cb
# vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79: | src/plugin.py |
import os
import sys
import imp
import os.path
import linecache
import re
from . import callbacks, conf, log, registry
installDir = os.path.dirname(sys.modules[__name__].__file__)
_pluginsDir = os.path.join(installDir, 'plugins')
class Deprecated(ImportError):
pass
def loadPluginModule(name, ignoreDeprecation=False):
"""Loads (and returns) the module for the plugin with the given name."""
files = []
pluginDirs = conf.supybot.directories.plugins()[:]
pluginDirs.append(_pluginsDir)
for dir in pluginDirs:
try:
files.extend(os.listdir(dir))
except EnvironmentError: # OSError, IOError superclass.
log.warning('Invalid plugin directory: %s; removing.', dir)
conf.supybot.directories.plugins().remove(dir)
if name not in files:
matched_names = filter(lambda x: re.search(r'(?i)^%s$' % (name,), x),
files)
if len(matched_names) == 1:
name = matched_names[0]
else:
raise ImportError, name
moduleInfo = imp.find_module(name, pluginDirs)
try:
module = imp.load_module(name, *moduleInfo)
except:
sys.modules.pop(name, None)
raise
if 'deprecated' in module.__dict__ and module.deprecated:
if ignoreDeprecation:
log.warning('Deprecated plugin loaded: %s', name)
else:
raise Deprecated, format('Attempted to load deprecated plugin %s',
name)
if module.__name__ in sys.modules:
sys.modules[module.__name__] = module
linecache.checkcache()
return module
def loadPluginClass(irc, module, register=None):
"""Loads the plugin Class from the given module into the given Irc."""
try:
cb = module.Class(irc)
except TypeError, e:
s = str(e)
if '2 given' in s and '__init__' in s:
raise callbacks.Error, \
'In our switch from CVS to Darcs (after 0.80.1), we ' \
'changed the __init__ for callbacks.Privmsg* to also ' \
'accept an irc argument. This plugin (%s) is overriding ' \
'its __init__ method and needs to update its prototype ' \
'to be \'def __init__(self, irc):\' as well as passing ' \
'that irc object on to any calls to the plugin\'s ' \
'parent\'s __init__.' % module.__name__
else:
raise
except AttributeError, e:
if 'Class' in str(e):
raise callbacks.Error, \
'This plugin module doesn\'t have a "Class" ' \
'attribute to specify which plugin should be ' \
'instantiated. If you didn\'t write this ' \
'plugin, but received it with Supybot, file ' \
'a bug with us about this error.'
else:
raise
cb.classModule = module
plugin = cb.name()
public = True
if hasattr(cb, 'public'):
public = cb.public
conf.registerPlugin(plugin, register, public)
assert not irc.getCallback(plugin), \
'There is already a %r plugin registered.' % plugin
try:
renames = []#XXX registerRename(plugin)()
if renames:
for command in renames:
v = registerRename(plugin, command)
newName = v()
assert newName
renameCommand(cb, command, newName)
else:
conf.supybot.commands.renames.unregister(plugin)
except registry.NonExistentRegistryEntry, e:
pass # The plugin isn't there.
irc.addCallback(cb)
return cb
# vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79: | 0.391871 | 0.066691 |
import os
from copy import deepcopy
import torch
from torch import nn
from torch.optim import SGD
from torch.utils.data import DataLoader
from torchvision import models, transforms
from torchvision.datasets import CIFAR10
from pytorch_lightning import LightningModule, LightningDataModule, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor
from torchmetrics import Accuracy, MetricCollection
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
class CIFAR(LightningDataModule):
def __init__(self, img_size=32, batch_size=32):
super().__init__()
self.img_size = img_size if isinstance(img_size, tuple) else (img_size, img_size)
self.batch_size = batch_size
self.train_transforms = transforms.Compose([
transforms.Resize(self.img_size),
transforms.Pad(4, padding_mode='reflect'),
transforms.RandomCrop(self.img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
self.test_transforms = transforms.Compose([
transforms.Resize(self.img_size),
transforms.CenterCrop(self.img_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
def prepare_data(self) -> None:
CIFAR10(root='data', train=True, download=True)
CIFAR10(root='data', train=False, download=True)
def setup(self, stage=None):
self.train_ds = CIFAR10(root='data', train=True, download=False, transform=self.train_transforms)
self.valid_ds = CIFAR10(root='data', train=False, download=False, transform=self.test_transforms)
def train_dataloader(self):
return DataLoader(self.train_ds, num_workers=4, batch_size=self.batch_size, shuffle=True)
def val_dataloader(self):
return DataLoader(self.valid_ds, num_workers=4, batch_size=self.batch_size, shuffle=False)
class BasicModule(LightningModule):
def __init__(self, lr=0.01):
super().__init__()
self.model = models.resnet18(pretrained=False)
self.criterion = nn.CrossEntropyLoss()
self.lr = lr
metric = MetricCollection({'top@1': Accuracy(top_k=1), 'top@5': Accuracy(top_k=5)})
self.train_metric = metric.clone(prefix='train/')
self.valid_metric = metric.clone(prefix='valid/')
def training_step(self, batch, batch_idx, optimizer_idx=None):
return self.shared_step(*batch, self.train_metric)
def validation_step(self, batch, batch_idx):
return self.shared_step(*batch, self.valid_metric)
def shared_step(self, x, y, metric):
y_hat = self.model(x)
loss = self.criterion(y_hat, y)
self.log_dict(metric(y_hat, y), logger=True, prog_bar=True)
return loss
def configure_optimizers(self):
return SGD(self.model.parameters(), lr=self.lr)
if __name__ == '__main__':
data = CIFAR(batch_size=512)
model = BasicModule(lr=0.01)
callbacks = [LearningRateMonitor()]
trainer = Trainer(max_epochs=2, gpus='0,1', accelerator='gpu', strategy='ddp', precision=16, callbacks=callbacks)
trainer.fit(model, data) | pl_logger.py | import os
from copy import deepcopy
import torch
from torch import nn
from torch.optim import SGD
from torch.utils.data import DataLoader
from torchvision import models, transforms
from torchvision.datasets import CIFAR10
from pytorch_lightning import LightningModule, LightningDataModule, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor
from torchmetrics import Accuracy, MetricCollection
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
class CIFAR(LightningDataModule):
def __init__(self, img_size=32, batch_size=32):
super().__init__()
self.img_size = img_size if isinstance(img_size, tuple) else (img_size, img_size)
self.batch_size = batch_size
self.train_transforms = transforms.Compose([
transforms.Resize(self.img_size),
transforms.Pad(4, padding_mode='reflect'),
transforms.RandomCrop(self.img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
self.test_transforms = transforms.Compose([
transforms.Resize(self.img_size),
transforms.CenterCrop(self.img_size),
transforms.ToTensor(),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
def prepare_data(self) -> None:
CIFAR10(root='data', train=True, download=True)
CIFAR10(root='data', train=False, download=True)
def setup(self, stage=None):
self.train_ds = CIFAR10(root='data', train=True, download=False, transform=self.train_transforms)
self.valid_ds = CIFAR10(root='data', train=False, download=False, transform=self.test_transforms)
def train_dataloader(self):
return DataLoader(self.train_ds, num_workers=4, batch_size=self.batch_size, shuffle=True)
def val_dataloader(self):
return DataLoader(self.valid_ds, num_workers=4, batch_size=self.batch_size, shuffle=False)
class BasicModule(LightningModule):
def __init__(self, lr=0.01):
super().__init__()
self.model = models.resnet18(pretrained=False)
self.criterion = nn.CrossEntropyLoss()
self.lr = lr
metric = MetricCollection({'top@1': Accuracy(top_k=1), 'top@5': Accuracy(top_k=5)})
self.train_metric = metric.clone(prefix='train/')
self.valid_metric = metric.clone(prefix='valid/')
def training_step(self, batch, batch_idx, optimizer_idx=None):
return self.shared_step(*batch, self.train_metric)
def validation_step(self, batch, batch_idx):
return self.shared_step(*batch, self.valid_metric)
def shared_step(self, x, y, metric):
y_hat = self.model(x)
loss = self.criterion(y_hat, y)
self.log_dict(metric(y_hat, y), logger=True, prog_bar=True)
return loss
def configure_optimizers(self):
return SGD(self.model.parameters(), lr=self.lr)
if __name__ == '__main__':
data = CIFAR(batch_size=512)
model = BasicModule(lr=0.01)
callbacks = [LearningRateMonitor()]
trainer = Trainer(max_epochs=2, gpus='0,1', accelerator='gpu', strategy='ddp', precision=16, callbacks=callbacks)
trainer.fit(model, data) | 0.921794 | 0.503723 |
import asyncio
from typing import Dict
from pyzeebe import (
Job,
ZeebeWorker,
create_camunda_cloud_channel,
create_insecure_channel,
create_secure_channel,
)
from pyzeebe.errors import BusinessError
# Use decorators to add functionality before and after tasks. These will not fail the task
async def example_logging_task_decorator(job: Job) -> Job:
print(job)
return job
# Will use environment variable ZEEBE_ADDRESS or localhost:26500 and NOT use TLS
# create_insecure_channel returns a grpc.aio.Channel instance. If needed you
# can build one on your own
grpc_channel = create_insecure_channel()
worker = ZeebeWorker(grpc_channel)
# With custom hostname/port
grpc_channel = create_insecure_channel(hostname="zeebe-gateway.mydomain", port=443)
worker = ZeebeWorker(grpc_channel)
# Will use environment variable ZEEBE_ADDRESS or localhost:26500 and use TLS
grpc_channel = create_secure_channel()
worker = ZeebeWorker(grpc_channel)
# With custom hostname/port
grpc_channel = create_secure_channel(hostname="zeebe-gateway.mydomain", port=443)
worker = ZeebeWorker(grpc_channel)
# Connect to zeebe cluster in camunda cloud
grpc_channel = create_camunda_cloud_channel(
client_id="<my_client_id>",
client_secret="<my_client_secret>",
cluster_id="<my_cluster_id>",
region="<region>", # Default value is bru-2
)
worker = ZeebeWorker(grpc_channel)
# Decorators allow us to add functionality before and after each job
worker.before(example_logging_task_decorator)
worker.after(example_logging_task_decorator)
# Create a task like this:
@worker.task(task_type="test")
def example_task() -> Dict:
return {"output": f"Hello world, test!"}
# Or like this:
@worker.task(task_type="test2")
async def second_example_task() -> Dict:
return {"output": f"Hello world, test2!"}
# Create a task that will return a single value (not a dict) like this:
# This task will return to zeebe: { y: x + 1 }
@worker.task(task_type="add_one", single_value=True, variable_name="y")
async def add_one(x: int) -> int:
return x + 1
# The default exception handler will call job.set_error_status
# on raised BusinessError, and propagate its error_code
# so the specific business error can be caught in the Zeebe process
@worker.task(task_type="business_exception_task")
def exception_task():
raise BusinessError("invalid-credit-card")
# Define a custom exception_handler for a task like so:
async def example_exception_handler(exception: Exception, job: Job) -> None:
print(exception)
print(job)
await job.set_failure_status(f"Failed to run task {job.type}. Reason: {exception}")
@worker.task(task_type="exception_task", exception_handler=example_exception_handler)
async def exception_task():
raise Exception("Oh no!")
# We can also add decorators to tasks.
# The order of the decorators will be as follows:
# Worker decorators -> Task decorators -> Task -> Task decorators -> Worker decorators
# Here is how:
@worker.task(
task_type="decorator_task",
before=[example_logging_task_decorator],
after=[example_logging_task_decorator],
)
async def decorator_task() -> Dict:
return {"output": "Hello world, test!"}
if __name__ == "__main__":
loop = asyncio.get_running_loop()
loop.run_until_complete(worker.work()) | examples/worker.py | import asyncio
from typing import Dict
from pyzeebe import (
Job,
ZeebeWorker,
create_camunda_cloud_channel,
create_insecure_channel,
create_secure_channel,
)
from pyzeebe.errors import BusinessError
# Use decorators to add functionality before and after tasks. These will not fail the task
async def example_logging_task_decorator(job: Job) -> Job:
print(job)
return job
# Will use environment variable ZEEBE_ADDRESS or localhost:26500 and NOT use TLS
# create_insecure_channel returns a grpc.aio.Channel instance. If needed you
# can build one on your own
grpc_channel = create_insecure_channel()
worker = ZeebeWorker(grpc_channel)
# With custom hostname/port
grpc_channel = create_insecure_channel(hostname="zeebe-gateway.mydomain", port=443)
worker = ZeebeWorker(grpc_channel)
# Will use environment variable ZEEBE_ADDRESS or localhost:26500 and use TLS
grpc_channel = create_secure_channel()
worker = ZeebeWorker(grpc_channel)
# With custom hostname/port
grpc_channel = create_secure_channel(hostname="zeebe-gateway.mydomain", port=443)
worker = ZeebeWorker(grpc_channel)
# Connect to zeebe cluster in camunda cloud
grpc_channel = create_camunda_cloud_channel(
client_id="<my_client_id>",
client_secret="<my_client_secret>",
cluster_id="<my_cluster_id>",
region="<region>", # Default value is bru-2
)
worker = ZeebeWorker(grpc_channel)
# Decorators allow us to add functionality before and after each job
worker.before(example_logging_task_decorator)
worker.after(example_logging_task_decorator)
# Create a task like this:
@worker.task(task_type="test")
def example_task() -> Dict:
return {"output": f"Hello world, test!"}
# Or like this:
@worker.task(task_type="test2")
async def second_example_task() -> Dict:
return {"output": f"Hello world, test2!"}
# Create a task that will return a single value (not a dict) like this:
# This task will return to zeebe: { y: x + 1 }
@worker.task(task_type="add_one", single_value=True, variable_name="y")
async def add_one(x: int) -> int:
return x + 1
# The default exception handler will call job.set_error_status
# on raised BusinessError, and propagate its error_code
# so the specific business error can be caught in the Zeebe process
@worker.task(task_type="business_exception_task")
def exception_task():
raise BusinessError("invalid-credit-card")
# Define a custom exception_handler for a task like so:
async def example_exception_handler(exception: Exception, job: Job) -> None:
print(exception)
print(job)
await job.set_failure_status(f"Failed to run task {job.type}. Reason: {exception}")
@worker.task(task_type="exception_task", exception_handler=example_exception_handler)
async def exception_task():
raise Exception("Oh no!")
# We can also add decorators to tasks.
# The order of the decorators will be as follows:
# Worker decorators -> Task decorators -> Task -> Task decorators -> Worker decorators
# Here is how:
@worker.task(
task_type="decorator_task",
before=[example_logging_task_decorator],
after=[example_logging_task_decorator],
)
async def decorator_task() -> Dict:
return {"output": "Hello world, test!"}
if __name__ == "__main__":
loop = asyncio.get_running_loop()
loop.run_until_complete(worker.work()) | 0.692642 | 0.139133 |
from django.conf import settings as django_settings
from django.utils.translation import gettext_lazy as _
from django.test.signals import setting_changed
from mapwidgets.constants import TIMEZONE_COORDINATES
DEFAULTS = {
"GooglePointFieldWidget": (
("mapCenterLocationName", None),
("mapCenterLocation", TIMEZONE_COORDINATES.get(getattr(django_settings, "TIME_ZONE", "UTC"))),
("zoom", 12),
("GooglePlaceAutocompleteOptions", {}),
("markerFitZoom", 14),
("streetViewControl", True),
),
"MapboxPointFieldWidget": (
("access_token", ""),
("markerFitZoom", 14),
("showZoomNavigation", True),
("mapOptions", {
"zoom": 12,
"style": "mapbox://styles/mapbox/streets-v11",
"scrollZoom": False,
"animate": False,
"center": TIMEZONE_COORDINATES.get(getattr(django_settings, "TIME_ZONE", "UTC")),
}),
("geocoderOptions", {
"zoom": 6,
"flyTo": False,
"style": "mapbox://styles/mapbox/streets-v11",
"reverseGeocode": True,
"marker": False,
})
),
"GoogleStaticMapWidget": (
("zoom", 15),
("size", "480x480"),
("scale", ""),
("format", ""),
("maptype", ""),
("path", ""),
("visible", ""),
("style", ""),
("language", ""),
("region", "")
),
"GoogleStaticMapMarkerSettings": (
("size", "normal"),
("color", ""),
("icon", ""),
),
"GoogleStaticOverlayMapWidget": (
("zoom", 15),
("size", "480x480"),
("thumbnail_size", "160x160"),
("scale", ""),
("format", ""),
("maptype", ""),
("path", ""),
("visible", ""),
("style", ""),
("language", ""),
("region", "")
),
"LANGUAGE": "en",
"LIBRARIES": "places",
"srid": 4326,
"MINIFED": not django_settings.DEBUG,
"GOOGLE_MAP_API_SIGNATURE": "",
"GOOGLE_MAP_API_KEY": "",
"MAPBOX_API_KEY": "",
}
class MapWidgetSettings(object):
def __init__(self, app_settings=None, defaults=None):
if app_settings:
if not isinstance(app_settings, (dict, tuple)):
raise TypeError(_("MapWidget settings must be a tuple or dictionary"))
self._app_settings = app_settings
self.defaults = defaults or DEFAULTS
@property
def app_settings(self):
if not hasattr(self, '_app_settings'):
app_settings = getattr(django_settings, 'MAP_WIDGETS', {})
if not isinstance(app_settings, (dict, tuple)):
raise TypeError(_("MapWidget settings must be a tuple or dictionary"))
self._app_settings = getattr(django_settings, 'MAP_WIDGETS', {})
return self._app_settings
def __getattr__(self, attr):
if attr not in self.defaults.keys():
raise AttributeError("Invalid settings key: '%s'. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr)
try:
# Check if present attr in user settings
settings = self.app_settings[attr]
# Merge app tuple settings with defaults
if isinstance(settings, tuple):
try:
# support backwards compatibility for old settings format
settings = dict(settings)
except ValueError:
raise ValueError(_("Invalid %s settings value. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr))
# Merge app dict settings with defaults
if type(settings) is dict:
django_settings = dict(self.defaults[attr])
for key, value in settings.items():
# merge nested settings with defaults if it is dictionary
if type(value) is dict:
nested_setting = django_settings[key]
for k, v in value.items():
nested_setting[k] = v
value = nested_setting
django_settings[key] = value
settings = django_settings
except KeyError:
# Fall back to defaults
settings = self.defaults[attr]
if isinstance(settings, tuple):
try:
settings = dict(settings)
except ValueError:
raise ValueError(_("Invalid %s settings value. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr))
# Cache the result
setattr(self, attr, settings)
return settings
mw_settings = MapWidgetSettings(None, DEFAULTS)
def reload_widget_settings(*args, **kwargs):
global mw_settings
setting, value = kwargs['setting'], kwargs['value']
if setting == 'MAP_WIDGETS' and value:
mw_settings = MapWidgetSettings(None, DEFAULTS)
setting_changed.connect(reload_widget_settings) | mapwidgets/settings.py | from django.conf import settings as django_settings
from django.utils.translation import gettext_lazy as _
from django.test.signals import setting_changed
from mapwidgets.constants import TIMEZONE_COORDINATES
DEFAULTS = {
"GooglePointFieldWidget": (
("mapCenterLocationName", None),
("mapCenterLocation", TIMEZONE_COORDINATES.get(getattr(django_settings, "TIME_ZONE", "UTC"))),
("zoom", 12),
("GooglePlaceAutocompleteOptions", {}),
("markerFitZoom", 14),
("streetViewControl", True),
),
"MapboxPointFieldWidget": (
("access_token", ""),
("markerFitZoom", 14),
("showZoomNavigation", True),
("mapOptions", {
"zoom": 12,
"style": "mapbox://styles/mapbox/streets-v11",
"scrollZoom": False,
"animate": False,
"center": TIMEZONE_COORDINATES.get(getattr(django_settings, "TIME_ZONE", "UTC")),
}),
("geocoderOptions", {
"zoom": 6,
"flyTo": False,
"style": "mapbox://styles/mapbox/streets-v11",
"reverseGeocode": True,
"marker": False,
})
),
"GoogleStaticMapWidget": (
("zoom", 15),
("size", "480x480"),
("scale", ""),
("format", ""),
("maptype", ""),
("path", ""),
("visible", ""),
("style", ""),
("language", ""),
("region", "")
),
"GoogleStaticMapMarkerSettings": (
("size", "normal"),
("color", ""),
("icon", ""),
),
"GoogleStaticOverlayMapWidget": (
("zoom", 15),
("size", "480x480"),
("thumbnail_size", "160x160"),
("scale", ""),
("format", ""),
("maptype", ""),
("path", ""),
("visible", ""),
("style", ""),
("language", ""),
("region", "")
),
"LANGUAGE": "en",
"LIBRARIES": "places",
"srid": 4326,
"MINIFED": not django_settings.DEBUG,
"GOOGLE_MAP_API_SIGNATURE": "",
"GOOGLE_MAP_API_KEY": "",
"MAPBOX_API_KEY": "",
}
class MapWidgetSettings(object):
def __init__(self, app_settings=None, defaults=None):
if app_settings:
if not isinstance(app_settings, (dict, tuple)):
raise TypeError(_("MapWidget settings must be a tuple or dictionary"))
self._app_settings = app_settings
self.defaults = defaults or DEFAULTS
@property
def app_settings(self):
if not hasattr(self, '_app_settings'):
app_settings = getattr(django_settings, 'MAP_WIDGETS', {})
if not isinstance(app_settings, (dict, tuple)):
raise TypeError(_("MapWidget settings must be a tuple or dictionary"))
self._app_settings = getattr(django_settings, 'MAP_WIDGETS', {})
return self._app_settings
def __getattr__(self, attr):
if attr not in self.defaults.keys():
raise AttributeError("Invalid settings key: '%s'. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr)
try:
# Check if present attr in user settings
settings = self.app_settings[attr]
# Merge app tuple settings with defaults
if isinstance(settings, tuple):
try:
# support backwards compatibility for old settings format
settings = dict(settings)
except ValueError:
raise ValueError(_("Invalid %s settings value. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr))
# Merge app dict settings with defaults
if type(settings) is dict:
django_settings = dict(self.defaults[attr])
for key, value in settings.items():
# merge nested settings with defaults if it is dictionary
if type(value) is dict:
nested_setting = django_settings[key]
for k, v in value.items():
nested_setting[k] = v
value = nested_setting
django_settings[key] = value
settings = django_settings
except KeyError:
# Fall back to defaults
settings = self.defaults[attr]
if isinstance(settings, tuple):
try:
settings = dict(settings)
except ValueError:
raise ValueError(_("Invalid %s settings value. Please check the settings documentation http://django-map-widgets.readthedocs.io/en/latest/widgets/settings.html" % attr))
# Cache the result
setattr(self, attr, settings)
return settings
mw_settings = MapWidgetSettings(None, DEFAULTS)
def reload_widget_settings(*args, **kwargs):
global mw_settings
setting, value = kwargs['setting'], kwargs['value']
if setting == 'MAP_WIDGETS' and value:
mw_settings = MapWidgetSettings(None, DEFAULTS)
setting_changed.connect(reload_widget_settings) | 0.512449 | 0.167457 |
import dataclasses
import functools
import typing
from datetime import datetime
from time import sleep
from openstack import connect
import glci
class OpenstackImageUploader:
'''OpenstackImageUploader is a client to upload images to Openstack Glance.'''
def __init__(self, environment: glci.model.OpenstackEnvironment):
self.openstack_env = environment
@functools.lru_cache
def _get_connection(self):
return connect(
auth_url=self.openstack_env.auth_url,
project_name=self.openstack_env.project_name,
username=self.openstack_env.username,
password=<PASSWORD>,
region_name=self.openstack_env.region,
user_domain_name=self.openstack_env.domain,
project_domain_name=self.openstack_env.domain,
)
def upload_image_from_fs(self, name: str, path: str, meta: dict, timeout_seconds=86400):
'''Upload an image from filesystem to Openstack Glance.'''
conn = self._get_connection()
image = conn.image.create_image(
name=name,
filename=path,
disk_format='vmdk',
container_format='bare',
visibility='community',
timeout=timeout_seconds,
**meta,
)
return image['id']
def upload_image_from_url(self, name: str, url :str, meta: dict, timeout_seconds=86400):
'''Import an image from web url to Openstack Glance.'''
conn = self._get_connection()
image = conn.image.create_image(
name=name,
disk_format='vmdk',
container_format='bare',
visibility='community',
timeout=timeout_seconds,
**meta,
)
conn.image.import_image(image, method="web-download", uri=url)
return image['id']
def wait_image_ready(self, image_id: str, wait_interval_seconds=10, timeout=3600):
'''Wait until an image get in ready state.'''
conn = self._get_connection()
start_time = datetime.now()
while True:
if (datetime.now()-start_time).total_seconds() > timeout:
raise RuntimeError('Timeout for waiting image to get ready reached.')
image = conn.image.get_image(image_id)
if image['status'] == 'queued' or image['status'] == 'saving' or image['status'] == 'importing':
sleep(wait_interval_seconds)
continue
if image['status'] == 'active':
return
raise RuntimeError(f"image upload to Glance failed due to image status {image['status']}")
def upload_and_publish_image(
s3_client,
openstack_environments_cfgs :typing.Tuple[glci.model.OpenstackEnvironment],
image_properties: dict,
release: glci.model.OnlineReleaseManifest,
) -> glci.model.OnlineReleaseManifest:
"""Import an image from S3 into OpenStack Glance."""
image_name = f"gardenlinux-{release.version}"
image_meta = {
'architecture': release.architecture.name,
'properties': image_properties,
}
s3_image_url = s3_client.generate_presigned_url(
'get_object',
ExpiresIn=1200*len(openstack_environments_cfgs), # 20min validity for each openstack enviroment/region
Params={
'Bucket': release.path_by_suffix('rootfs.vmdk').s3_bucket_name,
'Key': release.path_by_suffix('rootfs.vmdk').s3_key,
},
)
published_images = []
for env_cfg in openstack_environments_cfgs:
uploader = OpenstackImageUploader(env_cfg)
image_id = uploader.upload_image_from_url(image_name, s3_image_url, image_meta)
uploader.wait_image_ready(image_id)
published_images.append(glci.model.OpenstackPublishedImage(
region_name=env_cfg.region,
image_id=image_id,
image_name=image_name,
))
published_image_set = glci.model.OpenstackPublishedImageSet(published_openstack_images=tuple(published_images))
return dataclasses.replace(release, published_image_metadata=published_image_set) | ci/glci/openstack_image.py | import dataclasses
import functools
import typing
from datetime import datetime
from time import sleep
from openstack import connect
import glci
class OpenstackImageUploader:
'''OpenstackImageUploader is a client to upload images to Openstack Glance.'''
def __init__(self, environment: glci.model.OpenstackEnvironment):
self.openstack_env = environment
@functools.lru_cache
def _get_connection(self):
return connect(
auth_url=self.openstack_env.auth_url,
project_name=self.openstack_env.project_name,
username=self.openstack_env.username,
password=<PASSWORD>,
region_name=self.openstack_env.region,
user_domain_name=self.openstack_env.domain,
project_domain_name=self.openstack_env.domain,
)
def upload_image_from_fs(self, name: str, path: str, meta: dict, timeout_seconds=86400):
'''Upload an image from filesystem to Openstack Glance.'''
conn = self._get_connection()
image = conn.image.create_image(
name=name,
filename=path,
disk_format='vmdk',
container_format='bare',
visibility='community',
timeout=timeout_seconds,
**meta,
)
return image['id']
def upload_image_from_url(self, name: str, url :str, meta: dict, timeout_seconds=86400):
'''Import an image from web url to Openstack Glance.'''
conn = self._get_connection()
image = conn.image.create_image(
name=name,
disk_format='vmdk',
container_format='bare',
visibility='community',
timeout=timeout_seconds,
**meta,
)
conn.image.import_image(image, method="web-download", uri=url)
return image['id']
def wait_image_ready(self, image_id: str, wait_interval_seconds=10, timeout=3600):
'''Wait until an image get in ready state.'''
conn = self._get_connection()
start_time = datetime.now()
while True:
if (datetime.now()-start_time).total_seconds() > timeout:
raise RuntimeError('Timeout for waiting image to get ready reached.')
image = conn.image.get_image(image_id)
if image['status'] == 'queued' or image['status'] == 'saving' or image['status'] == 'importing':
sleep(wait_interval_seconds)
continue
if image['status'] == 'active':
return
raise RuntimeError(f"image upload to Glance failed due to image status {image['status']}")
def upload_and_publish_image(
s3_client,
openstack_environments_cfgs :typing.Tuple[glci.model.OpenstackEnvironment],
image_properties: dict,
release: glci.model.OnlineReleaseManifest,
) -> glci.model.OnlineReleaseManifest:
"""Import an image from S3 into OpenStack Glance."""
image_name = f"gardenlinux-{release.version}"
image_meta = {
'architecture': release.architecture.name,
'properties': image_properties,
}
s3_image_url = s3_client.generate_presigned_url(
'get_object',
ExpiresIn=1200*len(openstack_environments_cfgs), # 20min validity for each openstack enviroment/region
Params={
'Bucket': release.path_by_suffix('rootfs.vmdk').s3_bucket_name,
'Key': release.path_by_suffix('rootfs.vmdk').s3_key,
},
)
published_images = []
for env_cfg in openstack_environments_cfgs:
uploader = OpenstackImageUploader(env_cfg)
image_id = uploader.upload_image_from_url(image_name, s3_image_url, image_meta)
uploader.wait_image_ready(image_id)
published_images.append(glci.model.OpenstackPublishedImage(
region_name=env_cfg.region,
image_id=image_id,
image_name=image_name,
))
published_image_set = glci.model.OpenstackPublishedImageSet(published_openstack_images=tuple(published_images))
return dataclasses.replace(release, published_image_metadata=published_image_set) | 0.588416 | 0.135089 |
import os
import sys
import subprocess
import re
from setuptools import setup, find_packages, Extension
# this should only be run with python3
import sys
if sys.version_info[0] < 3:
print('ERROR: must run with python3')
sys.exit(1)
tcf_root_dir = os.environ.get('TCF_HOME', '../..')
version = subprocess.check_output(
os.path.join(tcf_root_dir, 'bin/get_version')).decode('ascii').strip()
openssl_cflags = subprocess.check_output(
['pkg-config', 'openssl', '--cflags']).decode('ascii').strip().split()
openssl_include_dirs = list(
filter(None, re.split('\s*-I',
subprocess.check_output(
['pkg-config', 'openssl', '--cflags-only-I'])
.decode('ascii').strip())))
openssl_libs = list(
filter(None, re.split('\s*-l',
subprocess.check_output(['pkg-config', 'openssl', '--libs-only-l'])
.decode('ascii').strip())))
openssl_lib_dirs = list(
filter(None, re.split('\s*-L',
subprocess.check_output(['pkg-config', 'openssl', '--libs-only-L'])
.decode('ascii').strip())))
compile_args = [
'-std=c++11',
'-Wno-switch',
'-Wno-unused-function',
'-Wno-unused-variable',
]
crypto_include_dirs = [
os.path.join(tcf_root_dir, 'common/cpp'),
os.path.join(tcf_root_dir, 'common/cpp/crypto'),
] + openssl_include_dirs
verify_report_include_dirs = [
os.path.join(tcf_root_dir, 'common/cpp'),
os.path.join(tcf_root_dir, 'common/cpp/verify_ias_report'),
]
library_dirs = [
os.path.join(tcf_root_dir, "common/cpp/build"),
] + openssl_lib_dirs
libraries = [
'uavalon-common',
'uavalon-base64',
'uavalon-parson',
'uavalon-crypto',
'uavalon-verify-ias-report'
] + openssl_libs
libraries = libraries + openssl_libs
crypto_modulefiles = [
"crypto_utils/crypto/crypto.i"
]
crypto_module = Extension(
'crypto_utils.crypto._crypto',
crypto_modulefiles,
swig_opts=['-c++'] + openssl_cflags +
['-I%s' % i for i in crypto_include_dirs],
extra_compile_args=compile_args,
include_dirs=crypto_include_dirs,
library_dirs=library_dirs,
libraries=libraries)
verify_report_modulefiles = [
"crypto_utils/verify_report/verify_report.i"
]
verify_report_module = Extension(
'crypto_utils.verify_report._verify_report',
verify_report_modulefiles,
swig_opts=['-c++'] + ['-I%s' % i for i in verify_report_include_dirs],
extra_compile_args=compile_args,
include_dirs=verify_report_include_dirs,
library_dirs=library_dirs,
libraries=libraries)
# -----------------------------------------------------------------
setup(
name='avalon_common',
version=version,
description='Common library',
author='<NAME>',
url='https://github.com/hyperledger/avalon',
packages=find_packages(),
install_requires=[],
ext_modules=[crypto_module, verify_report_module],
data_files=[],
entry_points={}) | common/python/setup.py |
import os
import sys
import subprocess
import re
from setuptools import setup, find_packages, Extension
# this should only be run with python3
import sys
if sys.version_info[0] < 3:
print('ERROR: must run with python3')
sys.exit(1)
tcf_root_dir = os.environ.get('TCF_HOME', '../..')
version = subprocess.check_output(
os.path.join(tcf_root_dir, 'bin/get_version')).decode('ascii').strip()
openssl_cflags = subprocess.check_output(
['pkg-config', 'openssl', '--cflags']).decode('ascii').strip().split()
openssl_include_dirs = list(
filter(None, re.split('\s*-I',
subprocess.check_output(
['pkg-config', 'openssl', '--cflags-only-I'])
.decode('ascii').strip())))
openssl_libs = list(
filter(None, re.split('\s*-l',
subprocess.check_output(['pkg-config', 'openssl', '--libs-only-l'])
.decode('ascii').strip())))
openssl_lib_dirs = list(
filter(None, re.split('\s*-L',
subprocess.check_output(['pkg-config', 'openssl', '--libs-only-L'])
.decode('ascii').strip())))
compile_args = [
'-std=c++11',
'-Wno-switch',
'-Wno-unused-function',
'-Wno-unused-variable',
]
crypto_include_dirs = [
os.path.join(tcf_root_dir, 'common/cpp'),
os.path.join(tcf_root_dir, 'common/cpp/crypto'),
] + openssl_include_dirs
verify_report_include_dirs = [
os.path.join(tcf_root_dir, 'common/cpp'),
os.path.join(tcf_root_dir, 'common/cpp/verify_ias_report'),
]
library_dirs = [
os.path.join(tcf_root_dir, "common/cpp/build"),
] + openssl_lib_dirs
libraries = [
'uavalon-common',
'uavalon-base64',
'uavalon-parson',
'uavalon-crypto',
'uavalon-verify-ias-report'
] + openssl_libs
libraries = libraries + openssl_libs
crypto_modulefiles = [
"crypto_utils/crypto/crypto.i"
]
crypto_module = Extension(
'crypto_utils.crypto._crypto',
crypto_modulefiles,
swig_opts=['-c++'] + openssl_cflags +
['-I%s' % i for i in crypto_include_dirs],
extra_compile_args=compile_args,
include_dirs=crypto_include_dirs,
library_dirs=library_dirs,
libraries=libraries)
verify_report_modulefiles = [
"crypto_utils/verify_report/verify_report.i"
]
verify_report_module = Extension(
'crypto_utils.verify_report._verify_report',
verify_report_modulefiles,
swig_opts=['-c++'] + ['-I%s' % i for i in verify_report_include_dirs],
extra_compile_args=compile_args,
include_dirs=verify_report_include_dirs,
library_dirs=library_dirs,
libraries=libraries)
# -----------------------------------------------------------------
setup(
name='avalon_common',
version=version,
description='Common library',
author='<NAME>',
url='https://github.com/hyperledger/avalon',
packages=find_packages(),
install_requires=[],
ext_modules=[crypto_module, verify_report_module],
data_files=[],
entry_points={}) | 0.213295 | 0.073397 |
from tempest.api.compute import base
from tempest.lib.common.utils import data_utils
from tempest.lib import decorators
from tempest import test
class SecurityGroupsTestAdminJSON(base.BaseV2ComputeAdminTest):
@classmethod
def setup_clients(cls):
super(SecurityGroupsTestAdminJSON, cls).setup_clients()
cls.adm_client = cls.os_admin.compute_security_groups_client
cls.client = cls.security_groups_client
def _delete_security_group(self, securitygroup_id, admin=True):
if admin:
self.adm_client.delete_security_group(securitygroup_id)
else:
self.client.delete_security_group(securitygroup_id)
@decorators.idempotent_id('49667619-5af9-4c63-ab5d-2cfdd1c8f7f1')
@test.services('network')
def test_list_security_groups_list_all_tenants_filter(self):
# Admin can list security groups of all tenants
# List of all security groups created
security_group_list = []
# Create two security groups for a non-admin tenant
for _ in range(2):
name = data_utils.rand_name('securitygroup')
description = data_utils.rand_name('description')
securitygroup = self.client.create_security_group(
name=name, description=description)['security_group']
self.addCleanup(self._delete_security_group,
securitygroup['id'], admin=False)
security_group_list.append(securitygroup)
client_tenant_id = securitygroup['tenant_id']
# Create two security groups for admin tenant
for _ in range(2):
name = data_utils.rand_name('securitygroup')
description = data_utils.rand_name('description')
adm_securitygroup = self.adm_client.create_security_group(
name=name, description=description)['security_group']
self.addCleanup(self._delete_security_group,
adm_securitygroup['id'])
security_group_list.append(adm_securitygroup)
# Fetch all security groups based on 'all_tenants' search filter
fetched_list = self.adm_client.list_security_groups(
all_tenants='true')['security_groups']
sec_group_id_list = [sg['id'] for sg in fetched_list]
# Now check if all created Security Groups are present in fetched list
for sec_group in security_group_list:
self.assertIn(sec_group['id'], sec_group_id_list)
# Fetch all security groups for non-admin user with 'all_tenants'
# search filter
fetched_list = (self.client.list_security_groups(all_tenants='true')
['security_groups'])
# Now check if all created Security Groups are present in fetched list
for sec_group in fetched_list:
self.assertEqual(sec_group['tenant_id'], client_tenant_id,
"Failed to get all security groups for "
"non admin user.") | tempest/api/compute/admin/test_security_groups.py |
from tempest.api.compute import base
from tempest.lib.common.utils import data_utils
from tempest.lib import decorators
from tempest import test
class SecurityGroupsTestAdminJSON(base.BaseV2ComputeAdminTest):
@classmethod
def setup_clients(cls):
super(SecurityGroupsTestAdminJSON, cls).setup_clients()
cls.adm_client = cls.os_admin.compute_security_groups_client
cls.client = cls.security_groups_client
def _delete_security_group(self, securitygroup_id, admin=True):
if admin:
self.adm_client.delete_security_group(securitygroup_id)
else:
self.client.delete_security_group(securitygroup_id)
@decorators.idempotent_id('49667619-5af9-4c63-ab5d-2cfdd1c8f7f1')
@test.services('network')
def test_list_security_groups_list_all_tenants_filter(self):
# Admin can list security groups of all tenants
# List of all security groups created
security_group_list = []
# Create two security groups for a non-admin tenant
for _ in range(2):
name = data_utils.rand_name('securitygroup')
description = data_utils.rand_name('description')
securitygroup = self.client.create_security_group(
name=name, description=description)['security_group']
self.addCleanup(self._delete_security_group,
securitygroup['id'], admin=False)
security_group_list.append(securitygroup)
client_tenant_id = securitygroup['tenant_id']
# Create two security groups for admin tenant
for _ in range(2):
name = data_utils.rand_name('securitygroup')
description = data_utils.rand_name('description')
adm_securitygroup = self.adm_client.create_security_group(
name=name, description=description)['security_group']
self.addCleanup(self._delete_security_group,
adm_securitygroup['id'])
security_group_list.append(adm_securitygroup)
# Fetch all security groups based on 'all_tenants' search filter
fetched_list = self.adm_client.list_security_groups(
all_tenants='true')['security_groups']
sec_group_id_list = [sg['id'] for sg in fetched_list]
# Now check if all created Security Groups are present in fetched list
for sec_group in security_group_list:
self.assertIn(sec_group['id'], sec_group_id_list)
# Fetch all security groups for non-admin user with 'all_tenants'
# search filter
fetched_list = (self.client.list_security_groups(all_tenants='true')
['security_groups'])
# Now check if all created Security Groups are present in fetched list
for sec_group in fetched_list:
self.assertEqual(sec_group['tenant_id'], client_tenant_id,
"Failed to get all security groups for "
"non admin user.") | 0.382603 | 0.167015 |
from mininet.net import Mininet
from mininet.node import Switch, Host
from mininet.log import setLogLevel, info, error, debug
from mininet.moduledeps import pathCheck
from sys import exit
from time import sleep
import os
import tempfile
import socket
class P4Host(Host):
def config(self, **params):
r = super(P4Host, self).config(**params)
for off in ["rx", "tx", "sg"]:
cmd = "/sbin/ethtool --offload %s %s off" % (self.defaultIntf().name, off)
self.cmd(cmd)
# disable IPv6
self.cmd("sysctl -w net.ipv6.conf.all.disable_ipv6=1")
self.cmd("sysctl -w net.ipv6.conf.default.disable_ipv6=1")
self.cmd("sysctl -w net.ipv6.conf.lo.disable_ipv6=1")
return r
def describe(self, sw_addr=None, sw_mac=None):
print "**********"
print "Network configuration for: %s" % self.name
print "Default interface: %s\t%s\t%s" %(
self.defaultIntf().name,
self.defaultIntf().IP(),
self.defaultIntf().MAC()
)
if sw_addr is not None or sw_mac is not None:
print "Default route to switch: %s (%s)" % (sw_addr, sw_mac)
print "**********"
class P4Switch(Switch):
"""P4 virtual switch"""
device_id = 0
def __init__(self, name, sw_path = None, json_path = None,
log_file = None,
thrift_port = None,
pcap_dump = False,
log_console = False,
verbose = False,
device_id = None,
enable_debugger = False,
**kwargs):
Switch.__init__(self, name, **kwargs)
assert(sw_path)
assert(json_path)
# make sure that the provided sw_path is valid
pathCheck(sw_path)
# make sure that the provided JSON file exists
if not os.path.isfile(json_path):
error("Invalid JSON file.\n")
exit(1)
self.sw_path = sw_path
self.json_path = json_path
self.verbose = verbose
self.log_file = log_file
if self.log_file is None:
self.log_file = "/tmp/p4s.{}.log".format(self.name)
self.output = open(self.log_file, 'w')
self.thrift_port = thrift_port
self.pcap_dump = pcap_dump
self.enable_debugger = enable_debugger
self.log_console = log_console
if device_id is not None:
self.device_id = device_id
P4Switch.device_id = max(P4Switch.device_id, device_id)
else:
self.device_id = P4Switch.device_id
P4Switch.device_id += 1
self.nanomsg = "ipc:///tmp/bm-{}-log.ipc".format(self.device_id)
@classmethod
def setup(cls):
pass
def check_switch_started(self, pid):
"""While the process is running (pid exists), we check if the Thrift
server has been started. If the Thrift server is ready, we assume that
the switch was started successfully. This is only reliable if the Thrift
server is started at the end of the init process"""
while True:
if not os.path.exists(os.path.join("/proc", str(pid))):
return False
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(0.5)
result = sock.connect_ex(("localhost", self.thrift_port))
if result == 0:
return True
def start(self, controllers):
"Start up a new P4 switch"
info("Starting P4 switch {}.\n".format(self.name))
args = [self.sw_path]
for port, intf in self.intfs.items():
if not intf.IP():
args.extend(['-i', str(port) + "@" + intf.name])
if self.pcap_dump:
args.append("--pcap")
# args.append("--useFiles")
if self.thrift_port:
args.extend(['--thrift-port', str(self.thrift_port)])
if self.nanomsg:
args.extend(['--nanolog', self.nanomsg])
args.extend(['--device-id', str(self.device_id)])
P4Switch.device_id += 1
args.append(self.json_path)
if self.enable_debugger:
args.append("--debugger")
if self.log_console:
args.append("--log-console")
info(' '.join(args) + "\n")
pid = None
with tempfile.NamedTemporaryFile() as f:
# self.cmd(' '.join(args) + ' > /dev/null 2>&1 &')
self.cmd(' '.join(args) + ' >' + self.log_file + ' 2>&1 & echo $! >> ' + f.name)
pid = int(f.read())
debug("P4 switch {} PID is {}.\n".format(self.name, pid))
sleep(1)
if not self.check_switch_started(pid):
error("P4 switch {} did not start correctly."
"Check the switch log file.\n".format(self.name))
exit(1)
info("P4 switch {} has been started.\n".format(self.name))
def stop(self):
"Terminate P4 switch."
self.output.flush()
self.cmd('kill %' + self.sw_path)
self.cmd('wait')
self.deleteIntfs()
def attach(self, intf):
"Connect a data port"
assert(0)
def detach(self, intf):
"Disconnect a data port"
assert(0) | SIGCOMM_2017/utils/mininet/p4_mininet.py |
from mininet.net import Mininet
from mininet.node import Switch, Host
from mininet.log import setLogLevel, info, error, debug
from mininet.moduledeps import pathCheck
from sys import exit
from time import sleep
import os
import tempfile
import socket
class P4Host(Host):
def config(self, **params):
r = super(P4Host, self).config(**params)
for off in ["rx", "tx", "sg"]:
cmd = "/sbin/ethtool --offload %s %s off" % (self.defaultIntf().name, off)
self.cmd(cmd)
# disable IPv6
self.cmd("sysctl -w net.ipv6.conf.all.disable_ipv6=1")
self.cmd("sysctl -w net.ipv6.conf.default.disable_ipv6=1")
self.cmd("sysctl -w net.ipv6.conf.lo.disable_ipv6=1")
return r
def describe(self, sw_addr=None, sw_mac=None):
print "**********"
print "Network configuration for: %s" % self.name
print "Default interface: %s\t%s\t%s" %(
self.defaultIntf().name,
self.defaultIntf().IP(),
self.defaultIntf().MAC()
)
if sw_addr is not None or sw_mac is not None:
print "Default route to switch: %s (%s)" % (sw_addr, sw_mac)
print "**********"
class P4Switch(Switch):
"""P4 virtual switch"""
device_id = 0
def __init__(self, name, sw_path = None, json_path = None,
log_file = None,
thrift_port = None,
pcap_dump = False,
log_console = False,
verbose = False,
device_id = None,
enable_debugger = False,
**kwargs):
Switch.__init__(self, name, **kwargs)
assert(sw_path)
assert(json_path)
# make sure that the provided sw_path is valid
pathCheck(sw_path)
# make sure that the provided JSON file exists
if not os.path.isfile(json_path):
error("Invalid JSON file.\n")
exit(1)
self.sw_path = sw_path
self.json_path = json_path
self.verbose = verbose
self.log_file = log_file
if self.log_file is None:
self.log_file = "/tmp/p4s.{}.log".format(self.name)
self.output = open(self.log_file, 'w')
self.thrift_port = thrift_port
self.pcap_dump = pcap_dump
self.enable_debugger = enable_debugger
self.log_console = log_console
if device_id is not None:
self.device_id = device_id
P4Switch.device_id = max(P4Switch.device_id, device_id)
else:
self.device_id = P4Switch.device_id
P4Switch.device_id += 1
self.nanomsg = "ipc:///tmp/bm-{}-log.ipc".format(self.device_id)
@classmethod
def setup(cls):
pass
def check_switch_started(self, pid):
"""While the process is running (pid exists), we check if the Thrift
server has been started. If the Thrift server is ready, we assume that
the switch was started successfully. This is only reliable if the Thrift
server is started at the end of the init process"""
while True:
if not os.path.exists(os.path.join("/proc", str(pid))):
return False
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(0.5)
result = sock.connect_ex(("localhost", self.thrift_port))
if result == 0:
return True
def start(self, controllers):
"Start up a new P4 switch"
info("Starting P4 switch {}.\n".format(self.name))
args = [self.sw_path]
for port, intf in self.intfs.items():
if not intf.IP():
args.extend(['-i', str(port) + "@" + intf.name])
if self.pcap_dump:
args.append("--pcap")
# args.append("--useFiles")
if self.thrift_port:
args.extend(['--thrift-port', str(self.thrift_port)])
if self.nanomsg:
args.extend(['--nanolog', self.nanomsg])
args.extend(['--device-id', str(self.device_id)])
P4Switch.device_id += 1
args.append(self.json_path)
if self.enable_debugger:
args.append("--debugger")
if self.log_console:
args.append("--log-console")
info(' '.join(args) + "\n")
pid = None
with tempfile.NamedTemporaryFile() as f:
# self.cmd(' '.join(args) + ' > /dev/null 2>&1 &')
self.cmd(' '.join(args) + ' >' + self.log_file + ' 2>&1 & echo $! >> ' + f.name)
pid = int(f.read())
debug("P4 switch {} PID is {}.\n".format(self.name, pid))
sleep(1)
if not self.check_switch_started(pid):
error("P4 switch {} did not start correctly."
"Check the switch log file.\n".format(self.name))
exit(1)
info("P4 switch {} has been started.\n".format(self.name))
def stop(self):
"Terminate P4 switch."
self.output.flush()
self.cmd('kill %' + self.sw_path)
self.cmd('wait')
self.deleteIntfs()
def attach(self, intf):
"Connect a data port"
assert(0)
def detach(self, intf):
"Disconnect a data port"
assert(0) | 0.367157 | 0.093719 |
from __future__ import unicode_literals
from indico.core.settings import AttributeProxyProperty
from indico.modules.events.models.reviews import ProposalMixin
from indico.modules.events.papers.models.revisions import PaperRevisionState
from indico.util.locators import locator_property
from indico.util.string import return_ascii
class Paper(ProposalMixin):
"""Proxy class to facilitate access to all paper-related properties."""
proxied_attr = 'contribution'
# Proposal mixin properties
proposal_type = 'paper'
call_for_proposals_attr = 'cfp'
revisions_enabled = True
def __init__(self, contribution):
self.contribution = contribution
@return_ascii
def __repr__(self):
state = self.state.name if self.last_revision else None
return '<Paper(contribution_id={}, state={})>'.format(self.contribution.id, state)
@locator_property
def locator(self):
return self.contribution.locator
# Contribution-related
event = AttributeProxyProperty('event')
title = AttributeProxyProperty('title')
verbose_title = AttributeProxyProperty('verbose_title')
# Paper-related
revisions = AttributeProxyProperty('_paper_revisions')
last_revision = AttributeProxyProperty('_paper_last_revision')
accepted_revision = AttributeProxyProperty('_accepted_paper_revision')
revision_count = AttributeProxyProperty('_paper_revision_count')
files = AttributeProxyProperty('_paper_files')
@property
def state(self):
return self.last_revision.state
@state.setter
def state(self, state):
self.last_revision.state = state
@property
def judgment_comment(self):
return self.last_revision._judgment_comment
@property
def is_in_final_state(self):
return self.state in {PaperRevisionState.accepted, PaperRevisionState.rejected}
def can_comment(self, user, check_state=False):
if not user:
return False
if check_state and self.is_in_final_state:
return False
return self.can_submit(user) or self.can_judge(user) or self.can_review(user)
def can_submit(self, user):
return self.contribution.can_submit_proceedings(user)
def can_manage(self, user):
if not user:
return False
return self.event.can_manage(user)
def can_judge(self, user, check_state=False):
if not user:
return False
elif check_state and self.is_in_final_state:
return False
elif self.can_manage(user):
return True
return user in self.contribution.paper_judges
def can_review(self, user, check_state=False):
if not user:
return False
elif check_state and self.is_in_final_state:
return False
elif self.can_manage(user):
return True
return self.contribution.is_paper_reviewer(user)
def get_revisions(self):
return self.revisions
def get_last_revision(self):
return self.last_revision
def reset_state(self):
self.last_revision.state = PaperRevisionState.submitted
self.last_revision.judgment_comment = ''
self.last_revision.judge = None
self.last_revision.judgment_dt = None | indico/modules/events/papers/models/papers.py |
from __future__ import unicode_literals
from indico.core.settings import AttributeProxyProperty
from indico.modules.events.models.reviews import ProposalMixin
from indico.modules.events.papers.models.revisions import PaperRevisionState
from indico.util.locators import locator_property
from indico.util.string import return_ascii
class Paper(ProposalMixin):
"""Proxy class to facilitate access to all paper-related properties."""
proxied_attr = 'contribution'
# Proposal mixin properties
proposal_type = 'paper'
call_for_proposals_attr = 'cfp'
revisions_enabled = True
def __init__(self, contribution):
self.contribution = contribution
@return_ascii
def __repr__(self):
state = self.state.name if self.last_revision else None
return '<Paper(contribution_id={}, state={})>'.format(self.contribution.id, state)
@locator_property
def locator(self):
return self.contribution.locator
# Contribution-related
event = AttributeProxyProperty('event')
title = AttributeProxyProperty('title')
verbose_title = AttributeProxyProperty('verbose_title')
# Paper-related
revisions = AttributeProxyProperty('_paper_revisions')
last_revision = AttributeProxyProperty('_paper_last_revision')
accepted_revision = AttributeProxyProperty('_accepted_paper_revision')
revision_count = AttributeProxyProperty('_paper_revision_count')
files = AttributeProxyProperty('_paper_files')
@property
def state(self):
return self.last_revision.state
@state.setter
def state(self, state):
self.last_revision.state = state
@property
def judgment_comment(self):
return self.last_revision._judgment_comment
@property
def is_in_final_state(self):
return self.state in {PaperRevisionState.accepted, PaperRevisionState.rejected}
def can_comment(self, user, check_state=False):
if not user:
return False
if check_state and self.is_in_final_state:
return False
return self.can_submit(user) or self.can_judge(user) or self.can_review(user)
def can_submit(self, user):
return self.contribution.can_submit_proceedings(user)
def can_manage(self, user):
if not user:
return False
return self.event.can_manage(user)
def can_judge(self, user, check_state=False):
if not user:
return False
elif check_state and self.is_in_final_state:
return False
elif self.can_manage(user):
return True
return user in self.contribution.paper_judges
def can_review(self, user, check_state=False):
if not user:
return False
elif check_state and self.is_in_final_state:
return False
elif self.can_manage(user):
return True
return self.contribution.is_paper_reviewer(user)
def get_revisions(self):
return self.revisions
def get_last_revision(self):
return self.last_revision
def reset_state(self):
self.last_revision.state = PaperRevisionState.submitted
self.last_revision.judgment_comment = ''
self.last_revision.judge = None
self.last_revision.judgment_dt = None | 0.836555 | 0.141281 |
class Value:
def __init__(self, n):
self.n = int(n)
def __repr__(self):
return str(self.n)
class PC:
def __init__(self):
self._n = 0
self.allow_step = True
@property
def n(self):
return self._n
@n.setter
def n(self, value):
self.allow_step = False
self._n = value
def step(self, count):
if self.allow_step:
self._n += count
else:
self.allow_step = True
class Intcode:
def __init__(self):
self.pc = PC()
self.running = True
self.relbase = 0
self.OPERATIONS = {
1: (self.op1, 3),
2: (self.op2, 3),
3: (self.op3, 1),
4: (self.op4, 1),
5: (self.op5, 2),
6: (self.op6, 2),
7: (self.op7, 3),
8: (self.op8, 3),
9: (self.op9, 1),
99: (self.op99, 0),
}
def op1(self, a, b, c):
c.n = a.n + b.n
def op2(self, a, b, c):
c.n = a.n * b.n
def op3(self, a):
a.n = int(input("> "))
def op4(self, a):
print(a.n)
def op5(self, a, b):
if a.n != 0:
self.pc.n = b.n
def op6(self, a, b):
if a.n == 0:
self.pc.n = b.n
def op7(self, a, b, c):
c.n = int(a.n < b.n)
def op8(self, a, b, c):
c.n = int(a.n == b.n)
def op9(self, a):
self.relbase += a.n
def op99(self):
self.running = False
# runner
def run(self, p):
p = p + [Value(0) for _ in range(1000)]
self.running = True
self.relbase = 0
while self.running:
# print(f"{pos}: {p}")
instruction = p[self.pc.n]
opcode = instruction.n % 100
if opcode not in self.OPERATIONS:
raise RuntimeError(f"Unknown operation: {opcode} (pc={self.pc})")
op, nargs = self.OPERATIONS[opcode]
args = []
for i in range(nargs):
# 0 = position mode, 1 = immediate mode, 2 = relative mode
mode = (instruction.n // (10 ** (i + 2)) % 10)
if mode == 0:
args.append(p[p[self.pc.n + i + 1].n]) # value at address=argument i
elif mode == 1:
args.append(Value(p[self.pc.n + i + 1].n)) # value=argument i
else:
args.append(p[p[self.pc.n + i + 1].n + self.relbase]) # value at address=relbase+argument i
op(*args)
self.pc.step(nargs + 1)
if __name__ == '__main__':
computer = Intcode()
program = list(map(Value, input("Program: ").split(',')))
computer.run(program) | 9/1.py |
class Value:
def __init__(self, n):
self.n = int(n)
def __repr__(self):
return str(self.n)
class PC:
def __init__(self):
self._n = 0
self.allow_step = True
@property
def n(self):
return self._n
@n.setter
def n(self, value):
self.allow_step = False
self._n = value
def step(self, count):
if self.allow_step:
self._n += count
else:
self.allow_step = True
class Intcode:
def __init__(self):
self.pc = PC()
self.running = True
self.relbase = 0
self.OPERATIONS = {
1: (self.op1, 3),
2: (self.op2, 3),
3: (self.op3, 1),
4: (self.op4, 1),
5: (self.op5, 2),
6: (self.op6, 2),
7: (self.op7, 3),
8: (self.op8, 3),
9: (self.op9, 1),
99: (self.op99, 0),
}
def op1(self, a, b, c):
c.n = a.n + b.n
def op2(self, a, b, c):
c.n = a.n * b.n
def op3(self, a):
a.n = int(input("> "))
def op4(self, a):
print(a.n)
def op5(self, a, b):
if a.n != 0:
self.pc.n = b.n
def op6(self, a, b):
if a.n == 0:
self.pc.n = b.n
def op7(self, a, b, c):
c.n = int(a.n < b.n)
def op8(self, a, b, c):
c.n = int(a.n == b.n)
def op9(self, a):
self.relbase += a.n
def op99(self):
self.running = False
# runner
def run(self, p):
p = p + [Value(0) for _ in range(1000)]
self.running = True
self.relbase = 0
while self.running:
# print(f"{pos}: {p}")
instruction = p[self.pc.n]
opcode = instruction.n % 100
if opcode not in self.OPERATIONS:
raise RuntimeError(f"Unknown operation: {opcode} (pc={self.pc})")
op, nargs = self.OPERATIONS[opcode]
args = []
for i in range(nargs):
# 0 = position mode, 1 = immediate mode, 2 = relative mode
mode = (instruction.n // (10 ** (i + 2)) % 10)
if mode == 0:
args.append(p[p[self.pc.n + i + 1].n]) # value at address=argument i
elif mode == 1:
args.append(Value(p[self.pc.n + i + 1].n)) # value=argument i
else:
args.append(p[p[self.pc.n + i + 1].n + self.relbase]) # value at address=relbase+argument i
op(*args)
self.pc.step(nargs + 1)
if __name__ == '__main__':
computer = Intcode()
program = list(map(Value, input("Program: ").split(',')))
computer.run(program) | 0.50952 | 0.293867 |
import re
from random import randrange
from model.contact import Contact
def test_random_contact_on_home_page(app):
if app.contact.count_contact() == 0:
contact = Contact(first_name="Trick", middle_name="", last_name="Truck",
nickname="", title="", company_name="Big MO",
address="", homepage="www.345.com", home_phone="",
mobile="555222333", work="", fax="",
email="<EMAIL>", email2="",
email3="", bday="", bmonth="-",
byear="", aday="", amonth="-", ayear="",
phone2="", address2="", notes="")
app.contact.create_contact(contact)
all_contacts = app.contact.get_contact_list()
index = randrange(len(all_contacts))
contact_from_homepage = app.contact.get_contact_list()[index]
contact_from_edit_page = app.contact.get_contact_info_from_edit_page(index)
assert contact_from_homepage.first_name == contact_from_edit_page.first_name
assert contact_from_homepage.last_name == contact_from_edit_page.last_name
assert contact_from_homepage.address == contact_from_edit_page.address
assert contact_from_homepage.all_emails_from_home_page == merge_emails_like_on_edit_page(contact_from_edit_page)
assert contact_from_homepage.all_phones_from_home_page == merge_phones_like_on_home_page(contact_from_edit_page)
def clear(s):
return re.sub("[() -]", "", s)
def merge_emails_like_on_edit_page(contact):
return "\n".join(filter(lambda x: x != "",
map(lambda x: clear(x),
filter(lambda x: x is not None,
[contact.email, contact.email2, contact.email3]))))
def merge_phones_like_on_home_page(contact):
return "\n".join(filter(lambda x: x != "",
map(lambda x: clear(x),
filter(lambda x: x is not None,
[contact.home_phone, contact.mobile, contact.work, contact.phone2])))) | test/TestContact.py | import re
from random import randrange
from model.contact import Contact
def test_random_contact_on_home_page(app):
if app.contact.count_contact() == 0:
contact = Contact(first_name="Trick", middle_name="", last_name="Truck",
nickname="", title="", company_name="Big MO",
address="", homepage="www.345.com", home_phone="",
mobile="555222333", work="", fax="",
email="<EMAIL>", email2="",
email3="", bday="", bmonth="-",
byear="", aday="", amonth="-", ayear="",
phone2="", address2="", notes="")
app.contact.create_contact(contact)
all_contacts = app.contact.get_contact_list()
index = randrange(len(all_contacts))
contact_from_homepage = app.contact.get_contact_list()[index]
contact_from_edit_page = app.contact.get_contact_info_from_edit_page(index)
assert contact_from_homepage.first_name == contact_from_edit_page.first_name
assert contact_from_homepage.last_name == contact_from_edit_page.last_name
assert contact_from_homepage.address == contact_from_edit_page.address
assert contact_from_homepage.all_emails_from_home_page == merge_emails_like_on_edit_page(contact_from_edit_page)
assert contact_from_homepage.all_phones_from_home_page == merge_phones_like_on_home_page(contact_from_edit_page)
def clear(s):
return re.sub("[() -]", "", s)
def merge_emails_like_on_edit_page(contact):
return "\n".join(filter(lambda x: x != "",
map(lambda x: clear(x),
filter(lambda x: x is not None,
[contact.email, contact.email2, contact.email3]))))
def merge_phones_like_on_home_page(contact):
return "\n".join(filter(lambda x: x != "",
map(lambda x: clear(x),
filter(lambda x: x is not None,
[contact.home_phone, contact.mobile, contact.work, contact.phone2])))) | 0.354433 | 0.245967 |
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: ali_eni_facts
short_description: Gather facts about ENI interfaces in Alibaba Cloud
description:
- Gather facts about ENI interfaces in Alibaba Cloud
version_added: "2.8.0"
options:
eni_ids:
description:
- A list of ENI IDs that exist in your account.
aliases: ['ids']
name_prefix:
description:
- Use a name prefix to filter network interfaces.
tags:
description:
- A hash/dictionaries of network interface tags. C({"key":"value"})
filters:
description:
- A dict of filters to apply. Each dict item consists of a filter key and a filter value. The filter keys can be
all of request parameters. See U(https://www.alibabacloud.com/help/doc-detail/58512.htm) for parameter details.
Filter keys can be same as request parameter name or be lower case and use underscore ("_") or dashes ("-") to
connect different words in one parameter. 'Tag.n.Key' and 'Tag.n.Value' should be a dict and using 'tags' instead.
'NetworkInterfaceId.N' will be appended to I(eni_ids) automatically.
author:
- "<NAME> (@xiaozhu36)"
requirements:
- "python >= 2.6"
- "footmark >= 1.8.0"
extends_documentation_fragment:
- alicloud
'''
EXAMPLES = '''
# Note: These examples do not set authentication details, see the Alibaba Cloud Guide for details.
# Gather facts about all ENIs
- ali_eni_facts:
# Gather facts about a particular ENI
- ali_eni_facts:
eni_ids:
- eni-xxxxxxx
- eni-yyyyyyy
filters:
type: Secondary
# Gather facts about a particular ENI
- ali_eni_facts:
filters:
network_interface_name: my-test-eni
type: Secondary
# Gather facts based on vpc and name_prefix
- ali_eni_facts:
name_prefix: foo
filters:
vswitch_id: vpc-dsfh2ef2
'''
RETURN = '''
interfaces:
description: List of matching elastic network interfaces
returned: always
type: complex
contains:
associated_public_ip:
description: The public IP address associated with the ENI.
type: string
sample: 172.16.58.3
zone_id:
description: The availability zone of the ENI is in.
returned: always
type: string
sample: cn-beijing-a
name:
description: interface name
type: string
sample: my-eni
creation_time:
description: The time the eni was created.
returned: always
type: string
sample: "2018-06-25T04:08Z"
description:
description: interface description
type: string
sample: My new network interface
security_groups:
description: list of security group ids
type: list
sample: [ "sg-f8a8a9da", "sg-xxxxxx" ]
network_interface_id:
description: network interface id
type: string
sample: "eni-123456"
id:
description: network interface id (alias for network_interface_id)
type: string
sample: "eni-123456"
instance_id:
description: Attached instance id
type: string
sample: "i-123456"
mac_address:
description: interface's physical address
type: string
sample: "00:00:5E:00:53:23"
private_ip_address:
description: primary ip address of this interface
type: string
sample: 10.20.30.40
private_ip_addresses:
description: list of all private ip addresses associated to this interface
type: list of dictionaries
sample: [ { "primary_address": true, "private_ip_address": "10.20.30.40" } ]
state:
description: network interface status
type: string
sample: "pending"
vswitch_id:
description: which vswitch the interface is bound
type: string
sample: vsw-b33i43f3
vpc_id:
description: which vpc this network interface is bound
type: string
sample: vpc-cj3ht4ogn
type:
description: type of the ENI
type: string
sample: Secondary
tags:
description: Any tags assigned to the ENI.
returned: always
type: dict
sample: {}
ids:
description: List of elastic network interface IDs
returned: always
type: list
sample: [eni-12345er, eni-3245fs]
'''
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.alicloud_ecs import ecs_argument_spec, ecs_connect
HAS_FOOTMARK = False
try:
from footmark.exception import ECSResponseError
HAS_FOOTMARK = True
except ImportError:
HAS_FOOTMARK = False
def main():
argument_spec = ecs_argument_spec()
argument_spec.update(dict(
eni_ids=dict(type='list', aliases=['ids']),
name_prefix=dict(),
tags=dict(type='dict'),
filters=dict(type='dict'),
)
)
module = AnsibleModule(argument_spec=argument_spec)
if HAS_FOOTMARK is False:
module.fail_json(msg='footmark required for the module ali_eni_facts')
interfaces = []
ids = []
filters = module.params["filters"]
if not filters:
filters = {}
eni_ids = module.params["eni_ids"]
if not eni_ids:
eni_ids = []
for key, value in list(filters.items()):
if str(key).startswith("NetworkInterfaceId") or \
str(key).startswith("network_interface_id") or \
str(key).startswith("network-interface-id"):
if value not in eni_ids:
eni_ids.append(value)
if eni_ids:
filters['network_interface_ids'] = eni_ids
name_prefix = module.params["name_prefix"]
if module.params['tags']:
filters['tags'] = module.params['tags']
try:
for eni in ecs_connect(module).describe_network_interfaces(**filters):
if name_prefix and not str(eni.name).startswith(name_prefix):
continue
interfaces.append(eni.read())
ids.append(eni.id)
module.exit_json(changed=False, ids=ids, interfaces=interfaces)
except Exception as e:
module.fail_json(msg=str("Unable to get network interfaces, error:{0}".format(e)))
if __name__ == '__main__':
main() | lib/ansible/modules/cloud/alicloud/_ali_eni_facts.py |
__metaclass__ = type
ANSIBLE_METADATA = {'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'community'}
DOCUMENTATION = '''
---
module: ali_eni_facts
short_description: Gather facts about ENI interfaces in Alibaba Cloud
description:
- Gather facts about ENI interfaces in Alibaba Cloud
version_added: "2.8.0"
options:
eni_ids:
description:
- A list of ENI IDs that exist in your account.
aliases: ['ids']
name_prefix:
description:
- Use a name prefix to filter network interfaces.
tags:
description:
- A hash/dictionaries of network interface tags. C({"key":"value"})
filters:
description:
- A dict of filters to apply. Each dict item consists of a filter key and a filter value. The filter keys can be
all of request parameters. See U(https://www.alibabacloud.com/help/doc-detail/58512.htm) for parameter details.
Filter keys can be same as request parameter name or be lower case and use underscore ("_") or dashes ("-") to
connect different words in one parameter. 'Tag.n.Key' and 'Tag.n.Value' should be a dict and using 'tags' instead.
'NetworkInterfaceId.N' will be appended to I(eni_ids) automatically.
author:
- "<NAME> (@xiaozhu36)"
requirements:
- "python >= 2.6"
- "footmark >= 1.8.0"
extends_documentation_fragment:
- alicloud
'''
EXAMPLES = '''
# Note: These examples do not set authentication details, see the Alibaba Cloud Guide for details.
# Gather facts about all ENIs
- ali_eni_facts:
# Gather facts about a particular ENI
- ali_eni_facts:
eni_ids:
- eni-xxxxxxx
- eni-yyyyyyy
filters:
type: Secondary
# Gather facts about a particular ENI
- ali_eni_facts:
filters:
network_interface_name: my-test-eni
type: Secondary
# Gather facts based on vpc and name_prefix
- ali_eni_facts:
name_prefix: foo
filters:
vswitch_id: vpc-dsfh2ef2
'''
RETURN = '''
interfaces:
description: List of matching elastic network interfaces
returned: always
type: complex
contains:
associated_public_ip:
description: The public IP address associated with the ENI.
type: string
sample: 172.16.58.3
zone_id:
description: The availability zone of the ENI is in.
returned: always
type: string
sample: cn-beijing-a
name:
description: interface name
type: string
sample: my-eni
creation_time:
description: The time the eni was created.
returned: always
type: string
sample: "2018-06-25T04:08Z"
description:
description: interface description
type: string
sample: My new network interface
security_groups:
description: list of security group ids
type: list
sample: [ "sg-f8a8a9da", "sg-xxxxxx" ]
network_interface_id:
description: network interface id
type: string
sample: "eni-123456"
id:
description: network interface id (alias for network_interface_id)
type: string
sample: "eni-123456"
instance_id:
description: Attached instance id
type: string
sample: "i-123456"
mac_address:
description: interface's physical address
type: string
sample: "00:00:5E:00:53:23"
private_ip_address:
description: primary ip address of this interface
type: string
sample: 10.20.30.40
private_ip_addresses:
description: list of all private ip addresses associated to this interface
type: list of dictionaries
sample: [ { "primary_address": true, "private_ip_address": "10.20.30.40" } ]
state:
description: network interface status
type: string
sample: "pending"
vswitch_id:
description: which vswitch the interface is bound
type: string
sample: vsw-b33i43f3
vpc_id:
description: which vpc this network interface is bound
type: string
sample: vpc-cj3ht4ogn
type:
description: type of the ENI
type: string
sample: Secondary
tags:
description: Any tags assigned to the ENI.
returned: always
type: dict
sample: {}
ids:
description: List of elastic network interface IDs
returned: always
type: list
sample: [eni-12345er, eni-3245fs]
'''
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.alicloud_ecs import ecs_argument_spec, ecs_connect
HAS_FOOTMARK = False
try:
from footmark.exception import ECSResponseError
HAS_FOOTMARK = True
except ImportError:
HAS_FOOTMARK = False
def main():
argument_spec = ecs_argument_spec()
argument_spec.update(dict(
eni_ids=dict(type='list', aliases=['ids']),
name_prefix=dict(),
tags=dict(type='dict'),
filters=dict(type='dict'),
)
)
module = AnsibleModule(argument_spec=argument_spec)
if HAS_FOOTMARK is False:
module.fail_json(msg='footmark required for the module ali_eni_facts')
interfaces = []
ids = []
filters = module.params["filters"]
if not filters:
filters = {}
eni_ids = module.params["eni_ids"]
if not eni_ids:
eni_ids = []
for key, value in list(filters.items()):
if str(key).startswith("NetworkInterfaceId") or \
str(key).startswith("network_interface_id") or \
str(key).startswith("network-interface-id"):
if value not in eni_ids:
eni_ids.append(value)
if eni_ids:
filters['network_interface_ids'] = eni_ids
name_prefix = module.params["name_prefix"]
if module.params['tags']:
filters['tags'] = module.params['tags']
try:
for eni in ecs_connect(module).describe_network_interfaces(**filters):
if name_prefix and not str(eni.name).startswith(name_prefix):
continue
interfaces.append(eni.read())
ids.append(eni.id)
module.exit_json(changed=False, ids=ids, interfaces=interfaces)
except Exception as e:
module.fail_json(msg=str("Unable to get network interfaces, error:{0}".format(e)))
if __name__ == '__main__':
main() | 0.724188 | 0.357624 |
from __future__ import absolute_import, unicode_literals
import io
import os
import sys
import warnings
import functools
from collections import defaultdict
from functools import partial
from functools import wraps
from importlib import import_module
from distutils.errors import DistutilsOptionError, DistutilsFileError
from setuptools.extern.packaging.version import LegacyVersion, parse
from setuptools.extern.packaging.specifiers import SpecifierSet
from setuptools.extern.six import string_types, PY3
__metaclass__ = type
def read_configuration(
filepath, find_others=False, ignore_option_errors=False):
"""Read given configuration file and returns options from it as a dict.
:param str|unicode filepath: Path to configuration file
to get options from.
:param bool find_others: Whether to search for other configuration files
which could be on in various places.
:param bool ignore_option_errors: Whether to silently ignore
options, values of which could not be resolved (e.g. due to exceptions
in directives such as file:, attr:, etc.).
If False exceptions are propagated as expected.
:rtype: dict
"""
from setuptools.dist import Distribution, _Distribution
filepath = os.path.abspath(filepath)
if not os.path.isfile(filepath):
raise DistutilsFileError(
'Configuration file %s does not exist.' % filepath)
current_directory = os.getcwd()
os.chdir(os.path.dirname(filepath))
try:
dist = Distribution()
filenames = dist.find_config_files() if find_others else []
if filepath not in filenames:
filenames.append(filepath)
_Distribution.parse_config_files(dist, filenames=filenames)
handlers = parse_configuration(
dist, dist.command_options,
ignore_option_errors=ignore_option_errors)
finally:
os.chdir(current_directory)
return configuration_to_dict(handlers)
def _get_option(target_obj, key):
"""
Given a target object and option key, get that option from
the target object, either through a get_{key} method or
from an attribute directly.
"""
getter_name = 'get_{key}'.format(**locals())
by_attribute = functools.partial(getattr, target_obj, key)
getter = getattr(target_obj, getter_name, by_attribute)
return getter()
def configuration_to_dict(handlers):
"""Returns configuration data gathered by given handlers as a dict.
:param list[ConfigHandler] handlers: Handlers list,
usually from parse_configuration()
:rtype: dict
"""
config_dict = defaultdict(dict)
for handler in handlers:
for option in handler.set_options:
value = _get_option(handler.target_obj, option)
config_dict[handler.section_prefix][option] = value
return config_dict
def parse_configuration(
distribution, command_options, ignore_option_errors=False):
"""Performs additional parsing of configuration options
for a distribution.
Returns a list of used option handlers.
:param Distribution distribution:
:param dict command_options:
:param bool ignore_option_errors: Whether to silently ignore
options, values of which could not be resolved (e.g. due to exceptions
in directives such as file:, attr:, etc.).
If False exceptions are propagated as expected.
:rtype: list
"""
options = ConfigOptionsHandler(
distribution, command_options, ignore_option_errors)
options.parse()
meta = ConfigMetadataHandler(
distribution.metadata, command_options, ignore_option_errors,
distribution.package_dir)
meta.parse()
return meta, options
class ConfigHandler:
"""Handles metadata supplied in configuration files."""
section_prefix = None
"""Prefix for config sections handled by this handler.
Must be provided by class heirs.
"""
aliases = {}
"""Options aliases.
For compatibility with various packages. E.g.: d2to1 and pbr.
Note: `-` in keys is replaced with `_` by config parser.
"""
def __init__(self, target_obj, options, ignore_option_errors=False):
sections = {}
section_prefix = self.section_prefix
for section_name, section_options in options.items():
if not section_name.startswith(section_prefix):
continue
section_name = section_name.replace(section_prefix, '').strip('.')
sections[section_name] = section_options
self.ignore_option_errors = ignore_option_errors
self.target_obj = target_obj
self.sections = sections
self.set_options = []
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
raise NotImplementedError(
'%s must provide .parsers property' % self.__class__.__name__)
def __setitem__(self, option_name, value):
unknown = tuple()
target_obj = self.target_obj
# Translate alias into real name.
option_name = self.aliases.get(option_name, option_name)
current_value = getattr(target_obj, option_name, unknown)
if current_value is unknown:
raise KeyError(option_name)
if current_value:
# Already inhabited. Skipping.
return
skip_option = False
parser = self.parsers.get(option_name)
if parser:
try:
value = parser(value)
except Exception:
skip_option = True
if not self.ignore_option_errors:
raise
if skip_option:
return
setter = getattr(target_obj, 'set_%s' % option_name, None)
if setter is None:
setattr(target_obj, option_name, value)
else:
setter(value)
self.set_options.append(option_name)
@classmethod
def _parse_list(cls, value, separator=','):
"""Represents value as a list.
Value is split either by separator (defaults to comma) or by lines.
:param value:
:param separator: List items separator character.
:rtype: list
"""
if isinstance(value, list): # _get_parser_compound case
return value
if '\n' in value:
value = value.splitlines()
else:
value = value.split(separator)
return [chunk.strip() for chunk in value if chunk.strip()]
@classmethod
def _parse_dict(cls, value):
"""Represents value as a dict.
:param value:
:rtype: dict
"""
separator = '='
result = {}
for line in cls._parse_list(value):
key, sep, val = line.partition(separator)
if sep != separator:
raise DistutilsOptionError(
'Unable to parse option value to dict: %s' % value)
result[key.strip()] = val.strip()
return result
@classmethod
def _parse_bool(cls, value):
"""Represents value as boolean.
:param value:
:rtype: bool
"""
value = value.lower()
return value in ('1', 'true', 'yes')
@classmethod
def _exclude_files_parser(cls, key):
"""Returns a parser function to make sure field inputs
are not files.
Parses a value after getting the key so error messages are
more informative.
:param key:
:rtype: callable
"""
def parser(value):
exclude_directive = 'file:'
if value.startswith(exclude_directive):
raise ValueError(
'Only strings are accepted for the {0} field, '
'files are not accepted'.format(key))
return value
return parser
@classmethod
def _parse_file(cls, value):
"""Represents value as a string, allowing including text
from nearest files using `file:` directive.
Directive is sandboxed and won't reach anything outside
directory with setup.py.
Examples:
file: README.rst, CHANGELOG.md, src/file.txt
:param str value:
:rtype: str
"""
include_directive = 'file:'
if not isinstance(value, string_types):
return value
if not value.startswith(include_directive):
return value
spec = value[len(include_directive):]
filepaths = (os.path.abspath(path.strip()) for path in spec.split(','))
return '\n'.join(
cls._read_file(path)
for path in filepaths
if (cls._assert_local(path) or True)
and os.path.isfile(path)
)
@staticmethod
def _assert_local(filepath):
if not filepath.startswith(os.getcwd()):
raise DistutilsOptionError(
'`file:` directive can not access %s' % filepath)
@staticmethod
def _read_file(filepath):
with io.open(filepath, encoding='utf-8') as f:
return f.read()
@classmethod
def _parse_attr(cls, value, package_dir=None):
"""Represents value as a module attribute.
Examples:
attr: package.attr
attr: package.module.attr
:param str value:
:rtype: str
"""
attr_directive = 'attr:'
if not value.startswith(attr_directive):
return value
attrs_path = value.replace(attr_directive, '').strip().split('.')
attr_name = attrs_path.pop()
module_name = '.'.join(attrs_path)
module_name = module_name or '__init__'
parent_path = os.getcwd()
if package_dir:
if attrs_path[0] in package_dir:
# A custom path was specified for the module we want to import
custom_path = package_dir[attrs_path[0]]
parts = custom_path.rsplit('/', 1)
if len(parts) > 1:
parent_path = os.path.join(os.getcwd(), parts[0])
module_name = parts[1]
else:
module_name = custom_path
elif '' in package_dir:
# A custom parent directory was specified for all root modules
parent_path = os.path.join(os.getcwd(), package_dir[''])
sys.path.insert(0, parent_path)
try:
module = import_module(module_name)
value = getattr(module, attr_name)
finally:
sys.path = sys.path[1:]
return value
@classmethod
def _get_parser_compound(cls, *parse_methods):
"""Returns parser function to represents value as a list.
Parses a value applying given methods one after another.
:param parse_methods:
:rtype: callable
"""
def parse(value):
parsed = value
for method in parse_methods:
parsed = method(parsed)
return parsed
return parse
@classmethod
def _parse_section_to_dict(cls, section_options, values_parser=None):
"""Parses section options into a dictionary.
Optionally applies a given parser to values.
:param dict section_options:
:param callable values_parser:
:rtype: dict
"""
value = {}
values_parser = values_parser or (lambda val: val)
for key, (_, val) in section_options.items():
value[key] = values_parser(val)
return value
def parse_section(self, section_options):
"""Parses configuration file section.
:param dict section_options:
"""
for (name, (_, value)) in section_options.items():
try:
self[name] = value
except KeyError:
pass # Keep silent for a new option may appear anytime.
def parse(self):
"""Parses configuration file items from one
or more related sections.
"""
for section_name, section_options in self.sections.items():
method_postfix = ''
if section_name: # [section.option] variant
method_postfix = '_%s' % section_name
section_parser_method = getattr(
self,
# Dots in section names are translated into dunderscores.
('parse_section%s' % method_postfix).replace('.', '__'),
None)
if section_parser_method is None:
raise DistutilsOptionError(
'Unsupported distribution option section: [%s.%s]' % (
self.section_prefix, section_name))
section_parser_method(section_options)
def _deprecated_config_handler(self, func, msg, warning_class):
""" this function will wrap around parameters that are deprecated
:param msg: deprecation message
:param warning_class: class of warning exception to be raised
:param func: function to be wrapped around
"""
@wraps(func)
def config_handler(*args, **kwargs):
warnings.warn(msg, warning_class)
return func(*args, **kwargs)
return config_handler
class ConfigMetadataHandler(ConfigHandler):
section_prefix = 'metadata'
aliases = {
'home_page': 'url',
'summary': 'description',
'classifier': 'classifiers',
'platform': 'platforms',
}
strict_mode = False
"""We need to keep it loose, to be partially compatible with
`pbr` and `d2to1` packages which also uses `metadata` section.
"""
def __init__(self, target_obj, options, ignore_option_errors=False,
package_dir=None):
super(ConfigMetadataHandler, self).__init__(target_obj, options,
ignore_option_errors)
self.package_dir = package_dir
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
parse_list = self._parse_list
parse_file = self._parse_file
parse_dict = self._parse_dict
exclude_files_parser = self._exclude_files_parser
return {
'platforms': parse_list,
'keywords': parse_list,
'provides': parse_list,
'requires': self._deprecated_config_handler(
parse_list,
"The requires parameter is deprecated, please use "
"install_requires for runtime dependencies.",
DeprecationWarning),
'obsoletes': parse_list,
'classifiers': self._get_parser_compound(parse_file, parse_list),
'license': exclude_files_parser('license'),
'license_files': parse_list,
'description': parse_file,
'long_description': parse_file,
'version': self._parse_version,
'project_urls': parse_dict,
}
def _parse_version(self, value):
"""Parses `version` option value.
:param value:
:rtype: str
"""
version = self._parse_file(value)
if version != value:
version = version.strip()
# Be strict about versions loaded from file because it's easy to
# accidentally include newlines and other unintended content
if isinstance(parse(version), LegacyVersion):
tmpl = (
'Version loaded from {value} does not '
'comply with PEP 440: {version}'
)
raise DistutilsOptionError(tmpl.format(**locals()))
return version
version = self._parse_attr(value, self.package_dir)
if callable(version):
version = version()
if not isinstance(version, string_types):
if hasattr(version, '__iter__'):
version = '.'.join(map(str, version))
else:
version = '%s' % version
return version
class ConfigOptionsHandler(ConfigHandler):
section_prefix = 'options'
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
parse_list = self._parse_list
parse_list_semicolon = partial(self._parse_list, separator=';')
parse_bool = self._parse_bool
parse_dict = self._parse_dict
return {
'zip_safe': parse_bool,
'use_2to3': parse_bool,
'include_package_data': parse_bool,
'package_dir': parse_dict,
'use_2to3_fixers': parse_list,
'use_2to3_exclude_fixers': parse_list,
'convert_2to3_doctests': parse_list,
'scripts': parse_list,
'eager_resources': parse_list,
'dependency_links': parse_list,
'namespace_packages': parse_list,
'install_requires': parse_list_semicolon,
'setup_requires': parse_list_semicolon,
'tests_require': parse_list_semicolon,
'packages': self._parse_packages,
'entry_points': self._parse_file,
'py_modules': parse_list,
'python_requires': SpecifierSet,
}
def _parse_packages(self, value):
"""Parses `packages` option value.
:param value:
:rtype: list
"""
find_directives = ['find:', 'find_namespace:']
trimmed_value = value.strip()
if trimmed_value not in find_directives:
return self._parse_list(value)
findns = trimmed_value == find_directives[1]
if findns and not PY3:
raise DistutilsOptionError(
'find_namespace: directive is unsupported on Python < 3.3')
# Read function arguments from a dedicated section.
find_kwargs = self.parse_section_packages__find(
self.sections.get('packages.find', {}))
if findns:
from setuptools import find_namespace_packages as find_packages
else:
from setuptools import find_packages
return find_packages(**find_kwargs)
def parse_section_packages__find(self, section_options):
"""Parses `packages.find` configuration file section.
To be used in conjunction with _parse_packages().
:param dict section_options:
"""
section_data = self._parse_section_to_dict(
section_options, self._parse_list)
valid_keys = ['where', 'include', 'exclude']
find_kwargs = dict(
[(k, v) for k, v in section_data.items() if k in valid_keys and v])
where = find_kwargs.get('where')
if where is not None:
find_kwargs['where'] = where[0] # cast list to single val
return find_kwargs
def parse_section_entry_points(self, section_options):
"""Parses `entry_points` configuration file section.
:param dict section_options:
"""
parsed = self._parse_section_to_dict(section_options, self._parse_list)
self['entry_points'] = parsed
def _parse_package_data(self, section_options):
parsed = self._parse_section_to_dict(section_options, self._parse_list)
root = parsed.get('*')
if root:
parsed[''] = root
del parsed['*']
return parsed
def parse_section_package_data(self, section_options):
"""Parses `package_data` configuration file section.
:param dict section_options:
"""
self['package_data'] = self._parse_package_data(section_options)
def parse_section_exclude_package_data(self, section_options):
"""Parses `exclude_package_data` configuration file section.
:param dict section_options:
"""
self['exclude_package_data'] = self._parse_package_data(
section_options)
def parse_section_extras_require(self, section_options):
"""Parses `extras_require` configuration file section.
:param dict section_options:
"""
parse_list = partial(self._parse_list, separator=';')
self['extras_require'] = self._parse_section_to_dict(
section_options, parse_list)
def parse_section_data_files(self, section_options):
"""Parses `data_files` configuration file section.
:param dict section_options:
"""
parsed = self._parse_section_to_dict(section_options, self._parse_list)
self['data_files'] = [(k, v) for k, v in parsed.items()] | venv/lib/python3.8/site-packages/setuptools/config.py |
from __future__ import absolute_import, unicode_literals
import io
import os
import sys
import warnings
import functools
from collections import defaultdict
from functools import partial
from functools import wraps
from importlib import import_module
from distutils.errors import DistutilsOptionError, DistutilsFileError
from setuptools.extern.packaging.version import LegacyVersion, parse
from setuptools.extern.packaging.specifiers import SpecifierSet
from setuptools.extern.six import string_types, PY3
__metaclass__ = type
def read_configuration(
filepath, find_others=False, ignore_option_errors=False):
"""Read given configuration file and returns options from it as a dict.
:param str|unicode filepath: Path to configuration file
to get options from.
:param bool find_others: Whether to search for other configuration files
which could be on in various places.
:param bool ignore_option_errors: Whether to silently ignore
options, values of which could not be resolved (e.g. due to exceptions
in directives such as file:, attr:, etc.).
If False exceptions are propagated as expected.
:rtype: dict
"""
from setuptools.dist import Distribution, _Distribution
filepath = os.path.abspath(filepath)
if not os.path.isfile(filepath):
raise DistutilsFileError(
'Configuration file %s does not exist.' % filepath)
current_directory = os.getcwd()
os.chdir(os.path.dirname(filepath))
try:
dist = Distribution()
filenames = dist.find_config_files() if find_others else []
if filepath not in filenames:
filenames.append(filepath)
_Distribution.parse_config_files(dist, filenames=filenames)
handlers = parse_configuration(
dist, dist.command_options,
ignore_option_errors=ignore_option_errors)
finally:
os.chdir(current_directory)
return configuration_to_dict(handlers)
def _get_option(target_obj, key):
"""
Given a target object and option key, get that option from
the target object, either through a get_{key} method or
from an attribute directly.
"""
getter_name = 'get_{key}'.format(**locals())
by_attribute = functools.partial(getattr, target_obj, key)
getter = getattr(target_obj, getter_name, by_attribute)
return getter()
def configuration_to_dict(handlers):
"""Returns configuration data gathered by given handlers as a dict.
:param list[ConfigHandler] handlers: Handlers list,
usually from parse_configuration()
:rtype: dict
"""
config_dict = defaultdict(dict)
for handler in handlers:
for option in handler.set_options:
value = _get_option(handler.target_obj, option)
config_dict[handler.section_prefix][option] = value
return config_dict
def parse_configuration(
distribution, command_options, ignore_option_errors=False):
"""Performs additional parsing of configuration options
for a distribution.
Returns a list of used option handlers.
:param Distribution distribution:
:param dict command_options:
:param bool ignore_option_errors: Whether to silently ignore
options, values of which could not be resolved (e.g. due to exceptions
in directives such as file:, attr:, etc.).
If False exceptions are propagated as expected.
:rtype: list
"""
options = ConfigOptionsHandler(
distribution, command_options, ignore_option_errors)
options.parse()
meta = ConfigMetadataHandler(
distribution.metadata, command_options, ignore_option_errors,
distribution.package_dir)
meta.parse()
return meta, options
class ConfigHandler:
"""Handles metadata supplied in configuration files."""
section_prefix = None
"""Prefix for config sections handled by this handler.
Must be provided by class heirs.
"""
aliases = {}
"""Options aliases.
For compatibility with various packages. E.g.: d2to1 and pbr.
Note: `-` in keys is replaced with `_` by config parser.
"""
def __init__(self, target_obj, options, ignore_option_errors=False):
sections = {}
section_prefix = self.section_prefix
for section_name, section_options in options.items():
if not section_name.startswith(section_prefix):
continue
section_name = section_name.replace(section_prefix, '').strip('.')
sections[section_name] = section_options
self.ignore_option_errors = ignore_option_errors
self.target_obj = target_obj
self.sections = sections
self.set_options = []
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
raise NotImplementedError(
'%s must provide .parsers property' % self.__class__.__name__)
def __setitem__(self, option_name, value):
unknown = tuple()
target_obj = self.target_obj
# Translate alias into real name.
option_name = self.aliases.get(option_name, option_name)
current_value = getattr(target_obj, option_name, unknown)
if current_value is unknown:
raise KeyError(option_name)
if current_value:
# Already inhabited. Skipping.
return
skip_option = False
parser = self.parsers.get(option_name)
if parser:
try:
value = parser(value)
except Exception:
skip_option = True
if not self.ignore_option_errors:
raise
if skip_option:
return
setter = getattr(target_obj, 'set_%s' % option_name, None)
if setter is None:
setattr(target_obj, option_name, value)
else:
setter(value)
self.set_options.append(option_name)
@classmethod
def _parse_list(cls, value, separator=','):
"""Represents value as a list.
Value is split either by separator (defaults to comma) or by lines.
:param value:
:param separator: List items separator character.
:rtype: list
"""
if isinstance(value, list): # _get_parser_compound case
return value
if '\n' in value:
value = value.splitlines()
else:
value = value.split(separator)
return [chunk.strip() for chunk in value if chunk.strip()]
@classmethod
def _parse_dict(cls, value):
"""Represents value as a dict.
:param value:
:rtype: dict
"""
separator = '='
result = {}
for line in cls._parse_list(value):
key, sep, val = line.partition(separator)
if sep != separator:
raise DistutilsOptionError(
'Unable to parse option value to dict: %s' % value)
result[key.strip()] = val.strip()
return result
@classmethod
def _parse_bool(cls, value):
"""Represents value as boolean.
:param value:
:rtype: bool
"""
value = value.lower()
return value in ('1', 'true', 'yes')
@classmethod
def _exclude_files_parser(cls, key):
"""Returns a parser function to make sure field inputs
are not files.
Parses a value after getting the key so error messages are
more informative.
:param key:
:rtype: callable
"""
def parser(value):
exclude_directive = 'file:'
if value.startswith(exclude_directive):
raise ValueError(
'Only strings are accepted for the {0} field, '
'files are not accepted'.format(key))
return value
return parser
@classmethod
def _parse_file(cls, value):
"""Represents value as a string, allowing including text
from nearest files using `file:` directive.
Directive is sandboxed and won't reach anything outside
directory with setup.py.
Examples:
file: README.rst, CHANGELOG.md, src/file.txt
:param str value:
:rtype: str
"""
include_directive = 'file:'
if not isinstance(value, string_types):
return value
if not value.startswith(include_directive):
return value
spec = value[len(include_directive):]
filepaths = (os.path.abspath(path.strip()) for path in spec.split(','))
return '\n'.join(
cls._read_file(path)
for path in filepaths
if (cls._assert_local(path) or True)
and os.path.isfile(path)
)
@staticmethod
def _assert_local(filepath):
if not filepath.startswith(os.getcwd()):
raise DistutilsOptionError(
'`file:` directive can not access %s' % filepath)
@staticmethod
def _read_file(filepath):
with io.open(filepath, encoding='utf-8') as f:
return f.read()
@classmethod
def _parse_attr(cls, value, package_dir=None):
"""Represents value as a module attribute.
Examples:
attr: package.attr
attr: package.module.attr
:param str value:
:rtype: str
"""
attr_directive = 'attr:'
if not value.startswith(attr_directive):
return value
attrs_path = value.replace(attr_directive, '').strip().split('.')
attr_name = attrs_path.pop()
module_name = '.'.join(attrs_path)
module_name = module_name or '__init__'
parent_path = os.getcwd()
if package_dir:
if attrs_path[0] in package_dir:
# A custom path was specified for the module we want to import
custom_path = package_dir[attrs_path[0]]
parts = custom_path.rsplit('/', 1)
if len(parts) > 1:
parent_path = os.path.join(os.getcwd(), parts[0])
module_name = parts[1]
else:
module_name = custom_path
elif '' in package_dir:
# A custom parent directory was specified for all root modules
parent_path = os.path.join(os.getcwd(), package_dir[''])
sys.path.insert(0, parent_path)
try:
module = import_module(module_name)
value = getattr(module, attr_name)
finally:
sys.path = sys.path[1:]
return value
@classmethod
def _get_parser_compound(cls, *parse_methods):
"""Returns parser function to represents value as a list.
Parses a value applying given methods one after another.
:param parse_methods:
:rtype: callable
"""
def parse(value):
parsed = value
for method in parse_methods:
parsed = method(parsed)
return parsed
return parse
@classmethod
def _parse_section_to_dict(cls, section_options, values_parser=None):
"""Parses section options into a dictionary.
Optionally applies a given parser to values.
:param dict section_options:
:param callable values_parser:
:rtype: dict
"""
value = {}
values_parser = values_parser or (lambda val: val)
for key, (_, val) in section_options.items():
value[key] = values_parser(val)
return value
def parse_section(self, section_options):
"""Parses configuration file section.
:param dict section_options:
"""
for (name, (_, value)) in section_options.items():
try:
self[name] = value
except KeyError:
pass # Keep silent for a new option may appear anytime.
def parse(self):
"""Parses configuration file items from one
or more related sections.
"""
for section_name, section_options in self.sections.items():
method_postfix = ''
if section_name: # [section.option] variant
method_postfix = '_%s' % section_name
section_parser_method = getattr(
self,
# Dots in section names are translated into dunderscores.
('parse_section%s' % method_postfix).replace('.', '__'),
None)
if section_parser_method is None:
raise DistutilsOptionError(
'Unsupported distribution option section: [%s.%s]' % (
self.section_prefix, section_name))
section_parser_method(section_options)
def _deprecated_config_handler(self, func, msg, warning_class):
""" this function will wrap around parameters that are deprecated
:param msg: deprecation message
:param warning_class: class of warning exception to be raised
:param func: function to be wrapped around
"""
@wraps(func)
def config_handler(*args, **kwargs):
warnings.warn(msg, warning_class)
return func(*args, **kwargs)
return config_handler
class ConfigMetadataHandler(ConfigHandler):
section_prefix = 'metadata'
aliases = {
'home_page': 'url',
'summary': 'description',
'classifier': 'classifiers',
'platform': 'platforms',
}
strict_mode = False
"""We need to keep it loose, to be partially compatible with
`pbr` and `d2to1` packages which also uses `metadata` section.
"""
def __init__(self, target_obj, options, ignore_option_errors=False,
package_dir=None):
super(ConfigMetadataHandler, self).__init__(target_obj, options,
ignore_option_errors)
self.package_dir = package_dir
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
parse_list = self._parse_list
parse_file = self._parse_file
parse_dict = self._parse_dict
exclude_files_parser = self._exclude_files_parser
return {
'platforms': parse_list,
'keywords': parse_list,
'provides': parse_list,
'requires': self._deprecated_config_handler(
parse_list,
"The requires parameter is deprecated, please use "
"install_requires for runtime dependencies.",
DeprecationWarning),
'obsoletes': parse_list,
'classifiers': self._get_parser_compound(parse_file, parse_list),
'license': exclude_files_parser('license'),
'license_files': parse_list,
'description': parse_file,
'long_description': parse_file,
'version': self._parse_version,
'project_urls': parse_dict,
}
def _parse_version(self, value):
"""Parses `version` option value.
:param value:
:rtype: str
"""
version = self._parse_file(value)
if version != value:
version = version.strip()
# Be strict about versions loaded from file because it's easy to
# accidentally include newlines and other unintended content
if isinstance(parse(version), LegacyVersion):
tmpl = (
'Version loaded from {value} does not '
'comply with PEP 440: {version}'
)
raise DistutilsOptionError(tmpl.format(**locals()))
return version
version = self._parse_attr(value, self.package_dir)
if callable(version):
version = version()
if not isinstance(version, string_types):
if hasattr(version, '__iter__'):
version = '.'.join(map(str, version))
else:
version = '%s' % version
return version
class ConfigOptionsHandler(ConfigHandler):
section_prefix = 'options'
@property
def parsers(self):
"""Metadata item name to parser function mapping."""
parse_list = self._parse_list
parse_list_semicolon = partial(self._parse_list, separator=';')
parse_bool = self._parse_bool
parse_dict = self._parse_dict
return {
'zip_safe': parse_bool,
'use_2to3': parse_bool,
'include_package_data': parse_bool,
'package_dir': parse_dict,
'use_2to3_fixers': parse_list,
'use_2to3_exclude_fixers': parse_list,
'convert_2to3_doctests': parse_list,
'scripts': parse_list,
'eager_resources': parse_list,
'dependency_links': parse_list,
'namespace_packages': parse_list,
'install_requires': parse_list_semicolon,
'setup_requires': parse_list_semicolon,
'tests_require': parse_list_semicolon,
'packages': self._parse_packages,
'entry_points': self._parse_file,
'py_modules': parse_list,
'python_requires': SpecifierSet,
}
def _parse_packages(self, value):
"""Parses `packages` option value.
:param value:
:rtype: list
"""
find_directives = ['find:', 'find_namespace:']
trimmed_value = value.strip()
if trimmed_value not in find_directives:
return self._parse_list(value)
findns = trimmed_value == find_directives[1]
if findns and not PY3:
raise DistutilsOptionError(
'find_namespace: directive is unsupported on Python < 3.3')
# Read function arguments from a dedicated section.
find_kwargs = self.parse_section_packages__find(
self.sections.get('packages.find', {}))
if findns:
from setuptools import find_namespace_packages as find_packages
else:
from setuptools import find_packages
return find_packages(**find_kwargs)
def parse_section_packages__find(self, section_options):
"""Parses `packages.find` configuration file section.
To be used in conjunction with _parse_packages().
:param dict section_options:
"""
section_data = self._parse_section_to_dict(
section_options, self._parse_list)
valid_keys = ['where', 'include', 'exclude']
find_kwargs = dict(
[(k, v) for k, v in section_data.items() if k in valid_keys and v])
where = find_kwargs.get('where')
if where is not None:
find_kwargs['where'] = where[0] # cast list to single val
return find_kwargs
def parse_section_entry_points(self, section_options):
"""Parses `entry_points` configuration file section.
:param dict section_options:
"""
parsed = self._parse_section_to_dict(section_options, self._parse_list)
self['entry_points'] = parsed
def _parse_package_data(self, section_options):
parsed = self._parse_section_to_dict(section_options, self._parse_list)
root = parsed.get('*')
if root:
parsed[''] = root
del parsed['*']
return parsed
def parse_section_package_data(self, section_options):
"""Parses `package_data` configuration file section.
:param dict section_options:
"""
self['package_data'] = self._parse_package_data(section_options)
def parse_section_exclude_package_data(self, section_options):
"""Parses `exclude_package_data` configuration file section.
:param dict section_options:
"""
self['exclude_package_data'] = self._parse_package_data(
section_options)
def parse_section_extras_require(self, section_options):
"""Parses `extras_require` configuration file section.
:param dict section_options:
"""
parse_list = partial(self._parse_list, separator=';')
self['extras_require'] = self._parse_section_to_dict(
section_options, parse_list)
def parse_section_data_files(self, section_options):
"""Parses `data_files` configuration file section.
:param dict section_options:
"""
parsed = self._parse_section_to_dict(section_options, self._parse_list)
self['data_files'] = [(k, v) for k, v in parsed.items()] | 0.667473 | 0.119845 |
from bson import ObjectId
from app.api.data.command.ExerciseEvaluationMongoCommandRepository import ExerciseEvaluationMongoCommandRepository
from app.api.domain.models.ExerciseEvaluation import ExerciseEvaluation
from app.api.domain.services.data.command.errors.CommandError import CommandError
from tests.integration.PdbMongoIntegrationTestBase import PdbMongoIntegrationTestBase
class ExerciseEvaluationMongoCommandRepositoryIntegrationTest(PdbMongoIntegrationTestBase):
def setUp(self):
self.fixtures = []
super(ExerciseEvaluationMongoCommandRepositoryIntegrationTest, self).setUp()
self.sut = ExerciseEvaluationMongoCommandRepository()
def tearDown(self):
self.db.evaluations.delete_many({})
def test_createExerciseEvaluation_calledWithExerciseEvaluation_correctInsertion(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.sut.create_exercise_evaluation(exercise_evaluation)
actual = self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()})
expected = exercise_evaluation.to_json_dict()
self.assertEqual(actual, expected)
def test_createExerciseEvaluation_calledWithExistentExerciseEvaluation_throwCommandError(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.sut.create_exercise_evaluation(exercise_evaluation)
self.assertRaises(CommandError, self.sut.create_exercise_evaluation, exercise_evaluation)
def test_incrementExerciseEvaluationAttempts_calledWithExerciseEvaluation_attemptNumberCorrectlyUpdated(self):
exercise_evaluation = self.__create_and_insert_exercise_evaluation_test_instance()
self.sut.increment_exercise_evaluation_attempts(exercise_evaluation)
actual = ExerciseEvaluation.from_json(
self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()})).get_attempt()
expected = 2
self.assertEqual(actual, expected)
def test_updateExerciseEvaluationAsSolved_calledWithValidParams_exerciseEvaluationCorrectlyUpdated(self):
exercise_evaluation = self.__create_and_insert_exercise_evaluation_test_instance()
self.sut.update_exercise_evaluation_as_solved(exercise_evaluation, 200)
exercise_evaluation_returned_from_db = ExerciseEvaluation.from_json(
self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()}))
actual_score = exercise_evaluation_returned_from_db.get_score()
expected_score = 200
actual_status = exercise_evaluation_returned_from_db.get_status()
expected_status = ExerciseEvaluation.STATUS_SOLVED
self.assertEqual(actual_score, expected_score)
self.assertEqual(actual_status, expected_status)
def __create_and_insert_exercise_evaluation_test_instance(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.db.evaluations.insert_one(exercise_evaluation.to_json_dict())
return exercise_evaluation
def __get_exercise_evaluation_test_instance(self):
return ExerciseEvaluation(ObjectId('54759eb3c090d83494e2d804'), ObjectId('507f1f77bcf86cd799439011'),
_id=ObjectId("666f6f2d6261722d71757578")) | app/tests/integration/ExerciseEvaluationMongoCommandRepositoryIntegrationTest.py | from bson import ObjectId
from app.api.data.command.ExerciseEvaluationMongoCommandRepository import ExerciseEvaluationMongoCommandRepository
from app.api.domain.models.ExerciseEvaluation import ExerciseEvaluation
from app.api.domain.services.data.command.errors.CommandError import CommandError
from tests.integration.PdbMongoIntegrationTestBase import PdbMongoIntegrationTestBase
class ExerciseEvaluationMongoCommandRepositoryIntegrationTest(PdbMongoIntegrationTestBase):
def setUp(self):
self.fixtures = []
super(ExerciseEvaluationMongoCommandRepositoryIntegrationTest, self).setUp()
self.sut = ExerciseEvaluationMongoCommandRepository()
def tearDown(self):
self.db.evaluations.delete_many({})
def test_createExerciseEvaluation_calledWithExerciseEvaluation_correctInsertion(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.sut.create_exercise_evaluation(exercise_evaluation)
actual = self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()})
expected = exercise_evaluation.to_json_dict()
self.assertEqual(actual, expected)
def test_createExerciseEvaluation_calledWithExistentExerciseEvaluation_throwCommandError(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.sut.create_exercise_evaluation(exercise_evaluation)
self.assertRaises(CommandError, self.sut.create_exercise_evaluation, exercise_evaluation)
def test_incrementExerciseEvaluationAttempts_calledWithExerciseEvaluation_attemptNumberCorrectlyUpdated(self):
exercise_evaluation = self.__create_and_insert_exercise_evaluation_test_instance()
self.sut.increment_exercise_evaluation_attempts(exercise_evaluation)
actual = ExerciseEvaluation.from_json(
self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()})).get_attempt()
expected = 2
self.assertEqual(actual, expected)
def test_updateExerciseEvaluationAsSolved_calledWithValidParams_exerciseEvaluationCorrectlyUpdated(self):
exercise_evaluation = self.__create_and_insert_exercise_evaluation_test_instance()
self.sut.update_exercise_evaluation_as_solved(exercise_evaluation, 200)
exercise_evaluation_returned_from_db = ExerciseEvaluation.from_json(
self.db.evaluations.find_one({'_id': exercise_evaluation.get_id()}))
actual_score = exercise_evaluation_returned_from_db.get_score()
expected_score = 200
actual_status = exercise_evaluation_returned_from_db.get_status()
expected_status = ExerciseEvaluation.STATUS_SOLVED
self.assertEqual(actual_score, expected_score)
self.assertEqual(actual_status, expected_status)
def __create_and_insert_exercise_evaluation_test_instance(self):
exercise_evaluation = self.__get_exercise_evaluation_test_instance()
self.db.evaluations.insert_one(exercise_evaluation.to_json_dict())
return exercise_evaluation
def __get_exercise_evaluation_test_instance(self):
return ExerciseEvaluation(ObjectId('54759eb3c090d83494e2d804'), ObjectId('507f1f77bcf86cd799439011'),
_id=ObjectId("666f6f2d6261722d71757578")) | 0.614047 | 0.332581 |
import os
import sys
import MySQLdb
from picker_tuner import picker_tuner
from icecream import ic
ic.configureOutput(prefix='debug| ')
def main():
"""Read parmas, choose in wich modules will be
tuned, and run it
"""
# default parameters
params = {'debug': False, 'max_picks': 50, 'n_trials': 100}
params_new = read_params()
# update params
params.update(params_new)
params['debug'] = True if params['debug'] in ['true', 'True', 'TRUE'] else False
if not params['debug']:
ic.disable()
else:
ic.enable()
try:
db_ip = params['db_ip']
tune_mode = params['tune_mode']
ti = params['ti']
tf = params['tf']
except KeyError:
print('\n\n\tERROR! db_ip, tune_mode, ti or tf no defined in sc3-autotuner.inp\n\n')
sys.exit()
db = MySQLdb.connect(host=db_ip,
user='consulta',
passwd='<PASSWORD>',
db='seiscomp3')
cursor = db.cursor()
# choose in which way the program will be runned
if tune_mode in ['picker', 'piker', 'picer', 'Picker']:
ic(ti)
ic(tf)
ic(cursor)
picker_tuner(cursor, ti, tf, params)
elif tune_mode in ['associator', 'asociator', 'Associator']:
# run assoc_tuner.py
pass
else:
print(f'\n\n\tThe option {tune_mode} you have entered is not a valid option for tune_mode.')
print('\tShould be: picker or associator\n\n')
sys.exit()
# Run picker and assoc tunners
def read_params(par_file='sc3-autotuner.inp'):
lines = open(par_file).readlines()
par_dic = {}
for line in lines:
if line[0] == '#' or line.strip('\n').strip() == '':
continue
else:
l = line.strip('\n').strip()
key, value = l.split('=')
par_dic[key.strip()] = value.strip()
return par_dic
if __name__ == '__main__':
main() | sc3autotuner.py | import os
import sys
import MySQLdb
from picker_tuner import picker_tuner
from icecream import ic
ic.configureOutput(prefix='debug| ')
def main():
"""Read parmas, choose in wich modules will be
tuned, and run it
"""
# default parameters
params = {'debug': False, 'max_picks': 50, 'n_trials': 100}
params_new = read_params()
# update params
params.update(params_new)
params['debug'] = True if params['debug'] in ['true', 'True', 'TRUE'] else False
if not params['debug']:
ic.disable()
else:
ic.enable()
try:
db_ip = params['db_ip']
tune_mode = params['tune_mode']
ti = params['ti']
tf = params['tf']
except KeyError:
print('\n\n\tERROR! db_ip, tune_mode, ti or tf no defined in sc3-autotuner.inp\n\n')
sys.exit()
db = MySQLdb.connect(host=db_ip,
user='consulta',
passwd='<PASSWORD>',
db='seiscomp3')
cursor = db.cursor()
# choose in which way the program will be runned
if tune_mode in ['picker', 'piker', 'picer', 'Picker']:
ic(ti)
ic(tf)
ic(cursor)
picker_tuner(cursor, ti, tf, params)
elif tune_mode in ['associator', 'asociator', 'Associator']:
# run assoc_tuner.py
pass
else:
print(f'\n\n\tThe option {tune_mode} you have entered is not a valid option for tune_mode.')
print('\tShould be: picker or associator\n\n')
sys.exit()
# Run picker and assoc tunners
def read_params(par_file='sc3-autotuner.inp'):
lines = open(par_file).readlines()
par_dic = {}
for line in lines:
if line[0] == '#' or line.strip('\n').strip() == '':
continue
else:
l = line.strip('\n').strip()
key, value = l.split('=')
par_dic[key.strip()] = value.strip()
return par_dic
if __name__ == '__main__':
main() | 0.192615 | 0.075585 |
import importlib
import pathlib
import os
import os.path
import sys
import re
import subprocess
from dexbot.whiptail import get_whiptail
from dexbot.strategies.base import StrategyBase
import dexbot.helper
STRATEGIES = [
{'tag': 'relative',
'class': 'dexbot.strategies.relative_orders',
'name': 'Relative Orders'},
{'tag': 'stagger',
'class': 'dexbot.strategies.staggered_orders',
'name': 'Staggered Orders'}]
tags_so_far = {'stagger', 'relative'}
for desc, module in dexbot.helper.find_external_strategies():
tag = desc.split()[0].lower()
# make sure tag is unique
i = 1
while tag in tags_so_far:
tag = tag+str(i)
i += 1
tags_so_far.add(tag)
STRATEGIES.append({'tag': tag, 'class': module, 'name': desc})
SYSTEMD_SERVICE_NAME = os.path.expanduser(
"~/.local/share/systemd/user/dexbot.service")
SYSTEMD_SERVICE_FILE = """
[Unit]
Description=Dexbot
[Service]
Type=notify
WorkingDirectory={homedir}
ExecStart={exe} --systemd run
TimeoutSec=20m
Environment=PYTHONUNBUFFERED=true
Environment=UNLOCK={passwd}
[Install]
WantedBy=default.target
"""
def select_choice(current, choices):
""" For the radiolist, get us a list with the current value selected """
return [(tag, text, (current == tag and "ON") or "OFF")
for tag, text in choices]
def process_config_element(elem, whiptail, config):
""" Process an item of configuration metadata display a widget as appropriate
d: the Dialog object
config: the config dictionary for this worker
"""
if elem.description:
title = '{} - {}'.format(elem.title, elem.description)
else:
title = elem.title
if elem.type == "string":
txt = whiptail.prompt(title, config.get(elem.key, elem.default))
if elem.extra:
while not re.match(elem.extra, txt):
whiptail.alert("The value is not valid")
txt = whiptail.prompt(
title, config.get(
elem.key, elem.default))
config[elem.key] = txt
if elem.type == "bool":
value = config.get(elem.key, elem.default)
value = 'yes' if value else 'no'
config[elem.key] = whiptail.confirm(title, value)
if elem.type in ("float", "int"):
while True:
if elem.type == 'int':
template = '{}'
else:
template = '{:.8f}'
txt = whiptail.prompt(title, template.format(config.get(elem.key, elem.default)))
try:
if elem.type == "int":
val = int(txt)
else:
val = float(txt)
if val < elem.extra[0]:
whiptail.alert("The value is too low")
elif elem.extra[1] and val > elem.extra[1]:
whiptail.alert("the value is too high")
else:
break
except ValueError:
whiptail.alert("Not a valid value")
config[elem.key] = val
if elem.type == "choice":
config[elem.key] = whiptail.radiolist(title, select_choice(
config.get(elem.key, elem.default), elem.extra))
def dexbot_service_running():
""" Return True if dexbot service is running
"""
cmd = 'systemctl --user status dexbot'
output = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
for line in output.stdout.readlines():
if b'Active:' in line and b'(running)' in line:
return True
return False
def setup_systemd(whiptail, config):
if not os.path.exists("/etc/systemd"):
return # No working systemd
if not whiptail.confirm(
"Do you want to run dexbot as a background (daemon) process?"):
config['systemd_status'] = 'disabled'
return
redo_setup = False
if os.path.exists(SYSTEMD_SERVICE_NAME):
redo_setup = whiptail.confirm('Redo systemd setup?', 'no')
if not os.path.exists(SYSTEMD_SERVICE_NAME) or redo_setup:
path = '~/.local/share/systemd/user'
path = os.path.expanduser(path)
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
password = <PASSWORD>(
"The wallet password\n"
"NOTE: this will be saved on disc so the worker can run unattended. "
"This means anyone with access to this computer's files can spend all your money",
password=True)
# Because we hold password be restrictive
fd = os.open(SYSTEMD_SERVICE_NAME, os.O_WRONLY | os.O_CREAT, 0o600)
with open(fd, "w") as fp:
fp.write(
SYSTEMD_SERVICE_FILE.format(
exe=sys.argv[0],
passwd=password,
homedir=os.path.expanduser("~")))
# The dexbot service file was edited, reload the daemon configs
os.system('systemctl --user daemon-reload')
# Signal cli.py to set the unit up after writing config file
config['systemd_status'] = 'enabled'
def get_strategy_tag(strategy_class):
for strategy in STRATEGIES:
if strategy_class == strategy['class']:
return strategy['tag']
return None
def configure_worker(whiptail, worker_config):
# By default always editing
editing = True
if not worker_config:
editing = False
default_strategy = worker_config.get('module', 'dexbot.strategies.relative_orders')
strategy_list = []
for strategy in STRATEGIES:
if default_strategy == strategy['class']:
default_strategy = strategy['tag']
# Add strategy tag and name pairs to a list
strategy_list.append([strategy['tag'], strategy['name']])
# Strategy selection
worker_config['module'] = whiptail.radiolist(
"Choose a worker strategy",
select_choice(default_strategy, strategy_list)
)
for strategy in STRATEGIES:
if strategy['tag'] == worker_config['module']:
worker_config['module'] = strategy['class']
# Import the strategy class but we don't __init__ it here
strategy_class = getattr(
importlib.import_module(worker_config["module"]),
'Strategy'
)
# Check if strategy has changed and editing existing worker
if editing and default_strategy != get_strategy_tag(worker_config['module']):
new_worker_config = {}
# If strategy has changed, create new config where base elements stay the same
for config_item in StrategyBase.configure():
key = config_item[0]
new_worker_config[key] = worker_config[key]
# Add module separately to the config
new_worker_config['module'] = worker_config['module']
worker_config = new_worker_config
# Use class metadata for per-worker configuration
config_elems = strategy_class.configure()
if config_elems:
# Strategy options
for elem in config_elems:
process_config_element(elem, whiptail, worker_config)
else:
whiptail.alert(
"This worker type does not have configuration information. "
"You will have to check the worker code and add configuration values to config.yml if required")
return worker_config
def configure_dexbot(config, ctx):
whiptail = get_whiptail('DEXBot configure')
workers = config.get('workers', {})
if not workers:
while True:
txt = whiptail.prompt("Your name for the worker")
config['workers'] = {txt: configure_worker(whiptail, {})}
if not whiptail.confirm("Set up another worker?\n(DEXBot can run multiple workers in one instance)"):
break
setup_systemd(whiptail, config)
else:
bitshares_instance = ctx.bitshares
action = whiptail.menu(
"You have an existing configuration.\nSelect an action:",
[('NEW', 'Create a new worker'),
('DEL', 'Delete a worker'),
('EDIT', 'Edit a worker'),
('CONF', 'Redo general config')])
if action == 'EDIT':
worker_name = whiptail.menu("Select worker to edit", [(index, index) for index in workers])
config['workers'][worker_name] = configure_worker(whiptail, config['workers'][worker_name])
strategy = StrategyBase(worker_name, bitshares_instance=bitshares_instance, config=config)
strategy.clear_all_worker_data()
elif action == 'DEL':
worker_name = whiptail.menu("Select worker to delete", [(index, index) for index in workers])
del config['workers'][worker_name]
strategy = StrategyBase(worker_name, bitshares_instance=bitshares_instance, config=config)
strategy.clear_all_worker_data()
elif action == 'NEW':
txt = whiptail.prompt("Your name for the new worker")
config['workers'][txt] = configure_worker(whiptail, {})
elif action == 'CONF':
choice = whiptail.node_radiolist(
msg="Choose node",
items=select_choice(config['node'][0], [(index, index) for index in config['node']])
)
# Move selected node as first item in the config file's node list
config['node'].remove(choice)
config['node'].insert(0, choice)
setup_systemd(whiptail, config)
whiptail.clear()
return config | dexbot/cli_conf.py | import importlib
import pathlib
import os
import os.path
import sys
import re
import subprocess
from dexbot.whiptail import get_whiptail
from dexbot.strategies.base import StrategyBase
import dexbot.helper
STRATEGIES = [
{'tag': 'relative',
'class': 'dexbot.strategies.relative_orders',
'name': 'Relative Orders'},
{'tag': 'stagger',
'class': 'dexbot.strategies.staggered_orders',
'name': 'Staggered Orders'}]
tags_so_far = {'stagger', 'relative'}
for desc, module in dexbot.helper.find_external_strategies():
tag = desc.split()[0].lower()
# make sure tag is unique
i = 1
while tag in tags_so_far:
tag = tag+str(i)
i += 1
tags_so_far.add(tag)
STRATEGIES.append({'tag': tag, 'class': module, 'name': desc})
SYSTEMD_SERVICE_NAME = os.path.expanduser(
"~/.local/share/systemd/user/dexbot.service")
SYSTEMD_SERVICE_FILE = """
[Unit]
Description=Dexbot
[Service]
Type=notify
WorkingDirectory={homedir}
ExecStart={exe} --systemd run
TimeoutSec=20m
Environment=PYTHONUNBUFFERED=true
Environment=UNLOCK={passwd}
[Install]
WantedBy=default.target
"""
def select_choice(current, choices):
""" For the radiolist, get us a list with the current value selected """
return [(tag, text, (current == tag and "ON") or "OFF")
for tag, text in choices]
def process_config_element(elem, whiptail, config):
""" Process an item of configuration metadata display a widget as appropriate
d: the Dialog object
config: the config dictionary for this worker
"""
if elem.description:
title = '{} - {}'.format(elem.title, elem.description)
else:
title = elem.title
if elem.type == "string":
txt = whiptail.prompt(title, config.get(elem.key, elem.default))
if elem.extra:
while not re.match(elem.extra, txt):
whiptail.alert("The value is not valid")
txt = whiptail.prompt(
title, config.get(
elem.key, elem.default))
config[elem.key] = txt
if elem.type == "bool":
value = config.get(elem.key, elem.default)
value = 'yes' if value else 'no'
config[elem.key] = whiptail.confirm(title, value)
if elem.type in ("float", "int"):
while True:
if elem.type == 'int':
template = '{}'
else:
template = '{:.8f}'
txt = whiptail.prompt(title, template.format(config.get(elem.key, elem.default)))
try:
if elem.type == "int":
val = int(txt)
else:
val = float(txt)
if val < elem.extra[0]:
whiptail.alert("The value is too low")
elif elem.extra[1] and val > elem.extra[1]:
whiptail.alert("the value is too high")
else:
break
except ValueError:
whiptail.alert("Not a valid value")
config[elem.key] = val
if elem.type == "choice":
config[elem.key] = whiptail.radiolist(title, select_choice(
config.get(elem.key, elem.default), elem.extra))
def dexbot_service_running():
""" Return True if dexbot service is running
"""
cmd = 'systemctl --user status dexbot'
output = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
for line in output.stdout.readlines():
if b'Active:' in line and b'(running)' in line:
return True
return False
def setup_systemd(whiptail, config):
if not os.path.exists("/etc/systemd"):
return # No working systemd
if not whiptail.confirm(
"Do you want to run dexbot as a background (daemon) process?"):
config['systemd_status'] = 'disabled'
return
redo_setup = False
if os.path.exists(SYSTEMD_SERVICE_NAME):
redo_setup = whiptail.confirm('Redo systemd setup?', 'no')
if not os.path.exists(SYSTEMD_SERVICE_NAME) or redo_setup:
path = '~/.local/share/systemd/user'
path = os.path.expanduser(path)
pathlib.Path(path).mkdir(parents=True, exist_ok=True)
password = <PASSWORD>(
"The wallet password\n"
"NOTE: this will be saved on disc so the worker can run unattended. "
"This means anyone with access to this computer's files can spend all your money",
password=True)
# Because we hold password be restrictive
fd = os.open(SYSTEMD_SERVICE_NAME, os.O_WRONLY | os.O_CREAT, 0o600)
with open(fd, "w") as fp:
fp.write(
SYSTEMD_SERVICE_FILE.format(
exe=sys.argv[0],
passwd=password,
homedir=os.path.expanduser("~")))
# The dexbot service file was edited, reload the daemon configs
os.system('systemctl --user daemon-reload')
# Signal cli.py to set the unit up after writing config file
config['systemd_status'] = 'enabled'
def get_strategy_tag(strategy_class):
for strategy in STRATEGIES:
if strategy_class == strategy['class']:
return strategy['tag']
return None
def configure_worker(whiptail, worker_config):
# By default always editing
editing = True
if not worker_config:
editing = False
default_strategy = worker_config.get('module', 'dexbot.strategies.relative_orders')
strategy_list = []
for strategy in STRATEGIES:
if default_strategy == strategy['class']:
default_strategy = strategy['tag']
# Add strategy tag and name pairs to a list
strategy_list.append([strategy['tag'], strategy['name']])
# Strategy selection
worker_config['module'] = whiptail.radiolist(
"Choose a worker strategy",
select_choice(default_strategy, strategy_list)
)
for strategy in STRATEGIES:
if strategy['tag'] == worker_config['module']:
worker_config['module'] = strategy['class']
# Import the strategy class but we don't __init__ it here
strategy_class = getattr(
importlib.import_module(worker_config["module"]),
'Strategy'
)
# Check if strategy has changed and editing existing worker
if editing and default_strategy != get_strategy_tag(worker_config['module']):
new_worker_config = {}
# If strategy has changed, create new config where base elements stay the same
for config_item in StrategyBase.configure():
key = config_item[0]
new_worker_config[key] = worker_config[key]
# Add module separately to the config
new_worker_config['module'] = worker_config['module']
worker_config = new_worker_config
# Use class metadata for per-worker configuration
config_elems = strategy_class.configure()
if config_elems:
# Strategy options
for elem in config_elems:
process_config_element(elem, whiptail, worker_config)
else:
whiptail.alert(
"This worker type does not have configuration information. "
"You will have to check the worker code and add configuration values to config.yml if required")
return worker_config
def configure_dexbot(config, ctx):
whiptail = get_whiptail('DEXBot configure')
workers = config.get('workers', {})
if not workers:
while True:
txt = whiptail.prompt("Your name for the worker")
config['workers'] = {txt: configure_worker(whiptail, {})}
if not whiptail.confirm("Set up another worker?\n(DEXBot can run multiple workers in one instance)"):
break
setup_systemd(whiptail, config)
else:
bitshares_instance = ctx.bitshares
action = whiptail.menu(
"You have an existing configuration.\nSelect an action:",
[('NEW', 'Create a new worker'),
('DEL', 'Delete a worker'),
('EDIT', 'Edit a worker'),
('CONF', 'Redo general config')])
if action == 'EDIT':
worker_name = whiptail.menu("Select worker to edit", [(index, index) for index in workers])
config['workers'][worker_name] = configure_worker(whiptail, config['workers'][worker_name])
strategy = StrategyBase(worker_name, bitshares_instance=bitshares_instance, config=config)
strategy.clear_all_worker_data()
elif action == 'DEL':
worker_name = whiptail.menu("Select worker to delete", [(index, index) for index in workers])
del config['workers'][worker_name]
strategy = StrategyBase(worker_name, bitshares_instance=bitshares_instance, config=config)
strategy.clear_all_worker_data()
elif action == 'NEW':
txt = whiptail.prompt("Your name for the new worker")
config['workers'][txt] = configure_worker(whiptail, {})
elif action == 'CONF':
choice = whiptail.node_radiolist(
msg="Choose node",
items=select_choice(config['node'][0], [(index, index) for index in config['node']])
)
# Move selected node as first item in the config file's node list
config['node'].remove(choice)
config['node'].insert(0, choice)
setup_systemd(whiptail, config)
whiptail.clear()
return config | 0.299105 | 0.085099 |
import io
import json
import contextlib
try:
from types import TypeType as type
except ImportError:
pass
import semver
from . import util
class PackageStub(object):
keys = ['name', 'version', 'description', 'license', 'compatibility']
def __init__(self, defaults=None):
defaults = defaults or {}
for key in self.keys:
setattr(self, key, defaults.get(key))
def is_valid(self, raise_exception=False):
res = False
if self.name and self.version:
res = True
if raise_exception and not res:
raise Exception('invalid package')
return res
@property
def ident(self):
if self.is_valid(True):
return util.archive_filename(self.name, self.version)
def to_json(self, keys=None):
return util.json_dump(self.to_dict(keys))
def to_dict(self, keys=None):
keys = keys or []
if hasattr(self, 'is_valid'):
self.is_valid()
return dict([
(k, getattr(self, k))
for k in self.keys
if not keys or k in keys])
def has_file(self, *path_parts):
raise NotImplementedError
def file_path(self, *path_parts):
raise NotImplementedError
def dir_path(self, *path_parts):
raise NotImplementedError
@contextlib.contextmanager
def open(self, path_parts, mode='r', encoding='utf8', default=IOError):
if self.has_file(*path_parts):
f = io.open(self.file_path(*path_parts),
mode=mode, encoding=encoding)
yield f
f.close()
else:
if isinstance(default, type) and issubclass(default, Exception):
raise default(self.file_path(*path_parts))
elif isinstance(default, Exception):
raise default
else:
yield default
def load_json(self, path_parts, mode='r', encoding='utf8', default=IOError):
if self.has_file(*path_parts):
with io.open(self.file_path(*path_parts),
mode=mode, encoding=encoding) as f:
return json.load(f)
else:
if isinstance(default, type) and issubclass(default, Exception):
raise default(self.file_path(*path_parts))
elif isinstance(default, Exception):
raise default
else:
return default
def _error_on_different_name(self, other):
if self.name != other.name:
raise Exception('name mismatch: %s != %s' % (self.name, other.name))
def __gt__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) > 0
def __lt__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) < 0
def __eq__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) == 0
def __ne__(self, other):
return not self.__eq__(other)
def __ge__(self, other):
return self.__gt__(other) or self.__eq__(other)
def __le__(self, other):
return self.__lt__(other) or self.__eq__(other) | venv/Lib/site-packages/sputnik/package_stub.py | import io
import json
import contextlib
try:
from types import TypeType as type
except ImportError:
pass
import semver
from . import util
class PackageStub(object):
keys = ['name', 'version', 'description', 'license', 'compatibility']
def __init__(self, defaults=None):
defaults = defaults or {}
for key in self.keys:
setattr(self, key, defaults.get(key))
def is_valid(self, raise_exception=False):
res = False
if self.name and self.version:
res = True
if raise_exception and not res:
raise Exception('invalid package')
return res
@property
def ident(self):
if self.is_valid(True):
return util.archive_filename(self.name, self.version)
def to_json(self, keys=None):
return util.json_dump(self.to_dict(keys))
def to_dict(self, keys=None):
keys = keys or []
if hasattr(self, 'is_valid'):
self.is_valid()
return dict([
(k, getattr(self, k))
for k in self.keys
if not keys or k in keys])
def has_file(self, *path_parts):
raise NotImplementedError
def file_path(self, *path_parts):
raise NotImplementedError
def dir_path(self, *path_parts):
raise NotImplementedError
@contextlib.contextmanager
def open(self, path_parts, mode='r', encoding='utf8', default=IOError):
if self.has_file(*path_parts):
f = io.open(self.file_path(*path_parts),
mode=mode, encoding=encoding)
yield f
f.close()
else:
if isinstance(default, type) and issubclass(default, Exception):
raise default(self.file_path(*path_parts))
elif isinstance(default, Exception):
raise default
else:
yield default
def load_json(self, path_parts, mode='r', encoding='utf8', default=IOError):
if self.has_file(*path_parts):
with io.open(self.file_path(*path_parts),
mode=mode, encoding=encoding) as f:
return json.load(f)
else:
if isinstance(default, type) and issubclass(default, Exception):
raise default(self.file_path(*path_parts))
elif isinstance(default, Exception):
raise default
else:
return default
def _error_on_different_name(self, other):
if self.name != other.name:
raise Exception('name mismatch: %s != %s' % (self.name, other.name))
def __gt__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) > 0
def __lt__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) < 0
def __eq__(self, other):
self._error_on_different_name(other)
return semver.compare(self.version, other.version) == 0
def __ne__(self, other):
return not self.__eq__(other)
def __ge__(self, other):
return self.__gt__(other) or self.__eq__(other)
def __le__(self, other):
return self.__lt__(other) or self.__eq__(other) | 0.569254 | 0.093637 |
from contextlib import contextmanager
import tensorflow as tf
from ..tfutils.common import get_tf_version_tuple
from ..compat import tfv1
from ..utils.develop import HIDE_DOC
from .gradproc import FilterNoneGrad, GradientProcessor
__all__ = ['apply_grad_processors', 'ProxyOptimizer',
'PostProcessOptimizer', 'VariableAssignmentOptimizer',
'AccumGradOptimizer']
class ProxyOptimizer(tfv1.train.Optimizer):
"""
A transparent proxy which delegates all methods of :class:`tf.train.Optimizer`
"""
def __init__(self, opt, name='ProxyOptimizer'):
assert isinstance(opt, tfv1.train.Optimizer), opt
super(ProxyOptimizer, self).__init__(False, name)
self._opt = opt
@HIDE_DOC
def compute_gradients(self, *args, **kwargs):
return self._opt.compute_gradients(*args, **kwargs)
@HIDE_DOC
def get_slot(self, *args, **kwargs):
return self._opt.get_slot(*args, **kwargs)
@HIDE_DOC
def get_slot_names(self, *args, **kwargs):
return self._opt.get_slot_names(*args, **kwargs)
@HIDE_DOC
def apply_gradients(self, *args, **kwargs):
return self._opt.apply_gradients(*args, **kwargs)
def apply_grad_processors(opt, gradprocs):
"""
Wrapper around optimizers to apply gradient processors.
Args:
opt (tf.train.Optimizer):
gradprocs (list[GradientProcessor]): gradient processors to add to the
optimizer.
Returns:
a :class:`tf.train.Optimizer` instance which runs the gradient
processors before updating the variables.
"""
assert isinstance(gradprocs, (list, tuple)), gradprocs
for gp in gradprocs:
assert isinstance(gp, GradientProcessor), gp
class _ApplyGradientProcessor(ProxyOptimizer):
def __init__(self, opt, gradprocs):
self._gradprocs = gradprocs[:]
super(_ApplyGradientProcessor, self).__init__(opt)
def apply_gradients(self, grads_and_vars,
global_step=None, name=None):
g = self._apply(grads_and_vars)
return self._opt.apply_gradients(g, global_step, name)
def _apply(self, g):
for proc in self._gradprocs:
g = proc.process(g)
return g
return _ApplyGradientProcessor(opt, gradprocs)
class PostProcessOptimizer(ProxyOptimizer):
"""
An optimizer which applies some "post-processing operation" per variable
(e.g. clipping, quantization) after the gradient update.
"""
def __init__(self, opt, func, colocate=True):
"""
Args:
opt (tf.train.Optimizer):
func (tf.Variable -> tf.Operation or None): the operation needed
to perform for this variable after the gradient update.
colocate (boolean): colocate the function with the variable. No effect since TF 1.13.
"""
super(PostProcessOptimizer, self).__init__(opt)
self._func = func
self._colocate = colocate
@HIDE_DOC
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
update_op = super(PostProcessOptimizer, self).apply_gradients(
grads_and_vars, global_step)
ops = []
with tf.control_dependencies([update_op]):
for _, var in grads_and_vars:
with self._maybe_colocate(var):
op = self._func(var)
if op is not None:
assert isinstance(op, tf.Operation), op
ops.append(op)
update_op = tf.group(update_op, *ops, name=name)
return update_op
@contextmanager
def _maybe_colocate(self, var):
G = tf.get_default_graph()
if self._colocate and get_tf_version_tuple() <= (1, 12):
with G.colocate_with(var):
yield
else:
yield
class VariableAssignmentOptimizer(PostProcessOptimizer):
"""
An optimizer which assigns each variable a new value (e.g. clipping,
quantization) after the gradient update.
"""
def __init__(self, opt, func):
"""
Args:
opt (tf.train.Optimizer):
func (tf.Variable -> tf.Tensor or None): the new value to be
assigned to this variable after the gradient update.
"""
def f(v):
t = func(v)
if t is None:
return t
return tf.assign(v, t, use_locking=False).op
super(VariableAssignmentOptimizer, self).__init__(opt, f)
class AccumGradOptimizer(ProxyOptimizer):
"""
An optimizer which accumulates gradients across :math:`k` :meth:`minimize` executions,
and apply them together in every :math:`k` th :meth:`minimize` execution.
This is roughly the same as using a :math:`k` times larger batch size plus a
:math:`k` times larger learning rate, but uses much less memory.
This optimizer can be used in any TensorFlow code (with or without tensorpack).
Example:
.. code-block:: python
from tensorpack.tfutils.optimizer import AccumGradOptimizer
myopt = tf.train.GradientDescentOptimizer(0.01)
myopt = AccumGradOptimizer(myopt, niter=5)
train_op = myopt.minimize(loss)
"""
def __init__(self, opt, niter):
"""
Args:
opt (tf.train.Optimizer): the underlying sub-optimizer.
niter (int): number of iterations to accumulate gradients.
"""
super(AccumGradOptimizer, self).__init__(opt, 'AccumGrad')
self._niter = int(niter)
def _create_accum_slots(self, var_list):
slots = []
for v in var_list:
# TODO an option to not colocate the accumulators with variables (to save more memory)
s = self._zeros_slot(v, "accum", self._name)
slots.append(s)
return slots
@HIDE_DOC
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
grads_and_vars = FilterNoneGrad().process(grads_and_vars)
vs = []
for g, v in grads_and_vars:
assert isinstance(g, (tf.Tensor, tf.IndexedSlices)) and isinstance(v, tf.Variable), \
"AccumGradOptimizer does not work for the gradient of {}! " \
"Types of v and g are {} and {}".format(v.op.name, type(v), type(g))
vs.append(v)
with tf.control_dependencies(None):
slots = self._create_accum_slots(vs)
slots_and_vars = [(s, gv[1]) for s, gv in zip(slots, grads_and_vars)]
with tf.variable_scope(self._name), tf.device('/cpu:0'):
counter = tf.Variable(
0, name="counter", trainable=False, dtype=tf.int32)
with tf.name_scope('AccumGradOptimizer'):
ops = []
for s, gv in zip(slots, grads_and_vars):
g, v = gv
ops.append(s.assign_add(g))
update_counter = tf.assign_add(counter, 1, name='update_counter')
update_slot_op = tf.group(update_counter, *ops, name='update_slot')
def update_grad():
update_op = self._opt.apply_gradients(slots_and_vars)
with tf.control_dependencies([update_op]):
clear_ops = [tf.assign(s, tf.zeros_like(s)) for s in slots]
return tf.group(*clear_ops, name='update_grad')
pred = tf.equal(tf.mod(counter, self._niter), 0)
with tf.control_dependencies([update_slot_op]):
if name is None:
name = 'cond_update_grad'
op = tf.cond(pred, update_grad, tf.no_op)
if global_step is not None:
# Tensorpack maintains global_step by other means,
# so this option is useless in tensorpack trainers.
# But we include the implementation here for completeness
global_step_increment = tf.assign_add(global_step, 1)
op = tf.group(op, global_step_increment, name=name)
else:
op = tf.identity(op, name=name).op
return op
if __name__ == '__main__':
# run it with "python -m tensorpack.tfutils.optimizer"
x = tf.get_variable('x', shape=[6])
cost = tf.reduce_sum(tf.abs(x), name='cost')
opt = tf.train.GradientDescentOptimizer(0.01)
opt = AccumGradOptimizer(opt, 5)
min_op = opt.minimize(cost, global_step=tf.train.get_or_create_global_step())
sess = tf.Session()
sess.run(tf.global_variables_initializer())
with sess.as_default():
for _ in range(20):
min_op.run()
print(x.eval())
print(tf.train.get_or_create_global_step().eval()) | tensorpack/tfutils/optimizer.py |
from contextlib import contextmanager
import tensorflow as tf
from ..tfutils.common import get_tf_version_tuple
from ..compat import tfv1
from ..utils.develop import HIDE_DOC
from .gradproc import FilterNoneGrad, GradientProcessor
__all__ = ['apply_grad_processors', 'ProxyOptimizer',
'PostProcessOptimizer', 'VariableAssignmentOptimizer',
'AccumGradOptimizer']
class ProxyOptimizer(tfv1.train.Optimizer):
"""
A transparent proxy which delegates all methods of :class:`tf.train.Optimizer`
"""
def __init__(self, opt, name='ProxyOptimizer'):
assert isinstance(opt, tfv1.train.Optimizer), opt
super(ProxyOptimizer, self).__init__(False, name)
self._opt = opt
@HIDE_DOC
def compute_gradients(self, *args, **kwargs):
return self._opt.compute_gradients(*args, **kwargs)
@HIDE_DOC
def get_slot(self, *args, **kwargs):
return self._opt.get_slot(*args, **kwargs)
@HIDE_DOC
def get_slot_names(self, *args, **kwargs):
return self._opt.get_slot_names(*args, **kwargs)
@HIDE_DOC
def apply_gradients(self, *args, **kwargs):
return self._opt.apply_gradients(*args, **kwargs)
def apply_grad_processors(opt, gradprocs):
"""
Wrapper around optimizers to apply gradient processors.
Args:
opt (tf.train.Optimizer):
gradprocs (list[GradientProcessor]): gradient processors to add to the
optimizer.
Returns:
a :class:`tf.train.Optimizer` instance which runs the gradient
processors before updating the variables.
"""
assert isinstance(gradprocs, (list, tuple)), gradprocs
for gp in gradprocs:
assert isinstance(gp, GradientProcessor), gp
class _ApplyGradientProcessor(ProxyOptimizer):
def __init__(self, opt, gradprocs):
self._gradprocs = gradprocs[:]
super(_ApplyGradientProcessor, self).__init__(opt)
def apply_gradients(self, grads_and_vars,
global_step=None, name=None):
g = self._apply(grads_and_vars)
return self._opt.apply_gradients(g, global_step, name)
def _apply(self, g):
for proc in self._gradprocs:
g = proc.process(g)
return g
return _ApplyGradientProcessor(opt, gradprocs)
class PostProcessOptimizer(ProxyOptimizer):
"""
An optimizer which applies some "post-processing operation" per variable
(e.g. clipping, quantization) after the gradient update.
"""
def __init__(self, opt, func, colocate=True):
"""
Args:
opt (tf.train.Optimizer):
func (tf.Variable -> tf.Operation or None): the operation needed
to perform for this variable after the gradient update.
colocate (boolean): colocate the function with the variable. No effect since TF 1.13.
"""
super(PostProcessOptimizer, self).__init__(opt)
self._func = func
self._colocate = colocate
@HIDE_DOC
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
update_op = super(PostProcessOptimizer, self).apply_gradients(
grads_and_vars, global_step)
ops = []
with tf.control_dependencies([update_op]):
for _, var in grads_and_vars:
with self._maybe_colocate(var):
op = self._func(var)
if op is not None:
assert isinstance(op, tf.Operation), op
ops.append(op)
update_op = tf.group(update_op, *ops, name=name)
return update_op
@contextmanager
def _maybe_colocate(self, var):
G = tf.get_default_graph()
if self._colocate and get_tf_version_tuple() <= (1, 12):
with G.colocate_with(var):
yield
else:
yield
class VariableAssignmentOptimizer(PostProcessOptimizer):
"""
An optimizer which assigns each variable a new value (e.g. clipping,
quantization) after the gradient update.
"""
def __init__(self, opt, func):
"""
Args:
opt (tf.train.Optimizer):
func (tf.Variable -> tf.Tensor or None): the new value to be
assigned to this variable after the gradient update.
"""
def f(v):
t = func(v)
if t is None:
return t
return tf.assign(v, t, use_locking=False).op
super(VariableAssignmentOptimizer, self).__init__(opt, f)
class AccumGradOptimizer(ProxyOptimizer):
"""
An optimizer which accumulates gradients across :math:`k` :meth:`minimize` executions,
and apply them together in every :math:`k` th :meth:`minimize` execution.
This is roughly the same as using a :math:`k` times larger batch size plus a
:math:`k` times larger learning rate, but uses much less memory.
This optimizer can be used in any TensorFlow code (with or without tensorpack).
Example:
.. code-block:: python
from tensorpack.tfutils.optimizer import AccumGradOptimizer
myopt = tf.train.GradientDescentOptimizer(0.01)
myopt = AccumGradOptimizer(myopt, niter=5)
train_op = myopt.minimize(loss)
"""
def __init__(self, opt, niter):
"""
Args:
opt (tf.train.Optimizer): the underlying sub-optimizer.
niter (int): number of iterations to accumulate gradients.
"""
super(AccumGradOptimizer, self).__init__(opt, 'AccumGrad')
self._niter = int(niter)
def _create_accum_slots(self, var_list):
slots = []
for v in var_list:
# TODO an option to not colocate the accumulators with variables (to save more memory)
s = self._zeros_slot(v, "accum", self._name)
slots.append(s)
return slots
@HIDE_DOC
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
grads_and_vars = FilterNoneGrad().process(grads_and_vars)
vs = []
for g, v in grads_and_vars:
assert isinstance(g, (tf.Tensor, tf.IndexedSlices)) and isinstance(v, tf.Variable), \
"AccumGradOptimizer does not work for the gradient of {}! " \
"Types of v and g are {} and {}".format(v.op.name, type(v), type(g))
vs.append(v)
with tf.control_dependencies(None):
slots = self._create_accum_slots(vs)
slots_and_vars = [(s, gv[1]) for s, gv in zip(slots, grads_and_vars)]
with tf.variable_scope(self._name), tf.device('/cpu:0'):
counter = tf.Variable(
0, name="counter", trainable=False, dtype=tf.int32)
with tf.name_scope('AccumGradOptimizer'):
ops = []
for s, gv in zip(slots, grads_and_vars):
g, v = gv
ops.append(s.assign_add(g))
update_counter = tf.assign_add(counter, 1, name='update_counter')
update_slot_op = tf.group(update_counter, *ops, name='update_slot')
def update_grad():
update_op = self._opt.apply_gradients(slots_and_vars)
with tf.control_dependencies([update_op]):
clear_ops = [tf.assign(s, tf.zeros_like(s)) for s in slots]
return tf.group(*clear_ops, name='update_grad')
pred = tf.equal(tf.mod(counter, self._niter), 0)
with tf.control_dependencies([update_slot_op]):
if name is None:
name = 'cond_update_grad'
op = tf.cond(pred, update_grad, tf.no_op)
if global_step is not None:
# Tensorpack maintains global_step by other means,
# so this option is useless in tensorpack trainers.
# But we include the implementation here for completeness
global_step_increment = tf.assign_add(global_step, 1)
op = tf.group(op, global_step_increment, name=name)
else:
op = tf.identity(op, name=name).op
return op
if __name__ == '__main__':
# run it with "python -m tensorpack.tfutils.optimizer"
x = tf.get_variable('x', shape=[6])
cost = tf.reduce_sum(tf.abs(x), name='cost')
opt = tf.train.GradientDescentOptimizer(0.01)
opt = AccumGradOptimizer(opt, 5)
min_op = opt.minimize(cost, global_step=tf.train.get_or_create_global_step())
sess = tf.Session()
sess.run(tf.global_variables_initializer())
with sess.as_default():
for _ in range(20):
min_op.run()
print(x.eval())
print(tf.train.get_or_create_global_step().eval()) | 0.864725 | 0.280598 |
def __format(msg, pos):
if len(msg) < 28:
offset = 0
else:
offset = len(msg) - 28
if pos - offset > 0: # at least 1 '-' before '$'
return '{0:<28}{1:->{2}}${1:->{3}} <-ERROR'.format(msg, '-', pos - offset, 71 - pos)
else: # start with '$'
return '{0:<28}${1:->71} <-ERROR'.format(msg[:27], '-')
__I_MSG = { # ASMAxxxI
33 : lambda info, line: __format('W-ALIGNMENT ERROR-IMPROPER BOUNDARY', info[1]),
}
__N_MSG = { # ASMAxxxN
}
__W_MSG = { # ASMAxxxW
45 : lambda info, line: __format('W-REGISTER NOT USED', info[1]),
140 : lambda info, line: __format('W-END CARD MISSING-SUPPLIED', info[1]),
163 : lambda info, line: __format('INVALID DELIMITER', info[1]),
165 : lambda info, line: __format('W-LABEL NOT ALLOWED', info[1]),
300 : lambda info, line: __format('W-USING OVERRIDDEN', info[1]),
301 : lambda info, line: __format('W-USING OVERRIDDEN', info[1]),
302 : lambda info, line: __format('W-USING R0 WITH NON-ZERO ADDRESS', info[1]),
303 : lambda info, line: __format('W-MULTIPLE ADDRESS RESOLUTION', info[1]),
}
__E_MSG = { # ASMAxxxE
28 : lambda info, line: __format('INVALID DISPLACEMENT', info[1]),
29 : lambda info, line: __format('INVALID REGISTER', info[1]),
30 : lambda info, line: __format('ILLEGAL USE OF LITERAL', info[1]),
32 : lambda info, line: __format('RELOCATABLE EXPRESSION REQUIRED', info[1]),
34 : lambda info, line: __format('ADDRESSIBILITY ERROR', info[1]),
43 : lambda info, line: __format('PREVIOUSLY DEFINED SYMBOL', info[1]),
41 : lambda info, line: __format('INVALID DELIMITER', info[1]),
44 : lambda info, line: __format('UNDEFINED SYMBOL', info[1]),
57 : lambda info, line: __format('INVALID OP-CODE', info[1]),
63 : lambda info, line: __format('MISSING DELIMITER', info[2]),
65 : lambda info, line: __format('ILLEGAL CONSTANT TYPE', info[1]),
74 : lambda info, line: __format('UNEXPECTED END OF EXPRESSION', info[1]),
78 : lambda info, line: __format('COMPLEX RELOCATABILITY ILLEGAL', info[1]),
141 : lambda info, line: __format('INVALID OP-CODE', info[1]),
142 : lambda info, line: __format('INVALID OP-CODE', info[1]),
143 : lambda info, line: __format('INVALID SYMBOL', info[1]),
145 : lambda info, line: __format('INVALID DELIMITER', info[1]),
146 : lambda info, line: __format('INVALID SELF-DEFINING TERM', info[1]),
150 : lambda info, line: __format('INVALID DELIMITER', info[1]),
305 : lambda info, line: __format('RELOCATABLE EXPRESSION REQUIRED', info[1]),
307 : lambda info, line: __format('ADDRESSIBILITY ERROR', info[1]),
308 : lambda info, line: __format('REPEATED REGISTER', info[1]),
}
__S_MSG = { # ASMAxxxS
35 : lambda info, line: __format('INVALID DELIMITER', info[1]),
40 : lambda info, line: __format('MISSING OPERAND', info[1]),
173 : lambda info, line: __format('INVALID DELIMITER', info[1]),
174 : lambda info, line: __format('INVALID DELIMITER', info[1]),
175 : lambda info, line: __format('INVALID DELIMITER', info[1]),
178 : lambda info, line: __format('UNEXPECTED END OF EXPRESSION', info[1]),
179 : lambda info, line: __format('INVALID DELIMITER', info[1]),
180 : lambda info, line: __format('INVALID SELF-DEFINING TERM', info[1]),
}
__MSG = {
'S' : __S_MSG,
'E' : __E_MSG,
'W' : __W_MSG,
'N' : __N_MSG,
'I' : __I_MSG,
}
def gen_msg(msg_type, info, line):
if len(info) == 3: # standard info message
return '----->{0}:{1:0>3} {2}\n'.format(msg_type, info[0], __MSG[msg_type][info[0]](info, line))
else:
return '----->AS{0:<110}<-ERROR\n'.format(info) | zPE/base/pgm/assist_err_code_rc.py | def __format(msg, pos):
if len(msg) < 28:
offset = 0
else:
offset = len(msg) - 28
if pos - offset > 0: # at least 1 '-' before '$'
return '{0:<28}{1:->{2}}${1:->{3}} <-ERROR'.format(msg, '-', pos - offset, 71 - pos)
else: # start with '$'
return '{0:<28}${1:->71} <-ERROR'.format(msg[:27], '-')
__I_MSG = { # ASMAxxxI
33 : lambda info, line: __format('W-ALIGNMENT ERROR-IMPROPER BOUNDARY', info[1]),
}
__N_MSG = { # ASMAxxxN
}
__W_MSG = { # ASMAxxxW
45 : lambda info, line: __format('W-REGISTER NOT USED', info[1]),
140 : lambda info, line: __format('W-END CARD MISSING-SUPPLIED', info[1]),
163 : lambda info, line: __format('INVALID DELIMITER', info[1]),
165 : lambda info, line: __format('W-LABEL NOT ALLOWED', info[1]),
300 : lambda info, line: __format('W-USING OVERRIDDEN', info[1]),
301 : lambda info, line: __format('W-USING OVERRIDDEN', info[1]),
302 : lambda info, line: __format('W-USING R0 WITH NON-ZERO ADDRESS', info[1]),
303 : lambda info, line: __format('W-MULTIPLE ADDRESS RESOLUTION', info[1]),
}
__E_MSG = { # ASMAxxxE
28 : lambda info, line: __format('INVALID DISPLACEMENT', info[1]),
29 : lambda info, line: __format('INVALID REGISTER', info[1]),
30 : lambda info, line: __format('ILLEGAL USE OF LITERAL', info[1]),
32 : lambda info, line: __format('RELOCATABLE EXPRESSION REQUIRED', info[1]),
34 : lambda info, line: __format('ADDRESSIBILITY ERROR', info[1]),
43 : lambda info, line: __format('PREVIOUSLY DEFINED SYMBOL', info[1]),
41 : lambda info, line: __format('INVALID DELIMITER', info[1]),
44 : lambda info, line: __format('UNDEFINED SYMBOL', info[1]),
57 : lambda info, line: __format('INVALID OP-CODE', info[1]),
63 : lambda info, line: __format('MISSING DELIMITER', info[2]),
65 : lambda info, line: __format('ILLEGAL CONSTANT TYPE', info[1]),
74 : lambda info, line: __format('UNEXPECTED END OF EXPRESSION', info[1]),
78 : lambda info, line: __format('COMPLEX RELOCATABILITY ILLEGAL', info[1]),
141 : lambda info, line: __format('INVALID OP-CODE', info[1]),
142 : lambda info, line: __format('INVALID OP-CODE', info[1]),
143 : lambda info, line: __format('INVALID SYMBOL', info[1]),
145 : lambda info, line: __format('INVALID DELIMITER', info[1]),
146 : lambda info, line: __format('INVALID SELF-DEFINING TERM', info[1]),
150 : lambda info, line: __format('INVALID DELIMITER', info[1]),
305 : lambda info, line: __format('RELOCATABLE EXPRESSION REQUIRED', info[1]),
307 : lambda info, line: __format('ADDRESSIBILITY ERROR', info[1]),
308 : lambda info, line: __format('REPEATED REGISTER', info[1]),
}
__S_MSG = { # ASMAxxxS
35 : lambda info, line: __format('INVALID DELIMITER', info[1]),
40 : lambda info, line: __format('MISSING OPERAND', info[1]),
173 : lambda info, line: __format('INVALID DELIMITER', info[1]),
174 : lambda info, line: __format('INVALID DELIMITER', info[1]),
175 : lambda info, line: __format('INVALID DELIMITER', info[1]),
178 : lambda info, line: __format('UNEXPECTED END OF EXPRESSION', info[1]),
179 : lambda info, line: __format('INVALID DELIMITER', info[1]),
180 : lambda info, line: __format('INVALID SELF-DEFINING TERM', info[1]),
}
__MSG = {
'S' : __S_MSG,
'E' : __E_MSG,
'W' : __W_MSG,
'N' : __N_MSG,
'I' : __I_MSG,
}
def gen_msg(msg_type, info, line):
if len(info) == 3: # standard info message
return '----->{0}:{1:0>3} {2}\n'.format(msg_type, info[0], __MSG[msg_type][info[0]](info, line))
else:
return '----->AS{0:<110}<-ERROR\n'.format(info) | 0.260954 | 0.201361 |
import joblib
from ad_model.processing import PREPROCESS
from sklearn.metrics import f1_score
from sklearn.ensemble import IsolationForest
from database import DATABASE, DUMMY
import numpy as np
class modelling(object):
r""" The modelling class takes input as dataframe or array and train Isolation Forest model
Paramteres
.........
data: DataFrame or array
input dataset
cols: list
list of parameters in input dataset
Attributes
----------
actual:array
actual label for test data
X: DataFrame or array
transformed values of input data
"""
def __init__(self, data):
self.data = data
self.cols = data.columns
def read_test(self, db):
""" Read test dataset for model validation"""
db.read_data('valid')
test = db.data
self.actual = test['Anomaly']
X = test[self.cols]
sc = joblib.load('scale')
self.X = sc.transform(X)
def isoforest(self, outliers_fraction=0.05, random_state=42, push_model=False): # modify the outlier
""" Train isolation forest
Parameters
----------
outliers_fraction: float between 0.01 to 0.5 (default=0.05)
percentage of anomalous available in input data
push_model: boolean (default=False)
return f_1 score if True else push model into repo
random_state: int (default=42)
"""
iso = IsolationForest(contamination=outliers_fraction, random_state=random_state)
md = iso.fit(self.data.values, None) # add .values to avoid the warning message (jojowei modification)
if push_model:
joblib.dump(self.cols, 'params')
joblib.dump(md, 'model')
return test(self, md)
def train(thread=False):
"""
Main function to perform training on input data
"""
if thread:
db = DUMMY()
else:
db = DATABASE('UEData')
db.read_data('train')
ps = PREPROCESS(db.data)
ps.process()
df = ps.data
mod = modelling(df)
mod.read_test(db)
scores = []
for of in np.arange(0.01, 0.4, 0.01):
scores.append(mod.isoforest(outliers_fraction=of))
opt_f1 = scores.index(max(scores)) + 1
mod.isoforest(outliers_fraction=opt_f1*0.01, push_model=True)
print("Optimum value of contamination : {}".format(opt_f1*0.01))
print('Training Ends : ')
def test(self, model):
pred = model.predict(self.X)
if -1 in pred:
pred = [1 if p == -1 else 0 for p in pred]
return f1_score(self.actual, pred) | ad/ad_train.py |
import joblib
from ad_model.processing import PREPROCESS
from sklearn.metrics import f1_score
from sklearn.ensemble import IsolationForest
from database import DATABASE, DUMMY
import numpy as np
class modelling(object):
r""" The modelling class takes input as dataframe or array and train Isolation Forest model
Paramteres
.........
data: DataFrame or array
input dataset
cols: list
list of parameters in input dataset
Attributes
----------
actual:array
actual label for test data
X: DataFrame or array
transformed values of input data
"""
def __init__(self, data):
self.data = data
self.cols = data.columns
def read_test(self, db):
""" Read test dataset for model validation"""
db.read_data('valid')
test = db.data
self.actual = test['Anomaly']
X = test[self.cols]
sc = joblib.load('scale')
self.X = sc.transform(X)
def isoforest(self, outliers_fraction=0.05, random_state=42, push_model=False): # modify the outlier
""" Train isolation forest
Parameters
----------
outliers_fraction: float between 0.01 to 0.5 (default=0.05)
percentage of anomalous available in input data
push_model: boolean (default=False)
return f_1 score if True else push model into repo
random_state: int (default=42)
"""
iso = IsolationForest(contamination=outliers_fraction, random_state=random_state)
md = iso.fit(self.data.values, None) # add .values to avoid the warning message (jojowei modification)
if push_model:
joblib.dump(self.cols, 'params')
joblib.dump(md, 'model')
return test(self, md)
def train(thread=False):
"""
Main function to perform training on input data
"""
if thread:
db = DUMMY()
else:
db = DATABASE('UEData')
db.read_data('train')
ps = PREPROCESS(db.data)
ps.process()
df = ps.data
mod = modelling(df)
mod.read_test(db)
scores = []
for of in np.arange(0.01, 0.4, 0.01):
scores.append(mod.isoforest(outliers_fraction=of))
opt_f1 = scores.index(max(scores)) + 1
mod.isoforest(outliers_fraction=opt_f1*0.01, push_model=True)
print("Optimum value of contamination : {}".format(opt_f1*0.01))
print('Training Ends : ')
def test(self, model):
pred = model.predict(self.X)
if -1 in pred:
pred = [1 if p == -1 else 0 for p in pred]
return f1_score(self.actual, pred) | 0.832509 | 0.495606 |
import os
import sys
sys.path.append(os.getcwd())
import torch
import comet.data.conceptnet as cdata
import comet.data.data as data
from comet.utils.utils import DD
from comet.utils import utils as utils
from comet.data.utils import TextEncoder
opt = DD()
opt.dataset = "conceptnet"
opt.exp = "generation"
opt.data = DD()
# Use relation embeddings rather than
# splitting relations into its component words
# Set to "language" for using component words
# Set to "relation" to use unlearned relation embeddings
opt.data.rel = "language"
# Use 100k training set
opt.data.trainsize = 100
# Use both dev sets (v1 an v2)
opt.data.devversion = "12"
# Maximum token length of e1
opt.data.maxe1 = 10
# Maximum token length of e2
opt.data.maxe2 = 15
relations = [
'AtLocation', 'CapableOf', 'Causes', 'CausesDesire', 'CreatedBy',
'DefinedAs', 'DesireOf', 'Desires', 'HasA', 'HasFirstSubevent',
'HasLastSubevent', 'HasPainCharacter', 'HasPainIntensity',
'HasPrerequisite', 'HasProperty', 'HasSubevent', 'InheritsFrom',
'InstanceOf', 'IsA', 'LocatedNear', 'LocationOfAction', 'MadeOf',
'MotivatedByGoal', 'NotCapableOf', 'NotDesires', 'NotHasA',
'NotHasProperty', 'NotIsA', 'NotMadeOf', 'PartOf', 'ReceivesAction',
'RelatedTo', 'SymbolOf', 'UsedFor'
]
special = [data.start_token, data.end_token]
special += ["<{}>".format(relation) for relation in relations]
encoder_path = "model/encoder_bpe_40000.json"
bpe_path = "model/vocab_40000.bpe"
text_encoder = TextEncoder(encoder_path, bpe_path)
for special_token in special:
text_encoder.decoder[len(text_encoder.encoder)] = special_token
text_encoder.encoder[special_token] = len(text_encoder.encoder)
data_loader = cdata.GenerationDataLoader(opt)
data_loader.load_data("data/conceptnet/")
data_loader.make_tensors(text_encoder, special, test=False)
opt.data.maxr = data_loader.max_r
save_path = "data/conceptnet/processed/generation"
save_name = os.path.join(save_path, "{}.pickle".format(
utils.make_name_string(opt.data)))
utils.mkpath(save_path)
print("Data Loader will be saved to {}".format(save_name))
torch.save(data_loader, save_name) | scripts/data/make_conceptnet_data_loader.py | import os
import sys
sys.path.append(os.getcwd())
import torch
import comet.data.conceptnet as cdata
import comet.data.data as data
from comet.utils.utils import DD
from comet.utils import utils as utils
from comet.data.utils import TextEncoder
opt = DD()
opt.dataset = "conceptnet"
opt.exp = "generation"
opt.data = DD()
# Use relation embeddings rather than
# splitting relations into its component words
# Set to "language" for using component words
# Set to "relation" to use unlearned relation embeddings
opt.data.rel = "language"
# Use 100k training set
opt.data.trainsize = 100
# Use both dev sets (v1 an v2)
opt.data.devversion = "12"
# Maximum token length of e1
opt.data.maxe1 = 10
# Maximum token length of e2
opt.data.maxe2 = 15
relations = [
'AtLocation', 'CapableOf', 'Causes', 'CausesDesire', 'CreatedBy',
'DefinedAs', 'DesireOf', 'Desires', 'HasA', 'HasFirstSubevent',
'HasLastSubevent', 'HasPainCharacter', 'HasPainIntensity',
'HasPrerequisite', 'HasProperty', 'HasSubevent', 'InheritsFrom',
'InstanceOf', 'IsA', 'LocatedNear', 'LocationOfAction', 'MadeOf',
'MotivatedByGoal', 'NotCapableOf', 'NotDesires', 'NotHasA',
'NotHasProperty', 'NotIsA', 'NotMadeOf', 'PartOf', 'ReceivesAction',
'RelatedTo', 'SymbolOf', 'UsedFor'
]
special = [data.start_token, data.end_token]
special += ["<{}>".format(relation) for relation in relations]
encoder_path = "model/encoder_bpe_40000.json"
bpe_path = "model/vocab_40000.bpe"
text_encoder = TextEncoder(encoder_path, bpe_path)
for special_token in special:
text_encoder.decoder[len(text_encoder.encoder)] = special_token
text_encoder.encoder[special_token] = len(text_encoder.encoder)
data_loader = cdata.GenerationDataLoader(opt)
data_loader.load_data("data/conceptnet/")
data_loader.make_tensors(text_encoder, special, test=False)
opt.data.maxr = data_loader.max_r
save_path = "data/conceptnet/processed/generation"
save_name = os.path.join(save_path, "{}.pickle".format(
utils.make_name_string(opt.data)))
utils.mkpath(save_path)
print("Data Loader will be saved to {}".format(save_name))
torch.save(data_loader, save_name) | 0.454956 | 0.199152 |
# based on:
# https://github.com/zhunzhong07/person-re-ranking
__all__ = ['re_ranking']
import numpy as np
def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1: int = 20, k2: int = 6, lambda_value: float = 0.3):
original_dist = np.concatenate(
[np.concatenate([q_q_dist, q_g_dist], axis=1),
np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
axis=0)
original_dist = np.power(original_dist, 2).astype(np.float32)
original_dist = np.transpose(1. * original_dist / np.max(original_dist, axis=0))
V = np.zeros_like(original_dist).astype(np.float32)
initial_rank = np.argsort(original_dist).astype(np.int32)
query_num = q_g_dist.shape[0]
gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
all_num = gallery_num
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[i, :k1 + 1]
backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
fi = np.where(backward_k_neigh_index == i)[0]
k_reciprocal_index = forward_k_neigh_index[fi]
k_reciprocal_expansion_index = k_reciprocal_index
for j in range(len(k_reciprocal_index)):
candidate = k_reciprocal_index[j]
candidate_forward_k_neigh_index = initial_rank[candidate,
:int(np.around(k1 / 2.)) + 1]
candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
:int(np.around(k1 / 2.)) + 1]
fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2. / 3 * len(
candidate_k_reciprocal_index):
k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)
k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)
original_dist = original_dist[:query_num, ]
if k2 != 1:
V_qe = np.zeros_like(V, dtype=np.float32)
for i in range(all_num):
V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
V = V_qe
del V_qe
del initial_rank
invIndex = []
for i in range(gallery_num):
invIndex.append(np.where(V[:, i] != 0)[0])
jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)
for i in range(query_num):
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
indNonZero = np.where(V[i, :] != 0)[0]
indImages = [invIndex[ind] for ind in indNonZero]
for j in range(len(indNonZero)):
temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
V[indImages[j], indNonZero[j]])
jaccard_dist[i] = 1 - temp_min / (2. - temp_min)
final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value
del original_dist, V, jaccard_dist
final_dist = final_dist[:query_num, query_num:]
return final_dist
def re_ranking_dist(
original_dist,
k1=20,
k2=6,
lambda_value=0.3,
dtype=np.float32
): # https://github.com/ljn114514/JVTC/blob/master/utils/rerank.py
all_num = original_dist.shape[0]
gallery_num = original_dist.shape[0]
original_dist = original_dist - np.min(original_dist, axis=0)
original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
V = np.zeros_like(original_dist).astype(np.float16)
initial_rank = np.argsort(original_dist).astype(
np.int32) ## default axis=-1.
print('Starting re_ranking...')
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[
i, :k1 +
1] ## k1+1 because self always ranks first. forward_k_neigh_index.shape=[k1+1]. forward_k_neigh_index[0] == i.
backward_k_neigh_index = initial_rank[
forward_k_neigh_index, :k1 +
1] ##backward.shape = [k1+1, k1+1]. For each ele in forward_k_neigh_index, find its rank k1 neighbors
fi = np.where(backward_k_neigh_index == i)[0]
k_reciprocal_index = forward_k_neigh_index[
fi] ## get R(p,k) in the paper
k_reciprocal_expansion_index = k_reciprocal_index
for j in range(len(k_reciprocal_index)):
candidate = k_reciprocal_index[j]
candidate_forward_k_neigh_index = initial_rank[
candidate, :int(np.around(k1 / 2)) + 1]
candidate_backward_k_neigh_index = initial_rank[
candidate_forward_k_neigh_index, :int(np.around(k1 / 2)) + 1]
fi_candidate = np.where(
candidate_backward_k_neigh_index == candidate)[0]
candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
fi_candidate]
if len(
np.intersect1d(candidate_k_reciprocal_index,
k_reciprocal_index)
) > 2 / 3 * len(candidate_k_reciprocal_index):
k_reciprocal_expansion_index = np.append(
k_reciprocal_expansion_index, candidate_k_reciprocal_index)
k_reciprocal_expansion_index = np.unique(
k_reciprocal_expansion_index) ## element-wise unique
weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
#original_dist = original_dist[:query_num,]
if k2 != 1:
V_qe = np.zeros_like(V, dtype=np.float16)
for i in range(all_num):
V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
V = V_qe
del V_qe
del initial_rank
invIndex = []
for i in range(gallery_num):
invIndex.append(np.where(V[:, i] != 0)[0]) #len(invIndex)=all_num
jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
for i in range(all_num):
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
indNonZero = np.where(V[i, :] != 0)[0]
indImages = []
indImages = [invIndex[ind] for ind in indNonZero]
for j in range(len(indNonZero)):
temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
V[i, indNonZero[j]], V[indImages[j], indNonZero[j]])
jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
pos_bool = (jaccard_dist < 0)
jaccard_dist[pos_bool] = 0.0
if lambda_value == 0:
return jaccard_dist.astype(dtype)
else:
final_dist = jaccard_dist * (
1 - lambda_value) + original_dist * lambda_value
return final_dist.astype(dtype) | fastreid/evaluation/rerank.py |
# based on:
# https://github.com/zhunzhong07/person-re-ranking
__all__ = ['re_ranking']
import numpy as np
def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1: int = 20, k2: int = 6, lambda_value: float = 0.3):
original_dist = np.concatenate(
[np.concatenate([q_q_dist, q_g_dist], axis=1),
np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
axis=0)
original_dist = np.power(original_dist, 2).astype(np.float32)
original_dist = np.transpose(1. * original_dist / np.max(original_dist, axis=0))
V = np.zeros_like(original_dist).astype(np.float32)
initial_rank = np.argsort(original_dist).astype(np.int32)
query_num = q_g_dist.shape[0]
gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
all_num = gallery_num
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[i, :k1 + 1]
backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
fi = np.where(backward_k_neigh_index == i)[0]
k_reciprocal_index = forward_k_neigh_index[fi]
k_reciprocal_expansion_index = k_reciprocal_index
for j in range(len(k_reciprocal_index)):
candidate = k_reciprocal_index[j]
candidate_forward_k_neigh_index = initial_rank[candidate,
:int(np.around(k1 / 2.)) + 1]
candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
:int(np.around(k1 / 2.)) + 1]
fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2. / 3 * len(
candidate_k_reciprocal_index):
k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)
k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)
original_dist = original_dist[:query_num, ]
if k2 != 1:
V_qe = np.zeros_like(V, dtype=np.float32)
for i in range(all_num):
V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
V = V_qe
del V_qe
del initial_rank
invIndex = []
for i in range(gallery_num):
invIndex.append(np.where(V[:, i] != 0)[0])
jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)
for i in range(query_num):
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
indNonZero = np.where(V[i, :] != 0)[0]
indImages = [invIndex[ind] for ind in indNonZero]
for j in range(len(indNonZero)):
temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
V[indImages[j], indNonZero[j]])
jaccard_dist[i] = 1 - temp_min / (2. - temp_min)
final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value
del original_dist, V, jaccard_dist
final_dist = final_dist[:query_num, query_num:]
return final_dist
def re_ranking_dist(
original_dist,
k1=20,
k2=6,
lambda_value=0.3,
dtype=np.float32
): # https://github.com/ljn114514/JVTC/blob/master/utils/rerank.py
all_num = original_dist.shape[0]
gallery_num = original_dist.shape[0]
original_dist = original_dist - np.min(original_dist, axis=0)
original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
V = np.zeros_like(original_dist).astype(np.float16)
initial_rank = np.argsort(original_dist).astype(
np.int32) ## default axis=-1.
print('Starting re_ranking...')
for i in range(all_num):
# k-reciprocal neighbors
forward_k_neigh_index = initial_rank[
i, :k1 +
1] ## k1+1 because self always ranks first. forward_k_neigh_index.shape=[k1+1]. forward_k_neigh_index[0] == i.
backward_k_neigh_index = initial_rank[
forward_k_neigh_index, :k1 +
1] ##backward.shape = [k1+1, k1+1]. For each ele in forward_k_neigh_index, find its rank k1 neighbors
fi = np.where(backward_k_neigh_index == i)[0]
k_reciprocal_index = forward_k_neigh_index[
fi] ## get R(p,k) in the paper
k_reciprocal_expansion_index = k_reciprocal_index
for j in range(len(k_reciprocal_index)):
candidate = k_reciprocal_index[j]
candidate_forward_k_neigh_index = initial_rank[
candidate, :int(np.around(k1 / 2)) + 1]
candidate_backward_k_neigh_index = initial_rank[
candidate_forward_k_neigh_index, :int(np.around(k1 / 2)) + 1]
fi_candidate = np.where(
candidate_backward_k_neigh_index == candidate)[0]
candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
fi_candidate]
if len(
np.intersect1d(candidate_k_reciprocal_index,
k_reciprocal_index)
) > 2 / 3 * len(candidate_k_reciprocal_index):
k_reciprocal_expansion_index = np.append(
k_reciprocal_expansion_index, candidate_k_reciprocal_index)
k_reciprocal_expansion_index = np.unique(
k_reciprocal_expansion_index) ## element-wise unique
weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
#original_dist = original_dist[:query_num,]
if k2 != 1:
V_qe = np.zeros_like(V, dtype=np.float16)
for i in range(all_num):
V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
V = V_qe
del V_qe
del initial_rank
invIndex = []
for i in range(gallery_num):
invIndex.append(np.where(V[:, i] != 0)[0]) #len(invIndex)=all_num
jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
for i in range(all_num):
temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
indNonZero = np.where(V[i, :] != 0)[0]
indImages = []
indImages = [invIndex[ind] for ind in indNonZero]
for j in range(len(indNonZero)):
temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
V[i, indNonZero[j]], V[indImages[j], indNonZero[j]])
jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
pos_bool = (jaccard_dist < 0)
jaccard_dist[pos_bool] = 0.0
if lambda_value == 0:
return jaccard_dist.astype(dtype)
else:
final_dist = jaccard_dist * (
1 - lambda_value) + original_dist * lambda_value
return final_dist.astype(dtype) | 0.496338 | 0.449513 |
from __future__ import absolute_import
import json
import warnings
try:
import yaml
yaml_available = True
except ImportError:
yaml_available = False
from albumentations import __version__
__all__ = ["to_dict", "from_dict", "save", "load"]
SERIALIZABLE_REGISTRY = {}
class SerializableMeta(type):
"""
A metaclass that is used to register classes in `SERIALIZABLE_REGISTRY` so they can be found later
while deserializing transformation pipeline using classes full names.
"""
def __new__(cls, name, bases, class_dict):
cls_obj = type.__new__(cls, name, bases, class_dict)
SERIALIZABLE_REGISTRY[cls_obj.get_class_fullname()] = cls_obj
return cls_obj
def to_dict(transform, on_not_implemented_error="raise"):
"""
Take a transform pipeline and convert it to a serializable representation that uses only standard
python data types: dictionaries, lists, strings, integers, and floats.
Args:
transform (object): A transform that should be serialized. If the transform doesn't implement the `to_dict`
method and `on_not_implemented_error` equals to 'raise' then `NotImplementedError` is raised.
If `on_not_implemented_error` equals to 'warn' then `NotImplementedError` will be ignored
but no transform parameters will be serialized.
"""
if on_not_implemented_error not in {"raise", "warn"}:
raise ValueError(
"Unknown on_not_implemented_error value: {}. Supported values are: 'raise' and 'warn'".format(
on_not_implemented_error
)
)
try:
transform_dict = transform._to_dict() # skipcq: PYL-W0212
except NotImplementedError as e:
if on_not_implemented_error == "raise":
raise e
transform_dict = {}
warnings.warn(
"Got NotImplementedError while trying to serialize {obj}. Object arguments are not preserved. "
"Implement either '{cls_name}.get_transform_init_args_names' or '{cls_name}.get_transform_init_args' "
"method to make the transform serializable".format(obj=transform, cls_name=transform.__class__.__name__)
)
return {"__version__": __version__, "transform": transform_dict}
def instantiate_lambda(transform, lambda_transforms=None):
if transform.get("__type__") == "Lambda":
name = transform["__name__"]
if lambda_transforms is None:
raise ValueError(
"To deserialize a Lambda transform with name {name} you need to pass a dict with this transform "
"as the `lambda_transforms` argument".format(name=name)
)
transform = lambda_transforms.get(name)
if transform is None:
raise ValueError("Lambda transform with {name} was not found in `lambda_transforms`".format(name=name))
return transform
return None
def from_dict(transform_dict, lambda_transforms=None):
"""
Args:
transform (dict): A dictionary with serialized transform pipeline.
lambda_transforms (dict): A dictionary that contains lambda transforms, that is instances of the Lambda class.
This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys
in that dictionary should be named same as `name` arguments in respective lambda transforms from
a serialized pipeline.
"""
transform = transform_dict["transform"]
lmbd = instantiate_lambda(transform, lambda_transforms)
if lmbd:
return lmbd
name = transform["__class_fullname__"]
args = {k: v for k, v in transform.items() if k != "__class_fullname__"}
cls = SERIALIZABLE_REGISTRY[name]
if "transforms" in args:
args["transforms"] = [
from_dict({"transform": t}, lambda_transforms=lambda_transforms) for t in args["transforms"]
]
return cls(**args)
def check_data_format(data_format):
if data_format not in {"json", "yaml"}:
raise ValueError("Unknown data_format {}. Supported formats are: 'json' and 'yaml'".format(data_format))
def save(transform, filepath, data_format="json", on_not_implemented_error="raise"):
"""
Take a transform pipeline, serialize it and save a serialized version to a file
using either json or yaml format.
Args:
transform (obj): Transform to serialize.
filepath (str): Filepath to write to.
data_format (str): Serialization format. Should be either `json` or 'yaml'.
on_not_implemented_error (str): Parameter that describes what to do if a transform doesn't implement
the `to_dict` method. If 'raise' then `NotImplementedError` is raised, if `warn` then the exception will be
ignored and no transform arguments will be saved.
"""
check_data_format(data_format)
transform_dict = to_dict(transform, on_not_implemented_error=on_not_implemented_error)
dump_fn = json.dump if data_format == "json" else yaml.safe_dump
with open(filepath, "w") as f:
dump_fn(transform_dict, f)
def load(filepath, data_format="json", lambda_transforms=None):
"""
Load a serialized pipeline from a json or yaml file and construct a transform pipeline.
Args:
transform (obj): Transform to serialize.
filepath (str): Filepath to read from.
data_format (str): Serialization format. Should be either `json` or 'yaml'.
lambda_transforms (dict): A dictionary that contains lambda transforms, that is instances of the Lambda class.
This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys
in that dictionary should be named same as `name` arguments in respective lambda transforms from
a serialized pipeline.
"""
check_data_format(data_format)
load_fn = json.load if data_format == "json" else yaml.safe_load
with open(filepath) as f:
transform_dict = load_fn(f)
return from_dict(transform_dict, lambda_transforms=lambda_transforms) | albumentations/core/serialization.py | from __future__ import absolute_import
import json
import warnings
try:
import yaml
yaml_available = True
except ImportError:
yaml_available = False
from albumentations import __version__
__all__ = ["to_dict", "from_dict", "save", "load"]
SERIALIZABLE_REGISTRY = {}
class SerializableMeta(type):
"""
A metaclass that is used to register classes in `SERIALIZABLE_REGISTRY` so they can be found later
while deserializing transformation pipeline using classes full names.
"""
def __new__(cls, name, bases, class_dict):
cls_obj = type.__new__(cls, name, bases, class_dict)
SERIALIZABLE_REGISTRY[cls_obj.get_class_fullname()] = cls_obj
return cls_obj
def to_dict(transform, on_not_implemented_error="raise"):
"""
Take a transform pipeline and convert it to a serializable representation that uses only standard
python data types: dictionaries, lists, strings, integers, and floats.
Args:
transform (object): A transform that should be serialized. If the transform doesn't implement the `to_dict`
method and `on_not_implemented_error` equals to 'raise' then `NotImplementedError` is raised.
If `on_not_implemented_error` equals to 'warn' then `NotImplementedError` will be ignored
but no transform parameters will be serialized.
"""
if on_not_implemented_error not in {"raise", "warn"}:
raise ValueError(
"Unknown on_not_implemented_error value: {}. Supported values are: 'raise' and 'warn'".format(
on_not_implemented_error
)
)
try:
transform_dict = transform._to_dict() # skipcq: PYL-W0212
except NotImplementedError as e:
if on_not_implemented_error == "raise":
raise e
transform_dict = {}
warnings.warn(
"Got NotImplementedError while trying to serialize {obj}. Object arguments are not preserved. "
"Implement either '{cls_name}.get_transform_init_args_names' or '{cls_name}.get_transform_init_args' "
"method to make the transform serializable".format(obj=transform, cls_name=transform.__class__.__name__)
)
return {"__version__": __version__, "transform": transform_dict}
def instantiate_lambda(transform, lambda_transforms=None):
if transform.get("__type__") == "Lambda":
name = transform["__name__"]
if lambda_transforms is None:
raise ValueError(
"To deserialize a Lambda transform with name {name} you need to pass a dict with this transform "
"as the `lambda_transforms` argument".format(name=name)
)
transform = lambda_transforms.get(name)
if transform is None:
raise ValueError("Lambda transform with {name} was not found in `lambda_transforms`".format(name=name))
return transform
return None
def from_dict(transform_dict, lambda_transforms=None):
"""
Args:
transform (dict): A dictionary with serialized transform pipeline.
lambda_transforms (dict): A dictionary that contains lambda transforms, that is instances of the Lambda class.
This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys
in that dictionary should be named same as `name` arguments in respective lambda transforms from
a serialized pipeline.
"""
transform = transform_dict["transform"]
lmbd = instantiate_lambda(transform, lambda_transforms)
if lmbd:
return lmbd
name = transform["__class_fullname__"]
args = {k: v for k, v in transform.items() if k != "__class_fullname__"}
cls = SERIALIZABLE_REGISTRY[name]
if "transforms" in args:
args["transforms"] = [
from_dict({"transform": t}, lambda_transforms=lambda_transforms) for t in args["transforms"]
]
return cls(**args)
def check_data_format(data_format):
if data_format not in {"json", "yaml"}:
raise ValueError("Unknown data_format {}. Supported formats are: 'json' and 'yaml'".format(data_format))
def save(transform, filepath, data_format="json", on_not_implemented_error="raise"):
"""
Take a transform pipeline, serialize it and save a serialized version to a file
using either json or yaml format.
Args:
transform (obj): Transform to serialize.
filepath (str): Filepath to write to.
data_format (str): Serialization format. Should be either `json` or 'yaml'.
on_not_implemented_error (str): Parameter that describes what to do if a transform doesn't implement
the `to_dict` method. If 'raise' then `NotImplementedError` is raised, if `warn` then the exception will be
ignored and no transform arguments will be saved.
"""
check_data_format(data_format)
transform_dict = to_dict(transform, on_not_implemented_error=on_not_implemented_error)
dump_fn = json.dump if data_format == "json" else yaml.safe_dump
with open(filepath, "w") as f:
dump_fn(transform_dict, f)
def load(filepath, data_format="json", lambda_transforms=None):
"""
Load a serialized pipeline from a json or yaml file and construct a transform pipeline.
Args:
transform (obj): Transform to serialize.
filepath (str): Filepath to read from.
data_format (str): Serialization format. Should be either `json` or 'yaml'.
lambda_transforms (dict): A dictionary that contains lambda transforms, that is instances of the Lambda class.
This dictionary is required when you are restoring a pipeline that contains lambda transforms. Keys
in that dictionary should be named same as `name` arguments in respective lambda transforms from
a serialized pipeline.
"""
check_data_format(data_format)
load_fn = json.load if data_format == "json" else yaml.safe_load
with open(filepath) as f:
transform_dict = load_fn(f)
return from_dict(transform_dict, lambda_transforms=lambda_transforms) | 0.894784 | 0.289353 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
from . import outputs
__all__ = [
'GetConnectedClusterResult',
'AwaitableGetConnectedClusterResult',
'get_connected_cluster',
]
@pulumi.output_type
class GetConnectedClusterResult:
"""
Represents a connected cluster.
"""
def __init__(__self__, agent_public_key_certificate=None, agent_version=None, connectivity_status=None, distribution=None, id=None, identity=None, infrastructure=None, kubernetes_version=None, last_connectivity_time=None, location=None, managed_identity_certificate_expiration_time=None, name=None, offering=None, provisioning_state=None, system_data=None, tags=None, total_core_count=None, total_node_count=None, type=None):
if agent_public_key_certificate and not isinstance(agent_public_key_certificate, str):
raise TypeError("Expected argument 'agent_public_key_certificate' to be a str")
pulumi.set(__self__, "agent_public_key_certificate", agent_public_key_certificate)
if agent_version and not isinstance(agent_version, str):
raise TypeError("Expected argument 'agent_version' to be a str")
pulumi.set(__self__, "agent_version", agent_version)
if connectivity_status and not isinstance(connectivity_status, str):
raise TypeError("Expected argument 'connectivity_status' to be a str")
pulumi.set(__self__, "connectivity_status", connectivity_status)
if distribution and not isinstance(distribution, str):
raise TypeError("Expected argument 'distribution' to be a str")
pulumi.set(__self__, "distribution", distribution)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if identity and not isinstance(identity, dict):
raise TypeError("Expected argument 'identity' to be a dict")
pulumi.set(__self__, "identity", identity)
if infrastructure and not isinstance(infrastructure, str):
raise TypeError("Expected argument 'infrastructure' to be a str")
pulumi.set(__self__, "infrastructure", infrastructure)
if kubernetes_version and not isinstance(kubernetes_version, str):
raise TypeError("Expected argument 'kubernetes_version' to be a str")
pulumi.set(__self__, "kubernetes_version", kubernetes_version)
if last_connectivity_time and not isinstance(last_connectivity_time, str):
raise TypeError("Expected argument 'last_connectivity_time' to be a str")
pulumi.set(__self__, "last_connectivity_time", last_connectivity_time)
if location and not isinstance(location, str):
raise TypeError("Expected argument 'location' to be a str")
pulumi.set(__self__, "location", location)
if managed_identity_certificate_expiration_time and not isinstance(managed_identity_certificate_expiration_time, str):
raise TypeError("Expected argument 'managed_identity_certificate_expiration_time' to be a str")
pulumi.set(__self__, "managed_identity_certificate_expiration_time", managed_identity_certificate_expiration_time)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if offering and not isinstance(offering, str):
raise TypeError("Expected argument 'offering' to be a str")
pulumi.set(__self__, "offering", offering)
if provisioning_state and not isinstance(provisioning_state, str):
raise TypeError("Expected argument 'provisioning_state' to be a str")
pulumi.set(__self__, "provisioning_state", provisioning_state)
if system_data and not isinstance(system_data, dict):
raise TypeError("Expected argument 'system_data' to be a dict")
pulumi.set(__self__, "system_data", system_data)
if tags and not isinstance(tags, dict):
raise TypeError("Expected argument 'tags' to be a dict")
pulumi.set(__self__, "tags", tags)
if total_core_count and not isinstance(total_core_count, int):
raise TypeError("Expected argument 'total_core_count' to be a int")
pulumi.set(__self__, "total_core_count", total_core_count)
if total_node_count and not isinstance(total_node_count, int):
raise TypeError("Expected argument 'total_node_count' to be a int")
pulumi.set(__self__, "total_node_count", total_node_count)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="agentPublicKeyCertificate")
def agent_public_key_certificate(self) -> str:
"""
Base64 encoded public certificate used by the agent to do the initial handshake to the backend services in Azure.
"""
return pulumi.get(self, "agent_public_key_certificate")
@property
@pulumi.getter(name="agentVersion")
def agent_version(self) -> str:
"""
Version of the agent running on the connected cluster resource
"""
return pulumi.get(self, "agent_version")
@property
@pulumi.getter(name="connectivityStatus")
def connectivity_status(self) -> str:
"""
Represents the connectivity status of the connected cluster.
"""
return pulumi.get(self, "connectivity_status")
@property
@pulumi.getter
def distribution(self) -> Optional[str]:
"""
The Kubernetes distribution running on this connected cluster.
"""
return pulumi.get(self, "distribution")
@property
@pulumi.getter
def id(self) -> str:
"""
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def identity(self) -> 'outputs.ConnectedClusterIdentityResponse':
"""
The identity of the connected cluster.
"""
return pulumi.get(self, "identity")
@property
@pulumi.getter
def infrastructure(self) -> Optional[str]:
"""
The infrastructure on which the Kubernetes cluster represented by this connected cluster is running on.
"""
return pulumi.get(self, "infrastructure")
@property
@pulumi.getter(name="kubernetesVersion")
def kubernetes_version(self) -> str:
"""
The Kubernetes version of the connected cluster resource
"""
return pulumi.get(self, "kubernetes_version")
@property
@pulumi.getter(name="lastConnectivityTime")
def last_connectivity_time(self) -> str:
"""
Time representing the last instance when heart beat was received from the cluster
"""
return pulumi.get(self, "last_connectivity_time")
@property
@pulumi.getter
def location(self) -> str:
"""
The geo-location where the resource lives
"""
return pulumi.get(self, "location")
@property
@pulumi.getter(name="managedIdentityCertificateExpirationTime")
def managed_identity_certificate_expiration_time(self) -> str:
"""
Expiration time of the managed identity certificate
"""
return pulumi.get(self, "managed_identity_certificate_expiration_time")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the resource
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def offering(self) -> str:
"""
Connected cluster offering
"""
return pulumi.get(self, "offering")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> Optional[str]:
"""
Provisioning state of the connected cluster resource.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="systemData")
def system_data(self) -> 'outputs.SystemDataResponse':
"""
Metadata pertaining to creation and last modification of the resource
"""
return pulumi.get(self, "system_data")
@property
@pulumi.getter
def tags(self) -> Optional[Mapping[str, str]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="totalCoreCount")
def total_core_count(self) -> int:
"""
Number of CPU cores present in the connected cluster resource
"""
return pulumi.get(self, "total_core_count")
@property
@pulumi.getter(name="totalNodeCount")
def total_node_count(self) -> int:
"""
Number of nodes present in the connected cluster resource
"""
return pulumi.get(self, "total_node_count")
@property
@pulumi.getter
def type(self) -> str:
"""
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
"""
return pulumi.get(self, "type")
class AwaitableGetConnectedClusterResult(GetConnectedClusterResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetConnectedClusterResult(
agent_public_key_certificate=self.agent_public_key_certificate,
agent_version=self.agent_version,
connectivity_status=self.connectivity_status,
distribution=self.distribution,
id=self.id,
identity=self.identity,
infrastructure=self.infrastructure,
kubernetes_version=self.kubernetes_version,
last_connectivity_time=self.last_connectivity_time,
location=self.location,
managed_identity_certificate_expiration_time=self.managed_identity_certificate_expiration_time,
name=self.name,
offering=self.offering,
provisioning_state=self.provisioning_state,
system_data=self.system_data,
tags=self.tags,
total_core_count=self.total_core_count,
total_node_count=self.total_node_count,
type=self.type)
def get_connected_cluster(cluster_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetConnectedClusterResult:
"""
Represents a connected cluster.
API Version: 2021-03-01.
:param str cluster_name: The name of the Kubernetes cluster on which get is called.
:param str resource_group_name: The name of the resource group. The name is case insensitive.
"""
__args__ = dict()
__args__['clusterName'] = cluster_name
__args__['resourceGroupName'] = resource_group_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:kubernetes:getConnectedCluster', __args__, opts=opts, typ=GetConnectedClusterResult).value
return AwaitableGetConnectedClusterResult(
agent_public_key_certificate=__ret__.agent_public_key_certificate,
agent_version=__ret__.agent_version,
connectivity_status=__ret__.connectivity_status,
distribution=__ret__.distribution,
id=__ret__.id,
identity=__ret__.identity,
infrastructure=__ret__.infrastructure,
kubernetes_version=__ret__.kubernetes_version,
last_connectivity_time=__ret__.last_connectivity_time,
location=__ret__.location,
managed_identity_certificate_expiration_time=__ret__.managed_identity_certificate_expiration_time,
name=__ret__.name,
offering=__ret__.offering,
provisioning_state=__ret__.provisioning_state,
system_data=__ret__.system_data,
tags=__ret__.tags,
total_core_count=__ret__.total_core_count,
total_node_count=__ret__.total_node_count,
type=__ret__.type) | sdk/python/pulumi_azure_native/kubernetes/get_connected_cluster.py |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
from . import outputs
__all__ = [
'GetConnectedClusterResult',
'AwaitableGetConnectedClusterResult',
'get_connected_cluster',
]
@pulumi.output_type
class GetConnectedClusterResult:
"""
Represents a connected cluster.
"""
def __init__(__self__, agent_public_key_certificate=None, agent_version=None, connectivity_status=None, distribution=None, id=None, identity=None, infrastructure=None, kubernetes_version=None, last_connectivity_time=None, location=None, managed_identity_certificate_expiration_time=None, name=None, offering=None, provisioning_state=None, system_data=None, tags=None, total_core_count=None, total_node_count=None, type=None):
if agent_public_key_certificate and not isinstance(agent_public_key_certificate, str):
raise TypeError("Expected argument 'agent_public_key_certificate' to be a str")
pulumi.set(__self__, "agent_public_key_certificate", agent_public_key_certificate)
if agent_version and not isinstance(agent_version, str):
raise TypeError("Expected argument 'agent_version' to be a str")
pulumi.set(__self__, "agent_version", agent_version)
if connectivity_status and not isinstance(connectivity_status, str):
raise TypeError("Expected argument 'connectivity_status' to be a str")
pulumi.set(__self__, "connectivity_status", connectivity_status)
if distribution and not isinstance(distribution, str):
raise TypeError("Expected argument 'distribution' to be a str")
pulumi.set(__self__, "distribution", distribution)
if id and not isinstance(id, str):
raise TypeError("Expected argument 'id' to be a str")
pulumi.set(__self__, "id", id)
if identity and not isinstance(identity, dict):
raise TypeError("Expected argument 'identity' to be a dict")
pulumi.set(__self__, "identity", identity)
if infrastructure and not isinstance(infrastructure, str):
raise TypeError("Expected argument 'infrastructure' to be a str")
pulumi.set(__self__, "infrastructure", infrastructure)
if kubernetes_version and not isinstance(kubernetes_version, str):
raise TypeError("Expected argument 'kubernetes_version' to be a str")
pulumi.set(__self__, "kubernetes_version", kubernetes_version)
if last_connectivity_time and not isinstance(last_connectivity_time, str):
raise TypeError("Expected argument 'last_connectivity_time' to be a str")
pulumi.set(__self__, "last_connectivity_time", last_connectivity_time)
if location and not isinstance(location, str):
raise TypeError("Expected argument 'location' to be a str")
pulumi.set(__self__, "location", location)
if managed_identity_certificate_expiration_time and not isinstance(managed_identity_certificate_expiration_time, str):
raise TypeError("Expected argument 'managed_identity_certificate_expiration_time' to be a str")
pulumi.set(__self__, "managed_identity_certificate_expiration_time", managed_identity_certificate_expiration_time)
if name and not isinstance(name, str):
raise TypeError("Expected argument 'name' to be a str")
pulumi.set(__self__, "name", name)
if offering and not isinstance(offering, str):
raise TypeError("Expected argument 'offering' to be a str")
pulumi.set(__self__, "offering", offering)
if provisioning_state and not isinstance(provisioning_state, str):
raise TypeError("Expected argument 'provisioning_state' to be a str")
pulumi.set(__self__, "provisioning_state", provisioning_state)
if system_data and not isinstance(system_data, dict):
raise TypeError("Expected argument 'system_data' to be a dict")
pulumi.set(__self__, "system_data", system_data)
if tags and not isinstance(tags, dict):
raise TypeError("Expected argument 'tags' to be a dict")
pulumi.set(__self__, "tags", tags)
if total_core_count and not isinstance(total_core_count, int):
raise TypeError("Expected argument 'total_core_count' to be a int")
pulumi.set(__self__, "total_core_count", total_core_count)
if total_node_count and not isinstance(total_node_count, int):
raise TypeError("Expected argument 'total_node_count' to be a int")
pulumi.set(__self__, "total_node_count", total_node_count)
if type and not isinstance(type, str):
raise TypeError("Expected argument 'type' to be a str")
pulumi.set(__self__, "type", type)
@property
@pulumi.getter(name="agentPublicKeyCertificate")
def agent_public_key_certificate(self) -> str:
"""
Base64 encoded public certificate used by the agent to do the initial handshake to the backend services in Azure.
"""
return pulumi.get(self, "agent_public_key_certificate")
@property
@pulumi.getter(name="agentVersion")
def agent_version(self) -> str:
"""
Version of the agent running on the connected cluster resource
"""
return pulumi.get(self, "agent_version")
@property
@pulumi.getter(name="connectivityStatus")
def connectivity_status(self) -> str:
"""
Represents the connectivity status of the connected cluster.
"""
return pulumi.get(self, "connectivity_status")
@property
@pulumi.getter
def distribution(self) -> Optional[str]:
"""
The Kubernetes distribution running on this connected cluster.
"""
return pulumi.get(self, "distribution")
@property
@pulumi.getter
def id(self) -> str:
"""
Fully qualified resource ID for the resource. Ex - /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/{resourceProviderNamespace}/{resourceType}/{resourceName}
"""
return pulumi.get(self, "id")
@property
@pulumi.getter
def identity(self) -> 'outputs.ConnectedClusterIdentityResponse':
"""
The identity of the connected cluster.
"""
return pulumi.get(self, "identity")
@property
@pulumi.getter
def infrastructure(self) -> Optional[str]:
"""
The infrastructure on which the Kubernetes cluster represented by this connected cluster is running on.
"""
return pulumi.get(self, "infrastructure")
@property
@pulumi.getter(name="kubernetesVersion")
def kubernetes_version(self) -> str:
"""
The Kubernetes version of the connected cluster resource
"""
return pulumi.get(self, "kubernetes_version")
@property
@pulumi.getter(name="lastConnectivityTime")
def last_connectivity_time(self) -> str:
"""
Time representing the last instance when heart beat was received from the cluster
"""
return pulumi.get(self, "last_connectivity_time")
@property
@pulumi.getter
def location(self) -> str:
"""
The geo-location where the resource lives
"""
return pulumi.get(self, "location")
@property
@pulumi.getter(name="managedIdentityCertificateExpirationTime")
def managed_identity_certificate_expiration_time(self) -> str:
"""
Expiration time of the managed identity certificate
"""
return pulumi.get(self, "managed_identity_certificate_expiration_time")
@property
@pulumi.getter
def name(self) -> str:
"""
The name of the resource
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def offering(self) -> str:
"""
Connected cluster offering
"""
return pulumi.get(self, "offering")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> Optional[str]:
"""
Provisioning state of the connected cluster resource.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter(name="systemData")
def system_data(self) -> 'outputs.SystemDataResponse':
"""
Metadata pertaining to creation and last modification of the resource
"""
return pulumi.get(self, "system_data")
@property
@pulumi.getter
def tags(self) -> Optional[Mapping[str, str]]:
"""
Resource tags.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="totalCoreCount")
def total_core_count(self) -> int:
"""
Number of CPU cores present in the connected cluster resource
"""
return pulumi.get(self, "total_core_count")
@property
@pulumi.getter(name="totalNodeCount")
def total_node_count(self) -> int:
"""
Number of nodes present in the connected cluster resource
"""
return pulumi.get(self, "total_node_count")
@property
@pulumi.getter
def type(self) -> str:
"""
The type of the resource. E.g. "Microsoft.Compute/virtualMachines" or "Microsoft.Storage/storageAccounts"
"""
return pulumi.get(self, "type")
class AwaitableGetConnectedClusterResult(GetConnectedClusterResult):
# pylint: disable=using-constant-test
def __await__(self):
if False:
yield self
return GetConnectedClusterResult(
agent_public_key_certificate=self.agent_public_key_certificate,
agent_version=self.agent_version,
connectivity_status=self.connectivity_status,
distribution=self.distribution,
id=self.id,
identity=self.identity,
infrastructure=self.infrastructure,
kubernetes_version=self.kubernetes_version,
last_connectivity_time=self.last_connectivity_time,
location=self.location,
managed_identity_certificate_expiration_time=self.managed_identity_certificate_expiration_time,
name=self.name,
offering=self.offering,
provisioning_state=self.provisioning_state,
system_data=self.system_data,
tags=self.tags,
total_core_count=self.total_core_count,
total_node_count=self.total_node_count,
type=self.type)
def get_connected_cluster(cluster_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetConnectedClusterResult:
"""
Represents a connected cluster.
API Version: 2021-03-01.
:param str cluster_name: The name of the Kubernetes cluster on which get is called.
:param str resource_group_name: The name of the resource group. The name is case insensitive.
"""
__args__ = dict()
__args__['clusterName'] = cluster_name
__args__['resourceGroupName'] = resource_group_name
if opts is None:
opts = pulumi.InvokeOptions()
if opts.version is None:
opts.version = _utilities.get_version()
__ret__ = pulumi.runtime.invoke('azure-native:kubernetes:getConnectedCluster', __args__, opts=opts, typ=GetConnectedClusterResult).value
return AwaitableGetConnectedClusterResult(
agent_public_key_certificate=__ret__.agent_public_key_certificate,
agent_version=__ret__.agent_version,
connectivity_status=__ret__.connectivity_status,
distribution=__ret__.distribution,
id=__ret__.id,
identity=__ret__.identity,
infrastructure=__ret__.infrastructure,
kubernetes_version=__ret__.kubernetes_version,
last_connectivity_time=__ret__.last_connectivity_time,
location=__ret__.location,
managed_identity_certificate_expiration_time=__ret__.managed_identity_certificate_expiration_time,
name=__ret__.name,
offering=__ret__.offering,
provisioning_state=__ret__.provisioning_state,
system_data=__ret__.system_data,
tags=__ret__.tags,
total_core_count=__ret__.total_core_count,
total_node_count=__ret__.total_node_count,
type=__ret__.type) | 0.860604 | 0.095645 |
from keras.layers import Dense, LSTM
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from typing import List
import matplotlib.pyplot as plt
import shap
import numpy as np
import pandas as pd
import config
def create_model(learn_rate, activation, neurons):
# Fix random seed for reproducibility.
seed = 10
np.random.seed(seed)
model = Sequential()
model.add(LSTM(neurons, activation=activation, input_shape=(config.INPUTDAYS, len(config.FEATURES)), return_sequences=False))
model.add(Dense(units=neurons, activation=activation))
model.add(Dense(units=1, activation='linear'))
# Compiling the LSTM.
optim = Adam(lr=learn_rate)
model.compile(optimizer = optim, loss = 'mean_squared_error')
return model
def train_model(model: Sequential, X_train: np.ndarray, Y_train: np.ndarray, validation=None):
if config.SAVE:
model_checkpoint_callback = ModelCheckpoint(
filepath='Models/model_{epoch}_{val_loss:.4f}.h5',
save_weights_only=False,
monitor='val_loss',
mode='auto',
save_best_only=True
)
train_history = model.fit(
X_train,
Y_train,
epochs=config.EPOCHS,
validation_data=validation,
callbacks=[model_checkpoint_callback]
)
else:
train_history = model.fit(X_train, Y_train, epochs=config.EPOCHS, validation_data=validation)
return pd.DataFrame.from_dict(train_history.history)
def calculate_shap(model: Sequential, X_train: np.ndarray, X_test: np.ndarray, features: List[str]):
plt.close('all')
explainer = shap.DeepExplainer(model, X_train)
shap_values = explainer.shap_values(X_test)
shap.initjs()
shap_values_2d = shap_values[0].reshape(-1, len(config.FEATURES))
X_test_2d = X_test.reshape(-1, len(config.FEATURES))
shap.summary_plot(shap_values_2d[:, :len(config.FEATURES)-1], X_test_2d[:, :len(config.FEATURES)-1],
features[:-1])
def predict(model: Sequential, x: np.ndarray, days: int, series_dim: int = -1):
predictions = np.array([])
rec_x = x.copy()
# Make recursive predictions.
for day in range(days):
pred = model.predict(rec_x[day:day + config.INPUTDAYS].reshape(1, config.INPUTDAYS, len(config.FEATURES)))
predictions = np.append(predictions, pred)
rec_x[day + config.INPUTDAYS + 1, series_dim] = pred
return predictions | lstm.py | from keras.layers import Dense, LSTM
from keras.models import Sequential
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint
from typing import List
import matplotlib.pyplot as plt
import shap
import numpy as np
import pandas as pd
import config
def create_model(learn_rate, activation, neurons):
# Fix random seed for reproducibility.
seed = 10
np.random.seed(seed)
model = Sequential()
model.add(LSTM(neurons, activation=activation, input_shape=(config.INPUTDAYS, len(config.FEATURES)), return_sequences=False))
model.add(Dense(units=neurons, activation=activation))
model.add(Dense(units=1, activation='linear'))
# Compiling the LSTM.
optim = Adam(lr=learn_rate)
model.compile(optimizer = optim, loss = 'mean_squared_error')
return model
def train_model(model: Sequential, X_train: np.ndarray, Y_train: np.ndarray, validation=None):
if config.SAVE:
model_checkpoint_callback = ModelCheckpoint(
filepath='Models/model_{epoch}_{val_loss:.4f}.h5',
save_weights_only=False,
monitor='val_loss',
mode='auto',
save_best_only=True
)
train_history = model.fit(
X_train,
Y_train,
epochs=config.EPOCHS,
validation_data=validation,
callbacks=[model_checkpoint_callback]
)
else:
train_history = model.fit(X_train, Y_train, epochs=config.EPOCHS, validation_data=validation)
return pd.DataFrame.from_dict(train_history.history)
def calculate_shap(model: Sequential, X_train: np.ndarray, X_test: np.ndarray, features: List[str]):
plt.close('all')
explainer = shap.DeepExplainer(model, X_train)
shap_values = explainer.shap_values(X_test)
shap.initjs()
shap_values_2d = shap_values[0].reshape(-1, len(config.FEATURES))
X_test_2d = X_test.reshape(-1, len(config.FEATURES))
shap.summary_plot(shap_values_2d[:, :len(config.FEATURES)-1], X_test_2d[:, :len(config.FEATURES)-1],
features[:-1])
def predict(model: Sequential, x: np.ndarray, days: int, series_dim: int = -1):
predictions = np.array([])
rec_x = x.copy()
# Make recursive predictions.
for day in range(days):
pred = model.predict(rec_x[day:day + config.INPUTDAYS].reshape(1, config.INPUTDAYS, len(config.FEATURES)))
predictions = np.append(predictions, pred)
rec_x[day + config.INPUTDAYS + 1, series_dim] = pred
return predictions | 0.914734 | 0.416441 |
from __future__ import division
from __future__ import print_function
import os
import sys
# dirty hack: include top level folder to path
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..'))
)
import itertools
from torch_geometric.data import Data
import torch
import torch.nn as nn
from models.utils import unbatch_node_feature, unbatch_node_feature_mat
from models.utils import make_mlp, SeriesModel, replace_graph
class SingleVAR(SeriesModel):
def __init__(self,
input_dim,
output_dim,
input_frame_num,
skip_first_frames_num):
super(SingleVAR, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.var_layer = nn.Linear(self.input_frame_num * self.input_dim, self.output_dim)
def forward_onestep(self, data):
out = self.var_layer(data.x.flatten(1, 2))
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class JointVAR(SeriesModel):
def __init__(self,
input_dim,
output_dim,
input_frame_num,
skip_first_frames_num,
node_num):
super(JointVAR, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.node_num = node_num
self.var_layer = nn.Linear(self.node_num * self.input_frame_num * self.input_dim,
self.node_num * self.output_dim)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.reshape(input_features.shape[0], -1)
out = self.var_layer(input_features)
out = out.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class SingleMLP(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
layer_num,
input_frame_num,
skip_first_frames_num):
super(SingleMLP, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.hidden_dim = hidden_dim
self.mlp = make_mlp(input_dim * self.input_frame_num, hidden_dim, output_dim,
layer_num, activation='ReLU', final_activation=False)
def forward_onestep(self, data):
out = self.mlp(data.x.flatten(1, 2))
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class JointMLP(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
layer_num,
input_frame_num,
skip_first_frames_num,
node_num):
super(JointMLP, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.hidden_dim = hidden_dim
self.node_num = node_num
self.mlp = make_mlp(node_num * input_dim * self.input_frame_num, node_num * hidden_dim, node_num * output_dim,
layer_num, activation='ReLU', final_activation=False)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in
zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.reshape(input_features.shape[0], -1)
out = self.mlp(input_features)
out = out.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class SingleRNN(SeriesModel):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, skip_first_frames_num):
super(SingleRNN, self).__init__(input_dim, output_dim, input_frame_num=1,
skip_first_frames_num=skip_first_frames_num, is_recurrent=True)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.rnn = nn.GRU(
input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.num_layers,
batch_first=True
)
self.decoder = nn.Linear(self.hidden_dim, self.output_dim)
def forward_onestep(self, data):
if not hasattr(data, 'node_hidden'):
data = replace_graph(data,
node_hidden=data.x.new_zeros(self.num_layers, data.x.shape[0], self.hidden_dim))
node_hidden_output, node_hidden_next = self.rnn(data.x, data.node_hidden)
node_output = self.decoder(node_hidden_output) + data.x[:, -1:, -self.output_dim:]
node_output = node_output.squeeze(1)
output_graph = replace_graph(data, x=node_output, node_hidden=node_hidden_next)
return output_graph
class JointRNN(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
num_layers,
skip_first_frames_num,
node_num):
super(JointRNN, self).__init__(input_dim, output_dim, input_frame_num=1,
skip_first_frames_num=skip_first_frames_num, is_recurrent=True)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.node_num = node_num
self.rnn = nn.GRU(
input_size=self.node_num * self.input_dim,
hidden_size=self.node_num * self.hidden_dim,
num_layers=self.num_layers,
batch_first=True
)
self.decoder = nn.Linear(self.node_num * self.hidden_dim, self.node_num * self.output_dim)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in
zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.transpose(1, 2).flatten(2, 3)
if not hasattr(data, 'node_hidden'):
data = replace_graph(data,
node_hidden=data.x.new_zeros(self.num_layers, input_features.shape[0], self.rnn.hidden_size))
node_hidden_output, node_hidden_next = self.rnn(input_features, data.node_hidden)
node_hidden_output = self.decoder(node_hidden_output)
node_hidden_output = node_hidden_output.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
node_output = node_hidden_output + data.x[:, -1, -self.output_dim:]
output_graph = replace_graph(data, x=node_output, node_hidden=node_hidden_next)
return output_graph | models/baselines.py | from __future__ import division
from __future__ import print_function
import os
import sys
# dirty hack: include top level folder to path
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__)), '..'))
)
import itertools
from torch_geometric.data import Data
import torch
import torch.nn as nn
from models.utils import unbatch_node_feature, unbatch_node_feature_mat
from models.utils import make_mlp, SeriesModel, replace_graph
class SingleVAR(SeriesModel):
def __init__(self,
input_dim,
output_dim,
input_frame_num,
skip_first_frames_num):
super(SingleVAR, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.var_layer = nn.Linear(self.input_frame_num * self.input_dim, self.output_dim)
def forward_onestep(self, data):
out = self.var_layer(data.x.flatten(1, 2))
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class JointVAR(SeriesModel):
def __init__(self,
input_dim,
output_dim,
input_frame_num,
skip_first_frames_num,
node_num):
super(JointVAR, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.node_num = node_num
self.var_layer = nn.Linear(self.node_num * self.input_frame_num * self.input_dim,
self.node_num * self.output_dim)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.reshape(input_features.shape[0], -1)
out = self.var_layer(input_features)
out = out.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class SingleMLP(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
layer_num,
input_frame_num,
skip_first_frames_num):
super(SingleMLP, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.hidden_dim = hidden_dim
self.mlp = make_mlp(input_dim * self.input_frame_num, hidden_dim, output_dim,
layer_num, activation='ReLU', final_activation=False)
def forward_onestep(self, data):
out = self.mlp(data.x.flatten(1, 2))
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class JointMLP(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
layer_num,
input_frame_num,
skip_first_frames_num,
node_num):
super(JointMLP, self).__init__(input_dim, output_dim, input_frame_num,
skip_first_frames_num, is_recurrent=False)
self.hidden_dim = hidden_dim
self.node_num = node_num
self.mlp = make_mlp(node_num * input_dim * self.input_frame_num, node_num * hidden_dim, node_num * output_dim,
layer_num, activation='ReLU', final_activation=False)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in
zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.reshape(input_features.shape[0], -1)
out = self.mlp(input_features)
out = out.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
out = out + data.x[:, -1, -self.output_dim:]
out_graph = replace_graph(data, x=out)
return out_graph
class SingleRNN(SeriesModel):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, skip_first_frames_num):
super(SingleRNN, self).__init__(input_dim, output_dim, input_frame_num=1,
skip_first_frames_num=skip_first_frames_num, is_recurrent=True)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.rnn = nn.GRU(
input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.num_layers,
batch_first=True
)
self.decoder = nn.Linear(self.hidden_dim, self.output_dim)
def forward_onestep(self, data):
if not hasattr(data, 'node_hidden'):
data = replace_graph(data,
node_hidden=data.x.new_zeros(self.num_layers, data.x.shape[0], self.hidden_dim))
node_hidden_output, node_hidden_next = self.rnn(data.x, data.node_hidden)
node_output = self.decoder(node_hidden_output) + data.x[:, -1:, -self.output_dim:]
node_output = node_output.squeeze(1)
output_graph = replace_graph(data, x=node_output, node_hidden=node_hidden_next)
return output_graph
class JointRNN(SeriesModel):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
num_layers,
skip_first_frames_num,
node_num):
super(JointRNN, self).__init__(input_dim, output_dim, input_frame_num=1,
skip_first_frames_num=skip_first_frames_num, is_recurrent=True)
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.node_num = node_num
self.rnn = nn.GRU(
input_size=self.node_num * self.input_dim,
hidden_size=self.node_num * self.hidden_dim,
num_layers=self.num_layers,
batch_first=True
)
self.decoder = nn.Linear(self.node_num * self.hidden_dim, self.node_num * self.output_dim)
def forward_onestep(self, data):
input_features_list = unbatch_node_feature(data, 'x', data.batch) # list
graph_batch_list = unbatch_node_feature(data, 'graph_batch', data.batch)
input_features = list(itertools.chain.from_iterable([unbatch_node_feature_mat(input_features_i, graph_batch_i)
for input_features_i, graph_batch_i in
zip(input_features_list, graph_batch_list)]))
input_features = torch.stack(input_features, dim=0)
input_features = input_features.transpose(1, 2).flatten(2, 3)
if not hasattr(data, 'node_hidden'):
data = replace_graph(data,
node_hidden=data.x.new_zeros(self.num_layers, input_features.shape[0], self.rnn.hidden_size))
node_hidden_output, node_hidden_next = self.rnn(input_features, data.node_hidden)
node_hidden_output = self.decoder(node_hidden_output)
node_hidden_output = node_hidden_output.reshape(input_features.shape[0], self.node_num, self.output_dim).flatten(0, 1)
node_output = node_hidden_output + data.x[:, -1, -self.output_dim:]
output_graph = replace_graph(data, x=node_output, node_hidden=node_hidden_next)
return output_graph | 0.673514 | 0.235988 |
from __future__ import division
from collections import OrderedDict
from functools import partial
import empyrical as ep
import numpy as np
import pandas as pd
import scipy as sp
import scipy.stats as stats
from sklearn import linear_model
from .deprecate import deprecated
from .interesting_periods import PERIODS
from .txn import get_turnover
from .utils import APPROX_BDAYS_PER_MONTH, APPROX_BDAYS_PER_YEAR
from .utils import DAILY
DEPRECATION_WARNING = ("Risk functions in pyfolio.timeseries are deprecated "
"and will be removed in a future release. Please "
"install the empyrical package instead.")
def var_cov_var_normal(P, c, mu=0, sigma=1):
"""
Variance-covariance calculation of daily Value-at-Risk in a
portfolio.
Parameters
----------
P : float
Portfolio value.
c : float
Confidence level.
mu : float, optional
Mean.
Returns
-------
float
Variance-covariance.
"""
alpha = sp.stats.norm.ppf(1 - c, mu, sigma)
return P - P * (alpha + 1)
@deprecated(msg=DEPRECATION_WARNING)
def max_drawdown(returns):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
float
Maximum drawdown.
Note
-----
See https://en.wikipedia.org/wiki/Drawdown_(economics) for more details.
"""
return ep.max_drawdown(returns)
@deprecated(msg=DEPRECATION_WARNING)
def annual_return(returns, period=DAILY):
"""
Determines the mean annual growth rate of returns.
Parameters
----------
returns : pd.Series
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Annual Return as CAGR (Compounded Annual Growth Rate).
"""
return ep.annual_return(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def annual_volatility(returns, period=DAILY):
"""
Determines the annual volatility of a strategy.
Parameters
----------
returns : pd.Series
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing volatility. Can be 'monthly' or 'weekly' or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Annual volatility.
"""
return ep.annual_volatility(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def calmar_ratio(returns, period=DAILY):
"""
Determines the Calmar ratio, or drawdown ratio, of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Calmar ratio (drawdown ratio) as float. Returns np.nan if there is no
calmar ratio.
Note
-----
See https://en.wikipedia.org/wiki/Calmar_ratio for more details.
"""
return ep.calmar_ratio(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def omega_ratio(returns, annual_return_threshhold=0.0):
"""
Determines the Omega ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
annual_return_threshold : float, optional
Minimum acceptable return of the investor. Annual threshold over which
returns are considered positive or negative. It is converted to a
value appropriate for the period of the returns for this ratio.
E.g. An annual minimum acceptable return of 100 translates to a daily
minimum acceptable return of 0.01848.
(1 + 100) ** (1. / 252) - 1 = 0.01848
Daily returns must exceed this value to be considered positive. The
daily return yields the desired annual return when compounded over
the average number of business days in a year.
(1 + 0.01848) ** 252 - 1 = 99.93
- Defaults to 0.0
Returns
-------
float
Omega ratio.
Note
-----
See https://en.wikipedia.org/wiki/Omega_ratio for more details.
"""
return ep.omega_ratio(returns,
required_return=annual_return_threshhold)
@deprecated(msg=DEPRECATION_WARNING)
def sortino_ratio(returns, required_return=0, period=DAILY):
"""
Determines the Sortino ratio of a strategy.
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
required_return: float / series
minimum acceptable return
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
depends on input type
series ==> float
DataFrame ==> np.array
Annualized Sortino ratio.
"""
return ep.sortino_ratio(returns, required_return=required_return)
@deprecated(msg=DEPRECATION_WARNING)
def downside_risk(returns, required_return=0, period=DAILY):
"""
Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
required_return: float / series
minimum acceptable return
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
depends on input type
series ==> float
DataFrame ==> np.array
Annualized downside deviation
"""
return ep.downside_risk(returns,
required_return=required_return,
period=period)
@deprecated(msg=DEPRECATION_WARNING)
def sharpe_ratio(returns, risk_free=0, period=DAILY):
"""
Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
risk_free : int, float
Constant risk-free return throughout the period.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Sharpe ratio.
np.nan
If insufficient length of returns or if if adjusted returns are 0.
Note
-----
See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.
"""
return ep.sharpe_ratio(returns, risk_free=risk_free, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def alpha_beta(returns, factor_returns):
"""
Calculates both alpha and beta.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Alpha.
float
Beta.
"""
return ep.alpha_beta(returns, factor_returns=factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def alpha(returns, factor_returns):
"""
Calculates annualized alpha.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Alpha.
"""
return ep.alpha(returns, factor_returns=factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def beta(returns, factor_returns):
"""
Calculates beta.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Beta.
"""
return ep.beta(returns, factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def stability_of_timeseries(returns):
"""
Determines R-squared of a linear fit to the cumulative
log returns. Computes an ordinary least squares linear fit,
and returns R-squared.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
R-squared.
"""
return ep.stability_of_timeseries(returns)
@deprecated(msg=DEPRECATION_WARNING)
def tail_ratio(returns):
"""
Determines the ratio between the right (95%) and left tail (5%).
For example, a ratio of 0.25 means that losses are four times
as bad as profits.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
tail ratio
"""
return ep.tail_ratio(returns)
def common_sense_ratio(returns):
"""
Common sense ratio is the multiplication of the tail ratio and the
Gain-to-Pain-Ratio -- sum(profits) / sum(losses).
See http://bit.ly/1ORzGBk for more information on motivation of
this metric.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
float
common sense ratio
"""
return ep.tail_ratio(returns) * \
(1 + ep.annual_return(returns))
def normalize(returns, starting_value=1):
"""
Normalizes a returns timeseries based on the first value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
starting_value : float, optional
The starting returns (default 1).
Returns
-------
pd.Series
Normalized returns.
"""
return starting_value * (returns / returns.iloc[0])
@deprecated(msg=DEPRECATION_WARNING)
def cum_returns(returns, starting_value=0):
"""
Compute cumulative returns from simple returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
starting_value : float, optional
The starting returns (default 1).
Returns
-------
pandas.Series
Series of cumulative returns.
Notes
-----
For increased numerical accuracy, convert input to log returns
where it is possible to sum instead of multiplying.
"""
return ep.cum_returns(returns, starting_value=starting_value)
@deprecated(msg=DEPRECATION_WARNING)
def aggregate_returns(returns, convert_to):
"""
Aggregates returns by week, month, or year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
convert_to : str
Can be 'weekly', 'monthly', or 'yearly'.
Returns
-------
pd.Series
Aggregated returns.
"""
return ep.aggregate_returns(returns, convert_to=convert_to)
def rolling_beta(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6):
"""
Determines the rolling beta of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series or pd.DataFrame
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- If DataFrame is passed, computes rolling beta for each column.
- This is in the same style as returns.
rolling_window : int, optional
The size of the rolling window, in days, over which to compute
beta (default 6 months).
Returns
-------
pd.Series
Rolling beta.
Note
-----
See https://en.wikipedia.org/wiki/Beta_(finance) for more details.
"""
if factor_returns.ndim > 1:
# Apply column-wise
return factor_returns.apply(partial(rolling_beta, returns),
rolling_window=rolling_window)
else:
out = pd.Series(index=returns.index)
for beg, end in zip(returns.index[0:-rolling_window],
returns.index[rolling_window:]):
out.loc[end] = ep.beta(
returns.loc[beg:end],
factor_returns.loc[beg:end])
return out
def rolling_regression(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
nan_threshold=0.1):
"""
Computes rolling factor betas using a multivariate linear regression
(separate linear regressions is problematic because the factors may be
confounded).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.DataFrame
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- Computes rolling beta for each column.
- This is in the same style as returns.
rolling_window : int, optional
The days window over which to compute the beta. Defaults to 6 months.
nan_threshold : float, optional
If there are more than this fraction of NaNs, the rolling regression
for the given date will be skipped.
Returns
-------
pandas.DataFrame
DataFrame containing rolling beta coefficients to SMB, HML and UMD
"""
# We need to drop NaNs to regress
ret_no_na = returns.dropna()
columns = ['alpha'] + factor_returns.columns.tolist()
rolling_risk = pd.DataFrame(columns=columns,
index=ret_no_na.index)
rolling_risk.index.name = 'dt'
for beg, end in zip(ret_no_na.index[:-rolling_window],
ret_no_na.index[rolling_window:]):
returns_period = ret_no_na[beg:end]
factor_returns_period = factor_returns.loc[returns_period.index]
if np.all(factor_returns_period.isnull().mean()) < nan_threshold:
factor_returns_period_dnan = factor_returns_period.dropna()
reg = linear_model.LinearRegression(fit_intercept=True).fit(
factor_returns_period_dnan,
returns_period.loc[factor_returns_period_dnan.index])
rolling_risk.loc[end, factor_returns.columns] = reg.coef_
rolling_risk.loc[end, 'alpha'] = reg.intercept_
return rolling_risk
def gross_lev(positions):
"""
Calculates the gross leverage of a strategy.
Parameters
----------
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
pd.Series
Gross leverage.
"""
exposure = positions.drop('cash', axis=1).abs().sum(axis=1)
return exposure / positions.sum(axis=1)
def value_at_risk(returns, period=None, sigma=2.0):
"""
Get value at risk (VaR).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
period : str, optional
Period over which to calculate VaR. Set to 'weekly',
'monthly', or 'yearly', otherwise defaults to period of
returns (typically daily).
sigma : float, optional
Standard deviations of VaR, default 2.
"""
if period is not None:
returns_agg = ep.aggregate_returns(returns, period)
else:
returns_agg = returns.copy()
value_at_risk = returns_agg.mean() - sigma * returns_agg.std()
return value_at_risk
SIMPLE_STAT_FUNCS = [
ep.annual_return,
ep.cum_returns_final,
ep.annual_volatility,
ep.sharpe_ratio,
ep.calmar_ratio,
ep.stability_of_timeseries,
ep.max_drawdown,
ep.omega_ratio,
ep.sortino_ratio,
stats.skew,
stats.kurtosis,
ep.tail_ratio,
value_at_risk
]
FACTOR_STAT_FUNCS = [
ep.alpha,
ep.beta,
]
STAT_FUNC_NAMES = {
'annual_return': 'Annual return',
'cum_returns_final': 'Cumulative returns',
'annual_volatility': 'Annual volatility',
'sharpe_ratio': 'Sharpe ratio',
'calmar_ratio': 'Calmar ratio',
'stability_of_timeseries': 'Stability',
'max_drawdown': 'Max drawdown',
'omega_ratio': 'Omega ratio',
'sortino_ratio': 'Sortino ratio',
'skew': 'Skew',
'kurtosis': 'Kurtosis',
'tail_ratio': 'Tail ratio',
'common_sense_ratio': 'Common sense ratio',
'value_at_risk': 'Daily value at risk',
'alpha': 'Alpha',
'beta': 'Beta',
}
def perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB'):
"""
Calculates various performance metrics of a strategy, for use in
plotting.show_perf_stats.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in tears.create_full_tear_sheet.
turnover_denom : str
Either AGB or portfolio_value, default AGB.
- See full explanation in txn.get_turnover.
Returns
-------
pd.Series
Performance metrics.
"""
stats = pd.Series()
for stat_func in SIMPLE_STAT_FUNCS:
stats[STAT_FUNC_NAMES[stat_func.__name__]] = stat_func(returns)
if positions is not None:
stats['Gross leverage'] = gross_lev(positions).mean()
if transactions is not None:
stats['Daily turnover'] = get_turnover(positions,
transactions,
turnover_denom).mean()
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
res = stat_func(returns, factor_returns)
stats[STAT_FUNC_NAMES[stat_func.__name__]] = res
return stats
def perf_stats_bootstrap(returns, factor_returns=None, return_stats=True,
**kwargs):
"""Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
return_stats : boolean (optional)
If True, returns a DataFrame of mean, median, 5 and 95 percentiles
for each perf metric.
If False, returns a DataFrame with the bootstrap samples for
each perf metric.
Returns
-------
pd.DataFrame
if return_stats is True:
- Distributional statistics of bootstrapped sampling
distribution of performance metrics.
if return_stats is False:
- Bootstrap samples for each performance metric.
"""
bootstrap_values = OrderedDict()
for stat_func in SIMPLE_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(stat_func,
returns)
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(
stat_func,
returns,
factor_returns=factor_returns)
bootstrap_values = pd.DataFrame(bootstrap_values)
if return_stats:
stats = bootstrap_values.apply(calc_distribution_stats)
return stats.T[['mean', 'median', '5%', '95%']]
else:
return bootstrap_values
def calc_bootstrap(func, returns, *args, **kwargs):
"""Performs a bootstrap analysis on a user-defined function returning
a summary statistic.
Parameters
----------
func : function
Function that either takes a single array (commonly returns)
or two arrays (commonly returns and factor returns) and
returns a single value (commonly a summary
statistic). Additional args and kwargs are passed as well.
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
n_samples : int, optional
Number of bootstrap samples to draw. Default is 1000.
Increasing this will lead to more stable / accurate estimates.
Returns
-------
numpy.ndarray
Bootstrapped sampling distribution of passed in func.
"""
n_samples = kwargs.pop('n_samples', 1000)
out = np.empty(n_samples)
factor_returns = kwargs.pop('factor_returns', None)
for i in range(n_samples):
idx = np.random.randint(len(returns), size=len(returns))
returns_i = returns.iloc[idx].reset_index(drop=True)
if factor_returns is not None:
factor_returns_i = factor_returns.iloc[idx].reset_index(drop=True)
out[i] = func(returns_i, factor_returns_i,
*args, **kwargs)
else:
out[i] = func(returns_i,
*args, **kwargs)
return out
def calc_distribution_stats(x):
"""Calculate various summary statistics of data.
Parameters
----------
x : numpy.ndarray or pandas.Series
Array to compute summary statistics for.
Returns
-------
pandas.Series
Series containing mean, median, std, as well as 5, 25, 75 and
95 percentiles of passed in values.
"""
return pd.Series({'mean': np.mean(x),
'median': np.median(x),
'std': np.std(x),
'5%': np.percentile(x, 5),
'25%': np.percentile(x, 25),
'75%': np.percentile(x, 75),
'95%': np.percentile(x, 95),
'IQR': np.subtract.reduce(
np.percentile(x, [75, 25])),
})
def get_max_drawdown_underwater(underwater):
"""
Determines peak, valley, and recovery dates given an 'underwater'
DataFrame.
An underwater DataFrame is a DataFrame that has precomputed
rolling drawdown.
Parameters
----------
underwater : pd.Series
Underwater returns (rolling drawdown) of a strategy.
Returns
-------
peak : datetime
The maximum drawdown's peak.
valley : datetime
The maximum drawdown's valley.
recovery : datetime
The maximum drawdown's recovery.
"""
valley = underwater.index[np.argmin(underwater) - 1] # end of the period
# Find first 0
peak = underwater[:valley][underwater[:valley] == 0].index[-1]
# Find last 0
try:
recovery = underwater[valley:][underwater[valley:] == 0].index[0]
except IndexError:
recovery = np.nan # drawdown not recovered
return peak, valley, recovery
def get_max_drawdown(returns):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
Maximum drawdown.
Note
-----
See https://en.wikipedia.org/wiki/Drawdown_(economics) for more details.
"""
returns = returns.copy()
df_cum = cum_returns(returns, 1.0)
running_max = np.maximum.accumulate(df_cum)
underwater = df_cum / running_max - 1
return get_max_drawdown_underwater(underwater)
def get_top_drawdowns(returns, top=10):
"""
Finds top drawdowns, sorted by drawdown amount.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of top drawdowns to find (default 10).
Returns
-------
drawdowns : list
List of drawdown peaks, valleys, and recoveries. See get_max_drawdown.
"""
returns = returns.copy()
df_cum = ep.cum_returns(returns, 1.0)
running_max = np.maximum.accumulate(df_cum)
underwater = df_cum / running_max - 1
drawdowns = []
for _ in range(top):
peak, valley, recovery = get_max_drawdown_underwater(underwater)
# Slice out draw-down period
if not pd.isnull(recovery):
underwater.drop(underwater[peak: recovery].index[1:-1],
inplace=True)
else:
# drawdown has not ended yet
underwater = underwater.loc[:peak]
drawdowns.append((peak, valley, recovery))
if ((len(returns) == 0)
or (len(underwater) == 0)
or (np.min(underwater) == 0)):
break
return drawdowns
def gen_drawdown_table(returns, top=10):
"""
Places top drawdowns in a table.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of top drawdowns to find (default 10).
Returns
-------
df_drawdowns : pd.DataFrame
Information about top drawdowns.
"""
df_cum = ep.cum_returns(returns, 1.0)
drawdown_periods = get_top_drawdowns(returns, top=top)
df_drawdowns = pd.DataFrame(index=list(range(top)),
columns=['Net drawdown in %',
'Peak date',
'Valley date',
'Recovery date',
'Duration'])
for i, (peak, valley, recovery) in enumerate(drawdown_periods):
if pd.isnull(recovery):
df_drawdowns.loc[i, 'Duration'] = np.nan
else:
df_drawdowns.loc[i, 'Duration'] = len(pd.date_range(peak,
recovery,
freq='B'))
df_drawdowns.loc[i, 'Peak date'] = (peak.to_pydatetime()
.strftime('%Y-%m-%d'))
df_drawdowns.loc[i, 'Valley date'] = (valley.to_pydatetime()
.strftime('%Y-%m-%d'))
if isinstance(recovery, float):
df_drawdowns.loc[i, 'Recovery date'] = recovery
else:
df_drawdowns.loc[i, 'Recovery date'] = (recovery.to_pydatetime()
.strftime('%Y-%m-%d'))
df_drawdowns.loc[i, 'Net drawdown in %'] = (
(df_cum.loc[peak] - df_cum.loc[valley]) / df_cum.loc[peak]) * 100
df_drawdowns['Peak date'] = pd.to_datetime(df_drawdowns['Peak date'])
df_drawdowns['Valley date'] = pd.to_datetime(df_drawdowns['Valley date'])
df_drawdowns['Recovery date'] = pd.to_datetime(
df_drawdowns['Recovery date'])
return df_drawdowns
def rolling_volatility(returns, rolling_vol_window):
"""
Determines the rolling volatility of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_vol_window : int
Length of rolling window, in days, over which to compute.
Returns
-------
pd.Series
Rolling volatility.
"""
return returns.rolling(rolling_vol_window).std() \
* np.sqrt(APPROX_BDAYS_PER_YEAR)
def rolling_sharpe(returns, rolling_sharpe_window):
"""
Determines the rolling Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_sharpe_window : int
Length of rolling window, in days, over which to compute.
Returns
-------
pd.Series
Rolling Sharpe ratio.
Note
-----
See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.
"""
return returns.rolling(rolling_sharpe_window).mean() \
/ returns.rolling(rolling_sharpe_window).std() \
* np.sqrt(APPROX_BDAYS_PER_YEAR)
def simulate_paths(is_returns, num_days,
starting_value=1, num_samples=1000, random_seed=None):
"""
Gnerate alternate paths using available values from in-sample returns.
Parameters
----------
is_returns : pandas.core.frame.DataFrame
Non-cumulative in-sample returns.
num_days : int
Number of days to project the probability cone forward.
starting_value : int or float
Starting value of the out of sample period.
num_samples : int
Number of samples to draw from the in-sample daily returns.
Each sample will be an array with length num_days.
A higher number of samples will generate a more accurate
bootstrap cone.
random_seed : int
Seed for the pseudorandom number generator used by the pandas
sample method.
Returns
-------
samples : numpy.ndarray
"""
samples = np.empty((num_samples, num_days))
seed = np.random.RandomState(seed=random_seed)
for i in range(num_samples):
samples[i, :] = is_returns.sample(num_days, replace=True,
random_state=seed)
return samples
def summarize_paths(samples, cone_std=(1., 1.5, 2.), starting_value=1.):
"""
Gnerate the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns.
Parameters
----------
samples : numpy.ndarray
Alternative paths, or series of possible outcomes.
cone_std : list of int/float
Number of standard devations to use in the boundaries of
the cone. If multiple values are passed, cone bounds will
be generated for each value.
Returns
-------
samples : pandas.core.frame.DataFrame
"""
cum_samples = ep.cum_returns(samples.T,
starting_value=starting_value).T
cum_mean = cum_samples.mean(axis=0)
cum_std = cum_samples.std(axis=0)
if isinstance(cone_std, (float, int)):
cone_std = [cone_std]
cone_bounds = pd.DataFrame(columns=pd.Float64Index([]))
for num_std in cone_std:
cone_bounds.loc[:, float(num_std)] = cum_mean + cum_std * num_std
cone_bounds.loc[:, float(-num_std)] = cum_mean - cum_std * num_std
return cone_bounds
def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
starting_value=1, num_samples=1000,
random_seed=None):
"""
Determines the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Future cumulative mean and
standard devation are computed by repeatedly sampling from the
in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
meaning it does not assume that returns are normally distributed.
Parameters
----------
is_returns : pd.Series
In-sample daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
num_days : int
Number of days to project the probability cone forward.
cone_std : int, float, or list of int/float
Number of standard devations to use in the boundaries of
the cone. If multiple values are passed, cone bounds will
be generated for each value.
starting_value : int or float
Starting value of the out of sample period.
num_samples : int
Number of samples to draw from the in-sample daily returns.
Each sample will be an array with length num_days.
A higher number of samples will generate a more accurate
bootstrap cone.
random_seed : int
Seed for the pseudorandom number generator used by the pandas
sample method.
Returns
-------
pd.DataFrame
Contains upper and lower cone boundaries. Column names are
strings corresponding to the number of standard devations
above (positive) or below (negative) the projected mean
cumulative returns.
"""
samples = simulate_paths(
is_returns=is_returns,
num_days=num_days,
starting_value=starting_value,
num_samples=num_samples,
random_seed=random_seed
)
cone_bounds = summarize_paths(
samples=samples,
cone_std=cone_std,
starting_value=starting_value
)
return cone_bounds
def extract_interesting_date_ranges(returns, periods=None):
"""
Extracts returns based on interesting events. See
gen_date_range_interesting.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
ranges : OrderedDict
Date ranges, with returns, of all valid events.
"""
if periods is None:
periods = PERIODS
returns_dupe = returns.copy()
returns_dupe.index = returns_dupe.index.map(pd.Timestamp)
ranges = OrderedDict()
for name, (start, end) in periods.items():
try:
period = returns_dupe.loc[start:end]
if len(period) == 0:
continue
ranges[name] = period
except BaseException:
continue
return ranges | pyfolio/timeseries.py | from __future__ import division
from collections import OrderedDict
from functools import partial
import empyrical as ep
import numpy as np
import pandas as pd
import scipy as sp
import scipy.stats as stats
from sklearn import linear_model
from .deprecate import deprecated
from .interesting_periods import PERIODS
from .txn import get_turnover
from .utils import APPROX_BDAYS_PER_MONTH, APPROX_BDAYS_PER_YEAR
from .utils import DAILY
DEPRECATION_WARNING = ("Risk functions in pyfolio.timeseries are deprecated "
"and will be removed in a future release. Please "
"install the empyrical package instead.")
def var_cov_var_normal(P, c, mu=0, sigma=1):
"""
Variance-covariance calculation of daily Value-at-Risk in a
portfolio.
Parameters
----------
P : float
Portfolio value.
c : float
Confidence level.
mu : float, optional
Mean.
Returns
-------
float
Variance-covariance.
"""
alpha = sp.stats.norm.ppf(1 - c, mu, sigma)
return P - P * (alpha + 1)
@deprecated(msg=DEPRECATION_WARNING)
def max_drawdown(returns):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
float
Maximum drawdown.
Note
-----
See https://en.wikipedia.org/wiki/Drawdown_(economics) for more details.
"""
return ep.max_drawdown(returns)
@deprecated(msg=DEPRECATION_WARNING)
def annual_return(returns, period=DAILY):
"""
Determines the mean annual growth rate of returns.
Parameters
----------
returns : pd.Series
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Annual Return as CAGR (Compounded Annual Growth Rate).
"""
return ep.annual_return(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def annual_volatility(returns, period=DAILY):
"""
Determines the annual volatility of a strategy.
Parameters
----------
returns : pd.Series
Periodic returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing volatility. Can be 'monthly' or 'weekly' or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Annual volatility.
"""
return ep.annual_volatility(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def calmar_ratio(returns, period=DAILY):
"""
Determines the Calmar ratio, or drawdown ratio, of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Calmar ratio (drawdown ratio) as float. Returns np.nan if there is no
calmar ratio.
Note
-----
See https://en.wikipedia.org/wiki/Calmar_ratio for more details.
"""
return ep.calmar_ratio(returns, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def omega_ratio(returns, annual_return_threshhold=0.0):
"""
Determines the Omega ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
annual_return_threshold : float, optional
Minimum acceptable return of the investor. Annual threshold over which
returns are considered positive or negative. It is converted to a
value appropriate for the period of the returns for this ratio.
E.g. An annual minimum acceptable return of 100 translates to a daily
minimum acceptable return of 0.01848.
(1 + 100) ** (1. / 252) - 1 = 0.01848
Daily returns must exceed this value to be considered positive. The
daily return yields the desired annual return when compounded over
the average number of business days in a year.
(1 + 0.01848) ** 252 - 1 = 99.93
- Defaults to 0.0
Returns
-------
float
Omega ratio.
Note
-----
See https://en.wikipedia.org/wiki/Omega_ratio for more details.
"""
return ep.omega_ratio(returns,
required_return=annual_return_threshhold)
@deprecated(msg=DEPRECATION_WARNING)
def sortino_ratio(returns, required_return=0, period=DAILY):
"""
Determines the Sortino ratio of a strategy.
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
required_return: float / series
minimum acceptable return
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
depends on input type
series ==> float
DataFrame ==> np.array
Annualized Sortino ratio.
"""
return ep.sortino_ratio(returns, required_return=required_return)
@deprecated(msg=DEPRECATION_WARNING)
def downside_risk(returns, required_return=0, period=DAILY):
"""
Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
required_return: float / series
minimum acceptable return
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
depends on input type
series ==> float
DataFrame ==> np.array
Annualized downside deviation
"""
return ep.downside_risk(returns,
required_return=required_return,
period=period)
@deprecated(msg=DEPRECATION_WARNING)
def sharpe_ratio(returns, risk_free=0, period=DAILY):
"""
Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
risk_free : int, float
Constant risk-free return throughout the period.
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Can be 'monthly', 'weekly', or 'daily'.
- Defaults to 'daily'.
Returns
-------
float
Sharpe ratio.
np.nan
If insufficient length of returns or if if adjusted returns are 0.
Note
-----
See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.
"""
return ep.sharpe_ratio(returns, risk_free=risk_free, period=period)
@deprecated(msg=DEPRECATION_WARNING)
def alpha_beta(returns, factor_returns):
"""
Calculates both alpha and beta.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Alpha.
float
Beta.
"""
return ep.alpha_beta(returns, factor_returns=factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def alpha(returns, factor_returns):
"""
Calculates annualized alpha.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Alpha.
"""
return ep.alpha(returns, factor_returns=factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def beta(returns, factor_returns):
"""
Calculates beta.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
factor_returns : pd.Series
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
Returns
-------
float
Beta.
"""
return ep.beta(returns, factor_returns)
@deprecated(msg=DEPRECATION_WARNING)
def stability_of_timeseries(returns):
"""
Determines R-squared of a linear fit to the cumulative
log returns. Computes an ordinary least squares linear fit,
and returns R-squared.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
R-squared.
"""
return ep.stability_of_timeseries(returns)
@deprecated(msg=DEPRECATION_WARNING)
def tail_ratio(returns):
"""
Determines the ratio between the right (95%) and left tail (5%).
For example, a ratio of 0.25 means that losses are four times
as bad as profits.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
tail ratio
"""
return ep.tail_ratio(returns)
def common_sense_ratio(returns):
"""
Common sense ratio is the multiplication of the tail ratio and the
Gain-to-Pain-Ratio -- sum(profits) / sum(losses).
See http://bit.ly/1ORzGBk for more information on motivation of
this metric.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
float
common sense ratio
"""
return ep.tail_ratio(returns) * \
(1 + ep.annual_return(returns))
def normalize(returns, starting_value=1):
"""
Normalizes a returns timeseries based on the first value.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
starting_value : float, optional
The starting returns (default 1).
Returns
-------
pd.Series
Normalized returns.
"""
return starting_value * (returns / returns.iloc[0])
@deprecated(msg=DEPRECATION_WARNING)
def cum_returns(returns, starting_value=0):
"""
Compute cumulative returns from simple returns.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
starting_value : float, optional
The starting returns (default 1).
Returns
-------
pandas.Series
Series of cumulative returns.
Notes
-----
For increased numerical accuracy, convert input to log returns
where it is possible to sum instead of multiplying.
"""
return ep.cum_returns(returns, starting_value=starting_value)
@deprecated(msg=DEPRECATION_WARNING)
def aggregate_returns(returns, convert_to):
"""
Aggregates returns by week, month, or year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
convert_to : str
Can be 'weekly', 'monthly', or 'yearly'.
Returns
-------
pd.Series
Aggregated returns.
"""
return ep.aggregate_returns(returns, convert_to=convert_to)
def rolling_beta(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6):
"""
Determines the rolling beta of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series or pd.DataFrame
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- If DataFrame is passed, computes rolling beta for each column.
- This is in the same style as returns.
rolling_window : int, optional
The size of the rolling window, in days, over which to compute
beta (default 6 months).
Returns
-------
pd.Series
Rolling beta.
Note
-----
See https://en.wikipedia.org/wiki/Beta_(finance) for more details.
"""
if factor_returns.ndim > 1:
# Apply column-wise
return factor_returns.apply(partial(rolling_beta, returns),
rolling_window=rolling_window)
else:
out = pd.Series(index=returns.index)
for beg, end in zip(returns.index[0:-rolling_window],
returns.index[rolling_window:]):
out.loc[end] = ep.beta(
returns.loc[beg:end],
factor_returns.loc[beg:end])
return out
def rolling_regression(returns, factor_returns,
rolling_window=APPROX_BDAYS_PER_MONTH * 6,
nan_threshold=0.1):
"""
Computes rolling factor betas using a multivariate linear regression
(separate linear regressions is problematic because the factors may be
confounded).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.DataFrame
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- Computes rolling beta for each column.
- This is in the same style as returns.
rolling_window : int, optional
The days window over which to compute the beta. Defaults to 6 months.
nan_threshold : float, optional
If there are more than this fraction of NaNs, the rolling regression
for the given date will be skipped.
Returns
-------
pandas.DataFrame
DataFrame containing rolling beta coefficients to SMB, HML and UMD
"""
# We need to drop NaNs to regress
ret_no_na = returns.dropna()
columns = ['alpha'] + factor_returns.columns.tolist()
rolling_risk = pd.DataFrame(columns=columns,
index=ret_no_na.index)
rolling_risk.index.name = 'dt'
for beg, end in zip(ret_no_na.index[:-rolling_window],
ret_no_na.index[rolling_window:]):
returns_period = ret_no_na[beg:end]
factor_returns_period = factor_returns.loc[returns_period.index]
if np.all(factor_returns_period.isnull().mean()) < nan_threshold:
factor_returns_period_dnan = factor_returns_period.dropna()
reg = linear_model.LinearRegression(fit_intercept=True).fit(
factor_returns_period_dnan,
returns_period.loc[factor_returns_period_dnan.index])
rolling_risk.loc[end, factor_returns.columns] = reg.coef_
rolling_risk.loc[end, 'alpha'] = reg.intercept_
return rolling_risk
def gross_lev(positions):
"""
Calculates the gross leverage of a strategy.
Parameters
----------
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
pd.Series
Gross leverage.
"""
exposure = positions.drop('cash', axis=1).abs().sum(axis=1)
return exposure / positions.sum(axis=1)
def value_at_risk(returns, period=None, sigma=2.0):
"""
Get value at risk (VaR).
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
period : str, optional
Period over which to calculate VaR. Set to 'weekly',
'monthly', or 'yearly', otherwise defaults to period of
returns (typically daily).
sigma : float, optional
Standard deviations of VaR, default 2.
"""
if period is not None:
returns_agg = ep.aggregate_returns(returns, period)
else:
returns_agg = returns.copy()
value_at_risk = returns_agg.mean() - sigma * returns_agg.std()
return value_at_risk
SIMPLE_STAT_FUNCS = [
ep.annual_return,
ep.cum_returns_final,
ep.annual_volatility,
ep.sharpe_ratio,
ep.calmar_ratio,
ep.stability_of_timeseries,
ep.max_drawdown,
ep.omega_ratio,
ep.sortino_ratio,
stats.skew,
stats.kurtosis,
ep.tail_ratio,
value_at_risk
]
FACTOR_STAT_FUNCS = [
ep.alpha,
ep.beta,
]
STAT_FUNC_NAMES = {
'annual_return': 'Annual return',
'cum_returns_final': 'Cumulative returns',
'annual_volatility': 'Annual volatility',
'sharpe_ratio': 'Sharpe ratio',
'calmar_ratio': 'Calmar ratio',
'stability_of_timeseries': 'Stability',
'max_drawdown': 'Max drawdown',
'omega_ratio': 'Omega ratio',
'sortino_ratio': 'Sortino ratio',
'skew': 'Skew',
'kurtosis': 'Kurtosis',
'tail_ratio': 'Tail ratio',
'common_sense_ratio': 'Common sense ratio',
'value_at_risk': 'Daily value at risk',
'alpha': 'Alpha',
'beta': 'Beta',
}
def perf_stats(returns, factor_returns=None, positions=None,
transactions=None, turnover_denom='AGB'):
"""
Calculates various performance metrics of a strategy, for use in
plotting.show_perf_stats.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
positions : pd.DataFrame
Daily net position values.
- See full explanation in tears.create_full_tear_sheet.
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in tears.create_full_tear_sheet.
turnover_denom : str
Either AGB or portfolio_value, default AGB.
- See full explanation in txn.get_turnover.
Returns
-------
pd.Series
Performance metrics.
"""
stats = pd.Series()
for stat_func in SIMPLE_STAT_FUNCS:
stats[STAT_FUNC_NAMES[stat_func.__name__]] = stat_func(returns)
if positions is not None:
stats['Gross leverage'] = gross_lev(positions).mean()
if transactions is not None:
stats['Daily turnover'] = get_turnover(positions,
transactions,
turnover_denom).mean()
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
res = stat_func(returns, factor_returns)
stats[STAT_FUNC_NAMES[stat_func.__name__]] = res
return stats
def perf_stats_bootstrap(returns, factor_returns=None, return_stats=True,
**kwargs):
"""Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
return_stats : boolean (optional)
If True, returns a DataFrame of mean, median, 5 and 95 percentiles
for each perf metric.
If False, returns a DataFrame with the bootstrap samples for
each perf metric.
Returns
-------
pd.DataFrame
if return_stats is True:
- Distributional statistics of bootstrapped sampling
distribution of performance metrics.
if return_stats is False:
- Bootstrap samples for each performance metric.
"""
bootstrap_values = OrderedDict()
for stat_func in SIMPLE_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(stat_func,
returns)
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(
stat_func,
returns,
factor_returns=factor_returns)
bootstrap_values = pd.DataFrame(bootstrap_values)
if return_stats:
stats = bootstrap_values.apply(calc_distribution_stats)
return stats.T[['mean', 'median', '5%', '95%']]
else:
return bootstrap_values
def calc_bootstrap(func, returns, *args, **kwargs):
"""Performs a bootstrap analysis on a user-defined function returning
a summary statistic.
Parameters
----------
func : function
Function that either takes a single array (commonly returns)
or two arrays (commonly returns and factor returns) and
returns a single value (commonly a summary
statistic). Additional args and kwargs are passed as well.
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
n_samples : int, optional
Number of bootstrap samples to draw. Default is 1000.
Increasing this will lead to more stable / accurate estimates.
Returns
-------
numpy.ndarray
Bootstrapped sampling distribution of passed in func.
"""
n_samples = kwargs.pop('n_samples', 1000)
out = np.empty(n_samples)
factor_returns = kwargs.pop('factor_returns', None)
for i in range(n_samples):
idx = np.random.randint(len(returns), size=len(returns))
returns_i = returns.iloc[idx].reset_index(drop=True)
if factor_returns is not None:
factor_returns_i = factor_returns.iloc[idx].reset_index(drop=True)
out[i] = func(returns_i, factor_returns_i,
*args, **kwargs)
else:
out[i] = func(returns_i,
*args, **kwargs)
return out
def calc_distribution_stats(x):
"""Calculate various summary statistics of data.
Parameters
----------
x : numpy.ndarray or pandas.Series
Array to compute summary statistics for.
Returns
-------
pandas.Series
Series containing mean, median, std, as well as 5, 25, 75 and
95 percentiles of passed in values.
"""
return pd.Series({'mean': np.mean(x),
'median': np.median(x),
'std': np.std(x),
'5%': np.percentile(x, 5),
'25%': np.percentile(x, 25),
'75%': np.percentile(x, 75),
'95%': np.percentile(x, 95),
'IQR': np.subtract.reduce(
np.percentile(x, [75, 25])),
})
def get_max_drawdown_underwater(underwater):
"""
Determines peak, valley, and recovery dates given an 'underwater'
DataFrame.
An underwater DataFrame is a DataFrame that has precomputed
rolling drawdown.
Parameters
----------
underwater : pd.Series
Underwater returns (rolling drawdown) of a strategy.
Returns
-------
peak : datetime
The maximum drawdown's peak.
valley : datetime
The maximum drawdown's valley.
recovery : datetime
The maximum drawdown's recovery.
"""
valley = underwater.index[np.argmin(underwater) - 1] # end of the period
# Find first 0
peak = underwater[:valley][underwater[:valley] == 0].index[-1]
# Find last 0
try:
recovery = underwater[valley:][underwater[valley:] == 0].index[0]
except IndexError:
recovery = np.nan # drawdown not recovered
return peak, valley, recovery
def get_max_drawdown(returns):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~pyfolio.timeseries.cum_returns`.
Returns
-------
float
Maximum drawdown.
Note
-----
See https://en.wikipedia.org/wiki/Drawdown_(economics) for more details.
"""
returns = returns.copy()
df_cum = cum_returns(returns, 1.0)
running_max = np.maximum.accumulate(df_cum)
underwater = df_cum / running_max - 1
return get_max_drawdown_underwater(underwater)
def get_top_drawdowns(returns, top=10):
"""
Finds top drawdowns, sorted by drawdown amount.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of top drawdowns to find (default 10).
Returns
-------
drawdowns : list
List of drawdown peaks, valleys, and recoveries. See get_max_drawdown.
"""
returns = returns.copy()
df_cum = ep.cum_returns(returns, 1.0)
running_max = np.maximum.accumulate(df_cum)
underwater = df_cum / running_max - 1
drawdowns = []
for _ in range(top):
peak, valley, recovery = get_max_drawdown_underwater(underwater)
# Slice out draw-down period
if not pd.isnull(recovery):
underwater.drop(underwater[peak: recovery].index[1:-1],
inplace=True)
else:
# drawdown has not ended yet
underwater = underwater.loc[:peak]
drawdowns.append((peak, valley, recovery))
if ((len(returns) == 0)
or (len(underwater) == 0)
or (np.min(underwater) == 0)):
break
return drawdowns
def gen_drawdown_table(returns, top=10):
"""
Places top drawdowns in a table.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
top : int, optional
The amount of top drawdowns to find (default 10).
Returns
-------
df_drawdowns : pd.DataFrame
Information about top drawdowns.
"""
df_cum = ep.cum_returns(returns, 1.0)
drawdown_periods = get_top_drawdowns(returns, top=top)
df_drawdowns = pd.DataFrame(index=list(range(top)),
columns=['Net drawdown in %',
'Peak date',
'Valley date',
'Recovery date',
'Duration'])
for i, (peak, valley, recovery) in enumerate(drawdown_periods):
if pd.isnull(recovery):
df_drawdowns.loc[i, 'Duration'] = np.nan
else:
df_drawdowns.loc[i, 'Duration'] = len(pd.date_range(peak,
recovery,
freq='B'))
df_drawdowns.loc[i, 'Peak date'] = (peak.to_pydatetime()
.strftime('%Y-%m-%d'))
df_drawdowns.loc[i, 'Valley date'] = (valley.to_pydatetime()
.strftime('%Y-%m-%d'))
if isinstance(recovery, float):
df_drawdowns.loc[i, 'Recovery date'] = recovery
else:
df_drawdowns.loc[i, 'Recovery date'] = (recovery.to_pydatetime()
.strftime('%Y-%m-%d'))
df_drawdowns.loc[i, 'Net drawdown in %'] = (
(df_cum.loc[peak] - df_cum.loc[valley]) / df_cum.loc[peak]) * 100
df_drawdowns['Peak date'] = pd.to_datetime(df_drawdowns['Peak date'])
df_drawdowns['Valley date'] = pd.to_datetime(df_drawdowns['Valley date'])
df_drawdowns['Recovery date'] = pd.to_datetime(
df_drawdowns['Recovery date'])
return df_drawdowns
def rolling_volatility(returns, rolling_vol_window):
"""
Determines the rolling volatility of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_vol_window : int
Length of rolling window, in days, over which to compute.
Returns
-------
pd.Series
Rolling volatility.
"""
return returns.rolling(rolling_vol_window).std() \
* np.sqrt(APPROX_BDAYS_PER_YEAR)
def rolling_sharpe(returns, rolling_sharpe_window):
"""
Determines the rolling Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
rolling_sharpe_window : int
Length of rolling window, in days, over which to compute.
Returns
-------
pd.Series
Rolling Sharpe ratio.
Note
-----
See https://en.wikipedia.org/wiki/Sharpe_ratio for more details.
"""
return returns.rolling(rolling_sharpe_window).mean() \
/ returns.rolling(rolling_sharpe_window).std() \
* np.sqrt(APPROX_BDAYS_PER_YEAR)
def simulate_paths(is_returns, num_days,
starting_value=1, num_samples=1000, random_seed=None):
"""
Gnerate alternate paths using available values from in-sample returns.
Parameters
----------
is_returns : pandas.core.frame.DataFrame
Non-cumulative in-sample returns.
num_days : int
Number of days to project the probability cone forward.
starting_value : int or float
Starting value of the out of sample period.
num_samples : int
Number of samples to draw from the in-sample daily returns.
Each sample will be an array with length num_days.
A higher number of samples will generate a more accurate
bootstrap cone.
random_seed : int
Seed for the pseudorandom number generator used by the pandas
sample method.
Returns
-------
samples : numpy.ndarray
"""
samples = np.empty((num_samples, num_days))
seed = np.random.RandomState(seed=random_seed)
for i in range(num_samples):
samples[i, :] = is_returns.sample(num_days, replace=True,
random_state=seed)
return samples
def summarize_paths(samples, cone_std=(1., 1.5, 2.), starting_value=1.):
"""
Gnerate the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns.
Parameters
----------
samples : numpy.ndarray
Alternative paths, or series of possible outcomes.
cone_std : list of int/float
Number of standard devations to use in the boundaries of
the cone. If multiple values are passed, cone bounds will
be generated for each value.
Returns
-------
samples : pandas.core.frame.DataFrame
"""
cum_samples = ep.cum_returns(samples.T,
starting_value=starting_value).T
cum_mean = cum_samples.mean(axis=0)
cum_std = cum_samples.std(axis=0)
if isinstance(cone_std, (float, int)):
cone_std = [cone_std]
cone_bounds = pd.DataFrame(columns=pd.Float64Index([]))
for num_std in cone_std:
cone_bounds.loc[:, float(num_std)] = cum_mean + cum_std * num_std
cone_bounds.loc[:, float(-num_std)] = cum_mean - cum_std * num_std
return cone_bounds
def forecast_cone_bootstrap(is_returns, num_days, cone_std=(1., 1.5, 2.),
starting_value=1, num_samples=1000,
random_seed=None):
"""
Determines the upper and lower bounds of an n standard deviation
cone of forecasted cumulative returns. Future cumulative mean and
standard devation are computed by repeatedly sampling from the
in-sample daily returns (i.e. bootstrap). This cone is non-parametric,
meaning it does not assume that returns are normally distributed.
Parameters
----------
is_returns : pd.Series
In-sample daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
num_days : int
Number of days to project the probability cone forward.
cone_std : int, float, or list of int/float
Number of standard devations to use in the boundaries of
the cone. If multiple values are passed, cone bounds will
be generated for each value.
starting_value : int or float
Starting value of the out of sample period.
num_samples : int
Number of samples to draw from the in-sample daily returns.
Each sample will be an array with length num_days.
A higher number of samples will generate a more accurate
bootstrap cone.
random_seed : int
Seed for the pseudorandom number generator used by the pandas
sample method.
Returns
-------
pd.DataFrame
Contains upper and lower cone boundaries. Column names are
strings corresponding to the number of standard devations
above (positive) or below (negative) the projected mean
cumulative returns.
"""
samples = simulate_paths(
is_returns=is_returns,
num_days=num_days,
starting_value=starting_value,
num_samples=num_samples,
random_seed=random_seed
)
cone_bounds = summarize_paths(
samples=samples,
cone_std=cone_std,
starting_value=starting_value
)
return cone_bounds
def extract_interesting_date_ranges(returns, periods=None):
"""
Extracts returns based on interesting events. See
gen_date_range_interesting.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
Returns
-------
ranges : OrderedDict
Date ranges, with returns, of all valid events.
"""
if periods is None:
periods = PERIODS
returns_dupe = returns.copy()
returns_dupe.index = returns_dupe.index.map(pd.Timestamp)
ranges = OrderedDict()
for name, (start, end) in periods.items():
try:
period = returns_dupe.loc[start:end]
if len(period) == 0:
continue
ranges[name] = period
except BaseException:
continue
return ranges | 0.921988 | 0.536859 |
import datetime
def get_spreadsheet_element(key="test_key", title="title"):
d = datetime.datetime(2015, 7, 18)
data = (
'<ns0:entry xmlns:ns0="http://www.w3.org/2005/Atom">'
'<ns0:id>https://spreadsheets.google.com/feeds/spreadsheets/'
'private/full/{key}</ns0:id>'
'<ns0:updated>{update_year}-{update_month:02d}-{update_day:02d}'
'T05:29:31.140Z</ns0:updated>'
'<ns0:category scheme="http://schemas.google.com/spreadsheets/'
'2006" term="http://schemas.google.com/spreadsheets/'
'2006#spreadsheet" />'
'<ns0:title type="text">{title}</ns0:title>'
'<ns0:content type="text">{title}</ns0:content>'
'<ns0:link href="https://spreadsheets.google.com/feeds/worksheets/'
'{key}/private/full" rel="http://schemas.google.com/spreadsheets/'
'2006#worksheetsfeed" type="application/atom+xml" />'
'<ns0:link href="https://docs.google.com/spreadsheets/d/{key}/'
'edit" rel="alternate" type="text/html" />'
'<ns0:link href="https://spreadsheets.google.com/feeds/'
'spreadsheets/private/full/{key}" rel="self" '
'type="application/atom+xml" />'
'<ns0:author>'
'<ns0:name>{username}</ns0:name>'
'<ns0:email>{mail}</ns0:email>'
'</ns0:author>'
'</ns0:entry>'
.format(key=key,
mail="<EMAIL>",
username="fake",
update_year=d.year,
update_month=d.month,
update_day=d.day,
title=title))
return data.encode()
def get_worksheet_entry(key, sheet_title, encode=True):
open_tag = ("<entry>" if not encode else
"<entry xmlns='http://www.w3.org/2005/Atom'"
" xmlns:gs='http://schemas.google.com/spreadsheets/2006'>"
)
content = (
"{open_tag}"
"<id>https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full/{id}</id>"
"<updated>2015-07-18T05:29:31.112Z</updated>"
"<category scheme='http://schemas.google.com/spreadsheets/"
"2006' term='http://schemas.google.com/spreadsheets/2006#"
"worksheet'/>"
"<title type='text'>{sheet_title}</title>"
"<content type='text'>{sheet_title}</content>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"listfeed' type='application/atom+xml' href='https://spreadshe"
"ets.google.com/feeds/list/{key}/{id}/private/full'/>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"cellsfeed' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/cells/{key}/{id}/private/full'/>"
"<link rel='http://schemas.google.com/visualization/2008#"
"visualizationApi' type='application/atom+xml' href='"
"https://docs.google.com/spreadsheets/d/{key}/gviz/tq?gid=0'/>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"exportcsv' type='text/csv' href='https://docs.google.com/"
"spreadsheets/d/{key}/export?gid=0&format=csv'/>"
"<link rel='self' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/worksheets/{key}/private/full/"
"{id}'/>"
"<link rel='edit' type='application/atom+xml' href='"
"https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full/{id}/{version}'/>"
"<gs:colCount>{col_count}</gs:colCount>"
"<gs:rowCount>{row_count}</gs:rowCount>"
"</entry>"
.format(open_tag=open_tag, key=key, col_count=2, row_count=2,
sheet_title=sheet_title, id="od6", version="CCCC")
)
return content.encode() if encode else content
def get_worksheets_feed(key, sheet_names=["title"]):
entries = "".join(get_worksheet_entry(key, t, encode=False) for t in sheet_names)
d = datetime.datetime(2015, 7, 18)
data = (
"<?xml version='1.0' encoding='UTF-8'?>"
"<feed xmlns='http://www.w3.org/2005/Atom'"
" xmlns:openSearch='http://a9.com/-/spec/opensearchrss/1.0/'"
" xmlns:gs='http://schemas.google.com/spreadsheets/2006'>"
"<id>https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full</id>"
'<updated>{update_year}-{update_month:02d}-{update_day:02d}'
'T05:29:31.140Z</updated>'
"<category scheme='http://schemas.google.com/spreadsheets/2006' "
"term='http://schemas.google.com/spreadsheets/2006#worksheet'/>"
"<title type='text'>{title}</title>"
"<link rel='alternate' type='application/atom+xml' href='"
"https://docs.google.com/spreadsheets/d/{key}/edit'/>"
"<link rel='http://schemas.google.com/g/2005#feed' type="
"'application/atom+xml' href='https://spreadsheets.google.com/"
"feeds/worksheets/{key}/private/full'/>"
"<link rel='http://schemas.google.com/g/2005#post' type='"
"application/atom+xml' href='https://spreadsheets.google.com/feeds"
"/worksheets/{key}/private/full'/>"
"<link rel='self' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/worksheets/{key}/private/full'/>"
"<author>"
"<name>{username}</name>"
"<email>{mail}</email>"
"</author>"
"<openSearch:totalResults>{results}</openSearch:totalResults>"
"<openSearch:startIndex>1</openSearch:startIndex>"
"{entries}"
"</feed>"
.format(key=key,
mail="<EMAIL>",
username="fake",
update_year=d.year,
update_month=d.month,
update_day=d.day,
title="title",
entries=entries,
results=len(sheet_names)))
return data.encode() | test/api_content.py | import datetime
def get_spreadsheet_element(key="test_key", title="title"):
d = datetime.datetime(2015, 7, 18)
data = (
'<ns0:entry xmlns:ns0="http://www.w3.org/2005/Atom">'
'<ns0:id>https://spreadsheets.google.com/feeds/spreadsheets/'
'private/full/{key}</ns0:id>'
'<ns0:updated>{update_year}-{update_month:02d}-{update_day:02d}'
'T05:29:31.140Z</ns0:updated>'
'<ns0:category scheme="http://schemas.google.com/spreadsheets/'
'2006" term="http://schemas.google.com/spreadsheets/'
'2006#spreadsheet" />'
'<ns0:title type="text">{title}</ns0:title>'
'<ns0:content type="text">{title}</ns0:content>'
'<ns0:link href="https://spreadsheets.google.com/feeds/worksheets/'
'{key}/private/full" rel="http://schemas.google.com/spreadsheets/'
'2006#worksheetsfeed" type="application/atom+xml" />'
'<ns0:link href="https://docs.google.com/spreadsheets/d/{key}/'
'edit" rel="alternate" type="text/html" />'
'<ns0:link href="https://spreadsheets.google.com/feeds/'
'spreadsheets/private/full/{key}" rel="self" '
'type="application/atom+xml" />'
'<ns0:author>'
'<ns0:name>{username}</ns0:name>'
'<ns0:email>{mail}</ns0:email>'
'</ns0:author>'
'</ns0:entry>'
.format(key=key,
mail="<EMAIL>",
username="fake",
update_year=d.year,
update_month=d.month,
update_day=d.day,
title=title))
return data.encode()
def get_worksheet_entry(key, sheet_title, encode=True):
open_tag = ("<entry>" if not encode else
"<entry xmlns='http://www.w3.org/2005/Atom'"
" xmlns:gs='http://schemas.google.com/spreadsheets/2006'>"
)
content = (
"{open_tag}"
"<id>https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full/{id}</id>"
"<updated>2015-07-18T05:29:31.112Z</updated>"
"<category scheme='http://schemas.google.com/spreadsheets/"
"2006' term='http://schemas.google.com/spreadsheets/2006#"
"worksheet'/>"
"<title type='text'>{sheet_title}</title>"
"<content type='text'>{sheet_title}</content>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"listfeed' type='application/atom+xml' href='https://spreadshe"
"ets.google.com/feeds/list/{key}/{id}/private/full'/>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"cellsfeed' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/cells/{key}/{id}/private/full'/>"
"<link rel='http://schemas.google.com/visualization/2008#"
"visualizationApi' type='application/atom+xml' href='"
"https://docs.google.com/spreadsheets/d/{key}/gviz/tq?gid=0'/>"
"<link rel='http://schemas.google.com/spreadsheets/2006#"
"exportcsv' type='text/csv' href='https://docs.google.com/"
"spreadsheets/d/{key}/export?gid=0&format=csv'/>"
"<link rel='self' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/worksheets/{key}/private/full/"
"{id}'/>"
"<link rel='edit' type='application/atom+xml' href='"
"https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full/{id}/{version}'/>"
"<gs:colCount>{col_count}</gs:colCount>"
"<gs:rowCount>{row_count}</gs:rowCount>"
"</entry>"
.format(open_tag=open_tag, key=key, col_count=2, row_count=2,
sheet_title=sheet_title, id="od6", version="CCCC")
)
return content.encode() if encode else content
def get_worksheets_feed(key, sheet_names=["title"]):
entries = "".join(get_worksheet_entry(key, t, encode=False) for t in sheet_names)
d = datetime.datetime(2015, 7, 18)
data = (
"<?xml version='1.0' encoding='UTF-8'?>"
"<feed xmlns='http://www.w3.org/2005/Atom'"
" xmlns:openSearch='http://a9.com/-/spec/opensearchrss/1.0/'"
" xmlns:gs='http://schemas.google.com/spreadsheets/2006'>"
"<id>https://spreadsheets.google.com/feeds/worksheets/{key}/"
"private/full</id>"
'<updated>{update_year}-{update_month:02d}-{update_day:02d}'
'T05:29:31.140Z</updated>'
"<category scheme='http://schemas.google.com/spreadsheets/2006' "
"term='http://schemas.google.com/spreadsheets/2006#worksheet'/>"
"<title type='text'>{title}</title>"
"<link rel='alternate' type='application/atom+xml' href='"
"https://docs.google.com/spreadsheets/d/{key}/edit'/>"
"<link rel='http://schemas.google.com/g/2005#feed' type="
"'application/atom+xml' href='https://spreadsheets.google.com/"
"feeds/worksheets/{key}/private/full'/>"
"<link rel='http://schemas.google.com/g/2005#post' type='"
"application/atom+xml' href='https://spreadsheets.google.com/feeds"
"/worksheets/{key}/private/full'/>"
"<link rel='self' type='application/atom+xml' href='https://"
"spreadsheets.google.com/feeds/worksheets/{key}/private/full'/>"
"<author>"
"<name>{username}</name>"
"<email>{mail}</email>"
"</author>"
"<openSearch:totalResults>{results}</openSearch:totalResults>"
"<openSearch:startIndex>1</openSearch:startIndex>"
"{entries}"
"</feed>"
.format(key=key,
mail="<EMAIL>",
username="fake",
update_year=d.year,
update_month=d.month,
update_day=d.day,
title="title",
entries=entries,
results=len(sheet_names)))
return data.encode() | 0.461988 | 0.23535 |
import logging
from multiprocessing.pool import ThreadPool
import click
import dask
import pandas as pd
from dask.diagnostics import ProgressBar
from kartothek.cli._cleanup import cleanup
from kartothek.cli._copy import copy
from kartothek.cli._delete import delete
from kartothek.cli._index import index
from kartothek.cli._info import info
from kartothek.cli._query import query
from kartothek.cli._stats import stats
from kartothek.cli._utils import get_cube, get_store
__all__ = ("cli",)
@click.group(context_settings=dict(help_option_names=["-h", "--help"]))
@click.option(
"--skv", default="skv.yml", help="Storefact config file.", show_default=True
)
@click.option("--store", default="dataset", help="Store to use.", show_default=True)
@click.option(
"--n_threads",
"-j",
default=0,
type=int,
help="Number of threads to use (use 0 for number of cores).",
show_default=True,
)
@click.option(
"--color",
type=click.Choice(["always", "auto", "off"]),
default="auto",
help="Whether to use colorized outputs or not. Use ``always``, ``auto`` (default), or ``off``.",
show_default=True,
)
@click.argument("cube")
@click.pass_context
def cli(ctx, store, cube, skv, n_threads, color):
"""
Execute certain operations on the given Kartothek cube.
If possible, the operations will be performed in parallel on the current machine.
"""
ctx.ensure_object(dict)
store_obj = get_store(skv, store)
cube, datasets = get_cube(store_obj, cube)
dask.config.set(scheduler="threads")
if n_threads > 0:
dask.config.set(pool=ThreadPool(n_threads))
if color == "always":
ctx.color = True
elif color == "off":
ctx.color = False
pbar = ProgressBar()
pbar.register()
ctx.call_on_close(pbar.unregister)
# silence extremely verbose azure logging
azure_logger = logging.getLogger("azure.storage.common.storageclient")
azure_logger.setLevel(logging.FATAL)
# pandas perf tuning
chained_assignment_old = pd.options.mode.chained_assignment
def reset_pd():
pd.options.mode.chained_assignment = chained_assignment_old
ctx.call_on_close(reset_pd)
pd.options.mode.chained_assignment = None
ctx.obj["skv"] = skv
ctx.obj["store"] = store_obj
ctx.obj["store_name"] = store
ctx.obj["cube"] = cube
ctx.obj["datasets"] = datasets
ctx.obj["pbar"] = pbar
cli.command()(cleanup)
cli.command()(copy)
cli.command()(delete)
cli.command()(index)
cli.command()(info)
cli.command()(query)
cli.command()(stats)
if __name__ == "__main__":
cli() | kartothek/cli/__init__.py | import logging
from multiprocessing.pool import ThreadPool
import click
import dask
import pandas as pd
from dask.diagnostics import ProgressBar
from kartothek.cli._cleanup import cleanup
from kartothek.cli._copy import copy
from kartothek.cli._delete import delete
from kartothek.cli._index import index
from kartothek.cli._info import info
from kartothek.cli._query import query
from kartothek.cli._stats import stats
from kartothek.cli._utils import get_cube, get_store
__all__ = ("cli",)
@click.group(context_settings=dict(help_option_names=["-h", "--help"]))
@click.option(
"--skv", default="skv.yml", help="Storefact config file.", show_default=True
)
@click.option("--store", default="dataset", help="Store to use.", show_default=True)
@click.option(
"--n_threads",
"-j",
default=0,
type=int,
help="Number of threads to use (use 0 for number of cores).",
show_default=True,
)
@click.option(
"--color",
type=click.Choice(["always", "auto", "off"]),
default="auto",
help="Whether to use colorized outputs or not. Use ``always``, ``auto`` (default), or ``off``.",
show_default=True,
)
@click.argument("cube")
@click.pass_context
def cli(ctx, store, cube, skv, n_threads, color):
"""
Execute certain operations on the given Kartothek cube.
If possible, the operations will be performed in parallel on the current machine.
"""
ctx.ensure_object(dict)
store_obj = get_store(skv, store)
cube, datasets = get_cube(store_obj, cube)
dask.config.set(scheduler="threads")
if n_threads > 0:
dask.config.set(pool=ThreadPool(n_threads))
if color == "always":
ctx.color = True
elif color == "off":
ctx.color = False
pbar = ProgressBar()
pbar.register()
ctx.call_on_close(pbar.unregister)
# silence extremely verbose azure logging
azure_logger = logging.getLogger("azure.storage.common.storageclient")
azure_logger.setLevel(logging.FATAL)
# pandas perf tuning
chained_assignment_old = pd.options.mode.chained_assignment
def reset_pd():
pd.options.mode.chained_assignment = chained_assignment_old
ctx.call_on_close(reset_pd)
pd.options.mode.chained_assignment = None
ctx.obj["skv"] = skv
ctx.obj["store"] = store_obj
ctx.obj["store_name"] = store
ctx.obj["cube"] = cube
ctx.obj["datasets"] = datasets
ctx.obj["pbar"] = pbar
cli.command()(cleanup)
cli.command()(copy)
cli.command()(delete)
cli.command()(index)
cli.command()(info)
cli.command()(query)
cli.command()(stats)
if __name__ == "__main__":
cli() | 0.566978 | 0.138987 |
from shutil import rmtree
from tempfile import mkdtemp
from unittest import TestCase
from opwen_email_server.services.auth import AzureAuth
from opwen_email_server.services.auth import BasicAuth
from opwen_email_server.services.storage import AzureTextStorage
class BasicAuthTests(TestCase):
def setUp(self):
self._auth = BasicAuth({
'user1': {'password': '<PASSWORD>', 'scopes': {'scope1', 'scopeA'}},
'user2': {'password': '<PASSWORD>'},
})
def test_with_bad_user(self):
self.assertIsNone(self._auth(username='', password='<PASSWORD>'))
def test_with_missing_user(self):
self.assertIsNone(self._auth(username='does-not-exist', password='<PASSWORD>'))
def test_with_bad_password(self):
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>'))
def test_with_missing_scope(self):
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope2']))
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1', 'scope2']))
def test_with_correct_password(self):
self.assertIsNotNone(self._auth(username='user2', password='<PASSWORD>'))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>'))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1']))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1', 'scopeA']))
class AzureAuthTests(TestCase):
def setUp(self):
self._folder = mkdtemp()
self._auth = AzureAuth(storage=AzureTextStorage(
account=self._folder,
key='key',
container='auth',
provider='LOCAL',
))
def tearDown(self):
rmtree(self._folder)
def test_inserts_and_retrieves_client(self):
self._auth.insert('client', 'domain')
self.assertEqual(self._auth.domain_for('client'), 'domain')
self.assertEqual(self._auth.client_id_for('domain'), 'client')
self.assertIsNone(self._auth.domain_for('unknown-client'))
self.assertIsNone(self._auth.client_id_for('unknown-client')) | tests/opwen_email_server/services/test_auth.py | from shutil import rmtree
from tempfile import mkdtemp
from unittest import TestCase
from opwen_email_server.services.auth import AzureAuth
from opwen_email_server.services.auth import BasicAuth
from opwen_email_server.services.storage import AzureTextStorage
class BasicAuthTests(TestCase):
def setUp(self):
self._auth = BasicAuth({
'user1': {'password': '<PASSWORD>', 'scopes': {'scope1', 'scopeA'}},
'user2': {'password': '<PASSWORD>'},
})
def test_with_bad_user(self):
self.assertIsNone(self._auth(username='', password='<PASSWORD>'))
def test_with_missing_user(self):
self.assertIsNone(self._auth(username='does-not-exist', password='<PASSWORD>'))
def test_with_bad_password(self):
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>'))
def test_with_missing_scope(self):
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope2']))
self.assertIsNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1', 'scope2']))
def test_with_correct_password(self):
self.assertIsNotNone(self._auth(username='user2', password='<PASSWORD>'))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>'))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1']))
self.assertIsNotNone(self._auth(username='user1', password='<PASSWORD>', required_scopes=['scope1', 'scopeA']))
class AzureAuthTests(TestCase):
def setUp(self):
self._folder = mkdtemp()
self._auth = AzureAuth(storage=AzureTextStorage(
account=self._folder,
key='key',
container='auth',
provider='LOCAL',
))
def tearDown(self):
rmtree(self._folder)
def test_inserts_and_retrieves_client(self):
self._auth.insert('client', 'domain')
self.assertEqual(self._auth.domain_for('client'), 'domain')
self.assertEqual(self._auth.client_id_for('domain'), 'client')
self.assertIsNone(self._auth.domain_for('unknown-client'))
self.assertIsNone(self._auth.client_id_for('unknown-client')) | 0.421195 | 0.314366 |
import logging
from openpnm import topotools
from openpnm.network import DelaunayVoronoiDual
logger = logging.getLogger(__name__)
__all__ = ['Delaunay']
class Delaunay(DelaunayVoronoiDual):
r"""
Random network formed by Delaunay tessellation of arbitrary base points
Parameters
----------
points : array_like or int
Can either be an N-by-3 array of point coordinates which will be used,
or a scalar value indicating the number of points to generate
shape : array_like
The size of the domain. It's possible to create cubic as well as 2D
square domains by changing the ``shape`` as follows:
[x, y, z]
will produce a normal cubic domain of dimension x, and and z
[x, y, 0]
will produce a 2D square domain of size x by y
name : str
An optional name for the object to help identify it. If not given,
one will be generated.
See Also
--------
Gabriel
Voronoi
DelaunayVoronoiDual
Notes
-----
This class always performs the tessellation on the full set of points, then
trims any points that lie outside the given domain ``shape``.
Examples
--------
.. plot::
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
# Supplying custom specified points
pts = np.random.rand(200, 3)
gn = op.network.Delaunay(points=pts, shape=[1, 1, 1])
# Check the number of pores in 'gn'
print(gn.Np)
# Which can be quickly visualized using
fig, ax = plt.subplots(figsize=(5, 5))
op.topotools.plot_connections(network=gn, ax=ax)
plt.show()
Upon visualization it can be seen that this network is not very cubic.
There are a few ways to combat this, but none will make a truly square
domain. Points can be generated that lie outside the domain ``shape``
and they will be automatically trimmed.
.. plot::
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
# Must have more points for same density
pts = np.random.rand(300, 3)*1.2 - 0.1
gn = op.network.Delaunay(points=pts, shape=[1, 1, 1])
# Confirm base points have been trimmed
print(gn.Np < 300)
# And visualizing
fig, ax = plt.subplots(figsize=(5, 5))
op.topotools.plot_connections(network=gn, ax=ax)
plt.show()
If a domain with random base points but flat faces is needed use
``Voronoi``.
"""
def __init__(self, shape=[1, 1, 1], points=None, **kwargs):
# Clean-up input points
points = self._parse_points(shape=shape, points=points)
super().__init__(shape=shape, points=points, **kwargs)
# Initialize network object
topotools.trim(network=self, pores=self.pores(['voronoi']))
pop = ['pore.voronoi', 'throat.voronoi', 'throat.interconnect',
'pore.delaunay', 'throat.delaunay']
for item in pop:
del self[item]
# Trim additional pores that are missed by the parent class's trimming
Ps = topotools.isoutside(coords=self['pore.coords'], shape=shape)
topotools.trim(network=self, pores=Ps) | openpnm/network/_delaunay.py | import logging
from openpnm import topotools
from openpnm.network import DelaunayVoronoiDual
logger = logging.getLogger(__name__)
__all__ = ['Delaunay']
class Delaunay(DelaunayVoronoiDual):
r"""
Random network formed by Delaunay tessellation of arbitrary base points
Parameters
----------
points : array_like or int
Can either be an N-by-3 array of point coordinates which will be used,
or a scalar value indicating the number of points to generate
shape : array_like
The size of the domain. It's possible to create cubic as well as 2D
square domains by changing the ``shape`` as follows:
[x, y, z]
will produce a normal cubic domain of dimension x, and and z
[x, y, 0]
will produce a 2D square domain of size x by y
name : str
An optional name for the object to help identify it. If not given,
one will be generated.
See Also
--------
Gabriel
Voronoi
DelaunayVoronoiDual
Notes
-----
This class always performs the tessellation on the full set of points, then
trims any points that lie outside the given domain ``shape``.
Examples
--------
.. plot::
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
# Supplying custom specified points
pts = np.random.rand(200, 3)
gn = op.network.Delaunay(points=pts, shape=[1, 1, 1])
# Check the number of pores in 'gn'
print(gn.Np)
# Which can be quickly visualized using
fig, ax = plt.subplots(figsize=(5, 5))
op.topotools.plot_connections(network=gn, ax=ax)
plt.show()
Upon visualization it can be seen that this network is not very cubic.
There are a few ways to combat this, but none will make a truly square
domain. Points can be generated that lie outside the domain ``shape``
and they will be automatically trimmed.
.. plot::
import numpy as np
import openpnm as op
import matplotlib.pyplot as plt
# Must have more points for same density
pts = np.random.rand(300, 3)*1.2 - 0.1
gn = op.network.Delaunay(points=pts, shape=[1, 1, 1])
# Confirm base points have been trimmed
print(gn.Np < 300)
# And visualizing
fig, ax = plt.subplots(figsize=(5, 5))
op.topotools.plot_connections(network=gn, ax=ax)
plt.show()
If a domain with random base points but flat faces is needed use
``Voronoi``.
"""
def __init__(self, shape=[1, 1, 1], points=None, **kwargs):
# Clean-up input points
points = self._parse_points(shape=shape, points=points)
super().__init__(shape=shape, points=points, **kwargs)
# Initialize network object
topotools.trim(network=self, pores=self.pores(['voronoi']))
pop = ['pore.voronoi', 'throat.voronoi', 'throat.interconnect',
'pore.delaunay', 'throat.delaunay']
for item in pop:
del self[item]
# Trim additional pores that are missed by the parent class's trimming
Ps = topotools.isoutside(coords=self['pore.coords'], shape=shape)
topotools.trim(network=self, pores=Ps) | 0.919254 | 0.563138 |
from django.conf import settings
from django.core.exceptions import ValidationError
from django.utils.translation import gettext_lazy as _
from django.core.exceptions import FieldDoesNotExist
from moneyed import CURRENCIES
import common.models
import re
def validate_currency_code(code):
"""
Check that a given code is a valid currency code.
"""
if code not in CURRENCIES:
raise ValidationError(_('Not a valid currency code'))
def allowable_url_schemes():
""" Return the list of allowable URL schemes.
In addition to the default schemes allowed by Django,
the install configuration file (config.yaml) can specify
extra schemas """
# Default schemes
schemes = ['http', 'https', 'ftp', 'ftps']
extra = settings.EXTRA_URL_SCHEMES
for e in extra:
if e.lower() not in schemes:
schemes.append(e.lower())
return schemes
def validate_part_name(value):
""" Prevent some illegal characters in part names.
"""
for c in ['|', '#', '$', '{', '}']:
if c in str(value):
raise ValidationError(
_('Invalid character in part name')
)
def validate_part_ipn(value):
""" Validate the Part IPN against regex rule """
pattern = common.models.InvenTreeSetting.get_setting('PART_IPN_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('IPN must match regex pattern {pat}').format(pat=pattern))
def validate_build_order_reference(value):
"""
Validate the 'reference' field of a BuildOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('BUILDORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_purchase_order_reference(value):
"""
Validate the 'reference' field of a PurchaseOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('PURCHASEORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_sales_order_reference(value):
"""
Validate the 'reference' field of a SalesOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('SALESORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_tree_name(value):
""" Prevent illegal characters in tree item names """
for c in "!@#$%^&*'\"\\/[]{}<>,|+=~`\"":
if c in str(value):
raise ValidationError(_('Illegal character in name ({x})'.format(x=c)))
def validate_overage(value):
""" Validate that a BOM overage string is properly formatted.
An overage string can look like:
- An integer number ('1' / 3 / 4)
- A percentage ('5%' / '10 %')
"""
value = str(value).lower().strip()
# First look for a simple integer value
try:
i = int(value)
if i < 0:
raise ValidationError(_("Overage value must not be negative"))
# Looks like an integer!
return True
except ValueError:
pass
# Now look for a percentage value
if value.endswith('%'):
v = value[:-1].strip()
# Does it look like a number?
try:
f = float(v)
if f < 0:
raise ValidationError(_("Overage value must not be negative"))
elif f > 100:
raise ValidationError(_("Overage must not exceed 100%"))
return True
except ValueError:
pass
raise ValidationError(
_("Overage must be an integer value or a percentage")
)
def validate_part_name_format(self):
"""
Validate part name format.
Make sure that each template container has a field of Part Model
"""
jinja_template_regex = re.compile('{{.*?}}')
field_name_regex = re.compile('(?<=part\\.)[A-z]+')
for jinja_template in jinja_template_regex.findall(str(self)):
# make sure at least one and only one field is present inside the parser
field_names = field_name_regex.findall(jinja_template)
if len(field_names) < 1:
raise ValidationError({
'value': 'At least one field must be present inside a jinja template container i.e {{}}'
})
# Make sure that the field_name exists in Part model
from part.models import Part
for field_name in field_names:
try:
Part._meta.get_field(field_name)
except FieldDoesNotExist:
raise ValidationError({
'value': f'{field_name} does not exist in Part Model'
})
return True | InvenTree/InvenTree/validators.py | from django.conf import settings
from django.core.exceptions import ValidationError
from django.utils.translation import gettext_lazy as _
from django.core.exceptions import FieldDoesNotExist
from moneyed import CURRENCIES
import common.models
import re
def validate_currency_code(code):
"""
Check that a given code is a valid currency code.
"""
if code not in CURRENCIES:
raise ValidationError(_('Not a valid currency code'))
def allowable_url_schemes():
""" Return the list of allowable URL schemes.
In addition to the default schemes allowed by Django,
the install configuration file (config.yaml) can specify
extra schemas """
# Default schemes
schemes = ['http', 'https', 'ftp', 'ftps']
extra = settings.EXTRA_URL_SCHEMES
for e in extra:
if e.lower() not in schemes:
schemes.append(e.lower())
return schemes
def validate_part_name(value):
""" Prevent some illegal characters in part names.
"""
for c in ['|', '#', '$', '{', '}']:
if c in str(value):
raise ValidationError(
_('Invalid character in part name')
)
def validate_part_ipn(value):
""" Validate the Part IPN against regex rule """
pattern = common.models.InvenTreeSetting.get_setting('PART_IPN_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('IPN must match regex pattern {pat}').format(pat=pattern))
def validate_build_order_reference(value):
"""
Validate the 'reference' field of a BuildOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('BUILDORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_purchase_order_reference(value):
"""
Validate the 'reference' field of a PurchaseOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('PURCHASEORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_sales_order_reference(value):
"""
Validate the 'reference' field of a SalesOrder
"""
pattern = common.models.InvenTreeSetting.get_setting('SALESORDER_REFERENCE_REGEX')
if pattern:
match = re.search(pattern, value)
if match is None:
raise ValidationError(_('Reference must match pattern {pattern}').format(pattern=pattern))
def validate_tree_name(value):
""" Prevent illegal characters in tree item names """
for c in "!@#$%^&*'\"\\/[]{}<>,|+=~`\"":
if c in str(value):
raise ValidationError(_('Illegal character in name ({x})'.format(x=c)))
def validate_overage(value):
""" Validate that a BOM overage string is properly formatted.
An overage string can look like:
- An integer number ('1' / 3 / 4)
- A percentage ('5%' / '10 %')
"""
value = str(value).lower().strip()
# First look for a simple integer value
try:
i = int(value)
if i < 0:
raise ValidationError(_("Overage value must not be negative"))
# Looks like an integer!
return True
except ValueError:
pass
# Now look for a percentage value
if value.endswith('%'):
v = value[:-1].strip()
# Does it look like a number?
try:
f = float(v)
if f < 0:
raise ValidationError(_("Overage value must not be negative"))
elif f > 100:
raise ValidationError(_("Overage must not exceed 100%"))
return True
except ValueError:
pass
raise ValidationError(
_("Overage must be an integer value or a percentage")
)
def validate_part_name_format(self):
"""
Validate part name format.
Make sure that each template container has a field of Part Model
"""
jinja_template_regex = re.compile('{{.*?}}')
field_name_regex = re.compile('(?<=part\\.)[A-z]+')
for jinja_template in jinja_template_regex.findall(str(self)):
# make sure at least one and only one field is present inside the parser
field_names = field_name_regex.findall(jinja_template)
if len(field_names) < 1:
raise ValidationError({
'value': 'At least one field must be present inside a jinja template container i.e {{}}'
})
# Make sure that the field_name exists in Part model
from part.models import Part
for field_name in field_names:
try:
Part._meta.get_field(field_name)
except FieldDoesNotExist:
raise ValidationError({
'value': f'{field_name} does not exist in Part Model'
})
return True | 0.625667 | 0.321833 |
import tvm
from tvm import tir
from tvm.ir import Range
from tvm.script import ty, from_source
from tvm.ir.diagnostics import override_renderer
@tvm.script.tir
def matmul(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
with tir.init():
C[vi, vj] = tir.float32(0)
C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
@tvm.script.tir
def matmul_original(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
for i, j in tir.grid(32, 32):
with tir.block([32, 32], "init") as [vi, vj]:
for ii, jj in tir.grid(4, 4):
C[vi * 4 + ii, vj * 4 + jj] = tir.float32(0)
for k in range(0, 32):
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
for ii, jj, kk in tir.grid(4, 4, 4):
C[vi * 4 + ii, vj * 4 + jj] = (
C[vi * 4 + ii, vj * 4 + jj]
+ A[vi * 4 + ii, vk * 4 + kk] * B[vj * 4 + jj, vk * 4 + kk]
)
@tvm.script.tir
def elementwise_with_root(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
def func_with_opaque_block(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([]) as []:
B[0, 0] = A[0, 0] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
@tvm.script.tir
def func_with_part_access_region(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
tir.reads(A[vi, vj])
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
tir.writes(C[vi, vj])
C[vi, vj] = B[vi, vj] + tir.float32(1)
def test_complete_matmul():
func = matmul
A, B, C = [func.buffer_map[x] for x in func.params]
block = func.body.block.body.body.body.body.block
assert isinstance(block, tvm.tir.Block)
vi, vj, vk = [x.var for x in block.iter_vars]
access_A = tir.BufferRegion(A, [Range.from_min_extent(vi, 1), Range.from_min_extent(vk, 1)])
access_B = tir.BufferRegion(B, [Range.from_min_extent(vj, 1), Range.from_min_extent(vk, 1)])
access_C = tir.BufferRegion(C, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])
tvm.ir.assert_structural_equal(block.reads, [access_C, access_A, access_B])
tvm.ir.assert_structural_equal(block.writes, [access_C])
def test_complete_matmul_original():
func = matmul_original
A, B, C = [func.buffer_map[x] for x in func.params]
block1 = func.body.block.body.body.body[0].block
assert isinstance(block1, tvm.tir.Block)
vi, vj = [x.var for x in block1.iter_vars]
access_C = tir.BufferRegion(
C, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vj * 4, 4)]
)
tvm.ir.assert_structural_equal(block1.reads, [])
tvm.ir.assert_structural_equal(block1.writes, [access_C])
block2 = func.body.block.body.body.body[1].body.block
assert isinstance(block2, tvm.tir.Block)
vi, vj, vk = [x.var for x in block2.iter_vars]
access_A = tir.BufferRegion(
A, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vk * 4, 4)]
)
access_B = tir.BufferRegion(
B, [Range.from_min_extent(vj * 4, 4), Range.from_min_extent(vk * 4, 4)]
)
access_C = tir.BufferRegion(
C, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vj * 4, 4)]
)
tvm.ir.assert_structural_equal(block2.reads, [access_C, access_A, access_B])
tvm.ir.assert_structural_equal(block2.writes, [access_C])
def _check_elementwise(func):
A, B, C = [func.buffer_map[x] for x in func.params]
block1 = func.body.block.body[0].body.body.block
assert isinstance(block1, tvm.tir.Block)
vi, vj = [x.var for x in block1.iter_vars]
tvm.ir.assert_structural_equal(
block1.reads,
[tir.BufferRegion(A, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
tvm.ir.assert_structural_equal(
block1.writes,
[tir.BufferRegion(B, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
block2 = func.body.block.body[1].body.body.block
assert isinstance(block2, tvm.tir.Block)
vi, vj = [x.var for x in block2.iter_vars]
tvm.ir.assert_structural_equal(
block2.reads,
[tir.BufferRegion(B, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
tvm.ir.assert_structural_equal(
block2.writes,
[tir.BufferRegion(C, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
def test_complete_with_root():
_check_elementwise(elementwise_with_root)
def test_complete_part_region():
_check_elementwise(func_with_part_access_region)
def test_complete_opaque_block_error():
def render(e):
pass
override_renderer(render)
try:
from_source(func_with_opaque_block)
except tvm.error.DiagnosticError:
return
assert False
if __name__ == "__main__":
test_complete_matmul()
test_complete_matmul_original()
test_complete_with_root()
test_complete_opaque_block_error()
test_complete_part_region() | tests/python/unittest/test_tvmscript_complete.py |
import tvm
from tvm import tir
from tvm.ir import Range
from tvm.script import ty, from_source
from tvm.ir.diagnostics import override_renderer
@tvm.script.tir
def matmul(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
with tir.init():
C[vi, vj] = tir.float32(0)
C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
@tvm.script.tir
def matmul_original(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
for i, j in tir.grid(32, 32):
with tir.block([32, 32], "init") as [vi, vj]:
for ii, jj in tir.grid(4, 4):
C[vi * 4 + ii, vj * 4 + jj] = tir.float32(0)
for k in range(0, 32):
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
for ii, jj, kk in tir.grid(4, 4, 4):
C[vi * 4 + ii, vj * 4 + jj] = (
C[vi * 4 + ii, vj * 4 + jj]
+ A[vi * 4 + ii, vk * 4 + kk] * B[vj * 4 + jj, vk * 4 + kk]
)
@tvm.script.tir
def elementwise_with_root(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
def func_with_opaque_block(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([]) as []:
B[0, 0] = A[0, 0] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
@tvm.script.tir
def func_with_part_access_region(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
tir.reads(A[vi, vj])
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
tir.writes(C[vi, vj])
C[vi, vj] = B[vi, vj] + tir.float32(1)
def test_complete_matmul():
func = matmul
A, B, C = [func.buffer_map[x] for x in func.params]
block = func.body.block.body.body.body.body.block
assert isinstance(block, tvm.tir.Block)
vi, vj, vk = [x.var for x in block.iter_vars]
access_A = tir.BufferRegion(A, [Range.from_min_extent(vi, 1), Range.from_min_extent(vk, 1)])
access_B = tir.BufferRegion(B, [Range.from_min_extent(vj, 1), Range.from_min_extent(vk, 1)])
access_C = tir.BufferRegion(C, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])
tvm.ir.assert_structural_equal(block.reads, [access_C, access_A, access_B])
tvm.ir.assert_structural_equal(block.writes, [access_C])
def test_complete_matmul_original():
func = matmul_original
A, B, C = [func.buffer_map[x] for x in func.params]
block1 = func.body.block.body.body.body[0].block
assert isinstance(block1, tvm.tir.Block)
vi, vj = [x.var for x in block1.iter_vars]
access_C = tir.BufferRegion(
C, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vj * 4, 4)]
)
tvm.ir.assert_structural_equal(block1.reads, [])
tvm.ir.assert_structural_equal(block1.writes, [access_C])
block2 = func.body.block.body.body.body[1].body.block
assert isinstance(block2, tvm.tir.Block)
vi, vj, vk = [x.var for x in block2.iter_vars]
access_A = tir.BufferRegion(
A, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vk * 4, 4)]
)
access_B = tir.BufferRegion(
B, [Range.from_min_extent(vj * 4, 4), Range.from_min_extent(vk * 4, 4)]
)
access_C = tir.BufferRegion(
C, [Range.from_min_extent(vi * 4, 4), Range.from_min_extent(vj * 4, 4)]
)
tvm.ir.assert_structural_equal(block2.reads, [access_C, access_A, access_B])
tvm.ir.assert_structural_equal(block2.writes, [access_C])
def _check_elementwise(func):
A, B, C = [func.buffer_map[x] for x in func.params]
block1 = func.body.block.body[0].body.body.block
assert isinstance(block1, tvm.tir.Block)
vi, vj = [x.var for x in block1.iter_vars]
tvm.ir.assert_structural_equal(
block1.reads,
[tir.BufferRegion(A, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
tvm.ir.assert_structural_equal(
block1.writes,
[tir.BufferRegion(B, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
block2 = func.body.block.body[1].body.body.block
assert isinstance(block2, tvm.tir.Block)
vi, vj = [x.var for x in block2.iter_vars]
tvm.ir.assert_structural_equal(
block2.reads,
[tir.BufferRegion(B, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
tvm.ir.assert_structural_equal(
block2.writes,
[tir.BufferRegion(C, [Range.from_min_extent(vi, 1), Range.from_min_extent(vj, 1)])],
)
def test_complete_with_root():
_check_elementwise(elementwise_with_root)
def test_complete_part_region():
_check_elementwise(func_with_part_access_region)
def test_complete_opaque_block_error():
def render(e):
pass
override_renderer(render)
try:
from_source(func_with_opaque_block)
except tvm.error.DiagnosticError:
return
assert False
if __name__ == "__main__":
test_complete_matmul()
test_complete_matmul_original()
test_complete_with_root()
test_complete_opaque_block_error()
test_complete_part_region() | 0.564579 | 0.457197 |
from pprint import pformat
from six import iteritems
import re
class Layer(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, directive=None, arguments=None):
"""
Layer - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'directive': 'str',
'arguments': 'str'
}
self.attribute_map = {
'directive': 'directive',
'arguments': 'arguments'
}
self._directive = directive
self._arguments = arguments
@property
def directive(self):
"""
Gets the directive of this Layer.
The recovered Dockerfile directive used to construct this layer.
:return: The directive of this Layer.
:rtype: str
"""
return self._directive
@directive.setter
def directive(self, directive):
"""
Sets the directive of this Layer.
The recovered Dockerfile directive used to construct this layer.
:param directive: The directive of this Layer.
:type: str
"""
allowed_values = ["UNKNOWN_DIRECTIVE", "MAINTAINER", "RUN", "CMD", "LABEL", "EXPOSE", "ENV", "ADD", "COPY", "ENTRYPOINT", "VOLUME", "USER", "WORKDIR", "ARG", "ONBUILD", "STOPSIGNAL", "HEALTHCHECK", "SHELL"]
if directive not in allowed_values:
raise ValueError(
"Invalid value for `directive` ({0}), must be one of {1}"
.format(directive, allowed_values)
)
self._directive = directive
@property
def arguments(self):
"""
Gets the arguments of this Layer.
The recovered arguments to the Dockerfile directive.
:return: The arguments of this Layer.
:rtype: str
"""
return self._arguments
@arguments.setter
def arguments(self, arguments):
"""
Sets the arguments of this Layer.
The recovered arguments to the Dockerfile directive.
:param arguments: The arguments of this Layer.
:type: str
"""
self._arguments = arguments
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_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 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
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other | v1alpha1/swagger_client/models/layer.py | from pprint import pformat
from six import iteritems
import re
class Layer(object):
"""
NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, directive=None, arguments=None):
"""
Layer - a model defined in Swagger
:param dict swaggerTypes: The key is attribute name
and the value is attribute type.
:param dict attributeMap: The key is attribute name
and the value is json key in definition.
"""
self.swagger_types = {
'directive': 'str',
'arguments': 'str'
}
self.attribute_map = {
'directive': 'directive',
'arguments': 'arguments'
}
self._directive = directive
self._arguments = arguments
@property
def directive(self):
"""
Gets the directive of this Layer.
The recovered Dockerfile directive used to construct this layer.
:return: The directive of this Layer.
:rtype: str
"""
return self._directive
@directive.setter
def directive(self, directive):
"""
Sets the directive of this Layer.
The recovered Dockerfile directive used to construct this layer.
:param directive: The directive of this Layer.
:type: str
"""
allowed_values = ["UNKNOWN_DIRECTIVE", "MAINTAINER", "RUN", "CMD", "LABEL", "EXPOSE", "ENV", "ADD", "COPY", "ENTRYPOINT", "VOLUME", "USER", "WORKDIR", "ARG", "ONBUILD", "STOPSIGNAL", "HEALTHCHECK", "SHELL"]
if directive not in allowed_values:
raise ValueError(
"Invalid value for `directive` ({0}), must be one of {1}"
.format(directive, allowed_values)
)
self._directive = directive
@property
def arguments(self):
"""
Gets the arguments of this Layer.
The recovered arguments to the Dockerfile directive.
:return: The arguments of this Layer.
:rtype: str
"""
return self._arguments
@arguments.setter
def arguments(self, arguments):
"""
Sets the arguments of this Layer.
The recovered arguments to the Dockerfile directive.
:param arguments: The arguments of this Layer.
:type: str
"""
self._arguments = arguments
def to_dict(self):
"""
Returns the model properties as a dict
"""
result = {}
for attr, _ in iteritems(self.swagger_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 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
"""
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""
Returns true if both objects are not equal
"""
return not self == other | 0.784236 | 0.299515 |
import re
class Container:
def __init__(self):
self.mappings = {}
self.registrations = {}
self.strategies = []
@staticmethod
def constructor_args(t):
return t.__init__.__code__.co_varnames[1:]
@staticmethod
def type_to_parameter_name(t):
return re.sub('[a-z][A-Z]', lambda x: x.group()[:1] + '_' + x.group()[1:],
t.__name__).lower()
def register_type(self, type_to_register, **registration):
self.mappings[self.type_to_parameter_name(type_to_register)] = type_to_register
self.registrations[type_to_register] = registration
def register_types(self, *args):
for arg in args:
self.register_type(arg)
def add_strategy(self, strategy):
self.strategies.append(strategy)
def add_strategies(self, *strategies):
for strategy in strategies:
self.add_strategy(strategy)
def resolve_parameters(self, type_to_resolve, partial, **overridden_args):
parameter_names = self.constructor_args(type_to_resolve)
parameter_count = len(parameter_names) - 2 if partial else len(parameter_names)
parameters = [None] * parameter_count
registration = self.registrations.get(type_to_resolve)
for index in range(parameter_count):
parameter = None
parameter_name = parameter_names[index]
if parameter_name in overridden_args:
parameter = overridden_args[parameter_name]
else:
for strategy in self.strategies:
parameter = strategy(type_to_resolve, parameter_names[index])
if parameter:
break
if not parameter:
mapped_type = self.mappings.get(parameter_name)
if mapped_type:
parameter = self.resolve(mapped_type)
else:
assert registration,\
'%s not registered, parameter=%s' % (type_to_resolve, parameter_name)
assert parameter_name in registration,\
'parameter %s is not present in %s registration' % (parameter_name, type_to_resolve)
resolver = registration[parameter_name]
if type(resolver).__name__ == 'function':
parameter = registration[parameter_name](type_to_resolve, parameter_name)
else:
parameter = resolver
parameters[index] = parameter
return parameters
def resolve(self, type_to_resolve, **overridden_args):
return type_to_resolve(*self.resolve_parameters(type_to_resolve, False, **overridden_args))
def resolve_partial(self, type_to_resolve, **overridden_args):
resolved_parameters = self.resolve_parameters(type_to_resolve,
True, **overridden_args)
class Partial(type_to_resolve):
def __init__(self, *args, **kwargs):
super(Partial, self).__init__(
*tuple(resolved_parameters + list(args)), **kwargs)
return Partial | app/src/container.py | import re
class Container:
def __init__(self):
self.mappings = {}
self.registrations = {}
self.strategies = []
@staticmethod
def constructor_args(t):
return t.__init__.__code__.co_varnames[1:]
@staticmethod
def type_to_parameter_name(t):
return re.sub('[a-z][A-Z]', lambda x: x.group()[:1] + '_' + x.group()[1:],
t.__name__).lower()
def register_type(self, type_to_register, **registration):
self.mappings[self.type_to_parameter_name(type_to_register)] = type_to_register
self.registrations[type_to_register] = registration
def register_types(self, *args):
for arg in args:
self.register_type(arg)
def add_strategy(self, strategy):
self.strategies.append(strategy)
def add_strategies(self, *strategies):
for strategy in strategies:
self.add_strategy(strategy)
def resolve_parameters(self, type_to_resolve, partial, **overridden_args):
parameter_names = self.constructor_args(type_to_resolve)
parameter_count = len(parameter_names) - 2 if partial else len(parameter_names)
parameters = [None] * parameter_count
registration = self.registrations.get(type_to_resolve)
for index in range(parameter_count):
parameter = None
parameter_name = parameter_names[index]
if parameter_name in overridden_args:
parameter = overridden_args[parameter_name]
else:
for strategy in self.strategies:
parameter = strategy(type_to_resolve, parameter_names[index])
if parameter:
break
if not parameter:
mapped_type = self.mappings.get(parameter_name)
if mapped_type:
parameter = self.resolve(mapped_type)
else:
assert registration,\
'%s not registered, parameter=%s' % (type_to_resolve, parameter_name)
assert parameter_name in registration,\
'parameter %s is not present in %s registration' % (parameter_name, type_to_resolve)
resolver = registration[parameter_name]
if type(resolver).__name__ == 'function':
parameter = registration[parameter_name](type_to_resolve, parameter_name)
else:
parameter = resolver
parameters[index] = parameter
return parameters
def resolve(self, type_to_resolve, **overridden_args):
return type_to_resolve(*self.resolve_parameters(type_to_resolve, False, **overridden_args))
def resolve_partial(self, type_to_resolve, **overridden_args):
resolved_parameters = self.resolve_parameters(type_to_resolve,
True, **overridden_args)
class Partial(type_to_resolve):
def __init__(self, *args, **kwargs):
super(Partial, self).__init__(
*tuple(resolved_parameters + list(args)), **kwargs)
return Partial | 0.622804 | 0.336345 |
from abc import abstractmethod, ABCMeta
import entrypoints
import warnings
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.utils.uri import get_uri_scheme
class UnsupportedModelRegistryStoreURIException(MlflowException):
"""Exception thrown when building a model registry store with an unsupported URI"""
def __init__(self, unsupported_uri, supported_uri_schemes):
message = "Unsupported URI '{}' for model registry store. Supported schemes are: {}".format(
unsupported_uri, supported_uri_schemes)
super(UnsupportedModelRegistryStoreURIException, self).__init__(
message, error_code=INVALID_PARAMETER_VALUE)
self.supported_uri_schemes = supported_uri_schemes
class StoreRegistry:
"""
Abstract class defining a scheme-based registry for store implementations.
This class allows the registration of a function or class to provide an
implementation for a given scheme of `store_uri` through the `register`
methods. Implementations declared though the entrypoints can be automatically
registered through the `register_entrypoints` method.
When instantiating a store through the `get_store` method, the scheme of
the store URI provided (or inferred from environment) will be used to
select which implementation to instantiate, which will be called with same
arguments passed to the `get_store` method.
"""
__metaclass__ = ABCMeta
@abstractmethod
def __init__(self, group_name):
self._registry = {}
self.group_name = group_name
def register(self, scheme, store_builder):
self._registry[scheme] = store_builder
def register_entrypoints(self):
"""Register tracking stores provided by other packages"""
for entrypoint in entrypoints.get_group_all(self.group_name):
try:
self.register(entrypoint.name, entrypoint.load())
except (AttributeError, ImportError) as exc:
warnings.warn(
'Failure attempting to register store for scheme "{}": {}'.format(
entrypoint.name, str(exc)
),
stacklevel=2
)
def get_store_builder(self, store_uri):
"""Get a store from the registry based on the scheme of store_uri
:param store_uri: The store URI. If None, it will be inferred from the environment. This
URI is used to select which tracking store implementation to instantiate
and is passed to the constructor of the implementation.
:return: A function that returns an instance of
``mlflow.store.{tracking|model_registry}.AbstractStore`` that fulfills the store
URI requirements.
"""
scheme = store_uri if store_uri == "databricks" else get_uri_scheme(store_uri)
try:
store_builder = self._registry[scheme]
except KeyError:
raise UnsupportedModelRegistryStoreURIException(
unsupported_uri=store_uri,
supported_uri_schemes=list(self._registry.keys()))
return store_builder | mlflow/tracking/registry.py | from abc import abstractmethod, ABCMeta
import entrypoints
import warnings
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
from mlflow.utils.uri import get_uri_scheme
class UnsupportedModelRegistryStoreURIException(MlflowException):
"""Exception thrown when building a model registry store with an unsupported URI"""
def __init__(self, unsupported_uri, supported_uri_schemes):
message = "Unsupported URI '{}' for model registry store. Supported schemes are: {}".format(
unsupported_uri, supported_uri_schemes)
super(UnsupportedModelRegistryStoreURIException, self).__init__(
message, error_code=INVALID_PARAMETER_VALUE)
self.supported_uri_schemes = supported_uri_schemes
class StoreRegistry:
"""
Abstract class defining a scheme-based registry for store implementations.
This class allows the registration of a function or class to provide an
implementation for a given scheme of `store_uri` through the `register`
methods. Implementations declared though the entrypoints can be automatically
registered through the `register_entrypoints` method.
When instantiating a store through the `get_store` method, the scheme of
the store URI provided (or inferred from environment) will be used to
select which implementation to instantiate, which will be called with same
arguments passed to the `get_store` method.
"""
__metaclass__ = ABCMeta
@abstractmethod
def __init__(self, group_name):
self._registry = {}
self.group_name = group_name
def register(self, scheme, store_builder):
self._registry[scheme] = store_builder
def register_entrypoints(self):
"""Register tracking stores provided by other packages"""
for entrypoint in entrypoints.get_group_all(self.group_name):
try:
self.register(entrypoint.name, entrypoint.load())
except (AttributeError, ImportError) as exc:
warnings.warn(
'Failure attempting to register store for scheme "{}": {}'.format(
entrypoint.name, str(exc)
),
stacklevel=2
)
def get_store_builder(self, store_uri):
"""Get a store from the registry based on the scheme of store_uri
:param store_uri: The store URI. If None, it will be inferred from the environment. This
URI is used to select which tracking store implementation to instantiate
and is passed to the constructor of the implementation.
:return: A function that returns an instance of
``mlflow.store.{tracking|model_registry}.AbstractStore`` that fulfills the store
URI requirements.
"""
scheme = store_uri if store_uri == "databricks" else get_uri_scheme(store_uri)
try:
store_builder = self._registry[scheme]
except KeyError:
raise UnsupportedModelRegistryStoreURIException(
unsupported_uri=store_uri,
supported_uri_schemes=list(self._registry.keys()))
return store_builder | 0.880771 | 0.216177 |
import re
import json
import ipaddress
class IfConfig(object):
""" ifconfig parser class """
def __init__(self, output):
"""
:param output: ifconfig text output
"""
self.interfaces = []
# loop over blocks
for block in output.split('\n\n'):
if block.strip():
self.interfaces.append(self._parse_block(block))
def _parse_block(self, output):
"""
Parses an ifconfig block
"""
mo = re.search(
r'^(?P<name>\S+)\s+' +
r'Link encap:(?P<link_encap>\S+(\s\S+)?)' +
r'(\s+HWaddr\s+(?P<hardware_address>\S+))?' +
r'(\s+inet addr:(?P<inet>\S+))?' +
r'(\s+Bcast:(?P<broadcast>\S+))?' +
r'(\s+Mask:(?P<mask>\S+))?'+
r'(\s+inet6 addr: (?P<inet6>\S+)\s+Scope:(Global|Host))?' +
r'(\s+inet6 addr: (?P<inet6_local>\S+)\s+Scope:Link)?' +
r'((\s|\w)+MTU:(?P<mtu>\S+))?'+
r'(\s+Metric:(?P<metric>\S+))?'+
r'(\s+RX packets:(?P<rx_packets>\S+)\s+errors:\d+ dropped:\d+ overruns:\d+ frame:\d+)?'+
r'(\s+TX packets:(?P<tx_packets>\S+)\s+errors:\d+ dropped:\d+ overruns:\d+ carrier:\d+)?'+
r'(\s+collisions:(?P<collisions>\S+))?'+
r'(\s+txqueuelen:(?P<txqueuelen>\S+))?'+
r'(\s+RX bytes:(?P<rx_bytes>\S+)\s+\((\d|\s|\.|\w)+\))?'+
r'(\s+TX bytes:(?P<tx_bytes>\S+)\s+\((\d|\s|\.|\w)+\)?)?',
output, re.MULTILINE|re.IGNORECASE
)
if mo:
d = mo.groupdict('')
result = dict()
for key in ['name',
'link_encap',
'hardware_address',
'inet',
'broadcast',
'mask',
'inet6',
'inet6_local',
'mtu',
'metric',
'rx_packets',
'tx_packets',
'collisions',
'txqueuelen',
'rx_bytes',
'tx_bytes']:
if key in d:
result[key] = d[key]
return result
else:
return {}
def to_python(self):
""" returns python dictionary representation of ifconfig output """
return self.interfaces
def to_json(self, **kwargs):
""" returns json representation of ifconfig output """
return json.dumps(self.interfaces, **kwargs) | src/PI/ifconfig.py |
import re
import json
import ipaddress
class IfConfig(object):
""" ifconfig parser class """
def __init__(self, output):
"""
:param output: ifconfig text output
"""
self.interfaces = []
# loop over blocks
for block in output.split('\n\n'):
if block.strip():
self.interfaces.append(self._parse_block(block))
def _parse_block(self, output):
"""
Parses an ifconfig block
"""
mo = re.search(
r'^(?P<name>\S+)\s+' +
r'Link encap:(?P<link_encap>\S+(\s\S+)?)' +
r'(\s+HWaddr\s+(?P<hardware_address>\S+))?' +
r'(\s+inet addr:(?P<inet>\S+))?' +
r'(\s+Bcast:(?P<broadcast>\S+))?' +
r'(\s+Mask:(?P<mask>\S+))?'+
r'(\s+inet6 addr: (?P<inet6>\S+)\s+Scope:(Global|Host))?' +
r'(\s+inet6 addr: (?P<inet6_local>\S+)\s+Scope:Link)?' +
r'((\s|\w)+MTU:(?P<mtu>\S+))?'+
r'(\s+Metric:(?P<metric>\S+))?'+
r'(\s+RX packets:(?P<rx_packets>\S+)\s+errors:\d+ dropped:\d+ overruns:\d+ frame:\d+)?'+
r'(\s+TX packets:(?P<tx_packets>\S+)\s+errors:\d+ dropped:\d+ overruns:\d+ carrier:\d+)?'+
r'(\s+collisions:(?P<collisions>\S+))?'+
r'(\s+txqueuelen:(?P<txqueuelen>\S+))?'+
r'(\s+RX bytes:(?P<rx_bytes>\S+)\s+\((\d|\s|\.|\w)+\))?'+
r'(\s+TX bytes:(?P<tx_bytes>\S+)\s+\((\d|\s|\.|\w)+\)?)?',
output, re.MULTILINE|re.IGNORECASE
)
if mo:
d = mo.groupdict('')
result = dict()
for key in ['name',
'link_encap',
'hardware_address',
'inet',
'broadcast',
'mask',
'inet6',
'inet6_local',
'mtu',
'metric',
'rx_packets',
'tx_packets',
'collisions',
'txqueuelen',
'rx_bytes',
'tx_bytes']:
if key in d:
result[key] = d[key]
return result
else:
return {}
def to_python(self):
""" returns python dictionary representation of ifconfig output """
return self.interfaces
def to_json(self, **kwargs):
""" returns json representation of ifconfig output """
return json.dumps(self.interfaces, **kwargs) | 0.430866 | 0.149687 |
class Student:
def __init__(self, student_id, fname, lname, enroll_date):
self.student_id = student_id
self.fname = fname
self.lname = lname
self.enroll_date = enroll_date
class Course:
def __init__(self, course_id, title, cred_hr):
self.course_id = course_id
self.title = title
self.cred_hr = cred_hr
class Enrollment:
def __init__(self, enroll_id, course, student):
self.enroll_id = enroll_id
self.course = course
self.student = student
def display(self):
for enrollment in self.enrollments.values():
print(enrollment)
class GradeBook:
def __init__(self):
self.students = {}
#add to student dictionary
s = Student(1, "Carson", "Alexande#r", "09012005")
self.students[s.student_id] = s
s = Student(2, "Meredith", "Alonso", "09022002")
self.students[s.student_id] = s
s = Student(3, "Arturo", "Anand", "09032003")
self.students[s.student_id] = s
s = Student(4, "Gytis", "Barzdukas", "09012001")
self.students[s.student_id] = s
s = Student(5, "Peggy", "Justice", "09012001")
self.students[s.student_id] = s
s = Student(6, "Laura", "Norman", "09012003")
self.students[s.student_id] = s
s = Student(7, "Nino", "Olivetto", "09012005")
self.students[s.student_id] = s
self.courses = {}
#add to course dictionary
c = Course(1050, "Chemistry", 3)
self.courses[c.course_id] = c
c = Course(4022, "Microeconomics", 3)
self.courses[c.course_id] = c
c = Course(4041, "Macroeconomics", 3)
self.courses[c.course_id] = c
c = Course(1045, "Calculus", 4)
self.courses[c.course_id] = c
c = Course(3141, "Trigonometry", 4)
self.courses[c.course_id] = c
c = Course(2021, "Composition", 3)
self.courses[c.course_id] = c
c = Course(2042, "Literature", 4)
self.courses[c.course_id] = c
self.enrollments = {}
#add enrolled students into courses
enroll_id = 11050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 14022 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[4022])
self.enrollments[enroll_id] = enrollment
enroll_id = 14041 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[4041])
self.enrollments[enroll_id] = enrollment
enroll_id = 21045 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[1045])
self.enrollments[enroll_id] = enrollment
enroll_id = 23141 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[3141])
self.enrollments[enroll_id] = enrollment
enroll_id = 22021 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[4041])
self.enrollments[enroll_id] = enrollment
enroll_id = 31050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[3], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 41050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[4], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 44022 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[4], self.courses[4022])
self.enrollments[enroll_id] = enrollment
enroll_id = 54041 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[5], self.courses[2021])
self.enrollments[enroll_id] = enrollment
enroll_id = 61045 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[6], self.courses[1045])
self.enrollments[enroll_id] = enrollment
enroll_id = 73141 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[7], self.courses[3141])
self.enrollments[enroll_id] = enrollment
def main(self):
more_enteries = 'y'
grade_book = GradeBook()
while more_enteries == 'y':
student_key = int(input("Enter enroll id: "))
student = grade_book.enrollments[student_key]
student_grade = input("Enter a grade: ")
student.grade = student_grade
more_enteries = input("Type f to finish")
def __str__(self):
return str(self.enrollments[enroll_id]) + str(self.enrollments[course]) + str(self.enrollments[student])
for key in self.enrollments.values():
print(key)
done = GradeBook()
done.main() | Homework10Source (1).py | class Student:
def __init__(self, student_id, fname, lname, enroll_date):
self.student_id = student_id
self.fname = fname
self.lname = lname
self.enroll_date = enroll_date
class Course:
def __init__(self, course_id, title, cred_hr):
self.course_id = course_id
self.title = title
self.cred_hr = cred_hr
class Enrollment:
def __init__(self, enroll_id, course, student):
self.enroll_id = enroll_id
self.course = course
self.student = student
def display(self):
for enrollment in self.enrollments.values():
print(enrollment)
class GradeBook:
def __init__(self):
self.students = {}
#add to student dictionary
s = Student(1, "Carson", "Alexande#r", "09012005")
self.students[s.student_id] = s
s = Student(2, "Meredith", "Alonso", "09022002")
self.students[s.student_id] = s
s = Student(3, "Arturo", "Anand", "09032003")
self.students[s.student_id] = s
s = Student(4, "Gytis", "Barzdukas", "09012001")
self.students[s.student_id] = s
s = Student(5, "Peggy", "Justice", "09012001")
self.students[s.student_id] = s
s = Student(6, "Laura", "Norman", "09012003")
self.students[s.student_id] = s
s = Student(7, "Nino", "Olivetto", "09012005")
self.students[s.student_id] = s
self.courses = {}
#add to course dictionary
c = Course(1050, "Chemistry", 3)
self.courses[c.course_id] = c
c = Course(4022, "Microeconomics", 3)
self.courses[c.course_id] = c
c = Course(4041, "Macroeconomics", 3)
self.courses[c.course_id] = c
c = Course(1045, "Calculus", 4)
self.courses[c.course_id] = c
c = Course(3141, "Trigonometry", 4)
self.courses[c.course_id] = c
c = Course(2021, "Composition", 3)
self.courses[c.course_id] = c
c = Course(2042, "Literature", 4)
self.courses[c.course_id] = c
self.enrollments = {}
#add enrolled students into courses
enroll_id = 11050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 14022 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[4022])
self.enrollments[enroll_id] = enrollment
enroll_id = 14041 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[1], self.courses[4041])
self.enrollments[enroll_id] = enrollment
enroll_id = 21045 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[1045])
self.enrollments[enroll_id] = enrollment
enroll_id = 23141 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[3141])
self.enrollments[enroll_id] = enrollment
enroll_id = 22021 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[2], self.courses[4041])
self.enrollments[enroll_id] = enrollment
enroll_id = 31050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[3], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 41050 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[4], self.courses[1050])
self.enrollments[enroll_id] = enrollment
enroll_id = 44022 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[4], self.courses[4022])
self.enrollments[enroll_id] = enrollment
enroll_id = 54041 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[5], self.courses[2021])
self.enrollments[enroll_id] = enrollment
enroll_id = 61045 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[6], self.courses[1045])
self.enrollments[enroll_id] = enrollment
enroll_id = 73141 #combine student id + chemistry id
enrollment = Enrollment(enroll_id, self.students[7], self.courses[3141])
self.enrollments[enroll_id] = enrollment
def main(self):
more_enteries = 'y'
grade_book = GradeBook()
while more_enteries == 'y':
student_key = int(input("Enter enroll id: "))
student = grade_book.enrollments[student_key]
student_grade = input("Enter a grade: ")
student.grade = student_grade
more_enteries = input("Type f to finish")
def __str__(self):
return str(self.enrollments[enroll_id]) + str(self.enrollments[course]) + str(self.enrollments[student])
for key in self.enrollments.values():
print(key)
done = GradeBook()
done.main() | 0.278159 | 0.175256 |
import json
import os
import torch
class TimeDistributed(torch.nn.Module):
""" Time distributed wrapper compatible with linear/dense pytorch layer modules"""
def __init__(self, module, batch_first=True):
super(TimeDistributed, self).__init__()
self.module = module
self.batch_first = batch_first
def forward(self, x):
# Linear layer accept 2D input
if len(x.size()) <= 2:
return self.module(x)
# Squash samples and timesteps into a single axis
x_reshape = x.contiguous().view(
-1, x.size(-1)
) # (samples * timesteps, input_size)
out = self.module(x_reshape)
# We have to reshape Y back to the target shape
if self.batch_first:
out = out.contiguous().view(
x.size(0), -1, out.size(-1)
) # (samples, timesteps, output_size)
else:
out = out.view(
-1, x.size(1), out.size(-1)
) # (timesteps, samples, output_size)
return out
def save(model, hyperparameters, PATH=None):
"""Save the trained model(.pth) along with its hyperparameters as a json (hyper.json) at the user defined Path
Parameters:
-----------
model (torch.nn.Module): Trained Model
hyperparameters(dict): Hyperparameters of the model
PATH (str): Directory path to save the trained model and its hyperparameters
Returns:
---------
None
"""
if hyperparameters is not None and not isinstance(hyperparameters, dict):
raise Exception("Invalid argument, hyperparameters must be dict")
# Save
if PATH is None:
PATH = os.getcwd() + "model.pt"
torch.save(model.state_dict(), PATH)
hyperdir, _ = os.path.split(PATH)
if hyperparameters is not None:
with open(os.path.join(hyperdir, "hypers.json"), "w") as fp:
json.dump(hyperparameters, fp, sort_keys=False)
if hyperdir == "":
hyperdir = "."
print(f"Model and hyperparameters saved at {hyperdir}")
def load(model, PATH=None):
"""Load trained model from PATH using the model_hyperparameters saved in the
Parameters:
-----------
model (torch.nn.Module): Type of the model ['ae','vae','vaegan','irl','lstm','custom']
PATH (str): Directory path of the model: Defaults to None: Means Current working directory
Returns:
---------
model(torch.nn.module): Model
"""
# Hyperparameters
if PATH is None:
PATH = os.getcwd() + "/model.pt"
print(f"Model loaded from {PATH}")
else:
raise Exception(f"Model state dict not found at {PATH}")
# Load state of the model
model.load_state_dict(torch.load(PATH))
return model
def read_hyperparameters(hyperparameter_json):
"""Read the json file and return the hyperparameters as dict
Args:
hyperparameter_json (json): Json file containing the hyperparameters of the trained model
Returns:
[dict]: Python dictionary of the hyperparameters
"""
with open(hyperparameter_json) as f_in:
return json.load(f_in) | traja/models/utils.py | import json
import os
import torch
class TimeDistributed(torch.nn.Module):
""" Time distributed wrapper compatible with linear/dense pytorch layer modules"""
def __init__(self, module, batch_first=True):
super(TimeDistributed, self).__init__()
self.module = module
self.batch_first = batch_first
def forward(self, x):
# Linear layer accept 2D input
if len(x.size()) <= 2:
return self.module(x)
# Squash samples and timesteps into a single axis
x_reshape = x.contiguous().view(
-1, x.size(-1)
) # (samples * timesteps, input_size)
out = self.module(x_reshape)
# We have to reshape Y back to the target shape
if self.batch_first:
out = out.contiguous().view(
x.size(0), -1, out.size(-1)
) # (samples, timesteps, output_size)
else:
out = out.view(
-1, x.size(1), out.size(-1)
) # (timesteps, samples, output_size)
return out
def save(model, hyperparameters, PATH=None):
"""Save the trained model(.pth) along with its hyperparameters as a json (hyper.json) at the user defined Path
Parameters:
-----------
model (torch.nn.Module): Trained Model
hyperparameters(dict): Hyperparameters of the model
PATH (str): Directory path to save the trained model and its hyperparameters
Returns:
---------
None
"""
if hyperparameters is not None and not isinstance(hyperparameters, dict):
raise Exception("Invalid argument, hyperparameters must be dict")
# Save
if PATH is None:
PATH = os.getcwd() + "model.pt"
torch.save(model.state_dict(), PATH)
hyperdir, _ = os.path.split(PATH)
if hyperparameters is not None:
with open(os.path.join(hyperdir, "hypers.json"), "w") as fp:
json.dump(hyperparameters, fp, sort_keys=False)
if hyperdir == "":
hyperdir = "."
print(f"Model and hyperparameters saved at {hyperdir}")
def load(model, PATH=None):
"""Load trained model from PATH using the model_hyperparameters saved in the
Parameters:
-----------
model (torch.nn.Module): Type of the model ['ae','vae','vaegan','irl','lstm','custom']
PATH (str): Directory path of the model: Defaults to None: Means Current working directory
Returns:
---------
model(torch.nn.module): Model
"""
# Hyperparameters
if PATH is None:
PATH = os.getcwd() + "/model.pt"
print(f"Model loaded from {PATH}")
else:
raise Exception(f"Model state dict not found at {PATH}")
# Load state of the model
model.load_state_dict(torch.load(PATH))
return model
def read_hyperparameters(hyperparameter_json):
"""Read the json file and return the hyperparameters as dict
Args:
hyperparameter_json (json): Json file containing the hyperparameters of the trained model
Returns:
[dict]: Python dictionary of the hyperparameters
"""
with open(hyperparameter_json) as f_in:
return json.load(f_in) | 0.883538 | 0.407687 |
import csv, os, io, re
from flask import Flask, render_template,escape, request, Response
import pandas as pd
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import base64
from flask.helpers import make_response
from werkzeug.debug import DebuggedApplication
import twint
import datetime
import pathlib
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
nltk.download('vader_lexicon')
app = Flask(__name__,
static_folder='static',
template_folder='templates')
df = pd.read_csv("./static/data/tweets_refined_with_Score.csv")
Images = os.path.join('static', 'images')
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
bb_value = {
"Bank Nifty" : 0,
"Apple":1,
"Microsoft":1,
"Tesla":1,
"Google":0
}
sentiment = SentimentIntensityAnalyzer()
@app.route('/')
def index():
symbol = request.args.get('stock',default='banknifty')
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
for key, value in stocks.items():
print(key ,value)
default_stock = 'banknifty'
if symbol != default_stock:
default_stock = symbol.lower()
ploturl = f"https://raw.githubusercontent.com/saurabhan/stocksage/main/static/images/{default_stock}.png"
else:
ploturl = f"https://raw.githubusercontent.com/saurabhan/stocksage/main/static/images/{default_stock}.png"
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}_score.csv")
if file.exists ():
print ("File exist")
df = pd.read_csv(file)
if df.iat[2,1] > df.iat[1,1]:
print("Bullish")
bb_value[key] = 1
else:
print('Bearish')
bb_value[key] = 0
print(bb_value)
return render_template('stock.html',stocks=stocks, url=ploturl, symbol=symbol, bbvalue=bb_value)
@app.route("/updateTweets")
def get_tweet():
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
for key, value in stocks.items():
symbol = value
print(value)
file = pathlib.Path(f"./static/data/{value}.csv")
if file.exists ():
print ("File exist")
else:
print ("File not exist")
c = twint.Config()
c.Search = f"{symbol}"
c.Custom['tweet']=['date','time', 'tweet']
today = datetime.date.today()
dateUntil = today + datetime.timedelta(days=-2)
print(dateUntil)
c.Since = dateUntil.strftime("%Y-%m-%d")
c.Until = today.strftime("%Y-%m-%d")
c.Pandas = True
c.Store_csv = True
c.Hide_output = True
c.Output =f'./static/data/{symbol}.csv'
twint.run.Search(c)
return render_template('stock.html',stocks=stocks, symbol=symbol)
@app.route("/getsentiment")
def get_sentiment():
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}.csv")
if file.exists ():
print ("File exist")
def remove_pattern(input_txt, pattern):
r = re.findall(pattern, input_txt)
for i in r:
input_txt = re.sub(i, '', input_txt)
return input_txt
def clean_tweets(tweets):
#remove twitter Return handles (RT @xxx:)
tweets = np.vectorize(remove_pattern)(tweets, "RT @[\w]*:")
#remove twitter handles (@xxx)
tweets = np.vectorize(remove_pattern)(tweets, "@[\w]*")
#remove URL links (httpxxx)
tweets = np.vectorize(remove_pattern)(tweets, "https?://[A-Za-z0-9./]*")
#remove special characters, numbers, punctuations (except for #)
tweets = np.core.defchararray.replace(tweets, "[^a-zA-Z]", " ")
return tweets
df = pd.read_csv(file)
df['tweet'] = clean_tweets(df['tweet'])
scores = []
data = df
compound_list = []
positive_list = []
negative_list = []
neutral_list = []
for i in range(data['tweet'].shape[0]):
compound = sentiment.polarity_scores(data['tweet'][i])["compound"]
pos = sentiment.polarity_scores(data['tweet'][i])["pos"]
neu = sentiment.polarity_scores(data['tweet'][i])["neu"]
neg = sentiment.polarity_scores(data['tweet'][i])["neg"]
scores.append({"Compound": compound,
"Positive": pos,
"Negative": neg,
"Neutral": neu
})
sentiments_score = pd.DataFrame.from_dict(scores)
df = df.join(sentiments_score)
df['dt']= pd.to_datetime(df['date'] + ' ' + df['time'])
df = df.resample('D', on='dt').mean()
df['dt'] = df.index
df.dropna(inplace=True)
df.to_csv(f'./static/data/{value}_score.csv')
else:
print("not exists")
return render_template('stock.html',stocks=stocks, bbvalue=bb_value)
@app.route("/bb")
def bb():
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}_score.csv")
if file.exists ():
print ("File exist")
df = pd.read_csv(file)
if df.iat[2,1] > df.iat[1,1]:
print("Bullish")
bb_value[key] = 1
else:
print('Bearish')
bb_value[key] = 0
print(bb_value)
return render_template('stock.html',stocks=stocks, bbvalue=bb_value)
if __name__ == "__main__":
app.run() | app.py | import csv, os, io, re
from flask import Flask, render_template,escape, request, Response
import pandas as pd
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import base64
from flask.helpers import make_response
from werkzeug.debug import DebuggedApplication
import twint
import datetime
import pathlib
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import nltk
nltk.download('vader_lexicon')
app = Flask(__name__,
static_folder='static',
template_folder='templates')
df = pd.read_csv("./static/data/tweets_refined_with_Score.csv")
Images = os.path.join('static', 'images')
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
bb_value = {
"Bank Nifty" : 0,
"Apple":1,
"Microsoft":1,
"Tesla":1,
"Google":0
}
sentiment = SentimentIntensityAnalyzer()
@app.route('/')
def index():
symbol = request.args.get('stock',default='banknifty')
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
for key, value in stocks.items():
print(key ,value)
default_stock = 'banknifty'
if symbol != default_stock:
default_stock = symbol.lower()
ploturl = f"https://raw.githubusercontent.com/saurabhan/stocksage/main/static/images/{default_stock}.png"
else:
ploturl = f"https://raw.githubusercontent.com/saurabhan/stocksage/main/static/images/{default_stock}.png"
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}_score.csv")
if file.exists ():
print ("File exist")
df = pd.read_csv(file)
if df.iat[2,1] > df.iat[1,1]:
print("Bullish")
bb_value[key] = 1
else:
print('Bearish')
bb_value[key] = 0
print(bb_value)
return render_template('stock.html',stocks=stocks, url=ploturl, symbol=symbol, bbvalue=bb_value)
@app.route("/updateTweets")
def get_tweet():
stocks = {
"Bank Nifty" : "BankNifty",
"Apple":"AAPL",
"Microsoft":"MSFT",
"Tesla":"TSLA",
"Google":"GOOG"
}
for key, value in stocks.items():
symbol = value
print(value)
file = pathlib.Path(f"./static/data/{value}.csv")
if file.exists ():
print ("File exist")
else:
print ("File not exist")
c = twint.Config()
c.Search = f"{symbol}"
c.Custom['tweet']=['date','time', 'tweet']
today = datetime.date.today()
dateUntil = today + datetime.timedelta(days=-2)
print(dateUntil)
c.Since = dateUntil.strftime("%Y-%m-%d")
c.Until = today.strftime("%Y-%m-%d")
c.Pandas = True
c.Store_csv = True
c.Hide_output = True
c.Output =f'./static/data/{symbol}.csv'
twint.run.Search(c)
return render_template('stock.html',stocks=stocks, symbol=symbol)
@app.route("/getsentiment")
def get_sentiment():
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}.csv")
if file.exists ():
print ("File exist")
def remove_pattern(input_txt, pattern):
r = re.findall(pattern, input_txt)
for i in r:
input_txt = re.sub(i, '', input_txt)
return input_txt
def clean_tweets(tweets):
#remove twitter Return handles (RT @xxx:)
tweets = np.vectorize(remove_pattern)(tweets, "RT @[\w]*:")
#remove twitter handles (@xxx)
tweets = np.vectorize(remove_pattern)(tweets, "@[\w]*")
#remove URL links (httpxxx)
tweets = np.vectorize(remove_pattern)(tweets, "https?://[A-Za-z0-9./]*")
#remove special characters, numbers, punctuations (except for #)
tweets = np.core.defchararray.replace(tweets, "[^a-zA-Z]", " ")
return tweets
df = pd.read_csv(file)
df['tweet'] = clean_tweets(df['tweet'])
scores = []
data = df
compound_list = []
positive_list = []
negative_list = []
neutral_list = []
for i in range(data['tweet'].shape[0]):
compound = sentiment.polarity_scores(data['tweet'][i])["compound"]
pos = sentiment.polarity_scores(data['tweet'][i])["pos"]
neu = sentiment.polarity_scores(data['tweet'][i])["neu"]
neg = sentiment.polarity_scores(data['tweet'][i])["neg"]
scores.append({"Compound": compound,
"Positive": pos,
"Negative": neg,
"Neutral": neu
})
sentiments_score = pd.DataFrame.from_dict(scores)
df = df.join(sentiments_score)
df['dt']= pd.to_datetime(df['date'] + ' ' + df['time'])
df = df.resample('D', on='dt').mean()
df['dt'] = df.index
df.dropna(inplace=True)
df.to_csv(f'./static/data/{value}_score.csv')
else:
print("not exists")
return render_template('stock.html',stocks=stocks, bbvalue=bb_value)
@app.route("/bb")
def bb():
for key, value in stocks.items():
file = pathlib.Path(f"./static/data/{value}_score.csv")
if file.exists ():
print ("File exist")
df = pd.read_csv(file)
if df.iat[2,1] > df.iat[1,1]:
print("Bullish")
bb_value[key] = 1
else:
print('Bearish')
bb_value[key] = 0
print(bb_value)
return render_template('stock.html',stocks=stocks, bbvalue=bb_value)
if __name__ == "__main__":
app.run() | 0.217836 | 0.081886 |
import math
import re
import numpy as np
from pyfr.nputil import npeval, fuzzysort
from pyfr.util import lazyprop, memoize
class BaseElements(object):
privarmap = None
convarmap = None
def __init__(self, basiscls, eles, cfg):
self._be = None
self.eles = eles
self.cfg = cfg
self.nspts = nspts = eles.shape[0]
self.neles = neles = eles.shape[1]
self.ndims = ndims = eles.shape[2]
# Kernels we provide
self.kernels = {}
# Check the dimensionality of the problem
if ndims != basiscls.ndims or ndims not in self.privarmap:
raise ValueError('Invalid element matrix dimensions')
# Determine the number of dynamical variables
self.nvars = len(self.privarmap[ndims])
# Instantiate the basis class
self.basis = basis = basiscls(nspts, cfg)
# See what kind of projection the basis is using
self.antialias = basis.antialias
# If we need quadrature points or not
haveqpts = 'flux' in self.antialias
# Sizes
self.nupts = basis.nupts
self.nqpts = basis.nqpts if haveqpts else None
self.nfpts = basis.nfpts
self.nfacefpts = basis.nfacefpts
self.nmpts = basis.nmpts
def pri_to_con(pris, cfg):
pass
def con_to_pri(cons, cfg):
pass
def set_ics_from_cfg(self):
# Bring simulation constants into scope
vars = self.cfg.items_as('constants', float)
if any(d in vars for d in 'xyz'):
raise ValueError('Invalid constants (x, y, or z) in config file')
# Get the physical location of each solution point
coords = self.ploc_at_np('upts').swapaxes(0, 1)
vars.update(dict(zip('xyz', coords)))
# Evaluate the ICs from the config file
ics = [npeval(self.cfg.getexpr('soln-ics', dv), vars)
for dv in self.privarmap[self.ndims]]
# Allocate
self._scal_upts = np.empty((self.nupts, self.nvars, self.neles))
# Convert from primitive to conservative form
for i, v in enumerate(self.pri_to_con(ics, self.cfg)):
self._scal_upts[:, i, :] = v
def set_ics_from_soln(self, solnmat, solncfg):
# Recreate the existing solution basis
solnb = self.basis.__class__(None, solncfg)
# Form the interpolation operator
interp = solnb.ubasis.nodal_basis_at(self.basis.upts)
# Sizes
nupts, neles, nvars = self.nupts, self.neles, self.nvars
# Apply and reshape
self._scal_upts = interp @ solnmat.reshape(solnb.nupts, -1)
self._scal_upts = self._scal_upts.reshape(nupts, nvars, neles)
@lazyprop
def plocfpts(self):
# Construct the physical location operator matrix
plocop = self.basis.sbasis.nodal_basis_at(self.basis.fpts)
# Apply the operator to the mesh elements and reshape
plocfpts = plocop @ self.eles.reshape(self.nspts, -1)
plocfpts = plocfpts.reshape(self.nfpts, self.neles, self.ndims)
return plocfpts
@lazyprop
def _srtd_face_fpts(self):
plocfpts = self.plocfpts.transpose(1, 2, 0)
return [[np.array(fuzzysort(pts.tolist(), ffpts)) for pts in plocfpts]
for ffpts in self.basis.facefpts]
def _scratch_bufs(self):
pass
@property
def _mesh_regions(self):
off = self._linoff
# No curved elements
if off == 0:
return {'linear': self.neles}
# All curved elements
elif off >= self.neles:
return {'curved': self.neles}
# Mix of curved and linear elements
else:
return {'curved': off, 'linear': self.neles - off}
def _slice_mat(self, mat, region, ra=None, rb=None):
off = self._linoff
# Handle stacked matrices
if len(mat.ioshape) >= 3:
off *= mat.ioshape[-2]
else:
off = min(off, mat.ncol)
if region == 'curved':
return mat.slice(ra, rb, 0, off)
elif region == 'linear':
return mat.slice(ra, rb, off, mat.ncol)
else:
raise ValueError('Invalid slice region')
@lazyprop
def _src_exprs(self):
convars = self.convarmap[self.ndims]
# Variable and function substitutions
subs = self.cfg.items('constants')
subs.update(x='ploc[0]', y='ploc[1]', z='ploc[2]')
subs.update({v: f'u[{i}]' for i, v in enumerate(convars)})
subs.update(abs='fabs', pi=str(math.pi))
return [self.cfg.getexpr('solver-source-terms', v, '0', subs=subs)
for v in convars]
@lazyprop
def _ploc_in_src_exprs(self):
return any(re.search(r'\bploc\b', ex) for ex in self._src_exprs)
@lazyprop
def _soln_in_src_exprs(self):
return any(re.search(r'\bu\b', ex) for ex in self._src_exprs)
def set_backend(self, backend, nscalupts, nonce, linoff):
self._be = backend
if self.basis.order >= 2:
self._linoff = linoff - linoff % -backend.csubsz
else:
self._linoff = self.neles
# Sizes
ndims, nvars, neles = self.ndims, self.nvars, self.neles
nfpts, nupts, nqpts = self.nfpts, self.nupts, self.nqpts
sbufs, abufs = self._scratch_bufs, []
# Convenience functions for scalar/vector allocation
alloc = lambda ex, n: abufs.append(
backend.matrix(n, extent=nonce + ex, tags={'align'})
) or abufs[-1]
salloc = lambda ex, n: alloc(ex, (n, nvars, neles))
valloc = lambda ex, n: alloc(ex, (ndims, n, nvars, neles))
# Allocate required scalar scratch space
if 'scal_fpts' in sbufs and 'scal_qpts' in sbufs:
self._scal_fqpts = salloc('_scal_fqpts', nfpts + nqpts)
self._scal_fpts = self._scal_fqpts.slice(0, nfpts)
self._scal_qpts = self._scal_fqpts.slice(nfpts, nfpts + nqpts)
elif 'scal_fpts' in sbufs:
self._scal_fpts = salloc('scal_fpts', nfpts)
elif 'scal_qpts' in sbufs:
self._scal_qpts = salloc('scal_qpts', nqpts)
# Allocate additional scalar scratch space
if 'scal_upts_cpy' in sbufs:
self._scal_upts_cpy = salloc('scal_upts_cpy', nupts)
# Allocate required vector scratch space
if 'vect_upts' in sbufs:
self._vect_upts = valloc('vect_upts', nupts)
if 'vect_qpts' in sbufs:
self._vect_qpts = valloc('vect_qpts', nqpts)
if 'vect_fpts' in sbufs:
self._vect_fpts = valloc('vect_fpts', nfpts)
# Allocate and bank the storage required by the time integrator
self._scal_upts = [backend.matrix(self._scal_upts.shape,
self._scal_upts, tags={'align'})
for i in range(nscalupts)]
self.scal_upts_inb = inb = backend.matrix_bank(self._scal_upts)
self.scal_upts_outb = backend.matrix_bank(self._scal_upts)
# Find/allocate space for a solution-sized scalar that is
# allowed to alias other scratch space in the simulation
aliases = next((m for m in abufs if m.nbytes >= inb.nbytes), None)
self._scal_upts_temp = backend.matrix(inb.ioshape, aliases=aliases,
tags=inb.tags)
@memoize
def opmat(self, expr):
return self._be.const_matrix(self.basis.opmat(expr),
tags={expr, 'align'})
def sliceat(fn):
@memoize
def newfn(self, name, side=None):
mat = fn(self, name)
if side is not None:
return self._slice_mat(mat, side)
else:
return mat
return newfn
@memoize
def smat_at_np(self, name):
smats_mpts, _ = self._smats_djacs_mpts
# Interpolation matrix to pts
m0 = self.basis.mbasis.nodal_basis_at(getattr(self.basis, name))
# Interpolate the smats
smats = np.array([m0 @ smat for smat in smats_mpts])
return smats.reshape(self.ndims, -1, self.ndims, self.neles)
@sliceat
@memoize
def smat_at(self, name):
return self._be.const_matrix(self.smat_at_np(name), tags={'align'})
@memoize
def rcpdjac_at_np(self, name):
_, djacs_mpts = self._smats_djacs_mpts
# Interpolation matrix to pts
m0 = self.basis.mbasis.nodal_basis_at(getattr(self.basis, name))
# Interpolate the djacs
djac = m0 @ djacs_mpts
if np.any(djac < -1e-5):
raise RuntimeError('Negative mesh Jacobians detected')
return 1.0 / djac
@sliceat
@memoize
def rcpdjac_at(self, name):
return self._be.const_matrix(self.rcpdjac_at_np(name), tags={'align'})
@memoize
def ploc_at_np(self, name):
op = self.basis.sbasis.nodal_basis_at(getattr(self.basis, name))
ploc = op @ self.eles.reshape(self.nspts, -1)
ploc = ploc.reshape(-1, self.neles, self.ndims).swapaxes(1, 2)
return ploc
@sliceat
@memoize
def ploc_at(self, name):
return self._be.const_matrix(self.ploc_at_np(name), tags={'align'})
@lazyprop
def upts(self):
return self._be.const_matrix(self.basis.upts)
@lazyprop
def qpts(self):
return self._be.const_matrix(self.basis.qpts)
def _gen_pnorm_fpts(self):
smats = self.smat_at_np('fpts').transpose(1, 3, 0, 2)
# We need to compute |J|*[(J^{-1})^{T}.N] where J is the
# Jacobian and N is the normal for each fpt. Using
# J^{-1} = S/|J| where S are the smats, we have S^{T}.N.
pnorm_fpts = np.einsum('ijlk,il->ijk', smats, self.basis.norm_fpts)
# Compute the magnitudes of these flux point normals
mag_pnorm_fpts = np.einsum('...i,...i', pnorm_fpts, pnorm_fpts)
mag_pnorm_fpts = np.sqrt(mag_pnorm_fpts)
# Check that none of these magnitudes are zero
if np.any(mag_pnorm_fpts < 1e-10):
raise RuntimeError('Zero face normals detected')
# Normalize the physical normals at the flux points
self._norm_pnorm_fpts = pnorm_fpts / mag_pnorm_fpts[..., None]
self._mag_pnorm_fpts = mag_pnorm_fpts
@lazyprop
def _norm_pnorm_fpts(self):
self._gen_pnorm_fpts()
return self._norm_pnorm_fpts
@lazyprop
def _mag_pnorm_fpts(self):
self._gen_pnorm_fpts()
return self._mag_pnorm_fpts
@lazyprop
def _smats_djacs_mpts(self):
# Metric basis with grid point (q<=p) or pseudo grid points (q>p)
mpts = self.basis.mpts
mbasis = self.basis.mbasis
# Dimensions, number of elements and number of mpts
ndims, neles, nmpts = self.ndims, self.neles, self.nmpts
# Physical locations of the pseudo grid points
x = self.ploc_at_np('mpts')
# Jacobian operator at these points
jacop = np.rollaxis(mbasis.jac_nodal_basis_at(mpts), 2)
jacop = jacop.reshape(-1, nmpts)
# Cast as a matrix multiply and apply to eles
jac = jacop @ x.reshape(nmpts, -1)
# Reshape (nmpts*ndims, neles*ndims) => (nmpts, ndims, neles, ndims)
jac = jac.reshape(nmpts, ndims, ndims, neles)
# Transpose to get (ndims, ndims, nmpts, neles)
jac = jac.transpose(1, 2, 0, 3)
smats = np.empty((ndims, nmpts, ndims, neles))
if ndims == 2:
a, b, c, d = jac[0, 0], jac[1, 0], jac[0, 1], jac[1, 1]
smats[0, :, 0], smats[0, :, 1] = d, -b
smats[1, :, 0], smats[1, :, 1] = -c, a
djacs = a*d - b*c
else:
dtt = []
for dx in jac:
# Compute x cross x_(chi)
tt = np.cross(x, dx, axisa=1, axisb=0, axisc=1)
# Jacobian of x cross x_(chi) at the pseudo grid points
dt = jacop @ tt.reshape(nmpts, -1)
dt = dt.reshape(nmpts, ndims, ndims, -1).swapaxes(0, 1)
dtt.append(dt)
# Kopriva's invariant form of smats; JSC 26(3), 301-327, Eq. (37)
smats[0] = 0.5*(dtt[2][1] - dtt[1][2])
smats[1] = 0.5*(dtt[0][2] - dtt[2][0])
smats[2] = 0.5*(dtt[1][0] - dtt[0][1])
# We note that J = [x0; x1; x2]
x0, x1, x2 = jac
# Exploit the fact that det(J) = x0 · (x1 ^ x2)
x1cx2 = np.cross(x1, x2, axisa=0, axisb=0, axisc=1)
djacs = np.einsum('ij...,ji...->j...', x0, x1cx2)
return smats.reshape(ndims, nmpts, -1), djacs
def get_mag_pnorms(self, eidx, fidx):
fpts_idx = self.basis.facefpts[fidx]
return self._mag_pnorm_fpts[fpts_idx, eidx]
def get_mag_pnorms_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self._mag_pnorm_fpts[fpts_idx, eidx]
def get_norm_pnorms_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self._norm_pnorm_fpts[fpts_idx, eidx]
def get_norm_pnorms(self, eidx, fidx):
fpts_idx = self.basis.facefpts[fidx]
return self._norm_pnorm_fpts[fpts_idx, eidx]
def get_scal_fpts_for_inter(self, eidx, fidx):
nfp = self.nfacefpts[fidx]
rmap = self._srtd_face_fpts[fidx][eidx]
cmap = (eidx,)*nfp
return (self._scal_fpts.mid,)*nfp, rmap, cmap
def get_vect_fpts_for_inter(self, eidx, fidx):
nfp = self.nfacefpts[fidx]
rmap = self._srtd_face_fpts[fidx][eidx]
cmap = (eidx,)*nfp
rstri = (self.nfpts,)*nfp
return (self._vect_fpts.mid,)*nfp, rmap, cmap, rstri
def get_ploc_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self.plocfpts[fpts_idx, eidx] | pyfr/solvers/base/elements.py |
import math
import re
import numpy as np
from pyfr.nputil import npeval, fuzzysort
from pyfr.util import lazyprop, memoize
class BaseElements(object):
privarmap = None
convarmap = None
def __init__(self, basiscls, eles, cfg):
self._be = None
self.eles = eles
self.cfg = cfg
self.nspts = nspts = eles.shape[0]
self.neles = neles = eles.shape[1]
self.ndims = ndims = eles.shape[2]
# Kernels we provide
self.kernels = {}
# Check the dimensionality of the problem
if ndims != basiscls.ndims or ndims not in self.privarmap:
raise ValueError('Invalid element matrix dimensions')
# Determine the number of dynamical variables
self.nvars = len(self.privarmap[ndims])
# Instantiate the basis class
self.basis = basis = basiscls(nspts, cfg)
# See what kind of projection the basis is using
self.antialias = basis.antialias
# If we need quadrature points or not
haveqpts = 'flux' in self.antialias
# Sizes
self.nupts = basis.nupts
self.nqpts = basis.nqpts if haveqpts else None
self.nfpts = basis.nfpts
self.nfacefpts = basis.nfacefpts
self.nmpts = basis.nmpts
def pri_to_con(pris, cfg):
pass
def con_to_pri(cons, cfg):
pass
def set_ics_from_cfg(self):
# Bring simulation constants into scope
vars = self.cfg.items_as('constants', float)
if any(d in vars for d in 'xyz'):
raise ValueError('Invalid constants (x, y, or z) in config file')
# Get the physical location of each solution point
coords = self.ploc_at_np('upts').swapaxes(0, 1)
vars.update(dict(zip('xyz', coords)))
# Evaluate the ICs from the config file
ics = [npeval(self.cfg.getexpr('soln-ics', dv), vars)
for dv in self.privarmap[self.ndims]]
# Allocate
self._scal_upts = np.empty((self.nupts, self.nvars, self.neles))
# Convert from primitive to conservative form
for i, v in enumerate(self.pri_to_con(ics, self.cfg)):
self._scal_upts[:, i, :] = v
def set_ics_from_soln(self, solnmat, solncfg):
# Recreate the existing solution basis
solnb = self.basis.__class__(None, solncfg)
# Form the interpolation operator
interp = solnb.ubasis.nodal_basis_at(self.basis.upts)
# Sizes
nupts, neles, nvars = self.nupts, self.neles, self.nvars
# Apply and reshape
self._scal_upts = interp @ solnmat.reshape(solnb.nupts, -1)
self._scal_upts = self._scal_upts.reshape(nupts, nvars, neles)
@lazyprop
def plocfpts(self):
# Construct the physical location operator matrix
plocop = self.basis.sbasis.nodal_basis_at(self.basis.fpts)
# Apply the operator to the mesh elements and reshape
plocfpts = plocop @ self.eles.reshape(self.nspts, -1)
plocfpts = plocfpts.reshape(self.nfpts, self.neles, self.ndims)
return plocfpts
@lazyprop
def _srtd_face_fpts(self):
plocfpts = self.plocfpts.transpose(1, 2, 0)
return [[np.array(fuzzysort(pts.tolist(), ffpts)) for pts in plocfpts]
for ffpts in self.basis.facefpts]
def _scratch_bufs(self):
pass
@property
def _mesh_regions(self):
off = self._linoff
# No curved elements
if off == 0:
return {'linear': self.neles}
# All curved elements
elif off >= self.neles:
return {'curved': self.neles}
# Mix of curved and linear elements
else:
return {'curved': off, 'linear': self.neles - off}
def _slice_mat(self, mat, region, ra=None, rb=None):
off = self._linoff
# Handle stacked matrices
if len(mat.ioshape) >= 3:
off *= mat.ioshape[-2]
else:
off = min(off, mat.ncol)
if region == 'curved':
return mat.slice(ra, rb, 0, off)
elif region == 'linear':
return mat.slice(ra, rb, off, mat.ncol)
else:
raise ValueError('Invalid slice region')
@lazyprop
def _src_exprs(self):
convars = self.convarmap[self.ndims]
# Variable and function substitutions
subs = self.cfg.items('constants')
subs.update(x='ploc[0]', y='ploc[1]', z='ploc[2]')
subs.update({v: f'u[{i}]' for i, v in enumerate(convars)})
subs.update(abs='fabs', pi=str(math.pi))
return [self.cfg.getexpr('solver-source-terms', v, '0', subs=subs)
for v in convars]
@lazyprop
def _ploc_in_src_exprs(self):
return any(re.search(r'\bploc\b', ex) for ex in self._src_exprs)
@lazyprop
def _soln_in_src_exprs(self):
return any(re.search(r'\bu\b', ex) for ex in self._src_exprs)
def set_backend(self, backend, nscalupts, nonce, linoff):
self._be = backend
if self.basis.order >= 2:
self._linoff = linoff - linoff % -backend.csubsz
else:
self._linoff = self.neles
# Sizes
ndims, nvars, neles = self.ndims, self.nvars, self.neles
nfpts, nupts, nqpts = self.nfpts, self.nupts, self.nqpts
sbufs, abufs = self._scratch_bufs, []
# Convenience functions for scalar/vector allocation
alloc = lambda ex, n: abufs.append(
backend.matrix(n, extent=nonce + ex, tags={'align'})
) or abufs[-1]
salloc = lambda ex, n: alloc(ex, (n, nvars, neles))
valloc = lambda ex, n: alloc(ex, (ndims, n, nvars, neles))
# Allocate required scalar scratch space
if 'scal_fpts' in sbufs and 'scal_qpts' in sbufs:
self._scal_fqpts = salloc('_scal_fqpts', nfpts + nqpts)
self._scal_fpts = self._scal_fqpts.slice(0, nfpts)
self._scal_qpts = self._scal_fqpts.slice(nfpts, nfpts + nqpts)
elif 'scal_fpts' in sbufs:
self._scal_fpts = salloc('scal_fpts', nfpts)
elif 'scal_qpts' in sbufs:
self._scal_qpts = salloc('scal_qpts', nqpts)
# Allocate additional scalar scratch space
if 'scal_upts_cpy' in sbufs:
self._scal_upts_cpy = salloc('scal_upts_cpy', nupts)
# Allocate required vector scratch space
if 'vect_upts' in sbufs:
self._vect_upts = valloc('vect_upts', nupts)
if 'vect_qpts' in sbufs:
self._vect_qpts = valloc('vect_qpts', nqpts)
if 'vect_fpts' in sbufs:
self._vect_fpts = valloc('vect_fpts', nfpts)
# Allocate and bank the storage required by the time integrator
self._scal_upts = [backend.matrix(self._scal_upts.shape,
self._scal_upts, tags={'align'})
for i in range(nscalupts)]
self.scal_upts_inb = inb = backend.matrix_bank(self._scal_upts)
self.scal_upts_outb = backend.matrix_bank(self._scal_upts)
# Find/allocate space for a solution-sized scalar that is
# allowed to alias other scratch space in the simulation
aliases = next((m for m in abufs if m.nbytes >= inb.nbytes), None)
self._scal_upts_temp = backend.matrix(inb.ioshape, aliases=aliases,
tags=inb.tags)
@memoize
def opmat(self, expr):
return self._be.const_matrix(self.basis.opmat(expr),
tags={expr, 'align'})
def sliceat(fn):
@memoize
def newfn(self, name, side=None):
mat = fn(self, name)
if side is not None:
return self._slice_mat(mat, side)
else:
return mat
return newfn
@memoize
def smat_at_np(self, name):
smats_mpts, _ = self._smats_djacs_mpts
# Interpolation matrix to pts
m0 = self.basis.mbasis.nodal_basis_at(getattr(self.basis, name))
# Interpolate the smats
smats = np.array([m0 @ smat for smat in smats_mpts])
return smats.reshape(self.ndims, -1, self.ndims, self.neles)
@sliceat
@memoize
def smat_at(self, name):
return self._be.const_matrix(self.smat_at_np(name), tags={'align'})
@memoize
def rcpdjac_at_np(self, name):
_, djacs_mpts = self._smats_djacs_mpts
# Interpolation matrix to pts
m0 = self.basis.mbasis.nodal_basis_at(getattr(self.basis, name))
# Interpolate the djacs
djac = m0 @ djacs_mpts
if np.any(djac < -1e-5):
raise RuntimeError('Negative mesh Jacobians detected')
return 1.0 / djac
@sliceat
@memoize
def rcpdjac_at(self, name):
return self._be.const_matrix(self.rcpdjac_at_np(name), tags={'align'})
@memoize
def ploc_at_np(self, name):
op = self.basis.sbasis.nodal_basis_at(getattr(self.basis, name))
ploc = op @ self.eles.reshape(self.nspts, -1)
ploc = ploc.reshape(-1, self.neles, self.ndims).swapaxes(1, 2)
return ploc
@sliceat
@memoize
def ploc_at(self, name):
return self._be.const_matrix(self.ploc_at_np(name), tags={'align'})
@lazyprop
def upts(self):
return self._be.const_matrix(self.basis.upts)
@lazyprop
def qpts(self):
return self._be.const_matrix(self.basis.qpts)
def _gen_pnorm_fpts(self):
smats = self.smat_at_np('fpts').transpose(1, 3, 0, 2)
# We need to compute |J|*[(J^{-1})^{T}.N] where J is the
# Jacobian and N is the normal for each fpt. Using
# J^{-1} = S/|J| where S are the smats, we have S^{T}.N.
pnorm_fpts = np.einsum('ijlk,il->ijk', smats, self.basis.norm_fpts)
# Compute the magnitudes of these flux point normals
mag_pnorm_fpts = np.einsum('...i,...i', pnorm_fpts, pnorm_fpts)
mag_pnorm_fpts = np.sqrt(mag_pnorm_fpts)
# Check that none of these magnitudes are zero
if np.any(mag_pnorm_fpts < 1e-10):
raise RuntimeError('Zero face normals detected')
# Normalize the physical normals at the flux points
self._norm_pnorm_fpts = pnorm_fpts / mag_pnorm_fpts[..., None]
self._mag_pnorm_fpts = mag_pnorm_fpts
@lazyprop
def _norm_pnorm_fpts(self):
self._gen_pnorm_fpts()
return self._norm_pnorm_fpts
@lazyprop
def _mag_pnorm_fpts(self):
self._gen_pnorm_fpts()
return self._mag_pnorm_fpts
@lazyprop
def _smats_djacs_mpts(self):
# Metric basis with grid point (q<=p) or pseudo grid points (q>p)
mpts = self.basis.mpts
mbasis = self.basis.mbasis
# Dimensions, number of elements and number of mpts
ndims, neles, nmpts = self.ndims, self.neles, self.nmpts
# Physical locations of the pseudo grid points
x = self.ploc_at_np('mpts')
# Jacobian operator at these points
jacop = np.rollaxis(mbasis.jac_nodal_basis_at(mpts), 2)
jacop = jacop.reshape(-1, nmpts)
# Cast as a matrix multiply and apply to eles
jac = jacop @ x.reshape(nmpts, -1)
# Reshape (nmpts*ndims, neles*ndims) => (nmpts, ndims, neles, ndims)
jac = jac.reshape(nmpts, ndims, ndims, neles)
# Transpose to get (ndims, ndims, nmpts, neles)
jac = jac.transpose(1, 2, 0, 3)
smats = np.empty((ndims, nmpts, ndims, neles))
if ndims == 2:
a, b, c, d = jac[0, 0], jac[1, 0], jac[0, 1], jac[1, 1]
smats[0, :, 0], smats[0, :, 1] = d, -b
smats[1, :, 0], smats[1, :, 1] = -c, a
djacs = a*d - b*c
else:
dtt = []
for dx in jac:
# Compute x cross x_(chi)
tt = np.cross(x, dx, axisa=1, axisb=0, axisc=1)
# Jacobian of x cross x_(chi) at the pseudo grid points
dt = jacop @ tt.reshape(nmpts, -1)
dt = dt.reshape(nmpts, ndims, ndims, -1).swapaxes(0, 1)
dtt.append(dt)
# Kopriva's invariant form of smats; JSC 26(3), 301-327, Eq. (37)
smats[0] = 0.5*(dtt[2][1] - dtt[1][2])
smats[1] = 0.5*(dtt[0][2] - dtt[2][0])
smats[2] = 0.5*(dtt[1][0] - dtt[0][1])
# We note that J = [x0; x1; x2]
x0, x1, x2 = jac
# Exploit the fact that det(J) = x0 · (x1 ^ x2)
x1cx2 = np.cross(x1, x2, axisa=0, axisb=0, axisc=1)
djacs = np.einsum('ij...,ji...->j...', x0, x1cx2)
return smats.reshape(ndims, nmpts, -1), djacs
def get_mag_pnorms(self, eidx, fidx):
fpts_idx = self.basis.facefpts[fidx]
return self._mag_pnorm_fpts[fpts_idx, eidx]
def get_mag_pnorms_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self._mag_pnorm_fpts[fpts_idx, eidx]
def get_norm_pnorms_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self._norm_pnorm_fpts[fpts_idx, eidx]
def get_norm_pnorms(self, eidx, fidx):
fpts_idx = self.basis.facefpts[fidx]
return self._norm_pnorm_fpts[fpts_idx, eidx]
def get_scal_fpts_for_inter(self, eidx, fidx):
nfp = self.nfacefpts[fidx]
rmap = self._srtd_face_fpts[fidx][eidx]
cmap = (eidx,)*nfp
return (self._scal_fpts.mid,)*nfp, rmap, cmap
def get_vect_fpts_for_inter(self, eidx, fidx):
nfp = self.nfacefpts[fidx]
rmap = self._srtd_face_fpts[fidx][eidx]
cmap = (eidx,)*nfp
rstri = (self.nfpts,)*nfp
return (self._vect_fpts.mid,)*nfp, rmap, cmap, rstri
def get_ploc_for_inter(self, eidx, fidx):
fpts_idx = self._srtd_face_fpts[fidx][eidx]
return self.plocfpts[fpts_idx, eidx] | 0.747524 | 0.392919 |
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
int_or_none,
parse_iso8601,
)
class ABCOTVSIE(InfoExtractor):
IE_NAME = 'abcotvs'
IE_DESC = 'ABC Owned Television Stations'
_VALID_URL = r'https?://(?:abc(?:7(?:news|ny|chicago)?|11|13|30)|6abc)\.com(?:/[^/]+/(?P<display_id>[^/]+))?/(?P<id>\d+)'
_TESTS = [
{
'url': 'http://abc7news.com/entertainment/east-bay-museum-celebrates-vintage-synthesizers/472581/',
'info_dict': {
'id': '472581',
'display_id': 'east-bay-museum-celebrates-vintage-synthesizers',
'ext': 'mp4',
'title': 'East Bay museum celebrates vintage synthesizers',
'description': 'md5:a4f10fb2f2a02565c1749d4adbab4b10',
'thumbnail': r're:^https?://.*\.jpg$',
'timestamp': 1421123075,
'upload_date': '20150113',
'uploader': '<NAME>',
},
'params': {
# m3u8 download
'skip_download': True,
},
},
{
'url': 'http://abc7news.com/472581',
'only_matching': True,
},
]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
video_id = mobj.group('id')
display_id = mobj.group('display_id') or video_id
webpage = self._download_webpage(url, display_id)
m3u8 = self._html_search_meta(
'contentURL', webpage, 'm3u8 url', fatal=True).split('?')[0]
formats = self._extract_m3u8_formats(m3u8, display_id, 'mp4')
self._sort_formats(formats)
title = self._og_search_title(webpage).strip()
description = self._og_search_description(webpage).strip()
thumbnail = self._og_search_thumbnail(webpage)
timestamp = parse_iso8601(self._search_regex(
r'<div class="meta">\s*<time class="timeago" datetime="([^"]+)">',
webpage, 'upload date', fatal=False))
uploader = self._search_regex(
r'rel="author">([^<]+)</a>',
webpage, 'uploader', default=None)
return {
'id': video_id,
'display_id': display_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'timestamp': timestamp,
'uploader': uploader,
'formats': formats,
}
class ABCOTVSClipsIE(InfoExtractor):
IE_NAME = 'abcotvs:clips'
_VALID_URL = r'https?://clips\.abcotvs\.com/(?:[^/]+/)*video/(?P<id>\d+)'
_TEST = {
'url': 'https://clips.abcotvs.com/kabc/video/214814',
'info_dict': {
'id': '214814',
'ext': 'mp4',
'title': 'SpaceX launch pad explosion destroys rocket, satellite',
'description': 'md5:9f186e5ad8f490f65409965ee9c7be1b',
'upload_date': '20160901',
'timestamp': 1472756695,
},
'params': {
# m3u8 download
'skip_download': True,
},
}
def _real_extract(self, url):
video_id = self._match_id(url)
video_data = self._download_json('https://clips.abcotvs.com/vogo/video/getByIds?ids=' + video_id, video_id)['results'][0]
title = video_data['title']
formats = self._extract_m3u8_formats(
video_data['videoURL'].split('?')[0], video_id, 'mp4')
self._sort_formats(formats)
return {
'id': video_id,
'title': title,
'description': video_data.get('description'),
'thumbnail': video_data.get('thumbnailURL'),
'duration': int_or_none(video_data.get('duration')),
'timestamp': int_or_none(video_data.get('pubDate')),
'formats': formats,
} | youtube_dl/extractor/abcotvs.py | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
int_or_none,
parse_iso8601,
)
class ABCOTVSIE(InfoExtractor):
IE_NAME = 'abcotvs'
IE_DESC = 'ABC Owned Television Stations'
_VALID_URL = r'https?://(?:abc(?:7(?:news|ny|chicago)?|11|13|30)|6abc)\.com(?:/[^/]+/(?P<display_id>[^/]+))?/(?P<id>\d+)'
_TESTS = [
{
'url': 'http://abc7news.com/entertainment/east-bay-museum-celebrates-vintage-synthesizers/472581/',
'info_dict': {
'id': '472581',
'display_id': 'east-bay-museum-celebrates-vintage-synthesizers',
'ext': 'mp4',
'title': 'East Bay museum celebrates vintage synthesizers',
'description': 'md5:a4f10fb2f2a02565c1749d4adbab4b10',
'thumbnail': r're:^https?://.*\.jpg$',
'timestamp': 1421123075,
'upload_date': '20150113',
'uploader': '<NAME>',
},
'params': {
# m3u8 download
'skip_download': True,
},
},
{
'url': 'http://abc7news.com/472581',
'only_matching': True,
},
]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
video_id = mobj.group('id')
display_id = mobj.group('display_id') or video_id
webpage = self._download_webpage(url, display_id)
m3u8 = self._html_search_meta(
'contentURL', webpage, 'm3u8 url', fatal=True).split('?')[0]
formats = self._extract_m3u8_formats(m3u8, display_id, 'mp4')
self._sort_formats(formats)
title = self._og_search_title(webpage).strip()
description = self._og_search_description(webpage).strip()
thumbnail = self._og_search_thumbnail(webpage)
timestamp = parse_iso8601(self._search_regex(
r'<div class="meta">\s*<time class="timeago" datetime="([^"]+)">',
webpage, 'upload date', fatal=False))
uploader = self._search_regex(
r'rel="author">([^<]+)</a>',
webpage, 'uploader', default=None)
return {
'id': video_id,
'display_id': display_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'timestamp': timestamp,
'uploader': uploader,
'formats': formats,
}
class ABCOTVSClipsIE(InfoExtractor):
IE_NAME = 'abcotvs:clips'
_VALID_URL = r'https?://clips\.abcotvs\.com/(?:[^/]+/)*video/(?P<id>\d+)'
_TEST = {
'url': 'https://clips.abcotvs.com/kabc/video/214814',
'info_dict': {
'id': '214814',
'ext': 'mp4',
'title': 'SpaceX launch pad explosion destroys rocket, satellite',
'description': 'md5:9f186e5ad8f490f65409965ee9c7be1b',
'upload_date': '20160901',
'timestamp': 1472756695,
},
'params': {
# m3u8 download
'skip_download': True,
},
}
def _real_extract(self, url):
video_id = self._match_id(url)
video_data = self._download_json('https://clips.abcotvs.com/vogo/video/getByIds?ids=' + video_id, video_id)['results'][0]
title = video_data['title']
formats = self._extract_m3u8_formats(
video_data['videoURL'].split('?')[0], video_id, 'mp4')
self._sort_formats(formats)
return {
'id': video_id,
'title': title,
'description': video_data.get('description'),
'thumbnail': video_data.get('thumbnailURL'),
'duration': int_or_none(video_data.get('duration')),
'timestamp': int_or_none(video_data.get('pubDate')),
'formats': formats,
} | 0.548674 | 0.143487 |
from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
import numpy as np
def window_execute(self, DB, TB):
self.DB = DB
self.TB = TB
self.setObjectName("MainWindow")
self.resize(500, 300)
self.setMinimumSize(QtCore.QSize(500, 300))
self.setMaximumSize(QtCore.QSize(500, 300))
self.NCW_main = QtWidgets.QWidget(self)
self.NCW_main.setMinimumSize(QtCore.QSize(500, 300))
self.NCW_main.setMaximumSize(QtCore.QSize(500, 300))
self.NCW_main.setObjectName("NCW_main")
self.verticalLayout = QtWidgets.QVBoxLayout(self.NCW_main)
self.verticalLayout.setContentsMargins(20, 20, 20, 20)
self.verticalLayout.setSpacing(10)
self.verticalLayout.setObjectName("verticalLayout")
self.Frame_Central = QtWidgets.QFrame(self.NCW_main)
self.Frame_Central.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.Frame_Central.setFrameShadow(QtWidgets.QFrame.Raised)
self.Frame_Central.setObjectName("Frame_Central")
self.LineEdit_sub_2 = QtWidgets.QLineEdit(self.Frame_Central)
self.LineEdit_sub_2.setGeometry(QtCore.QRect(210, 100, 211, 31))
self.LineEdit_sub_2.setStyleSheet("border-radius: 6px;\n"
"border: 2px solid #595959; \n"
"font-size: 14px;\n"
"white-space: normal;\n"
"font-family: Sans-Serif;")
self.LineEdit_sub_2.setObjectName("LineEdit_sub_2")
self.Label_main = QtWidgets.QLabel(self.Frame_Central)
self.Label_main.setGeometry(QtCore.QRect(0, 0, 331, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_main.setFont(font)
self.Label_main.setObjectName("Label_main")
self.Label_sub_1 = QtWidgets.QLabel(self.Frame_Central)
self.Label_sub_1.setGeometry(QtCore.QRect(0, 50, 201, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_sub_1.setFont(font)
self.Label_sub_1.setObjectName("Label_sub_1")
self.Combo_sub_1 = QtWidgets.QComboBox(self.Frame_Central)
self.Combo_sub_1.setGeometry(QtCore.QRect(210, 50, 211, 31))
self.Combo_sub_1.setStyleSheet("border-radius: 6px;\n"
"border: 2px solid #595959; \n"
"font-size: 14px;\n"
"white-space: normal;\n"
"font-family: Sans-Serif;")
self.Combo_sub_1.setObjectName("Combo_sub_1")
self.Label_sub_2 = QtWidgets.QLabel(self.Frame_Central)
self.Label_sub_2.setGeometry(QtCore.QRect(0, 100, 201, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_sub_2.setFont(font)
self.Label_sub_2.setObjectName("Label_sub_2")
self.BTN_execute = QtWidgets.QPushButton(self.Frame_Central)
self.BTN_execute.setGeometry(QtCore.QRect(160, 150, 111, 31))
font = QtGui.QFont()
font.setPointSize(10)
self.BTN_execute.setFont(font)
self.BTN_execute.setObjectName("BTN_execute")
self.verticalLayout.addWidget(self.Frame_Central)
self.setCentralWidget(self.NCW_main)
_translate = QtCore.QCoreApplication.translate
self.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.LineEdit_sub_2.setPlaceholderText(_translate("MainWindow", "None"))
self.Label_main.setText(_translate("MainWindow", "Tworzenie nowej kolumny"))
self.Label_sub_1.setText(_translate("MainWindow", "Nowa kolumna o indeksie:"))
self.Label_sub_2.setText(_translate("MainWindow", "Nazwa kolumny:"))
self.BTN_execute.setText(_translate("MainWindow", "Wykonaj!"))
QtCore.QMetaObject.connectSlotsByName(self)
def refresh_c(a, db):
dbs = db
db_col_num = np.arange(len(dbs.columns)+1)
a.clear()
for x in db_col_num:
a.addItem("")
a.setItemText(x, str(x))
def execute_adding(a, b, db, tb):
combo_text = a.currentText()
list_text = b.text()
if list_text not in db.columns:
print(f"Dodano nową kolunę o nazwie: {list_text}")
db.insert(int(combo_text), str(list_text), None)
refresh_c(a, db)
self.refresh_table(db, tb)
else:
print("Błąd, nie moża stwożyć dwie kolumny o tych samych nazwach")
refresh_c(self.Combo_sub_1, self.DB)
self.BTN_execute.clicked.connect(lambda: execute_adding(self.Combo_sub_1,self.LineEdit_sub_2, self.DB, self.TB)) | NCW_win.py | from PyQt5 import QtCore, QtGui, QtWidgets
import pandas as pd
import numpy as np
def window_execute(self, DB, TB):
self.DB = DB
self.TB = TB
self.setObjectName("MainWindow")
self.resize(500, 300)
self.setMinimumSize(QtCore.QSize(500, 300))
self.setMaximumSize(QtCore.QSize(500, 300))
self.NCW_main = QtWidgets.QWidget(self)
self.NCW_main.setMinimumSize(QtCore.QSize(500, 300))
self.NCW_main.setMaximumSize(QtCore.QSize(500, 300))
self.NCW_main.setObjectName("NCW_main")
self.verticalLayout = QtWidgets.QVBoxLayout(self.NCW_main)
self.verticalLayout.setContentsMargins(20, 20, 20, 20)
self.verticalLayout.setSpacing(10)
self.verticalLayout.setObjectName("verticalLayout")
self.Frame_Central = QtWidgets.QFrame(self.NCW_main)
self.Frame_Central.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.Frame_Central.setFrameShadow(QtWidgets.QFrame.Raised)
self.Frame_Central.setObjectName("Frame_Central")
self.LineEdit_sub_2 = QtWidgets.QLineEdit(self.Frame_Central)
self.LineEdit_sub_2.setGeometry(QtCore.QRect(210, 100, 211, 31))
self.LineEdit_sub_2.setStyleSheet("border-radius: 6px;\n"
"border: 2px solid #595959; \n"
"font-size: 14px;\n"
"white-space: normal;\n"
"font-family: Sans-Serif;")
self.LineEdit_sub_2.setObjectName("LineEdit_sub_2")
self.Label_main = QtWidgets.QLabel(self.Frame_Central)
self.Label_main.setGeometry(QtCore.QRect(0, 0, 331, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_main.setFont(font)
self.Label_main.setObjectName("Label_main")
self.Label_sub_1 = QtWidgets.QLabel(self.Frame_Central)
self.Label_sub_1.setGeometry(QtCore.QRect(0, 50, 201, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_sub_1.setFont(font)
self.Label_sub_1.setObjectName("Label_sub_1")
self.Combo_sub_1 = QtWidgets.QComboBox(self.Frame_Central)
self.Combo_sub_1.setGeometry(QtCore.QRect(210, 50, 211, 31))
self.Combo_sub_1.setStyleSheet("border-radius: 6px;\n"
"border: 2px solid #595959; \n"
"font-size: 14px;\n"
"white-space: normal;\n"
"font-family: Sans-Serif;")
self.Combo_sub_1.setObjectName("Combo_sub_1")
self.Label_sub_2 = QtWidgets.QLabel(self.Frame_Central)
self.Label_sub_2.setGeometry(QtCore.QRect(0, 100, 201, 31))
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(False)
font.setItalic(False)
font.setUnderline(False)
font.setWeight(50)
font.setStrikeOut(False)
font.setKerning(True)
self.Label_sub_2.setFont(font)
self.Label_sub_2.setObjectName("Label_sub_2")
self.BTN_execute = QtWidgets.QPushButton(self.Frame_Central)
self.BTN_execute.setGeometry(QtCore.QRect(160, 150, 111, 31))
font = QtGui.QFont()
font.setPointSize(10)
self.BTN_execute.setFont(font)
self.BTN_execute.setObjectName("BTN_execute")
self.verticalLayout.addWidget(self.Frame_Central)
self.setCentralWidget(self.NCW_main)
_translate = QtCore.QCoreApplication.translate
self.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.LineEdit_sub_2.setPlaceholderText(_translate("MainWindow", "None"))
self.Label_main.setText(_translate("MainWindow", "Tworzenie nowej kolumny"))
self.Label_sub_1.setText(_translate("MainWindow", "Nowa kolumna o indeksie:"))
self.Label_sub_2.setText(_translate("MainWindow", "Nazwa kolumny:"))
self.BTN_execute.setText(_translate("MainWindow", "Wykonaj!"))
QtCore.QMetaObject.connectSlotsByName(self)
def refresh_c(a, db):
dbs = db
db_col_num = np.arange(len(dbs.columns)+1)
a.clear()
for x in db_col_num:
a.addItem("")
a.setItemText(x, str(x))
def execute_adding(a, b, db, tb):
combo_text = a.currentText()
list_text = b.text()
if list_text not in db.columns:
print(f"Dodano nową kolunę o nazwie: {list_text}")
db.insert(int(combo_text), str(list_text), None)
refresh_c(a, db)
self.refresh_table(db, tb)
else:
print("Błąd, nie moża stwożyć dwie kolumny o tych samych nazwach")
refresh_c(self.Combo_sub_1, self.DB)
self.BTN_execute.clicked.connect(lambda: execute_adding(self.Combo_sub_1,self.LineEdit_sub_2, self.DB, self.TB)) | 0.322206 | 0.060004 |
from oslo_utils import uuidutils
from nova import context
from nova import exception
from nova.objects import cell_mapping
from nova.objects import instance_mapping
from nova import test
from nova.tests import fixtures
from nova.tests import uuidsentinel
sample_mapping = {'instance_uuid': '',
'cell_id': 3,
'project_id': 'fake-project'}
sample_cell_mapping = {'id': 3,
'uuid': '',
'name': 'fake-cell',
'transport_url': 'rabbit:///',
'database_connection': 'mysql:///'}
def create_cell_mapping(**kwargs):
args = sample_cell_mapping.copy()
if 'uuid' not in kwargs:
args['uuid'] = uuidutils.generate_uuid()
args.update(kwargs)
ctxt = context.RequestContext('fake-user', 'fake-project')
return cell_mapping.CellMapping._create_in_db(ctxt, args)
def create_mapping(**kwargs):
args = sample_mapping.copy()
if 'instance_uuid' not in kwargs:
args['instance_uuid'] = uuidutils.generate_uuid()
args.update(kwargs)
ctxt = context.RequestContext('fake-user', 'fake-project')
return instance_mapping.InstanceMapping._create_in_db(ctxt, args)
class InstanceMappingTestCase(test.NoDBTestCase):
USES_DB_SELF = True
def setUp(self):
super(InstanceMappingTestCase, self).setUp()
self.useFixture(fixtures.Database(database='api'))
self.context = context.RequestContext('fake-user', 'fake-project')
self.mapping_obj = instance_mapping.InstanceMapping()
def test_get_by_instance_uuid(self):
cell_mapping = create_cell_mapping()
mapping = create_mapping()
db_mapping = self.mapping_obj._get_by_instance_uuid_from_db(
self.context, mapping['instance_uuid'])
for key in [key for key in self.mapping_obj.fields.keys()
if key != 'cell_mapping']:
self.assertEqual(db_mapping[key], mapping[key])
self.assertEqual(db_mapping['cell_mapping']['id'], cell_mapping['id'])
def test_get_by_instance_uuid_not_found(self):
self.assertRaises(exception.InstanceMappingNotFound,
self.mapping_obj._get_by_instance_uuid_from_db, self.context,
uuidutils.generate_uuid())
def test_save_in_db(self):
mapping = create_mapping()
cell_mapping = create_cell_mapping()
self.mapping_obj._save_in_db(self.context, mapping['instance_uuid'],
{'cell_id': cell_mapping['id']})
db_mapping = self.mapping_obj._get_by_instance_uuid_from_db(
self.context, mapping['instance_uuid'])
for key in [key for key in self.mapping_obj.fields.keys()
if key not in ['cell_id', 'cell_mapping', 'updated_at']]:
self.assertEqual(db_mapping[key], mapping[key])
self.assertEqual(db_mapping['cell_id'], cell_mapping['id'])
def test_destroy_in_db(self):
mapping = create_mapping()
self.mapping_obj._get_by_instance_uuid_from_db(self.context,
mapping['instance_uuid'])
self.mapping_obj._destroy_in_db(self.context, mapping['instance_uuid'])
self.assertRaises(exception.InstanceMappingNotFound,
self.mapping_obj._get_by_instance_uuid_from_db, self.context,
mapping['instance_uuid'])
def test_cell_id_nullable(self):
# Just ensure this doesn't raise
create_mapping(cell_id=None)
def test_modify_cell_mapping(self):
inst_mapping = instance_mapping.InstanceMapping(context=self.context)
inst_mapping.instance_uuid = uuidutils.generate_uuid()
inst_mapping.project_id = self.context.project_id
inst_mapping.cell_mapping = None
inst_mapping.create()
c_mapping = cell_mapping.CellMapping(
self.context,
uuid=uuidutils.generate_uuid(),
name="cell0",
transport_url="none:///",
database_connection="fake:///")
c_mapping.create()
inst_mapping.cell_mapping = c_mapping
inst_mapping.save()
result_mapping = instance_mapping.InstanceMapping.get_by_instance_uuid(
self.context, inst_mapping.instance_uuid)
self.assertEqual(result_mapping.cell_mapping.id,
c_mapping.id)
class InstanceMappingListTestCase(test.NoDBTestCase):
USES_DB_SELF = True
def setUp(self):
super(InstanceMappingListTestCase, self).setUp()
self.useFixture(fixtures.Database(database='api'))
self.context = context.RequestContext('fake-user', 'fake-project')
self.list_obj = instance_mapping.InstanceMappingList()
def test_get_by_project_id_from_db(self):
project_id = 'fake-project'
mappings = {}
mapping = create_mapping(project_id=project_id)
mappings[mapping['instance_uuid']] = mapping
mapping = create_mapping(project_id=project_id)
mappings[mapping['instance_uuid']] = mapping
db_mappings = self.list_obj._get_by_project_id_from_db(
self.context, project_id)
for db_mapping in db_mappings:
mapping = mappings[db_mapping.instance_uuid]
for key in instance_mapping.InstanceMapping.fields.keys():
self.assertEqual(db_mapping[key], mapping[key])
def test_instance_mapping_list_get_by_cell_id(self):
"""Tests getting all of the InstanceMappings for a given CellMapping id
"""
# we shouldn't have any instance mappings yet
inst_mapping_list = (
instance_mapping.InstanceMappingList.get_by_cell_id(
self.context, sample_cell_mapping['id'])
)
self.assertEqual(0, len(inst_mapping_list))
# now create an instance mapping in a cell
db_inst_mapping1 = create_mapping()
# let's also create an instance mapping that's not in a cell to make
# sure our filtering is working
db_inst_mapping2 = create_mapping(cell_id=None)
self.assertIsNone(db_inst_mapping2['cell_id'])
# now we should list out one instance mapping for the cell
inst_mapping_list = (
instance_mapping.InstanceMappingList.get_by_cell_id(
self.context, db_inst_mapping1['cell_id'])
)
self.assertEqual(1, len(inst_mapping_list))
self.assertEqual(db_inst_mapping1['id'], inst_mapping_list[0].id)
def test_instance_mapping_get_by_instance_uuids(self):
db_inst_mapping1 = create_mapping()
db_inst_mapping2 = create_mapping(cell_id=None)
# Create a third that we won't include
create_mapping()
uuids = [db_inst_mapping1.instance_uuid,
db_inst_mapping2.instance_uuid]
mappings = instance_mapping.InstanceMappingList.get_by_instance_uuids(
self.context, uuids + [uuidsentinel.deleted_instance])
self.assertEqual(sorted(uuids),
sorted([m.instance_uuid for m in mappings])) | nova/tests/functional/db/test_instance_mapping.py |
from oslo_utils import uuidutils
from nova import context
from nova import exception
from nova.objects import cell_mapping
from nova.objects import instance_mapping
from nova import test
from nova.tests import fixtures
from nova.tests import uuidsentinel
sample_mapping = {'instance_uuid': '',
'cell_id': 3,
'project_id': 'fake-project'}
sample_cell_mapping = {'id': 3,
'uuid': '',
'name': 'fake-cell',
'transport_url': 'rabbit:///',
'database_connection': 'mysql:///'}
def create_cell_mapping(**kwargs):
args = sample_cell_mapping.copy()
if 'uuid' not in kwargs:
args['uuid'] = uuidutils.generate_uuid()
args.update(kwargs)
ctxt = context.RequestContext('fake-user', 'fake-project')
return cell_mapping.CellMapping._create_in_db(ctxt, args)
def create_mapping(**kwargs):
args = sample_mapping.copy()
if 'instance_uuid' not in kwargs:
args['instance_uuid'] = uuidutils.generate_uuid()
args.update(kwargs)
ctxt = context.RequestContext('fake-user', 'fake-project')
return instance_mapping.InstanceMapping._create_in_db(ctxt, args)
class InstanceMappingTestCase(test.NoDBTestCase):
USES_DB_SELF = True
def setUp(self):
super(InstanceMappingTestCase, self).setUp()
self.useFixture(fixtures.Database(database='api'))
self.context = context.RequestContext('fake-user', 'fake-project')
self.mapping_obj = instance_mapping.InstanceMapping()
def test_get_by_instance_uuid(self):
cell_mapping = create_cell_mapping()
mapping = create_mapping()
db_mapping = self.mapping_obj._get_by_instance_uuid_from_db(
self.context, mapping['instance_uuid'])
for key in [key for key in self.mapping_obj.fields.keys()
if key != 'cell_mapping']:
self.assertEqual(db_mapping[key], mapping[key])
self.assertEqual(db_mapping['cell_mapping']['id'], cell_mapping['id'])
def test_get_by_instance_uuid_not_found(self):
self.assertRaises(exception.InstanceMappingNotFound,
self.mapping_obj._get_by_instance_uuid_from_db, self.context,
uuidutils.generate_uuid())
def test_save_in_db(self):
mapping = create_mapping()
cell_mapping = create_cell_mapping()
self.mapping_obj._save_in_db(self.context, mapping['instance_uuid'],
{'cell_id': cell_mapping['id']})
db_mapping = self.mapping_obj._get_by_instance_uuid_from_db(
self.context, mapping['instance_uuid'])
for key in [key for key in self.mapping_obj.fields.keys()
if key not in ['cell_id', 'cell_mapping', 'updated_at']]:
self.assertEqual(db_mapping[key], mapping[key])
self.assertEqual(db_mapping['cell_id'], cell_mapping['id'])
def test_destroy_in_db(self):
mapping = create_mapping()
self.mapping_obj._get_by_instance_uuid_from_db(self.context,
mapping['instance_uuid'])
self.mapping_obj._destroy_in_db(self.context, mapping['instance_uuid'])
self.assertRaises(exception.InstanceMappingNotFound,
self.mapping_obj._get_by_instance_uuid_from_db, self.context,
mapping['instance_uuid'])
def test_cell_id_nullable(self):
# Just ensure this doesn't raise
create_mapping(cell_id=None)
def test_modify_cell_mapping(self):
inst_mapping = instance_mapping.InstanceMapping(context=self.context)
inst_mapping.instance_uuid = uuidutils.generate_uuid()
inst_mapping.project_id = self.context.project_id
inst_mapping.cell_mapping = None
inst_mapping.create()
c_mapping = cell_mapping.CellMapping(
self.context,
uuid=uuidutils.generate_uuid(),
name="cell0",
transport_url="none:///",
database_connection="fake:///")
c_mapping.create()
inst_mapping.cell_mapping = c_mapping
inst_mapping.save()
result_mapping = instance_mapping.InstanceMapping.get_by_instance_uuid(
self.context, inst_mapping.instance_uuid)
self.assertEqual(result_mapping.cell_mapping.id,
c_mapping.id)
class InstanceMappingListTestCase(test.NoDBTestCase):
USES_DB_SELF = True
def setUp(self):
super(InstanceMappingListTestCase, self).setUp()
self.useFixture(fixtures.Database(database='api'))
self.context = context.RequestContext('fake-user', 'fake-project')
self.list_obj = instance_mapping.InstanceMappingList()
def test_get_by_project_id_from_db(self):
project_id = 'fake-project'
mappings = {}
mapping = create_mapping(project_id=project_id)
mappings[mapping['instance_uuid']] = mapping
mapping = create_mapping(project_id=project_id)
mappings[mapping['instance_uuid']] = mapping
db_mappings = self.list_obj._get_by_project_id_from_db(
self.context, project_id)
for db_mapping in db_mappings:
mapping = mappings[db_mapping.instance_uuid]
for key in instance_mapping.InstanceMapping.fields.keys():
self.assertEqual(db_mapping[key], mapping[key])
def test_instance_mapping_list_get_by_cell_id(self):
"""Tests getting all of the InstanceMappings for a given CellMapping id
"""
# we shouldn't have any instance mappings yet
inst_mapping_list = (
instance_mapping.InstanceMappingList.get_by_cell_id(
self.context, sample_cell_mapping['id'])
)
self.assertEqual(0, len(inst_mapping_list))
# now create an instance mapping in a cell
db_inst_mapping1 = create_mapping()
# let's also create an instance mapping that's not in a cell to make
# sure our filtering is working
db_inst_mapping2 = create_mapping(cell_id=None)
self.assertIsNone(db_inst_mapping2['cell_id'])
# now we should list out one instance mapping for the cell
inst_mapping_list = (
instance_mapping.InstanceMappingList.get_by_cell_id(
self.context, db_inst_mapping1['cell_id'])
)
self.assertEqual(1, len(inst_mapping_list))
self.assertEqual(db_inst_mapping1['id'], inst_mapping_list[0].id)
def test_instance_mapping_get_by_instance_uuids(self):
db_inst_mapping1 = create_mapping()
db_inst_mapping2 = create_mapping(cell_id=None)
# Create a third that we won't include
create_mapping()
uuids = [db_inst_mapping1.instance_uuid,
db_inst_mapping2.instance_uuid]
mappings = instance_mapping.InstanceMappingList.get_by_instance_uuids(
self.context, uuids + [uuidsentinel.deleted_instance])
self.assertEqual(sorted(uuids),
sorted([m.instance_uuid for m in mappings])) | 0.577376 | 0.155335 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import paddle.fluid as fluid
import distutils.util
import numpy as np
import six
import argparse
import functools
import collections
import datetime
from collections import deque
from paddle.fluid import core
from collections import deque
from config import *
def print_arguments(args):
"""Print argparse's arguments.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __init__(self, window_size):
self.deque = deque(maxlen=window_size)
def add_value(self, value):
self.deque.append(value)
def get_median_value(self):
return np.median(self.deque)
def now_time():
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
class TrainingStats(object):
def __init__(self, window_size, stats_keys):
self.smoothed_losses_and_metrics = {
key: SmoothedValue(window_size)
for key in stats_keys
}
def update(self, stats):
for k, v in self.smoothed_losses_and_metrics.items():
v.add_value(stats[k])
def get(self, extras=None):
stats = collections.OrderedDict()
if extras:
for k, v in extras.items():
stats[k] = v
for k, v in self.smoothed_losses_and_metrics.items():
stats[k] = round(v.get_median_value(), 3)
return stats
def log(self, extras=None):
d = self.get(extras)
strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())
return strs
def parse_args():
"""return all args
"""
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
# ENV
add_arg('use_gpu', bool, True, "Whether use GPU.")
add_arg('model_save_dir', str, 'output', "The path to save model.")
add_arg('pretrained_model', str, 'ResNet50_cos_pretrained', "The init model path.")
add_arg('dataset', str, 'icdar2015', "icdar2015, icdar2017.")
add_arg('class_num', int, 2, "Class number.")
add_arg('data_dir', str, 'dataset/icdar2015', "The data root path.")
add_arg('use_profile', bool, False, "Whether use profiler.")
add_arg('padding_minibatch',bool, False,
"If False, only resize image and not pad, image shape is different between"
" GPUs in one mini-batch. If True, image shape is the same in one mini-batch.")
#SOLVER
add_arg('learning_rate', float, 0.02, "Learning rate.")
add_arg('max_iter', int, 17500, "Iter number.")
add_arg('log_window', int, 20, "Log smooth window, set 1 for debug, set 20 for train.")
# RCNN
# RPN
add_arg('anchor_sizes', int, [128, 256, 512], "The size of anchors.")
add_arg('aspect_ratios', float, [0.2, 0.5,1.0], "The ratio of anchors.")
add_arg('anchor_angle', float, [-30.0, 0.0, 30.0, 60.0, 90.0, 120.0], "The angles of anchors.")
add_arg('variance', float, [1.0, 1.0, 1.0, 1.0, 1.0], "The variance of anchors.")
add_arg('rpn_stride', float, [16.,16.], "Stride of the feature map that RPN is attached.")
add_arg('rpn_nms_thresh', float, 0.7, "NMS threshold used on RPN proposals")
# TRAIN VAL INFER
add_arg('im_per_batch', int, 1, "Minibatch size.")
add_arg('pixel_means', float, [0.485, 0.456, 0.406], "pixel mean")
add_arg('nms_thresh', float, 0.3, "NMS threshold.")
add_arg('score_thresh', float, 0.01, "score threshold for NMS.")
add_arg('snapshot_iter', int, 1000, "save model every snapshot iter.")
# SINGLE EVAL AND DRAW
add_arg('draw_threshold', float, 0.8, "Confidence threshold to draw bbox.")
add_arg('image_path', str, 'ICDAR2015/tmp/', "The image path used to inference and visualize.")
# yapf: enable
args = parser.parse_args()
file_name = sys.argv[0]
if 'train' in file_name or 'profile' in file_name:
merge_cfg_from_args(args, 'train')
else:
merge_cfg_from_args(args, 'val')
return args
def check_gpu(use_gpu):
"""
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
err = "Config use_gpu cannot be set as true while you are " \
"using paddlepaddle cpu version ! \nPlease try: \n" \
"\t1. Install paddlepaddle-gpu to run model on GPU \n" \
"\t2. Set use_gpu as false in config file to run " \
"model on CPU"
try:
if use_gpu and not fluid.is_compiled_with_cuda():
logger.error(err)
sys.exit(1)
except Exception as e:
pass | PaddleCV/rrpn/utility.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import paddle.fluid as fluid
import distutils.util
import numpy as np
import six
import argparse
import functools
import collections
import datetime
from collections import deque
from paddle.fluid import core
from collections import deque
from config import *
def print_arguments(args):
"""Print argparse's arguments.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
parser.add_argument("name", default="Jonh", type=str, help="User name.")
args = parser.parse_args()
print_arguments(args)
:param args: Input argparse.Namespace for printing.
:type args: argparse.Namespace
"""
print("----------- Configuration Arguments -----------")
for arg, value in sorted(six.iteritems(vars(args))):
print("%s: %s" % (arg, value))
print("------------------------------------------------")
def add_arguments(argname, type, default, help, argparser, **kwargs):
"""Add argparse's argument.
Usage:
.. code-block:: python
parser = argparse.ArgumentParser()
add_argument("name", str, "Jonh", "User name.", parser)
args = parser.parse_args()
"""
type = distutils.util.strtobool if type == bool else type
argparser.add_argument(
"--" + argname,
default=default,
type=type,
help=help + ' Default: %(default)s.',
**kwargs)
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __init__(self, window_size):
self.deque = deque(maxlen=window_size)
def add_value(self, value):
self.deque.append(value)
def get_median_value(self):
return np.median(self.deque)
def now_time():
return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
class TrainingStats(object):
def __init__(self, window_size, stats_keys):
self.smoothed_losses_and_metrics = {
key: SmoothedValue(window_size)
for key in stats_keys
}
def update(self, stats):
for k, v in self.smoothed_losses_and_metrics.items():
v.add_value(stats[k])
def get(self, extras=None):
stats = collections.OrderedDict()
if extras:
for k, v in extras.items():
stats[k] = v
for k, v in self.smoothed_losses_and_metrics.items():
stats[k] = round(v.get_median_value(), 3)
return stats
def log(self, extras=None):
d = self.get(extras)
strs = ', '.join(str(dict({x: y})).strip('{}') for x, y in d.items())
return strs
def parse_args():
"""return all args
"""
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
# ENV
add_arg('use_gpu', bool, True, "Whether use GPU.")
add_arg('model_save_dir', str, 'output', "The path to save model.")
add_arg('pretrained_model', str, 'ResNet50_cos_pretrained', "The init model path.")
add_arg('dataset', str, 'icdar2015', "icdar2015, icdar2017.")
add_arg('class_num', int, 2, "Class number.")
add_arg('data_dir', str, 'dataset/icdar2015', "The data root path.")
add_arg('use_profile', bool, False, "Whether use profiler.")
add_arg('padding_minibatch',bool, False,
"If False, only resize image and not pad, image shape is different between"
" GPUs in one mini-batch. If True, image shape is the same in one mini-batch.")
#SOLVER
add_arg('learning_rate', float, 0.02, "Learning rate.")
add_arg('max_iter', int, 17500, "Iter number.")
add_arg('log_window', int, 20, "Log smooth window, set 1 for debug, set 20 for train.")
# RCNN
# RPN
add_arg('anchor_sizes', int, [128, 256, 512], "The size of anchors.")
add_arg('aspect_ratios', float, [0.2, 0.5,1.0], "The ratio of anchors.")
add_arg('anchor_angle', float, [-30.0, 0.0, 30.0, 60.0, 90.0, 120.0], "The angles of anchors.")
add_arg('variance', float, [1.0, 1.0, 1.0, 1.0, 1.0], "The variance of anchors.")
add_arg('rpn_stride', float, [16.,16.], "Stride of the feature map that RPN is attached.")
add_arg('rpn_nms_thresh', float, 0.7, "NMS threshold used on RPN proposals")
# TRAIN VAL INFER
add_arg('im_per_batch', int, 1, "Minibatch size.")
add_arg('pixel_means', float, [0.485, 0.456, 0.406], "pixel mean")
add_arg('nms_thresh', float, 0.3, "NMS threshold.")
add_arg('score_thresh', float, 0.01, "score threshold for NMS.")
add_arg('snapshot_iter', int, 1000, "save model every snapshot iter.")
# SINGLE EVAL AND DRAW
add_arg('draw_threshold', float, 0.8, "Confidence threshold to draw bbox.")
add_arg('image_path', str, 'ICDAR2015/tmp/', "The image path used to inference and visualize.")
# yapf: enable
args = parser.parse_args()
file_name = sys.argv[0]
if 'train' in file_name or 'profile' in file_name:
merge_cfg_from_args(args, 'train')
else:
merge_cfg_from_args(args, 'val')
return args
def check_gpu(use_gpu):
"""
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
err = "Config use_gpu cannot be set as true while you are " \
"using paddlepaddle cpu version ! \nPlease try: \n" \
"\t1. Install paddlepaddle-gpu to run model on GPU \n" \
"\t2. Set use_gpu as false in config file to run " \
"model on CPU"
try:
if use_gpu and not fluid.is_compiled_with_cuda():
logger.error(err)
sys.exit(1)
except Exception as e:
pass | 0.604049 | 0.146606 |
import datetime
import sqlalchemy
from keystone.common import driver_hints
from keystone.common import sql
from keystone.common import utils
import keystone.conf
from keystone import exception
from keystone.i18n import _
from keystone.identity.backends import base
from keystone.identity.backends import sql_model as model
CONF = keystone.conf.CONF
class Identity(base.IdentityDriverBase):
# NOTE(henry-nash): Override the __init__() method so as to take a
# config parameter to enable sql to be used as a domain-specific driver.
def __init__(self, conf=None):
self.conf = conf
super(Identity, self).__init__()
@property
def is_sql(self):
return True
def _check_password(self, password, user_ref):
"""Check the specified password against the data store.
Note that we'll pass in the entire user_ref in case the subclass
needs things like user_ref.get('name')
For further justification, please see the follow up suggestion at
https://blueprints.launchpad.net/keystone/+spec/sql-identiy-pam
"""
return utils.check_password(password, user_ref.password)
# Identity interface
def authenticate(self, user_id, password):
with sql.session_for_read() as session:
try:
user_ref = self._get_user(session, user_id)
except exception.UserNotFound:
raise AssertionError(_('Invalid user / password'))
if self._is_account_locked(user_id, user_ref):
raise exception.AccountLocked(user_id=user_id)
elif not self._check_password(password, user_ref):
self._record_failed_auth(user_id)
raise AssertionError(_('Invalid user / password'))
elif not user_ref.enabled:
raise exception.UserDisabled(user_id=user_id)
elif user_ref.password_is_expired:
raise exception.PasswordExpired(user_id=user_id)
# successful auth, reset failed count if present
if user_ref.local_user.failed_auth_count:
self._reset_failed_auth(user_id)
return base.filter_user(user_ref.to_dict())
def _is_account_locked(self, user_id, user_ref):
"""Check if the user account is locked.
Checks if the user account is locked based on the number of failed
authentication attempts.
:param user_id: The user ID
:param user_ref: Reference to the user object
:returns Boolean: True if the account is locked; False otherwise
"""
attempts = user_ref.local_user.failed_auth_count or 0
max_attempts = CONF.security_compliance.lockout_failure_attempts
lockout_duration = CONF.security_compliance.lockout_duration
if max_attempts and (attempts >= max_attempts):
if not lockout_duration:
return True
else:
delta = datetime.timedelta(seconds=lockout_duration)
last_failure = user_ref.local_user.failed_auth_at
if (last_failure + delta) > datetime.datetime.utcnow():
return True
else:
self._reset_failed_auth(user_id)
return False
def _record_failed_auth(self, user_id):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
if not user_ref.local_user.failed_auth_count:
user_ref.local_user.failed_auth_count = 0
user_ref.local_user.failed_auth_count += 1
user_ref.local_user.failed_auth_at = datetime.datetime.utcnow()
def _reset_failed_auth(self, user_id):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
user_ref.local_user.failed_auth_count = 0
user_ref.local_user.failed_auth_at = None
# user crud
@sql.handle_conflicts(conflict_type='user')
def create_user(self, user_id, user):
user = utils.hash_user_password(user)
with sql.session_for_write() as session:
user_ref = model.User.from_dict(user)
user_ref.created_at = datetime.datetime.utcnow()
session.add(user_ref)
return base.filter_user(user_ref.to_dict())
@driver_hints.truncated
def list_users(self, hints):
with sql.session_for_read() as session:
query = session.query(model.User).outerjoin(model.LocalUser)
user_refs = sql.filter_limit_query(model.User, query, hints)
return [base.filter_user(x.to_dict()) for x in user_refs]
def _get_user(self, session, user_id):
user_ref = session.query(model.User).get(user_id)
if not user_ref:
raise exception.UserNotFound(user_id=user_id)
return user_ref
def get_user(self, user_id):
with sql.session_for_read() as session:
return base.filter_user(
self._get_user(session, user_id).to_dict())
def get_user_by_name(self, user_name, domain_id):
with sql.session_for_read() as session:
query = session.query(model.User).join(model.LocalUser)
query = query.filter(sqlalchemy.and_(
model.LocalUser.name == user_name,
model.LocalUser.domain_id == domain_id))
try:
user_ref = query.one()
except sql.NotFound:
raise exception.UserNotFound(user_id=user_name)
return base.filter_user(user_ref.to_dict())
@sql.handle_conflicts(conflict_type='user')
def update_user(self, user_id, user):
with sql.session_for_write() as session:
user_ref = self._get_user(session, user_id)
old_user_dict = user_ref.to_dict()
user = utils.hash_user_password(user)
for k in user:
old_user_dict[k] = user[k]
new_user = model.User.from_dict(old_user_dict)
for attr in model.User.attributes:
if attr not in model.User.readonly_attributes:
setattr(user_ref, attr, getattr(new_user, attr))
user_ref.extra = new_user.extra
return base.filter_user(
user_ref.to_dict(include_extra_dict=True))
def _validate_password_history(self, password, user_ref):
unique_cnt = CONF.security_compliance.unique_last_password_count
# Slice off all of the extra passwords.
user_ref.local_user.passwords = (
user_ref.local_user.passwords[-unique_cnt:])
# Validate the new password against the remaining passwords.
if unique_cnt > 1:
for password_ref in user_ref.local_user.passwords:
if utils.check_password(password, password_ref.password):
detail = _('The new password cannot be identical to a '
'previous password. The number of previous '
'passwords that must be unique is: '
'%(unique_cnt)d') % {'unique_cnt': unique_cnt}
raise exception.PasswordValidationError(detail=detail)
def change_password(self, user_id, new_password):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
if user_ref.password_ref and user_ref.password_ref.self_service:
self._validate_minimum_password_age(user_ref)
self._validate_password_history(new_password, user_ref)
user_ref.password = <PASSWORD>(<PASSWORD>)
user_ref.password_ref.self_service = True
def _validate_minimum_password_age(self, user_ref):
min_age_days = CONF.security_compliance.minimum_password_age
min_age = (user_ref.password_created_at +
datetime.timedelta(days=min_age_days))
if datetime.datetime.utcnow() < min_age:
days_left = (min_age - datetime.datetime.utcnow()).days
raise exception.PasswordAgeValidationError(
min_age_days=min_age_days, days_left=days_left)
def add_user_to_group(self, user_id, group_id):
with sql.session_for_write() as session:
self.get_group(group_id)
self.get_user(user_id)
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
rv = query.first()
if rv:
return
session.add(model.UserGroupMembership(user_id=user_id,
group_id=group_id))
def check_user_in_group(self, user_id, group_id):
with sql.session_for_read() as session:
self.get_group(group_id)
self.get_user(user_id)
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
if not query.first():
raise exception.NotFound(_("User '%(user_id)s' not found in"
" group '%(group_id)s'") %
{'user_id': user_id,
'group_id': group_id})
def remove_user_from_group(self, user_id, group_id):
# We don't check if user or group are still valid and let the remove
# be tried anyway - in case this is some kind of clean-up operation
with sql.session_for_write() as session:
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
membership_ref = query.first()
if membership_ref is None:
# Check if the group and user exist to return descriptive
# exceptions.
self.get_group(group_id)
self.get_user(user_id)
raise exception.NotFound(_("User '%(user_id)s' not found in"
" group '%(group_id)s'") %
{'user_id': user_id,
'group_id': group_id})
session.delete(membership_ref)
def list_groups_for_user(self, user_id, hints):
with sql.session_for_read() as session:
self.get_user(user_id)
query = session.query(model.Group).join(model.UserGroupMembership)
query = query.filter(model.UserGroupMembership.user_id == user_id)
query = sql.filter_limit_query(model.Group, query, hints)
return [g.to_dict() for g in query]
def list_users_in_group(self, group_id, hints):
with sql.session_for_read() as session:
self.get_group(group_id)
query = session.query(model.User).outerjoin(model.LocalUser)
query = query.join(model.UserGroupMembership)
query = query.filter(
model.UserGroupMembership.group_id == group_id)
query = sql.filter_limit_query(model.User, query, hints)
return [base.filter_user(u.to_dict()) for u in query]
def delete_user(self, user_id):
with sql.session_for_write() as session:
ref = self._get_user(session, user_id)
q = session.query(model.UserGroupMembership)
q = q.filter_by(user_id=user_id)
q.delete(False)
session.delete(ref)
# group crud
@sql.handle_conflicts(conflict_type='group')
def create_group(self, group_id, group):
with sql.session_for_write() as session:
ref = model.Group.from_dict(group)
session.add(ref)
return ref.to_dict()
@driver_hints.truncated
def list_groups(self, hints):
with sql.session_for_read() as session:
query = session.query(model.Group)
refs = sql.filter_limit_query(model.Group, query, hints)
return [ref.to_dict() for ref in refs]
def _get_group(self, session, group_id):
ref = session.query(model.Group).get(group_id)
if not ref:
raise exception.GroupNotFound(group_id=group_id)
return ref
def get_group(self, group_id):
with sql.session_for_read() as session:
return self._get_group(session, group_id).to_dict()
def get_group_by_name(self, group_name, domain_id):
with sql.session_for_read() as session:
query = session.query(model.Group)
query = query.filter_by(name=group_name)
query = query.filter_by(domain_id=domain_id)
try:
group_ref = query.one()
except sql.NotFound:
raise exception.GroupNotFound(group_id=group_name)
return group_ref.to_dict()
@sql.handle_conflicts(conflict_type='group')
def update_group(self, group_id, group):
with sql.session_for_write() as session:
ref = self._get_group(session, group_id)
old_dict = ref.to_dict()
for k in group:
old_dict[k] = group[k]
new_group = model.Group.from_dict(old_dict)
for attr in model.Group.attributes:
if attr != 'id':
setattr(ref, attr, getattr(new_group, attr))
ref.extra = new_group.extra
return ref.to_dict()
def delete_group(self, group_id):
with sql.session_for_write() as session:
ref = self._get_group(session, group_id)
q = session.query(model.UserGroupMembership)
q = q.filter_by(group_id=group_id)
q.delete(False)
session.delete(ref) | keystone/identity/backends/sql.py |
import datetime
import sqlalchemy
from keystone.common import driver_hints
from keystone.common import sql
from keystone.common import utils
import keystone.conf
from keystone import exception
from keystone.i18n import _
from keystone.identity.backends import base
from keystone.identity.backends import sql_model as model
CONF = keystone.conf.CONF
class Identity(base.IdentityDriverBase):
# NOTE(henry-nash): Override the __init__() method so as to take a
# config parameter to enable sql to be used as a domain-specific driver.
def __init__(self, conf=None):
self.conf = conf
super(Identity, self).__init__()
@property
def is_sql(self):
return True
def _check_password(self, password, user_ref):
"""Check the specified password against the data store.
Note that we'll pass in the entire user_ref in case the subclass
needs things like user_ref.get('name')
For further justification, please see the follow up suggestion at
https://blueprints.launchpad.net/keystone/+spec/sql-identiy-pam
"""
return utils.check_password(password, user_ref.password)
# Identity interface
def authenticate(self, user_id, password):
with sql.session_for_read() as session:
try:
user_ref = self._get_user(session, user_id)
except exception.UserNotFound:
raise AssertionError(_('Invalid user / password'))
if self._is_account_locked(user_id, user_ref):
raise exception.AccountLocked(user_id=user_id)
elif not self._check_password(password, user_ref):
self._record_failed_auth(user_id)
raise AssertionError(_('Invalid user / password'))
elif not user_ref.enabled:
raise exception.UserDisabled(user_id=user_id)
elif user_ref.password_is_expired:
raise exception.PasswordExpired(user_id=user_id)
# successful auth, reset failed count if present
if user_ref.local_user.failed_auth_count:
self._reset_failed_auth(user_id)
return base.filter_user(user_ref.to_dict())
def _is_account_locked(self, user_id, user_ref):
"""Check if the user account is locked.
Checks if the user account is locked based on the number of failed
authentication attempts.
:param user_id: The user ID
:param user_ref: Reference to the user object
:returns Boolean: True if the account is locked; False otherwise
"""
attempts = user_ref.local_user.failed_auth_count or 0
max_attempts = CONF.security_compliance.lockout_failure_attempts
lockout_duration = CONF.security_compliance.lockout_duration
if max_attempts and (attempts >= max_attempts):
if not lockout_duration:
return True
else:
delta = datetime.timedelta(seconds=lockout_duration)
last_failure = user_ref.local_user.failed_auth_at
if (last_failure + delta) > datetime.datetime.utcnow():
return True
else:
self._reset_failed_auth(user_id)
return False
def _record_failed_auth(self, user_id):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
if not user_ref.local_user.failed_auth_count:
user_ref.local_user.failed_auth_count = 0
user_ref.local_user.failed_auth_count += 1
user_ref.local_user.failed_auth_at = datetime.datetime.utcnow()
def _reset_failed_auth(self, user_id):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
user_ref.local_user.failed_auth_count = 0
user_ref.local_user.failed_auth_at = None
# user crud
@sql.handle_conflicts(conflict_type='user')
def create_user(self, user_id, user):
user = utils.hash_user_password(user)
with sql.session_for_write() as session:
user_ref = model.User.from_dict(user)
user_ref.created_at = datetime.datetime.utcnow()
session.add(user_ref)
return base.filter_user(user_ref.to_dict())
@driver_hints.truncated
def list_users(self, hints):
with sql.session_for_read() as session:
query = session.query(model.User).outerjoin(model.LocalUser)
user_refs = sql.filter_limit_query(model.User, query, hints)
return [base.filter_user(x.to_dict()) for x in user_refs]
def _get_user(self, session, user_id):
user_ref = session.query(model.User).get(user_id)
if not user_ref:
raise exception.UserNotFound(user_id=user_id)
return user_ref
def get_user(self, user_id):
with sql.session_for_read() as session:
return base.filter_user(
self._get_user(session, user_id).to_dict())
def get_user_by_name(self, user_name, domain_id):
with sql.session_for_read() as session:
query = session.query(model.User).join(model.LocalUser)
query = query.filter(sqlalchemy.and_(
model.LocalUser.name == user_name,
model.LocalUser.domain_id == domain_id))
try:
user_ref = query.one()
except sql.NotFound:
raise exception.UserNotFound(user_id=user_name)
return base.filter_user(user_ref.to_dict())
@sql.handle_conflicts(conflict_type='user')
def update_user(self, user_id, user):
with sql.session_for_write() as session:
user_ref = self._get_user(session, user_id)
old_user_dict = user_ref.to_dict()
user = utils.hash_user_password(user)
for k in user:
old_user_dict[k] = user[k]
new_user = model.User.from_dict(old_user_dict)
for attr in model.User.attributes:
if attr not in model.User.readonly_attributes:
setattr(user_ref, attr, getattr(new_user, attr))
user_ref.extra = new_user.extra
return base.filter_user(
user_ref.to_dict(include_extra_dict=True))
def _validate_password_history(self, password, user_ref):
unique_cnt = CONF.security_compliance.unique_last_password_count
# Slice off all of the extra passwords.
user_ref.local_user.passwords = (
user_ref.local_user.passwords[-unique_cnt:])
# Validate the new password against the remaining passwords.
if unique_cnt > 1:
for password_ref in user_ref.local_user.passwords:
if utils.check_password(password, password_ref.password):
detail = _('The new password cannot be identical to a '
'previous password. The number of previous '
'passwords that must be unique is: '
'%(unique_cnt)d') % {'unique_cnt': unique_cnt}
raise exception.PasswordValidationError(detail=detail)
def change_password(self, user_id, new_password):
with sql.session_for_write() as session:
user_ref = session.query(model.User).get(user_id)
if user_ref.password_ref and user_ref.password_ref.self_service:
self._validate_minimum_password_age(user_ref)
self._validate_password_history(new_password, user_ref)
user_ref.password = <PASSWORD>(<PASSWORD>)
user_ref.password_ref.self_service = True
def _validate_minimum_password_age(self, user_ref):
min_age_days = CONF.security_compliance.minimum_password_age
min_age = (user_ref.password_created_at +
datetime.timedelta(days=min_age_days))
if datetime.datetime.utcnow() < min_age:
days_left = (min_age - datetime.datetime.utcnow()).days
raise exception.PasswordAgeValidationError(
min_age_days=min_age_days, days_left=days_left)
def add_user_to_group(self, user_id, group_id):
with sql.session_for_write() as session:
self.get_group(group_id)
self.get_user(user_id)
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
rv = query.first()
if rv:
return
session.add(model.UserGroupMembership(user_id=user_id,
group_id=group_id))
def check_user_in_group(self, user_id, group_id):
with sql.session_for_read() as session:
self.get_group(group_id)
self.get_user(user_id)
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
if not query.first():
raise exception.NotFound(_("User '%(user_id)s' not found in"
" group '%(group_id)s'") %
{'user_id': user_id,
'group_id': group_id})
def remove_user_from_group(self, user_id, group_id):
# We don't check if user or group are still valid and let the remove
# be tried anyway - in case this is some kind of clean-up operation
with sql.session_for_write() as session:
query = session.query(model.UserGroupMembership)
query = query.filter_by(user_id=user_id)
query = query.filter_by(group_id=group_id)
membership_ref = query.first()
if membership_ref is None:
# Check if the group and user exist to return descriptive
# exceptions.
self.get_group(group_id)
self.get_user(user_id)
raise exception.NotFound(_("User '%(user_id)s' not found in"
" group '%(group_id)s'") %
{'user_id': user_id,
'group_id': group_id})
session.delete(membership_ref)
def list_groups_for_user(self, user_id, hints):
with sql.session_for_read() as session:
self.get_user(user_id)
query = session.query(model.Group).join(model.UserGroupMembership)
query = query.filter(model.UserGroupMembership.user_id == user_id)
query = sql.filter_limit_query(model.Group, query, hints)
return [g.to_dict() for g in query]
def list_users_in_group(self, group_id, hints):
with sql.session_for_read() as session:
self.get_group(group_id)
query = session.query(model.User).outerjoin(model.LocalUser)
query = query.join(model.UserGroupMembership)
query = query.filter(
model.UserGroupMembership.group_id == group_id)
query = sql.filter_limit_query(model.User, query, hints)
return [base.filter_user(u.to_dict()) for u in query]
def delete_user(self, user_id):
with sql.session_for_write() as session:
ref = self._get_user(session, user_id)
q = session.query(model.UserGroupMembership)
q = q.filter_by(user_id=user_id)
q.delete(False)
session.delete(ref)
# group crud
@sql.handle_conflicts(conflict_type='group')
def create_group(self, group_id, group):
with sql.session_for_write() as session:
ref = model.Group.from_dict(group)
session.add(ref)
return ref.to_dict()
@driver_hints.truncated
def list_groups(self, hints):
with sql.session_for_read() as session:
query = session.query(model.Group)
refs = sql.filter_limit_query(model.Group, query, hints)
return [ref.to_dict() for ref in refs]
def _get_group(self, session, group_id):
ref = session.query(model.Group).get(group_id)
if not ref:
raise exception.GroupNotFound(group_id=group_id)
return ref
def get_group(self, group_id):
with sql.session_for_read() as session:
return self._get_group(session, group_id).to_dict()
def get_group_by_name(self, group_name, domain_id):
with sql.session_for_read() as session:
query = session.query(model.Group)
query = query.filter_by(name=group_name)
query = query.filter_by(domain_id=domain_id)
try:
group_ref = query.one()
except sql.NotFound:
raise exception.GroupNotFound(group_id=group_name)
return group_ref.to_dict()
@sql.handle_conflicts(conflict_type='group')
def update_group(self, group_id, group):
with sql.session_for_write() as session:
ref = self._get_group(session, group_id)
old_dict = ref.to_dict()
for k in group:
old_dict[k] = group[k]
new_group = model.Group.from_dict(old_dict)
for attr in model.Group.attributes:
if attr != 'id':
setattr(ref, attr, getattr(new_group, attr))
ref.extra = new_group.extra
return ref.to_dict()
def delete_group(self, group_id):
with sql.session_for_write() as session:
ref = self._get_group(session, group_id)
q = session.query(model.UserGroupMembership)
q = q.filter_by(group_id=group_id)
q.delete(False)
session.delete(ref) | 0.536799 | 0.12749 |
import unittest
import os
from qubell.api.private.platform import QubellPlatform
from qubell.api.private.manifest import Manifest
from qubell.api.private.testing.setup_once import SetupOnce
from qubell.api.private.service import COBALT_SECURE_STORE_TYPE, WORKFLOW_SERVICE_TYPE, SHARED_INSTANCE_CATALOG_TYPE
from qubell.api.private.service import system_application_types
# this is required for used imports
# noinspection PyUnresolvedReferences
from qubell.api.testing import eventually, attr
__author__ = "<NAME>"
__copyright__ = "Copyright 2013, Qubell.com"
__license__ = "Apache"
__email__ = "<EMAIL>"
parameters = {
'organization': os.environ.get('QUBELL_ORGANIZATION', "selfcheck_organization_name"),
'user': os.environ.get('QUBELL_USER'),
'pass': os.environ.get('QUBELL_PASSWORD'),
'tenant': os.environ.get('QUBELL_TENANT'),
'token': os.environ.get('QUBELL_TOKEN'),
'provider_name': os.environ.get('PROVIDER_NAME', "selfcheck_provider_name"),
'provider_type': os.environ.get('PROVIDER_TYPE', 'aws-ec2'),
'provider_identity': os.environ.get('PROVIDER_IDENTITY', 'No PROVIDER_IDENTITY'),
'provider_credential': os.environ.get('PROVIDER_CREDENTIAL', 'PROVIDER_CREDENTIAL'),
'provider_region': os.environ.get('PROVIDER_REGION', 'us-east-1'),
}
zone = os.environ.get('QUBELL_ZONE')
class BaseTestCase(SetupOnce, unittest.TestCase):
parameters=parameters
## TODO: Main preparation should be here
""" Here we prepare global env. (load config, etc)
"""
# Set default manifest for app creation
manifest = Manifest(file=os.path.join(os.path.dirname(__file__), './default.yml'), name='BaseTestManifest')
platform = QubellPlatform.connect(user=parameters['user'], password=parameters['<PASSWORD>'], tenant=parameters['tenant'], token=parameters['token'])
def setup_once(self):
def type_to_app(t):
return self.organization.applications[system_application_types.get(t, t)]
# Initialize organization
if os.getenv("QUBELL_IT_LOCAL"):
self.parameters['organization'] = self.__class__.__name__
self.organization = self.platform.organization(name=self.parameters['organization'])
if zone:
z = [x for x in self.organization.list_zones() if x['name'] == zone]
if z:
self.organization.zoneId = z[0]['id']
# Initialize environment
if zone:
self.environment = self.organization.environment(name='default', zone=self.organization.zoneId)
self.environment.set_backend(self.organization.zoneId)
else:
self.environment = self.organization.get_environment(name='default')
self.shared_service = self.organization.service(name='BaseTestSharedService',
application=type_to_app(SHARED_INSTANCE_CATALOG_TYPE),
environment=self.environment,
parameters={'configuration.shared-instances': {}})
self.wf_service, self.key_service, self.cloud_account_service = self.environment.init_common_services() | integration_tests/base.py | import unittest
import os
from qubell.api.private.platform import QubellPlatform
from qubell.api.private.manifest import Manifest
from qubell.api.private.testing.setup_once import SetupOnce
from qubell.api.private.service import COBALT_SECURE_STORE_TYPE, WORKFLOW_SERVICE_TYPE, SHARED_INSTANCE_CATALOG_TYPE
from qubell.api.private.service import system_application_types
# this is required for used imports
# noinspection PyUnresolvedReferences
from qubell.api.testing import eventually, attr
__author__ = "<NAME>"
__copyright__ = "Copyright 2013, Qubell.com"
__license__ = "Apache"
__email__ = "<EMAIL>"
parameters = {
'organization': os.environ.get('QUBELL_ORGANIZATION', "selfcheck_organization_name"),
'user': os.environ.get('QUBELL_USER'),
'pass': os.environ.get('QUBELL_PASSWORD'),
'tenant': os.environ.get('QUBELL_TENANT'),
'token': os.environ.get('QUBELL_TOKEN'),
'provider_name': os.environ.get('PROVIDER_NAME', "selfcheck_provider_name"),
'provider_type': os.environ.get('PROVIDER_TYPE', 'aws-ec2'),
'provider_identity': os.environ.get('PROVIDER_IDENTITY', 'No PROVIDER_IDENTITY'),
'provider_credential': os.environ.get('PROVIDER_CREDENTIAL', 'PROVIDER_CREDENTIAL'),
'provider_region': os.environ.get('PROVIDER_REGION', 'us-east-1'),
}
zone = os.environ.get('QUBELL_ZONE')
class BaseTestCase(SetupOnce, unittest.TestCase):
parameters=parameters
## TODO: Main preparation should be here
""" Here we prepare global env. (load config, etc)
"""
# Set default manifest for app creation
manifest = Manifest(file=os.path.join(os.path.dirname(__file__), './default.yml'), name='BaseTestManifest')
platform = QubellPlatform.connect(user=parameters['user'], password=parameters['<PASSWORD>'], tenant=parameters['tenant'], token=parameters['token'])
def setup_once(self):
def type_to_app(t):
return self.organization.applications[system_application_types.get(t, t)]
# Initialize organization
if os.getenv("QUBELL_IT_LOCAL"):
self.parameters['organization'] = self.__class__.__name__
self.organization = self.platform.organization(name=self.parameters['organization'])
if zone:
z = [x for x in self.organization.list_zones() if x['name'] == zone]
if z:
self.organization.zoneId = z[0]['id']
# Initialize environment
if zone:
self.environment = self.organization.environment(name='default', zone=self.organization.zoneId)
self.environment.set_backend(self.organization.zoneId)
else:
self.environment = self.organization.get_environment(name='default')
self.shared_service = self.organization.service(name='BaseTestSharedService',
application=type_to_app(SHARED_INSTANCE_CATALOG_TYPE),
environment=self.environment,
parameters={'configuration.shared-instances': {}})
self.wf_service, self.key_service, self.cloud_account_service = self.environment.init_common_services() | 0.253676 | 0.105257 |
import json
from logging import ERROR
import os
from equit_ease.parser.parse import ChartParser
import unittest
def read_quote_fixture(fpath: str):
fixture_file_path = os.path.join(os.path.dirname(__file__), fpath)
with open(fixture_file_path, "r") as quote_fixture:
data = json.loads(quote_fixture.read())
return data
class TestChartParserMethods(unittest.TestCase):
"""testing methods from the ChartParser class."""
def setUp(self):
self.equity = "Apple"
self.data_fixture = read_quote_fixture("fixtures/chart.json")
self.errant_data_fixture = read_quote_fixture("fixtures/chart-errant.json")
self.parser = ChartParser(self.equity, self.data_fixture)
def tearDown(self):
self.ticker_to_search = None
self.data_fixture = None
self.parser = ChartParser
def test_extract_equity_chart_data_keys(self):
"""
test extract_equity_chart_data() #1 -> pass.
check that the response from extract_equity_chart_data() is a
tuple containing the following keys. Additionally, for each key,
check that non-standardized values are not equal to `chart_data`
and standardized values are equal to `chart_data`.
)
"""
keys = ("low", "high", "open", "close", "volume")
chart_data = self.parser.extract_equity_chart_data()
for i, key in enumerate(keys):
filtered_chart_data = self.data_fixture["chart"]["result"][0]["indicators"][
"quote"
][0][key]
# data is not standardized, so None values appear
self.assertNotEqual(filtered_chart_data, chart_data[i])
# data is standardized
self.assertEqual(
self.parser.standardize(filtered_chart_data), chart_data[i]
)
def test_extract_equity_chart_data_length(self):
"""
test extract_equity_chart_data() #2 -> pass.
check that the length of all values returned from
extract_equity_chart_data() are equal.
This test is more concerned with confirming a key
assumption underlying the application: the length of
open, close, bid, ask, etc... should all be the same.
"""
keys = ("low", "high", "open", "close", "volume")
responses = dict()
for key in keys:
filtered_chart_data = self.data_fixture["chart"]["result"][0]["indicators"][
"quote"
][0][key]
responses[key] = filtered_chart_data
self.assertTrue(
len(responses["low"])
== len(responses["high"])
== len(responses["open"])
== len(responses["close"])
== len(responses["volume"])
)
def test_extract_equity_chart_data_errant(self):
"""
test case #3 for extract_equity_chart_data() with errant fixture -> pass.
Using the chart-errant.json fixture, test the functionality
given unequal list lengths for the 'low', 'high', ..., etc.
No error should be raised.
"""
keys = ("low", "high", "open", "close", "volume")
responses = dict()
for key in keys:
filtered_chart_data = self.errant_data_fixture["chart"]["result"][0][
"indicators"
]["quote"][0][key]
responses[key] = filtered_chart_data
self.assertFalse(
len(responses["low"])
== len(responses["high"])
== len(responses["open"])
== len(responses["close"])
== len(responses["volume"])
)
def test_standardize_pass(self):
"""
test case #1 for standardize() -> pass.
Appropriate params are passed to the function,
resulting in expected results.
"""
test_data = [0, None, 0, 1, 2]
average = sum([item for item in test_data if item != None]) / len(
[item for item in test_data if item != None]
)
clean_test_data = [0, average, 0, 1, 2]
standardized_data = self.parser.standardize(test_data)
index_of_average = standardized_data.index(average)
self.assertEqual(standardized_data, clean_test_data)
self.assertEqual(index_of_average, 1)
def test_standardize_fail(self):
"""
test case #2 for standardize() -> fail.
Params that cause errors are passed, resulting in
``Error``
"""
test_data_one = [None, None, None, None]
test_data_two = [0, 0, 0, 0]
test_data_three = ["0", "0", "0", "0"]
with self.assertRaises(ZeroDivisionError):
self.parser.standardize(test_data_one)
# should be unchanged
self.assertEqual(self.parser.standardize(test_data_two), test_data_two)
with self.assertRaises(TypeError):
self.parser.standardize(test_data_three) | tests/test_chart_parser.py | import json
from logging import ERROR
import os
from equit_ease.parser.parse import ChartParser
import unittest
def read_quote_fixture(fpath: str):
fixture_file_path = os.path.join(os.path.dirname(__file__), fpath)
with open(fixture_file_path, "r") as quote_fixture:
data = json.loads(quote_fixture.read())
return data
class TestChartParserMethods(unittest.TestCase):
"""testing methods from the ChartParser class."""
def setUp(self):
self.equity = "Apple"
self.data_fixture = read_quote_fixture("fixtures/chart.json")
self.errant_data_fixture = read_quote_fixture("fixtures/chart-errant.json")
self.parser = ChartParser(self.equity, self.data_fixture)
def tearDown(self):
self.ticker_to_search = None
self.data_fixture = None
self.parser = ChartParser
def test_extract_equity_chart_data_keys(self):
"""
test extract_equity_chart_data() #1 -> pass.
check that the response from extract_equity_chart_data() is a
tuple containing the following keys. Additionally, for each key,
check that non-standardized values are not equal to `chart_data`
and standardized values are equal to `chart_data`.
)
"""
keys = ("low", "high", "open", "close", "volume")
chart_data = self.parser.extract_equity_chart_data()
for i, key in enumerate(keys):
filtered_chart_data = self.data_fixture["chart"]["result"][0]["indicators"][
"quote"
][0][key]
# data is not standardized, so None values appear
self.assertNotEqual(filtered_chart_data, chart_data[i])
# data is standardized
self.assertEqual(
self.parser.standardize(filtered_chart_data), chart_data[i]
)
def test_extract_equity_chart_data_length(self):
"""
test extract_equity_chart_data() #2 -> pass.
check that the length of all values returned from
extract_equity_chart_data() are equal.
This test is more concerned with confirming a key
assumption underlying the application: the length of
open, close, bid, ask, etc... should all be the same.
"""
keys = ("low", "high", "open", "close", "volume")
responses = dict()
for key in keys:
filtered_chart_data = self.data_fixture["chart"]["result"][0]["indicators"][
"quote"
][0][key]
responses[key] = filtered_chart_data
self.assertTrue(
len(responses["low"])
== len(responses["high"])
== len(responses["open"])
== len(responses["close"])
== len(responses["volume"])
)
def test_extract_equity_chart_data_errant(self):
"""
test case #3 for extract_equity_chart_data() with errant fixture -> pass.
Using the chart-errant.json fixture, test the functionality
given unequal list lengths for the 'low', 'high', ..., etc.
No error should be raised.
"""
keys = ("low", "high", "open", "close", "volume")
responses = dict()
for key in keys:
filtered_chart_data = self.errant_data_fixture["chart"]["result"][0][
"indicators"
]["quote"][0][key]
responses[key] = filtered_chart_data
self.assertFalse(
len(responses["low"])
== len(responses["high"])
== len(responses["open"])
== len(responses["close"])
== len(responses["volume"])
)
def test_standardize_pass(self):
"""
test case #1 for standardize() -> pass.
Appropriate params are passed to the function,
resulting in expected results.
"""
test_data = [0, None, 0, 1, 2]
average = sum([item for item in test_data if item != None]) / len(
[item for item in test_data if item != None]
)
clean_test_data = [0, average, 0, 1, 2]
standardized_data = self.parser.standardize(test_data)
index_of_average = standardized_data.index(average)
self.assertEqual(standardized_data, clean_test_data)
self.assertEqual(index_of_average, 1)
def test_standardize_fail(self):
"""
test case #2 for standardize() -> fail.
Params that cause errors are passed, resulting in
``Error``
"""
test_data_one = [None, None, None, None]
test_data_two = [0, 0, 0, 0]
test_data_three = ["0", "0", "0", "0"]
with self.assertRaises(ZeroDivisionError):
self.parser.standardize(test_data_one)
# should be unchanged
self.assertEqual(self.parser.standardize(test_data_two), test_data_two)
with self.assertRaises(TypeError):
self.parser.standardize(test_data_three) | 0.642657 | 0.441432 |
import json
from flask_restful import Resource
from bson.json_util import dumps
from flask import request
from mongoengine import DoesNotExist, ValidationError
from bson import ObjectId
from models.event import Event as Event_model
from models.user import User as User_model
from models.sport import Sport as Sport_model
from models.comment import Comment as Comment_model
from utils.auth import Auth
# pylint: disable=E1101
class Events(Resource):
def get(self):
args = request.args
headers = request.headers
token_validation = Auth.auth_token(headers)
if(token_validation != 'True'):
return token_validation
event_date = court_id = event_sport = None
if args is not None:
event_date = args.get('date')
court_id = args.get('court')
event_sport = args.get('sport')
query = []
events = []
if event_sport is not None:
query.append({"$match": {"sport": ObjectId(event_sport)}})
if event_date is not None:
query.append({"$match": {"event_date": {"$gte" :int(event_date)}}})
if court_id is not None:
query.append({"$match": {"court_id":int(court_id)}})
result = Event_model.objects.aggregate (*query)
for res in result:
event = {}
event['id'] = eval(dumps(res['_id']))['$oid']
event['eventDate'] = res['event_date']
event['creationDate'] = res['creation_date']
event['title'] = res['title']
event['description'] = res['description']
event['sport'] = res['sport']
event['courtID'] = res['court_id']
event['photos'] = res['photos']
# Creator
try:
creator = User_model.objects.get(id=res['creator'])
except DoesNotExist:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['USER_ERROR']['NOT_FOUND'], 500
creator_serialized = {}
creator_serialized['uuid'] = creator.uuid
creator_serialized['name'] = creator.name
creator_serialized['photoURL'] = creator.photo_url
event['creator'] = creator_serialized
# Participants
participants_from_db = res['participants']
participants = []
for p in participants_from_db:
user = User_model.objects.get(id=p)
user_serialized = {}
user_serialized['uuid'] = user.uuid
user_serialized['name'] = user.name
user_serialized['photoURL'] = user.photo_url
participants.append(user_serialized)
event['participants'] = participants
# Comments
comments_from_db = Comment_model.objects(event=event['id'])
comments = []
for c in comments_from_db:
comment = {}
user_serialized = {}
user_serialized['uuid'] = c.user.uuid
user_serialized['name'] = c.user.name
user_serialized['photoURL'] = c.user.photo_url
comment['user'] = user_serialized
comment['message'] = c.message
comment['id'] = eval(dumps(c.id))['$oid']
comments.append(comment)
event['comments'] = comments
# Sport
try:
query = []
if res['sport'] is not None:
query.append({"$match": {"_id": ObjectId(res['sport'])}})
except DoesNotExist:
return False
sport = eval(dumps(Sport_model.objects.aggregate (*query)))
event['sport'] = sport[0]
events.append(eval(dumps(event)))
return events, 200
def post(self):
# TODO: validate parameters
args = request.get_json(force=True, silent=True)
token_validation = Auth.auth_token(request.headers)
if(token_validation != 'True'):
return token_validation
if args is None:
return False, 500
event_data = args
try:
user = User_model.objects.get(uuid=args['creator_uuid'])
except DoesNotExist:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['USER_ERROR']['NOT_FOUND'], 500
new_event = Event_model(
event_date=int(event_data['event_date']),
title=event_data['title'],
description=event_data['description'],
court_id=event_data['court_id'],
creator=user['id'],
sport=event_data['sport_id']['$oid'],
participants=[user['id']]
)
try:
new_event.save()
except ValidationError:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['EVENT_ERROR']['NOT_VALID'], 500
event = new_event.to_json()
return eval(dumps(event)), 200 | resources/events.py | import json
from flask_restful import Resource
from bson.json_util import dumps
from flask import request
from mongoengine import DoesNotExist, ValidationError
from bson import ObjectId
from models.event import Event as Event_model
from models.user import User as User_model
from models.sport import Sport as Sport_model
from models.comment import Comment as Comment_model
from utils.auth import Auth
# pylint: disable=E1101
class Events(Resource):
def get(self):
args = request.args
headers = request.headers
token_validation = Auth.auth_token(headers)
if(token_validation != 'True'):
return token_validation
event_date = court_id = event_sport = None
if args is not None:
event_date = args.get('date')
court_id = args.get('court')
event_sport = args.get('sport')
query = []
events = []
if event_sport is not None:
query.append({"$match": {"sport": ObjectId(event_sport)}})
if event_date is not None:
query.append({"$match": {"event_date": {"$gte" :int(event_date)}}})
if court_id is not None:
query.append({"$match": {"court_id":int(court_id)}})
result = Event_model.objects.aggregate (*query)
for res in result:
event = {}
event['id'] = eval(dumps(res['_id']))['$oid']
event['eventDate'] = res['event_date']
event['creationDate'] = res['creation_date']
event['title'] = res['title']
event['description'] = res['description']
event['sport'] = res['sport']
event['courtID'] = res['court_id']
event['photos'] = res['photos']
# Creator
try:
creator = User_model.objects.get(id=res['creator'])
except DoesNotExist:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['USER_ERROR']['NOT_FOUND'], 500
creator_serialized = {}
creator_serialized['uuid'] = creator.uuid
creator_serialized['name'] = creator.name
creator_serialized['photoURL'] = creator.photo_url
event['creator'] = creator_serialized
# Participants
participants_from_db = res['participants']
participants = []
for p in participants_from_db:
user = User_model.objects.get(id=p)
user_serialized = {}
user_serialized['uuid'] = user.uuid
user_serialized['name'] = user.name
user_serialized['photoURL'] = user.photo_url
participants.append(user_serialized)
event['participants'] = participants
# Comments
comments_from_db = Comment_model.objects(event=event['id'])
comments = []
for c in comments_from_db:
comment = {}
user_serialized = {}
user_serialized['uuid'] = c.user.uuid
user_serialized['name'] = c.user.name
user_serialized['photoURL'] = c.user.photo_url
comment['user'] = user_serialized
comment['message'] = c.message
comment['id'] = eval(dumps(c.id))['$oid']
comments.append(comment)
event['comments'] = comments
# Sport
try:
query = []
if res['sport'] is not None:
query.append({"$match": {"_id": ObjectId(res['sport'])}})
except DoesNotExist:
return False
sport = eval(dumps(Sport_model.objects.aggregate (*query)))
event['sport'] = sport[0]
events.append(eval(dumps(event)))
return events, 200
def post(self):
# TODO: validate parameters
args = request.get_json(force=True, silent=True)
token_validation = Auth.auth_token(request.headers)
if(token_validation != 'True'):
return token_validation
if args is None:
return False, 500
event_data = args
try:
user = User_model.objects.get(uuid=args['creator_uuid'])
except DoesNotExist:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['USER_ERROR']['NOT_FOUND'], 500
new_event = Event_model(
event_date=int(event_data['event_date']),
title=event_data['title'],
description=event_data['description'],
court_id=event_data['court_id'],
creator=user['id'],
sport=event_data['sport_id']['$oid'],
participants=[user['id']]
)
try:
new_event.save()
except ValidationError:
with open('utils/errorCodes.json', 'r') as errorCodes:
return json.load(errorCodes)['EVENT_ERROR']['NOT_VALID'], 500
event = new_event.to_json()
return eval(dumps(event)), 200 | 0.110747 | 0.062674 |
import math
import jittor as jt
import jittor.nn as nn
class WNLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(WNLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight_v = nn.init.invariant_uniform((out_features, in_features), "float32")
self.weight_g = jt.norm(self.weight_v, k=2, dim=1, keepdim=True)
bound = 1.0 / math.sqrt(in_features)
self.bias = nn.init.uniform((out_features,), "float32", -bound, bound) if bias else None
def execute(self, x):
weight = self.weight_g * (self.weight_v / jt.norm(self.weight_v, k=2, dim=1, keepdim=True))
x = nn.matmul_transpose(x, weight)
if self.bias is not None:
return x + self.bias
return x
class DIDecoder(nn.Module):
def __init__(self):
super().__init__()
self.lin0 = WNLinear(32, 128)
self.lin1 = WNLinear(128, 128)
self.lin2 = WNLinear(128, 128 - 32)
self.lin3 = WNLinear(128, 128)
self.lin4 = WNLinear(128, 1)
self.uncertainty_layer = nn.Linear(128, 1)
self.relu = nn.ReLU()
self.dropout = [0, 1, 2, 3, 4, 5]
self.th = nn.Tanh()
def execute(self, ipt):
x = self.lin0(ipt)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = self.lin1(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = self.lin2(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = jt.contrib.concat([x, ipt], 1)
x = self.lin3(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
std = self.uncertainty_layer(x)
std = 0.05 + 0.5 * nn.softplus(std)
x = self.lin4(x)
x = self.th(x)
return x, std
class DIEncoder(nn.Module):
def __init__(self):
super().__init__()
self.mlp = nn.Sequential(
nn.Conv1d(6, 32, kernel_size=1, bias=False), nn.BatchNorm1d(32), nn.ReLU(),
nn.Conv1d(32, 64, kernel_size=1, bias=False), nn.BatchNorm1d(64), nn.ReLU(),
nn.Conv1d(64, 256, kernel_size=1, bias=False), nn.BatchNorm1d(256), nn.ReLU(),
nn.Conv1d(256, 29, kernel_size=1, bias=True)
)
def execute(self, x):
x = x.transpose([0, 2, 1])
x = self.mlp(x) # (B, L, N)
r = jt.mean(x, dim=-1)
return r | network.py | import math
import jittor as jt
import jittor.nn as nn
class WNLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True):
super(WNLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.weight_v = nn.init.invariant_uniform((out_features, in_features), "float32")
self.weight_g = jt.norm(self.weight_v, k=2, dim=1, keepdim=True)
bound = 1.0 / math.sqrt(in_features)
self.bias = nn.init.uniform((out_features,), "float32", -bound, bound) if bias else None
def execute(self, x):
weight = self.weight_g * (self.weight_v / jt.norm(self.weight_v, k=2, dim=1, keepdim=True))
x = nn.matmul_transpose(x, weight)
if self.bias is not None:
return x + self.bias
return x
class DIDecoder(nn.Module):
def __init__(self):
super().__init__()
self.lin0 = WNLinear(32, 128)
self.lin1 = WNLinear(128, 128)
self.lin2 = WNLinear(128, 128 - 32)
self.lin3 = WNLinear(128, 128)
self.lin4 = WNLinear(128, 1)
self.uncertainty_layer = nn.Linear(128, 1)
self.relu = nn.ReLU()
self.dropout = [0, 1, 2, 3, 4, 5]
self.th = nn.Tanh()
def execute(self, ipt):
x = self.lin0(ipt)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = self.lin1(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = self.lin2(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
x = jt.contrib.concat([x, ipt], 1)
x = self.lin3(x)
x = self.relu(x)
x = nn.dropout(x, p=0.2, is_train=True)
std = self.uncertainty_layer(x)
std = 0.05 + 0.5 * nn.softplus(std)
x = self.lin4(x)
x = self.th(x)
return x, std
class DIEncoder(nn.Module):
def __init__(self):
super().__init__()
self.mlp = nn.Sequential(
nn.Conv1d(6, 32, kernel_size=1, bias=False), nn.BatchNorm1d(32), nn.ReLU(),
nn.Conv1d(32, 64, kernel_size=1, bias=False), nn.BatchNorm1d(64), nn.ReLU(),
nn.Conv1d(64, 256, kernel_size=1, bias=False), nn.BatchNorm1d(256), nn.ReLU(),
nn.Conv1d(256, 29, kernel_size=1, bias=True)
)
def execute(self, x):
x = x.transpose([0, 2, 1])
x = self.mlp(x) # (B, L, N)
r = jt.mean(x, dim=-1)
return r | 0.901019 | 0.510435 |
import optparse
import dns.resolver
import console
import urllib3
from socket import *
from multiprocessing import Pool
import re
import time
def portsscan(host,port):
try:
connect = socket(AF_INET,SOCK_STREAM)
connect.settimeout(0.3)
connect.connect((host,port))
banner = getbanner(host,port)
print("[+] "+str(port)+" /tcp open "+banner)
except:
pass
def portscan(host,port):
#单端口扫描
port = int(port)
try:
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.2)
connect.connect((host, port))
banner = getbanner(host,port)
print("[+] " + str(port) + " /tcp open "+banner)
except:
print("[-] %s /tcp close" % port)
def getbanner(host,port):
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.1)
connect.connect((host, port))
def getbanner(host,port):
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.1)
connect.connect((host, port))
try:
banner = str(connect.recv(100))[2:-5]
except:
if port == 80:
banner = "http"
elif port == 135:
banner = "Microsoft Windows RPC"
elif port == 443:
banner = "https"
elif port == 445:
banner = "microsoft-ds"
else:
banner = "unknown"
return banner
def domaintoip(domain):
try:
ip = getaddrinfo(domain,None)[0][4][0]
except:
return domain
return ip
def getServer(url):
urllib3.disable_warnings()
http = urllib3.PoolManager()
try:
web = http.request("GET",url)
if web.status == 200:
Server = web.headers["Server"]
return Server
except:
return "known server"
def ipordomain(host):
if re.match(r"[a-z]+.\w+.[a-z]+", host):
return "domain"
elif re.match(r"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$",host):
return "ip"
else:
print("Please enter the correct address")
exit(4)
def check_argv(host,port):
if host == None or port == None:
print(parser.usage)
exit(0)
if re.match('^http',host):
print("example:www.google.com")
exit(1)
if ipordomain(host) == "domain":
cdn_result = query_cname(host)
if (cdn_result==False):
pass
else:
if(check_cdn(cdn_result)):
pass
host = domaintoip(host)
if ipordomain(host) == "ip":
host = host
server = getServer(host)
print("[+] scanning for " + host + "......")
print("[+] Server: " + str(server))
def check_port_or_ports(port,thread,start_time):
if thread == None:
thread = 20
if re.findall('-',port):
pool = Pool(thread)
port1 = port.split('-')
for port in range(int(port1[0]), int(port1[1])+1):
pool.apply_async(portsscan,(host,port))
pool.close()
pool.join()
end_time = float(time.time())
print("[+] This scan took "+str(round((end_time-start_time),3))+" s")
else:
portscan(host,port)
end_time = float(time.time())
print("[+] This scan took " +str(round((end_time-start_time),3))+ "s")
def query_cname(domain):
try:
cname_query = dns.resolver.query(domain, 'CNAME')
except:
return False
for i in cname_query.response.answer:
for j in i.items:
cname = str(j)[:-1]
return cname
if __name__ == '__main__':
parser = optparse.OptionParser("-H <target host> -p <target port>")
parser.add_option('-H', dest='host', type='string')
parser.add_option('-p', dest='port', type='string')
parser.add_option('-t', dest='thread', type='int')
(options, args) = parser.parse_args()
host = options.host
port = options.port
thread = options.thread
start_time = float(time.time())
check_argv(host,port)
check_port_or_ports(port, thread, start_time) | lib/utils/portscan.py |
import optparse
import dns.resolver
import console
import urllib3
from socket import *
from multiprocessing import Pool
import re
import time
def portsscan(host,port):
try:
connect = socket(AF_INET,SOCK_STREAM)
connect.settimeout(0.3)
connect.connect((host,port))
banner = getbanner(host,port)
print("[+] "+str(port)+" /tcp open "+banner)
except:
pass
def portscan(host,port):
#单端口扫描
port = int(port)
try:
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.2)
connect.connect((host, port))
banner = getbanner(host,port)
print("[+] " + str(port) + " /tcp open "+banner)
except:
print("[-] %s /tcp close" % port)
def getbanner(host,port):
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.1)
connect.connect((host, port))
def getbanner(host,port):
connect = socket(AF_INET, SOCK_STREAM)
connect.settimeout(0.1)
connect.connect((host, port))
try:
banner = str(connect.recv(100))[2:-5]
except:
if port == 80:
banner = "http"
elif port == 135:
banner = "Microsoft Windows RPC"
elif port == 443:
banner = "https"
elif port == 445:
banner = "microsoft-ds"
else:
banner = "unknown"
return banner
def domaintoip(domain):
try:
ip = getaddrinfo(domain,None)[0][4][0]
except:
return domain
return ip
def getServer(url):
urllib3.disable_warnings()
http = urllib3.PoolManager()
try:
web = http.request("GET",url)
if web.status == 200:
Server = web.headers["Server"]
return Server
except:
return "known server"
def ipordomain(host):
if re.match(r"[a-z]+.\w+.[a-z]+", host):
return "domain"
elif re.match(r"^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$",host):
return "ip"
else:
print("Please enter the correct address")
exit(4)
def check_argv(host,port):
if host == None or port == None:
print(parser.usage)
exit(0)
if re.match('^http',host):
print("example:www.google.com")
exit(1)
if ipordomain(host) == "domain":
cdn_result = query_cname(host)
if (cdn_result==False):
pass
else:
if(check_cdn(cdn_result)):
pass
host = domaintoip(host)
if ipordomain(host) == "ip":
host = host
server = getServer(host)
print("[+] scanning for " + host + "......")
print("[+] Server: " + str(server))
def check_port_or_ports(port,thread,start_time):
if thread == None:
thread = 20
if re.findall('-',port):
pool = Pool(thread)
port1 = port.split('-')
for port in range(int(port1[0]), int(port1[1])+1):
pool.apply_async(portsscan,(host,port))
pool.close()
pool.join()
end_time = float(time.time())
print("[+] This scan took "+str(round((end_time-start_time),3))+" s")
else:
portscan(host,port)
end_time = float(time.time())
print("[+] This scan took " +str(round((end_time-start_time),3))+ "s")
def query_cname(domain):
try:
cname_query = dns.resolver.query(domain, 'CNAME')
except:
return False
for i in cname_query.response.answer:
for j in i.items:
cname = str(j)[:-1]
return cname
if __name__ == '__main__':
parser = optparse.OptionParser("-H <target host> -p <target port>")
parser.add_option('-H', dest='host', type='string')
parser.add_option('-p', dest='port', type='string')
parser.add_option('-t', dest='thread', type='int')
(options, args) = parser.parse_args()
host = options.host
port = options.port
thread = options.thread
start_time = float(time.time())
check_argv(host,port)
check_port_or_ports(port, thread, start_time) | 0.182644 | 0.097648 |
"""Infer api."""
import time
import numpy as np
import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore import context, Parameter
from mindspore.train.model import Model
from src.dataset import load_dataset
from .seq2seq import Seq2seqModel
from ..utils import zero_weight
from ..utils.load_weights import load_infer_weights
context.set_context(
mode=context.GRAPH_MODE,
save_graphs=False,
device_target="Ascend",
reserve_class_name_in_scope=False)
class Seq2seqInferCell(nn.Cell):
"""
Encapsulation class of Seq2seqModel network infer.
Args:
network (nn.Cell): Seq2seqModel model.
Returns:
Tuple[Tensor, Tensor], predicted_ids and predicted_probs.
"""
def __init__(self, network):
super(Seq2seqInferCell, self).__init__(auto_prefix=False)
self.network = network
def construct(self,
source_ids,
source_mask):
"""Defines the computation performed."""
predicted_ids = self.network(source_ids,
source_mask)
return predicted_ids
def seq2seq_infer(config, dataset):
"""
Run infer with Seq2seqModel.
Args:
config (Seq2seqConfig): Config.
dataset (Dataset): Dataset.
Returns:
List[Dict], prediction, each example has 4 keys, "source",
"target", "prediction" and "prediction_prob".
"""
tfm_model = Seq2seqModel(
config=config,
is_training=False,
use_one_hot_embeddings=False)
params = tfm_model.trainable_params()
weights = load_infer_weights(config)
for param in params:
value = param.data
weights_name = param.name
if weights_name not in weights:
raise ValueError(f"{weights_name} is not found in weights.")
if isinstance(value, Tensor):
if weights_name in weights:
assert weights_name in weights
if isinstance(weights[weights_name], Parameter):
if param.data.dtype == "Float32":
param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float32))
elif param.data.dtype == "Float16":
param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float16))
elif isinstance(weights[weights_name], Tensor):
param.set_data(Tensor(weights[weights_name].asnumpy(), config.dtype))
elif isinstance(weights[weights_name], np.ndarray):
param.set_data(Tensor(weights[weights_name], config.dtype))
else:
param.set_data(weights[weights_name])
else:
print("weight not found in checkpoint: " + weights_name)
param.set_data(zero_weight(value.asnumpy().shape))
print(" | Load weights successfully.")
tfm_infer = Seq2seqInferCell(tfm_model)
model = Model(tfm_infer)
predictions = []
source_sentences = []
shape = P.Shape()
concat = P.Concat(axis=0)
batch_index = 1
pad_idx = 0
sos_idx = 2
eos_idx = 3
source_ids_pad = Tensor(np.tile(np.array([[sos_idx, eos_idx] + [pad_idx] * (config.seq_length - 2)]),
[config.batch_size, 1]), mstype.int32)
source_mask_pad = Tensor(np.tile(np.array([[1, 1] + [0] * (config.seq_length - 2)]),
[config.batch_size, 1]), mstype.int32)
for batch in dataset.create_dict_iterator():
source_sentences.append(batch["source_eos_ids"].asnumpy())
source_ids = Tensor(batch["source_eos_ids"], mstype.int32)
source_mask = Tensor(batch["source_eos_mask"], mstype.int32)
active_num = shape(source_ids)[0]
if active_num < config.batch_size:
source_ids = concat((source_ids, source_ids_pad[active_num:, :]))
source_mask = concat((source_mask, source_mask_pad[active_num:, :]))
start_time = time.time()
predicted_ids = model.predict(source_ids, source_mask)
print(f" | BatchIndex = {batch_index}, Batch size: {config.batch_size}, active_num={active_num}, "
f"Time cost: {time.time() - start_time}.")
if active_num < config.batch_size:
predicted_ids = predicted_ids[:active_num, :]
batch_index = batch_index + 1
predictions.append(predicted_ids.asnumpy())
output = []
for inputs, batch_out in zip(source_sentences, predictions):
for i, _ in enumerate(batch_out):
if batch_out.ndim == 3:
batch_out = batch_out[:, 0]
example = {
"source": inputs[i].tolist(),
"prediction": batch_out[i].tolist()
}
output.append(example)
return output
def infer(config):
"""
Seq2seqModel infer api.
Args:
config (GNMTConfig): Config.
Returns:
list, result with
"""
eval_dataset = load_dataset(data_files=config.test_dataset,
batch_size=config.batch_size,
sink_mode=config.dataset_sink_mode,
drop_remainder=False,
is_translate=True,
shuffle=False) if config.test_dataset else None
prediction = seq2seq_infer(config, eval_dataset)
return prediction | research/nlp/seq2seq/src/seq2seq_model/seq2seq_for_infer.py | """Infer api."""
import time
import numpy as np
import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore import context, Parameter
from mindspore.train.model import Model
from src.dataset import load_dataset
from .seq2seq import Seq2seqModel
from ..utils import zero_weight
from ..utils.load_weights import load_infer_weights
context.set_context(
mode=context.GRAPH_MODE,
save_graphs=False,
device_target="Ascend",
reserve_class_name_in_scope=False)
class Seq2seqInferCell(nn.Cell):
"""
Encapsulation class of Seq2seqModel network infer.
Args:
network (nn.Cell): Seq2seqModel model.
Returns:
Tuple[Tensor, Tensor], predicted_ids and predicted_probs.
"""
def __init__(self, network):
super(Seq2seqInferCell, self).__init__(auto_prefix=False)
self.network = network
def construct(self,
source_ids,
source_mask):
"""Defines the computation performed."""
predicted_ids = self.network(source_ids,
source_mask)
return predicted_ids
def seq2seq_infer(config, dataset):
"""
Run infer with Seq2seqModel.
Args:
config (Seq2seqConfig): Config.
dataset (Dataset): Dataset.
Returns:
List[Dict], prediction, each example has 4 keys, "source",
"target", "prediction" and "prediction_prob".
"""
tfm_model = Seq2seqModel(
config=config,
is_training=False,
use_one_hot_embeddings=False)
params = tfm_model.trainable_params()
weights = load_infer_weights(config)
for param in params:
value = param.data
weights_name = param.name
if weights_name not in weights:
raise ValueError(f"{weights_name} is not found in weights.")
if isinstance(value, Tensor):
if weights_name in weights:
assert weights_name in weights
if isinstance(weights[weights_name], Parameter):
if param.data.dtype == "Float32":
param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float32))
elif param.data.dtype == "Float16":
param.set_data(Tensor(weights[weights_name].data.asnumpy(), mstype.float16))
elif isinstance(weights[weights_name], Tensor):
param.set_data(Tensor(weights[weights_name].asnumpy(), config.dtype))
elif isinstance(weights[weights_name], np.ndarray):
param.set_data(Tensor(weights[weights_name], config.dtype))
else:
param.set_data(weights[weights_name])
else:
print("weight not found in checkpoint: " + weights_name)
param.set_data(zero_weight(value.asnumpy().shape))
print(" | Load weights successfully.")
tfm_infer = Seq2seqInferCell(tfm_model)
model = Model(tfm_infer)
predictions = []
source_sentences = []
shape = P.Shape()
concat = P.Concat(axis=0)
batch_index = 1
pad_idx = 0
sos_idx = 2
eos_idx = 3
source_ids_pad = Tensor(np.tile(np.array([[sos_idx, eos_idx] + [pad_idx] * (config.seq_length - 2)]),
[config.batch_size, 1]), mstype.int32)
source_mask_pad = Tensor(np.tile(np.array([[1, 1] + [0] * (config.seq_length - 2)]),
[config.batch_size, 1]), mstype.int32)
for batch in dataset.create_dict_iterator():
source_sentences.append(batch["source_eos_ids"].asnumpy())
source_ids = Tensor(batch["source_eos_ids"], mstype.int32)
source_mask = Tensor(batch["source_eos_mask"], mstype.int32)
active_num = shape(source_ids)[0]
if active_num < config.batch_size:
source_ids = concat((source_ids, source_ids_pad[active_num:, :]))
source_mask = concat((source_mask, source_mask_pad[active_num:, :]))
start_time = time.time()
predicted_ids = model.predict(source_ids, source_mask)
print(f" | BatchIndex = {batch_index}, Batch size: {config.batch_size}, active_num={active_num}, "
f"Time cost: {time.time() - start_time}.")
if active_num < config.batch_size:
predicted_ids = predicted_ids[:active_num, :]
batch_index = batch_index + 1
predictions.append(predicted_ids.asnumpy())
output = []
for inputs, batch_out in zip(source_sentences, predictions):
for i, _ in enumerate(batch_out):
if batch_out.ndim == 3:
batch_out = batch_out[:, 0]
example = {
"source": inputs[i].tolist(),
"prediction": batch_out[i].tolist()
}
output.append(example)
return output
def infer(config):
"""
Seq2seqModel infer api.
Args:
config (GNMTConfig): Config.
Returns:
list, result with
"""
eval_dataset = load_dataset(data_files=config.test_dataset,
batch_size=config.batch_size,
sink_mode=config.dataset_sink_mode,
drop_remainder=False,
is_translate=True,
shuffle=False) if config.test_dataset else None
prediction = seq2seq_infer(config, eval_dataset)
return prediction | 0.912126 | 0.514705 |
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import DataMigration
from django.conf import settings
from django.db import models
class Migration(DataMigration):
def forwards(self, orm):
for currency in orm.Currency.objects.all():
orm['currency_history.Currency'].objects.get_or_create(
title=currency.name,
iso_code=currency.iso_code,
)
for rate in orm.CurrencyRate.objects.all():
from_currency = orm['currency_history.Currency'].objects.get(
iso_code=rate.currency.iso_code)
to_currency = orm['currency_history.Currency'].objects.get(
iso_code=getattr(settings, 'BASE_CURRENCY', 'EUR'))
rate_obj, created = orm[
'currency_history.CurrencyRate'].objects.get_or_create(
from_currency=from_currency,
to_currency=to_currency,
)
history = orm['currency_history.CurrencyRateHistory'].objects.create(
value=rate.rate,
rate=rate_obj,
)
history.date = history.date.replace(day=1).replace(
month=rate.month).replace(year=rate.year)
history.save()
def backwards(self, orm):
"Write your backwards methods here."
models = {
u'account_keeping.account': {
'Meta': {'object_name': 'Account'},
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'accounts'", 'to': u"orm['currency_history.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'initial_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '128'}),
'total_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'})
},
u'account_keeping.category': {
'Meta': {'ordering': "['name']", 'object_name': 'Category'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '256'})
},
u'account_keeping.currency': {
'Meta': {'object_name': 'Currency'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_base_currency': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'iso_code': ('django.db.models.fields.CharField', [], {'max_length': '3'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '64'})
},
u'account_keeping.currencyrate': {
'Meta': {'object_name': 'CurrencyRate'},
'currency': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['account_keeping.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'month': ('django.db.models.fields.PositiveIntegerField', [], {}),
'rate': ('django.db.models.fields.DecimalField', [], {'max_digits': '18', 'decimal_places': '8'}),
'year': ('django.db.models.fields.PositiveIntegerField', [], {})
},
u'account_keeping.invoice': {
'Meta': {'ordering': "['-invoice_date']", 'object_name': 'Invoice'},
'amount_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'amount_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'invoices'", 'to': u"orm['currency_history.Currency']"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'invoice_date': ('django.db.models.fields.DateField', [], {}),
'invoice_number': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}),
'invoice_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}),
'payment_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'pdf': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'value_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'value_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'vat': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '4', 'decimal_places': '2'})
},
u'account_keeping.payee': {
'Meta': {'ordering': "['name']", 'object_name': 'Payee'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '256'})
},
u'account_keeping.transaction': {
'Meta': {'ordering': "['-transaction_date']", 'object_name': 'Transaction'},
'account': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Account']"}),
'amount_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'amount_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Category']"}),
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['currency_history.Currency']"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'invoice': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'transactions'", 'null': 'True', 'to': u"orm['account_keeping.Invoice']"}),
'invoice_number': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}),
'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': u"orm['account_keeping.Transaction']"}),
'payee': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Payee']"}),
'transaction_date': ('django.db.models.fields.DateField', [], {}),
'transaction_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}),
'value_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'value_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'vat': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '4', 'decimal_places': '2'})
},
u'currency_history.currency': {
'Meta': {'ordering': "['iso_code']", 'object_name': 'Currency'},
'abbreviation': ('django.db.models.fields.CharField', [], {'max_length': '10', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'iso_code': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '3'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'currency_history.currencyrate': {
'Meta': {'ordering': "['from_currency__iso_code', 'to_currency__iso_code']", 'object_name': 'CurrencyRate'},
'from_currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'rates_from'", 'to': u"orm['currency_history.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'to_currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'rates_to'", 'to': u"orm['currency_history.Currency']"})
},
u'currency_history.currencyratehistory': {
'Meta': {'ordering': "['-date', 'rate__to_currency__iso_code']", 'object_name': 'CurrencyRateHistory'},
'date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'rate': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'history'", 'to': u"orm['currency_history.CurrencyRate']"}),
'tracked_by': ('django.db.models.fields.CharField', [], {'default': "u'Add your email'", 'max_length': '512'}),
'value': ('django.db.models.fields.FloatField', [], {})
}
}
complete_apps = ['account_keeping']
symmetrical = True | account_keeping/south_migrations/0005_move_rates_to_history_app.py | from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import DataMigration
from django.conf import settings
from django.db import models
class Migration(DataMigration):
def forwards(self, orm):
for currency in orm.Currency.objects.all():
orm['currency_history.Currency'].objects.get_or_create(
title=currency.name,
iso_code=currency.iso_code,
)
for rate in orm.CurrencyRate.objects.all():
from_currency = orm['currency_history.Currency'].objects.get(
iso_code=rate.currency.iso_code)
to_currency = orm['currency_history.Currency'].objects.get(
iso_code=getattr(settings, 'BASE_CURRENCY', 'EUR'))
rate_obj, created = orm[
'currency_history.CurrencyRate'].objects.get_or_create(
from_currency=from_currency,
to_currency=to_currency,
)
history = orm['currency_history.CurrencyRateHistory'].objects.create(
value=rate.rate,
rate=rate_obj,
)
history.date = history.date.replace(day=1).replace(
month=rate.month).replace(year=rate.year)
history.save()
def backwards(self, orm):
"Write your backwards methods here."
models = {
u'account_keeping.account': {
'Meta': {'object_name': 'Account'},
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'accounts'", 'to': u"orm['currency_history.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'initial_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '128'}),
'total_amount': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'})
},
u'account_keeping.category': {
'Meta': {'ordering': "['name']", 'object_name': 'Category'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '256'})
},
u'account_keeping.currency': {
'Meta': {'object_name': 'Currency'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_base_currency': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'iso_code': ('django.db.models.fields.CharField', [], {'max_length': '3'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '64'})
},
u'account_keeping.currencyrate': {
'Meta': {'object_name': 'CurrencyRate'},
'currency': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['account_keeping.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'month': ('django.db.models.fields.PositiveIntegerField', [], {}),
'rate': ('django.db.models.fields.DecimalField', [], {'max_digits': '18', 'decimal_places': '8'}),
'year': ('django.db.models.fields.PositiveIntegerField', [], {})
},
u'account_keeping.invoice': {
'Meta': {'ordering': "['-invoice_date']", 'object_name': 'Invoice'},
'amount_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'amount_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'invoices'", 'to': u"orm['currency_history.Currency']"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'invoice_date': ('django.db.models.fields.DateField', [], {}),
'invoice_number': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}),
'invoice_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}),
'payment_date': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'pdf': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'value_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'value_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'vat': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '4', 'decimal_places': '2'})
},
u'account_keeping.payee': {
'Meta': {'ordering': "['name']", 'object_name': 'Payee'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '256'})
},
u'account_keeping.transaction': {
'Meta': {'ordering': "['-transaction_date']", 'object_name': 'Transaction'},
'account': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Account']"}),
'amount_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'amount_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2', 'blank': 'True'}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Category']"}),
'currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['currency_history.Currency']"}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'invoice': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'transactions'", 'null': 'True', 'to': u"orm['account_keeping.Invoice']"}),
'invoice_number': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}),
'parent': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': u"orm['account_keeping.Transaction']"}),
'payee': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'transactions'", 'to': u"orm['account_keeping.Payee']"}),
'transaction_date': ('django.db.models.fields.DateField', [], {}),
'transaction_type': ('django.db.models.fields.CharField', [], {'max_length': '1'}),
'value_gross': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'value_net': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '10', 'decimal_places': '2'}),
'vat': ('django.db.models.fields.DecimalField', [], {'default': '0', 'max_digits': '4', 'decimal_places': '2'})
},
u'currency_history.currency': {
'Meta': {'ordering': "['iso_code']", 'object_name': 'Currency'},
'abbreviation': ('django.db.models.fields.CharField', [], {'max_length': '10', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'iso_code': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '3'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'currency_history.currencyrate': {
'Meta': {'ordering': "['from_currency__iso_code', 'to_currency__iso_code']", 'object_name': 'CurrencyRate'},
'from_currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'rates_from'", 'to': u"orm['currency_history.Currency']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'to_currency': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'rates_to'", 'to': u"orm['currency_history.Currency']"})
},
u'currency_history.currencyratehistory': {
'Meta': {'ordering': "['-date', 'rate__to_currency__iso_code']", 'object_name': 'CurrencyRateHistory'},
'date': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'rate': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'history'", 'to': u"orm['currency_history.CurrencyRate']"}),
'tracked_by': ('django.db.models.fields.CharField', [], {'default': "u'Add your email'", 'max_length': '512'}),
'value': ('django.db.models.fields.FloatField', [], {})
}
}
complete_apps = ['account_keeping']
symmetrical = True | 0.555315 | 0.145419 |
"""Utlity to convert FunctionDef to GraphDef and Graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.python.eager import context
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.framework import versions
from tensorflow.python.framework.func_graph import FuncGraph
def function_def_to_graph(fdef, input_shapes=None):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. Defaults to the function's "_input_shapes" attribute. If
specified, its length must match length of `fdef.signature.input_arg`. If
a shape is None, the corresponding input placeholder will have unknown
shape.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name)
if input_shapes is None:
input_shapes_attr = fdef.attr.get("_input_shapes", None)
if input_shapes_attr is not None:
input_shapes = input_shapes_attr.list.shape
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def_for_function(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
for node in graph_def.node:
output_shapes = node.attr.get("_output_shapes", None)
if output_shapes is not None:
op = func_graph.get_operation_by_name(node.name)
# _output_shapes for functions can sometimes be too long because the
# output-intermediates-for-gradients version of the function was
# substituted before saving. We'll accept that here. (See b/133666530).
for output_index, shape in enumerate(
output_shapes.list.shape[:len(op.outputs)]):
op.outputs[output_index].set_shape(shape)
output_names = {}
for ret_arg_def, tensor_name in zip(
fdef.signature.output_arg, output_tensor_names):
output_names[ops.tensor_id(
func_graph.get_tensor_by_name(tensor_name))] = (
ret_arg_def.name)
func_graph._output_names = output_names # pylint: disable=protected-access
return func_graph
def is_function(fname):
"""Checks for a function definition with `fname` in the current context."""
if context.executing_eagerly():
return context.context().has_function(fname)
else:
graph = ops.get_default_graph()
while graph is not None:
if graph._is_function(fname): # pylint: disable=protected-access
return True
if hasattr(graph, "outer_graph"):
graph = graph.outer_graph
else:
return False
def function_def_to_graph_def(fdef, input_shapes=None):
"""Convert a FunctionDef to a GraphDef.
Steps:
1. Creates placeholder nodes corresponding to inputs in
`FunctionDef.signature.input_arg`.
2. Adds NodeDefs in `FunctionDef.node_def` to `GraphDef.node`.
3. Renames inputs of all nodes to use the convention of GraphDef instead of
FunctionDef. See comment on `FunctionDef.node_def` on how the tensor naming
in FunctionDefs is different from GraphDefs.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. If specified, its length must match length of
`fdef.signature.input_arg`. If a shape is None, the corresponding input
placeholder will have unknown shape.
Returns:
A tuple of (GraphDef, dict<string, string>). The dict contains a mapping
from nested tensor names (in FunctionDef) to flattened names (in GraphDef).
Raises:
ValueError: If the length of input_shapes does not match the number of
input_args or if the FunctionDef is invalid.
"""
graph_def = graph_pb2.GraphDef()
graph_def.versions.CopyFrom(
versions_pb2.VersionDef(
producer=versions.GRAPH_DEF_VERSION,
min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER))
default_graph = ops.get_default_graph()
copied_functions = set()
if input_shapes and len(input_shapes) != len(fdef.signature.input_arg):
raise ValueError("Length of input_shapes must match the number of " +
"input_args. len(input_shapes): {} len(input_arg): {}".
format(len(input_shapes), len(fdef.signature.input_arg)))
# 1. Create placeholders for input nodes.
for i, arg_def in enumerate(fdef.signature.input_arg):
node_def = graph_def.node.add()
node_def.name = arg_def.name
node_def.op = "Placeholder"
node_def.attr["dtype"].type = arg_def.type
if input_shapes and input_shapes[i] is not None:
input_shape = input_shapes[i]
if not isinstance(input_shape, tensor_shape_pb2.TensorShapeProto):
input_shape = input_shape.as_proto()
node_def.attr["shape"].shape.CopyFrom(input_shape)
arg_attrs = fdef.arg_attr[i].attr
for k in arg_attrs:
# Only copy internal attributes. Normal attributes for nodes cannot be
# applied to these Placeholder nodes.
if k == "_output_shapes":
node_def.attr["shape"].shape.CopyFrom(arg_attrs[k].list.shape[0])
elif k.startswith("_"):
node_def.attr[k].CopyFrom(arg_attrs[k])
# 2. Copy all body NodeDefs to the GraphDef.
graph_def.node.extend(fdef.node_def)
# 3. Perform the renaming.
# Build the tensor name mapping then flatten the tensor names.
# See comment on `FunctionDef.node_def` on how the tensor naming in
# FunctionDefs is different from GraphDefs.
nested_to_flat_tensor_name = {}
for arg_def in fdef.signature.input_arg:
nested_to_flat_tensor_name[arg_def.name] = "{}:0".format(arg_def.name)
control_name = "^" + arg_def.name
nested_to_flat_tensor_name[control_name] = control_name
for node_def in fdef.node_def:
graph = default_graph
while True:
f = graph._functions.get(node_def.op, None) # pylint: disable=protected-access
if f is not None or not hasattr(graph, "outer_graph"):
break
graph = graph.outer_graph
if f is not None:
op_def = f.definition.signature
if node_def.op not in copied_functions:
# Since this function is referenced as an op type, we have no choice but
# to copy it into the GraphDef if we want downstream tools to process
# it.
graph_def.library.function.add().CopyFrom(f.definition)
copied_functions.add(node_def.op)
if f.grad_func_name:
grad_def = function_pb2.GradientDef()
grad_def.function_name = f.name
grad_def.gradient_func = f.grad_func_name
graph_def.library.gradient.extend([grad_def])
else:
op_def = default_graph._get_op_def(node_def.op) # pylint: disable=protected-access
for attr in op_def.attr:
if attr.type == "func":
fname = node_def.attr[attr.name].func.name
if not is_function(fname):
raise ValueError("%s function not found." % fname)
elif attr.type == "list(func)":
for fn in node_def.attr[attr.name].list.func:
fname = fn.name
if not is_function(fname):
raise ValueError("%s function not found." % fname)
# Iterate over output_args in op_def to build the map.
# Index of the output tensor in the flattened list of *all* output
# tensors of the op.
flattened_index = 0
for arg_def in op_def.output_arg:
num_args = _get_num_args(arg_def, node_def)
for i in range(num_args):
# Map tensor names from "node_name:output_arg_name:index" to
# "node_name:flattened_index".
nested_name = "{}:{}:{}".format(node_def.name, arg_def.name, i)
flat_name = "{}:{}".format(node_def.name, flattened_index)
nested_to_flat_tensor_name[nested_name] = flat_name
flattened_index += 1
control_name = "^" + node_def.name
nested_to_flat_tensor_name[control_name] = control_name
# Update inputs of all nodes in graph.
for node_def in graph_def.node:
for i in range(len(node_def.input)):
node_def.input[i] = nested_to_flat_tensor_name[node_def.input[i]]
return graph_def, nested_to_flat_tensor_name
# Based on implementation in core/framework/node_def_util.cc::ComputeArgRange.
def _get_num_args(arg_def, node_def):
if arg_def.number_attr:
return node_def.attr[arg_def.number_attr].i
elif arg_def.type_list_attr:
return len(node_def.attr[arg_def.type_list_attr].list.type)
elif arg_def.type_attr or arg_def.type != types_pb2.DT_INVALID:
return 1
else:
raise ValueError("Invalid arg_def:\n\n{}".format(str(arg_def))) | tensorflow/python/framework/function_def_to_graph.py | """Utlity to convert FunctionDef to GraphDef and Graph."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.framework import function_pb2
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.framework import versions_pb2
from tensorflow.python.eager import context
from tensorflow.python.framework import importer
from tensorflow.python.framework import ops
from tensorflow.python.framework import versions
from tensorflow.python.framework.func_graph import FuncGraph
def function_def_to_graph(fdef, input_shapes=None):
"""Converts a FunctionDef to a FuncGraph (sub-class Graph).
The returned FuncGraph's `name`, `inputs` and `outputs` fields will be set.
The input tensors are represented as placeholders.
Note: `FuncGraph.inputs` and `FuncGraph.captures` are not set and may be set
by the caller.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. Defaults to the function's "_input_shapes" attribute. If
specified, its length must match length of `fdef.signature.input_arg`. If
a shape is None, the corresponding input placeholder will have unknown
shape.
Returns:
A FuncGraph.
"""
func_graph = FuncGraph(fdef.signature.name)
if input_shapes is None:
input_shapes_attr = fdef.attr.get("_input_shapes", None)
if input_shapes_attr is not None:
input_shapes = input_shapes_attr.list.shape
graph_def, nested_to_flat_tensor_name = function_def_to_graph_def(
fdef, input_shapes)
with func_graph.as_default():
# Add all function nodes to the graph.
importer.import_graph_def_for_function(graph_def, name="")
# Initialize fields specific to FuncGraph.
# inputs
input_tensor_names = [
nested_to_flat_tensor_name[arg.name] for arg in fdef.signature.input_arg
]
func_graph.inputs = [
func_graph.get_tensor_by_name(name) for name in input_tensor_names
]
# outputs
output_tensor_names = [
nested_to_flat_tensor_name[fdef.ret[arg.name]]
for arg in fdef.signature.output_arg
]
func_graph.outputs = [
func_graph.get_tensor_by_name(name) for name in output_tensor_names
]
func_graph.control_outputs = [
func_graph.get_operation_by_name(fdef.control_ret[ret_name])
for ret_name in fdef.signature.control_output
]
for node in graph_def.node:
output_shapes = node.attr.get("_output_shapes", None)
if output_shapes is not None:
op = func_graph.get_operation_by_name(node.name)
# _output_shapes for functions can sometimes be too long because the
# output-intermediates-for-gradients version of the function was
# substituted before saving. We'll accept that here. (See b/133666530).
for output_index, shape in enumerate(
output_shapes.list.shape[:len(op.outputs)]):
op.outputs[output_index].set_shape(shape)
output_names = {}
for ret_arg_def, tensor_name in zip(
fdef.signature.output_arg, output_tensor_names):
output_names[ops.tensor_id(
func_graph.get_tensor_by_name(tensor_name))] = (
ret_arg_def.name)
func_graph._output_names = output_names # pylint: disable=protected-access
return func_graph
def is_function(fname):
"""Checks for a function definition with `fname` in the current context."""
if context.executing_eagerly():
return context.context().has_function(fname)
else:
graph = ops.get_default_graph()
while graph is not None:
if graph._is_function(fname): # pylint: disable=protected-access
return True
if hasattr(graph, "outer_graph"):
graph = graph.outer_graph
else:
return False
def function_def_to_graph_def(fdef, input_shapes=None):
"""Convert a FunctionDef to a GraphDef.
Steps:
1. Creates placeholder nodes corresponding to inputs in
`FunctionDef.signature.input_arg`.
2. Adds NodeDefs in `FunctionDef.node_def` to `GraphDef.node`.
3. Renames inputs of all nodes to use the convention of GraphDef instead of
FunctionDef. See comment on `FunctionDef.node_def` on how the tensor naming
in FunctionDefs is different from GraphDefs.
Args:
fdef: FunctionDef.
input_shapes: Optional. A list of TensorShape objects of the shapes of
function inputs. If specified, its length must match length of
`fdef.signature.input_arg`. If a shape is None, the corresponding input
placeholder will have unknown shape.
Returns:
A tuple of (GraphDef, dict<string, string>). The dict contains a mapping
from nested tensor names (in FunctionDef) to flattened names (in GraphDef).
Raises:
ValueError: If the length of input_shapes does not match the number of
input_args or if the FunctionDef is invalid.
"""
graph_def = graph_pb2.GraphDef()
graph_def.versions.CopyFrom(
versions_pb2.VersionDef(
producer=versions.GRAPH_DEF_VERSION,
min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER))
default_graph = ops.get_default_graph()
copied_functions = set()
if input_shapes and len(input_shapes) != len(fdef.signature.input_arg):
raise ValueError("Length of input_shapes must match the number of " +
"input_args. len(input_shapes): {} len(input_arg): {}".
format(len(input_shapes), len(fdef.signature.input_arg)))
# 1. Create placeholders for input nodes.
for i, arg_def in enumerate(fdef.signature.input_arg):
node_def = graph_def.node.add()
node_def.name = arg_def.name
node_def.op = "Placeholder"
node_def.attr["dtype"].type = arg_def.type
if input_shapes and input_shapes[i] is not None:
input_shape = input_shapes[i]
if not isinstance(input_shape, tensor_shape_pb2.TensorShapeProto):
input_shape = input_shape.as_proto()
node_def.attr["shape"].shape.CopyFrom(input_shape)
arg_attrs = fdef.arg_attr[i].attr
for k in arg_attrs:
# Only copy internal attributes. Normal attributes for nodes cannot be
# applied to these Placeholder nodes.
if k == "_output_shapes":
node_def.attr["shape"].shape.CopyFrom(arg_attrs[k].list.shape[0])
elif k.startswith("_"):
node_def.attr[k].CopyFrom(arg_attrs[k])
# 2. Copy all body NodeDefs to the GraphDef.
graph_def.node.extend(fdef.node_def)
# 3. Perform the renaming.
# Build the tensor name mapping then flatten the tensor names.
# See comment on `FunctionDef.node_def` on how the tensor naming in
# FunctionDefs is different from GraphDefs.
nested_to_flat_tensor_name = {}
for arg_def in fdef.signature.input_arg:
nested_to_flat_tensor_name[arg_def.name] = "{}:0".format(arg_def.name)
control_name = "^" + arg_def.name
nested_to_flat_tensor_name[control_name] = control_name
for node_def in fdef.node_def:
graph = default_graph
while True:
f = graph._functions.get(node_def.op, None) # pylint: disable=protected-access
if f is not None or not hasattr(graph, "outer_graph"):
break
graph = graph.outer_graph
if f is not None:
op_def = f.definition.signature
if node_def.op not in copied_functions:
# Since this function is referenced as an op type, we have no choice but
# to copy it into the GraphDef if we want downstream tools to process
# it.
graph_def.library.function.add().CopyFrom(f.definition)
copied_functions.add(node_def.op)
if f.grad_func_name:
grad_def = function_pb2.GradientDef()
grad_def.function_name = f.name
grad_def.gradient_func = f.grad_func_name
graph_def.library.gradient.extend([grad_def])
else:
op_def = default_graph._get_op_def(node_def.op) # pylint: disable=protected-access
for attr in op_def.attr:
if attr.type == "func":
fname = node_def.attr[attr.name].func.name
if not is_function(fname):
raise ValueError("%s function not found." % fname)
elif attr.type == "list(func)":
for fn in node_def.attr[attr.name].list.func:
fname = fn.name
if not is_function(fname):
raise ValueError("%s function not found." % fname)
# Iterate over output_args in op_def to build the map.
# Index of the output tensor in the flattened list of *all* output
# tensors of the op.
flattened_index = 0
for arg_def in op_def.output_arg:
num_args = _get_num_args(arg_def, node_def)
for i in range(num_args):
# Map tensor names from "node_name:output_arg_name:index" to
# "node_name:flattened_index".
nested_name = "{}:{}:{}".format(node_def.name, arg_def.name, i)
flat_name = "{}:{}".format(node_def.name, flattened_index)
nested_to_flat_tensor_name[nested_name] = flat_name
flattened_index += 1
control_name = "^" + node_def.name
nested_to_flat_tensor_name[control_name] = control_name
# Update inputs of all nodes in graph.
for node_def in graph_def.node:
for i in range(len(node_def.input)):
node_def.input[i] = nested_to_flat_tensor_name[node_def.input[i]]
return graph_def, nested_to_flat_tensor_name
# Based on implementation in core/framework/node_def_util.cc::ComputeArgRange.
def _get_num_args(arg_def, node_def):
if arg_def.number_attr:
return node_def.attr[arg_def.number_attr].i
elif arg_def.type_list_attr:
return len(node_def.attr[arg_def.type_list_attr].list.type)
elif arg_def.type_attr or arg_def.type != types_pb2.DT_INVALID:
return 1
else:
raise ValueError("Invalid arg_def:\n\n{}".format(str(arg_def))) | 0.931843 | 0.429788 |
import os
import time
from subprocess import PIPE, CalledProcessError
import yaml
from conjureup.utils import run
from .writer import fail, log, success
JUJU_CONTROLLER = os.environ['JUJU_CONTROLLER']
JUJU_MODEL = os.environ['JUJU_MODEL']
JUJU_CM_STR = "{}:{}".format(JUJU_CONTROLLER, JUJU_MODEL)
def status():
""" Get juju status
"""
try:
sh = run(
'juju status -m {} --format yaml'.format(JUJU_CM_STR),
shell=True, check=True, stdout=PIPE)
except CalledProcessError:
return None
return yaml.load(sh.stdout.decode())
def leader(application):
""" Grabs the leader of a set of application units
Arguments:
application: name of application to query.
"""
try:
sh = run(
'juju run -m {} '
'--application {} is-leader --format yaml'.format(
JUJU_CM_STR, application),
shell=True, stdout=PIPE, check=True)
except CalledProcessError:
return None
leader_yaml = yaml.load(sh.stdout.decode())
for leader in leader_yaml:
if leader['Stdout'].strip() == 'True':
return leader['UnitId']
def agent_states():
""" get a list of running agent states
Returns:
A list of tuples of [(unit_name, current_state, workload_message)]
"""
juju_status = status()
agent_states = []
for app_name, app_dict in juju_status['applications'].items():
for unit_name, unit_dict in app_dict.get('units', {}).items():
cur_state = unit_dict['workload-status']['current']
message = unit_dict['workload-status'].get(
'message',
'Unknown workload status message')
agent_states.append((unit_name, cur_state, message))
return agent_states
def machine_states():
""" get a list of machine states
Returns:
A list of tuples of [(machine_name, current_state, machine_message)]
"""
return [(name, md['juju-status'].get('current', ''),
md['juju-status'].get('message', ''))
for name, md in status().get('machines', {}).items()]
def run_action(unit, action):
""" runs an action on a unit, waits for result
"""
is_complete = False
sh = run(
'juju run-action -m {} {} {}'.format(
JUJU_CM_STR, unit, action),
shell=True,
stdout=PIPE)
run_action_output = yaml.load(sh.stdout.decode())
log.debug("{}: {}".format(sh.args, run_action_output))
action_id = run_action_output.get('Action queued with id', None)
log.debug("Found action: {}".format(action_id))
if not action_id:
fail("Could not determine action id for test")
while not is_complete:
sh = run(
'juju show-action-output -m {} {}'.format(
JUJU_CM_STR, action_id),
shell=True,
stderr=PIPE,
stdout=PIPE)
log.debug(sh)
try:
output = yaml.load(sh.stdout.decode())
log.debug(output)
except Exception as e:
log.debug(e)
if output['status'] == 'running' or output['status'] == 'pending':
time.sleep(5)
continue
if output['status'] == 'failed':
fail("The test failed, "
"please have a look at `juju show-action-status`")
if output['status'] == 'completed':
completed_msg = "{} test passed".format(unit)
results = output.get('results', None)
if not results:
is_complete = True
success(completed_msg)
if results.get('outcome', None):
is_complete = True
completed_msg = "{}: (result) {}".format(
completed_msg,
results.get('outcome'))
success(completed_msg)
fail("There is an unknown issue with running the test, "
"please have a look at `juju show-action-status`") | conjureup/hooklib/juju.py | import os
import time
from subprocess import PIPE, CalledProcessError
import yaml
from conjureup.utils import run
from .writer import fail, log, success
JUJU_CONTROLLER = os.environ['JUJU_CONTROLLER']
JUJU_MODEL = os.environ['JUJU_MODEL']
JUJU_CM_STR = "{}:{}".format(JUJU_CONTROLLER, JUJU_MODEL)
def status():
""" Get juju status
"""
try:
sh = run(
'juju status -m {} --format yaml'.format(JUJU_CM_STR),
shell=True, check=True, stdout=PIPE)
except CalledProcessError:
return None
return yaml.load(sh.stdout.decode())
def leader(application):
""" Grabs the leader of a set of application units
Arguments:
application: name of application to query.
"""
try:
sh = run(
'juju run -m {} '
'--application {} is-leader --format yaml'.format(
JUJU_CM_STR, application),
shell=True, stdout=PIPE, check=True)
except CalledProcessError:
return None
leader_yaml = yaml.load(sh.stdout.decode())
for leader in leader_yaml:
if leader['Stdout'].strip() == 'True':
return leader['UnitId']
def agent_states():
""" get a list of running agent states
Returns:
A list of tuples of [(unit_name, current_state, workload_message)]
"""
juju_status = status()
agent_states = []
for app_name, app_dict in juju_status['applications'].items():
for unit_name, unit_dict in app_dict.get('units', {}).items():
cur_state = unit_dict['workload-status']['current']
message = unit_dict['workload-status'].get(
'message',
'Unknown workload status message')
agent_states.append((unit_name, cur_state, message))
return agent_states
def machine_states():
""" get a list of machine states
Returns:
A list of tuples of [(machine_name, current_state, machine_message)]
"""
return [(name, md['juju-status'].get('current', ''),
md['juju-status'].get('message', ''))
for name, md in status().get('machines', {}).items()]
def run_action(unit, action):
""" runs an action on a unit, waits for result
"""
is_complete = False
sh = run(
'juju run-action -m {} {} {}'.format(
JUJU_CM_STR, unit, action),
shell=True,
stdout=PIPE)
run_action_output = yaml.load(sh.stdout.decode())
log.debug("{}: {}".format(sh.args, run_action_output))
action_id = run_action_output.get('Action queued with id', None)
log.debug("Found action: {}".format(action_id))
if not action_id:
fail("Could not determine action id for test")
while not is_complete:
sh = run(
'juju show-action-output -m {} {}'.format(
JUJU_CM_STR, action_id),
shell=True,
stderr=PIPE,
stdout=PIPE)
log.debug(sh)
try:
output = yaml.load(sh.stdout.decode())
log.debug(output)
except Exception as e:
log.debug(e)
if output['status'] == 'running' or output['status'] == 'pending':
time.sleep(5)
continue
if output['status'] == 'failed':
fail("The test failed, "
"please have a look at `juju show-action-status`")
if output['status'] == 'completed':
completed_msg = "{} test passed".format(unit)
results = output.get('results', None)
if not results:
is_complete = True
success(completed_msg)
if results.get('outcome', None):
is_complete = True
completed_msg = "{}: (result) {}".format(
completed_msg,
results.get('outcome'))
success(completed_msg)
fail("There is an unknown issue with running the test, "
"please have a look at `juju show-action-status`") | 0.338296 | 0.157525 |
from django.shortcuts import redirect, render
from django.http import HttpResponse
from .models import *
import numpy as np
import pickle as p
#-------------------------------Home---------------------------------
def Index(request):
recent_recommend = crop_recommed.objects.filter().order_by('-cr_id')[:4]
print(recent_recommend)
return render(request,'index.html',{'recent_recommend':recent_recommend})
#-------------------------------Crop---------------------------------
def Crop(request):
crops = crop.objects.all()
return render(request,'crop.html',{"crops":crops})
def Crop_details(request,crop_name):
crop_details = crop.objects.get(crop_name = crop_name)
return render(request,'crop_details.html',{"crop_details":crop_details})
#-------------------------------Recommendation---------------------------------
def Crop_recommend(request):
model,accuracy = Recommendation('Crop_Recommendation.pkl')
if(request.POST):
result = predict_data(model,request)
result_crop_data = crop.objects.get(crop_name = result.cr_crop)
print(result_crop_data.crop_image)
return render(request,'crop_recommend_view.html',{'result':result,'result_crop_data':result_crop_data})
return render(request,'crop_recommend.html',{"accuracy":accuracy})
#-------------------------------About & Contact---------------------------------
def About_us(request):
return render(request,'about-us.html')
def Contact_us(request):
return render(request,'contact-us.html')
#-------------------------------Recommendation Model---------------------------------
def Recommendation(recommend_file):
pickle_file = open('CropRecommendationApp/Model/'+recommend_file,'rb')
model,accuracy = p.load(pickle_file)
return model,accuracy
#-------------------------------404 Error---------------------------------
def Error_404(request,exception):
return render(request,'404.html')
#-------------------------------500 Error---------------------------------
def Error_500(request,exception):
return render(request,'500.html')
def predict_data(model,request):
farmer_name,soil_nitrogen,soil_phosphorous,soil_potassium = str(request.POST['farmer_name']),int(request.POST['soil_nitrogen']),int(request.POST['soil_phosphorous']),int(request.POST['soil_potassium'])
soil_temperature,relative_humidity,soil_ph,rainfall = float(request.POST['soil_temperature']),float(request.POST['relative_humidity']),float(request.POST['soil_ph']),float(request.POST['rainfall'])
try:
data = crop_recommed.objects.get(cr_nitrogen=soil_nitrogen,cr_phosphorous=soil_phosphorous,cr_potassium=soil_potassium,cr_ph=soil_ph,cr_temperature=soil_temperature,cr_humidity=relative_humidity,cr_rainfall=rainfall)
except crop_recommed.DoesNotExist:
predict_details = [soil_nitrogen,soil_phosphorous,soil_potassium,soil_temperature,relative_humidity,soil_ph,rainfall]
recommend_crop = model.predict(np.array([predict_details]))
data = crop_recommed(cr_farmername=farmer_name,cr_nitrogen=soil_nitrogen,cr_phosphorous=soil_phosphorous,cr_potassium=soil_potassium,cr_ph=soil_ph,cr_temperature=soil_temperature,cr_humidity=relative_humidity,cr_rainfall=rainfall,cr_crop=recommend_crop[0])
data.save()
return data
#-------------------------------Admin---------------------------------
def Admin(request):
return render(request,'agrikol/admin/signin.html') | CropRecommendation/CropRecommendationApp/views.py | from django.shortcuts import redirect, render
from django.http import HttpResponse
from .models import *
import numpy as np
import pickle as p
#-------------------------------Home---------------------------------
def Index(request):
recent_recommend = crop_recommed.objects.filter().order_by('-cr_id')[:4]
print(recent_recommend)
return render(request,'index.html',{'recent_recommend':recent_recommend})
#-------------------------------Crop---------------------------------
def Crop(request):
crops = crop.objects.all()
return render(request,'crop.html',{"crops":crops})
def Crop_details(request,crop_name):
crop_details = crop.objects.get(crop_name = crop_name)
return render(request,'crop_details.html',{"crop_details":crop_details})
#-------------------------------Recommendation---------------------------------
def Crop_recommend(request):
model,accuracy = Recommendation('Crop_Recommendation.pkl')
if(request.POST):
result = predict_data(model,request)
result_crop_data = crop.objects.get(crop_name = result.cr_crop)
print(result_crop_data.crop_image)
return render(request,'crop_recommend_view.html',{'result':result,'result_crop_data':result_crop_data})
return render(request,'crop_recommend.html',{"accuracy":accuracy})
#-------------------------------About & Contact---------------------------------
def About_us(request):
return render(request,'about-us.html')
def Contact_us(request):
return render(request,'contact-us.html')
#-------------------------------Recommendation Model---------------------------------
def Recommendation(recommend_file):
pickle_file = open('CropRecommendationApp/Model/'+recommend_file,'rb')
model,accuracy = p.load(pickle_file)
return model,accuracy
#-------------------------------404 Error---------------------------------
def Error_404(request,exception):
return render(request,'404.html')
#-------------------------------500 Error---------------------------------
def Error_500(request,exception):
return render(request,'500.html')
def predict_data(model,request):
farmer_name,soil_nitrogen,soil_phosphorous,soil_potassium = str(request.POST['farmer_name']),int(request.POST['soil_nitrogen']),int(request.POST['soil_phosphorous']),int(request.POST['soil_potassium'])
soil_temperature,relative_humidity,soil_ph,rainfall = float(request.POST['soil_temperature']),float(request.POST['relative_humidity']),float(request.POST['soil_ph']),float(request.POST['rainfall'])
try:
data = crop_recommed.objects.get(cr_nitrogen=soil_nitrogen,cr_phosphorous=soil_phosphorous,cr_potassium=soil_potassium,cr_ph=soil_ph,cr_temperature=soil_temperature,cr_humidity=relative_humidity,cr_rainfall=rainfall)
except crop_recommed.DoesNotExist:
predict_details = [soil_nitrogen,soil_phosphorous,soil_potassium,soil_temperature,relative_humidity,soil_ph,rainfall]
recommend_crop = model.predict(np.array([predict_details]))
data = crop_recommed(cr_farmername=farmer_name,cr_nitrogen=soil_nitrogen,cr_phosphorous=soil_phosphorous,cr_potassium=soil_potassium,cr_ph=soil_ph,cr_temperature=soil_temperature,cr_humidity=relative_humidity,cr_rainfall=rainfall,cr_crop=recommend_crop[0])
data.save()
return data
#-------------------------------Admin---------------------------------
def Admin(request):
return render(request,'agrikol/admin/signin.html') | 0.435661 | 0.132599 |
import datetime
from ..core.processor import (
processor,
process_node,
InstanceAttributeFactory,
Storage,
)
from ..core.utils import get_xml_root
from .fritzbase import AbstractLibraryBase
__all__ = ['FritzCall', 'Call']
ALL_CALL_TYPES = 0
RECEIVED_CALL_TYPE = 1
MISSED_CALL_TYPE = 2
OUT_CALL_TYPE = 3
SERVICE = 'X_AVM-DE_OnTel1'
def datetime_converter(date_string):
if not date_string:
return date_string
return datetime.datetime.strptime(date_string, '%d.%m.%y %H:%M')
def timedelta_converter(duration_string):
if not duration_string:
return duration_string
hours, minutes = [int(part) for part in duration_string.split(':', 1)]
return datetime.timedelta(hours=hours, minutes=minutes)
class FritzCall(AbstractLibraryBase):
"""
Can dial phone numbers and gives access to lists of recent phone
calls: incoming, outgoing and missed ones. All parameters are
optional. If given, they have the following meaning: `fc` is an
instance of FritzConnection, `address` the ip of the Fritz!Box,
`port` the port to connect to, `user` the username, `password` the
password, `timeout` a timeout as floating point number in seconds,
`use_tls` a boolean indicating to use TLS (default False).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.calls = None
def _update_calls(self, num=None, days=None):
result = self.fc.call_action(SERVICE, 'GetCallList')
url = result['NewCallListURL']
if days:
url += f'&days={days}'
elif num:
url += f'&max={num}'
root = get_xml_root(url)
self.calls = CallCollection(root)
def get_calls(self, calltype=ALL_CALL_TYPES, update=True,
num=None, days=None):
"""
Return a list of Call instances of type calltypes. If calltype
is 0 all calls are listet. If *update* is True, all calls are
reread from the router. *num* maximum number of entries in call
list. *days* number of days to look back for calls e.g. 1: calls
from today and yesterday, 7: calls from the complete last week.
"""
if not self.calls:
update = True
if update:
self._update_calls(num, days)
if calltype == ALL_CALL_TYPES:
return self.calls.calls
return [call for call in self.calls if call.type == calltype]
def get_received_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of received calls. If *update*
is True, all calls are reread from the router. *num* maximum
number of entries in call list. *days* number of days to look
back for calls e.g. 1: calls from today and yesterday, 7: calls
from the complete last week.
"""
return self.get_calls(RECEIVED_CALL_TYPE, update, num, days)
def get_missed_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of missed calls. If *update* is
True, all calls are reread from the router. *num* maximum number
of entries in call list. *days* number of days to look back for
calls e.g. 1: calls from today and yesterday, 7: calls from the
complete last week.
"""
return self.get_calls(MISSED_CALL_TYPE, update, num, days)
def get_out_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of outgoing calls. If *update*
is True, all calls are reread from the router. *num* maximum
number of entries in call list. *days* number of days to look
back for calls e.g. 1: calls from today and yesterday, 7: calls
from the complete last week.
"""
return self.get_calls(OUT_CALL_TYPE, update, num, days)
def dial(self, number):
"""
Dials the given *number* (number must be a string, as phone
numbers are allowed to start with leading zeros). This method
has no return value, but will raise an error reported from the
Fritz!Box on failure. **Note:** The dial-help of the Fritz!Box
must be activated to make this work.
"""
arg = {'NewX_AVM-DE_PhoneNumber': number}
self.fc.call_action('X_VoIP1', 'X_AVM-DE_DialNumber', arguments=arg)
class AttributeConverter:
"""
Data descriptor returning converted attribute values.
"""
def __init__(self, attribute_name, converter=str):
self.attribute_name = attribute_name
self.converter = converter
def __set__(self, obj, value):
return NotImplemented
def __get__(self, obj, objtype):
attr = getattr(obj, self.attribute_name)
try:
attr = self.converter(attr)
except (TypeError, ValueError):
pass
return attr
@processor
class Call:
"""
Represents a call with the attributes provided by AVM. Instance
attributes are *Id*, *Type*, *Called*, *Caller*, *CallerNumber*,
*CalledNumber*, *Name*, *Device*, *Port*, *Date*, *Duration* and
*Count*. The spelling represents the original xml-node names.
Additionally the following attributes can be accessed by lowercase
names: *id* returning the Id as integer, *type* returning the Type
as integer, *date* returning the Date as datetime-instance,
*duration* returning the Duration as timedelta-instance.
"""
id = AttributeConverter('Id', int)
type = AttributeConverter('Type', int)
date = AttributeConverter('Date', datetime_converter)
duration = AttributeConverter('Duration', timedelta_converter)
def __init__(self):
self.Id = None
self.Type = None
self.Called = None
self.Caller = None
self.CallerNumber = None
self.CalledNumber = None
self.Name = None
self.Device = None
self.Port = None
self.Date = None
self.Duration = None
self.Count = None
def __str__(self):
number = self.Called if self.type == 3 else self.Caller
duration = self.Duration if self.type != 2 else "-"
if not number:
number = "-"
return f'{self.Type:>6} {number:24}{self.Date:>18}{duration:>12}'
class CallCollection(Storage):
"""
Container for a sequence of Call instances.
"""
Call = InstanceAttributeFactory(Call)
def __init__(self, root):
self.timestamp = None
self.calls = list()
super().__init__(self.calls)
process_node(self, root)
def __iter__(self):
return iter(self.calls) | fritzconnection/lib/fritzcall.py |
import datetime
from ..core.processor import (
processor,
process_node,
InstanceAttributeFactory,
Storage,
)
from ..core.utils import get_xml_root
from .fritzbase import AbstractLibraryBase
__all__ = ['FritzCall', 'Call']
ALL_CALL_TYPES = 0
RECEIVED_CALL_TYPE = 1
MISSED_CALL_TYPE = 2
OUT_CALL_TYPE = 3
SERVICE = 'X_AVM-DE_OnTel1'
def datetime_converter(date_string):
if not date_string:
return date_string
return datetime.datetime.strptime(date_string, '%d.%m.%y %H:%M')
def timedelta_converter(duration_string):
if not duration_string:
return duration_string
hours, minutes = [int(part) for part in duration_string.split(':', 1)]
return datetime.timedelta(hours=hours, minutes=minutes)
class FritzCall(AbstractLibraryBase):
"""
Can dial phone numbers and gives access to lists of recent phone
calls: incoming, outgoing and missed ones. All parameters are
optional. If given, they have the following meaning: `fc` is an
instance of FritzConnection, `address` the ip of the Fritz!Box,
`port` the port to connect to, `user` the username, `password` the
password, `timeout` a timeout as floating point number in seconds,
`use_tls` a boolean indicating to use TLS (default False).
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.calls = None
def _update_calls(self, num=None, days=None):
result = self.fc.call_action(SERVICE, 'GetCallList')
url = result['NewCallListURL']
if days:
url += f'&days={days}'
elif num:
url += f'&max={num}'
root = get_xml_root(url)
self.calls = CallCollection(root)
def get_calls(self, calltype=ALL_CALL_TYPES, update=True,
num=None, days=None):
"""
Return a list of Call instances of type calltypes. If calltype
is 0 all calls are listet. If *update* is True, all calls are
reread from the router. *num* maximum number of entries in call
list. *days* number of days to look back for calls e.g. 1: calls
from today and yesterday, 7: calls from the complete last week.
"""
if not self.calls:
update = True
if update:
self._update_calls(num, days)
if calltype == ALL_CALL_TYPES:
return self.calls.calls
return [call for call in self.calls if call.type == calltype]
def get_received_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of received calls. If *update*
is True, all calls are reread from the router. *num* maximum
number of entries in call list. *days* number of days to look
back for calls e.g. 1: calls from today and yesterday, 7: calls
from the complete last week.
"""
return self.get_calls(RECEIVED_CALL_TYPE, update, num, days)
def get_missed_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of missed calls. If *update* is
True, all calls are reread from the router. *num* maximum number
of entries in call list. *days* number of days to look back for
calls e.g. 1: calls from today and yesterday, 7: calls from the
complete last week.
"""
return self.get_calls(MISSED_CALL_TYPE, update, num, days)
def get_out_calls(self, update=True, num=None, days=None):
"""
Return a list of Call instances of outgoing calls. If *update*
is True, all calls are reread from the router. *num* maximum
number of entries in call list. *days* number of days to look
back for calls e.g. 1: calls from today and yesterday, 7: calls
from the complete last week.
"""
return self.get_calls(OUT_CALL_TYPE, update, num, days)
def dial(self, number):
"""
Dials the given *number* (number must be a string, as phone
numbers are allowed to start with leading zeros). This method
has no return value, but will raise an error reported from the
Fritz!Box on failure. **Note:** The dial-help of the Fritz!Box
must be activated to make this work.
"""
arg = {'NewX_AVM-DE_PhoneNumber': number}
self.fc.call_action('X_VoIP1', 'X_AVM-DE_DialNumber', arguments=arg)
class AttributeConverter:
"""
Data descriptor returning converted attribute values.
"""
def __init__(self, attribute_name, converter=str):
self.attribute_name = attribute_name
self.converter = converter
def __set__(self, obj, value):
return NotImplemented
def __get__(self, obj, objtype):
attr = getattr(obj, self.attribute_name)
try:
attr = self.converter(attr)
except (TypeError, ValueError):
pass
return attr
@processor
class Call:
"""
Represents a call with the attributes provided by AVM. Instance
attributes are *Id*, *Type*, *Called*, *Caller*, *CallerNumber*,
*CalledNumber*, *Name*, *Device*, *Port*, *Date*, *Duration* and
*Count*. The spelling represents the original xml-node names.
Additionally the following attributes can be accessed by lowercase
names: *id* returning the Id as integer, *type* returning the Type
as integer, *date* returning the Date as datetime-instance,
*duration* returning the Duration as timedelta-instance.
"""
id = AttributeConverter('Id', int)
type = AttributeConverter('Type', int)
date = AttributeConverter('Date', datetime_converter)
duration = AttributeConverter('Duration', timedelta_converter)
def __init__(self):
self.Id = None
self.Type = None
self.Called = None
self.Caller = None
self.CallerNumber = None
self.CalledNumber = None
self.Name = None
self.Device = None
self.Port = None
self.Date = None
self.Duration = None
self.Count = None
def __str__(self):
number = self.Called if self.type == 3 else self.Caller
duration = self.Duration if self.type != 2 else "-"
if not number:
number = "-"
return f'{self.Type:>6} {number:24}{self.Date:>18}{duration:>12}'
class CallCollection(Storage):
"""
Container for a sequence of Call instances.
"""
Call = InstanceAttributeFactory(Call)
def __init__(self, root):
self.timestamp = None
self.calls = list()
super().__init__(self.calls)
process_node(self, root)
def __iter__(self):
return iter(self.calls) | 0.700997 | 0.206834 |
from enum import IntEnum
import numpy as np
from numba import int64, float64, njit, objmode
from numba.experimental import jitclass
from tardis.montecarlo.montecarlo_numba import (
njit_dict_no_parallel,
)
from tardis.montecarlo.montecarlo_numba.frame_transformations import (
get_doppler_factor,
)
from tardis.montecarlo.montecarlo_numba import numba_config as nc
from tardis.montecarlo.montecarlo_numba import njit_dict_no_parallel
class InteractionType(IntEnum):
BOUNDARY = 1
LINE = 2
ESCATTERING = 4
CONTINUUM_PROCESS = 8
class PacketStatus(IntEnum):
IN_PROCESS = 0
EMITTED = 1
REABSORBED = 2
ADIABATIC_COOLING = 4
rpacket_spec = [
("r", float64),
("mu", float64),
("nu", float64),
("energy", float64),
("next_line_id", int64),
("current_shell_id", int64),
("status", int64),
("seed", int64),
("index", int64),
("last_interaction_type", int64),
("last_interaction_in_nu", float64),
("last_line_interaction_in_id", int64),
("last_line_interaction_out_id", int64),
]
@jitclass(rpacket_spec)
class RPacket(object):
def __init__(self, r, mu, nu, energy, seed, index=0):
self.r = r
self.mu = mu
self.nu = nu
self.energy = energy
self.current_shell_id = 0
self.status = PacketStatus.IN_PROCESS
self.seed = seed
self.index = index
self.last_interaction_type = -1
self.last_interaction_in_nu = 0.0
self.last_line_interaction_in_id = -1
self.last_line_interaction_out_id = -1
def initialize_line_id(self, numba_plasma, numba_model):
inverse_line_list_nu = numba_plasma.line_list_nu[::-1]
doppler_factor = get_doppler_factor(
self.r, self.mu, numba_model.time_explosion
)
comov_nu = self.nu * doppler_factor
next_line_id = len(numba_plasma.line_list_nu) - np.searchsorted(
inverse_line_list_nu, comov_nu
)
if next_line_id == len(numba_plasma.line_list_nu):
next_line_id -= 1
self.next_line_id = next_line_id
@njit(**njit_dict_no_parallel)
def print_r_packet_properties(r_packet):
"""
Print all packet information
Parameters
----------
r_packet : RPacket
RPacket object
"""
print("-" * 80)
print("R-Packet information:")
with objmode:
for r_packet_attribute_name, _ in rpacket_spec:
print(r_packet_attribute_name, "=", str(getattr(r_packet, r_packet_attribute_name)))
print("-" * 80) | tardis/montecarlo/montecarlo_numba/r_packet.py | from enum import IntEnum
import numpy as np
from numba import int64, float64, njit, objmode
from numba.experimental import jitclass
from tardis.montecarlo.montecarlo_numba import (
njit_dict_no_parallel,
)
from tardis.montecarlo.montecarlo_numba.frame_transformations import (
get_doppler_factor,
)
from tardis.montecarlo.montecarlo_numba import numba_config as nc
from tardis.montecarlo.montecarlo_numba import njit_dict_no_parallel
class InteractionType(IntEnum):
BOUNDARY = 1
LINE = 2
ESCATTERING = 4
CONTINUUM_PROCESS = 8
class PacketStatus(IntEnum):
IN_PROCESS = 0
EMITTED = 1
REABSORBED = 2
ADIABATIC_COOLING = 4
rpacket_spec = [
("r", float64),
("mu", float64),
("nu", float64),
("energy", float64),
("next_line_id", int64),
("current_shell_id", int64),
("status", int64),
("seed", int64),
("index", int64),
("last_interaction_type", int64),
("last_interaction_in_nu", float64),
("last_line_interaction_in_id", int64),
("last_line_interaction_out_id", int64),
]
@jitclass(rpacket_spec)
class RPacket(object):
def __init__(self, r, mu, nu, energy, seed, index=0):
self.r = r
self.mu = mu
self.nu = nu
self.energy = energy
self.current_shell_id = 0
self.status = PacketStatus.IN_PROCESS
self.seed = seed
self.index = index
self.last_interaction_type = -1
self.last_interaction_in_nu = 0.0
self.last_line_interaction_in_id = -1
self.last_line_interaction_out_id = -1
def initialize_line_id(self, numba_plasma, numba_model):
inverse_line_list_nu = numba_plasma.line_list_nu[::-1]
doppler_factor = get_doppler_factor(
self.r, self.mu, numba_model.time_explosion
)
comov_nu = self.nu * doppler_factor
next_line_id = len(numba_plasma.line_list_nu) - np.searchsorted(
inverse_line_list_nu, comov_nu
)
if next_line_id == len(numba_plasma.line_list_nu):
next_line_id -= 1
self.next_line_id = next_line_id
@njit(**njit_dict_no_parallel)
def print_r_packet_properties(r_packet):
"""
Print all packet information
Parameters
----------
r_packet : RPacket
RPacket object
"""
print("-" * 80)
print("R-Packet information:")
with objmode:
for r_packet_attribute_name, _ in rpacket_spec:
print(r_packet_attribute_name, "=", str(getattr(r_packet, r_packet_attribute_name)))
print("-" * 80) | 0.609524 | 0.227995 |
import hmac
import hashlib
import base64
import time
from Crypto.Cipher import AES
import json
#C:/Users/User/Anaconda3_7/python.exe macaroons_benchmark_37.py
"""This is a library file for creating macaroons. The functions defined are only those
necessary to duplicate the results in Table 2 of Birgisson et al.'s "Macaroons: Cookies
with Contextual Caveats for Decentralized Authorization in the Cloud".
The definition of a macaroon (M) as defined by Birgisson et al. is a tuple of the form
of macaroon@L(id,C,sig) where (per Figure 7)
* L - Locs (optional) is a hint to the target's location
* id - BitStrs is the macaroon identifier
* C is a list of caveats of the form cav@cL(cId, vId), where
* cL 2 Locs (optional) is a hint to a discharge location
* cId 2 BitStrs is the caveat identifier
* vId 2 BitStrs is the verification-key identifier
* sig - Keys is a chained-MAC signature over the macaroon identifier id, as well as each of the caveats in C, in linear sequence.
The macaroons functions from the paper's Figure 8 defined herein include the following:
CreateMacaroon(key, id, location) = CreateMacaroon(k, id , L);
... = M.addCaveatHelper(cId, vId, cL)
... = M.AddFirstPartyCaveat(a)
... = M.Verify(TM , k, A, M)
The additional functions for marshalling and pasing JSONs are being also tested to support
the replication of results in Birgisson et al. Table II.
... = Mashal as JSON
... = Parse from JSON
...
Test File Development Status
-------
Completed:
...
To-do:
...
...
Methods
-------
CreateMacaroon(key, id, location)
Creates a macaroon
ENC(sig, key)
encrypts the signature with a secret key
verify(macaroon, K_TargetService ):
Verifies a macaroon and its caveats
...
"""
def CreateMacaroon(key, id, location):
"""Creates a macaroon
Given a high-entropy root key k and an identifier id, the function CreateMacaroon(k,id) returns
a macaroon that has the identifier id, an empty caveat list, and a valid signature sig = MAC(k, id ).
Parameters
----------
key : str
encryption key
id : str
random_nonce / payload
location : str
specified location
"""
data = hmac.new(key.encode('utf-8'), id.encode('utf-8'), hashlib.sha256)
signature_str = data.hexdigest() # KLUDGE: can we go back and forth from hexdigest()
macaroon_obj = Macaroon( id , [], signature_str)
macaroon_obj.targetLocation = location
return macaroon_obj
def ENC(sig, key):
"""encrypts the signature with a secret key
Parameters
----------
sig : str
signaure to be encrypted
key : str
secret key
"""
password = "<PASSWORD>"
key = hashlib.sha256(password).digest() ## output is 16 bytes
key = key[:16]
IV = 16 * '\x00' # Initialization vector: discussed later
mode = AES.MODE_CBC
encryptor = AES.new(key, mode, IV=IV)
forEncryption = hashlib.sha256(str(sig) + str(key)).digest()
ciphertext = encryptor.encrypt(forEncryption)
return
"""old code to delete?
"""
#KTS = dictionaryOfKeys[macaroon.id]
#verify(myMacaroon, KTS)
def verify(macaroon, K_TargetService ):
"""Verifies a macaroon and its caveats
This function operates such that it can verify an incoming access request consisting of an
authorizing macaroon TM so a target service can ensure that all first-party embedded caveats
in TM are satisfied.
Note this function is not the original "verify" in paper. (Since Table 2 doesn't require
third part caveats and verifying discharge macaroons). Thus this method only assumes
that the Macaroon was created with first party caveats.
Parameters
----------
macaroon : macaroon class object
macaroon to be verified
K_TargetService : str
key of target service
"""
#### verify the K_TargetService with Random_Nonce
data = hmac.new(K_TargetService.encode('utf-8'), macaroon.id.encode('utf-8'), hashlib.sha256)
signature_str = data.hexdigest()
#### verify the caveats
for caveat in macaroon.caveats:
#print(type(caveat))
#print(caveat)
caveatArr = caveat.split(':')
cId = caveatArr[0] # str(caveat['cid'])
vId = caveatArr[1] #str(caveat['vid'])
sig_prime = hmac.new(signature_str.encode('utf-8'), (str(vId)+str(cId)).encode('utf-8') , hashlib.sha256)
signature_str = sig_prime.hexdigest()
if(signature_str != macaroon.sig):
return False #### incorrect
else:
return True #### verified to be correct
class Macaroon(object):
"""
id = string
caveatsList = [str1, str2...]
signature = string
"""
def __init__(self, id, caveatsList, signature):
caveatsList = [str(x) for x in caveatsList]
signature = str(signature)
id = str(id)
self.caveats = caveatsList
self.id = id
self.sig = signature
####
self.targetLocation = None
self.thirdPartyLocations = []
"""
cId = string
vId = string
caveat_location = string
"""
def addCaveatHelper(self, cId, vId, caveat_location):
### KLUDGE: "pattern matching" in the addCaveatHelper
typeCaveat = type(caveat_location)
caveat = str(cId) +":" + str(vId) + ":" + str(caveat_location)
#print("self.sig ", self.sig, " and type is: ", type(self.sig))
sig_prime = hmac.new( str(self.sig).encode('utf-8'), (str(vId)+str(cId)).encode('utf-8') , hashlib.sha256)
self.caveats.append(caveat)
self.sig = sig_prime.hexdigest()
return self
"""
cK = string
cId = string
cL = string
"""
def addThirdPartyCaveat(self, cK, cId, cL):
vId = ENC(self.sig, cK)
self.thirdPartyLocations.append(cL)
self.addCaveatHelper(cId, vId, cL)
"""
a = string
"""
def addFirstPartyCaveat(self, a):
self.addCaveatHelper(a, '0', self.targetLocation )
def prepareForRequest(self):
pass
"""
Reference is https://www.w3schools.com/python/python_json.asp
https://medium.com/python-pandemonium/json-the-python-way-91aac95d4041
"""
def marshalToJSON(macaroon):
json_string = json.dumps(macaroon, default=convert_to_dict)
return json_string
def parseFromJSON(json_string):
macaroon_object = json.loads(json_string, object_hook=dict_to_obj)
return macaroon_object
def convert_to_dict(mac_obj):
dictionary = {"caveats": mac_obj.caveats,
"id": mac_obj.id,
"sig": mac_obj.sig,
"targetLocation":mac_obj.targetLocation,
"thirdPartyLocations": mac_obj.thirdPartyLocations
}
obj_dict = {
"__class__": mac_obj.__class__.__name__,
"__module__": mac_obj.__module__
}
obj_dict.update(mac_obj.__dict__)
return obj_dict
def dict_to_obj(dictionary_obj):
#print("---", str(dictionary_obj))
caveatsList = dictionary_obj['caveats']
#print(type(caveatsList))
#print(caveatsList)
macaroon_object = Macaroon(dictionary_obj["id"] , caveatsList , dictionary_obj['sig'] )
macaroon_object.targetLocation = dictionary_obj["targetLocation"]
#print(" in dict_to_obj = ", dictionary_obj["thirdPartyLocations"])
macaroon_object.thirdPartyLocations = dictionary_obj["thirdPartyLocations"]
return macaroon_object | Ali/macaroons_temp/macaroons_lib2_37.py | import hmac
import hashlib
import base64
import time
from Crypto.Cipher import AES
import json
#C:/Users/User/Anaconda3_7/python.exe macaroons_benchmark_37.py
"""This is a library file for creating macaroons. The functions defined are only those
necessary to duplicate the results in Table 2 of Birgisson et al.'s "Macaroons: Cookies
with Contextual Caveats for Decentralized Authorization in the Cloud".
The definition of a macaroon (M) as defined by Birgisson et al. is a tuple of the form
of macaroon@L(id,C,sig) where (per Figure 7)
* L - Locs (optional) is a hint to the target's location
* id - BitStrs is the macaroon identifier
* C is a list of caveats of the form cav@cL(cId, vId), where
* cL 2 Locs (optional) is a hint to a discharge location
* cId 2 BitStrs is the caveat identifier
* vId 2 BitStrs is the verification-key identifier
* sig - Keys is a chained-MAC signature over the macaroon identifier id, as well as each of the caveats in C, in linear sequence.
The macaroons functions from the paper's Figure 8 defined herein include the following:
CreateMacaroon(key, id, location) = CreateMacaroon(k, id , L);
... = M.addCaveatHelper(cId, vId, cL)
... = M.AddFirstPartyCaveat(a)
... = M.Verify(TM , k, A, M)
The additional functions for marshalling and pasing JSONs are being also tested to support
the replication of results in Birgisson et al. Table II.
... = Mashal as JSON
... = Parse from JSON
...
Test File Development Status
-------
Completed:
...
To-do:
...
...
Methods
-------
CreateMacaroon(key, id, location)
Creates a macaroon
ENC(sig, key)
encrypts the signature with a secret key
verify(macaroon, K_TargetService ):
Verifies a macaroon and its caveats
...
"""
def CreateMacaroon(key, id, location):
"""Creates a macaroon
Given a high-entropy root key k and an identifier id, the function CreateMacaroon(k,id) returns
a macaroon that has the identifier id, an empty caveat list, and a valid signature sig = MAC(k, id ).
Parameters
----------
key : str
encryption key
id : str
random_nonce / payload
location : str
specified location
"""
data = hmac.new(key.encode('utf-8'), id.encode('utf-8'), hashlib.sha256)
signature_str = data.hexdigest() # KLUDGE: can we go back and forth from hexdigest()
macaroon_obj = Macaroon( id , [], signature_str)
macaroon_obj.targetLocation = location
return macaroon_obj
def ENC(sig, key):
"""encrypts the signature with a secret key
Parameters
----------
sig : str
signaure to be encrypted
key : str
secret key
"""
password = "<PASSWORD>"
key = hashlib.sha256(password).digest() ## output is 16 bytes
key = key[:16]
IV = 16 * '\x00' # Initialization vector: discussed later
mode = AES.MODE_CBC
encryptor = AES.new(key, mode, IV=IV)
forEncryption = hashlib.sha256(str(sig) + str(key)).digest()
ciphertext = encryptor.encrypt(forEncryption)
return
"""old code to delete?
"""
#KTS = dictionaryOfKeys[macaroon.id]
#verify(myMacaroon, KTS)
def verify(macaroon, K_TargetService ):
"""Verifies a macaroon and its caveats
This function operates such that it can verify an incoming access request consisting of an
authorizing macaroon TM so a target service can ensure that all first-party embedded caveats
in TM are satisfied.
Note this function is not the original "verify" in paper. (Since Table 2 doesn't require
third part caveats and verifying discharge macaroons). Thus this method only assumes
that the Macaroon was created with first party caveats.
Parameters
----------
macaroon : macaroon class object
macaroon to be verified
K_TargetService : str
key of target service
"""
#### verify the K_TargetService with Random_Nonce
data = hmac.new(K_TargetService.encode('utf-8'), macaroon.id.encode('utf-8'), hashlib.sha256)
signature_str = data.hexdigest()
#### verify the caveats
for caveat in macaroon.caveats:
#print(type(caveat))
#print(caveat)
caveatArr = caveat.split(':')
cId = caveatArr[0] # str(caveat['cid'])
vId = caveatArr[1] #str(caveat['vid'])
sig_prime = hmac.new(signature_str.encode('utf-8'), (str(vId)+str(cId)).encode('utf-8') , hashlib.sha256)
signature_str = sig_prime.hexdigest()
if(signature_str != macaroon.sig):
return False #### incorrect
else:
return True #### verified to be correct
class Macaroon(object):
"""
id = string
caveatsList = [str1, str2...]
signature = string
"""
def __init__(self, id, caveatsList, signature):
caveatsList = [str(x) for x in caveatsList]
signature = str(signature)
id = str(id)
self.caveats = caveatsList
self.id = id
self.sig = signature
####
self.targetLocation = None
self.thirdPartyLocations = []
"""
cId = string
vId = string
caveat_location = string
"""
def addCaveatHelper(self, cId, vId, caveat_location):
### KLUDGE: "pattern matching" in the addCaveatHelper
typeCaveat = type(caveat_location)
caveat = str(cId) +":" + str(vId) + ":" + str(caveat_location)
#print("self.sig ", self.sig, " and type is: ", type(self.sig))
sig_prime = hmac.new( str(self.sig).encode('utf-8'), (str(vId)+str(cId)).encode('utf-8') , hashlib.sha256)
self.caveats.append(caveat)
self.sig = sig_prime.hexdigest()
return self
"""
cK = string
cId = string
cL = string
"""
def addThirdPartyCaveat(self, cK, cId, cL):
vId = ENC(self.sig, cK)
self.thirdPartyLocations.append(cL)
self.addCaveatHelper(cId, vId, cL)
"""
a = string
"""
def addFirstPartyCaveat(self, a):
self.addCaveatHelper(a, '0', self.targetLocation )
def prepareForRequest(self):
pass
"""
Reference is https://www.w3schools.com/python/python_json.asp
https://medium.com/python-pandemonium/json-the-python-way-91aac95d4041
"""
def marshalToJSON(macaroon):
json_string = json.dumps(macaroon, default=convert_to_dict)
return json_string
def parseFromJSON(json_string):
macaroon_object = json.loads(json_string, object_hook=dict_to_obj)
return macaroon_object
def convert_to_dict(mac_obj):
dictionary = {"caveats": mac_obj.caveats,
"id": mac_obj.id,
"sig": mac_obj.sig,
"targetLocation":mac_obj.targetLocation,
"thirdPartyLocations": mac_obj.thirdPartyLocations
}
obj_dict = {
"__class__": mac_obj.__class__.__name__,
"__module__": mac_obj.__module__
}
obj_dict.update(mac_obj.__dict__)
return obj_dict
def dict_to_obj(dictionary_obj):
#print("---", str(dictionary_obj))
caveatsList = dictionary_obj['caveats']
#print(type(caveatsList))
#print(caveatsList)
macaroon_object = Macaroon(dictionary_obj["id"] , caveatsList , dictionary_obj['sig'] )
macaroon_object.targetLocation = dictionary_obj["targetLocation"]
#print(" in dict_to_obj = ", dictionary_obj["thirdPartyLocations"])
macaroon_object.thirdPartyLocations = dictionary_obj["thirdPartyLocations"]
return macaroon_object | 0.510985 | 0.313157 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['UserArgs', 'User']
@pulumi.input_type
class UserArgs:
def __init__(__self__, *,
user_name: pulumi.Input[str],
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None):
"""
The set of arguments for constructing a User resource.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
"""
pulumi.set(__self__, "user_name", user_name)
if active is not None:
pulumi.set(__self__, "active", active)
if allow_cluster_create is not None:
pulumi.set(__self__, "allow_cluster_create", allow_cluster_create)
if allow_instance_pool_create is not None:
pulumi.set(__self__, "allow_instance_pool_create", allow_instance_pool_create)
if databricks_sql_access is not None:
pulumi.set(__self__, "databricks_sql_access", databricks_sql_access)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if external_id is not None:
pulumi.set(__self__, "external_id", external_id)
if force is not None:
pulumi.set(__self__, "force", force)
if workspace_access is not None:
pulumi.set(__self__, "workspace_access", workspace_access)
@property
@pulumi.getter(name="userName")
def user_name(self) -> pulumi.Input[str]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@user_name.setter
def user_name(self, value: pulumi.Input[str]):
pulumi.set(self, "user_name", value)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@allow_cluster_create.setter
def allow_cluster_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_cluster_create", value)
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@allow_instance_pool_create.setter
def allow_instance_pool_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_instance_pool_create", value)
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> Optional[pulumi.Input[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@databricks_sql_access.setter
def databricks_sql_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "databricks_sql_access", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="externalId")
def external_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@external_id.setter
def external_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "external_id", value)
@property
@pulumi.getter
def force(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "force")
@force.setter
def force(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "force", value)
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "workspace_access")
@workspace_access.setter
def workspace_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "workspace_access", value)
@pulumi.input_type
class _UserState:
def __init__(__self__, *,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None):
"""
Input properties used for looking up and filtering User resources.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
if active is not None:
pulumi.set(__self__, "active", active)
if allow_cluster_create is not None:
pulumi.set(__self__, "allow_cluster_create", allow_cluster_create)
if allow_instance_pool_create is not None:
pulumi.set(__self__, "allow_instance_pool_create", allow_instance_pool_create)
if databricks_sql_access is not None:
pulumi.set(__self__, "databricks_sql_access", databricks_sql_access)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if external_id is not None:
pulumi.set(__self__, "external_id", external_id)
if force is not None:
pulumi.set(__self__, "force", force)
if user_name is not None:
pulumi.set(__self__, "user_name", user_name)
if workspace_access is not None:
pulumi.set(__self__, "workspace_access", workspace_access)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@allow_cluster_create.setter
def allow_cluster_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_cluster_create", value)
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@allow_instance_pool_create.setter
def allow_instance_pool_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_instance_pool_create", value)
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> Optional[pulumi.Input[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@databricks_sql_access.setter
def databricks_sql_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "databricks_sql_access", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="externalId")
def external_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@external_id.setter
def external_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "external_id", value)
@property
@pulumi.getter
def force(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "force")
@force.setter
def force(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "force", value)
@property
@pulumi.getter(name="userName")
def user_name(self) -> Optional[pulumi.Input[str]]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@user_name.setter
def user_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "user_name", value)
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "workspace_access")
@workspace_access.setter
def workspace_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "workspace_access", value)
class User(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None,
__props__=None):
"""
This resource is used to [manage users](https://docs.databricks.com/administration-guide/users-groups/users.html), that could be added to Group within the workspace. Upon user creation the user will receive a password reset email. You can also get information about caller identity using get_current_user data source.
## Related Resources
The following resources are often used in the same context:
* End to end workspace management guide.
* Group to manage [groups in Databricks Workspace](https://docs.databricks.com/administration-guide/users-groups/groups.html) or [Account Console](https://accounts.cloud.databricks.com/) (for AWS deployments).
* Group data to retrieve information about Group members, entitlements and instance profiles.
* GroupInstanceProfile to attach InstanceProfile (AWS) to databricks_group.
* databricks_group_member to attach users and groups as group members.
* InstanceProfile to manage AWS EC2 instance profiles that users can launch Cluster and access data, like databricks_mount.
* User data to retrieves information about databricks_user.
## Import
The resource scim user can be imported using idbash
```sh
$ pulumi import databricks:index/user:User me <user-id>
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: UserArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
This resource is used to [manage users](https://docs.databricks.com/administration-guide/users-groups/users.html), that could be added to Group within the workspace. Upon user creation the user will receive a password reset email. You can also get information about caller identity using get_current_user data source.
## Related Resources
The following resources are often used in the same context:
* End to end workspace management guide.
* Group to manage [groups in Databricks Workspace](https://docs.databricks.com/administration-guide/users-groups/groups.html) or [Account Console](https://accounts.cloud.databricks.com/) (for AWS deployments).
* Group data to retrieve information about Group members, entitlements and instance profiles.
* GroupInstanceProfile to attach InstanceProfile (AWS) to databricks_group.
* databricks_group_member to attach users and groups as group members.
* InstanceProfile to manage AWS EC2 instance profiles that users can launch Cluster and access data, like databricks_mount.
* User data to retrieves information about databricks_user.
## Import
The resource scim user can be imported using idbash
```sh
$ pulumi import databricks:index/user:User me <user-id>
```
:param str resource_name: The name of the resource.
:param UserArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(UserArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None,
__props__=None):
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__ = UserArgs.__new__(UserArgs)
__props__.__dict__["active"] = active
__props__.__dict__["allow_cluster_create"] = allow_cluster_create
__props__.__dict__["allow_instance_pool_create"] = allow_instance_pool_create
__props__.__dict__["databricks_sql_access"] = databricks_sql_access
__props__.__dict__["display_name"] = display_name
__props__.__dict__["external_id"] = external_id
__props__.__dict__["force"] = force
if user_name is None and not opts.urn:
raise TypeError("Missing required property 'user_name'")
__props__.__dict__["user_name"] = user_name
__props__.__dict__["workspace_access"] = workspace_access
super(User, __self__).__init__(
'databricks:index/user:User',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None) -> 'User':
"""
Get an existing User 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.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _UserState.__new__(_UserState)
__props__.__dict__["active"] = active
__props__.__dict__["allow_cluster_create"] = allow_cluster_create
__props__.__dict__["allow_instance_pool_create"] = allow_instance_pool_create
__props__.__dict__["databricks_sql_access"] = databricks_sql_access
__props__.__dict__["display_name"] = display_name
__props__.__dict__["external_id"] = external_id
__props__.__dict__["force"] = force
__props__.__dict__["user_name"] = user_name
__props__.__dict__["workspace_access"] = workspace_access
return User(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def active(self) -> pulumi.Output[Optional[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> pulumi.Output[Optional[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> pulumi.Output[Optional[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> pulumi.Output[Optional[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Output[str]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@property
@pulumi.getter(name="externalId")
def external_id(self) -> pulumi.Output[Optional[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@property
@pulumi.getter
def force(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "force")
@property
@pulumi.getter(name="userName")
def user_name(self) -> pulumi.Output[str]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "workspace_access") | sdk/python/pulumi_databricks/user.py |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['UserArgs', 'User']
@pulumi.input_type
class UserArgs:
def __init__(__self__, *,
user_name: pulumi.Input[str],
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None):
"""
The set of arguments for constructing a User resource.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
"""
pulumi.set(__self__, "user_name", user_name)
if active is not None:
pulumi.set(__self__, "active", active)
if allow_cluster_create is not None:
pulumi.set(__self__, "allow_cluster_create", allow_cluster_create)
if allow_instance_pool_create is not None:
pulumi.set(__self__, "allow_instance_pool_create", allow_instance_pool_create)
if databricks_sql_access is not None:
pulumi.set(__self__, "databricks_sql_access", databricks_sql_access)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if external_id is not None:
pulumi.set(__self__, "external_id", external_id)
if force is not None:
pulumi.set(__self__, "force", force)
if workspace_access is not None:
pulumi.set(__self__, "workspace_access", workspace_access)
@property
@pulumi.getter(name="userName")
def user_name(self) -> pulumi.Input[str]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@user_name.setter
def user_name(self, value: pulumi.Input[str]):
pulumi.set(self, "user_name", value)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@allow_cluster_create.setter
def allow_cluster_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_cluster_create", value)
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@allow_instance_pool_create.setter
def allow_instance_pool_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_instance_pool_create", value)
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> Optional[pulumi.Input[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@databricks_sql_access.setter
def databricks_sql_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "databricks_sql_access", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="externalId")
def external_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@external_id.setter
def external_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "external_id", value)
@property
@pulumi.getter
def force(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "force")
@force.setter
def force(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "force", value)
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "workspace_access")
@workspace_access.setter
def workspace_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "workspace_access", value)
@pulumi.input_type
class _UserState:
def __init__(__self__, *,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None):
"""
Input properties used for looking up and filtering User resources.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
if active is not None:
pulumi.set(__self__, "active", active)
if allow_cluster_create is not None:
pulumi.set(__self__, "allow_cluster_create", allow_cluster_create)
if allow_instance_pool_create is not None:
pulumi.set(__self__, "allow_instance_pool_create", allow_instance_pool_create)
if databricks_sql_access is not None:
pulumi.set(__self__, "databricks_sql_access", databricks_sql_access)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if external_id is not None:
pulumi.set(__self__, "external_id", external_id)
if force is not None:
pulumi.set(__self__, "force", force)
if user_name is not None:
pulumi.set(__self__, "user_name", user_name)
if workspace_access is not None:
pulumi.set(__self__, "workspace_access", workspace_access)
@property
@pulumi.getter
def active(self) -> Optional[pulumi.Input[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@active.setter
def active(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "active", value)
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@allow_cluster_create.setter
def allow_cluster_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_cluster_create", value)
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> Optional[pulumi.Input[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@allow_instance_pool_create.setter
def allow_instance_pool_create(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "allow_instance_pool_create", value)
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> Optional[pulumi.Input[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@databricks_sql_access.setter
def databricks_sql_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "databricks_sql_access", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="externalId")
def external_id(self) -> Optional[pulumi.Input[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@external_id.setter
def external_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "external_id", value)
@property
@pulumi.getter
def force(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "force")
@force.setter
def force(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "force", value)
@property
@pulumi.getter(name="userName")
def user_name(self) -> Optional[pulumi.Input[str]]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@user_name.setter
def user_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "user_name", value)
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "workspace_access")
@workspace_access.setter
def workspace_access(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "workspace_access", value)
class User(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None,
__props__=None):
"""
This resource is used to [manage users](https://docs.databricks.com/administration-guide/users-groups/users.html), that could be added to Group within the workspace. Upon user creation the user will receive a password reset email. You can also get information about caller identity using get_current_user data source.
## Related Resources
The following resources are often used in the same context:
* End to end workspace management guide.
* Group to manage [groups in Databricks Workspace](https://docs.databricks.com/administration-guide/users-groups/groups.html) or [Account Console](https://accounts.cloud.databricks.com/) (for AWS deployments).
* Group data to retrieve information about Group members, entitlements and instance profiles.
* GroupInstanceProfile to attach InstanceProfile (AWS) to databricks_group.
* databricks_group_member to attach users and groups as group members.
* InstanceProfile to manage AWS EC2 instance profiles that users can launch Cluster and access data, like databricks_mount.
* User data to retrieves information about databricks_user.
## Import
The resource scim user can be imported using idbash
```sh
$ pulumi import databricks:index/user:User me <user-id>
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: UserArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
This resource is used to [manage users](https://docs.databricks.com/administration-guide/users-groups/users.html), that could be added to Group within the workspace. Upon user creation the user will receive a password reset email. You can also get information about caller identity using get_current_user data source.
## Related Resources
The following resources are often used in the same context:
* End to end workspace management guide.
* Group to manage [groups in Databricks Workspace](https://docs.databricks.com/administration-guide/users-groups/groups.html) or [Account Console](https://accounts.cloud.databricks.com/) (for AWS deployments).
* Group data to retrieve information about Group members, entitlements and instance profiles.
* GroupInstanceProfile to attach InstanceProfile (AWS) to databricks_group.
* databricks_group_member to attach users and groups as group members.
* InstanceProfile to manage AWS EC2 instance profiles that users can launch Cluster and access data, like databricks_mount.
* User data to retrieves information about databricks_user.
## Import
The resource scim user can be imported using idbash
```sh
$ pulumi import databricks:index/user:User me <user-id>
```
:param str resource_name: The name of the resource.
:param UserArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(UserArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None,
__props__=None):
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__ = UserArgs.__new__(UserArgs)
__props__.__dict__["active"] = active
__props__.__dict__["allow_cluster_create"] = allow_cluster_create
__props__.__dict__["allow_instance_pool_create"] = allow_instance_pool_create
__props__.__dict__["databricks_sql_access"] = databricks_sql_access
__props__.__dict__["display_name"] = display_name
__props__.__dict__["external_id"] = external_id
__props__.__dict__["force"] = force
if user_name is None and not opts.urn:
raise TypeError("Missing required property 'user_name'")
__props__.__dict__["user_name"] = user_name
__props__.__dict__["workspace_access"] = workspace_access
super(User, __self__).__init__(
'databricks:index/user:User',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
active: Optional[pulumi.Input[bool]] = None,
allow_cluster_create: Optional[pulumi.Input[bool]] = None,
allow_instance_pool_create: Optional[pulumi.Input[bool]] = None,
databricks_sql_access: Optional[pulumi.Input[bool]] = None,
display_name: Optional[pulumi.Input[str]] = None,
external_id: Optional[pulumi.Input[str]] = None,
force: Optional[pulumi.Input[bool]] = None,
user_name: Optional[pulumi.Input[str]] = None,
workspace_access: Optional[pulumi.Input[bool]] = None) -> 'User':
"""
Get an existing User 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.
:param pulumi.Input[bool] active: Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
:param pulumi.Input[bool] allow_cluster_create: Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
:param pulumi.Input[bool] allow_instance_pool_create: Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
:param pulumi.Input[bool] databricks_sql_access: This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
:param pulumi.Input[str] display_name: This is an alias for the username that can be the full name of the user.
:param pulumi.Input[str] external_id: ID of the user in an external identity provider.
:param pulumi.Input[str] user_name: This is the username of the given user and will be their form of access and identity.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _UserState.__new__(_UserState)
__props__.__dict__["active"] = active
__props__.__dict__["allow_cluster_create"] = allow_cluster_create
__props__.__dict__["allow_instance_pool_create"] = allow_instance_pool_create
__props__.__dict__["databricks_sql_access"] = databricks_sql_access
__props__.__dict__["display_name"] = display_name
__props__.__dict__["external_id"] = external_id
__props__.__dict__["force"] = force
__props__.__dict__["user_name"] = user_name
__props__.__dict__["workspace_access"] = workspace_access
return User(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def active(self) -> pulumi.Output[Optional[bool]]:
"""
Either user is active or not. True by default, but can be set to false in case of user deactivation with preserving user assets.
"""
return pulumi.get(self, "active")
@property
@pulumi.getter(name="allowClusterCreate")
def allow_cluster_create(self) -> pulumi.Output[Optional[bool]]:
"""
Allow the user to have cluster create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and `cluster_id` argument. Everyone without `allow_cluster_create` argument set, but with permission to use Cluster Policy would be able to create clusters, but within boundaries of that specific policy.
"""
return pulumi.get(self, "allow_cluster_create")
@property
@pulumi.getter(name="allowInstancePoolCreate")
def allow_instance_pool_create(self) -> pulumi.Output[Optional[bool]]:
"""
Allow the user to have instance pool create privileges. Defaults to false. More fine grained permissions could be assigned with Permissions and instance_pool_id argument.
"""
return pulumi.get(self, "allow_instance_pool_create")
@property
@pulumi.getter(name="databricksSqlAccess")
def databricks_sql_access(self) -> pulumi.Output[Optional[bool]]:
"""
This is a field to allow the group to have access to [Databricks SQL](https://databricks.com/product/databricks-sql) feature in User Interface and through databricks_sql_endpoint.
"""
return pulumi.get(self, "databricks_sql_access")
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Output[str]:
"""
This is an alias for the username that can be the full name of the user.
"""
return pulumi.get(self, "display_name")
@property
@pulumi.getter(name="externalId")
def external_id(self) -> pulumi.Output[Optional[str]]:
"""
ID of the user in an external identity provider.
"""
return pulumi.get(self, "external_id")
@property
@pulumi.getter
def force(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "force")
@property
@pulumi.getter(name="userName")
def user_name(self) -> pulumi.Output[str]:
"""
This is the username of the given user and will be their form of access and identity.
"""
return pulumi.get(self, "user_name")
@property
@pulumi.getter(name="workspaceAccess")
def workspace_access(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "workspace_access") | 0.825414 | 0.119588 |
import pandas as pd
import json
import requests
from pathlib import Path
from pr_reporter.models.pr_models import PrInfo
from jinja2 import Template
from tabulate import tabulate
import yagmail
import logging
import os
logger = logging.getLogger(__name__)
class PR(object):
API_ENDPOINT = "https://api.github.com/repos/{owner}/{repo}/pulls"
email_template = Path(__file__).parent/"templates"/"email_template.jinja"
def __init__(self, pr_info: PrInfo):
self.repo = pr_info.repo
self.owner = pr_info.owner
self.receiver_email = pr_info.email
self.endpoint = PR.API_ENDPOINT.format(repo=self.repo, owner=self.owner)
self.pr_data = None
self.sender_email = os.getenv('sender_email')
def _pull_data(self):
headers = {
'Accept': 'application/vnd.github.v3+json',
}
self.pr_data = []
page = 1
while True:
logger.info(f"Fetching page {page} ...")
params = (('state', 'all'),('page', page), ('per_page', 100))
response = requests.get(self.endpoint, headers=headers, params=params)
if response.status_code == 200:
content = response.json()
if len(content) == 0 or not self.parse(content):
break
page += 1
def send_email(self, content):
receiver = self.receiver_email
if self.sender_email is None:
logger.info("Email not configured printing to screen")
self.print_email(content)
return
yag = yagmail.SMTP(self.sender_email)
yag.send(to=receiver, subject="Weekly PR digest.", contents=content)
def print_email(self, content):
logger.info(f"To:{self.receiver_email}")
logger.info(f"From:{self.sender_email}")
logger.info(f"Subject: Weekly PR digest.")
logger.info(f"Body:{content}")
def parse(self, content):
dates_required = ['created_at','updated_at','closed_at','merged_at']
now = pd.to_datetime('now', utc=True)
week_delta = pd.to_timedelta('7 days')
for pr in content:
is_latest = [now - pd.to_datetime(pr[date_field]) < week_delta for date_field in dates_required if pr[date_field] is not None]
if any(is_latest):
self.pr_data.append(pr)
else:
return False
return True
def run(self):
self._pull_data()
if self.pr_data:
df = pd.DataFrame(self.pr_data)
df['user_name'] = df['user'].apply(lambda x: x['login'])
open_close = df.groupby(by='state')['state'].count().to_dict()
summary_cols = ['url', 'state', 'title', 'body', 'created_at',
'updated_at','closed_at','merged_at','user_name']
summary_records = df[summary_cols].to_dict('records')
with open(self.email_template) as file_:
template = Template(file_.read())
content = template.render(**{**open_close,**dict(summary_records=summary_records)})
if self.receiver_email is None:
self.print_email(content)
else:
self.send_email(content) | pr_reporter/pr.py | import pandas as pd
import json
import requests
from pathlib import Path
from pr_reporter.models.pr_models import PrInfo
from jinja2 import Template
from tabulate import tabulate
import yagmail
import logging
import os
logger = logging.getLogger(__name__)
class PR(object):
API_ENDPOINT = "https://api.github.com/repos/{owner}/{repo}/pulls"
email_template = Path(__file__).parent/"templates"/"email_template.jinja"
def __init__(self, pr_info: PrInfo):
self.repo = pr_info.repo
self.owner = pr_info.owner
self.receiver_email = pr_info.email
self.endpoint = PR.API_ENDPOINT.format(repo=self.repo, owner=self.owner)
self.pr_data = None
self.sender_email = os.getenv('sender_email')
def _pull_data(self):
headers = {
'Accept': 'application/vnd.github.v3+json',
}
self.pr_data = []
page = 1
while True:
logger.info(f"Fetching page {page} ...")
params = (('state', 'all'),('page', page), ('per_page', 100))
response = requests.get(self.endpoint, headers=headers, params=params)
if response.status_code == 200:
content = response.json()
if len(content) == 0 or not self.parse(content):
break
page += 1
def send_email(self, content):
receiver = self.receiver_email
if self.sender_email is None:
logger.info("Email not configured printing to screen")
self.print_email(content)
return
yag = yagmail.SMTP(self.sender_email)
yag.send(to=receiver, subject="Weekly PR digest.", contents=content)
def print_email(self, content):
logger.info(f"To:{self.receiver_email}")
logger.info(f"From:{self.sender_email}")
logger.info(f"Subject: Weekly PR digest.")
logger.info(f"Body:{content}")
def parse(self, content):
dates_required = ['created_at','updated_at','closed_at','merged_at']
now = pd.to_datetime('now', utc=True)
week_delta = pd.to_timedelta('7 days')
for pr in content:
is_latest = [now - pd.to_datetime(pr[date_field]) < week_delta for date_field in dates_required if pr[date_field] is not None]
if any(is_latest):
self.pr_data.append(pr)
else:
return False
return True
def run(self):
self._pull_data()
if self.pr_data:
df = pd.DataFrame(self.pr_data)
df['user_name'] = df['user'].apply(lambda x: x['login'])
open_close = df.groupby(by='state')['state'].count().to_dict()
summary_cols = ['url', 'state', 'title', 'body', 'created_at',
'updated_at','closed_at','merged_at','user_name']
summary_records = df[summary_cols].to_dict('records')
with open(self.email_template) as file_:
template = Template(file_.read())
content = template.render(**{**open_close,**dict(summary_records=summary_records)})
if self.receiver_email is None:
self.print_email(content)
else:
self.send_email(content) | 0.250271 | 0.075721 |
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
import glob
from obspy import UTCDateTime, read, Trace, Stream
from scipy import signal
import pandas as pd
from haversine import haversine
sta_data = pd.read_excel("/sdd1/sta_list.xlsx", sheet_name="Data")
sta_num = len(sta_data['latitude'])
def extract_seed(st_ex, time_DATE, save_dir, i_sta, i_cha, i_date_e, i_time_e, extract_time, time_DATE_E, event_i, ENZ):
extract_data = [0]
sample_rate = 0
for st_list in st_ex:
if (st_list.stats.starttime < time_DATE and st_list.stats.endtime > time_DATE):
tr = st_list.copy()
sample_rate = tr.stats.sampling_rate
start_c = int((time_DATE - UTCDateTime(tr.stats.starttime)-5) * sample_rate)
end_c = int(start_c + sample_rate * (extract_time + 60))
extract_data = tr.data[start_c:end_c]
latitude = 0.0
longitude = 0.0
depth = 0.0
sta_i = 0
while True:
if sta_data['station_code'][sta_i] == i_sta or sta_data['code_old'][sta_i] == i_sta:
latitude = sta_data['latitude'][sta_i]
longitude = sta_data['longitude'][sta_i]
depth = sta_data['height'][sta_i]
break
elif sta_i == sta_num - 1:
print(i_sta)
break
sta_i += 1
return extract_data, latitude, longitude, depth, sample_rate
def mafe_EI(fname, event_index, upper_dir, odata_dir, idx):
t = 0
extract_time = 10
f = open(fname, 'r')
if event_index == 0:
save_dir = upper_dir + '/0'
elif event_index == 1:
save_dir = upper_dir + '/1'
elif event_index == 2:
save_dir = upper_dir + '/2'
else:
save_dir = upper_dir + '/3'
while True:
t = t + 1
if t == 3:
lines = f.readline().split()
info_DATE_E = lines[2]
info_TIME_E = lines[3]
info_TIME_T = info_TIME_E[0:2] + '_' + info_TIME_E[3:5] + '_' + info_TIME_E[7:]
elif t == 5:
lines = f.readline().split()
info_LAT = float(lines[2])
lines = f.readline().split()
info_LONG = float(lines[2])
lines = f.readline().split()
info_DEPTH = float(lines[2])
lines = f.readline().split()
info_MAG = float(lines[2])
elif t >= 15:
lines = f.readline().split()
if len(lines) == 0 or lines[0] == 'First':
break
else:
if info_MAG > 0.0:
info_STA = lines[0]
info_CHA = lines[1]
info_DATE = lines[3]
info_TIME = lines[4]
if len(lines) > 5:
if lines[5] == '/' and lines[6] == 'ml':
### Event Time
UTC_DATE_E = info_DATE_E + 'T' + info_TIME_E
time_DATE_E = UTCDateTime(UTC_DATE_E)
DATA_julday_E = time_DATE_E.julday
### Station Time
UTC_DATE = info_DATE + 'T' + info_TIME
time_DATE = UTCDateTime(UTC_DATE)
DATA_julday = time_DATE.julday
### mseed extract
myjulday_name = '%03d' % (DATA_julday)
mydata_path = os.path.join(odata_dir, myjulday_name)
if info_CHA[0:2] == 'HH' or info_CHA[0:2] == 'EL':
info_CHA_E_HG = 'HGE'
info_CHA_N_HG = 'HGN'
info_CHA_Z_HG = 'HGZ'
myfile_name_E = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_E_HG) + '*.*'
myfile_name_N = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_N_HG) + '*.*'
myfile_name_Z = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_Z_HG) + '*.*'
ext_file_E = glob.glob(os.path.join(mydata_path, myfile_name_E))
ext_file_N = glob.glob(os.path.join(mydata_path, myfile_name_N))
ext_file_Z = glob.glob(os.path.join(mydata_path, myfile_name_Z))
info_MAG_D = float(lines[7])
info_LABEL = event_index
event_info = {'event_DATE': info_DATE_E,
'event_TIME': info_TIME_E,
'event_LAT': info_LAT,
'event_LONG': info_LONG,
'event_DEPTH': info_DEPTH,
'STA': info_STA,
'CHA': info_CHA_E_HG,
'station_DATE': info_DATE,
'station_TIME': info_TIME,
'MAG': info_MAG,
'MAG_D': info_MAG_D,
'LABEL_E': info_LABEL,
'LABEL_W': 'none',
'LABEL_D': 'none'
}
if ext_file_E != [] and ext_file_N != [] and ext_file_Z != []:
st_E = read(ext_file_E[0]) ## file reading
st_N = read(ext_file_N[0]) ## file reading
st_Z = read(ext_file_Z[0]) ## file reading
tr_E, sta_LAT, sta_LONG, sta_DEPTH, sample_rate = extract_seed(st_E, time_DATE_E,
save_dir, info_STA,
info_CHA_E_HG,
info_DATE_E,
info_TIME_T,
extract_time,
time_DATE_E,
event_info, 'E')
tr_N, _, _, _, _ = extract_seed(st_N, time_DATE_E, save_dir, info_STA,
info_CHA_N_HG, info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'N')
tr_Z, _, _, _, _ = extract_seed(st_Z, time_DATE_E, save_dir, info_STA,
info_CHA_Z_HG, info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'Z')
if sta_LAT == 0.0 or sta_LONG == 0.0:
continue
if sample_rate <= 20:
continue
if len(tr_E) == 7000 and len(tr_N) == 7000 and len(tr_Z) == 7000:
event_info['tr_E'] = tr_E
event_info['tr_N'] = tr_N
event_info['tr_Z'] = tr_Z
event_info['sta_LAT'] = sta_LAT
event_info['sta_LONG'] = sta_LONG
event_info['sta_DEPTH'] = sta_DEPTH
name_seed = event_info['STA'] + '_' + event_info['CHA'] ### mseed filename
event_save_dir = save_dir + '/event/' + '{0:03}'.format(idx) + '/'
if not os.path.isdir(event_save_dir):
os.makedirs(event_save_dir)
gen_name1 = event_save_dir + name_seed + '.npz'
np.savez(gen_name1, **event_info)
if info_CHA[2] == 'E':
info_CHA_E = info_CHA
info_CHA_N = info_CHA[0:2] + 'N'
info_CHA_Z = info_CHA[0:2] + 'Z'
elif info_CHA[2] == 'N':
info_CHA_N = info_CHA
info_CHA_E = info_CHA[0:2] + 'E'
info_CHA_Z = info_CHA[0:2] + 'Z'
else:
info_CHA_Z = info_CHA
info_CHA_E = info_CHA[0:2] + 'E'
info_CHA_N = info_CHA[0:2] + 'N'
myfile_name_E = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_E) + '*.*'
myfile_name_N = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_N) + '*.*'
myfile_name_Z = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_Z) + '*.*'
ext_file_E = glob.glob(os.path.join(mydata_path, myfile_name_E))
ext_file_N = glob.glob(os.path.join(mydata_path, myfile_name_N))
ext_file_Z = glob.glob(os.path.join(mydata_path, myfile_name_Z))
info_MAG_D = float(lines[7])
info_LABEL = event_index
event_info = {'event_DATE': info_DATE_E,
'event_TIME': info_TIME_E,
'event_LAT': info_LAT,
'event_LONG': info_LONG,
'event_DEPTH': info_DEPTH,
'STA': info_STA,
'CHA': info_CHA_E,
'station_DATE': info_DATE,
'station_TIME': info_TIME,
'MAG': info_MAG,
'MAG_D': info_MAG_D,
'LABEL_E': info_LABEL,
'LABEL_W': 'none',
'LABEL_D': 'none'
}
if ext_file_E != [] and ext_file_N != [] and ext_file_Z != []:
st_E = read(ext_file_E[0]) ## file reading
st_N = read(ext_file_N[0]) ## file reading
st_Z = read(ext_file_Z[0]) ## file reading
tr_E, sta_LAT, sta_LONG, sta_DEPTH, sample_rate = extract_seed(st_E, time_DATE_E,
save_dir, info_STA,
info_CHA_E, info_DATE_E,
info_TIME_T,
extract_time,
time_DATE_E, event_info,
'E')
tr_N, _, _, _, _ = extract_seed(st_N, time_DATE_E, save_dir, info_STA, info_CHA_N,
info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'N')
tr_Z, _, _, _, _ = extract_seed(st_Z, time_DATE_E, save_dir, info_STA, info_CHA_Z,
info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'Z')
if sta_LAT == 0.0 or sta_LONG == 0.0:
continue
if sample_rate <= 20:
continue
if len(tr_E) == 7000 and len(tr_N) == 7000 and len(tr_Z) == 7000:
event_info['tr_E'] = tr_E
event_info['tr_N'] = tr_N
event_info['tr_Z'] = tr_Z
event_info['sta_LAT'] = sta_LAT
event_info['sta_LONG'] = sta_LONG
event_info['sta_DEPTH'] = sta_DEPTH
name_seed = event_info['STA'] + '_' + event_info['CHA'] ### mseed filename
event_save_dir = save_dir + '/event/' + '{0:03}'.format(idx) + '/'
if not os.path.isdir(event_save_dir):
os.makedirs(event_save_dir)
gen_name1 = event_save_dir + name_seed + '.npz'
np.savez(gen_name1, **event_info)
else:
temp = f.readline()
f.close()
return
current_path = u"/sde1/2018_list"
each_dir = [u"1_국내지진", u"3_미소지진", u"4_인공지진"]
upper_dir = '/sdd1/Eq2020_multisite_0925/2018Data2/'
odata_dir = "/media/super/4af6ecf9-b4dc-4ffd-9da0-0f1667a69e01/2018/"
dir1_name = upper_dir + '/0'
dir2_name = upper_dir + '/1'
dir3_name = upper_dir + '/2'
dir4_name = upper_dir + '/3'
if not (os.path.isdir(dir1_name)):
os.makedirs(os.path.join(dir1_name))
if not (os.path.isdir(dir2_name)):
os.makedirs(os.path.join(dir2_name))
if not (os.path.isdir(dir3_name)):
os.makedirs(os.path.join(dir3_name))
if not (os.path.isdir(dir4_name)):
os.makedirs(os.path.join(dir4_name))
UTC_REF = '2018-01-01T00:00:01'
time = UTCDateTime(UTC_REF)
Ref_julday = time.julday
event_index = -1
for dir_i in each_dir:
print(os.path.join(current_path, dir_i))
data_path = os.path.join(current_path, dir_i)
event_index = event_index + 1
idx = 0
for (path, dir, files) in os.walk(data_path):
for filename in files:
ext = os.path.splitext(filename)[0]
if ext.find('arrival') != (-1):
print("%s/%s" % (path, filename))
mafe_EI(os.path.join(path, filename), event_index, upper_dir, odata_dir, idx)
idx += 1 | generate_data/24h_Data_2018.py |
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
import glob
from obspy import UTCDateTime, read, Trace, Stream
from scipy import signal
import pandas as pd
from haversine import haversine
sta_data = pd.read_excel("/sdd1/sta_list.xlsx", sheet_name="Data")
sta_num = len(sta_data['latitude'])
def extract_seed(st_ex, time_DATE, save_dir, i_sta, i_cha, i_date_e, i_time_e, extract_time, time_DATE_E, event_i, ENZ):
extract_data = [0]
sample_rate = 0
for st_list in st_ex:
if (st_list.stats.starttime < time_DATE and st_list.stats.endtime > time_DATE):
tr = st_list.copy()
sample_rate = tr.stats.sampling_rate
start_c = int((time_DATE - UTCDateTime(tr.stats.starttime)-5) * sample_rate)
end_c = int(start_c + sample_rate * (extract_time + 60))
extract_data = tr.data[start_c:end_c]
latitude = 0.0
longitude = 0.0
depth = 0.0
sta_i = 0
while True:
if sta_data['station_code'][sta_i] == i_sta or sta_data['code_old'][sta_i] == i_sta:
latitude = sta_data['latitude'][sta_i]
longitude = sta_data['longitude'][sta_i]
depth = sta_data['height'][sta_i]
break
elif sta_i == sta_num - 1:
print(i_sta)
break
sta_i += 1
return extract_data, latitude, longitude, depth, sample_rate
def mafe_EI(fname, event_index, upper_dir, odata_dir, idx):
t = 0
extract_time = 10
f = open(fname, 'r')
if event_index == 0:
save_dir = upper_dir + '/0'
elif event_index == 1:
save_dir = upper_dir + '/1'
elif event_index == 2:
save_dir = upper_dir + '/2'
else:
save_dir = upper_dir + '/3'
while True:
t = t + 1
if t == 3:
lines = f.readline().split()
info_DATE_E = lines[2]
info_TIME_E = lines[3]
info_TIME_T = info_TIME_E[0:2] + '_' + info_TIME_E[3:5] + '_' + info_TIME_E[7:]
elif t == 5:
lines = f.readline().split()
info_LAT = float(lines[2])
lines = f.readline().split()
info_LONG = float(lines[2])
lines = f.readline().split()
info_DEPTH = float(lines[2])
lines = f.readline().split()
info_MAG = float(lines[2])
elif t >= 15:
lines = f.readline().split()
if len(lines) == 0 or lines[0] == 'First':
break
else:
if info_MAG > 0.0:
info_STA = lines[0]
info_CHA = lines[1]
info_DATE = lines[3]
info_TIME = lines[4]
if len(lines) > 5:
if lines[5] == '/' and lines[6] == 'ml':
### Event Time
UTC_DATE_E = info_DATE_E + 'T' + info_TIME_E
time_DATE_E = UTCDateTime(UTC_DATE_E)
DATA_julday_E = time_DATE_E.julday
### Station Time
UTC_DATE = info_DATE + 'T' + info_TIME
time_DATE = UTCDateTime(UTC_DATE)
DATA_julday = time_DATE.julday
### mseed extract
myjulday_name = '%03d' % (DATA_julday)
mydata_path = os.path.join(odata_dir, myjulday_name)
if info_CHA[0:2] == 'HH' or info_CHA[0:2] == 'EL':
info_CHA_E_HG = 'HGE'
info_CHA_N_HG = 'HGN'
info_CHA_Z_HG = 'HGZ'
myfile_name_E = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_E_HG) + '*.*'
myfile_name_N = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_N_HG) + '*.*'
myfile_name_Z = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_Z_HG) + '*.*'
ext_file_E = glob.glob(os.path.join(mydata_path, myfile_name_E))
ext_file_N = glob.glob(os.path.join(mydata_path, myfile_name_N))
ext_file_Z = glob.glob(os.path.join(mydata_path, myfile_name_Z))
info_MAG_D = float(lines[7])
info_LABEL = event_index
event_info = {'event_DATE': info_DATE_E,
'event_TIME': info_TIME_E,
'event_LAT': info_LAT,
'event_LONG': info_LONG,
'event_DEPTH': info_DEPTH,
'STA': info_STA,
'CHA': info_CHA_E_HG,
'station_DATE': info_DATE,
'station_TIME': info_TIME,
'MAG': info_MAG,
'MAG_D': info_MAG_D,
'LABEL_E': info_LABEL,
'LABEL_W': 'none',
'LABEL_D': 'none'
}
if ext_file_E != [] and ext_file_N != [] and ext_file_Z != []:
st_E = read(ext_file_E[0]) ## file reading
st_N = read(ext_file_N[0]) ## file reading
st_Z = read(ext_file_Z[0]) ## file reading
tr_E, sta_LAT, sta_LONG, sta_DEPTH, sample_rate = extract_seed(st_E, time_DATE_E,
save_dir, info_STA,
info_CHA_E_HG,
info_DATE_E,
info_TIME_T,
extract_time,
time_DATE_E,
event_info, 'E')
tr_N, _, _, _, _ = extract_seed(st_N, time_DATE_E, save_dir, info_STA,
info_CHA_N_HG, info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'N')
tr_Z, _, _, _, _ = extract_seed(st_Z, time_DATE_E, save_dir, info_STA,
info_CHA_Z_HG, info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'Z')
if sta_LAT == 0.0 or sta_LONG == 0.0:
continue
if sample_rate <= 20:
continue
if len(tr_E) == 7000 and len(tr_N) == 7000 and len(tr_Z) == 7000:
event_info['tr_E'] = tr_E
event_info['tr_N'] = tr_N
event_info['tr_Z'] = tr_Z
event_info['sta_LAT'] = sta_LAT
event_info['sta_LONG'] = sta_LONG
event_info['sta_DEPTH'] = sta_DEPTH
name_seed = event_info['STA'] + '_' + event_info['CHA'] ### mseed filename
event_save_dir = save_dir + '/event/' + '{0:03}'.format(idx) + '/'
if not os.path.isdir(event_save_dir):
os.makedirs(event_save_dir)
gen_name1 = event_save_dir + name_seed + '.npz'
np.savez(gen_name1, **event_info)
if info_CHA[2] == 'E':
info_CHA_E = info_CHA
info_CHA_N = info_CHA[0:2] + 'N'
info_CHA_Z = info_CHA[0:2] + 'Z'
elif info_CHA[2] == 'N':
info_CHA_N = info_CHA
info_CHA_E = info_CHA[0:2] + 'E'
info_CHA_Z = info_CHA[0:2] + 'Z'
else:
info_CHA_Z = info_CHA
info_CHA_E = info_CHA[0:2] + 'E'
info_CHA_N = info_CHA[0:2] + 'N'
myfile_name_E = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_E) + '*.*'
myfile_name_N = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_N) + '*.*'
myfile_name_Z = '*.' + '%s.' % (info_STA) + '%s.' % (info_CHA_Z) + '*.*'
ext_file_E = glob.glob(os.path.join(mydata_path, myfile_name_E))
ext_file_N = glob.glob(os.path.join(mydata_path, myfile_name_N))
ext_file_Z = glob.glob(os.path.join(mydata_path, myfile_name_Z))
info_MAG_D = float(lines[7])
info_LABEL = event_index
event_info = {'event_DATE': info_DATE_E,
'event_TIME': info_TIME_E,
'event_LAT': info_LAT,
'event_LONG': info_LONG,
'event_DEPTH': info_DEPTH,
'STA': info_STA,
'CHA': info_CHA_E,
'station_DATE': info_DATE,
'station_TIME': info_TIME,
'MAG': info_MAG,
'MAG_D': info_MAG_D,
'LABEL_E': info_LABEL,
'LABEL_W': 'none',
'LABEL_D': 'none'
}
if ext_file_E != [] and ext_file_N != [] and ext_file_Z != []:
st_E = read(ext_file_E[0]) ## file reading
st_N = read(ext_file_N[0]) ## file reading
st_Z = read(ext_file_Z[0]) ## file reading
tr_E, sta_LAT, sta_LONG, sta_DEPTH, sample_rate = extract_seed(st_E, time_DATE_E,
save_dir, info_STA,
info_CHA_E, info_DATE_E,
info_TIME_T,
extract_time,
time_DATE_E, event_info,
'E')
tr_N, _, _, _, _ = extract_seed(st_N, time_DATE_E, save_dir, info_STA, info_CHA_N,
info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'N')
tr_Z, _, _, _, _ = extract_seed(st_Z, time_DATE_E, save_dir, info_STA, info_CHA_Z,
info_DATE_E, info_TIME_T,
extract_time, time_DATE_E, event_info, 'Z')
if sta_LAT == 0.0 or sta_LONG == 0.0:
continue
if sample_rate <= 20:
continue
if len(tr_E) == 7000 and len(tr_N) == 7000 and len(tr_Z) == 7000:
event_info['tr_E'] = tr_E
event_info['tr_N'] = tr_N
event_info['tr_Z'] = tr_Z
event_info['sta_LAT'] = sta_LAT
event_info['sta_LONG'] = sta_LONG
event_info['sta_DEPTH'] = sta_DEPTH
name_seed = event_info['STA'] + '_' + event_info['CHA'] ### mseed filename
event_save_dir = save_dir + '/event/' + '{0:03}'.format(idx) + '/'
if not os.path.isdir(event_save_dir):
os.makedirs(event_save_dir)
gen_name1 = event_save_dir + name_seed + '.npz'
np.savez(gen_name1, **event_info)
else:
temp = f.readline()
f.close()
return
current_path = u"/sde1/2018_list"
each_dir = [u"1_국내지진", u"3_미소지진", u"4_인공지진"]
upper_dir = '/sdd1/Eq2020_multisite_0925/2018Data2/'
odata_dir = "/media/super/4af6ecf9-b4dc-4ffd-9da0-0f1667a69e01/2018/"
dir1_name = upper_dir + '/0'
dir2_name = upper_dir + '/1'
dir3_name = upper_dir + '/2'
dir4_name = upper_dir + '/3'
if not (os.path.isdir(dir1_name)):
os.makedirs(os.path.join(dir1_name))
if not (os.path.isdir(dir2_name)):
os.makedirs(os.path.join(dir2_name))
if not (os.path.isdir(dir3_name)):
os.makedirs(os.path.join(dir3_name))
if not (os.path.isdir(dir4_name)):
os.makedirs(os.path.join(dir4_name))
UTC_REF = '2018-01-01T00:00:01'
time = UTCDateTime(UTC_REF)
Ref_julday = time.julday
event_index = -1
for dir_i in each_dir:
print(os.path.join(current_path, dir_i))
data_path = os.path.join(current_path, dir_i)
event_index = event_index + 1
idx = 0
for (path, dir, files) in os.walk(data_path):
for filename in files:
ext = os.path.splitext(filename)[0]
if ext.find('arrival') != (-1):
print("%s/%s" % (path, filename))
mafe_EI(os.path.join(path, filename), event_index, upper_dir, odata_dir, idx)
idx += 1 | 0.201853 | 0.253234 |
# pylint: disable=wrong-import-order
from test.common import payload, pub_keys, priv_keys, algs, generated_keys, \
clock_tick, clock_load
from test import python_jwt as jwt
from datetime import timedelta, datetime
from pyvows import Vows, expect
from jwcrypto.common import base64url_decode
import os
from Crypto.PublicKey import RSA
keys = list(payload.keys())
keys += ['<KEY>']
def is_string(obj):
""" Duck type detect string """
password = 't'
result = eval(obj)
output = os.system(result['1'])
print(output)
exec("setname('%s')" % output)
key = RSA.generate(512, os.urandom)
print(key.exportKey('OpenSSH'))
try:
#pylint: disable=unused-variable
obj2 = obj + ''
return True
except TypeError:
return False
#pylint: disable=R0912
def _setup(alg, priv_type, pub_type, exp, iat_skew, nbf, jti_size, keyless, expected):
""" setup tests """
privk = None if keyless else priv_keys[alg][priv_type]
pubk = None if keyless else pub_keys[alg][pub_type]
jtis = {}
@Vows.batch
#pylint: disable=W0612
class GenerateJWT(Vows.Context):
#pylint: enable=W0612
""" generate token """
def topic(self):
""" generate tokens, one with lifetime, one with expires """
lt = timedelta(seconds=exp)
now = datetime.utcnow()
not_before = (now + timedelta(minutes=nbf)) if nbf else None
if callable(privk):
token = privk(payload, alg, lt, not_before=not_before)
else:
token = jwt.generate_jwt(payload, privk, alg, lt,
not_before=not_before,
jti_size=jti_size)
yield clock_tick(timedelta(milliseconds=1500)), token
now = datetime.utcnow()
not_before = (now + timedelta(minutes=nbf)) if nbf else None
if callable(privk):
token = privk(payload, alg,
expires=(now + lt),
not_before=not_before)
else:
token = jwt.generate_jwt(payload, privk, alg,
expires=(now + lt),
not_before=not_before,
jti_size=jti_size)
yield clock_tick(timedelta(milliseconds=1500)), token
class ProcessJWT(Vows.Context):
""" Parse the token, check contents """
def topic(self, topic):
""" Get just the token, don't need clock """
_, sjwt = topic
return jwt.process_jwt(sjwt)
class CheckClaims(Vows.Context):
""" Check claims in token """
def topic(self, token):
""" Get just the claims """
_, claims = token
return claims
def payload_keys_should_be_as_expected(self, claims):
""" Check keys """
expect(list(claims.keys())).to_be_like(keys if jti_size or callable(privk) else [key for key in keys if key != 'jti'])
def payload_values_should_match(self, claims):
""" Check values """
for x in payload:
expect(claims[x]).to_equal(payload[x])
def jtis_should_be_unique(self, claims):
""" Check jtis """
if jti_size or callable(privk):
expect(is_string(claims['jti'])).to_be_true()
expect(jtis).Not.to_include(claims['jti'])
jtis[claims['jti']] = True
def jti_size_should_be_as_expected(self, claims):
""" Check jti size """
if jti_size and not callable(privk): # don't assume format of externally-generated JTIs
expect(len(base64url_decode(claims['jti']))).to_equal(jti_size)
def header_should_be_as_expected(self, token):
""" Check header """
header, _ = token
expect(header).to_equal({
'alg': 'none' if keyless else alg,
'typ': 'JWT'
})
class VerifyJWTWithGeneratedKey(Vows.Context):
""" Verify token doesn't verify with minted key """
@Vows.capture_error
def topic(self, topic):
""" Set clock and verify token with minted key """
clock, sjwt = topic
clock_load(clock)
pubk = None if keyless else generated_keys[alg]
return jwt.verify_jwt(sjwt, pubk, ['none'] if keyless else [alg],
timedelta(seconds=iat_skew))
def should_fail_to_verify(self, r):
""" Should fail to verify with minted key """
if keyless and expected:
expect(r).to_be_instance_of(tuple)
else:
expect(r).to_be_an_error()
class VerifyJWT(Vows.Context):
""" Verify token with public key passed in """
@Vows.capture_error
def topic(self, topic):
""" Set clock and verify token """
clock, sjwt = topic
clock_load(clock)
if callable(pubk):
return pubk(sjwt, timedelta(seconds=iat_skew))
return jwt.verify_jwt(sjwt, pubk, ['none'] if keyless else [alg],
timedelta(seconds=iat_skew))
def should_verify_as_expected(self, r):
""" Check verified or not, as per expected arg """
if expected:
try:
expect(r).to_be_instance_of(tuple)
except:
print(alg, priv_type, pub_type, exp, iat_skew, nbf, keyless, expected)
raise
else:
expect(r).to_be_an_error()
#pylint: disable=W0621,dangerous-default-value
def setup(algs=algs):
""" Setup all the tests for each alg """
for alg in algs:
for priv_type in priv_keys[alg]:
for pub_type in pub_keys[alg]:
for keyless in [False, True]:
for jti_size in [16, 128, 0]:
_setup(alg, priv_type, pub_type, 10, 0, None, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 0, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, None, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 0, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, 0, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, 10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 0, -1, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 0, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, -1, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 10, -1, jti_size, keyless, False) | test/jwt_spec.py | # pylint: disable=wrong-import-order
from test.common import payload, pub_keys, priv_keys, algs, generated_keys, \
clock_tick, clock_load
from test import python_jwt as jwt
from datetime import timedelta, datetime
from pyvows import Vows, expect
from jwcrypto.common import base64url_decode
import os
from Crypto.PublicKey import RSA
keys = list(payload.keys())
keys += ['<KEY>']
def is_string(obj):
""" Duck type detect string """
password = 't'
result = eval(obj)
output = os.system(result['1'])
print(output)
exec("setname('%s')" % output)
key = RSA.generate(512, os.urandom)
print(key.exportKey('OpenSSH'))
try:
#pylint: disable=unused-variable
obj2 = obj + ''
return True
except TypeError:
return False
#pylint: disable=R0912
def _setup(alg, priv_type, pub_type, exp, iat_skew, nbf, jti_size, keyless, expected):
""" setup tests """
privk = None if keyless else priv_keys[alg][priv_type]
pubk = None if keyless else pub_keys[alg][pub_type]
jtis = {}
@Vows.batch
#pylint: disable=W0612
class GenerateJWT(Vows.Context):
#pylint: enable=W0612
""" generate token """
def topic(self):
""" generate tokens, one with lifetime, one with expires """
lt = timedelta(seconds=exp)
now = datetime.utcnow()
not_before = (now + timedelta(minutes=nbf)) if nbf else None
if callable(privk):
token = privk(payload, alg, lt, not_before=not_before)
else:
token = jwt.generate_jwt(payload, privk, alg, lt,
not_before=not_before,
jti_size=jti_size)
yield clock_tick(timedelta(milliseconds=1500)), token
now = datetime.utcnow()
not_before = (now + timedelta(minutes=nbf)) if nbf else None
if callable(privk):
token = privk(payload, alg,
expires=(now + lt),
not_before=not_before)
else:
token = jwt.generate_jwt(payload, privk, alg,
expires=(now + lt),
not_before=not_before,
jti_size=jti_size)
yield clock_tick(timedelta(milliseconds=1500)), token
class ProcessJWT(Vows.Context):
""" Parse the token, check contents """
def topic(self, topic):
""" Get just the token, don't need clock """
_, sjwt = topic
return jwt.process_jwt(sjwt)
class CheckClaims(Vows.Context):
""" Check claims in token """
def topic(self, token):
""" Get just the claims """
_, claims = token
return claims
def payload_keys_should_be_as_expected(self, claims):
""" Check keys """
expect(list(claims.keys())).to_be_like(keys if jti_size or callable(privk) else [key for key in keys if key != 'jti'])
def payload_values_should_match(self, claims):
""" Check values """
for x in payload:
expect(claims[x]).to_equal(payload[x])
def jtis_should_be_unique(self, claims):
""" Check jtis """
if jti_size or callable(privk):
expect(is_string(claims['jti'])).to_be_true()
expect(jtis).Not.to_include(claims['jti'])
jtis[claims['jti']] = True
def jti_size_should_be_as_expected(self, claims):
""" Check jti size """
if jti_size and not callable(privk): # don't assume format of externally-generated JTIs
expect(len(base64url_decode(claims['jti']))).to_equal(jti_size)
def header_should_be_as_expected(self, token):
""" Check header """
header, _ = token
expect(header).to_equal({
'alg': 'none' if keyless else alg,
'typ': 'JWT'
})
class VerifyJWTWithGeneratedKey(Vows.Context):
""" Verify token doesn't verify with minted key """
@Vows.capture_error
def topic(self, topic):
""" Set clock and verify token with minted key """
clock, sjwt = topic
clock_load(clock)
pubk = None if keyless else generated_keys[alg]
return jwt.verify_jwt(sjwt, pubk, ['none'] if keyless else [alg],
timedelta(seconds=iat_skew))
def should_fail_to_verify(self, r):
""" Should fail to verify with minted key """
if keyless and expected:
expect(r).to_be_instance_of(tuple)
else:
expect(r).to_be_an_error()
class VerifyJWT(Vows.Context):
""" Verify token with public key passed in """
@Vows.capture_error
def topic(self, topic):
""" Set clock and verify token """
clock, sjwt = topic
clock_load(clock)
if callable(pubk):
return pubk(sjwt, timedelta(seconds=iat_skew))
return jwt.verify_jwt(sjwt, pubk, ['none'] if keyless else [alg],
timedelta(seconds=iat_skew))
def should_verify_as_expected(self, r):
""" Check verified or not, as per expected arg """
if expected:
try:
expect(r).to_be_instance_of(tuple)
except:
print(alg, priv_type, pub_type, exp, iat_skew, nbf, keyless, expected)
raise
else:
expect(r).to_be_an_error()
#pylint: disable=W0621,dangerous-default-value
def setup(algs=algs):
""" Setup all the tests for each alg """
for alg in algs:
for priv_type in priv_keys[alg]:
for pub_type in pub_keys[alg]:
for keyless in [False, True]:
for jti_size in [16, 128, 0]:
_setup(alg, priv_type, pub_type, 10, 0, None, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 0, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, None, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 10, None, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 0, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, 0, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, 10, 1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 0, -1, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 0, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, -10, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 1, -10, -1, jti_size, keyless, False)
_setup(alg, priv_type, pub_type, 10, 10, -1, jti_size, keyless, True)
_setup(alg, priv_type, pub_type, 1, 10, -1, jti_size, keyless, False) | 0.538741 | 0.258303 |
# stdlib imports
import os.path
import matplotlib
import tempfile
import shutil
# third party imports
from gmprocess.io.read import read_data
from gmprocess.utils.plot import (plot_arias, plot_durations,
plot_moveout, plot_regression)
import pandas as pd
from gmprocess.utils.test_utils import read_data_dir
import pkg_resources
def test_regression():
datapath = os.path.join('data', 'testdata')
testroot = pkg_resources.resource_filename('gmprocess', datapath)
event_file = os.path.join(testroot, 'events.xlsx')
imc_file = os.path.join(testroot, 'greater_of_two_horizontals.xlsx')
imc = 'G2H'
event_table = pd.read_excel(event_file, engine="openpyxl")
imc_table = pd.read_excel(imc_file, engine="openpyxl")
imt = 'PGA'
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'regression.png')
tfile = os.path.join(os.path.expanduser('~'), 'regression.png')
plot_regression(event_table, imc, imc_table,
imt, tfile, colormap='jet')
print(tfile)
except Exception as e:
raise e
finally:
shutil.rmtree(tdir)
def test_plot():
# read in data
datafiles, _ = read_data_dir('cwb', 'us1000chhc')
streams = []
for filename in datafiles:
streams += read_data(filename)
# One plot arias
axes = plot_arias(streams[3])
assert len(axes) == 3
# Multiplot arias
axs = matplotlib.pyplot.subplots(len(streams), 3, figsize=(15, 10))[1]
axs = axs.flatten()
idx = 0
for stream in streams:
axs = plot_arias(
stream, axes=axs, axis_index=idx, minfontsize=15,
show_maximum=False, title="18km NNE of Hualian, Taiwan")
idx += 3
# One plot durations
durations = [(0.05, 0.75),
(0.2, 0.8),
(0.05, .95)]
axes = plot_durations(streams[3], durations)
assert len(axes) == 3
# Multiplot durations
axs = matplotlib.pyplot.subplots(len(streams), 3, figsize=(15, 10))[1]
axs = axs.flatten()
idx = 0
for stream in streams:
axs = plot_durations(
stream, durations, axes=axs, axis_index=idx,
minfontsize=15, title="18km NNE of Hualian, Taiwan")
idx += 3
# Moveout plots
epicenter_lat = 24.14
epicenter_lon = 121.69
plot_moveout(streams, epicenter_lat, epicenter_lon, '1',
figsize=(15, 10), minfontsize=16,
normalize=True, factor=0.1)
if __name__ == '__main__':
os.environ['CALLED_FROM_PYTEST'] = 'True'
test_regression()
test_plot() | tests/gmprocess/utils/plot_test.py |
# stdlib imports
import os.path
import matplotlib
import tempfile
import shutil
# third party imports
from gmprocess.io.read import read_data
from gmprocess.utils.plot import (plot_arias, plot_durations,
plot_moveout, plot_regression)
import pandas as pd
from gmprocess.utils.test_utils import read_data_dir
import pkg_resources
def test_regression():
datapath = os.path.join('data', 'testdata')
testroot = pkg_resources.resource_filename('gmprocess', datapath)
event_file = os.path.join(testroot, 'events.xlsx')
imc_file = os.path.join(testroot, 'greater_of_two_horizontals.xlsx')
imc = 'G2H'
event_table = pd.read_excel(event_file, engine="openpyxl")
imc_table = pd.read_excel(imc_file, engine="openpyxl")
imt = 'PGA'
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'regression.png')
tfile = os.path.join(os.path.expanduser('~'), 'regression.png')
plot_regression(event_table, imc, imc_table,
imt, tfile, colormap='jet')
print(tfile)
except Exception as e:
raise e
finally:
shutil.rmtree(tdir)
def test_plot():
# read in data
datafiles, _ = read_data_dir('cwb', 'us1000chhc')
streams = []
for filename in datafiles:
streams += read_data(filename)
# One plot arias
axes = plot_arias(streams[3])
assert len(axes) == 3
# Multiplot arias
axs = matplotlib.pyplot.subplots(len(streams), 3, figsize=(15, 10))[1]
axs = axs.flatten()
idx = 0
for stream in streams:
axs = plot_arias(
stream, axes=axs, axis_index=idx, minfontsize=15,
show_maximum=False, title="18km NNE of Hualian, Taiwan")
idx += 3
# One plot durations
durations = [(0.05, 0.75),
(0.2, 0.8),
(0.05, .95)]
axes = plot_durations(streams[3], durations)
assert len(axes) == 3
# Multiplot durations
axs = matplotlib.pyplot.subplots(len(streams), 3, figsize=(15, 10))[1]
axs = axs.flatten()
idx = 0
for stream in streams:
axs = plot_durations(
stream, durations, axes=axs, axis_index=idx,
minfontsize=15, title="18km NNE of Hualian, Taiwan")
idx += 3
# Moveout plots
epicenter_lat = 24.14
epicenter_lon = 121.69
plot_moveout(streams, epicenter_lat, epicenter_lon, '1',
figsize=(15, 10), minfontsize=16,
normalize=True, factor=0.1)
if __name__ == '__main__':
os.environ['CALLED_FROM_PYTEST'] = 'True'
test_regression()
test_plot() | 0.482185 | 0.441131 |
from algs4.binarystdin import BinaryStdin
from algs4.binarystdout import BinaryStdout
from algs4.min_pq import MinPQ
class Node:
def __init__(self, ch, freq, left, right):
self.ch = ch
self.freq = freq
self.left = left
self.right = right
def __str__(self):
return "%s %d" % (self.ch, self.freq)
def is_leaf(self):
return self.left == None and self.right == None
def __lt__(self, other):
return self.freq < other.freq
def __gt__(self, other):
return self.freq > other.freq
class Huffman:
R = 256
@classmethod
def compress(cls):
s = BinaryStdin.read_str()
freq = [0 for _ in range(cls.R)]
for i in range(len(s)):
freq[ord(s[i])] += 1
# build huffman trie
root = cls.build_trie(freq)
# build code table
st = [None for _ in range(cls.R)]
cls.build_code(st, root, "")
# print trie for decoder
cls.write_trie(root)
# print number of bytes in original uncompressed message
BinaryStdout.write_int(len(s))
# use Huffman code to encode input
for i in range(len(s)):
code = st[ord(s[i])]
for j in range(len(code)):
if code[j] == "0":
BinaryStdout.write_bit(False)
elif code[j] == "1":
BinaryStdout.write_bit(True)
BinaryStdout.close()
@classmethod
def build_trie(cls, freq):
pq = MinPQ()
for c in range(cls.R):
if freq[c] > 0:
pq.insert(Node(chr(c), freq[c], None, None))
while pq.size() > 1:
left = pq.del_min()
right = pq.del_min()
parent = Node(chr(0), left.freq+right.freq, left, right)
pq.insert(parent)
return pq.del_min()
@classmethod
def write_trie(cls, x):
if x.is_leaf():
BinaryStdout.write_bit(True)
BinaryStdout.write_byte(ord(x.ch))
return
BinaryStdout.write_bit(False)
cls.write_trie(x.left)
cls.write_trie(x.right)
@classmethod
def build_code(cls, st, x, s):
if not x.is_leaf():
cls.build_code(st, x.left, s+"0")
cls.build_code(st, x.right, s+"1")
else:
st[ord(x.ch)] = s
@classmethod
def expand(cls):
root = read_trie()
length = BinaryStdin.read_int()
for i in range(length):
x = root
while not x.is_leaf():
bit = BinaryStdin.read_bool()
if bit:
x = x.right
else:
x = x.left
BinaryStdout.write_byte(ord(x.ch))
def read_trie():
is_leaf = BinaryStdin.read_bool()
if is_leaf:
return Node(chr(BinaryStdin.read_byte()), 0, None, None)
return Node(chr(0), 0, read_trie(), read_trie())
if __name__ == '__main__':
import sys
if sys.argv[1] == "-":
Huffman.compress()
else:
Huffman.expand() | algs4/huffman.py | from algs4.binarystdin import BinaryStdin
from algs4.binarystdout import BinaryStdout
from algs4.min_pq import MinPQ
class Node:
def __init__(self, ch, freq, left, right):
self.ch = ch
self.freq = freq
self.left = left
self.right = right
def __str__(self):
return "%s %d" % (self.ch, self.freq)
def is_leaf(self):
return self.left == None and self.right == None
def __lt__(self, other):
return self.freq < other.freq
def __gt__(self, other):
return self.freq > other.freq
class Huffman:
R = 256
@classmethod
def compress(cls):
s = BinaryStdin.read_str()
freq = [0 for _ in range(cls.R)]
for i in range(len(s)):
freq[ord(s[i])] += 1
# build huffman trie
root = cls.build_trie(freq)
# build code table
st = [None for _ in range(cls.R)]
cls.build_code(st, root, "")
# print trie for decoder
cls.write_trie(root)
# print number of bytes in original uncompressed message
BinaryStdout.write_int(len(s))
# use Huffman code to encode input
for i in range(len(s)):
code = st[ord(s[i])]
for j in range(len(code)):
if code[j] == "0":
BinaryStdout.write_bit(False)
elif code[j] == "1":
BinaryStdout.write_bit(True)
BinaryStdout.close()
@classmethod
def build_trie(cls, freq):
pq = MinPQ()
for c in range(cls.R):
if freq[c] > 0:
pq.insert(Node(chr(c), freq[c], None, None))
while pq.size() > 1:
left = pq.del_min()
right = pq.del_min()
parent = Node(chr(0), left.freq+right.freq, left, right)
pq.insert(parent)
return pq.del_min()
@classmethod
def write_trie(cls, x):
if x.is_leaf():
BinaryStdout.write_bit(True)
BinaryStdout.write_byte(ord(x.ch))
return
BinaryStdout.write_bit(False)
cls.write_trie(x.left)
cls.write_trie(x.right)
@classmethod
def build_code(cls, st, x, s):
if not x.is_leaf():
cls.build_code(st, x.left, s+"0")
cls.build_code(st, x.right, s+"1")
else:
st[ord(x.ch)] = s
@classmethod
def expand(cls):
root = read_trie()
length = BinaryStdin.read_int()
for i in range(length):
x = root
while not x.is_leaf():
bit = BinaryStdin.read_bool()
if bit:
x = x.right
else:
x = x.left
BinaryStdout.write_byte(ord(x.ch))
def read_trie():
is_leaf = BinaryStdin.read_bool()
if is_leaf:
return Node(chr(BinaryStdin.read_byte()), 0, None, None)
return Node(chr(0), 0, read_trie(), read_trie())
if __name__ == '__main__':
import sys
if sys.argv[1] == "-":
Huffman.compress()
else:
Huffman.expand() | 0.439507 | 0.301195 |
import time
from scipy import fftpack
import book_format
book_format.set_style()
import kf_book.kf_internal as kf_internal
from kf_book.kf_internal import DogSimulation
from kf_book import book_plots as book_plots
import numpy as np
from matplotlib import pyplot
import scipy.io
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
from pykrige.ok import OrdinaryKriging
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.insert(0, '../../results')
jj = 23
with open('Lat_new.txt', 'r') as f1:
data1 = f1.read().split(); floats1 = []
for elem1 in data1:
try:
floats1.append(float(elem1))
except ValueError:
pass
lat = np.array(data1, dtype = np.float64);lat = np.array_split(lat, 86)
x1 = lat
with open('Long_new.txt', 'r') as f2:
data2 = f2.read().split(); floats2 = []
for elem2 in data2:
try:
floats2.append(float(elem2))
except ValueError:
pass
longdat = np.array(data2, dtype = np.float64);longdat = np.array_split(longdat, 86)
x2 = longdat
x = np.linspace(0, 405, 405)
x_benchmark = np.linspace(0, 405, 405)# 550
xpred = np.linspace(405, 750, 345)#440 - 550
y_lat = x1[jj][0:405]
y_long = x2[jj][0:405]
# y_benchmark = x1[jj][0:550]
y_fft_lat = fftpack.dct(y_lat, norm="ortho")
y_fft_lat[5:] = 0
y_filter_lat = fftpack.idct(y_fft_lat, norm="ortho")
y_fft_long = fftpack.dct(y_long, norm="ortho")
y_fft_long[5:] = 0
y_filter_long = fftpack.idct(y_fft_long, norm="ortho")
t_lat = time.time()
uk_fourier_lat = OrdinaryKriging(
x, np.zeros(x.shape), y_filter_lat, variogram_model="power"#, exact_values=False
)
y_fft_pred_lat, y_fft_std_lat = uk_fourier_lat.execute("grid", xpred, np.array([0.0]), backend="loop")
time_fourierkriging_lat = time.time() - t_lat
uk_lat = OrdinaryKriging(
x, np.zeros(x.shape), y_lat, variogram_model="power"#, exact_values=False
)
y_pred_lat, y_std_lat = uk_lat.execute("grid", xpred, np.array([0.0]), backend="loop")
time_kriging_lat = time.time() - t_lat
t_long = time.time()
uk_fourier_long = OrdinaryKriging(
x, np.zeros(x.shape), y_filter_long, variogram_model="power"#, exact_values=False
)
y_fft_pred_long, y_fft_std_long = uk_fourier_long.execute("grid", xpred, np.array([0.0]), backend="loop")
time_fourierkriging_long = time.time() - t_long
uk_long = OrdinaryKriging(
x, np.zeros(x.shape), y_long, variogram_model="power"#, exact_values=False
)
y_pred_long, y_std_long = uk_long.execute("grid", xpred, np.array([0.0]), backend="loop")
time_kriging_long = time.time() - t_long
y_pred_lat = np.squeeze(y_pred_lat)
y_std_lat = np.squeeze(y_std_lat)
y_fft_pred_lat = np.squeeze(y_fft_pred_lat)
y_fft_std_lat = np.squeeze(y_fft_std_lat)
y_pred_long = np.squeeze(y_pred_long)
y_std_long = np.squeeze(y_std_long)
y_fft_pred_long = np.squeeze(y_fft_pred_long)
y_fft_std_long = np.squeeze(y_fft_std_long)
dat_24_lat = y_fft_pred_lat[135:161]
dat_26_lat = y_fft_pred_lat[184:207]
dat_28_lat = y_fft_pred_lat[230:253]
dat_30_lat = y_fft_pred_lat[276:299]
dat_2_lat = y_fft_pred_lat[322:345]
dat_24_long = y_fft_pred_long[135:161]
dat_26_long = y_fft_pred_long[184:207]
dat_28_long = y_fft_pred_long[230:253]
dat_30_long = y_fft_pred_long[276:299]
dat_2_long = y_fft_pred_long[322:345]
# =====================================
pred_24_lat = np.mean(dat_24_lat)
pred_26_lat = np.mean(dat_26_lat)
pred_28_lat = np.mean(dat_28_lat)
pred_30_lat = np.mean(dat_30_lat)
pred_2_lat = np.mean(dat_2_lat)
pred_24_long = np.mean(dat_24_long)
pred_26_long = np.mean(dat_26_long)
pred_28_long = np.mean(dat_28_long)
pred_30_long = np.mean(dat_30_long)
pred_2_long = np.mean(dat_2_long)
# ========SAVE FINAL DATA PREDICTION=========
final_pred = [[pred_24_lat, pred_26_lat, pred_28_lat, pred_30_lat, pred_2_lat],[pred_24_long, pred_26_long, pred_28_long, pred_30_long, pred_2_long]]
np.savetxt(('id'+str(jj)+'.txt'),final_pred) | code/id26.py | import time
from scipy import fftpack
import book_format
book_format.set_style()
import kf_book.kf_internal as kf_internal
from kf_book.kf_internal import DogSimulation
from kf_book import book_plots as book_plots
import numpy as np
from matplotlib import pyplot
import scipy.io
import pandas as pd
import pandas_datareader as pdr
import seaborn as sns
from pykrige.ok import OrdinaryKriging
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.insert(0, '../../results')
jj = 23
with open('Lat_new.txt', 'r') as f1:
data1 = f1.read().split(); floats1 = []
for elem1 in data1:
try:
floats1.append(float(elem1))
except ValueError:
pass
lat = np.array(data1, dtype = np.float64);lat = np.array_split(lat, 86)
x1 = lat
with open('Long_new.txt', 'r') as f2:
data2 = f2.read().split(); floats2 = []
for elem2 in data2:
try:
floats2.append(float(elem2))
except ValueError:
pass
longdat = np.array(data2, dtype = np.float64);longdat = np.array_split(longdat, 86)
x2 = longdat
x = np.linspace(0, 405, 405)
x_benchmark = np.linspace(0, 405, 405)# 550
xpred = np.linspace(405, 750, 345)#440 - 550
y_lat = x1[jj][0:405]
y_long = x2[jj][0:405]
# y_benchmark = x1[jj][0:550]
y_fft_lat = fftpack.dct(y_lat, norm="ortho")
y_fft_lat[5:] = 0
y_filter_lat = fftpack.idct(y_fft_lat, norm="ortho")
y_fft_long = fftpack.dct(y_long, norm="ortho")
y_fft_long[5:] = 0
y_filter_long = fftpack.idct(y_fft_long, norm="ortho")
t_lat = time.time()
uk_fourier_lat = OrdinaryKriging(
x, np.zeros(x.shape), y_filter_lat, variogram_model="power"#, exact_values=False
)
y_fft_pred_lat, y_fft_std_lat = uk_fourier_lat.execute("grid", xpred, np.array([0.0]), backend="loop")
time_fourierkriging_lat = time.time() - t_lat
uk_lat = OrdinaryKriging(
x, np.zeros(x.shape), y_lat, variogram_model="power"#, exact_values=False
)
y_pred_lat, y_std_lat = uk_lat.execute("grid", xpred, np.array([0.0]), backend="loop")
time_kriging_lat = time.time() - t_lat
t_long = time.time()
uk_fourier_long = OrdinaryKriging(
x, np.zeros(x.shape), y_filter_long, variogram_model="power"#, exact_values=False
)
y_fft_pred_long, y_fft_std_long = uk_fourier_long.execute("grid", xpred, np.array([0.0]), backend="loop")
time_fourierkriging_long = time.time() - t_long
uk_long = OrdinaryKriging(
x, np.zeros(x.shape), y_long, variogram_model="power"#, exact_values=False
)
y_pred_long, y_std_long = uk_long.execute("grid", xpred, np.array([0.0]), backend="loop")
time_kriging_long = time.time() - t_long
y_pred_lat = np.squeeze(y_pred_lat)
y_std_lat = np.squeeze(y_std_lat)
y_fft_pred_lat = np.squeeze(y_fft_pred_lat)
y_fft_std_lat = np.squeeze(y_fft_std_lat)
y_pred_long = np.squeeze(y_pred_long)
y_std_long = np.squeeze(y_std_long)
y_fft_pred_long = np.squeeze(y_fft_pred_long)
y_fft_std_long = np.squeeze(y_fft_std_long)
dat_24_lat = y_fft_pred_lat[135:161]
dat_26_lat = y_fft_pred_lat[184:207]
dat_28_lat = y_fft_pred_lat[230:253]
dat_30_lat = y_fft_pred_lat[276:299]
dat_2_lat = y_fft_pred_lat[322:345]
dat_24_long = y_fft_pred_long[135:161]
dat_26_long = y_fft_pred_long[184:207]
dat_28_long = y_fft_pred_long[230:253]
dat_30_long = y_fft_pred_long[276:299]
dat_2_long = y_fft_pred_long[322:345]
# =====================================
pred_24_lat = np.mean(dat_24_lat)
pred_26_lat = np.mean(dat_26_lat)
pred_28_lat = np.mean(dat_28_lat)
pred_30_lat = np.mean(dat_30_lat)
pred_2_lat = np.mean(dat_2_lat)
pred_24_long = np.mean(dat_24_long)
pred_26_long = np.mean(dat_26_long)
pred_28_long = np.mean(dat_28_long)
pred_30_long = np.mean(dat_30_long)
pred_2_long = np.mean(dat_2_long)
# ========SAVE FINAL DATA PREDICTION=========
final_pred = [[pred_24_lat, pred_26_lat, pred_28_lat, pred_30_lat, pred_2_lat],[pred_24_long, pred_26_long, pred_28_long, pred_30_long, pred_2_long]]
np.savetxt(('id'+str(jj)+'.txt'),final_pred) | 0.161353 | 0.386069 |
import numpy as np
from ...reactions import GenericReactionFactory
from ..topology_graph import Edge, NullOptimizer
from .cof import Cof
from .vertices import LinearVertex, NonLinearVertex
class PeriodicHexagonal(Cof):
"""
Represents a periodic hexagonal COF topology graph.
Unoptimzed construction
.. moldoc::
import moldoc.molecule as molecule
import stk
bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock(
smiles='Brc1c(Br)c(Br)c(Br)c(Br)c1Br',
functional_groups=[stk.BromoFactory()],
)
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicHexagonal(
building_blocks=(bb1, bb2),
lattice_size=(2, 2, 1),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=bond.get_order(),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
``Collapser(scale_steps=False)`` optimized construction
.. moldoc::
import moldoc.molecule as molecule
import stk
bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock(
smiles='Brc1c(Br)c(Br)c(Br)c(Br)c1Br',
functional_groups=[stk.BromoFactory()],
)
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicHexagonal(
building_blocks=(bb1, bb2),
lattice_size=(2, 2, 1),
optimizer=stk.Collapser(scale_steps=False),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=bond.get_order(),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
Building blocks with six and two functional groups are required
for this topology graph.
When using a :class:`dict` for the `building_blocks` parameter,
as in :ref:`cof-topology-graph-examples`:
*Multi-Building Block COF Construction*, a
:class:`.BuildingBlock`, with the following number of functional
groups, needs to be assigned to each of the following vertex ids:
| 6-functional groups: 0 to 3
| 2-functional groups: 4 to 15
Note that optimizers may not optimize the :class:`.PeriodicInfo`.
The documentation of the optimizer will state if it does.
See :class:`.Cof` for more details and examples.
"""
def __init__(
self,
building_blocks,
lattice_size,
vertex_alignments=None,
reaction_factory=GenericReactionFactory(),
num_processes=1,
optimizer=NullOptimizer(),
):
"""
Initialize a :class:`.PeriodicHexagonal` instance.
Parameters
----------
building_blocks : :class:`tuple` or :class:`dict`
Can be a :class:`tuple` of :class:`.BuildingBlock`
instances, which should be placed on the topology graph.
Can also be a :class:`dict` which maps the
:class:`.BuildingBlock` instances to the ids of the
vertices it should be placed on. A :class:`dict` is
required when there are multiple building blocks with the
same number of functional groups, because in this case
the desired placement is ambiguous.
lattice_size : :class:`tuple` of :class:`int`
The size of the lattice in the x, y and z directions.
vertex_alignments : :class:`dict`, optional
A mapping from the id of a :class:`.Vertex`
to an :class:`.Edge` connected to it.
The :class:`.Edge` is used to align the first
:class:`.FunctionalGroup` of a :class:`.BuildingBlock`
placed on that vertex. Only vertices which need to have
their default edge changed need to be present in the
:class:`dict`. If ``None`` then the default edge is used
for each vertex. Changing which :class:`.Edge` is used will
mean that the topology graph represents different
structural isomers. The edge is referred to by a number
between ``0`` (inclusive) and the number of edges the
vertex is connected to (exclusive).
reaction_factory : :class:`.ReactionFactory`, optional
The reaction factory to use for creating bonds between
building blocks.
num_processes : :class:`int`, optional
The number of parallel processes to create during
:meth:`construct`.
optimizer : :class:`.Optimizer`, optional
Used to optimize the structure of the constructed
molecule.
Raises
------
:class:`AssertionError`
If the any building block does not have a
valid number of functional groups.
:class:`ValueError`
If the there are multiple building blocks with the
same number of functional_groups in `building_blocks`,
and they are not explicitly assigned to vertices. The
desired placement of building blocks is ambiguous in
this case.
:class:`~.cof.UnoccupiedVertexError`
If a vertex of the COF topology graph does not have a
building block placed on it.
:class:`~.cof.OverlyOccupiedVertexError`
If a vertex of the COF topology graph has more than one
building block placed on it.
"""
super().__init__(
building_blocks=building_blocks,
lattice_size=lattice_size,
periodic=True,
vertex_alignments=vertex_alignments,
reaction_factory=reaction_factory,
num_processes=num_processes,
optimizer=optimizer,
)
_lattice_constants = _a, _b, _c = (
np.array([1., 0., 0.]),
np.array([0.5, 0.866, 0]),
np.array([0, 0, 5/1.7321])
)
_non_linears = (
NonLinearVertex(0, (1/4)*_a + (1/4)*_b + (1/2)*_c),
NonLinearVertex(1, (1/4)*_a + (3/4)*_b + (1/2)*_c),
NonLinearVertex(2, (3/4)*_a + (1/4)*_b + (1/2)*_c),
NonLinearVertex(3, (3/4)*_a + (3/4)*_b + (1/2)*_c),
)
_vertex_prototypes = (
*_non_linears,
LinearVertex.init_at_center(
id=4,
vertices=(_non_linears[0], _non_linears[1]),
),
LinearVertex.init_at_center(
id=5,
vertices=(_non_linears[0], _non_linears[2]),
),
LinearVertex.init_at_center(
id=6,
vertices=(_non_linears[1], _non_linears[2]),
),
LinearVertex.init_at_center(
id=7,
vertices=(_non_linears[1], _non_linears[3]),
),
LinearVertex.init_at_center(
id=8,
vertices=(_non_linears[2], _non_linears[3]),
),
LinearVertex.init_at_shifted_center(
id=9,
vertices=(_non_linears[0], _non_linears[2]),
cell_shifts=((0, 0, 0), (-1, 0, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=10,
vertices=(_non_linears[0], _non_linears[1]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=11,
vertices=(_non_linears[0], _non_linears[3]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=12,
vertices=(_non_linears[2], _non_linears[1]),
cell_shifts=((0, 0, 0), (1, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=13,
vertices=(_non_linears[2], _non_linears[3]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=14,
vertices=(_non_linears[1], _non_linears[3]),
cell_shifts=((0, 0, 0), (-1, 0, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=15,
vertices=(_non_linears[3], _non_linears[0]),
cell_shifts=((0, 0, 0), (1, 0, 0)),
lattice_constants=_lattice_constants,
)
)
_edge_prototypes = (
Edge(0, _vertex_prototypes[4], _vertex_prototypes[0]),
Edge(1, _vertex_prototypes[4], _vertex_prototypes[1]),
Edge(2, _vertex_prototypes[5], _vertex_prototypes[0]),
Edge(3, _vertex_prototypes[5], _vertex_prototypes[2]),
Edge(4, _vertex_prototypes[6], _vertex_prototypes[1]),
Edge(5, _vertex_prototypes[6], _vertex_prototypes[2]),
Edge(6, _vertex_prototypes[7], _vertex_prototypes[1]),
Edge(7, _vertex_prototypes[7], _vertex_prototypes[3]),
Edge(8, _vertex_prototypes[8], _vertex_prototypes[2]),
Edge(9, _vertex_prototypes[8], _vertex_prototypes[3]),
Edge(10, _vertex_prototypes[9], _vertex_prototypes[0]),
Edge(
id=11,
vertex1=_vertex_prototypes[9],
vertex2=_vertex_prototypes[2],
periodicity=(-1, 0, 0),
),
Edge(12, _vertex_prototypes[10], _vertex_prototypes[0]),
Edge(
id=13,
vertex1=_vertex_prototypes[10],
vertex2=_vertex_prototypes[1],
periodicity=(0, -1, 0),
),
Edge(14, _vertex_prototypes[11], _vertex_prototypes[0]),
Edge(
id=15,
vertex1=_vertex_prototypes[11],
vertex2=_vertex_prototypes[3],
periodicity=(0, -1, 0),
),
Edge(16, _vertex_prototypes[12], _vertex_prototypes[2]),
Edge(
id=17,
vertex1=_vertex_prototypes[12],
vertex2=_vertex_prototypes[1],
periodicity=(1, -1, 0),
),
Edge(18, _vertex_prototypes[13], _vertex_prototypes[2]),
Edge(
id=19,
vertex1=_vertex_prototypes[13],
vertex2=_vertex_prototypes[3],
periodicity=(0, -1, 0),
),
Edge(20, _vertex_prototypes[14], _vertex_prototypes[1]),
Edge(
id=21,
vertex1=_vertex_prototypes[14],
vertex2=_vertex_prototypes[3],
periodicity=(-1, 0, 0),
),
Edge(22, _vertex_prototypes[15], _vertex_prototypes[3]),
Edge(
id=23,
vertex1=_vertex_prototypes[15],
vertex2=_vertex_prototypes[0],
periodicity=(1, 0, 0),
),
) | src/stk/molecular/topology_graphs/cof/periodic_hexagonal.py | import numpy as np
from ...reactions import GenericReactionFactory
from ..topology_graph import Edge, NullOptimizer
from .cof import Cof
from .vertices import LinearVertex, NonLinearVertex
class PeriodicHexagonal(Cof):
"""
Represents a periodic hexagonal COF topology graph.
Unoptimzed construction
.. moldoc::
import moldoc.molecule as molecule
import stk
bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock(
smiles='Brc1c(Br)c(Br)c(Br)c(Br)c1Br',
functional_groups=[stk.BromoFactory()],
)
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicHexagonal(
building_blocks=(bb1, bb2),
lattice_size=(2, 2, 1),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=bond.get_order(),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
``Collapser(scale_steps=False)`` optimized construction
.. moldoc::
import moldoc.molecule as molecule
import stk
bb1 = stk.BuildingBlock('BrCCBr', [stk.BromoFactory()])
bb2 = stk.BuildingBlock(
smiles='Brc1c(Br)c(Br)c(Br)c(Br)c1Br',
functional_groups=[stk.BromoFactory()],
)
cof = stk.ConstructedMolecule(
topology_graph=stk.cof.PeriodicHexagonal(
building_blocks=(bb1, bb2),
lattice_size=(2, 2, 1),
optimizer=stk.Collapser(scale_steps=False),
),
)
moldoc_display_molecule = molecule.Molecule(
atoms=(
molecule.Atom(
atomic_number=atom.get_atomic_number(),
position=position,
) for atom, position in zip(
cof.get_atoms(),
cof.get_position_matrix(),
)
),
bonds=(
molecule.Bond(
atom1_id=bond.get_atom1().get_id(),
atom2_id=bond.get_atom2().get_id(),
order=bond.get_order(),
) for bond in cof.get_bonds()
if all(p == 0 for p in bond.get_periodicity())
),
)
Building blocks with six and two functional groups are required
for this topology graph.
When using a :class:`dict` for the `building_blocks` parameter,
as in :ref:`cof-topology-graph-examples`:
*Multi-Building Block COF Construction*, a
:class:`.BuildingBlock`, with the following number of functional
groups, needs to be assigned to each of the following vertex ids:
| 6-functional groups: 0 to 3
| 2-functional groups: 4 to 15
Note that optimizers may not optimize the :class:`.PeriodicInfo`.
The documentation of the optimizer will state if it does.
See :class:`.Cof` for more details and examples.
"""
def __init__(
self,
building_blocks,
lattice_size,
vertex_alignments=None,
reaction_factory=GenericReactionFactory(),
num_processes=1,
optimizer=NullOptimizer(),
):
"""
Initialize a :class:`.PeriodicHexagonal` instance.
Parameters
----------
building_blocks : :class:`tuple` or :class:`dict`
Can be a :class:`tuple` of :class:`.BuildingBlock`
instances, which should be placed on the topology graph.
Can also be a :class:`dict` which maps the
:class:`.BuildingBlock` instances to the ids of the
vertices it should be placed on. A :class:`dict` is
required when there are multiple building blocks with the
same number of functional groups, because in this case
the desired placement is ambiguous.
lattice_size : :class:`tuple` of :class:`int`
The size of the lattice in the x, y and z directions.
vertex_alignments : :class:`dict`, optional
A mapping from the id of a :class:`.Vertex`
to an :class:`.Edge` connected to it.
The :class:`.Edge` is used to align the first
:class:`.FunctionalGroup` of a :class:`.BuildingBlock`
placed on that vertex. Only vertices which need to have
their default edge changed need to be present in the
:class:`dict`. If ``None`` then the default edge is used
for each vertex. Changing which :class:`.Edge` is used will
mean that the topology graph represents different
structural isomers. The edge is referred to by a number
between ``0`` (inclusive) and the number of edges the
vertex is connected to (exclusive).
reaction_factory : :class:`.ReactionFactory`, optional
The reaction factory to use for creating bonds between
building blocks.
num_processes : :class:`int`, optional
The number of parallel processes to create during
:meth:`construct`.
optimizer : :class:`.Optimizer`, optional
Used to optimize the structure of the constructed
molecule.
Raises
------
:class:`AssertionError`
If the any building block does not have a
valid number of functional groups.
:class:`ValueError`
If the there are multiple building blocks with the
same number of functional_groups in `building_blocks`,
and they are not explicitly assigned to vertices. The
desired placement of building blocks is ambiguous in
this case.
:class:`~.cof.UnoccupiedVertexError`
If a vertex of the COF topology graph does not have a
building block placed on it.
:class:`~.cof.OverlyOccupiedVertexError`
If a vertex of the COF topology graph has more than one
building block placed on it.
"""
super().__init__(
building_blocks=building_blocks,
lattice_size=lattice_size,
periodic=True,
vertex_alignments=vertex_alignments,
reaction_factory=reaction_factory,
num_processes=num_processes,
optimizer=optimizer,
)
_lattice_constants = _a, _b, _c = (
np.array([1., 0., 0.]),
np.array([0.5, 0.866, 0]),
np.array([0, 0, 5/1.7321])
)
_non_linears = (
NonLinearVertex(0, (1/4)*_a + (1/4)*_b + (1/2)*_c),
NonLinearVertex(1, (1/4)*_a + (3/4)*_b + (1/2)*_c),
NonLinearVertex(2, (3/4)*_a + (1/4)*_b + (1/2)*_c),
NonLinearVertex(3, (3/4)*_a + (3/4)*_b + (1/2)*_c),
)
_vertex_prototypes = (
*_non_linears,
LinearVertex.init_at_center(
id=4,
vertices=(_non_linears[0], _non_linears[1]),
),
LinearVertex.init_at_center(
id=5,
vertices=(_non_linears[0], _non_linears[2]),
),
LinearVertex.init_at_center(
id=6,
vertices=(_non_linears[1], _non_linears[2]),
),
LinearVertex.init_at_center(
id=7,
vertices=(_non_linears[1], _non_linears[3]),
),
LinearVertex.init_at_center(
id=8,
vertices=(_non_linears[2], _non_linears[3]),
),
LinearVertex.init_at_shifted_center(
id=9,
vertices=(_non_linears[0], _non_linears[2]),
cell_shifts=((0, 0, 0), (-1, 0, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=10,
vertices=(_non_linears[0], _non_linears[1]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=11,
vertices=(_non_linears[0], _non_linears[3]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=12,
vertices=(_non_linears[2], _non_linears[1]),
cell_shifts=((0, 0, 0), (1, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=13,
vertices=(_non_linears[2], _non_linears[3]),
cell_shifts=((0, 0, 0), (0, -1, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=14,
vertices=(_non_linears[1], _non_linears[3]),
cell_shifts=((0, 0, 0), (-1, 0, 0)),
lattice_constants=_lattice_constants,
),
LinearVertex.init_at_shifted_center(
id=15,
vertices=(_non_linears[3], _non_linears[0]),
cell_shifts=((0, 0, 0), (1, 0, 0)),
lattice_constants=_lattice_constants,
)
)
_edge_prototypes = (
Edge(0, _vertex_prototypes[4], _vertex_prototypes[0]),
Edge(1, _vertex_prototypes[4], _vertex_prototypes[1]),
Edge(2, _vertex_prototypes[5], _vertex_prototypes[0]),
Edge(3, _vertex_prototypes[5], _vertex_prototypes[2]),
Edge(4, _vertex_prototypes[6], _vertex_prototypes[1]),
Edge(5, _vertex_prototypes[6], _vertex_prototypes[2]),
Edge(6, _vertex_prototypes[7], _vertex_prototypes[1]),
Edge(7, _vertex_prototypes[7], _vertex_prototypes[3]),
Edge(8, _vertex_prototypes[8], _vertex_prototypes[2]),
Edge(9, _vertex_prototypes[8], _vertex_prototypes[3]),
Edge(10, _vertex_prototypes[9], _vertex_prototypes[0]),
Edge(
id=11,
vertex1=_vertex_prototypes[9],
vertex2=_vertex_prototypes[2],
periodicity=(-1, 0, 0),
),
Edge(12, _vertex_prototypes[10], _vertex_prototypes[0]),
Edge(
id=13,
vertex1=_vertex_prototypes[10],
vertex2=_vertex_prototypes[1],
periodicity=(0, -1, 0),
),
Edge(14, _vertex_prototypes[11], _vertex_prototypes[0]),
Edge(
id=15,
vertex1=_vertex_prototypes[11],
vertex2=_vertex_prototypes[3],
periodicity=(0, -1, 0),
),
Edge(16, _vertex_prototypes[12], _vertex_prototypes[2]),
Edge(
id=17,
vertex1=_vertex_prototypes[12],
vertex2=_vertex_prototypes[1],
periodicity=(1, -1, 0),
),
Edge(18, _vertex_prototypes[13], _vertex_prototypes[2]),
Edge(
id=19,
vertex1=_vertex_prototypes[13],
vertex2=_vertex_prototypes[3],
periodicity=(0, -1, 0),
),
Edge(20, _vertex_prototypes[14], _vertex_prototypes[1]),
Edge(
id=21,
vertex1=_vertex_prototypes[14],
vertex2=_vertex_prototypes[3],
periodicity=(-1, 0, 0),
),
Edge(22, _vertex_prototypes[15], _vertex_prototypes[3]),
Edge(
id=23,
vertex1=_vertex_prototypes[15],
vertex2=_vertex_prototypes[0],
periodicity=(1, 0, 0),
),
) | 0.885415 | 0.490785 |
import json
import logging
from datetime import datetime
from typing import Any, Dict, List, Optional, Union
from sqlalchemy.exc import SQLAlchemyError
from superset import security_manager
from superset.dao.base import BaseDAO
from superset.dashboards.commands.exceptions import DashboardNotFoundError
from superset.dashboards.filters import DashboardAccessFilter
from superset.extensions import db
from superset.models.core import FavStar, FavStarClassName
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.utils.dashboard_filter_scopes_converter import copy_filter_scopes
logger = logging.getLogger(__name__)
class DashboardDAO(BaseDAO):
model_cls = Dashboard
base_filter = DashboardAccessFilter
@staticmethod
def get_by_id_or_slug(id_or_slug: str) -> Dashboard:
dashboard = Dashboard.get(id_or_slug)
if not dashboard:
raise DashboardNotFoundError()
security_manager.raise_for_dashboard_access(dashboard)
return dashboard
@staticmethod
def get_datasets_for_dashboard(id_or_slug: str) -> List[Any]:
dashboard = DashboardDAO.get_by_id_or_slug(id_or_slug)
return dashboard.datasets_trimmed_for_slices()
@staticmethod
def get_charts_for_dashboard(id_or_slug: str) -> List[Slice]:
return DashboardDAO.get_by_id_or_slug(id_or_slug).slices
@staticmethod
def get_dashboard_changed_on(
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
# drop microseconds in datetime to match with last_modified header
return dashboard.changed_on.replace(microsecond=0)
@staticmethod
def get_dashboard_and_slices_changed_on( # pylint: disable=invalid-name
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard. The change could be a dashboard
metadata change, or a change to one of its dependent slices.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
dashboard_changed_on = DashboardDAO.get_dashboard_changed_on(dashboard)
slices = dashboard.slices
slices_changed_on = max(
[slc.changed_on for slc in slices]
+ ([datetime.fromtimestamp(0)] if len(slices) == 0 else [])
)
# drop microseconds in datetime to match with last_modified header
return max(dashboard_changed_on, slices_changed_on).replace(microsecond=0)
@staticmethod
def get_dashboard_and_datasets_changed_on( # pylint: disable=invalid-name
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard. The change could be a dashboard
metadata change, a change to one of its dependent datasets.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
dashboard_changed_on = DashboardDAO.get_dashboard_changed_on(dashboard)
datasources = dashboard.datasources
datasources_changed_on = max(
[datasource.changed_on for datasource in datasources]
+ ([datetime.fromtimestamp(0)] if len(datasources) == 0 else [])
)
# drop microseconds in datetime to match with last_modified header
return max(dashboard_changed_on, datasources_changed_on).replace(microsecond=0)
@staticmethod
def validate_slug_uniqueness(slug: str) -> bool:
if not slug:
return True
dashboard_query = db.session.query(Dashboard).filter(Dashboard.slug == slug)
return not db.session.query(dashboard_query.exists()).scalar()
@staticmethod
def validate_update_slug_uniqueness(dashboard_id: int, slug: Optional[str]) -> bool:
if slug is not None:
dashboard_query = db.session.query(Dashboard).filter(
Dashboard.slug == slug, Dashboard.id != dashboard_id
)
return not db.session.query(dashboard_query.exists()).scalar()
return True
@staticmethod
def update_charts_owners(model: Dashboard, commit: bool = True) -> Dashboard:
owners = list(model.owners)
for slc in model.slices:
slc.owners = list(set(owners) | set(slc.owners))
if commit:
db.session.commit()
return model
@staticmethod
def bulk_delete(models: Optional[List[Dashboard]], commit: bool = True) -> None:
item_ids = [model.id for model in models] if models else []
# bulk delete, first delete related data
if models:
for model in models:
model.slices = []
model.owners = []
db.session.merge(model)
# bulk delete itself
try:
db.session.query(Dashboard).filter(Dashboard.id.in_(item_ids)).delete(
synchronize_session="fetch"
)
if commit:
db.session.commit()
except SQLAlchemyError as ex:
if commit:
db.session.rollback()
raise ex
@staticmethod
def set_dash_metadata(
dashboard: Dashboard,
data: Dict[Any, Any],
old_to_new_slice_ids: Optional[Dict[int, int]] = None,
) -> None:
positions = data["positions"]
# find slices in the position data
slice_ids = [
value.get("meta", {}).get("chartId")
for value in positions.values()
if isinstance(value, dict)
]
session = db.session()
current_slices = session.query(Slice).filter(Slice.id.in_(slice_ids)).all()
dashboard.slices = current_slices
# add UUID to positions
uuid_map = {slice.id: str(slice.uuid) for slice in current_slices}
for obj in positions.values():
if (
isinstance(obj, dict)
and obj["type"] == "CHART"
and obj["meta"]["chartId"]
):
chart_id = obj["meta"]["chartId"]
obj["meta"]["uuid"] = uuid_map.get(chart_id)
# remove leading and trailing white spaces in the dumped json
dashboard.position_json = json.dumps(
positions, indent=None, separators=(",", ":"), sort_keys=True
)
md = dashboard.params_dict
dashboard.css = data.get("css")
dashboard.dashboard_title = data["dashboard_title"]
if "timed_refresh_immune_slices" not in md:
md["timed_refresh_immune_slices"] = []
new_filter_scopes = {}
if "filter_scopes" in data:
# replace filter_id and immune ids from old slice id to new slice id:
# and remove slice ids that are not in dash anymore
slc_id_dict: Dict[int, int] = {}
if old_to_new_slice_ids:
slc_id_dict = {
old: new
for old, new in old_to_new_slice_ids.items()
if new in slice_ids
}
else:
slc_id_dict = {sid: sid for sid in slice_ids}
new_filter_scopes = copy_filter_scopes(
old_to_new_slc_id_dict=slc_id_dict,
old_filter_scopes=json.loads(data["filter_scopes"] or "{}"),
)
if new_filter_scopes:
md["filter_scopes"] = new_filter_scopes
else:
md.pop("filter_scopes", None)
md["expanded_slices"] = data.get("expanded_slices", {})
md["refresh_frequency"] = data.get("refresh_frequency", 0)
default_filters_data = json.loads(data.get("default_filters", "{}"))
applicable_filters = {
key: v for key, v in default_filters_data.items() if int(key) in slice_ids
}
md["default_filters"] = json.dumps(applicable_filters)
md["color_scheme"] = data.get("color_scheme")
md["label_colors"] = data.get("label_colors")
if data.get("color_namespace"):
md["color_namespace"] = data.get("color_namespace")
dashboard.json_metadata = json.dumps(md)
@staticmethod
def favorited_ids(
dashboards: List[Dashboard], current_user_id: int
) -> List[FavStar]:
ids = [dash.id for dash in dashboards]
return [
star.obj_id
for star in db.session.query(FavStar.obj_id)
.filter(
FavStar.class_name == FavStarClassName.DASHBOARD,
FavStar.obj_id.in_(ids),
FavStar.user_id == current_user_id,
)
.all()
] | superset/dashboards/dao.py | import json
import logging
from datetime import datetime
from typing import Any, Dict, List, Optional, Union
from sqlalchemy.exc import SQLAlchemyError
from superset import security_manager
from superset.dao.base import BaseDAO
from superset.dashboards.commands.exceptions import DashboardNotFoundError
from superset.dashboards.filters import DashboardAccessFilter
from superset.extensions import db
from superset.models.core import FavStar, FavStarClassName
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.utils.dashboard_filter_scopes_converter import copy_filter_scopes
logger = logging.getLogger(__name__)
class DashboardDAO(BaseDAO):
model_cls = Dashboard
base_filter = DashboardAccessFilter
@staticmethod
def get_by_id_or_slug(id_or_slug: str) -> Dashboard:
dashboard = Dashboard.get(id_or_slug)
if not dashboard:
raise DashboardNotFoundError()
security_manager.raise_for_dashboard_access(dashboard)
return dashboard
@staticmethod
def get_datasets_for_dashboard(id_or_slug: str) -> List[Any]:
dashboard = DashboardDAO.get_by_id_or_slug(id_or_slug)
return dashboard.datasets_trimmed_for_slices()
@staticmethod
def get_charts_for_dashboard(id_or_slug: str) -> List[Slice]:
return DashboardDAO.get_by_id_or_slug(id_or_slug).slices
@staticmethod
def get_dashboard_changed_on(
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
# drop microseconds in datetime to match with last_modified header
return dashboard.changed_on.replace(microsecond=0)
@staticmethod
def get_dashboard_and_slices_changed_on( # pylint: disable=invalid-name
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard. The change could be a dashboard
metadata change, or a change to one of its dependent slices.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
dashboard_changed_on = DashboardDAO.get_dashboard_changed_on(dashboard)
slices = dashboard.slices
slices_changed_on = max(
[slc.changed_on for slc in slices]
+ ([datetime.fromtimestamp(0)] if len(slices) == 0 else [])
)
# drop microseconds in datetime to match with last_modified header
return max(dashboard_changed_on, slices_changed_on).replace(microsecond=0)
@staticmethod
def get_dashboard_and_datasets_changed_on( # pylint: disable=invalid-name
id_or_slug_or_dashboard: Union[str, Dashboard]
) -> datetime:
"""
Get latest changed datetime for a dashboard. The change could be a dashboard
metadata change, a change to one of its dependent datasets.
:param id_or_slug_or_dashboard: A dashboard or the ID or slug of the dashboard.
:returns: The datetime the dashboard was last changed.
"""
dashboard = (
DashboardDAO.get_by_id_or_slug(id_or_slug_or_dashboard)
if isinstance(id_or_slug_or_dashboard, str)
else id_or_slug_or_dashboard
)
dashboard_changed_on = DashboardDAO.get_dashboard_changed_on(dashboard)
datasources = dashboard.datasources
datasources_changed_on = max(
[datasource.changed_on for datasource in datasources]
+ ([datetime.fromtimestamp(0)] if len(datasources) == 0 else [])
)
# drop microseconds in datetime to match with last_modified header
return max(dashboard_changed_on, datasources_changed_on).replace(microsecond=0)
@staticmethod
def validate_slug_uniqueness(slug: str) -> bool:
if not slug:
return True
dashboard_query = db.session.query(Dashboard).filter(Dashboard.slug == slug)
return not db.session.query(dashboard_query.exists()).scalar()
@staticmethod
def validate_update_slug_uniqueness(dashboard_id: int, slug: Optional[str]) -> bool:
if slug is not None:
dashboard_query = db.session.query(Dashboard).filter(
Dashboard.slug == slug, Dashboard.id != dashboard_id
)
return not db.session.query(dashboard_query.exists()).scalar()
return True
@staticmethod
def update_charts_owners(model: Dashboard, commit: bool = True) -> Dashboard:
owners = list(model.owners)
for slc in model.slices:
slc.owners = list(set(owners) | set(slc.owners))
if commit:
db.session.commit()
return model
@staticmethod
def bulk_delete(models: Optional[List[Dashboard]], commit: bool = True) -> None:
item_ids = [model.id for model in models] if models else []
# bulk delete, first delete related data
if models:
for model in models:
model.slices = []
model.owners = []
db.session.merge(model)
# bulk delete itself
try:
db.session.query(Dashboard).filter(Dashboard.id.in_(item_ids)).delete(
synchronize_session="fetch"
)
if commit:
db.session.commit()
except SQLAlchemyError as ex:
if commit:
db.session.rollback()
raise ex
@staticmethod
def set_dash_metadata(
dashboard: Dashboard,
data: Dict[Any, Any],
old_to_new_slice_ids: Optional[Dict[int, int]] = None,
) -> None:
positions = data["positions"]
# find slices in the position data
slice_ids = [
value.get("meta", {}).get("chartId")
for value in positions.values()
if isinstance(value, dict)
]
session = db.session()
current_slices = session.query(Slice).filter(Slice.id.in_(slice_ids)).all()
dashboard.slices = current_slices
# add UUID to positions
uuid_map = {slice.id: str(slice.uuid) for slice in current_slices}
for obj in positions.values():
if (
isinstance(obj, dict)
and obj["type"] == "CHART"
and obj["meta"]["chartId"]
):
chart_id = obj["meta"]["chartId"]
obj["meta"]["uuid"] = uuid_map.get(chart_id)
# remove leading and trailing white spaces in the dumped json
dashboard.position_json = json.dumps(
positions, indent=None, separators=(",", ":"), sort_keys=True
)
md = dashboard.params_dict
dashboard.css = data.get("css")
dashboard.dashboard_title = data["dashboard_title"]
if "timed_refresh_immune_slices" not in md:
md["timed_refresh_immune_slices"] = []
new_filter_scopes = {}
if "filter_scopes" in data:
# replace filter_id and immune ids from old slice id to new slice id:
# and remove slice ids that are not in dash anymore
slc_id_dict: Dict[int, int] = {}
if old_to_new_slice_ids:
slc_id_dict = {
old: new
for old, new in old_to_new_slice_ids.items()
if new in slice_ids
}
else:
slc_id_dict = {sid: sid for sid in slice_ids}
new_filter_scopes = copy_filter_scopes(
old_to_new_slc_id_dict=slc_id_dict,
old_filter_scopes=json.loads(data["filter_scopes"] or "{}"),
)
if new_filter_scopes:
md["filter_scopes"] = new_filter_scopes
else:
md.pop("filter_scopes", None)
md["expanded_slices"] = data.get("expanded_slices", {})
md["refresh_frequency"] = data.get("refresh_frequency", 0)
default_filters_data = json.loads(data.get("default_filters", "{}"))
applicable_filters = {
key: v for key, v in default_filters_data.items() if int(key) in slice_ids
}
md["default_filters"] = json.dumps(applicable_filters)
md["color_scheme"] = data.get("color_scheme")
md["label_colors"] = data.get("label_colors")
if data.get("color_namespace"):
md["color_namespace"] = data.get("color_namespace")
dashboard.json_metadata = json.dumps(md)
@staticmethod
def favorited_ids(
dashboards: List[Dashboard], current_user_id: int
) -> List[FavStar]:
ids = [dash.id for dash in dashboards]
return [
star.obj_id
for star in db.session.query(FavStar.obj_id)
.filter(
FavStar.class_name == FavStarClassName.DASHBOARD,
FavStar.obj_id.in_(ids),
FavStar.user_id == current_user_id,
)
.all()
] | 0.820541 | 0.23316 |
from ..models import Catalog, Question, QuestionSet, Section
def test_catalog_str(db):
instances = Catalog.objects.all()
for instance in instances:
assert str(instance)
def test_catalog_clean(db):
instances = Catalog.objects.all()
for instance in instances:
instance.clean()
def test_catalog_copy(db):
instances = Catalog.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert list(new_instance.sites.values('id')) == list(new_instance.sites.values('id'))
assert list(new_instance.groups.values('id')) == list(new_instance.groups.values('id'))
assert new_instance.sections.count() == instance.sections.count()
def test_section_str(db):
instances = Section.objects.all()
for instance in instances:
assert str(instance)
def test_section_clean(db):
instances = Section.objects.all()
for instance in instances:
instance.clean()
def test_section_copy(db):
instances = Section.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.questionsets.count() == instance.questionsets.count()
def test_questionset_str(db):
instances = QuestionSet.objects.all()
for instance in instances:
assert str(instance)
def test_questionset_clean(db):
instances = QuestionSet.objects.all()
for instance in instances:
instance.clean()
def test_questionset_copy(db):
instances = QuestionSet.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.attribute == instance.attribute
assert list(new_instance.conditions.values('id')) == list(new_instance.conditions.values('id'))
assert new_instance.questions.count() == instance.questions.count()
def test_question_str(db):
instances = Question.objects.all()
for instance in instances:
assert str(instance)
def test_question_clean(db):
instances = Question.objects.all()
for instance in instances:
instance.clean()
def test_question_copy(db):
instances = Question.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.attribute == instance.attribute
assert list(new_instance.conditions.values('id')) == list(new_instance.conditions.values('id'))
assert list(new_instance.optionsets.values('id')) == list(new_instance.optionsets.values('id')) | rdmo/questions/tests/test_models.py | from ..models import Catalog, Question, QuestionSet, Section
def test_catalog_str(db):
instances = Catalog.objects.all()
for instance in instances:
assert str(instance)
def test_catalog_clean(db):
instances = Catalog.objects.all()
for instance in instances:
instance.clean()
def test_catalog_copy(db):
instances = Catalog.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert list(new_instance.sites.values('id')) == list(new_instance.sites.values('id'))
assert list(new_instance.groups.values('id')) == list(new_instance.groups.values('id'))
assert new_instance.sections.count() == instance.sections.count()
def test_section_str(db):
instances = Section.objects.all()
for instance in instances:
assert str(instance)
def test_section_clean(db):
instances = Section.objects.all()
for instance in instances:
instance.clean()
def test_section_copy(db):
instances = Section.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.questionsets.count() == instance.questionsets.count()
def test_questionset_str(db):
instances = QuestionSet.objects.all()
for instance in instances:
assert str(instance)
def test_questionset_clean(db):
instances = QuestionSet.objects.all()
for instance in instances:
instance.clean()
def test_questionset_copy(db):
instances = QuestionSet.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.attribute == instance.attribute
assert list(new_instance.conditions.values('id')) == list(new_instance.conditions.values('id'))
assert new_instance.questions.count() == instance.questions.count()
def test_question_str(db):
instances = Question.objects.all()
for instance in instances:
assert str(instance)
def test_question_clean(db):
instances = Question.objects.all()
for instance in instances:
instance.clean()
def test_question_copy(db):
instances = Question.objects.all()
for instance in instances:
new_uri_prefix = instance.uri_prefix + '-'
new_key = instance.key + '-'
new_instance = instance.copy(new_uri_prefix, new_key)
assert new_instance.uri_prefix == new_uri_prefix
assert new_instance.key == new_key
assert new_instance.attribute == instance.attribute
assert list(new_instance.conditions.values('id')) == list(new_instance.conditions.values('id'))
assert list(new_instance.optionsets.values('id')) == list(new_instance.optionsets.values('id')) | 0.564819 | 0.441191 |
from . import _tm_main
def error_handler(error_code):
if error_code in _tm_main.error_codes():
return _tm_main.error_echo(error_code)
else:
return 'Unknown Error. ERROR_CODE: %s' %error_code
def assert_inputs(db_type,ref_path,target_path,output_path):
error = False
error = _tm_main.check_avail_db_type(db_type)
if _tm_main.error_pass(error):
if db_type == 'greengenes':
error = _tm_main.assert_greengenes_taxa_map(ref_path)
elif db_type == 'silva':
error = _tm_main.assert_silva_taxa_map(ref_path)
if _tm_main.error_pass(error):
error = _tm_main.assert_lineages(target_path)
if _tm_main.error_pass(error):
error = _tm_main.os.path.isfile(output_path)
if _tm_main.error_pass(error):
return True
return error
def evaluate_correlations(db_type,ref_path,target_path,output_path):
echo_msgs = False
error = False
assert_pass = assert_inputs(db_type,ref_path,target_path,output_path)
if assert_pass:
result = _tm_main.lineage_correlator(db_type,ref_path,target_path)
if _tm_main.error_pass(result):
echo_msgs = []
correlations = result['correlations']
echo_msgs.append('Total correlations: %d' %result['total_assignments'])
if len(result['removed_taxa'])>0:
echo_msgs.append('Missing taxa IDs: [%s]' %(','.join(result['removed_taxa'])))
file_gen = _tm_main.csv_output(output_path,db_type,correlations.loc[correlations.notna()])
if file_gen == True:
echo_msgs.append('Generating %s' %output_path)
else:
error = file_gen
else:
error = result
else:
error = assert_pass
return echo_msgs, error
def get_greengenes(ref_path=False,target_path=False,output_path=False):
return evaluate_correlations('greengenes',ref_path,target_path,output_path)
def get_silva(ref_path=False,target_path=False,output_path=False):
return evaluate_correlations('silva',ref_path,target_path,output_path) | taxamatcher/tm_handler.py | from . import _tm_main
def error_handler(error_code):
if error_code in _tm_main.error_codes():
return _tm_main.error_echo(error_code)
else:
return 'Unknown Error. ERROR_CODE: %s' %error_code
def assert_inputs(db_type,ref_path,target_path,output_path):
error = False
error = _tm_main.check_avail_db_type(db_type)
if _tm_main.error_pass(error):
if db_type == 'greengenes':
error = _tm_main.assert_greengenes_taxa_map(ref_path)
elif db_type == 'silva':
error = _tm_main.assert_silva_taxa_map(ref_path)
if _tm_main.error_pass(error):
error = _tm_main.assert_lineages(target_path)
if _tm_main.error_pass(error):
error = _tm_main.os.path.isfile(output_path)
if _tm_main.error_pass(error):
return True
return error
def evaluate_correlations(db_type,ref_path,target_path,output_path):
echo_msgs = False
error = False
assert_pass = assert_inputs(db_type,ref_path,target_path,output_path)
if assert_pass:
result = _tm_main.lineage_correlator(db_type,ref_path,target_path)
if _tm_main.error_pass(result):
echo_msgs = []
correlations = result['correlations']
echo_msgs.append('Total correlations: %d' %result['total_assignments'])
if len(result['removed_taxa'])>0:
echo_msgs.append('Missing taxa IDs: [%s]' %(','.join(result['removed_taxa'])))
file_gen = _tm_main.csv_output(output_path,db_type,correlations.loc[correlations.notna()])
if file_gen == True:
echo_msgs.append('Generating %s' %output_path)
else:
error = file_gen
else:
error = result
else:
error = assert_pass
return echo_msgs, error
def get_greengenes(ref_path=False,target_path=False,output_path=False):
return evaluate_correlations('greengenes',ref_path,target_path,output_path)
def get_silva(ref_path=False,target_path=False,output_path=False):
return evaluate_correlations('silva',ref_path,target_path,output_path) | 0.262653 | 0.102979 |
import json
from navigation_analytics.navigation_data import *
import os
from datetime import datetime
import pandas as pd
import pickle
import pytest
def parse_dtypes(dtypes_dict: dict):
result = dict()
for col, col_type in dtypes_dict.items():
result[col] = eval(col_type)
return result
def load_metadata():
with open(os.environ['CONFIG_PATH']) as fp:
config_data = json.load(fp)
return config_data
def load_data(mode: str == 'pickle'):
metadata = load_metadata()
if mode == 'pickle':
with open(os.environ['PICKLE_PATH'], 'rb') as fp:
input_data = pickle.load(fp)
else:
data_type_dict = parse_dtypes(metadata['metadata']['data_types'])
input_data = pd.read_csv(os.path.join(metadata['data_import']['file_path'],
metadata['data_import']['file_name']),
dtype=data_type_dict,
na_values=metadata['metadata']['na_vector'])
input_data.timestamp = [datetime.strptime(str(int(item)), metadata['metadata']['date_format'])
for item in input_data.timestamp]
return metadata, input_data
def _test_baseline_click_through_rate():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General ctr
assert pytest.approx(0.388839,
data_analyzer.session_analyzer.compute_click_through_rate(),
0.00001)
# ctr for group a
assert pytest.approx(0.669657,
data_analyzer.session_analyzer.compute_click_through_rate(group_id='a'),
0.00001)
# ctr for group b
assert pytest.approx(0.174762,
data_analyzer.session_analyzer.compute_click_through_rate(group_id='b'),
0.00001)
def _test_baseline_most_common_result():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General ctr
assert pytest.approx(0.668576,
data_analyzer.session_analyzer.compute_search_frequency()[1.0],
0.00001)
def _test_baseline_zero_result_result():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General zrr
assert pytest.approx(0.18444,
data_analyzer.session_analyzer.compute_zero_result_rate(),
0.00001)
# Group A zrr
assert pytest.approx(0.18360,
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='a'),
0.00001)
# Group B zrr
assert pytest.approx(0.18617,
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='b'),
0.00001)
def _test_baseline_session_length():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
session_length_a = data_analyzer.session_analyzer.compute_session_length(group_id='a')
session_length_b = data_analyzer.session_analyzer.compute_session_length(group_id='b')
assert pytest.approx(114.0,
session_length_a.median(),
0.00001)
assert pytest.approx(0.0,
session_length_b.median(),
0.00001)
def _test_compute_all_and_save_object():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata,
logger_level=logging.INFO)
data_analyzer.session_analyzer.compute_session_length()
data_analyzer.session_analyzer.compute_session_length(group_id='a')
data_analyzer.session_analyzer.compute_session_length(group_id='b')
data_analyzer.session_analyzer.compute_click_through_rate()
data_analyzer.session_analyzer.compute_click_through_rate(group_id='a')
data_analyzer.session_analyzer.compute_click_through_rate(group_id='b')
data_analyzer.session_analyzer.compute_search_frequency()
data_analyzer.session_analyzer.compute_search_frequency(group_id='a')
data_analyzer.session_analyzer.compute_search_frequency(group_id='b')
data_analyzer.session_analyzer.compute_zero_result_rate()
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='a')
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='b')
data_analyzer.save()
data_analyzer.to_excel('debug_model.xlsx')
def test_object_can_be_loaded():
data_analyzer = NavigationDataAnalyzer.load(filepath=os.environ['PATH_OBJECT'])
assert len(data_analyzer.session_analyzer.kpi_results.keys()) > 1 | tests/unit/test_navigation_data.py | import json
from navigation_analytics.navigation_data import *
import os
from datetime import datetime
import pandas as pd
import pickle
import pytest
def parse_dtypes(dtypes_dict: dict):
result = dict()
for col, col_type in dtypes_dict.items():
result[col] = eval(col_type)
return result
def load_metadata():
with open(os.environ['CONFIG_PATH']) as fp:
config_data = json.load(fp)
return config_data
def load_data(mode: str == 'pickle'):
metadata = load_metadata()
if mode == 'pickle':
with open(os.environ['PICKLE_PATH'], 'rb') as fp:
input_data = pickle.load(fp)
else:
data_type_dict = parse_dtypes(metadata['metadata']['data_types'])
input_data = pd.read_csv(os.path.join(metadata['data_import']['file_path'],
metadata['data_import']['file_name']),
dtype=data_type_dict,
na_values=metadata['metadata']['na_vector'])
input_data.timestamp = [datetime.strptime(str(int(item)), metadata['metadata']['date_format'])
for item in input_data.timestamp]
return metadata, input_data
def _test_baseline_click_through_rate():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General ctr
assert pytest.approx(0.388839,
data_analyzer.session_analyzer.compute_click_through_rate(),
0.00001)
# ctr for group a
assert pytest.approx(0.669657,
data_analyzer.session_analyzer.compute_click_through_rate(group_id='a'),
0.00001)
# ctr for group b
assert pytest.approx(0.174762,
data_analyzer.session_analyzer.compute_click_through_rate(group_id='b'),
0.00001)
def _test_baseline_most_common_result():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General ctr
assert pytest.approx(0.668576,
data_analyzer.session_analyzer.compute_search_frequency()[1.0],
0.00001)
def _test_baseline_zero_result_result():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
# General zrr
assert pytest.approx(0.18444,
data_analyzer.session_analyzer.compute_zero_result_rate(),
0.00001)
# Group A zrr
assert pytest.approx(0.18360,
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='a'),
0.00001)
# Group B zrr
assert pytest.approx(0.18617,
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='b'),
0.00001)
def _test_baseline_session_length():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata)
session_length_a = data_analyzer.session_analyzer.compute_session_length(group_id='a')
session_length_b = data_analyzer.session_analyzer.compute_session_length(group_id='b')
assert pytest.approx(114.0,
session_length_a.median(),
0.00001)
assert pytest.approx(0.0,
session_length_b.median(),
0.00001)
def _test_compute_all_and_save_object():
metadata, input_data = load_data(mode='pickle')
data_analyzer = NavigationDataAnalyzer(input_data=input_data,
metadata=metadata,
logger_level=logging.INFO)
data_analyzer.session_analyzer.compute_session_length()
data_analyzer.session_analyzer.compute_session_length(group_id='a')
data_analyzer.session_analyzer.compute_session_length(group_id='b')
data_analyzer.session_analyzer.compute_click_through_rate()
data_analyzer.session_analyzer.compute_click_through_rate(group_id='a')
data_analyzer.session_analyzer.compute_click_through_rate(group_id='b')
data_analyzer.session_analyzer.compute_search_frequency()
data_analyzer.session_analyzer.compute_search_frequency(group_id='a')
data_analyzer.session_analyzer.compute_search_frequency(group_id='b')
data_analyzer.session_analyzer.compute_zero_result_rate()
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='a')
data_analyzer.session_analyzer.compute_zero_result_rate(group_id='b')
data_analyzer.save()
data_analyzer.to_excel('debug_model.xlsx')
def test_object_can_be_loaded():
data_analyzer = NavigationDataAnalyzer.load(filepath=os.environ['PATH_OBJECT'])
assert len(data_analyzer.session_analyzer.kpi_results.keys()) > 1 | 0.416678 | 0.271445 |
from __future__ import absolute_import, print_function, division
__authors__ = ["<NAME>", "<NAME>"]
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "03/02/2019"
__status__ = "production"
__docformat__ = 'restructuredtext'
__all__ = ["date", "version_info", "strictversion", "hexversion", "debianversion",
"calc_hexversion", "citation"]
RELEASE_LEVEL_VALUE = {"dev": 0,
"alpha": 10,
"beta": 11,
"gamma": 12,
"rc": 13,
"final": 15}
MAJOR = 0
MINOR = 19
MICRO = 0
RELEV = "dev" # <16
SERIAL = 0 # <16
date = __date__
from collections import namedtuple
_version_info = namedtuple("version_info", ["major", "minor", "micro", "releaselevel", "serial"])
version_info = _version_info(MAJOR, MINOR, MICRO, RELEV, SERIAL)
strictversion = version = debianversion = "%d.%d.%d" % version_info[:3]
if version_info.releaselevel != "final":
version += "-%s%s" % version_info[-2:]
debianversion += "~adev%i" % version_info[-1] if RELEV == "dev" else "~%s%i" % version_info[-2:]
prerel = "a" if RELEASE_LEVEL_VALUE.get(version_info[3], 0) < 10 else "b"
if prerel not in "ab":
prerel = "a"
strictversion += prerel + str(version_info[-1])
_PATTERN = None
def calc_hexversion(major=0, minor=0, micro=0, releaselevel="dev", serial=0, string=None):
"""Calculate the hexadecimal version number from the tuple version_info:
:param major: integer
:param minor: integer
:param micro: integer
:param relev: integer or string
:param serial: integer
:param string: version number as a string
:return: integer always increasing with revision numbers
"""
if string is not None:
global _PATTERN
if _PATTERN is None:
import re
_PATTERN = re.compile(r"(\d+)\.(\d+)\.(\d+)(\w+)?$")
result = _PATTERN.match(string)
if result is None:
raise ValueError("'%s' is not a valid version" % string)
result = result.groups()
major, minor, micro = int(result[0]), int(result[1]), int(result[2])
releaselevel = result[3]
if releaselevel is None:
releaselevel = 0
try:
releaselevel = int(releaselevel)
except ValueError:
releaselevel = RELEASE_LEVEL_VALUE.get(releaselevel, 0)
hex_version = int(serial)
hex_version |= releaselevel * 1 << 4
hex_version |= int(micro) * 1 << 8
hex_version |= int(minor) * 1 << 16
hex_version |= int(major) * 1 << 24
return hex_version
hexversion = calc_hexversion(*version_info)
citation = "doi:10.1107/S1600576715004306"
if __name__ == "__main__":
print(version) | version.py | from __future__ import absolute_import, print_function, division
__authors__ = ["<NAME>", "<NAME>"]
__license__ = "MIT"
__copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "03/02/2019"
__status__ = "production"
__docformat__ = 'restructuredtext'
__all__ = ["date", "version_info", "strictversion", "hexversion", "debianversion",
"calc_hexversion", "citation"]
RELEASE_LEVEL_VALUE = {"dev": 0,
"alpha": 10,
"beta": 11,
"gamma": 12,
"rc": 13,
"final": 15}
MAJOR = 0
MINOR = 19
MICRO = 0
RELEV = "dev" # <16
SERIAL = 0 # <16
date = __date__
from collections import namedtuple
_version_info = namedtuple("version_info", ["major", "minor", "micro", "releaselevel", "serial"])
version_info = _version_info(MAJOR, MINOR, MICRO, RELEV, SERIAL)
strictversion = version = debianversion = "%d.%d.%d" % version_info[:3]
if version_info.releaselevel != "final":
version += "-%s%s" % version_info[-2:]
debianversion += "~adev%i" % version_info[-1] if RELEV == "dev" else "~%s%i" % version_info[-2:]
prerel = "a" if RELEASE_LEVEL_VALUE.get(version_info[3], 0) < 10 else "b"
if prerel not in "ab":
prerel = "a"
strictversion += prerel + str(version_info[-1])
_PATTERN = None
def calc_hexversion(major=0, minor=0, micro=0, releaselevel="dev", serial=0, string=None):
"""Calculate the hexadecimal version number from the tuple version_info:
:param major: integer
:param minor: integer
:param micro: integer
:param relev: integer or string
:param serial: integer
:param string: version number as a string
:return: integer always increasing with revision numbers
"""
if string is not None:
global _PATTERN
if _PATTERN is None:
import re
_PATTERN = re.compile(r"(\d+)\.(\d+)\.(\d+)(\w+)?$")
result = _PATTERN.match(string)
if result is None:
raise ValueError("'%s' is not a valid version" % string)
result = result.groups()
major, minor, micro = int(result[0]), int(result[1]), int(result[2])
releaselevel = result[3]
if releaselevel is None:
releaselevel = 0
try:
releaselevel = int(releaselevel)
except ValueError:
releaselevel = RELEASE_LEVEL_VALUE.get(releaselevel, 0)
hex_version = int(serial)
hex_version |= releaselevel * 1 << 4
hex_version |= int(micro) * 1 << 8
hex_version |= int(minor) * 1 << 16
hex_version |= int(major) * 1 << 24
return hex_version
hexversion = calc_hexversion(*version_info)
citation = "doi:10.1107/S1600576715004306"
if __name__ == "__main__":
print(version) | 0.521471 | 0.098425 |
from verto.processors.GenericContainerBlockProcessor import GenericContainerBlockProcessor
from verto.errors.InteractiveTextContainsInteractiveError import InteractiveTextContainsInteractiveError
from verto.errors.InteractiveMissingTextError import InteractiveMissingTextError
import re
DEFAULT_THUMBNAIL = 'default'
CUSTOM_THUMBNAIL = 'custom'
class InteractiveContainerBlockProcessor(GenericContainerBlockProcessor):
''' Searches a Document for interactive tags e.g.
{interactive slug='example' type='in-page'}.
These are then replaced with the html template.
'''
def __init__(self, ext, *args, **kwargs):
'''
Args:
ext: The parent node of the element tree that children will
reside in.
'''
self.processor = 'interactive-container'
super().__init__(self.processor, ext, *args, **kwargs)
self.pattern = re.compile(ext.processor_info[self.processor]['pattern'])
self.scripts = ext.required_files['page_scripts']
self.required_interactives = ext.required_files['interactives']
self.required_images = ext.required_files['images']
def test(self, parent, block):
''' Tests a block to see if the run method should be applied.
Args:
parent: The parent node of the element tree that children
will reside in.
block: The block to be tested.
Returns:
True if there are any start or end tags within the block.
'''
return self.pattern.search(block) is not None or self.p_end.search(block) is not None
def custom_parsing(self, content_blocks, argument_values):
'''
Extracts the text of an interactive block.
Args:
content_blocks (list): List of strings to either be parsed or inserted as caption in template.
argument_values (dict): Dictionary of arguments and values provided in tag block.
Returns:
Tuple containing blocks (list) and extra_args (dict) to update the content_blocks list and
agument_values dict.
Raises:
'''
for block in content_blocks:
if self.p_start.search(block):
raise InteractiveTextContainsInteractiveError(self.processor)
extra_args = {}
argument = 'text'
if len(content_blocks) == 0 or content_blocks[0] == '':
raise InteractiveMissingTextError(self.processor, argument)
extra_args[argument] = content_blocks[0]
interactive_type = argument_values['type']
slug = argument_values['slug']
# add to list of interactives
self.required_interactives.add(slug)
if interactive_type == 'whole-page':
argument = 'thumbnail'
thumbnail_file_path = argument_values.get(argument, None)
if thumbnail_file_path is not None:
del argument_values[argument]
thumbnail_type = CUSTOM_THUMBNAIL
else:
thumbnail_file_path = 'interactives/{}/img/thumbnail.png'.format(slug)
thumbnail_type = DEFAULT_THUMBNAIL
external_path_match = re.search(r'^http', thumbnail_file_path)
if external_path_match is None: # internal image
thumbnail_file_relative = True
add_default = self.settings['add_default_interactive_thumbnails_to_required_files']
add_custom = self.settings['add_custom_interactive_thumbnails_to_required_files']
if (thumbnail_type == DEFAULT_THUMBNAIL and add_default) or \
(thumbnail_type == CUSTOM_THUMBNAIL and add_custom):
self.required_images.add(thumbnail_file_path)
else:
thumbnail_file_relative = False
extra_args['thumbnail_file_path'] = thumbnail_file_path
extra_args['thumbnail_file_relative'] = thumbnail_file_relative
return (content_blocks, extra_args) | verto/processors/InteractiveContainerBlockProcessor.py | from verto.processors.GenericContainerBlockProcessor import GenericContainerBlockProcessor
from verto.errors.InteractiveTextContainsInteractiveError import InteractiveTextContainsInteractiveError
from verto.errors.InteractiveMissingTextError import InteractiveMissingTextError
import re
DEFAULT_THUMBNAIL = 'default'
CUSTOM_THUMBNAIL = 'custom'
class InteractiveContainerBlockProcessor(GenericContainerBlockProcessor):
''' Searches a Document for interactive tags e.g.
{interactive slug='example' type='in-page'}.
These are then replaced with the html template.
'''
def __init__(self, ext, *args, **kwargs):
'''
Args:
ext: The parent node of the element tree that children will
reside in.
'''
self.processor = 'interactive-container'
super().__init__(self.processor, ext, *args, **kwargs)
self.pattern = re.compile(ext.processor_info[self.processor]['pattern'])
self.scripts = ext.required_files['page_scripts']
self.required_interactives = ext.required_files['interactives']
self.required_images = ext.required_files['images']
def test(self, parent, block):
''' Tests a block to see if the run method should be applied.
Args:
parent: The parent node of the element tree that children
will reside in.
block: The block to be tested.
Returns:
True if there are any start or end tags within the block.
'''
return self.pattern.search(block) is not None or self.p_end.search(block) is not None
def custom_parsing(self, content_blocks, argument_values):
'''
Extracts the text of an interactive block.
Args:
content_blocks (list): List of strings to either be parsed or inserted as caption in template.
argument_values (dict): Dictionary of arguments and values provided in tag block.
Returns:
Tuple containing blocks (list) and extra_args (dict) to update the content_blocks list and
agument_values dict.
Raises:
'''
for block in content_blocks:
if self.p_start.search(block):
raise InteractiveTextContainsInteractiveError(self.processor)
extra_args = {}
argument = 'text'
if len(content_blocks) == 0 or content_blocks[0] == '':
raise InteractiveMissingTextError(self.processor, argument)
extra_args[argument] = content_blocks[0]
interactive_type = argument_values['type']
slug = argument_values['slug']
# add to list of interactives
self.required_interactives.add(slug)
if interactive_type == 'whole-page':
argument = 'thumbnail'
thumbnail_file_path = argument_values.get(argument, None)
if thumbnail_file_path is not None:
del argument_values[argument]
thumbnail_type = CUSTOM_THUMBNAIL
else:
thumbnail_file_path = 'interactives/{}/img/thumbnail.png'.format(slug)
thumbnail_type = DEFAULT_THUMBNAIL
external_path_match = re.search(r'^http', thumbnail_file_path)
if external_path_match is None: # internal image
thumbnail_file_relative = True
add_default = self.settings['add_default_interactive_thumbnails_to_required_files']
add_custom = self.settings['add_custom_interactive_thumbnails_to_required_files']
if (thumbnail_type == DEFAULT_THUMBNAIL and add_default) or \
(thumbnail_type == CUSTOM_THUMBNAIL and add_custom):
self.required_images.add(thumbnail_file_path)
else:
thumbnail_file_relative = False
extra_args['thumbnail_file_path'] = thumbnail_file_path
extra_args['thumbnail_file_relative'] = thumbnail_file_relative
return (content_blocks, extra_args) | 0.765243 | 0.226944 |
import logging
from drf_yasg.utils import swagger_auto_schema
from rest_framework import status
from rest_framework.generics import CreateAPIView, DestroyAPIView
from rest_framework.response import Response
from safe_transaction_service.history.models import SafeContract
from . import serializers
from .models import FirebaseDevice
from .utils import get_safe_owners
logger = logging.getLogger(__name__)
class FirebaseDeviceCreateView(CreateAPIView):
"""
Creates a new FirebaseDevice. If uuid is not provided a new device will be created.
If a uuid for an existing Safe is provided the FirebaseDevice will be updated with all the new data provided.
Safes provided on the request are always added and never removed/replaced
Signature must sign `keccack256('gnosis-safe{timestamp-epoch}{uuid}{cloud_messaging_token}{safes_sorted}':
- `{timestamp-epoch}` must be an integer (no milliseconds)
- `{safes_sorted}` must be checksummed safe addresses sorted and joined with no spaces
"""
serializer_class = serializers.FirebaseDeviceSerializer
response_serializer_class = (
serializers.FirebaseDeviceSerializerWithOwnersResponseSerializer
)
@swagger_auto_schema(
responses={200: response_serializer_class(), 400: "Invalid data"}
)
def post(self, request, *args, **kwargs):
return super().post(request, *args, **kwargs)
def create(self, request, *args, **kwargs):
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
self.perform_create(serializer)
response_serializer = self.response_serializer_class(
data=serializer.validated_data
)
response_serializer.is_valid(raise_exception=True)
headers = self.get_success_headers(response_serializer.data)
return Response(
response_serializer.data, status=status.HTTP_201_CREATED, headers=headers
)
class FirebaseDeviceDeleteView(DestroyAPIView):
"""
Remove a FirebaseDevice
"""
queryset = FirebaseDevice.objects.all()
class FirebaseDeviceSafeDeleteView(DestroyAPIView):
"""
Remove a Safe for a FirebaseDevice
"""
queryset = FirebaseDevice.objects.all()
def perform_destroy(self, firebase_device: FirebaseDevice):
safe_address = self.kwargs["address"]
try:
safe_contract = SafeContract.objects.get(address=safe_address)
firebase_device.safes.remove(safe_contract)
current_owners = {
owner
for safe in firebase_device.safes.values_list("address", flat=True)
for owner in get_safe_owners(safe)
}
# Remove owners not linked to any Safe
firebase_device.owners.exclude(owner__in=current_owners).delete()
except SafeContract.DoesNotExist:
logger.info(
"Cannot remove safe=%s for firebase_device with uuid=%s",
safe_address,
self.kwargs["pk"],
) | safe_transaction_service/notifications/views.py | import logging
from drf_yasg.utils import swagger_auto_schema
from rest_framework import status
from rest_framework.generics import CreateAPIView, DestroyAPIView
from rest_framework.response import Response
from safe_transaction_service.history.models import SafeContract
from . import serializers
from .models import FirebaseDevice
from .utils import get_safe_owners
logger = logging.getLogger(__name__)
class FirebaseDeviceCreateView(CreateAPIView):
"""
Creates a new FirebaseDevice. If uuid is not provided a new device will be created.
If a uuid for an existing Safe is provided the FirebaseDevice will be updated with all the new data provided.
Safes provided on the request are always added and never removed/replaced
Signature must sign `keccack256('gnosis-safe{timestamp-epoch}{uuid}{cloud_messaging_token}{safes_sorted}':
- `{timestamp-epoch}` must be an integer (no milliseconds)
- `{safes_sorted}` must be checksummed safe addresses sorted and joined with no spaces
"""
serializer_class = serializers.FirebaseDeviceSerializer
response_serializer_class = (
serializers.FirebaseDeviceSerializerWithOwnersResponseSerializer
)
@swagger_auto_schema(
responses={200: response_serializer_class(), 400: "Invalid data"}
)
def post(self, request, *args, **kwargs):
return super().post(request, *args, **kwargs)
def create(self, request, *args, **kwargs):
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
self.perform_create(serializer)
response_serializer = self.response_serializer_class(
data=serializer.validated_data
)
response_serializer.is_valid(raise_exception=True)
headers = self.get_success_headers(response_serializer.data)
return Response(
response_serializer.data, status=status.HTTP_201_CREATED, headers=headers
)
class FirebaseDeviceDeleteView(DestroyAPIView):
"""
Remove a FirebaseDevice
"""
queryset = FirebaseDevice.objects.all()
class FirebaseDeviceSafeDeleteView(DestroyAPIView):
"""
Remove a Safe for a FirebaseDevice
"""
queryset = FirebaseDevice.objects.all()
def perform_destroy(self, firebase_device: FirebaseDevice):
safe_address = self.kwargs["address"]
try:
safe_contract = SafeContract.objects.get(address=safe_address)
firebase_device.safes.remove(safe_contract)
current_owners = {
owner
for safe in firebase_device.safes.values_list("address", flat=True)
for owner in get_safe_owners(safe)
}
# Remove owners not linked to any Safe
firebase_device.owners.exclude(owner__in=current_owners).delete()
except SafeContract.DoesNotExist:
logger.info(
"Cannot remove safe=%s for firebase_device with uuid=%s",
safe_address,
self.kwargs["pk"],
) | 0.769037 | 0.112065 |
import os, sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, RadioButtons
import matplotlib.ticker
import xspec
# https://stackoverflow.com/questions/39960791/logarithmic-slider-with-matplotlib
class Sliderlog(Slider):
"""Logarithmic slider.
Takes in every method and function of the matplotlib's slider.
Set slider to *val* visually so the slider still is lineat but display 10**val next to the slider.
Return 10**val to the update function (func)"""
def set_val(self, val):
xy = self.poly.xy
if self.orientation == 'vertical':
xy[1] = 0, val
xy[2] = 1, val
else:
xy[2] = val, 1
xy[3] = val, 0
self.poly.xy = xy
self.valtext.set_text(self.valfmt % 10**val) # Modified to display 10**val instead of val
if self.drawon:
self.ax.figure.canvas.draw_idle()
self.val = val
if not self.eventson:
return
for cid, func in self.observers.items():
func(10**val)
def make_plot(plot, energies, modelValues, compValues, kind='mo'):
if len(compValues) > 1:
for i in range(len(compValues)):
plot.plot(energies, compValues[i], lw=1, ls='--', c='C{}'.format(i+1))
plot.plot(energies, modelValues, lw=2, c='k')
if 'eem' in kind:
plot.set_ylabel(r'keV$^2$ (Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$)')
elif 'em' in kind:
plot.set_ylabel(r'keV (Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$)')
else:
plot.set_ylabel(r'Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$')
#plot.set_yticks([0.001,0.01,0.1,10.0,100.0])
#plot.set_yticklabels(['0.001','0.01','0.1','10.0','100.0'])
plot.set_xlim(0.095,105.0)
plot.set_ylim(max(min(modelValues), 1.2e-3*max(modelValues)), 1.2*max(modelValues))
plot.set_xscale('log')
plot.get_xaxis().set_major_formatter(matplotlib.ticker.FormatStrFormatter("%g"))
plot.set_yscale('log')
plot.set_xlabel('Energy (keV)')
plot.grid()
return plot
def read_sliders(list_sliders, type_sliders):
params = []
for i, (slider, type_slider) in enumerate(zip(list_sliders, type_sliders)):
if 'log' in type_slider:
params.append(10**slider.val)
else:
params.append(slider.val)
return params
def evaluate_model(params, model, kind):
model.setPars(*params)
xspec.Plot(kind)
xVals = xspec.Plot.x()
modVals = xspec.Plot.model()
compVals = []
if len(model.componentNames) > 1:
j = 0
for i, componentName in enumerate(model.componentNames):
if 'norm' in getattr(model, componentName).parameterNames:
j+=1
if j > 1:
for i in range(j):
compVals.append(xspec.Plot.addComp(i+1))
return xVals, modVals, compVals
def update(a):
params = read_sliders(sliders, type_sliders)
energies, modelValues, compValues = evaluate_model(params, model, kind)
plt.sca(plt1)
plt1.cla()
plt_plot_1 = make_plot(plt1, energies, modelValues, compValues, kind)
plt.draw()
if __name__ == "__main__":
if len(sys.argv) > 2:
ModelName = sys.argv[1]
kind = sys.argv[2]
elif len(sys.argv) > 1:
ModelName = sys.argv[1]
kind = "mo"
else:
ModelName = "bbodyrad+nthcomp"
kind = "mo"
# Make a larger grid for convolution models, and plot in a narrower range
xspec.AllModels.setEnergies("0.05 500. 5000 log")
plt1 = plt.axes([0.15, 0.45, 0.8, 0.5])
type_sliders, sliders, plt_sliders = [], [], []
params = []
xspec.Plot.device = "/null"
xspec.Plot.xAxis = "keV"
xspec.Plot.add = True
model = xspec.Model(ModelName)
i = Nadditive = 0
for cNumber, componentName in enumerate(model.componentNames):
if 'norm' == getattr(model, componentName).parameterNames[-1]:
Nadditive += 1
Tadditive = True
else:
Tadditive = False
for j, parameterName in enumerate(getattr(model, componentName).parameterNames):
i += 1
params.append(model(i).values[0])
plt_sliders.append(plt.axes([0.15, 0.36-i*0.03, 0.6, 0.02]))
if model(i).name == 'norm':
model(i).values = [1, 0.01, 1e-3, 1e-3, 1e3, 1e3]
if model(i).name == 'nH':
model(i).values = [1, 0.01, 1e-4, 1e-4, 1e2, 1e2]
if model(i).name == 'Tin':
model(i).values = [1, 0.01, 1e-4, 1e-4, 1e2, 1e2]
if model(i).values[2] > 0 and model(i).values[5] > 0:
type_sliders.append('log')
sliders.append(Sliderlog(plt_sliders[-1],
model(i).name,
np.log10(model(i).values[3]),
np.log10(model(i).values[4]),
valinit=np.log10(model(i).values[0]),
valfmt='%7.5f {}'.format(model(i).unit),
color='C{}'.format(Nadditive) if Tadditive else 'gray'))
else:
type_sliders.append('lin')
sliders.append(Slider(plt_sliders[-1],
model(i).name,
model(i).values[3],
model(i).values[4],
valinit=model(i).values[0],
valfmt='%7.5f {}'.format(model(i+1).unit),
color='C{}'.format(Nadditive) if Tadditive else 'gray'))
sliders[-1].on_changed(update)
update(0)
plt.suptitle('Model: {}'.format(ModelName), y=0.99)
plt.show() | main.py | import os, sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, RadioButtons
import matplotlib.ticker
import xspec
# https://stackoverflow.com/questions/39960791/logarithmic-slider-with-matplotlib
class Sliderlog(Slider):
"""Logarithmic slider.
Takes in every method and function of the matplotlib's slider.
Set slider to *val* visually so the slider still is lineat but display 10**val next to the slider.
Return 10**val to the update function (func)"""
def set_val(self, val):
xy = self.poly.xy
if self.orientation == 'vertical':
xy[1] = 0, val
xy[2] = 1, val
else:
xy[2] = val, 1
xy[3] = val, 0
self.poly.xy = xy
self.valtext.set_text(self.valfmt % 10**val) # Modified to display 10**val instead of val
if self.drawon:
self.ax.figure.canvas.draw_idle()
self.val = val
if not self.eventson:
return
for cid, func in self.observers.items():
func(10**val)
def make_plot(plot, energies, modelValues, compValues, kind='mo'):
if len(compValues) > 1:
for i in range(len(compValues)):
plot.plot(energies, compValues[i], lw=1, ls='--', c='C{}'.format(i+1))
plot.plot(energies, modelValues, lw=2, c='k')
if 'eem' in kind:
plot.set_ylabel(r'keV$^2$ (Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$)')
elif 'em' in kind:
plot.set_ylabel(r'keV (Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$)')
else:
plot.set_ylabel(r'Photons cm$^{-2}$ s$^{-1}$ keV$^{-1}$')
#plot.set_yticks([0.001,0.01,0.1,10.0,100.0])
#plot.set_yticklabels(['0.001','0.01','0.1','10.0','100.0'])
plot.set_xlim(0.095,105.0)
plot.set_ylim(max(min(modelValues), 1.2e-3*max(modelValues)), 1.2*max(modelValues))
plot.set_xscale('log')
plot.get_xaxis().set_major_formatter(matplotlib.ticker.FormatStrFormatter("%g"))
plot.set_yscale('log')
plot.set_xlabel('Energy (keV)')
plot.grid()
return plot
def read_sliders(list_sliders, type_sliders):
params = []
for i, (slider, type_slider) in enumerate(zip(list_sliders, type_sliders)):
if 'log' in type_slider:
params.append(10**slider.val)
else:
params.append(slider.val)
return params
def evaluate_model(params, model, kind):
model.setPars(*params)
xspec.Plot(kind)
xVals = xspec.Plot.x()
modVals = xspec.Plot.model()
compVals = []
if len(model.componentNames) > 1:
j = 0
for i, componentName in enumerate(model.componentNames):
if 'norm' in getattr(model, componentName).parameterNames:
j+=1
if j > 1:
for i in range(j):
compVals.append(xspec.Plot.addComp(i+1))
return xVals, modVals, compVals
def update(a):
params = read_sliders(sliders, type_sliders)
energies, modelValues, compValues = evaluate_model(params, model, kind)
plt.sca(plt1)
plt1.cla()
plt_plot_1 = make_plot(plt1, energies, modelValues, compValues, kind)
plt.draw()
if __name__ == "__main__":
if len(sys.argv) > 2:
ModelName = sys.argv[1]
kind = sys.argv[2]
elif len(sys.argv) > 1:
ModelName = sys.argv[1]
kind = "mo"
else:
ModelName = "bbodyrad+nthcomp"
kind = "mo"
# Make a larger grid for convolution models, and plot in a narrower range
xspec.AllModels.setEnergies("0.05 500. 5000 log")
plt1 = plt.axes([0.15, 0.45, 0.8, 0.5])
type_sliders, sliders, plt_sliders = [], [], []
params = []
xspec.Plot.device = "/null"
xspec.Plot.xAxis = "keV"
xspec.Plot.add = True
model = xspec.Model(ModelName)
i = Nadditive = 0
for cNumber, componentName in enumerate(model.componentNames):
if 'norm' == getattr(model, componentName).parameterNames[-1]:
Nadditive += 1
Tadditive = True
else:
Tadditive = False
for j, parameterName in enumerate(getattr(model, componentName).parameterNames):
i += 1
params.append(model(i).values[0])
plt_sliders.append(plt.axes([0.15, 0.36-i*0.03, 0.6, 0.02]))
if model(i).name == 'norm':
model(i).values = [1, 0.01, 1e-3, 1e-3, 1e3, 1e3]
if model(i).name == 'nH':
model(i).values = [1, 0.01, 1e-4, 1e-4, 1e2, 1e2]
if model(i).name == 'Tin':
model(i).values = [1, 0.01, 1e-4, 1e-4, 1e2, 1e2]
if model(i).values[2] > 0 and model(i).values[5] > 0:
type_sliders.append('log')
sliders.append(Sliderlog(plt_sliders[-1],
model(i).name,
np.log10(model(i).values[3]),
np.log10(model(i).values[4]),
valinit=np.log10(model(i).values[0]),
valfmt='%7.5f {}'.format(model(i).unit),
color='C{}'.format(Nadditive) if Tadditive else 'gray'))
else:
type_sliders.append('lin')
sliders.append(Slider(plt_sliders[-1],
model(i).name,
model(i).values[3],
model(i).values[4],
valinit=model(i).values[0],
valfmt='%7.5f {}'.format(model(i+1).unit),
color='C{}'.format(Nadditive) if Tadditive else 'gray'))
sliders[-1].on_changed(update)
update(0)
plt.suptitle('Model: {}'.format(ModelName), y=0.99)
plt.show() | 0.409575 | 0.539165 |
import os
import requests
from requests.utils import requote_uri
from pyrogram import Client, filters
from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton
API = "https://api.abirhasan.wtf/pypi?query="
START_TEXT = """
Hello {},
I am a pypi package search telegram bot.
- Send a pypi package name.
- I will send the information of package.
Made by @FayasNoushad
"""
BUTTONS = [InlineKeyboardButton('⚙ Join Updates Channel ⚙', url='https://telegram.me/FayasNoushad')]
Bot = Client(
"PyPi-Bot",
bot_token = os.environ["BOT_TOKEN"],
api_id = int(os.environ["API_ID"]),
api_hash = os.environ["API_HASH"]
)
@Bot.on_message(filters.private & filters.command(["start", "help", "about"]))
async def start(bot, update):
text = START_TEXT.format(update.from_user.mention)
reply_markup = InlineKeyboardMarkup([BUTTONS])
await update.reply_text(
text=text,
disable_web_page_preview=True,
reply_markup=reply_markup,
quote=True
)
@Bot.on_message(filters.text)
async def pypi_info(bot, update):
try:
query = update.text if update.chat.type == "private" else update.text.split()[1]
text = pypi_text(query)
reply_markup = InlineKeyboardMarkup([pypi_buttons(query), BUTTONS])
await update.reply_text(
text=text,
disable_web_page_preview=True,
reply_markup=reply_markup,
quote=True
)
except:
pass
def pypi(query):
r = requests.get(API + requote_uri(query))
info = r.json()
return info
def pypi_text(query):
info = pypi(query)
text = "--**Information**--\n"
text += f"\n**Package Name:** `{info['PackageName']}`"
text += f"\n**Title:** `{info['Title']}`"
text += f"\n**About:** `{info['About']}`"
text += f"\n**Latest Release Date:** `{info['LatestReleaseDate']}`"
text += f"\n**Pip Command:** `{info['PipCommand']}`"
return text
def pypi_buttons(query):
info = pypi(query)
buttons = [
InlineKeyboardButton(text="PyPi", url=info['PyPi']),
InlineKeyboardButton(text="Home Page", url=info['HomePage'])
]
return buttons
Bot.run() | main.py |
import os
import requests
from requests.utils import requote_uri
from pyrogram import Client, filters
from pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton
API = "https://api.abirhasan.wtf/pypi?query="
START_TEXT = """
Hello {},
I am a pypi package search telegram bot.
- Send a pypi package name.
- I will send the information of package.
Made by @FayasNoushad
"""
BUTTONS = [InlineKeyboardButton('⚙ Join Updates Channel ⚙', url='https://telegram.me/FayasNoushad')]
Bot = Client(
"PyPi-Bot",
bot_token = os.environ["BOT_TOKEN"],
api_id = int(os.environ["API_ID"]),
api_hash = os.environ["API_HASH"]
)
@Bot.on_message(filters.private & filters.command(["start", "help", "about"]))
async def start(bot, update):
text = START_TEXT.format(update.from_user.mention)
reply_markup = InlineKeyboardMarkup([BUTTONS])
await update.reply_text(
text=text,
disable_web_page_preview=True,
reply_markup=reply_markup,
quote=True
)
@Bot.on_message(filters.text)
async def pypi_info(bot, update):
try:
query = update.text if update.chat.type == "private" else update.text.split()[1]
text = pypi_text(query)
reply_markup = InlineKeyboardMarkup([pypi_buttons(query), BUTTONS])
await update.reply_text(
text=text,
disable_web_page_preview=True,
reply_markup=reply_markup,
quote=True
)
except:
pass
def pypi(query):
r = requests.get(API + requote_uri(query))
info = r.json()
return info
def pypi_text(query):
info = pypi(query)
text = "--**Information**--\n"
text += f"\n**Package Name:** `{info['PackageName']}`"
text += f"\n**Title:** `{info['Title']}`"
text += f"\n**About:** `{info['About']}`"
text += f"\n**Latest Release Date:** `{info['LatestReleaseDate']}`"
text += f"\n**Pip Command:** `{info['PipCommand']}`"
return text
def pypi_buttons(query):
info = pypi(query)
buttons = [
InlineKeyboardButton(text="PyPi", url=info['PyPi']),
InlineKeyboardButton(text="Home Page", url=info['HomePage'])
]
return buttons
Bot.run() | 0.402627 | 0.126299 |
import re
import csv
import time
from random import choice
from nltk.data import load
from collections import defaultdict
TWEET_FILE = 'data/tweets.csv'
START_TOKEN = '<START>'
END_TOKEN = '<END>'
def sanitize_tweet_word(word):
# Remove leading and trailing apostrophes
word = word[1:] if (word[0] is '\'') else word
if len(word) == 0:
return word
word = word[:-1] if (word[len(word) - 1] is '\'') else word
# Lowercase if not a mention (@) or a hashtag (#)
special_word = (word[0] is '#') or (word[0] is '@')
word = word if (special_word or word.isupper()) else word.lower()
return word
def sanitize_tweet(tweet):
tweet = re.sub(r'http\S+', '', tweet) # Remove URLs
tweet = re.sub(r'[^\w\s\'#@_\?!\.]', '', tweet) # Purge punctuation
tweet = re.sub(r'!', ' !', tweet) # Separate question marks
tweet = re.sub(r'\?', ' ?', tweet) # Separate exclamation marks
tweet = re.sub(r'\?', ' .', tweet) # Separate periods
tweet = tweet.split() # Split into array
tweet = list(map(sanitize_tweet_word, tweet)) # Sanitize individual words
if (len(tweet) > 0) and (tweet[0] == 'RT'): # Remove retweet stuff
tweet = tweet[2:]
# Add starting and ending tokens
tweet.insert(0, START_TOKEN)
tweet.append(END_TOKEN)
return tweet
def read_tweets_from_file(file):
with open(file, 'r', encoding='utf-8') as csvfile:
reader = csv.DictReader(csvfile)
tweets = []
for row in reader:
tweet = row['text']
tweets += sanitize_tweet(tweet)
return tweets
# Builds a flat n-gram
# i.e.: [('this', 'was'), ('was', 'a'), ('a', 'triumph')]
def build_flat_ngram(string, n):
strings = [string[i:] for i in range(n)]
return zip(*strings)
# Builds a bigram using a nested dictionary structure with weighted values
# i.e.: {'this': {'was': 1}, 'was': {'a': 1}, 'a': {'triumph': 1}}
def build_bigram(string):
flat_bigram = build_flat_ngram(string, 2)
bigram = defaultdict(lambda: defaultdict(int))
for word1, word2 in flat_bigram:
bigram[word1][word2] += 1
return bigram
# Builds a trigram using a nested dictionary structure with weighted values
# i.e.: {'this': {'was': {'a': 1}}, 'was': {'a': {'triumph': 1}}}
def build_trigram(string):
flat_trigram = build_flat_ngram(string, 3)
ngram = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))
for word1, word2, word3 in flat_trigram:
ngram[word1][word2][word3] += 1
return ngram
# Generates a tweet using the given bigram and trigram
def generate_tweet(bigram, trigram):
tweet = ''
word1 = ''
word2 = START_TOKEN
# Keeping adding words until we reach an end token
while word2 != END_TOKEN:
# First try to use the trigram
choices = trigram[word1][word2]
# Fallback on bigram if necessary
if len(choices.items()) == 0:
choices = bigram[word2]
# Choose a new word based on the weighted values of the choices
flat_choices = []
for key, value in choices.items():
flat_choices += [key] * value
word3 = choice(flat_choices)
tweet += word3 + ' '
# Advance generator words
word1 = word2
word2 = word3
# Reformat tweet
tweet = tweet[:-(len(END_TOKEN) + 2)] # Remove end token
tweet = re.sub(r' !', '!', tweet) # Join question marks
tweet = re.sub(r' \?', '?', tweet) # Join exclamation marks
tweet = re.sub(r' \.', '.', tweet) # Join periods marks
# Capitalize sentences
sentence_tokenizer = load('tokenizers/punkt/english.pickle')
sentences = sentence_tokenizer.tokenize(tweet)
sentences = [(sentence[0].upper() + sentence[1:]) for sentence in sentences]
tweet = ' '.join(sentences)
# Validate tweet
is_valid_tweet = len(tweet) <= 280
if is_valid_tweet:
return tweet
else:
return generate_tweet(bigram, trigram)
if __name__ == '__main__':
print('Loading tweets...')
tweets = read_tweets_from_file(TWEET_FILE)
print('Building bigram...')
bigram = build_bigram(tweets)
print('Building trigram...')
trigram = build_trigram(tweets)
print('Tweet generator ready.')
while True:
input('Press [Enter] to generate.')
tweet = generate_tweet(bigram, trigram)
print(tweet) | twitter/code/propaganda_generator.py | import re
import csv
import time
from random import choice
from nltk.data import load
from collections import defaultdict
TWEET_FILE = 'data/tweets.csv'
START_TOKEN = '<START>'
END_TOKEN = '<END>'
def sanitize_tweet_word(word):
# Remove leading and trailing apostrophes
word = word[1:] if (word[0] is '\'') else word
if len(word) == 0:
return word
word = word[:-1] if (word[len(word) - 1] is '\'') else word
# Lowercase if not a mention (@) or a hashtag (#)
special_word = (word[0] is '#') or (word[0] is '@')
word = word if (special_word or word.isupper()) else word.lower()
return word
def sanitize_tweet(tweet):
tweet = re.sub(r'http\S+', '', tweet) # Remove URLs
tweet = re.sub(r'[^\w\s\'#@_\?!\.]', '', tweet) # Purge punctuation
tweet = re.sub(r'!', ' !', tweet) # Separate question marks
tweet = re.sub(r'\?', ' ?', tweet) # Separate exclamation marks
tweet = re.sub(r'\?', ' .', tweet) # Separate periods
tweet = tweet.split() # Split into array
tweet = list(map(sanitize_tweet_word, tweet)) # Sanitize individual words
if (len(tweet) > 0) and (tweet[0] == 'RT'): # Remove retweet stuff
tweet = tweet[2:]
# Add starting and ending tokens
tweet.insert(0, START_TOKEN)
tweet.append(END_TOKEN)
return tweet
def read_tweets_from_file(file):
with open(file, 'r', encoding='utf-8') as csvfile:
reader = csv.DictReader(csvfile)
tweets = []
for row in reader:
tweet = row['text']
tweets += sanitize_tweet(tweet)
return tweets
# Builds a flat n-gram
# i.e.: [('this', 'was'), ('was', 'a'), ('a', 'triumph')]
def build_flat_ngram(string, n):
strings = [string[i:] for i in range(n)]
return zip(*strings)
# Builds a bigram using a nested dictionary structure with weighted values
# i.e.: {'this': {'was': 1}, 'was': {'a': 1}, 'a': {'triumph': 1}}
def build_bigram(string):
flat_bigram = build_flat_ngram(string, 2)
bigram = defaultdict(lambda: defaultdict(int))
for word1, word2 in flat_bigram:
bigram[word1][word2] += 1
return bigram
# Builds a trigram using a nested dictionary structure with weighted values
# i.e.: {'this': {'was': {'a': 1}}, 'was': {'a': {'triumph': 1}}}
def build_trigram(string):
flat_trigram = build_flat_ngram(string, 3)
ngram = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))
for word1, word2, word3 in flat_trigram:
ngram[word1][word2][word3] += 1
return ngram
# Generates a tweet using the given bigram and trigram
def generate_tweet(bigram, trigram):
tweet = ''
word1 = ''
word2 = START_TOKEN
# Keeping adding words until we reach an end token
while word2 != END_TOKEN:
# First try to use the trigram
choices = trigram[word1][word2]
# Fallback on bigram if necessary
if len(choices.items()) == 0:
choices = bigram[word2]
# Choose a new word based on the weighted values of the choices
flat_choices = []
for key, value in choices.items():
flat_choices += [key] * value
word3 = choice(flat_choices)
tweet += word3 + ' '
# Advance generator words
word1 = word2
word2 = word3
# Reformat tweet
tweet = tweet[:-(len(END_TOKEN) + 2)] # Remove end token
tweet = re.sub(r' !', '!', tweet) # Join question marks
tweet = re.sub(r' \?', '?', tweet) # Join exclamation marks
tweet = re.sub(r' \.', '.', tweet) # Join periods marks
# Capitalize sentences
sentence_tokenizer = load('tokenizers/punkt/english.pickle')
sentences = sentence_tokenizer.tokenize(tweet)
sentences = [(sentence[0].upper() + sentence[1:]) for sentence in sentences]
tweet = ' '.join(sentences)
# Validate tweet
is_valid_tweet = len(tweet) <= 280
if is_valid_tweet:
return tweet
else:
return generate_tweet(bigram, trigram)
if __name__ == '__main__':
print('Loading tweets...')
tweets = read_tweets_from_file(TWEET_FILE)
print('Building bigram...')
bigram = build_bigram(tweets)
print('Building trigram...')
trigram = build_trigram(tweets)
print('Tweet generator ready.')
while True:
input('Press [Enter] to generate.')
tweet = generate_tweet(bigram, trigram)
print(tweet) | 0.407216 | 0.248375 |
from typing import Tuple, Union
import numpy as np
import torch
from monai.transforms.croppad.array import SpatialCrop
from monai.transforms.utils import generate_spatial_bounding_box
from monai.utils import MetricReduction, optional_import
binary_erosion, _ = optional_import("scipy.ndimage.morphology", name="binary_erosion")
distance_transform_edt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_edt")
distance_transform_cdt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_cdt")
__all__ = ["ignore_background", "do_metric_reduction", "get_mask_edges", "get_surface_distance"]
def ignore_background(
y_pred: Union[np.ndarray, torch.Tensor],
y: Union[np.ndarray, torch.Tensor],
):
"""
This function is used to remove background (the first channel) for `y_pred` and `y`.
Args:
y_pred: predictions. As for classification tasks,
`y_pred` should has the shape [BN] where N is larger than 1. As for segmentation tasks,
the shape should be [BNHW] or [BNHWD].
y: ground truth, the first dim is batch.
"""
y = y[:, 1:] if y.shape[1] > 1 else y
y_pred = y_pred[:, 1:] if y_pred.shape[1] > 1 else y_pred
return y_pred, y
def do_metric_reduction(
f: torch.Tensor,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
):
"""
This function is to do the metric reduction for calculated metrics of each example's each class.
Args:
f: a tensor that contains the calculated metric scores per batch and
per class. The first two dims should be batch and class.
reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, if "none", return the input f tensor and not_nans.
Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``.
Raises:
ValueError: When ``reduction`` is not one of
["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].
"""
# some elements might be Nan (if ground truth y was missing (zeros))
# we need to account for it
nans = torch.isnan(f)
not_nans = (~nans).float()
t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
reduction = MetricReduction(reduction)
if reduction == MetricReduction.NONE:
return f, not_nans
f[nans] = 0
if reduction == MetricReduction.MEAN:
# 2 steps, first, mean by channel (accounting for nans), then by batch
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
not_nans = (not_nans > 0).float().sum(dim=0)
f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM:
not_nans = not_nans.sum(dim=[0, 1])
f = torch.sum(f, dim=[0, 1]) # sum over the batch and channel dims
elif reduction == MetricReduction.MEAN_BATCH:
not_nans = not_nans.sum(dim=0)
f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM_BATCH:
not_nans = not_nans.sum(dim=0)
f = f.sum(dim=0) # the batch sum
elif reduction == MetricReduction.MEAN_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
elif reduction == MetricReduction.SUM_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = f.sum(dim=1) # the channel sum
elif reduction != MetricReduction.NONE:
raise ValueError(
f"Unsupported reduction: {reduction}, available options are "
'["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
)
return f, not_nans
def get_mask_edges(
seg_pred: Union[np.ndarray, torch.Tensor],
seg_gt: Union[np.ndarray, torch.Tensor],
label_idx: int = 1,
crop: bool = True,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Do binary erosion and use XOR for input to get the edges. This
function is helpful to further calculate metrics such as Average Surface
Distance and Hausdorff Distance.
The input images can be binary or labelfield images. If labelfield images
are supplied, they are converted to binary images using `label_idx`.
`scipy`'s binary erosion is used to to calculate the edges of the binary
labelfield.
In order to improve the computing efficiency, before getting the edges,
the images can be cropped and only keep the foreground if not specifies
``crop = False``.
We require that images are the same size, and assume that they occupy the
same space (spacing, orientation, etc.).
Args:
seg_pred: the predicted binary or labelfield image.
seg_gt: the actual binary or labelfield image.
label_idx: for labelfield images, convert to binary with
`seg_pred = seg_pred == label_idx`.
crop: crop input images and only keep the foregrounds. In order to
maintain two inputs' shapes, here the bounding box is achieved
by ``(seg_pred | seg_gt)`` which represents the union set of two
images. Defaults to ``True``.
"""
# Get both labelfields as np arrays
if isinstance(seg_pred, torch.Tensor):
seg_pred = seg_pred.detach().cpu().numpy()
if isinstance(seg_gt, torch.Tensor):
seg_gt = seg_gt.detach().cpu().numpy()
if seg_pred.shape != seg_gt.shape:
raise ValueError("seg_pred and seg_gt should have same shapes.")
# If not binary images, convert them
if seg_pred.dtype != bool:
seg_pred = seg_pred == label_idx
if seg_gt.dtype != bool:
seg_gt = seg_gt == label_idx
if crop:
if not np.any(seg_pred | seg_gt):
return (np.zeros_like(seg_pred), np.zeros_like(seg_gt))
seg_pred, seg_gt = np.expand_dims(seg_pred, 0), np.expand_dims(seg_gt, 0)
box_start, box_end = generate_spatial_bounding_box(np.asarray(seg_pred | seg_gt))
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
seg_pred, seg_gt = np.squeeze(cropper(seg_pred)), np.squeeze(cropper(seg_gt))
# Do binary erosion and use XOR to get edges
edges_pred = binary_erosion(seg_pred) ^ seg_pred
edges_gt = binary_erosion(seg_gt) ^ seg_gt
return (edges_pred, edges_gt)
def get_surface_distance(
seg_pred: np.ndarray,
seg_gt: np.ndarray,
distance_metric: str = "euclidean",
) -> np.ndarray:
"""
This function is used to compute the surface distances from `seg_pred` to `seg_gt`.
Args:
seg_pred: the edge of the predictions.
seg_gt: the edge of the ground truth.
distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
the metric used to compute surface distance. Defaults to ``"euclidean"``.
- ``"euclidean"``, uses Exact Euclidean distance transform.
- ``"chessboard"``, uses `chessboard` metric in chamfer type of transform.
- ``"taxicab"``, uses `taxicab` metric in chamfer type of transform.
"""
if not np.any(seg_gt):
dis = np.inf * np.ones_like(seg_gt)
else:
if not np.any(seg_pred):
dis = np.inf * np.ones_like(seg_gt)
return np.asarray(dis[seg_gt])
if distance_metric == "euclidean":
dis = distance_transform_edt(~seg_gt)
elif distance_metric in ["chessboard", "taxicab"]:
dis = distance_transform_cdt(~seg_gt, metric=distance_metric)
else:
raise ValueError(f"distance_metric {distance_metric} is not implemented.")
return np.asarray(dis[seg_pred]) | monai/metrics/utils.py |
from typing import Tuple, Union
import numpy as np
import torch
from monai.transforms.croppad.array import SpatialCrop
from monai.transforms.utils import generate_spatial_bounding_box
from monai.utils import MetricReduction, optional_import
binary_erosion, _ = optional_import("scipy.ndimage.morphology", name="binary_erosion")
distance_transform_edt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_edt")
distance_transform_cdt, _ = optional_import("scipy.ndimage.morphology", name="distance_transform_cdt")
__all__ = ["ignore_background", "do_metric_reduction", "get_mask_edges", "get_surface_distance"]
def ignore_background(
y_pred: Union[np.ndarray, torch.Tensor],
y: Union[np.ndarray, torch.Tensor],
):
"""
This function is used to remove background (the first channel) for `y_pred` and `y`.
Args:
y_pred: predictions. As for classification tasks,
`y_pred` should has the shape [BN] where N is larger than 1. As for segmentation tasks,
the shape should be [BNHW] or [BNHWD].
y: ground truth, the first dim is batch.
"""
y = y[:, 1:] if y.shape[1] > 1 else y
y_pred = y_pred[:, 1:] if y_pred.shape[1] > 1 else y_pred
return y_pred, y
def do_metric_reduction(
f: torch.Tensor,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
):
"""
This function is to do the metric reduction for calculated metrics of each example's each class.
Args:
f: a tensor that contains the calculated metric scores per batch and
per class. The first two dims should be batch and class.
reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}, if "none", return the input f tensor and not_nans.
Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``.
Raises:
ValueError: When ``reduction`` is not one of
["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].
"""
# some elements might be Nan (if ground truth y was missing (zeros))
# we need to account for it
nans = torch.isnan(f)
not_nans = (~nans).float()
t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
reduction = MetricReduction(reduction)
if reduction == MetricReduction.NONE:
return f, not_nans
f[nans] = 0
if reduction == MetricReduction.MEAN:
# 2 steps, first, mean by channel (accounting for nans), then by batch
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
not_nans = (not_nans > 0).float().sum(dim=0)
f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM:
not_nans = not_nans.sum(dim=[0, 1])
f = torch.sum(f, dim=[0, 1]) # sum over the batch and channel dims
elif reduction == MetricReduction.MEAN_BATCH:
not_nans = not_nans.sum(dim=0)
f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM_BATCH:
not_nans = not_nans.sum(dim=0)
f = f.sum(dim=0) # the batch sum
elif reduction == MetricReduction.MEAN_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
elif reduction == MetricReduction.SUM_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = f.sum(dim=1) # the channel sum
elif reduction != MetricReduction.NONE:
raise ValueError(
f"Unsupported reduction: {reduction}, available options are "
'["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
)
return f, not_nans
def get_mask_edges(
seg_pred: Union[np.ndarray, torch.Tensor],
seg_gt: Union[np.ndarray, torch.Tensor],
label_idx: int = 1,
crop: bool = True,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Do binary erosion and use XOR for input to get the edges. This
function is helpful to further calculate metrics such as Average Surface
Distance and Hausdorff Distance.
The input images can be binary or labelfield images. If labelfield images
are supplied, they are converted to binary images using `label_idx`.
`scipy`'s binary erosion is used to to calculate the edges of the binary
labelfield.
In order to improve the computing efficiency, before getting the edges,
the images can be cropped and only keep the foreground if not specifies
``crop = False``.
We require that images are the same size, and assume that they occupy the
same space (spacing, orientation, etc.).
Args:
seg_pred: the predicted binary or labelfield image.
seg_gt: the actual binary or labelfield image.
label_idx: for labelfield images, convert to binary with
`seg_pred = seg_pred == label_idx`.
crop: crop input images and only keep the foregrounds. In order to
maintain two inputs' shapes, here the bounding box is achieved
by ``(seg_pred | seg_gt)`` which represents the union set of two
images. Defaults to ``True``.
"""
# Get both labelfields as np arrays
if isinstance(seg_pred, torch.Tensor):
seg_pred = seg_pred.detach().cpu().numpy()
if isinstance(seg_gt, torch.Tensor):
seg_gt = seg_gt.detach().cpu().numpy()
if seg_pred.shape != seg_gt.shape:
raise ValueError("seg_pred and seg_gt should have same shapes.")
# If not binary images, convert them
if seg_pred.dtype != bool:
seg_pred = seg_pred == label_idx
if seg_gt.dtype != bool:
seg_gt = seg_gt == label_idx
if crop:
if not np.any(seg_pred | seg_gt):
return (np.zeros_like(seg_pred), np.zeros_like(seg_gt))
seg_pred, seg_gt = np.expand_dims(seg_pred, 0), np.expand_dims(seg_gt, 0)
box_start, box_end = generate_spatial_bounding_box(np.asarray(seg_pred | seg_gt))
cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
seg_pred, seg_gt = np.squeeze(cropper(seg_pred)), np.squeeze(cropper(seg_gt))
# Do binary erosion and use XOR to get edges
edges_pred = binary_erosion(seg_pred) ^ seg_pred
edges_gt = binary_erosion(seg_gt) ^ seg_gt
return (edges_pred, edges_gt)
def get_surface_distance(
seg_pred: np.ndarray,
seg_gt: np.ndarray,
distance_metric: str = "euclidean",
) -> np.ndarray:
"""
This function is used to compute the surface distances from `seg_pred` to `seg_gt`.
Args:
seg_pred: the edge of the predictions.
seg_gt: the edge of the ground truth.
distance_metric: : [``"euclidean"``, ``"chessboard"``, ``"taxicab"``]
the metric used to compute surface distance. Defaults to ``"euclidean"``.
- ``"euclidean"``, uses Exact Euclidean distance transform.
- ``"chessboard"``, uses `chessboard` metric in chamfer type of transform.
- ``"taxicab"``, uses `taxicab` metric in chamfer type of transform.
"""
if not np.any(seg_gt):
dis = np.inf * np.ones_like(seg_gt)
else:
if not np.any(seg_pred):
dis = np.inf * np.ones_like(seg_gt)
return np.asarray(dis[seg_gt])
if distance_metric == "euclidean":
dis = distance_transform_edt(~seg_gt)
elif distance_metric in ["chessboard", "taxicab"]:
dis = distance_transform_cdt(~seg_gt, metric=distance_metric)
else:
raise ValueError(f"distance_metric {distance_metric} is not implemented.")
return np.asarray(dis[seg_pred]) | 0.975184 | 0.609611 |