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07c5ea304ad9e83dcc3f691f719157c2c8a1317f | 85 | py | Python | app/auth/__init__.py | louisenje/Pitches | 11248906ffa7d1b6ed6c47db0c5e7bd3b4768825 | [
"Unlicense"
] | null | null | null | app/auth/__init__.py | louisenje/Pitches | 11248906ffa7d1b6ed6c47db0c5e7bd3b4768825 | [
"Unlicense"
] | null | null | null | app/auth/__init__.py | louisenje/Pitches | 11248906ffa7d1b6ed6c47db0c5e7bd3b4768825 | [
"Unlicense"
] | null | null | null | from flask import Blueprint
auth=Blueprint('auth',__name__)
from .import views,forms | 21.25 | 31 | 0.811765 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0.070588 |
07c638a7630e99e901331aada0e29b538ff7310d | 1,482 | py | Python | forms/QRGenerator.py | Rono-Barto-Co/Project-QR | e80fc5a41f25542038c090311844912790cb1478 | [
"MIT"
] | 3 | 2019-07-04T03:27:06.000Z | 2019-09-06T08:52:35.000Z | forms/QRGenerator.py | Rono-Barto-Co/Project-QR | e80fc5a41f25542038c090311844912790cb1478 | [
"MIT"
] | null | null | null | forms/QRGenerator.py | Rono-Barto-Co/Project-QR | e80fc5a41f25542038c090311844912790cb1478 | [
"MIT"
] | null | null | null | from flask_wtf import FlaskForm
from wtforms import StringField, SubmitField, SelectField
from wtforms.validators import DataRequired
class QRGenerator(FlaskForm):
code_content = StringField('Content', validators=[DataRequired()])
code_size = SelectField('Size', choices=[('15', 'Size'),
('5', '5'),
('10', '10'),
('15', '15'),
('20', '20'),
('25', '25'),
('30', '30')])
code_color = SelectField('Colour', choices=[('white', 'Colour'),
("white", "White"),
('yellow', "Yellow"),
('lime', "Green"),
("#ffa500", "Orange")])
code_correction = SelectField('Error Correction', choices=[("H", "Error Correction"),
("H", "H"),
("L", "L"),
("M", "M"),
("Q", "Q")])
code_image = StringField('Image URL')
generate_code = SubmitField('Generate QR Code')
| 54.888889 | 89 | 0.323887 | 1,345 | 0.907557 | 0 | 0 | 0 | 0 | 0 | 0 | 246 | 0.165992 |
07c6a477f6bfebee04a539e1d02b2df95226ab91 | 1,259 | py | Python | Quiz/m2_advanced_quants/l5_volatility/volatility_estimation.py | jcrangel/AI-for-Trading | c3b865e992f8eb8deda91e7641428eef1d343636 | [
"Apache-2.0"
] | null | null | null | Quiz/m2_advanced_quants/l5_volatility/volatility_estimation.py | jcrangel/AI-for-Trading | c3b865e992f8eb8deda91e7641428eef1d343636 | [
"Apache-2.0"
] | null | null | null | Quiz/m2_advanced_quants/l5_volatility/volatility_estimation.py | jcrangel/AI-for-Trading | c3b865e992f8eb8deda91e7641428eef1d343636 | [
"Apache-2.0"
] | null | null | null | import pandas as pd
import numpy as np
def estimate_volatility(prices, l):
"""Create an exponential moving average model of the volatility of a stock
price, and return the most recent (last) volatility estimate.
Parameters
----------
prices : pandas.Series
A series of adjusted closing prices for a stock.
l : float
The 'lambda' parameter of the exponential moving average model. Making
this value smaller will cause the model to weight older terms less
relative to more recent terms.
Returns
-------
last_vol : float
The last element of your exponential moving averge volatility model series.
"""
# TODO: Implement the exponential moving average volatility model and return the last value.
return prices.ewm(alpha=(1-l)).mean()[-1]
def test_run(filename='data.csv'):
"""Test run get_most_volatile() with stock prices from a file."""
prices = pd.read_csv(filename, parse_dates=[
'date'], index_col='date', squeeze=True)
print("Most recent volatility estimate: {:.6f}".format(estimate_volatility(prices, 0.7)))
# print(estimate_volatility(prices, 0.7))
if __name__ == '__main__':
test_run()
| 31.475 | 96 | 0.659253 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 898 | 0.713264 |
07c6e4237d951b4abd908ba105b7d327ebecef98 | 484 | py | Python | jp.atcoder/abc122/abc122_c/9516079.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-09T03:06:25.000Z | 2022-02-09T03:06:25.000Z | jp.atcoder/abc122/abc122_c/9516079.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | 1 | 2022-02-05T22:53:18.000Z | 2022-02-09T01:29:30.000Z | jp.atcoder/abc122/abc122_c/9516079.py | kagemeka/atcoder-submissions | 91d8ad37411ea2ec582b10ba41b1e3cae01d4d6e | [
"MIT"
] | null | null | null | import sys
n, q = map(int, sys.stdin.readline().split())
s = '$' + sys.stdin.readline().rstrip()
lr = zip(*[map(int, sys.stdin.read().split())] * 2)
def main():
res = [None] * (n + 1); res[0] = 0
prev = '$'
for i in range(1, n+1):
res[i] = res[i-1]
res[i] += (prev == 'A' and s[i] == 'C') & 1
prev = s[i]
for l, r in lr:
yield res[r] - res[l]
if __name__ == '__main__':
ans = main()
print(*ans, sep='\n')
| 23.047619 | 52 | 0.452479 | 0 | 0 | 251 | 0.518595 | 0 | 0 | 0 | 0 | 26 | 0.053719 |
07c71ca4f5171ffec7ba2da60f8676b83050c69c | 2,262 | py | Python | Decoder.py | gokulsg/Attention-is-all-you-need-implementation-from-scratch | f5eb591d169cbef3ef8b066d8d462fee11badc3b | [
"MIT"
] | 1 | 2022-02-19T03:51:38.000Z | 2022-02-19T03:51:38.000Z | Decoder.py | gokulsg/Attention-is-all-you-need-implementation-from-scratch | f5eb591d169cbef3ef8b066d8d462fee11badc3b | [
"MIT"
] | null | null | null | Decoder.py | gokulsg/Attention-is-all-you-need-implementation-from-scratch | f5eb591d169cbef3ef8b066d8d462fee11badc3b | [
"MIT"
] | null | null | null | import torch
import torch.nn as nn
from DecoderLayer import DecoderLayer
import math
class Decoder(nn.Module):
def __init__(self, output_dim, embed_dim, num_layers, num_heads, expand_dim, dropout, device, max_length = 30):
super().__init__()
self.tok_embedding = nn.Embedding(output_dim, embed_dim)
#self.pos_embedding = nn.Embedding(max_length, embed_dim)
self.pos_embedding = nn.Embedding.from_pretrained(self.get_positional_encoding(max_length, embed_dim))
self.layers = nn.ModuleList([DecoderLayer(embed_dim, num_heads, expand_dim, dropout) for _ in range(num_layers)])
self.fc_out = nn.Linear(embed_dim, output_dim)
self.dropout = nn.Dropout(dropout)
self.scale = torch.sqrt(torch.FloatTensor([embed_dim])).to(device)
self.device = device
def forward(self, trg, enc_src, trg_mask, src_mask):
#trg = [batch size, trg len]
#enc_src = [batch size, src len, embed dim]
#trg_mask = [batch size, 1, trg len, trg len]
#src_mask = [batch size, 1, 1, src len]
batch_size = trg.shape[0]
trg_len = trg.shape[1]
pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
#pos = [batch size, trg len]
trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))
#trg = [batch size, trg len, embed dim]
for layer in self.layers:
trg = layer(trg, enc_src, trg_mask, src_mask)
#trg = [batch size, trg len, embed dim]
output = self.fc_out(trg)
#output = [batch size, trg len, output dim]
return output
def get_positional_encoding(self, max_seq_len, embed_dim):
pos_enc = torch.zeros(max_seq_len, embed_dim)
position = torch.arange(0, max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))
pos_enc[:, 0::2] = torch.sin(position * div_term)
pos_enc[:, 1::2] = torch.cos(position * div_term)
return pos_enc | 42.679245 | 122 | 0.597259 | 2,171 | 0.95977 | 0 | 0 | 0 | 0 | 0 | 0 | 370 | 0.163572 |
07c821ec1109c1e7667cac57580d2ee557fe694c | 901 | py | Python | salt/grains/nxos.py | babs/salt | c536ea716d5308880b244e7980f4b659d86fc104 | [
"Apache-2.0"
] | 9,425 | 2015-01-01T05:59:24.000Z | 2022-03-31T20:44:05.000Z | salt/grains/nxos.py | babs/salt | c536ea716d5308880b244e7980f4b659d86fc104 | [
"Apache-2.0"
] | 33,507 | 2015-01-01T00:19:56.000Z | 2022-03-31T23:48:20.000Z | salt/grains/nxos.py | babs/salt | c536ea716d5308880b244e7980f4b659d86fc104 | [
"Apache-2.0"
] | 5,810 | 2015-01-01T19:11:45.000Z | 2022-03-31T02:37:20.000Z | """
Grains for Cisco NX-OS minions
.. versionadded:: 2016.11.0
For documentation on setting up the nxos proxy minion look in the documentation
for :mod:`salt.proxy.nxos<salt.proxy.nxos>`.
"""
import logging
import salt.utils.nxos
import salt.utils.platform
from salt.exceptions import NxosClientError
log = logging.getLogger(__name__)
__proxyenabled__ = ["nxos"]
__virtualname__ = "nxos"
def __virtual__():
try:
salt.utils.nxos.version_info()
except NxosClientError as err:
return False, err
return __virtualname__
def system_information(proxy=None):
if salt.utils.platform.is_proxy():
if proxy is None:
return {}
if proxy["nxos.initialized"]() is False:
return {}
return {"nxos": proxy["nxos.grains"]()}
else:
data = salt.utils.nxos.version_info()
return salt.utils.nxos.system_info(data)
| 21.97561 | 79 | 0.675916 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 242 | 0.26859 |
07c8e1478041c57a7ff67d2397fd305a48517875 | 3,356 | py | Python | tmsproviderapisdk/tms_device.py | tvip/tmsproviderapisdk | f385ddb0d7e87e7a62d1caef3e2c9769e844a4a1 | [
"MIT"
] | null | null | null | tmsproviderapisdk/tms_device.py | tvip/tmsproviderapisdk | f385ddb0d7e87e7a62d1caef3e2c9769e844a4a1 | [
"MIT"
] | null | null | null | tmsproviderapisdk/tms_device.py | tvip/tmsproviderapisdk | f385ddb0d7e87e7a62d1caef3e2c9769e844a4a1 | [
"MIT"
] | null | null | null | from typing import List, Optional, Tuple
from tmsproviderapisdk.tms_extended_model import TmsExtendedModel
class TmsDevice(TmsExtendedModel):
_path_url = "/devices/"
def __init__(self, unique_id: str, account: int, device_id: int = None, ipaddr: str = None, mac: str = None,
remote_custom_field: str = None, comment: str = None, last_online: str = None, last_fw_ver: str = None,
first_online: str = None, use_nat: bool = False, operation_system: str = None, udpxy_addr: str = None,
device_type: int = None, provider: int = None):
self.unique_id = unique_id
self.account = account
self.id = device_id
self.ipaddr = ipaddr
self.mac = mac
self.remote_custom_field = remote_custom_field
self.comment = comment
self.last_online = last_online
self.last_fw_ver = last_fw_ver
self.first_online = first_online
self.use_nat = use_nat
self.operation_system = operation_system
self.udpxy_addr = udpxy_addr
self.device_type = device_type
self.provider = provider
@staticmethod
def _dict_to_object(device_dict: dict) -> object:
device = TmsDevice(
unique_id=device_dict["unique_id"],
device_id=device_dict["id"],
ipaddr=device_dict["ipaddr"],
mac=device_dict["mac"],
remote_custom_field=device_dict["remote_custom_field"],
comment=device_dict["comment"],
last_online=device_dict["last_online"],
last_fw_ver=device_dict["last_fw_ver"],
first_online=device_dict["first_online"],
use_nat=device_dict["use_nat"],
operation_system=device_dict["operation_system"],
udpxy_addr=device_dict["udpxy_addr"],
device_type=device_dict["device_type"],
provider=device_dict["provider"],
account=device_dict["account"]
)
return device
@classmethod
def get_list(cls, account: int = None, device_type: int = None, limit: int = 50, provider: int = None,
quick_search: str = "", remote_custom_field: str = None, sort: str = "", start: int = 0,
unique_id: str = "") -> Optional[Tuple[List[object], int]]:
devices = super().get_list(start=start, limit=limit, account=account, device_type=device_type,
provider=provider, quick_search=quick_search,
remote_custom_field=remote_custom_field, sort=sort, unique_id=unique_id)
return devices
def __str__(self):
return """id:{}, ipaddr:{}, mac:{}, unique_id:{}, remote_custom_field: {}, comment: {}, last_online: {}, \
last_fw_ver: {}, first_online: {}, use_nat: {}, operation_system: {}, \
udpxy_addr: {}, device_type: {}, provider: {}, account: {}""".format(
self.id,
self.ipaddr,
self.mac,
self.unique_id,
self.remote_custom_field,
self.comment,
self.last_online,
self.last_fw_ver,
self.first_online,
self.use_nat,
self.operation_system,
self.udpxy_addr,
self.device_type,
self.provider,
self.account
)
| 40.926829 | 120 | 0.598033 | 3,246 | 0.967223 | 0 | 0 | 1,481 | 0.441299 | 0 | 0 | 435 | 0.129619 |
07ca1045f3d1ae1412959e210adfbad3cf54eff0 | 7,676 | py | Python | fealty/fields.py | eddiejessup/fealty | 03745eb98d85bc2a5d08920773ab9c4515462d30 | [
"BSD-3-Clause"
] | null | null | null | fealty/fields.py | eddiejessup/fealty | 03745eb98d85bc2a5d08920773ab9c4515462d30 | [
"BSD-3-Clause"
] | null | null | null | fealty/fields.py | eddiejessup/fealty | 03745eb98d85bc2a5d08920773ab9c4515462d30 | [
"BSD-3-Clause"
] | null | null | null | """
A class hierarchy relating to fields of all kinds.
"""
from __future__ import print_function, division
import numpy as np
from ciabatta.meta import make_repr_str
from fealty import lattice, field_numerics, walled_field_numerics
class Space(object):
def __init__(self, L, dim):
self.L = L
self.dim = dim
@property
def L_half(self):
return self.L / 2.0
@property
def A(self):
return self.L ** self.dim
def iterate(self, *args, **kwargs):
pass
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim)]
return make_repr_str(self, fs)
class Field(Space):
def __init__(self, L, dim, dx):
Space.__init__(self, L, dim)
self.M = int(round(self.L / dx))
@property
def dx(self):
return self.L / self.M
@property
def A_i(self):
return self.M ** self.dim
@property
def dA(self):
return self.dx ** self.dim
def density_field(self, r):
return density(r, self.L, self.dx)
def r_to_i(self, r):
return lattice.r_to_i(r, self.L, self.dx)
def i_to_r(self, i):
return lattice.i_to_r(i, self.L, self.dx)
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx)]
return make_repr_str(self, fs)
class Scalar(Field):
def __init__(self, L, dim, dx, a_0=0.0):
Field.__init__(self, L, dim, dx)
self.a = np.ones(self.dim * (self.M,), dtype=np.float) * a_0
def grad(self):
return _grad(self.a, self.dx)
def grad_i(self, r):
return _grad_i(self.a, self.r_to_i(r), self.dx)
def laplacian(self):
return _laplace(self.a, self.dx)
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),
('a_0', self.a_0)]
return make_repr_str(self, fs)
class Diffusing(Scalar):
def __init__(self, L, dim, dx, D, dt, a_0=0.0):
Scalar.__init__(self, L, dim, dx, a_0=a_0)
self.D = D
self.dt = dt
if self.D > self.dx ** 2 / (2.0 * self.dim * self.dt):
raise Exception('Unstable diffusion constant')
def iterate(self):
self.a += self.D * self.laplacian() * self.dt
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),
('D', self.D), ('dt', self.dt), ('a_0', self.a_0)]
return make_repr_str(self, fs)
class WalledScalar(Scalar):
def __init__(self, L, dim, dx, walls, a_0=0.0):
Scalar.__init__(self, L, dim, dx, a_0=a_0)
self.walls = walls
# Make field zero-valued where obstructed
self.a *= np.logical_not(self.walls)
def grad(self):
return _walled_grad(self.a, self.dx, self.walls)
def grad_i(self, r):
return _walled_grad_i(self.a, self.r_to_i(r), self.dx,
self.walls)
def laplacian(self):
return _walled_laplace(self.a, self.dx, self.walls)
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),
('walls', self.walls), ('a_0', self.a_0)]
return make_repr_str(self, fs)
# Note, inheritance order matters to get walled grad & laplacian call
# (see diamond problem on wikipedia and how python handles it)
class WalledDiffusing(WalledScalar, Diffusing):
def __init__(self, L, dim, dx, walls, D, dt, a_0=0.0):
Diffusing.__init__(self, L, dim, dx, D, dt, a_0=a_0)
WalledScalar.__init__(self, L, dim, dx, walls, a_0=a_0)
def __repr__(self):
fs = [('L', self.L), ('dim', self.dim), ('dx', self.dx),
('walls', self.walls), ('D', self.D), ('dt', self.dt),
('a_0', self.a_0)]
return make_repr_str(self, fs)
def density(r, L, dx):
assert r.ndim == 2
M = int(round(L / dx))
dx = L / M
inds = lattice.r_to_i(r, L, dx)
f = np.zeros(r.shape[1] * (M,), dtype=np.int)
if f.ndim == 1:
field_numerics.density_1d(inds, f)
elif f.ndim == 2:
field_numerics.density_2d(inds, f)
elif f.ndim == 3:
field_numerics.density_3d(inds, f)
else:
raise Exception('Density calc not implemented in this dimension')
return f / dx ** r.shape[1]
def _laplace(field, dx):
assert dx > 0.0
laplace = np.empty_like(field)
if field.ndim == 1:
field_numerics.laplace_1d(field, laplace, dx)
elif field.ndim == 2:
field_numerics.laplace_2d(field, laplace, dx)
elif field.ndim == 3:
field_numerics.laplace_3d(field, laplace, dx)
else:
raise Exception('Laplacian not implemented in this dimension')
return laplace
def _grad_i(field, inds, dx):
assert dx > 0.0
assert inds.ndim == 2
assert field.ndim == inds.shape[1]
grad_i = np.empty(inds.shape, dtype=field.dtype)
if field.ndim == 1:
field_numerics.grad_i_1d(field, inds, grad_i, dx)
elif field.ndim == 2:
field_numerics.grad_i_2d(field, inds, grad_i, dx)
elif field.ndim == 3:
field_numerics.grad_i_3d(field, grad_i, dx)
else:
raise Exception("Grad_i not implemented in this dimension")
return grad_i
def _grad(field, dx):
assert dx > 0.0
grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)
if field.ndim == 1:
field_numerics.grad_1d(field, grad, dx)
elif field.ndim == 2:
field_numerics.grad_2d(field, grad, dx)
elif field.ndim == 3:
field_numerics.grad_3d(field, grad, dx)
else:
raise Exception('Grad not implemented in this dimension')
return grad
def _div(field, dx):
assert dx > 0.0
div = np.empty(field.shape[:-1], dtype=field.dtype)
if field.ndim == 2:
field_numerics.div_1d(field, div, dx)
elif field.ndim == 3:
field_numerics.div_2d(field, div, dx)
elif field.ndim == 4:
field_numerics.div_3d(field, div, dx)
else:
raise Exception('Divergence not implemented in this dimension')
return div
def _walled_grad(field, dx, walls):
assert field.shape == walls.shape
assert dx > 0.0
grad = np.empty(field.shape + (field.ndim,), dtype=field.dtype)
if field.ndim == 1:
walled_field_numerics.grad_1d(field, grad, dx, walls)
elif field.ndim == 2:
walled_field_numerics.grad_2d(field, grad, dx, walls)
elif field.ndim == 3:
walled_field_numerics.grad_3d(field, grad, dx, walls)
else:
raise Exception("Walled grad not implemented in this dimension")
return grad
def _walled_grad_i(field, inds, dx, walls):
assert field.shape == walls.shape
assert dx > 0.0
assert inds.ndim == 2
assert field.ndim == inds.shape[1]
grad_i = np.empty(inds.shape, dtype=field.dtype)
if field.ndim == 1:
walled_field_numerics.grad_i_1d(field, inds, grad_i, dx, walls)
elif field.ndim == 2:
walled_field_numerics.grad_i_2d(field, inds, grad_i, dx, walls)
elif field.ndim == 3:
walled_field_numerics.grad_i_3d(field, inds, grad_i, dx, walls)
else:
raise Exception("Walled Grad_i not implemented in this dimension")
return grad_i
def _walled_laplace(field, dx, walls):
assert field.shape == walls.shape
assert dx > 0.0
laplace = np.empty_like(field)
if field.ndim == 1:
walled_field_numerics.laplace_1d(field, laplace, dx, walls)
elif field.ndim == 2:
walled_field_numerics.laplace_2d(field, laplace, dx, walls)
elif field.ndim == 3:
walled_field_numerics.laplace_3d(field, laplace, dx, walls)
else:
raise Exception('Laplacian not implemented in this dimension')
return laplace
| 29.186312 | 74 | 0.60839 | 3,388 | 0.441376 | 0 | 0 | 301 | 0.039213 | 0 | 0 | 737 | 0.096014 |
07cb2143ea47874c58c2e1ce2165dfb24f78e2e2 | 583 | py | Python | examples/example_django/example_django/asgi.py | cpascariello/aleph-vm | 1b4920bec211ef3bd379e9359f57f06b9308c1a1 | [
"MIT"
] | 19 | 2021-04-13T14:01:06.000Z | 2022-02-19T00:45:05.000Z | examples/example_django/example_django/asgi.py | cpascariello/aleph-vm | 1b4920bec211ef3bd379e9359f57f06b9308c1a1 | [
"MIT"
] | 65 | 2021-04-14T09:02:24.000Z | 2022-03-29T07:56:03.000Z | examples/example_django/example_django/asgi.py | cpascariello/aleph-vm | 1b4920bec211ef3bd379e9359f57f06b9308c1a1 | [
"MIT"
] | 9 | 2021-06-01T23:03:28.000Z | 2022-02-17T20:24:57.000Z | """
ASGI config for example_django project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/
"""
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "example_django.settings")
application = get_asgi_application()
os.system("/usr/bin/python3 /opt/code/manage.py migrate")
os.system("/usr/bin/python3 /opt/code/manage.py "
"loaddata /opt/code/blog/fixtures/default_articles.json")
| 26.5 | 78 | 0.766724 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 418 | 0.716981 |
07cc54388c8061e52f8dc1aa33c14d904afe5143 | 3,964 | py | Python | lectures/extensions/hyperbolic_discounting/replication_code/src/analysis/get_bivariate_distr_data.py | loikein/ekw-lectures | a2f5436f10515ab26eab323fca8c37c91bdc5dcd | [
"MIT"
] | 4 | 2019-11-15T15:21:27.000Z | 2020-07-08T15:04:30.000Z | lectures/extensions/hyperbolic_discounting/replication_code/src/analysis/get_bivariate_distr_data.py | loikein/ekw-lectures | a2f5436f10515ab26eab323fca8c37c91bdc5dcd | [
"MIT"
] | 9 | 2019-11-18T15:54:36.000Z | 2020-07-14T13:56:53.000Z | lectures/extensions/hyperbolic_discounting/replication_code/src/analysis/get_bivariate_distr_data.py | loikein/ekw-lectures | a2f5436f10515ab26eab323fca8c37c91bdc5dcd | [
"MIT"
] | 3 | 2021-01-25T15:41:30.000Z | 2021-09-21T08:51:36.000Z | """Generate values of Method of Simulated Moments criterion function.
Given observed moments and weighting matrix in `OUT_ANALYSIS`, "msm_estimation",
generate values of Method of Simulated Moments criterion function for combinations
of discount factor and present bias values.
The goal is to study the bivariate distribution of the time preference parameters
around the combination of true parameter values.
"""
import itertools
import numpy as np
import pandas as pd
import respy as rp
import yaml
from bld.project_paths import project_paths_join as ppj
from src.library.compute_moments import _replace_nans
from src.library.compute_moments import calc_restricted_choice_probabilities
from src.library.compute_moments import calc_restricted_wage_distribution
from src.library.compute_moments import calc_unrestricted_choice_probabilities
from src.library.compute_moments import calc_unrestricted_wage_distribution
from src.library.compute_moments import calc_very_restricted_choice_probabilities
from src.library.compute_moments import calc_very_restricted_wage_distribution
from src.library.housekeeping import _load_pickle
from src.library.housekeeping import _temporary_working_directory
from tqdm import tqdm
def get_bivariate_distribution(params, crit_func, grid_delta, grid_beta):
"""Compute value of criterion function.
Args:
params (pd.DataFrame): DataFrame containing model parameters.
crit_func (dict): Dictionary containing model options.
grid_delta (np.array): Values of discount factor.
grid_beta (np.array): Values of present-bias parameter.
Returns:
pd.DataFrame
"""
results = []
for beta, delta in tqdm(itertools.product(grid_beta, grid_delta)):
params_ = params.copy()
params_.loc[("beta", "beta"), "value"] = beta
params_.loc[("delta", "delta"), "value"] = delta
val = crit_func(params_)
result = {"beta": beta, "delta": delta, "val": val}
results.append(result)
return pd.DataFrame.from_dict(results)
if __name__ == "__main__":
# load params
params = pd.read_csv(
ppj("IN_MODEL_SPECS", "params_hyp.csv"),
sep=";",
index_col=["category", "name"],
)
params["value"] = params["value"].astype(float)
# load options
with open(ppj("IN_MODEL_SPECS", "options_hyp.yaml")) as options:
options = yaml.safe_load(options)
# get empirical moments
empirical_moments = _load_pickle(ppj("OUT_ANALYSIS", "msm_estimation", "moments_hyp.pickle"))
# get weighting matrix
weighting_matrix = _load_pickle(
ppj("OUT_ANALYSIS", "msm_estimation", "weighting_matrix_hyp.pickle")
)
calc_moments = {
"Choice Probabilities Very Restricted": calc_very_restricted_choice_probabilities,
"Choice Probabilities Restricted": calc_restricted_choice_probabilities,
"Choice Probabilities Unrestricted": calc_unrestricted_choice_probabilities,
"Wage Distribution Very Restricted": calc_very_restricted_wage_distribution,
"Wage Distribution Restricted": calc_restricted_wage_distribution,
"Wage Distribution Unrestricted": calc_unrestricted_wage_distribution,
}
with _temporary_working_directory(snippet="heatmap"):
# get criterion function
weighted_sum_squared_errors = rp.get_moment_errors_func(
params=params,
options=options,
calc_moments=calc_moments,
replace_nans=_replace_nans,
empirical_moments=empirical_moments,
weighting_matrix=weighting_matrix,
)
# get bivariate distribution results
results = get_bivariate_distribution(
crit_func=weighted_sum_squared_errors,
params=params,
grid_delta=np.arange(0.945, 0.9625, 0.0025),
grid_beta=np.arange(0.75, 1.05, 0.01),
)
results.to_csv(ppj("OUT_ANALYSIS", "heatmap.csv"))
| 36.703704 | 97 | 0.726791 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,411 | 0.355954 |
07cdddcbeb3cb4de7a49a05fd2f00ba890200e08 | 180 | py | Python | space_game/events/KeyPressedEvent.py | Iwomichu/probable-giggle | 2af5ed83a60d65ec9d509c217cb5fcb880d5dbcc | [
"MIT"
] | 1 | 2020-11-30T11:21:21.000Z | 2020-11-30T11:21:21.000Z | space_game/events/KeyPressedEvent.py | Iwomichu/probable-giggle | 2af5ed83a60d65ec9d509c217cb5fcb880d5dbcc | [
"MIT"
] | 5 | 2020-11-03T16:46:49.000Z | 2021-01-24T14:29:24.000Z | space_game/events/KeyPressedEvent.py | Iwomichu/probable-giggle | 2af5ed83a60d65ec9d509c217cb5fcb880d5dbcc | [
"MIT"
] | null | null | null | from dataclasses import dataclass
from space_game.domain_names import KeyId
from space_game.events.Event import Event
@dataclass
class KeyPressedEvent(Event):
key_id: KeyId
| 18 | 41 | 0.822222 | 47 | 0.261111 | 0 | 0 | 58 | 0.322222 | 0 | 0 | 0 | 0 |
07cebbfa2f7886d8fbfa5e82f77ae6da2ef52744 | 703 | py | Python | src/oncall/messengers/teams_messenger.py | navoday-91/oncall | 0a977f06bbf308978d0d2c2b46e0aca23937ca9a | [
"BSD-2-Clause"
] | 857 | 2017-05-03T00:59:10.000Z | 2022-03-29T06:45:23.000Z | src/oncall/messengers/teams_messenger.py | navoday-91/oncall | 0a977f06bbf308978d0d2c2b46e0aca23937ca9a | [
"BSD-2-Clause"
] | 142 | 2017-05-03T02:00:58.000Z | 2022-03-25T20:58:11.000Z | src/oncall/messengers/teams_messenger.py | navoday-91/oncall | 0a977f06bbf308978d0d2c2b46e0aca23937ca9a | [
"BSD-2-Clause"
] | 218 | 2017-05-03T02:04:56.000Z | 2022-03-25T18:28:04.000Z | import pymsteams
import logging
from oncall.constants import TEAMS_SUPPORT
class teams_messenger(object):
supports = frozenset([TEAMS_SUPPORT])
def __init__(self, config):
self.webhook = config['webhook']
def send(self, message):
heading = message.get("subject")
final_message = "User: " + message.get("user") + " Message: " + message.get("body")
try:
myTeamsMessage = pymsteams.connectorcard(self.webhook)
myTeamsMessage.title(str(heading))
myTeamsMessage.text(str(final_message))
myTeamsMessage.send()
except:
logging.info("An issue occured while sending message to teams messenger")
| 30.565217 | 91 | 0.651494 | 625 | 0.889047 | 0 | 0 | 0 | 0 | 0 | 0 | 109 | 0.15505 |
07cf43823d8f9a2b976bcf3456b4ba03ebef67d9 | 11,632 | py | Python | python/chronos/src/bigdl/chronos/autots/model/auto_prophet.py | Laniakea94/BigDL | 4d01734086dda893a7f08ba53251dc3c5c8ecfd1 | [
"Apache-2.0"
] | 3 | 2021-07-14T01:28:47.000Z | 2022-03-02T01:16:32.000Z | python/chronos/src/bigdl/chronos/autots/model/auto_prophet.py | liangs6212/BigDL | 3c89ff7e8bbdc713110536c18099506811cd2b3a | [
"Apache-2.0"
] | null | null | null | python/chronos/src/bigdl/chronos/autots/model/auto_prophet.py | liangs6212/BigDL | 3c89ff7e8bbdc713110536c18099506811cd2b3a | [
"Apache-2.0"
] | null | null | null | # +
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either exp'
# ress or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import pandas as pd
import warnings
from bigdl.chronos.model.prophet import ProphetBuilder, ProphetModel
from bigdl.chronos.autots.utils import recalculate_n_sampling
# -
class AutoProphet:
def __init__(self,
changepoint_prior_scale=None,
seasonality_prior_scale=None,
holidays_prior_scale=None,
seasonality_mode=None,
changepoint_range=None,
metric='mse',
logs_dir="/tmp/auto_prophet_logs",
cpus_per_trial=1,
name="auto_prophet",
remote_dir=None,
load_dir=None,
**prophet_config
):
"""
Create an automated Prophet Model.
User need to specify either the exact value or the search space of the
Prophet model hyperparameters. For details of the Prophet model hyperparameters, refer to
https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
:param changepoint_prior_scale: Int or hp sampling function from an integer space
for hyperparameter changepoint_prior_scale for the Prophet model.
For hp sampling, see bigdl.chronos.orca.automl.hp for more details.
e.g. hp.loguniform(0.001, 0.5).
:param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
Prophet model.
e.g. hp.loguniform(0.01, 10).
:param holidays_prior_scale: hyperparameter holidays_prior_scale for the
Prophet model.
e.g. hp.loguniform(0.01, 10).
:param seasonality_mode: hyperparameter seasonality_mode for the
Prophet model.
e.g. hp.choice(['additive', 'multiplicative']).
:param changepoint_range: hyperparameter changepoint_range for the
Prophet model.
e.g. hp.uniform(0.8, 0.95).
:param metric: String. The evaluation metric name to optimize. e.g. "mse"
:param logs_dir: Local directory to save logs and results. It defaults to
"/tmp/auto_prophet_logs"
:param cpus_per_trial: Int. Number of cpus for each trial. It defaults to 1.
:param name: name of the AutoProphet. It defaults to "auto_prophet"
:param remote_dir: String. Remote directory to sync training results and checkpoints. It
defaults to None and doesn't take effects while running in local. While running in
cluster, it defaults to "hdfs:///tmp/{name}".
:param load_dir: Load the ckpt from load_dir. The value defaults to None.
:param prophet_config: Other Prophet hyperparameters.
"""
if load_dir:
self.best_model = ProphetModel()
self.best_model.restore(load_dir)
try:
from bigdl.orca.automl.auto_estimator import AutoEstimator
import bigdl.orca.automl.hp as hp
self.search_space = {
"changepoint_prior_scale": hp.grid_search([0.005, 0.05, 0.1, 0.5])
if changepoint_prior_scale is None
else changepoint_prior_scale,
"seasonality_prior_scale": hp.grid_search([0.01, 0.1, 1.0, 10.0])
if seasonality_prior_scale is None
else seasonality_prior_scale,
"holidays_prior_scale": hp.loguniform(0.01, 10)
if holidays_prior_scale is None
else holidays_prior_scale,
"seasonality_mode": hp.choice(['additive', 'multiplicative'])
if seasonality_mode is None
else seasonality_mode,
"changepoint_range": hp.uniform(0.8, 0.95)
if changepoint_range is None
else changepoint_range
}
self.search_space.update(prophet_config) # update other configs
self.metric = metric
model_builder = ProphetBuilder()
self.auto_est = AutoEstimator(model_builder=model_builder,
logs_dir=logs_dir,
resources_per_trial={"cpu": cpus_per_trial},
remote_dir=remote_dir,
name=name)
except ImportError:
warnings.warn("You need to install `bigdl-orca[automl]` to use `fit` function.")
def fit(self,
data,
cross_validation=True,
expect_horizon=None,
freq=None,
metric_threshold=None,
n_sampling=16,
search_alg=None,
search_alg_params=None,
scheduler=None,
scheduler_params=None,
):
"""
Automatically fit the model and search for the best hyperparameters.
:param data: training data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param cross_validation: bool, if the eval result comes from cross_validation.
The value is set to True by default. Setting this option to False to
speed up the process.
:param expect_horizon: int, validation data will be automatically splited from training
data, and expect_horizon is the horizon you may need to use once the mode is fitted.
The value defaults to None, where 10% of training data will be taken
as the validation data.
:param freq: the freqency of the training dataframe. the frequency can be anything from the
pandas list of frequency strings here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliasesDefaulted
to None, where an unreliable frequency will be infer implicitly.
:param metric_threshold: a trial will be terminated when metric threshold is met
:param n_sampling: Number of trials to evaluate in total. Defaults to 16.
If hp.grid_search is in search_space, the grid will be run n_sampling of trials
and round up n_sampling according to hp.grid_search.
If this is -1, (virtually) infinite samples are generated
until a stopping condition is met.
:param search_alg: str, all supported searcher provided by ray tune
(i.e."variant_generator", "random", "ax", "dragonfly", "skopt",
"hyperopt", "bayesopt", "bohb", "nevergrad", "optuna", "zoopt" and
"sigopt")
:param search_alg_params: extra parameters for searcher algorithm besides search_space,
metric and searcher mode
:param scheduler: str, all supported scheduler provided by ray tune
:param scheduler_params: parameters for scheduler
"""
if expect_horizon is None:
expect_horizon = int(0.1*len(data))
if freq is None:
assert len(data) >= 2, "The training dataframe should contains more than 2 records."
assert pd.api.types.is_datetime64_any_dtype(data["ds"].dtypes), \
"The 'ds' col should be in datetime 64 type, or you need to set `freq` in fit."
self._freq = data["ds"].iloc[1] - data["ds"].iloc[0]
else:
self._freq = pd.Timedelta(freq)
expect_horizon_str = str(self._freq * expect_horizon)
self.search_space.update({"expect_horizon": expect_horizon_str,
"cross_validation": cross_validation})
train_data = data if cross_validation else data[:len(data)-expect_horizon]
validation_data = None if cross_validation else data[len(data)-expect_horizon:]
n_sampling = recalculate_n_sampling(self.search_space,
n_sampling) if n_sampling != -1 else -1
self.auto_est.fit(data=train_data,
validation_data=validation_data,
metric=self.metric,
metric_threshold=metric_threshold,
n_sampling=n_sampling,
search_space=self.search_space,
search_alg=search_alg,
search_alg_params=search_alg_params,
scheduler=scheduler,
scheduler_params=scheduler_params
)
# use the best config to fit a new prophet model on whole data
self.best_model = ProphetBuilder().build(self.auto_est.get_best_config())
self.best_model.model.fit(data)
def predict(self, horizon=1, freq="D", ds_data=None):
"""
Predict using the best model after HPO.
:param horizon: the number of steps forward to predict
:param freq: the freqency of the predicted dataframe, defaulted to day("D"),
the frequency can be anything from the pandas list of frequency strings here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
:param ds_data: a dataframe that has 1 column 'ds' indicating date.
"""
if self.best_model.model is None:
raise RuntimeError(
"You must call fit or restore first before calling predict!")
return self.best_model.predict(horizon=horizon, freq=freq, ds_data=ds_data)
def evaluate(self, data, metrics=['mse']):
"""
Evaluate using the best model after HPO.
:param data: evaluation data, a pandas dataframe with Td rows,
and 2 columns, with column 'ds' indicating date and column 'y' indicating value
and Td is the time dimension
:param metrics: A list contains metrics for test/valid data.
"""
if data is None:
raise ValueError("Input invalid data of None")
if self.best_model.model is None:
raise RuntimeError(
"You must call fit or restore first before calling evaluate!")
return self.best_model.evaluate(target=data,
metrics=metrics)
def save(self, checkpoint_file):
"""
Save the best model after HPO.
:param checkpoint_file: The location you want to save the best model, should be a json file
"""
if self.best_model.model is None:
raise RuntimeError(
"You must call fit or restore first before calling save!")
self.best_model.save(checkpoint_file)
def restore(self, checkpoint_file):
"""
Restore the best model after HPO.
:param checkpoint_file: The checkpoint file location you want to load the best model.
"""
self.best_model.restore(checkpoint_file)
def get_best_model(self):
"""
Get the best Prophet model.
"""
return self.best_model.model
| 48.066116 | 121 | 0.616145 | 10,862 | 0.933803 | 0 | 0 | 0 | 0 | 0 | 0 | 6,656 | 0.572215 |
07cfa7c073692f7ff81f78defb2e3541ec3803be | 13,313 | py | Python | nf/flows.py | arita37/normalizing-flows | c9896656bfd2007b0c17b801c0fe068560127301 | [
"MIT"
] | 1 | 2019-11-11T05:40:30.000Z | 2019-11-11T05:40:30.000Z | nf/flows.py | arita37/normalizing-flows | c9896656bfd2007b0c17b801c0fe068560127301 | [
"MIT"
] | null | null | null | nf/flows.py | arita37/normalizing-flows | c9896656bfd2007b0c17b801c0fe068560127301 | [
"MIT"
] | null | null | null | import math
import numpy as np
import scipy as sp
import scipy.linalg
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from nf.utils import unconstrained_RQS
# supported non-linearities: note that the function must be invertible
functional_derivatives = {
torch.tanh: lambda x: 1 - torch.pow(torch.tanh(x), 2),
F.leaky_relu: lambda x: (x > 0).type(torch.FloatTensor) + \
(x < 0).type(torch.FloatTensor) * -0.01,
F.elu: lambda x: (x > 0).type(torch.FloatTensor) + \
(x < 0).type(torch.FloatTensor) * torch.exp(x)
}
class Planar(nn.Module):
"""
Planar flow.
z = f(x) = x + u h(wᵀx + b)
[Rezende and Mohamed, 2015]
"""
def __init__(self, dim, nonlinearity=torch.tanh):
super().__init__()
self.h = nonlinearity
self.w = nn.Parameter(torch.Tensor(dim))
self.u = nn.Parameter(torch.Tensor(dim))
self.b = nn.Parameter(torch.Tensor(1))
self.reset_parameters(dim)
def reset_parameters(self, dim):
init.uniform_(self.w, -math.sqrt(1/dim), math.sqrt(1/dim))
init.uniform_(self.u, -math.sqrt(1/dim), math.sqrt(1/dim))
init.uniform_(self.b, -math.sqrt(1/dim), math.sqrt(1/dim))
def forward(self, x):
"""
Given x, returns z and the log-determinant log|df/dx|.
Returns
-------
"""
if self.h in (F.elu, F.leaky_relu):
u = self.u
elif self.h == torch.tanh:
scal = torch.log(1+torch.exp(self.w @ self.u)) - self.w @ self.u - 1
u = self.u + scal * self.w / torch.norm(self.w)
else:
raise NotImplementedError("Non-linearity is not supported.")
lin = torch.unsqueeze(x @ self.w, 1) + self.b
z = x + u * self.h(lin)
phi = functional_derivatives[self.h](lin) * self.w
log_det = torch.log(torch.abs(1 + phi @ u) + 1e-4)
return z, log_det
def backward(self, z):
raise NotImplementedError("Planar flow has no algebraic inverse.")
class Radial(nn.Module):
"""
Radial flow.
z = f(x) = = x + β h(α, r)(z − z0)
[Rezende and Mohamed 2015]
"""
def __init__(self, dim):
super().__init__()
self.x0 = nn.Parameter(torch.Tensor(dim))
self.log_alpha = nn.Parameter(torch.Tensor(1))
self.beta = nn.Parameter(torch.Tensor(1))
def reset_parameters(dim):
init.uniform_(self.z0, -math.sqrt(1/dim), math.sqrt(1/dim))
init.uniform_(self.log_alpha, -math.sqrt(1/dim), math.sqrt(1/dim))
init.uniform_(self.beta, -math.sqrt(1/dim), math.sqrt(1/dim))
def forward(self, x):
"""
Given x, returns z and the log-determinant log|df/dx|.
"""
m, n = x.shape
r = torch.norm(x - self.x0)
h = 1 / (torch.exp(self.log_alpha) + r)
beta = -torch.exp(self.log_alpha) + torch.log(1 + torch.exp(self.beta))
z = x + beta * h * (x - self.x0)
log_det = (n - 1) * torch.log(1 + beta * h) + \
torch.log(1 + beta * h - \
beta * r / (torch.exp(self.log_alpha) + r) ** 2)
return z, log_det
class FCNN(nn.Module):
"""
Simple fully connected neural network.
"""
def __init__(self, in_dim, out_dim, hidden_dim):
super().__init__()
self.network = nn.Sequential(
nn.Linear(in_dim, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, out_dim),
)
def forward(self, x):
return self.network(x)
class RealNVP(nn.Module):
"""
Non-volume preserving flow.
[Dinh et. al. 2017]
"""
def __init__(self, dim, hidden_dim = 8, base_network=FCNN):
super().__init__()
self.dim = dim
self.t1 = base_network(dim // 2, dim // 2, hidden_dim)
self.s1 = base_network(dim // 2, dim // 2, hidden_dim)
self.t2 = base_network(dim // 2, dim // 2, hidden_dim)
self.s2 = base_network(dim // 2, dim // 2, hidden_dim)
def forward(self, x):
lower, upper = x[:,:self.dim // 2], x[:,self.dim // 2:]
t1_transformed = self.t1(lower)
s1_transformed = self.s1(lower)
upper = t1_transformed + upper * torch.exp(s1_transformed)
t2_transformed = self.t2(upper)
s2_transformed = self.s2(upper)
lower = t2_transformed + lower * torch.exp(s2_transformed)
z = torch.cat([lower, upper], dim=1)
log_det = torch.sum(s1_transformed, dim=1) + \
torch.sum(s2_transformed, dim=1)
return z, log_det
def backward(self, z):
lower, upper = z[:,:self.dim // 2], z[:,self.dim // 2:]
t2_transformed = self.t2(upper)
s2_transformed = self.s2(upper)
lower = (lower - t2_transformed) * torch.exp(-s2_transformed)
t1_transformed = self.t1(lower)
s1_transformed = self.s1(lower)
upper = (upper - t1_transformed) * torch.exp(-s1_transformed)
x = torch.cat([lower, upper], dim=1)
log_det = torch.sum(-s1_transformed, dim=1) + \
torch.sum(-s2_transformed, dim=1)
return x, log_det
class MAF(nn.Module):
"""
Masked auto-regressive flow.
[Papamakarios et al. 2018]
"""
def __init__(self, dim, hidden_dim = 8, base_network=FCNN):
super().__init__()
self.dim = dim
self.layers = nn.ModuleList()
self.initial_param = nn.Parameter(torch.Tensor(2))
for i in range(1, dim):
self.layers += [base_network(i, 2, hidden_dim)]
self.reset_parameters()
def reset_parameters(self):
init.uniform_(self.initial_param, -math.sqrt(0.5), math.sqrt(0.5))
def forward(self, x):
z = torch.zeros_like(x)
log_det = torch.zeros(z.shape[0])
for i in range(self.dim):
if i == 0:
mu, alpha = self.initial_param[0], self.initial_param[1]
else:
out = self.layers[i - 1](x[:, :i])
mu, alpha = out[:, 0], out[:, 1]
z[:, i] = (x[:, i] - mu) / torch.exp(alpha)
log_det -= alpha
return z.flip(dims=(1,)), log_det
def backward(self, z):
x = torch.zeros_like(z)
log_det = torch.zeros(z.shape[0])
z = z.flip(dims=(1,))
for i in range(self.dim):
if i == 0:
mu, alpha = self.initial_param[0], self.initial_param[1]
else:
out = self.layers[i - 1](x[:, :i])
mu, alpha = out[:, 0], out[:, 1]
x[:, i] = mu + torch.exp(alpha) * z[:, i]
log_det += alpha
return x, log_det
class ActNorm(nn.Module):
"""
ActNorm layer.
[Kingma and Dhariwal, 2018.]
"""
def __init__(self, dim):
super().__init__()
self.dim = dim
self.mu = nn.Parameter(torch.zeros(dim, dtype = torch.float))
self.log_sigma = nn.Parameter(torch.zeros(dim, dtype = torch.float))
def forward(self, x):
z = x * torch.exp(self.log_sigma) + self.mu
log_det = torch.sum(self.log_sigma)
return z, log_det
def backward(self, z):
x = (z - self.mu) / torch.exp(self.log_sigma)
log_det = -torch.sum(self.log_sigma)
return x, log_det
class OneByOneConv(nn.Module):
"""
Invertible 1x1 convolution.
[Kingma and Dhariwal, 2018.]
"""
def __init__(self, dim):
super().__init__()
self.dim = dim
W, _ = sp.linalg.qr(np.random.randn(dim, dim))
P, L, U = sp.linalg.lu(W)
self.P = torch.tensor(P, dtype = torch.float)
self.L = nn.Parameter(torch.tensor(L, dtype = torch.float))
self.S = nn.Parameter(torch.tensor(np.diag(U), dtype = torch.float))
self.U = nn.Parameter(torch.triu(torch.tensor(U, dtype = torch.float),
diagonal = 1))
self.W_inv = None
def forward(self, x):
L = torch.tril(self.L, diagonal = -1) + torch.diag(torch.ones(self.dim))
U = torch.triu(self.U, diagonal = 1)
z = x @ self.P @ L @ (U + torch.diag(self.S))
log_det = torch.sum(torch.log(torch.abs(self.S)))
return z, log_det
def backward(self, z):
if not self.W_inv:
L = torch.tril(self.L, diagonal = -1) + \
torch.diag(torch.ones(self.dim))
U = torch.triu(self.U, diagonal = 1)
W = self.P @ L @ (U + torch.diag(self.S))
self.W_inv = torch.inverse(W)
x = z @ self.W_inv
log_det = -torch.sum(torch.log(torch.abs(self.S)))
return x, log_det
class NSF_AR(nn.Module):
"""
Neural spline flow, auto-regressive.
[Durkan et al. 2019]
"""
def __init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network = FCNN):
super().__init__()
self.dim = dim
self.K = K
self.B = B
self.layers = nn.ModuleList()
self.init_param = nn.Parameter(torch.Tensor(3 * K - 1))
for i in range(1, dim):
self.layers += [base_network(i, 3 * K - 1, hidden_dim)]
self.reset_parameters()
def reset_parameters(self):
init.uniform_(self.init_param, - 1 / 2, 1 / 2)
def forward(self, x):
z = torch.zeros_like(x)
log_det = torch.zeros(z.shape[0])
for i in range(self.dim):
if i == 0:
init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1)
W, H, D = torch.split(init_param, self.K, dim = 1)
else:
out = self.layers[i - 1](x[:, :i])
W, H, D = torch.split(out, self.K, dim = 1)
W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
z[:, i], ld = unconstrained_RQS(
x[:, i], W, H, D, inverse=False, tail_bound=self.B)
log_det += ld
return z, log_det
def backward(self, z):
x = torch.zeros_like(z)
log_det = torch.zeros(x.shape[0])
for i in range(self.dim):
if i == 0:
init_param = self.init_param.expand(x.shape[0], 3 * self.K - 1)
W, H, D = torch.split(init_param, self.K, dim = 1)
else:
out = self.layers[i - 1](x[:, :i])
W, H, D = torch.split(out, self.K, dim = 1)
W, H = torch.softmax(W, dim = 1), torch.softmax(H, dim = 1)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
x[:, i], ld = unconstrained_RQS(
z[:, i], W, H, D, inverse = True, tail_bound = self.B)
log_det += ld
return x, log_det
class NSF_CL(nn.Module):
"""
Neural spline flow, coupling layer.
[Durkan et al. 2019]
"""
def __init__(self, dim, K = 5, B = 3, hidden_dim = 8, base_network = FCNN):
super().__init__()
self.dim = dim
self.K = K
self.B = B
self.f1 = base_network(dim // 2, (3 * K - 1) * dim // 2, hidden_dim)
self.f2 = base_network(dim // 2, (3 * K - 1) * dim // 2, hidden_dim)
def forward(self, x):
log_det = torch.zeros(x.shape[0])
lower, upper = x[:, :self.dim // 2], x[:, self.dim // 2:]
out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)
W, H, D = torch.split(out, self.K, dim = 2)
W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
upper, ld = unconstrained_RQS(
upper, W, H, D, inverse=False, tail_bound=self.B)
log_det += torch.sum(ld, dim = 1)
out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)
W, H, D = torch.split(out, self.K, dim = 2)
W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
lower, ld = unconstrained_RQS(
lower, W, H, D, inverse=False, tail_bound=self.B)
log_det += torch.sum(ld, dim = 1)
return torch.cat([lower, upper], dim = 1), log_det
def backward(self, z):
log_det = torch.zeros(z.shape[0])
lower, upper = z[:, :self.dim // 2], z[:, self.dim // 2:]
out = self.f2(upper).reshape(-1, self.dim // 2, 3 * self.K - 1)
W, H, D = torch.split(out, self.K, dim = 2)
W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
lower, ld = unconstrained_RQS(
lower, W, H, D, inverse=True, tail_bound=self.B)
log_det += torch.sum(ld, dim = 1)
out = self.f1(lower).reshape(-1, self.dim // 2, 3 * self.K - 1)
W, H, D = torch.split(out, self.K, dim = 2)
W, H = torch.softmax(W, dim = 2), torch.softmax(H, dim = 2)
W, H = 2 * self.B * W, 2 * self.B * H
D = F.softplus(D)
upper, ld = unconstrained_RQS(
upper, W, H, D, inverse = True, tail_bound = self.B)
log_det += torch.sum(ld, dim = 1)
return torch.cat([lower, upper], dim = 1), log_det
| 35.312997 | 80 | 0.533539 | 12,669 | 0.951198 | 0 | 0 | 0 | 0 | 0 | 0 | 1,033 | 0.077558 |
07d1d3b232129762c8438710d6931bc551a36122 | 5,286 | py | Python | ioos_qc/config_creator/fx_parser.py | HakaiInstitute/ioos_qc | dfb28ee404a17c8355747b792fba0471093953c4 | [
"Apache-2.0"
] | null | null | null | ioos_qc/config_creator/fx_parser.py | HakaiInstitute/ioos_qc | dfb28ee404a17c8355747b792fba0471093953c4 | [
"Apache-2.0"
] | null | null | null | ioos_qc/config_creator/fx_parser.py | HakaiInstitute/ioos_qc | dfb28ee404a17c8355747b792fba0471093953c4 | [
"Apache-2.0"
] | 1 | 2021-01-20T23:20:06.000Z | 2021-01-20T23:20:06.000Z | # module pyparsing.py
#
# Copyright (c) 2003-2019 Paul T. McGuire
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
from pyparsing import (
Literal,
Word,
Group,
Forward,
alphas,
alphanums,
Regex,
CaselessKeyword,
Suppress,
delimitedList,
)
import math
import operator
# map operator symbols to corresponding arithmetic operations
epsilon = 1e-12
opn = {
"+": operator.add,
"-": operator.sub,
"*": operator.mul,
"/": operator.truediv,
"^": operator.pow,
}
fn = {
"sin": math.sin,
"cos": math.cos,
"tan": math.tan,
"exp": math.exp,
"abs": abs,
"trunc": lambda a: int(a),
"round": round,
"sgn": lambda a: -1 if a < -epsilon else 1 if a > epsilon else 0,
}
exprStack = []
def push_first(toks):
exprStack.append(toks[0])
def push_unary_minus(toks):
for t in toks:
if t == "-":
exprStack.append("unary -")
else:
break
def BNF():
"""
expop :: '^'
multop :: '*' | '/'
addop :: '+' | '-'
integer :: ['+' | '-'] '0'..'9'+
atom :: PI | E | real | fn '(' expr ')' | '(' expr ')'
factor :: atom [ expop factor ]*
term :: factor [ multop factor ]*
expr :: term [ addop term ]*
"""
# use CaselessKeyword for e and pi, to avoid accidentally matching
# functions that start with 'e' or 'pi' (such as 'exp'); Keyword
# and CaselessKeyword only match whole words
e = CaselessKeyword("E")
pi = CaselessKeyword("PI")
# fnumber = Combine(Word("+-"+nums, nums) +
# Optional("." + Optional(Word(nums))) +
# Optional(e + Word("+-"+nums, nums)))
# or use provided pyparsing_common.number, but convert back to str:
# fnumber = ppc.number().addParseAction(lambda t: str(t[0]))
fnumber = Regex(r"[+-]?\d+(?:\.\d*)?(?:[eE][+-]?\d+)?")
ident = Word(alphas, alphanums + "_$")
plus, minus, mult, div = map(Literal, "+-*/")
lpar, rpar = map(Suppress, "()")
addop = plus | minus
multop = mult | div
expop = Literal("^")
expr = Forward()
expr_list = delimitedList(Group(expr))
# add parse action that replaces the function identifier with a (name, number of args) tuple
fn_call = (ident + lpar - Group(expr_list) + rpar).setParseAction(
lambda t: t.insert(0, (t.pop(0), len(t[0])))
)
atom = (
addop[...]
+ (
(fn_call | pi | e | fnumber | ident).setParseAction(push_first)
| Group(lpar + expr + rpar)
)
).setParseAction(push_unary_minus)
# by defining exponentiation as "atom [ ^ factor ]..." instead of "atom [ ^ atom ]...", we get right-to-left
# exponents, instead of left-to-right that is, 2^3^2 = 2^(3^2), not (2^3)^2.
factor = Forward()
factor <<= atom + (expop + factor).setParseAction(push_first)[...]
term = factor + (multop + factor).setParseAction(push_first)[...]
expr <<= term + (addop + term).setParseAction(push_first)[...]
bnf = expr
return bnf
def evaluate_stack(s, stats):
op, num_args = s.pop(), 0
if isinstance(op, tuple):
op, num_args = op
if op == "unary -":
return -evaluate_stack(s, stats)
if op in "+-*/^":
# note: operands are pushed onto the stack in reverse order
op2 = evaluate_stack(s, stats)
op1 = evaluate_stack(s, stats)
return opn[op](op1, op2)
elif op == "PI":
return math.pi # 3.1415926535
elif op == "E":
return math.e # 2.718281828
elif op == "mean":
return stats['mean']
elif op == "min":
return stats['min']
elif op == "max":
return stats['max']
elif op == "std":
return stats['std']
elif op in fn:
# note: args are pushed onto the stack in reverse order
args = reversed([evaluate_stack(s, stats) for _ in range(num_args)])
return fn[op](*args)
elif op[0].isalpha():
raise Exception("invalid identifier '%s'" % op)
else:
return float(op)
def eval_fx(fx, stats):
"""Given fx and stats ('min', 'max', 'mean', 'std') return the result"""
_ = BNF().parseString(fx, parseAll=True)
val = evaluate_stack(exprStack[:], stats)
return val
| 31.278107 | 112 | 0.600643 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,632 | 0.497919 |
07d274563189ebc57a38c1571e12c09ed638080d | 18,828 | py | Python | scanlogger.py | pythonhacker/pyscanlogd | 64d6ad38127243e5c422be7f899ecfa802e1ad21 | [
"BSD-3-Clause"
] | 1 | 2021-04-03T22:15:06.000Z | 2021-04-03T22:15:06.000Z | scanlogger.py | pythonhacker/pyscanlogd | 64d6ad38127243e5c422be7f899ecfa802e1ad21 | [
"BSD-3-Clause"
] | null | null | null | scanlogger.py | pythonhacker/pyscanlogd | 64d6ad38127243e5c422be7f899ecfa802e1ad21 | [
"BSD-3-Clause"
] | 2 | 2020-12-18T20:06:21.000Z | 2021-04-08T02:47:40.000Z | # -- coding: utf-8
#!/usr/bin/env python
"""
pyscanlogger: Port scan detector/logger tool, inspired
by scanlogd {http://www.openwall.com/scanlogd} but with
added ability to log slow port-scans.
Features
1. Detects all stealth (half-open) and full-connect scans.
2. Detects Idle scan and logs it correctly using correlation!
3. Detects SCTP scan.
4. Detects slow port-scans also.
Modification History
Mar 17 2010 - Cleaned up code to publish to google.
Apr 8 2010 - Better detection of TCP full-connect scan without
spurious and incorrect logging. Better logging
functions.
Licensed under GNU GPL v3.0.
"""
import sys, os
import dpkt, pcap
import struct
import socket
import time
import threading
import optparse
import entry
import timerlist
__author__ = "pythonhacker"
__maintainer__ = "pythonhacker"
__version__ = '0.5.1'
__modified__ = 'Thu Apr 8 19:21:11 IST 2010'
# UDP - in progress...
SCAN_TIMEOUT = 5
WEIGHT_THRESHOLD = 25
PIDFILE="/var/run/pyscanlogger.pid"
# TCP flag constants
TH_URG=dpkt.tcp.TH_URG
TH_ACK=dpkt.tcp.TH_ACK
TH_PSH=dpkt.tcp.TH_PUSH
TH_RST=dpkt.tcp.TH_RST
TH_SYN=dpkt.tcp.TH_SYN
TH_FIN=dpkt.tcp.TH_FIN
# Protocols
TCP=dpkt.tcp.TCP
UDP=dpkt.udp.UDP
SCTP=dpkt.sctp.SCTP
get_timestamp = lambda : time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())
ip2quad = lambda x: socket.inet_ntoa(struct.pack('I', x))
scan_ip2quad = lambda scan: map(ip2quad, [scan.src, scan.dst])
class ScanLogger(object):
""" Port scan detector/logger """
# TCP flags to scan type mapping
scan_types = {0: 'TCP null',
TH_FIN: 'TCP fin',
TH_SYN: 'TCP syn', TH_SYN|TH_RST: 'TCP syn',
TH_ACK: 'TCP ack',
TH_URG|TH_PSH|TH_FIN: 'TCP x-mas',
TH_URG|TH_PSH|TH_FIN|TH_ACK: 'TCP x-mas',
TH_SYN|TH_FIN: 'TCP syn/fin',
TH_FIN|TH_ACK: 'TCP fin/ack',
TH_SYN|TH_ACK: 'TCP full-connect',
TH_URG|TH_PSH|TH_ACK|TH_RST|TH_SYN|TH_FIN: 'TCP all-flags',
TH_SYN|TH_ACK|TH_RST: 'TCP full-connect',
# Not a scan
TH_RST|TH_ACK: 'reply'}
def __init__(self, timeout, threshold, maxsize, daemon=True, logfile='/var/log/scanlog'):
self.scans = entry.EntryLog(maxsize)
self.long_scans = entry.EntryLog(maxsize)
# Port scan weight threshold
self.threshold = threshold
# Timeout for scan entries
self.timeout = timeout
# Long-period scan timeouts
self.timeout_l = 3600
# Long-period scan threshold
self.threshold_l = self.threshold/2
# Daemonize ?
self.daemon = daemon
# Log file
try:
self.scanlog = open(logfile,'a')
print >> sys.stderr, 'Scan logs will be saved to %s' % logfile
except (IOError, OSError), (errno, strerror):
print >> sys.stderr, "Error opening scan log file %s => %s" % (logfile, strerror)
self.scanlog = None
# Recent scans - this list allows to keep scan information
# upto last 'n' seconds, so as to not call duplicate scans
# in the same time-period. 'n' is 60 sec by default.
# Since entries time out in 60 seconds, max size is equal
# to maximum such entries possible in 60 sec - assuming
# a scan occurs at most every 5 seconds, this would be 12.
self.recent_scans = timerlist.TimerList(12, 60.0)
def hash_func(self, addr):
""" Hash a host address """
value = addr
h = 0
while value:
# print value
h ^= value
value = value >> 9
return h & (8192-1)
def mix(self, a, b, c):
a -= b; a -= c; a ^= (c>>13)
b -= c; b -= a; b ^= (a<<8)
c -= a; c -= b; c ^= (b>>13)
a -= b; a -= c; a ^= (c>>12)
b -= c; b -= a; b ^= (a<<16)
c -= a; c -= b; c ^= (b>>5)
a -= b; a -= c; a ^= (c>>3)
b -= c; b -= a; b ^= (a<<10)
c -= a; c -= b; c ^= (b>>15)
return abs(c)
def host_hash(self, src, dst):
""" Hash mix two host addresses """
return self.hash_func(self.mix(src, dst, 0xffffff))
def log(self, msg):
""" Log a message to console and/or log file """
line = '[%s]: %s' % (get_timestamp(), msg)
if self.scanlog:
self.scanlog.write(line + '\n')
self.scanlog.flush()
if not self.daemon:
print >> sys.stderr, line
def log_scan(self, scan, continuation=False, slow_scan=False, unsure=False):
""" Log the scan to file and/or console """
srcip, dstip = scan_ip2quad(scan)
ports = ','.join([str(port) for port in scan.ports])
if not continuation:
tup = [scan.type,scan.flags_or,srcip,dstip, ports]
if not slow_scan:
if scan.type != 'Idle':
line = '%s scan (flags:%d) from %s to %s (ports:%s)'
else:
tup.append(ip2quad(scan.zombie))
line = '%s scan (flags: %d) from %s to %s (ports: %s) using zombie host %s'
else:
tup.append(scan.time_avg)
if unsure:
line = 'Possible slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'
else:
line = 'Slow %s scan (flags:%d) from %s to %s (ports:%s), average timediff %.2fs'
else:
tup = [scan.type, srcip,dstip, ports]
if not slow_scan:
if scan.type != 'Idle':
line = 'Continuation of %s scan from %s to %s (ports:%s)'
else:
tup.append(ip2quad(scan.zombie))
line = 'Continuation of %s scan from %s to %s (ports: %s) using zombie host %s'
else:
tup.append(scan.time_avg)
line = 'Continuation of slow %s scan from %s to %s (ports:%s), average timediff %.2fs'
msg = line % tuple(tup)
self.log(msg)
def update_ports(self, scan, dport, flags):
scan.flags_or |= flags
if dport in scan.ports:
return
# Add weight for port
if dport < 1024:
scan.weight += 3
else:
scan.weight += 1
scan.ports.append(dport)
def inspect_scan(self, scan, slow_scan=False):
# Sure scan
is_scan = ((slow_scan and scan.weight >= self.threshold_l) or (not slow_scan and scan.weight >= self.threshold))
# Possible scan
maybe_scan = (slow_scan and len(scan.ports)>=3 and len(scan.timediffs)>=4 and (scan.weight < self.threshold_l))
not_scan = False
if is_scan or maybe_scan:
scan.logged = True
if scan.proto==TCP:
idle_scan = False
if scan.flags_or==TH_RST:
# None does scan using RST, however this could be
# return packets from a zombie host to the scanning
# host when a scanning host is doing an idle scan.
# Basically
# A -scanning host
# B - zombie host
# C - target host
# If A does an idle scan on C with B as zombie,
# it will appear to C as if B is syn scanning it
# and later we could get an apparent RST "scan"
# from B to A
# Correlation: If 'RST scan' detected from X to Y
# See if there was a SYN scan recently from host
# X to host Z. Then actually Y is idle scanning
# Z
dummy_scans, idle_ports = [], []
for item in reversed(self.recent_scans):
rscan = item[1]
if rscan.src==scan.src and rscan.flags_or==TH_SYN and ((rscan.timestamp - scan.timestamp)<30):
idle_scan = True
idle_ports.append(rscan.ports)
dummy_scans.append(item)
if idle_scan:
scan.src = scan.dst
scan.dst = rscan.dst
scan.zombie = rscan.src
scan.type = 'Idle'
scan.ports = idle_ports
# for d in dummy_scans:
# self.recent_scans.remove(d)
else:
# Remove entry
if slow_scan:
del self.long_scans[scan.hash]
else:
del self.scans[scan.hash]
return False
else:
scan.type = self.scan_types.get(scan.flags_or,'unknown')
if scan.type in ('', 'reply'):
not_scan = True
# If we see scan flags 22 from A->B, make sure that
# there was no recent full-connect scan from B->A, if
# so this is spurious and should be ignored.
if scan.flags_or == (TH_SYN|TH_ACK|TH_RST) and len(self.recent_scans):
recent1 = self.recent_scans[-1:-2:-1]
for recent in recent1:
# Was not a scan, skip
if not recent.is_scan: continue
if recent.type == 'TCP full-connect' and ((scan.src == recent.dst) and (scan.dst == recent.src)):
# Spurious
self.log("Ignoring spurious TCP full-connect scan from %s" % ' to '.join(scan_ip2quad(scan)))
not_scan = True
break
# If this is a syn scan, see if there was a recent idle scan
# with this as zombie, then ignore it...
elif scan.flags_or == TH_SYN and len(self.recent_scans):
# Try last 1 scans
recent1 = self.recent_scans[-1:-2:-1]
for recent in recent1:
if recent.type=='Idle' and scan.src==recent.zombie:
self.log('Ignoring mis-interpreted syn scan from zombie host %s' % ' to '.join(scan_ip2quad(scan)))
break
# Reply from B->A for full-connect scan from A->B
elif (recent.type == 'reply' and ((scan.src == recent.dst) and (scan.dst == recent.src))):
scan.type = 'TCP full-connect'
break
elif scan.proto==UDP:
scan.type = 'UDP'
# Reset flags for UDP scan
scan.flags_or = 0
elif scan.proto==SCTP:
if scan.chunk_type==1:
scan.type = 'SCTP Init'
elif scan.chunk_type==10:
scan.type = 'SCTP COOKIE_ECHO'
# See if this was logged recently
scanentry = entry.RecentScanEntry(scan, not not_scan)
if scanentry not in self.recent_scans:
continuation=False
self.recent_scans.append(scanentry)
else:
continuation=True
if not not_scan:
self.log_scan(scan, continuation=continuation, slow_scan=slow_scan, unsure=maybe_scan)
# Remove entry
if slow_scan:
del self.long_scans[scan.hash]
else:
del self.scans[scan.hash]
return True
else:
return False
def process(self, pkt):
if not hasattr(pkt, 'ip'):
return
ip = pkt.ip
# Ignore non-tcp, non-udp packets
if type(ip.data) not in (TCP, UDP, SCTP):
return
pload = ip.data
src,dst,dport,flags = int(struct.unpack('I',ip.src)[0]),int(struct.unpack('I', ip.dst)[0]),int(pload.dport),0
proto = type(pload)
if proto == TCP: flags = pload.flags
key = self.host_hash(src,dst)
curr=time.time()
# Keep dropping old entries
self.recent_scans.collect()
if key in self.scans:
scan = self.scans[key]
if scan.src != src:
# Skip packets in reverse direction or invalid protocol
return
timediff = curr - scan.timestamp
# Update only if not too old, else skip and remove entry
if (timediff > self.timeout):
# Add entry in long_scans if timediff not larger
# than longscan timeout
prev = self.scans[key].timestamp
if timediff<=self.timeout_l:
if key not in self.long_scans:
lscan = entry.ScanEntry(key)
lscan.src = src
lscan.dst = dst
lscan.timestamp = curr
lscan.timediffs.append(curr - prev)
lscan.flags_or |= flags
lscan.ports.append(dport)
lscan.proto = proto
self.long_scans[key] = lscan
else:
lscan = self.long_scans[key]
lscan.timestamp = curr
lscan.flags_or |= flags
lscan.timediffs.append(curr - prev)
lscan.update_time_sd()
self.update_ports(lscan, dport, flags)
if lscan.time_sd<2:
# SD is less than 2, possible slow scan
# update port weights...
# print 'Weight=>',lscan.weight
if not self.inspect_scan(lscan, True):
# Not a scan, check # of entries - if too many
# then this is a regular network activity
# but not a scan, so remove entry
if len(lscan.timediffs)>=10:
# print lscan.src, lscan.timediffs, lscan.time_sd
print 'Removing',key,lscan.src,'since not a scan'
del self.long_scans[key]
elif len(lscan.timediffs)>2:
# More than 2 entries, but SD is too large,
# delete the entry
# print 'Removing',key,lscan.src,'since SD is',lscan.time_sd
del self.long_scans[key]
else:
# Too large timeout, remove key
del self.long_scans[key]
del self.scans[key]
return
if scan.logged: return
scan.timestamp = curr
self.update_ports(scan, dport, flags)
self.inspect_scan(scan)
else:
# Add new entry
scan = entry.ScanEntry(key)
scan.src = src
scan.dst = dst
scan.timestamp = curr
scan.flags_or |= flags
if proto==SCTP:
scan.chunk_type = pload.chunks[0].type
scan.ports.append(dport)
scan.proto = proto
self.scans[key] = scan
def loop(self):
pc = pcap.pcap()
decode = { pcap.DLT_LOOP:dpkt.loopback.Loopback,
pcap.DLT_NULL:dpkt.loopback.Loopback,
pcap.DLT_EN10MB:dpkt.ethernet.Ethernet } [pc.datalink()]
try:
print 'listening on %s: %s' % (pc.name, pc.filter)
for ts, pkt in pc:
self.process(decode(pkt))
except KeyboardInterrupt:
if not self.daemon:
nrecv, ndrop, nifdrop = pc.stats()
print '\n%d packets received by filter' % nrecv
print '%d packets dropped by kernel' % ndrop
def run_daemon(self):
# Disconnect from tty
try:
pid = os.fork()
if pid>0:
sys.exit(0)
except OSError, e:
print >>sys.stderr, "fork #1 failed", e
sys.exit(1)
os.setsid()
os.umask(0)
# Second fork
try:
pid = os.fork()
if pid>0:
open(PIDFILE,'w').write(str(pid))
sys.exit(0)
except OSError, e:
print >>sys.stderr, "fork #2 failed", e
sys.exit(1)
self.loop()
def run(self):
# If dameon, then create a new thread and wait for it
if self.daemon:
print 'Daemonizing...'
self.run_daemon()
else:
# Run in foreground
self.loop()
def main():
if os.geteuid() != 0:
sys.exit("You must be super-user to run this program")
o=optparse.OptionParser()
o.add_option("-d", "--daemonize", dest="daemon", help="Daemonize",
action="store_true", default=False)
o.add_option("-f", "--logfile", dest="logfile", help="File to save logs to",
default="/var/log/scanlog")
options, args = o.parse_args()
s=ScanLogger(SCAN_TIMEOUT, WEIGHT_THRESHOLD, 8192, options.daemon, options.logfile)
s.run()
if __name__ == '__main__':
main()
| 37.8833 | 132 | 0.468929 | 16,718 | 0.887933 | 0 | 0 | 0 | 0 | 0 | 0 | 4,753 | 0.252443 |
07d2a6693b06381833978cbf344d430f2c2b518b | 4,539 | py | Python | src/util/util.py | ashleylst/DSDmodel | 4276c832e0335539aef2ae2b33e23719957a3f08 | [
"MIT"
] | 1 | 2021-07-26T17:30:16.000Z | 2021-07-26T17:30:16.000Z | src/util/util.py | ashleylst/DSDmodel | 4276c832e0335539aef2ae2b33e23719957a3f08 | [
"MIT"
] | null | null | null | src/util/util.py | ashleylst/DSDmodel | 4276c832e0335539aef2ae2b33e23719957a3f08 | [
"MIT"
] | 2 | 2020-06-29T15:43:22.000Z | 2021-05-12T08:25:03.000Z | from itertools import combinations
import copy
def get_reverse(n):
if n == 1:
return 0
else:
return 1
def get_edge_info(e):
v = [0 for i in range(2)]
n = [0 for i in range(2)]
t = 0
for x in e:
v[t], n[t] = x
t += 1
return v, n
def sort_e_by_domain(val):
return val[0][1]
def sort_by_strand(val):
return val[0][0]
def check_edge_in_tuplelist(edge, tpl):
for i in tpl:
if edge in i:
return True
return False
def compare(a, b):
return (a > b) - (a < b)
def flip(i):
if i == 0:
i = 1
elif i == 1:
i = 0
return i
def get_free_domains(limits, blocks, bound):
limits = sorted(limits)
interval = limits[1] - limits[0]
for i in blocks:
if limits[1] > i > limits[0]:
tmp = abs(bound - i)
if tmp < interval:
interval = tmp
return interval
def get_combinations(oldlen, newlen, cursor, indexlist):
combold = list(combinations(indexlist[cursor:oldlen], 2))
combself = [(i, i) for i in range(0, oldlen)]
combnew = []
if oldlen != newlen:
for i in range(0, oldlen):
for j in range(oldlen, newlen):
combnew.append((i, j))
return combold + combnew + combself
def get_migrate_nodes(edges, indices, startstrand):
d = []
for i in indices:
vi, ni = get_edge_info(edges[i][0])
if vi[0] == startstrand:
d.append(ni[0])
else:
d.append(ni[1])
d.sort()
return d
def check_following_migration(edges, p=0):
"""
:param edges:
:return:
"""
e = copy.copy(edges)
visited = [False for _ in e]
miggroup = []
cnt = -1
for i in range(0, len(e)):
if visited[i]:
continue
e[i] = list(e[i])
e[i][p] = list(e[i][p])
t1 = sorted(e[i][p], key=lambda tup: tup[0])
if not visited[i]:
visited[i] = True
miggroup.append([i])
cnt += 1
for j in range(0, len(e)):
if j != i and not visited[j]:
e[j] = list(e[j])
e[j][p] = list(e[j][p])
t2 = sorted(e[j][p], key=lambda tup: tup[0])
if (t2[0][0] != t1[0][0]) or (t2[1][0] != t1[1][0]):
continue
for num in range(0, len(miggroup[cnt])):
t1 = sorted(e[miggroup[cnt][num]][p], key=lambda tup: tup[0])
if (t1[0][1] + 1 == t2[0][1] and t1[1][1] - 1 == t2[1][1]) \
or (t1[0][1] - 1 == t2[0][1] and t1[1][1] + 1 == t2[1][1]):
visited[j] = True
miggroup[cnt].append(j)
break
return miggroup
def get_absdist(domain1, domain2):
"""
:param domain1:
:param domain2:
:return:
"""
return abs(domain1[1] - domain2[1])
def get_closet_domain_to_target(target, domains):
"""
:param target:
:param domains:
:return:
"""
closet = 10000
closetd = ()
for i in domains:
dist = get_absdist(i, target)
if dist < closet:
closet = dist
closetd = i
return closetd
def get_domains_on_2sides(target1, target2, domains1, domains2):
"""
:param target1:
:param target2:
:param domains1:
:param domains2:
:return:
"""
if target1[0] == domains1[0][0]:
closetd1 = get_closet_domain_to_target(target1, domains1)
elif target2[0] == domains1[0][0]:
closetd1 = get_closet_domain_to_target(target2, domains1)
if target1[0] == domains2[0][0]:
closetd2 = get_closet_domain_to_target(target1, domains2)
elif target2[0] == domains2[0][0]:
closetd2 = get_closet_domain_to_target(target2, domains2)
return closetd1, closetd2
def get_closest_target(domains, targets):
"""
:return:
"""
domains = sorted(domains, key=lambda tup: tup[1])
mindist = 10000
mint = None
for t in targets:
dist = min(get_absdist(t, domains[0]), get_absdist(t, domains[len(domains) - 1]))
if dist < mindist:
mint = t
return mint
def check_continuity(a, b):
for i in a:
for j in b:
if i + 1 == j or i - 1 == j:
return i, j
return None
def check_bond_existence(d1, d2, l1, l2):
for i in range(len(l1)):
if d1 == l1[i] and d2 == l2[i]:
return True
return False
| 22.695 | 89 | 0.516193 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 304 | 0.066975 |
07d5b427e69bdc09287f1c66c02797e0db0b274b | 1,218 | py | Python | examples/question_answering/qa_sparse_train.py | ebell495/nn_pruning | 41263ab898117a639f3f219c23a4cecc8bc0e3f3 | [
"Apache-2.0"
] | 250 | 2021-02-22T15:50:04.000Z | 2022-03-31T08:12:02.000Z | examples/question_answering/qa_sparse_train.py | vuiseng9/nn_pruning | 8f4a14dd63d621483cbc1bc4eb34600d66e9e71b | [
"Apache-2.0"
] | 28 | 2021-02-22T15:54:34.000Z | 2022-03-17T08:57:38.000Z | examples/question_answering/qa_sparse_train.py | vuiseng9/nn_pruning | 8f4a14dd63d621483cbc1bc4eb34600d66e9e71b | [
"Apache-2.0"
] | 31 | 2021-02-22T16:07:17.000Z | 2022-03-28T09:17:24.000Z | # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Sparse Fine-tuning the library models for question answering.
"""
# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
from nn_pruning.sparse_trainer import SparseTrainer
from .qa_train import QATrainer
# SparseTrainer should appear first in the base classes, as its functions must override QATrainer and its base classes (Trainer)
class QASparseTrainer(SparseTrainer, QATrainer):
def __init__(self, sparse_args, *args, **kwargs):
QATrainer.__init__(self, *args, **kwargs)
SparseTrainer.__init__(self, sparse_args)
| 43.5 | 128 | 0.769294 | 202 | 0.165846 | 0 | 0 | 0 | 0 | 0 | 0 | 912 | 0.748768 |
07d6951331a7c029a2698e9098ae813a023bef49 | 10,729 | py | Python | ironic/drivers/modules/ilo/raid.py | armohamm/ironic | 21093ca886ed736a7a25bf5e71e05d41e132fd2f | [
"Apache-2.0"
] | 2 | 2019-06-17T21:37:53.000Z | 2020-07-11T03:58:39.000Z | ironic/drivers/modules/ilo/raid.py | armohamm/ironic | 21093ca886ed736a7a25bf5e71e05d41e132fd2f | [
"Apache-2.0"
] | 5 | 2019-08-14T06:46:03.000Z | 2021-12-13T20:01:25.000Z | ironic/drivers/modules/ilo/raid.py | armohamm/ironic | 21093ca886ed736a7a25bf5e71e05d41e132fd2f | [
"Apache-2.0"
] | 6 | 2019-06-13T12:49:33.000Z | 2021-04-17T16:33:19.000Z | # Copyright 2018 Hewlett Packard Enterprise Development LP
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
iLO5 RAID specific methods
"""
from ironic_lib import metrics_utils
from oslo_log import log as logging
from oslo_utils import importutils
from ironic.common import exception
from ironic.common.i18n import _
from ironic.common import raid
from ironic.common import states
from ironic.conductor import utils as manager_utils
from ironic import conf
from ironic.drivers import base
from ironic.drivers.modules import deploy_utils
from ironic.drivers.modules.ilo import common as ilo_common
LOG = logging.getLogger(__name__)
CONF = conf.CONF
METRICS = metrics_utils.get_metrics_logger(__name__)
ilo_error = importutils.try_import('proliantutils.exception')
class Ilo5RAID(base.RAIDInterface):
"""Implementation of OOB RAIDInterface for iLO5."""
def get_properties(self):
"""Return the properties of the interface."""
return ilo_common.REQUIRED_PROPERTIES
def _set_clean_failed(self, task, msg, exc):
LOG.error("RAID configuration job failed for node %(node)s. "
"Message: '%(message)s'.",
{'node': task.node.uuid, 'message': msg})
task.node.last_error = msg
task.process_event('fail')
def _set_driver_internal_true_value(self, task, *keys):
driver_internal_info = task.node.driver_internal_info
for key in keys:
driver_internal_info[key] = True
task.node.driver_internal_info = driver_internal_info
task.node.save()
def _set_driver_internal_false_value(self, task, *keys):
driver_internal_info = task.node.driver_internal_info
for key in keys:
driver_internal_info[key] = False
task.node.driver_internal_info = driver_internal_info
task.node.save()
def _pop_driver_internal_values(self, task, *keys):
driver_internal_info = task.node.driver_internal_info
for key in keys:
driver_internal_info.pop(key, None)
task.node.driver_internal_info = driver_internal_info
task.node.save()
def _prepare_for_read_raid(self, task, raid_step):
deploy_opts = deploy_utils.build_agent_options(task.node)
task.driver.boot.prepare_ramdisk(task, deploy_opts)
manager_utils.node_power_action(task, states.REBOOT)
if raid_step == 'create_raid':
self._set_driver_internal_true_value(
task, 'ilo_raid_create_in_progress')
else:
self._set_driver_internal_true_value(
task, 'ilo_raid_delete_in_progress')
self._set_driver_internal_true_value(task, 'cleaning_reboot')
self._set_driver_internal_false_value(task, 'skip_current_clean_step')
@METRICS.timer('Ilo5RAID.create_configuration')
@base.clean_step(priority=0, abortable=False, argsinfo={
'create_root_volume': {
'description': (
'This specifies whether to create the root volume. '
'Defaults to `True`.'
),
'required': False
},
'create_nonroot_volumes': {
'description': (
'This specifies whether to create the non-root volumes. '
'Defaults to `True`.'
),
'required': False
}
})
def create_configuration(self, task, create_root_volume=True,
create_nonroot_volumes=True):
"""Create a RAID configuration on a bare metal using agent ramdisk.
This method creates a RAID configuration on the given node.
:param task: a TaskManager instance.
:param create_root_volume: If True, a root volume is created
during RAID configuration. Otherwise, no root volume is
created. Default is True.
:param create_nonroot_volumes: If True, non-root volumes are
created. If False, no non-root volumes are created. Default
is True.
:raises: MissingParameterValue, if node.target_raid_config is missing
or was found to be empty after skipping root volume and/or non-root
volumes.
:raises: NodeCleaningFailure, on failure to execute step.
"""
node = task.node
target_raid_config = raid.filter_target_raid_config(
node, create_root_volume=create_root_volume,
create_nonroot_volumes=create_nonroot_volumes)
driver_internal_info = node.driver_internal_info
driver_internal_info['target_raid_config'] = target_raid_config
LOG.debug("Calling OOB RAID create_configuration for node %(node)s "
"with the following target RAID configuration: %(target)s",
{'node': node.uuid, 'target': target_raid_config})
ilo_object = ilo_common.get_ilo_object(node)
try:
# Raid configuration in progress, checking status
if not driver_internal_info.get('ilo_raid_create_in_progress'):
ilo_object.create_raid_configuration(target_raid_config)
self._prepare_for_read_raid(task, 'create_raid')
return states.CLEANWAIT
else:
# Raid configuration is done, updating raid_config
raid_conf = (
ilo_object.read_raid_configuration(
raid_config=target_raid_config))
if len(raid_conf['logical_disks']):
raid.update_raid_info(node, raid_conf)
LOG.debug("Node %(uuid)s raid create clean step is done.",
{'uuid': node.uuid})
self._pop_driver_internal_values(
task, 'ilo_raid_create_in_progress',
'cleaning_reboot', 'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
else:
# Raid configuration failed
msg = "Unable to create raid"
self._pop_driver_internal_values(
task, 'ilo_raid_create_in_progress',
'cleaning_reboot', 'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
raise exception.NodeCleaningFailure(
"Clean step create_configuration failed "
"on node %(node)s with error: %(err)s" %
{'node': node.uuid, 'err': msg})
except ilo_error.IloError as ilo_exception:
operation = (_("Failed to create raid configuration on node %s")
% node.uuid)
self._pop_driver_internal_values(task,
'ilo_raid_create_in_progress',
'cleaning_reboot',
'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
self._set_clean_failed(task, operation, ilo_exception)
@METRICS.timer('Ilo5RAID.delete_configuration')
@base.clean_step(priority=0, abortable=False)
def delete_configuration(self, task):
"""Delete the RAID configuration.
:param task: a TaskManager instance containing the node to act on.
:raises: NodeCleaningFailure, on failure to execute step.
"""
node = task.node
LOG.debug("OOB RAID delete_configuration invoked for node %s.",
node.uuid)
driver_internal_info = node.driver_internal_info
ilo_object = ilo_common.get_ilo_object(node)
try:
# Raid configuration in progress, checking status
if not driver_internal_info.get('ilo_raid_delete_in_progress'):
ilo_object.delete_raid_configuration()
self._prepare_for_read_raid(task, 'delete_raid')
return states.CLEANWAIT
else:
# Raid configuration is done, updating raid_config
raid_conf = ilo_object.read_raid_configuration()
if not len(raid_conf['logical_disks']):
node.raid_config = {}
LOG.debug("Node %(uuid)s raid delete clean step is done.",
{'uuid': node.uuid})
self._pop_driver_internal_values(
task, 'ilo_raid_delete_in_progress',
'cleaning_reboot', 'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
else:
# Raid configuration failed
msg = ("Unable to delete this logical disks: %s" %
raid_conf['logical_disks'])
self._pop_driver_internal_values(
task, 'ilo_raid_delete_in_progress',
'cleaning_reboot', 'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
raise exception.NodeCleaningFailure(
"Clean step delete_configuration failed "
"on node %(node)s with error: %(err)s" %
{'node': node.uuid, 'err': msg})
except ilo_error.IloLogicalDriveNotFoundError:
LOG.info("No logical drive found to delete on node %(node)s",
{'node': node.uuid})
except ilo_error.IloError as ilo_exception:
operation = (_("Failed to delete raid configuration on node %s")
% node.uuid)
self._pop_driver_internal_values(task,
'ilo_raid_delete_in_progress',
'cleaning_reboot',
'skip_current_clean_step')
node.driver_internal_info = driver_internal_info
node.save()
self._set_clean_failed(task, operation, ilo_exception)
| 45.461864 | 79 | 0.609004 | 9,456 | 0.88135 | 0 | 0 | 7,435 | 0.692982 | 0 | 0 | 3,737 | 0.348308 |
07d7992d7ae8299b452c378aa6d4664a38bab354 | 1,252 | py | Python | src/petronia/aid/bootstrap/__init__.py | groboclown/petronia | 486338023d19cee989e92f0c5692680f1a37811f | [
"MIT"
] | 19 | 2017-06-21T10:28:24.000Z | 2021-12-31T11:49:28.000Z | src/petronia/aid/bootstrap/__init__.py | groboclown/petronia | 486338023d19cee989e92f0c5692680f1a37811f | [
"MIT"
] | 10 | 2016-11-11T18:57:57.000Z | 2021-02-01T15:33:43.000Z | src/petronia/aid/bootstrap/__init__.py | groboclown/petronia | 486338023d19cee989e92f0c5692680f1a37811f | [
"MIT"
] | 3 | 2017-09-17T03:29:35.000Z | 2019-06-03T10:43:08.000Z |
"""
Common Petronia imports for bootstrap parts of an extension.
This should be imported along with the `simp` module.
"""
from ...base.bus import (
EventBus,
ListenerRegistrar,
ListenerSetup,
QueuePriority,
ExtensionMetadataStruct,
register_event,
EVENT_WILDCARD,
TARGET_WILDCARD,
QUEUE_EVENT_NORMAL,
QUEUE_EVENT_HIGH,
QUEUE_EVENT_IO,
QUEUE_EVENT_TYPES
)
from ...base.participant import (
create_singleton_identity,
NOT_PARTICIPANT,
)
from ...base.events import (
# These are generally just bootstrap events.
DisposeCompleteEvent,
as_dispose_complete_listener,
RequestDisposeEvent,
as_request_dispose_listener,
SystemStartedEvent,
as_system_started_listener,
)
from ...base.events.bus import (
EventProtectionModel,
GLOBAL_EVENT_PROTECTION,
INTERNAL_EVENT_PROTECTION,
PRODUCE_EVENT_PROTECTION,
CONSUME_EVENT_PROTECTION,
REQUEST_EVENT_PROTECTION,
RESPONSE_EVENT_PROTECTION,
)
from ...core.extensions.api import ANY_VERSION
from ...core.shutdown.api import (
SystemShutdownEvent,
as_system_shutdown_listener,
SystemShutdownFinalizeEvent,
as_system_shutdown_finalize_listener,
TARGET_ID_SYSTEM_SHUTDOWN,
)
| 19.261538 | 60 | 0.747604 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 167 | 0.133387 |
07d9159827c315605286d46a4f7de494b7d7489e | 3,251 | py | Python | examples/dump-properties.py | zachjweiner/pyopencl | 4e2e4f3150c331680e6d9e36c59290411e4a0c40 | [
"Apache-2.0"
] | 7 | 2020-11-23T09:26:17.000Z | 2021-01-26T08:23:30.000Z | examples/dump-properties.py | zachjweiner/pyopencl | 4e2e4f3150c331680e6d9e36c59290411e4a0c40 | [
"Apache-2.0"
] | null | null | null | examples/dump-properties.py | zachjweiner/pyopencl | 4e2e4f3150c331680e6d9e36c59290411e4a0c40 | [
"Apache-2.0"
] | 1 | 2020-12-14T05:12:10.000Z | 2020-12-14T05:12:10.000Z | import pyopencl as cl
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-s", "--short", action="store_true",
help="don't print all device properties")
(options, args) = parser.parse_args()
def print_info(obj, info_cls):
for info_name in sorted(dir(info_cls)):
if not info_name.startswith("_") and info_name != "to_string":
info = getattr(info_cls, info_name)
try:
info_value = obj.get_info(info)
except:
info_value = "<error>"
if (info_cls == cl.device_info and info_name == "PARTITION_TYPES_EXT"
and isinstance(info_value, list)):
print("{}: {}".format(info_name, [
cl.device_partition_property_ext.to_string(v,
"<unknown device partition property %d>")
for v in info_value]))
else:
try:
print(f"{info_name}: {info_value}")
except:
print("%s: <error>" % info_name)
for platform in cl.get_platforms():
print(75*"=")
print(platform)
print(75*"=")
if not options.short:
print_info(platform, cl.platform_info)
for device in platform.get_devices():
if not options.short:
print(75*"-")
print(device)
if not options.short:
print(75*"-")
print_info(device, cl.device_info)
ctx = cl.Context([device])
for mf in [
cl.mem_flags.READ_ONLY,
#cl.mem_flags.READ_WRITE,
#cl.mem_flags.WRITE_ONLY
]:
for itype in [
cl.mem_object_type.IMAGE2D,
cl.mem_object_type.IMAGE3D
]:
try:
formats = cl.get_supported_image_formats(ctx, mf, itype)
except:
formats = "<error>"
else:
def str_chd_type(chdtype):
result = cl.channel_type.to_string(chdtype,
"<unknown channel data type %d>")
result = result.replace("_INT", "")
result = result.replace("UNSIGNED", "U")
result = result.replace("SIGNED", "S")
result = result.replace("NORM", "N")
result = result.replace("FLOAT", "F")
return result
formats = ", ".join(
"{}-{}".format(
cl.channel_order.to_string(iform.channel_order,
"<unknown channel order 0x%x>"),
str_chd_type(iform.channel_data_type))
for iform in formats)
print("{} {} FORMATS: {}\n".format(
cl.mem_object_type.to_string(itype),
cl.mem_flags.to_string(mf),
formats))
del ctx
| 38.702381 | 83 | 0.450015 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 408 | 0.1255 |
07da573d0a32afbfa2ea160efcad1a4e21eef4c3 | 42,106 | py | Python | interfaces/acados_template/acados_template/acados_ocp_solver.py | jgillis/acados | 3119e2dda636a8358fbd52247eb0163a167cbc97 | [
"BSD-2-Clause"
] | 1 | 2020-07-30T10:54:35.000Z | 2020-07-30T10:54:35.000Z | interfaces/acados_template/acados_template/acados_ocp_solver.py | jgillis/acados | 3119e2dda636a8358fbd52247eb0163a167cbc97 | [
"BSD-2-Clause"
] | null | null | null | interfaces/acados_template/acados_template/acados_ocp_solver.py | jgillis/acados | 3119e2dda636a8358fbd52247eb0163a167cbc97 | [
"BSD-2-Clause"
] | null | null | null | # -*- coding: future_fstrings -*-
#
# Copyright 2019 Gianluca Frison, Dimitris Kouzoupis, Robin Verschueren,
# Andrea Zanelli, Niels van Duijkeren, Jonathan Frey, Tommaso Sartor,
# Branimir Novoselnik, Rien Quirynen, Rezart Qelibari, Dang Doan,
# Jonas Koenemann, Yutao Chen, Tobias Schöls, Jonas Schlagenhauf, Moritz Diehl
#
# This file is part of acados.
#
# The 2-Clause BSD License
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.;
#
import sys, os, json
import numpy as np
from ctypes import *
from casadi import CasadiMeta, Function, SX
from copy import deepcopy
from .generate_c_code_explicit_ode import generate_c_code_explicit_ode
from .generate_c_code_implicit_ode import generate_c_code_implicit_ode
from .generate_c_code_gnsf import generate_c_code_gnsf
from .generate_c_code_constraint import generate_c_code_constraint
from .generate_c_code_nls_cost import generate_c_code_nls_cost
from .generate_c_code_external_cost import generate_c_code_external_cost
from .acados_ocp import AcadosOcp
from .acados_model import acados_model_strip_casadi_symbolics
from .utils import is_column, is_empty, casadi_length, render_template, acados_class2dict,\
format_class_dict, ocp_check_against_layout, np_array_to_list, make_model_consistent,\
set_up_imported_gnsf_model
def make_ocp_dims_consistent(acados_ocp):
dims = acados_ocp.dims
cost = acados_ocp.cost
constraints = acados_ocp.constraints
model = acados_ocp.model
opts = acados_ocp.solver_options
# nx
if is_column(model.x):
dims.nx = casadi_length(model.x)
else:
raise Exception('model.x should be column vector!')
# nu
if is_empty(model.u):
dims.nu = 0
else:
dims.nu = casadi_length(model.u)
# nz
if is_empty(model.z):
dims.nz = 0
else:
dims.nz = casadi_length(model.z)
# np
if is_empty(model.p):
dims.np = 0
else:
dims.np = casadi_length(model.p)
if acados_ocp.parameter_values.shape[0] != dims.np:
raise Exception('inconsistent dimension np, regarding model.p and parameter_values.')
## cost
# path
if cost.cost_type == 'LINEAR_LS':
ny = cost.W.shape[0]
if cost.Vx.shape[0] != ny or cost.Vu.shape[0] != ny:
raise Exception('inconsistent dimension ny, regarding W, Vx, Vu.' + \
f'\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}]\n')
if dims.nz != 0 and cost.Vz.shape[0] != ny:
raise Exception('inconsistent dimension ny, regarding W, Vx, Vu, Vz.' + \
f'\nGot W[{cost.W.shape}], Vx[{cost.Vx.shape}], Vu[{cost.Vu.shape}], Vz[{cost.Vz.shape}]\n')
if cost.Vx.shape[1] != dims.nx and ny != 0:
raise Exception('inconsistent dimension: Vx should have nx columns.')
if cost.Vu.shape[1] != dims.nu and ny != 0:
raise Exception('inconsistent dimension: Vu should have nu columns.')
if cost.yref.shape[0] != ny:
raise Exception('inconsistent dimension: regarding W, yref.' + \
f'\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\n')
dims.ny = ny
elif cost.cost_type == 'NONLINEAR_LS':
ny = cost.W.shape[0]
if is_empty(model.cost_y_expr) and ny != 0:
raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')
elif casadi_length(model.cost_y_expr) != ny:
raise Exception('inconsistent dimension ny: regarding W, cost_y_expr.')
if cost.yref.shape[0] != ny:
raise Exception('inconsistent dimension: regarding W, yref.' + \
f'\nGot W[{cost.W.shape}], yref[{cost.yref.shape}]\n')
dims.ny = ny
# terminal
if cost.cost_type_e == 'LINEAR_LS':
ny_e = cost.W_e.shape[0]
if cost.Vx_e.shape[0] != ny_e:
raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.' + \
f'\nGot W_e[{cost.W_e.shape}], Vx_e[{cost.Vx_e.shape}]')
if cost.Vx_e.shape[1] != dims.nx and ny_e != 0:
raise Exception('inconsistent dimension: Vx_e should have nx columns.')
if cost.yref_e.shape[0] != ny_e:
raise Exception('inconsistent dimension: regarding W_e, yref_e.')
dims.ny_e = ny_e
elif cost.cost_type_e == 'NONLINEAR_LS':
ny_e = cost.W_e.shape[0]
if is_empty(model.cost_y_expr_e) and ny_e != 0:
raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')
elif casadi_length(model.cost_y_expr_e) != ny_e:
raise Exception('inconsistent dimension ny_e: regarding W_e, cost_y_expr_e.')
if cost.yref_e.shape[0] != ny_e:
raise Exception('inconsistent dimension: regarding W_e, yref_e.')
dims.ny_e = ny_e
## constraints
# initial
if (constraints.lbx_0 == [] and constraints.ubx_0 == []):
dims.nbx_0 = 0
else:
this_shape = constraints.lbx_0.shape
other_shape = constraints.ubx_0.shape
if not this_shape == other_shape:
raise Exception('lbx_0, ubx_0 have different shapes!')
if not is_column(constraints.lbx_0):
raise Exception('lbx_0, ubx_0 must be column vectors!')
dims.nbx_0 = constraints.lbx_0.size
if all(constraints.lbx_0 == constraints.ubx_0):
dims.nbxe_0 = dims.nbx_0
# path
nbx = constraints.idxbx.shape[0]
if constraints.ubx.shape[0] != nbx or constraints.lbx.shape[0] != nbx:
raise Exception('inconsistent dimension nbx, regarding idxbx, ubx, lbx.')
else:
dims.nbx = nbx
nbu = constraints.idxbu.shape[0]
if constraints.ubu.shape[0] != nbu or constraints.lbu.shape[0] != nbu:
raise Exception('inconsistent dimension nbu, regarding idxbu, ubu, lbu.')
else:
dims.nbu = nbu
ng = constraints.lg.shape[0]
if constraints.ug.shape[0] != ng or constraints.C.shape[0] != ng \
or constraints.D.shape[0] != ng:
raise Exception('inconsistent dimension ng, regarding lg, ug, C, D.')
else:
dims.ng = ng
if not is_empty(model.con_h_expr):
nh = casadi_length(model.con_h_expr)
else:
nh = 0
if constraints.uh.shape[0] != nh or constraints.lh.shape[0] != nh:
raise Exception('inconsistent dimension nh, regarding lh, uh, con_h_expr.')
else:
dims.nh = nh
if is_empty(model.con_phi_expr):
dims.nphi = 0
dims.nr = 0
else:
dims.nphi = casadi_length(model.con_phi_expr)
if is_empty(model.con_r_expr):
raise Exception('convex over nonlinear constraints: con_r_expr but con_phi_expr is nonempty')
else:
dims.nr = casadi_length(model.con_r_expr)
# terminal
nbx_e = constraints.idxbx_e.shape[0]
if constraints.ubx_e.shape[0] != nbx_e or constraints.lbx_e.shape[0] != nbx_e:
raise Exception('inconsistent dimension nbx_e, regarding idxbx_e, ubx_e, lbx_e.')
else:
dims.nbx_e = nbx_e
ng_e = constraints.lg_e.shape[0]
if constraints.ug_e.shape[0] != ng_e or constraints.C_e.shape[0] != ng_e:
raise Exception('inconsistent dimension ng_e, regarding_e lg_e, ug_e, C_e.')
else:
dims.ng_e = ng_e
if not is_empty(model.con_h_expr_e):
nh_e = casadi_length(model.con_h_expr_e)
else:
nh_e = 0
if constraints.uh_e.shape[0] != nh_e or constraints.lh_e.shape[0] != nh_e:
raise Exception('inconsistent dimension nh_e, regarding lh_e, uh_e, con_h_expr_e.')
else:
dims.nh_e = nh_e
if is_empty(model.con_phi_expr_e):
dims.nphi_e = 0
dims.nr_e = 0
else:
dims.nphi_e = casadi_length(model.con_phi_expr_e)
if is_empty(model.con_r_expr_e):
raise Exception('convex over nonlinear constraints: con_r_expr_e but con_phi_expr_e is nonempty')
else:
dims.nr_e = casadi_length(model.con_r_expr_e)
# Slack dimensions
nsbx = constraints.idxsbx.shape[0]
if is_empty(constraints.lsbx):
constraints.lsbx = np.zeros((nsbx,))
elif constraints.lsbx.shape[0] != nsbx:
raise Exception('inconsistent dimension nsbx, regarding idxsbx, lsbx.')
if is_empty(constraints.usbx):
constraints.usbx = np.zeros((nsbx,))
elif constraints.usbx.shape[0] != nsbx:
raise Exception('inconsistent dimension nsbx, regarding idxsbx, usbx.')
dims.nsbx = nsbx
nsbu = constraints.idxsbu.shape[0]
if is_empty(constraints.lsbu):
constraints.lsbu = np.zeros((nsbu,))
elif constraints.lsbu.shape[0] != nsbu:
raise Exception('inconsistent dimension nsbu, regarding idxsbu, lsbu.')
if is_empty(constraints.usbu):
constraints.usbu = np.zeros((nsbu,))
elif constraints.usbu.shape[0] != nsbu:
raise Exception('inconsistent dimension nsbu, regarding idxsbu, usbu.')
dims.nsbu = nsbu
nsh = constraints.idxsh.shape[0]
if is_empty(constraints.lsh):
constraints.lsh = np.zeros((nsh,))
elif constraints.lsh.shape[0] != nsh:
raise Exception('inconsistent dimension nsh, regarding idxsh, lsh.')
if is_empty(constraints.ush):
constraints.ush = np.zeros((nsh,))
elif constraints.ush.shape[0] != nsh:
raise Exception('inconsistent dimension nsh, regarding idxsh, ush.')
dims.nsh = nsh
nsphi = constraints.idxsphi.shape[0]
if is_empty(constraints.lsphi):
constraints.lsphi = np.zeros((nsphi,))
elif constraints.lsphi.shape[0] != nsphi:
raise Exception('inconsistent dimension nsphi, regarding idxsphi, lsphi.')
if is_empty(constraints.usphi):
constraints.usphi = np.zeros((nsphi,))
elif constraints.usphi.shape[0] != nsphi:
raise Exception('inconsistent dimension nsphi, regarding idxsphi, usphi.')
dims.nsphi = nsphi
nsg = constraints.idxsg.shape[0]
if is_empty(constraints.lsg):
constraints.lsg = np.zeros((nsg,))
elif constraints.lsg.shape[0] != nsg:
raise Exception('inconsistent dimension nsg, regarding idxsg, lsg.')
if is_empty(constraints.usg):
constraints.usg = np.zeros((nsg,))
elif constraints.usg.shape[0] != nsg:
raise Exception('inconsistent dimension nsg, regarding idxsg, usg.')
dims.nsg = nsg
ns = nsbx + nsbu + nsh + nsg + nsphi
wrong_field = ""
if cost.Zl.shape[0] != ns:
wrong_field = "Zl"
dim = cost.Zl.shape[0]
elif cost.Zu.shape[0] != ns:
wrong_field = "Zu"
dim = cost.Zu.shape[0]
elif cost.zl.shape[0] != ns:
wrong_field = "zl"
dim = cost.zl.shape[0]
elif cost.zu.shape[0] != ns:
wrong_field = "zu"
dim = cost.zu.shape[0]
if wrong_field != "":
raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \n\t'\
+ f'Detected ns = {ns} = nsbx + nsbu + nsg + nsh + nsphi.\n\t'\
+ f'With nsbx = {nsbx}, nsbu = {nsbu}, nsg = {nsg}, nsh = {nsh}, nsphi = {nsphi}')
dims.ns = ns
nsbx_e = constraints.idxsbx_e.shape[0]
if is_empty(constraints.lsbx_e):
constraints.lsbx_e = np.zeros((nsbx_e,))
elif constraints.lsbx_e.shape[0] != nsbx_e:
raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, lsbx_e.')
if is_empty(constraints.usbx_e):
constraints.usbx_e = np.zeros((nsbx_e,))
elif constraints.usbx_e.shape[0] != nsbx_e:
raise Exception('inconsistent dimension nsbx_e, regarding idxsbx_e, usbx_e.')
dims.nsbx_e = nsbx_e
nsh_e = constraints.idxsh_e.shape[0]
if is_empty(constraints.lsh_e):
constraints.lsh_e = np.zeros((nsh_e,))
elif constraints.lsh_e.shape[0] != nsh_e:
raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, lsh_e.')
if is_empty(constraints.ush_e):
constraints.ush_e = np.zeros((nsh_e,))
elif constraints.ush_e.shape[0] != nsh_e:
raise Exception('inconsistent dimension nsh_e, regarding idxsh_e, ush_e.')
dims.nsh_e = nsh_e
nsg_e = constraints.idxsg_e.shape[0]
if is_empty(constraints.lsg_e):
constraints.lsg_e = np.zeros((nsg_e,))
elif constraints.lsg_e.shape[0] != nsg_e:
raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, lsg_e.')
if is_empty(constraints.usg_e):
constraints.usg_e = np.zeros((nsg_e,))
elif constraints.usg_e.shape[0] != nsg_e:
raise Exception('inconsistent dimension nsg_e, regarding idxsg_e, usg_e.')
dims.nsg_e = nsg_e
nsphi_e = constraints.idxsphi_e.shape[0]
if is_empty(constraints.lsphi_e):
constraints.lsphi_e = np.zeros((nsphi_e,))
elif constraints.lsphi_e.shape[0] != nsphi_e:
raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, lsphi_e.')
if is_empty(constraints.usphi_e):
constraints.usphi_e = np.zeros((nsphi_e,))
elif constraints.usphi_e.shape[0] != nsphi_e:
raise Exception('inconsistent dimension nsphi_e, regarding idxsphi_e, usphi_e.')
dims.nsphi_e = nsphi_e
# terminal
ns_e = nsbx_e + nsh_e + nsg_e + nsphi_e
wrong_field = ""
if cost.Zl_e.shape[0] != ns_e:
wrong_field = "Zl_e"
dim = cost.Zl_e.shape[0]
elif cost.Zu_e.shape[0] != ns_e:
wrong_field = "Zu_e"
dim = cost.Zu_e.shape[0]
elif cost.zl_e.shape[0] != ns_e:
wrong_field = "zl_e"
dim = cost.zl_e.shape[0]
elif cost.zu_e.shape[0] != ns_e:
wrong_field = "zu_e"
dim = cost.zu_e.shape[0]
if wrong_field != "":
raise Exception(f'Inconsistent size for field {wrong_field}, with dimension {dim}, \n\t'\
+ f'Detected ns_e = {ns_e} = nsbx_e + nsg_e + nsh_e + nsphi_e.\n\t'\
+ f'With nsbx_e = {nsbx_e}, nsg_e = {nsg_e}, nsh_e = {nsh_e}, nsphi_e = {nsphi_e}')
dims.ns_e = ns_e
# discretization
if is_empty(opts.time_steps) and is_empty(opts.shooting_nodes):
# uniform discretization
opts.time_steps = opts.tf / dims.N * np.ones((dims.N,))
elif not is_empty(opts.shooting_nodes):
if np.shape(opts.shooting_nodes)[0] != dims.N+1:
raise Exception('inconsistent dimension N, regarding shooting_nodes.')
time_steps = np.zeros((dims.N,))
for i in range(dims.N):
time_steps[i] = opts.shooting_nodes[i+1] - opts.shooting_nodes[i]
opts.time_steps = time_steps
elif (not is_empty(opts.time_steps)) and (not is_empty(opts.shooting_nodes)):
Exception('Please provide either time_steps or shooting_nodes for nonuniform discretization')
tf = np.sum(opts.time_steps)
if (tf - opts.tf) / tf > 1e-15:
raise Exception(f'Inconsistent discretization: {opts.tf}'\
f' = tf != sum(opts.time_steps) = {tf}.')
def get_ocp_nlp_layout():
current_module = sys.modules[__name__]
acados_path = os.path.dirname(current_module.__file__)
with open(acados_path + '/acados_layout.json', 'r') as f:
ocp_nlp_layout = json.load(f)
return ocp_nlp_layout
def ocp_formulation_json_dump(acados_ocp, json_file='acados_ocp_nlp.json'):
# Load acados_ocp_nlp structure description
ocp_layout = get_ocp_nlp_layout()
# Copy input ocp object dictionary
ocp_nlp_dict = dict(deepcopy(acados_ocp).__dict__)
# TODO: maybe make one funciton with formatting
for acados_struct, v in ocp_layout.items():
# skip non dict attributes
if not isinstance(v, dict): continue
# setattr(ocp_nlp, acados_struct, dict(getattr(acados_ocp, acados_struct).__dict__))
# Copy ocp object attributes dictionaries
ocp_nlp_dict[acados_struct]=dict(getattr(acados_ocp, acados_struct).__dict__)
ocp_nlp_dict = format_class_dict(ocp_nlp_dict)
# strip symbolics
ocp_nlp_dict['model'] = acados_model_strip_casadi_symbolics(ocp_nlp_dict['model'])
# strip shooting_nodes
ocp_nlp_dict['solver_options'].pop('shooting_nodes', None)
dims_dict = acados_class2dict(acados_ocp.dims)
ocp_check_against_layout(ocp_nlp_dict, dims_dict)
with open(json_file, 'w') as f:
json.dump(ocp_nlp_dict, f, default=np_array_to_list, indent=4, sort_keys=True)
def ocp_formulation_json_load(json_file='acados_ocp_nlp.json'):
# Load acados_ocp_nlp structure description
ocp_layout = get_ocp_nlp_layout()
with open(json_file, 'r') as f:
ocp_nlp_json = json.load(f)
ocp_nlp_dict = json2dict(ocp_nlp_json, ocp_nlp_json['dims'])
# Instantiate AcadosOcp object
acados_ocp = AcadosOcp()
# load class dict
acados_ocp.__dict__ = ocp_nlp_dict
# laod class attributes dict, dims, constraints, etc
for acados_struct, v in ocp_layout.items():
# skip non dict attributes
if not isinstance(v, dict): continue
acados_attribute = getattr(acados_ocp, acados_struct)
acados_attribute.__dict__ = ocp_nlp_dict[acados_struct]
setattr(acados_ocp, acados_struct, acados_attribute)
return acados_ocp
def ocp_generate_external_functions(acados_ocp, model):
model = make_model_consistent(model)
if acados_ocp.solver_options.integrator_type == 'ERK':
# explicit model -- generate C code
generate_c_code_explicit_ode(model)
elif acados_ocp.solver_options.integrator_type == 'IRK':
# implicit model -- generate C code
opts = dict(generate_hess=1)
generate_c_code_implicit_ode(model, opts)
elif acados_ocp.solver_options.integrator_type == 'GNSF':
generate_c_code_gnsf(model)
else:
raise Exception("ocp_generate_external_functions: unknown integrator type.")
if acados_ocp.solver_options.hessian_approx == 'EXACT':
opts = dict(generate_hess=1)
else:
opts = dict(generate_hess=0)
if acados_ocp.dims.nphi > 0 or acados_ocp.dims.nh > 0:
generate_c_code_constraint(model, model.name, False, opts)
if acados_ocp.dims.nphi_e > 0 or acados_ocp.dims.nh_e > 0:
generate_c_code_constraint(model, model.name, True, opts)
# dummy matrices
if not acados_ocp.cost.cost_type == 'LINEAR_LS':
acados_ocp.cost.Vx = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nx))
acados_ocp.cost.Vu = np.zeros((acados_ocp.dims.ny, acados_ocp.dims.nu))
if not acados_ocp.cost.cost_type_e == 'LINEAR_LS':
acados_ocp.cost.Vx_e = np.zeros((acados_ocp.dims.ny_e, acados_ocp.dims.nx))
if acados_ocp.cost.cost_type == 'NONLINEAR_LS':
generate_c_code_nls_cost(model, model.name, False)
elif acados_ocp.cost.cost_type == 'EXTERNAL':
generate_c_code_external_cost(model, False)
if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':
generate_c_code_nls_cost(model, model.name, True)
elif acados_ocp.cost.cost_type_e == 'EXTERNAL':
generate_c_code_external_cost(model, True)
def ocp_render_templates(acados_ocp, json_file):
name = acados_ocp.model.name
# setting up loader and environment
json_path = '{cwd}/{json_file}'.format(
cwd=os.getcwd(),
json_file=json_file)
if not os.path.exists(json_path):
raise Exception('{} not found!'.format(json_path))
template_dir = 'c_generated_code/'
## Render templates
in_file = 'main.in.c'
out_file = 'main_{}.c'.format(name)
render_template(in_file, out_file, template_dir, json_path)
in_file = 'acados_solver.in.c'
out_file = 'acados_solver_{}.c'.format(name)
render_template(in_file, out_file, template_dir, json_path)
in_file = 'acados_solver.in.h'
out_file = 'acados_solver_{}.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
in_file = 'Makefile.in'
out_file = 'Makefile'
render_template(in_file, out_file, template_dir, json_path)
in_file = 'acados_solver_sfun.in.c'
out_file = 'acados_solver_sfunction_{}.c'.format(name)
render_template(in_file, out_file, template_dir, json_path)
in_file = 'make_sfun.in.m'
out_file = 'make_sfun.m'
render_template(in_file, out_file, template_dir, json_path)
in_file = 'acados_sim_solver.in.c'
out_file = 'acados_sim_solver_{}.c'.format(name)
render_template(in_file, out_file, template_dir, json_path)
in_file = 'acados_sim_solver.in.h'
out_file = 'acados_sim_solver_{}.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
## folder model
template_dir = 'c_generated_code/{}_model/'.format(name)
in_file = 'model.in.h'
out_file = '{}_model.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# constraints on convex over nonlinear function
if acados_ocp.constraints.constr_type == 'BGP' and acados_ocp.dims.nphi > 0:
# constraints on outer function
template_dir = 'c_generated_code/{}_constraints/'.format(name)
in_file = 'phi_constraint.in.h'
out_file = '{}_phi_constraint.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# terminal constraints on convex over nonlinear function
if acados_ocp.constraints.constr_type_e == 'BGP' and acados_ocp.dims.nphi_e > 0:
# terminal constraints on outer function
template_dir = 'c_generated_code/{}_constraints/'.format(name)
in_file = 'phi_e_constraint.in.h'
out_file = '{}_phi_e_constraint.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# nonlinear constraints
if acados_ocp.constraints.constr_type == 'BGH' and acados_ocp.dims.nh > 0:
template_dir = 'c_generated_code/{}_constraints/'.format(name)
in_file = 'h_constraint.in.h'
out_file = '{}_h_constraint.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# terminal nonlinear constraints
if acados_ocp.constraints.constr_type_e == 'BGH' and acados_ocp.dims.nh_e > 0:
template_dir = 'c_generated_code/{}_constraints/'.format(name)
in_file = 'h_e_constraint.in.h'
out_file = '{}_h_e_constraint.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# nonlinear cost function
if acados_ocp.cost.cost_type == 'NONLINEAR_LS':
template_dir = 'c_generated_code/{}_cost/'.format(name)
in_file = 'cost_y_fun.in.h'
out_file = '{}_cost_y_fun.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# terminal nonlinear cost function
if acados_ocp.cost.cost_type_e == 'NONLINEAR_LS':
template_dir = 'c_generated_code/{}_cost/'.format(name)
in_file = 'cost_y_e_fun.in.h'
out_file = '{}_cost_y_e_fun.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# external cost
if acados_ocp.cost.cost_type == 'EXTERNAL':
template_dir = 'c_generated_code/{}_cost/'.format(name)
in_file = 'external_cost.in.h'
out_file = '{}_external_cost.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
# external cost - terminal
if acados_ocp.cost.cost_type_e == 'EXTERNAL':
template_dir = 'c_generated_code/{}_cost/'.format(name)
in_file = 'external_cost_e.in.h'
out_file = '{}_external_cost_e.h'.format(name)
render_template(in_file, out_file, template_dir, json_path)
class AcadosOcpSolver:
"""
class to interact with the acados ocp solver C object
"""
def __init__(self, acados_ocp, json_file='acados_ocp_nlp.json'):
self.solver_created = False
model = acados_ocp.model
# make dims consistent
make_ocp_dims_consistent(acados_ocp)
if acados_ocp.solver_options.integrator_type == 'GNSF':
set_up_imported_gnsf_model(acados_ocp)
# set integrator time automatically
acados_ocp.solver_options.Tsim = acados_ocp.solver_options.time_steps[0]
# generate external functions
ocp_generate_external_functions(acados_ocp, model)
# dump to json
ocp_formulation_json_dump(acados_ocp, json_file)
# render templates
ocp_render_templates(acados_ocp, json_file)
## Compile solver
os.chdir('c_generated_code')
os.system('make clean_ocp_shared_lib')
os.system('make ocp_shared_lib')
os.chdir('..')
self.shared_lib_name = 'c_generated_code/libacados_ocp_solver_' + model.name + '.so'
# get
self.shared_lib = CDLL(self.shared_lib_name)
self.shared_lib.acados_create()
self.solver_created = True
self.shared_lib.acados_get_nlp_opts.restype = c_void_p
self.nlp_opts = self.shared_lib.acados_get_nlp_opts()
self.shared_lib.acados_get_nlp_dims.restype = c_void_p
self.nlp_dims = self.shared_lib.acados_get_nlp_dims()
self.shared_lib.acados_get_nlp_config.restype = c_void_p
self.nlp_config = self.shared_lib.acados_get_nlp_config()
self.shared_lib.acados_get_nlp_out.restype = c_void_p
self.nlp_out = self.shared_lib.acados_get_nlp_out()
self.shared_lib.acados_get_nlp_in.restype = c_void_p
self.nlp_in = self.shared_lib.acados_get_nlp_in()
self.shared_lib.acados_get_nlp_solver.restype = c_void_p
self.nlp_solver = self.shared_lib.acados_get_nlp_solver()
self.acados_ocp = acados_ocp
def solve(self):
"""
solve the ocp with current input
"""
status = self.shared_lib.acados_solve()
return status
def get(self, stage_, field_):
"""
get the last solution of the solver:
:param stage: integer corresponding to shooting node
:param field_: string in ['x', 'u', 'z', 'pi', 'lam', 't', 'sl', 'su',]
.. note:: regarding lam, t: \n
the inequalities are internally organized in the following order: \n
[ lbu lbx lg lh lphi ubu ubx ug uh uphi; \n
lsbu lsbx lsg lsh lsphi usbu usbx usg ush usphi]
.. note:: pi: multipliers for dynamics equality constraints \n
lam: multipliers for inequalities \n
t: slack variables corresponding to evaluation of all inequalities (at the solution) \n
sl: slack variables of soft lower inequality constraints \n
su: slack variables of soft upper inequality constraints \n
"""
out_fields = ['x', 'u', 'z', 'pi', 'lam', 't']
mem_fields = ['sl', 'su']
field = field_
field = field.encode('utf-8')
if (field_ not in out_fields + mem_fields):
raise Exception('AcadosOcpSolver.get(): {} is an invalid argument.\
\n Possible values are {}. Exiting.'.format(field_, out_fields + mem_fields))
self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p]
self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int
dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage_, field)
out = np.ascontiguousarray(np.zeros((dims,)), dtype=np.float64)
out_data = cast(out.ctypes.data, POINTER(c_double))
if (field_ in out_fields):
self.shared_lib.ocp_nlp_out_get.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_out_get(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage_, field, out_data)
elif field_ in mem_fields:
self.shared_lib.ocp_nlp_get_at_stage.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_get_at_stage(self.nlp_config, \
self.nlp_dims, self.nlp_solver, stage_, field, out_data)
return out
def print_statistics(self):
stat = self.get_stats("statistics")
if self.acados_ocp.solver_options.nlp_solver_type == 'SQP':
print('\niter\tres_stat\tres_eq\t\tres_ineq\tres_comp\tqp_stat\tqp_iter')
if stat.shape[0]>7:
print('\tqp_res_stat\tqp_res_eq\tqp_res_ineq\tqp_res_comp')
for jj in range(stat.shape[1]):
print('{:d}\t{:e}\t{:e}\t{:e}\t{:e}\t{:d}\t{:d}'.format( \
int(stat[0][jj]), stat[1][jj], stat[2][jj], \
stat[3][jj], stat[4][jj], int(stat[5][jj]), int(stat[6][jj])))
if stat.shape[0]>7:
print('\t{:e}\t{:e}\t{:e}\t{:e}'.format( \
stat[7][jj], stat[8][jj], stat[9][jj], stat[10][jj]))
print('\n')
elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':
print('\niter\tqp_stat\tqp_iter')
if stat.shape[0]>3:
print('\tqp_res_stat\tqp_res_eq\tqp_res_ineq\tqp_res_comp')
for jj in range(stat.shape[1]):
print('{:d}\t{:d}\t{:d}'.format( int(stat[0][jj]), int(stat[1][jj]), int(stat[2][jj])))
if stat.shape[0]>3:
print('\t{:e}\t{:e}\t{:e}\t{:e}'.format( \
stat[3][jj], stat[4][jj], stat[5][jj], stat[6][jj]))
print('\n')
return
def get_stats(self, field_):
"""
get the information of the last solver call:
:param field_: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter']
"""
fields = ['time_tot', # total cpu time previous call
'time_lin', # cpu time for linearization
'time_sim', # cpu time for integrator
'time_sim_ad', # cpu time for integrator contribution of external function calls
'time_sim_la', # cpu time for integrator contribution of linear algebra
'time_qp', # cpu time qp solution
'time_qp_solver_call', # cpu time inside qp solver (without converting the QP)
'time_qp_xcond',
'time_reg', # cpu time regularization
'sqp_iter', # number of SQP iterations
'statistics', # table with info about last iteration
'stat_m',
'stat_n',
]
field = field_
field = field.encode('utf-8')
if (field_ not in fields):
raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\
\n Possible values are {}. Exiting.'.format(fields, fields))
if field_ in ['sqp_iter', 'stat_m', 'stat_n']:
out = np.ascontiguousarray(np.zeros((1,)), dtype=np.int64)
out_data = cast(out.ctypes.data, POINTER(c_int64))
elif field_ == 'statistics':
sqp_iter = self.get_stats("sqp_iter")
stat_m = self.get_stats("stat_m")
stat_n = self.get_stats("stat_n")
min_size = min([stat_m, sqp_iter+1])
out = np.ascontiguousarray(
np.zeros( (stat_n[0]+1, min_size[0]) ), dtype=np.float64)
out_data = cast(out.ctypes.data, POINTER(c_double))
else:
out = np.ascontiguousarray(np.zeros((1,)), dtype=np.float64)
out_data = cast(out.ctypes.data, POINTER(c_double))
self.shared_lib.ocp_nlp_get.argtypes = [c_void_p, c_void_p, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, out_data)
return out
# Note: this function should not be used anymore, better use cost_set, constraints_set
def set(self, stage_, field_, value_):
cost_fields = ['y_ref', 'yref']
constraints_fields = ['lbx', 'ubx', 'lbu', 'ubu']
out_fields = ['x', 'u', 'pi', 'lam', 't']
# cast value_ to avoid conversion issues
value_ = value_.astype(float)
field = field_
field = field.encode('utf-8')
stage = c_int(stage_)
# treat parameters separately
if field_ is 'p':
self.shared_lib.acados_update_params.argtypes = [c_int, POINTER(c_double)]
self.shared_lib.acados_update_params.restype = c_int
value_data = cast(value_.ctypes.data, POINTER(c_double))
self.shared_lib.acados_update_params(stage, value_data, value_.shape[0])
else:
if field_ not in constraints_fields + cost_fields + out_fields:
raise Exception("AcadosOcpSolver.set(): {} is not a valid argument.\
\nPossible values are {}. Exiting.".format(field, \
constraints_fields + cost_fields + out_fields + ['p']))
self.shared_lib.ocp_nlp_dims_get_from_attr.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p]
self.shared_lib.ocp_nlp_dims_get_from_attr.restype = c_int
dims = self.shared_lib.ocp_nlp_dims_get_from_attr(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage_, field)
if value_.shape[0] != dims:
msg = 'AcadosOcpSolver.set(): mismatching dimension for field "{}" '.format(field_)
msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0])
raise Exception(msg)
value_data = cast(value_.ctypes.data, POINTER(c_double))
value_data_p = cast((value_data), c_void_p)
if field_ in constraints_fields:
self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
elif field_ in cost_fields:
self.shared_lib.ocp_nlp_cost_model_set.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
elif field_ in out_fields:
self.shared_lib.ocp_nlp_out_set.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_out_set(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage, field, value_data_p)
return
def cost_set(self, stage_, field_, value_):
"""
set numerical data in the cost module of the solver:
:param stage_: integer corresponding to shooting node
:param field_: string, e.g. 'yref', 'W', 'ext_cost_num_hess'
:param value_: of appropriate size
"""
# cast value_ to avoid conversion issues
value_ = value_.astype(float)
field = field_
field = field.encode('utf-8')
stage = c_int(stage_)
self.shared_lib.ocp_nlp_cost_dims_get_from_attr.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]
self.shared_lib.ocp_nlp_cost_dims_get_from_attr.restype = c_int
dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)
dims_data = cast(dims.ctypes.data, POINTER(c_int))
self.shared_lib.ocp_nlp_cost_dims_get_from_attr(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage_, field, dims_data)
value_shape = value_.shape
if len(value_shape) == 1:
value_shape = (value_shape[0], 0)
if value_shape != tuple(dims):
raise Exception('AcadosOcpSolver.cost_set(): mismatching dimension', \
' for field "{}" with dimension {} (you have {})'.format( \
field_, tuple(dims), value_shape))
value_data = cast(value_.ctypes.data, POINTER(c_double))
value_data_p = cast((value_data), c_void_p)
self.shared_lib.ocp_nlp_cost_model_set.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_cost_model_set(self.nlp_config, \
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
return
def constraints_set(self, stage_, field_, value_):
"""
set numerical data in the constraint module of the solver:
Parameters:
:param stage_: integer corresponding to shooting node
:param field_: string, e.g. 'lbx'
:param value_: of appropriate size
"""
# cast value_ to avoid conversion issues
value_ = value_.astype(float)
field = field_
field = field.encode('utf-8')
stage = c_int(stage_)
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, POINTER(c_int)]
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr.restype = c_int
dims = np.ascontiguousarray(np.zeros((2,)), dtype=np.intc)
dims_data = cast(dims.ctypes.data, POINTER(c_int))
self.shared_lib.ocp_nlp_constraint_dims_get_from_attr(self.nlp_config, \
self.nlp_dims, self.nlp_out, stage_, field, dims_data)
value_shape = value_.shape
if len(value_shape) == 1:
value_shape = (value_shape[0], 0)
if value_shape != tuple(dims):
raise Exception('AcadosOcpSolver.constraints_set(): mismatching dimension' \
' for field "{}" with dimension {} (you have {})'.format(field_, tuple(dims), value_shape))
value_data = cast(value_.ctypes.data, POINTER(c_double))
value_data_p = cast((value_data), c_void_p)
self.shared_lib.ocp_nlp_constraints_model_set.argtypes = \
[c_void_p, c_void_p, c_void_p, c_int, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_constraints_model_set(self.nlp_config, \
self.nlp_dims, self.nlp_in, stage, field, value_data_p)
return
def options_set(self, field_, value_):
"""
set options of the solver:
Parameters:
:param field_: string, e.g. 'print_level', 'rti_phase', 'initialize_t_slacks', 'step_length'
:param value_: of type int, float
"""
int_fields = ['print_level', 'rti_phase', 'initialize_t_slacks']
double_fields = ['step_length']
string_fields = ['globalization']
if field_ in int_fields:
if not isinstance(value_, int):
raise Exception('solver option {} must be of type int. You have {}.'.format(field_, type(value_)))
else:
value_ctypes = c_int(value_)
elif field_ in double_fields:
if not isinstance(value_, float):
raise Exception('solver option {} must be of type float. You have {}.'.format(field_, type(value_)))
else:
value_ctypes = c_double(value_)
elif field_ in string_fields:
if not isinstance(value_, str):
raise Exception('solver option {} must be of type str. You have {}.'.format(field_, type(value_)))
else:
value_ctypes = value_.encode('utf-8')
if field_ == 'rti_phase':
if value_ < 0 or value_ > 2:
raise Exception('AcadosOcpSolver.solve(): argument \'rti_phase\' can '
'take only values 0, 1, 2 for SQP-RTI-type solvers')
if self.acados_ocp.solver_options.nlp_solver_type != 'SQP_RTI' and value_ > 0:
raise Exception('AcadosOcpSolver.solve(): argument \'rti_phase\' can '
'take only value 0 for SQP-type solvers')
field = field_
field = field.encode('utf-8')
if field_ in string_fields:
self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \
[c_void_p, c_void_p, c_char_p, c_char_p]
self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \
self.nlp_opts, field, value_ctypes)
else:
self.shared_lib.ocp_nlp_solver_opts_set.argtypes = \
[c_void_p, c_void_p, c_char_p, c_void_p]
self.shared_lib.ocp_nlp_solver_opts_set(self.nlp_config, \
self.nlp_opts, field, byref(value_ctypes))
return
def __del__(self):
if self.solver_created:
self.shared_lib.acados_free()
del self.shared_lib
# NOTE: DLL cannot be easily unloaded!!!
# see https://stackoverflow.com/questions/359498/how-can-i-unload-a-dll-using-ctypes-in-python
# while isLoaded(self.shared_lib_name):
# dlclose(handle)
| 40.525505 | 175 | 0.644682 | 17,462 | 0.414705 | 0 | 0 | 0 | 0 | 0 | 0 | 12,470 | 0.29615 |
07da9e40651ed6fdc8a2ae109d81653125f5c378 | 5,128 | py | Python | noise/estimation/PCA/analyticNoiseEstimation_PCA.py | MaikWischow/Camera-Condition-Monitoring | 910f9192d6309a6803ab76c346269fa5029c38e6 | [
"MIT"
] | 3 | 2021-12-10T14:52:23.000Z | 2022-03-22T08:48:08.000Z | noise/estimation/PCA/analyticNoiseEstimation_PCA.py | MaikWischow/Camera-Condition-Monitoring | 910f9192d6309a6803ab76c346269fa5029c38e6 | [
"MIT"
] | null | null | null | noise/estimation/PCA/analyticNoiseEstimation_PCA.py | MaikWischow/Camera-Condition-Monitoring | 910f9192d6309a6803ab76c346269fa5029c38e6 | [
"MIT"
] | 2 | 2021-12-13T08:12:52.000Z | 2022-01-17T14:00:48.000Z | import numpy as np
import cv2
import sys
import os
import glob
def im2patch(im, pch_size, stride=1):
'''
Transform image to patches.
Input:
im: 3 x H x W or 1 X H x W image, numpy format
pch_size: (int, int) tuple or integer
stride: (int, int) tuple or integer
'''
if isinstance(pch_size, tuple):
pch_H, pch_W = pch_size
elif isinstance(pch_size, int):
pch_H = pch_W = pch_size
else:
sys.exit('The input of pch_size must be a integer or a int tuple!')
if isinstance(stride, tuple):
stride_H, stride_W = stride
elif isinstance(stride, int):
stride_H = stride_W = stride
else:
sys.exit('The input of stride must be a integer or a int tuple!')
C, H, W = im.shape
num_H = len(range(0, H-pch_H+1, stride_H))
num_W = len(range(0, W-pch_W+1, stride_W))
num_pch = num_H * num_W
pch = np.zeros((C, pch_H*pch_W, num_pch), dtype=im.dtype)
kk = 0
for ii in range(pch_H):
for jj in range(pch_W):
temp = im[:, ii:H-pch_H+ii+1:stride_H, jj:W-pch_W+jj+1:stride_W]
pch[:, kk, :] = temp.reshape((C, num_pch))
kk += 1
return pch.reshape((C, pch_H, pch_W, num_pch))
def noise_estimate(im, pch_size=8):
'''
Implement of noise level estimation of the following paper:
Chen G , Zhu F , Heng P A . An Efficient Statistical Method for Image Noise Level Estimation[C]// 2015 IEEE International Conference
on Computer Vision (ICCV). IEEE Computer Society, 2015.
Input:
im: the noise image, H x W x 3 or H x W numpy tensor, range [0,1]
pch_size: patch_size
Output:
noise_level: the estimated noise level
'''
if im.ndim == 3:
im = im.transpose((2, 0, 1))
else:
im = np.expand_dims(im, axis=0)
# image to patch
pch = im2patch(im, pch_size, 3) # C x pch_size x pch_size x num_pch tensor
num_pch = pch.shape[3]
pch = pch.reshape((-1, num_pch)) # d x num_pch matrix
d = pch.shape[0]
mu = pch.mean(axis=1, keepdims=True) # d x 1
X = pch - mu
sigma_X = np.matmul(X, X.transpose()) / num_pch
sig_value, _ = np.linalg.eigh(sigma_X)
sig_value.sort()
for ii in range(-1, -d-1, -1):
tau = np.mean(sig_value[:ii])
if np.sum(sig_value[:ii]>tau) == np.sum(sig_value[:ii] < tau):
return np.sqrt(tau)
def run(imgPath, patchSize, internalNumPatches, dirOut, saveResults=True):
"""
Estimates the standard deviation of (additive white gaussian) noise of image patches.
The noise is estimated patch by patch.
Based on: "An Efficient Statistical Method for Image Noise Level Estimation" (2015)
:param imgPath: Path to the input image.
:param patchSize: Image patch size.
:param internalNumPatches: Internal number of sub-image-patches.
:param dirOut: Directory where to save the noise estimation results.
:param saveResults: Whether to save the estimation results or not.
:return: None
"""
# Load image
img = np.array(cv2.imread(imgPath))
try:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = img / 255.0
h, w = img.shape
psize = min(min(patchSize, h), w)
psize -= psize % 2
patch_step = psize
shift_factor = 2
# Result array
estimatedNoiseMap = np.zeros([h, w], dtype=np.int8)
rangex = range(0, w, patch_step)
rangey = range(0, h, patch_step)
for start_x in rangex:
for start_y in rangey:
end_x = start_x + psize
end_y = start_y + psize
if end_x > w:
end_x = w
end_x = shift_factor * ((end_x) // shift_factor)
start_x = end_x - psize
if end_y > h:
end_y = h
end_y = shift_factor * ((end_y) // shift_factor)
start_y = end_y - psize
tileM = img[start_y:end_y, start_x:end_x]
h_, w_ = tileM.shape
sigma = noise_estimate(tileM, internalNumPatches) * 255.0
estimatedNoiseMap[start_y :start_y + h_, start_x : start_x + w_] = sigma
if saveResults:
if dirOut is not None:
imgName = imgPath.split(os.sep)[-1].split(".")[0]
dirOut = os.path.join(dirOut)
if not os.path.exists(dirOut):
os.makedirs(dirOut)
noiseMapPath = os.path.join(dirOut, imgName + ".npz")
if not os.path.exists(noiseMapPath):
np.savez_compressed(noiseMapPath, estimatedNoiseMap)
return estimatedNoiseMap
except:
return None
# Example
# if __name__ == '__main__':
# dirIn = r"../../../data/udacity/img/GT"
# dirOut = r"../../../data/udacity/labels_noise_patchwise/PCA"
# imgFileEnding = ".jpg"
# for imgPath in glob.glob(os.path.join(dirIn, "*" + imgFileEnding)):
# run(imgPath, 128, 8, dirOut) | 34.884354 | 136 | 0.576248 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,712 | 0.333853 |
07dab8d1754575bc1f3f83e4e0cadea3c8dcd3af | 8,104 | py | Python | src/biotite/application/application.py | claudejrogers/biotite | 3635bc9071506ecb85ddd9b1dbe6a430295e060e | [
"BSD-3-Clause"
] | null | null | null | src/biotite/application/application.py | claudejrogers/biotite | 3635bc9071506ecb85ddd9b1dbe6a430295e060e | [
"BSD-3-Clause"
] | null | null | null | src/biotite/application/application.py | claudejrogers/biotite | 3635bc9071506ecb85ddd9b1dbe6a430295e060e | [
"BSD-3-Clause"
] | null | null | null | # This source code is part of the Biotite package and is distributed
# under the 3-Clause BSD License. Please see 'LICENSE.rst' for further
# information.
__name__ = "biotite.application"
__author__ = "Patrick Kunzmann"
__all__ = ["Application", "AppStateError", "TimeoutError", "VersionError",
"AppState", "requires_state"]
import abc
import time
from functools import wraps
from enum import Flag, auto
class AppState(Flag):
"""
This enum type represents the app states of an application.
"""
CREATED = auto()
RUNNING = auto()
FINISHED = auto()
JOINED = auto()
CANCELLED = auto()
def requires_state(app_state):
"""
A decorator for methods of :class:`Application` subclasses that
raises an :class:`AppStateError` in case the method is called, when
the :class:`Application` is not in the specified :class:`AppState`
`app_state`.
Parameters
----------
app_state : AppState
The required app state.
Examples
--------
Raises :class:`AppStateError` when `function` is called,
if :class:`Application` is not in one of the specified states:
>>> @requires_state(AppState.RUNNING | AppState.FINISHED)
... def function(self):
... pass
"""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
# First parameter of method is always 'self'
instance = args[0]
if not instance._state & app_state:
raise AppStateError(
f"The application is in {instance.get_app_state()} state, "
f"but {app_state} state is required"
)
return func(*args, **kwargs)
return wrapper
return decorator
class Application(metaclass=abc.ABCMeta):
"""
This class is a wrapper around an external piece of runnable
software in any sense. Subclasses of this abstract base class
specify the respective kind of software and the way of interacting
with it.
Every :class:`Application` runs through a different app states
(instances of enum :class:`AppState`) from its creation until its
termination:
Directly after its instantiation the app is in the *CREATED* state.
In this state further parameters can be set for the application run.
After the user calls the :func:`start()` method, the app state is
set to *RUNNING* and the :class:`Application` type specific
:func:`run()` method is called.
When the application finishes the AppState changes to *FINISHED*.
This is checked via the :class:`Application` type specific
:func:`is_finished()` method.
The user can now call the :func:`join()` method, concluding the
application in the *JOINED* state and making the results of the
application accessible by executing the :class:`Application`
type specific :func:`evaluate()` method.
Furthermore this executes the :class:`Application` type specific
:func:`clean_up()` method.
:func:`join()` can even be called in the *RUNNING* state:
This will constantly check :func:`is_finished()` and will directly
go into the *JOINED* state as soon as the application reaches the
*FINISHED* state.
Calling the :func:`cancel()` method while the application is
*RUNNING* or *FINISHED* leaves the application in the *CANCELLED*
state.
This triggers the :func:`clean_up()` method, too, but there are no
accessible results.
If a method is called in an unsuitable app state, an
:class:`AppStateError` is called.
The application run behaves like an additional thread: Between the
call of :func:`start()` and :func:`join()` other Python code can be
executed, while the application runs in the background.
"""
def __init__(self):
self._state = AppState.CREATED
@requires_state(AppState.CREATED)
def start(self):
"""
Start the application run and set its state to *RUNNING*.
This can only be done from the *CREATED* state.
"""
self.run()
self._start_time = time.time()
self._state = AppState.RUNNING
@requires_state(AppState.RUNNING | AppState.FINISHED)
def join(self, timeout=None):
"""
Conclude the application run and set its state to *JOINED*.
This can only be done from the *RUNNING* or *FINISHED* state.
If the application is *FINISHED* the joining process happens
immediately, if otherwise the application is *RUNNING*, this
method waits until the application is *FINISHED*.
Parameters
----------
timeout : float, optional
If this parameter is specified, the :class:`Application`
only waits for finishing until this value (in seconds) runs
out.
After this time is exceeded a :class:`TimeoutError` is
raised and the application is cancelled.
Raises
------
TimeoutError
If the joining process exceeds the `timeout` value.
"""
time.sleep(self.wait_interval())
while self.get_app_state() != AppState.FINISHED:
if timeout is not None and time.time()-self._start_time > timeout:
self.cancel()
raise TimeoutError(
f"The application expired its timeout "
f"({timeout:.1f} s)"
)
else:
time.sleep(self.wait_interval())
time.sleep(self.wait_interval())
try:
self.evaluate()
except AppStateError:
raise
except:
self._state = AppState.CANCELLED
raise
else:
self._state = AppState.JOINED
self.clean_up()
@requires_state(AppState.RUNNING | AppState.FINISHED)
def cancel(self):
"""
Cancel the application when in *RUNNING* or *FINISHED* state.
"""
self._state = AppState.CANCELLED
self.clean_up()
def get_app_state(self):
"""
Get the current app state.
Returns
-------
app_state : AppState
The current app state.
"""
if self._state == AppState.RUNNING:
if self.is_finished():
self._state = AppState.FINISHED
return self._state
@abc.abstractmethod
def run(self):
"""
Commence the application run. Called in :func:`start()`.
PROTECTED: Override when inheriting.
"""
pass
@abc.abstractmethod
def is_finished(self):
"""
Check if the application has finished.
PROTECTED: Override when inheriting.
Returns
-------
finished : bool
True of the application has finished, false otherwise
"""
pass
@abc.abstractmethod
def wait_interval(self):
"""
The time interval of :func:`is_finished()` calls in the joining
process.
PROTECTED: Override when inheriting.
Returns
-------
interval : float
Time (in seconds) between calls of :func:`is_finished()` in
:func:`join()`
"""
pass
@abc.abstractmethod
def evaluate(self):
"""
Evaluate application results. Called in :func:`join()`.
PROTECTED: Override when inheriting.
"""
pass
def clean_up(self):
"""
Do clean up work after the application terminates.
PROTECTED: Optionally override when inheriting.
"""
pass
class AppStateError(Exception):
"""
Indicate that the application lifecycle was violated.
"""
pass
class TimeoutError(Exception):
"""
Indicate that the application's timeout expired.
"""
pass
class VersionError(Exception):
"""
Indicate that the application's version is invalid.
"""
pass | 31.169231 | 79 | 0.604516 | 6,536 | 0.806515 | 0 | 0 | 3,705 | 0.457182 | 0 | 0 | 5,525 | 0.681762 |
07db7794b6d98e1a7fc4b7e5727cce2c98e1474f | 8,162 | py | Python | python/paddle/tensor/attribute.py | douch/Paddle | 81c40722869935d6e897f4b1aeb6e6f67606188a | [
"Apache-2.0"
] | 1 | 2021-12-27T02:40:41.000Z | 2021-12-27T02:40:41.000Z | python/paddle/tensor/attribute.py | LiYuRio/Paddle | dbd6e2df9d074973b7ee177e2d6b96ed2318008e | [
"Apache-2.0"
] | null | null | null | python/paddle/tensor/attribute.py | LiYuRio/Paddle | dbd6e2df9d074973b7ee177e2d6b96ed2318008e | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from ..framework import core
from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype
# TODO: define functions to get tensor attributes
from ..fluid.layers import rank # noqa: F401
from ..fluid.layers import shape # noqa: F401
import paddle
from paddle import _C_ops
from paddle.static import Variable
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
__all__ = []
def _complex_to_real_dtype(dtype):
if dtype == core.VarDesc.VarType.COMPLEX64:
return core.VarDesc.VarType.FP32
elif dtype == core.VarDesc.VarType.COMPLEX128:
return core.VarDesc.VarType.FP64
else:
return dtype
def _real_to_complex_dtype(dtype):
if dtype == core.VarDesc.VarType.FP32:
return core.VarDesc.VarType.COMPLEX64
elif dtype == core.VarDesc.VarType.FP64:
return core.VarDesc.VarType.COMPLEX128
else:
return dtype
def is_complex(x):
"""Return whether x is a tensor of complex data type(complex64 or complex128).
Args:
x (Tensor): The input tensor.
Returns:
bool: True if the data type of the input is complex data type, otherwise false.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([1 + 2j, 3 + 4j])
print(paddle.is_complex(x))
# True
x = paddle.to_tensor([1.1, 1.2])
print(paddle.is_complex(x))
# False
x = paddle.to_tensor([1, 2, 3])
print(paddle.is_complex(x))
# False
"""
if not isinstance(x, (paddle.Tensor, paddle.static.Variable)):
raise TypeError("Expected Tensor, but received type of x: {}".format(
type(x)))
dtype = x.dtype
is_complex_dtype = (dtype == core.VarDesc.VarType.COMPLEX64 or
dtype == core.VarDesc.VarType.COMPLEX128)
return is_complex_dtype
def is_floating_point(x):
"""
Returns whether the dtype of `x` is one of paddle.float64, paddle.float32, paddle.float16, and paddle.bfloat16.
Args:
x (Tensor): The input tensor.
Returns:
bool: True if the dtype of `x` is floating type, otherwise false.
Examples:
.. code-block:: python
import paddle
x = paddle.arange(1., 5., dtype='float32')
y = paddle.arange(1, 5, dtype='int32')
print(paddle.is_floating_point(x))
# True
print(paddle.is_floating_point(y))
# False
"""
if not isinstance(x, (paddle.Tensor, paddle.static.Variable)):
raise TypeError("Expected Tensor, but received type of x: {}".format(
type(x)))
dtype = x.dtype
is_fp_dtype = (dtype == core.VarDesc.VarType.FP32 or
dtype == core.VarDesc.VarType.FP64 or
dtype == core.VarDesc.VarType.FP16 or
dtype == core.VarDesc.VarType.BF16)
return is_fp_dtype
def is_integer(x):
"""Return whether x is a tensor of integeral data type.
Args:
x (Tensor): The input tensor.
Returns:
bool: True if the data type of the input is integer data type, otherwise false.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([1 + 2j, 3 + 4j])
print(paddle.is_integer(x))
# False
x = paddle.to_tensor([1.1, 1.2])
print(paddle.is_integer(x))
# False
x = paddle.to_tensor([1, 2, 3])
print(paddle.is_integer(x))
# True
"""
if not isinstance(x, (paddle.Tensor, paddle.static.Variable)):
raise TypeError("Expected Tensor, but received type of x: {}".format(
type(x)))
dtype = x.dtype
is_int_dtype = (dtype == core.VarDesc.VarType.UINT8 or
dtype == core.VarDesc.VarType.INT8 or
dtype == core.VarDesc.VarType.INT16 or
dtype == core.VarDesc.VarType.INT32 or
dtype == core.VarDesc.VarType.INT64)
return is_int_dtype
def real(x, name=None):
"""
Returns a new tensor containing real values of the input tensor.
Args:
x (Tensor): the input tensor, its data type could be complex64 or complex128.
name (str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns:
Tensor: a tensor containing real values of the input tensor.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor(
[[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]])
# Tensor(shape=[2, 3], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
# [[(1+6j), (2+5j), (3+4j)],
# [(4+3j), (5+2j), (6+1j)]])
real_res = paddle.real(x)
# Tensor(shape=[2, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[1., 2., 3.],
# [4., 5., 6.]])
real_t = x.real()
# Tensor(shape=[2, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[1., 2., 3.],
# [4., 5., 6.]])
"""
if in_dygraph_mode():
return _C_ops.final_state_real(x)
if _in_legacy_dygraph():
return _C_ops.real(x)
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'real')
helper = LayerHelper('real', **locals())
out = helper.create_variable_for_type_inference(
dtype=_complex_to_real_dtype(helper.input_dtype()))
helper.append_op(type='real', inputs={'X': x}, outputs={'Out': out})
return out
def imag(x, name=None):
"""
Returns a new tensor containing imaginary values of input tensor.
Args:
x (Tensor): the input tensor, its data type could be complex64 or complex128.
name (str, optional): The default value is None. Normally there is no need for
user to set this property. For more information, please refer to :ref:`api_guide_Name` .
Returns:
Tensor: a tensor containing imaginary values of the input tensor.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor(
[[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]])
# Tensor(shape=[2, 3], dtype=complex64, place=CUDAPlace(0), stop_gradient=True,
# [[(1+6j), (2+5j), (3+4j)],
# [(4+3j), (5+2j), (6+1j)]])
imag_res = paddle.imag(x)
# Tensor(shape=[2, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[6., 5., 4.],
# [3., 2., 1.]])
imag_t = x.imag()
# Tensor(shape=[2, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=True,
# [[6., 5., 4.],
# [3., 2., 1.]])
"""
if in_dygraph_mode():
return _C_ops.final_state_imag(x)
if _in_legacy_dygraph():
return _C_ops.imag(x)
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'imag')
helper = LayerHelper('imag', **locals())
out = helper.create_variable_for_type_inference(
dtype=_complex_to_real_dtype(helper.input_dtype()))
helper.append_op(type='imag', inputs={'X': x}, outputs={'Out': out})
return out
| 33.178862 | 115 | 0.590787 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5,163 | 0.632566 |
07de8a38f3e34f5218e591e882773caeb8f88eba | 1,256 | py | Python | ocdsmerge/exceptions.py | open-contracting/ocds-merge | 80c7cb380d191c75f88feefd34b607bc0de13ee1 | [
"BSD-3-Clause"
] | 4 | 2017-09-06T06:14:09.000Z | 2019-07-05T13:11:40.000Z | ocdsmerge/exceptions.py | open-contracting/ocds-merge | 80c7cb380d191c75f88feefd34b607bc0de13ee1 | [
"BSD-3-Clause"
] | 20 | 2017-11-25T02:29:41.000Z | 2020-01-08T18:45:49.000Z | ocdsmerge/exceptions.py | open-contracting/ocds-merge | 80c7cb380d191c75f88feefd34b607bc0de13ee1 | [
"BSD-3-Clause"
] | 1 | 2018-11-07T14:05:12.000Z | 2018-11-07T14:05:12.000Z | class OCDSMergeError(Exception):
"""Base class for exceptions from within this package"""
class MissingDateKeyError(OCDSMergeError, KeyError):
"""Raised when a release is missing a 'date' key"""
def __init__(self, key, message):
self.key = key
self.message = message
def __str__(self):
return str(self.message)
class NonObjectReleaseError(OCDSMergeError, TypeError):
"""Raised when a release is not an object"""
class NullDateValueError(OCDSMergeError, TypeError):
"""Raised when a release has a null 'date' value"""
class NonStringDateValueError(OCDSMergeError, TypeError):
"""Raised when a release has a non-string 'date' value"""
class InconsistentTypeError(OCDSMergeError, TypeError):
"""Raised when a path is a literal and an object in different releases"""
class OCDSMergeWarning(UserWarning):
"""Base class for warnings from within this package"""
class DuplicateIdValueWarning(OCDSMergeWarning):
"""Used when at least two objects in the same array have the same value for the 'id' field"""
def __init__(self, path, id, message):
self.path = path
self.id = id
self.message = message
def __str__(self):
return str(self.message)
| 27.304348 | 97 | 0.700637 | 1,234 | 0.982484 | 0 | 0 | 0 | 0 | 0 | 0 | 479 | 0.381369 |
07dedb4b0df0aef6400f3a4ca8f79f22e2a6cabd | 2,990 | py | Python | appcodec.py | guardhunt/TelemterRC | 679f99b317ecc6cbef6e022ae861cde18594f6a0 | [
"MIT"
] | null | null | null | appcodec.py | guardhunt/TelemterRC | 679f99b317ecc6cbef6e022ae861cde18594f6a0 | [
"MIT"
] | null | null | null | appcodec.py | guardhunt/TelemterRC | 679f99b317ecc6cbef6e022ae861cde18594f6a0 | [
"MIT"
] | null | null | null | import evdev
import time
import struct
class appcodec():
def __init__(self):
self.device = evdev.InputDevice("/dev/input/event2")
self.capabilities = self.device.capabilities(verbose=True)
self.capaRAW = self.device.capabilities(absinfo=False)
self.config = {}
self.state = {}
def build(self):
"""build state dictionary for controller"""
#build config dictionary by code and name
for key, value in self.capabilities.items():
for element in value:
if type(element[0]) is tuple:
self.config[element[0][1]] = element[0][0]
elif type(element[0]) is list:
self.config[element[1]] = element[0][0]
elif ("SYN" in str(element[0])) or ("FF" in str(element[0])):
pass
else:
self.config[element[1]] = element[0]
#build state dictionary from raw codes
for code in self.capaRAW[1]:
self.state[self.config[code]] = 0
for code in self.capaRAW[3]:
self.state[self.config[code]] = 0
print("waiting for event")
for event in self.device.read_loop():
if event.type == evdev.ecodes.EV_KEY or event.type == evdev.ecodes.EV_ABS:
return(self.update_state(event))
def update_state(self, event):
self.state[self.config[event.code]] = event.value
buttons1_state = 0
buttons1_state = buttons1_state | self.state["BTN_A"]
buttons1_state = buttons1_state | self.state["BTN_B"] << 1
buttons1_state = buttons1_state | self.state["BTN_NORTH"] << 2
buttons1_state = buttons1_state | self.state["BTN_WEST"] << 3
buttons2_state = 0
buttons2_state = buttons2_state | self.state["BTN_START"]
buttons2_state = buttons2_state | self.state["BTN_MODE"] << 1
buttons2_state = buttons2_state | self.state["BTN_SELECT"] << 2
buttons2_state = buttons2_state | self.state["BTN_TR"] << 3
buttons2_state = buttons2_state | self.state["BTN_TL"] << 4
packet = struct.pack('6h2c', self.state["ABS_X"], self.state["ABS_Y"], self.state["ABS_RX"], self.state["ABS_RY"], self.state["ABS_HAT0X"], self.state["ABS_HAT0Y"], buttons1_state.to_bytes(1, byteorder="big"), buttons2_state.to_bytes(1, byteorder="big"))
return packet
def decode(self, packet):
buttons = []
state = packet[14:30]
state = struct.unpack('6h2B2c', state)
buttons1 = state[8]
buttons2 = state[9]
holder1 = '{0:06b}'.format(int.from_bytes(buttons1, byteorder="big"))
holder2 = '{0:05b}'.format(int.from_bytes(buttons2, byteorder="big"))
for i in holder1:
buttons.append(int(i))
for i in holder2:
buttons.append(int(i))
state = list(state[ :7]) + buttons
return state
| 37.375 | 262 | 0.585619 | 2,949 | 0.986288 | 0 | 0 | 0 | 0 | 0 | 0 | 357 | 0.119398 |
07dee507ce31e115b2b94a29d53cdc5c3d4bd0df | 2,316 | py | Python | scripts/examples/OpenMV/16-Codes/find_barcodes.py | jiskra/openmv | a0f321836f77f94d8118910598dcdb79eb784d58 | [
"MIT"
] | 1,761 | 2015-07-10T23:14:17.000Z | 2022-03-30T07:49:49.000Z | scripts/examples/OpenMV/16-Codes/find_barcodes.py | jiskra/openmv | a0f321836f77f94d8118910598dcdb79eb784d58 | [
"MIT"
] | 487 | 2015-07-07T23:21:20.000Z | 2022-03-30T17:13:22.000Z | scripts/examples/OpenMV/16-Codes/find_barcodes.py | jiskra/openmv | a0f321836f77f94d8118910598dcdb79eb784d58 | [
"MIT"
] | 882 | 2015-08-01T08:34:19.000Z | 2022-03-30T07:36:23.000Z | # Barcode Example
#
# This example shows off how easy it is to detect bar codes using the
# OpenMV Cam M7. Barcode detection does not work on the M4 Camera.
import sensor, image, time, math
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.VGA) # High Res!
sensor.set_windowing((640, 80)) # V Res of 80 == less work (40 for 2X the speed).
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must turn this off to prevent image washout...
sensor.set_auto_whitebal(False) # must turn this off to prevent image washout...
clock = time.clock()
# Barcode detection can run at the full 640x480 resolution of your OpenMV Cam's
# OV7725 camera module. Barcode detection will also work in RGB565 mode but at
# a lower resolution. That said, barcode detection requires a higher resolution
# to work well so it should always be run at 640x480 in grayscale...
def barcode_name(code):
if(code.type() == image.EAN2):
return "EAN2"
if(code.type() == image.EAN5):
return "EAN5"
if(code.type() == image.EAN8):
return "EAN8"
if(code.type() == image.UPCE):
return "UPCE"
if(code.type() == image.ISBN10):
return "ISBN10"
if(code.type() == image.UPCA):
return "UPCA"
if(code.type() == image.EAN13):
return "EAN13"
if(code.type() == image.ISBN13):
return "ISBN13"
if(code.type() == image.I25):
return "I25"
if(code.type() == image.DATABAR):
return "DATABAR"
if(code.type() == image.DATABAR_EXP):
return "DATABAR_EXP"
if(code.type() == image.CODABAR):
return "CODABAR"
if(code.type() == image.CODE39):
return "CODE39"
if(code.type() == image.PDF417):
return "PDF417"
if(code.type() == image.CODE93):
return "CODE93"
if(code.type() == image.CODE128):
return "CODE128"
while(True):
clock.tick()
img = sensor.snapshot()
codes = img.find_barcodes()
for code in codes:
img.draw_rectangle(code.rect())
print_args = (barcode_name(code), code.payload(), (180 * code.rotation()) / math.pi, code.quality(), clock.fps())
print("Barcode %s, Payload \"%s\", rotation %f (degrees), quality %d, FPS %f" % print_args)
if not codes:
print("FPS %f" % clock.fps())
| 35.090909 | 121 | 0.638169 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 814 | 0.351468 |
07df8ef3eee337410a8a9059f852b86e3f074f99 | 102 | py | Python | Python/factorial.py | devaslooper/Code-Overflow | d7d55ea0f5015bccb5c4100c4240464fcda8504a | [
"MIT"
] | null | null | null | Python/factorial.py | devaslooper/Code-Overflow | d7d55ea0f5015bccb5c4100c4240464fcda8504a | [
"MIT"
] | null | null | null | Python/factorial.py | devaslooper/Code-Overflow | d7d55ea0f5015bccb5c4100c4240464fcda8504a | [
"MIT"
] | null | null | null | n=int(input("Enter number "))
fact=1
for i in range(1,n+1):
fact=fact*i
print("Factorial is ",fact)
| 17 | 29 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 0.294118 |
07e01a30113159cdf23415b99ef5107294e27bab | 606 | py | Python | mundo 3/099.py | thiagofreitascarneiro/Curso-de-Python---Curso-em-Video | 0342e482780b5a1c6f78cddd51d9bfad785c79fa | [
"MIT"
] | 1 | 2021-08-04T13:21:22.000Z | 2021-08-04T13:21:22.000Z | mundo 3/099.py | thiagofreitascarneiro/Curso-de-Python---Curso-em-Video | 0342e482780b5a1c6f78cddd51d9bfad785c79fa | [
"MIT"
] | null | null | null | mundo 3/099.py | thiagofreitascarneiro/Curso-de-Python---Curso-em-Video | 0342e482780b5a1c6f78cddd51d9bfad785c79fa | [
"MIT"
] | null | null | null | import time
# O * é para desempacotar o paramêtro. Permite atribuir inumeros parametros.
def maior(* num):
contador = maior = 0
print('Analisando os valores passados...')
for v in num:
contador = contador + 1
print(f'{v} ', end='', flush=True)
time.sleep(0.3)
if contador == 1:
maior = v
else:
if v > maior:
maior = v
print(f'Foram informado o total de {len(num)}')
print(f'O maior valor informado foi {max(num)}')
print(30 * '-')
maior(2, 1, 7)
maior(5, 4, 7, 9, 2)
maior(1, 4, 7, 20, 2)
maior(0)
| 23.307692 | 76 | 0.544554 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 206 | 0.338816 |
07e0af37e19269400d7c0cf5ac0dc3b1672e18e4 | 10,752 | py | Python | tests/test_packed_to_padded.py | theycallmepeter/pytorch3d_PBR | bc83c23969ff7843fc05d2da001952b368926174 | [
"BSD-3-Clause"
] | null | null | null | tests/test_packed_to_padded.py | theycallmepeter/pytorch3d_PBR | bc83c23969ff7843fc05d2da001952b368926174 | [
"BSD-3-Clause"
] | null | null | null | tests/test_packed_to_padded.py | theycallmepeter/pytorch3d_PBR | bc83c23969ff7843fc05d2da001952b368926174 | [
"BSD-3-Clause"
] | null | null | null | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from common_testing import TestCaseMixin, get_random_cuda_device
from pytorch3d.ops import packed_to_padded, padded_to_packed
from pytorch3d.structures.meshes import Meshes
class TestPackedToPadded(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(1)
@staticmethod
def init_meshes(
num_meshes: int = 10,
num_verts: int = 1000,
num_faces: int = 3000,
device: str = "cpu",
):
device = torch.device(device)
verts_list = []
faces_list = []
for _ in range(num_meshes):
verts = torch.rand((num_verts, 3), dtype=torch.float32, device=device)
faces = torch.randint(
num_verts, size=(num_faces, 3), dtype=torch.int64, device=device
)
verts_list.append(verts)
faces_list.append(faces)
meshes = Meshes(verts_list, faces_list)
return meshes
@staticmethod
def packed_to_padded_python(inputs, first_idxs, max_size, device):
"""
PyTorch implementation of packed_to_padded function.
"""
num_meshes = first_idxs.size(0)
D = inputs.shape[1] if inputs.dim() == 2 else 0
if D == 0:
inputs_padded = torch.zeros((num_meshes, max_size), device=device)
else:
inputs_padded = torch.zeros((num_meshes, max_size, D), device=device)
for m in range(num_meshes):
s = first_idxs[m]
if m == num_meshes - 1:
f = inputs.shape[0]
else:
f = first_idxs[m + 1]
inputs_padded[m, :f] = inputs[s:f]
return inputs_padded
@staticmethod
def padded_to_packed_python(inputs, first_idxs, num_inputs, device):
"""
PyTorch implementation of padded_to_packed function.
"""
num_meshes = inputs.size(0)
D = inputs.shape[2] if inputs.dim() == 3 else 0
if D == 0:
inputs_packed = torch.zeros((num_inputs,), device=device)
else:
inputs_packed = torch.zeros((num_inputs, D), device=device)
for m in range(num_meshes):
s = first_idxs[m]
if m == num_meshes - 1:
f = num_inputs
else:
f = first_idxs[m + 1]
inputs_packed[s:f] = inputs[m, :f]
return inputs_packed
def _test_packed_to_padded_helper(self, D, device):
"""
Check the results from packed_to_padded and PyTorch implementations
are the same.
"""
meshes = self.init_meshes(16, 100, 300, device=device)
faces = meshes.faces_packed()
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
max_faces = meshes.num_faces_per_mesh().max().item()
if D == 0:
values = torch.rand((faces.shape[0],), device=device, requires_grad=True)
else:
values = torch.rand((faces.shape[0], D), device=device, requires_grad=True)
values_torch = values.detach().clone()
values_torch.requires_grad = True
values_padded = packed_to_padded(
values, mesh_to_faces_packed_first_idx, max_faces
)
values_padded_torch = TestPackedToPadded.packed_to_padded_python(
values_torch, mesh_to_faces_packed_first_idx, max_faces, device
)
# check forward
self.assertClose(values_padded, values_padded_torch)
# check backward
if D == 0:
grad_inputs = torch.rand((len(meshes), max_faces), device=device)
else:
grad_inputs = torch.rand((len(meshes), max_faces, D), device=device)
values_padded.backward(grad_inputs)
grad_outputs = values.grad
values_padded_torch.backward(grad_inputs)
grad_outputs_torch1 = values_torch.grad
grad_outputs_torch2 = TestPackedToPadded.padded_to_packed_python(
grad_inputs, mesh_to_faces_packed_first_idx, values.size(0), device=device
)
self.assertClose(grad_outputs, grad_outputs_torch1)
self.assertClose(grad_outputs, grad_outputs_torch2)
def test_packed_to_padded_flat_cpu(self):
self._test_packed_to_padded_helper(0, "cpu")
def test_packed_to_padded_D1_cpu(self):
self._test_packed_to_padded_helper(1, "cpu")
def test_packed_to_padded_D16_cpu(self):
self._test_packed_to_padded_helper(16, "cpu")
def test_packed_to_padded_flat_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper(0, device)
def test_packed_to_padded_D1_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper(1, device)
def test_packed_to_padded_D16_cuda(self):
device = get_random_cuda_device()
self._test_packed_to_padded_helper(16, device)
def _test_padded_to_packed_helper(self, D, device):
"""
Check the results from packed_to_padded and PyTorch implementations
are the same.
"""
meshes = self.init_meshes(16, 100, 300, device=device)
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
num_faces_per_mesh = meshes.num_faces_per_mesh()
max_faces = num_faces_per_mesh.max().item()
if D == 0:
values = torch.rand((len(meshes), max_faces), device=device)
else:
values = torch.rand((len(meshes), max_faces, D), device=device)
for i, num in enumerate(num_faces_per_mesh):
values[i, num:] = 0
values.requires_grad = True
values_torch = values.detach().clone()
values_torch.requires_grad = True
values_packed = padded_to_packed(
values, mesh_to_faces_packed_first_idx, num_faces_per_mesh.sum().item()
)
values_packed_torch = TestPackedToPadded.padded_to_packed_python(
values_torch,
mesh_to_faces_packed_first_idx,
num_faces_per_mesh.sum().item(),
device,
)
# check forward
self.assertClose(values_packed, values_packed_torch)
# check backward
if D == 0:
grad_inputs = torch.rand((num_faces_per_mesh.sum().item()), device=device)
else:
grad_inputs = torch.rand(
(num_faces_per_mesh.sum().item(), D), device=device
)
values_packed.backward(grad_inputs)
grad_outputs = values.grad
values_packed_torch.backward(grad_inputs)
grad_outputs_torch1 = values_torch.grad
grad_outputs_torch2 = TestPackedToPadded.packed_to_padded_python(
grad_inputs, mesh_to_faces_packed_first_idx, values.size(1), device=device
)
self.assertClose(grad_outputs, grad_outputs_torch1)
self.assertClose(grad_outputs, grad_outputs_torch2)
def test_padded_to_packed_flat_cpu(self):
self._test_padded_to_packed_helper(0, "cpu")
def test_padded_to_packed_D1_cpu(self):
self._test_padded_to_packed_helper(1, "cpu")
def test_padded_to_packed_D16_cpu(self):
self._test_padded_to_packed_helper(16, "cpu")
def test_padded_to_packed_flat_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper(0, device)
def test_padded_to_packed_D1_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper(1, device)
def test_padded_to_packed_D16_cuda(self):
device = get_random_cuda_device()
self._test_padded_to_packed_helper(16, device)
def test_invalid_inputs_shapes(self, device="cuda:0"):
with self.assertRaisesRegex(ValueError, "input can only be 2-dimensional."):
values = torch.rand((100, 50, 2), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
packed_to_padded(values, first_idxs, 100)
with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
values = torch.rand((100,), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
padded_to_packed(values, first_idxs, 20)
with self.assertRaisesRegex(ValueError, "input can only be 3-dimensional."):
values = torch.rand((100, 50, 2, 2), device=device)
first_idxs = torch.tensor([0, 80], dtype=torch.int64, device=device)
padded_to_packed(values, first_idxs, 20)
@staticmethod
def packed_to_padded_with_init(
num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
):
meshes = TestPackedToPadded.init_meshes(
num_meshes, num_verts, num_faces, device
)
faces = meshes.faces_packed()
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
max_faces = meshes.num_faces_per_mesh().max().item()
if num_d == 0:
values = torch.rand((faces.shape[0],), device=meshes.device)
else:
values = torch.rand((faces.shape[0], num_d), device=meshes.device)
torch.cuda.synchronize()
def out():
packed_to_padded(values, mesh_to_faces_packed_first_idx, max_faces)
torch.cuda.synchronize()
return out
@staticmethod
def packed_to_padded_with_init_torch(
num_meshes: int, num_verts: int, num_faces: int, num_d: int, device: str = "cpu"
):
meshes = TestPackedToPadded.init_meshes(
num_meshes, num_verts, num_faces, device
)
faces = meshes.faces_packed()
mesh_to_faces_packed_first_idx = meshes.mesh_to_faces_packed_first_idx()
max_faces = meshes.num_faces_per_mesh().max().item()
if num_d == 0:
values = torch.rand((faces.shape[0],), device=meshes.device)
else:
values = torch.rand((faces.shape[0], num_d), device=meshes.device)
torch.cuda.synchronize()
def out():
TestPackedToPadded.packed_to_padded_python(
values, mesh_to_faces_packed_first_idx, max_faces, device
)
torch.cuda.synchronize()
return out
| 39.384615 | 89 | 0.628255 | 10,324 | 0.960193 | 0 | 0 | 3,877 | 0.360584 | 0 | 0 | 815 | 0.0758 |
07e0ba1b41406f219565825c5f80d1ee0cfaded5 | 8,638 | py | Python | easyric/tests/test_io_geotiff.py | HowcanoeWang/EasyRIC | a3420bc7b1e0f1013411565cf0e66dd2d2ba5371 | [
"MIT"
] | 12 | 2021-01-25T07:11:52.000Z | 2022-02-14T11:57:03.000Z | easyric/tests/test_io_geotiff.py | HowcanoeWang/EasyRIC | a3420bc7b1e0f1013411565cf0e66dd2d2ba5371 | [
"MIT"
] | null | null | null | easyric/tests/test_io_geotiff.py | HowcanoeWang/EasyRIC | a3420bc7b1e0f1013411565cf0e66dd2d2ba5371 | [
"MIT"
] | null | null | null | import pyproj
import pytest
import numpy as np
from easyric.io import geotiff, shp
from skimage.io import imread
from skimage.color import rgb2gray
import matplotlib.pyplot as plt
def test_prase_header_string_width():
out_dict = geotiff._prase_header_string("* 256 image_width (1H) 13503")
assert out_dict['width'] == 13503
def test_prase_header_string_length():
out_dict = geotiff._prase_header_string("* 257 image_length (1H) 19866")
assert out_dict['length'] == 19866
def test_prase_header_string_scale():
in_str = "* 33550 model_pixel_scale (3d) (0.0029700000000000004, 0.0029700000000000004, 0"
out_dict = geotiff._prase_header_string(in_str)
assert out_dict['scale'] == (0.0029700000000000004, 0.0029700000000000004)
def test_prase_header_string_tie_point():
in_str = "* 33922 model_tie_point (6d) (0.0, 0.0, 0.0, 368090.77975000005, 3956071.13823,"
out_dict = geotiff._prase_header_string(in_str)
assert out_dict['tie_point'] == (368090.77975000005, 3956071.13823)
in_str = "* 33922 model_tie_point (6d) (0.0, 0.0, 0.0, 368090.77975000005, 3956071.13823, 0"
out_dict = geotiff._prase_header_string(in_str)
assert out_dict['tie_point'] == (368090.77975000005, 3956071.13823)
def test_prase_header_string_nodata():
out_dict = geotiff._prase_header_string("* 42113 gdal_nodata (7s) b'-10000'")
assert out_dict['nodata'] == -10000
def test_prase_header_string_proj_normal(capsys):
in_str = "* 34737 geo_ascii_params (30s) b'WGS 84 / UTM zone 54N|WGS 84|'"
out_dict = geotiff._prase_header_string(in_str)
captured = capsys.readouterr()
assert f"[io][geotiff][GeoCorrd] Comprehense [{in_str}]" in captured.out
assert out_dict['proj'] == pyproj.CRS.from_epsg(32654)
def test_prase_header_string_proj_error(capsys):
# should raise error because WGS 84 / UTM ... should be full
out_dict = geotiff._prase_header_string("* 34737 geo_ascii_params (30s) b'UTM zone 54N|WGS 84|'")
captured = capsys.readouterr()
assert '[io][geotiff][GeoCorrd] Generation failed, because [Input is not a CRS: UTM zone 54N]' in captured.out
assert out_dict['proj'] == None
def test_get_imarray_without_header(capsys):
pass
def test_get_imarray_with_header(capsys):
pass
def test_point_query_one_point():
point = (368023.004, 3955500.669)
out = geotiff.point_query(r'file/pix4d.diy/hasu_tanashi_20170525_Ins1RGB_30m_dsm.tif', point)
np.testing.assert_almost_equal(out, np.float32(97.45558), decimal=3)
def test_point_query_numpy_points():
points = np.asarray([[368022.581, 3955501.054], [368024.032, 3955500.465]])
out = geotiff.point_query(r'file/pix4d.diy/hasu_tanashi_20170525_Ins1RGB_30m_dsm.tif', points)
expected = np.asarray([97.624344, 97.59617])
np.testing.assert_almost_equal(out, expected, decimal=3)
def test_point_query_list_numpy_points():
points = np.asarray([[368022.581, 3955501.054], [368024.032, 3955500.465]])
point = np.asarray([[368023.004, 3955500.669]])
p_list = [point, points]
expected = [np.asarray([97.45558]), np.asarray([97.624344, 97.59617])]
out = geotiff.point_query(r'file/pix4d.diy/hasu_tanashi_20170525_Ins1RGB_30m_dsm.tif', p_list)
assert type(expected) == type(out)
np.testing.assert_almost_equal(expected[0], out[0], decimal=3)
np.testing.assert_almost_equal(expected[1], out[1], decimal=3)
def test_point_query_wrong_types():
# [TODO]
pass
def test_point_query_input_ndarray():
# [Todo]
pass
def test_mean_values(capsys):
mean_ht = geotiff.mean_values(r'file/pix4d.diy/hasu_tanashi_20170525_Ins1RGB_30m_dsm.tif')
captured = capsys.readouterr()
# When not convert to float, mean_values = 97.562584
# assert mean_ht == np.float32(97.562584)
np.testing.assert_almost_equal(mean_ht, np.float32(97.562584), decimal=3)
# another case that not working in previous version:
# Cannot convert np.nan to int, fixed by astype(float)
mean_ht = geotiff.mean_values(r'file/tiff_test/2_12.tif')
captured = capsys.readouterr()
np.testing.assert_almost_equal(mean_ht, np.float(72.31657466298653), decimal=3)
def test_gis2pixel2gis():
geo_head_txt = """
TIFF file: 200423_G_M600pro_transparent_mosaic_group1.tif, 411 MiB, little endian, bigtiff
Series 0: 31255x19436x4, uint8, YXS, 1 pages, not mem-mappable
Page 0: 31255x19436x4, uint8, 8 bit, rgb, lzw
* 256 image_width (1H) 19436
* 257 image_length (1H) 31255
* 258 bits_per_sample (4H) (8, 8, 8, 8)
* 259 compression (1H) 5
* 262 photometric (1H) 2
* 273 strip_offsets (31255Q) (500650, 501114, 501578, 502042, 502506, 502970, 5
* 277 samples_per_pixel (1H) 4
* 278 rows_per_strip (1H) 1
* 279 strip_byte_counts (31255Q) (464, 464, 464, 464, 464, 464, 464, 464, 464,
* 284 planar_configuration (1H) 1
* 305 software (12s) b'pix4dmapper'
* 317 predictor (1H) 2
* 338 extra_samples (1H) 2
* 339 sample_format (4H) (1, 1, 1, 1)
* 33550 model_pixel_scale (3d) (0.001, 0.001, 0.0)
* 33922 model_tie_point (6d) (0.0, 0.0, 0.0, 484576.70205, 3862285.5109300003,
* 34735 geo_key_directory (32H) (1, 1, 0, 7, 1024, 0, 1, 1, 1025, 0, 1, 1, 1026
* 34737 geo_ascii_params (30s) b'WGS 84 / UTM zone 53N|WGS 84|'
"""
gis_coord = np.asarray([[ 484593.67474654, 3862259.42413431],
[ 484593.41064743, 3862259.92582402],
[ 484593.64841806, 3862260.06515117],
[ 484593.93077419, 3862259.55455913],
[ 484593.67474654, 3862259.42413431]])
header = geotiff._prase_header_string(geo_head_txt)
expected_pixel = np.asarray([[16972, 26086],
[16708, 25585],
[16946, 25445],
[17228, 25956],
[16972, 26086]])
pixel_coord = geotiff.geo2pixel(gis_coord, header)
np.testing.assert_almost_equal(pixel_coord, expected_pixel)
gis_revert = geotiff.pixel2geo(pixel_coord, header)
np.testing.assert_almost_equal(gis_revert, gis_coord, decimal=3)
def test_is_roi_type():
roi1 = np.asarray([[123, 456], [456, 789]])
roi2 = [roi1, roi1]
roi_wrong_1 = (123, 345)
roi_wrong_2 = [123, 456]
roi_wrong_3 = [[123, 345], [456, 789]]
roi1_out = geotiff._is_roi_type(roi1)
assert roi1_out == [roi1]
roi2_out = geotiff._is_roi_type(roi2)
assert roi2_out == roi2
with pytest.raises(TypeError) as errinfo:
roi_w1_out = geotiff._is_roi_type(roi_wrong_1)
assert 'Only numpy.ndarray points and list contains numpy.ndarray points are supported' in str(errinfo.value)
with pytest.raises(TypeError) as errinfo:
roi_w2_out = geotiff._is_roi_type(roi_wrong_2)
assert 'Only list contains numpy.ndarray points are supported' in str(errinfo.value)
with pytest.raises(TypeError) as errinfo:
roi_w3_out = geotiff._is_roi_type(roi_wrong_3)
assert 'Only list contains numpy.ndarray points are supported' in str(errinfo.value)
def test_imarray_clip_2d_rgb_rgba():
photo_path = 'file/pix4d.diy/photos/DJI_0174.JPG'
roi = np.asarray([[2251, 1223], [2270, 1270], [2227, 1263], [2251, 1223]])
fig, ax = plt.subplots(1,3, figsize=(12,4))
# -----------------------------------------------
imarray_rgb = imread(photo_path)
assert imarray_rgb.shape == (3456, 4608, 3)
im_out_rgb, offsets_rgb = geotiff.imarray_clip(imarray_rgb, roi)
ax[1].imshow(im_out_rgb / 255)
ax[1].set_title('rgb')
# -----------------------------------------------
imarray_2d = rgb2gray(imarray_rgb)
assert imarray_2d.shape == (3456, 4608)
im_out_2d, offsets_2d = geotiff.imarray_clip(imarray_2d, roi)
ax[0].imshow(im_out_2d, cmap='gray')
ax[0].set_title('gray')
# -----------------------------------------------
imarray_rgba = np.dstack((imarray_rgb, np.ones((3456, 4608)) * 255))
assert imarray_rgba.shape == (3456, 4608, 4)
im_out_rgba, offsets = geotiff.imarray_clip(imarray_rgba, roi)
ax[2].imshow(im_out_rgba/255)
ax[2].set_title('rgba')
plt.show()
def test_clip_roi_pixel():
poly = shp.read_shp2d('file/shp_test/test.shp')
poly_pixel = geotiff.geo2pixel(poly['0'], geotiff.get_header('file/tiff_test/2_12.tif'))
imarray, offset = geotiff.clip_roi(poly_pixel, 'file/tiff_test/2_12.tif', is_geo=False)
assert len(imarray) == 1
def test_clip_roi_geo():
poly = shp.read_shp2d('file/shp_test/test.shp')
imarray, offset = geotiff.clip_roi(poly['0'], 'file/tiff_test/2_12.tif', is_geo=True)
assert len(imarray) == 1 | 37.232759 | 117 | 0.683376 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,735 | 0.316624 |
07e0cae8b52445fc39f360d69691eeb8e3e8f35e | 9,833 | py | Python | src/ebay_rest/api/buy_marketplace_insights/models/item_location.py | matecsaj/ebay_rest | dd23236f39e05636eff222f99df1e3699ce47d4a | [
"MIT"
] | 3 | 2021-12-12T04:28:03.000Z | 2022-03-10T03:29:18.000Z | src/ebay_rest/api/buy_marketplace_insights/models/item_location.py | jdavv/ebay_rest | 20fc88c6aefdae9ab90f9c1330e79abddcd750cd | [
"MIT"
] | 33 | 2021-06-16T20:44:36.000Z | 2022-03-30T14:55:06.000Z | src/ebay_rest/api/buy_marketplace_insights/models/item_location.py | jdavv/ebay_rest | 20fc88c6aefdae9ab90f9c1330e79abddcd750cd | [
"MIT"
] | 7 | 2021-06-03T09:30:23.000Z | 2022-03-08T19:51:33.000Z | # coding: utf-8
"""
Marketplace Insights API
<a href=\"https://developer.ebay.com/api-docs/static/versioning.html#limited\" target=\"_blank\"> <img src=\"/cms/img/docs/partners-api.svg\" class=\"legend-icon partners-icon\" title=\"Limited Release\" alt=\"Limited Release\" />(Limited Release)</a> The Marketplace Insights API provides the ability to search for sold items on eBay by keyword, GTIN, category, and product and returns the of sales history of those items. # noqa: E501
OpenAPI spec version: v1_beta.2.2
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class ItemLocation(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'address_line1': 'str',
'address_line2': 'str',
'city': 'str',
'country': 'str',
'county': 'str',
'postal_code': 'str',
'state_or_province': 'str'
}
attribute_map = {
'address_line1': 'addressLine1',
'address_line2': 'addressLine2',
'city': 'city',
'country': 'country',
'county': 'county',
'postal_code': 'postalCode',
'state_or_province': 'stateOrProvince'
}
def __init__(self, address_line1=None, address_line2=None, city=None, country=None, county=None, postal_code=None, state_or_province=None): # noqa: E501
"""ItemLocation - a model defined in Swagger""" # noqa: E501
self._address_line1 = None
self._address_line2 = None
self._city = None
self._country = None
self._county = None
self._postal_code = None
self._state_or_province = None
self.discriminator = None
if address_line1 is not None:
self.address_line1 = address_line1
if address_line2 is not None:
self.address_line2 = address_line2
if city is not None:
self.city = city
if country is not None:
self.country = country
if county is not None:
self.county = county
if postal_code is not None:
self.postal_code = postal_code
if state_or_province is not None:
self.state_or_province = state_or_province
@property
def address_line1(self):
"""Gets the address_line1 of this ItemLocation. # noqa: E501
The first line of the street address. # noqa: E501
:return: The address_line1 of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._address_line1
@address_line1.setter
def address_line1(self, address_line1):
"""Sets the address_line1 of this ItemLocation.
The first line of the street address. # noqa: E501
:param address_line1: The address_line1 of this ItemLocation. # noqa: E501
:type: str
"""
self._address_line1 = address_line1
@property
def address_line2(self):
"""Gets the address_line2 of this ItemLocation. # noqa: E501
The second line of the street address. This field may contain such values as an apartment or suite number. # noqa: E501
:return: The address_line2 of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._address_line2
@address_line2.setter
def address_line2(self, address_line2):
"""Sets the address_line2 of this ItemLocation.
The second line of the street address. This field may contain such values as an apartment or suite number. # noqa: E501
:param address_line2: The address_line2 of this ItemLocation. # noqa: E501
:type: str
"""
self._address_line2 = address_line2
@property
def city(self):
"""Gets the city of this ItemLocation. # noqa: E501
The city in which the item is located. # noqa: E501
:return: The city of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._city
@city.setter
def city(self, city):
"""Sets the city of this ItemLocation.
The city in which the item is located. # noqa: E501
:param city: The city of this ItemLocation. # noqa: E501
:type: str
"""
self._city = city
@property
def country(self):
"""Gets the country of this ItemLocation. # noqa: E501
The two-letter <a href=\"https://www.iso.org/iso-3166-country-codes.html\">ISO 3166</a> standard code that indicates the country in which the item is located. For implementation help, refer to <a href='https://developer.ebay.com/api-docs/buy/marketplace_insights/types/ba:CountryCodeEnum'>eBay API documentation</a> # noqa: E501
:return: The country of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._country
@country.setter
def country(self, country):
"""Sets the country of this ItemLocation.
The two-letter <a href=\"https://www.iso.org/iso-3166-country-codes.html\">ISO 3166</a> standard code that indicates the country in which the item is located. For implementation help, refer to <a href='https://developer.ebay.com/api-docs/buy/marketplace_insights/types/ba:CountryCodeEnum'>eBay API documentation</a> # noqa: E501
:param country: The country of this ItemLocation. # noqa: E501
:type: str
"""
self._country = country
@property
def county(self):
"""Gets the county of this ItemLocation. # noqa: E501
The county in which the item is located. # noqa: E501
:return: The county of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._county
@county.setter
def county(self, county):
"""Sets the county of this ItemLocation.
The county in which the item is located. # noqa: E501
:param county: The county of this ItemLocation. # noqa: E501
:type: str
"""
self._county = county
@property
def postal_code(self):
"""Gets the postal_code of this ItemLocation. # noqa: E501
The postal code (or zip code in US) where the item is located.<br /> <br /><span class=\"tablenote\"> <b> Note: </b>Beginning in late January 2020, the displayed postal code will be masked to all users. Different countries will mask postal/zip codes in slightly different ways, but an example would be <code>951**</code>.</span> # noqa: E501
:return: The postal_code of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._postal_code
@postal_code.setter
def postal_code(self, postal_code):
"""Sets the postal_code of this ItemLocation.
The postal code (or zip code in US) where the item is located.<br /> <br /><span class=\"tablenote\"> <b> Note: </b>Beginning in late January 2020, the displayed postal code will be masked to all users. Different countries will mask postal/zip codes in slightly different ways, but an example would be <code>951**</code>.</span> # noqa: E501
:param postal_code: The postal_code of this ItemLocation. # noqa: E501
:type: str
"""
self._postal_code = postal_code
@property
def state_or_province(self):
"""Gets the state_or_province of this ItemLocation. # noqa: E501
The state or province in which the item is located. # noqa: E501
:return: The state_or_province of this ItemLocation. # noqa: E501
:rtype: str
"""
return self._state_or_province
@state_or_province.setter
def state_or_province(self, state_or_province):
"""Sets the state_or_province of this ItemLocation.
The state or province in which the item is located. # noqa: E501
:param state_or_province: The state_or_province of this ItemLocation. # noqa: E501
:type: str
"""
self._state_or_province = state_or_province
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.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
if issubclass(ItemLocation, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, ItemLocation):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| 34.992883 | 442 | 0.616699 | 9,169 | 0.932472 | 0 | 0 | 5,635 | 0.57307 | 0 | 0 | 5,975 | 0.607648 |
07e1c7308c17fb9509fe646f19b12d537b946ccf | 502 | py | Python | fractionalKnapsack.py | aadishgoel2013/Algos-with-Python | 19541607c8ede9a76a8cbbe047e01343080cfd5b | [
"Apache-2.0"
] | 6 | 2017-11-12T05:13:27.000Z | 2022-03-13T07:58:31.000Z | fractionalKnapsack.py | aadishgoel2013/Algos-with-Python | 19541607c8ede9a76a8cbbe047e01343080cfd5b | [
"Apache-2.0"
] | null | null | null | fractionalKnapsack.py | aadishgoel2013/Algos-with-Python | 19541607c8ede9a76a8cbbe047e01343080cfd5b | [
"Apache-2.0"
] | null | null | null | # Fractional Knapsack
wt = [40,50,30,10,10,40,30]
pro = [30,20,20,25,5,35,15]
n = len(wt)
data = [ (i,pro[i],wt[i]) for i in range(n) ]
bag = 100
data.sort(key=lambda x: x[1]/x[2], reverse=True)
profit=0
ans=[]
i=0
while i<n:
if data[i][2]<=bag:
bag-=data[i][2]
ans.append(data[i][0])
profit+=data[i][1]
i+=1
else:
break
if i<n:
ans.append(data[i][0])
profit += (bag*data[i][1])/data[i][2]
print(profit,ans)
| 17.310345 | 49 | 0.498008 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0.043825 |
07e2537b3e43653ce0616ed6421ef634050042c8 | 3,085 | py | Python | pysrc/classifier.py | CrackerCat/xed | 428712c28e831573579b7f749db63d3a58dcdbd9 | [
"Apache-2.0"
] | 1,261 | 2016-12-16T14:29:30.000Z | 2022-03-30T20:21:25.000Z | pysrc/classifier.py | CrackerCat/xed | 428712c28e831573579b7f749db63d3a58dcdbd9 | [
"Apache-2.0"
] | 190 | 2016-12-17T13:44:09.000Z | 2022-03-27T09:28:13.000Z | pysrc/classifier.py | CrackerCat/xed | 428712c28e831573579b7f749db63d3a58dcdbd9 | [
"Apache-2.0"
] | 155 | 2016-12-16T22:17:20.000Z | 2022-02-16T20:53:59.000Z | #!/usr/bin/env python
# -*- python -*-
#BEGIN_LEGAL
#
#Copyright (c) 2019 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#END_LEGAL
from __future__ import print_function
import re
import genutil
import codegen
def _emit_function(fe, isa_sets, name):
fo = codegen.function_object_t('xed_classify_{}'.format(name))
fo.add_arg('const xed_decoded_inst_t* d')
fo.add_code_eol(' const xed_isa_set_enum_t isa_set = xed_decoded_inst_get_isa_set(d)')
# FIXME: 2017-07-14 optimization: could use a static array for faster checking, smaller code
switch = codegen.c_switch_generator_t('isa_set', fo)
isa_sets_sorted = sorted(isa_sets)
for c in isa_sets_sorted:
switch.add_case('XED_ISA_SET_{}'.format(c.upper()),[],do_break=False)
if len(isa_sets) > 0:
switch.add('return 1;')
switch.add_default(['return 0;'], do_break=False)
switch.finish()
fo.emit_file_emitter(fe)
def work(agi):
sse_isa_sets = set([])
avx_isa_sets = set([])
avx512_isa_sets = set([])
avx512_kmask_op = set([])
for generator in agi.generator_list:
for ii in generator.parser_output.instructions:
if genutil.field_check(ii, 'iclass'):
if re.search('AVX512',ii.isa_set):
avx512_isa_sets.add(ii.isa_set)
if re.search('KOP',ii.isa_set):
avx512_kmask_op.add(ii.isa_set)
elif re.search('AVX',ii.isa_set) or ii.isa_set in ['F16C', 'FMA']:
avx_isa_sets.add(ii.isa_set)
elif re.search('SSE',ii.isa_set) or ii.isa_set in ['AES','PCLMULQDQ']:
# Exclude MMX instructions that come in with SSE2 &
# SSSE3. The several purely MMX instr in SSE are
# "SSE-opcodes" with memop operands. One can look for
# those with SSE2MMX and SSSE3MMX xed isa_sets.
#
# Also exclude the SSE_PREFETCH operations; Those are
# just memops.
if (not re.search('MMX',ii.isa_set) and not re.search('PREFETCH',ii.isa_set)
and not re.search('X87',ii.isa_set) and not re.search('MWAIT',ii.isa_set)):
sse_isa_sets.add(ii.isa_set)
fe = agi.open_file('xed-classifiers.c') # xed_file_emitter_t
_emit_function(fe, avx512_isa_sets, 'avx512')
_emit_function(fe, avx512_kmask_op, 'avx512_maskop')
_emit_function(fe, avx_isa_sets, 'avx')
_emit_function(fe, sse_isa_sets, 'sse')
fe.close()
return
| 39.551282 | 96 | 0.647326 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1,324 | 0.429173 |
07e2d1b8a7c46e378298b64b296fe93ed48acbf5 | 1,828 | py | Python | tests/integration/api/test_target_groups.py | lanz/Tenable.io-SDK-for-Python | e81a61c369ac103d1524b0898153a569536a131e | [
"MIT"
] | 90 | 2017-02-02T18:36:17.000Z | 2022-02-05T17:58:50.000Z | tests/integration/api/test_target_groups.py | lanz/Tenable.io-SDK-for-Python | e81a61c369ac103d1524b0898153a569536a131e | [
"MIT"
] | 64 | 2017-02-03T00:54:00.000Z | 2020-08-06T14:06:50.000Z | tests/integration/api/test_target_groups.py | lanz/Tenable.io-SDK-for-Python | e81a61c369ac103d1524b0898153a569536a131e | [
"MIT"
] | 49 | 2017-02-03T01:01:00.000Z | 2022-02-25T13:25:28.000Z | import pytest
from tenable_io.api.target_groups import TargetListEditRequest
from tenable_io.api.models import TargetGroup, TargetGroupList
@pytest.mark.vcr()
def test_target_groups_create(new_target_group):
assert isinstance(new_target_group, TargetGroup), u'The `create` method did not return type `TargetGroup`.'
@pytest.mark.vcr()
def test_target_groups_details(client, new_target_group):
target_group = new_target_group
details = client.target_groups_api.details(target_group.id)
assert isinstance(details, TargetGroup), u'The `details` method did not return type `TargetGroup`.'
assert details.id == target_group.id, u'Expected the `details` response to match the requested target group.'
@pytest.mark.vcr()
def test_target_groups_list(client):
target_groups = client.target_groups_api.list()
assert isinstance(target_groups, TargetGroupList), u'The `details` method did not return type `TargetGroup`.'
for group in target_groups.target_groups:
assert isinstance(group, TargetGroup), u'Expected a list of type `TargetGroup`.'
@pytest.mark.vcr()
def test_target_groups_delete(client, new_target_group):
assert client.target_groups_api.delete(new_target_group.id), u'The target group was not deleted.'
@pytest.mark.vcr()
def test_target_groups_edit(client, new_target_group):
target_group = new_target_group
edited_name = 'test_target_group_edit'
edited_group = client.target_groups_api.edit(TargetListEditRequest(name=edited_name), target_group.id)
assert isinstance(edited_group, TargetGroup), u'The `edit` method did not return type `TargetGroup`.'
assert edited_group.id == target_group.id, u'Expected the edited target group to match the requested target group.'
assert edited_group.name == edited_name, u'Expected the name to be updated.'
| 44.585366 | 119 | 0.784464 | 0 | 0 | 0 | 0 | 1,672 | 0.914661 | 0 | 0 | 507 | 0.277352 |
07e312b03c9acc9dee5930a523090053e38045ca | 85,560 | py | Python | Installation/nnAudio/Spectrogram.py | tasercake/nnAudio | 5edc37b7b73674598d533261314429b875ba285d | [
"MIT"
] | null | null | null | Installation/nnAudio/Spectrogram.py | tasercake/nnAudio | 5edc37b7b73674598d533261314429b875ba285d | [
"MIT"
] | null | null | null | Installation/nnAudio/Spectrogram.py | tasercake/nnAudio | 5edc37b7b73674598d533261314429b875ba285d | [
"MIT"
] | null | null | null | """
Module containing all the spectrogram classes
"""
# 0.2.0
import torch
import torch.nn as nn
from torch.nn.functional import conv1d, conv2d, fold
import numpy as np
from time import time
from nnAudio.librosa_functions import *
from nnAudio.utils import *
sz_float = 4 # size of a float
epsilon = 10e-8 # fudge factor for normalization
### --------------------------- Spectrogram Classes ---------------------------###
class STFT(torch.nn.Module):
"""This function is to calculate the short-time Fourier transform (STFT) of the input signal.
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred automatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
Parameters
----------
n_fft : int
The window size. Default value is 2048.
freq_bins : int
Number of frequency bins. Default is ``None``, which means ``n_fft//2+1`` bins.
hop_length : int
The hop (or stride) size. Default value is ``None`` which is equivalent to ``n_fft//4``.
window : str
The windowing function for STFT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
freq_scale : 'linear', 'log', or 'no'
Determine the spacing between each frequency bin. When `linear` or `log` is used,
the bin spacing can be controlled by ``fmin`` and ``fmax``. If 'no' is used, the bin will
start at 0Hz and end at Nyquist frequency with linear spacing.
center : bool
Putting the STFT keneral at the center of the time-step or not. If ``False``, the time
index is the beginning of the STFT kernel, if ``True``, the time index is the center of
the STFT kernel. Default value if ``True``.
pad_mode : str
The padding method. Default value is 'reflect'.
inverse : bool
To activate the iSTFT module or not. By default, it is False to save GPU memory.
fmin : int
The starting frequency for the lowest frequency bin. If freq_scale is ``no``, this argument
does nothing.
fmax : int
The ending frequency for the highest frequency bin. If freq_scale is ``no``, this argument
does nothing.
sr : int
The sampling rate for the input audio. It is used to calucate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
trainable : bool
Determine if the STFT kenrels are trainable or not. If ``True``, the gradients for STFT
kernels will also be caluclated and the STFT kernels will be updated during model training.
Default value is ``False``
output_format : str
Control the spectrogram output type, either ``Magnitude``, ``Complex``, or ``Phase``.
The output_format can also be changed during the ``forward`` method.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints
device : str
Choose which device to initialize this layer. Default value is 'cpu'
Returns
-------
spectrogram : torch.tensor
It returns a tensor of spectrograms.
``shape = (num_samples, freq_bins,time_steps)`` if ``output_format='Magnitude'``;
``shape = (num_samples, freq_bins,time_steps, 2)`` if ``output_format='Complex' or 'Phase'``;
Examples
--------
>>> spec_layer = Spectrogram.STFT()
>>> specs = spec_layer(x)
"""
def __init__(self, n_fft=2048, win_length=None, freq_bins=None, hop_length=None, window='hann',
freq_scale='no', center=True, pad_mode='reflect', iSTFT=False,
fmin=50, fmax=6000, sr=22050, trainable=False,
output_format="Complex", verbose=True):
super().__init__()
# Trying to make the default setting same as librosa
if win_length==None: win_length = n_fft
if hop_length==None: hop_length = int(win_length // 4)
self.output_format = output_format
self.trainable = trainable
self.stride = hop_length
self.center = center
self.pad_mode = pad_mode
self.n_fft = n_fft
self.freq_bins = freq_bins
self.trainable = trainable
self.pad_amount = self.n_fft // 2
self.window = window
self.win_length = win_length
self.iSTFT = iSTFT
self.trainable = trainable
start = time()
# Create filter windows for stft
kernel_sin, kernel_cos, self.bins2freq, self.bin_list, window_mask = create_fourier_kernels(n_fft,
win_length=win_length,
freq_bins=freq_bins,
window=window,
freq_scale=freq_scale,
fmin=fmin,
fmax=fmax,
sr=sr,
verbose=verbose)
kernel_sin = torch.tensor(kernel_sin, dtype=torch.float)
kernel_cos = torch.tensor(kernel_cos, dtype=torch.float)
# In this way, the inverse kernel and the forward kernel do not share the same memory...
kernel_sin_inv = torch.cat((kernel_sin, -kernel_sin[1:-1].flip(0)), 0)
kernel_cos_inv = torch.cat((kernel_cos, kernel_cos[1:-1].flip(0)), 0)
if iSTFT:
self.register_buffer('kernel_sin_inv', kernel_sin_inv.unsqueeze(-1))
self.register_buffer('kernel_cos_inv', kernel_cos_inv.unsqueeze(-1))
# Making all these variables nn.Parameter, so that the model can be used with nn.Parallel
# self.kernel_sin = torch.nn.Parameter(self.kernel_sin, requires_grad=self.trainable)
# self.kernel_cos = torch.nn.Parameter(self.kernel_cos, requires_grad=self.trainable)
# Applying window functions to the Fourier kernels
window_mask = torch.tensor(window_mask)
wsin = kernel_sin * window_mask
wcos = kernel_cos * window_mask
if self.trainable==False:
self.register_buffer('wsin', wsin)
self.register_buffer('wcos', wcos)
if self.trainable==True:
wsin = torch.nn.Parameter(wsin, requires_grad=self.trainable)
wcos = torch.nn.Parameter(wcos, requires_grad=self.trainable)
self.register_parameter('wsin', wsin)
self.register_parameter('wcos', wcos)
# Prepare the shape of window mask so that it can be used later in inverse
self.register_buffer('window_mask', window_mask.unsqueeze(0).unsqueeze(-1))
if verbose==True:
print("STFT kernels created, time used = {:.4f} seconds".format(time()-start))
else:
pass
def forward(self, x, output_format=None):
"""
Convert a batch of waveforms to spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
output_format : str
Control the type of spectrogram to be return. Can be either ``Magnitude`` or ``Complex`` or ``Phase``.
Default value is ``Complex``.
"""
output_format = output_format or self.output_format
self.num_samples = x.shape[-1]
x = broadcast_dim(x)
if self.center:
if self.pad_mode == 'constant':
padding = nn.ConstantPad1d(self.pad_amount, 0)
elif self.pad_mode == 'reflect':
if self.num_samples < self.pad_amount:
raise AssertionError("Signal length shorter than reflect padding length (n_fft // 2).")
padding = nn.ReflectionPad1d(self.pad_amount)
x = padding(x)
spec_imag = conv1d(x, self.wsin, stride=self.stride)
spec_real = conv1d(x, self.wcos, stride=self.stride) # Doing STFT by using conv1d
# remove redundant parts
spec_real = spec_real[:, :self.freq_bins, :]
spec_imag = spec_imag[:, :self.freq_bins, :]
if output_format=='Magnitude':
spec = spec_real.pow(2) + spec_imag.pow(2)
if self.trainable==True:
return torch.sqrt(spec+1e-8) # prevent Nan gradient when sqrt(0) due to output=0
else:
return torch.sqrt(spec)
elif output_format=='Complex':
return torch.stack((spec_real,-spec_imag), -1) # Remember the minus sign for imaginary part
elif output_format=='Phase':
return torch.atan2(-spec_imag+0.0,spec_real) # +0.0 removes -0.0 elements, which leads to error in calculating phase
def inverse(self, X, onesided=True, length=None, refresh_win=True):
"""
This function is same as the :func:`~nnAudio.Spectrogram.iSTFT` class,
which is to convert spectrograms back to waveforms.
It only works for the complex value spectrograms. If you have the magnitude spectrograms,
please use :func:`~nnAudio.Spectrogram.Griffin_Lim`.
Parameters
----------
onesided : bool
If your spectrograms only have ``n_fft//2+1`` frequency bins, please use ``onesided=True``,
else use ``onesided=False``
length : int
To make sure the inverse STFT has the same output length of the original waveform, please
set `length` as your intended waveform length. By default, ``length=None``,
which will remove ``n_fft//2`` samples from the start and the end of the output.
refresh_win : bool
Recalculating the window sum square. If you have an input with fixed number of timesteps,
you can increase the speed by setting ``refresh_win=False``. Else please keep ``refresh_win=True``
"""
if (hasattr(self, 'kernel_sin_inv') != True) or (hasattr(self, 'kernel_cos_inv') != True):
raise NameError("Please activate the iSTFT module by setting `iSTFT=True` if you want to use `inverse`")
assert X.dim()==4 , "Inverse iSTFT only works for complex number," \
"make sure our tensor is in the shape of (batch, freq_bins, timesteps, 2)."\
"\nIf you have a magnitude spectrogram, please consider using Griffin-Lim."
if onesided:
X = extend_fbins(X) # extend freq
X_real, X_imag = X[:, :, :, 0], X[:, :, :, 1]
# broadcast dimensions to support 2D convolution
X_real_bc = X_real.unsqueeze(1)
X_imag_bc = X_imag.unsqueeze(1)
a1 = conv2d(X_real_bc, self.kernel_cos_inv, stride=(1,1))
b2 = conv2d(X_imag_bc, self.kernel_sin_inv, stride=(1,1))
# compute real and imag part. signal lies in the real part
real = a1 - b2
real = real.squeeze(-2)*self.window_mask
# Normalize the amplitude with n_fft
real /= (self.n_fft)
# Overlap and Add algorithm to connect all the frames
real = overlap_add(real, self.stride)
# Prepare the window sumsqure for division
# Only need to create this window once to save time
# Unless the input spectrograms have different time steps
if hasattr(self, 'w_sum')==False or refresh_win==True:
self.w_sum = torch_window_sumsquare(self.window_mask.flatten(), X.shape[2], self.stride, self.n_fft).flatten()
self.nonzero_indices = (self.w_sum>1e-10)
else:
pass
real[:, self.nonzero_indices] = real[:,self.nonzero_indices].div(self.w_sum[self.nonzero_indices])
# Remove padding
if length is None:
if self.center:
real = real[:, self.pad_amount:-self.pad_amount]
else:
if self.center:
real = real[:, self.pad_amount:self.pad_amount + length]
else:
real = real[:, :length]
return real
def extra_repr(self) -> str:
return 'n_fft={}, Fourier Kernel size={}, iSTFT={}, trainable={}'.format(
self.n_fft, (*self.wsin.shape,), self.iSTFT, self.trainable
)
class MelSpectrogram(torch.nn.Module):
"""This function is to calculate the Melspectrogram of the input signal.
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred automatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
Parameters
----------
sr : int
The sampling rate for the input audio.
It is used to calculate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
n_fft : int
The window size for the STFT. Default value is 2048
n_mels : int
The number of Mel filter banks. The filter banks maps the n_fft to mel bins.
Default value is 128.
hop_length : int
The hop (or stride) size. Default value is 512.
window : str
The windowing function for STFT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
center : bool
Putting the STFT keneral at the center of the time-step or not. If ``False``,
the time index is the beginning of the STFT kernel, if ``True``, the time index is the
center of the STFT kernel. Default value if ``True``.
pad_mode : str
The padding method. Default value is 'reflect'.
htk : bool
When ``False`` is used, the Mel scale is quasi-logarithmic. When ``True`` is used, the
Mel scale is logarithmic. The default value is ``False``.
fmin : int
The starting frequency for the lowest Mel filter bank.
fmax : int
The ending frequency for the highest Mel filter bank.
trainable_mel : bool
Determine if the Mel filter banks are trainable or not. If ``True``, the gradients for Mel
filter banks will also be calculated and the Mel filter banks will be updated during model
training. Default value is ``False``.
trainable_STFT : bool
Determine if the STFT kenrels are trainable or not. If ``True``, the gradients for STFT
kernels will also be caluclated and the STFT kernels will be updated during model training.
Default value is ``False``.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints.
device : str
Choose which device to initialize this layer. Default value is 'cpu'.
Returns
-------
spectrogram : torch.tensor
It returns a tensor of spectrograms. shape = ``(num_samples, freq_bins,time_steps)``.
Examples
--------
>>> spec_layer = Spectrogram.MelSpectrogram()
>>> specs = spec_layer(x)
"""
def __init__(self, sr=22050, n_fft=2048, n_mels=128, hop_length=512,
window='hann', center=True, pad_mode='reflect', power=2.0, htk=False,
fmin=0.0, fmax=None, norm=1, trainable_mel=False, trainable_STFT=False,
verbose=True, **kwargs):
super().__init__()
self.stride = hop_length
self.center = center
self.pad_mode = pad_mode
self.n_fft = n_fft
self.power = power
self.trainable_mel = trainable_mel
self.trainable_STFT = trainable_STFT
self.verbose = verbose
# Preparing for the stft layer. No need for center
self.stft = STFT(n_fft=n_fft, freq_bins=None, hop_length=hop_length, window=window,
freq_scale='no', center=center, pad_mode=pad_mode, sr=sr, trainable=trainable_STFT,
output_format="Magnitude", verbose=verbose, **kwargs)
# Create filter windows for stft
start = time()
# Creating kernel for mel spectrogram
start = time()
mel_basis = mel(sr, n_fft, n_mels, fmin, fmax, htk=htk, norm=norm)
mel_basis = torch.tensor(mel_basis)
if verbose==True:
print("STFT filter created, time used = {:.4f} seconds".format(time()-start))
print("Mel filter created, time used = {:.4f} seconds".format(time()-start))
else:
pass
if trainable_mel:
# Making everything nn.Parameter, so that this model can support nn.DataParallel
mel_basis = torch.nn.Parameter(mel_basis, requires_grad=trainable_mel)
self.register_parameter('mel_basis', mel_basis)
else:
self.register_buffer('mel_basis', mel_basis)
# if trainable_mel==True:
# self.mel_basis = torch.nn.Parameter(self.mel_basis)
# if trainable_STFT==True:
# self.wsin = torch.nn.Parameter(self.wsin)
# self.wcos = torch.nn.Parameter(self.wcos)
def forward(self, x):
"""
Convert a batch of waveforms to Mel spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
x = broadcast_dim(x)
spec = self.stft(x, output_format='Magnitude')**self.power
melspec = torch.matmul(self.mel_basis, spec)
return melspec
def extra_repr(self) -> str:
return 'Mel filter banks size = {}, trainable_mel={}'.format(
(*self.mel_basis.shape,), self.trainable_mel, self.trainable_STFT
)
def to_stft(self, melspec, max_steps=1000, loss_threshold=1e-8, grad_threshold=1e-7, random_start=False, sgd_kwargs=None, eps=1e-12, return_extras=False, verbose=None):
"""
Best-attempt spectrogram inversion
"""
def loss_fn(pred, target):
pred = pred.unsqueeze(1) if pred.ndim == 3 else pred
target = target.unsqueeze(1) if target.ndim == 3 else target
loss = (pred - target).pow(2).sum(-2).mean()
return loss
verbose = verbose or self.verbose
# SGD arguments
default_sgd_kwargs = dict(lr=1e3, momentum=0.9)
if sgd_kwargs:
default_sgd_kwargs.update(sgd_kwargs)
sgd_kwargs = default_sgd_kwargs
mel_basis = self.mel_basis.detach()
shape = melspec.shape
batch_size, n_mels, time = shape[0], shape[-2], shape[-1]
_, n_freq = mel_basis.shape
melspec = melspec.detach().view(-1, n_mels, time)
if random_start:
pred_stft_shape = (batch_size, n_freq, time)
pred_stft = torch.zeros(*pred_stft_shape, dtype=torch.float32, device=mel_basis.device).normal_().clamp_(eps)
else:
pred_stft = (torch.pinverse(mel_basis) @ melspec).clamp(eps)
pred_stft = nn.Parameter(pred_stft, requires_grad=True)
sgd_kwargs["lr"] = sgd_kwargs["lr"] * batch_size
optimizer = torch.optim.SGD([pred_stft], **sgd_kwargs)
losses = []
for i in range(max_steps):
optimizer.zero_grad()
pred_mel = mel_basis @ pred_stft
loss = loss_fn(pred_mel, melspec)
losses.append(loss.item())
loss.backward()
optimizer.step()
# Check conditions
if not loss.isfinite():
raise OverflowError("Overflow encountered in Mel -> STFT optimization")
if loss_threshold and loss < loss_threshold:
if verbose:
print(f"Target error of {loss_threshold} reached. Stopping optimization.")
break
if grad_threshold and pred_stft.grad.max() < grad_threshold:
if verbose:
print(f"Target max gradient of {grad_threshold} reached. Stopping optimization.")
break
pred_stft = pred_stft.detach().clamp(eps) ** 0.5
pred_stft = pred_stft.view((*shape[:-2], n_freq, time))
if return_extras:
return pred_stft, pred_mel.detach(), losses
return pred_stft
def inverse(self, melspec, mel_inversion_params=None, stft_inversion_params=None):
default_mel_inversion_params = {}
default_stft_inversion_params = {}
mel_inversion_params = mel_inversion_params or {}
stft_inversion_params = stft_inversion_params or {}
if mel_inversion_params:
mel_inversion_params = {**default_mel_inversion_params, **mel_inversion_params}
if stft_inversion_params:
stft_inversion_params = {**default_stft_inversion_params, **stft_inversion_params}
recon_stft = self.to_stft(melspec, **mel_inversion_params)
recon_audio = self.stft.inverse(recon_stft, **stft_inversion_params)
return recon_audio
class MFCC(torch.nn.Module):
"""This function is to calculate the Mel-frequency cepstral coefficients (MFCCs) of the input signal.
This algorithm first extracts Mel spectrograms from the audio clips,
then the discrete cosine transform is calcuated to obtain the final MFCCs.
Therefore, the Mel spectrogram part can be made trainable using
``trainable_mel`` and ``trainable_STFT``.
It only support type-II DCT at the moment. Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred autommatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
Parameters
----------
sr : int
The sampling rate for the input audio. It is used to calculate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
n_mfcc : int
The number of Mel-frequency cepstral coefficients
norm : string
The default value is 'ortho'. Normalization for DCT basis
**kwargs
Other arguments for Melspectrogram such as n_fft, n_mels, hop_length, and window
Returns
-------
MFCCs : torch.tensor
It returns a tensor of MFCCs. shape = ``(num_samples, n_mfcc, time_steps)``.
Examples
--------
>>> spec_layer = Spectrogram.MFCC()
>>> mfcc = spec_layer(x)
"""
def __init__(self, sr=22050, n_mfcc=20, norm='ortho', verbose=True, ref=1.0, amin=1e-10, top_db=80.0, **kwargs):
super().__init__()
self.melspec_layer = MelSpectrogram(sr=sr, verbose=verbose, **kwargs)
self.m_mfcc = n_mfcc
# attributes that will be used for _power_to_db
if amin <= 0:
raise ParameterError('amin must be strictly positive')
amin = torch.tensor([amin])
ref = torch.abs(torch.tensor([ref]))
self.register_buffer('amin', amin)
self.register_buffer('ref', ref)
self.top_db = top_db
self.n_mfcc = n_mfcc
def _power_to_db(self, S):
'''
Refer to https://librosa.github.io/librosa/_modules/librosa/core/spectrum.html#power_to_db
for the original implmentation.
'''
log_spec = 10.0 * torch.log10(torch.max(S, self.amin))
log_spec -= 10.0 * torch.log10(torch.max(self.amin, self.ref))
if self.top_db is not None:
if self.top_db < 0:
raise ParameterError('top_db must be non-negative')
# make the dim same as log_spec so that it can be broadcasted
batch_wise_max = log_spec.flatten(1).max(1)[0].unsqueeze(1).unsqueeze(1)
log_spec = torch.max(log_spec, batch_wise_max - self.top_db)
return log_spec
def _dct(self, x, norm=None):
'''
Refer to https://github.com/zh217/torch-dct for the original implmentation.
'''
x = x.permute(0,2,1) # make freq the last axis, since dct applies to the frequency axis
x_shape = x.shape
N = x_shape[-1]
v = torch.cat([x[:, :, ::2], x[:, :, 1::2].flip([2])], dim=2)
Vc = torch.rfft(v, 1, onesided=False)
# TODO: Can make the W_r and W_i trainable here
k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
W_r = torch.cos(k)
W_i = torch.sin(k)
V = Vc[:, :, :, 0] * W_r - Vc[:, :, :, 1] * W_i
if norm == 'ortho':
V[:, :, 0] /= np.sqrt(N) * 2
V[:, :, 1:] /= np.sqrt(N / 2) * 2
V = 2 * V
return V.permute(0,2,1) # swapping back the time axis and freq axis
def forward(self, x):
"""
Convert a batch of waveforms to MFCC.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
x = self.melspec_layer(x)
x = self._power_to_db(x)
x = self._dct(x, norm='ortho')[:,:self.m_mfcc,:]
return x
def extra_repr(self) -> str:
return 'n_mfcc = {}'.format(
(self.n_mfcc)
)
class CQT1992(torch.nn.Module):
"""
This alogrithm uses the method proposed in [1]. Please refer to :func:`~nnAudio.Spectrogram.CQT1992v2` for a more
computational and memory efficient version.
[1] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of a
constant Q transform.” (1992).
This function is to calculate the CQT of the input signal.
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred autommatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
Parameters
----------
sr : int
The sampling rate for the input audio. It is used to calucate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
hop_length : int
The hop (or stride) size. Default value is 512.
fmin : float
The frequency for the lowest CQT bin. Default is 32.70Hz, which coresponds to the note C0.
fmax : float
The frequency for the highest CQT bin. Default is ``None``, therefore the higest CQT bin is
inferred from the ``n_bins`` and ``bins_per_octave``.
If ``fmax`` is not ``None``, then the argument ``n_bins`` will be ignored and ``n_bins``
will be calculated automatically. Default is ``None``
n_bins : int
The total numbers of CQT bins. Default is 84. Will be ignored if ``fmax`` is not ``None``.
bins_per_octave : int
Number of bins per octave. Default is 12.
trainable_STFT : bool
Determine if the time to frequency domain transformation kernel for the input audio is trainable or not.
Default is ``False``
trainable_CQT : bool
Determine if the frequency domain CQT kernel is trainable or not.
Default is ``False``
norm : int
Normalization for the CQT kernels. ``1`` means L1 normalization, and ``2`` means L2 normalization.
Default is ``1``, which is same as the normalization used in librosa.
window : str
The windowing function for CQT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
center : bool
Putting the CQT keneral at the center of the time-step or not. If ``False``, the time index is
the beginning of the CQT kernel, if ``True``, the time index is the center of the CQT kernel.
Default value if ``True``.
pad_mode : str
The padding method. Default value is 'reflect'.
trainable : bool
Determine if the CQT kernels are trainable or not. If ``True``, the gradients for CQT kernels
will also be caluclated and the CQT kernels will be updated during model training.
Default value is ``False``.
output_format : str
Determine the return type.
``Magnitude`` will return the magnitude of the STFT result, shape = ``(num_samples, freq_bins,time_steps)``;
``Complex`` will return the STFT result in complex number, shape = ``(num_samples, freq_bins,time_steps, 2)``;
``Phase`` will return the phase of the STFT reuslt, shape = ``(num_samples, freq_bins,time_steps, 2)``.
The complex number is stored as ``(real, imag)`` in the last axis. Default value is 'Magnitude'.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints
Returns
-------
spectrogram : torch.tensor
It returns a tensor of spectrograms.
shape = ``(num_samples, freq_bins,time_steps)`` if ``output_format='Magnitude'``;
shape = ``(num_samples, freq_bins,time_steps, 2)`` if ``output_format='Complex' or 'Phase'``;
Examples
--------
>>> spec_layer = Spectrogram.CQT1992v2()
>>> specs = spec_layer(x)
"""
def __init__(self, sr=22050, hop_length=512, fmin=220, fmax=None, n_bins=84,
trainable_STFT=False, trainable_CQT=False, bins_per_octave=12,
output_format='Complex', norm=1, window='hann', center=True, pad_mode='reflect'):
super().__init__()
# norm arg is not functioning
self.hop_length = hop_length
self.center = center
self.pad_mode = pad_mode
self.norm = norm
self.output_format = output_format
# creating kernels for CQT
Q = 1/(2**(1/bins_per_octave)-1)
print("Creating CQT kernels ...", end='\r')
start = time()
cqt_kernels, self.kernel_width, lenghts = create_cqt_kernels(Q,
sr,
fmin,
n_bins,
bins_per_octave,
norm,
window,
fmax)
self.register_buffer('lenghts', lenghts)
cqt_kernels = fft(cqt_kernels)[:,:self.kernel_width//2+1]
print("CQT kernels created, time used = {:.4f} seconds".format(time()-start))
# creating kernels for stft
# self.cqt_kernels_real*=lenghts.unsqueeze(1)/self.kernel_width # Trying to normalize as librosa
# self.cqt_kernels_imag*=lenghts.unsqueeze(1)/self.kernel_width
print("Creating STFT kernels ...", end='\r')
start = time()
kernel_sin, kernel_cos, self.bins2freq, _, window = create_fourier_kernels(self.kernel_width,
window='ones',
freq_scale='no')
# Converting kernels from numpy arrays to torch tensors
wsin = torch.tensor(kernel_sin * window)
wcos = torch.tensor(kernel_cos * window)
cqt_kernels_real = torch.tensor(cqt_kernels.real.astype(np.float32))
cqt_kernels_imag = torch.tensor(cqt_kernels.imag.astype(np.float32))
if trainable_STFT:
wsin = torch.nn.Parameter(wsin, requires_grad=trainable_kernels)
wcos = torch.nn.Parameter(wcos, requires_grad=trainable_kernels)
self.register_parameter('wsin', wsin)
self.register_parameter('wcos', wcos)
else:
self.register_buffer('wsin', wsin)
self.register_buffer('wcos', wcos)
if trainable_CQT:
cqt_kernels_real = torch.nn.Parameter(cqt_kernels_real, requires_grad=trainable_kernels)
cqt_kernels_imag = torch.nn.Parameter(cqt_kernels_imag, requires_grad=trainable_kernels)
self.register_parameter('cqt_kernels_real', cqt_kernels_real)
self.register_parameter('cqt_kernels_imag', cqt_kernels_imag)
else:
self.register_buffer('cqt_kernels_real', cqt_kernels_real)
self.register_buffer('cqt_kernels_imag', cqt_kernels_imag)
print("STFT kernels created, time used = {:.4f} seconds".format(time()-start))
def forward(self, x, output_format=None):
"""
Convert a batch of waveforms to CQT spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
output_format = output_format or self.output_format
x = broadcast_dim(x)
if self.center:
if self.pad_mode == 'constant':
padding = nn.ConstantPad1d(self.kernel_width//2, 0)
elif self.pad_mode == 'reflect':
padding = nn.ReflectionPad1d(self.kernel_width//2)
x = padding(x)
# STFT
fourier_real = conv1d(x, self.wcos, stride=self.hop_length)
fourier_imag = conv1d(x, self.wsin, stride=self.hop_length)
# CQT
CQT_real, CQT_imag = complex_mul((self.cqt_kernels_real, self.cqt_kernels_imag),
(fourier_real, fourier_imag))
CQT = torch.stack((CQT_real,-CQT_imag),-1)
if self.norm:
CQT = CQT/self.kernel_width*torch.sqrt(self.lenghts.view(-1,1,1))
else:
CQT = CQT*torch.sqrt(self.lenghts.view(-1,1,1))
if output_format=='Magnitude':
# Getting CQT Amplitude
return torch.sqrt(CQT.pow(2).sum(-1))
elif output_format=='Complex':
return CQT
elif output_format=='Phase':
phase_real = torch.cos(torch.atan2(CQT_imag,CQT_real))
phase_imag = torch.sin(torch.atan2(CQT_imag,CQT_real))
return torch.stack((phase_real,phase_imag), -1)
def extra_repr(self) -> str:
return 'STFT kernel size = {}, CQT kernel size = {}'.format(
(*self.wcos.shape,), (*self.cqt_kernels_real.shape,)
)
class CQT2010(torch.nn.Module):
"""
This algorithm is using the resampling method proposed in [1].
Instead of convoluting the STFT results with a gigantic CQT kernel covering the full frequency
spectrum, we make a small CQT kernel covering only the top octave.
Then we keep downsampling the input audio by a factor of 2 to convoluting it with the
small CQT kernel. Everytime the input audio is downsampled, the CQT relative to the downsampled
input is equavalent to the next lower octave.
The kernel creation process is still same as the 1992 algorithm. Therefore, we can reuse the code
from the 1992 alogrithm [2]
[1] Schörkhuber, Christian. “CONSTANT-Q TRANSFORM TOOLBOX FOR MUSIC PROCESSING.” (2010).
[2] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of a
constant Q transform.” (1992).
early downsampling factor is to downsample the input audio to reduce the CQT kernel size.
The result with and without early downsampling are more or less the same except in the very low
frequency region where freq < 40Hz.
"""
def __init__(self, sr=22050, hop_length=512, fmin=32.70, fmax=None, n_bins=84, bins_per_octave=12,
norm=True, basis_norm=1, window='hann', pad_mode='reflect', trainable_STFT=False,
trainable_CQT=False, output_format='Complex', earlydownsample=True, verbose=True):
super().__init__()
self.norm = norm # Now norm is used to normalize the final CQT result by dividing n_fft
# basis_norm is for normalizing basis
self.hop_length = hop_length
self.pad_mode = pad_mode
self.n_bins = n_bins
self.output_format = output_format
self.earlydownsample = earlydownsample # TODO: activate early downsampling later if possible
# This will be used to calculate filter_cutoff and creating CQT kernels
Q = 1/(2**(1/bins_per_octave)-1)
# Creating lowpass filter and make it a torch tensor
if verbose==True:
print("Creating low pass filter ...", end='\r')
start = time()
lowpass_filter = torch.tensor(create_lowpass_filter(
band_center = 0.5,
kernelLength=256,
transitionBandwidth=0.001
)
)
# Broadcast the tensor to the shape that fits conv1d
self.register_buffer('lowpass_filter', lowpass_filter[None,None,:])
if verbose==True:
print("Low pass filter created, time used = {:.4f} seconds".format(time()-start))
# Calculate num of filter requires for the kernel
# n_octaves determines how many resampling requires for the CQT
n_filters = min(bins_per_octave, n_bins)
self.n_octaves = int(np.ceil(float(n_bins) / bins_per_octave))
# print("n_octaves = ", self.n_octaves)
# Calculate the lowest frequency bin for the top octave kernel
self.fmin_t = fmin*2**(self.n_octaves-1)
remainder = n_bins % bins_per_octave
# print("remainder = ", remainder)
if remainder==0:
# Calculate the top bin frequency
fmax_t = self.fmin_t*2**((bins_per_octave-1)/bins_per_octave)
else:
# Calculate the top bin frequency
fmax_t = self.fmin_t*2**((remainder-1)/bins_per_octave)
self.fmin_t = fmax_t/2**(1-1/bins_per_octave) # Adjusting the top minium bins
if fmax_t > sr/2:
raise ValueError('The top bin {}Hz has exceeded the Nyquist frequency, \
please reduce the n_bins'.format(fmax_t))
if self.earlydownsample == True: # Do early downsampling if this argument is True
if verbose==True:
print("Creating early downsampling filter ...", end='\r')
start = time()
sr, self.hop_length, self.downsample_factor, early_downsample_filter, \
self.earlydownsample = get_early_downsample_params(sr,
hop_length,
fmax_t,
Q,
self.n_octaves,
verbose)
self.register_buffer('early_downsample_filter', early_downsample_filter)
if verbose==True:
print("Early downsampling filter created, \
time used = {:.4f} seconds".format(time()-start))
else:
self.downsample_factor=1.
# Preparing CQT kernels
if verbose==True:
print("Creating CQT kernels ...", end='\r')
start = time()
# print("Q = {}, fmin_t = {}, n_filters = {}".format(Q, self.fmin_t, n_filters))
basis, self.n_fft, _ = create_cqt_kernels(Q,
sr,
self.fmin_t,
n_filters,
bins_per_octave,
norm=basis_norm,
topbin_check=False)
# This is for the normalization in the end
freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.float(bins_per_octave))
lenghts = np.ceil(Q * sr / freqs)
lenghts = torch.tensor(lenghts).float()
self.register_buffer('lenghts', lenghts)
self.basis=basis
fft_basis = fft(basis)[:,:self.n_fft//2+1] # Convert CQT kenral from time domain to freq domain
# These cqt_kernel is already in the frequency domain
cqt_kernels_real = torch.tensor(fft_basis.real.astype(np.float32))
cqt_kernels_imag = torch.tensor(fft_basis.imag.astype(np.float32))
if verbose==True:
print("CQT kernels created, time used = {:.4f} seconds".format(time()-start))
# print("Getting cqt kernel done, n_fft = ",self.n_fft)
# Preparing kernels for Short-Time Fourier Transform (STFT)
# We set the frequency range in the CQT filter instead of here.
if verbose==True:
print("Creating STFT kernels ...", end='\r')
start = time()
kernel_sin, kernel_cos, self.bins2freq, _, window = create_fourier_kernels(self.n_fft, window='ones', freq_scale='no')
wsin = kernel_sin * window
wcos = kernel_cos * window
wsin = torch.tensor(wsin)
wcos = torch.tensor(wcos)
if verbose==True:
print("STFT kernels created, time used = {:.4f} seconds".format(time()-start))
if trainable_STFT:
wsin = torch.nn.Parameter(wsin, requires_grad=trainable_kernels)
wcos = torch.nn.Parameter(wcos, requires_grad=trainable_kernels)
self.register_parameter('wsin', wsin)
self.register_parameter('wcos', wcos)
else:
self.register_buffer('wsin', wsin)
self.register_buffer('wcos', wcos)
if trainable_CQT:
cqt_kernels_real = torch.nn.Parameter(cqt_kernels_real, requires_grad=trainable_kernels)
cqt_kernels_imag = torch.nn.Parameter(cqt_kernels_imag, requires_grad=trainable_kernels)
self.register_parameter('cqt_kernels_real', cqt_kernels_real)
self.register_parameter('cqt_kernels_imag', cqt_kernels_imag)
else:
self.register_buffer('cqt_kernels_real', cqt_kernels_real)
self.register_buffer('cqt_kernels_imag', cqt_kernels_imag)
# If center==True, the STFT window will be put in the middle, and paddings at the beginning
# and ending are required.
if self.pad_mode == 'constant':
self.padding = nn.ConstantPad1d(self.n_fft//2, 0)
elif self.pad_mode == 'reflect':
self.padding = nn.ReflectionPad1d(self.n_fft//2)
def forward(self,x, output_format=None):
"""
Convert a batch of waveforms to CQT spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
output_format = output_format or self.output_format
x = broadcast_dim(x)
if self.earlydownsample==True:
x = downsampling_by_n(x, self.early_downsample_filter, self.downsample_factor)
hop = self.hop_length
CQT = get_cqt_complex(x, self.wcos, self.wsin, hop, self.padding) # Getting the top octave CQT
x_down = x # Preparing a new variable for downsampling
for i in range(self.n_octaves-1):
hop = hop//2
x_down = downsampling_by_2(x_down, self.lowpass_filter)
CQT1 = get_cqt_complex(x_down, self.wcos, self.wsin, hop, self.padding)
CQT = torch.cat((CQT1, CQT),1)
CQT = CQT[:,-self.n_bins:,:] # Removing unwanted top bins
if self.norm:
CQT = CQT/self.n_fft*torch.sqrt(self.lenghts.view(-1,1,1))
else:
CQT = CQT*torch.sqrt(self.lenghts.view(-1,1,1))
# Normalizing the output with the downsampling factor, 2**(self.n_octaves-1)
# is make it same mag as 1992
CQT = CQT*self.downsample_factor
if output_format=='Magnitude':
# Getting CQT Amplitude
return torch.sqrt(CQT.pow(2).sum(-1))
elif output_format=='Complex':
return CQT
elif output_format=='Phase':
phase_real = torch.cos(torch.atan2(CQT[:,:,:,1],CQT[:,:,:,0]))
phase_imag = torch.sin(torch.atan2(CQT[:,:,:,1],CQT[:,:,:,0]))
return torch.stack((phase_real,phase_imag), -1)
def extra_repr(self) -> str:
return 'STFT kernel size = {}, CQT kernel size = {}'.format(
(*self.wcos.shape,), (*self.cqt_kernels_real.shape,)
)
class CQT1992v2(torch.nn.Module):
"""This function is to calculate the CQT of the input signal.
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred autommatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
This alogrithm uses the method proposed in [1]. I slightly modify it so that it runs faster
than the original 1992 algorithm, that is why I call it version 2.
[1] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of a
constant Q transform.” (1992).
Parameters
----------
sr : int
The sampling rate for the input audio. It is used to calucate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
hop_length : int
The hop (or stride) size. Default value is 512.
fmin : float
The frequency for the lowest CQT bin. Default is 32.70Hz, which coresponds to the note C0.
fmax : float
The frequency for the highest CQT bin. Default is ``None``, therefore the higest CQT bin is
inferred from the ``n_bins`` and ``bins_per_octave``.
If ``fmax`` is not ``None``, then the argument ``n_bins`` will be ignored and ``n_bins``
will be calculated automatically. Default is ``None``
n_bins : int
The total numbers of CQT bins. Default is 84. Will be ignored if ``fmax`` is not ``None``.
bins_per_octave : int
Number of bins per octave. Default is 12.
norm : int
Normalization for the CQT kernels. ``1`` means L1 normalization, and ``2`` means L2 normalization.
Default is ``1``, which is same as the normalization used in librosa.
window : str
The windowing function for CQT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
center : bool
Putting the CQT keneral at the center of the time-step or not. If ``False``, the time index is
the beginning of the CQT kernel, if ``True``, the time index is the center of the CQT kernel.
Default value if ``True``.
pad_mode : str
The padding method. Default value is 'reflect'.
trainable : bool
Determine if the CQT kernels are trainable or not. If ``True``, the gradients for CQT kernels
will also be caluclated and the CQT kernels will be updated during model training.
Default value is ``False``.
output_format : str
Determine the return type.
``Magnitude`` will return the magnitude of the STFT result, shape = ``(num_samples, freq_bins,time_steps)``;
``Complex`` will return the STFT result in complex number, shape = ``(num_samples, freq_bins,time_steps, 2)``;
``Phase`` will return the phase of the STFT reuslt, shape = ``(num_samples, freq_bins,time_steps, 2)``.
The complex number is stored as ``(real, imag)`` in the last axis. Default value is 'Magnitude'.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints
Returns
-------
spectrogram : torch.tensor
It returns a tensor of spectrograms.
shape = ``(num_samples, freq_bins,time_steps)`` if ``output_format='Magnitude'``;
shape = ``(num_samples, freq_bins,time_steps, 2)`` if ``output_format='Complex' or 'Phase'``;
Examples
--------
>>> spec_layer = Spectrogram.CQT1992v2()
>>> specs = spec_layer(x)
"""
def __init__(self, sr=22050, hop_length=512, fmin=32.70, fmax=None, n_bins=84,
bins_per_octave=12, norm=1, window='hann', center=True, pad_mode='reflect',
trainable=False, output_format='Magnitude', verbose=True):
super().__init__()
# norm arg is not functioning
self.trainable = trainable
self.hop_length = hop_length
self.center = center
self.pad_mode = pad_mode
self.output_format = output_format
# creating kernels for CQT
Q = 1/(2**(1/bins_per_octave)-1)
if verbose==True:
print("Creating CQT kernels ...", end='\r')
start = time()
cqt_kernels, self.kernel_width, lenghts = create_cqt_kernels(Q,
sr,
fmin,
n_bins,
bins_per_octave,
norm,
window,
fmax)
self.register_buffer('lenghts', lenghts)
cqt_kernels_real = torch.tensor(cqt_kernels.real).unsqueeze(1)
cqt_kernels_imag = torch.tensor(cqt_kernels.imag).unsqueeze(1)
if trainable:
cqt_kernels_real = torch.nn.Parameter(cqt_kernels_real, requires_grad=trainable_kernels)
cqt_kernels_imag = torch.nn.Parameter(cqt_kernels_imag, requires_grad=trainable_kernels)
self.register_parameter('cqt_kernels_real', cqt_kernels_real)
self.register_parameter('cqt_kernels_imag', cqt_kernels_imag)
else:
self.register_buffer('cqt_kernels_real', cqt_kernels_real)
self.register_buffer('cqt_kernels_imag', cqt_kernels_imag)
if verbose==True:
print("CQT kernels created, time used = {:.4f} seconds".format(time()-start))
def forward(self,x, output_format=None):
"""
Convert a batch of waveforms to CQT spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
output_format = output_format or self.output_format
x = broadcast_dim(x)
if self.center:
if self.pad_mode == 'constant':
padding = nn.ConstantPad1d(self.kernel_width//2, 0)
elif self.pad_mode == 'reflect':
padding = nn.ReflectionPad1d(self.kernel_width//2)
x = padding(x)
# CQT
CQT_real = conv1d(x, self.cqt_kernels_real, stride=self.hop_length) * \
torch.sqrt(self.lenghts.view(-1,1))
CQT_imag = -conv1d(x, self.cqt_kernels_imag, stride=self.hop_length) * \
torch.sqrt(self.lenghts.view(-1,1))
if output_format=='Magnitude':
if self.trainable==False:
# Getting CQT Amplitude
CQT = torch.sqrt(CQT_real.pow(2)+CQT_imag.pow(2))
else:
CQT = torch.sqrt(CQT_real.pow(2)+CQT_imag.pow(2)+1e-8)
return CQT
elif output_format=='Complex':
return torch.stack((CQT_real,CQT_imag),-1)
elif output_format=='Phase':
phase_real = torch.cos(torch.atan2(CQT_imag,CQT_real))
phase_imag = torch.sin(torch.atan2(CQT_imag,CQT_real))
return torch.stack((phase_real,phase_imag), -1)
def forward_manual(self,x):
"""
Method for debugging
"""
x = broadcast_dim(x)
if self.center:
if self.pad_mode == 'constant':
padding = nn.ConstantPad1d(self.kernel_width//2, 0)
elif self.pad_mode == 'reflect':
padding = nn.ReflectionPad1d(self.kernel_width//2)
x = padding(x)
# CQT
CQT_real = conv1d(x, self.cqt_kernels_real, stride=self.hop_length)
CQT_imag = conv1d(x, self.cqt_kernels_imag, stride=self.hop_length)
# Getting CQT Amplitude
CQT = torch.sqrt(CQT_real.pow(2)+CQT_imag.pow(2))
return CQT*torch.sqrt(self.lenghts.view(-1,1))
class CQT2010v2(torch.nn.Module):
"""This function is to calculate the CQT of the input signal.
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
The correct shape will be inferred autommatically if the input follows these 3 shapes.
Most of the arguments follow the convention from librosa.
This class inherits from ``torch.nn.Module``, therefore, the usage is same as ``torch.nn.Module``.
This alogrithm uses the resampling method proposed in [1].
Instead of convoluting the STFT results with a gigantic CQT kernel covering the full frequency
spectrum, we make a small CQT kernel covering only the top octave. Then we keep downsampling the
input audio by a factor of 2 to convoluting it with the small CQT kernel.
Everytime the input audio is downsampled, the CQT relative to the downsampled input is equivalent
to the next lower octave.
The kernel creation process is still same as the 1992 algorithm. Therefore, we can reuse the
code from the 1992 alogrithm [2]
[1] Schörkhuber, Christian. “CONSTANT-Q TRANSFORM TOOLBOX FOR MUSIC PROCESSING.” (2010).
[2] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of a
constant Q transform.” (1992).
Early downsampling factor is to downsample the input audio to reduce the CQT kernel size.
The result with and without early downsampling are more or less the same except in the very low
frequency region where freq < 40Hz.
Parameters
----------
sr : int
The sampling rate for the input audio. It is used to calucate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
hop_length : int
The hop (or stride) size. Default value is 512.
fmin : float
The frequency for the lowest CQT bin. Default is 32.70Hz, which coresponds to the note C0.
fmax : float
The frequency for the highest CQT bin. Default is ``None``, therefore the higest CQT bin is
inferred from the ``n_bins`` and ``bins_per_octave``. If ``fmax`` is not ``None``, then the
argument ``n_bins`` will be ignored and ``n_bins`` will be calculated automatically.
Default is ``None``
n_bins : int
The total numbers of CQT bins. Default is 84. Will be ignored if ``fmax`` is not ``None``.
bins_per_octave : int
Number of bins per octave. Default is 12.
norm : bool
Normalization for the CQT result.
basis_norm : int
Normalization for the CQT kernels. ``1`` means L1 normalization, and ``2`` means L2 normalization.
Default is ``1``, which is same as the normalization used in librosa.
window : str
The windowing function for CQT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'
pad_mode : str
The padding method. Default value is 'reflect'.
trainable : bool
Determine if the CQT kernels are trainable or not. If ``True``, the gradients for CQT kernels
will also be caluclated and the CQT kernels will be updated during model training.
Default value is ``False``
output_format : str
Determine the return type.
'Magnitude' will return the magnitude of the STFT result, shape = ``(num_samples, freq_bins, time_steps)``;
'Complex' will return the STFT result in complex number, shape = ``(num_samples, freq_bins, time_steps, 2)``;
'Phase' will return the phase of the STFT reuslt, shape = ``(num_samples, freq_bins,time_steps, 2)``.
The complex number is stored as ``(real, imag)`` in the last axis. Default value is 'Magnitude'.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints.
device : str
Choose which device to initialize this layer. Default value is 'cpu'.
Returns
-------
spectrogram : torch.tensor
It returns a tensor of spectrograms.
shape = ``(num_samples, freq_bins,time_steps)`` if ``output_format='Magnitude'``;
shape = ``(num_samples, freq_bins,time_steps, 2)`` if ``output_format='Complex' or 'Phase'``;
Examples
--------
>>> spec_layer = Spectrogram.CQT2010v2()
>>> specs = spec_layer(x)
"""
# To DO:
# need to deal with the filter and other tensors
def __init__(self, sr=22050, hop_length=512, fmin=32.70, fmax=None, n_bins=84,
bins_per_octave=12, norm=True, basis_norm=1, window='hann', pad_mode='reflect',
earlydownsample=True, trainable=False, output_format='Magnitude', verbose=True):
super().__init__()
self.norm = norm # Now norm is used to normalize the final CQT result by dividing n_fft
# basis_norm is for normalizing basis
self.hop_length = hop_length
self.pad_mode = pad_mode
self.n_bins = n_bins
self.earlydownsample = earlydownsample # We will activate early downsampling later if possible
self.trainable = trainable
self.output_format = output_format
# It will be used to calculate filter_cutoff and creating CQT kernels
Q = 1/(2**(1/bins_per_octave)-1)
# Creating lowpass filter and make it a torch tensor
if verbose==True:
print("Creating low pass filter ...", end='\r')
start = time()
# self.lowpass_filter = torch.tensor(
# create_lowpass_filter(
# band_center = 0.50,
# kernelLength=256,
# transitionBandwidth=0.001))
lowpass_filter = torch.tensor(create_lowpass_filter(
band_center = 0.50,
kernelLength=256,
transitionBandwidth=0.001)
)
# Broadcast the tensor to the shape that fits conv1d
self.register_buffer('lowpass_filter', lowpass_filter[None,None,:])
if verbose==True:
print("Low pass filter created, time used = {:.4f} seconds".format(time()-start))
# Caluate num of filter requires for the kernel
# n_octaves determines how many resampling requires for the CQT
n_filters = min(bins_per_octave, n_bins)
self.n_octaves = int(np.ceil(float(n_bins) / bins_per_octave))
if verbose==True:
print("num_octave = ", self.n_octaves)
# Calculate the lowest frequency bin for the top octave kernel
self.fmin_t = fmin*2**(self.n_octaves-1)
remainder = n_bins % bins_per_octave
# print("remainder = ", remainder)
if remainder==0:
# Calculate the top bin frequency
fmax_t = self.fmin_t*2**((bins_per_octave-1)/bins_per_octave)
else:
# Calculate the top bin frequency
fmax_t = self.fmin_t*2**((remainder-1)/bins_per_octave)
self.fmin_t = fmax_t/2**(1-1/bins_per_octave) # Adjusting the top minium bins
if fmax_t > sr/2:
raise ValueError('The top bin {}Hz has exceeded the Nyquist frequency, \
please reduce the n_bins'.format(fmax_t))
if self.earlydownsample == True: # Do early downsampling if this argument is True
if verbose==True:
print("Creating early downsampling filter ...", end='\r')
start = time()
sr, self.hop_length, self.downsample_factor, early_downsample_filter, \
self.earlydownsample = get_early_downsample_params(sr,
hop_length,
fmax_t,
Q,
self.n_octaves,
verbose)
self.register_buffer('early_downsample_filter', early_downsample_filter)
if verbose==True:
print("Early downsampling filter created, \
time used = {:.4f} seconds".format(time()-start))
else:
self.downsample_factor=1.
# Preparing CQT kernels
if verbose==True:
print("Creating CQT kernels ...", end='\r')
start = time()
basis, self.n_fft, lenghts = create_cqt_kernels(Q,
sr,
self.fmin_t,
n_filters,
bins_per_octave,
norm=basis_norm,
topbin_check=False)
# For normalization in the end
freqs = fmin * 2.0 ** (np.r_[0:n_bins] / np.float(bins_per_octave))
lenghts = np.ceil(Q * sr / freqs)
lenghts = torch.tensor(lenghts).float()
self.register_buffer('lenghts', lenghts)
self.basis = basis
# These cqt_kernel is already in the frequency domain
cqt_kernels_real = torch.tensor(basis.real.astype(np.float32)).unsqueeze(1)
cqt_kernels_imag = torch.tensor(basis.imag.astype(np.float32)).unsqueeze(1)
if trainable:
cqt_kernels_real = torch.nn.Parameter(cqt_kernels_real, requires_grad=trainable_kernels)
cqt_kernels_imag = torch.nn.Parameter(cqt_kernels_imag, requires_grad=trainable_kernels)
self.register_parameter('cqt_kernels_real', cqt_kernels_real)
self.register_parameter('cqt_kernels_imag', cqt_kernels_imag)
else:
self.register_buffer('cqt_kernels_real', cqt_kernels_real)
self.register_buffer('cqt_kernels_imag', cqt_kernels_imag)
if verbose==True:
print("CQT kernels created, time used = {:.4f} seconds".format(time()-start))
# print("Getting cqt kernel done, n_fft = ",self.n_fft)
# If center==True, the STFT window will be put in the middle, and paddings at the beginning
# and ending are required.
if self.pad_mode == 'constant':
self.padding = nn.ConstantPad1d(self.n_fft//2, 0)
elif self.pad_mode == 'reflect':
self.padding = nn.ReflectionPad1d(self.n_fft//2)
def forward(self,x,output_format=None):
"""
Convert a batch of waveforms to CQT spectrograms.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
output_format = output_format or self.output_format
x = broadcast_dim(x)
if self.earlydownsample==True:
x = downsampling_by_n(x, self.early_downsample_filter, self.downsample_factor)
hop = self.hop_length
CQT = get_cqt_complex(x, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding) # Getting the top octave CQT
x_down = x # Preparing a new variable for downsampling
for i in range(self.n_octaves-1):
hop = hop//2
x_down = downsampling_by_2(x_down, self.lowpass_filter)
CQT1 = get_cqt_complex(x_down, self.cqt_kernels_real, self.cqt_kernels_imag, hop, self.padding)
CQT = torch.cat((CQT1, CQT),1)
CQT = CQT[:,-self.n_bins:,:] # Removing unwanted bottom bins
# print("downsample_factor = ",self.downsample_factor)
# print(CQT.shape)
# print(self.lenghts.view(-1,1).shape)
# Normalizing the output with the downsampling factor, 2**(self.n_octaves-1) is make it
# same mag as 1992
CQT = CQT*self.downsample_factor
# Normalize again to get same result as librosa
CQT = CQT*torch.sqrt(self.lenghts.view(-1,1,1))
if output_format=='Magnitude':
if self.trainable==False:
# Getting CQT Amplitude
return torch.sqrt(CQT.pow(2).sum(-1))
else:
return torch.sqrt(CQT.pow(2).sum(-1)+1e-8)
elif output_format=='Complex':
return CQT
elif output_format=='Phase':
phase_real = torch.cos(torch.atan2(CQT[:,:,:,1],CQT[:,:,:,0]))
phase_imag = torch.sin(torch.atan2(CQT[:,:,:,1],CQT[:,:,:,0]))
return torch.stack((phase_real,phase_imag), -1)
class CQT(CQT1992v2):
"""An abbreviation for :func:`~nnAudio.Spectrogram.CQT1992v2`. Please refer to the :func:`~nnAudio.Spectrogram.CQT1992v2` documentation"""
pass
# The section below is for developing purpose
# Please don't use the following classes
#
class DFT(torch.nn.Module):
"""
Experimental feature before `torch.fft` was made avaliable.
The inverse function only works for 1 single frame. i.e. input shape = (batch, n_fft, 1)
"""
def __init__(self, n_fft=2048, freq_bins=None, hop_length=512,
window='hann', freq_scale='no', center=True, pad_mode='reflect',
fmin=50, fmax=6000, sr=22050):
super().__init__()
self.stride = hop_length
self.center = center
self.pad_mode = pad_mode
self.n_fft = n_fft
# Create filter windows for stft
wsin, wcos, self.bins2freq = create_fourier_kernels(n_fft=n_fft,
freq_bins=n_fft,
window=window,
freq_scale=freq_scale,
fmin=fmin,
fmax=fmax,
sr=sr)
self.wsin = torch.tensor(wsin, dtype=torch.float)
self.wcos = torch.tensor(wcos, dtype=torch.float)
def forward(self,x):
"""
Convert a batch of waveforms to spectrums.
Parameters
----------
x : torch tensor
Input signal should be in either of the following shapes.\n
1. ``(len_audio)``\n
2. ``(num_audio, len_audio)``\n
3. ``(num_audio, 1, len_audio)``
It will be automatically broadcast to the right shape
"""
x = broadcast_dim(x)
if self.center:
if self.pad_mode == 'constant':
padding = nn.ConstantPad1d(self.n_fft//2, 0)
elif self.pad_mode == 'reflect':
padding = nn.ReflectionPad1d(self.n_fft//2)
x = padding(x)
imag = conv1d(x, self.wsin, stride=self.stride)
real = conv1d(x, self.wcos, stride=self.stride)
return (real, -imag)
def inverse(self,x_real,x_imag):
"""
Convert a batch of waveforms to CQT spectrograms.
Parameters
----------
x_real : torch tensor
Real part of the signal.
x_imag : torch tensor
Imaginary part of the signal.
"""
x_real = broadcast_dim(x_real)
x_imag = broadcast_dim(x_imag)
x_real.transpose_(1,2) # Prepare the right shape to do inverse
x_imag.transpose_(1,2) # Prepare the right shape to do inverse
# if self.center:
# if self.pad_mode == 'constant':
# padding = nn.ConstantPad1d(self.n_fft//2, 0)
# elif self.pad_mode == 'reflect':
# padding = nn.ReflectionPad1d(self.n_fft//2)
# x_real = padding(x_real)
# x_imag = padding(x_imag)
# Watch out for the positive and negative signs
# ifft = e^(+2\pi*j)*X
# ifft(X_real) = (a1, a2)
# ifft(X_imag)*1j = (b1, b2)*1j
# = (-b2, b1)
a1 = conv1d(x_real, self.wcos, stride=self.stride)
a2 = conv1d(x_real, self.wsin, stride=self.stride)
b1 = conv1d(x_imag, self.wcos, stride=self.stride)
b2 = conv1d(x_imag, self.wsin, stride=self.stride)
imag = a2+b1
real = a1-b2
return (real/self.n_fft, imag/self.n_fft)
class iSTFT(torch.nn.Module):
"""This class is to convert spectrograms back to waveforms. It only works for the complex value spectrograms.
If you have the magnitude spectrograms, please use :func:`~nnAudio.Spectrogram.Griffin_Lim`.
The parameters (e.g. n_fft, window) need to be the same as the STFT in order to obtain the correct inverse.
If trainability is not required, it is recommended to use the ``inverse`` method under the ``STFT`` class
to save GPU/RAM memory.
When ``trainable=True`` and ``freq_scale!='no'``, there is no guarantee that the inverse is perfect, please
use with extra care.
Parameters
----------
n_fft : int
The window size. Default value is 2048.
freq_bins : int
Number of frequency bins. Default is ``None``, which means ``n_fft//2+1`` bins
Please make sure the value is the same as the forward STFT.
hop_length : int
The hop (or stride) size. Default value is ``None`` which is equivalent to ``n_fft//4``.
Please make sure the value is the same as the forward STFT.
window : str
The windowing function for iSTFT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
Please make sure the value is the same as the forward STFT.
freq_scale : 'linear', 'log', or 'no'
Determine the spacing between each frequency bin. When `linear` or `log` is used,
the bin spacing can be controlled by ``fmin`` and ``fmax``. If 'no' is used, the bin will
start at 0Hz and end at Nyquist frequency with linear spacing.
Please make sure the value is the same as the forward STFT.
center : bool
Putting the iSTFT keneral at the center of the time-step or not. If ``False``, the time
index is the beginning of the iSTFT kernel, if ``True``, the time index is the center of
the iSTFT kernel. Default value if ``True``.
Please make sure the value is the same as the forward STFT.
fmin : int
The starting frequency for the lowest frequency bin. If freq_scale is ``no``, this argument
does nothing. Please make sure the value is the same as the forward STFT.
fmax : int
The ending frequency for the highest frequency bin. If freq_scale is ``no``, this argument
does nothing. Please make sure the value is the same as the forward STFT.
sr : int
The sampling rate for the input audio. It is used to calucate the correct ``fmin`` and ``fmax``.
Setting the correct sampling rate is very important for calculating the correct frequency.
trainable_kernels : bool
Determine if the STFT kenrels are trainable or not. If ``True``, the gradients for STFT
kernels will also be caluclated and the STFT kernels will be updated during model training.
Default value is ``False``.
trainable_window : bool
Determine if the window function is trainable or not.
Default value is ``False``.
verbose : bool
If ``True``, it shows layer information. If ``False``, it suppresses all prints.
device : str
Choose which device to initialize this layer. Default value is 'cpu'.
Returns
-------
spectrogram : torch.tensor
It returns a batch of waveforms.
Examples
--------
>>> spec_layer = Spectrogram.iSTFT()
>>> specs = spec_layer(x)
"""
def __init__(self, n_fft=2048, win_length=None, freq_bins=None, hop_length=None, window='hann',
freq_scale='no', center=True, fmin=50, fmax=6000, sr=22050, trainable_kernels=False,
trainable_window=False, verbose=True, refresh_win=True):
super().__init__()
# Trying to make the default setting same as librosa
if win_length==None: win_length = n_fft
if hop_length==None: hop_length = int(win_length // 4)
self.n_fft = n_fft
self.win_length = win_length
self.stride = hop_length
self.center = center
self.pad_amount = self.n_fft // 2
self.refresh_win = refresh_win
start = time()
# Create the window function and prepare the shape for batch-wise-time-wise multiplication
# Create filter windows for inverse
kernel_sin, kernel_cos, _, _, window_mask = create_fourier_kernels(n_fft,
win_length=win_length,
freq_bins=n_fft,
window=window,
freq_scale=freq_scale,
fmin=fmin,
fmax=fmax,
sr=sr,
verbose=False)
window_mask = get_window(window,int(win_length), fftbins=True)
# For inverse, the Fourier kernels do not need to be windowed
window_mask = torch.tensor(window_mask).unsqueeze(0).unsqueeze(-1)
# kernel_sin and kernel_cos have the shape (freq_bins, 1, n_fft, 1) to support 2D Conv
kernel_sin = torch.tensor(kernel_sin, dtype=torch.float).unsqueeze(-1)
kernel_cos = torch.tensor(kernel_cos, dtype=torch.float).unsqueeze(-1)
# Decide if the Fourier kernels are trainable
if trainable_kernels:
# Making all these variables trainable
kernel_sin = torch.nn.Parameter(kernel_sin, requires_grad=trainable_kernels)
kernel_cos = torch.nn.Parameter(kernel_cos, requires_grad=trainable_kernels)
self.register_parameter('kernel_sin', kernel_sin)
self.register_parameter('kernel_cos', kernel_cos)
else:
self.register_buffer('kernel_sin', kernel_sin)
self.register_buffer('kernel_cos', kernel_cos)
# Decide if the window function is trainable
if trainable_window:
window_mask = torch.nn.Parameter(window_mask, requires_grad=trainable_window)
self.register_parameter('window_mask', window_mask)
else:
self.register_buffer('window_mask', window_mask)
if verbose==True:
print("iSTFT kernels created, time used = {:.4f} seconds".format(time()-start))
else:
pass
def forward(self, X, onesided=False, length=None, refresh_win=None):
"""
If your spectrograms only have ``n_fft//2+1`` frequency bins, please use ``onesided=True``,
else use ``onesided=False``
To make sure the inverse STFT has the same output length of the original waveform, please
set `length` as your intended waveform length. By default, ``length=None``,
which will remove ``n_fft//2`` samples from the start and the end of the output.
If your input spectrograms X are of the same length, please use ``refresh_win=None`` to increase
computational speed.
"""
if refresh_win==None:
refresh_win=self.refresh_win
assert X.dim()==4 , "Inverse iSTFT only works for complex number," \
"make sure our tensor is in the shape of (batch, freq_bins, timesteps, 2)"
# If the input spectrogram contains only half of the n_fft
# Use extend_fbins function to get back another half
if onesided:
X = extend_fbins(X) # extend freq
X_real, X_imag = X[:, :, :, 0], X[:, :, :, 1]
# broadcast dimensions to support 2D convolution
X_real_bc = X_real.unsqueeze(1)
X_imag_bc = X_imag.unsqueeze(1)
a1 = conv2d(X_real_bc, self.kernel_cos, stride=(1,1))
b2 = conv2d(X_imag_bc, self.kernel_sin, stride=(1,1))
# compute real and imag part. signal lies in the real part
real = a1 - b2
real = real.squeeze(-2)*self.window_mask
# Normalize the amplitude with n_fft
real /= (self.n_fft)
# Overlap and Add algorithm to connect all the frames
real = overlap_add(real, self.stride)
# Prepare the window sumsqure for division
# Only need to create this window once to save time
# Unless the input spectrograms have different time steps
if hasattr(self, 'w_sum')==False or refresh_win==True:
self.w_sum = torch_window_sumsquare(self.window_mask.flatten(), X.shape[2], self.stride, self.n_fft).flatten()
self.nonzero_indices = (self.w_sum>1e-10)
else:
pass
real[:, self.nonzero_indices] = real[:,self.nonzero_indices].div(self.w_sum[self.nonzero_indices])
# Remove padding
if length is None:
if self.center:
real = real[:, self.pad_amount:-self.pad_amount]
else:
if self.center:
real = real[:, self.pad_amount:self.pad_amount + length]
else:
real = real[:, :length]
return real
class Griffin_Lim(torch.nn.Module):
"""
Converting Magnitude spectrograms back to waveforms based on the "fast Griffin-Lim"[1].
This Griffin Lim is a direct clone from librosa.griffinlim.
[1] Perraudin, N., Balazs, P., & Søndergaard, P. L. “A fast Griffin-Lim algorithm,”
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (pp. 1-4), Oct. 2013.
Parameters
----------
n_fft : int
The window size. Default value is 2048.
n_iter=32 : int
The number of iterations for Griffin-Lim. The default value is ``32``
hop_length : int
The hop (or stride) size. Default value is ``None`` which is equivalent to ``n_fft//4``.
Please make sure the value is the same as the forward STFT.
window : str
The windowing function for iSTFT. It uses ``scipy.signal.get_window``, please refer to
scipy documentation for possible windowing functions. The default value is 'hann'.
Please make sure the value is the same as the forward STFT.
center : bool
Putting the iSTFT keneral at the center of the time-step or not. If ``False``, the time
index is the beginning of the iSTFT kernel, if ``True``, the time index is the center of
the iSTFT kernel. Default value if ``True``.
Please make sure the value is the same as the forward STFT.
momentum : float
The momentum for the update rule. The default value is ``0.99``.
device : str
Choose which device to initialize this layer. Default value is 'cpu'
"""
def __init__(self,
n_fft,
n_iter=32,
hop_length=None,
win_length=None,
window='hann',
center=True,
pad_mode='reflect',
momentum=0.99,
device='cpu'):
super().__init__()
self.n_fft = n_fft
self.win_length = win_length
self.n_iter = n_iter
self.center = center
self.pad_mode = pad_mode
self.momentum = momentum
self.device = device
if win_length==None:
self.win_length=n_fft
else:
self.win_length=win_length
if hop_length==None:
self.hop_length = n_fft//4
else:
self.hop_length = hop_length
# Creating window function for stft and istft later
self.w = torch.tensor(get_window(window,
int(self.win_length),
fftbins=True),
device=device).float()
def forward(self, S):
"""
Convert a batch of magnitude spectrograms to waveforms.
Parameters
----------
S : torch tensor
Spectrogram of the shape ``(batch, n_fft//2+1, timesteps)``
"""
assert S.dim()==3 , "Please make sure your input is in the shape of (batch, freq_bins, timesteps)"
# Initializing Random Phase
rand_phase = torch.randn(*S.shape, device=self.device)
angles = torch.empty((*S.shape,2), device=self.device)
angles[:, :,:,0] = torch.cos(2 * np.pi * rand_phase)
angles[:,:,:,1] = torch.sin(2 * np.pi * rand_phase)
# Initializing the rebuilt magnitude spectrogram
rebuilt = torch.zeros(*angles.shape, device=self.device)
for _ in range(self.n_iter):
tprev = rebuilt # Saving previous rebuilt magnitude spec
# spec2wav conversion
# print(f'win_length={self.win_length}\tw={self.w.shape}')
inverse = torch.istft(S.unsqueeze(-1) * angles,
self.n_fft,
self.hop_length,
win_length=self.win_length,
window=self.w,
center=self.center)
# wav2spec conversion
rebuilt = torch.stft(inverse,
self.n_fft,
self.hop_length,
win_length=self.win_length,
window=self.w,
pad_mode=self.pad_mode)
# Phase update rule
angles[:,:,:] = rebuilt[:,:,:] - (self.momentum / (1 + self.momentum)) * tprev[:,:,:]
# Phase normalization
angles = angles.div(torch.sqrt(angles.pow(2).sum(-1)).unsqueeze(-1) + 1e-16) # normalizing the phase
# Using the final phase to reconstruct the waveforms
inverse = torch.istft(S.unsqueeze(-1) * angles,
self.n_fft,
self.hop_length,
win_length=self.win_length,
window=self.w,
center=self.center)
return inverse
| 41.695906 | 172 | 0.593233 | 85,031 | 0.993457 | 0 | 0 | 0 | 0 | 0 | 0 | 43,422 | 0.50732 |
07e4a4e5e49ff1a01f2886f954c1382ba8822f86 | 9,352 | py | Python | train.py | hui-won/KoBART_Project | 105608997473abc669d777c588d56382efb524c6 | [
"MIT"
] | 13 | 2020-12-30T15:09:08.000Z | 2022-01-02T08:11:18.000Z | train.py | hui-won/KoBART_Project | 105608997473abc669d777c588d56382efb524c6 | [
"MIT"
] | 2 | 2021-11-21T11:49:31.000Z | 2022-03-18T05:09:13.000Z | train.py | hui-won/KoBART_Project | 105608997473abc669d777c588d56382efb524c6 | [
"MIT"
] | 1 | 2021-06-15T01:24:18.000Z | 2021-06-15T01:24:18.000Z | import argparse
import logging
import os
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
from pytorch_lightning import loggers as pl_loggers
from torch.utils.data import DataLoader, Dataset
from dataset import KoBARTSummaryDataset
from transformers import BartForConditionalGeneration, PreTrainedTokenizerFast
from transformers.optimization import AdamW, get_cosine_schedule_with_warmup
from kobart import get_pytorch_kobart_model, get_kobart_tokenizer
parser = argparse.ArgumentParser(description='KoBART translation')
parser.add_argument('--checkpoint_path',
type=str,
help='checkpoint path')
logger = logging.getLogger()
logger.setLevel(logging.INFO)
class ArgsBase():
@staticmethod
def add_model_specific_args(parent_parser):
parser = argparse.ArgumentParser(
parents=[parent_parser], add_help=False)
parser.add_argument('--train_file',
type=str,
default='data/train.tsv',
help='train file')
parser.add_argument('--test_file',
type=str,
default='data/test.tsv',
help='test file')
parser.add_argument('--batch_size',
type=int,
default=28,
help='')
parser.add_argument('--max_len',
type=int,
default=512,
help='max seq len')
return parser
class KobartSummaryModule(pl.LightningDataModule):
def __init__(self, train_file,
test_file, tok,
max_len=512,
batch_size=8,
num_workers=5):
super().__init__()
self.batch_size = batch_size
self.max_len = max_len
self.train_file_path = train_file
self.test_file_path = test_file
if tok is None:
self.tok = get_kobart_tokenizer()
else:
self.tok = tok
self.num_workers = num_workers
@staticmethod
def add_model_specific_args(parent_parser):
parser = argparse.ArgumentParser(
parents=[parent_parser], add_help=False)
parser.add_argument('--num_workers',
type=int,
default=5,
help='num of worker for dataloader')
return parser
# OPTIONAL, called for every GPU/machine (assigning state is OK)
def setup(self, stage):
# split dataset
self.train = KoBARTSummaryDataset(self.train_file_path,
self.tok,
self.max_len)
self.test = KoBARTSummaryDataset(self.test_file_path,
self.tok,
self.max_len)
def train_dataloader(self):
train = DataLoader(self.train,
batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=True)
return train
def val_dataloader(self):
val = DataLoader(self.test,
batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=False)
return val
def test_dataloader(self):
test = DataLoader(self.test,
batch_size=self.batch_size,
num_workers=self.num_workers, shuffle=False)
return test
class Base(pl.LightningModule):
def __init__(self, hparams, **kwargs) -> None:
super(Base, self).__init__()
self.hparams = hparams
@staticmethod
def add_model_specific_args(parent_parser):
# add model specific args
parser = argparse.ArgumentParser(
parents=[parent_parser], add_help=False)
parser.add_argument('--batch-size',
type=int,
default=14,
help='batch size for training (default: 96)')
parser.add_argument('--lr',
type=float,
default=3e-5,
help='The initial learning rate')
parser.add_argument('--warmup_ratio',
type=float,
default=0.1,
help='warmup ratio')
parser.add_argument('--model_path',
type=str,
default=None,
help='kobart model path')
return parser
def configure_optimizers(self):
# Prepare optimizer
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(
nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(
nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=self.hparams.lr, correct_bias=False)
# warm up lr
num_workers = (self.hparams.gpus if self.hparams.gpus is not None else 1) * (self.hparams.num_nodes if self.hparams.num_nodes is not None else 1)
data_len = len(self.train_dataloader().dataset)
logging.info(f'number of workers {num_workers}, data length {data_len}')
num_train_steps = int(data_len / (self.hparams.batch_size * num_workers) * self.hparams.max_epochs)
logging.info(f'num_train_steps : {num_train_steps}')
num_warmup_steps = int(num_train_steps * self.hparams.warmup_ratio)
logging.info(f'num_warmup_steps : {num_warmup_steps}')
scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=num_warmup_steps, num_training_steps=num_train_steps)
lr_scheduler = {'scheduler': scheduler,
'monitor': 'loss', 'interval': 'step',
'frequency': 1}
return [optimizer], [lr_scheduler]
class KoBARTConditionalGeneration(Base):
def __init__(self, hparams, **kwargs):
super(KoBARTConditionalGeneration, self).__init__(hparams, **kwargs)
self.model = BartForConditionalGeneration.from_pretrained(get_pytorch_kobart_model())
self.model.train()
self.bos_token = '<s>'
self.eos_token = '</s>'
self.pad_token_id = 0
self.tokenizer = get_kobart_tokenizer()
def forward(self, inputs):
attention_mask = inputs['input_ids'].ne(self.pad_token_id).float()
decoder_attention_mask = inputs['decoder_input_ids'].ne(self.pad_token_id).float()
return self.model(input_ids=inputs['input_ids'],
attention_mask=attention_mask,
decoder_input_ids=inputs['decoder_input_ids'],
decoder_attention_mask=decoder_attention_mask,
labels=inputs['labels'], return_dict=True)
def training_step(self, batch, batch_idx):
outs = self(batch)
loss = outs.loss
self.log('train_loss', loss, prog_bar=True)
return loss
def validation_step(self, batch, batch_idx):
outs = self(batch)
loss = outs['loss']
return (loss)
def validation_epoch_end(self, outputs):
losses = []
for loss in outputs:
losses.append(loss)
self.log('val_loss', torch.stack(losses).mean(), prog_bar=True)
if __name__ == '__main__':
parser = Base.add_model_specific_args(parser)
parser = ArgsBase.add_model_specific_args(parser)
parser = KobartSummaryModule.add_model_specific_args(parser)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
logging.info(args)
model = KoBARTConditionalGeneration(args)
dm = KobartSummaryModule(args.train_file,
args.test_file,
None,
max_len=args.max_len,
batch_size=args.batch_size,
num_workers=args.num_workers)
checkpoint_callback = pl.callbacks.ModelCheckpoint(monitor='val_loss',
dirpath=args.default_root_dir,
filename='model_chp/{epoch:02d}-{val_loss:.3f}',
verbose=True,
save_last=True,
mode='min',
save_top_k=-1,
prefix='kobart_translation')
tb_logger = pl_loggers.TensorBoardLogger(os.path.join(args.default_root_dir, 'tb_logs'))
lr_logger = pl.callbacks.LearningRateMonitor()
trainer = pl.Trainer.from_argparse_args(args, logger=tb_logger,
callbacks=[checkpoint_callback, lr_logger])
trainer.fit(model, dm)
| 39.627119 | 153 | 0.559773 | 7,034 | 0.752139 | 0 | 0 | 2,182 | 0.233319 | 0 | 0 | 978 | 0.104577 |
07e4f4a4fe370f9d6aeaae97b9bd2ee2d9364898 | 11,945 | py | Python | homeassistant/components/shelly/sensor.py | RavensburgOP/core | 0ea76e848b182ca0ebb0fdb54558f7f733898ad7 | [
"Apache-2.0"
] | 1 | 2019-08-28T00:54:28.000Z | 2019-08-28T00:54:28.000Z | homeassistant/components/shelly/sensor.py | RavensburgOP/core | 0ea76e848b182ca0ebb0fdb54558f7f733898ad7 | [
"Apache-2.0"
] | 71 | 2020-07-14T09:08:56.000Z | 2022-03-31T06:01:47.000Z | homeassistant/components/shelly/sensor.py | Vaarlion/core | f3de8b9f28de01abf72c0f5bb0b457eb1841f201 | [
"Apache-2.0"
] | null | null | null | """Sensor for Shelly."""
from __future__ import annotations
from datetime import timedelta
import logging
from typing import Final, cast
import aioshelly
from homeassistant.components import sensor
from homeassistant.components.sensor import SensorEntity
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import (
CONCENTRATION_PARTS_PER_MILLION,
DEGREE,
ELECTRIC_CURRENT_AMPERE,
ELECTRIC_POTENTIAL_VOLT,
ENERGY_KILO_WATT_HOUR,
LIGHT_LUX,
PERCENTAGE,
POWER_WATT,
SIGNAL_STRENGTH_DECIBELS_MILLIWATT,
)
from homeassistant.core import HomeAssistant
from homeassistant.helpers.entity_platform import AddEntitiesCallback
from homeassistant.helpers.typing import StateType
from homeassistant.util import dt
from . import ShellyDeviceWrapper
from .const import LAST_RESET_NEVER, LAST_RESET_UPTIME, SHAIR_MAX_WORK_HOURS
from .entity import (
BlockAttributeDescription,
RestAttributeDescription,
ShellyBlockAttributeEntity,
ShellyRestAttributeEntity,
ShellySleepingBlockAttributeEntity,
async_setup_entry_attribute_entities,
async_setup_entry_rest,
)
from .utils import get_device_uptime, temperature_unit
_LOGGER: Final = logging.getLogger(__name__)
SENSORS: Final = {
("device", "battery"): BlockAttributeDescription(
name="Battery",
unit=PERCENTAGE,
device_class=sensor.DEVICE_CLASS_BATTERY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
removal_condition=lambda settings, _: settings.get("external_power") == 1,
),
("device", "deviceTemp"): BlockAttributeDescription(
name="Device Temperature",
unit=temperature_unit,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_TEMPERATURE,
state_class=sensor.STATE_CLASS_MEASUREMENT,
default_enabled=False,
),
("emeter", "current"): BlockAttributeDescription(
name="Current",
unit=ELECTRIC_CURRENT_AMPERE,
value=lambda value: value,
device_class=sensor.DEVICE_CLASS_CURRENT,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("light", "power"): BlockAttributeDescription(
name="Power",
unit=POWER_WATT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_POWER,
state_class=sensor.STATE_CLASS_MEASUREMENT,
default_enabled=False,
),
("device", "power"): BlockAttributeDescription(
name="Power",
unit=POWER_WATT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_POWER,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("emeter", "power"): BlockAttributeDescription(
name="Power",
unit=POWER_WATT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_POWER,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("emeter", "voltage"): BlockAttributeDescription(
name="Voltage",
unit=ELECTRIC_POTENTIAL_VOLT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_VOLTAGE,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("emeter", "powerFactor"): BlockAttributeDescription(
name="Power Factor",
unit=PERCENTAGE,
value=lambda value: round(value * 100, 1),
device_class=sensor.DEVICE_CLASS_POWER_FACTOR,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("relay", "power"): BlockAttributeDescription(
name="Power",
unit=POWER_WATT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_POWER,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("roller", "rollerPower"): BlockAttributeDescription(
name="Power",
unit=POWER_WATT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_POWER,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("device", "energy"): BlockAttributeDescription(
name="Energy",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 60 / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
last_reset=LAST_RESET_UPTIME,
),
("emeter", "energy"): BlockAttributeDescription(
name="Energy",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
last_reset=LAST_RESET_NEVER,
),
("emeter", "energyReturned"): BlockAttributeDescription(
name="Energy Returned",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
last_reset=LAST_RESET_NEVER,
),
("light", "energy"): BlockAttributeDescription(
name="Energy",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 60 / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
default_enabled=False,
last_reset=LAST_RESET_UPTIME,
),
("relay", "energy"): BlockAttributeDescription(
name="Energy",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 60 / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
last_reset=LAST_RESET_UPTIME,
),
("roller", "rollerEnergy"): BlockAttributeDescription(
name="Energy",
unit=ENERGY_KILO_WATT_HOUR,
value=lambda value: round(value / 60 / 1000, 2),
device_class=sensor.DEVICE_CLASS_ENERGY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
last_reset=LAST_RESET_UPTIME,
),
("sensor", "concentration"): BlockAttributeDescription(
name="Gas Concentration",
unit=CONCENTRATION_PARTS_PER_MILLION,
icon="mdi:gauge",
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("sensor", "extTemp"): BlockAttributeDescription(
name="Temperature",
unit=temperature_unit,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_TEMPERATURE,
state_class=sensor.STATE_CLASS_MEASUREMENT,
available=lambda block: cast(bool, block.extTemp != 999),
),
("sensor", "humidity"): BlockAttributeDescription(
name="Humidity",
unit=PERCENTAGE,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_HUMIDITY,
state_class=sensor.STATE_CLASS_MEASUREMENT,
available=lambda block: cast(bool, block.extTemp != 999),
),
("sensor", "luminosity"): BlockAttributeDescription(
name="Luminosity",
unit=LIGHT_LUX,
device_class=sensor.DEVICE_CLASS_ILLUMINANCE,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("sensor", "tilt"): BlockAttributeDescription(
name="Tilt",
unit=DEGREE,
icon="mdi:angle-acute",
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("relay", "totalWorkTime"): BlockAttributeDescription(
name="Lamp Life",
unit=PERCENTAGE,
icon="mdi:progress-wrench",
value=lambda value: round(100 - (value / 3600 / SHAIR_MAX_WORK_HOURS), 1),
extra_state_attributes=lambda block: {
"Operational hours": round(block.totalWorkTime / 3600, 1)
},
),
("adc", "adc"): BlockAttributeDescription(
name="ADC",
unit=ELECTRIC_POTENTIAL_VOLT,
value=lambda value: round(value, 1),
device_class=sensor.DEVICE_CLASS_VOLTAGE,
state_class=sensor.STATE_CLASS_MEASUREMENT,
),
("sensor", "sensorOp"): BlockAttributeDescription(
name="Operation",
icon="mdi:cog-transfer",
value=lambda value: value,
extra_state_attributes=lambda block: {"self_test": block.selfTest},
),
}
REST_SENSORS: Final = {
"rssi": RestAttributeDescription(
name="RSSI",
unit=SIGNAL_STRENGTH_DECIBELS_MILLIWATT,
value=lambda status, _: status["wifi_sta"]["rssi"],
device_class=sensor.DEVICE_CLASS_SIGNAL_STRENGTH,
state_class=sensor.STATE_CLASS_MEASUREMENT,
default_enabled=False,
),
"uptime": RestAttributeDescription(
name="Uptime",
value=get_device_uptime,
device_class=sensor.DEVICE_CLASS_TIMESTAMP,
default_enabled=False,
),
}
async def async_setup_entry(
hass: HomeAssistant,
config_entry: ConfigEntry,
async_add_entities: AddEntitiesCallback,
) -> None:
"""Set up sensors for device."""
if config_entry.data["sleep_period"]:
await async_setup_entry_attribute_entities(
hass, config_entry, async_add_entities, SENSORS, ShellySleepingSensor
)
else:
await async_setup_entry_attribute_entities(
hass, config_entry, async_add_entities, SENSORS, ShellySensor
)
await async_setup_entry_rest(
hass, config_entry, async_add_entities, REST_SENSORS, ShellyRestSensor
)
class ShellySensor(ShellyBlockAttributeEntity, SensorEntity):
"""Represent a shelly sensor."""
def __init__(
self,
wrapper: ShellyDeviceWrapper,
block: aioshelly.Block,
attribute: str,
description: BlockAttributeDescription,
) -> None:
"""Initialize sensor."""
super().__init__(wrapper, block, attribute, description)
self._last_value: float | None = None
if description.last_reset == LAST_RESET_NEVER:
self._attr_last_reset = dt.utc_from_timestamp(0)
elif description.last_reset == LAST_RESET_UPTIME:
self._attr_last_reset = (
dt.utcnow() - timedelta(seconds=wrapper.device.status["uptime"])
).replace(second=0, microsecond=0)
@property
def state(self) -> StateType:
"""Return value of sensor."""
if (
self.description.last_reset == LAST_RESET_UPTIME
and self.attribute_value is not None
):
value = cast(float, self.attribute_value)
if self._last_value and self._last_value > value:
self._attr_last_reset = dt.utcnow().replace(second=0, microsecond=0)
_LOGGER.info("Energy reset detected for entity %s", self.name)
self._last_value = value
return self.attribute_value
@property
def state_class(self) -> str | None:
"""State class of sensor."""
return self.description.state_class
@property
def unit_of_measurement(self) -> str | None:
"""Return unit of sensor."""
return cast(str, self._unit)
class ShellyRestSensor(ShellyRestAttributeEntity, SensorEntity):
"""Represent a shelly REST sensor."""
@property
def state(self) -> StateType:
"""Return value of sensor."""
return self.attribute_value
@property
def state_class(self) -> str | None:
"""State class of sensor."""
return self.description.state_class
@property
def unit_of_measurement(self) -> str | None:
"""Return unit of sensor."""
return self.description.unit
class ShellySleepingSensor(ShellySleepingBlockAttributeEntity, SensorEntity):
"""Represent a shelly sleeping sensor."""
@property
def state(self) -> StateType:
"""Return value of sensor."""
if self.block is not None:
return self.attribute_value
return self.last_state
@property
def state_class(self) -> str | None:
"""State class of sensor."""
return self.description.state_class
@property
def unit_of_measurement(self) -> str | None:
"""Return unit of sensor."""
return cast(str, self._unit)
| 34.226361 | 84 | 0.669904 | 2,721 | 0.227794 | 0 | 0 | 1,665 | 0.139389 | 640 | 0.053579 | 1,322 | 0.110674 |
07e5b14fe954fccf9ada38a8fb44f9dd227c6830 | 1,301 | py | Python | tests/web/config.py | zcqian/biothings.api | 61c0300317cf2ac7db8310b5b5741ad9b08c4163 | [
"Apache-2.0"
] | null | null | null | tests/web/config.py | zcqian/biothings.api | 61c0300317cf2ac7db8310b5b5741ad9b08c4163 | [
"Apache-2.0"
] | null | null | null | tests/web/config.py | zcqian/biothings.api | 61c0300317cf2ac7db8310b5b5741ad9b08c4163 | [
"Apache-2.0"
] | null | null | null | """
Web settings to override for testing.
"""
import os
from biothings.web.settings.default import QUERY_KWARGS
# *****************************************************************************
# Elasticsearch Variables
# *****************************************************************************
ES_INDEX = 'bts_test'
ES_DOC_TYPE = 'gene'
ES_SCROLL_SIZE = 60
# *****************************************************************************
# User Input Control
# *****************************************************************************
# use a smaller size for testing
QUERY_KWARGS['GET']['facet_size']['default'] = 3
QUERY_KWARGS['GET']['facet_size']['max'] = 5
QUERY_KWARGS['POST']['q']['jsoninput'] = True
# *****************************************************************************
# Elasticsearch Query Builder
# *****************************************************************************
ALLOW_RANDOM_QUERY = True
ALLOW_NESTED_AGGS = True
USERQUERY_DIR = os.path.join(os.path.dirname(__file__), 'userquery')
# *****************************************************************************
# Endpoints Specifics
# *****************************************************************************
STATUS_CHECK = {
'id': '1017',
'index': 'bts_test',
'doc_type': '_all'
}
| 34.236842 | 79 | 0.362798 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 946 | 0.727133 |
07e700a1cf3d3463190722de4956e44165a923c7 | 1,969 | py | Python | InvenTree/InvenTree/management/commands/rebuild_thumbnails.py | rocheparadox/InvenTree | 76c1e936db78424e0d6953c4062eb32863e302c6 | [
"MIT"
] | 656 | 2017-03-29T22:06:14.000Z | 2022-03-30T11:23:52.000Z | InvenTree/InvenTree/management/commands/rebuild_thumbnails.py | rocheparadox/InvenTree | 76c1e936db78424e0d6953c4062eb32863e302c6 | [
"MIT"
] | 1,545 | 2017-04-10T23:26:04.000Z | 2022-03-31T18:32:10.000Z | InvenTree/InvenTree/management/commands/rebuild_thumbnails.py | fablabbcn/InvenTree | 1d7ea7716cc96c6ffd151c822b01cd1fb5dcfecd | [
"MIT"
] | 196 | 2017-03-28T03:06:21.000Z | 2022-03-28T11:53:29.000Z | """
Custom management command to rebuild thumbnail images
- May be required after importing a new dataset, for example
"""
import os
import logging
from PIL import UnidentifiedImageError
from django.core.management.base import BaseCommand
from django.conf import settings
from django.db.utils import OperationalError, ProgrammingError
from company.models import Company
from part.models import Part
logger = logging.getLogger("inventree-thumbnails")
class Command(BaseCommand):
"""
Rebuild all thumbnail images
"""
def rebuild_thumbnail(self, model):
"""
Rebuild the thumbnail specified by the "image" field of the provided model
"""
if not model.image:
return
img = model.image
url = img.thumbnail.name
loc = os.path.join(settings.MEDIA_ROOT, url)
if not os.path.exists(loc):
logger.info(f"Generating thumbnail image for '{img}'")
try:
model.image.render_variations(replace=False)
except FileNotFoundError:
logger.error(f"ERROR: Image file '{img}' is missing")
except UnidentifiedImageError:
logger.error(f"ERROR: Image file '{img}' is not a valid image")
def handle(self, *args, **kwargs):
logger.setLevel(logging.INFO)
logger.info("Rebuilding Part thumbnails")
for part in Part.objects.exclude(image=None):
try:
self.rebuild_thumbnail(part)
except (OperationalError, ProgrammingError):
logger.error("ERROR: Database read error.")
break
logger.info("Rebuilding Company thumbnails")
for company in Company.objects.exclude(image=None):
try:
self.rebuild_thumbnail(company)
except (OperationalError, ProgrammingError):
logger.error("ERROR: abase read error.")
break
| 27.732394 | 82 | 0.630269 | 1,509 | 0.766379 | 0 | 0 | 0 | 0 | 0 | 0 | 530 | 0.269172 |
07e88f36bd18f9a9dc8241de858cfab239c3ca4a | 1,758 | py | Python | cogs/carbon.py | Baracchino-Della-Scuola/Bot | 65c1ef37ca9eae5d104de7d7de5cc58cc138402d | [
"MIT"
] | 6 | 2021-12-18T10:15:01.000Z | 2022-03-25T18:11:04.000Z | cogs/carbon.py | Baracchino-Della-Scuola/Bot | 65c1ef37ca9eae5d104de7d7de5cc58cc138402d | [
"MIT"
] | 3 | 2022-01-13T12:44:46.000Z | 2022-02-21T17:40:52.000Z | cogs/carbon.py | Baracchino-Della-Scuola/Bot | 65c1ef37ca9eae5d104de7d7de5cc58cc138402d | [
"MIT"
] | 1 | 2022-02-14T21:54:07.000Z | 2022-02-14T21:54:07.000Z | import discord
from discord.ext import commands
import urllib.parse
from .constants import themes, controls, languages, fonts, escales
import os
from pathlib import Path
from typing import Any
# from pyppeteer import launch
from io import *
import requests
def encode_url(text: str) -> str:
first_encoding = urllib.parse.quote(text, safe="*()")
return urllib.parse.quote(first_encoding, safe="*") # Carbonsh encodes text twice
def hex_to_rgb(hex: str) -> tuple:
"""
Args:
hex (str):
"""
return tuple(int(hex.lstrip("#")[i : i + 2], 16) for i in (0, 2, 4))
def parse_bg(background) -> str:
if background == "":
return "rgba(171, 184, 195, 1)"
elif background[0] == "#" or "(" not in background:
return f"rgba{hex_to_rgb(background) + (1,)}"
return background
def int_to_px(number) -> str:
return f"{number}px"
def int_to_percent(number) -> str:
return f"{number}%"
def trim_url(text: str) -> str:
if len(text) < 2000:
return text
if "%25" not in text:
return text[:2000]
if text[:2003][:-3] == "%25":
return text[:2000]
last_percent = text[:2000].rindex("%25")
return text[:last_percent]
_carbon_url = "https://carbonnowsh.herokuapp.com/"
def code_to_url(code: str) -> str:
return f"{_carbon_url}?&code={trim_url(encode_url(code))}"
class Carbon(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.command()
async def carbonate(self, ctx, *, code):
carbon_url = code_to_url(code)
r = requests.get(carbon_url)
b = BytesIO(r.content)
await ctx.send(file=discord.File(fp=b, filename="code.png"))
async def setup(bot):
await bot.add_cog(Carbon(bot))
| 21.975 | 86 | 0.633675 | 327 | 0.186007 | 0 | 0 | 242 | 0.137656 | 274 | 0.155859 | 317 | 0.180319 |
07e994b02286199ddba77a78c4751e4388520310 | 267 | py | Python | examples/show_artist.py | jimcortez/spotipy_twisted | 49ff2a4a5a5a9b3184b22adbe068eb91a38f3102 | [
"MIT"
] | null | null | null | examples/show_artist.py | jimcortez/spotipy_twisted | 49ff2a4a5a5a9b3184b22adbe068eb91a38f3102 | [
"MIT"
] | null | null | null | examples/show_artist.py | jimcortez/spotipy_twisted | 49ff2a4a5a5a9b3184b22adbe068eb91a38f3102 | [
"MIT"
] | null | null | null | # shows artist info for a URN or URL
import spotipy_twisted
import sys
import pprint
if len(sys.argv) > 1:
urn = sys.argv[1]
else:
urn = 'spotify:artist:3jOstUTkEu2JkjvRdBA5Gu'
sp = spotipy_twisted.Spotify()
artist = sp.artist(urn)
pprint.pprint(artist)
| 15.705882 | 49 | 0.726592 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 75 | 0.280899 |
07ea3ff52f1fa71b79053f13390d47944be9bd66 | 499 | py | Python | examples/mcp3xxx_mcp3002_single_ended_simpletest.py | sommersoft/Adafruit_CircuitPython_MCP3xxx | 94088a7e2b30f1b34e8a5fd7076075d88aad460b | [
"MIT"
] | null | null | null | examples/mcp3xxx_mcp3002_single_ended_simpletest.py | sommersoft/Adafruit_CircuitPython_MCP3xxx | 94088a7e2b30f1b34e8a5fd7076075d88aad460b | [
"MIT"
] | null | null | null | examples/mcp3xxx_mcp3002_single_ended_simpletest.py | sommersoft/Adafruit_CircuitPython_MCP3xxx | 94088a7e2b30f1b34e8a5fd7076075d88aad460b | [
"MIT"
] | null | null | null | import busio
import digitalio
import board
import adafruit_mcp3xxx.mcp3002 as MCP
from adafruit_mcp3xxx.analog_in import AnalogIn
# create the spi bus
spi = busio.SPI(clock=board.SCK, MISO=board.MISO, MOSI=board.MOSI)
# create the cs (chip select)
cs = digitalio.DigitalInOut(board.D5)
# create the mcp object
mcp = MCP.MCP3002(spi, cs)
# create an analog input channel on pin 0
chan = AnalogIn(mcp, MCP.P0)
print("Raw ADC Value: ", chan.value)
print("ADC Voltage: " + str(chan.voltage) + "V")
| 23.761905 | 66 | 0.747495 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 148 | 0.296593 |
07eb8c54a1c0d882798ebdd645e52dda754bb70e | 759 | py | Python | glue/core/data_factories/tables.py | rosteen/glue | ed71979f8e0e41f993a2363b3b5a8f8c3167a130 | [
"BSD-3-Clause"
] | 550 | 2015-01-08T13:51:06.000Z | 2022-03-31T11:54:47.000Z | glue/core/data_factories/tables.py | mmorys/glue | b58ced518ba6f56c59a4e03ffe84afa47235e193 | [
"BSD-3-Clause"
] | 1,362 | 2015-01-03T19:15:52.000Z | 2022-03-30T13:23:11.000Z | glue/core/data_factories/tables.py | mmorys/glue | b58ced518ba6f56c59a4e03ffe84afa47235e193 | [
"BSD-3-Clause"
] | 142 | 2015-01-08T13:08:00.000Z | 2022-03-18T13:25:57.000Z | from glue.core.data_factories.helpers import has_extension
from glue.config import data_factory
__all__ = ['tabular_data']
@data_factory(label="ASCII Table",
identifier=has_extension('csv txt tsv tbl dat '
'csv.gz txt.gz tbl.bz '
'dat.gz'),
priority=1)
def tabular_data(path, **kwargs):
from glue.core.data_factories.astropy_table import astropy_tabular_data
from glue.core.data_factories.pandas import pandas_read_table
for fac in [astropy_tabular_data, pandas_read_table]:
try:
return fac(path, **kwargs)
except Exception:
pass
else:
raise IOError("Could not parse file: %s" % path)
| 33 | 75 | 0.613966 | 0 | 0 | 0 | 0 | 631 | 0.831357 | 0 | 0 | 106 | 0.139657 |
07ebcae81863c1e60bd65e743d7f7961451a23cf | 2,895 | py | Python | code_doc/views/author_views.py | coordt/code_doc | c2fac64ac3ad61952a2d9f036727166741f9aff9 | [
"BSD-3-Clause"
] | null | null | null | code_doc/views/author_views.py | coordt/code_doc | c2fac64ac3ad61952a2d9f036727166741f9aff9 | [
"BSD-3-Clause"
] | null | null | null | code_doc/views/author_views.py | coordt/code_doc | c2fac64ac3ad61952a2d9f036727166741f9aff9 | [
"BSD-3-Clause"
] | null | null | null | from django.shortcuts import render
from django.http import Http404
from django.views.generic.edit import UpdateView
from django.views.generic import ListView, View
from django.contrib.auth.decorators import login_required
from django.contrib.auth.models import User
from django.utils.decorators import method_decorator
import logging
from ..models.projects import Project
from ..models.authors import Author
from ..forms import AuthorForm
from .permission_helpers import PermissionOnObjectViewMixin
# logger for this file
logger = logging.getLogger(__name__)
class AuthorListView(ListView):
"""A generic view of the authors in a list"""
paginate_by = 10
template_name = "code_doc/authors/author_list.html"
context_object_name = "authors"
model = Author
def detail_author(request, author_id):
try:
author = Author.objects.get(pk=author_id)
except Author.DoesNotExist:
raise Http404
project_list = Project.objects.filter(authors=author)
coauthor_list = (
Author.objects.filter(project__in=project_list).distinct().exclude(pk=author_id)
)
return render(
request,
"code_doc/authors/author_details.html",
{
"project_list": project_list,
"author": author,
"user": request.user,
"coauthor_list": coauthor_list,
},
)
class AuthorUpdateView(PermissionOnObjectViewMixin, UpdateView):
"""View for editing information about an Author
.. note:: in order to be able to edit an Author, the user should have the
'code_doc.author_edit' permission on the Author object.
"""
form_class = AuthorForm
model = Author
permissions_on_object = ("code_doc.author_edit",)
permissions_object_getter = "get_author_from_request"
template_name = "code_doc/authors/author_edit.html"
pk_url_kwarg = "author_id"
def get_author_from_request(self, request, *args, **kwargs):
# TODO check if needed
try:
return Author.objects.get(pk=kwargs["author_id"])
except Author.DoesNotExist:
logger.warning(
"[AuthorUpdateView] non existent Author with id %s", kwargs["author_id"]
)
return None
class MaintainerProfileView(View):
"""Manages the views associated to the maintainers"""
@method_decorator(login_required)
def get(self, request, maintainer_id):
try:
maintainer = User.objects.get(pk=maintainer_id)
except Project.DoesNotExist:
raise Http404
projects = Project.objects.filter(administrators=maintainer)
return render(
request,
"code_doc/maintainer_details.html",
{"projects": projects, "maintainer": maintainer},
)
@method_decorator(login_required)
def post(self, request):
pass
| 28.382353 | 88 | 0.680484 | 1,729 | 0.597237 | 0 | 0 | 521 | 0.179965 | 0 | 0 | 697 | 0.24076 |
07ee95bf0289bb4f328ba250a0e725c6cb917270 | 2,073 | py | Python | d00dfeed/analyses/print_sloc_per_soc.py | rehosting/rehosting_sok | 499b625c8aa60020f311df97a6253820982f20d4 | [
"MIT"
] | 4 | 2021-09-17T02:37:08.000Z | 2022-02-15T01:44:41.000Z | d00dfeed/analyses/print_sloc_per_soc.py | rehosting/rehosting_sok | 499b625c8aa60020f311df97a6253820982f20d4 | [
"MIT"
] | null | null | null | d00dfeed/analyses/print_sloc_per_soc.py | rehosting/rehosting_sok | 499b625c8aa60020f311df97a6253820982f20d4 | [
"MIT"
] | null | null | null | # External deps
import os, sys, json
from pathlib import Path
from typing import Dict, List
# Internal deps
os.chdir(sys.path[0])
sys.path.append("..")
import df_common as dfc
import analyses_common as ac
# Generated files directory
GEN_FILE_DIR = str(Path(__file__).resolve().parent.parent) + os.sep + "generated_files" # TODO: ugly parent.parent pathing
if os.path.exists(GEN_FILE_DIR):
sys.path.append(GEN_FILE_DIR)
if os.path.exists(os.path.join(GEN_FILE_DIR, "sloc_cnt.py")):
from sloc_cnt import DRIVER_NAME_TO_SLOC
else:
print("Error: no SLOC file! Run \'df_analyze.py\' with \'--linux-src-dir\'")
sys.exit(1)
if __name__ == "__main__":
json_files = ac.argparse_and_get_files("Graph SLOC/SoC data")
soc_sloc_by_arch: Dict[str, List[int]] = {}
print("Gathering SLOC average by arch...")
from graph_dd_sloc_by_arch import get_sloc_avg_and_list_by_arch
cmp_by_arch = ac.build_dict_two_lvl_cnt(json_files, dfc.JSON_ARC, dfc.JSON_CMP_STR)
avg_sloc_by_arch, sloc_list_by_arch = get_sloc_avg_and_list_by_arch(cmp_by_arch, verbose = False)
# Collection
print("Iterating DTBs/SoCs...")
for dtb_json in json_files:
with open(dtb_json) as json_file:
data = json.load(json_file)
soc_sloc = 0
arch = data[dfc.JSON_ARC]
cmp_strs = data[dfc.JSON_CMP_STR]
# Total SLOC for this SoC
for cmp_str in cmp_strs:
driver_sloc = dfc.cmp_str_to_sloc(cmp_str)
if not driver_sloc: # Closed-source driver
driver_sloc = avg_sloc_by_arch[arch]
soc_sloc += driver_sloc
#print("{}: {}".format(cmp_str, driver_sloc))
if arch not in soc_sloc_by_arch:
soc_sloc_by_arch[arch] = []
else:
soc_sloc_by_arch[arch].append(soc_sloc)
print("{} ({}): {}".format(dtb_json.split(os.sep)[-1], arch, soc_sloc))
# Final stats
ac.print_mean_median_std_dev_for_dict_of_lists(soc_sloc_by_arch,
"\nSloc Per Soc, format: [arch : (mean, median, std_dev)]\n")
| 32.904762 | 122 | 0.673903 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 474 | 0.228654 |
07eea84b8f7990a608b685c1a60f3250095ce8a2 | 1,271 | py | Python | mingpt/lr_decay.py | asigalov61/minGPT | b4f8d57aaf1bb5c64d480f8005b73d39b075ae4b | [
"MIT"
] | 18 | 2020-09-10T02:29:38.000Z | 2022-03-16T03:17:35.000Z | mingpt/lr_decay.py | asigalov61/minGPT | b4f8d57aaf1bb5c64d480f8005b73d39b075ae4b | [
"MIT"
] | null | null | null | mingpt/lr_decay.py | asigalov61/minGPT | b4f8d57aaf1bb5c64d480f8005b73d39b075ae4b | [
"MIT"
] | 7 | 2020-08-20T16:35:38.000Z | 2022-01-10T21:57:49.000Z | import math
import pytorch_lightning as pl
class LearningRateDecayCallback(pl.Callback):
def __init__(self, learning_rate, warmup_tokens=375e6, final_tokens=260e9, lr_decay=True):
super().__init__()
self.learning_rate = learning_rate
self.tokens = 0
self.final_tokens = final_tokens
self.lr_decay = lr_decay
self.warmup_tokens = warmup_tokens
def on_train_batch_end(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
optimizer = trainer.optimizers[0]
_, y = batch
if self.lr_decay:
self.tokens += (y >= 0).sum() # number of tokens processed this step (i.e. label is not -100)
if self.tokens < self.warmup_tokens:
# linear warmup
lr_mult = float(self.tokens) / float(max(1, self.warmup_tokens))
else:
# cosine learning rate decay
progress = float(self.tokens - self.warmup_tokens) / float(
max(1, self.final_tokens - self.warmup_tokens))
lr_mult = max(0.1, 0.5 * (1.0 + math.cos(math.pi * progress)))
lr = self.learning_rate * lr_mult
for param_group in optimizer.param_groups:
param_group['lr'] = lr | 41 | 106 | 0.609756 | 1,226 | 0.964595 | 0 | 0 | 0 | 0 | 0 | 0 | 110 | 0.086546 |
07ef68929a367d76f0cb572e51ac36b254e815b0 | 40,518 | py | Python | apprise/config/ConfigBase.py | calvinbui/apprise | a5510790baf5aa1d74afabab25ff57d6b2304d56 | [
"MIT"
] | null | null | null | apprise/config/ConfigBase.py | calvinbui/apprise | a5510790baf5aa1d74afabab25ff57d6b2304d56 | [
"MIT"
] | null | null | null | apprise/config/ConfigBase.py | calvinbui/apprise | a5510790baf5aa1d74afabab25ff57d6b2304d56 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
#
# Copyright (C) 2020 Chris Caron <[email protected]>
# All rights reserved.
#
# This code is licensed under the MIT License.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files(the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and / or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions :
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import os
import re
import six
import yaml
import time
from .. import plugins
from ..AppriseAsset import AppriseAsset
from ..URLBase import URLBase
from ..common import ConfigFormat
from ..common import CONFIG_FORMATS
from ..common import ContentIncludeMode
from ..utils import GET_SCHEMA_RE
from ..utils import parse_list
from ..utils import parse_bool
from ..utils import parse_urls
from . import SCHEMA_MAP
# Test whether token is valid or not
VALID_TOKEN = re.compile(
r'(?P<token>[a-z0-9][a-z0-9_]+)', re.I)
class ConfigBase(URLBase):
"""
This is the base class for all supported configuration sources
"""
# The Default Encoding to use if not otherwise detected
encoding = 'utf-8'
# The default expected configuration format unless otherwise
# detected by the sub-modules
default_config_format = ConfigFormat.TEXT
# This is only set if the user overrides the config format on the URL
# this should always initialize itself as None
config_format = None
# Don't read any more of this amount of data into memory as there is no
# reason we should be reading in more. This is more of a safe guard then
# anything else. 128KB (131072B)
max_buffer_size = 131072
# By default all configuration is not includable using the 'include'
# line found in configuration files.
allow_cross_includes = ContentIncludeMode.NEVER
# the config path manages the handling of relative include
config_path = os.getcwd()
def __init__(self, cache=True, recursion=0, insecure_includes=False,
**kwargs):
"""
Initialize some general logging and common server arguments that will
keep things consistent when working with the configurations that
inherit this class.
By default we cache our responses so that subsiquent calls does not
cause the content to be retrieved again. For local file references
this makes no difference at all. But for remote content, this does
mean more then one call can be made to retrieve the (same) data. This
method can be somewhat inefficient if disabled. Only disable caching
if you understand the consequences.
You can alternatively set the cache value to an int identifying the
number of seconds the previously retrieved can exist for before it
should be considered expired.
recursion defines how deep we recursively handle entries that use the
`include` keyword. This keyword requires us to fetch more configuration
from another source and add it to our existing compilation. If the
file we remotely retrieve also has an `include` reference, we will only
advance through it if recursion is set to 2 deep. If set to zero
it is off. There is no limit to how high you set this value. It would
be recommended to keep it low if you do intend to use it.
insecure_include by default are disabled. When set to True, all
Apprise Config files marked to be in STRICT mode are treated as being
in ALWAYS mode.
Take a file:// based configuration for example, only a file:// based
configuration can include another file:// based one. because it is set
to STRICT mode. If an http:// based configuration file attempted to
include a file:// one it woul fail. However this include would be
possible if insecure_includes is set to True.
There are cases where a self hosting apprise developer may wish to load
configuration from memory (in a string format) that contains 'include'
entries (even file:// based ones). In these circumstances if you want
these 'include' entries to be honored, this value must be set to True.
"""
super(ConfigBase, self).__init__(**kwargs)
# Tracks the time the content was last retrieved on. This place a role
# for cases where we are not caching our response and are required to
# re-retrieve our settings.
self._cached_time = None
# Tracks previously loaded content for speed
self._cached_servers = None
# Initialize our recursion value
self.recursion = recursion
# Initialize our insecure_includes flag
self.insecure_includes = insecure_includes
if 'encoding' in kwargs:
# Store the encoding
self.encoding = kwargs.get('encoding')
if 'format' in kwargs \
and isinstance(kwargs['format'], six.string_types):
# Store the enforced config format
self.config_format = kwargs.get('format').lower()
if self.config_format not in CONFIG_FORMATS:
# Simple error checking
err = 'An invalid config format ({}) was specified.'.format(
self.config_format)
self.logger.warning(err)
raise TypeError(err)
# Set our cache flag; it can be True or a (positive) integer
try:
self.cache = cache if isinstance(cache, bool) else int(cache)
if self.cache < 0:
err = 'A negative cache value ({}) was specified.'.format(
cache)
self.logger.warning(err)
raise TypeError(err)
except (ValueError, TypeError):
err = 'An invalid cache value ({}) was specified.'.format(cache)
self.logger.warning(err)
raise TypeError(err)
return
def servers(self, asset=None, **kwargs):
"""
Performs reads loaded configuration and returns all of the services
that could be parsed and loaded.
"""
if not self.expired():
# We already have cached results to return; use them
return self._cached_servers
# Our cached response object
self._cached_servers = list()
# read() causes the child class to do whatever it takes for the
# config plugin to load the data source and return unparsed content
# None is returned if there was an error or simply no data
content = self.read(**kwargs)
if not isinstance(content, six.string_types):
# Set the time our content was cached at
self._cached_time = time.time()
# Nothing more to do; return our empty cache list
return self._cached_servers
# Our Configuration format uses a default if one wasn't one detected
# or enfored.
config_format = \
self.default_config_format \
if self.config_format is None else self.config_format
# Dynamically load our parse_ function based on our config format
fn = getattr(ConfigBase, 'config_parse_{}'.format(config_format))
# Initialize our asset object
asset = asset if isinstance(asset, AppriseAsset) else self.asset
# Execute our config parse function which always returns a tuple
# of our servers and our configuration
servers, configs = fn(content=content, asset=asset)
self._cached_servers.extend(servers)
# Configuration files were detected; recursively populate them
# If we have been configured to do so
for url in configs:
if self.recursion > 0:
# Attempt to acquire the schema at the very least to allow
# our configuration based urls.
schema = GET_SCHEMA_RE.match(url)
if schema is None:
# Plan B is to assume we're dealing with a file
schema = 'file'
if not os.path.isabs(url):
# We're dealing with a relative path; prepend
# our current config path
url = os.path.join(self.config_path, url)
url = '{}://{}'.format(schema, URLBase.quote(url))
else:
# Ensure our schema is always in lower case
schema = schema.group('schema').lower()
# Some basic validation
if schema not in SCHEMA_MAP:
ConfigBase.logger.warning(
'Unsupported include schema {}.'.format(schema))
continue
# Parse our url details of the server object as dictionary
# containing all of the information parsed from our URL
results = SCHEMA_MAP[schema].parse_url(url)
if not results:
# Failed to parse the server URL
self.logger.warning(
'Unparseable include URL {}'.format(url))
continue
# Handle cross inclusion based on allow_cross_includes rules
if (SCHEMA_MAP[schema].allow_cross_includes ==
ContentIncludeMode.STRICT
and schema not in self.schemas()
and not self.insecure_includes) or \
SCHEMA_MAP[schema].allow_cross_includes == \
ContentIncludeMode.NEVER:
# Prevent the loading if insecure base protocols
ConfigBase.logger.warning(
'Including {}:// based configuration is prohibited. '
'Ignoring URL {}'.format(schema, url))
continue
# Prepare our Asset Object
results['asset'] = asset
# No cache is required because we're just lumping this in
# and associating it with the cache value we've already
# declared (prior to our recursion)
results['cache'] = False
# Recursion can never be parsed from the URL; we decrement
# it one level
results['recursion'] = self.recursion - 1
# Insecure Includes flag can never be parsed from the URL
results['insecure_includes'] = self.insecure_includes
try:
# Attempt to create an instance of our plugin using the
# parsed URL information
cfg_plugin = SCHEMA_MAP[results['schema']](**results)
except Exception as e:
# the arguments are invalid or can not be used.
self.logger.warning(
'Could not load include URL: {}'.format(url))
self.logger.debug('Loading Exception: {}'.format(str(e)))
continue
# if we reach here, we can now add this servers found
# in this configuration file to our list
self._cached_servers.extend(
cfg_plugin.servers(asset=asset))
# We no longer need our configuration object
del cfg_plugin
else:
self.logger.debug(
'Recursion limit reached; ignoring Include URL: %s' % url)
if self._cached_servers:
self.logger.info('Loaded {} entries from {}'.format(
len(self._cached_servers), self.url()))
else:
self.logger.warning(
'Failed to load Apprise configuration from {}'.format(
self.url()))
# Set the time our content was cached at
self._cached_time = time.time()
return self._cached_servers
def read(self):
"""
This object should be implimented by the child classes
"""
return None
def expired(self):
"""
Simply returns True if the configuration should be considered
as expired or False if content should be retrieved.
"""
if isinstance(self._cached_servers, list) and self.cache:
# We have enough reason to look further into our cached content
# and verify it has not expired.
if self.cache is True:
# we have not expired, return False
return False
# Verify our cache time to determine whether we will get our
# content again.
age_in_sec = time.time() - self._cached_time
if age_in_sec <= self.cache:
# We have not expired; return False
return False
# If we reach here our configuration should be considered
# missing and/or expired.
return True
@staticmethod
def parse_url(url, verify_host=True):
"""Parses the URL and returns it broken apart into a dictionary.
This is very specific and customized for Apprise.
Args:
url (str): The URL you want to fully parse.
verify_host (:obj:`bool`, optional): a flag kept with the parsed
URL which some child classes will later use to verify SSL
keys (if SSL transactions take place). Unless under very
specific circumstances, it is strongly recomended that
you leave this default value set to True.
Returns:
A dictionary is returned containing the URL fully parsed if
successful, otherwise None is returned.
"""
results = URLBase.parse_url(url, verify_host=verify_host)
if not results:
# We're done; we failed to parse our url
return results
# Allow overriding the default config format
if 'format' in results['qsd']:
results['format'] = results['qsd'].get('format')
if results['format'] not in CONFIG_FORMATS:
URLBase.logger.warning(
'Unsupported format specified {}'.format(
results['format']))
del results['format']
# Defines the encoding of the payload
if 'encoding' in results['qsd']:
results['encoding'] = results['qsd'].get('encoding')
# Our cache value
if 'cache' in results['qsd']:
# First try to get it's integer value
try:
results['cache'] = int(results['qsd']['cache'])
except (ValueError, TypeError):
# No problem, it just isn't an integer; now treat it as a bool
# instead:
results['cache'] = parse_bool(results['qsd']['cache'])
return results
@staticmethod
def detect_config_format(content, **kwargs):
"""
Takes the specified content and attempts to detect the format type
The function returns the actual format type if detected, otherwise
it returns None
"""
# Detect Format Logic:
# - A pound/hashtag (#) is alawys a comment character so we skip over
# lines matched here.
# - Detection begins on the first non-comment and non blank line
# matched.
# - If we find a string followed by a colon, we know we're dealing
# with a YAML file.
# - If we find a string that starts with a URL, or our tag
# definitions (accepting commas) followed by an equal sign we know
# we're dealing with a TEXT format.
# Define what a valid line should look like
valid_line_re = re.compile(
r'^\s*(?P<line>([;#]+(?P<comment>.*))|'
r'(?P<text>((?P<tag>[ \t,a-z0-9_-]+)=)?[a-z0-9]+://.*)|'
r'((?P<yaml>[a-z0-9]+):.*))?$', re.I)
try:
# split our content up to read line by line
content = re.split(r'\r*\n', content)
except TypeError:
# content was not expected string type
ConfigBase.logger.error(
'Invalid Apprise configuration specified.')
return None
# By default set our return value to None since we don't know
# what the format is yet
config_format = None
# iterate over each line of the file to attempt to detect it
# stop the moment a the type has been determined
for line, entry in enumerate(content, start=1):
result = valid_line_re.match(entry)
if not result:
# Invalid syntax
ConfigBase.logger.error(
'Undetectable Apprise configuration found '
'based on line {}.'.format(line))
# Take an early exit
return None
# Attempt to detect configuration
if result.group('yaml'):
config_format = ConfigFormat.YAML
ConfigBase.logger.debug(
'Detected YAML configuration '
'based on line {}.'.format(line))
break
elif result.group('text'):
config_format = ConfigFormat.TEXT
ConfigBase.logger.debug(
'Detected TEXT configuration '
'based on line {}.'.format(line))
break
# If we reach here, we have a comment entry
# Adjust default format to TEXT
config_format = ConfigFormat.TEXT
return config_format
@staticmethod
def config_parse(content, asset=None, config_format=None, **kwargs):
"""
Takes the specified config content and loads it based on the specified
config_format. If a format isn't specified, then it is auto detected.
"""
if config_format is None:
# Detect the format
config_format = ConfigBase.detect_config_format(content)
if not config_format:
# We couldn't detect configuration
ConfigBase.logger.error('Could not detect configuration')
return (list(), list())
if config_format not in CONFIG_FORMATS:
# Invalid configuration type specified
ConfigBase.logger.error(
'An invalid configuration format ({}) was specified'.format(
config_format))
return (list(), list())
# Dynamically load our parse_ function based on our config format
fn = getattr(ConfigBase, 'config_parse_{}'.format(config_format))
# Execute our config parse function which always returns a list
return fn(content=content, asset=asset)
@staticmethod
def config_parse_text(content, asset=None):
"""
Parse the specified content as though it were a simple text file only
containing a list of URLs.
Return a tuple that looks like (servers, configs) where:
- servers contains a list of loaded notification plugins
- configs contains a list of additional configuration files
referenced.
You may also optionally associate an asset with the notification.
The file syntax is:
#
# pound/hashtag allow for line comments
#
# One or more tags can be idenified using comma's (,) to separate
# them.
<Tag(s)>=<URL>
# Or you can use this format (no tags associated)
<URL>
# you can also use the keyword 'include' and identify a
# configuration location (like this file) which will be included
# as additional configuration entries when loaded.
include <ConfigURL>
"""
# A list of loaded Notification Services
servers = list()
# A list of additional configuration files referenced using
# the include keyword
configs = list()
# Define what a valid line should look like
valid_line_re = re.compile(
r'^\s*(?P<line>([;#]+(?P<comment>.*))|'
r'(\s*(?P<tags>[^=]+)=|=)?\s*'
r'(?P<url>[a-z0-9]{2,9}://.*)|'
r'include\s+(?P<config>.+))?\s*$', re.I)
try:
# split our content up to read line by line
content = re.split(r'\r*\n', content)
except TypeError:
# content was not expected string type
ConfigBase.logger.error(
'Invalid Apprise TEXT based configuration specified.')
return (list(), list())
for line, entry in enumerate(content, start=1):
result = valid_line_re.match(entry)
if not result:
# Invalid syntax
ConfigBase.logger.error(
'Invalid Apprise TEXT configuration format found '
'{} on line {}.'.format(entry, line))
# Assume this is a file we shouldn't be parsing. It's owner
# can read the error printed to screen and take action
# otherwise.
return (list(), list())
url, config = result.group('url'), result.group('config')
if not (url or config):
# Comment/empty line; do nothing
continue
if config:
ConfigBase.logger.debug('Include URL: {}'.format(config))
# Store our include line
configs.append(config.strip())
continue
# Acquire our url tokens
results = plugins.url_to_dict(url)
if results is None:
# Failed to parse the server URL
ConfigBase.logger.warning(
'Unparseable URL {} on line {}.'.format(url, line))
continue
# Build a list of tags to associate with the newly added
# notifications if any were set
results['tag'] = set(parse_list(result.group('tags')))
# Prepare our Asset Object
results['asset'] = \
asset if isinstance(asset, AppriseAsset) else AppriseAsset()
try:
# Attempt to create an instance of our plugin using the
# parsed URL information
plugin = plugins.SCHEMA_MAP[results['schema']](**results)
# Create log entry of loaded URL
ConfigBase.logger.debug('Loaded URL: {}'.format(plugin.url()))
except Exception as e:
# the arguments are invalid or can not be used.
ConfigBase.logger.warning(
'Could not load URL {} on line {}.'.format(
url, line))
ConfigBase.logger.debug('Loading Exception: %s' % str(e))
continue
# if we reach here, we successfully loaded our data
servers.append(plugin)
# Return what was loaded
return (servers, configs)
@staticmethod
def config_parse_yaml(content, asset=None):
"""
Parse the specified content as though it were a yaml file
specifically formatted for Apprise.
Return a tuple that looks like (servers, configs) where:
- servers contains a list of loaded notification plugins
- configs contains a list of additional configuration files
referenced.
You may optionally associate an asset with the notification.
"""
# A list of loaded Notification Services
servers = list()
# A list of additional configuration files referenced using
# the include keyword
configs = list()
try:
# Load our data (safely)
result = yaml.load(content, Loader=yaml.SafeLoader)
except (AttributeError,
yaml.parser.ParserError,
yaml.error.MarkedYAMLError) as e:
# Invalid content
ConfigBase.logger.error(
'Invalid Apprise YAML data specified.')
ConfigBase.logger.debug(
'YAML Exception:{}{}'.format(os.linesep, e))
return (list(), list())
if not isinstance(result, dict):
# Invalid content
ConfigBase.logger.error(
'Invalid Apprise YAML based configuration specified.')
return (list(), list())
# YAML Version
version = result.get('version', 1)
if version != 1:
# Invalid syntax
ConfigBase.logger.error(
'Invalid Apprise YAML version specified {}.'.format(version))
return (list(), list())
#
# global asset object
#
asset = asset if isinstance(asset, AppriseAsset) else AppriseAsset()
tokens = result.get('asset', None)
if tokens and isinstance(tokens, dict):
for k, v in tokens.items():
if k.startswith('_') or k.endswith('_'):
# Entries are considered reserved if they start or end
# with an underscore
ConfigBase.logger.warning(
'Ignored asset key "{}".'.format(k))
continue
if not (hasattr(asset, k) and
isinstance(getattr(asset, k),
(bool, six.string_types))):
# We can't set a function or non-string set value
ConfigBase.logger.warning(
'Invalid asset key "{}".'.format(k))
continue
if v is None:
# Convert to an empty string
v = ''
if (isinstance(v, (bool, six.string_types))
and isinstance(getattr(asset, k), bool)):
# If the object in the Asset is a boolean, then
# we want to convert the specified string to
# match that.
setattr(asset, k, parse_bool(v))
elif isinstance(v, six.string_types):
# Set our asset object with the new value
setattr(asset, k, v.strip())
else:
# we must set strings with a string
ConfigBase.logger.warning(
'Invalid asset value to "{}".'.format(k))
continue
#
# global tag root directive
#
global_tags = set()
tags = result.get('tag', None)
if tags and isinstance(tags, (list, tuple, six.string_types)):
# Store any preset tags
global_tags = set(parse_list(tags))
#
# include root directive
#
includes = result.get('include', None)
if isinstance(includes, six.string_types):
# Support a single inline string or multiple ones separated by a
# comma and/or space
includes = parse_urls(includes)
elif not isinstance(includes, (list, tuple)):
# Not a problem; we simply have no includes
includes = list()
# Iterate over each config URL
for no, url in enumerate(includes):
if isinstance(url, six.string_types):
# Support a single inline string or multiple ones separated by
# a comma and/or space
configs.extend(parse_urls(url))
elif isinstance(url, dict):
# Store the url and ignore arguments associated
configs.extend(u for u in url.keys())
#
# urls root directive
#
urls = result.get('urls', None)
if not isinstance(urls, (list, tuple)):
# Not a problem; we simply have no urls
urls = list()
# Iterate over each URL
for no, url in enumerate(urls):
# Our results object is what we use to instantiate our object if
# we can. Reset it to None on each iteration
results = list()
if isinstance(url, six.string_types):
# We're just a simple URL string...
schema = GET_SCHEMA_RE.match(url)
if schema is None:
# Log invalid entries so that maintainer of config
# config file at least has something to take action
# with.
ConfigBase.logger.warning(
'Invalid URL {}, entry #{}'.format(url, no + 1))
continue
# We found a valid schema worthy of tracking; store it's
# details:
_results = plugins.url_to_dict(url)
if _results is None:
ConfigBase.logger.warning(
'Unparseable URL {}, entry #{}'.format(
url, no + 1))
continue
# add our results to our global set
results.append(_results)
elif isinstance(url, dict):
# We are a url string with additional unescaped options. In
# this case we want to iterate over all of our options so we
# can at least tell the end user what entries were ignored
# due to errors
if six.PY2:
it = url.iteritems()
else: # six.PY3
it = iter(url.items())
# Track the URL to-load
_url = None
# Track last acquired schema
schema = None
for key, tokens in it:
# Test our schema
_schema = GET_SCHEMA_RE.match(key)
if _schema is None:
# Log invalid entries so that maintainer of config
# config file at least has something to take action
# with.
ConfigBase.logger.warning(
'Ignored entry {} found under urls, entry #{}'
.format(key, no + 1))
continue
# Store our schema
schema = _schema.group('schema').lower()
# Store our URL and Schema Regex
_url = key
if _url is None:
# the loop above failed to match anything
ConfigBase.logger.warning(
'Unsupported URL, entry #{}'.format(no + 1))
continue
_results = plugins.url_to_dict(_url)
if _results is None:
# Setup dictionary
_results = {
# Minimum requirements
'schema': schema,
}
if isinstance(tokens, (list, tuple, set)):
# populate and/or override any results populated by
# parse_url()
for entries in tokens:
# Copy ourselves a template of our parsed URL as a base
# to work with
r = _results.copy()
# We are a url string with additional unescaped options
if isinstance(entries, dict):
if six.PY2:
_url, tokens = next(url.iteritems())
else: # six.PY3
_url, tokens = next(iter(url.items()))
# Tags you just can't over-ride
if 'schema' in entries:
del entries['schema']
# support our special tokens (if they're present)
if schema in plugins.SCHEMA_MAP:
entries = ConfigBase.__extract_special_tokens(
schema, entries)
# Extend our dictionary with our new entries
r.update(entries)
# add our results to our global set
results.append(r)
elif isinstance(tokens, dict):
# support our special tokens (if they're present)
if schema in plugins.SCHEMA_MAP:
tokens = ConfigBase.__extract_special_tokens(
schema, tokens)
# Copy ourselves a template of our parsed URL as a base to
# work with
r = _results.copy()
# add our result set
r.update(tokens)
# add our results to our global set
results.append(r)
else:
# add our results to our global set
results.append(_results)
else:
# Unsupported
ConfigBase.logger.warning(
'Unsupported Apprise YAML entry #{}'.format(no + 1))
continue
# Track our entries
entry = 0
while len(results):
# Increment our entry count
entry += 1
# Grab our first item
_results = results.pop(0)
# tag is a special keyword that is managed by Apprise object.
# The below ensures our tags are set correctly
if 'tag' in _results:
# Tidy our list up
_results['tag'] = \
set(parse_list(_results['tag'])) | global_tags
else:
# Just use the global settings
_results['tag'] = global_tags
for key in list(_results.keys()):
# Strip out any tokens we know that we can't accept and
# warn the user
match = VALID_TOKEN.match(key)
if not match:
ConfigBase.logger.warning(
'Ignoring invalid token ({}) found in YAML '
'configuration entry #{}, item #{}'
.format(key, no + 1, entry))
del _results[key]
ConfigBase.logger.trace(
'URL #{}: {} unpacked as:{}{}'
.format(no + 1, url, os.linesep, os.linesep.join(
['{}="{}"'.format(k, a)
for k, a in _results.items()])))
# Prepare our Asset Object
_results['asset'] = asset
try:
# Attempt to create an instance of our plugin using the
# parsed URL information
plugin = plugins.SCHEMA_MAP[_results['schema']](**_results)
# Create log entry of loaded URL
ConfigBase.logger.debug(
'Loaded URL: {}'.format(plugin.url()))
except Exception as e:
# the arguments are invalid or can not be used.
ConfigBase.logger.warning(
'Could not load Apprise YAML configuration '
'entry #{}, item #{}'
.format(no + 1, entry))
ConfigBase.logger.debug('Loading Exception: %s' % str(e))
continue
# if we reach here, we successfully loaded our data
servers.append(plugin)
return (servers, configs)
def pop(self, index=-1):
"""
Removes an indexed Notification Service from the stack and returns it.
By default, the last element of the list is removed.
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
# Pop the element off of the stack
return self._cached_servers.pop(index)
@staticmethod
def __extract_special_tokens(schema, tokens):
"""
This function takes a list of tokens and updates them to no longer
include any special tokens such as +,-, and :
- schema must be a valid schema of a supported plugin type
- tokens must be a dictionary containing the yaml entries parsed.
The idea here is we can post process a set of tokens provided in
a YAML file where the user provided some of the special keywords.
We effectivley look up what these keywords map to their appropriate
value they're expected
"""
# Create a copy of our dictionary
tokens = tokens.copy()
for kw, meta in plugins.SCHEMA_MAP[schema]\
.template_kwargs.items():
# Determine our prefix:
prefix = meta.get('prefix', '+')
# Detect any matches
matches = \
{k[1:]: str(v) for k, v in tokens.items()
if k.startswith(prefix)}
if not matches:
# we're done with this entry
continue
if not isinstance(tokens.get(kw, None), dict):
# Invalid; correct it
tokens[kw] = dict()
# strip out processed tokens
tokens = {k: v for k, v in tokens.items()
if not k.startswith(prefix)}
# Update our entries
tokens[kw].update(matches)
# Return our tokens
return tokens
def __getitem__(self, index):
"""
Returns the indexed server entry associated with the loaded
notification servers
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
return self._cached_servers[index]
def __iter__(self):
"""
Returns an iterator to our server list
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
return iter(self._cached_servers)
def __len__(self):
"""
Returns the total number of servers loaded
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
return len(self._cached_servers)
def __bool__(self):
"""
Allows the Apprise object to be wrapped in an Python 3.x based 'if
statement'. True is returned if our content was downloaded correctly.
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
return True if self._cached_servers else False
def __nonzero__(self):
"""
Allows the Apprise object to be wrapped in an Python 2.x based 'if
statement'. True is returned if our content was downloaded correctly.
"""
if not isinstance(self._cached_servers, list):
# Generate ourselves a list of content we can pull from
self.servers()
return True if self._cached_servers else False
| 38.116651 | 79 | 0.550768 | 38,784 | 0.957204 | 0 | 0 | 24,488 | 0.604373 | 0 | 0 | 18,989 | 0.468656 |
07f0b2b68417d129704d340d100e569555824ebc | 977 | py | Python | ffmpeg_util.py | manuel-fischer/ScrollRec | ec5662d3f61630f939613481290a166133d23a20 | [
"MIT"
] | null | null | null | ffmpeg_util.py | manuel-fischer/ScrollRec | ec5662d3f61630f939613481290a166133d23a20 | [
"MIT"
] | null | null | null | ffmpeg_util.py | manuel-fischer/ScrollRec | ec5662d3f61630f939613481290a166133d23a20 | [
"MIT"
] | null | null | null | import sys
import subprocess
from subprocess import Popen, PIPE
AV_LOG_QUIET = "quiet"
AV_LOG_PANIC = "panic"
AV_LOG_FATAL = "fatal"
AV_LOG_ERROR = "error"
AV_LOG_WARNING = "warning"
AV_LOG_INFO = "info"
AV_LOG_VERBOSE = "verbose"
AV_LOG_DEBUG = "debug"
ffmpeg_loglevel = AV_LOG_ERROR
IS_WIN32 = 'win32' in str(sys.platform).lower()
SUBPROCESS_ARGS = {}
if IS_WIN32:
startupinfo = subprocess.STARTUPINFO()
startupinfo.dwFlags = subprocess.CREATE_NEW_CONSOLE | subprocess.STARTF_USESHOWWINDOW
startupinfo.wShowWindow = subprocess.SW_HIDE
SUBPROCESS_ARGS['startupinfo'] = startupinfo
def popen_ffmpeg(inner_args):
cmd = [
'ffmpeg',
*inner_args,
# logging
'-loglevel', ffmpeg_loglevel,
'-hide_banner',
]
process = Popen(cmd, stdout=PIPE, stderr=PIPE, **SUBPROCESS_ARGS)
stdout, stderr = process.communicate()
print(stderr.decode(), end='', file=sys.stderr)
return stdout, stderr | 24.425 | 89 | 0.698055 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 123 | 0.125896 |
07f0cc1a2096d19875fd8be0522fd82d52ff8b5c | 128 | py | Python | setup.py | rizar/CLOSURE | 57f80d4e89fa281830bb9c8b6a7a2498747e727a | [
"MIT"
] | 14 | 2019-12-08T04:38:57.000Z | 2021-07-13T15:46:57.000Z | setup.py | rizar/CLOSURE | 57f80d4e89fa281830bb9c8b6a7a2498747e727a | [
"MIT"
] | null | null | null | setup.py | rizar/CLOSURE | 57f80d4e89fa281830bb9c8b6a7a2498747e727a | [
"MIT"
] | 2 | 2020-09-01T10:21:27.000Z | 2021-01-15T02:38:25.000Z | from setuptools import setup
setup(
name="nmn-iwp",
version="0.1",
keywords="",
packages=["vr", "vr.models"]
)
| 14.222222 | 32 | 0.585938 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 0.242188 |
07f1195aa55500ccfbdb1eb16ce8a5e553bfeb5d | 11,381 | py | Python | analysis_tools/PYTHON_RICARDO/output_ingress_egress/scripts/uniform_grid.py | lefevre-fraser/openmeta-mms | 08f3115e76498df1f8d70641d71f5c52cab4ce5f | [
"MIT"
] | null | null | null | analysis_tools/PYTHON_RICARDO/output_ingress_egress/scripts/uniform_grid.py | lefevre-fraser/openmeta-mms | 08f3115e76498df1f8d70641d71f5c52cab4ce5f | [
"MIT"
] | null | null | null | analysis_tools/PYTHON_RICARDO/output_ingress_egress/scripts/uniform_grid.py | lefevre-fraser/openmeta-mms | 08f3115e76498df1f8d70641d71f5c52cab4ce5f | [
"MIT"
] | null | null | null | """ Represent a triangulated surface using a 3D boolean grid"""
import logging
import numpy as np
from rpl.tools.ray_tracing.bsp_tree_poly import BSP_Element
from rpl.tools.geometry import geom_utils
import data_io
class BSP_Grid(object):
def __init__(self, node_array, tris, allocate_step=100000):
"""
Store the triangles with an enumeration so that even when they are subdivided their
identity is not lost.
"""
tri_nums = np.arange(len(tris), dtype=np.int32).reshape((len(tris), 1))
minus_ones = -np.ones((len(tris), 6), dtype=np.int32)
self.tris = np.hstack((tris, minus_ones, tri_nums))
self.allocate_step = allocate_step
self.node_array = node_array # Reference to the full list of nodes
self._resize()
self.next_free = len(node_array)
self.split_cache = np.zeros(len(self.tris), dtype=np.int32)
def _resize(self):
"""
Increase node array size by the allocate_step amount.
"""
self.array_size = len(self.node_array) + self.allocate_step
self.node_array = np.concatenate((self.node_array, np.zeros((self.allocate_step, 3))))
def add_node(self, node):
"""
Adds a new node to the end of the node array (expanding if required). Returns the index of
the newly added node.
"""
if self.next_free == self.array_size:
self._resize()
self.node_array[self.next_free] = node
self.next_free += 1
return self.next_free - 1
def prepare_add(self, num_add_nodes):
"""
Make sure that ``num_add_nodes`` can be added later without needing a resize.
Useful if adding nodes from within cython where resizing is tricky.
"""
if self.next_free + num_add_nodes >= self.array_size:
self._resize()
return self.next_free
def make_grid(veh_surfs, settings):
"""
Make coordinates of voxelated grid based on overall list of vehicle surfaces
"""
## Find overall bounding box
x_min, x_max = 1e30, -1e30
y_min, y_max = 1e30, -1e30
z_min, z_max = 1e30, -1e30
for key, veh_surf in veh_surfs.items():
x_min, x_max = min(x_min, np.min(veh_surf["x"])), max(x_max, np.max(veh_surf["x"]))
y_min, y_max = min(y_min, np.min(veh_surf["y"])), max(y_max, np.max(veh_surf["y"]))
z_min, z_max = min(z_min, np.min(veh_surf["z"])), max(z_max, np.max(veh_surf["z"]))
x_min, x_max = x_min - settings["voxel_size"], x_max + settings["voxel_size"]
y_min, y_max = y_min - settings["voxel_size"], y_max + settings["voxel_size"]
z_min, z_max = z_min - settings["voxel_size"], z_max + settings["voxel_size"]
###########################################
# Create the uniformly spaced grid points
x_grid = np.arange(x_min, x_max + settings["voxel_size"], settings["voxel_size"])
y_grid = np.arange(y_min, y_max + settings["voxel_size"], settings["voxel_size"])
z_grid = np.arange(z_min, z_max + settings["voxel_size"], settings["voxel_size"])
return x_grid, y_grid, z_grid
def convert_geom(veh_surf, tr_mat):
"""
Rotate nodes using provided transformation matrix; convert xyz node dict to nodes array
"""
veh_surf["nodes"] = np.vstack((veh_surf["x"], veh_surf["y"], veh_surf["z"])).T
veh_surf['nodes'] = np.dot(veh_surf['nodes'], tr_mat[:3, :3])
veh_surf["x"] = veh_surf['nodes'][:, 0]
veh_surf["y"] = veh_surf['nodes'][:, 1]
veh_surf["z"] = veh_surf['nodes'][:, 2]
return veh_surf
def find_occupied_voxels(surf, surf_mask, voxel_data):
"""
Voxels with any triangle from ``surf`` are considered occupied and or'ed with ``group_mask``.
If the supplied ``occupied_voxels`` is None a voxel array is created and returned.
"""
nodes = surf["nodes"]
tris = surf["tris"]
x_pts, y_pts, z_pts = [voxel_data[k] for k in ("x_grid", "y_grid", "z_grid")]
vox_size = voxel_data["vox_size"]
## Find the local extents of this part
min_x, max_x = np.min(surf["x"]) - vox_size, np.max(surf["x"]) + vox_size
min_y, max_y = np.min(surf["y"]) - vox_size, np.max(surf["y"]) + vox_size
min_z, max_z = np.min(surf["z"]) - vox_size, np.max(surf["z"]) + vox_size
b_tree = BSP_Grid(nodes, tris)
# Create BSP tree elements- we're not using a tree, but we are using some of the functions
b_x_root = BSP_Element(b_tree.tris, b_tree)
size_i, size_j, size_k = len(x_pts), len(y_pts), len(z_pts)
## Create the occupied voxels if none were supplied
if voxel_data["value"] is None:
voxel_data["value"] = np.zeros((size_i - 1, size_j - 1, size_k - 1), dtype=np.uint32)
occupied_voxels = voxel_data["value"]
## The [1:] is because to make n voxels in a given direction we need n-1 splits
for i, x_pos in enumerate(x_pts[1:]):
if x_pos < min_x: continue
if x_pos > max_x: break
b_above_x, b_below_x = b_x_root.split_at(0, x_pos)
b_y_root = b_below_x
for j, y_pos in enumerate(y_pts[1:]):
if b_y_root is None:
break
if y_pos < min_y: continue
if y_pos > max_y: break
b_above_y, b_below_y = b_y_root.split_at(1, y_pos)
b_z_root = b_below_y
for k, z_pos in enumerate(z_pts[1:]):
if b_z_root is None:
break
if z_pos < min_z: continue
if z_pos > max_z: break
b_above_z, b_below_z = b_z_root.split_at(2, z_pos)
if not (b_below_z and (len(b_below_z.tris) == 0)):
## There is at least part of triangle here so mark as occupied
occupied_voxels[i, j, k] |= surf_mask
b_z_root = b_above_z
b_y_root = b_above_y
b_x_root = b_above_x
return voxel_data
#############
# Main code
def main(vehicle_comp_coords, tr_mat, voxel_masks, settings):
"""
Perform voxelization for all vehicle geometries in a list of parts. Combine on a uniform grid.
"""
for key, veh_surf in vehicle_comp_coords.items():
# Convert coordinates and find overall best bounding box
veh_surf = convert_geom(veh_surf, tr_mat)
x_grid, y_grid, z_grid = make_grid(vehicle_comp_coords, settings)
voxel_data = {"x_grid": x_grid,
"y_grid": y_grid,
"z_grid": z_grid,
"vox_size": settings["voxel_size"],
"csys_trans": tr_mat,
"value": None}
for key, veh_surf in vehicle_comp_coords.items():
# Build up the voxel_data
logging.debug("Sampling component: {}".format(key))
## Default mask is 1 for anything not in an identified set
surf_mask = 1
for mask, geo_set in voxel_masks.items():
if veh_surf['part_class'] in geo_set:
surf_mask |= mask
voxel_data = find_occupied_voxels(veh_surf, surf_mask, voxel_data)
return voxel_data
if __name__ == "__main__":
from rpl.tools.api import test_bench_api as tb_api
SETTINGS = tb_api.load_settings("settings.js")
DOORS = {'Hatch_Assembly_Rear_Ramp', 'Hatch_Assembly_Personnel_Door'}
HATCHES = {'Hatch_Assembly_Driver_Commander', 'Hatch_Assembly_Cargo'}
HULLS = {"Hull_Assembly_Parametric", 'Hull_Assembly_Example_With_Connector'}
MANIKINS = {"Manikin"}
# Special labels applied to specific types of voxels
VOXEL_LABELS = {2: HULLS,
4: DOORS,
8: HATCHES,
16: MANIKINS}
vehicle_surfs = tb_api.load_geometry(tb_api.get_all_geom_set() - MANIKINS, single_file=False)
# Modify node coords so object aligns with cartesian axes of occ voxel grid, +z=up
# Vector to rotate around is cross product of current z axis and sfc normal
veh_up = np.array([0., 1., 0.])
rot_around = np.cross(veh_up, np.array([0, 0, 1]))
rot_ang = -np.arccos(veh_up[2])
tr_mat = geom_utils.rotation_about_vector(rot_around, rot_ang)
# voxel_data = main(vehicle_surfs, tr_mat, VOXEL_LABELS, SETTINGS)
vox_veh_folder = r"voxelated_models/vehicles/{}/{}".format(SETTINGS["run_id"],
SETTINGS["voxel_size"])
vox_veh_file = "voxels_{}_vox{}_hacked".format(SETTINGS["run_id"],
SETTINGS["voxel_size"])
try:
voxel_data = data_io.load_array(vox_veh_folder, vox_veh_file, True)
except:
voxel_data = main(vehicle_surfs, tr_mat, VOXEL_LABELS, SETTINGS)
from mayavi import mlab
xo, yo, zo = np.where(voxel_data["value"] == 1)
plot_vehicle = mlab.points3d(voxel_data["x_grid"][xo],
voxel_data["y_grid"][yo],
voxel_data["z_grid"][zo],
color=(0.9, 0.9, 0.9),
scale_mode="none", scale_factor=voxel_data["vox_size"],
mode='cube', opacity=1)
xo, yo, zo = np.where(voxel_data["value"] & 2)
plot_vehicle = mlab.points3d(voxel_data["x_grid"][xo],
voxel_data["y_grid"][yo],
voxel_data["z_grid"][zo],
color=(1, 1, 1),
scale_mode="none", scale_factor=voxel_data["vox_size"],
mode='cube', opacity=0.05)
xo, yo, zo = np.where(voxel_data["value"] & 4)
plot_vehicle = mlab.points3d(voxel_data["x_grid"][xo],
voxel_data["y_grid"][yo],
voxel_data["z_grid"][zo],
color=(1.0, 0.5, 0.5),
scale_mode="none", scale_factor=voxel_data["vox_size"],
mode='cube', opacity=1)
xo, yo, zo = np.where(voxel_data["value"] & 8)
plot_vehicle = mlab.points3d(voxel_data["x_grid"][xo],
voxel_data["y_grid"][yo],
voxel_data["z_grid"][zo],
color=(0.6, 0.6, 1.0),
scale_mode="none", scale_factor=voxel_data["vox_size"],
mode='cube', opacity=1)
# No manikins included, no need to plot them
# xo, yo, zo = np.where(voxel_data["value"] & 16)
# plot_vehicle = mlab.points3d(voxel_data["x_grid"][xo],
# voxel_data["y_grid"][yo],
# voxel_data["z_grid"][zo],
# color=(0.5, 1.0, 0.8),
# scale_mode="none", scale_factor=voxel_data["vox_size"],
# mode='cube', opacity=1.0)
mlab.show()
# Save the voxelated model of the vehicle (sans door and other excluded parts)
data_io.save_multi_array(vox_veh_folder, vox_veh_file, voxel_data) | 42.30855 | 99 | 0.567613 | 1,739 | 0.152799 | 0 | 0 | 0 | 0 | 0 | 0 | 3,535 | 0.310605 |
07f12eb8f08aef21196193b3111071cb20b8013a | 1,884 | py | Python | silver_bullet/crypto.py | Hojung-Jeong/Silver-Bullet-Encryption-Tool | 5ea29b3cd78cf7488e0cbdcf4ea60d7c9151c2a7 | [
"Apache-2.0"
] | null | null | null | silver_bullet/crypto.py | Hojung-Jeong/Silver-Bullet-Encryption-Tool | 5ea29b3cd78cf7488e0cbdcf4ea60d7c9151c2a7 | [
"Apache-2.0"
] | null | null | null | silver_bullet/crypto.py | Hojung-Jeong/Silver-Bullet-Encryption-Tool | 5ea29b3cd78cf7488e0cbdcf4ea60d7c9151c2a7 | [
"Apache-2.0"
] | null | null | null | '''
>List of functions
1. encrypt(user_input,passphrase) - Encrypt the given string with the given passphrase. Returns cipher text and locked pad.
2. decrypt(cipher_text,locked_pad,passphrase) - Decrypt the cipher text encrypted with SBET. It requires cipher text, locked pad, and passphrase.
'''
# CODE ========================================================================
import zlib
import random
from hashlib import sha1
from silver_bullet.TRNG import trlist
from silver_bullet.contain_value import contain
ascii_value=256
def ciphering(target_list,pad,decrypt=False):
result=[]
for counter in range(len(pad)):
if decrypt==False:
operated=contain(target_list[counter]+pad[counter],ascii_value)
else:
operated=contain(int(target_list[counter])-pad[counter],ascii_value)
result.append(operated)
return result
def locker(pad,passphrase):
cutter=round(len(passphrase)/2)
splited=[passphrase[:cutter],passphrase[cutter:]]
locker=[0 for counter in range(len(pad))]
for element in splited:
bloated_seed=sha1(element.encode()).hexdigest()
random.seed(bloated_seed)
locker=[contain(random.randrange(ascii_value)+element,ascii_value) for element in locker]
holder=[]
for counter in range(len(pad)):
operated=int(pad[counter])^locker[counter]
holder.append(operated)
return holder
def encrypt(user_input,passphrase):
compressed=zlib.compress(user_input.encode())
ui_listed=list(compressed)
pad=trlist(len(ui_listed),ascii_value)
ct=ciphering(ui_listed,pad)
lp=locker(pad,passphrase)
cipher_text=' '.join(map(str,ct))
locked_pad=' '.join(map(str,lp))
return cipher_text, locked_pad
def decrypt(cipher_text,locked_pad,passphrase):
ct=cipher_text.split(' ')
lp=locked_pad.split(' ')
pad=locker(lp,passphrase)
pt=ciphering(ct,pad,True)
byted=bytes(pt)
decompressed=zlib.decompress(byted).decode()
return decompressed | 24.789474 | 146 | 0.735669 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 389 | 0.206476 |
07f1ea6de606abc50abb899228cdc43831fa522e | 876 | py | Python | pyfire/errors.py | RavidLevi98/pyfire | 404ae2082fd5be3ef652b3e15a66ad0d79b7a1b5 | [
"BSD-3-Clause"
] | null | null | null | pyfire/errors.py | RavidLevi98/pyfire | 404ae2082fd5be3ef652b3e15a66ad0d79b7a1b5 | [
"BSD-3-Clause"
] | 1 | 2021-05-22T21:34:44.000Z | 2021-05-22T21:34:44.000Z | pyfire/errors.py | RavidLevi98/pyfire | 404ae2082fd5be3ef652b3e15a66ad0d79b7a1b5 | [
"BSD-3-Clause"
] | 1 | 2021-05-22T21:21:11.000Z | 2021-05-22T21:21:11.000Z | # -*- coding: utf-8 -*-
"""
pyfire.errors
~~~~~~~~~~~~~~~~~~~~~~
Holds the global used base errors
:copyright: 2011 by the pyfire Team, see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
import xml.etree.ElementTree as ET
class XMPPProtocolError(Exception):
"""Base class for all errors that can be
sent via XMPP Protocol to peer
"""
def __init__(self, error_element, error_namespace, error_name=None):
self.error_name = error_name
self.element = ET.Element(error_element)
self.element.set("xmlns", error_namespace)
# per default all errors are recoverable
self.unrecoverable = False
def __unicode__(self):
if self.error_name is not None:
self.element.append(ET.Element(self.error_name))
return unicode(ET.tostring(self.element))
| 28.258065 | 72 | 0.651826 | 600 | 0.684932 | 0 | 0 | 0 | 0 | 0 | 0 | 368 | 0.420091 |
07f21adedf8ef7aa0ba52361a9cf4372ad43ac9a | 4,967 | py | Python | app/nextMoveLogic.py | thekitbag/starter-snake-python | 48d12d2fa61ecfc976cd5750316b1db49a641f7f | [
"MIT"
] | null | null | null | app/nextMoveLogic.py | thekitbag/starter-snake-python | 48d12d2fa61ecfc976cd5750316b1db49a641f7f | [
"MIT"
] | null | null | null | app/nextMoveLogic.py | thekitbag/starter-snake-python | 48d12d2fa61ecfc976cd5750316b1db49a641f7f | [
"MIT"
] | null | null | null | import random
class Status(object):
def getHeadPosition(gamedata):
me = gamedata['you']
my_position = me['body']
head = my_position[0]
return head
def getMyLength(gamedata):
me = gamedata['you']
my_position = me['body']
if my_position[0] == my_position[1] == my_position[2]:
return 1
elif my_position[1] == my_position[2]:
return 2
else: return len(my_position)
def getMyDirection(gamedata):
me = gamedata['you']
my_position = me['body']
if Status.getMyLength(gamedata) == 1:
return 'none'
elif my_position[0]['x'] > my_position[1]['x']:
return 'right'
elif my_position[0]['x'] < my_position[1]['x']:
return 'left'
elif my_position[0]['x'] == my_position[1]['x'] and my_position[0]['y'] < my_position[1]['y']:
return 'up'
else: return 'down'
def getHealth(gamedata):
pass
def getBoardSize(gamedata):
board_height = gamedata['board']['height']
board_width = gamedata['board']['width']
dimensions = {'height': board_height, 'width': board_width}
return dimensions
def getFoodPositions(gamedata):
pass
def getSnakesPositions(gamedata):
pass
class Assess(object):
def wallProximity(gamedata):
"""returns proximity to a wall
either parallel to, head-on or corner"""
head = Status.getHeadPosition(gamedata)
board_size = Status.getBoardSize(gamedata)
direction = Status.getMyDirection(gamedata)
height = board_size['height'] - 1
width = board_size['width'] - 1
#corners
if head['x'] == 0 and head['y'] == 0:
return {'type': 'corner', 'identifier': 'top left', 'direction': direction}
elif head['x'] == 0 and head['y'] == height:
return {'type': 'corner', 'identifier': 'bottom left', 'direction': direction}
elif head['x'] == width and head['y'] == 0:
return {'type': 'corner', 'identifier': 'top right', 'direction': direction}
elif head['x'] == width and head['y'] == height:
return {'type': 'corner', 'identifier': 'bottom right', 'direction': direction}
#headons
elif head['x'] == 0 and direction == 'left':
return {'type': 'head-on', 'identifier': 'left', 'direction': direction}
elif head['y'] == 0 and direction == 'up':
return {'type': 'head-on', 'identifier': 'top', 'direction': direction}
elif head['x'] == width and direction == 'right':
return {'type': 'head-on', 'identifier': 'right', 'direction': direction}
elif head['y'] == height and direction == 'down':
return {'type': 'head-on', 'identifier': 'bottom', 'direction': direction}
#parrallels
elif head['x'] == 0 and direction == 'up' or head['x'] == 0 and direction == 'down':
return {'type': 'parallel', 'identifier': 'left', 'direction': direction}
elif head['y'] == 0 and direction == 'right' or head['y'] == 0 and direction =='left':
return {'type': 'parallel', 'identifier': 'top', 'direction': direction}
elif head['x'] == width and direction =='down' or head['x'] == width and direction == 'up':
return {'type': 'parallel', 'identifier': 'right', 'direction': direction}
elif head['y'] == height and direction == 'left' or head['y'] == height and direction == 'right':
return {'type': 'parallel', 'identifier': 'bottom', 'direction': direction}
else: return False
def ownBodyProximity(gamedata):
pass
def killPossible(gamedata):
pass
def smallerSnakeNearby(gamedata):
pass
def biggerSnakeNearby(gamedata):
pass
def foodNearby(gamedata):
pass
class Action(object):
def avoidDeath():
pass
def chaseFood():
pass
def fleeSnake():
pass
def chaseSnake():
pass
class Decision(object):
def chooseBestOption(gamedata):
options = ['up', 'down', 'right', 'left']
current_direction = Status.getMyDirection(gamedata)
#first go
if current_direction == 'none':
choice = random.choice(options)
#remove opposite direction
if current_direction == 'up':
options.remove('down')
if current_direction == 'down':
options.remove('up')
if current_direction == 'right':
options.remove('left')
if current_direction == 'left':
options.remove('right')
#no danger keep going
if Assess.wallProximity(gamedata) == False:
choice = current_direction
#in a corner
elif Assess.wallProximity(gamedata)['type'] == 'corner':
options.remove(current_direction)
if Assess.wallProximity(gamedata)['identifier'][0] == 't' and Assess.wallProximity(gamedata)['identifier'][4] == 'l':
if 'up' in options:
choice = 'down'
else: choice = 'right'
elif Assess.wallProximity(gamedata)['identifier'][0] == 't' and Assess.wallProximity(gamedata)['identifier'][4] == 'r':
if 'up' in options:
choice = 'down'
else: choice = 'left'
#headon
elif Assess.wallProximity(gamedata)['type'] == 'head-on':
options.remove(current_direction)
choice = random.choice(options)
#parallel
elif Assess.wallProximity(gamedata)['type'] == 'parallel':
choice = current_direction
else: print("shit")
print(options)
return choice
| 29.217647 | 122 | 0.655124 | 4,933 | 0.993155 | 0 | 0 | 0 | 0 | 0 | 0 | 1,237 | 0.249044 |
07f27b728b22aae57de29b0aad696e2f245d31dd | 2,921 | py | Python | generator/util.py | gbtami/lichess-puzzler | e7338b35f592481141acefe39c7aaa444b26aa9e | [
"MIT"
] | 1 | 2021-02-20T11:21:53.000Z | 2021-02-20T11:21:53.000Z | generator/util.py | gbtami/lichess-puzzler | e7338b35f592481141acefe39c7aaa444b26aa9e | [
"MIT"
] | null | null | null | generator/util.py | gbtami/lichess-puzzler | e7338b35f592481141acefe39c7aaa444b26aa9e | [
"MIT"
] | null | null | null | from dataclasses import dataclass
import math
import chess
import chess.engine
from model import EngineMove, NextMovePair
from chess import Color, Board
from chess.pgn import GameNode
from chess.engine import SimpleEngine, Score
nps = []
def material_count(board: Board, side: Color) -> int:
values = { chess.PAWN: 1, chess.KNIGHT: 3, chess.BISHOP: 3, chess.ROOK: 5, chess.QUEEN: 9 }
return sum(len(board.pieces(piece_type, side)) * value for piece_type, value in values.items())
def material_diff(board: Board, side: Color) -> int:
return material_count(board, side) - material_count(board, not side)
def is_up_in_material(board: Board, side: Color) -> bool:
return material_diff(board, side) > 0
def get_next_move_pair(engine: SimpleEngine, node: GameNode, winner: Color, limit: chess.engine.Limit) -> NextMovePair:
info = engine.analyse(node.board(), multipv = 2, limit = limit)
global nps
nps.append(info[0]["nps"])
nps = nps[-20:]
# print(info)
best = EngineMove(info[0]["pv"][0], info[0]["score"].pov(winner))
second = EngineMove(info[1]["pv"][0], info[1]["score"].pov(winner)) if len(info) > 1 else None
return NextMovePair(node, winner, best, second)
def avg_knps():
global nps
return round(sum(nps) / len(nps) / 1000) if nps else 0
def win_chances(score: Score) -> float:
"""
winning chances from -1 to 1 https://graphsketch.com/?eqn1_color=1&eqn1_eqn=100+*+%282+%2F+%281+%2B+exp%28-0.004+*+x%29%29+-+1%29&eqn2_color=2&eqn2_eqn=&eqn3_color=3&eqn3_eqn=&eqn4_color=4&eqn4_eqn=&eqn5_color=5&eqn5_eqn=&eqn6_color=6&eqn6_eqn=&x_min=-1000&x_max=1000&y_min=-100&y_max=100&x_tick=100&y_tick=10&x_label_freq=2&y_label_freq=2&do_grid=0&do_grid=1&bold_labeled_lines=0&bold_labeled_lines=1&line_width=4&image_w=850&image_h=525
"""
mate = score.mate()
if mate is not None:
return 1 if mate > 0 else -1
cp = score.score()
return 2 / (1 + math.exp(-0.004 * cp)) - 1 if cp is not None else 0
CORRESP_TIME = 999999
def reject_by_time_control(line: str, has_master: bool, master_only: bool, bullet: bool, mates: bool) -> bool:
if not line.startswith("[TimeControl "):
return False
if master_only and not has_master:
return True
try:
seconds, increment = line[1:][:-2].split()[1].replace("\"", "").split("+")
total = int(seconds) + int(increment) * 40
if master_only or mates:
if bullet:
return total < 30 or total >= 160
else:
return total < 160 or total >= 480
else:
return total < (160 if has_master else 480)
except:
return True
def exclude_rating(line: str, mates: bool) -> bool:
if not line.startswith("[WhiteElo ") and not line.startswith("[BlackElo "):
return False
try:
return int(line[11:15]) < (1501 if mates else 1600)
except:
return True
| 38.946667 | 442 | 0.66176 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 542 | 0.185553 |
07f699666466b24dd921c96e35a918f2fe5e627b | 293 | py | Python | sleep.py | SkylerHoward/O | 989246a5cdc297ab9f76cb6b26daebd799a03741 | [
"MIT"
] | null | null | null | sleep.py | SkylerHoward/O | 989246a5cdc297ab9f76cb6b26daebd799a03741 | [
"MIT"
] | null | null | null | sleep.py | SkylerHoward/O | 989246a5cdc297ab9f76cb6b26daebd799a03741 | [
"MIT"
] | null | null | null | import time, morning
from datetime import datetime
def main():
while True:
a = time.mktime(datetime.now().timetuple())
n = datetime.now()
if n.hour == 6 and (n.minute-(n.minute%5)) == 15:
return morning.main()
time.sleep(300 - (time.mktime(datetime.now().timetuple())-a)) | 29.3 | 63 | 0.648464 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
07f6a33d952a989c19f3efa056df22e95ace1f20 | 4,526 | py | Python | tests/unit/commands/local/start_lambda/test_cli.py | ourobouros/aws-sam-cli | 3fba861f5106d604fde6d023923a9b83377a35d9 | [
"Apache-2.0"
] | 2 | 2018-11-09T04:43:41.000Z | 2018-11-20T06:39:45.000Z | tests/unit/commands/local/start_lambda/test_cli.py | ourobouros/aws-sam-cli | 3fba861f5106d604fde6d023923a9b83377a35d9 | [
"Apache-2.0"
] | null | null | null | tests/unit/commands/local/start_lambda/test_cli.py | ourobouros/aws-sam-cli | 3fba861f5106d604fde6d023923a9b83377a35d9 | [
"Apache-2.0"
] | null | null | null | from unittest import TestCase
from mock import patch, Mock
from samcli.commands.local.start_lambda.cli import do_cli as start_lambda_cli
from samcli.commands.local.cli_common.user_exceptions import UserException
from samcli.commands.validate.lib.exceptions import InvalidSamDocumentException
from samcli.commands.local.lib.exceptions import OverridesNotWellDefinedError
class TestCli(TestCase):
def setUp(self):
self.template = "template"
self.env_vars = "env-vars"
self.debug_port = 123
self.debug_args = "args"
self.debugger_path = "/test/path"
self.docker_volume_basedir = "basedir"
self.docker_network = "network"
self.log_file = "logfile"
self.skip_pull_image = True
self.profile = "profile"
self.region = "region"
self.parameter_overrides = {}
self.host = "host"
self.port = 123
@patch("samcli.commands.local.start_lambda.cli.InvokeContext")
@patch("samcli.commands.local.start_lambda.cli.LocalLambdaService")
def test_cli_must_setup_context_and_start_service(self, local_lambda_service_mock,
invoke_context_mock):
# Mock the __enter__ method to return a object inside a context manager
context_mock = Mock()
invoke_context_mock.return_value.__enter__.return_value = context_mock
service_mock = Mock()
local_lambda_service_mock.return_value = service_mock
self.call_cli()
invoke_context_mock.assert_called_with(template_file=self.template,
function_identifier=None,
env_vars_file=self.env_vars,
docker_volume_basedir=self.docker_volume_basedir,
docker_network=self.docker_network,
log_file=self.log_file,
skip_pull_image=self.skip_pull_image,
aws_profile=self.profile,
debug_port=self.debug_port,
debug_args=self.debug_args,
debugger_path=self.debugger_path,
aws_region=self.region,
parameter_overrides=self.parameter_overrides)
local_lambda_service_mock.assert_called_with(lambda_invoke_context=context_mock,
port=self.port,
host=self.host)
service_mock.start.assert_called_with()
@patch("samcli.commands.local.start_lambda.cli.InvokeContext")
def test_must_raise_user_exception_on_invalid_sam_template(self, invoke_context_mock):
invoke_context_mock.side_effect = InvalidSamDocumentException("bad template")
with self.assertRaises(UserException) as context:
self.call_cli()
msg = str(context.exception)
expected = "bad template"
self.assertEquals(msg, expected)
@patch("samcli.commands.local.start_lambda.cli.InvokeContext")
def test_must_raise_user_exception_on_invalid_env_vars(self, invoke_context_mock):
invoke_context_mock.side_effect = OverridesNotWellDefinedError("bad env vars")
with self.assertRaises(UserException) as context:
self.call_cli()
msg = str(context.exception)
expected = "bad env vars"
self.assertEquals(msg, expected)
def call_cli(self):
start_lambda_cli(ctx=None,
host=self.host,
port=self.port,
template=self.template,
env_vars=self.env_vars,
debug_port=self.debug_port,
debug_args=self.debug_args,
debugger_path=self.debugger_path,
docker_volume_basedir=self.docker_volume_basedir,
docker_network=self.docker_network,
log_file=self.log_file,
skip_pull_image=self.skip_pull_image,
profile=self.profile,
region=self.region,
parameter_overrides=self.parameter_overrides)
| 45.26 | 96 | 0.579319 | 4,152 | 0.917366 | 0 | 0 | 2,786 | 0.615555 | 0 | 0 | 436 | 0.096332 |
07f8b674abd2c96105f1e6a593d1a4ae299e109a | 5,389 | py | Python | restapi/services/Utils/test_getters.py | Varun487/CapstoneProject_TradingSystem | b21e3f2c6c5e75596927666bf65294a2014babcf | [
"MIT"
] | 3 | 2022-01-10T01:39:00.000Z | 2022-01-11T13:17:36.000Z | restapi/services/Utils/test_getters.py | Varun487/CapstoneProject_TradingSystem | b21e3f2c6c5e75596927666bf65294a2014babcf | [
"MIT"
] | null | null | null | restapi/services/Utils/test_getters.py | Varun487/CapstoneProject_TradingSystem | b21e3f2c6c5e75596927666bf65294a2014babcf | [
"MIT"
] | 1 | 2022-01-09T07:30:36.000Z | 2022-01-09T07:30:36.000Z | from django.test import TestCase
import pandas as pd
from .getters import Getter
from .converter import Converter
from strategies.models import Company
from strategies.models import IndicatorType
class GetDataTestCase(TestCase):
def setUp(self) -> None:
# Dummy company data
Company.objects.create(name='abc', ticker='ABC', description='desc')
Company.objects.create(name='abcd', ticker='ABCD', description='desc')
Company.objects.create(name='abce', ticker='ABCE', description='desc')
# Dummy indicator data
IndicatorType.objects.create(name='abc', description='desc')
IndicatorType.objects.create(name='abcd', description='desc')
IndicatorType.objects.create(name='abce', description='desc')
self.param_list_company = {"name": "abc", "ticker": 'ABC', "description": 'desc'}
self.param_list_indicator_type = {"name": "abc", "description": 'desc'}
def test_input_none(self):
"""No inputs are given"""
self.assertEquals(Getter().table_name, None)
self.assertEquals(Getter().param_list, None)
self.assertEquals(Getter().df_flag, False)
def test_input_all(self):
"""All inputs provided"""
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list=self.param_list_company).table_name,
Company)
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list=self.param_list_company).param_list,
self.param_list_company)
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list=self.param_list_company).df_flag, True)
def test_input_df_flag(self):
"""Only df_flag input is provided"""
self.assertEquals(Getter(df_flag=True).df_flag, True)
self.assertEquals(Getter(df_flag=False).df_flag, False)
def test_get_data_correct_obj_list(self):
"""Whether it returns correct obj list when input is correct"""
# Returns correct object list for company
self.assertEquals(Getter(table_name=Company, df_flag=False, param_list=self.param_list_company).get_data(),
list(Company.objects.filter(**self.param_list_company)))
self.assertEquals(Getter(table_name=Company, param_list={"description": 'desc'}).get_data(),
list(Company.objects.filter(**{"description": 'desc'})))
self.assertEquals(Getter(table_name=Company, param_list={"name": "abcd"}).get_data(),
list(Company.objects.filter(**{"name": "abcd"})))
# Returns correct object list for Indicator
self.assertEquals(
Getter(table_name=IndicatorType, df_flag=False, param_list=self.param_list_indicator_type).get_data(),
list(IndicatorType.objects.filter(**self.param_list_indicator_type)))
self.assertEquals(Getter(table_name=IndicatorType, param_list={"description": 'desc'}).get_data(),
list(IndicatorType.objects.filter(**{"description": 'desc'})))
self.assertEquals(Getter(table_name=IndicatorType, param_list={"name": "abcd"}).get_data(),
list(IndicatorType.objects.filter(**{"name": "abcd"})))
def test_get_data_correct_df(self):
"""Whether it returns correct df when input is correct"""
# Returns correct df for company
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list=self.param_list_company).get_data()
.equals(
Converter(obj_list=list(Company.objects.filter(**self.param_list_company))).to_df()
), True)
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list={"description": 'desc'}).get_data()
.equals(
Converter(obj_list=list(Company.objects.filter(**{"description": 'desc'}))).to_df()
), True)
self.assertEquals(Getter(table_name=Company, df_flag=True, param_list={"name": "abcd"}).get_data()
.equals(
Converter(obj_list=list(Company.objects.filter(**{"name": "abcd"}))).to_df()
), True)
# Returns correct df for indicator type
self.assertEquals(
Getter(table_name=IndicatorType, df_flag=True, param_list=self.param_list_indicator_type).get_data()
.equals(
Converter(obj_list=list(IndicatorType.objects.filter(**self.param_list_indicator_type))).to_df()
), True)
self.assertEquals(Getter(table_name=IndicatorType, df_flag=True, param_list={"description": 'desc'}).get_data()
.equals(
Converter(obj_list=list(IndicatorType.objects.filter(**{"description": 'desc'}))).to_df()
), True)
self.assertEquals(Getter(table_name=IndicatorType, df_flag=True, param_list={"name": "abcd"}).get_data()
.equals(
Converter(obj_list=list(IndicatorType.objects.filter(**{"name": "abcd"}))).to_df()
), True)
def test_get_data_invalid_inputs(self):
self.assertRaises(TypeError,
Getter(table_name="IndicatorTyp", df_flag=True, param_list={"name": "abcd"}).get_data)
self.assertRaises(TypeError, Getter(table_name=IndicatorType, param_list={"nam": "abcd"}).get_data)
self.assertRaises(TypeError, Getter(table_name=Company, param_list={"name": "abcd", "res": "abcd"}))
| 52.833333 | 119 | 0.660419 | 5,187 | 0.962516 | 0 | 0 | 0 | 0 | 0 | 0 | 871 | 0.161626 |
07f928cd0ad75195469f95ed414958ac002210c7 | 3,376 | py | Python | pytorch_toolkit/ote/ote/modules/trainers/mmdetection.py | abhatikar/training_extensions | 1c96e0f5f39688f8b79735e8dfa90646afc3d5e6 | [
"Apache-2.0"
] | 2 | 2021-01-07T05:09:17.000Z | 2021-10-15T05:13:46.000Z | pytorch_toolkit/ote/ote/modules/trainers/mmdetection.py | abhatikar/training_extensions | 1c96e0f5f39688f8b79735e8dfa90646afc3d5e6 | [
"Apache-2.0"
] | 9 | 2021-09-08T03:12:59.000Z | 2022-03-12T00:57:19.000Z | pytorch_toolkit/ote/ote/modules/trainers/mmdetection.py | abhatikar/training_extensions | 1c96e0f5f39688f8b79735e8dfa90646afc3d5e6 | [
"Apache-2.0"
] | null | null | null | """
Copyright (c) 2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import json
import logging
import subprocess
import tempfile
from ote import MMDETECTION_TOOLS
from .base import BaseTrainer
from ..registry import TRAINERS
@TRAINERS.register_module()
class MMDetectionTrainer(BaseTrainer):
def __init__(self):
super(MMDetectionTrainer, self).__init__()
def _get_tools_dir(self):
return MMDETECTION_TOOLS
def _add_extra_args(self, cfg, config_path, update_config):
if self.__is_clustering_needed(cfg):
update_config = self.__cluster(cfg, config_path, update_config)
return update_config
@staticmethod
def __is_clustering_needed(cfg):
if cfg.total_epochs > 0:
return False
if not hasattr(cfg.model, 'bbox_head') or not cfg.model.bbox_head.type == 'SSDHead':
return False
if not cfg.model.bbox_head.anchor_generator.type == 'SSDAnchorGeneratorClustered':
return False
return True
@staticmethod
def __cluster(cfg, config_path, update_config):
logging.info('Clustering started...')
widths = cfg.model.bbox_head.anchor_generator.widths
n_clust = 0
for w in widths:
n_clust += len(w) if isinstance(w, (list, tuple)) else 1
n_clust = ' --n_clust ' + str(n_clust)
group_as = ''
if isinstance(widths[0], (list, tuple)):
group_as = ' --group_as ' + ' '.join([str(len(w)) for w in widths])
config = ' --config ' + config_path
tmp_file = tempfile.NamedTemporaryFile(delete=False)
out = f' --out {tmp_file.name}'
if 'pipeline' in cfg.data.train:
img_shape = [t for t in cfg.data.train.pipeline if t['type'] == 'Resize'][0][
'img_scale']
else:
img_shape = [t for t in cfg.data.train.dataset.pipeline if t['type'] == 'Resize'][0][
'img_scale']
img_shape = f' --image_size_wh {img_shape[0]} {img_shape[1]}'
subprocess.run(f'python {MMDETECTION_TOOLS}/cluster_boxes.py'
f'{config}'
f'{n_clust}'
f'{group_as}'
f'{update_config}'
f'{img_shape}'
f'{out}'.split(' '), check=True)
with open(tmp_file.name) as src_file:
content = json.load(src_file)
widths, heights = content['widths'], content['heights']
if not update_config:
update_config = ' --update_config'
update_config += f' model.bbox_head.anchor_generator.widths={str(widths).replace(" ", "")}'
update_config += f' model.bbox_head.anchor_generator.heights={str(heights).replace(" ", "")}'
logging.info('... clustering completed.')
return update_config
| 34.44898 | 101 | 0.627073 | 2,604 | 0.771327 | 0 | 0 | 2,632 | 0.779621 | 0 | 0 | 1,167 | 0.345675 |
07fabd24f913f0cde7669692291156d001f2e833 | 1,979 | py | Python | svn-go-stats/transform.py | BT-OpenSource/bt-betalab | af5a1b0d778c1746312149f62da0c4159f387293 | [
"MIT"
] | 1 | 2021-03-02T10:44:07.000Z | 2021-03-02T10:44:07.000Z | svn-go-stats/transform.py | BT-OpenSource/bt-betalab | af5a1b0d778c1746312149f62da0c4159f387293 | [
"MIT"
] | null | null | null | svn-go-stats/transform.py | BT-OpenSource/bt-betalab | af5a1b0d778c1746312149f62da0c4159f387293 | [
"MIT"
] | null | null | null | import sys
import json
import subprocess
import re
import statistics
def get_complexity():
# Load the cyclomatic complexity info
cyclostats = subprocess.check_output(['./gocyclo', 'repo']).decode("utf-8")
results = re.findall('([0-9]+)\s([^\s]+)\s([^\s]+)\s([^:]+):([0-9]+):([0-9]+)', cyclostats)
# Setup a dictionary in which to keep track of the complixities
# for each file
files = {}
# Build an array of complexities for each file
for result in results:
if result[3] in files:
files[result[3]].append(int(result[0]))
else:
files[result[3]] = [int(result[0])]
# Pick out the median value (picking the highest of the two
# middle entries if needed) for each file
for name, values in files.items():
files[name] = statistics.median_high(values)
return files
def get_duplicate_const_strings():
# Load the const string duplication info
cyclostats = subprocess.check_output(['./goconst', './repo/...']).decode("utf-8")
results = re.findall('([^:]+).+ other occurrence\(s\) of \"(.+)\" found in: ([^:]+).+\n?', cyclostats)
files = {}
# Build an array containing the number of potentially duplicated
# constants by file
for result in results:
if result[0] in files:
files[result[0]] = files[result[0]]+1
else:
files[result[0]] = 1
return files
# Main service body
if __name__ == "__main__":
complexity = get_complexity()
duplicate_const_strings = get_duplicate_const_strings()
files = set()
files.update(complexity.keys())
files.update(duplicate_const_strings.keys())
result = []
for f in files:
result.append({
'filename': f,
'cyclomaticComplexity': complexity[f] if f in complexity else 0,
'duplicateConstStrings': duplicate_const_strings[f] if f in duplicate_const_strings else 0
})
print(json.dumps(result)) | 29.102941 | 106 | 0.623042 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 647 | 0.326933 |
07fb390e2fe8908e8e3a429d629ca30f1d77df66 | 11,225 | py | Python | test/test_python_errors.py | yangyangxcf/parso | e496b07b6342f6182225a60aad6031d7ad08f24d | [
"PSF-2.0"
] | null | null | null | test/test_python_errors.py | yangyangxcf/parso | e496b07b6342f6182225a60aad6031d7ad08f24d | [
"PSF-2.0"
] | null | null | null | test/test_python_errors.py | yangyangxcf/parso | e496b07b6342f6182225a60aad6031d7ad08f24d | [
"PSF-2.0"
] | null | null | null | """
Testing if parso finds syntax errors and indentation errors.
"""
import sys
import warnings
import pytest
import parso
from parso._compatibility import is_pypy
from .failing_examples import FAILING_EXAMPLES, indent, build_nested
if is_pypy:
# The errors in PyPy might be different. Just skip the module for now.
pytestmark = pytest.mark.skip()
def _get_error_list(code, version=None):
grammar = parso.load_grammar(version=version)
tree = grammar.parse(code)
return list(grammar.iter_errors(tree))
def assert_comparison(code, error_code, positions):
errors = [(error.start_pos, error.code) for error in _get_error_list(code)]
assert [(pos, error_code) for pos in positions] == errors
@pytest.mark.parametrize('code', FAILING_EXAMPLES)
def test_python_exception_matches(code):
wanted, line_nr = _get_actual_exception(code)
errors = _get_error_list(code)
actual = None
if errors:
error, = errors
actual = error.message
assert actual in wanted
# Somehow in Python3.3 the SyntaxError().lineno is sometimes None
assert line_nr is None or line_nr == error.start_pos[0]
def test_non_async_in_async():
"""
This example doesn't work with FAILING_EXAMPLES, because the line numbers
are not always the same / incorrect in Python 3.8.
"""
if sys.version_info[:2] < (3, 5):
pytest.skip()
# Raises multiple errors in previous versions.
code = 'async def foo():\n def nofoo():[x async for x in []]'
wanted, line_nr = _get_actual_exception(code)
errors = _get_error_list(code)
if errors:
error, = errors
actual = error.message
assert actual in wanted
if sys.version_info[:2] < (3, 8):
assert line_nr == error.start_pos[0]
else:
assert line_nr == 0 # For whatever reason this is zero in Python 3.8+
@pytest.mark.parametrize(
('code', 'positions'), [
('1 +', [(1, 3)]),
('1 +\n', [(1, 3)]),
('1 +\n2 +', [(1, 3), (2, 3)]),
('x + 2', []),
('[\n', [(2, 0)]),
('[\ndef x(): pass', [(2, 0)]),
('[\nif 1: pass', [(2, 0)]),
('1+?', [(1, 2)]),
('?', [(1, 0)]),
('??', [(1, 0)]),
('? ?', [(1, 0)]),
('?\n?', [(1, 0), (2, 0)]),
('? * ?', [(1, 0)]),
('1 + * * 2', [(1, 4)]),
('?\n1\n?', [(1, 0), (3, 0)]),
]
)
def test_syntax_errors(code, positions):
assert_comparison(code, 901, positions)
@pytest.mark.parametrize(
('code', 'positions'), [
(' 1', [(1, 0)]),
('def x():\n 1\n 2', [(3, 0)]),
('def x():\n 1\n 2', [(3, 0)]),
('def x():\n1', [(2, 0)]),
]
)
def test_indentation_errors(code, positions):
assert_comparison(code, 903, positions)
def _get_actual_exception(code):
with warnings.catch_warnings():
# We don't care about warnings where locals/globals misbehave here.
# It's as simple as either an error or not.
warnings.filterwarnings('ignore', category=SyntaxWarning)
try:
compile(code, '<unknown>', 'exec')
except (SyntaxError, IndentationError) as e:
wanted = e.__class__.__name__ + ': ' + e.msg
line_nr = e.lineno
except ValueError as e:
# The ValueError comes from byte literals in Python 2 like '\x'
# that are oddly enough not SyntaxErrors.
wanted = 'SyntaxError: (value error) ' + str(e)
line_nr = None
else:
assert False, "The piece of code should raise an exception."
# SyntaxError
# Python 2.6 has a bit different error messages here, so skip it.
if sys.version_info[:2] == (2, 6) and wanted == 'SyntaxError: unexpected EOF while parsing':
wanted = 'SyntaxError: invalid syntax'
if wanted == 'SyntaxError: non-keyword arg after keyword arg':
# The python 3.5+ way, a bit nicer.
wanted = 'SyntaxError: positional argument follows keyword argument'
elif wanted == 'SyntaxError: assignment to keyword':
return [wanted, "SyntaxError: can't assign to keyword",
'SyntaxError: cannot assign to __debug__'], line_nr
elif wanted == 'SyntaxError: assignment to None':
# Python 2.6 does has a slightly different error.
wanted = 'SyntaxError: cannot assign to None'
elif wanted == 'SyntaxError: can not assign to __debug__':
# Python 2.6 does has a slightly different error.
wanted = 'SyntaxError: cannot assign to __debug__'
elif wanted == 'SyntaxError: can use starred expression only as assignment target':
# Python 3.4/3.4 have a bit of a different warning than 3.5/3.6 in
# certain places. But in others this error makes sense.
return [wanted, "SyntaxError: can't use starred expression here"], line_nr
elif wanted == 'SyntaxError: f-string: unterminated string':
wanted = 'SyntaxError: EOL while scanning string literal'
elif wanted == 'SyntaxError: f-string expression part cannot include a backslash':
return [
wanted,
"SyntaxError: EOL while scanning string literal",
"SyntaxError: unexpected character after line continuation character",
], line_nr
elif wanted == "SyntaxError: f-string: expecting '}'":
wanted = 'SyntaxError: EOL while scanning string literal'
elif wanted == 'SyntaxError: f-string: empty expression not allowed':
wanted = 'SyntaxError: invalid syntax'
elif wanted == "SyntaxError: f-string expression part cannot include '#'":
wanted = 'SyntaxError: invalid syntax'
elif wanted == "SyntaxError: f-string: single '}' is not allowed":
wanted = 'SyntaxError: invalid syntax'
return [wanted], line_nr
def test_default_except_error_postition():
# For this error the position seemed to be one line off, but that doesn't
# really matter.
code = 'try: pass\nexcept: pass\nexcept X: pass'
wanted, line_nr = _get_actual_exception(code)
error, = _get_error_list(code)
assert error.message in wanted
assert line_nr != error.start_pos[0]
# I think this is the better position.
assert error.start_pos[0] == 2
def test_statically_nested_blocks():
def build(code, depth):
if depth == 0:
return code
new_code = 'if 1:\n' + indent(code)
return build(new_code, depth - 1)
def get_error(depth, add_func=False):
code = build('foo', depth)
if add_func:
code = 'def bar():\n' + indent(code)
errors = _get_error_list(code)
if errors:
assert errors[0].message == 'SyntaxError: too many statically nested blocks'
return errors[0]
return None
assert get_error(19) is None
assert get_error(19, add_func=True) is None
assert get_error(20)
assert get_error(20, add_func=True)
def test_future_import_first():
def is_issue(code, *args):
code = code % args
return bool(_get_error_list(code))
i1 = 'from __future__ import division'
i2 = 'from __future__ import absolute_import'
assert not is_issue(i1)
assert not is_issue(i1 + ';' + i2)
assert not is_issue(i1 + '\n' + i2)
assert not is_issue('"";' + i1)
assert not is_issue('"";' + i1)
assert not is_issue('""\n' + i1)
assert not is_issue('""\n%s\n%s', i1, i2)
assert not is_issue('""\n%s;%s', i1, i2)
assert not is_issue('"";%s;%s ', i1, i2)
assert not is_issue('"";%s\n%s ', i1, i2)
assert is_issue('1;' + i1)
assert is_issue('1\n' + i1)
assert is_issue('"";1\n' + i1)
assert is_issue('""\n%s\nfrom x import a\n%s', i1, i2)
assert is_issue('%s\n""\n%s', i1, i2)
def test_named_argument_issues(works_not_in_py):
message = works_not_in_py.get_error_message('def foo(*, **dict): pass')
message = works_not_in_py.get_error_message('def foo(*): pass')
if works_not_in_py.version.startswith('2'):
assert message == 'SyntaxError: invalid syntax'
else:
assert message == 'SyntaxError: named arguments must follow bare *'
works_not_in_py.assert_no_error_in_passing('def foo(*, name): pass')
works_not_in_py.assert_no_error_in_passing('def foo(bar, *, name=1): pass')
works_not_in_py.assert_no_error_in_passing('def foo(bar, *, name=1, **dct): pass')
def test_escape_decode_literals(each_version):
"""
We are using internal functions to assure that unicode/bytes escaping is
without syntax errors. Here we make a bit of quality assurance that this
works through versions, because the internal function might change over
time.
"""
def get_msg(end, to=1):
base = "SyntaxError: (unicode error) 'unicodeescape' " \
"codec can't decode bytes in position 0-%s: " % to
return base + end
def get_msgs(escape):
return (get_msg('end of string in escape sequence'),
get_msg(r"truncated %s escape" % escape))
error, = _get_error_list(r'u"\x"', version=each_version)
assert error.message in get_msgs(r'\xXX')
error, = _get_error_list(r'u"\u"', version=each_version)
assert error.message in get_msgs(r'\uXXXX')
error, = _get_error_list(r'u"\U"', version=each_version)
assert error.message in get_msgs(r'\UXXXXXXXX')
error, = _get_error_list(r'u"\N{}"', version=each_version)
assert error.message == get_msg(r'malformed \N character escape', to=2)
error, = _get_error_list(r'u"\N{foo}"', version=each_version)
assert error.message == get_msg(r'unknown Unicode character name', to=6)
# Finally bytes.
error, = _get_error_list(r'b"\x"', version=each_version)
wanted = r'SyntaxError: (value error) invalid \x escape'
if sys.version_info >= (3, 0):
# The positioning information is only available in Python 3.
wanted += ' at position 0'
assert error.message == wanted
def test_too_many_levels_of_indentation():
assert not _get_error_list(build_nested('pass', 99))
assert _get_error_list(build_nested('pass', 100))
base = 'def x():\n if x:\n'
assert not _get_error_list(build_nested('pass', 49, base=base))
assert _get_error_list(build_nested('pass', 50, base=base))
@pytest.mark.parametrize(
'code', [
"f'{*args,}'",
r'f"\""',
r'f"\\\""',
r'fr"\""',
r'fr"\\\""',
r"print(f'Some {x:.2f} and some {y}')",
]
)
def test_valid_fstrings(code):
assert not _get_error_list(code, version='3.6')
@pytest.mark.parametrize(
('code', 'message'), [
("f'{1+}'", ('invalid syntax')),
(r'f"\"', ('invalid syntax')),
(r'fr"\"', ('invalid syntax')),
]
)
def test_invalid_fstrings(code, message):
"""
Some fstring errors are handled differntly in 3.6 and other versions.
Therefore check specifically for these errors here.
"""
error, = _get_error_list(code, version='3.6')
assert message in error.message
@pytest.mark.parametrize(
'code', [
"from foo import (\nbar,\n rab,\n)",
"from foo import (bar, rab, )",
]
)
def test_trailing_comma(code):
errors = _get_error_list(code)
assert not errors
| 34.860248 | 96 | 0.625568 | 0 | 0 | 0 | 0 | 2,285 | 0.203563 | 0 | 0 | 4,327 | 0.385479 |
07fb5058c7a297096cbf1ff7f21aedcf66b7d3ad | 985 | py | Python | shogitk/usikif.py | koji-hirono/pytk-shogi-replayer | a10819a797faecbee5c7b0654beb3694eb522840 | [
"MIT"
] | null | null | null | shogitk/usikif.py | koji-hirono/pytk-shogi-replayer | a10819a797faecbee5c7b0654beb3694eb522840 | [
"MIT"
] | null | null | null | shogitk/usikif.py | koji-hirono/pytk-shogi-replayer | a10819a797faecbee5c7b0654beb3694eb522840 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from shogitk.shogi import Coords, Move, BLACK, WHITE, DROP, PROMOTE
RANKNUM = {
'a': 1,
'b': 2,
'c': 3,
'd': 4,
'e': 5,
'f': 6,
'g': 7,
'h': 8,
'i': 9
}
def decoder(f):
color = [BLACK, WHITE]
step = 0
for line in f:
line = line.strip()
if line[0] == '[':
pass
elif line[0].isdigit():
src = Coords(int(line[0]), RANKNUM[line[1]])
dst = Coords(int(line[2]), RANKNUM[line[3]])
if line[-1] == '+':
modifier = PROMOTE
else:
modifier = None
yield Move(color[step & 1], dst, src, None, modifier=modifier)
step += 1
elif line[0].isupper():
dst = Coords(int(line[2]), RANKNUM[line[3]])
yield Move(color[step & 1], dst, None, line[0], modifier=DROP)
step += 1
| 25.921053 | 74 | 0.450761 | 0 | 0 | 692 | 0.702538 | 0 | 0 | 0 | 0 | 56 | 0.056853 |
07fca3ec2f3896a49c6703b50dabc9ec79e258a9 | 2,160 | py | Python | etherbank_cli/oracles.py | ideal-money/etherbank-cli | d957daa13aa951331cadc35c246c1ce8459ca8df | [
"BSD-2-Clause"
] | 1 | 2018-12-29T02:12:25.000Z | 2018-12-29T02:12:25.000Z | etherbank_cli/oracles.py | ideal-money/etherbank-cli | d957daa13aa951331cadc35c246c1ce8459ca8df | [
"BSD-2-Clause"
] | 5 | 2018-12-20T12:45:39.000Z | 2019-01-08T06:16:01.000Z | etherbank_cli/oracles.py | ideal-money/etherbank-cli | d957daa13aa951331cadc35c246c1ce8459ca8df | [
"BSD-2-Clause"
] | null | null | null | import click
from . import utils
@click.group()
def main():
"Simple CLI for oracles to work with Ether dollar"
pass
@main.command()
@click.option('--ether-price', type=float, help="The ether price in ether dollar")
@click.option('--collateral-ratio', type=float, help="The collateral ratio")
@click.option(
'--liquidation-duration',
type=int,
help="The liquidation duration in minutes")
@click.option(
'--private-key',
callback=utils.check_account,
help='The privat key to sign the transaction')
def vote(ether_price, collateral_ratio, liquidation_duration, private_key):
"Vote on the variable for setting up Ether Bank"
assert [ether_price, collateral_ratio, liquidation_duration
].count(None) == 2, "You should set one variable per vote"
if ether_price:
var_code = 0
value = int(ether_price * 100)
elif collateral_ratio:
var_code = 1
value = int(collateral_ratio * 1000)
elif liquidation_duration:
var_code = 2
value = liquidation_duration * 60
func = utils.contracts['oracles'].functions.vote(var_code, value)
tx_hash = utils.send_transaction(func, 0, private_key)
return tx_hash
@main.command()
@click.option('--oracle', required=True, help="The oracle's address")
@click.option('--score', type=int, required=True, help="The oracle's score")
@click.option(
'--private-key',
callback=utils.check_account,
help='The privat key to sign the transaction')
def set_score(oracle, score, private_key):
"Edit oracle's score"
oracle = utils.w3.toChecksumAddress(oracle)
func = utils.contracts['oracles'].functions.setScore(oracle, score)
tx_hash = utils.send_transaction(func, 0, private_key)
return tx_hash
@main.command()
@click.option(
'--private-key',
callback=utils.check_account,
help='The privat key to sign the transaction')
def finish_recruiting(private_key):
"Set recruiting as finished"
func = utils.contracts['oracles'].functions.finishRecruiting()
tx_hash = utils.send_transaction(func, 0, private_key)
return tx_hash
if __name__ == '__main__':
main()
| 30 | 82 | 0.697222 | 0 | 0 | 0 | 0 | 2,075 | 0.960648 | 0 | 0 | 599 | 0.277315 |
07fd108f6337b8e7a88da0155cf318b6098e4ae4 | 2,585 | py | Python | src/grader/machine.py | MrKaStep/csc230-grader | 559846f4d921c5c4be6b6e9ba8629fb24b448e41 | [
"MIT"
] | null | null | null | src/grader/machine.py | MrKaStep/csc230-grader | 559846f4d921c5c4be6b6e9ba8629fb24b448e41 | [
"MIT"
] | null | null | null | src/grader/machine.py | MrKaStep/csc230-grader | 559846f4d921c5c4be6b6e9ba8629fb24b448e41 | [
"MIT"
] | null | null | null | import getpass
from plumbum import local
from plumbum.machines.paramiko_machine import ParamikoMachine
from plumbum.path.utils import copy
def _once(f):
res = None
def wrapped(*args, **kwargs):
nonlocal res
if res is None:
res = f(*args, **kwargs)
return res
return wrapped
@_once
def get_remote_machine_with_password(host, user):
password = getpass.getpass(prompt=f"Password for {user}@{host}: ", stream=None)
rem = ParamikoMachine(host, user=user, password=password)
return rem
@_once
def get_remote_machine(host, user, keyfile):
rem = ParamikoMachine(host, user=user, keyfile=keyfile)
return rem
def get_local_machine():
return local
def with_machine_rule(cls):
old_init = cls.__init__
def new_init(self, config):
if "machine" not in config:
machine_type = "local"
else:
machine_type = config["machine"]["type"]
if machine_type == "local":
self.machine = get_local_machine()
self.files_to_copy = None
elif machine_type == "remote":
if "keyfile" in config["machine"]:
self.machine = get_remote_machine(config["machine"]["host"], config["machine"]["user"], config["machine"]["keyfile"])
else:
self.machine = get_remote_machine_with_password(config["machine"]["host"], config["machine"]["user"])
self.files_to_copy = config["machine"].get("files_to_copy")
else:
raise ValueError(f"Invalid machine type: {config['machine']['type']}")
self.machine_type = machine_type
old_init(self, config)
cls.__init__ = new_init
old_apply = cls.apply
def new_apply(self, project):
with self.machine.tempdir() as tempdir:
project_path = tempdir / "project"
project_path.mkdir()
existing_files = set([f.name for f in project.root.list()])
if self.files_to_copy:
for fname in self.files_to_copy:
if fname in existing_files:
copy(project.root / fname, project_path / fname)
else:
for f in project.files():
if f.name in existing_files:
copy(f.path, project_path / f.name)
with self.machine.cwd(project_path):
self.session = self.machine.session()
self.session.run(f"cd {project_path}")
return old_apply(self, project)
cls.apply = new_apply
return cls
| 32.3125 | 133 | 0.600387 | 0 | 0 | 0 | 0 | 344 | 0.133075 | 0 | 0 | 278 | 0.107544 |
07fdb99131cb0d8251646bea304d79ad58fc7ab6 | 157 | py | Python | Mundo 1/Ex33.py | legna7/Python | 52e0b642d1b7acc592ec82dd360c5697fb0765db | [
"MIT"
] | null | null | null | Mundo 1/Ex33.py | legna7/Python | 52e0b642d1b7acc592ec82dd360c5697fb0765db | [
"MIT"
] | null | null | null | Mundo 1/Ex33.py | legna7/Python | 52e0b642d1b7acc592ec82dd360c5697fb0765db | [
"MIT"
] | null | null | null | salario = float(input('digite o seu salario: '))
aumento = (salario + (salario * 15)/100 if salario <= 1250 else salario + (salario * 10)/100)
print(aumento) | 52.333333 | 93 | 0.687898 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24 | 0.152866 |
07ff0da6e717ab9585c2e512803b8604ff985d37 | 2,793 | py | Python | tests/test_tree.py | andreax79/airflow-code-editor | 031170387496bbc6d540179c6c2f1765e1e70694 | [
"Apache-2.0"
] | 194 | 2019-08-06T13:03:11.000Z | 2022-03-25T15:29:29.000Z | tests/test_tree.py | andreax79/airflow-code-editor | 031170387496bbc6d540179c6c2f1765e1e70694 | [
"Apache-2.0"
] | 29 | 2019-08-23T16:07:17.000Z | 2022-03-31T03:43:47.000Z | tests/test_tree.py | andreax79/airflow-code-editor | 031170387496bbc6d540179c6c2f1765e1e70694 | [
"Apache-2.0"
] | 32 | 2019-08-15T12:13:37.000Z | 2022-03-31T17:27:24.000Z | #!/usr/bin/env python
import os
import os.path
import airflow
import airflow.plugins_manager
from airflow import configuration
from flask import Flask
from unittest import TestCase, main
from airflow_code_editor.commons import PLUGIN_NAME
from airflow_code_editor.tree import (
get_tree,
)
assert airflow.plugins_manager
app = Flask(__name__)
class TestTree(TestCase):
def setUp(self):
self.root_dir = os.path.dirname(os.path.realpath(__file__))
configuration.conf.set(PLUGIN_NAME, 'git_init_repo', 'False')
configuration.conf.set(PLUGIN_NAME, 'root_directory', self.root_dir)
def test_tree(self):
with app.app_context():
t = get_tree()
self.assertTrue(len(t) > 0)
self.assertTrue('git' in (x['id'] for x in t))
def test_tags(self):
with app.app_context():
t = get_tree("tags")
self.assertIsNotNone(t)
def test_local_branches(self):
with app.app_context():
t = get_tree("local-branches")
self.assertIsNotNone(t)
def test_remote_branches(self):
with app.app_context():
t = get_tree("remote-branches")
self.assertIsNotNone(t)
def test_files(self):
with app.app_context():
t = get_tree("files")
self.assertTrue(
len([x.get('id') for x in t if x.get('id') == 'test_utils.py']) == 1
)
t = get_tree("files/folder")
self.assertTrue(len([x.get('id') for x in t if x.get('id') == '1']) == 1)
def test_git(self):
with app.app_context():
t = get_tree("git/HEAD")
self.assertTrue(t is not None)
class TestTreeGitDisabled(TestCase):
def setUp(self):
self.root_dir = os.path.dirname(os.path.realpath(__file__))
configuration.conf.set(PLUGIN_NAME, 'git_init_repo', 'False')
configuration.conf.set(PLUGIN_NAME, 'root_directory', self.root_dir)
configuration.conf.set(PLUGIN_NAME, 'git_enabled', 'False')
def test_tree(self):
with app.app_context():
t = get_tree()
self.assertTrue(len(t) > 0)
self.assertTrue('git' not in (x['id'] for x in t))
t = get_tree("tags")
self.assertEqual(t, [])
t = get_tree("local-branches")
self.assertEqual(t, [])
t = get_tree("remote-branches")
self.assertEqual(t, [])
t = get_tree("files")
self.assertTrue(
len([x.get('id') for x in t if x.get('id') == 'test_utils.py']) == 1
)
t = get_tree("files/folder")
self.assertTrue(len([x.get('id') for x in t if x.get('id') == '1']) == 1)
if __name__ == '__main__':
main()
| 31.382022 | 85 | 0.583602 | 2,398 | 0.858575 | 0 | 0 | 0 | 0 | 0 | 0 | 343 | 0.122807 |
07ff31219d3e42ddfa090b695c0d4b6ede8d31e9 | 2,826 | py | Python | examples/token_freshness.py | greenape/flask-jwt-extended | 11ac3bf0937ee199aea7d6dc47c748bef9bf1d2f | [
"MIT"
] | 2 | 2021-03-20T01:55:08.000Z | 2021-11-14T12:20:23.000Z | examples/token_freshness.py | greenape/flask-jwt-extended | 11ac3bf0937ee199aea7d6dc47c748bef9bf1d2f | [
"MIT"
] | 1 | 2020-08-06T23:02:45.000Z | 2020-09-26T01:36:21.000Z | examples/token_freshness.py | greenape/flask-jwt-extended | 11ac3bf0937ee199aea7d6dc47c748bef9bf1d2f | [
"MIT"
] | 1 | 2020-10-28T20:09:00.000Z | 2020-10-28T20:09:00.000Z | from quart import Quart, jsonify, request
from quart_jwt_extended import (
JWTManager,
jwt_required,
create_access_token,
jwt_refresh_token_required,
create_refresh_token,
get_jwt_identity,
fresh_jwt_required,
)
app = Quart(__name__)
app.config["JWT_SECRET_KEY"] = "super-secret" # Change this!
jwt = JWTManager(app)
# Standard login endpoint. Will return a fresh access token and
# a refresh token
@app.route("/login", methods=["POST"])
async def login():
username = (await request.get_json()).get("username", None)
password = (await request.get_json()).get("password", None)
if username != "test" or password != "test":
return {"msg": "Bad username or password"}, 401
# create_access_token supports an optional 'fresh' argument,
# which marks the token as fresh or non-fresh accordingly.
# As we just verified their username and password, we are
# going to mark the token as fresh here.
ret = {
"access_token": create_access_token(identity=username, fresh=True),
"refresh_token": create_refresh_token(identity=username),
}
return ret, 200
# Refresh token endpoint. This will generate a new access token from
# the refresh token, but will mark that access token as non-fresh,
# as we do not actually verify a password in this endpoint.
@app.route("/refresh", methods=["POST"])
@jwt_refresh_token_required
async def refresh():
current_user = get_jwt_identity()
new_token = create_access_token(identity=current_user, fresh=False)
ret = {"access_token": new_token}
return ret, 200
# Fresh login endpoint. This is designed to be used if we need to
# make a fresh token for a user (by verifying they have the
# correct username and password). Unlike the standard login endpoint,
# this will only return a new access token, so that we don't keep
# generating new refresh tokens, which entirely defeats their point.
@app.route("/fresh-login", methods=["POST"])
async def fresh_login():
username = (await request.get_json()).get("username", None)
password = (await request.get_json()).get("password", None)
if username != "test" or password != "test":
return {"msg": "Bad username or password"}, 401
new_token = create_access_token(identity=username, fresh=True)
ret = {"access_token": new_token}
return ret, 200
# Any valid JWT can access this endpoint
@app.route("/protected", methods=["GET"])
@jwt_required
async def protected():
username = get_jwt_identity()
return dict(logged_in_as=username), 200
# Only fresh JWTs can access this endpoint
@app.route("/protected-fresh", methods=["GET"])
@fresh_jwt_required
async def protected_fresh():
username = get_jwt_identity()
return dict(fresh_logged_in_as=username), 200
if __name__ == "__main__":
app.run()
| 33.247059 | 75 | 0.714084 | 0 | 0 | 0 | 0 | 1,727 | 0.611111 | 1,450 | 0.513093 | 1,223 | 0.432767 |
07ff9a1f5ea3709124ad1fb347eb63a9284a7ad2 | 195 | py | Python | env/lib/python3.7/site-packages/tinvest/typedefs.py | umchemurziev/Practics | 82b49f9d58e67f1ecff9e6303e7d914bc1905730 | [
"MIT"
] | 1 | 2021-03-29T18:47:32.000Z | 2021-03-29T18:47:32.000Z | env/lib/python3.7/site-packages/tinvest/typedefs.py | umchemurziev/Practics | 82b49f9d58e67f1ecff9e6303e7d914bc1905730 | [
"MIT"
] | null | null | null | env/lib/python3.7/site-packages/tinvest/typedefs.py | umchemurziev/Practics | 82b49f9d58e67f1ecff9e6303e7d914bc1905730 | [
"MIT"
] | null | null | null | from datetime import datetime
from typing import Any, Dict, Union
__all__ = 'AnyDict'
AnyDict = Dict[str, Any] # pragma: no mutate
datetime_or_str = Union[datetime, str] # pragma: no mutate
| 21.666667 | 59 | 0.738462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 47 | 0.241026 |
07ffdb3c18cae37c2fe662c5c84ed5398af39b35 | 1,345 | py | Python | keras/linear/model/pipeline_train.py | PipelineAI/models | d8df07877aa8b10ce9b84983bb440af75e84dca7 | [
"Apache-2.0"
] | 44 | 2017-11-17T06:19:05.000Z | 2021-11-03T06:00:56.000Z | keras/linear/model/pipeline_train.py | PipelineAI/models | d8df07877aa8b10ce9b84983bb440af75e84dca7 | [
"Apache-2.0"
] | 3 | 2018-08-09T14:28:17.000Z | 2018-09-10T03:32:42.000Z | keras/linear/model/pipeline_train.py | PipelineAI/models | d8df07877aa8b10ce9b84983bb440af75e84dca7 | [
"Apache-2.0"
] | 21 | 2017-11-18T15:12:12.000Z | 2020-08-15T07:08:33.000Z | import os
os.environ['KERAS_BACKEND'] = 'theano'
os.environ['THEANO_FLAGS'] = 'floatX=float32,device=cpu'
import cloudpickle as pickle
import pipeline_invoke
import pandas as pd
import numpy as np
import keras
from keras.layers import Input, Dense
from keras.models import Model
from keras.models import save_model, load_model
from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer
if __name__ == '__main__':
df = pd.read_csv("../input/training/training.csv")
df["People per Television"] = pd.to_numeric(df["People per Television"],errors='coerce')
df = df.dropna()
x = df["People per Television"].values.reshape(-1,1).astype(np.float64)
y = df["People per Physician"].values.reshape(-1,1).astype(np.float64)
# min-max -1,1
sc = MinMaxScaler(feature_range=(-1,1))
x_ = sc.fit_transform(x)
y_ = sc.fit_transform(y)
inputs = Input(shape=(1,))
preds = Dense(1,activation='linear')(inputs)
model = Model(inputs=inputs,outputs=preds)
sgd = keras.optimizers.SGD()
model.compile(optimizer=sgd ,loss='mse')
model.fit(x_,y_, batch_size=1, verbose=1, epochs=10, shuffle=False)
save_model(model, 'state/keras_theano_linear_model_state.h5')
# model_pkl_path = 'model.pkl'
# with open(model_pkl_path, 'wb') as fh:
# pickle.dump(pipeline_invoke, fh)
| 30.568182 | 92 | 0.709294 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 392 | 0.29145 |
580077f8f713a612aa61ab64e08f6fd83f19a081 | 1,454 | py | Python | tests/effects/test_cheerlights.py | RatJuggler/led-shim-effects | 3c63f5f2ce3f35f52e784489deb9212757c18cd2 | [
"MIT"
] | 1 | 2021-04-17T16:18:14.000Z | 2021-04-17T16:18:14.000Z | tests/effects/test_cheerlights.py | RatJuggler/led-shim-effects | 3c63f5f2ce3f35f52e784489deb9212757c18cd2 | [
"MIT"
] | 12 | 2019-07-26T18:01:56.000Z | 2019-08-31T15:35:17.000Z | tests/effects/test_cheerlights.py | RatJuggler/led-shim-demo | 3c63f5f2ce3f35f52e784489deb9212757c18cd2 | [
"MIT"
] | null | null | null | from unittest import TestCase
from unittest.mock import Mock, patch
import sys
sys.modules['smbus'] = Mock() # Mock the hardware layer to avoid errors.
from ledshimdemo.canvas import Canvas
from ledshimdemo.effects.cheerlights import CheerLightsEffect
class TestCheerLights(TestCase):
TEST_CANVAS_SIZE = 3 # type: int
def test_cheerlight_call(self):
canvas = Canvas(self.TEST_CANVAS_SIZE)
effect = CheerLightsEffect(canvas)
self.assertIsNone(effect.get_colour_from_channel("http://ejiferfneciudwedwojcmeiocnw.com"))
@patch('ledshimdemo.effects.cheerlights.CheerLightsEffect.get_colour_from_channel', return_value=None)
def test_effect_failed_cheerlights(self, patch_function):
canvas = Canvas(self.TEST_CANVAS_SIZE)
effect = CheerLightsEffect(canvas)
effect.compose()
patch_function.assert_called_once()
for i in range(canvas.get_size()):
self.assertEqual(canvas.get_pixel(i), canvas.BLANK_PIXEL)
def test_effect_working_cheerlights(self):
canvas = Canvas(self.TEST_CANVAS_SIZE)
effect = CheerLightsEffect(canvas)
# Must check before and after in case it changes during the test.
before = effect.get_colour_from_channel(effect.URL)
effect.compose()
after = effect.get_colour_from_channel(effect.URL)
self.assertRegex(repr(effect), "^CheerLights\\(Colour:({0}|{1})\\)$".format(before, after))
| 39.297297 | 106 | 0.72696 | 1,196 | 0.822558 | 0 | 0 | 436 | 0.299862 | 0 | 0 | 277 | 0.190509 |
5800997c4a49cdfc01a368bf3ebf423b84d98d2c | 7,074 | py | Python | figures/Figure_7/02_generate_images.py | Jhsmit/ColiCoords-Paper | 7b92e67600930f64859d14867113b6de3edf1379 | [
"MIT"
] | 2 | 2019-05-12T12:06:50.000Z | 2020-11-11T16:44:49.000Z | figures/Figure_7/02_generate_images.py | Jhsmit/ColiCoords-Paper | 7b92e67600930f64859d14867113b6de3edf1379 | [
"MIT"
] | null | null | null | figures/Figure_7/02_generate_images.py | Jhsmit/ColiCoords-Paper | 7b92e67600930f64859d14867113b6de3edf1379 | [
"MIT"
] | 2 | 2019-06-17T16:00:56.000Z | 2020-02-07T22:17:47.000Z | from colicoords.synthetic_data import add_readout_noise, draw_poisson
from colicoords import load
import numpy as np
import mahotas as mh
from tqdm import tqdm
import os
import tifffile
def chunk_list(l, sizes):
prev = 0
for s in sizes:
result = l[prev:prev+s]
prev += s
yield result
def generate_images(cell_list, num_images, cell_per_img, cell_per_img_std, shape):
nums = np.round(np.random.normal(cell_per_img, cell_per_img_std, num_images)).astype(int)
nums = nums[nums > 0]
assert sum(nums) < len(cell_list), 'Not enough cells'
chunked = [chunk for chunk in tqdm(chunk_list(cell_list, nums))]
dicts = [generate_image(cells, shape) for cells in tqdm(chunked)]
out_dict = {}
for i, d in enumerate(dicts):
for k, v in d.items():
if 'storm' in k:
v['frame'] = i + 1
if k in out_dict:
out_dict[k] = np.append(out_dict[k], v)
else:
out_dict[k] = v
else:
if k in out_dict:
out_dict[k][i] = v
else:
out_dict[k] = np.zeros((num_images, *shape))
out_dict[k][i] = v
return out_dict
def generate_image(cells, shape, max_dist=5):
thetas = 360 * np.random.rand(len(cells))
data_list = [cell.data.rotate(theta) for cell, theta in zip(cells, thetas)]
assert all([data.names == data_list[0].names for data in data_list]), 'All cells must have the same data elements'
out_dict = {name: np.zeros(shape) for name, dclass in zip(data_list[0].names, data_list[0].dclasses) if dclass != 'storm'}
for i, data in enumerate(data_list):
valid_position = False
while not valid_position:
pos_x = int(np.round(shape[1] * np.random.rand()))
pos_y = int(np.round(shape[0] * np.random.rand()))
min1 = pos_y - int(np.floor(data.shape[0]))
max1 = min1 + data.shape[0]
min2 = pos_x - int(np.floor(data.shape[1]))
max2 = min2 + data.shape[1]
# Crop the data for when the cell is on the border of the image
d_min1 = np.max([0 - min1, 0])
d_max1 = np.min([data.shape[0] + (shape[0] - pos_y), data.shape[0]])
d_min2 = np.max([0 - min2, 0])
d_max2 = np.min([data.shape[1] + (shape[1] - pos_x), data.shape[1]])
data_cropped = data[d_min1:d_max1, d_min2:d_max2]
# Limit image position to the edges of the image
min1 = np.max([min1, 0])
max1 = np.min([max1, shape[0]])
min2 = np.max([min2, 0])
max2 = np.min([max2, shape[1]])
temp_binary = np.zeros(shape)
temp_binary[min1:max1, min2:max2] = data_cropped.binary_img
out_binary = (out_dict['binary'] > 0).astype(int)
distance_map = mh.distance(1 - out_binary, metric='euclidean')
if np.any(distance_map[temp_binary.astype(bool)] < max_dist):
continue
valid_position = True
for name in data.names:
data_elem = data_cropped.data_dict[name]
if data_elem.dclass == 'storm':
data_elem['x'] += min2
data_elem['y'] += min1
xmax, ymax = shape[1], shape[0]
bools = (data_elem['x'] < 0) + (data_elem['x'] > xmax) + (data_elem['y'] < 0) + (data_elem['y'] > ymax)
data_out = data_elem[~bools].copy()
if name in out_dict:
out_dict[name] = np.append(out_dict[name], data_out)
else:
out_dict[name] = data_out
continue
elif data_elem.dclass == 'binary':
out_dict[name][min1:max1, min2:max2] += ((i+1)*data_elem)
else:
out_dict[name][min1:max1, min2:max2] += data_elem
return out_dict
def gen_image_from_storm(storm_table, shape, sigma=1.54, sigma_std=0.3):
xmax = shape[1]
ymax = shape[0]
step = 1
xi = np.arange(step / 2, xmax, step)
yi = np.arange(step / 2, ymax, step)
x_coords = np.repeat(xi, len(yi)).reshape(len(xi), len(yi)).T
y_coords = np.repeat(yi, len(xi)).reshape(len(yi), len(xi))
x, y = storm_table['x'], storm_table['y']
img = np.zeros_like(x_coords)
intensities = storm_table['intensity']
sigma = sigma * np.ones_like(x) if not sigma_std else np.random.normal(sigma, sigma_std, size=len(x))
for _sigma, _int, _x, _y in zip(sigma, intensities, x, y):
img += _int * np.exp(-(((_x - x_coords) / _sigma) ** 2 + ((_y - y_coords) / _sigma) ** 2) / 2)
return img
def gen_im(data_dir):
"""Generate microscopy images from a list of cell objects by placing them randomly oriented in the image."""
cell_list = load(os.path.join(data_dir, 'cell_obj', 'cells_final_selected.hdf5'))
out_dict = generate_images(cell_list, 1000, 10, 3, (512, 512))
if not os.path.exists(os.path.join(data_dir, 'images')):
os.mkdir(os.path.join(data_dir, 'images'))
np.save(os.path.join(data_dir, 'images', 'binary.npy'), out_dict['binary'])
np.save(os.path.join(data_dir, 'images', 'brightfield.npy'), out_dict['brightfield'])
np.save(os.path.join(data_dir, 'images', 'foci_inner.npy'), out_dict['foci_inner'])
np.save(os.path.join(data_dir, 'images', 'foci_outer.npy'), out_dict['foci_outer'])
np.save(os.path.join(data_dir, 'images', 'storm_inner.npy'), out_dict['storm_inner'])
np.save(os.path.join(data_dir, 'images', 'storm_outer.npy'), out_dict['storm_outer'])
tifffile.imsave(os.path.join(data_dir, 'images', 'binary.tif'), out_dict['binary'])
tifffile.imsave(os.path.join(data_dir, 'images', 'brightfield.tif'), out_dict['brightfield'])
tifffile.imsave(os.path.join(data_dir, 'images', 'foci_inner.tif'), out_dict['foci_inner'])
tifffile.imsave(os.path.join(data_dir, 'images', 'foci_outer.tif'), out_dict['foci_outer'])
np.savetxt(os.path.join(data_dir, 'images', 'storm_inner.txt'), out_dict['storm_inner'])
np.savetxt(os.path.join(data_dir, 'images', 'storm_outer.txt'), out_dict['storm_inner'])
def noise_bf(data_dir):
"""add poissonian and readout noise to brightfield images"""
noise = 20
img_stack = np.load(os.path.join(data_dir, 'images', 'brightfield.npy'))
for photons in [10000, 1000, 500]:
ratio = 1.0453 # ratio between 'background' (no cells) and cell wall
img = (photons*(ratio-1))*img_stack + photons
img = draw_poisson(img)
img = add_readout_noise(img, noise)
tifffile.imsave(os.path.join(data_dir, 'images', 'bf_noise_{}_photons.tif'.format(photons)), img)
np.save(os.path.join(data_dir, 'images', 'bf_noise_{}_photons.npy'.format(photons)), img)
if __name__ == '__main__':
np.random.seed(42)
data_dir = r'.'
if not os.path.exists(os.path.join(data_dir, 'images')):
os.mkdir(os.path.join(data_dir, 'images'))
gen_im(data_dir)
noise_bf(data_dir)
| 40.890173 | 126 | 0.602771 | 0 | 0 | 129 | 0.018236 | 0 | 0 | 0 | 0 | 1,086 | 0.15352 |
5800bf043c25e136451e6eec4f012f681fa98619 | 336 | py | Python | epiphancloud/models/settings.py | epiphan-video/epiphancloud_api | 8799591ea4d3a7285976e0038716b23c9ffe7061 | [
"MIT"
] | null | null | null | epiphancloud/models/settings.py | epiphan-video/epiphancloud_api | 8799591ea4d3a7285976e0038716b23c9ffe7061 | [
"MIT"
] | null | null | null | epiphancloud/models/settings.py | epiphan-video/epiphancloud_api | 8799591ea4d3a7285976e0038716b23c9ffe7061 | [
"MIT"
] | null | null | null | class DeviceSettings:
def __init__(self, settings):
self._id = settings["id"]
self._title = settings["title"]
self._type = settings["type"]["name"]
self._value = settings["value"]
@property
def id(self):
return self._id
@property
def value(self):
return self._value
| 22.4 | 45 | 0.583333 | 335 | 0.997024 | 0 | 0 | 108 | 0.321429 | 0 | 0 | 30 | 0.089286 |
580134063c60e1903557dccde046d7a394258b01 | 319 | py | Python | dictionary.py | SchmitzAndrew/OSS-101-example | 1efecd4c5bfef4495904568d11e3f8d0a5ed9bd0 | [
"MIT"
] | null | null | null | dictionary.py | SchmitzAndrew/OSS-101-example | 1efecd4c5bfef4495904568d11e3f8d0a5ed9bd0 | [
"MIT"
] | null | null | null | dictionary.py | SchmitzAndrew/OSS-101-example | 1efecd4c5bfef4495904568d11e3f8d0a5ed9bd0 | [
"MIT"
] | null | null | null | word = input("Enter a word: ")
if word == "a":
print("one; any")
elif word == "apple":
print("familiar, round fleshy fruit")
elif word == "rhinoceros":
print("large thick-skinned animal with one or two horns on its nose")
else:
print("That word must not exist. This dictionary is very comprehensive.")
| 29 | 77 | 0.667712 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 206 | 0.645768 |
58023a0843c7993ed1535e882c1755e33a7a8544 | 116 | py | Python | solved_bronze/num11720.py | ilmntr/white_study | 51d69d122b07e9a0922dddb134bff4ec79077eb9 | [
"MIT"
] | null | null | null | solved_bronze/num11720.py | ilmntr/white_study | 51d69d122b07e9a0922dddb134bff4ec79077eb9 | [
"MIT"
] | null | null | null | solved_bronze/num11720.py | ilmntr/white_study | 51d69d122b07e9a0922dddb134bff4ec79077eb9 | [
"MIT"
] | null | null | null | cnt = int(input())
num = list(map(int, input()))
sum = 0
for i in range(len(num)):
sum = sum + num[i]
print(sum) | 19.333333 | 29 | 0.586207 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
58035ad02fa85d7c60de0ef4d5c14279175bc2ac | 566 | py | Python | setup.py | sdnhub/kube-navi | d16a9289ba7261011e6c8d19c48cdc9bd533e629 | [
"Apache-2.0"
] | null | null | null | setup.py | sdnhub/kube-navi | d16a9289ba7261011e6c8d19c48cdc9bd533e629 | [
"Apache-2.0"
] | null | null | null | setup.py | sdnhub/kube-navi | d16a9289ba7261011e6c8d19c48cdc9bd533e629 | [
"Apache-2.0"
] | null | null | null | from distutils.core import setup
setup(
name = 'kube_navi',
packages = ['kube_navi'], # this must be the same as the name above
version = '0.1',
description = 'Kubernetes resource discovery toolkit',
author = 'Srini Seetharaman',
author_email = '[email protected]',
url = 'https://github.com/sdnhub/kube-navi', # use the URL to the github repo
download_url = 'https://github.com/sdnhub/kube-navi/archive/0.1.tar.gz', # I'll explain this in a second
keywords = ['testing', 'logging', 'example'], # arbitrary keywords
classifiers = [],
)
| 40.428571 | 106 | 0.69788 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 358 | 0.632509 |
5804951a8f92330526763d3f11395d318d54d180 | 10,444 | py | Python | flink-ai-flow/ai_flow/metric/utils.py | MarvinMiao/flink-ai-extended | e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | 1 | 2020-12-12T15:21:05.000Z | 2020-12-12T15:21:05.000Z | flink-ai-flow/ai_flow/metric/utils.py | MarvinMiao/flink-ai-extended | e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | 1 | 2021-01-30T11:28:37.000Z | 2021-01-30T11:28:37.000Z | flink-ai-flow/ai_flow/metric/utils.py | MarvinMiao/flink-ai-extended | e45eecf2deea6976ba3d7ba821ffb8d9ce0a17f4 | [
"Apache-2.0",
"BSD-2-Clause",
"MIT",
"ECL-2.0",
"BSD-3-Clause"
] | null | null | null | #
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
import ast
from typing import Text, Optional, Union, List
from ai_flow.rest_endpoint.protobuf.metric_service_pb2 import MetricMetaResponse, ListMetricMetaResponse, \
MetricSummaryResponse, ListMetricSummaryResponse
from ai_flow.rest_endpoint.service import int64Value, stringValue
from ai_flow.common.properties import Properties
from ai_flow.meta.metric_meta import MetricMeta, MetricType, MetricSummary
from ai_flow.rest_endpoint.protobuf.message_pb2 import MetricMetaProto, MetricSummaryProto, MetricTypeProto, ReturnCode, \
SUCCESS, RESOURCE_DOES_NOT_EXIST
from ai_flow.store.db.db_model import SqlMetricMeta, SqlMetricSummary
from ai_flow.store.db.db_model import MongoMetricSummary, MongoMetricMeta
def table_to_metric_meta(metric_meta_result) -> MetricMeta:
properties = metric_meta_result.properties
if properties is not None:
properties = ast.literal_eval(properties)
return MetricMeta(uuid=metric_meta_result.uuid,
name=metric_meta_result.name,
dataset_id=metric_meta_result.dataset_id,
model_name=metric_meta_result.model_name,
model_version=metric_meta_result.model_version,
job_id=metric_meta_result.job_id,
start_time=metric_meta_result.start_time,
end_time=metric_meta_result.end_time,
metric_type=MetricType.value_of(metric_meta_result.metric_type),
uri=metric_meta_result.uri,
tags=metric_meta_result.tags,
metric_description=metric_meta_result.metric_description,
properties=properties)
def table_to_metric_summary(metric_summary_result) -> MetricSummary:
return MetricSummary(uuid=metric_summary_result.uuid,
metric_id=metric_summary_result.metric_id,
metric_key=metric_summary_result.metric_key,
metric_value=metric_summary_result.metric_value)
def metric_meta_to_table(name: Text,
dataset_id: int,
model_name: Optional[Text],
model_version: Optional[Text],
job_id: int,
start_time: int,
end_time: int,
metric_type: MetricType,
uri: Text,
tags: Text,
metric_description: Text,
properties: Properties,
store_type: Text = 'SqlAlchemyStore'):
if properties is not None:
properties = str(properties)
if store_type == 'MongoStore':
_class = MongoMetricMeta
else:
_class = SqlMetricMeta
return _class(name=name,
dataset_id=dataset_id,
model_name=model_name,
model_version=model_version,
job_id=job_id,
start_time=start_time,
end_time=end_time,
metric_type=metric_type.value,
uri=uri,
tags=tags,
metric_description=metric_description,
properties=properties)
def metric_summary_to_table(metric_id: int,
metric_key: Text,
metric_value: Text,
store_type: Text = 'SqlAlchemyStore'):
if store_type == 'MongoStore':
_class = MongoMetricSummary
else:
_class = SqlMetricSummary
return _class(metric_id=metric_id,
metric_key=metric_key,
metric_value=metric_value)
def metric_meta_to_proto(metric_meta: MetricMeta) -> MetricMetaProto:
if metric_meta.metric_type == MetricType.DATASET:
metric_type = MetricTypeProto.DATASET
else:
metric_type = MetricTypeProto.MODEL
return MetricMetaProto(uuid=metric_meta.uuid,
name=stringValue(metric_meta.name),
dataset_id=int64Value(metric_meta.dataset_id),
model_name=stringValue(metric_meta.model_name),
model_version=stringValue(metric_meta.model_version),
job_id=int64Value(metric_meta.job_id),
start_time=int64Value(metric_meta.start_time),
end_time=int64Value(metric_meta.end_time),
metric_type=metric_type,
uri=stringValue(metric_meta.uri),
tags=stringValue(metric_meta.tags),
metric_description=stringValue(metric_meta.metric_description),
properties=metric_meta.properties)
def metric_summary_to_proto(metric_summary: MetricSummary) -> MetricSummaryProto:
return MetricSummaryProto(uuid=metric_summary.uuid,
metric_id=int64Value(metric_summary.metric_id),
metric_key=stringValue(metric_summary.metric_key),
metric_value=stringValue(metric_summary.metric_value))
def proto_to_metric_meta(metric_meta_proto: MetricMetaProto) -> MetricMeta:
if MetricTypeProto.DATASET == metric_meta_proto.metric_type:
metric_type = MetricType.DATASET
else:
metric_type = MetricType.MODEL
return MetricMeta(uuid=metric_meta_proto.uuid,
name=metric_meta_proto.name.value,
dataset_id=metric_meta_proto.dataset_id.value,
model_name=metric_meta_proto.model_name.value,
model_version=metric_meta_proto.model_version.value,
job_id=metric_meta_proto.job_id.value,
start_time=metric_meta_proto.start_time.value,
end_time=metric_meta_proto.end_time.value,
metric_type=metric_type,
uri=metric_meta_proto.uri.value if metric_meta_proto.HasField('uri') else None,
tags=metric_meta_proto.tags.value if metric_meta_proto.HasField('tags') else None,
metric_description=metric_meta_proto.metric_description.value
if metric_meta_proto.HasField('metric_description') else None,
properties=metric_meta_proto.properties
)
def proto_to_metric_summary(metric_summary_proto: MetricSummaryProto) -> MetricSummary:
return MetricSummary(uuid=metric_summary_proto.uuid,
metric_id=metric_summary_proto.metric_id.value,
metric_key=metric_summary_proto.metric_key.value
if metric_summary_proto.HasField('metric_key') else None,
metric_value=metric_summary_proto.metric_value.value
if metric_summary_proto.HasField('metric_value') else None
)
def _warp_metric_meta_response(metric_meta: Optional[MetricMeta]) -> MetricMetaResponse:
if metric_meta is not None:
return MetricMetaResponse(return_code=0, return_msg=ReturnCode.Name(SUCCESS).lower(),
metric_meta=metric_meta_to_proto(metric_meta))
else:
return MetricMetaResponse(return_code=1,
return_msg=ReturnCode.Name(RESOURCE_DOES_NOT_EXIST).lower(),
metric_meta=None)
def _warp_list_metric_meta_response(metric_meta: Union[None, MetricMeta, List[MetricMeta]]) -> MetricMetaResponse:
if metric_meta is not None:
if isinstance(metric_meta, MetricMeta):
return ListMetricMetaResponse(return_code=0, return_msg=ReturnCode.Name(SUCCESS).lower(),
metric_meta=[metric_meta_to_proto(metric_meta)])
else:
res = []
for meta in metric_meta:
res.append(metric_meta_to_proto(meta))
return ListMetricMetaResponse(return_code=0, return_msg=ReturnCode.Name(SUCCESS).lower(),
metric_meta=res)
else:
return ListMetricMetaResponse(return_code=1,
return_msg=ReturnCode.Name(RESOURCE_DOES_NOT_EXIST).lower(),
metric_meta=None)
def _warp_metric_summary_response(metric_summary: Optional[MetricSummary]) -> MetricSummaryResponse:
if metric_summary is not None:
return MetricSummaryResponse(return_code=0, return_msg=ReturnCode.Name(SUCCESS).lower(),
metric_summary=metric_summary_to_proto(metric_summary))
else:
return MetricSummaryResponse(return_code=1,
return_msg=ReturnCode.Name(RESOURCE_DOES_NOT_EXIST).lower(),
metric_summary=None)
def _warp_list_metric_summary_response(metric_summary: Optional[List[MetricSummary]]) -> ListMetricSummaryResponse:
if metric_summary is not None:
res = []
for summary in metric_summary:
res.append(metric_summary_to_proto(summary))
return ListMetricSummaryResponse(return_code=0, return_msg=ReturnCode.Name(SUCCESS).lower(),
metric_summary=res)
else:
return ListMetricSummaryResponse(return_code=1,
return_msg=ReturnCode.Name(RESOURCE_DOES_NOT_EXIST).lower(),
metric_summary=None)
| 49.032864 | 122 | 0.627633 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 886 | 0.084833 |
58055aabc65a23b166d03e3a5c7b5b2ffaa06173 | 3,154 | py | Python | src/moduels/gui/Tab_Help.py | HaujetZhao/Caps_Writer | f2b2038a2c0984a1d356f024cbac421fe594601a | [
"MIT"
] | 234 | 2020-07-10T11:23:09.000Z | 2022-03-31T09:41:40.000Z | src/moduels/gui/Tab_Help.py | HaujetZhao/Caps_Writer | f2b2038a2c0984a1d356f024cbac421fe594601a | [
"MIT"
] | 9 | 2020-07-11T08:31:11.000Z | 2022-03-01T04:30:08.000Z | src/moduels/gui/Tab_Help.py | HaujetZhao/Caps_Writer | f2b2038a2c0984a1d356f024cbac421fe594601a | [
"MIT"
] | 23 | 2020-07-14T08:58:44.000Z | 2022-03-17T06:38:10.000Z | # -*- coding: UTF-8 -*-
from PySide2.QtWidgets import QWidget, QPushButton, QVBoxLayout
from PySide2.QtCore import Signal
from moduels.component.NormalValue import 常量
from moduels.component.SponsorDialog import SponsorDialog
import os, webbrowser
class Tab_Help(QWidget):
状态栏消息 = Signal(str, int)
def __init__(self):
super().__init__()
self.initElement() # 先初始化各个控件
self.initSlots() # 再将各个控件连接到信号槽
self.initLayout() # 然后布局
self.initValue() # 再定义各个控件的值
def initElement(self):
self.打开帮助按钮 = QPushButton(self.tr('打开帮助文档'))
self.ffmpegMannualNoteButton = QPushButton(self.tr('查看作者的 FFmpeg 笔记'))
self.openVideoHelpButtone = QPushButton(self.tr('查看视频教程'))
self.openGiteePage = QPushButton(self.tr(f'当前版本是 v{常量.软件版本}，到 Gitee 检查新版本'))
self.openGithubPage = QPushButton(self.tr(f'当前版本是 v{常量.软件版本}，到 Github 检查新版本'))
self.linkToDiscussPage = QPushButton(self.tr('加入 QQ 群'))
self.tipButton = QPushButton(self.tr('打赏作者'))
self.masterLayout = QVBoxLayout()
def initSlots(self):
self.打开帮助按钮.clicked.connect(self.openHelpDocument)
self.ffmpegMannualNoteButton.clicked.connect(lambda: webbrowser.open(self.tr(r'https://hacpai.com/article/1595480295489')))
self.openVideoHelpButtone.clicked.connect(lambda: webbrowser.open(self.tr(r'https://www.bilibili.com/video/BV12A411p73r/')))
self.openGiteePage.clicked.connect(lambda: webbrowser.open(self.tr(r'https://gitee.com/haujet/CapsWriter/releases')))
self.openGithubPage.clicked.connect(lambda: webbrowser.open(self.tr(r'https://github.com/HaujetZhao/CapsWriter/releases')))
self.linkToDiscussPage.clicked.connect(lambda: webbrowser.open(
self.tr(r'https://qm.qq.com/cgi-bin/qm/qr?k=DgiFh5cclAElnELH4mOxqWUBxReyEVpm&jump_from=webapi')))
self.tipButton.clicked.connect(lambda: SponsorDialog(self))
def initLayout(self):
self.setLayout(self.masterLayout)
# self.masterLayout.addWidget(self.打开帮助按钮)
# self.masterLayout.addWidget(self.ffmpegMannualNoteButton)
self.masterLayout.addWidget(self.openVideoHelpButtone)
self.masterLayout.addWidget(self.openGiteePage)
self.masterLayout.addWidget(self.openGithubPage)
self.masterLayout.addWidget(self.linkToDiscussPage)
self.masterLayout.addWidget(self.tipButton)
def initValue(self):
self.打开帮助按钮.setMaximumHeight(100)
self.ffmpegMannualNoteButton.setMaximumHeight(100)
self.openVideoHelpButtone.setMaximumHeight(100)
self.openGiteePage.setMaximumHeight(100)
self.openGithubPage.setMaximumHeight(100)
self.linkToDiscussPage.setMaximumHeight(100)
self.tipButton.setMaximumHeight(100)
def openHelpDocument(self):
try:
if 常量.系统平台 == 'Darwin':
import shlex
os.system("open " + shlex.quote(self.tr("./misc/Docs/README_zh.html")))
elif 常量.系统平台 == 'Windows':
os.startfile(os.path.realpath(self.tr('./misc/Docs/README_zh.html')))
except:
print('未能打开帮助文档')
| 45.057143 | 132 | 0.69499 | 3,190 | 0.925711 | 0 | 0 | 0 | 0 | 0 | 0 | 863 | 0.250435 |
5805a2c8d616906daf19682b40baa91f10a88715 | 1,845 | py | Python | app/routes/register.py | AuFeld/COAG | 3874a9c1c6ceb908a6bbabfb49e2c701d8e54f20 | [
"MIT"
] | 1 | 2021-06-03T10:29:12.000Z | 2021-06-03T10:29:12.000Z | app/routes/register.py | AuFeld/COAG | 3874a9c1c6ceb908a6bbabfb49e2c701d8e54f20 | [
"MIT"
] | 45 | 2021-06-05T14:47:09.000Z | 2022-03-30T06:16:44.000Z | app/routes/register.py | AuFeld/COAG | 3874a9c1c6ceb908a6bbabfb49e2c701d8e54f20 | [
"MIT"
] | null | null | null | from typing import Callable, Optional, Type, cast
from fastapi import APIRouter, HTTPException, Request, status
from app.models import users
from app.common.user import ErrorCode, run_handler
from app.users.user import (
CreateUserProtocol,
InvalidPasswordException,
UserAlreadyExists,
ValidatePasswordProtocol,
)
def get_register_router(
create_user: CreateUserProtocol,
user_model: Type[users.BaseUser],
user_create_model: Type[users.BaseUserCreate],
after_register: Optional[Callable[[users.UD, Request], None]] = None,
validate_password: Optional[ValidatePasswordProtocol] = None,
) -> APIRouter:
"""Generate a router with the register route."""
router = APIRouter()
@router.post(
"/register", response_model=user_model, status_code=status.HTTP_201_CREATED
)
async def register(request: Request, user: user_create_model): # type: ignore
user = cast(users.BaseUserCreate, user) # Prevent mypy complain
if validate_password:
try:
await validate_password(user.password, user)
except InvalidPasswordException as e:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail={
"code": ErrorCode.REGISTER_INVALID_PASSWORD,
"reason": e.reason,
},
)
try:
created_user = await create_user(user, safe=True)
except UserAlreadyExists:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ErrorCode.REGISTER_USER_ALREADY_EXISTS,
)
if after_register:
await run_handler(after_register, created_user, request)
return created_user
return router
| 32.946429 | 83 | 0.648238 | 0 | 0 | 0 | 0 | 1,101 | 0.596748 | 993 | 0.538211 | 110 | 0.059621 |
5805ce50d417618b337a1e60276ff06de0f997f8 | 1,425 | py | Python | utils/visual.py | xizaoqu/Panoptic-PolarNet | 8ce05f437f54e030eac7de150f43caab2810cfbb | [
"BSD-3-Clause"
] | 90 | 2021-03-30T08:02:15.000Z | 2022-03-30T03:29:56.000Z | utils/visual.py | xizaoqu/Panoptic-PolarNet | 8ce05f437f54e030eac7de150f43caab2810cfbb | [
"BSD-3-Clause"
] | 11 | 2021-04-01T02:29:08.000Z | 2022-03-04T07:30:50.000Z | utils/visual.py | xizaoqu/Panoptic-PolarNet | 8ce05f437f54e030eac7de150f43caab2810cfbb | [
"BSD-3-Clause"
] | 21 | 2021-04-01T09:29:38.000Z | 2022-03-28T01:36:02.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import cv2
import numpy as np
def flow_to_img(flow, normalize=True):
"""Convert flow to viewable image, using color hue to encode flow vector orientation, and color saturation to
encode vector length. This is similar to the OpenCV tutorial on dense optical flow, except that they map vector
length to the value plane of the HSV color model, instead of the saturation plane, as we do here.
Args:
flow: optical flow
normalize: Normalize flow to 0..255
Returns:
img: viewable representation of the dense optical flow in RGB format
Ref:
https://github.com/philferriere/tfoptflow/blob/33e8a701e34c8ce061f17297d40619afbd459ade/tfoptflow/optflow.py
"""
hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8)
flow_magnitude, flow_angle = cv2.cartToPolar(flow[..., 0].astype(np.float32), flow[..., 1].astype(np.float32))
# A couple times, we've gotten NaNs out of the above...
nans = np.isnan(flow_magnitude)
if np.any(nans):
nans = np.where(nans)
flow_magnitude[nans] = 0.
# Normalize
hsv[..., 0] = flow_angle * 180 / np.pi / 2
if normalize is True:
hsv[..., 1] = cv2.normalize(flow_magnitude, None, 0, 255, cv2.NORM_MINMAX)
else:
hsv[..., 1] = flow_magnitude
hsv[..., 2] = 255
img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
return img | 39.583333 | 116 | 0.665263 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 743 | 0.521404 |
5806fd8ba37feb4c4d823dfb9c4c105ed07bdd0c | 624 | py | Python | DistributedRL/Gateway/build/Code/sim/Parser/LAI/GreenIndex.py | zhkmxx9302013/SoftwarePilot | 826098465b800085774946c20a7a283f369f1d21 | [
"MIT"
] | 4 | 2019-03-20T17:46:01.000Z | 2019-03-31T17:32:44.000Z | DistributedRL/Gateway/build/Code/sim/Parser/LAI/GreenIndex.py | zhkmxx9302013/SoftwarePilot | 826098465b800085774946c20a7a283f369f1d21 | [
"MIT"
] | null | null | null | DistributedRL/Gateway/build/Code/sim/Parser/LAI/GreenIndex.py | zhkmxx9302013/SoftwarePilot | 826098465b800085774946c20a7a283f369f1d21 | [
"MIT"
] | null | null | null | import argparse
from PIL import Image, ImageStat
import math
parser = argparse.ArgumentParser()
parser.add_argument('fname')
parser.add_argument('pref', default="", nargs="?")
args = parser.parse_args()
im = Image.open(args.fname)
RGB = im.convert('RGB')
imWidth, imHeight = im.size
ratg = 1.2
ratgb = 1.66
ming = 10
ratr = 2
speed = 8
leafcount = 0
total = 0
for i in range(0, int(imWidth/speed)):
for j in range(0, int(imHeight/speed)):
R,G,B = RGB.getpixel((i*speed,j*speed))
if R*ratg < G and B*ratgb < G and B*ratr < R:
leafcount = leafcount + 1
total = total+1
print("LAI="+str(float(leafcount)/total))
| 20.8 | 50 | 0.684295 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 29 | 0.046474 |
58077bea9c4435d13d9ff119348291eadd3323f7 | 4,561 | py | Python | reports/heliosV1/python/heliosStorageStats/heliosStorageStats.py | ped998/scripts | 0dcaaf47f9676210e1c972a5d59d8d0de82a1d93 | [
"Apache-2.0"
] | null | null | null | reports/heliosV1/python/heliosStorageStats/heliosStorageStats.py | ped998/scripts | 0dcaaf47f9676210e1c972a5d59d8d0de82a1d93 | [
"Apache-2.0"
] | null | null | null | reports/heliosV1/python/heliosStorageStats/heliosStorageStats.py | ped998/scripts | 0dcaaf47f9676210e1c972a5d59d8d0de82a1d93 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
"""cluster storage stats for python"""
# import pyhesity wrapper module
from pyhesity import *
from datetime import datetime
import codecs
# command line arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-v', '--vip', type=str, default='helios.cohesity.com') # cluster to connect to
parser.add_argument('-u', '--username', type=str, required=True) # username
parser.add_argument('-d', '--domain', type=str, default='local') # (optional) domain - defaults to local
parser.add_argument('-pwd', '--password', type=str, default=None) # optional password
parser.add_argument('-n', '--unit', type=str, choices=['GiB', 'TiB', 'gib', 'tib'], default='TiB')
args = parser.parse_args()
vip = args.vip
username = args.username
domain = args.domain
password = args.password
unit = args.unit
if unit.lower() == 'tib':
multiplier = 1024 * 1024 * 1024 * 1024
unit = 'TiB'
else:
multiplier = 1024 * 1024 * 1024
unit = 'GiB'
def toUnits(value):
return round(float(value) / multiplier, 1)
# authenticate
apiauth(vip=vip, username=username, domain=domain, password=password, useApiKey=True, noretry=True)
# outfile
now = datetime.now()
# cluster = api('get', 'cluster')
dateString = now.strftime("%Y-%m-%d")
outfile = 'heliosStorageStats-%s.csv' % dateString
f = codecs.open(outfile, 'w')
# headings
f.write('Date,Capacity (%s),Consumed (%s),Free (%s),Used %%,Data In (%s),Data Written (%s),Storage Reduction,Data Reduction\n' % (unit, unit, unit, unit, unit))
stats = {}
def parseStats(clusterName, dataPoint, statName):
if clusterName not in stats.keys():
stats[clusterName] = {}
stats[clusterName][statName] = dataPoint['data']['int64Value']
endMsecs = dateToUsecs(now.strftime("%Y-%m-%d %H:%M:%S")) / 1000
startMsecs = (timeAgo(2, 'days')) / 1000
print('\nGathering cluster stats:\n')
for cluster in heliosClusters():
heliosCluster(cluster)
print(' %s' % cluster['name'])
capacityStats = api('get', 'statistics/timeSeriesStats?endTimeMsecs=%s&entityId=%s&metricName=kCapacityBytes&metricUnitType=0&range=day&rollupFunction=average&rollupIntervalSecs=86400&schemaName=kBridgeClusterStats&startTimeMsecs=%s' % (endMsecs, cluster['clusterId'], startMsecs))
consumedStats = api('get', 'statistics/timeSeriesStats?startTimeMsecs=%s&schemaName=kBridgeClusterTierPhysicalStats&metricName=kMorphedUsageBytes&rollupIntervalSecs=86400&rollupFunction=latest&entityIdList=%s:Local&endTimeMsecs=%s' % (startMsecs, cluster['clusterId'], endMsecs))
dataInStats = api('get', 'statistics/timeSeriesStats?startTimeMsecs=%s&schemaName=ApolloV2ClusterStats&metricName=BrickBytesLogical&rollupIntervalSecs=86400&rollupFunction=latest&entityIdList=%s (ID %s)&endTimeMsecs=%s' % (startMsecs, cluster['name'], cluster['clusterId'], endMsecs))
dataWrittenStats = api('get', 'statistics/timeSeriesStats?startTimeMsecs=%s&schemaName=ApolloV2ClusterStats&metricName=ChunkBytesMorphed&rollupIntervalSecs=86400&rollupFunction=latest&entityIdList=%s (ID %s)&endTimeMsecs=%s' % (startMsecs, cluster['name'], cluster['clusterId'], endMsecs))
logicalSizeStats = api('get', 'statistics/timeSeriesStats?startTimeMsecs=%s&schemaName=kBridgeClusterLogicalStats&metricName=kUnmorphedUsageBytes&rollupIntervalSecs=86400&rollupFunction=latest&entityIdList=%s&endTimeMsecs=%s' % (startMsecs, cluster['clusterId'], endMsecs))
parseStats(cluster['name'], capacityStats['dataPointVec'][0], 'capacity')
parseStats(cluster['name'], consumedStats['dataPointVec'][0], 'consumed')
parseStats(cluster['name'], dataInStats['dataPointVec'][0], 'dataIn')
parseStats(cluster['name'], dataWrittenStats['dataPointVec'][0], 'dataWritten')
parseStats(cluster['name'], logicalSizeStats['dataPointVec'][0], 'logicalSize')
for clusterName in sorted(stats.keys()):
capacity = stats[clusterName]['capacity']
consumed = stats[clusterName]['consumed']
dataIn = stats[clusterName]['dataIn']
dataWritten = stats[clusterName]['dataWritten']
logicalSize = stats[clusterName]['logicalSize']
free = capacity - consumed
pctUsed = round(100 * consumed / capacity, 0)
storageReduction = round(float(logicalSize) / consumed, 1)
dataReduction = round(float(dataIn) / dataWritten, 1)
f.write('"%s","%s","%s","%s","%s","%s","%s","%s","%s"\n' % (clusterName, toUnits(capacity), toUnits(consumed), toUnits(free), pctUsed, toUnits(dataIn), toUnits(dataWritten), storageReduction, dataReduction))
f.close()
print('\nOutput saved to %s\n' % outfile)
| 49.043011 | 293 | 0.726814 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,021 | 0.443105 |
580792f2d4c1bf5c14b84d5f807f69b1126aead4 | 5,422 | py | Python | src/advanceoperate/malimgthread.py | zengrx/S.M.A.R.T | 47a9abe89008e9b34f9b9d057656dbf3fb286456 | [
"MIT"
] | 10 | 2017-07-11T01:08:28.000Z | 2021-05-07T01:49:00.000Z | src/advanceoperate/malimgthread.py | YanqiangHuang/S.M.A.R.T | 47a9abe89008e9b34f9b9d057656dbf3fb286456 | [
"MIT"
] | null | null | null | src/advanceoperate/malimgthread.py | YanqiangHuang/S.M.A.R.T | 47a9abe89008e9b34f9b9d057656dbf3fb286456 | [
"MIT"
] | 6 | 2017-05-02T14:27:15.000Z | 2017-05-15T05:56:40.000Z | #coding=utf-8
from PyQt4 import QtCore
import os, glob, numpy, sys
from PIL import Image
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import BallTree
from sklearn import cross_validation
from sklearn.utils import shuffle
import sklearn
import leargist
import cPickle
import random
import sys
reload(sys)
sys.setdefaultencoding( "utf-8" )
class ValidationResult(QtCore.QThread):
finishSignal = QtCore.pyqtSignal(list)
def __init__(self, parent=None):
super(ValidationResult, self).__init__(parent)
def getClassifyLabel(self):
X = numpy.load("./datafiles/img_features.npy") # 特征
y = numpy.load("./datafiles/img_labels.npy") # 标签
n = cPickle.load(open("./datafiles/img.p","rb")) # 标号
l = cPickle.load(open("./datafiles/imglabel.p", "rb")) # [家族号, 家族中序号, 文件名, 总序号]
return X, y, n ,l
'''
准备绘制矩阵的数据
@X:特征矩阵
@y:标签
@n:所有样本家族名称
@l:对应家族个数
'''
def prepareData2Matrix(self, X, y, n, l):
n_samples, useless = X.shape
p = range(n_samples)
random.seed(random.random())
random.shuffle(p)
X, y = X[p], y[p] # 打乱数组
kfold = 10 # 10重
skf = StratifiedKFold(y,kfold)
skfind = [None] * len(skf)
cnt = 0
for train_index in skf:
skfind[cnt] = train_index
cnt += 1
list_fams = n
cache = []
no_imgs = []
for l_list in l:
if 0 == l_list[1]:
# print l[l_list[3] - 1]
# print l_list
cache.append(l[l_list[3] - 1][1] + 1)
no_imgs = cache[1:len(cache)]
no_imgs.append(cache[0])
# print no_imgs # 输出所有家族包含文件个数
conf_mat = numpy.zeros((len(no_imgs), len(no_imgs))) # 初始化矩阵
n_neighbors = 5
# 10-fold Cross Validation
for i in range(kfold):
train_indices = skfind[i][0]
test_indices = skfind[i][1]
clf = []
clf = KNeighborsClassifier(n_neighbors, weights='distance')
X_train = X[train_indices]
y_train = y[train_indices]
X_test = X[test_indices]
y_test = y[test_indices]
# Training
import time
tic = time.time()
clf.fit(X_train,y_train)
toc = time.time()
print "training time= ", toc-tic # roughly 2.5 secs
# Testing
y_predict = []
tic = time.time()
y_predict = clf.predict(X_test) # output is labels and not indices
toc = time.time()
print "testing time = ", toc-tic # roughly 0.3 secs
# Compute confusion matrix
cm = []
cm = confusion_matrix(y_test,y_predict)
conf_mat = conf_mat + cm
return conf_mat, no_imgs, list_fams
def run(self):
print "start draw"
X, y, n, l = self.getClassifyLabel()
cm, nimg, listf = self.prepareData2Matrix(X, y, n, l)
msg = [cm, nimg, listf]
self.finishSignal.emit(msg)
class MalwareImageClass(QtCore.QThread):
malwarSignal = QtCore.pyqtSignal(int, list)
concluSignal = QtCore.pyqtSignal(int, list)
def __init__(self, filename, parent=None):
super(MalwareImageClass, self).__init__(parent)
self.filename = str(filename)#.encode('cp936')
self.feature = ''
'''
获取训练结果
特征,标签,文件名称及相应的序号
'''
def getClassifyLabel(self):
X = numpy.load("./datafiles/img_features.npy") # 特征
y = numpy.load("./datafiles/img_labels.npy") # 标签
n = cPickle.load(open("./datafiles/img.p","rb")) # 标号
l = cPickle.load(open("./datafiles/imglabel.p", "rb")) # [家族号, 家族中序号, 文件名, 总序号]
return X, y, n ,l
'''
对图片进行分类
train@训练集特征
label@训练集标签
'''
def classifyImage(self, feature_X, label_y, number):
im = Image.open(self.filename)
im1 = im.resize((64,64), Image.ANTIALIAS); # 转换为64x64
des = leargist.color_gist(im1); # 960 values
feature = des[0:320]; # 生成灰阶图，只需要前320内容
query_feature = feature.reshape(1, -1)
self.feature = query_feature
# 获取特征和标签
X = feature_X
y = label_y
n = number
n_neighbors = 5; # better to have this at the start of the code
knn = KNeighborsClassifier(n_neighbors, weights='distance')
knn.fit(X, y)
num = int(knn.predict(query_feature))
classname = n[num]
proba = knn.predict_proba(query_feature)
msg = [num, classname, proba]
self.malwarSignal.emit(1, msg)
'''
balltrees寻找数据集中最相近的样本
返回距离值及样本标签号
'''
def findMostSimilarImg(self, feature_X, serial):
X = feature_X
b = BallTree(X)
# 5个最相近的样本
dist, ind = b.query(self.feature, k=3)
print dist, ind
ind = ind[0]
# print ind
l = serial
imgs = []
for rank in ind:
# print rank
for name in l:
if rank == name[3]:
# print name
imgs.append(name[2])
self.concluSignal.emit(2, imgs)
def run(self):
X, y, n ,l = self.getClassifyLabel()
self.classifyImage(X, y, n)
self.findMostSimilarImg(X, l)
| 31.34104 | 87 | 0.568241 | 5,311 | 0.91854 | 0 | 0 | 0 | 0 | 0 | 0 | 1,349 | 0.23331 |
5807aa2f208ead7fccb84b1ddddcf37b4c200266 | 12,661 | py | Python | tests/unittests/command_parse/test_stream.py | itamarhaber/iredis | 61208aab34c731f88232abd2cacdf0e075e701f2 | [
"BSD-3-Clause"
] | 1,857 | 2019-08-09T02:36:13.000Z | 2022-03-31T05:53:09.000Z | tests/unittests/command_parse/test_stream.py | itamarhaber/iredis | 61208aab34c731f88232abd2cacdf0e075e701f2 | [
"BSD-3-Clause"
] | 281 | 2019-08-08T16:00:59.000Z | 2022-03-07T14:05:36.000Z | tests/unittests/command_parse/test_stream.py | itamarhaber/iredis | 61208aab34c731f88232abd2cacdf0e075e701f2 | [
"BSD-3-Clause"
] | 105 | 2019-08-09T04:36:12.000Z | 2022-02-03T13:27:29.000Z | def test_xrange(judge_command):
judge_command(
"XRANGE somestream - +",
{"command": "XRANGE", "key": "somestream", "stream_id": ["-", "+"]},
)
judge_command(
"XRANGE somestream 1526985054069 1526985055069",
{
"command": "XRANGE",
"key": "somestream",
"stream_id": ["1526985054069", "1526985055069"],
},
)
judge_command(
"XRANGE somestream 1526985054069 1526985055069-10",
{
"command": "XRANGE",
"key": "somestream",
"stream_id": ["1526985054069", "1526985055069-10"],
},
)
judge_command(
"XRANGE somestream 1526985054069 1526985055069-10 count 10",
{
"command": "XRANGE",
"key": "somestream",
"stream_id": ["1526985054069", "1526985055069-10"],
"count_const": "count",
"count": "10",
},
)
def test_xgroup_create(judge_command):
judge_command(
"XGROUP CREATE mykey mygroup 123",
{
"command": "XGROUP",
"stream_create": "CREATE",
"key": "mykey",
"group": "mygroup",
"stream_id": "123",
},
)
judge_command(
"XGROUP CREATE mykey mygroup $",
{
"command": "XGROUP",
"stream_create": "CREATE",
"key": "mykey",
"group": "mygroup",
"stream_id": "$",
},
)
# short of a parameter
judge_command("XGROUP CREATE mykey mygroup", None)
judge_command("XGROUP CREATE mykey", None)
def test_xgroup_setid(judge_command):
judge_command(
"XGROUP SETID mykey mygroup 123",
{
"command": "XGROUP",
"stream_setid": "SETID",
"key": "mykey",
"group": "mygroup",
"stream_id": "123",
},
)
judge_command(
"XGROUP SETID mykey mygroup $",
{
"command": "XGROUP",
"stream_setid": "SETID",
"key": "mykey",
"group": "mygroup",
"stream_id": "$",
},
)
# two subcommand together shouldn't match
judge_command("XGROUP CREATE mykey mygroup 123 SETID mykey mygroup $", None)
def test_xgroup_destroy(judge_command):
judge_command(
"XGROUP destroy mykey mygroup",
{
"command": "XGROUP",
"stream_destroy": "destroy",
"key": "mykey",
"group": "mygroup",
},
)
judge_command("XGROUP destroy mykey", None)
judge_command("XGROUP DESTROY mykey mygroup $", None)
def test_xgroup_delconsumer(judge_command):
judge_command(
"XGROUP delconsumer mykey mygroup myconsumer",
{
"command": "XGROUP",
"stream_delconsumer": "delconsumer",
"key": "mykey",
"group": "mygroup",
"consumer": "myconsumer",
},
)
judge_command(
"XGROUP delconsumer mykey mygroup $",
{
"command": "XGROUP",
"stream_delconsumer": "delconsumer",
"key": "mykey",
"group": "mygroup",
"consumer": "$",
},
)
judge_command("XGROUP delconsumer mykey mygroup", None)
def test_xgroup_stream(judge_command):
judge_command(
"XACK mystream group1 123123",
{
"command": "XACK",
"key": "mystream",
"group": "group1",
"stream_id": "123123",
},
)
judge_command(
"XACK mystream group1 123123 111",
{"command": "XACK", "key": "mystream", "group": "group1", "stream_id": "111"},
)
def test_xinfo(judge_command):
judge_command(
"XINFO consumers mystream mygroup",
{
"command": "XINFO",
"stream_consumers": "consumers",
"key": "mystream",
"group": "mygroup",
},
)
judge_command(
"XINFO GROUPS mystream",
{"command": "XINFO", "stream_groups": "GROUPS", "key": "mystream"},
)
judge_command(
"XINFO STREAM mystream",
{"command": "XINFO", "stream": "STREAM", "key": "mystream"},
)
judge_command("XINFO HELP", {"command": "XINFO", "help": "HELP"})
judge_command("XINFO consumers mystream mygroup GROUPS mystream", None)
judge_command("XINFO groups mystream mygroup", None)
def test_xinfo_with_full(judge_command):
judge_command(
"XINFO STREAM mystream FULL",
{
"command": "XINFO",
"stream": "STREAM",
"key": "mystream",
"full_const": "FULL",
},
)
judge_command(
"XINFO STREAM mystream FULL count 10",
{
"command": "XINFO",
"stream": "STREAM",
"key": "mystream",
"full_const": "FULL",
"count_const": "count",
"count": "10",
},
)
def test_xpending(judge_command):
judge_command(
"XPENDING mystream group55",
{"command": "XPENDING", "key": "mystream", "group": "group55"},
)
judge_command(
"XPENDING mystream group55 myconsumer",
{
"command": "XPENDING",
"key": "mystream",
"group": "group55",
"consumer": "myconsumer",
},
)
judge_command(
"XPENDING mystream group55 - + 10",
{
"command": "XPENDING",
"key": "mystream",
"group": "group55",
"stream_id": ["-", "+"],
"count": "10",
},
)
judge_command(
"XPENDING mystream group55 - + 10 myconsumer",
{
"command": "XPENDING",
"key": "mystream",
"group": "group55",
"stream_id": ["-", "+"],
"count": "10",
"consumer": "myconsumer",
},
)
judge_command("XPENDING mystream group55 - + ", None)
def test_xadd(judge_command):
judge_command(
"xadd mystream MAXLEN ~ 1000 * key value",
{
"command": "xadd",
"key": "mystream",
"maxlen": "MAXLEN",
"approximately": "~",
"count": "1000",
"sfield": "key",
"svalue": "value",
"stream_id": "*",
},
)
# test for MAXLEN option
judge_command(
"xadd mystream MAXLEN 1000 * key value",
{
"command": "xadd",
"key": "mystream",
"maxlen": "MAXLEN",
"count": "1000",
"sfield": "key",
"svalue": "value",
"stream_id": "*",
},
)
judge_command(
"xadd mystream * key value",
{
"command": "xadd",
"key": "mystream",
"sfield": "key",
"svalue": "value",
"stream_id": "*",
},
)
# spcify stream id
judge_command(
"xadd mystream 123-123 key value",
{
"command": "xadd",
"key": "mystream",
"sfield": "key",
"svalue": "value",
"stream_id": "123-123",
},
)
judge_command(
"xadd mystream 123-123 key value foo bar hello world",
{
"command": "xadd",
"key": "mystream",
"sfield": "hello",
"svalue": "world",
"stream_id": "123-123",
},
)
def test_xtrim(judge_command):
judge_command(
" XTRIM mystream MAXLEN 2",
{"command": "XTRIM", "key": "mystream", "maxlen": "MAXLEN", "count": "2"},
)
judge_command(
" XTRIM mystream MAXLEN ~ 2",
{
"command": "XTRIM",
"key": "mystream",
"maxlen": "MAXLEN",
"count": "2",
"approximately": "~",
},
)
judge_command(" XTRIM mystream", None)
def test_xdel(judge_command):
judge_command(
"XDEL mystream 1581165000000 1549611229000 1581060831000",
{"command": "XDEL", "key": "mystream", "stream_id": "1581060831000"},
)
judge_command(
"XDEL mystream 1581165000000",
{"command": "XDEL", "key": "mystream", "stream_id": "1581165000000"},
)
def test_xclaim(judge_command):
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "1526569498055-0",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 123 456 789",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "789",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 IDEL 300",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": ["3600000", "300"],
"stream_id": "1526569498055-0",
"idel": "IDEL",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 retrycount 7",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "1526569498055-0",
"retrycount": "retrycount",
"count": "7",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 TIME 123456789",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "1526569498055-0",
"time": "TIME",
"timestamp": "123456789",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 FORCE",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "1526569498055-0",
"force": "FORCE",
},
)
judge_command(
"XCLAIM mystream mygroup Alice 3600000 1526569498055-0 JUSTID",
{
"command": "XCLAIM",
"key": "mystream",
"group": "mygroup",
"consumer": "Alice",
"millisecond": "3600000",
"stream_id": "1526569498055-0",
"justid": "JUSTID",
},
)
def test_xread(judge_command):
judge_command(
"XREAD COUNT 2 STREAMS mystream writers 0-0 0-0",
{
"command": "XREAD",
"count_const": "COUNT",
"count": "2",
"streams": "STREAMS",
# FIXME current grammar can't support multiple tokens
# so the ids will be recongized to keys.
"keys": "mystream writers 0-0",
"stream_id": "0-0",
},
)
judge_command(
"XREAD COUNT 2 BLOCK 1000 STREAMS mystream writers 0-0 0-0",
{
"command": "XREAD",
"count_const": "COUNT",
"count": "2",
"streams": "STREAMS",
"keys": "mystream writers 0-0",
"block": "BLOCK",
"millisecond": "1000",
"stream_id": "0-0",
},
)
def test_xreadgroup(judge_command):
judge_command(
"XREADGROUP GROUP mygroup1 Bob COUNT 1 BLOCK 100 NOACK STREAMS key1 1 key2 2",
{
"command": "XREADGROUP",
"stream_group": "GROUP",
"group": "mygroup1",
"consumer": "Bob",
"count_const": "COUNT",
"count": "1",
"block": "BLOCK",
"millisecond": "100",
"noack": "NOACK",
"streams": "STREAMS",
"keys": "key1 1 key2",
"stream_id": "2",
},
)
judge_command(
"XREADGROUP GROUP mygroup1 Bob STREAMS key1 1 key2 2",
{
"command": "XREADGROUP",
"stream_group": "GROUP",
"group": "mygroup1",
"consumer": "Bob",
"streams": "STREAMS",
"keys": "key1 1 key2",
"stream_id": "2",
},
)
judge_command("XREADGROUP GROUP group consumer", None)
| 27.523913 | 86 | 0.477766 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6,226 | 0.491746 |
6af1bee4f8dfc29969377047eaf5953641fb77f7 | 4,823 | py | Python | tests/test_find_forks/test_find_forks.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 41 | 2015-05-15T14:37:42.000Z | 2022-02-05T01:52:00.000Z | tests/test_find_forks/test_find_forks.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 12 | 2015-05-15T22:10:36.000Z | 2021-12-05T14:21:58.000Z | tests/test_find_forks/test_find_forks.py | ivan2kh/find_forks | 409251282a85da48445afc03c5a1797df393ca95 | [
"MIT"
] | 16 | 2015-05-15T14:44:33.000Z | 2020-11-18T00:54:18.000Z | # coding: utf-8
"""test_find_fork."""
# pylint: disable=no-self-use
from __future__ import absolute_import, division, print_function, unicode_literals
from os import path
import unittest
from six import PY3
from find_forks.__init__ import CONFIG
from find_forks.find_forks import add_forks, determine_names, find_forks, main
from .__init__ import BASEPATH
if PY3:
from unittest.mock import patch, MagicMock, Mock # pylint: disable=no-name-in-module
else:
from mock import patch, MagicMock, Mock
class FindForksCommon(unittest.TestCase):
@staticmethod
def make_mock(json_response):
"""Used in test_interesting.py."""
response_mock = MagicMock()
response_mock.read = Mock(return_value=json_response)
if PY3:
response_mock.status = 200
response_mock.getheader = Mock(return_value='<https://api.github.com/repos/frost-nzcr4/find_forks/forks?page=2>; rel="next", '
'<https://api.github.com/repos/frost-nzcr4/find_forks/forks?page=3>; rel="last"')
else:
response_mock.code = 200
response_mock.info = Mock(return_value=(('link', '<https://api.github.com/repos/frost-nzcr4/find_forks/forks?page=2>; rel="next", '
'<https://api.github.com/repos/frost-nzcr4/find_forks/forks?page=3>; rel="last"'), ))
return response_mock
def make_test(self, response_mock):
"""Used in test_interesting.py."""
url = 'https://github.com/frost-nzcr4/find_forks'
with patch('find_forks.find_forks.urllib.request.urlopen', return_value=response_mock) as urlopen_mock:
with patch('find_forks.git_wrapper.subprocess.call', return_value=None):
self.assertEqual(add_forks(url), 'https://api.github.com/repos/frost-nzcr4/find_forks/forks?page=2')
urlopen_mock.assert_called_once_with(url, timeout=6)
if PY3:
response_mock.status = 404
else:
response_mock.code = 404
self.assertIsNone(add_forks(url))
class FindForksTest(FindForksCommon):
def test_add_forks(self):
self.assertIsNone(add_forks('httttps://unavailable!url'))
with open(path.join(BASEPATH, 'fixture/response.json'), 'rb') as fixture:
json_response = fixture.read()
response_mock = self.make_mock(json_response)
self.make_test(response_mock)
def test_determine_names(self):
"""To run this test you'll need to prepare git first, run:
git remote add test-origin-1 https://github.com/frost-nzcr4/find_forks.git
git remote add test-origin-2 https://github.com/yagmort/symfony1.git
git remote add test-origin-3 [email protected]:tjerkw/Android-SlideExpandableListView.git
"""
user, repo = determine_names()
self.assertEqual(user, 'frost-nzcr4')
self.assertEqual(repo, 'find_forks')
user, repo = determine_names('test-origin-1')
self.assertEqual(user, 'frost-nzcr4')
self.assertEqual(repo, 'webmoney')
user, repo = determine_names('test-origin-2')
self.assertEqual(user, 'yagmort')
self.assertEqual(repo, 'symfony1')
user, repo = determine_names('test-origin-3')
self.assertEqual(user, 'tjerkw')
self.assertEqual(repo, 'Android-SlideExpandableListView')
with self.assertRaises(RuntimeError):
user, repo = determine_names('name-with-an-error')
def test_find_forks(self):
sent_args = {
'per_page': 99,
'start_page': 3
}
url = 'https://api.github.com/repos/frost-nzcr4/find_forks/forks?per_page=%s&page=%s' % (sent_args['per_page'], sent_args['start_page'])
with patch('find_forks.git_wrapper.subprocess.call', return_value=None) as call_mock:
with patch('find_forks.find_forks.add_forks', return_value=None) as add_forks_mock:
find_forks(**sent_args)
add_forks_mock.assert_called_once_with(url)
call_mock.assert_called_once()
def test_main(self):
with patch('find_forks.find_forks.find_forks', return_value=None) as find_forks_mock:
main()
sent_args = CONFIG.copy()
sent_args.update({'user': None, 'repo': None, 'no_fetch': False})
find_forks_mock.assert_called_once_with(**sent_args)
# Test __version__ exceptions.
find_forks_mock = MagicMock(side_effect=SystemError())
del find_forks_mock.__version__
modules = {
'find_forks.__init__': find_forks_mock
}
with patch.dict('sys.modules', modules):
self.assertRaises(ImportError, main)
| 41.577586 | 146 | 0.644412 | 4,308 | 0.89322 | 0 | 0 | 879 | 0.182252 | 0 | 0 | 1,562 | 0.323865 |
6af1e67adc2134fb57f91c04b0e1763048fc52e2 | 15,853 | py | Python | neutron/agent/l3/dvr_router.py | insequent/neutron | 2b1c4f121e3e8ba1c5eb2ba6661bf6326e1507c5 | [
"Apache-2.0"
] | null | null | null | neutron/agent/l3/dvr_router.py | insequent/neutron | 2b1c4f121e3e8ba1c5eb2ba6661bf6326e1507c5 | [
"Apache-2.0"
] | null | null | null | neutron/agent/l3/dvr_router.py | insequent/neutron | 2b1c4f121e3e8ba1c5eb2ba6661bf6326e1507c5 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2015 Openstack Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import binascii
import netaddr
from oslo_log import log as logging
from oslo_utils import excutils
from neutron.agent.l3 import dvr_fip_ns
from neutron.agent.l3 import dvr_snat_ns
from neutron.agent.l3 import router_info as router
from neutron.agent.linux import ip_lib
from neutron.common import constants as l3_constants
from neutron.common import utils as common_utils
from neutron.i18n import _LE
LOG = logging.getLogger(__name__)
# xor-folding mask used for IPv6 rule index
MASK_30 = 0x3fffffff
class DvrRouter(router.RouterInfo):
def __init__(self, agent, host, *args, **kwargs):
super(DvrRouter, self).__init__(*args, **kwargs)
self.agent = agent
self.host = host
self.floating_ips_dict = {}
self.snat_iptables_manager = None
# Linklocal subnet for router and floating IP namespace link
self.rtr_fip_subnet = None
self.dist_fip_count = None
self.snat_namespace = None
def get_floating_ips(self):
"""Filter Floating IPs to be hosted on this agent."""
floating_ips = super(DvrRouter, self).get_floating_ips()
return [i for i in floating_ips if i['host'] == self.host]
def get_snat_interfaces(self):
return self.router.get(l3_constants.SNAT_ROUTER_INTF_KEY, [])
def get_snat_int_device_name(self, port_id):
long_name = dvr_snat_ns.SNAT_INT_DEV_PREFIX + port_id
return long_name[:self.driver.DEV_NAME_LEN]
def _handle_fip_nat_rules(self, interface_name, action):
"""Configures NAT rules for Floating IPs for DVR.
Remove all the rules. This is safe because if
use_namespaces is set as False then the agent can
only configure one router, otherwise each router's
NAT rules will be in their own namespace.
"""
self.iptables_manager.ipv4['nat'].empty_chain('POSTROUTING')
self.iptables_manager.ipv4['nat'].empty_chain('snat')
# Add back the jump to float-snat
self.iptables_manager.ipv4['nat'].add_rule('snat', '-j $float-snat')
# And add them back if the action is add_rules
if action == 'add_rules' and interface_name:
rule = ('POSTROUTING', '! -i %(interface_name)s '
'! -o %(interface_name)s -m conntrack ! '
'--ctstate DNAT -j ACCEPT' %
{'interface_name': interface_name})
self.iptables_manager.ipv4['nat'].add_rule(*rule)
self.iptables_manager.apply()
def floating_ip_added_dist(self, fip, fip_cidr):
"""Add floating IP to FIP namespace."""
floating_ip = fip['floating_ip_address']
fixed_ip = fip['fixed_ip_address']
rule_pr = self.fip_ns.allocate_rule_priority()
self.floating_ips_dict[floating_ip] = rule_pr
fip_2_rtr_name = self.fip_ns.get_int_device_name(self.router_id)
ip_rule = ip_lib.IPRule(namespace=self.ns_name)
ip_rule.rule.add(fixed_ip, dvr_fip_ns.FIP_RT_TBL, rule_pr)
#Add routing rule in fip namespace
fip_ns_name = self.fip_ns.get_name()
rtr_2_fip, _ = self.rtr_fip_subnet.get_pair()
device = ip_lib.IPDevice(fip_2_rtr_name, namespace=fip_ns_name)
device.route.add_route(fip_cidr, str(rtr_2_fip.ip))
interface_name = (
self.fip_ns.get_ext_device_name(
self.fip_ns.agent_gateway_port['id']))
ip_lib.send_garp_for_proxyarp(fip_ns_name,
interface_name,
floating_ip,
self.agent_conf.send_arp_for_ha)
# update internal structures
self.dist_fip_count = self.dist_fip_count + 1
def floating_ip_removed_dist(self, fip_cidr):
"""Remove floating IP from FIP namespace."""
floating_ip = fip_cidr.split('/')[0]
rtr_2_fip_name = self.fip_ns.get_rtr_ext_device_name(self.router_id)
fip_2_rtr_name = self.fip_ns.get_int_device_name(self.router_id)
if self.rtr_fip_subnet is None:
self.rtr_fip_subnet = self.fip_ns.local_subnets.allocate(
self.router_id)
rtr_2_fip, fip_2_rtr = self.rtr_fip_subnet.get_pair()
fip_ns_name = self.fip_ns.get_name()
if floating_ip in self.floating_ips_dict:
rule_pr = self.floating_ips_dict[floating_ip]
ip_rule = ip_lib.IPRule(namespace=self.ns_name)
ip_rule.rule.delete(floating_ip, dvr_fip_ns.FIP_RT_TBL, rule_pr)
self.fip_ns.deallocate_rule_priority(rule_pr)
#TODO(rajeev): Handle else case - exception/log?
device = ip_lib.IPDevice(fip_2_rtr_name, namespace=fip_ns_name)
device.route.delete_route(fip_cidr, str(rtr_2_fip.ip))
# check if this is the last FIP for this router
self.dist_fip_count = self.dist_fip_count - 1
if self.dist_fip_count == 0:
#remove default route entry
device = ip_lib.IPDevice(rtr_2_fip_name, namespace=self.ns_name)
ns_ip = ip_lib.IPWrapper(namespace=fip_ns_name)
device.route.delete_gateway(str(fip_2_rtr.ip),
table=dvr_fip_ns.FIP_RT_TBL)
self.fip_ns.local_subnets.release(self.router_id)
self.rtr_fip_subnet = None
ns_ip.del_veth(fip_2_rtr_name)
is_last = self.fip_ns.unsubscribe(self.router_id)
if is_last:
# TODO(Carl) I can't help but think that another router could
# come in and want to start using this namespace while this is
# destroying it. The two could end up conflicting on
# creating/destroying interfaces and such. I think I'd like a
# semaphore to sync creation/deletion of this namespace.
self.fip_ns.delete()
self.fip_ns = None
def add_floating_ip(self, fip, interface_name, device):
if not self._add_fip_addr_to_device(fip, device):
return l3_constants.FLOATINGIP_STATUS_ERROR
# Special Handling for DVR - update FIP namespace
ip_cidr = common_utils.ip_to_cidr(fip['floating_ip_address'])
self.floating_ip_added_dist(fip, ip_cidr)
return l3_constants.FLOATINGIP_STATUS_ACTIVE
def remove_floating_ip(self, device, ip_cidr):
super(DvrRouter, self).remove_floating_ip(device, ip_cidr)
self.floating_ip_removed_dist(ip_cidr)
def create_snat_namespace(self):
# TODO(mlavalle): in the near future, this method should contain the
# code in the L3 agent that creates a gateway for a dvr. The first step
# is to move the creation of the snat namespace here
self.snat_namespace = dvr_snat_ns.SnatNamespace(self.router['id'],
self.agent_conf,
self.driver,
self.use_ipv6)
self.snat_namespace.create()
return self.snat_namespace
def delete_snat_namespace(self):
# TODO(mlavalle): in the near future, this method should contain the
# code in the L3 agent that removes an external gateway for a dvr. The
# first step is to move the deletion of the snat namespace here
self.snat_namespace.delete()
self.snat_namespace = None
def _get_internal_port(self, subnet_id):
"""Return internal router port based on subnet_id."""
router_ports = self.router.get(l3_constants.INTERFACE_KEY, [])
for port in router_ports:
fips = port['fixed_ips']
for f in fips:
if f['subnet_id'] == subnet_id:
return port
def _update_arp_entry(self, ip, mac, subnet_id, operation):
"""Add or delete arp entry into router namespace for the subnet."""
port = self._get_internal_port(subnet_id)
# update arp entry only if the subnet is attached to the router
if not port:
return
try:
# TODO(mrsmith): optimize the calls below for bulk calls
interface_name = self.get_internal_device_name(port['id'])
device = ip_lib.IPDevice(interface_name, namespace=self.ns_name)
if operation == 'add':
device.neigh.add(ip, mac)
elif operation == 'delete':
device.neigh.delete(ip, mac)
except Exception:
with excutils.save_and_reraise_exception():
LOG.exception(_LE("DVR: Failed updating arp entry"))
def _set_subnet_arp_info(self, port):
"""Set ARP info retrieved from Plugin for existing ports."""
if 'id' not in port['subnet']:
return
subnet_id = port['subnet']['id']
# TODO(Carl) Can we eliminate the need to make this RPC while
# processing a router.
subnet_ports = self.agent.get_ports_by_subnet(subnet_id)
for p in subnet_ports:
if p['device_owner'] not in l3_constants.ROUTER_INTERFACE_OWNERS:
for fixed_ip in p['fixed_ips']:
self._update_arp_entry(fixed_ip['ip_address'],
p['mac_address'],
subnet_id,
'add')
def _map_internal_interfaces(self, int_port, snat_ports):
"""Return the SNAT port for the given internal interface port."""
fixed_ip = int_port['fixed_ips'][0]
subnet_id = fixed_ip['subnet_id']
match_port = [p for p in snat_ports if
p['fixed_ips'][0]['subnet_id'] == subnet_id]
if match_port:
return match_port[0]
else:
LOG.error(_LE('DVR: no map match_port found!'))
@staticmethod
def _get_snat_idx(ip_cidr):
"""Generate index for DVR snat rules and route tables.
The index value has to be 32 bits or less but more than the system
generated entries i.e. 32768. For IPv4 use the numeric value of the
cidr. For IPv6 generate a crc32 bit hash and xor-fold to 30 bits.
Use the freed range to extend smaller values so that they become
greater than system generated entries.
"""
net = netaddr.IPNetwork(ip_cidr)
if net.version == 6:
# the crc32 & 0xffffffff is for Python 2.6 and 3.0 compatibility
snat_idx = binascii.crc32(ip_cidr) & 0xffffffff
# xor-fold the hash to reserve upper range to extend smaller values
snat_idx = (snat_idx >> 30) ^ (snat_idx & MASK_30)
if snat_idx < 32768:
snat_idx = snat_idx + MASK_30
else:
snat_idx = net.value
return snat_idx
def _snat_redirect_add(self, gateway, sn_port, sn_int):
"""Adds rules and routes for SNAT redirection."""
try:
ip_cidr = sn_port['ip_cidr']
snat_idx = self._get_snat_idx(ip_cidr)
ns_ipr = ip_lib.IPRule(namespace=self.ns_name)
ns_ipd = ip_lib.IPDevice(sn_int, namespace=self.ns_name)
ns_ipwrapr = ip_lib.IPWrapper(namespace=self.ns_name)
ns_ipd.route.add_gateway(gateway, table=snat_idx)
ns_ipr.rule.add(ip_cidr, snat_idx, snat_idx)
ns_ipwrapr.netns.execute(['sysctl', '-w', 'net.ipv4.conf.%s.'
'send_redirects=0' % sn_int])
except Exception:
LOG.exception(_LE('DVR: error adding redirection logic'))
def _snat_redirect_remove(self, gateway, sn_port, sn_int):
"""Removes rules and routes for SNAT redirection."""
try:
ip_cidr = sn_port['ip_cidr']
snat_idx = self._get_snat_idx(ip_cidr)
ns_ipr = ip_lib.IPRule(namespace=self.ns_name)
ns_ipd = ip_lib.IPDevice(sn_int, namespace=self.ns_name)
ns_ipd.route.delete_gateway(gateway, table=snat_idx)
ns_ipr.rule.delete(ip_cidr, snat_idx, snat_idx)
except Exception:
LOG.exception(_LE('DVR: removed snat failed'))
def get_gw_port_host(self):
host = self.router.get('gw_port_host')
if not host:
LOG.debug("gw_port_host missing from router: %s",
self.router['id'])
return host
def internal_network_added(self, port):
super(DvrRouter, self).internal_network_added(port)
ex_gw_port = self.get_ex_gw_port()
if not ex_gw_port:
return
snat_ports = self.get_snat_interfaces()
sn_port = self._map_internal_interfaces(port, snat_ports)
if not sn_port:
return
interface_name = self.get_internal_device_name(port['id'])
self._snat_redirect_add(sn_port['fixed_ips'][0]['ip_address'],
port,
interface_name)
# TODO(Carl) This is a sign that dvr needs two router classes.
is_this_snat_host = (self.agent_conf.agent_mode == 'dvr_snat' and
self.get_gw_port_host() == self.host)
if not is_this_snat_host:
return
ns_name = dvr_snat_ns.SnatNamespace.get_snat_ns_name(self.router['id'])
self._set_subnet_info(sn_port)
interface_name = self.get_snat_int_device_name(sn_port['id'])
self._internal_network_added(
ns_name,
sn_port['network_id'],
sn_port['id'],
sn_port['ip_cidr'],
sn_port['mac_address'],
interface_name,
dvr_snat_ns.SNAT_INT_DEV_PREFIX)
self._set_subnet_arp_info(port)
def _dvr_internal_network_removed(self, port):
if not self.ex_gw_port:
return
sn_port = self._map_internal_interfaces(port, self.snat_ports)
if not sn_port:
return
# DVR handling code for SNAT
interface_name = self.get_internal_device_name(port['id'])
self._snat_redirect_remove(sn_port['fixed_ips'][0]['ip_address'],
port,
interface_name)
is_this_snat_host = (self.agent_conf.agent_mode == 'dvr_snat' and
self.ex_gw_port['binding:host_id'] == self.host)
if not is_this_snat_host:
return
snat_interface = (
self.get_snat_int_device_name(sn_port['id']))
ns_name = self.snat_namespace.name
prefix = dvr_snat_ns.SNAT_INT_DEV_PREFIX
if ip_lib.device_exists(snat_interface, namespace=ns_name):
self.driver.unplug(snat_interface, namespace=ns_name,
prefix=prefix)
def internal_network_removed(self, port):
self._dvr_internal_network_removed(port)
super(DvrRouter, self).internal_network_removed(port)
def get_floating_agent_gw_interface(self, ext_net_id):
"""Filter Floating Agent GW port for the external network."""
fip_ports = self.router.get(l3_constants.FLOATINGIP_AGENT_INTF_KEY, [])
return next(
(p for p in fip_ports if p['network_id'] == ext_net_id), None)
| 43.196185 | 79 | 0.6272 | 14,730 | 0.929162 | 0 | 0 | 966 | 0.060935 | 0 | 0 | 4,212 | 0.265691 |
6af266c300822127b8933d07e4d514dfddafdca3 | 575 | py | Python | sider/warnings.py | PCManticore/sider | cd11b38b2a1bf1ea3600eb287abfe3c2b40c67c1 | [
"MIT"
] | 19 | 2015-01-17T18:24:36.000Z | 2022-02-05T06:33:41.000Z | sider/warnings.py | PCManticore/sider | cd11b38b2a1bf1ea3600eb287abfe3c2b40c67c1 | [
"MIT"
] | 5 | 2016-01-13T14:19:44.000Z | 2016-06-01T18:50:36.000Z | sider/warnings.py | PCManticore/sider | cd11b38b2a1bf1ea3600eb287abfe3c2b40c67c1 | [
"MIT"
] | 6 | 2015-08-16T10:32:09.000Z | 2019-11-29T09:57:06.000Z | """:mod:`sider.warnings` --- Warning categories
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This module defines several custom warning category classes.
"""
class SiderWarning(Warning):
"""All warning classes used by Sider extend this base class."""
class PerformanceWarning(SiderWarning, RuntimeWarning):
"""The category for warnings about performance worries. Operations
that warn this category would work but be inefficient.
"""
class TransactionWarning(SiderWarning, RuntimeWarning):
"""The category for warnings about transactions."""
| 25 | 71 | 0.683478 | 402 | 0.69913 | 0 | 0 | 0 | 0 | 0 | 0 | 411 | 0.714783 |
6af312eaf5ecd0a0c44737d1362c73e2f2338489 | 2,512 | py | Python | kadal/query.py | Bucolo/Kadal | a0085f15df4f8ebbf5ec4cd4344e207773c6b498 | [
"MIT"
] | 1 | 2022-03-12T15:04:01.000Z | 2022-03-12T15:04:01.000Z | kadal/query.py | Bucolo/Kadal | a0085f15df4f8ebbf5ec4cd4344e207773c6b498 | [
"MIT"
] | null | null | null | kadal/query.py | Bucolo/Kadal | a0085f15df4f8ebbf5ec4cd4344e207773c6b498 | [
"MIT"
] | null | null | null | MEDIA_SEARCH = """
query ($search: String, $type: MediaType, $exclude: MediaFormat, $isAdult: Boolean) {
Media(search: $search, type: $type, format_not: $exclude, isAdult: $isAdult) {
id
type
format
title {
english
romaji
native
}
synonyms
status
description
startDate {
year
month
day
}
endDate {
year
month
day
}
episodes
chapters
volumes
coverImage {
large
color
}
bannerImage
genres
averageScore
siteUrl
isAdult
nextAiringEpisode {
timeUntilAiring
episode
}
}
}
"""
MEDIA_BY_ID = """
query ($id: Int, $type: MediaType) {
Media(id: $id, type: $type) {
id
type
format
title {
english
romaji
native
}
synonyms
status
description
startDate {
year
month
day
}
endDate {
year
month
day
}
episodes
chapters
coverImage {
large
color
}
bannerImage
genres
averageScore
siteUrl
isAdult
nextAiringEpisode {
timeUntilAiring
episode
}
}
}
"""
MEDIA_PAGED = """
query (
$id: Int,
$page: Int,
$perPage: Int,
$search: String,
$type: MediaType,
$sort: [MediaSort] = [SEARCH_MATCH],
$exclude: MediaFormat,
$isAdult: Boolean
) {
Page(page: $page, perPage: $perPage) {
media(id: $id, search: $search, type: $type, sort: $sort, format_not: $exclude, isAdult: $isAdult) {
id
type
format
title {
english
romaji
native
}
synonyms
status
description
startDate {
year
month
day
}
endDate {
year
month
day
}
episodes
chapters
volumes
coverImage {
large
color
}
bannerImage
genres
averageScore
siteUrl
isAdult
popularity
}
}
}
"""
USER_SEARCH = """
query ($search: String) {
User(search: $search) {
id
name
html_about: about(asHtml: true)
about
avatar {
large
}
bannerImage
siteUrl
stats {
watchedTime
chaptersRead
}
}
}
"""
USER_BY_ID = """
query ($id: Int) {
User(id: $id) {
id
name
html_about: about(asHtml: true)
about
avatar {
large
}
bannerImage
siteUrl
stats {
watchedTime
chaptersRead
}
}
}
"""
| 14.03352 | 104 | 0.515525 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2,433 | 0.968551 |
6af335ed4a4087ef091d5830d5a795b074596342 | 1,032 | py | Python | sandbox/test/testChainop.py | turkeydonkey/nzmath3 | a48ae9efcf0d9ad1485c2e9863c948a7f1b20311 | [
"BSD-3-Clause"
] | 1 | 2021-05-26T19:22:17.000Z | 2021-05-26T19:22:17.000Z | sandbox/test/testChainop.py | turkeydonkey/nzmath3 | a48ae9efcf0d9ad1485c2e9863c948a7f1b20311 | [
"BSD-3-Clause"
] | null | null | null | sandbox/test/testChainop.py | turkeydonkey/nzmath3 | a48ae9efcf0d9ad1485c2e9863c948a7f1b20311 | [
"BSD-3-Clause"
] | null | null | null | import unittest
import operator
import sandbox.chainop as chainop
class BasicChainTest (unittest.TestCase):
def testBasicChain(self):
double = lambda x: x * 2
self.assertEqual(62, chainop.basic_chain((operator.add, double), 2, 31))
square = lambda x: x ** 2
self.assertEqual(2**31, chainop.basic_chain((operator.mul, square), 2, 31))
class MultiChainTest (unittest.TestCase):
def testMultiChain(self):
double = lambda x: x * 2
self.assertEqual([62, 93], chainop.multi_chains((operator.add, double), (2, 3), 31))
square = lambda x: x ** 2
self.assertEqual([2**31, 3**31], chainop.multi_chains((operator.mul, square), [2, 3], 31))
def suite(suffix="Test"):
suite = unittest.TestSuite()
all_names = globals()
for name in all_names:
if name.endswith(suffix):
suite.addTest(unittest.makeSuite(all_names[name], "test"))
return suite
if __name__ == '__main__':
runner = unittest.TextTestRunner()
runner.run(suite())
| 31.272727 | 98 | 0.650194 | 633 | 0.613372 | 0 | 0 | 0 | 0 | 0 | 0 | 22 | 0.021318 |
6af3beecef5460df43fd5570a5ba8ce1f6a0d13d | 1,131 | py | Python | labs_final/lab5/experiments/run_trpo_pendulum.py | mrmotallebi/berkeley-deeprl-bootcamp | 9257c693724c38edfa4571e3510667ca168b7ca1 | [
"MIT"
] | 3 | 2018-03-26T14:13:11.000Z | 2020-07-23T22:26:28.000Z | labs_final/lab5/experiments/run_trpo_pendulum.py | mrmotallebi/berkeley-deeprl-bootcamp | 9257c693724c38edfa4571e3510667ca168b7ca1 | [
"MIT"
] | null | null | null | labs_final/lab5/experiments/run_trpo_pendulum.py | mrmotallebi/berkeley-deeprl-bootcamp | 9257c693724c38edfa4571e3510667ca168b7ca1 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
import chainer
from algs import trpo
from env_makers import EnvMaker
from models import GaussianMLPPolicy, MLPBaseline
from utils import SnapshotSaver
import numpy as np
import os
import logger
log_dir = "data/local/trpo-pendulum"
np.random.seed(42)
# Clean up existing logs
os.system("rm -rf {}".format(log_dir))
with logger.session(log_dir):
env_maker = EnvMaker('Pendulum-v0')
env = env_maker.make()
policy = GaussianMLPPolicy(
observation_space=env.observation_space,
action_space=env.action_space,
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=chainer.functions.tanh,
)
baseline = MLPBaseline(
observation_space=env.observation_space,
action_space=env.action_space,
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=chainer.functions.tanh,
)
trpo(
env=env,
env_maker=env_maker,
n_envs=16,
policy=policy,
baseline=baseline,
batch_size=10000,
n_iters=100,
snapshot_saver=SnapshotSaver(log_dir),
)
| 24.586957 | 51 | 0.678161 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 95 | 0.083996 |
6af3f2a17f291a65e5f9b17cbe9f19d00752f642 | 2,098 | py | Python | jtyoui/regular/regexengine.py | yy1244/Jtyoui | d3c212ed9d6ffa6b37a8ca49098ab59c89216f09 | [
"MIT"
] | 1 | 2019-12-05T09:46:51.000Z | 2019-12-05T09:46:51.000Z | jtyoui/regular/regexengine.py | yy1244/Jtyoui | d3c212ed9d6ffa6b37a8ca49098ab59c89216f09 | [
"MIT"
] | null | null | null | jtyoui/regular/regexengine.py | yy1244/Jtyoui | d3c212ed9d6ffa6b37a8ca49098ab59c89216f09 | [
"MIT"
] | null | null | null | #!/usr/bin/python3.7
# -*- coding: utf-8 -*-
# @Time : 2019/12/2 10:17
# @Author: [email protected]
"""
正则解析器
"""
try:
import xml.etree.cElementTree as et
except ModuleNotFoundError:
import xml.etree.ElementTree as et
import re
class RegexEngine:
def __init__(self, xml, str_):
"""加载正则表。正则表为xml
:param xml: 正则表的位置
:param str_: 要匹配的字符串
"""
self._string = str_
self._root = et.parse(xml).getroot()
self.re = ''
self.data = []
def select(self, tag):
"""根据xml的tag来实现不同的正则提取
:param tag: xml的tag标签
:return: 正则提取的数据
"""
root = self._root.find(tag)
attrib = root.attrib
if attrib.get('part', 'False').lower() == 'true':
self._part_tag(root)
return list(filter(lambda x: x[1], self.data))
else:
sf = self._no_part(root)
self.re = ''.join(self.data) + sf
return re.findall(self.re, self._string)
def _no_part(self, tags):
"""tag标签不分开抽取"""
for tag in tags:
if tag:
if tag.attrib.get('must', 'true').lower() == 'true':
self.data.append(self.re)
self.re = ''
self.re = '(?:' + self._no_part(tag) + ')'
else:
self.re = self._no_part(tag)
else:
attrib = tag.attrib
text = tag.text.strip()
if attrib.get('must', 'true').lower() == 'true':
self.re = '(?:' + text + ')'
else:
self.re += '(?:' + text + ')?'
return self.re
def _part_tag(self, tags):
"""tag标签分开提取"""
for tag in tags:
if tag:
self._part_tag(tag)
else:
self.data.append((tag.tag, re.findall(tag.text.strip(), self._string)))
@property
def string(self):
return self._string
@string.setter
def string(self, str_):
self._string = str_
self.re, self.data = '', []
| 27.246753 | 87 | 0.479981 | 1,979 | 0.889039 | 0 | 0 | 165 | 0.074124 | 0 | 0 | 526 | 0.236298 |
6af4d5fe8b77a49f0cbdce7b5f8e3248894cc3b5 | 5,117 | py | Python | proglearn/transformers.py | rflperry/ProgLearn | 9f799b4a8cf2157ba40b04842dc88eaf646e6420 | [
"MIT"
] | null | null | null | proglearn/transformers.py | rflperry/ProgLearn | 9f799b4a8cf2157ba40b04842dc88eaf646e6420 | [
"MIT"
] | 1 | 2020-11-25T19:21:54.000Z | 2020-11-25T19:21:54.000Z | proglearn/transformers.py | rflperry/ProgLearn | 9f799b4a8cf2157ba40b04842dc88eaf646e6420 | [
"MIT"
] | null | null | null | """
Main Author: Will LeVine
Corresponding Email: [email protected]
"""
import keras
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
from .base import BaseTransformer
class NeuralClassificationTransformer(BaseTransformer):
"""
A class used to transform data from a category to a specialized representation.
Parameters
----------
network : object
A neural network used in the classification transformer.
euclidean_layer_idx : int
An integer to represent the final layer of the transformer.
optimizer : str or keras.optimizers instance
An optimizer used when compiling the neural network.
loss : str, default="categorical_crossentropy"
A loss function used when compiling the neural network.
pretrained : bool, default=False
A boolean used to identify if the network is pretrained.
compile_kwargs : dict, default={"metrics": ["acc"]}
A dictionary containing metrics for judging network performance.
fit_kwargs : dict, default={
"epochs": 100,
"callbacks": [keras.callbacks.EarlyStopping(patience=5, monitor="val_acc")],
"verbose": False,
"validation_split": 0.33,
},
A dictionary to hold epochs, callbacks, verbose, and validation split for the network.
Attributes
----------
encoder_ : object
A Keras model with inputs and outputs based on the network attribute.
Output layers are determined by the euclidean_layer_idx parameter.
"""
def __init__(
self,
network,
euclidean_layer_idx,
optimizer,
loss="categorical_crossentropy",
pretrained=False,
compile_kwargs={"metrics": ["acc"]},
fit_kwargs={
"epochs": 100,
"callbacks": [keras.callbacks.EarlyStopping(patience=5, monitor="val_acc")],
"verbose": False,
"validation_split": 0.33,
},
):
self.network = keras.models.clone_model(network)
self.encoder_ = keras.models.Model(
inputs=self.network.inputs,
outputs=self.network.layers[euclidean_layer_idx].output,
)
self.pretrained = pretrained
self.optimizer = optimizer
self.loss = loss
self.compile_kwargs = compile_kwargs
self.fit_kwargs = fit_kwargs
def fit(self, X, y):
"""
Fits the transformer to data X with labels y.
Parameters
----------
X : ndarray
Input data matrix.
y : ndarray
Output (i.e. response data matrix).
Returns
-------
self : NeuralClassificationTransformer
The object itself.
"""
check_X_y(X, y)
_, y = np.unique(y, return_inverse=True)
# more typechecking
self.network.compile(
loss=self.loss, optimizer=self.optimizer, **self.compile_kwargs
)
self.network.fit(X, keras.utils.to_categorical(y), **self.fit_kwargs)
return self
def transform(self, X):
"""
Performs inference using the transformer.
Parameters
----------
X : ndarray
Input data matrix.
Returns
-------
X_transformed : ndarray
The transformed input.
Raises
------
NotFittedError
When the model is not fitted.
"""
check_is_fitted(self)
check_array(X)
return self.encoder_.predict(X)
class TreeClassificationTransformer(BaseTransformer):
"""
A class used to transform data from a category to a specialized representation.
Parameters
----------
kwargs : dict, default={}
A dictionary to contain parameters of the tree.
Attributes
----------
transformer : sklearn.tree.DecisionTreeClassifier
an internal sklearn DecisionTreeClassifier
"""
def __init__(self, kwargs={}):
self.kwargs = kwargs
def fit(self, X, y):
"""
Fits the transformer to data X with labels y.
Parameters
----------
X : ndarray
Input data matrix.
y : ndarray
Output (i.e. response data matrix).
Returns
-------
self : TreeClassificationTransformer
The object itself.
"""
X, y = check_X_y(X, y)
self.transformer_ = DecisionTreeClassifier(**self.kwargs).fit(X, y)
return self
def transform(self, X):
"""
Performs inference using the transformer.
Parameters
----------
X : ndarray
Input data matrix.
Returns
-------
X_transformed : ndarray
The transformed input.
Raises
------
NotFittedError
When the model is not fitted.
"""
check_is_fitted(self)
X = check_array(X)
return self.transformer_.apply(X)
| 26.931579 | 94 | 0.587063 | 4,843 | 0.946453 | 0 | 0 | 0 | 0 | 0 | 0 | 3,234 | 0.632011 |
6af5766ae43b84c8b76547fb51e5b56cfdb7f3af | 9,900 | py | Python | morphelia/external/saphire.py | marx-alex/Morphelia | 809278b07f1a535789455d54df3cbddc850d609c | [
"MIT"
] | null | null | null | morphelia/external/saphire.py | marx-alex/Morphelia | 809278b07f1a535789455d54df3cbddc850d609c | [
"MIT"
] | null | null | null | morphelia/external/saphire.py | marx-alex/Morphelia | 809278b07f1a535789455d54df3cbddc850d609c | [
"MIT"
] | null | null | null | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.collections as mcoll
from matplotlib.ticker import MaxNLocator
plt.style.use('seaborn-darkgrid')
class BaseTraj:
def __init__(self, model, X):
self.model = model
assert len(X.shape) == 2, f"X should be 2-d, instead got shape {X.shape}"
self.X = X
self.means = self.model.means_.copy()
self.states = self.model.predict(X)
self.n_states = len(np.unique(self.states))
self.trans = self.model.transmat_.copy()
def rho_dt_bins(self, rho, theta, dt, bins=12):
"""
Bin rho values and dwell time on polar coordinates.
:param rho:
:param theta:
:param dt:
:param bins:
:return:
"""
bins = np.linspace(-np.pi, np.pi, bins+1)
bin_means = (bins[:-1] + bins[1:]) / 2
bin_ix = np.digitize(theta, bins)
bin_rd = [rho[(bin_ix == i) & (rho > 0)].mean()
if len(rho[(bin_ix == i) & (rho > 0)]) > 0 else
0 for i in range(1, len(bins))]
bin_dt = [dt[(bin_ix == i) & (dt > 0)].sum()
if len(dt[(bin_ix == i) & (dt > 0)]) > 0 else
0 for i in range(1, len(bins))]
return bin_means, bin_rd, bin_dt
def transition_vectors(self):
"""
Transition vectors between states on polar coordinates.
:return:
"""
mu_x, mu_y = self.means[:, 0], self.means[:, 1]
mu_x_dist = mu_x - mu_x[:, np.newaxis]
mu_y_dist = mu_y - mu_y[:, np.newaxis]
dist_vect = np.column_stack((mu_x_dist.flatten(), mu_y_dist.flatten()))
trans_rho, trans_theta = self.cart2pol(dist_vect)
trans_rho = (trans_rho.reshape((self.n_states, self.n_states)) * self.design_transition()).flatten()
return trans_rho, trans_theta
def design_transition(self, thresh=0.1):
design_trans = self.trans
diag_ix = np.diag_indices(len(design_trans))
design_trans[diag_ix] = 0
design_trans[design_trans < thresh] = 0
design_trans[design_trans >= thresh] = 1
return design_trans
def norm_trans_time(self):
"""
Normalized transition time.
:return:
"""
unique, counts = np.unique(self.states, return_counts=True)
sort_ix = unique.argsort()
counts = counts[sort_ix]
# normalize by transition probability
dt = (counts * self.design_transition()).flatten()
return dt / dt.sum()
def norm_state_time(self):
"""
Normalized state time.
:return:
"""
unique, counts = np.unique(self.states, return_counts=True)
sort_ix = unique.argsort()
counts = counts[sort_ix]
return counts / counts.sum()
@staticmethod
def cart2pol(arr):
"""
Cartesion space to polar space.
Args:
arr (numpy.array): Array of shape [n_state x dims]
"""
x, y = arr[:, 0], arr[:, 1]
rho = np.sqrt(x ** 2 + y ** 2)
theta = np.arctan2(y, x)
return rho, theta
class PhenoSign(BaseTraj):
"""Phenotypic Signature class."""
def __init__(self, model, X):
super(PhenoSign, self).__init__(model, X)
self.bin_means, self.signature = self.get_signature()
def get_signature(self):
"""
Calculate phenotypic signature for a given model.
:return: bin_means, array of shape [4 x n_bins] with
1. state radial distances
2. state dwell times
3. transition distances
3. transition dwell times
"""
# states
mu_rho, mu_theta = self.cart2pol(self.means)
state_dt = self.norm_state_time()
bin_means_1, state_rd_bins, state_dt_bins = self.rho_dt_bins(mu_rho, mu_theta, state_dt)
# transitions
trans_rho, trans_theta = self.transition_vectors()
trans_dt = self.norm_trans_time()
bin_means_2, trans_rd_bins, trans_dt_bins = self.rho_dt_bins(trans_rho, trans_theta, trans_dt)
assert (bin_means_1 == bin_means_2).all(), "state and transition vectors are binned differently and can" \
"not be concatenated."
return bin_means_1, np.vstack((state_rd_bins, state_dt_bins, trans_rd_bins, trans_dt_bins))
class Saphire(PhenoSign):
"""Implementation of the SAPHIRE algorithm for plotting Hidden Markov Models.
Gordonov S, Hwang MK, Wells A, Gertler FB, Lauffenburger DA,
Bathe M. Time series modeling of live-cell shape dynamics for
image-based phenotypic profiling. Integr Biol (Camb). 2016;8(1):73-90.
"""
def __init__(self, model, X):
super(Saphire, self).__init__(model, X)
def plot_traj(self, projection='cartesian', ymax=None):
"""
Plot cell trajectory.
Args:
projection (str): cartesian or polar.
ymax (int)
"""
avail_proj = ['cartesian', 'polar']
projection = projection.lower()
assert projection in avail_proj, f"projection unknown: {projection}"
if projection == 'cartesian':
projection = None
cmap = plt.get_cmap('binary')
cmap = truncate_colormap(cmap, minval=0.2)
if projection == 'polar':
y, x = self.cart2pol(self.X)
y_mu, x_mu = self.cart2pol(self.means)
else:
x, y = self.X[:, 0], self.X[:, 1]
x_mu, y_mu = self.means[:, 0], self.means[:, 1]
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw={'projection': projection})
ax.scatter(x, y,
c=self.states, cmap='Set1', zorder=2)
traj = ax.scatter(x_mu, y_mu,
c=np.unique(self.states), cmap='Set1',
s=200, zorder=2, edgecolor='black', alpha=0.6)
legend = ax.legend(*traj.legend_elements(),
loc="upper right", bbox_to_anchor=(1.2, 0.94),
title="States")
ax.add_artist(legend)
if ymax is not None:
ax.set_ylim(0, ymax)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
colorline(x, y, cmap=cmap, zorder=1)
norm = mpl.colors.Normalize(vmin=0, vmax=48)
cax = fig.add_axes([0.94, 0.15, 0.05, 0.3])
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax,
orientation='vertical', label='Time')
plt.show()
return fig, ax
def plot_states(self, ymax=None):
"""
Plot cell states.
"""
bin_rd, bin_dt = self.signature[0, :], self.signature[1, :]
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw={'projection': 'polar'})
cmap = plt.get_cmap("Oranges")
N = 12
width = (2 * np.pi) / N
ax.bar(self.bin_means, bin_rd, width=width, color=cmap(bin_dt))
if ymax is not None:
ax.set_ylim(0, ymax)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cax = fig.add_axes([0.94, 0.15, 0.05, 0.3])
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax,
orientation='vertical', label='Increasing state dwell time',
ticks=[0, 0.5, 1])
return fig, ax
def plot_transition(self, ymax=None):
"""
Plot transition between cell states.
"""
bin_rd, bin_dt = self.signature[2, :], self.signature[3, :]
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw={'projection': 'polar'})
cmap = plt.get_cmap("Blues")
N = 12
width = (2 * np.pi) / N
ax.bar(self.bin_means, bin_rd, width=width, color=cmap(bin_dt))
if ymax is not None:
ax.set_ylim(0, ymax)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
norm = mpl.colors.Normalize(vmin=0, vmax=1)
cax = fig.add_axes([0.94, 0.15, 0.05, 0.3])
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax,
orientation='vertical', label='Increasing transition dwell time',
ticks=[0, 0.5, 1])
return fig, ax
def colorline(x, y, z=None, cmap=plt.get_cmap('copper'), norm=plt.Normalize(0.0, 1.0),
linewidth=3, alpha=1.0, zorder=1):
"""
Plot a colored line with coordinates x and y
Optionally specify colors in the array z
Optionally specify a colormap, a norm function and a line width
"""
# Default colors equally spaced on [0,1]:
if z is None:
z = np.linspace(0.0, 1.0, len(x))
# Special case if a single number:
if not hasattr(z, "__iter__"): # to check for numerical input -- this is a hack
z = np.array([z])
z = np.asarray(z)
segments = make_segments(x, y)
lc = mcoll.LineCollection(segments, array=z, cmap=cmap, norm=norm,
linewidth=linewidth, alpha=alpha, zorder=zorder)
ax = plt.gca()
ax.add_collection(lc)
return lc
def make_segments(x, y):
"""
Create list of line segments from x and y coordinates, in the correct format
for LineCollection: an array of the form numlines x (points per line) x 2 (x
and y) array
"""
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
return segments
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
'''
https://stackoverflow.com/a/18926541
'''
if isinstance(cmap, str):
cmap = plt.get_cmap(cmap)
new_cmap = mpl.colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
| 34.256055 | 114 | 0.58101 | 8,115 | 0.819697 | 0 | 0 | 308 | 0.031111 | 0 | 0 | 2,460 | 0.248485 |
6af5ea523e6e4b25159d80c12448780bfd106c8c | 4,824 | py | Python | account/views.py | Stfuncode/food-beverage-investigator | 0fea4943a5c2634068dc04118f83742327937c25 | [
"MIT"
] | null | null | null | account/views.py | Stfuncode/food-beverage-investigator | 0fea4943a5c2634068dc04118f83742327937c25 | [
"MIT"
] | null | null | null | account/views.py | Stfuncode/food-beverage-investigator | 0fea4943a5c2634068dc04118f83742327937c25 | [
"MIT"
] | null | null | null | import imp
from venv import create
from django.shortcuts import render, redirect
from django.views import View
from django.views.generic import (
ListView,
)
from account.models import *
from account.forms import *
from data.models import *
from django.contrib.auth import login as auth_login
from django.contrib.auth.models import auth
from django.contrib import messages
from django.contrib.auth.mixins import PermissionRequiredMixin, LoginRequiredMixin
# Create your views here.
def login(request):
if request.method == "POST":
form = loginForm(data=request.POST)
if form.is_valid():
user = form.get_user()
auth_login(request, user)
print("succesful login")
remember_me = form.cleaned_data["remember_me"]
if remember_me:
request.session.set_expiry(1209600)
return redirect("home")
else:
messages.warning(request, 'There is an issue with your login processes')
return redirect("login")
else:
form = loginForm()
create_form = createUserForm()
context = {
"form": form,
"create_form": create_form
}
return render(request, "login.html", context)
def logout(request):
auth.logout(request)
return redirect("login")
def register(request):
if request.method == "POST":
create_form = createUserForm(data=request.POST)
if create_form.is_valid():
user = create_form.save(commit=False)
user.save()
messages.success(request, "User created successfully!")
return redirect("login")
else:
messages.error(request, "User creation failed")
else:
create_form = createUserForm()
return render(request, "login.html", {"create_form": create_form})
def homepage(request):
user = Account.objects.filter(is_superuser=False).count()
rest = Restaurant.objects.all().count()
rating = RestaurantReview.objects.exclude(rating__isnull=True).count()
review = RestaurantReview.objects.exclude(review__isnull=True).count()
context = {
"user_count" : user,
"rest_count" : rest,
"rating_count" : rating,
"review_count" : review,
}
return render(request, "home.html", context)
class ViewUserView(View, LoginRequiredMixin, PermissionRequiredMixin):
permission_required = 'accounts.view_account'
raise_exception = True
def post(self, request, pk, *args, **kwargs):
user = Account.objects.get(account_id=pk)
form = viewUserForm(request.POST, instance=user)
return redirect("userlist")
def get(self, request, pk, *args, **kwargs):
user = Account.objects.get(account_id=pk)
form = viewUserForm(instance=user)
context = {
"form": form,
"pk": pk
}
return render(request, "profile.html", context)
class EditUserView(View, LoginRequiredMixin, PermissionRequiredMixin):
permission_required = 'accounts.change_account'
raise_exception = True
def post(self, request, pk, *args, **kwargs):
user = Account.objects.get(account_id=pk)
form = editUserForm(request.POST, instance=user)
if form.is_valid():
user = form.save()
role = request.POST.get("role")
user.save()
messages.success(request, "Successfully updated profile!")
return redirect(f'/viewUser/{user.account_id}')
else:
form = editUserForm(instance=user)
extra_context = {
"form": form,
}
print('something wrong')
messages.error(request, "Invalid input! Please input a valid information.")
return render(request, "editUser.html", extra_context)
def get(self, request, pk, *args, **kwargs):
user = Account.objects.get(account_id=pk)
form = editUserForm(instance=user)
extra_context = {
"form": form,
}
return render(request, "editUser.html", extra_context)
class UserListView(LoginRequiredMixin, PermissionRequiredMixin, ListView):
permission_required = 'accounts.view_account'
template_name = "userList.html"
queryset = Account.objects.all()
class UpdateProfilePicView(View, LoginRequiredMixin, PermissionRequiredMixin):
permission_required = 'accounts.change_account'
raise_exception = True
def post(self, request, pk, *args, **kwargs):
user = Account.objects.get(account_id=pk)
user.profile_pic = request.FILES.get('profile-pic')
user.save()
return redirect('viewUser', pk)
def deleteUser(request, event_id):
event = Account.objects.get(pk=event_id)
event.delete()
return redirect('userlist') | 33.5 | 87 | 0.645522 | 2,353 | 0.487769 | 0 | 0 | 0 | 0 | 0 | 0 | 662 | 0.137231 |
6af67173be2103fc04ef7a7c51b006d1f866e003 | 2,697 | py | Python | fpds/client.py | mgradowski/aiproject | 855332bd982bef2530ad935a209ae8be35963165 | [
"MIT"
] | null | null | null | fpds/client.py | mgradowski/aiproject | 855332bd982bef2530ad935a209ae8be35963165 | [
"MIT"
] | null | null | null | fpds/client.py | mgradowski/aiproject | 855332bd982bef2530ad935a209ae8be35963165 | [
"MIT"
] | null | null | null | import cv2
import aiohttp
import asyncio
import concurrent.futures
import argparse
import numpy as np
async def camera_source(ws: aiohttp.ClientWebSocketResponse, threadpool: concurrent.futures.ThreadPoolExecutor, src_id: int=0):
loop = asyncio.get_running_loop()
try:
src = await loop.run_in_executor(threadpool, lambda: cv2.VideoCapture(src_id))
while True:
_, im = await loop.run_in_executor(threadpool, src.read)
im = cv2.resize(im, (640, 384))
enc_param = [int(cv2.IMWRITE_JPEG_QUALITY), 40]
_, im = await loop.run_in_executor(threadpool, lambda: cv2.imencode('.jpg', im, enc_param))
await ws.send_bytes(im.tobytes())
except asyncio.CancelledError:
pass
finally:
src.release()
async def preview_window(queue: asyncio.Queue, threadpool: concurrent.futures.ThreadPoolExecutor):
loop = asyncio.get_running_loop()
try:
while True:
im = await queue.get()
im = np.frombuffer(im, dtype=np.uint8)
im = await loop.run_in_executor(threadpool, lambda: cv2.imdecode(im, cv2.IMREAD_ANYCOLOR))
cv2.imshow('fpds_remote_preview', im)
cv2.waitKey(1)
except asyncio.CancelledError:
pass
finally:
cv2.destroyAllWindows()
async def run_client(
ws: aiohttp.ClientWebSocketResponse,
threadpool: concurrent.futures.ThreadPoolExecutor
) -> None:
# --
dst_queue = asyncio.Queue(maxsize=1)
src_task = asyncio.create_task(camera_source(ws, threadpool))
dst_task = asyncio.create_task(preview_window(dst_queue, threadpool))
try:
while True:
im = await ws.receive_bytes()
await dst_queue.put(im)
except asyncio.CancelledError:
await ws.send_str('close')
src_task.cancel()
dst_task.cancel()
await asyncio.wait([src_task, dst_task])
async def amain(url: str):
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as threadpool:
async with aiohttp.ClientSession() as session, session.ws_connect(url) as ws:
await run_client(ws, threadpool)
def main():
parser = argparse.ArgumentParser('fpds.client')
parser.add_argument('url', type=str, help='WebSocket endpoint of fpds.server e.g. http://localhost:8181/fpds')
args = parser.parse_args()
loop = asyncio.get_event_loop()
task = loop.create_task(amain(args.url))
try:
loop.run_until_complete(task)
except KeyboardInterrupt:
task.cancel()
loop.run_until_complete(asyncio.wait_for(task, timeout=None))
finally:
loop.close()
if __name__ == '__main__':
main()
| 35.025974 | 127 | 0.668891 | 0 | 0 | 0 | 0 | 0 | 0 | 2,052 | 0.760845 | 133 | 0.049314 |
6af759aad1d331394cb7f013c9559f17569541f2 | 3,619 | py | Python | Giveme5W1H/extractor/tools/key_value_cache.py | bkrrr/Giveme5W | 657738781fe387d76e6e0da35ed009ccf81f4290 | [
"Apache-2.0"
] | 410 | 2018-05-02T12:53:02.000Z | 2022-03-28T16:11:34.000Z | Giveme5W1H/extractor/tools/key_value_cache.py | bkrrr/Giveme5W | 657738781fe387d76e6e0da35ed009ccf81f4290 | [
"Apache-2.0"
] | 51 | 2018-05-02T13:53:19.000Z | 2022-03-22T00:16:39.000Z | Giveme5W1H/extractor/tools/key_value_cache.py | TU-Berlin/Giveme5W1H | b1586328393a50acde86015d22f78a4c15bf2f34 | [
"Apache-2.0"
] | 81 | 2018-05-29T14:03:27.000Z | 2022-02-08T08:59:38.000Z | import logging
import os
import pickle
import sys
import threading
import time
from typing import List
from Giveme5W1H.extractor.root import path
from Giveme5W1H.extractor.tools.util import bytes_2_human_readable
class KeyValueCache(object):
def __init__(self, cache_path):
"""
:param cache_path: path to cache, must be relative to the root.py file
"""
self.log = logging.getLogger('GiveMe5W')
# resolve path relative to the path file
self._cache_path = path(cache_path)
# ad a meaningful extension
self._cache_path = self._cache_path + '.prickle'
self._cache = {}
if cache_path and os.path.isfile(self._cache_path) and os.path.getsize(self._cache_path) > 0:
# reload cache object form disc, if any
with open(self._cache_path, 'rb') as ff:
self._cache = pickle.load(ff)
self.log.debug('KeyValueCache: ' + self._cache_path + ' restored')
self.log_stats()
else:
self._cache = {}
self._lock = threading.Lock()
def log_stats(self):
# size is not considering child's
self.log.info(self._cache_path + ' entries: ' + str(len(self._cache)) + ' size: ' + bytes_2_human_readable(
sys.getsizeof(self._cache)))
def persist(self):
with open(self._cache_path, 'wb') as f:
pickle.dump(self._cache, f, pickle.HIGHEST_PROTOCOL)
def cache(self, key: str, value: object):
"""
None values are considered as invalid results (ToughRequest) is producing none for exceptions
set -1 if you want to store "No distance"
:param key:
:param value:
:return:
"""
self._lock.acquire()
if value is not None:
self._cache[key] = self._pack(value);
self.log.debug(self._cache_path + ' CACHED: ' + str(key) + ': ' + str(value))
self.persist()
self._lock.release()
def get(self, key):
"""
Read cache entries
:param key:
:return:
"""
self._lock.acquire()
result = None
value = self._cache.get(key)
if value is not None:
self.log.debug(self._cache_path + ' LOADED: ' + str(key) + ': ' + str(value))
result = self._unpack(value)
self._lock.release()
return result
def get_complex(self, list_of_keys: List[str]):
"""
Read complex cache entries
"""
return self.get(self._get_id(list_of_keys))
def cache_complex(self, list_of_keys: List[str], value):
"""
helper to cache multi (string)key values.
They are sorted before concatenation, therefore an order is determined.
"""
self.cache(self._get_id(list_of_keys), value)
def _get_id(self, list_of_keys: List[str]):
"""
sorts list_of_keys, concatenates with # for readability
:param list_of_keys:
:return:
"""
sorted(list_of_keys)
return "#".join(list_of_keys)
def _pack(self, value):
"""
cache tracks the age of an entry, may be helpful in the future
:param value:
:return:
"""
return [value, str(time.time())]
def _unpack(self, value):
"""
removes the timestamp around the cached value, if any
:param value:
:return:
"""
# there are some old entries without timestamp
if isinstance(value, str) or isinstance(value, int):
return value
return value[0]
| 30.931624 | 115 | 0.585797 | 3,402 | 0.940039 | 0 | 0 | 0 | 0 | 0 | 0 | 1,255 | 0.346781 |
6af7f07129b756fc33dfdd705556d009ef89fe63 | 3,121 | py | Python | nsst_translate_corpus.py | AlexanderJenke/nsst | 75f6afa39568c72c9c513ac0313db33b80bb67d5 | [
"Apache-2.0"
] | null | null | null | nsst_translate_corpus.py | AlexanderJenke/nsst | 75f6afa39568c72c9c513ac0313db33b80bb67d5 | [
"Apache-2.0"
] | null | null | null | nsst_translate_corpus.py | AlexanderJenke/nsst | 75f6afa39568c72c9c513ac0313db33b80bb67d5 | [
"Apache-2.0"
] | null | null | null | from argparse import ArgumentParser
from tqdm import tqdm
import NSST
from nsst_translate import best_transition_sequence
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("--nsst_file", default="output/nsst_tss20_th4_nSt100_Q0.pkl", help="nsst file")
parser.add_argument("--src_lang", default="output/europarl-v7.de-en.de.clean")
parser.add_argument("--tgt_lang", default="output/europarl-v7.de-en.en.clean")
parser.add_argument("--enforce_n_reg", default=True)
parser.add_argument("--output", default=f"output/nsst_stat_nreg_100Q0.csv")
args = parser.parse_args()
args.enforce_n_final_reg = False
# load NSST
nsst = NSST.NSST()
nsst.load(args.nsst_file)
args.nsst = nsst
# open files
src_file = open(args.src_lang, 'r')
tgt_file = open(args.tgt_lang, 'r')
output_file = open(args.output, 'w')
# iterate over sentences, first 4096 -> test sentences
for src, tgt, _ in tqdm(list(zip(src_file, tgt_file, range(4096))), desc="Processing sentences"):
# remove line breaks
src = src[:-1]
tgt = tgt[:-1]
# try to translate
try:
# prepare tokenisations
token_src = [nsst.tokenization_src[word] if word in nsst.tokenization_src else 0
for word in src.split(" ") if len(word)]
token_tgt = [nsst.tokenization_tgt[word] if word in nsst.tokenization_tgt else 0
for word in tgt.split(" ") if len(word)]
# run nsst
args.input = src
args.token_src = token_src
result = best_transition_sequence(args)
# get best result
pred = sorted((k for k in result
if ('Qf' in args.nsst_file or not args.enforce_n_final_reg or len(k[1]) == 1)
and ('Q0' in args.nsst_file or k[0] == -1)
),
key=lambda x: x[2],
reverse=True)[0]
n_res = len(result)
q, reg, prob = pred
# write to csv
if not len(reg): # catch empty registers
continue
token_pred = [w for w in reg[0].split(' ') if len(w)]
pred_str = ""
for t in token_pred:
pred_str += f"{nsst.tokenization_tgt_lut[int(t)]} "
token_src_str = ""
for t in token_src:
token_src_str += f"{t} "
token_tgt_str = ""
for t in token_tgt:
token_tgt_str += f"{t} "
token_pred_str = ""
for t in token_pred:
token_pred_str += f"{t} "
print(f"{src};{token_src_str[:-1]};"
f"{tgt};{token_tgt_str[:-1]};"
f"{pred_str};{token_pred_str[:-1]};"
f"{prob};{len(reg)};{n_res}",
file=output_file)
output_file.flush()
except RuntimeError:
pass
# close files
src_file.close()
tgt_file.close()
output_file.close()
| 32.852632 | 104 | 0.544056 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 681 | 0.218199 |
6afa2508436ad02c7fe767127789a72b5fa053d8 | 382 | py | Python | 10 Days of Statistics/Day 1/Standard Deviation.py | dhyanpatel110/HACKERRANK | 949b1ff468ff3487663bf063a8fe6cdfb9dea26b | [
"Apache-2.0"
] | null | null | null | 10 Days of Statistics/Day 1/Standard Deviation.py | dhyanpatel110/HACKERRANK | 949b1ff468ff3487663bf063a8fe6cdfb9dea26b | [
"Apache-2.0"
] | null | null | null | 10 Days of Statistics/Day 1/Standard Deviation.py | dhyanpatel110/HACKERRANK | 949b1ff468ff3487663bf063a8fe6cdfb9dea26b | [
"Apache-2.0"
] | null | null | null | # Import library
import math
# Define functionts
def mean(data):
return sum(data) / len(data)
def stddev(data, size):
sum = 0
for i in range(size):
sum = sum + (data[i] - mean(data)) ** 2
return math.sqrt(sum / size)
# Set data
size = int(input())
numbers = list(map(int, input().split()))
# Get standard deviation
print(round(stddev(numbers, size), 1))
| 19.1 | 47 | 0.63089 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 69 | 0.180628 |
6afb588f82055ac18339fc17c00162ed0a0496d8 | 314 | py | Python | Homework/Hw4/Solution/problem5a.py | jmsevillam/Herramientas-Computacionales-UniAndes | 957338873bd6a17201dfd4629c7edd5760e2271d | [
"MIT"
] | null | null | null | Homework/Hw4/Solution/problem5a.py | jmsevillam/Herramientas-Computacionales-UniAndes | 957338873bd6a17201dfd4629c7edd5760e2271d | [
"MIT"
] | null | null | null | Homework/Hw4/Solution/problem5a.py | jmsevillam/Herramientas-Computacionales-UniAndes | 957338873bd6a17201dfd4629c7edd5760e2271d | [
"MIT"
] | 5 | 2019-05-27T13:35:51.000Z | 2020-09-30T15:19:39.000Z | def decode(word1,word2,code):
if len(word1)==1:
code+=word1+word2
return code
else:
code+=word1[0]+word2[0]
return decode(word1[1:],word2[1:],code)
Alice='Ti rga eoe esg o h ore"ermetsCmuainls'
Bob='hspormdcdsamsaefrtecus Hraina optcoae"'
print(decode(Alice,Bob,''))
| 24.153846 | 47 | 0.630573 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 82 | 0.261146 |
6afbbfc0a8b4d96b676b80363b2e541af846b662 | 7,415 | py | Python | pychron/lasers/power/composite_calibration_manager.py | ASUPychron/pychron | dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76 | [
"Apache-2.0"
] | 31 | 2016-03-07T02:38:17.000Z | 2022-02-14T18:23:43.000Z | pychron/lasers/power/composite_calibration_manager.py | ASUPychron/pychron | dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76 | [
"Apache-2.0"
] | 1,626 | 2015-01-07T04:52:35.000Z | 2022-03-25T19:15:59.000Z | pychron/lasers/power/composite_calibration_manager.py | UIllinoisHALPychron/pychron | f21b79f4592a9fb9dc9a4cb2e4e943a3885ededc | [
"Apache-2.0"
] | 26 | 2015-05-23T00:10:06.000Z | 2022-03-07T16:51:57.000Z | # ===============================================================================
# Copyright 2012 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================
# ============= enthought library imports =======================
from __future__ import absolute_import
from traits.api import HasTraits, Instance, DelegatesTo, Button, List, Any, Float
from traitsui.api import View, Item, VGroup, HGroup, Group, spring, TabularEditor
# ============= standard library imports ========================
import pickle
import os
from numpy import polyval
# ============= local library imports ==========================
from pychron.managers.manager import Manager
from pychron.database.selectors.power_calibration_selector import (
PowerCalibrationSelector,
)
from pychron.database.adapters.power_calibration_adapter import PowerCalibrationAdapter
from pychron.paths import paths
from pychron.graph.graph import Graph
from pychron.hardware.meter_calibration import MeterCalibration
"""
use a dbselector to select data
"""
class BoundsSelector(HasTraits):
graph = Instance(Graph)
def traits_view(self):
v = View(
Item("graph", show_label=False, style="custom"),
buttons=["OK", "Cancel"],
kind="livemodal",
)
return v
class CompositeCalibrationManager(Manager):
db = Instance(PowerCalibrationAdapter)
selector = Instance(PowerCalibrationSelector)
append = Button
replace = Button
load_graph = Button
save = Button
selected_calibrations = List
selected = Any
results = DelegatesTo("selector")
graph = Instance(Graph)
dclicked = Any
parent_name = "FusionsDiode"
power = Float
input = Float
def _power_changed(self):
pc = self._load_calibration()
pc
if pc is not None:
self.input, _ = pc.get_input(self.power)
def _load_calibration(self):
try:
p = self._get_calibration_path()
with open(p, "rb") as f:
pc = pickle.load(f)
except:
return
return pc
def _dclicked_changed(self):
s = self.selected
if s is not None:
s.bounds = None
s.load_graph()
s.graph.add_range_selector()
bc = BoundsSelector(graph=s.graph)
info = bc.edit_traits()
if info.result:
bounds = s.graph.plots[0].default_index.metadata["selections"]
s.bounds = bounds
s.calibration_bounds = (
polyval(s.coefficients, bounds[0]),
polyval(s.coefficients, bounds[1]),
)
def _append_fired(self):
s = self.selector.selected
if s is not None:
for si in s:
trs = list(si.traits().keys()).remove("graph")
self.selected_calibrations.append(si.clone_traits(traits=trs))
def _replace_fired(self):
s = self.selector.selected
trs = list(s.traits().keys()).remove("graph")
self.selected_calibrations = s.clone_traits(traits=trs)
def _save_fired(self):
self._dump_calibration()
def _dump_calibration(self):
pc = MeterCalibration()
coeffs = []
bounds = []
for s in self.selected_calibrations:
coeffs.append(s.coefficients)
bounds.append(s.calibration_bounds)
pc.coefficients = coeffs
pc.bounds = bounds
p = self._get_calibration_path()
self.info("saving calibration to {}".format(p))
with open(p, "wb") as f:
pickle.dump(pc, f)
def _get_calibration_path(self):
p = os.path.join(
paths.hidden_dir, "{}_power_calibration".format(self.parent_name)
)
return p
def _load_graph_fired(self):
g = self.graph
g.clear()
# g.new_plot(zoom=True, pan=True,
# padding=[40, 10, 10, 40]
# )
has_bounds = False
for i, s in enumerate(self.selected_calibrations):
if s.bounds:
has_bounds = True
elif has_bounds:
g.clear()
self._plot_factory(g)
self.warning_dialog("{} does not have its bounds set".format(s.rid))
break
s.load_graph(graph=g, new_plot=i == 0)
g.redraw()
def traits_view(self):
selector_grp = Group(Item("selector", style="custom", show_label=False))
transfer_grp = VGroup(
spring,
VGroup(Item("append", show_label=False), Item("replace", show_label=False)),
spring,
)
editor = TabularEditor(
adapter=self.selector.tabular_adapter(),
editable=False,
dclicked="object.dclicked",
selected="object.selected",
)
selected_grp = Item("selected_calibrations", editor=editor, show_label=False)
data_tab = Group(
HGroup(selector_grp, transfer_grp, selected_grp),
show_border=True,
label="Data",
)
process_tab = Group(
HGroup(
Item("power"),
Item("input", format_str=" %0.3f ", style="readonly"),
spring,
Item("save", show_label=False),
Item("load_graph", show_label=False),
),
Item("graph", style="custom", show_label=False),
show_border=True,
label="Process",
)
v = View(
VGroup(data_tab, process_tab),
resizable=True,
title="Composite {} Power Calibration".format(self.parent_name),
)
return v
def _graph_default(self):
g = Graph(
container_dict={
# 'fill_padding':True,
# 'bgcolor':'red',
"padding": 5
}
)
self._plot_factory(g)
return g
def _plot_factory(self, graph):
graph.new_plot(
zoom=True,
pan=True,
padding=[50, 10, 10, 40],
xtitle="Setpoint (%)",
ytitle="Measured Power (W)",
)
def _db_default(self):
if self.parent_name == "FusionsDiode":
name = paths.diodelaser_db
else:
name = paths.co2laser_db
db = PowerCalibrationAdapter(name=name, kind="sqlite")
db.connect()
return db
def _selector_default(self):
return self.db._selector_factory()
if __name__ == "__main__":
ccm = CompositeCalibrationManager()
ccm.configure_traits()
# ============= EOF =============================================
| 30.142276 | 88 | 0.552124 | 5,650 | 0.761969 | 0 | 0 | 0 | 0 | 0 | 0 | 1,692 | 0.228186 |
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