content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from turtle import Screen
# Cannot inherit from Screen class in Turtle, so each Screen method must be recreated here
# PUBLIC METHODS (recreated Screen methods)
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from .user import Base, User | [
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] | 4 | 7 |
# encoding: utf-8
from __future__ import division
import sys
import os
import time
import datetime
import pandas as pd
import numpy as np
import math
CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))
ADD_PATH = "%s/../"%(CURRENT_DIR)
sys.path.append(ADD_PATH)
from tools.mail import MyEmail
from tools.html import html_with_style
DATA_PATH = "%s/../data/mysql" % (CURRENT_DIR)
send_str = ''
# load data for max_day
if __name__ == '__main__':
max_day = sys.argv[1]
max_day = int(max_day)
if max_day > 2:
title = '订单漏斗汇总'
df, day_list = load_data(max_day)
elif max_day == 2:
title = '订单漏斗监控'
df, day_list = load_data(max_day)
df = df.T
mail = MyEmail()
send_str += html_with_style(df) + '<br>'
to_list = ['[email protected]']
# to_list = ['[email protected]', '[email protected]']
title = title + day_list[0]
mail.sendemail(send_str, title , to_list)
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] | 2.183445 | 447 |
# 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.
import mock
from oslo_log import log as logging
from oslo_utils import uuidutils
import testtools
from neutron.common import utils as common_utils
from neutron.services.trunk.drivers.openvswitch.agent import trunk_manager
from neutron.services.trunk.drivers.openvswitch import utils
from neutron.tests.common import conn_testers
from neutron.tests.common import helpers
from neutron.tests.common import net_helpers
from neutron.tests.functional import base
from neutron.tests.functional import constants as test_constants
LOG = logging.getLogger(__name__)
VLAN_RANGE = set(range(test_constants.VLAN_COUNT))
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] | 3.471429 | 350 |
from starwars.resolvers import resolvers
from starwars.sdl import STARWARSTIFLETTE
__all__ = ["STARWARSTIFLETTE"]
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import json
import multiprocessing
import time
from rx.testing import marbles
m = marbles
from pyshared.core.ref import LocalSharedResourcesManager
from pyshared.core.ref import ResourcesManagerListenerAdapter
from pyshared.core.ref import default_command_mapper
from pyshared.core.rx import ReactiveSharedResourcesServer
from pyshared.core.rx import TCPServer
from pyshared.core.rx import TCPServerConnection
from pyshared.core.utils import map_debug, fdebug
from rx import Observable
from rx.concurrency import ThreadPoolScheduler
address = '0.0.0.0'
optimal_thread_count = multiprocessing.cpu_count() + 1
pool_scheduler = ThreadPoolScheduler(optimal_thread_count)
if __name__ == '__main__':
try:
server, manager = main()
except Exception as ex:
print(ex)
exit(-1)
try:
while True:
print('...')
time.sleep(1)
except KeyboardInterrupt as ex:
del manager
server.stop()
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"""This file summarizes functionality for the construction of ConstraintNet with
output-constraints in form of convex polytopes. It is possible to constrain
output-parts to different convex polytopes independently.
The functionality for modelling the output-constraints consists namely of:
- Functors to create a tensor representation g(s) of the constraint
parameter s. The functor can be selected via the option
opts.opts2constr_para_repr. E.g. opts.opts2constr_para_repr =
'opts2const_feat_planes' would call the function opts2const_feat_planes
which instantiate the functor ConstFeatPlanes. ConstFeatPlanes is then
used for g(s).
- Functors to tranform the constraint parameter s into a vertex
representation. The vertex representation consists of vertices which
describe the convex polytope(s). The functor can be selected via the option
opts.opts2constr_para_trf. E.g. opts.opts2constr_para_trf = 'opts2v_polys_bb' would call
the function opts2v_polys_bb which instantiate the functor VPolysBB.
VPolysBB creates the vertice representation for bounding box
constraints.
We define the following format for the vertex representation v_polys:
v_polys (list): [out_part_1, out_part_2, ...]
v_polys is a list with length equal to the number of output parts
which should be independently constrained. Each list element in
v_polys corresponds to one output part.
out_part_i (list): [v_convex_poly_1]
Each out_part_i in v_polys is a list of length 1 and the element
corresponds to the vertice representation for the convex polytope of
the constraint for this part. In future this list could be longer
than one to model non convex polytopes by a set of convex polytopes.
v_convex_poly_1 (torch tensor): shape (N, n_v, dim_v)
The vertice representation for a convex polytope is given by a torch
tensor with shape (N, n_v, dim_v). N is the batch size, n_v the
number of vertices and dim_v the dimension of the vertices. The
entries are given by the coordinates of the vertices.
- PyTorch modules for the constraint-guard layer, i.e. the mapping from the
intermediate representation z to the constrained output region. The
module can be selected via the option opts.opts2constr_guard_layer. For
the considered convex polytope constraints in this file, the PyTorch
module "Polys" can be selected via opts.opts2constr_guard_layer =
'opts2polys'. Polys constrains different output parts to different
convex polytopes. For each output part, the number of vertices of the
convex polytope must be added to opts.polys_convex_polys_v_n, the
dimension of the vertices to opts.polys_convex_polys_v_dim and a 1 to
opts.polys_output_parts (in future non-convex polytopes might be defined
and then this number would be the number of convex polytopes it consists
of). E.g. consider an output-constraint consisting of three independent
constraints for three output parts. Furthermore, the constraint for the
first part is a convex polytope in 1d with 2 vertices, the constraint
for the second part is a convex polytope in 2d with 3 vertices and the
constraint for the third part is a convex polytope in 3d with 5
vertices. Then the options should be set to
opts.polys_convex_polys_v_n = [2, 3, 5]
opts.polys_convex_polys_v_dim = [1, 2, 3]
opts.polys_output_parts = [1, 1, 1]
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def opts2const_feat_planes(opts):
"""Creates ConstFeatPlanes functor by calling its constructor with
options from opts.
Args:
opts (obj): Namespace object with options.
Returns:
const_feat_planes (obj): Instantiated ConstFeatPlanes functor.
"""
return ConstFeatPlanes(
opts.const_feat_planes_h,
opts.const_feat_planes_w,
opts.const_feat_planes_n_channels,
opts.const_feat_planes_repeat_channels,
opts.const_feat_planes_norm_factor
)
class ConstFeatPlanes:
"""This functor generates a tensor representation g(s) of the constraint
parameter s.
Each component of the constraint parameter s corresponds to
<repeat_channels> channels of the generated tensor. The assigned channels
have entries with a constant value and are given by a constraint parameter
rescaled with a factor. The height and width of the tensor can be specified.
"""
def __init__(self, h, w, n_channels, repeat_channels=1, norm_factor=1.):
"""Initialization for setting parameters.
Args:
h (int): Height of channels.
w (int): Width of channels.
n_channels (int): Number of channels of generated tensor.
Specified number must match
n_channels = <length of constraint parameter) * repeat_channels
repeat_channels (int): The channel for a constraint parameter can be
replicated <repeat_channels> times.
norm_factor (float or list): Factor to normalize the values of the
tensor. If float, all constraint parameter components are
rescaled with this factor. If list, the length of the list must
match the number of constraint parameter components and the list
elements must be of type float. Each constraint parameter
component is then rescaled with the corresponding factor in the
list.
"""
self.h = h
self.w = w
self.n_channels = n_channels
self.repeat_channels = repeat_channels
#extract number of constraint parameter components
n_constr_para = int(n_channels / repeat_channels)
if not n_channels == n_constr_para * repeat_channels:
raise ValueError('Number of channels in constraint parameter \
tensor representation must be a \
multiple of repeat_channels. But n_channels={n_channels} \
and repeat_channels={repeat_channels}'.format(
n_channels = n_channels,
repeat_channels = repeat_channels)
)
#convert norm_factor scalar in list format
self.norm_factor = norm_factor
if isinstance(self.norm_factor, float):
norm_factor_value = self.norm_factor
self.norm_factor = []
for i in range(n_constr_para):
self.norm_factor.append(norm_factor_value)
if len(self.norm_factor)==1:
norm_factor_value = self.norm_factor[0]
self.norm_factor = []
for i in range(n_constr_para):
self.norm_factor.append(norm_factor_value)
if not len(self.norm_factor) * repeat_channels == n_channels:
raise ValueError('Number of norm factors for constr_para must \
match n_channels / repeat_channels. But \
len(norm_factor)={len_norm} is not equal to \
n_channels / repeat_channels = {n_channels} / \
{repeat_channels}'.format(
len_norm = len(self.norm_factor),
n_channels = n_channels,
repeat_channels = repeat_channels)
)
def __call__(self, constr_para):
"""Functor to create tensor representation g(s).
Args:
constr_para (obj): Pytorch tensor of shape (N, n_constr_para)
which specifies the output-constraint.
Returns:
constr_para_repr (obj): Pytorch tensor for tensor representation
g(s) of the constraint parameter with shape
(N, c_constr_para_repr, H, W).
"""
if not self.n_channels == constr_para.shape[1] * self.repeat_channels:
raise ValueError('Number of channels of the tensor representation \
of the constraint parameter must match with \
the number of constraint parameter components times \
repeat_channel. But n_channels={n_channels} is \
not equal to n_constr_para * repeat_channels = \
{n_constr_para} * {repeat_channels}'.format(
n_channels = self.n_channels,
n_constr_para = constr_para.shape[1],
repeat_channels = self.repeat_channels)
)
#create region feature tensor of correct shape
constr_para_repr = constr_para.new(
constr_para.shape[0],
self.n_channels,
self.h,
self.w
)
#fill the constr_features tensor with the normed constr_para
for i, sample in enumerate(constr_para):
for j, para in enumerate(sample):
j_in = j * self.repeat_channels
for l in range(self.repeat_channels):
constr_para_repr[i, j_in + l,:,:] = para * self.norm_factor[j]
return constr_para_repr
def opts2v_polys_bb_rel(opts):
"""Creates VPolysBbRel functor by calling its constructor with options
from opts.
Args:
opts (obj): Namespace object with options.
Returns:
v_polys_bb_rel (obj): Instantiated VPolysBbRel functor.
"""
return VPolysBbRel()
class VPolysBbRel:
"""This functor generates the vertex representation v_polys for bounding box
constraints in combination with constraints for the relative relations (The
eyes are above the nose and the left eye is in fact left with respect to the
right eye).
"""
def __call__(self, constr_para):
"""The functor gets the constraint parameter and generates the
corresponding vertices representation.
Args:
constr_para (obj): Torch tensor for the constraint
parameter with shape (N, n_constr_para=4). There are 4
constraint parameter components, they are ordered in the
following way (l_x, u_x, l_y, u_y). They encode the positions of
the boundaries of the bounding box:
l_x: left/ lower x
u_x: right/ upper x
l_y: upper/ lower y (y coordinates start with 0 at the top
of the image)
u_y: lower/ upper y (y coordinates start with 0 at the top
of the image)
Returns:
v_polys (obj): Vertex representation of the constraint parameter.
The output y for the neural network is ordered in the following
way: (x_nose, x_lefteye, y_righteye, y_lefteye, y_righteye,
y_nose)
"""
#1d polytope for x_nose
#shape poly_1d (N, n_v=2, dim_v=1), dim_vertices: x_nose
poly_1d = constr_para.new(constr_para.shape[0], 2, 1)
#v_1 = (l_x)
poly_1d[:, 0, 0] = constr_para[:, 0]
#v_2 = (u_x)
poly_1d[:, 1, 0] = constr_para[:, 1]
#2d polytope for x_lefteye and x_righteye
#shape (N, n_v=3, dim_v=2)
#dim_vertices: x_lefteye, x_righteye
poly_2d = constr_para.new(constr_para.shape[0], 3, 2)
#v_1 = (l_x, l_x)
poly_2d[:, 0, 0] = constr_para[:, 0]
poly_2d[:, 0, 1] = constr_para[:, 0]
#v_2 = (l_x, u_x)
poly_2d[:, 1, 0] = constr_para[:, 0]
poly_2d[:, 1, 1] = constr_para[:, 1]
#v_3 = (u_x, u_x)
poly_2d[:, 2, 0] = constr_para[:, 1]
poly_2d[:, 2, 1] = constr_para[:, 1]
#3-d polytope for y_lefteye, y_righteye and y_nose
#shape (N, n_v=5, dim_v=3)
#dim_vertices: y_lefteye, y_righteye, y_nose
poly_3d = constr_para.new(constr_para.shape[0], 5, 3)
#v_1 = (l_y, l_y, l_y)
poly_3d[:, 0, 0] = constr_para[:, 2]
poly_3d[:, 0, 1] = constr_para[:, 2]
poly_3d[:, 0, 2] = constr_para[:, 2]
#v_2 = (l_y, l_y, u_y)
poly_3d[:, 1, 0] = constr_para[:, 2]
poly_3d[:, 1, 1] = constr_para[:, 2]
poly_3d[:, 1, 2] = constr_para[:, 3]
#v_3 = (l_y, u_y, u_y)
poly_3d[:, 2, 0] = constr_para[:, 2]
poly_3d[:, 2, 1] = constr_para[:, 3]
poly_3d[:, 2, 2] = constr_para[:, 3]
#v_4 = (u_y, u_y, u_y)
poly_3d[:, 3, 0] = constr_para[:, 3]
poly_3d[:, 3, 1] = constr_para[:, 3]
poly_3d[:, 3, 2] = constr_para[:, 3]
#v_5 = (u_y, l_y, u_y)
poly_3d[:, 4, 0] = constr_para[:, 3]
poly_3d[:, 4, 1] = constr_para[:, 2]
poly_3d[:, 4, 2] = constr_para[:, 3]
v_polys = [[poly_1d,], [poly_2d,], [poly_3d,]]
return v_polys
def opts2v_polys_bb(opts):
"""Creates VPolysBb functor by calling its constructor with options
from opts.
Args:
opts (obj): Namespace object with options.
Returns:
v_polys_bb (obj): Instantiated VPolysBb functor.
"""
return VPolysBb(opts.lm_ordering_lm_order)
class VPolysBb:
"""This functor generates the vertex representation v_polys for bounding
box constraints, i.e. the landmarks for left eye, right eye and nose are
constrained to a bounding box.
"""
def __init__(self, lm_ordering_lm_order):
"""Initialization.
Args:
lm_ordering_lm_order (list): Order of the landmarks for the output
of the neural network. E.g. ['nose_x', 'lefteye_x', ... ].
"""
self.lm_ordering_lm_order = lm_ordering_lm_order
def __call__(self, constr_para):
"""The functor gets the constraint parameter and generates the
vertex representation.
Args:
constr_para (obj): Torch tensor containing the constraint
parameters with shape (N, n_constr_para=4). There are 4
constraint parameters, they are ordered in the following way
(l_x, u_x, l_y, u_y). They encode the positions of the
boundaries of the bounding box of the face detector:
l_x: left/ lower x
u_x: right/ upper x
l_y: upper/ lower y (y coordinates start with 0 at the top
of the image)
u_y: lower/ upper y (y coordinates start with 0 at the top
of the image)
Returns:
v_polys (obj): Vertice representation of the constraint parameters.
"""
v_polys = []
for lm in self.lm_ordering_lm_order:
if '_x' in lm:
poly_1d = constr_para.new(constr_para.shape[0], 2, 1)
# v_1 = (l_x)
poly_1d[:, 0, 0] = constr_para[:, 0]
# v_2 = (u_x)
poly_1d[:, 1, 0] = constr_para[:, 1]
elif '_y' in lm:
poly_1d = constr_para.new(constr_para.shape[0], 2, 1)
# v_1 = (l_y)
poly_1d[:, 0, 0] = constr_para[:, 2]
# v_2 = (u_y)
poly_1d[:, 1, 0] = constr_para[:, 3]
v_polys.append([poly_1d])
return v_polys
def opts2v_polys_2d_convex_poly(opts):
"""
parameters from opts.
Args:
opts (obj): Namespace object returned by parser with settings.
Returns:
opts2v_polys_lm_xy (obj): Instantiated VPolysLmXY functor.
"""
return VPolys2DConvexPoly()
class VPolys2DConvexPoly:
"""This functor generates the vertex representation v_polys for constraints
in form of one 2d-convex polytope.
We assume that the constraint parameter consists of concatenated vertices
coordinates (x0,y0, ..., xN,yN) of the convex polytope.
"""
def __call__(self, constr_para):
"""The functor gets the constraint parameter and generates the
corresponding vertices representation.
Args:
constr_para (obj): Torch tensor for the constraint
parameter with shape (N, n_constr_para). We assume that the
constraint parameter consists of concatenated vertices
coordinates (x0,y0, ..., xN,yN) of the convex polytope.
Returns:
v_polys (obj): Vertice representation of the constraint parameter.
The output dimensions: (x_lm, y_lm)
"""
#2-d polytope for landmark within triangle constraint
#shape (N, n_vertices, dim_vertices)
#dim_vertices: x, y
n_vertices = int(constr_para.shape[1] / 2)
poly_2d = constr_para.new(constr_para.shape[0], n_vertices, 2)
for v in range(n_vertices):
poly_2d[:, v, 0] = constr_para[:, 2*v]
poly_2d[:, v, 1] = constr_para[:, 2*v+1]
v_polys = [[poly_2d,]]
return v_polys
class ConvexPoly(nn.Module):
""" This nn.Module maps a latent vector in R^N to an output region defined
by a convex polytope with a fixed number of vertices. The shape of this
convex polytope is passed as additional input to this module.
"""
def __init__(self, convex_poly_format):
"""Informs the instance about the expected format of convex polytopes.
Args:
convex_poly_format (tuple): Tuple (n_v, dim_v) with two entries for
the number of vertices n_v of and for the dimension of the
convex polytope dim_v.
"""
super(ConvexPoly, self).__init__()
self.convex_poly_format = convex_poly_format
self.dim_z = self.convex_poly_format2dim_z(convex_poly_format)
self.dim_out = self.convex_poly_format2dim_out(convex_poly_format)
@staticmethod
def convex_poly_format2dim_z(convex_poly_format):
"""Extracts the required dimensions for the intermediate variable z from
convex_poly_format.
Args:
convex_poly_format (tuple): Tuples (n_v, dim_v) with number of
vertices and number of dimensions of convex polytope.
Returns:
dim_out (int): Number of output dimensions for given
convex_poly_format.
"""
return convex_poly_format[0]
@staticmethod
def convex_poly_format2dim_out(convex_poly_format):
"""Extracts the number of output dimensions from convex_poly_format.
Args:
convex_poly_format (tuple): Tuples (n_v, dim_v) with number of
vertices and number of dimensions of convex polytope.
Returns:
dim_out (int): Number of output dimensions for given
convex_poly_format.
"""
return convex_poly_format[1]
@staticmethod
def v_convex_poly2convex_poly_format(v_convex_poly):
"""Extract the convex_poly_format from vertex representation of convex
polytope.
Args:
v_convex_poly (obj): Torch tensor representing the vertex
representation of the convex polytope.
Returns:
v_convex_poly_format (tuple): Tuple of the number of vertices and
the dimension (n_v, dim_v).
"""
n_v = v_convex_poly.shape[1]
dim_v = v_convex_poly.shape[2]
return (n_v, dim_v)
def forward(self, z, v_convex_poly):
"""
Args:
z (obj): Torch tensor with latent representation. Shape
(N, n_v)
v_convex_poly (obj): Pytorch tensor with convex polytope
representation. Shape (N, n_v, dim_v).
Returns:
out (obj): Torch tensor with shape (N, dim_v). Each output is
within convex polytope specified by v_convex_poly.
"""
#check convex_poly_format
obs_convex_poly_format = self.v_convex_poly2convex_poly_format(v_convex_poly)
if not self.convex_poly_format == obs_convex_poly_format:
raise TypeError('Expected convex_poly_format does not match \
observed one.')
if not z.shape[1] == self.dim_z:
raise TypeError('Expected {z_dim} dimensions for latent \
representation but observed {z_dim_nn}.'.format(
z_dim = self.dim_z,
z_dim_nn = z.shape[1])
)
#shape of z: (N, n_v)
p = F.softmax(z)
#change shape to: (N, 1, n_v)
p = p.view(
p.shape[0],
-1,
p.shape[1]
)
#p(N, 1, n_v) * v_convex_poly (N, n_v, dim_v)
#= out (N, 1, dim_v)
out = torch.bmm(p, v_convex_poly)
#out (N, dim_v)
out = out.view(out.shape[0], -1)
#check output dimensions
if not out.shape[1] == self.dim_out:
raise TypeError('Expected {dim_out} output dimensions but observed \
{dim_out_nn}.'.format(
dim_out = self.dim_out,
dim_out_nn = out.shape[1])
)
return out
class Poly(nn.Module):
"""This nn.Module maps an intermediate variable z to an output region in
form of a polytope which is defined by several convex polytopes.
The shape of the non convex polytope is passed by a number of convex
polytopes as additional input.
Note: The functionality for non-convex polytopes is not considered in the
paper and focus of future research.
"""
def __init__(self, poly_format):
"""Generates information about the required dimension of the
intermediate variable z and the output dimension via poly_format.
Args:
poly_format (list): List of tuples (n_v, dim_v) for each convex
polytope which is part of the total polytope.
"""
super(Poly, self).__init__()
#ConvexPoly nn.Module is used for several polytopes
self.convex_polys = []
for convex_poly_format in poly_format:
self.convex_polys.append(ConvexPoly(convex_poly_format))
self.poly_format = poly_format
#number of convex polytopes
self.n_convex_poly = self.poly_format2n_convex_poly(poly_format)
#expected dimension of the latent representation
self.dim_z = self.poly_format2dim_z(poly_format)
#expected dimensions of the output
self.dim_out = self.poly_format2dim_out(poly_format)
if self.n_convex_poly == 0:
raise TypeError('Polytope must be constructed by at least one \
convex polytope.')
@staticmethod
def poly_format2dim_z(poly_format):
"""Extracts the required dimensions of the intermediate variable z from
poly_format.
Args:
poly_format (list): List of tuples (n_v, dim_v) for each convex
polytope which is part of the total polytope.
Returns:
dim_z (int): Number of latent vector dimensions for given
poly_format.
"""
#dimension of the latent representation
dim_z = 0
for convex_poly_format in poly_format:
dim_z += ConvexPoly.convex_poly_format2dim_z(convex_poly_format)
#if the polytope is described by more than one convex polytope a
#softmax is added and
n_convex_poly = Poly.poly_format2n_convex_poly(poly_format)
if n_convex_poly > 1:
self.dim_z += n_convex_poly
return dim_z
@staticmethod
def poly_format2dim_out(poly_format):
"""Extracts the number of output dimensions from poly_format.
Args:
poly_format (list): List of tuples (n_v, dim_v) for each convex
polytope which is part of the total polytope.
Returns:
dim_out (int): Number of output dimensions for given poly_format.
"""
#dimensions of the output
dim_out = 0
for convex_poly_format in poly_format:
dim_out += ConvexPoly.convex_poly_format2dim_out(convex_poly_format)
#if the polytope is described by more than one convex polytope a
#softmax is added and
n_convex_poly = Poly.poly_format2n_convex_poly(poly_format)
if n_convex_poly > 1:
self.dim_out += n_convex_poly
return dim_out
@staticmethod
def poly_format2n_convex_poly(poly_format):
"""Extracts the number of convex polytopes from poly_format.
Args:
poly_format (list): List of tuples (n_v, dim_v) for each convex
polytope which is part of the total polytope.
Returns:
n_convex_poly (int): Number of convex polytopes for given
poly_format.
"""
return len(poly_format)
@staticmethod
def v_poly2poly_format(v_poly):
"""Extract the polytope format from vertex description of several
convex polytopes.
Args:
v_poly (list): List of convex polytope vertice representations
which describe an eventually non-convex polytope. v_poly
consists of torch tensor elements.
Returns:
poly_format (list): List of tuples (N, n_v, dim_v) of convex
polytopes.
"""
poly_format = []
for v_convex_poly in v_poly:
convex_poly_format = ConvexPoly.v_convex_poly2convex_poly_format(v_convex_poly)
poly_format.append(convex_poly_format)
return poly_format
def forward(self, z, v_poly):
"""
Args:
z (obj): Torch tensor with intermediate representation with shape
(N,dim_z).
dim_z = sum of number of vertices of convex polytopes + number
of convex polytopes when this number is at least two.
v_poly (list): List of Torch tensors (N, n_v, dim_v) representing
convex polytopes. [ (), (), ...]
Returns:
out (obj): Torch tensor of shape (N, n_out). n_out = sum of
dimensions of each convex polytope + number of convex polytopes
when this number is at least two. Format is
(p_1, ..., p_K, y_1_1, .. y_1_L, ..., y_k_1, .. y_K_M)
p_1, ... p_K: probabilities for each convex polytope when
number of convex polytopes is greater equal 2. Otherwise these
probabilities are discarded. y_i_j: Coordinate j within convex
polytope i.
"""
if not z.shape[1] == self.dim_z:
raise TypeError('Dimension of latent representation in nn is \
{dim_z_nn} and required for polytope is {dim_z_poly}. \
They should be equal.'.format(
dim_z_nn = z.shape[1],
dim_z_poly = self.dim_z)
)
if not self.poly_format == self.v_poly2poly_format(v_poly):
raise TypeError('Expectet poly_format, i.e. number of convex \
polytopes, number of vertices and their dimensions, does \
not match with passed vertices representation of \
polytope.')
#add probabilities for each convex polytope to the output when number
#of them is greater or equal two.
out = z.new(z.shape[0], self.dim_out)
z_current_idx = 0
out_current_idx = 0
if self.n_convex_poly > 1:
#shape: (N, n_convex_poly)
out = F.softmax(z[:,0:self.n_convex_poly])
z_current_idx = self.n_convex_poly
out_current_idx = self.n_convex_poly
for i, convex_poly in enumerate(self.convex_polys):
v_convex_poly = v_poly[i]
out[:, out_current_idx: out_current_idx + convex_poly.dim_out] = \
convex_poly(
z[:, z_current_idx: z_current_idx + convex_poly.dim_z],
v_convex_poly)
z_current_idx += convex_poly.dim_z
out_current_idx += convex_poly.dim_out
return out
def opts2polys(opts):
"""Creates Polys nn.Modules by calling its constructor with options from
opts.
Args:
opts (obj): Namespace object with options.
Returns:
polys (obj): Instantiated Polys nn.Module.
"""
#e.g. poly_formats = [[(2,1)],[(3,2)],[(5,3)]]
#opts.polys_convex_polys_v_n = 2, 3, 5
#opts.polys_convex_polys_v_dim = 1, 2, 3
#opts.polys_output_parts = 1, 1, 1
if not len(opts.polys_convex_polys_v_n) == len(opts.polys_convex_polys_v_dim):
raise TypeError('Number of list elements in opts.polys_convex_polys_v_n \
and opts.polys_convex_polys_v_dim must be equal but is not.')
if not len(opts.polys_convex_polys_v_n) == len(opts.polys_output_parts):
raise TypeError('Number of list elements in opts.polys_convex_polys_v_n \
and opts.polys_output_parts must be equal but is not.')
poly_formats = []
current_idx = 0
for n_convex_polys in opts.polys_output_parts:
poly_format = []
for i_convex_poly in range(n_convex_polys):
v_n = opts.polys_convex_polys_v_n[i_convex_poly + current_idx]
v_dim = opts.polys_convex_polys_v_dim[i_convex_poly + current_idx]
poly_format.append((v_n, v_dim))
current_idx += n_convex_polys
poly_formats.append(poly_format)
print('Polys loaded as constraint-guard layer.')
return Polys(poly_formats)
class Polys(nn.Module):
"""Constraint-guard layer to constrain output-parts to polytopes. Currently
we consider only convex polytopes.
Different output parts are constrained to different polytopes
independently. These polytopes are passed to this functor as additional
input in the vertices format v_polys.
"""
def __init__(self, poly_formats):
"""Generates information about required dimensions for intermediate
representation and output dimensions.
Args:
poly_formats (list): List of poly_format objects (see Poly) for
the different polytopes of the output parts.
"""
super(Polys, self).__init__()
self.poly_formats = poly_formats
self.polys = []
for poly_format in self.poly_formats:
poly = Poly(poly_format)
self.polys.append(poly)
#expected number of dimensions for intermediate variable z
self.dim_z = self.poly_formats2dim_z(poly_formats)
#expected number of ouput dimensions
self.dim_out = self.poly_formats2dim_out(poly_formats)
@staticmethod
def poly_formats2dim_z(poly_formats):
"""Extracts the number of required dimensions of the intermediate
variable z from poly_formats.
Args:
poly_formats (list): List of poly_format objects (see Poly) for
the different polytopes of the output parts.
Returns:
dim_z (int): Number of required dimensions of intermediate variable.
"""
dim_z = 0
for poly_format in poly_formats:
dim_z += Poly.poly_format2dim_z(poly_format)
return dim_z
@staticmethod
def poly_formats2dim_out(poly_formats):
"""Extracts the number of output dimensions from poly_formats.
Args:
poly_formats (list): List of poly_format objects (see Poly) for
the different polytopes of the output parts.
Returns:
dim_out (int): Number of output dimensions for given poly_format.
"""
dim_out = 0
for poly_format in poly_formats:
dim_out += Poly.poly_format2dim_out(poly_format)
return dim_out
@staticmethod
def v_polys2poly_formats(v_polys):
"""Extract the polytope formats from vertex representation of several
polytopes.
Args:
v_polys (list): List of polytope vertex representations. v_polys
consists of list elements.
Returns:
poly_formats (list): List of poly_format elements.
"""
poly_formats = []
for v_poly in v_polys:
poly_format = Poly.v_poly2poly_format(v_poly)
poly_formats.append(poly_format)
return poly_formats
def forward(self, z, v_polys):
"""
Args:
z (obj): Torch tensor with latent representation. Shape (N, n_z).
v_polys (list): List with polytope description for different output
parts.
Returns:
out (obj): Torch tensor with
"""
#check correct shape of latent representation
if not z.shape[1] == self.dim_z:
raise TypeError('Dimension of intermediate representation z is \
{dim_z_nn}, but {dim_z} was expected.'.format(
dim_z_nn = z.shape[1],
dim_z = self.dim_z)
)
#check if v_polys maps with expected poly_formats
if not self.v_polys2poly_formats(v_polys) == self.poly_formats:
raise TypeError('Expected format of v_polys given by poly_formats \
does not match observed format inferred from v_polys. \n \
poly_formats: {poly_formats} \n \
v_polys2poly_formats(v_polys): {polys2polys_format}'.format(
poly_formats=self.poly_formats,
polys2polys_format=self.v_polys2poly_formats(v_polys)
))
#output tensor with required dimension
y = z.new(z.shape[0], self.dim_out)
z_current_idx = 0
y_current_idx = 0
for i, poly in enumerate(self.polys):
dim_z_i = poly.dim_z
dim_y_i = poly.dim_out
v_poly = v_polys[i]
y[:, y_current_idx: y_current_idx + dim_y_i] = \
poly(z[:, z_current_idx: z_current_idx + dim_z_i], v_poly)
z_current_idx += dim_z_i
y_current_idx += dim_y_i
return y
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220,
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220,
220,
220,
220,
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1441,
331,
198
] | 2.089081 | 16,558 |
import tkinter
from FireFoxCookieMonster import *
from tkinter import *
from tkinter.ttk import *
# simple function to retrieve the cookies into a dictionary
if __name__ == "__main__":
root = Tk()
# MainApplication(root).pack(side='top', fill='both', expand=True)
MainApplication(root)
root.mainloop()
| [
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628,
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220,
6808,
13,
12417,
26268,
3419,
198
] | 3.009174 | 109 |
#!/usr/bin/env python
import string
import sys
import mtbl
if __name__ == '__main__':
if not len(sys.argv) == 3:
sys.stderr.write('Usage: %s <TXT FILE> <MTBL FILE>\n' % sys.argv[0])
sys.exit(1)
main(sys.argv[1], sys.argv[2])
| [
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198
] | 2.032258 | 124 |
import numpy as np
from gnuradio import gr
| [
11748,
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] | 2.363636 | 22 |
# Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
import re
import pytest
import semver
from responsibleai._tools.shared.versions import CausalVersions
| [
2,
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] | 4.045455 | 44 |
import sys
import random
import h5pyd
import numpy as np
#
# Extracts a time series for the NCEP dataset
#
# choose random x,y coordinate for the time series
shape = (7850, 720, 1440)
x_index = random.randint(0, shape[2]-1)
y_index = random.randint(0, shape[1]-1)
end_index = shape[0]
if len(sys.argv) > 1:
if sys.argv[1] in ("-h", "--help"):
print("Usage python NCEP_TimeSeries.py [end_index] [x_index] [y_index]")
print(" end_index: [1,7850]")
print(" x_index: [0, 1439]")
print(" y_index: [0, 719]")
sys.exit(1)
end_index = int(sys.argv[1])
if len(sys.argv) > 2:
x_index = int(sys.argv[2])
if len(sys.argv) > 3:
y_index = int(sys.argv[3])
f = h5pyd.File("hdf5://shared/NASA/NCEP3/ncep3.he5", "r")
tair2m = f["/HDFEOS/GRIDS/NCEP/Data Fields/Tair_2m"]
fill_value = tair2m.attrs['_FillValue'][0]
print(f"Getting time series at point ({x_index}, {y_index}) for slices: 0-{end_index}")
tseries = tair2m[0:end_index, y_index, x_index]
non_fill = tseries[tseries != fill_value]
if len(non_fill) == 0:
print("no non_fill values returned!")
sys.exit(1)
print("done!")
tseries_mean = np.mean(non_fill)
tseries_min = np.min(non_fill)
tseries_max = np.max(non_fill)
print(f"Mean: {tseries_mean:.2f} Min: {tseries_min:.2f} Max: {tseries_max:.2f}")
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7,
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1,
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220,
628,
628
] | 2.101881 | 638 |
from django.conf import settings
import os
RAVEPAY_SECRET_KEY = getattr(
settings, "RAVEPAY_SECRET_KEY", os.getenv("RAVEPAY_SECRET_KEY", "")
)
RAVEPAY_PUBLIC_KEY = getattr(
settings, "RAVEPAY_PUBLIC_KEY", os.getenv("RAVEPAY_PUBLIC_KEY", "")
)
ALLOWED_HOSTS = getattr(settings, "ALLOWED_HOSTS", [])
RAVEPAY_WEBHOOK_DOMAIN = getattr(settings, "RAVEPAY_WEBHOOK_DOMAIN", None)
if RAVEPAY_WEBHOOK_DOMAIN:
ALLOWED_HOSTS.append(RAVEPAY_WEBHOOK_DOMAIN)
RAVEPAY_FAILED_URL = getattr(settings, "RAVEPAY_FAILED_URL", "ravepay:failed_page")
RAVEPAY_SUCCESS_URL = getattr(settings, "RAVEPAY_SUCCESS_URL", "ravepay:success_page")
TEST_RAVEPAY_API_URL = "https://ravesandboxapi.flutterwave.com/flwv3-pug/getpaidx"
RAVEPAY_API_URL = "https://api.ravepay.co/flwv3-pug/getpaidx"
RAVEPAY_MODAL_TITLE = getattr(
settings, "RAVEPAY_MODAL_TITLE", os.getenv("RAVEPAY_MODAL_TITLE", "Test Account")
)
RAVEPAY_MODAL_LOGO = getattr(
settings, "RAVEPAY_MODAL_LOGO", os.getenv("RAVEPAY_MODAL_LOGO", "")
)
RAVEPAY_LIB_MODULE = getattr(settings, "RAVEPAY_LIB_MODULE", "ravepay.utils")
RAVEPAY_WEBHOOK_HASH = getattr(settings, "RAVEPAY_WEBHOOK_HASH", "DJANGO_RAVEPAY")
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628
] | 2.130515 | 544 |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
""" Test EOH """
import unittest
from test.aqua.common import QiskitAquaTestCase
from qiskit import BasicAer
from qiskit.aqua.operators import MatrixOperator
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.components.initial_states import Custom
from qiskit.aqua.algorithms import EOH
class TestEOH(QiskitAquaTestCase):
"""Evolution tests."""
def test_eoh(self):
""" EOH test """
size = 2
aqua_globals.random_seed = 0
temp = aqua_globals.random.random_sample((2 ** size, 2 ** size))
h_1 = temp + temp.T
qubit_op = MatrixOperator(matrix=h_1)
temp = aqua_globals.random.random_sample((2 ** size, 2 ** size))
h_1 = temp + temp.T
evo_op = MatrixOperator(matrix=h_1)
state_in = Custom(size, state='random')
evo_time = 1
num_time_slices = 100
eoh = EOH(qubit_op, state_in, evo_op, evo_time=evo_time, num_time_slices=num_time_slices)
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, shots=1)
# self.log.debug('state_out:\n\n')
ret = eoh.run(quantum_instance)
self.log.debug('Evaluation result: %s', ret)
if __name__ == '__main__':
unittest.main()
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198
] | 2.505587 | 716 |
"""dataset_add_created_at
Revision ID: 26e723d032b1
Revises: 30474ebed7a2
Create Date: 2022-03-16 15:50:00.805743
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = "26e723d032b1"
down_revision = "30474ebed7a2"
branch_labels = None
depends_on = None
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from rest_framework import serializers
from rest_framework.serializers import HyperlinkedIdentityField
from users.models import User
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import cv2
import numpy as np
img_path = r'C:\Users\evan\Documents\GitHub\OCR-Project\BAKR\Crop_IMG__1651113545.png'
import cv2;
import numpy as np;
# Read image
image = cv2.imread(img_path)
cv2.waitKey(0)
# Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Find Canny edges
edged = cv2.Canny(gray, 20, 20)
cv2.waitKey(0)
# Finding Contours
# Use a copy of the image e.g. edged.copy()
# since findContours alters the image
contours, hierarchy = cv2.findContours(edged,
cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Threshold.
# Set values equal to or above 220 to 0.
# Set values below 220 to 255.
th, im_th = cv2.threshold(image, 220, 255, cv2.THRESH_BINARY_INV);
# Copy the thresholded image.
im_floodfill = im_th.copy()
# Mask used to flood filling.
# Notice the size needs to be 2 pixels than the image.
h, w = im_th.shape[:2]
mask = np.zeros((h+2, w+2), np.uint8)
# Floodfill from point (0, 0)
cv2.floodFill(im_floodfill, mask, (0,0), 255);
# Invert floodfilled image
im_floodfill_inv = cv2.bitwise_not(im_floodfill)
# Combine the two images to get the foreground.
im_out = im_th | im_floodfill_inv
# Display images.
cv2.imshow("Thresholded Image", im_th)
cv2.imshow("Floodfilled Image", im_floodfill)
cv2.imshow("Inverted Floodfilled Image", im_floodfill_inv)
cv2.imshow("Foreground", im_out)
cv2.waitKey(0) | [
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] | 2.42029 | 552 |
# encoding: utf8
#
# Copyright (C) 2014 Google Inc.
#
# This file is part of ycmd.
#
# ycmd is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# ycmd is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ycmd. If not, see <http://www.gnu.org/licenses/>.
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
# Not installing aliases from python-future; it's unreliable and slow.
from builtins import * # noqa
from ycmd.utils import ( ByteOffsetToCodepointOffset,
CodepointOffsetToByteOffset,
ToUnicode,
ToBytes,
SplitLines )
from ycmd.identifier_utils import StartOfLongestIdentifierEndingAtIndex
from ycmd.request_validation import EnsureRequestValid
# TODO: Change the custom computed (and other) keys to be actual properties on
# the object.
def CompletionStartColumn( line_value, column_num, filetype ):
"""Returns the 1-based byte index where the completion query should start.
So if the user enters:
foo.bar^
with the cursor being at the location of the caret (so the character *AFTER*
'r'), then the starting column would be the index of the letter 'b'.
NOTE: if the line contains multi-byte characters, then the result is not
the 'character' index (see CompletionStartCodepoint for that), and therefore
it is not safe to perform any character-relevant arithmetic on the result
of this method."""
return CodepointOffsetToByteOffset(
ToUnicode( line_value ),
CompletionStartCodepoint( line_value, column_num, filetype ) )
def CompletionStartCodepoint( line_value, column_num, filetype ):
"""Returns the 1-based codepoint index where the completion query should
start. So if the user enters:
ƒøø.∫å®^
with the cursor being at the location of the caret (so the character *AFTER*
'®'), then the starting column would be the index of the character '∫'
(i.e. 5, not its byte index)."""
# NOTE: column_num and other numbers on the wire are byte indices, but we need
# to walk codepoints for identifier checks.
codepoint_column_num = ByteOffsetToCodepointOffset( line_value, column_num )
unicode_line_value = ToUnicode( line_value )
# -1 and then +1 to account for difference betwen 0-based and 1-based
# indices/columns
codepoint_start_column = StartOfLongestIdentifierEndingAtIndex(
unicode_line_value, codepoint_column_num - 1, filetype ) + 1
return codepoint_start_column
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"""CertDB is a database managing X.509 certificates."""
__all__ = (
'CertDB',
'CertDBReadOnly',
'CertFileDB',
'CertFileDBReadOnly',
'CertNotAvailableError',
'CertInvalidError',
'CompositeCertDB',
'CompositeCertDBReadOnly',
)
__version__ = '1.1'
__author__ = 'Radim Podola'
from .cert_db import CertNotAvailableError, CertInvalidError, CertDB, CertDBReadOnly
from .cert_file_db import CertFileDBReadOnly, CertFileDB
from .composite_cert_db import CompositeCertDB, CompositeCertDBReadOnly
| [
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# -*- coding: utf-8 -*-
"""
``units`` classes manage the conversion of units for MPCPy variables. See
documentation on ``variables`` for more information.
"""
from abc import ABCMeta, abstractmethod
import numpy as np
#%% Display unit abstract interface
#%% Display unit quantity implementation
#%% Boolean display unit implementation
#%% Temperature display unit implementation
#%% Power display unit implementation
#%% Energy display unit implementation
#%% Power Flux display unit implementation
#%% Energy Intensity display unit implementation
#%% Pressure display unit implementation
#%% Dimensionless Ratio display unit implementation
#%% Angle display unit implementation
#%% Time display unit implementation
#%% Mass display unit implementation
#%% Length display unit implementation
#%% Area display unit implementation
#%% Volume display unit implementation
#%% Mass Flow display unit implementation
#%% Volumetric Flow display unit implementation
#%% Velocity display unit implementation
#%% Illuminance display unit implementation
#%% Luminance display unit implementation
#%% EnergyPrice unit implementation
#%% PowerPrice unit implementation
#%% Specific heat capacity unit implementation
#%% Heat capacity unit implementation
#%% Heat capacity coefficient unit implementation
#%% Heat resistance unit implementation
#%% Heat resistance coefficient unit implementation
#%% Heat transfer coefficient unit implementation
#%% Density unit implementation | [
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198,
2,
16626,
360,
6377,
4326,
7822,
220,
220,
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220,
220
] | 2.807692 | 650 |
import numpy as np
from pymgt.tindex import generate_directions
from pymgt.tindex import projection_index
from pymgt.tindex import Projectable
from pymgt.tindex import jarque_bera_index
from pymgt.tindex import shapiro_index
from pymgt.tindex import anderson_index
from pymgt.tindex import ks_index
| [
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] | 3.030303 | 99 |
import os
import unittest
from hone.utils import csv_utils
dirname = os.path.dirname(__file__)
csv_A_path = os.path.join(dirname, "data", "small_cats_dataset.csv")
csv_B_path = os.path.join(dirname, "data", "comma_test.csv")
small_csv = csv_utils.CSVUtils(csv_A_path)
comma_csv = csv_utils.CSVUtils(csv_B_path)
if __name__ == '__main__':
unittest.main()
| [
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] | 2.305732 | 157 |
import gym
import torch
import numpy as np
from networks import ActorNetwork
import argparse
from torch.autograd import Variable
if __name__=='__main__':
main() | [
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] | 3.543478 | 46 |
from wolverine.module import MicroModule
| [
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# -*- coding: utf-8 -*-
# Generated by Django 1.11.7 on 2018-03-20 13:35
from __future__ import unicode_literals
import django.core.validators
from django.db import migrations, models
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# -*- coding: utf-8 -*-
"""
Copyright (c) 2022 Colin Curtain
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.
Author: Colin Curtain (ccbogel)
https://github.com/ccbogel/QualCoder
"""
import datetime
import logging
import os
import shutil
import sqlite3
from PyQt5 import QtWidgets
from .helpers import Message
path = os.path.abspath(os.path.dirname(__file__))
logger = logging.getLogger(__name__)
def exception_handler(exception_type, value, tb_obj):
""" Global exception handler useful in GUIs.
tb_obj: exception.__traceback__ """
tb = '\n'.join(traceback.format_tb(tb_obj))
text = 'Traceback (most recent call last):\n' + tb + '\n' + exception_type.__name__ + ': ' + str(value)
print(text)
logger.error(_("Uncaught exception: ") + text)
QtWidgets.QMessageBox.critical(None, _('Uncaught Exception'), text)
class MergeProjects:
""" Merge one external Qualcoder project (source) database into existing project (destination).
Copies unmatched files from source project folders to destination project folders.
Adds new (unmatched) source categories to destination database.
Adds new (unmatched) source code names to destination database.
Adds journals and stored_sql to destination database, only if they have unique names,
Adds text codings, text annotations, image codings, av codings to destination database.
Adds cases and case_text (links to text file segments and images and A/V)
Add attributes for files and cases.
Existing attribute values in destination are not over-written, unless already blank
"""
app = None
path_d = "" # Path to destination project folder
conn_d = None
path_s = "" # Path to source project folder
conn_s = None
source_s = [] # source text from Source project
code_text_s = [] # coded text segments from Source project
annotations_s = [] # annotations from Source project
journals_s = []
stored_sql_s = []
summary_msg = ""
code_image_s = [] # coded image areas from Source project
code_av_s = [] # coded A/V segments from Source project
codes_s = [] # codes from Source project
categories_s = [] # code cats from Source project
attribute_types_s = [] # For new attributes that are not existing in the destination database
attributes_s = [] # values for Case and File attributes
cases_s = [] # cases
case_text_s = [] # case text and links to non-text files
projects_merged = False
def insert_categories(self):
""" Insert categories into destination code_cat table.
The categories have already been filtered to remove any names that match names
in the destination database.
"""
cur_d = self.conn_d.cursor()
# Insert top level categories
remove_list = []
for c in self.categories_s:
if c['supercatname'] is None:
self.summary_msg += _("Adding top level category: ") + c['name'] + "\n"
cur_d.execute("insert into code_cat (name,memo,owner,date,supercatid) values(?,?,?,?,?)",
(c['name'], c['memo'], c['owner'], c['date'], c['supercatid']))
self.conn_d.commit()
remove_list.append(c)
for item in remove_list:
self.categories_s.remove(item)
''' Add sub-categories. look at each unmatched category, iterate through
to add as child, then remove from the list '''
count = 0
while len(self.categories_s) > 0 and count < 1000:
remove_list = []
for c in self.categories_s:
# This needs to be repeated as it is changes
cur_d.execute("select catid from code_cat where name=?", [c['supercatname']])
res_category = cur_d.fetchone()
if res_category is not None:
remove_list.append(c)
sql = "insert into code_cat (name, memo, owner, date, supercatid) values (?,?,?,?,?)"
cur_d.execute(sql, [c['name'], c['memo'], c['owner'], c['date'], res_category[0]])
self.conn_d.commit()
self.summary_msg += _("Adding sub-category: " + c['name']) + " --> " + c['supercatname'] + "\n"
for item in remove_list:
self.categories_s.remove(item)
count += 1
if len(self.categories_s) > 0:
self.summary_msg += str(len(self.categories_s)) + _(" categories not added") + "\n"
print("Categories NOT added:\n", self.categories_s)
logger.debug("Categories NOT added:\n" + str(self.categories_s))
def update_code_cid_and_insert_code(self):
""" Update the cid to the one already in Destination.code_name.
Check for no matches and insert these into the Destination.code_name table.
"""
cur_d = self.conn_d.cursor()
cur_d.execute("select name, catid from code_cat")
dest_categories = cur_d.fetchall()
sql = "select cid, name from code_name"
cur_d.execute(sql)
res = cur_d.fetchall()
for code_dest in res:
for code_source in self.codes_s:
if code_source['name'] == code_dest[1]:
code_source['newcid'] = code_dest[0]
# Insert unmatched code names
for code_s in self.codes_s:
if code_s['newcid'] == -1:
# Fill category id using matching category name
for cat in dest_categories:
if cat[0] == code_s['catname']:
code_s['catid'] = cat[1]
cur_d.execute("insert into code_name (name,memo,owner,date,catid,color) values(?,?,?,?,?,?)",
(code_s['name'], code_s['memo'], code_s['owner'], code_s['date'], code_s['catid'],
code_s['color']))
self.conn_d.commit()
cur_d.execute("select last_insert_rowid()")
cid = cur_d.fetchone()[0]
code_s['newcid'] = cid
self.summary_msg += _("Adding code name: ") + code_s['name'] + "\n"
# Update code_text, code_image, code_av cids to destination values
for code_s in self.codes_s:
for coding_text in self.code_text_s:
if coding_text['cid'] == code_s['cid']:
coding_text['newcid'] = code_s['newcid']
for coding_image in self.code_image_s:
if coding_image['cid'] == code_s['cid']:
coding_image['newcid'] = code_s['newcid']
for coding_av in self.code_av_s:
if coding_av['cid'] == code_s['cid']:
coding_av['newcid'] = code_s['newcid']
def insert_coding_and_journal_data(self):
""" Coding fid and cid have been updated, annotation fid has been updated.
Insert code_text, code_image, code_av, journal and stored_sql data into Destination project. """
cur_d = self.conn_d.cursor()
# Earlier db versions did not have unique journal name
# Need to identify duplicate journal names and not import them
cur_d.execute("select name from journal")
j_names_res = cur_d.fetchall()
j_names = []
for j in j_names_res:
j_names.append(j[0])
for j in self.journals_s:
# Possible to have two identical journal names in earlier db versions
if j['name'] not in j_names:
cur_d.execute("insert into journal (name, jentry, date, owner) values(?,?,?,?)",
(j['name'], j['jentry'], j['date'], j['owner']))
self.summary_msg += _("Adding journal: ") + j['name'] + "\n"
self.conn_d.commit()
for s in self.stored_sql_s:
# Cannot have two identical stored_sql titles, using 'or ignore'
cur_d.execute("insert or ignore into stored_sql (title, description, grouper, ssql) values(?,?,?,?)",
(s['title'], s['description'], s['grouper'], s['ssql']))
self.conn_d.commit()
for c in self.code_text_s:
cur_d.execute("insert or ignore into code_text (cid,fid,seltext,pos0,pos1,owner,\
memo,date, important) values(?,?,?,?,?,?,?,?,?)", (c['newcid'], c['newfid'],
c['seltext'], c['pos0'], c['pos1'], c['owner'],
c['memo'], c['date'], c['important']))
self.conn_d.commit()
if len(self.code_text_s) > 0:
self.summary_msg += _("Merging coded text") + "\n"
for a in self.annotations_s:
cur_d.execute("insert or ignore into annotation (fid,pos0,pos1,memo,owner,date) values(?,?,?,?,?,?)",
[a["newfid"], a["pos0"], a["pos1"], a["memo"], a["owner"], a["date"]])
self.conn_d.commit()
if len(self.annotations_s) > 0:
self.summary_msg += _("Merging annotations") + "\n"
for c in self.code_image_s:
cur_d.execute(
"insert or ignore into code_image (cid, id,x1,y1,width,height,memo,owner,date,important) values(?,?,?,?,?,?,?,?,?,?)",
[c["newcid"], c["newfid"], c["x1"], c["y1"], c["width"], c["height"], c["memo"], c["owner"], c["date"],
c["important"]])
self.conn_d.commit()
if len(self.code_image_s) > 0:
self.summary_msg += _("Merging coded image areas") + "\n"
for c in self.code_av_s:
cur_d.execute(
"insert or ignore into code_av (cid, id,pos0,pos1,memo,owner,date,important) values(?,?,?,?,?,?,?,?)",
[c["newcid"], c["newfid"], c["pos0"], c["pos1"], c["memo"], c["owner"], c["date"], c["important"]])
self.conn_d.commit()
if len(self.code_av_s) > 0:
self.summary_msg += _("Merging coded audio/video segments") + "\n"
def insert_cases(self):
""" Insert case data into destination.
First remove all existing matching case names and the associated case text data.
"""
cur_d = self.app.conn.cursor()
# Remove all duplicate cases and case text lists from source data
cur_d.execute("select name from cases")
res_cases_dest = cur_d.fetchall()
existing_case_names = []
for r in res_cases_dest:
existing_case_names.append(r[0])
remove_case_list = []
for case_s in self.cases_s:
if case_s['name'] in existing_case_names:
remove_case_list.append(case_s)
removed_case_text_list = []
for removed_case in remove_case_list:
self.cases_s.remove(removed_case)
for case_text in self.case_text_s:
if case_text['caseid'] == removed_case['caseid']:
removed_case_text_list.append(case_text)
for removed_case_text in removed_case_text_list:
self.case_text_s.remove(removed_case_text)
# Insert new cases into destination
new_case_ids = []
for case_s in self.cases_s:
cur_d.execute("insert into cases (name, memo, owner, date) values (?,?,?,?)",
[case_s['name'], case_s['memo'], case_s['owner'], case_s['date']])
self.app.conn.commit()
cur_d.execute("select last_insert_rowid()")
case_id = cur_d.fetchone()[0]
case_s['newcaseid'] = case_id
new_case_ids.append(case_id)
self.summary_msg += _("Adding case: ") + case_s['name'] + "\n"
# Update newcaseid and newfid in case_text
for case_text in self.case_text_s:
for case_s in self.cases_s:
if case_s['caseid'] == case_text['caseid']:
case_text['newcaseid'] = case_s['newcaseid']
for file_ in self.source_s:
if case_text['fid'] == file_['newid']:
case_text['newfid'] = file_['newid']
# Insert case text if newfileid is not -1 and newcaseid is not -1
for c in self.case_text_s:
if c['newcaseid'] > -1 and c['newfid'] > -1:
cur_d.execute("insert into case_text (caseid,fid,pos0,pos1) values(?,?,?,?)",
[c['newcaseid'], c['newfid'], c['pos0'], c['pos1']])
self.app.conn.commit()
# Create attribute placeholders for the destination case attributes
now_date = datetime.datetime.now().astimezone().strftime("%Y-%m-%d %H:%M:%S")
sql_attribute_types = 'select name from attribute_type where caseOrFile ="case"'
cur_d.execute(sql_attribute_types)
res_attr_types = cur_d.fetchall()
sql_attribute = "insert into attribute (name, attr_type, value, id, date, owner) values(?,'file','',?,?,?)"
for id_ in new_case_ids:
for attribute_name in res_attr_types:
cur_d.execute(sql_attribute, [attribute_name[0], id_, now_date, self.app.settings['codername']])
self.app.conn.commit()
def insert_sources_get_new_file_ids(self):
""" Insert Source.source into Destination.source, unless source file name is already present.
update newfid in source_s and code_text_s.
Update the av_text_id link to link A/V to the corresponding transcript.
"""
new_source_file_ids = []
cur_d = self.conn_d.cursor()
for src in self.source_s:
cur_d.execute("select id, length(fulltext) from source where name=?", [src['name']])
res = cur_d.fetchone()
if res is not None:
# Existing same named source file is in the destination database
src['newid'] = res[0]
# Warn user if the source and destination fulltexts are different lengths
# Occurs if one of the texts was edited or replaced
if len(src['fulltext']) != res[1]:
msg = _("Warning! Inaccurate coding positions. Text lengths different for same text file: ")
msg += src['name'] + "\n"
msg += _("Import project file text length: ") + str(len(src['fulltext'])) + " "
msg += _("Destination project file text length: ") + str(res[1]) + "\n"
self.summary_msg += msg
else:
# To update the av_text_id after all new ids have been generated
cur_d.execute(
"insert into source(name,fulltext,mediapath,memo,owner,date, av_text_id) values(?,?,?,?,?,?,?)",
(src['name'], src['fulltext'], src['mediapath'], src['memo'], src['owner'], src['date'], None))
self.conn_d.commit()
cur_d.execute("select last_insert_rowid()")
id_ = cur_d.fetchone()[0]
src['newid'] = id_
new_source_file_ids.append(id_)
# Need to find matching av_text_filename to get its id to link as the av_text_id
for src in self.source_s:
if src['av_text_filename'] != "":
cur_d.execute("select id from source where name=?", [src['av_text_filename']])
res = cur_d.fetchone()
if res is not None:
cur_d.execute("update source set av_text_id=? where id=?", [res[0], src['id']])
self.conn_d.commit()
# Create attribute placeholders for the destination file attributes
now_date = datetime.datetime.now().astimezone().strftime("%Y-%m-%d %H:%M:%S")
sql_attribute_types = 'select name from attribute_type where caseOrFile ="file"'
cur_d.execute(sql_attribute_types)
res_attr_types = cur_d.fetchall()
sql_attribute = "insert into attribute (name, attr_type, value, id, date, owner) values(?,'file','',?,?,?)"
for id_ in new_source_file_ids:
for attribute_name in res_attr_types:
cur_d.execute(sql_attribute, [attribute_name[0], id_, now_date, self.app.settings['codername']])
self.app.conn.commit()
def update_coding_file_ids(self):
""" Update the file ids in the codings and annotations data. """
for src in self.source_s:
for c_text in self.code_text_s:
if c_text['fid'] == src['id']:
c_text['newfid'] = src['newid']
for an in self.annotations_s:
if an['fid'] == src['id']:
an['newfid'] = src['newid']
for c_img in self.code_image_s:
if c_img['fid'] == src['id']:
c_img['newfid'] = src['newid']
for c_av in self.code_av_s:
if c_av['fid'] == src['id']:
c_av['newfid'] = src['newid']
def copy_source_files_into_destination(self):
""" Copy source files into destination project.
Do not copy over existing files.
"""
folders = ["audio", "documents", "images", "video"]
for folder_name in folders:
dir_ = self.path_s + "/" + folder_name
files = os.listdir(dir_)
for f in files:
if not os.path.exists(self.app.project_path + "/" + folder_name + "/" + f):
try:
shutil.copyfile(dir_ + "/" + f, self.app.project_path + "/" + folder_name + "/" + f)
self.summary_msg += _("File copied: ") + f + "\n"
except shutil.SameFileError:
pass
except PermissionError:
self.summary_msg += f + " " + _("NOT copied. Permission error")
def insert_new_attribute_types(self):
""" Insert new attribute types for cases and files.
Insert placeholders for the new attribute types.
To be performed after Cases and files have been inserted.
"""
cur_d = self.app.conn.cursor()
cur_d.execute("select id from source")
res_file_ids = cur_d.fetchall()
cur_d.execute("select caseid from cases")
res_case_ids = cur_d.fetchall()
# Insert new attribute type and placeholder in attribute table
for a in self.attribute_types_s:
cur_d.execute("insert into attribute_type (name,date,owner,memo,caseOrFile, valuetype) values(?,?,?,?,?,?)",
(a['name'], a['date'], a['owner'], a['memo'], a['caseOrFile'], a['valuetype']))
self.app.conn.commit()
self.summary_msg += _("Adding attribute (") + a['caseOrFile'] + "): " + a['name'] + "\n"
# Create attribute placeholders for new attributes, does NOT create for existing destination attributes
if a['caseOrFile'] == "file":
for id_ in res_file_ids:
sql = "insert into attribute (name, value, id, attr_type, date, owner) values (?,?,?,?,?,?)"
cur_d.execute(sql, (a['name'], "", id_[0], "file", a['date'], a['owner']))
self.app.conn.commit()
if a['caseOrFile'] == "case":
for id_ in res_case_ids:
sql = "insert into attribute (name, value, id, attr_type, date, owner) values (?,?,?,?,?,?)"
cur_d.execute(sql, (a['name'], "", id_[0], "case", a['date'], a['owner']))
self.app.conn.commit()
def insert_attributes(self):
""" Insert new attribute values for files and cases.
Need to use destination file and case ids.
Example attribute:
{'name': 'age', 'attr_type': 'file', 'value': '100', 'id': 4, 'newid': -1, 'date': '2022-03-14 10:35:27', 'owner': 'default'}
"""
# Only update if value does not over-write an existing placeholder attribute value
sql_update = "update attribute set value=? where name=? and id=? and attr_type=? and value=''"
# Insert if a placeholder is missing
sql_insert = "insert into attribute (name,id,attr_type,value,date,owner) values (?,?,?,?,?,?)"
attribute_count = 0
cur_d = self.app.conn.cursor()
for a in self.attributes_s:
if a['attr_type'] == "file":
source_dict = next((item for item in self.source_s if item["id"] == a['id']), {'newid': -1})
a['newid'] = source_dict['newid']
if a['attr_type'] == "case":
case_dict = next((item for item in self.cases_s if item["caseid"] == a['id']), {'newcaseid': -1})
a['newid'] = case_dict['newcaseid']
# Only update or insert value does not over-write an existing placeholder attribute value
if a['newid'] != -1:
# Check placeholder exists, if not then insert values
cur_d.execute("select * from attribute where name=? and id=? and attr_type=?",
[a['name'], a['newid'], a['attr_type']])
res = cur_d.fetchall()
if not res:
cur_d.execute(sql_insert, (a['name'], a['newid'], a['attr_type'],a['value'], a['date'], a['owner']))
self.app.conn.commit()
attribute_count += 1
else:
cur_d.execute(sql_update, (a['value'], a['name'], a['newid'], a['attr_type']))
self.app.conn.commit()
attribute_count += 1
if attribute_count > 0:
self.summary_msg += _("Added attribute values for cases and files: n=") + str(attribute_count) + "\n"
def get_source_data(self):
""" Load the database data into Lists of Dictionaries.
return:
True or False if data was able to be loaded
"""
self.journals_s = []
self.stored_sql_s = []
self.codes_s = []
self.categories_s = []
self.code_text_s = []
self.annotations_s = []
self.code_image_s = []
self.code_av_s = []
self.cases_s = []
self.case_text_s = []
self.attribute_types_s = []
self.attributes_s = []
cur_s = self.conn_s.cursor()
# Database version must be v5 or higher
cur_s.execute("select databaseversion from project")
version = cur_s.fetchone()
if version[0] in ("v1", "v2", "v3", "v4"):
self.summary_msg += _("Need to update the source project database.") + "\n"
self.summary_msg += _("Please open the source project using QualCoder. Then close the project.") + "\n"
self.summary_msg += _("This will update the database schema. Then try merging again.")
self.summary_msg += _("Project not merged") + "\n"
return False
# Journal data
sql_journal = "select name, jentry, date, owner from journal"
cur_s.execute(sql_journal)
res_journals = cur_s.fetchall()
for i in res_journals:
src = {"name": i[0], "jentry": i[1], "date": i[2], "owner": i[3]}
self.journals_s.append(src)
# Stored sql data
sql_stored_sql = "select title, description, grouper, ssql from stored_sql"
cur_s.execute(sql_stored_sql)
res_stored_sqls = cur_s.fetchall()
for i in res_stored_sqls:
src = {"title": i[0], "description": i[1], "grouper": i[2], "ssql": i[3]}
self.stored_sql_s.append(src)
# Source data
sql_source = "select id, name, fulltext,mediapath,memo,owner,date,av_text_id from source"
cur_s.execute(sql_source)
res_source = cur_s.fetchall()
# Later update av_text_id
for i in res_source:
src = {"id": i[0], "newid": -1, "name": i[1], "fulltext": i[2], "mediapath": i[3], "memo": i[4],
"owner": i[5], "date": i[6], "av_text_id": i[7], "av_text_filename": ""}
self.source_s.append(src)
# The av_text_id is not enough to recreate linkages. Need the referenced text file name.
for i in self.source_s:
if i['av_text_id'] is not None:
cur_s.execute("select name from source where id=?", [i['av_text_id']])
res = cur_s.fetchone()
if res is not None:
i['av_text_filename'] = res[0]
# Category data
sql_codecats = "select catid, supercatid, name, memo, owner, date from code_cat"
cur_s.execute(sql_codecats)
res_codecats = cur_s.fetchall()
for i in res_codecats:
ccat = {"catid": i[0], "supercatid": i[1], "supercatname": None,
"name": i[2], "memo": i[3], "owner": i[4], "date": i[5], }
self.categories_s.append(ccat)
# Remove categories from the source list, that are already present in the destination database
cur_d = self.app.conn.cursor()
cur_d.execute("select name from code_cat")
res_dest_catnames = cur_d.fetchall()
dest_cat_names_list = []
for r in res_dest_catnames:
dest_cat_names_list.append(r[0])
temp_source_cats = []
for cat in self.categories_s:
if cat['name'] not in dest_cat_names_list:
temp_source_cats.append(cat)
self.categories_s = temp_source_cats
# Add reference to linked supercat using category name
for cat in self.categories_s:
cur_s.execute("select name from code_cat where catid=?", [cat['supercatid']])
res = cur_s.fetchone()
if res is not None:
cat['supercatname'] = res[0]
# Code data
sql_codenames = "select cid, name, memo, owner, date, color, catid from code_name"
cur_s.execute(sql_codenames)
res_codes = cur_s.fetchall()
for i in res_codes:
code_s = {"cid": i[0], "newcid": -1, "name": i[1], "memo": i[2], "owner": i[3], "date": i[4], "color": i[5],
"catid": i[6], "catname": None}
self.codes_s.append(code_s)
# Get and fill category name if code is in a category
for code_s in self.codes_s:
cur_s.execute("select name from code_cat where catid=?", [code_s['catid']])
res = cur_s.fetchone()
if res is not None:
code_s['catname'] = res[0]
# Code text data
sql_codetext = "select cid, fid, seltext, pos0, pos1, owner, date, memo, important from code_text"
cur_s.execute(sql_codetext)
res_codetext = cur_s.fetchall()
for i in res_codetext:
ct = {"cid": i[0], "newcid": -1, "fid": i[1], "newfid": -1, "seltext": i[2], "pos0": i[3], "pos1": i[4],
"owner": i[5], "date": i[6], "memo": i[7], "important": i[8]}
self.code_text_s.append(ct)
# Text annotations data
sql_annotations = "select fid, pos0, pos1, memo, owner, date from annotation"
cur_s.execute(sql_annotations)
res_annot = cur_s.fetchall()
for i in res_annot:
an = {"fid": i[0], "newfid": -1, "pos0": i[1], "pos1": i[2], "memo": i[3], "owner": i[4], "date": i[5]}
self.annotations_s.append(an)
# Code image data
sql_code_img = "select cid, id, x1, y1, width, height, memo, date, owner, important from code_image"
cur_s.execute(sql_code_img)
res_code_img = cur_s.fetchall()
for i in res_code_img:
cimg = {"cid": i[0], "newcid": -1, "fid": i[1], "newfid": -1, "x1": i[2], "y1": i[3],
"width": i[4], "height": i[5], "memo": i[6], "date": i[7], "owner": i[8], "important": i[9]}
self.code_image_s.append(cimg)
# Code AV data
sql_code_av = "select cid, id, pos0, pos1, owner, date, memo, important from code_av"
cur_s.execute(sql_code_av)
res_code_av = cur_s.fetchall()
for i in res_code_av:
c_av = {"cid": i[0], "newcid": -1, "fid": i[1], "newfid": -1, "pos0": i[2], "pos1": i[3],
"owner": i[4], "date": i[5], "memo": i[6], "important": i[7]}
self.code_av_s.append(c_av)
# Case data
sql_cases = "select caseid, name, memo, owner, date from cases"
cur_s.execute(sql_cases)
res_cases = cur_s.fetchall()
for i in res_cases:
c = {"caseid": i[0], "newcaseid": -1, "name": i[1], "memo": i[2], "owner": i[3], "date": i[4]}
self.cases_s.append(c)
sql_case_text = "select caseid, fid, pos0, pos1 from case_text"
cur_s.execute(sql_case_text)
res_case_text = cur_s.fetchall()
for i in res_case_text:
c = {"caseid": i[0], "newcaseid": -1, "fid": i[1], "newfid": -1, "pos0": i[2], "pos1": i[3]}
self.case_text_s.append(c)
# Attribute type data
sql_attr_type = "select name, memo, date, owner, caseOrFile, valuetype from attribute_type"
cur_s.execute(sql_attr_type)
res_attr_type_s = cur_s.fetchall()
keys = 'name', 'memo', 'date', 'owner', 'caseOrFile', 'valuetype'
temp_attribute_types_s = []
for row in res_attr_type_s:
temp_attribute_types_s.append(dict(zip(keys, row)))
# Remove matching attribute type names
cur_d = self.app.conn.cursor()
cur_d.execute("select name from attribute_type")
res_attr_name_dest = cur_d.fetchall()
attribute_names_dest = []
for r in res_attr_name_dest:
attribute_names_dest.append(r[0])
self.attribute_types_s = []
for r in temp_attribute_types_s:
if r['name'] not in attribute_names_dest:
self.attribute_types_s.append(r)
# Attribute data
sql_attributes = "select name, attr_type, value, id, date ,owner from attribute"
cur_s.execute(sql_attributes)
res_attributes = cur_s.fetchall()
for i in res_attributes:
attribute = {"name": i[0], "attr_type": i[1], "value": i[2], "id": i[3], "newid": -1, "date": i[4], "owner": i[5]}
self.attributes_s.append(attribute)
return True
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220,
220,
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220,
220,
220,
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1441,
6407,
198
] | 2.127876 | 14,647 |
import os
print("Hello",os.getenv('NAME'))
print("Your age is",os.getenv('AGE'))
| [
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] | 2.5625 | 32 |
import os
import re
import yaml
from .exceptions import MiuraException
import logging
logger = logging.getLogger(__name__)
def load_file_or_directory(path):
"""
given a path, determine if the path is a file or directory, and
yield a list of absolute file paths
"""
assert os.path.exists(path), "{0} does not exist!".format(path)
absolute_path = os.path.abspath(path)
if not os.path.isdir(path):
yield absolute_path
else:
for root, dirs, file_paths in os.walk(path):
for file_path in file_paths:
yield os.path.join(root, file_path)
def retrieve_data(file_paths):
"""
passed an iterable list of file_paths, loop through all of them and
generate a dictionary containing all the context
"""
data_dict = {}
for file_path in file_paths:
with open(file_path) as fh:
try:
content = yaml.load(fh.read())
except yaml.YAMLError as e:
raise MiuraException(
"Unable to parse yaml at {0}: \n {1}".format(
file_path,
str(e)
))
if not isinstance(content, dict):
raise MiuraException(
"{0} is does not translate to a dictionary!".format(file_path)
)
data_dict.update(content)
return data_dict
def filter_data(data, filter_dict):
""" filter a data dictionary for values only matching the filter """
for key, match_string in filter_dict.items():
if key not in data:
logger.warning("{0} doesn't match a top level key".format(key))
continue
values = data[key]
matcher = re.compile(match_string)
if isinstance(values, list):
values = [v for v in values if matcher.search(v)]
elif isinstance(values, dict):
values = dict((k, v) for k, v in values.items() if matcher.search(k))
else:
raise MiuraException("cannot filter a {0}".format(type(values)))
data[key] = values
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58,
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198
] | 2.203762 | 957 |
import ast
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
import os.path
import pandas as pd
import seaborn as sns
import operator as op
from functools import reduce
from setup import DIR # DIR to get data to plot
from setup import DIR2 # DIR
FILE_GENERAL = "output_file-"
FILE_GENERAL_CMP = "output_file_comparison-"
EXTENSION = ".out"
d = 0.05
FILE_2 = "output_file"
# binomial cofficient, n choosr r
# calculate binary entropy of Bernoulli(p)
# function to parse output file that contains both MGT and LP accuracy and time results
| [
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] | 3.131455 | 213 |
AUTH_USER_MODEL = 'users.User'
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
DJOSER = {
'HIDE_USERS': False,
'LOGIN_FIELD': 'email',
'SERIALIZERS': {
'user_create': 'users.api.serializers.UserCreateSerializer',
'user': 'users.api.serializers.UserSerializer',
'current_user': 'users.api.serializers.UserSerializer',
},
'PERMISSIONS': {
'user_list': ['rest_framework.permissions.AllowAny'],
'user': ['rest_framework.permissions.IsAuthenticated'],
},
}
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] | 2.328125 | 384 |
import re
import sys
import logging
from collections import Counter
# TODO use https://bitbucket.org/spirit/guess_language
# for language detection | [
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from datetime import timedelta
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.mgmt.media import AzureMediaServices
from azure.storage.blob import BlobServiceClient
from azure.mgmt.media.models import (
Asset,
Transform,
TransformOutput,
StandardEncoderPreset,
AacAudio,
AacAudioProfile,
H264Video,
H264Complexity,
H264Layer,
Mp4Format,
Filters,
Rectangle,
VideoOverlay,
Job,
JobInputs,
JobInputAsset,
JobOutputAsset,
OnErrorType,
Priority
)
import os
#Timer for checking job progress
import time
#Get environment variables
load_dotenv()
# Get the default Azure credential from the environment variables AZURE_CLIENT_ID and AZURE_CLIENT_SECRET and AZURE_TENTANT_ID
default_credential = DefaultAzureCredential()
# Get the environment variables SUBSCRIPTIONID, RESOURCEGROUP and ACCOUNTNAME
subscription_id = os.getenv('SUBSCRIPTIONID')
resource_group = os.getenv('RESOURCEGROUP')
account_name = os.getenv('ACCOUNTNAME')
# The file you want to upload. For this example, the file is placed under Media folder.
# The file ignite.mp4 has been provided for you.
source_file_location = os.chdir("../../Media/")
source_file = "ignite.mp4"
# This is a random string that will be added to the naming of things so that you don't have to keep doing this during testing
uniqueness = "encodeOverlayPng"
# Use the following PNG image to overlay on top of the video
overlay_file = "AzureMediaService.png"
overlay_label = "overlayCloud"
# Set the attributes of the input Asset using the random number
in_asset_name = 'inputassetName' + uniqueness
in_alternate_id = 'inputALTid' + uniqueness
in_description = 'inputdescription' + uniqueness
# Create an Asset object
# The asset_id will be used for the container parameter for the storage SDK after the asset is created by the AMS client.
in_asset = Asset(alternate_id=in_alternate_id, description=in_description)
# Create the JobInput for the PNG Image Overlay
overlay_asset_name = 'overlayassetName' + uniqueness
overlay_asset_alternate_id = 'inputALTid' + uniqueness
overlay_asset_description = 'inputdescription' + uniqueness
# Create an Asset object for PNG Image overlay
overlay_in_asset = Asset(alternate_id=overlay_asset_alternate_id, description=overlay_asset_description)
# Set the attributes of the output Asset using the random number
out_asset_name = 'outputassetName' + uniqueness
out_alternate_id = 'outputALTid' + uniqueness
out_description = 'outputdescription' + uniqueness
# Create Ouput Asset object
out_asset = Asset(alternate_id=out_alternate_id, description=out_description)
# The AMS Client
print("Creating AMS Client")
client = AzureMediaServices(default_credential, subscription_id)
# Create an input Asset
print(f"Creating input asset {in_asset_name}")
input_asset = client.assets.create_or_update(resource_group, account_name, in_asset_name, in_asset)
# An AMS asset is a container with a specific id that has "asset-" prepended to the GUID.
# So, you need to create the asset id to identify it as the container
# where Storage is to upload the video (as a block blob)
in_container = 'asset-' + input_asset.asset_id
# Create an Overlay input Asset
print(f"Creating input asset {overlay_asset_name}")
overlay_asset = client.assets.create_or_update(resource_group, account_name, overlay_asset_name, overlay_in_asset)
# # An AMS asset is a container with a specific id that has "asset-" prepended to the GUID.
# # So, you need to create the asset id to identify it as the container
# # where Storage is to upload the video (as a block blob)
overlay_container = 'asset-' + overlay_asset.asset_id
# create an output Asset
print(f"Creating output asset {out_asset_name}")
output_asset = client.assets.create_or_update(resource_group, account_name, out_asset_name, out_asset)
### Use the Storage SDK to upload the video ###
print(f"Uploading the file {source_file}")
blob_service_client = BlobServiceClient.from_connection_string(os.getenv('STORAGEACCOUNTCONNECTION'))
blob_client = blob_service_client.get_blob_client(in_container, source_file)
working_dir = os.getcwd()
print(f"Current working directory: {working_dir}")
upload_file_path = os.path.join(working_dir, source_file)
# WARNING: Depending on where you are launching the sample from, the path here could be off, and not include the BasicEncoding folder.
# Adjust the path as needed depending on how you are launching this python sample file.
# Upload the video to storage as a block blob
with open(upload_file_path, "rb") as data:
blob_client.upload_blob(data)
### Use the Storage SDK to upload the Overlay file
print(f"Uploading the file {overlay_file}")
blob_service_client = BlobServiceClient.from_connection_string(os.getenv('STORAGEACCOUNTCONNECTION'))
blob_client = blob_service_client.get_blob_client(overlay_container, overlay_file)
working_dir = os.getcwd()
print(f"Current working directory: {working_dir}")
upload_file_path = os.path.join(working_dir, overlay_file)
# WARNING: Depending on where you are launching the sample from, the path here could be off, and not include the BasicEncoding folder.
# Adjust the path as needed depending on how you are launching this python sample file.
# Upload the video to storage as a block blob
with open(upload_file_path, "rb") as data:
blob_client.upload_blob(data)
transform_name = 'H264EncodingOverlayImagePng'
# Create a new BuiltIn Standard encoding Transform for H264 ContentAware Constrained
print(f"Creating Standard Encoding transform named: {transform_name}")
# For this snippet, we are using 'StandardEncoderPreset' with Overlay Image
transform_output = TransformOutput(
preset = StandardEncoderPreset(
codecs=[
AacAudio(
channels=2,
sampling_rate=48000,
bitrate=128000,
profile=AacAudioProfile.AAC_LC
),
H264Video(
key_frame_interval=timedelta(seconds=2),
complexity=H264Complexity.BALANCED,
layers=[
H264Layer(
bitrate=3600000,
width="1280",
height="720",
label="HD-3600kbps"
),
H264Layer(
bitrate=1600000,
width="960",
height="540",
label="SD-1600kbps"
)
]
)
],
# Specify the format for the output files - one for video + audio, and another for the thumbnails
formats=[
Mp4Format(filename_pattern="Video-{Basename}-{Label}-{Bitrate}{Extension}")
],
filters=Filters(
overlays=[
VideoOverlay(
input_label=overlay_label, # same label that is used in the JobInput to identify which file in the asset is the actual overlay image .png file.
position=Rectangle(left="10%", top="10%"), # left and top position of the overlay in absolute pixel or percentage relative to the source video resolution.
# You can also set the height and width of the rectangle to draw into, but there is known problem here.
# If you use % for the top and left (or any of these) you have to stick with % for all or you will get a job configuration Error
# Also, it can alter your aspect ratio when using percentages, so you have to know the source video size in relation to the source image to
# provide the proper image size. Recommendation is to just use the right size image for the source video here and avoid passing in height and width for now.
# height: (if above is percentage based, this has to be also! Otherwise pixels are allowed. No mixing. )
# width: (if above is percentage based, this has to be also! Otherwise pixels are allowed No mixing. )
opacity=0.75, # Sets the blending opacity value to make the image slightly transparent over the video
start=timedelta(seconds=0), # Start at beginning of the video
fade_in_duration=timedelta(seconds=2), # 2 second fade in
fade_out_duration=timedelta(seconds=2), # 2 second fade out
end=timedelta(seconds=5) # end the fade out at 5 seconds on the timeline... fade will begin 2 seconds before this end time
)
]
)
),
# What should we do with the job if there is an error?
on_error=OnErrorType.STOP_PROCESSING_JOB,
# What is the relative priority of this job to others? Normal, high or low?
relative_priority=Priority.NORMAL
)
print("Creating encoding transform...")
# Adding transform details
my_transform = Transform()
my_transform.description="A simple custom H264 encoding transform that overlays a PNG image on the video source"
my_transform.outputs = [transform_output]
print(f"Creating transform {transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=transform_name,
parameters=my_transform)
print(f"{transform_name} created (or updated if it existed already). ")
job_name = 'MyEncodingH264OverlayImagePng'+ uniqueness
print(f"Creating Encoding264OverlayImagePng job {job_name}")
files = (source_file, overlay_file)
# Create Video Input Asset
job_video_input_asset = JobInputAsset(asset_name=in_asset_name)
job_input_overlay = JobInputAsset(
asset_name=overlay_asset_name,
label=overlay_label # Order does not matter here, it is the "label" used on the Filter and the jobInput Overlay that is important!
)
# Create a list of job inputs - we will add both the video and overlay image assets here as the inputs to the job.
job_inputs=[
job_video_input_asset,
job_input_overlay
]
# Create Job Output Asset
outputs = JobOutputAsset(asset_name=out_asset_name)
# Create Job object and then create Trasnform Job
the_job = Job(input=JobInputs(inputs=job_inputs), outputs=[outputs], correlation_data={ "propertyname": "string" })
job: Job = client.jobs.create(resource_group, account_name, transform_name, job_name, parameters=the_job)
# Check Job State
job_state = client.jobs.get(resource_group, account_name, transform_name, job_name)
# First check
print("First job check")
print(job_state.state)
# Check the state of the job every 10 seconds. Adjust time_in_seconds = <how often you want to check for job state>
time_in_seconds = 10
countdown(int(time_in_seconds))
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7,
2435,
62,
259,
62,
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4008,
198
] | 3.018378 | 3,428 |
from reinforcement._version import __version__
name = "reinforcement"
| [
6738,
37414,
13557,
9641,
1330,
11593,
9641,
834,
198,
198,
3672,
796,
366,
260,
259,
13442,
1,
198
] | 3.944444 | 18 |
import os
import json
import collections
import numpy as np
import pandas as pd
# in the standard lib
# like regexes but with less annoying wildcards
import re
from fnmatch import fnmatch
try:
import webcolors
except BaseException:
webcolors = None
def expand_wildcard(df, key, values):
"""
Expand finish wildcards from our actual material list.
Parameters
------------
df : pandas.DataFrame
Contains data.
values : [str]
Values to expand wildcards into
Returns
-------------
expanded : pandas.DataFrame
Copy of data with wildcards expanded.
"""
# do matching
# row indexes to drop from result
drop = []
# rows to append to the result
append = []
# iterate through each row
for i, row in df.iterrows():
# possibly a pattern
pattern = row[key]
# if no wildcard continue
if pd.isna(pattern) or '*' not in pattern:
continue
# we have wildcard so drop the original row
drop.append(i)
# go through each candidate material
for cand in values:
if pd.isna(cand):
continue
# if the wildcards match append it
if match(candidate=cand, pattern=pattern):
# get a copy of the row
c = row.copy()
# replace the wildcard material with an actual material
# if the material was specifically set don't override
if 'Process' in df.columns:
same = df[df.Material == cand]
if len(same) > 0 and any(same.Process == row.Process):
continue
# if c.Material in df.Material:
# continue
c[key] = cand
# append row as a dic
append.append(c.to_dict())
if len(drop) > 0:
result = df.drop(drop)
else:
result = df.copy()
if len(append) > 0:
# drop old rows with wildcard and append new rows
result = result.append(append)
return result
def check_machine(section, has_machines, shopId=None):
"""
Make sure the generated data includes every
specified machine.
"""
collected = []
for k, v in section.items():
if not k.startswith('machine_'):
continue
collected.extend(v['values'].keys())
if shopId is not None:
for machine in v['values'].values():
if 'shopId' not in machine:
machine['shopId'] = shopId
assert set(collected) == set(has_machines)
def inch_label(value):
"""
Create a thickness label from an inch thickness.
Parameters
------------
value : float
Thickness in inches
Returns
--------------
label : str
Label text
"""
label = '{:0.3f}" ({:0.2f} mm)'.format(
value,
np.round(value * 25.4, 2))
return label
def material_section(df, mac, has_machines, data=None):
"""
Convert a dataframe from our materials spreadsheet
to the options structure format.
Parameters
-------------
df : pandas.DataFrame
Contains material information
Returns
--------------
section : [dict]
Contains material filter information
"""
doable = set(mac[mac.Machine.isin(
has_machines.keys())].Material)
# set stock to zero if it was left unset
df['Stock Quantity'] = df['Stock Quantity'].fillna(0)
all_materials = set()
# save result to list of dict
result = [{'filter': {'process': ['cots']}}]
# all materials available in sheet form
all_sheet = set()
all_thick = set()
# group items to compress list
for values, frame in df.groupby(['Material',
'Stock Quantity',
'Extents']):
# expand out tuple
material, stock, ext = values
if material not in doable:
continue
# get the cutters for this material
cutters = (mac[mac.Material == material])
# get the subset of cutters this shop has
cutters_ok = cutters.Machine.isin(has_machines.keys())
cutters = cutters[cutters_ok]
# find the max thickness for this material
thick_max = cutters['Max Thickness'].max()
# only list thicknesses below our threshold
thicks = frame.Thickness[frame.Thickness < thick_max]
if len(thicks) == 0:
continue
# get thickness array for every grouped value
thick = ['{:0.3f}'.format(np.round(float(i), 3))
for i in thicks]
all_sheet.add(material)
all_materials.add(material)
all_thick.update([
'{:0.3f}'.format(np.round(float(i), 3))
for i in frame.Thickness])
# is the material stocked
is_stock = bool(stock > 1e-8)
# add a material filter with thicknesses
result.append(
{'filter':
{'stock': is_stock,
'thickness': thick,
'process': ['bent', 'flat', 'roll'],
'material': material},
'props': {'name': material,
'stock': is_stock}})
try:
# value might be NaN, just bail
extents = [int(i) for i in ext.split('x')]
assert len(extents) == 2
result[-1]['props']['extents'] = extents
except BaseException:
pass
for proc in ['turn', 'mill', 'add']:
mat_turn = list(mac.Material[mac.Process == proc])
result.append(
{'filter':
{'stock': False,
'process': proc,
'material': mat_turn}})
all_materials.add('custom')
result.append(
{'filter':
{'stock': False,
'process': 'manual',
'material': list(all_materials)}})
return result
def finish_section(df, all_color_keys):
"""
Convert an excel dataframe to our materials format.
Parameters
-------------
df : pandas.DataFrame
Contains finish data
Returns
------------
result : [dict]
Contains finish information in options format
"""
non_cots = ['flat', 'bent', 'roll', 'turn', 'mill', 'manual']
result = [{'filter': {'process': 'cots'}}]
for value, frame in df.dropna(
subset=['Material', 'Finish']).groupby(['Colors', 'Finish']):
materials = list(set(frame.Material))
color, finish = value
if not finish.startswith('layer'):
materials.append('custom')
proc = non_cots
else:
proc = 'add'
# replace wildcard with our all color list
if '*' in color:
colors = all_color_keys
else:
colors = color
# if colors is a string, strip it for labels
if not isinstance(colors, list):
colors = [i.strip().lower().replace(' ', '_')
for i in colors.split(',')]
colors = [i for i in colors if len(i) > 0]
result.append(
{'filter':
{'finish': finish,
'material': materials,
'process': proc,
'color': colors}})
return result
def machine_section(df, has_machines=None):
"""
Convert a dataframe from our materials spreadsheet
to the options structure format.
Parameters
-------------
df : pandas.DataFrame
Contains machineinformation
Returns
--------------
section : [dict]
Contains machine filter information
"""
# fill columns we don't care about
df['Feed Model'].fillna('', inplace=True)
df['Pierce Model'].fillna('', inplace=True)
df['Max Thickness'].fillna(20, inplace=True)
# first group materials with the same keys
keys = ['Machine',
'Process',
'Feed Model',
'Pierce Model',
'Max Thickness']
# pandas really monkey fucks mixed string/empty data
process = np.array(df['Process'])
material = np.array(df['Material'])
machine = np.array(df['Machine'])
# store result
result = collections.defaultdict(list)
# start with fixed filters
all_proc = set(['flat', 'turn', 'bent', 'mill', 'roll', 'add'])
for proc in all_proc:
filtered = all_proc.difference({proc})
filtered.add('cots')
filtered.add('manual')
if proc == 'flat':
filtered.difference_update(['bent', 'roll'])
result[f'machine_{proc}'].append(
{'filter': {'process': list(filtered)}})
for values, frame in df.groupby(keys):
machine, process, feed, pierce, thick = values
# all the materials with the same feeds
# i.e.: ['steel_a36', 'steel_a588']
# if this machine isn't included skip it
if has_machines is not None and not any(
m in machine for m in has_machines):
continue
material = list(set(frame.Material))
thick = float(thick)
top_key = 'machine_{}'.format(process)
if process == 'flat':
result[top_key].append(
{'filter':
{'machine_flat': machine,
'process': ['bent', 'flat', 'roll'],
'material': material},
'props':
{'feedModel': json.loads(feed),
'plungeModel': json.loads(pierce),
'maxThick': thick,
'name': machine}})
elif process == 'add':
result[top_key].append(
{'filter':
{top_key: machine,
'process': process,
'material': material},
'props': {'name': machine,
'cycleModel': json.loads(feed)}})
elif process == 'mill':
# roughing is f(r) = seconds per in^3
rough = json.loads(frame['Rough Model'].values[0])
finish = json.loads(frame['Finish Model'].values[0])
result[top_key].append(
{'filter':
{'machine_mill': machine,
'process': 'mill',
'material': material},
'props':
{'roughModel': rough,
'finishModel': finish,
'name': machine}})
elif process in ['bent', 'roll']:
result[top_key].append(
{'filter':
{top_key: machine,
'process': process,
'material': material},
'props':
{'maxThick': thick,
'name': machine}})
else:
result[top_key].append(
{'filter':
{top_key: machine,
'process': process,
'material': material},
'props': {'name': machine}})
return result
def flat_to_dict(frame):
"""
Expand a DataFrame containing Key and Value columns
into a dict.
Parameters
------------
frame : DataFrame
Contains Key and Value column, Key is a/b/c
Returns
----------
flat : dict
Reflects keys and values in frame
"""
result = {}
for _, row in frame.dropna(subset=['Key', 'Value']).iterrows():
try:
value = json.loads(row.Value)
except BaseException:
value = row.Value
multiset(result, row.Key, value)
return result
def multiset(target, key, value):
"""
Set keys multiple layers deep in a target dict.
Parameters
-------------
target : dict
Location to set new keys into
key : str
Key to set, in the form 'a/b/c'
will be available as target[a][b][c] = value
value : any
Will be added to target dictionary
Returns
------------
target : dict
The original dict object
"""
keys = key.split('/')
current = target
for i, key in enumerate(keys):
last = i >= len(keys) - 1
if key not in current:
if last:
current[key] = value
else:
current[key] = {}
current = current[key]
return target
def cost_density(materials, pad_factor=1.0):
"""
Calculate the cost density of materials from sheet goods.
Parameters
--------------
material : pandas.DataFrame
Contains material information
pad_factor : float
How much to pad material cost
Returns
-------------
updates : dict
Keys are in 'a/b/a' format for multiset
"""
# to calculate cost density we need price and volume
updates = {}
for label, m in materials.groupby('Material'):
# what are the extents of the sheet in inches
ext = np.ones((len(m.Extents), 2)) * np.nan
for index, e in enumerate(m.Extents):
try:
ext[index] = np.array(
e.split('x'), dtype=np.float64).reshape(2)
except BaseException:
pass
# how much was each sheet
usd = np.array(m.Price)
# how thick is each sheet
thick = np.array(m.Thickness)
# what is the volume in inches
volume = ext[:, 0] * ext[:, 1] * thick
# what is the $/in^3 of the material
ratio = usd / volume
# what is the value saved under
key = 'material/{}/usdCubicInch'.format(label)
# which had every field populated
ok = np.isfinite(ratio)
ratio[~ok] = np.median(ratio[ok])
# create an interpolatable model for cost density
ordering = thick.argsort()
model = {'type': 'table',
'keys': thick[ordering].tolist(),
'values': (ratio[ordering] * pad_factor).tolist()}
# should have fixed all nonsense
assert np.isfinite(ratio).all()
assert np.isfinite(thick).all()
updates[key] = model
return updates
def check_materials(materials, required_mat=None, doable=None):
"""
Check to make sure every material has required keys
"""
def convertUsd(item):
"""
USD accepts a model, not a single value.
"""
if isinstance(item, dict):
return item
# return a constant-value polynomial model
value = float(item)
model = {'type': 'polynomial',
'values': value}
return model
required_data = [
('label', str),
('blurb', str),
('link', str),
('lbPerCubicInch', float),
('usdCubicInch', convertUsd)]
if 'custom' in materials:
materials['custom']['lbPerCubicInch'] = None
materials['custom']['usdCubicInch'] = None
# keys we're removing
try:
for mat_key, data in materials.items():
if mat_key == 'custom':
assert all(k in data for k, _ in required_data)
continue
for key, convert in required_data:
check = convert(data[key])
if convert == float:
assert check > 0.001
elif convert == str:
assert len(check) > 0
except BaseException as E:
print('failed on material: {}'.format(mat_key))
print('missing {}: {}\n'.format(key, list(data.keys())))
raise(E)
# make sure all required materials are included
if required_mat is not None:
for r in required_mat:
if r not in materials:
raise ValueError(f'no data for required material {r}!')
# remove any material that isn't doable
# for key in list(materials.keys()):
# if key not in doable:
# materials.pop(key)
def generate_basic(processes=None,
template=None):
"""
A very basic helper which will use just a list of supported
machines.
"""
def generate_shopdoc(*args):
"""
Load a series of file objects to generate a shop
document. Data will override previous file objects
so that you can provide a base template containing
most information and then a sparser one with just
shop-specific data.
Parameters
-------------
*args : file-like object with Excel data
Load into a Pandas DataFrame
Returns
-------------
shopdoc : dict
Completed shop document.
"""
# load excel file and parse all sheets into DataFrame
xls = [{n: x.parse(n) for n in x.sheet_names}
for x in [pd.ExcelFile(
os.path.abspath(os.path.expanduser(a)),
engine='openpyxl') for a in args]]
return xls
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220,
220,
220,
220,
329,
4572,
287,
410,
17816,
27160,
6,
4083,
27160,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
705,
24643,
7390,
6,
407,
287,
4572,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4572,
17816,
24643,
7390,
20520,
796,
6128,
7390,
198,
220,
220,
220,
6818,
900,
7,
4033,
12609,
8,
6624,
900,
7,
10134,
62,
76,
620,
1127,
8,
628,
198,
4299,
11111,
62,
18242,
7,
8367,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
13610,
257,
20735,
6167,
422,
281,
11111,
20735,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
10541,
198,
220,
220,
220,
1988,
1058,
12178,
198,
220,
220,
220,
220,
220,
45816,
1108,
287,
8331,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
26171,
198,
220,
220,
220,
6167,
1058,
965,
198,
220,
220,
220,
220,
220,
36052,
2420,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6167,
796,
705,
90,
25,
15,
13,
18,
69,
36786,
37913,
25,
15,
13,
17,
69,
92,
8085,
8,
4458,
18982,
7,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
11,
198,
220,
220,
220,
220,
220,
220,
220,
45941,
13,
744,
7,
8367,
1635,
1679,
13,
19,
11,
362,
4008,
198,
220,
220,
220,
1441,
6167,
628,
198,
4299,
2587,
62,
5458,
7,
7568,
11,
8352,
11,
468,
62,
76,
620,
1127,
11,
1366,
28,
14202,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
38240,
257,
1366,
14535,
422,
674,
5696,
30117,
198,
220,
220,
220,
284,
262,
3689,
4645,
5794,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
32501,
198,
220,
220,
220,
47764,
1058,
19798,
292,
13,
6601,
19778,
198,
220,
220,
220,
220,
220,
49850,
2587,
1321,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
26171,
198,
220,
220,
220,
2665,
1058,
685,
11600,
60,
198,
220,
220,
220,
220,
220,
49850,
2587,
8106,
1321,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
466,
540,
796,
900,
7,
20285,
58,
20285,
13,
37573,
13,
45763,
7,
198,
220,
220,
220,
220,
220,
220,
220,
468,
62,
76,
620,
1127,
13,
13083,
3419,
25295,
17518,
8,
628,
220,
220,
220,
1303,
900,
4283,
284,
6632,
611,
340,
373,
1364,
555,
2617,
198,
220,
220,
220,
47764,
17816,
26207,
39789,
20520,
796,
47764,
17816,
26207,
39789,
6,
4083,
20797,
2616,
7,
15,
8,
628,
220,
220,
220,
477,
62,
33665,
82,
796,
900,
3419,
198,
220,
220,
220,
1303,
3613,
1255,
284,
1351,
286,
8633,
198,
220,
220,
220,
1255,
796,
685,
90,
6,
24455,
10354,
1391,
6,
14681,
10354,
37250,
66,
1747,
20520,
11709,
60,
628,
220,
220,
220,
1303,
477,
5696,
1695,
287,
9629,
1296,
198,
220,
220,
220,
477,
62,
21760,
796,
900,
3419,
198,
220,
220,
220,
477,
62,
400,
624,
796,
900,
3419,
198,
220,
220,
220,
1303,
1448,
3709,
284,
27413,
1351,
198,
220,
220,
220,
329,
3815,
11,
5739,
287,
47764,
13,
8094,
1525,
7,
17816,
17518,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
26207,
39789,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
11627,
658,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
4292,
503,
46545,
198,
220,
220,
220,
220,
220,
220,
220,
2587,
11,
4283,
11,
1070,
796,
3815,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2587,
407,
287,
466,
540,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
2005,
1010,
329,
428,
2587,
198,
220,
220,
220,
220,
220,
220,
220,
2005,
1010,
796,
357,
20285,
58,
20285,
13,
17518,
6624,
2587,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
262,
24637,
286,
2005,
1010,
428,
6128,
468,
198,
220,
220,
220,
220,
220,
220,
220,
2005,
1010,
62,
482,
796,
2005,
1010,
13,
37573,
13,
45763,
7,
10134,
62,
76,
620,
1127,
13,
13083,
28955,
198,
220,
220,
220,
220,
220,
220,
220,
2005,
1010,
796,
2005,
1010,
58,
8968,
1010,
62,
482,
60,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1064,
262,
3509,
20735,
329,
428,
2587,
198,
220,
220,
220,
220,
220,
220,
220,
6546,
62,
9806,
796,
2005,
1010,
17816,
11518,
45816,
1108,
6,
4083,
9806,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
691,
1351,
20735,
274,
2174,
674,
11387,
198,
220,
220,
220,
220,
220,
220,
220,
294,
3378,
796,
5739,
13,
817,
624,
1108,
58,
14535,
13,
817,
624,
1108,
1279,
6546,
62,
9806,
60,
628,
220,
220,
220,
220,
220,
220,
220,
611,
18896,
7,
400,
3378,
8,
6624,
657,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2555,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
651,
20735,
7177,
329,
790,
32824,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
6546,
796,
37250,
90,
25,
15,
13,
18,
69,
92,
4458,
18982,
7,
37659,
13,
744,
7,
22468,
7,
72,
828,
513,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
294,
3378,
60,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
21760,
13,
2860,
7,
33665,
8,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
33665,
82,
13,
2860,
7,
33665,
8,
198,
220,
220,
220,
220,
220,
220,
220,
477,
62,
400,
624,
13,
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26933,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
90,
25,
15,
13,
18,
69,
92,
4458,
18982,
7,
37659,
13,
744,
7,
22468,
7,
72,
828,
513,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
1312,
287,
5739,
13,
817,
624,
1108,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
318,
262,
2587,
42070,
198,
220,
220,
220,
220,
220,
220,
220,
318,
62,
13578,
796,
20512,
7,
13578,
1875,
352,
68,
12,
23,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
751,
257,
2587,
8106,
351,
20735,
274,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
13,
33295,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
24455,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
13578,
10354,
318,
62,
13578,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
400,
624,
1108,
10354,
6546,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14681,
10354,
37250,
46119,
3256,
705,
38568,
3256,
705,
2487,
6,
4357,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
33665,
10354,
2587,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1676,
862,
10354,
1391,
6,
3672,
10354,
2587,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
13578,
10354,
318,
62,
13578,
11709,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1988,
1244,
307,
11013,
45,
11,
655,
12274,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1070,
658,
796,
685,
600,
7,
72,
8,
329,
1312,
287,
1070,
13,
35312,
10786,
87,
11537,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
6818,
18896,
7,
2302,
658,
8,
6624,
362,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
12,
16,
7131,
6,
1676,
862,
6,
7131,
6,
2302,
658,
20520,
796,
1070,
658,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7308,
16922,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
628,
220,
220,
220,
329,
13834,
287,
37250,
15344,
3256,
705,
17805,
3256,
705,
2860,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
2603,
62,
15344,
796,
1351,
7,
20285,
13,
17518,
58,
20285,
13,
18709,
6624,
13834,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
1255,
13,
33295,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
24455,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
13578,
10354,
10352,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14681,
10354,
13834,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
33665,
10354,
2603,
62,
15344,
11709,
8,
628,
220,
220,
220,
477,
62,
33665,
82,
13,
2860,
10786,
23144,
11537,
198,
220,
220,
220,
1255,
13,
33295,
7,
198,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
24455,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
13578,
10354,
10352,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14681,
10354,
705,
805,
723,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
33665,
10354,
1351,
7,
439,
62,
33665,
82,
8,
11709,
8,
628,
220,
220,
220,
1441,
1255,
628,
198,
4299,
5461,
62,
5458,
7,
7568,
11,
477,
62,
8043,
62,
13083,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
38240,
281,
27336,
1366,
14535,
284,
674,
5696,
5794,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
32501,
198,
220,
220,
220,
47764,
1058,
19798,
292,
13,
6601,
19778,
198,
220,
220,
220,
220,
220,
49850,
5461,
1366,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
10541,
198,
220,
220,
220,
1255,
1058,
685,
11600,
60,
198,
220,
220,
220,
220,
220,
49850,
5461,
1321,
287,
3689,
5794,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1729,
62,
66,
1747,
796,
37250,
38568,
3256,
705,
46119,
3256,
705,
2487,
3256,
705,
15344,
3256,
705,
17805,
3256,
705,
805,
723,
20520,
198,
220,
220,
220,
1255,
796,
685,
90,
6,
24455,
10354,
1391,
6,
14681,
10354,
705,
66,
1747,
6,
11709,
60,
198,
220,
220,
220,
329,
1988,
11,
5739,
287,
47764,
13,
14781,
2616,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24637,
28,
17816,
17518,
3256,
705,
48658,
20520,
737,
8094,
1525,
7,
17816,
5216,
669,
3256,
705,
48658,
20520,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
5696,
796,
1351,
7,
2617,
7,
14535,
13,
17518,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
3124,
11,
5461,
796,
1988,
628,
220,
220,
220,
220,
220,
220,
220,
611,
407,
5461,
13,
9688,
2032,
342,
10786,
29289,
6,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
5696,
13,
33295,
10786,
23144,
11537,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
13834,
796,
1729,
62,
66,
1747,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
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14681,
10354,
1429,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
33665,
10354,
2587,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1676,
862,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
9806,
817,
624,
10354,
6546,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
3672,
10354,
4572,
11709,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1255,
58,
4852,
62,
2539,
4083,
33295,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
6,
24455,
10354,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1391,
4852,
62,
2539,
25,
4572,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
14681,
10354,
1429,
11,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
33665,
10354,
2587,
5512,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
1676,
862,
10354,
1391,
6,
3672,
10354,
4572,
11709,
8,
628,
220,
220,
220,
1441,
1255,
628,
198,
4299,
6228,
62,
1462,
62,
11600,
7,
14535,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
49368,
257,
6060,
19778,
7268,
7383,
290,
11052,
15180,
198,
220,
220,
220,
656,
257,
8633,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
10541,
198,
220,
220,
220,
5739,
1058,
6060,
19778,
198,
220,
220,
220,
220,
220,
49850,
7383,
290,
11052,
5721,
11,
7383,
318,
257,
14,
65,
14,
66,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
24200,
438,
198,
220,
220,
220,
6228,
1058,
8633,
198,
220,
220,
220,
220,
220,
36901,
82,
8251,
290,
3815,
287,
5739,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
1255,
796,
23884,
198,
220,
220,
220,
329,
4808,
11,
5752,
287,
5739,
13,
14781,
2616,
7,
7266,
2617,
28,
17816,
9218,
3256,
705,
11395,
20520,
737,
2676,
8516,
33529,
198,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
33918,
13,
46030,
7,
808,
13,
11395,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2845,
7308,
16922,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
5752,
13,
11395,
198,
220,
220,
220,
220,
220,
220,
220,
1963,
271,
316,
7,
20274,
11,
5752,
13,
9218,
11,
1988,
8,
628,
220,
220,
220,
1441,
1255,
628,
198,
4299,
1963,
271,
316,
7,
16793,
11,
1994,
11,
1988,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
5345,
8251,
3294,
11685,
2769,
287,
257,
2496,
8633,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
32501,
198,
220,
220,
220,
2496,
1058,
8633,
198,
220,
220,
220,
220,
220,
13397,
284,
900,
649,
8251,
656,
198,
220,
220,
220,
1994,
1058,
965,
198,
220,
220,
220,
220,
220,
7383,
284,
900,
11,
287,
262,
1296,
705,
64,
14,
65,
14,
66,
6,
198,
220,
220,
220,
220,
220,
481,
307,
1695,
355,
2496,
58,
64,
7131,
65,
7131,
66,
60,
796,
1988,
198,
220,
220,
220,
1988,
1058,
597,
198,
220,
220,
220,
220,
220,
2561,
307,
2087,
284,
2496,
22155,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
10541,
198,
220,
220,
220,
2496,
1058,
8633,
198,
220,
220,
220,
220,
220,
383,
2656,
8633,
2134,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
8251,
796,
1994,
13,
35312,
10786,
14,
11537,
198,
220,
220,
220,
1459,
796,
2496,
198,
220,
220,
220,
329,
1312,
11,
1994,
287,
27056,
378,
7,
13083,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
938,
796,
1312,
18189,
18896,
7,
13083,
8,
532,
352,
198,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
407,
287,
1459,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
938,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1459,
58,
2539,
60,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1459,
58,
2539,
60,
796,
23884,
198,
220,
220,
220,
220,
220,
220,
220,
1459,
796,
1459,
58,
2539,
60,
198,
220,
220,
220,
1441,
2496,
628,
198,
198,
4299,
1575,
62,
43337,
7,
33665,
82,
11,
14841,
62,
31412,
28,
16,
13,
15,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
27131,
378,
262,
1575,
12109,
286,
5696,
422,
9629,
7017,
13,
628,
220,
220,
220,
40117,
198,
220,
220,
220,
220,
26171,
198,
220,
220,
220,
2587,
1058,
19798,
292,
13,
6601,
19778,
198,
220,
220,
220,
220,
220,
49850,
2587,
1321,
198,
220,
220,
220,
14841,
62,
31412,
1058,
12178,
198,
220,
220,
220,
220,
220,
1374,
881,
284,
14841,
2587,
1575,
628,
220,
220,
220,
16409,
198,
220,
220,
220,
220,
32501,
198,
220,
220,
220,
5992,
1058,
8633,
198,
220,
220,
220,
220,
220,
26363,
389,
287,
705,
64,
14,
65,
14,
64,
6,
5794,
329,
1963,
271,
316,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
1303,
284,
15284,
1575,
12109,
356,
761,
2756,
290,
6115,
198,
220,
220,
220,
5992,
796,
23884,
198,
220,
220,
220,
329,
6167,
11,
285,
287,
5696,
13,
8094,
1525,
10786,
17518,
6,
2599,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
644,
389,
262,
1070,
658,
286,
262,
9629,
287,
8331,
198,
220,
220,
220,
220,
220,
220,
220,
1070,
796,
45941,
13,
1952,
19510,
11925,
7,
76,
13,
11627,
658,
828,
362,
4008,
1635,
45941,
13,
12647,
198,
220,
220,
220,
220,
220,
220,
220,
329,
6376,
11,
304,
287,
27056,
378,
7,
76,
13,
11627,
658,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1949,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1070,
58,
9630,
60,
796,
45941,
13,
18747,
7,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
304,
13,
35312,
10786,
87,
33809,
288,
4906,
28,
37659,
13,
22468,
2414,
737,
3447,
1758,
7,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2845,
7308,
16922,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1208,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
703,
881,
373,
1123,
9629,
198,
220,
220,
220,
220,
220,
220,
220,
514,
67,
796,
45941,
13,
18747,
7,
76,
13,
18124,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
703,
6546,
318,
1123,
9629,
198,
220,
220,
220,
220,
220,
220,
220,
6546,
796,
45941,
13,
18747,
7,
76,
13,
817,
624,
1108,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
644,
318,
262,
6115,
287,
8331,
198,
220,
220,
220,
220,
220,
220,
220,
6115,
796,
1070,
58,
45299,
657,
60,
1635,
1070,
58,
45299,
352,
60,
1635,
6546,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
644,
318,
262,
720,
14,
259,
61,
18,
286,
262,
2587,
198,
220,
220,
220,
220,
220,
220,
220,
8064,
796,
514,
67,
1220,
6115,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
644,
318,
262,
1988,
7448,
739,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
705,
33665,
14,
90,
92,
14,
385,
67,
43632,
291,
818,
354,
4458,
18982,
7,
18242,
8,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
543,
550,
790,
2214,
22331,
198,
220,
220,
220,
220,
220,
220,
220,
12876,
796,
45941,
13,
4468,
9504,
7,
10366,
952,
8,
198,
220,
220,
220,
220,
220,
220,
220,
8064,
58,
93,
482,
60,
796,
45941,
13,
1150,
666,
7,
10366,
952,
58,
482,
12962,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
2251,
281,
39555,
21156,
2746,
329,
1575,
12109,
198,
220,
220,
220,
220,
220,
220,
220,
16216,
796,
6546,
13,
22046,
419,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
1391,
6,
4906,
10354,
705,
11487,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
13083,
10354,
6546,
58,
34555,
4083,
83,
349,
396,
22784,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
27160,
10354,
357,
10366,
952,
58,
34555,
60,
1635,
14841,
62,
31412,
737,
83,
349,
396,
3419,
92,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
815,
423,
5969,
477,
18149,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
45941,
13,
4468,
9504,
7,
10366,
952,
737,
439,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
6818,
45941,
13,
4468,
9504,
7,
400,
624,
737,
439,
3419,
628,
220,
220,
220,
220,
220,
220,
220,
5992,
58,
2539,
60,
796,
2746,
628,
220,
220,
220,
1441,
5992,
628,
198,
4299,
2198,
62,
33665,
82,
7,
33665,
82,
11,
2672,
62,
6759,
28,
14202,
11,
466,
540,
28,
14202,
2599,
198,
220,
220,
220,
37227,
198,
220,
220,
220,
6822,
284,
787,
1654,
790,
2587,
468,
2672,
8251,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
825,
10385,
5842,
67,
7,
9186,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
11403,
18178,
257,
2746,
11,
407,
257,
2060,
1988,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
39098,
7,
9186,
11,
8633,
2599,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2378,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
1441,
257,
6937,
12,
8367,
745,
6213,
49070,
2746,
198,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
12178,
7,
9186,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2746,
796,
1391,
6,
4906,
10354,
705,
35428,
26601,
498,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
27160,
10354,
1988,
92,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2746,
628,
220,
220,
220,
2672,
62,
7890,
796,
685,
198,
220,
220,
220,
220,
220,
220,
220,
19203,
18242,
3256,
965,
828,
198,
220,
220,
220,
220,
220,
220,
220,
19203,
2436,
5945,
3256,
965,
828,
198,
220,
220,
220,
220,
220,
220,
220,
19203,
8726,
3256,
965,
828,
198,
220,
220,
220,
220,
220,
220,
220,
19203,
23160,
5990,
43632,
291,
818,
354,
3256,
12178,
828,
198,
220,
220,
220,
220,
220,
220,
220,
19203,
385,
67,
43632,
291,
818,
354,
3256,
10385,
5842,
67,
15437,
628,
220,
220,
220,
611,
705,
23144,
6,
287,
5696,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5696,
17816,
23144,
6,
7131,
6,
23160,
5990,
43632,
291,
818,
354,
20520,
796,
6045,
198,
220,
220,
220,
220,
220,
220,
220,
5696,
17816,
23144,
6,
7131,
6,
385,
67,
43632,
291,
818,
354,
20520,
796,
6045,
628,
220,
220,
220,
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198
] | 2.196337 | 7,645 |
#!/usr/bin/env python
# coding: utf-8
# Twitter: @xrths
# www.xrths.fr
# Importation des librairies.
import os
from configparser import ConfigParser
| [
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] | 2.732143 | 56 |
# -*- coding: utf-8 -*-
import datetime
from functools import total_ordering
import os
import stat
from .search_record import SearchRecord, FA_DIREC, FA_ARCH
@total_ordering
# Sort, directories first, then files
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] | 3.112676 | 71 |
'''
File name : rdf_RePlAce.py
Author : Jinwook Jung
Created on : Tue 30 Jul 2019 10:31:18 PM EDT
Last modified : 2020-01-06 15:22:58
Description :
'''
import subprocess, os, sys, random, yaml, time
from subprocess import Popen, PIPE, CalledProcessError
# FIXME
sys.path.insert(0, '../../../src/stage.py')
from stage import *
| [
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11537,
198,
6738,
3800,
1330,
1635,
628,
628
] | 2.391026 | 156 |
from django.apps import AppConfig
| [
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] | 3.888889 | 9 |
from tkinter import *
import mysql.connector
root = Tk()
root.title("Doidera")
root.geometry("400x400+200+200")
my_db = mysql.connector.connect(
host="localhost",
user="root",
passwd="YourPassword",
database="doidera"
)
# Criar um cursor e inicializa-lo
my_cursor = my_db.cursor()
# Criar tabela
comando_sql = "CREATE TABLE clientes(" \
"first_name VARCHAR(255), " \
"last_name VARCHAR(255), " \
"zipcode INT(10), " \
"price_paid DECIMAL(10, 2), " \
"user_id INT AUTO_INCREMENT PRIMARY KEY)"
my_cursor.execute(comando_sql)
root.mainloop()
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220,
220,
220,
366,
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62,
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17828,
47044,
46,
62,
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2200,
10979,
4810,
3955,
13153,
35374,
16725,
198,
1820,
62,
66,
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13,
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7,
785,
25440,
62,
25410,
8,
198,
198,
15763,
13,
12417,
26268,
3419,
198
] | 2.128378 | 296 |
"""Config flow for Gree."""
from homeassistant import config_entries
from homeassistant.helpers import config_entry_flow
from .bridge import DeviceHelper
from .const import DOMAIN
async def _async_has_devices(hass) -> bool:
"""Return if there are devices that can be discovered."""
devices = await DeviceHelper.find_devices()
return len(devices) > 0
config_entry_flow.register_discovery_flow(
DOMAIN, "Gree Climate", _async_has_devices, config_entries.CONN_CLASS_LOCAL_POLL
)
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62,
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62,
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3069,
198,
8,
198
] | 3.125786 | 159 |
import os
import sys
import django
import pytest
import pac # noqa
from .helpers import sanity
@sanity
@sanity
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] | 2.857143 | 42 |
import math
import matplotlib.pyplot as plt
print("Hello World!")
x = math.sqrt(30)
print(x)
plt.plot([1,2,3,4,5])
plt.show() | [
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# Generated by Django 2.1.2 on 2019-04-08 11:55
from django.db import migrations, models
import django.db.models.deletion
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] | 2.818182 | 44 |
#!/usr/bin/env python
import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import spline
import os
from config import failure_class_name_to_id
data_type = "SIM"
filepath = "/Users/sklaw_mba/Desktop/ex/dr_juan_proj/workshop/data_cooker_code/parse_rcbht_data/my_training_data/"+data_type+"_HIRO_ONE_SA_ERROR_CHARAC_prob"
failure_class = failure_class_name_to_id.keys()
all_mat = {}
for fc in failure_class:
file_name = "training_set_of_failure_class_"+fc
try:
mat = np.genfromtxt(os.path.join(filepath, file_name), dtype='string', delimiter=',')
except IOError:
print "no data for class", fc
continue
#a half for training, a half for test
if len(mat.shape) == 1:
mat = mat.reshape((1, mat.shape[0]))
#shuffle all these trails before collecting them
all_mat[fc] = mat
if __name__ == "__main__":
from optparse import OptionParser
usage = "usage: %prog -m METHOD"
parser = OptionParser(usage=usage)
parser.add_option("-m", "--method",
action="store", type="string", dest="method",
help="training method, svm or logistic or gaussiannb")
(options, args) = parser.parse_args()
if options.method is None:
parser.error("you have to provide a method in -m or --method")
from trainer_common import CommonTrainer
ct = CommonTrainer(all_mat, "data_type_"+data_type+"_model_for_failure_class_", options.method)
while True:
ct.run_one_training()
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] | 2.344311 | 668 |
"""Preprocess."""
import numpy as np
import menpo
from menpofit.fitter import align_shape_with_bounding_box
from menpodetect.opencv import detect
import common.utils
import face_alignment.utils
__DETECTOR__ = detect.load_opencv_frontal_face_detector()
class Preprocess(object):
"""Preprocess."""
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# Copyright 2018 Waseda University (Nelson Yalta)
# 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 pyimagesource.abscoeff import AbsCoeff
from pyimagesource.room_resp import ISM_RoomResp
import logging
import numpy as np
class Room_Impulse_Response(object):
"""Environmental parameters for image-source method simulation
Room_Impulse_Response(args)
This function can be used as a template for the definition of the
different parameters for an image-source method simulation, typically
providing inputs to the functions 'ISM_RIR_bank.m' as well as
'fast_ISM_RIR_bank.m' (Lehmann & Johansson's ISM implementations). This
function returns the structure 'SetupStruc' with the following fields:
sampling_freq: sampling frequency in Hz.
room: 1-by-3 array of enclosure dimensions (in m),
[x_length y_length z_length].
mic_pos: N-by-3 matrix, [x1 y1 z1; x2 y2 z2; ...] positions of N
microphones in the environment (in m).
source_trajectory: M-by-3 matrix, [x1 y1 z1; x2 y2 z2; ...] positions of M
source trajectory points in the environment (in m).
reverberation: list of two scalar values [a, b]
where a is the reverberation type (60 or 20) and b
is the desired reverberation time (in s).
c: (optional) sound velocity (in m/s).
abs_weights: (optional) 1-by-6 vector of absorption coefficients weights,
[w_x1 w_x2 w_y1 w_y2 w_z1 w_z2].
method: Type of Algorithm for reverberation.
verbose: Verbosity control
processes: number of subprocess to calculate the impulses.
The structure field 'c' is optional in the sense that the various
functions developed in relation to Lehmann & Johansson's ISM
implementation assume a sound velocity of 343 m/s by default. If defined
in the function below, the field 'SetupStruc.c' will take precedence and
override the default value with another setting.
The field 'abs_weight' corresponds to the relative weights of each of the
six absorption coefficients resulting from the desired reverberation time
T60. For instance, defining 'abs_weights' as [0.8 0.8 1 1 0.6 0.6] will
result in the absorption coefficients (alpha) for the walls in the
x-dimension being 20% smaller compared to the y-dimension walls, whereas
the floor and ceiling will end up with absorption coefficients 40%
smaller (e.g., to simulate the effects of a concrete floor and ceiling).
Note that setting some of the 'abs_weight' parameters to 1 does NOT mean
that the corresponding walls will end up with a total absorption! If the
field 'abs_weight' is omitted, the various functions developed in
relation to Lehmann & Johansson's ISM implementation will set the
'abs_weight' parameter to [1 1 1 1 1 1], which will lead to uniform
absorption coefficients for all room boundaries.
The reverberation list may contain one of the two fields '60' or
'20'. 60 corresponds to the time required by the impulse response to
decay by 60dB, whereas 20 is defined as the time required for the
impulse response to decay from -5 to -25dB. Simply define either one of
these fields in the file below. Set this value to 0 for anechoic
environments (direct path only).
"""
def bank(self, Delta_dB=50.0):
"""Function [RIR_cell] = ISM_RIR_bank(setupstruc,RIRFileName,varargin)
ISM_RIR_bank Bank of RIRs using Lehmann & Johansson's image-source method
[RIR_CELL] = ISM_RIR_bank(SETUP_STRUC,RIR_FILE_NAME)
This function generates a bank of room impulse responses (RIRs) for a
particular user-defined room setup, using Lehmann and Johansson's
implementation of the image-source method (see: "Prediction of energy
decay in room impulse responses simulated with an image-source model", J.
Acoust. Soc. Am., vol. 124(1), pp. 269-277, July 2008). The input
SETUP_STRUC is a structure of enviromental parameters containing the
following fields:
Fs: sampling frequency (in Hz).
room: 1-by-3 vector of enclosure dimensions (in m),
[x_length y_length z_length].
mic_pos: N-by-3 matrix, [x1 y1 z1; x2 y2 z2; ...] positions of N
microphones (in m).
src_traj: M-by-3 matrix, [x1 y1 z1; x2 y2 z2; ...] positions of M
source trajectory points (in m).
reverberation (T20 or T60): scalar value (in s), desired reverberation time.
c: (optional) sound velocity (in m/s).
abs_weights: (optional) 1-by-6 vector of absorption coefficients weights,
[w_x1 w_x2 w_y1 w_y2 w_z1 w_z2].
If the field SETUP_STRUC.c is undefined, the function assumes a default
value of sound velocity of 343 m/s.
The field 'abs_weight' corresponds to the relative weights of each of the
six absorption coefficients resulting from the desired reverberation time.
For instance, defining 'abs_weights' as [1 1 0.8 0.8 0.6 0.6] will result
in the absorption coefficients (alpha) for the walls in the y-dimension
being 20% smaller compared to the x-dimension walls, whereas the floor
and ceiling will end up with absorption coefficients 40% smaller (e.g.,
to simulate the effects of a concrete floor and ceiling). If this field
is omitted, the parameter 'abs_weight' will default to [1 1 1 1 1 1],
which leads to uniform absorption coefficients for all room boundaries.
The structure SETUP_STRUC may contain one of the two fields 'T60' or
'T20'. This function will automatically determine which reverberation
type is used and compute the desired room absorption coefficients
accordingly. T20 is defined as the time required for the impulse response
energy to decay from -5 to -25dB, whereas T60 corresponds to the time
required by the impulse response energy to decay by 60dB. Setting the
corresponding field value to 0 achieves anechoic impulse responses
(direct path only).
In addition, a number of other (optional) parameters can be set using a
series of 'argument'--value pairs. The following parameters (arguments)
can be used:
'Delta_dB': scalar (in dB), parameter determining how much the resulting
impulse response is cropped: the impulse response is
computed until the time index where its overall energy
content has decreased by 'Delta_dB' decibels, after which
the computations stop. Not relevant if the reverberation
time is set to 0 (anechoic case). Defaults to 50.
This function returns a 2-dimensional cell array RIR_CELL containing the
RIRs for each source trajectory point and each microphone, organised as
follows: RIR_CELL{mic_index,traj_index}. The resulting filter length
may differ slightly in each computed RIR.
This function also saves the computation results on file. The argument
RIR_FILE_NAME determines the name of the .mat file where the variable
RIR_CELL is to be saved. If a file already exists with the same name as
the input argument, the user will be prompted to determine whether the
file is to be overwritten or not. The given parameter RIR_FILE_NAME can
be a full access path to the desired file. If no access path is given,
the file is saved in the current working directory.
"""
if self.abs_weights is None:
self.abs_weights = np.ones((1, 6))
elif self.abs_weights.shape[1] != 6:
logging.warning('The given weights is not an array of 6, the values will be set to 1')
self.abs_weights = np.ones((1, 6))
if self.c is None:
self.c = 343.0
if self.reverberation[0] == 60:
alpha = AbsCoeff('t60', self.reverberation[1], self.room, self.abs_weights, self.method, self.c)
elif self.reverberation[0] == 20:
alpha = AbsCoeff('t20', self.reverberation[1], self.room, self.abs_weights, self.method, self.c)
else:
raise ValueError('Missing T60 or T20 field.')
rttype = self.reverberation[0]
rtval = self.reverberation[1]
beta = np.sqrt(1 - alpha)
nMics = self.mic_pos.shape[0] # number of microphones
nSPts = self.source_trajectory.shape[0] # number of source trajectory points
# -=:=- Compute RIR bank -=:=-
RIR_cell = np.empty((nMics, nSPts), dtype=object) # pre-allocate cell array
logging.info('Computing room impulse responses. ')
mics_range = range(nMics)
if self.processes > 1:
from pathos.multiprocessing import ProcessingPool as Pool
this_map = Pool(node=self.processes).map
X_src = (self.mic_pos[mm, :] for mm in mics_range for tt in range(nSPts))
X_rcv = (self.source_trajectory[tt, :] for mm in mics_range for tt in range(nSPts))
m_freq = (self.sampling_freq for mm in mics_range for tt in range(nSPts))
m_beta = (beta for mm in mics_range for tt in range(nSPts))
m_rttype = (rttype for mm in mics_range for tt in range(nSPts))
m_rtval = (rtval for mm in mics_range for tt in range(nSPts))
m_room = (self.room for mm in mics_range for tt in range(nSPts))
m_c = (self.c for mm in mics_range for tt in range(nSPts))
m_Delta_dB = (Delta_dB for mm in mics_range for tt in range(nSPts))
imps = this_map(ISM_RoomResp, m_freq,
m_beta, m_rttype, m_rtval,
X_src, X_rcv, m_room,
m_c, m_Delta_dB)
for mm in mics_range:
for tt in range(nSPts):
RIR_cell[mm, tt] = imps[mm * nSPts + tt]
logging.info('Room impulse responses completed. ')
else:
if self.verbose:
from tqdm import tqdm
mics_range = tqdm(mics_range)
for mm in mics_range:
X_rcv = self.mic_pos[mm, :]
# compute ISM room impulse response for each source-receiver combinations
for tt in range(nSPts):
X_src = self.source_trajectory[tt, :]
RIR_cell[mm, tt] = ISM_RoomResp(self.sampling_freq,
beta, rttype, rtval,
X_src, X_rcv, self.room,
self.c, Delta_dB)
self.RIR_cell = RIR_cell
return RIR_cell
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62,
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13,
23,
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220,
4013,
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13,
3345,
13,
1703,
1539,
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13,
19755,
7,
16,
828,
9788,
13,
38249,
12,
27019,
11,
2901,
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220,
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1708,
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25,
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220,
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376,
82,
25,
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25,
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12,
18,
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220,
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220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
685,
87,
62,
13664,
331,
62,
13664,
1976,
62,
13664,
4083,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12314,
62,
1930,
25,
399,
12,
1525,
12,
18,
17593,
11,
685,
87,
16,
331,
16,
1976,
16,
26,
2124,
17,
331,
17,
1976,
17,
26,
2644,
60,
6116,
286,
399,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
46952,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12351,
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9535,
73,
25,
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12,
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12,
18,
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11,
685,
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16,
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16,
1976,
16,
26,
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17,
1976,
17,
26,
2644,
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48134,
341,
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51,
1238,
393,
309,
1899,
2599,
16578,
283,
1988,
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259,
264,
828,
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640,
13,
198,
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220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
269,
25,
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8,
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357,
259,
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14,
82,
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2352,
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12,
21,
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685,
86,
62,
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62,
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17,
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62,
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1002,
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2214,
25823,
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62,
18601,
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13,
66,
318,
28721,
11,
262,
2163,
18533,
257,
4277,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
286,
2128,
15432,
286,
37290,
285,
14,
82,
13,
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23,
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23,
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21,
657,
13,
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12,
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852,
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4,
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262,
4314,
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220,
220,
220,
220,
220,
220,
220,
290,
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from typing import Optional, Text, List, Mapping, Callable, Tuple, Union, Counter
import tensorflow as tf
import tensorflow_probability.python.distributions as tfd
from ...utils import types as ts
from ...utils import keys
def f_child_sample_factory(beta: ts.Float = 0):
"""
Args:
beta: softmax temperature. Higher means more random
Returns:
function `f_child_sample`
"""
return f_child_sample
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"""
Cross-docking truck data.
This data is generated by a generate_dataset.py script.
Created: Feb 18, 2019 at 07:30:02 PM
Copyright (c) 2022, Krerkkiat Chusap
This souce code is licensed under BSD 3-Clause "New" or "Revised" License (see LICENSE for details).
"""
from pathlib import Path
# Problem data.
name = Path(__file__).stem
inbound_gate_count = 10
outbound_gate_count = 10
# Parameters used to generate this data.
number_of_total_product_types = 15
product_per_truck_rate = 0.35
possible_inbound_total_product = [250, 340]
# Truck data.
_inbound_truck_raw_data = [
[0, 55, 0, 0, 0, 0, 89, 0, 2, 0, 0, 70, 111, 0, 13],
[50, 0, 0, 0, 0, 115, 0, 0, 0, 0, 58, 0, 27, 0, 0],
[23, 0, 40, 23, 0, 41, 0, 0, 100, 0, 0, 0, 0, 113, 0],
[0, 0, 78, 0, 0, 71, 0, 0, 49, 21, 29, 0, 0, 92, 0],
[0, 0, 0, 15, 0, 0, 40, 0, 109, 0, 0, 0, 86, 0, 0],
[0, 0, 11, 0, 0, 0, 0, 0, 0, 0, 0, 43, 97, 0, 99],
[0, 43, 0, 0, 44, 0, 0, 0, 0, 95, 0, 0, 0, 68, 0],
[0, 0, 0, 0, 0, 8, 32, 0, 0, 132, 78, 0, 0, 0, 0],
[0, 0, 38, 0, 0, 0, 0, 0, 0, 34, 0, 0, 140, 0, 38],
[0, 0, 0, 207, 0, 0, 16, 0, 1, 0, 0, 0, 26, 0, 0],
[39, 0, 0, 59, 53, 0, 0, 0, 0, 48, 138, 0, 3, 0, 0],
[0, 0, 47, 18, 0, 0, 14, 0, 0, 0, 171, 0, 0, 0, 0],
[118, 0, 0, 22, 52, 0, 28, 72, 0, 0, 0, 48, 0, 0, 0],
[0, 16, 90, 0, 0, 0, 99, 20, 0, 81, 0, 0, 0, 34, 0],
[0, 27, 0, 59, 0, 0, 0, 0, 0, 0, 9, 70, 137, 0, 38],
[0, 138, 102, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[7, 0, 25, 0, 0, 0, 17, 0, 0, 0, 0, 0, 0, 13, 278],
[0, 67, 31, 0, 0, 0, 0, 13, 71, 0, 0, 72, 86, 0, 0],
[0, 0, 134, 0, 0, 7, 0, 0, 4, 139, 0, 31, 0, 25, 0],
[120, 0, 42, 47, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0],
[116, 97, 0, 26, 0, 9, 16, 76, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 5, 143, 0, 0, 0, 0, 0, 88, 0, 14, 0],
[0, 10, 183, 0, 0, 39, 11, 0, 0, 0, 0, 0, 41, 56, 0],
[0, 0, 0, 0, 0, 44, 178, 0, 0, 71, 0, 0, 35, 0, 12],
[4, 0, 0, 0, 0, 148, 0, 0, 95, 0, 3, 0, 0, 0, 0],
[40, 0, 0, 2, 0, 0, 0, 0, 73, 0, 0, 15, 176, 0, 34],
[0, 0, 64, 0, 33, 0, 51, 0, 0, 0, 0, 102, 0, 0, 0],
[0, 0, 0, 0, 9, 0, 164, 0, 0, 0, 0, 0, 0, 63, 14],
[0, 6, 0, 0, 0, 0, 0, 0, 16, 11, 125, 56, 126, 0, 0],
[0, 93, 59, 0, 0, 0, 57, 0, 50, 0, 43, 0, 0, 0, 38],
]
_outbound_truck_raw_data = [
[13, 4, 12, 20, 0, 18, 67, 5, 2, 49, 15, 2, 24, 10, 9],
[28, 1, 4, 15, 4, 24, 0, 5, 7, 10, 6, 1, 10, 2, 2],
[18, 12, 13, 4, 4, 5, 4, 1, 43, 17, 10, 3, 16, 1, 6],
[17, 13, 9, 13, 2, 1, 42, 4, 0, 16, 27, 1, 5, 1, 4],
[13, 10, 10, 7, 0, 10, 4, 12, 24, 7, 17, 16, 35, 1, 6],
[10, 12, 1, 5, 2, 10, 15, 7, 1, 5, 0, 15, 4, 33, 1],
[40, 0, 34, 6, 1, 9, 12, 1, 4, 4, 8, 11, 72, 13, 6],
[5, 25, 10, 1, 0, 19, 3, 1, 2, 2, 2, 1, 0, 5, 6],
[9, 15, 17, 2, 1, 12, 38, 2, 23, 1, 11, 7, 4, 1, 19],
[4, 5, 41, 6, 1, 7, 16, 3, 1, 19, 15, 24, 18, 14, 5],
[3, 2, 19, 10, 3, 16, 18, 0, 2, 2, 1, 2, 37, 12, 16],
[0, 8, 8, 11, 3, 5, 3, 4, 1, 3, 12, 3, 2, 21, 16],
[19, 15, 27, 39, 3, 16, 1, 1, 2, 24, 18, 10, 12, 6, 0],
[3, 12, 12, 2, 0, 3, 46, 1, 18, 21, 55, 8, 9, 20, 61],
[5, 19, 8, 3, 0, 3, 32, 3, 0, 7, 3, 11, 3, 3, 16],
[2, 0, 16, 5, 12, 14, 23, 3, 12, 12, 10, 4, 4, 22, 1],
[4, 33, 19, 1, 2, 0, 16, 0, 3, 2, 27, 28, 49, 14, 11],
[7, 42, 17, 9, 3, 4, 44, 0, 8, 6, 2, 9, 1, 11, 23],
[1, 9, 19, 2, 6, 27, 1, 1, 4, 16, 18, 30, 23, 19, 21],
[18, 18, 14, 9, 11, 10, 26, 1, 19, 5, 14, 12, 64, 22, 27],
[18, 1, 16, 20, 2, 5, 10, 1, 27, 6, 36, 3, 37, 4, 6],
[1, 4, 22, 3, 3, 16, 16, 1, 23, 8, 5, 32, 8, 4, 7],
[9, 23, 34, 9, 3, 15, 6, 2, 15, 8, 12, 9, 21, 1, 2],
[26, 20, 17, 5, 1, 8, 17, 1, 11, 19, 2, 26, 10, 11, 7],
[3, 8, 15, 15, 7, 4, 22, 10, 18, 4, 1, 5, 29, 3, 24],
[22, 29, 31, 2, 2, 12, 23, 1, 4, 1, 1, 33, 16, 2, 27],
[8, 13, 16, 5, 3, 0, 4, 18, 20, 23, 26, 13, 8, 8, 8],
[9, 45, 4, 8, 8, 24, 41, 1, 63, 18, 3, 18, 17, 9, 4],
[0, 20, 3, 2, 13, 8, 4, 1, 19, 5, 21, 0, 74, 5, 28],
[4, 6, 2, 2, 5, 12, 0, 1, 2, 12, 15, 7, 7, 14, 1],
[6, 2, 47, 53, 3, 15, 9, 5, 0, 6, 12, 10, 40, 3, 48],
[9, 6, 5, 30, 1, 17, 6, 0, 18, 9, 65, 29, 4, 1, 3],
[0, 14, 5, 1, 7, 4, 11, 4, 1, 3, 3, 6, 24, 15, 1],
[9, 5, 20, 15, 4, 3, 16, 1, 5, 11, 50, 29, 1, 4, 4],
[4, 6, 17, 0, 7, 56, 30, 4, 2, 27, 26, 1, 10, 26, 5],
[16, 15, 20, 1, 7, 36, 7, 2, 1, 4, 1, 1, 3, 13, 13],
[5, 7, 17, 13, 0, 35, 9, 6, 6, 13, 11, 0, 7, 3, 7],
[12, 2, 15, 3, 5, 10, 34, 5, 3, 10, 3, 13, 82, 21, 12],
[10, 3, 26, 3, 7, 1, 19, 2, 20, 72, 26, 35, 54, 3, 8],
[1, 15, 74, 12, 2, 18, 1, 0, 19, 5, 30, 2, 5, 17, 8],
[37, 2, 11, 3, 1, 8, 1, 2, 5, 15, 5, 1, 4, 1, 8],
[5, 9, 24, 11, 4, 13, 25, 0, 13, 5, 4, 2, 12, 4, 11],
[26, 14, 33, 10, 12, 5, 47, 5, 1, 2, 2, 1, 82, 6, 8],
[6, 3, 6, 24, 5, 11, 1, 2, 8, 36, 0, 13, 60, 7, 0],
[12, 5, 11, 21, 3, 0, 6, 3, 7, 3, 3, 19, 1, 6, 6],
[8, 2, 36, 14, 2, 37, 28, 12, 46, 8, 12, 31, 11, 6, 22],
[3, 6, 76, 7, 9, 10, 2, 6, 18, 29, 4, 9, 8, 13, 9],
[13, 4, 14, 2, 5, 10, 11, 15, 18, 2, 2, 7, 24, 15, 7],
[6, 6, 13, 24, 1, 2, 34, 14, 1, 16, 1, 32, 23, 6, 0],
[10, 2, 4, 0, 6, 17, 2, 1, 0, 24, 1, 10, 17, 16, 14],
]
# Derived data.
inbound_truck_count = len(_inbound_truck_raw_data)
outbound_truck_count = len(_outbound_truck_raw_data)
total_truck_count = inbound_truck_count + outbound_truck_count
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62,
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503,
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62,
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30915,
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198
] | 1.738188 | 3,090 |
import pandas as pd
import numpy as np
import datetime as dt
#Debug library functions
#Add a new row to dfDebug
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] | 3.166667 | 36 |
from unittest import TestCase
from RLTest.debuggers import Valgrind
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import numpy as np
a = range(100)
A = np.array(a).reshape(len(a)//2, 2)
A = A.ravel().view([('col1', 'i8'), ('col2', 'i8'), ]
).astype([('col1', 'i4'), ('col2', 'i8'), ])
print(A[:5])
# array([(0, 1), (2, 3), (4, 5), (6, 7), (8, 9)],
# dtype=[('col1', '<i4'), ('col2', '<i8')])
print(A.dtype)
# dtype([('col1', '<i4'), ('col2', '<i8')])
print(A['col1']*A['col2'])
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] | 1.798165 | 218 |
from examples.settings import settings
from office365.runtime.auth.authentication_context import AuthenticationContext
from office365.sharepoint.client_context import ClientContext
def read_folder_and_files():
"""Read a folder example"""
list_obj = ctx.web.lists.get_by_title(listTitle)
folder = list_obj.root_folder
ctx.load(folder)
ctx.execute_query()
print("List url: {0}".format(folder.properties["ServerRelativeUrl"]))
files = folder.files
ctx.load(files)
ctx.execute_query()
for cur_file in files:
print("File name: {0}".format(cur_file.properties["Name"]))
folders = ctx.web.folders
ctx.load(folders)
ctx.execute_query()
for folder in folders:
print("Folder name: {0}".format(folder.properties["Name"]))
if __name__ == '__main__':
ctx_auth = AuthenticationContext(url=settings['url'])
if ctx_auth.acquire_token_for_user(username=settings['username'], password=settings['password']):
ctx = ClientContext(settings['url'], ctx_auth)
listTitle = "Documents"
read_folder_and_files()
else:
print(ctx_auth.get_last_error())
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] | 2.688525 | 427 |
# Base classes
class _ScandinavianStemmer(object):
"""
This subclass encapsulates a method for defining the string region R1.
It is used by the Danish, Norwegian, and Swedish stemmer.
"""
def _r1_scandinavian(self, word, vowels):
"""
Return the region R1 that is used by the Scandinavian stemmers.
R1 is the region after the first non-vowel following a vowel,
or is the null region at the end of the word if there is no
such non-vowel. But then R1 is adjusted so that the region
before it contains at least three letters.
:param word: The word whose region R1 is determined.
:type word: str or unicode
:param vowels: The vowels of the respective language that are
used to determine the region R1.
:type vowels: unicode
:return: the region R1 for the respective word.
:rtype: unicode
:note: This helper method is invoked by the respective stem method of
the subclasses DanishStemmer, NorwegianStemmer, and
SwedishStemmer. It is not to be invoked directly!
"""
r1 = ""
for i in range(1, len(word)):
if word[i] not in vowels and word[i - 1] in vowels:
if len(word[:i + 1]) < 3 and len(word[:i + 1]) > 0:
r1 = word[3:]
elif len(word[:i + 1]) >= 3:
r1 = word[i + 1:]
else:
return word
break
return r1
class _StandardStemmer(object):
"""
This subclass encapsulates two methods for defining the standard versions
of the string regions R1, R2, and RV.
"""
def _r1r2_standard(self, word, vowels):
"""
Return the standard interpretations of the string regions R1 and R2.
R1 is the region after the first non-vowel following a vowel,
or is the null region at the end of the word if there is no
such non-vowel.
R2 is the region after the first non-vowel following a vowel
in R1, or is the null region at the end of the word if there
is no such non-vowel.
:param word: The word whose regions R1 and R2 are determined.
:type word: str or unicode
:param vowels: The vowels of the respective language that are
used to determine the regions R1 and R2.
:type vowels: unicode
:return: (r1,r2), the regions R1 and R2 for the respective word.
:rtype: tuple
:note: This helper method is invoked by the respective stem method of
the subclasses DutchStemmer, FinnishStemmer,
FrenchStemmer, GermanStemmer, ItalianStemmer,
PortugueseStemmer, RomanianStemmer, and SpanishStemmer.
It is not to be invoked directly!
:note: A detailed description of how to define R1 and R2
can be found at http://snowball.tartarus.org/texts/r1r2.html
"""
r1 = ""
r2 = ""
for i in range(1, len(word)):
if word[i] not in vowels and word[i - 1] in vowels:
r1 = word[i + 1:]
break
for i in range(1, len(r1)):
if r1[i] not in vowels and r1[i - 1] in vowels:
r2 = r1[i + 1:]
break
return (r1, r2)
def _rv_standard(self, word, vowels):
"""
Return the standard interpretation of the string region RV.
If the second letter is a consonant, RV is the region after the
next following vowel. If the first two letters are vowels, RV is
the region after the next following consonant. Otherwise, RV is
the region after the third letter.
:param word: The word whose region RV is determined.
:type word: str or unicode
:param vowels: The vowels of the respective language that are
used to determine the region RV.
:type vowels: unicode
:return: the region RV for the respective word.
:rtype: unicode
:note: This helper method is invoked by the respective stem method of
the subclasses ItalianStemmer, PortugueseStemmer,
RomanianStemmer, and SpanishStemmer. It is not to be
invoked directly!
"""
rv = ""
if len(word) >= 2:
if word[1] not in vowels:
for i in range(2, len(word)):
if word[i] in vowels:
rv = word[i + 1:]
break
elif word[:2] in vowels:
for i in range(2, len(word)):
if word[i] not in vowels:
rv = word[i + 1:]
break
else:
rv = word[3:]
return rv
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] | 2.176865 | 2,239 |
import sqlalchemy as sa
from sqlalchemy.orm import relationship
from myopenpantry.extensions.database import db
# ingredients are a base part of recipes. ex "chicken breast"
# association tables will be used to join these with inventory items, as well as the amount needed in recipes
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import unittest
from csrv.model import errors
from csrv.model import events
from csrv.model import test_base
from csrv.model import timing_phases
from csrv.model.cards.corp import card01065
if __name__ == '__main__':
unittest.main()
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] | 2.86747 | 83 |
import unittest
from Training.observer_abilities import *
if __name__ == '__main__':
unittest.main()
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#!/usr/bin/env python
# coding: utf-8
# <a id='top'></a>
#
#
# # $\texttt{GiRaFFEfood}$: Initial data for $\texttt{GiRaFFE}$
#
# ## Aligned Rotator
#
# $$\label{top}$$
#
# This module provides another initial data option for $\texttt{GiRaFFE}$. This is a flat-spacetime test with initial data $$A_{\phi} = \frac{\mu \varpi}{r^3},$$ where $\mu = B_p R_{\rm NS} / 2$, $R_{\rm NS}$ is the neutron star radius, and $\varpi = \sqrt{x^2+y^2}$ is the cylindrical radius. We let $A_r = A_\theta = 0$.
#
# Additionally, the drift velocity $v^i = \Omega \textbf{e}_z \times \textbf{r} = [ijk] \Omega \textbf{e}^j_z x^k$, where $[ijk]$ is the Levi-Civita permutation symbol and $\textbf{e}^i_z = (0,0,1)$.
# <a id='preliminaries'></a>
#
# ### Steps 0-1: Preliminaries
# $$\label{preliminaries}$$
#
# \[Back to [top](#top)\]
#
# Here, we will import the NRPy+ core modules and set the reference metric to Cartesian, set commonly used NRPy+ parameters, and set C parameters that will be set from outside the code eventually generated from these expressions. We will also set up a parameter to determine what initial data is set up, although it won't do much yet.
# Step 0: Import the NRPy+ core modules and set the reference metric to Cartesian
import NRPy_param_funcs as par
import indexedexp as ixp
import sympy as sp # SymPy: The Python computer algebra package upon which NRPy+ depends
import reference_metric as rfm
par.set_parval_from_str("reference_metric::CoordSystem","Cartesian")
rfm.reference_metric()
# Step 1a: Set commonly used parameters.
thismodule = __name__
B_p_aligned_rotator,R_NS_aligned_rotator = par.Cparameters("REAL",thismodule,
# B_p_aligned_rotator = the intensity of the magnetic field and
# R_NS_aligned_rotator= "Neutron star" radius
["B_p_aligned_rotator","R_NS_aligned_rotator"],
[1e-5, 1.0])
# The angular velocity of the "neutron star"
Omega_aligned_rotator = par.Cparameters("REAL",thismodule,"Omega_aligned_rotator",1e3)
# <a id='step2'></a>
#
# ### Step 2: Set the vectors A in Spherical coordinates
# $$\label{step2}$$
#
# \[Back to [top](#top)\]
#
# We will first build the fundamental vector $A_i$ in spherical coordinates (see [Table 3](https://arxiv.org/pdf/1704.00599.pdf)). Note that we use reference_metric.py to set $r$ and $\theta$ in terms of Cartesian coordinates; this will save us a step later when we convert to Cartesian coordinates. So, we set
# \begin{align}
# A_{\phi} &= \frac{\mu \varpi}{r^3}, \\
# \end{align}
# with $\mu = B_p R_{\rm NS} / 2$, $R_{\rm NS}$ is the neutron star radius, and $\varpi = \sqrt{x^2+y^2}$
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] | 2.377946 | 1,188 |
""" Implementing word2vec using the skipgram model using a logistic regression
as probability function. """
import tensorflow as tf
import numpy as np
import zipfile
import math
from . import directory_util, download
import os
import collections
dataset_name = 'text8.zip'
def read_data(filename):
""" Step 1 Read data. """
filepath = os.path.join(directory_util.get_data_dir(), dataset_name)
with zipfile.ZipFile(filepath) as f:
print 'FILES IN THE ZIP: ', f.namelist()
contents = f.read(f.namelist()[0])
try:
print 'SAMPLE CONTENTS OF FILE', contents[:100]
except:
print '!!CANNOT PRINT SAMPLE'
data = tf.compat.as_str(contents).split() # TODO what is package compat?
return data
download.download_data(dataset_name)
vocabulary = read_data(dataset_name)
vocabulary_size = len(vocabulary) # Taking all the words in the vocabulary!
print 'VOCABULARY SIZE: ', vocabulary_size, 'SAMPLE', vocabulary[:10]
vocabulary_size = 50000 # change so that you only take part of it.
def build_dataset(vocabulary, vocabulary_size):
""" To count the occurences of words and mark the most common words
as UNKOWN /UNK"""
count = [['UNK', -1]]
# select the top common ones.
count.extend(collections.Counter(vocabulary).most_common(vocabulary_size - 1))
print 'COUNT: ', count[:10]
wordDictionary = {}
# Build dictionary with name and count
for word, _ in count:
wordDictionary[word] = len(wordDictionary)
data = []
unknown_count = 0
for word in vocabulary:
index = wordDictionary.get(word, 0) # Rare words. TODO dig into this.
if (index == 0):
unknown_count += 1
data.append(index)
count[0][1] = unknown_count
reverseWordDictionary = dict(zip(wordDictionary.values(), wordDictionary.keys()))
return data, count, wordDictionary, reverseWordDictionary
# reverseWordDictionary => Index to Word mapping.
# wordDictionary => Name to Index mapping
# count = Top vocabulary_size-1 common words. (Unkown is one of them)
# data = The original data mapped to integers. ( If there is a word that doesn't exist in the map; the data integer would be 0)
data, count, wordDictionary, reverseWordDictionary = build_dataset(vocabulary, vocabulary_size)
del vocabulary # Don't need the vocabulary any more. Since the dictionary are there.
print 'MOST COMMON DATA: ', count[:10]
print 'SAMPLE DATA: ', data[:10], [reverseWordDictionary.get(index) for index in data[:10]]
data_index = 0 # global variable to keep track of till where the data was read last time.
def generate_batch(batch_size, window_size): # TODO improve generating a batch by using some randomness in the process.
"""
Generate a batch for training from the dataset ( data )
:returns
data: The data / center words.
labels: the target/ context words.
"""
global data_index
batch = np.ndarray((batch_size), dtype=np.int32)
labels = np.ndarray((batch_size, 1), dtype=np.int32)
current_batch_index = 0
for batch_index in range(batch_size): # These many elements are required in both the batch and labels.
if data_index + 2 * window_size >= len(data):
data_index = 0
center_word_index = data_index + window_size
context_word_indices = [index for index in range(data_index, data_index + 2 * window_size + 1) if
index != center_word_index]
for context_word_index in context_word_indices:
batch[current_batch_index] = data[center_word_index]
labels[current_batch_index, 0] = data[context_word_index]
current_batch_index += 1
if current_batch_index >= batch_size:
break
if current_batch_index >= batch_size:
break
# Shift the window by 1
data_index += 1
return batch, labels
def skipgram(vocabulary_size=50000, embedding_size=128):
"""Run skip gram model for a dataset."""
batch_size = 128 # Run sufficient batch_size until the loss is minimized
num_iterations = 100000 # loop for training.
graph = tf.Graph() # To construct a tensorflow Graph
with graph.as_default():
with tf.name_scope('inputs'):
# Placeholders for inputs center word (Center words) => context words
# (labels / what this model is trying to adjust to).
train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
with tf.device('/cpu:0'):
with tf.name_scope('embeddings'):
# Get embedding
embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], # Word vectors.
-1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs) # Retrieve the embeddings for the input
with tf.name_scope('weights'):
nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
with tf.name_scope('biases'):
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,
biases=nce_biases,
labels=train_labels,
inputs=embed, # Optimize these embeddings from
num_sampled=64, # Number of negatives to sample.
num_classes=vocabulary_size))
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
for batch_num in xrange(num_iterations):
inputs, labels = generate_batch(batch_size, window_size=1)
_, current_loss, current_embeddings = session.run([optimizer, loss, embeddings], feed_dict={
train_inputs: inputs,
train_labels: labels
})
print 'LOSS', current_loss
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3601,
705,
43,
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3256,
1459,
62,
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198
] | 2.366311 | 2,719 |
# 09_Três amigos, Carlos, André e Felipe. decidiram rachar igualmente a conta de um bar.
# Faça um algoritmo para ler o valor total da conta e imprimir quanto cada um deve
# pagar, mas faça com que Carlos e André não paguem centavos. Ex: uma conta de
# R$101,53 resulta em R$33,00 para Carlos, R$33,00 para André e R$35,53 para Felipe.
conta = float(input('Qual o valor da conta? '))
carlos = int(conta / 3)
andre = int(conta / 3)
felipe = float(conta - (carlos + andre))
print('Carlos paga: %.2f '% (carlos))
print('André paga: %.2f '% (andre))
print('Felipe paga: %.2f '% (felipe)) | [
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4,
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] | 2.368421 | 247 |
##############################################
# head-1-2-3-4(tail) <---enqueue
# dequeue---^
##############################################
# 测试函数
if __name__ == '__main__':
# 运行测试函数
test()
# 1
# 2
# 3
# 4
# None
| [
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] | 2.130435 | 115 |
from icevision.models.ultralytics import yolov5
| [
6738,
4771,
10178,
13,
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13,
586,
1373,
88,
14094,
1330,
331,
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20,
198
] | 3 | 16 |
from django import forms
from .models import CustomUser | [
6738,
42625,
14208,
1330,
5107,
198,
6738,
764,
27530,
1330,
8562,
12982
] | 4.583333 | 12 |
from uecp.commands.base import UECPCommand
from uecp.commands.bidirectional import (
MessageAcknowledgementCommand,
RequestCommand,
ResponseCode,
)
from uecp.commands.clock_control import (
RealTimeClockCorrectionSetCommand,
RealTimeClockEnabledSetCommand,
RealTimeClockSetCommand,
)
from uecp.commands.control_n_setup import (
CommunicationMode,
CommunicationModeSetCommand,
DataSetSelectCommand,
EncoderAddressSetCommand,
SiteAddressSetCommand,
SiteEncoderAddressSetCommandMode,
)
from uecp.commands.mixins import InvalidDataSetNumber, InvalidProgrammeServiceNumber
from uecp.commands.rds_control import (
RDSEnabledSetCommand,
RDSLevelSetCommand,
RDSPhaseSetCommand,
)
from uecp.commands.rds_message import (
DecoderInformationSetCommand,
InvalidNumberOfTransmissions,
InvalidProgrammeIdentification,
InvalidProgrammeServiceName,
InvalidProgrammeTypeName,
ProgrammeIdentificationSetCommand,
ProgrammeServiceNameSetCommand,
ProgrammeType,
ProgrammeTypeNameSetCommand,
ProgrammeTypeSetCommand,
RadioText,
RadioTextBufferConfiguration,
RadioTextSetCommand,
TrafficAnnouncementProgrammeSetCommand,
)
| [
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] | 3.068182 | 396 |
import os
from datetime import datetime, timedelta, date
from django.contrib.auth.models import User
from django.contrib.contenttypes.models import ContentType
from django.contrib.sites.models import Site
from django.core.files import File
from django.core.management import call_command
from django.core.management.base import BaseCommand
from django.utils.timezone import now
from ads.models import Banner
from content.models import Page, Redirect, SiteSettings, BlogPost
from resources.models import StyleSheet
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] | 3.76087 | 138 |
import numpy as np
__all__ = [
"img_2x2",
"mask_2x2",
"img_3x3_rgb",
"img_3x3",
"img_3x4",
"mask_3x3",
"mask_3x4",
"img_5x5",
"mask_5x5",
"img_6x6",
"img_6x6_lc",
"img_6x6_rgb",
"mask_6x6",
"img_7x7",
"cube_3x3x3",
]
def img_2x2():
"""
Generates a 2x2 grayscale image (uint8)
Returns
-------
out : ndarray
2x2x1 uint8 image
"""
return np.array([[1, 0], [1, 1]]).reshape((2, 2, 1)).astype(np.uint8)
def mask_2x2():
"""
Generates 2x2 mask (doesn't have the 3rd dimension compare to an image).
Returns
-------
out : ndarray
2x2 mask, uint8
"""
return np.array([[1, 0], [0, 1]]).reshape((2, 2)).astype(np.uint8)
def img_3x4():
"""
Generates a grayscale image 3x4
Returns
-------
out : ndarray
3x4x1 uint8 image
"""
img = np.array([[1, 1, 1, 0], [1, 0, 1, 1], [1, 1, 1, 1]]).reshape((3, 4, 1)).astype(np.uint8) * 255
return img
def mask_3x4():
"""
Generates a mask 3x4
Returns
-------
out : ndarray
3x4 uint8 image
"""
mask = np.array([[0, 1, 1, 1], [0, 1, 1, 0], [0, 1, 1, 0]]).reshape((3, 4)).astype(np.uint8)
return mask
def img_3x3():
"""
Generates a grayscale image 3x4
Returns
-------
out : ndarray
3x4x1 uint8 image
"""
img = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 1]]).reshape((3, 3, 1)).astype(np.uint8)
return img
def img_3x3_rgb():
"""
Generates a grayscale image 3x4
Returns
-------
out : ndarray
3x4x1 uint8 image
"""
img = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 1]]).reshape((3, 3, 1)).astype(np.uint8)
return np.dstack((img, img, img)) * 255
def mask_3x3():
"""
Generates a image+mask 3x4
Returns
-------
out : ndarray
3x4 uint8 image
"""
mask = np.array([[1, 1, 1], [1, 1, 1], [0, 1, 1]]).reshape((3, 3)).astype(np.uint8)
return mask
def img_5x5():
"""
Generates a gs image 5x5. It is all ones, besides the edges
Returns
-------
out : ndarray
5x5 uint8 image
"""
img = np.ones((5, 5, 1))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return img.astype(np.uint8)
def mask_5x5():
"""
Generates a mask 5x5. It is all ones, besides the edges
Returns
-------
out : ndarray
5x5 uint8 image
"""
img = np.ones((5, 5))
img[:, :2] = 2
img[:, -2:] = 2
img[:2, :] = 2
img[-2, :] = 2
return img.astype(np.uint8)
def img_6x6():
"""
Generates a gs image 5x5. It is all ones, besides the edges
Returns
-------
out : ndarray
6x6 uint8 image
"""
img = np.ones((6, 6, 1))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return img.astype(np.uint8) * 255
def img_7x7():
"""
Generates a gs image 7x7. It is all ones, besides the edges
Returns
-------
out : ndarray
6x6 uint8 image
"""
img = np.ones((7, 7, 1))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return img.astype(np.uint8) * 255
def mask_6x6():
"""
Generates a mask 6x6. It is all ones, besides the edges
Returns
-------
out : ndarray
3x5 uint8 image
"""
img = np.ones((6, 6))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return img.astype(np.uint8)
def img_6x6_rgb():
"""
Generates an RGB image 6x6. It is all 255, besides the edges
Returns
-------
out : ndarray
6x6 uint8 image
"""
img = np.ones((6, 6, 1))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return np.dstack((img, img, img)).astype(np.uint8) * 255
def img_6x6_lc():
"""
Generates an RGB image 6x6. It is all 7x7, besides the edges (low contrast)
Returns
-------
out : ndarray
6x6 uint8 image
"""
img = np.ones((6, 6, 1))
img[:, 0] = 0
img[:, -1] = 0
img[0, :] = 0
img[-1, :] = 0
return np.dstack((img, img, img)).astype(np.uint8) * 127
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] | 1.951515 | 2,145 |
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
"""
emit_log.py published log messages are going to be broadcast to all the receives
"""
# Pika is a pure-Python implementation of the AMQP 0-9-1 protocol
import pika
import sys
# guest user can only connect via localhost
#credentials = pika.PlainCredentials('guest', 'guest')
credentials = pika.PlainCredentials('pi', 'macintosh')
connection = pika.BlockingConnection(pika.ConnectionParameters(host='192.168.31.156',
port=5672,
virtual_host='/',
credentials=credentials))
channel = connection.channel()
# declare the exchange
channel.exchange_declare(exchange='logs',
exchange_type='fanout')
message = ' '.join(sys.argv[1:]) or "info: Hello World!"
channel.basic_publish(exchange='logs',
routing_key='',
body=message,
)
print("[x] Sent %r" %message)
connection.close()
"""
Please keep in mind that this and other tutorials are, well, tutorials, They demonstrate one new concept at a time and may
intentionally oversimplify some things and leave out others. For example topics such as connection management, error handling,
connection recovery, concurrency and metric collection are largely omitted for the sake of brevity. Such simplified code
should not be considered production ready.
""" | [
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] | 2.396226 | 636 |
"""
Ask the (testing) token service for a token.
Run with `python -m lta.solicit_token`.
"""
import asyncio
from rest_tools.client import RestClient # type: ignore
from rest_tools.server import from_environment # type: ignore
EXPECTED_CONFIG = {
'LTA_AUTH_ROLE': None,
'TOKEN_SERVICE_URL': None,
}
async def solicit_token(url, scope):
"""Obtain a service token from the token service."""
rc = RestClient(url, "")
result = await rc.request("GET", f"/token?scope={scope}")
print(result["access"])
if __name__ == '__main__':
config = from_environment(EXPECTED_CONFIG)
asyncio.run(solicit_token(config["TOKEN_SERVICE_URL"], config["LTA_AUTH_ROLE"]))
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] | 2.711462 | 253 |
# Copyright 2018 The Texar 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.
"""Sentence convolutional classifier config.
This is (approximately) the config of the paper:
(Kim) Convolutional Neural Networks for Sentence Classification
https://arxiv.org/pdf/1408.5882.pdf
"""
# pylint: disable=invalid-name, too-few-public-methods, missing-docstring
import copy
num_epochs = 15
train_data = {
"batch_size": 50,
"datasets": [
{
"files": "./data/sst2.train.sentences.txt",
"vocab_file": "./data/sst2.vocab",
# Discards samples with length > 56
"max_seq_length": 56,
"length_filter_mode": "discard",
# Do not append BOS/EOS tokens to the sentences
"bos_token": "",
"eos_token": "",
"data_name": "x"
},
{
"files": "./data/sst2.train.labels.txt",
"data_type": "int",
"data_name": "y"
}
]
}
# The val and test data have the same config with the train data, except
# for the file names
val_data = copy.deepcopy(train_data)
val_data["datasets"][0]["files"] = "./data/sst2.dev.sentences.txt"
val_data["datasets"][1]["files"] = "./data/sst2.dev.labels.txt"
test_data = copy.deepcopy(train_data)
test_data["datasets"][0]["files"] = "./data/sst2.test.sentences.txt"
test_data["datasets"][1]["files"] = "./data/sst2.test.labels.txt"
# Word embedding
emb = {
"dim": 300
}
# Classifier
clas = {
"num_conv_layers": 1,
"filters": 100,
"kernel_size": [3, 4, 5],
"conv_activation": "relu",
"pooling": "MaxPooling1D",
"num_dense_layers": 0,
"dropout_conv": [1],
"dropout_rate": 0.5,
"num_classes": 2
}
# Optimization
# Just use the default config, e.g., Adam Optimizer
opt = {}
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] | 2.436516 | 953 |
# Given a positive integer n, find the least number of perfect square numbers (for example, 1, 4, 9, 16, ...) which sum to n.
# Example 1:
# Input: n = 12
# Output: 3
# Explanation: 12 = 4 + 4 + 4.
# Example 2:
# Input: n = 13
# Output: 2
# Explanation: 13 = 4 + 9.
# 二刷 200627
# M1. 蛮力算法 TLE
# M2. DP
# M3. BFS
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from bs4 import BeautifulSoup
from lxml import etree
import json
# xpath需要定期更换，否则数据抓取不全
def overview_info_parse(parse_str:str = ''):
"""
传入一个待解析的字符串
"""
html_info = etree.HTML(parse_str,parser=None)
try:
#=======================开始解析字段=======================
Price = html_info.xpath('string(//*[@id="ContentPlaceHolder1_tr_valuepertoken"]/div/div[1]/span)').strip()
Fully_Diluted_Market_Cap = html_info.xpath('string(//*[@id="pricebutton"])').strip()
Max_Total_Supply = html_info.xpath('string(//*[@id="ContentPlaceHolder1_divSummary"]/div[1]/div[1]/div/div[2]/div[2]/div[2]/span)').strip()
Holders = html_info.xpath('string(//*[@id="ContentPlaceHolder1_tr_tokenHolders"]/div/div[2]/div)').strip()
Transfers = ''# 在另一个接口当中才有这个数据
Volume_24H = html_info.xpath('string(//*[@id="tokenInfo"]/div/table/tbody/tr[1]/td[3])').strip()
Market_Capitalization = html_info.xpath('string(//*[@id="tokenInfo"]/div/table/tbody/tr[2]/td[3])').strip()
Circulating_Supply = html_info.xpath('string(//*[@id="tokenInfo"]/div/table/tbody/tr[3]/td[3])').strip()
#=======================解析字段结束=======================
except:
return {'status':False,
'wrong_reason':"Error parsing the file, please change the XPATH parsing path."}
Dict_info = {
"status":True,
"info_name":"basic information",
"info_list":[{
"name": "Price",
"value":Price
},{
"name":"Fully_Diluted_Market_Cap",
"value":Fully_Diluted_Market_Cap
},{
"name":"Max_Total_Supply",
"value":Max_Total_Supply
},{
"name":"Holders",
"value":Holders
},{
"name":"Volume_24H",
"value":Volume_24H
},{
"name":"Market_Capitalization",
"value":Market_Capitalization
},{
"name":"Circulating_Supply",
"value":Circulating_Supply
}]
}
return json.dumps(Dict_info)
def overview_info_transfers_parse(parse_str:str = ''):
"""
传入一个待解析的字符串
"""
soup = BeautifulSoup(parse_str,features="lxml")
all_info_list = []
key_list = ['Txn_Hash','Method','time1','time2','From','To','Quantitiy']
for tr in soup.find_all("tr"):
list_info = []
for i in tr.find_all("td"):
if i.text != '':
if "..." not in i.text:
list_info.append(i.text)
else:
list_info.append(i.span['title'])
if len(list_info) == 7:
all_info_list.append(dict(zip(key_list,list_info)))
return all_info_list | [
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] | 1.871654 | 1,457 |
from marshmallow import Schema, fields
from marshmallow.validate import Length
from iot_api.user_api.repository.AppKeysRepository import MAX_PER_ORGANIZATION | [
6738,
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1330,
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11,
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198,
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4677,
40729,
6207,
13264,
1330,
25882,
62,
18973,
62,
1581,
45028,
14887,
6234
] | 3.488889 | 45 |
import logging
import shlex
from kubeyard.commands.devel import BaseDevelCommand
logger = logging.getLogger(__name__)
class BuildCommand(BaseDevelCommand):
"""
Builds docker image required to run tests and deployment. Can be overridden in <project_dir>/sripts/build.
If kubeyard is set up in development mode it uses minikube as docker host.
"""
custom_script_name = 'build'
context_vars = ['image_context', 'docker_args']
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] | 3.026846 | 149 |
#!/usr/bin/python
import MySQLdb as mdb
import psycopg2
import psycopg2.extras
import psycopg2.extensions
from psycopg2 import OperationalError
import sys
import argparse
parser = argparse.ArgumentParser(description='Generate an SQL file to update the ontology terms in the core database based on the matching terms in the ontology database.')
parser.add_argument('--division', help="The EG division (eg EnsemblFungi)", default="EnsemblFungi")
parser.add_argument('--species', help="The dataset species name (eg schizosaccharomyces_pombe)", default="schizosaccharomyces_pombe")
parser.add_argument('--eg_release', type=int, help="EG release version (eg 21)", default=21)
parser.add_argument('--e_release', type=int, help="Ensembl release version (eg 74)", default=74)
parser.add_argument('--assembly', type=int, help="Species assembly (eg 2)", default=2)
parser.add_argument('--chado_release', type=int, help="Chado dump version release number", default=41)
parser.add_argument('--dbhost', help="Core database host", default="mysql-cluster-eg-prod-3.ebi.ac.uk")
parser.add_argument('--dbport', type=int, help="Core database port", default=4243)
parser.add_argument('--dbchadohost', help="Core database host", default="postgres-eg-pombe.ebi.ac.uk")
parser.add_argument('--dbchadoport', type=int, help="Core database port", default=5432)
parser.add_argument('--dbchadouser', help="Core database username", default="ensrw")
parser.add_argument('--dbchadopass', help="Core database password", default="xxxxx")
parser.add_argument('--file', help="Output file. Default is 'data/sql/update_gene_names_synonyms_test.sql'", default="data/sql/update_gene_names_synonyms_test.sql")
args = parser.parse_args()
division = args.division # 'EnsemblFungi'
species = args.species # 'schizosaccharomyces_pombe'
eg_release = args.eg_release # 18
e_release = args.e_release # 71
chado_release = args.chado_release # 35
assembly = args.assembly # 2
sppdb = species + "_core_" + str(eg_release) + "_" + str(e_release) + "_" + str(assembly)
chadodb = 'pombase_chado_v' + str(args.chado_release)
pguser = args.dbchadouser
pgpass = args.dbchadopass
conp = None
conm = None
chado = dict()
eg = dict()
print args.dbchadohost, args.dbchadoport, chadodb
try:
conp = psycopg2.connect(host=args.dbchadohost, port=args.dbchadoport, user=args.dbchadouser, password=args.dbchadopass, database=chadodb)
cur = conp.cursor()
cur.execute("SELECT feature.name, feature.uniquename, ARRAY_TO_STRING(ARRAY_AGG(synonym.name),',') FROM feature JOIN organism on (feature.organism_id=organism.organism_id) JOIN cvterm ON (feature.type_id=cvterm.cvterm_id) JOIN cv on (cvterm.cv_id=cv.cv_id) LEFT JOIN feature_synonym ON feature.feature_id=feature_synonym.feature_id LEFT JOIN synonym ON feature_synonym.synonym_id=synonym.synonym_id WHERE cvterm.name in ('gene', 'pseudogene') AND cv.name='sequence' AND organism.genus || '_' || organism.species = '" + species.capitalize() + "' GROUP BY feature.name, feature.uniquename;")
rows = cur.fetchall()
cur.close()
for row in rows:
stable_id = row[1]
name = row[0]
if name == None:
name = stable_id
synonym = row[2]
if synonym == None:
synonym = ''
else:
synonym = row[2].split(',')
chado[stable_id] = {'name': name, 'synonyms':synonym}
except Exception, e:
print "PostgreSQL Error %s: %s" % (e.pgcode,e.pgerror)
print e
sys.exit(1)
finally:
if conp:
conp.close()
#fi = open('/homes/mcdowall/Documents/Code/python/Spombe_EG17_GeneName_Synonym_v33.tsv', 'r')
#for line in fi:
# line = line.replace('\n', '')
# sline = line.split('\t')
# eg[sline[1]] = {'name': sline[0], 'synonyms':sline[2].split(',')}
#fi.close()
print args.dbhost, args.dbport, sppdb
try:
conm = mdb.connect(host=args.dbhost, port=args.dbport, user='ensro', db=sppdb)
cur = conm.cursor()
#cur.execute('UPDATE xref JOIN gene ON (gene.stable_id=xref.dbprimary_acc) SET gene.display_xref_id=xref.xref_id WHERE external_db_id=50642;')
cur.execute("SELECT xref.display_label, gene.stable_id, GROUP_CONCAT(external_synonym.synonym SEPARATOR ','), gene.display_xref_id FROM gene LEFT JOIN xref ON (gene.display_xref_id=xref.xref_id) LEFT JOIN external_synonym ON (xref.xref_id=external_synonym.xref_id) GROUP BY xref.display_label, gene.stable_id;")
rows = cur.fetchall()
cur.close()
for row in rows:
stable_id = row[1]
name = row[0]
if row[3] != None and name == '':
name = stable_id
elif row[3] == None:
name = ''
synonym = row[2]
if synonym == None:
synonym = ''
else:
synonym = row[2].split(',')
eg[stable_id] = {'name': name, 'synonyms':synonym}
except mdb.Error, e:
print "MySQL Error %d: %s" % (e.args[0],e.args[1])
sys.exit(1)
finally:
if conm:
conm.close()
#print list(set(chado.keys()) - set(eg.keys()))
#print list(set(eg.keys()) - set(chado.keys()))
print 'Loading Update file ...'
f = open(args.file, 'w')
for k in chado.keys():
if (eg.has_key(k)):
#if (chado[k]['name'] != eg[k]['name'] and chado[k]['name'] != ''):
if (chado[k]['name'] != eg[k]['name']):
print 'Name Diff:', k, chado[k]['name'], eg[k]['name']
f.write('# Name change for ' + k + ' from ' + eg[k]['name'] + ' to ' + chado[k]['name'] + "\n")
if (eg[k]['name'] != ''):
f.write('UPDATE gene, xref SET xref.display_label=\'' + chado[k]['name'] + '\' WHERE gene.display_xref_id=xref.xref_id AND gene.stable_id=\'' + k + '\'; # Was ' + eg[k]['name'] + "\n")
else:
f.write('INSERT INTO xref (external_db_id, dbprimary_acc, display_label, version, description, info_type) VALUES (50642, \'' + k + '\', \'' + chado[k]['name'] + '\', 0, \'' + k + '\', "DIRECT");' + "\n")
#f.write('LAST_INSERT_ID();' + "\n")
#f.write('UPDATE gene, xref SET gene.display_xref_id=LAST_INSERT_ID() WHERE gene.stable_id=\'' + k + '\'; # Was NULL' + "\n")
#print 'UPDATE spombe_eg_gene__translation__main SET display_label_1074=\'' + chado[k]['name'] + '\', display_label_1074_r1=\'' + chado[k]['name'] + '\' WHERE stable_id_1023=\'' + k + '\';'
#print 'UPDATE spombe_eg_gene__transcript__main SET display_label_1074=\'' + chado[k]['name'] + '\', display_label_1074_r1=\'' + chado[k]['name'] + '\' WHERE stable_id_1023=\'' + k + '\';'
#print 'UPDATE spombe_eg_gene__ox_pombase_gene__dm SET display_label_1074=\'' + chado[k]['name'] + '\' WHERE dbprimary_acc_1074=\'' + k + '\';'
#print 'UPDATE spombe_eg_gene__ox_pombase_gene_name__dm SET display_label_1074=\'' + chado[k]['name'] + '\' WHERE dbprimary_acc_1074=\'' + k + '\';'
#print 'UPDATE spombe_eg_gene__gene__main SET display_label_1074=\'' + chado[k]['name'] + '\' WHERE stable_id_1023=\'' + k + '\';'
setdiff = set(chado[k]['synonyms'])^set(eg[k]['synonyms'])
if (len(setdiff) > 0):
#print '# Synonyms for ' + k
if (chado[k]['name'] != eg[k]['name'] and eg[k]['name'] != '' and chado[k]['name'] in set(eg[k]['synonyms'])):
f.write('UPDATE gene, xref, external_synonym SET external_synonym.synonym=\'' + eg[k]['name'] + '\' WHERE external_synonym.synonym=\'' + chado[k]['name'] + '\' AND gene.display_xref_id=xref.xref_id AND xref.xref_id=external_synonym.xref_id AND gene.stable_id=\'' + k + '\'; # Was ' + chado[k]['name'] + "\n")
for s in set(chado[k]['synonyms'])-set(eg[k]['synonyms']):
if ((chado[k]['name'] != eg[k]['name'] and chado[k]['name'] in set(eg[k]['synonyms'])) or s==''):
continue
f.write('INSERT INTO external_synonym SELECT xref.xref_id, \'' + s + '\' FROM gene, xref WHERE gene.display_xref_id=xref.xref_id AND gene.stable_id=\'' + k + '\';' + "\n")
for s in set(eg[k]['synonyms'])-set(chado[k]['synonyms']):
if (s==k or s=='' or (chado[k]['name'] != eg[k]['name'] and chado[k]['name'] in set(eg[k]['synonyms']))):
continue
f.write('DELETE external_synonym.* FROM xref JOIN external_synonym ON (xref.xref_id=external_synonym.xref_id) WHERE external_synonym.synonym=\'' + s + '\' AND gene.stable_id=\'' + k + '\';' + "\n")
#print 'Synonym Diff:', k, '|', chado[k]['name'], eg[k]['name'], '|', chado[k]['synonyms'], eg[k]['synonyms']
f.write('UPDATE xref JOIN gene ON (gene.stable_id=xref.dbprimary_acc) SET gene.display_xref_id=xref.xref_id WHERE external_db_id=50642;')
#Make sure that all genes that have a gene name is also reflected in the name of th etranscript
f.write('update gene join transcript using (gene_id) join xref x1 on (gene.display_xref_id=x1.xref_id) join xref x2 on (transcript.display_xref_id=x2.xref_id) set x2.display_label=x1.display_label where x1.dbprimary_acc!=x1.display_label and x1.display_label!=x2.display_label;')
f.close()
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] | 2.388678 | 3,692 |
import numpy as np
from torch import nn
from rlkit.launchers.experiments.disentanglement import (
contextual_encoder_distance_launcher as cedl,
)
from rlkit.torch.core import PyTorchModule
from rlkit.torch.distributions import MultivariateDiagonalNormal
from rlkit.torch.networks import (
BasicCNN,
Flatten,
Mlp,
ConcatMultiHeadedMlp,
Reshape,
)
from rlkit.torch.networks import basic
from rlkit.torch.networks.dcnn import BasicDCNN
from rlkit.torch.networks.mlp import MultiHeadedMlp
from rlkit.torch.networks.stochastic.distribution_generator import (
BernoulliGenerator,
Gaussian,
IndependentGenerator,
)
from rlkit.torch.vae.vae_torch_trainer import VAE
import rlkit.torch.pytorch_util as ptu
from rlkit.torch.sets.fancy_vae_architecture import (
get_fancy_vae,
)
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] | 2.580442 | 317 |
import matplotlib
import matplotlib.pyplot as mpl
from dataclasses import dataclass
from typing import Callable, List
import os
import numpy
import pkg_resources
##
@dataclass
| [
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] | 3.254545 | 55 |
"""
# -*- coding:utf-8 -*-
# @Time : 2019/5/4 20:24
# @Author : KUN LU @ SXU
# @Email : [email protected]
# @Github : lukun199
"""
from __future__ import division
#import sys
import os, time
# import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import glob
import cv2
#from skimage.measure import compare_ssim as ssim
#from skimage.measure import compare_psnr as psnr
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--input', '-i',default='./test')
parser.add_argument('--result', '-r',default='./res')
args = parser.parse_args()
checkpoint_dir = './ckpt/' # @lk199_sub1
input_dir = args.input
result_dir = args.result
print(tf.__version__) # 1.13.1
# -----------------------------------------#settings and preparations----------
sess = tf.Session()
in_image = tf.placeholder(tf.float32, [None, None, None, 3])
gt_image = tf.placeholder(tf.float32, [None, None, None, 3]) # The channel dimension of the inputs should be defined. Found `None`
uf_out = buildmodel(in_image)
# =------------------------------updates--------------------------------
time_elapsed = 0
with tf.Session() as sess:
saver = tf.compat.v1.train.Saver()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded ' + ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path) # 恢复ckpt用于训练
# stats_graph(sess.graph)
# --------------------------------------------------------------------#
eval_fns = glob.glob(input_dir + '*.*')
for N in range(len(eval_fns)):
temp_train = np.array(cv2.imread(eval_fns[N])) # different!!!
temp_train = temp_train/255.0
# ---------------------------------------------------------------------#
train_data = temp_train.reshape(1, temp_train.shape[0], temp_train.shape[1], temp_train.shape[2])
st = time.time()
[out] = sess.run([uf_out], feed_dict={in_image: train_data})
time_elapsed += time.time() - st
print('%s' % eval_fns[N])
[_, name] = os.path.split(eval_fns[N])
suffix = name[name.find('.') + 1:]
name = name[:name.find('.')]
output = np.array(out[0])
output = output.reshape(output.shape[0], output.shape[1], output.shape[2])
output = output*255.0
output_rueslt = np.array(output)
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
cv2.imwrite(result_dir + name + '_TBEFN.png', output_rueslt)
print('total processing time: ', time_elapsed)
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23350,
7587,
640,
25,
46083,
640,
62,
417,
28361,
8,
201,
198,
201,
198
] | 2.38537 | 1,121 |
# -*- coding: utf-8 -*-
# import des fonctions de temps pour attendre
import time
## import de grovepi adresse 4
import grovepi
## import du grovepi adresse 3
import grovepi3
## import de la division reele qui retourne un chiffre a virgule
from operator import truediv
## fonction de stabilisation de la hauteur
## fonction de stabilisation de l avancement
## fonction de decollage
## fonction d aterrisage
## fonction de stabilisation complete
| [
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22492,
1849,
69,
261,
596,
390,
14349,
5612,
1844,
198
] | 2.846626 | 163 |
import logging
from pyvisdk.exceptions import InvalidArgumentError
########################################
# Automatically generated, do not edit.
########################################
log = logging.getLogger(__name__)
def VmPortGroupProfile(vim, *args, **kwargs):
'''The VmPortGroupProfile data object represents the subprofile for a port group
that will be used by virtual machines. Use the policy list for access to
configuration data for the virtual machine port group profile. Use the property
list for access to subprofiles, if any.vSphere Servers use Network managed
objects to represent virtual machine port groups in the vSphere inventory.'''
obj = vim.client.factory.create('{urn:vim25}VmPortGroupProfile')
# do some validation checking...
if (len(args) + len(kwargs)) < 6:
raise IndexError('Expected at least 7 arguments got: %d' % len(args))
required = [ 'key', 'name', 'networkPolicy', 'vlan', 'vswitch', 'enabled' ]
optional = [ 'policy', 'profileTypeName', 'profileVersion', 'property', 'dynamicProperty',
'dynamicType' ]
for name, arg in zip(required+optional, args):
setattr(obj, name, arg)
for name, value in kwargs.items():
if name in required + optional:
setattr(obj, name, value)
else:
raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional)))
return obj
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5298,
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1,
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22179,
7,
35827,
1343,
11902,
22305,
628,
220,
220,
220,
1441,
26181,
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] | 3.155508 | 463 |
import json
import boto3
from botocore.exceptions import ClientError
import os
from slack import WebClient
from slack.errors import SlackApiError
boto_session = boto3.session.Session()
es_client = boto_session.client('es')
ssm_client = boto_session.client('ssm')
iam_client = boto_session.client('iam')
| [
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275,
2069,
62,
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13,
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10786,
1789,
11537,
198
] | 3.134021 | 97 |
from raiden.exceptions import RaidenUnrecoverableError
from raiden.utils.formatting import format_block_id
from raiden.utils.typing import BlockIdentifier, NoReturn
def raise_on_call_returned_empty(given_block_identifier: BlockIdentifier) -> NoReturn:
"""Format a message and raise RaidenUnrecoverableError."""
# We know that the given address has code because this is checked
# in the constructor
msg = (
f"Either the given address is for a different smart contract, "
f"or the contract was not yet deployed at the block "
f"{format_block_id(given_block_identifier)}. Either way this call "
f"should never have happened."
)
raise RaidenUnrecoverableError(msg)
| [
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3022,
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5298,
12448,
268,
3118,
260,
9631,
540,
12331,
7,
19662,
8,
198
] | 3.07265 | 234 |
#Debangshu Roy
#WAP to add input no. in inches and display in cms to program list
oldvalue = int(input("Enter your number in inches "))
inches(oldvalue)
| [
2,
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2,
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198,
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] | 3.163265 | 49 |
from django.apps import AppConfig
| [
6738,
42625,
14208,
13,
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1330,
2034,
16934,
628
] | 3.888889 | 9 |
from __future__ import absolute_import
from django import forms
from django.contrib import messages
from django.core.urlresolvers import reverse
from sentry.models import (
OrganizationMember, OrganizationMemberType, Project, Team
)
from sentry.web.forms.add_project import AddProjectForm
from sentry.web.frontend.base import OrganizationView
ERR_NO_TEAMS = 'You cannot create a new project because there are no teams to assign it to.'
# TODO(dcramer): I'm 95% certain the access is incorrect here as it would
# be probably validating against global org access, and all we care about is
# team admin
| [
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46,
7,
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314,
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4,
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355,
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11,
290,
477,
356,
1337,
546,
318,
198,
220,
220,
220,
1303,
1074,
13169,
198
] | 3.616279 | 172 |
from builtins import SystemError
from pathlib import Path
from sys import argv
from typing import Union
if len(argv) == 1:
day = int(input('day? '))
else:
day = int(argv[1])
day_folder = Path(f'day{day}/')
make_files(day_folder, 'data.txt', 'part1.py', 'part2.py', 'notes.md')
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import functools
from typing import Callable, Awaitable, Any
HandlerType = Callable[..., Awaitable[Any]]
from .exceptions import UmountAction
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] | 3.311111 | 45 |
# Copyright 2021 Google LLC
#
# 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
#
# https://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 absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from smore.models.kg_reasoning import KGReasoning
from smore.common.modules import Regularizer
from smore.common.embedding.sparse_embed import SparseEmbedding
from smore.common.torchext.dist_func.beta_dist import BetaDist, beta_kl, beta_l2, beta_fisher_approx, naive_beta_fisher_approx, naive_beta_kl, naive_beta_l2
import torch.distributions.kl as torch_kl
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] | 3.444118 | 340 |
# -*- coding: utf-8 -*-
from pyvoxel.log import Log
from pyvoxel.manager import Manager
if __name__ == '__main__':
log_test()
Log.error('Error')
p = Manager.auto_load()
print(p)
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# pixelsplines.py
# Pixel-integrated sline utilities.
# Written by A. Bolton, U. of Utah, 2010-2013.
import numpy as n
from scipy import linalg as la
from scipy import sparse as sp
from scipy import special as sf
def compute_duck_slopes(pixbound, flux):
"""
Compute the slope of the illuminating quadratic spline at
the locations of the 'ducks', i.e., the pixel boundaries,
given the integrated flux per unit baseline within the pixels.
ARGUMENTS:
pixbound: (npix + 1) ndarray of pixel boundaries, in units of
wavelength or log-wavelength or frequency or whatever you like.
flux: (npix) ndarray of spectral flux (energy or counts) per
abscissa unit, averaged over the extent of the pixel
RETURNS:
an (npix+1) ndarray of the slope of the underlying/illuminating
flux per unit abscissa spectrum at the position of the pixel
boundaries, a.k.a. 'ducks'. The end conditions are taken to
be zero slope, so the exterior points of the output are zeros.
"""
npix = len(flux)
# Test for correct argument dimensions:
if (len(pixbound) - npix) != 1:
print 'Need one more element in pixbound than in flux!'
return 0
# The array of "delta-x" values:
dxpix = pixbound[1:] - pixbound[:-1]
# Test for monotonif increase:
if dxpix.min() <= 0.:
print 'Pixel boundaries not monotonically increasing!'
return 0
# Encode the tridiagonal matrix that needs to be solved:
maindiag = (dxpix[:-1] + dxpix[1:]) / 3.
offdiag = dxpix[1:-1] / 6.
upperdiag = n.append(0., offdiag)
lowerdiag = n.append(offdiag, 0.)
band_matrix = n.vstack((upperdiag, maindiag, lowerdiag))
# The right-hand side:
rhs = flux[1:] - flux[:-1]
# Solve the banded matrix and return:
acoeff = la.solve_banded((1,1), band_matrix, rhs)
acoeff = n.append(n.append(0., acoeff), 0.)
return acoeff
def cen2bound(pixelcen):
"""
Convenience function to do the obvious thing to transform
pixel centers to pixel boundaries.
"""
pixbound = 0.5 * (pixelcen[1:] + pixelcen[:-1])
lo_val = 2. * pixbound[0] - pixbound[1]
hi_val = 2. * pixbound[-1] - pixbound[-2]
pixbound = n.append(n.append(lo_val, pixbound), hi_val)
return pixbound
def gauss_blur_matrix(pixbound, sig_conv):
"""
Function to generate a Gaussian blurring matrix for a pixelized
spectrum, from specified pixel boundaries and 'sigma' vector.
The matrix will be flux-conserving if the spectrum to which it is
applied has units of 'counts per unit x', and pixbound and sig_conv
both have units of x.
pixbound should have one more element than sig_conv.
Output is a scipy sparse matrix that can implement the blurring as:
blurflux = gauss_blur_matrix * flux
where 'flux' has the same dimensions as 'sig_conv'.
"""
# Derived values and error checks:
npix = len(pixbound) - 1
if (len(sig_conv) != npix):
raise PixSplineError('Need one more element in pixbound than in \
sig_conv!')
if (sig_conv.min() <= 0.):
raise PixSplineError('sig_conv must be > 0 everywhere!')
xcen = 0.5 * (pixbound[1:] + pixbound[:-1])
dxpix = pixbound[1:] - pixbound[:-1]
if (dxpix.min() <= 0.):
raise PixSplineError('Pixel boundaries not monotonically increasing!')
# Which "new" pixels does each "old" pixel touch?
# Let's go +/- 6 sigma for all:
sig_width = 6.0
# A minor correction factor to preserve flux conservation:
cfact = 1./sf.erf(sig_width / n.sqrt(2.))
xblur_lo = xcen - sig_width * sig_conv
xblur_hi = xcen + sig_width * sig_conv
bin_lo = n.digitize(xblur_lo, pixbound) - 1
bin_hi = n.digitize(xblur_hi, pixbound) - 1
# Restrict the ranges:
#xblur_lo = n.where((xblur_lo > pixbound[0]), xblur_lo, pixbound[0])
#xblur_lo = n.where((xblur_lo < pixbound[-1]), xblur_lo, pixbound[-1])
#xblur_hi = n.where((xblur_hi > pixbound[0]), xblur_hi, pixbound[0])
#xblur_hi = n.where((xblur_hi < pixbound[-1]), xblur_hi, pixbound[-1])
bin_lo = n.where((bin_lo >= 0), bin_lo, 0)
#bin_lo = n.where((bin_lo < npix), bin_lo, npix-1)
#bin_hi = n.where((bin_hi >= 0), bin_hi, 0)
bin_hi = n.where((bin_hi < npix), bin_hi, npix-1)
# Compute total number of non-zero elements in the broadening matrix:
n_each = bin_hi - bin_lo + 1
n_entries = n_each.sum()
ij = n.zeros((2, n_entries), dtype=long)
v_vec = n.zeros(n_entries, dtype=float)
# Loop over pixels in the "old" spectrum:
pcount = 0L
roottwo = n.sqrt(2.)
bin_vec = n.arange(npix, dtype=long)
for k in range(npix):
xbound = pixbound[bin_lo[k]:bin_hi[k]+2]
# Gaussian integral in terms of error function:
erf_terms = cfact * 0.5 * sf.erf((xbound - xcen[k]) / (roottwo *
sig_conv[k]))
erf_int = (erf_terms[1:] - erf_terms[:-1]) * \
dxpix[k] / dxpix[bin_lo[k]:bin_hi[k]+1]
ij[0,pcount:pcount+n_each[k]] = bin_vec[bin_lo[k]:bin_hi[k]+1]
ij[1,pcount:pcount+n_each[k]] = k
v_vec[pcount:pcount+n_each[k]] = erf_int
pcount += n_each[k]
conv_matrix = sp.coo_matrix((v_vec, ij), shape=(npix,npix))
return conv_matrix.tocsr()
class PixelSpline:
"""
Pixel Spline object class.
Initialize as follows:
PS = PixelSpline(pixbound, flux)
where
pixbound = array of pixel boundaries in baseline units
and
flux = array of specific flux values in baseline units.
Assumptions:
'pixbound' should have one more element than 'flux', and
units of 'flux' are -per-unit-baseline, for the baseline
units in which pixbound is expressed, averaged over the
extent of each pixel.
"""
def point_evaluate(self, xnew, missing=0.):
"""
Evaluate underlying pixel spline at array of points
BUG: input currently needs to be at least 1D array.
"""
# Initialize output array:
outflux = 0. * self.flux[0] * xnew + missing
# Digitize into bins:
bin_idx = n.digitize(xnew, self.pixbound)
# Find the indices of those that are actually in-bounds:
wh_in = n.where((bin_idx > 0) * (bin_idx < len(self.pixbound)))
if len(wh_in[0]) == 0:
return outflux
xnew_in = xnew[wh_in]
idx_in = bin_idx[wh_in] - 1
# The pixel centers as per the algorithm in use:
adiff = self.duckslopes[idx_in+1] - self.duckslopes[idx_in]
asum = self.duckslopes[idx_in+1] + self.duckslopes[idx_in]
xdiff = xnew_in - self.xcen[idx_in]
fluxvals = adiff * xdiff**2 / (2. * self.dxpix[idx_in]) + asum * xdiff \
/ 2. + self.flux[idx_in] - adiff * self.dxpix[idx_in] / 24.
outflux[wh_in] = fluxvals
return outflux
def resample(self, pb_new):
"""
Method to resample a pixelspline analytically onto a new
set of pixel boundaries.
"""
npix_new = len(pb_new) - 1
xnew_lo = pb_new[:-1].copy()
xnew_hi = pb_new[1:].copy()
# Test for monotonic:
new_fulldx = xnew_hi - xnew_lo
if new_fulldx.min() <= 0.:
raise PixSplineError('New pixel boundaries not monotonically \
increasing!')
# Digitize the new boundaries into the original bins:
bin_idx = n.digitize(pb_new, self.pixbound) - 1
bin_lo = bin_idx[:-1].copy()
bin_hi = bin_idx[1:].copy()
# Array for accumulating new counts:
new_counts = n.zeros(npix_new, dtype=self.flux.dtype)
# Array for accumulating new pixel widths by pieces.
# Only used for debugging so far, but may be useful in future.
#new_dxpix = n.zeros(npix_new, dtype=self.flux.dtype)
# For convenience, we define the following.
# Careful not to modify them... they are views, not copies!
xold_lo = self.pixbound[:-1]
xold_hi = self.pixbound[1:]
# 4 cases to cover:
# Case 1: both bin_hi and bin_lo in the same bin:
wh_this = n.where((bin_hi == bin_lo) * (bin_lo >= 0) * \
(bin_hi < self.npix))
if (len(wh_this[0]) > 0):
dx_this = xnew_hi[wh_this] - xnew_lo[wh_this]
avgval_this = self.subpixel_average(bin_lo[wh_this],
xnew_lo[wh_this],
xnew_hi[wh_this])
#new_dxpix[wh_this] += dx_this
new_counts[wh_this] += avgval_this * dx_this
# Case 2: more than one bin, lower segment:
wh_this = n.where((bin_hi > bin_lo) * (bin_lo >= 0))
if (len(wh_this[0]) > 0):
dx_this = xold_hi[bin_lo[wh_this]] - xnew_lo[wh_this]
avgval_this = self.subpixel_average(bin_lo[wh_this],
xnew_lo[wh_this],
xold_hi[bin_lo[wh_this]])
#new_dxpix[wh_this] += dx_this
new_counts[wh_this] += avgval_this * dx_this
# Case 3: more than one bin, upper segment:
wh_this = n.where((bin_hi > bin_lo) * (bin_hi < self.npix))
if (len(wh_this[0]) > 0):
dx_this = xnew_hi[wh_this] - xold_lo[bin_hi[wh_this]]
avgval_this = self.subpixel_average(bin_hi[wh_this],
xold_lo[bin_hi[wh_this]],
xnew_hi[wh_this])
#new_dxpix[wh_this] += dx_this
new_counts[wh_this] += avgval_this * dx_this
# Case 4: enire bins covered, whole pixels:
wh_this = n.where(bin_hi > (bin_lo+1))
nwhole = len(wh_this[0])
if (nwhole > 0):
pcounts = self.flux * self.dxpix
icounts_this = n.array([pcounts[bin_lo[wh_this[0][ii]]+1:\
bin_hi[wh_this[0][ii]]].sum()
for ii in range(nwhole)])
#new_dxpix[wh_this] += dx_this
new_counts[wh_this] += icounts_this
# Divide out for average and return:
return new_counts / new_fulldx
class WeightedRebinCoadder:
"""
Objet class for weighted rebinning and coaddition of spectra
Initialize as follows:
WRC = WeighedRebinCoadder(fluxes, invvars, pixbounds)
where
fluxes = list of arrays of specific flux values
invvars = list of arrays of associated inverse variances
pixbounds = list of arrays of pixel boundaries in baseline units
"""
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] | 2.046381 | 5,347 |
import os, os.path, pathlib, shutil
from Stat import Stat
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] | 3.222222 | 18 |
n = int(input())
solution = [[0 for i in range(n)] for j in range(n)]
for j in range(n) :
nQueen(0, j, n, solution)
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] | 2.326923 | 52 |
import re
from typing import Any, Dict
import requests
from celery import Task
from django.conf import settings
from posthog.celery import app
from posthog.models import Action, Event, Team
from posthog.tasks.webhooks import determine_webhook_type, get_formatted_message
@app.task(ignore_result=True, bind=True, max_retries=3)
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# Copyright (c) 2009-2011 Denis Bilenko. See LICENSE for details.
"""Managing greenlets in a group.
The :class:`Group` class in this module abstracts a group of running greenlets.
When a greenlet dies, it's automatically removed from the group.
The :class:`Pool` which a subclass of :class:`Group` provides a way to limit
concurrency: its :meth:`spawn <Pool.spawn>` method blocks if the number of
greenlets in the pool has already reached the limit, until there is a free slot.
"""
import sys
from bisect import insort_right
from gevent.hub import GreenletExit, getcurrent, kill as _kill, PY3
from gevent.greenlet import joinall, Greenlet
from gevent.timeout import Timeout
from gevent.event import Event
from gevent.lock import Semaphore, DummySemaphore
__all__ = ['Group', 'Pool']
class Group(object):
"""Maintain a group of greenlets that are still running.
Links to each item and removes it upon notification.
"""
greenlet_class = Greenlet
# def close(self):
# """Prevents any more tasks from being submitted to the pool"""
# self.add = RaiseException("This %s has been closed" % self.__class__.__name__)
def apply(self, func, args=None, kwds=None):
"""Equivalent of the apply() builtin function. It blocks till the result is ready."""
if args is None:
args = ()
if kwds is None:
kwds = {}
if getcurrent() in self:
return func(*args, **kwds)
else:
return self.spawn(func, *args, **kwds).get()
def apply_async(self, func, args=None, kwds=None, callback=None):
"""A variant of the apply() method which returns a Greenlet object.
If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready
callback is applied to it (unless the call failed)."""
if args is None:
args = ()
if kwds is None:
kwds = {}
if self.full():
# cannot call spawn() directly because it will block
return Greenlet.spawn(self.apply_cb, func, args, kwds, callback)
else:
greenlet = self.spawn(func, *args, **kwds)
if callback is not None:
greenlet.link(pass_value(callback))
return greenlet
def map_async(self, func, iterable, callback=None):
"""
A variant of the map() method which returns a Greenlet object.
If callback is specified then it should be a callable which accepts a
single argument.
"""
return Greenlet.spawn(self.map_cb, func, iterable, callback)
def imap(self, func, iterable):
"""An equivalent of itertools.imap()"""
return IMap.spawn(func, iterable, spawn=self.spawn)
def imap_unordered(self, func, iterable):
"""The same as imap() except that the ordering of the results from the
returned iterator should be considered in arbitrary order."""
return IMapUnordered.spawn(func, iterable, spawn=self.spawn)
_SKIP = object()
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] | 2.69352 | 1,142 |
import os
import configparser
import redis
from passlib.context import CryptContext
from app.util.log_util2 import get_path, get_logger
from pydantic import BaseSettings, Field
# from fastapi.logger import logger
# from logging.handlers import RotatingFileHandler
import logging
formatter = logging.Formatter(
"(%(asctime)s.%(msecs)03d) %(levelname)s (%(thread)d) - %(message)s", "%Y-%m-%d %H:%M:%S")
# import re
# import yaml
#
# path_matcher = re.compile(r'.*\$\{((^}^{)+)\}.*')
#
#
# def path_constructor(loader, node):
# return os.path.expandvars(node.value)
#
#
# class EnvVarLoader(yaml.SafeLoader):
# pass
#
#
# EnvVarLoader.add_implicit_resolver('!path', path_matcher, None)
# EnvVarLoader.add_constructor('!path', path_constructor)
#
# # _env = os.environ.get('CFG_ENV')
# _config_path = os.path.abspath(os.path.join(os.path.abspath(os.path.dirname(__file__)), os.pardir)) + 'configs.yml'
# conf_doc = yaml.load(open(_config_path), Loader=EnvVarLoader)
# conf = conf_doc.get(_env)
# 读取配置信息
file_path = os.path.abspath(__file__)
par_dir = os.path.dirname(file_path)
profile = os.environ.get('DEPLOY_ENVIRONMENT', 'local')
if profile == 'remote':
config_name = 'config_remote.ini'
else:
config_name = 'config.ini'
conf_path = os.path.join(par_dir, 'config', config_name)
print("conf_path:" + conf_path)
conf_doc = configparser.ConfigParser()
conf_doc.read(conf_path)
setting = Settings()
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# Functions that can generate attribute values.
#
# These are functions that can be used in the GenerateFuncAttribute() class
# (see module generator.py). They generate values according to some internal
# functionality.
#
# The requirement of any such functions are:
# 1) that it must return a string
# 2) it can have been 0 and 5 parameters
#
#
# Examples of such functions are:
# - Australian telephone numbers
# - Japanese telephone numbers
# - Credit card numbers
# - US social security numbers
# - Japanese social security numbers
# - Uniformly distributed age values between 0 and 100
# - Normally distributed age values between 0 and 110
# etc.
# Peter Christen and Dinusha Vatsalan, January-March 2012
# =============================================================================
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# =============================================================================
import random
import basefunctions, generator
import csv
# -----------------------------------------------------------------------------
#
def generate_phone_number_australia():
"""Randomly generate an Australian telephone number made of a two-digit area
code and an eight-digit number made of two blocks of four digits (with a
space between). For example: `02 1234 5678'
For details see: http://en.wikipedia.org/wiki/ \
Telephone_numbers_in_Australia#Personal_numbers_.2805.29
"""
area_code = random.choice(['02', '03', '04', '07', '08'])
number1 = random.randint(1,9999)
number2 = random.randint(1,9999)
oz_phone_str = str(area_code)+' '+str(number1).zfill(4)+' '+ \
str(number2).zfill(4)
assert len(oz_phone_str) == 12
assert oz_phone_str[0] == '0'
return oz_phone_str
# -----------------------------------------------------------------------------
#
def generate_phone_number_american():
"""Randomly generate an American telephone number made of a three-digit area
code and an seven-digit number made of two blocks, 3 and 4 digits (with a
space between). For example: `202 234 5678'
http://en.wikipedia.org/wiki/List_of_North_American_Numbering_Plan_area_codes
"""
#modify with look-up or full list
area_code = random.choice(['202', '212', '215', '412', '812'])
number1 = random.randint(1,999)
number2 = random.randint(1,9999)
#.zfill will pad zeros to the left of digits, 1 become 001 w/ zfill(3)
us_phone_str = str(area_code)+' '+str(number1).zfill(3)+' '+ \
str(number2).zfill(4)
assert len(us_phone_str) == 12
return us_phone_str
# -----------------------------------------------------------------------------
#
def generate_credit_card_number():
"""Randomly generate a credit card made of four four-digit numbers (with a
space between each number group). For example: '1234 5678 9012 3456'
For details see: http://en.wikipedia.org/wiki/Bank_card_number
"""
number1 = random.randint(1,9999)
assert number1 > 0
number2 = random.randint(1,9999)
assert number2 > 0
number3 = random.randint(1,9999)
assert number3 > 0
number4 = random.randint(1,9999)
assert number4 > 0
cc_str = str(number1).zfill(4)+' '+str(number2).zfill(4)+' '+ \
str(number3).zfill(4)+' '+str(number4).zfill(4)
assert len(cc_str) == 19
return cc_str
# -----------------------------------------------------------------------------
#
def generate_social_security_number():
"""Randomly generate a social security number.
For example: '234 78 9012'
Update to reflect state, date of birth info
consider: http://www.pnas.org/content/106/27/10975.full.pdf
"""
number1 = random.randint(1,999)
assert number1 > 0
number2 = random.randint(1,99)
assert number2 > 0
number3 = random.randint(1,9999)
assert number3 > 0
ss_str = str(number1).zfill(3)+' '+str(number2).zfill(2)+' '+ \
str(number3).zfill(4)
assert len(ss_str) == 11
return ss_str
# -----------------------------------------------------------------------------
#
def generate_drivers_license_num():
# need revision
# Based on dc format only
"""Randomly generate a drivers license number. 7-digit or 9-digit
For example: '2512235' or '682019423'
Update to reflect state infor
consider: http://http://adr-inc.com/PDFs/State_DLFormats.pdf
According to this paper, DOB info is encoded in drivers license
We should take this into consideration for further update
"http://www.highprogrammer.com/alan/numbers/index.html"
"""
number1 = random.randint(1,9999999)
assert number1 > 0
number2 = random.randint(1,999999999)
assert number2 > 0
ss_str1 = str(number1).zfill(7)
assert len(ss_str1) == 7
ss_str2 = str(number2).zfill(9)
assert len(ss_str1) == 9
return random.choice([ss_str1, ss_str2])
# -----------------------------------------------------------------------------
#
def generate_passport_num():
"""Randomly generate a us passport number(9-digit number).
For example: '203941429'
"""
number1 = random.randint(1,999999999)
assert number1 > 0
passport_str = str(number1).zfill(9)
assert len(passport_str) == 9
return passport_str
# -----------------------------------------------------------------------------
#
def generate_email_address(fname="Bohan", lname="Zhang"):
"""Randomly generate a email address
Update middle name and nickname
Update frequency table: http://www.ryansolutions.com/blog/2013/email-domains/
"""
unicode_encoding_used = 'ascii'
basefunctions.check_is_string('fname', fname)
basefunctions.check_is_string('lname', lname)
domain_name = random.choice(["@gmail.com","@hotmail.com","@yahoo.com","@aol.com",
"@live.com","@msn.com", "@comcast.com"])
add1 = fname[0] + "." + lname + domain_name
add2 = fname + "." + lname + domain_name
add3 = fname[0] + lname + domain_name
add4 = fname + lname[0] + domain_name
add5 = fname + domain_name
add = random.choice([add1, add2, add3, add4, add5])
return add
# -----------------------------------------------------------------------------
#
def generate_name_suffix():
"""Randomly generate a name suffix. Assumes that 10% has a suffix'
"""
#modify with look-up or full list
rand = random.random()
if rand <= 0.10:
suffix = random.choice(['Jr.', 'Snr.', 'I', 'II', 'III'])
else:
suffix = ""
return suffix
# -----------------------------------------------------------------------------
#
def generate_gender():
"""Randomly generate a gender."""
gender = random.choice(['Male', 'Female'])
return gender
def gender(g):
'gender'
return g
# -----------------------------------------------------------------------------
def generate_firstname(gender = 'Female'):
"""randomly generate a name"""
if gender == 'Female':
gname = generator.GenerateFreqAttribute(attribute_name = 'given-name',
freq_file_name = os.path.abspath('lookup_files/firstname_female.csv'),
has_header_line = False,
unicode_encoding = unicode_encoding_used)
if gender == 'Male':
gname= generator.GenerateFreqAttribute(attribute_name = 'given-name',
freq_file_name = os.path.abspath('lookup_files/firstname_male.csv'),
has_header_line = False,
unicode_encoding = unicode_encoding_used)
return gname
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
# -----------------------------------------------------------------------------
#
# -----------------------------------------------------------------------------
#
def generate_name_prefix_m():
"""Randomly generate a name prefix."""
prefix = random.choice(['Mr', ""])
return prefix
# -----------------------------------------------------------------------------
#
def generate_primary():
"""Randomly generate a field for a primary key"""
primary = "1"
return primary
# -----------------------------------------------------------------------------
#
def generate_name_prefix_f():
"""Randomly generate a name prefix.
"""
prefix = random.choice(['Miss', 'Mrs', 'Ms', ""])
return prefix
# -----------------------------------------------------------------------------
#
def generate_prefix_from_gender(gender):
"""Generate prefix using gender
Jamie's Test code but not currently used in generate_data_english
as of 2_8"""
if gender == "Male":
prefix = random.choice(['Mr', ""])
if gender == "Female":
prefix = random.choice(['Miss', 'Mrs', 'Ms', ""])
return prefix
#
#-------------------------------------------------------------------------------
def generate_nickname():
"""Randomly generate a nickname. Assumes that 5% has a nickname'
"""
import random
#modify with look-up or full list
rand = random.random()
if rand <= .05:
nickname = random.choice(['A', 'B', 'C'])
else:
nickname = ""
return nickname
def race(r):
'race'
return r
def hispanic(h):
'hispanic'
return h
#-------------------------------------------------------------------------------
# Jamie to add new marital status from Census Bureau distribution:
# http://www.census.gov/compendia/statab/cats/population.html
def marriage(age):
"Probabilities taken from US Cencus Bureau"
import random
numb = random.random()
items = ["Single", "Married", "Separated", "Widowed", "Divorced", ""]
age_bracket = [18, 19, 24, 29, 34, 39, 44, 54, 66, 74, 120]
j = 0
if age < int(age_bracket[0]):
mstatus = random.choice(['Single', ""])
elif age < int(age_bracket[1]):
cum = [0.953, 0.983, 0.994, 0.996]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[2]):
cum = [0.793, 0.967, 0.985, 0.986]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[3]):
cum = [0.478, 0.919, 0.954, 0.958]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[4]):
cum = [0.271, 0.87, 0.909, 0.914]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[5]):
cum = [0.177, 0.832, 0.871, 0.88]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[6]):
cum = [0.138, 0.804, 0.839, 0.855]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < int(age_bracket[6]):
cum = [0.11, 0.759, 0.793, 0.828]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < age_bracket[7]:
cum = [0.071, 0.717, 0.74, 0.822]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
elif age < age_bracket[8]:
cum = [0.051, 0.597, 0.611, 0.85]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
else:
cum = [0.039, 0.355, 0.362, 0.93]
if numb < cum[0]:
mstatus = items[0]
elif numb < cum[1]:
mstatus = items[1]
elif numb < cum[2]:
mstatus = items[2]
elif numb < cum[3]:
mstatus = items[3]
else:
mstatus = items[4]
return mstatus
#-------------------------------------------------------------------------------
#""" Generate Fake DOB - Need to pass age which isn't working. See comments below.
#For now I'm passing in a dummy age"""
def generate_DOB(age=65):
"""Randomly generate a month & date for DOB """
import random
birth_month = random.randint(1,12)
if birth_month == "1" or "3" or "5" or "7" or "8" or "10" or "12":
birth_day = random.randint(1,31)
if birth_month == "2":
birth_day = random.randint(1,28)
else:
birth_day = random.randint(1,30)
"""Can not use the age generator function here for some reason but this code
worked on generate_data_english.py. For now, passing dummy age into the function
to make it work for the time being. I did input reference to import generator in
the beginning of the program but got stuck on 'unicode_encoding'
age = generator.GenerateFreqAlt(attribute_name = 'agejy',
freq_file_name = 'lookup_files/age_gender_ratio_female.csv',
has_header_line = False,
unicode_encoding = unicode_encoding_used) """
from time import gmtime, strftime
year_system = strftime ("%Y", gmtime())
year_from_age = int(year_system) - age
DOB = str(birth_month) +'/' + str(birth_day) + '/' + str(year_from_age)
return DOB
# -----------------------------------------------------------------------------
def generate_uniform_value(min_val, max_val, val_type):
"""Randomly generate a numerical value according to a uniform distribution
between the minimum and maximum values given.
The value type can be set as 'int', so a string formatted as an integer
value is returned; or as 'float1' to 'float9', in which case a string
formatted as floating-point value with the specified number of digits
behind the comma is returned.
Note that for certain situations and string formats a value outside the
set range might be returned. For example, if min_val=100.25 and
val_type='float1' the rounding can result in a string value '100.2' to
be returned.
Suitable minimum and maximum values need to be selected to prevent such a
situation.
"""
basefunctions.check_is_number('min_val', min_val)
basefunctions.check_is_number('max_val', max_val)
assert min_val < max_val
r = random.uniform(min_val, max_val)
return basefunctions.float_to_str(r, val_type)
# -----------------------------------------------------------------------------
#
def generate_uniform_age(min_val, max_val):
"""Randomly generate an age value (returned as integer) according to a
uniform distribution between the minimum and maximum values given.
This function is simple a shorthand for:
generate_uniform_value(min_val, max_val, 'int')
"""
assert min_val >= 0
assert max_val <= 130
return generate_uniform_value(min_val, max_val, 'int')
# -----------------------------------------------------------------------------
def generate_normal_value(mu, sigma, min_val, max_val, val_type):
"""Randomly generate a numerical value according to a normal distribution
with the mean (mu) and standard deviation (sigma) given.
A minimum and maximum allowed value can given as additional parameters,
if set to None then no minimum and/or maximum limit is set.
The value type can be set as 'int', so a string formatted as an integer
value is returned; or as 'float1' to 'float9', in which case a string
formatted as floating-point value with the specified number of digits
behind the comma is returned.
"""
basefunctions.check_is_number('mu', mu)
basefunctions.check_is_number('sigma', sigma)
assert sigma > 0.0
if (min_val != None):
basefunctions.check_is_number('min_val', min_val)
assert min_val <= mu
if (max_val != None):
basefunctions.check_is_number('max_val', max_val)
assert max_val >= mu
if ((min_val != None) and (max_val != None)):
assert min_val < max_val
if (min_val != None) or (max_val != None):
in_range = False # For testing if the random value is with the range
else:
in_range = True
r = random.normalvariate(mu, sigma)
while (in_range == False):
if ((min_val == None) or ((min_val != None) and (r >= min_val))):
in_range = True
if ((max_val != None) and (r > max_val)):
in_range = False
if (in_range == True):
r_str = basefunctions.float_to_str(r, val_type)
r_test = float(r_str)
if (min_val != None) and (r_test < min_val):
in_range = False
if (max_val != None) and (r_test > max_val):
in_range = False
if (in_range == False):
r = random.normalvariate(mu, sigma)
if (min_val != None):
assert r >= min_val
if (max_val != None):
assert r <= max_val
return basefunctions.float_to_str(r, val_type)
# -----------------------------------------------------------------------------
#
def generate_normal_age(mu, sigma, min_val, max_val):
"""Randomly generate an age value (returned as integer) according to a
normal distribution following the mean and standard deviation values
given, and limited to age values between (including) the minimum and
maximum values given.
This function is simple a shorthand for:
generate_normal_value(mu, sigma, min_val, max_val, 'int')
"""
assert min_val >= 0
assert max_val <= 130
age = generate_normal_value(mu, sigma, min_val, max_val, 'int')
while ((int(age) < min_val) or (int(age) > max_val)):
age = generate_normal_value(mu, sigma, min_val, max_val, 'int')
return age
def attrgenfunct_log(num_test=10):
'log for attrgenfunct'
with open('attrgenfunct_log.txt', 'w') as output:
output.write( 'Generate %d Australian telephone numbers:' % (num_test))
for i in range(num_test):
output.write(' ' + generate_phone_number_australia()+',')
output.write('\n')
output.write( 'Generate %d credit card numbers:' % (num_test))
for i in range(num_test):
output.write(' '+generate_credit_card_number()+',')
output.write('\n')
output.write( 'Generate %d uniformly distributed integer numbers between -100' % \
(num_test) + ' and -5:')
for i in range(num_test):
output.write( ' ' + generate_uniform_value(-100, -5, 'int'),)
output.write('\n')
output.write( 'Generate %d uniformly distributed floating-point numbers with ' % \
(num_test) + '3 digits between -55 and 55:')
for i in range(num_test):
output.write( ' ' + generate_uniform_value(-55, 55, 'float3'))
output.write('\n')
output.write( 'Generate %d uniformly distributed floating-point numbers with ' % \
(num_test) + '7 digits between 147 and 9843:')
for i in range(num_test):
output.write( ' ' + generate_uniform_value(147, 9843, 'float7'))
output.write('\n')
output.write( 'Generate %d uniformly distributed age values between 0 and 120:' % \
(num_test))
for i in range(num_test):
output.write( ' ' + generate_uniform_age(0, 120))
output.write('\n')
output.write( 'Generate %d uniformly distributed age values between 18 and 65:' % \
(num_test))
for i in range(num_test):
output.write( ' ' + generate_uniform_age(18, 65))
output.write('\n')
output.write( 'Generate %d normally distributed integer numbers between -200' % \
(num_test) + ' and -3 with mean -50 and standard deviation 44:')
for i in range(num_test):
output.write( ' ' + generate_normal_value(-50, 44, -200, -3, 'int'))
output.write('\n')
output.write( 'Generate %d normally distributed floating-point numbers with ' % \
(num_test) + '5 digits between -100 and 100 and with mean 22 and ' + \
'standard deviation 74:')
for i in range(num_test):
output.write( ' ' + generate_normal_value(22, 74, -100, 100, 'float5'))
output.write('\n')
output.write( 'Generate %d normally distributed floating-point numbers with ' % \
(num_test) + '9 digits with mean 22 and standard deviation 74:')
for i in range(num_test):
output.write( ' ' + generate_normal_value(22, 74, min_val=None, max_val= None,
val_type='float9'))
output.write('\n')
output.write( 'Generate %d normally distributed floating-point numbers with ' % \
(num_test) + '2 digits with mean 22 and standard deviation 24 that' + \
' are larger than 10:')
for i in range(num_test):
output.write( ' ' + generate_normal_value(22, 74, min_val=10, max_val=None,
val_type='float2'))
output.write('\n')
output.write( 'Generate %d normally distributed floating-point numbers with ' % \
(num_test) + '4 digits with mean 22 and standard deviation 24 that' + \
' are smaller than 30:')
for i in range(num_test):
output.write( ' ' + generate_normal_value(22, 74, min_val=None, max_val=40,
val_type='float4'))
output.write('\n')
output.write( 'Generate %d normally distributed age values between 0 and 120' % \
(num_test) + ' with mean 45 and standard deviation 22:')
for i in range(num_test):
output.write( ' ' + generate_normal_age(45, 22, 0, 120))
output.write('\n')
output.write( 'Generate %d normally distributed age values between 18 and 65' % \
(num_test) + ' with mean 30 and standard deviation 10:')
for i in range(num_test):
output.write( ' ' + generate_normal_age(30, 10, 18, 65))
# =============================================================================
# If called from command line perform some examples: Generate values
#
if (__name__ == '__main__'):
attrgenfunct_log() | [
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201,
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2,
10097,
24305,
201,
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2,
37811,
2980,
378,
33482,
8410,
33,
532,
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284,
1208,
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2125,
470,
1762,
13,
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2,
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62,
8424,
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13,
25192,
600,
7,
16,
11,
1065,
8,
201,
198,
220,
611,
4082,
62,
8424,
6624,
366,
16,
1,
393,
366,
18,
1,
393,
366,
20,
1,
393,
366,
22,
1,
393,
366,
23,
1,
393,
366,
940,
1,
393,
366,
1065,
1298,
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220,
220,
4082,
62,
820,
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4738,
13,
25192,
600,
7,
16,
11,
3132,
8,
201,
198,
220,
611,
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62,
8424,
6624,
366,
17,
1298,
201,
198,
220,
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220,
4082,
62,
820,
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4738,
13,
25192,
600,
7,
16,
11,
2078,
8,
201,
198,
220,
2073,
25,
201,
198,
220,
220,
220,
4082,
62,
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796,
4738,
13,
25192,
600,
7,
16,
11,
1270,
8,
201,
198,
220,
220,
201,
198,
220,
37227,
6090,
407,
779,
262,
2479,
17301,
2163,
994,
329,
617,
1738,
475,
428,
2438,
201,
198,
220,
3111,
319,
7716,
62,
7890,
62,
39126,
13,
9078,
13,
220,
1114,
783,
11,
6427,
31548,
2479,
656,
262,
2163,
201,
198,
220,
284,
787,
340,
670,
329,
262,
640,
852,
13,
220,
314,
750,
5128,
4941,
284,
1330,
17301,
287,
201,
198,
220,
262,
3726,
286,
262,
1430,
475,
1392,
7819,
319,
705,
46903,
1098,
62,
12685,
7656,
6,
220,
201,
198,
220,
220,
201,
198,
220,
2479,
796,
17301,
13,
8645,
378,
20366,
80,
29161,
7,
42348,
62,
3672,
796,
705,
496,
73,
88,
3256,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2030,
80,
62,
7753,
62,
3672,
796,
705,
5460,
929,
62,
16624,
14,
496,
62,
8388,
62,
10366,
952,
62,
24724,
13,
40664,
3256,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
468,
62,
25677,
62,
1370,
796,
10352,
11,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
28000,
1098,
62,
12685,
7656,
796,
28000,
1098,
62,
12685,
7656,
62,
1484,
8,
37227,
201,
198,
201,
198,
220,
422,
640,
1330,
308,
76,
2435,
11,
965,
31387,
201,
198,
220,
614,
62,
10057,
796,
965,
31387,
5855,
4,
56,
1600,
308,
76,
2435,
28955,
201,
198,
220,
614,
62,
6738,
62,
496,
796,
493,
7,
1941,
62,
10057,
8,
532,
2479,
201,
198,
220,
8410,
33,
796,
965,
7,
24280,
62,
8424,
8,
1343,
26488,
6,
1343,
965,
7,
24280,
62,
820,
8,
1343,
31051,
6,
1343,
965,
7,
1941,
62,
6738,
62,
496,
8,
201,
198,
220,
1441,
8410,
33,
201,
198,
220,
220,
201,
198,
201,
198,
2,
16529,
32501,
201,
198,
201,
198,
4299,
7716,
62,
403,
6933,
62,
8367,
7,
1084,
62,
2100,
11,
3509,
62,
2100,
11,
1188,
62,
4906,
2599,
201,
198,
220,
37227,
29531,
306,
7716,
257,
29052,
1988,
1864,
284,
257,
8187,
6082,
201,
198,
220,
220,
220,
220,
1022,
262,
5288,
290,
5415,
3815,
1813,
13,
201,
198,
201,
198,
220,
220,
220,
220,
383,
1988,
2099,
460,
307,
900,
355,
705,
600,
3256,
523,
257,
4731,
39559,
355,
281,
18253,
201,
198,
220,
220,
220,
220,
1988,
318,
4504,
26,
393,
355,
705,
22468,
16,
6,
284,
705,
22468,
24,
3256,
287,
543,
1339,
257,
4731,
201,
198,
220,
220,
220,
220,
39559,
355,
12462,
12,
4122,
1988,
351,
262,
7368,
1271,
286,
19561,
201,
198,
220,
220,
220,
220,
2157,
262,
39650,
318,
4504,
13,
201,
198,
201,
198,
220,
220,
220,
220,
5740,
326,
329,
1728,
7445,
290,
4731,
17519,
257,
1988,
2354,
262,
201,
198,
220,
220,
220,
220,
900,
2837,
1244,
307,
4504,
13,
1114,
1672,
11,
611,
949,
62,
2100,
28,
3064,
13,
1495,
290,
201,
198,
220,
220,
220,
220,
1188,
62,
4906,
11639,
22468,
16,
6,
262,
38185,
460,
1255,
287,
257,
4731,
1988,
705,
3064,
13,
17,
6,
284,
201,
198,
220,
220,
220,
220,
307,
4504,
13,
201,
198,
201,
198,
220,
220,
220,
220,
1778,
4674,
5288,
290,
5415,
3815,
761,
284,
307,
6163,
284,
2948,
884,
257,
201,
198,
220,
220,
220,
220,
3074,
13,
201,
198,
220,
37227,
201,
198,
201,
198,
220,
2779,
12543,
2733,
13,
9122,
62,
271,
62,
17618,
10786,
1084,
62,
2100,
3256,
949,
62,
2100,
8,
201,
198,
220,
2779,
12543,
2733,
13,
9122,
62,
271,
62,
17618,
10786,
9806,
62,
2100,
3256,
3509,
62,
2100,
8,
201,
198,
220,
6818,
949,
62,
2100,
1279,
3509,
62,
2100,
201,
198,
201,
198,
220,
374,
796,
4738,
13,
403,
6933,
7,
1084,
62,
2100,
11,
3509,
62,
2100,
8,
201,
198,
201,
198,
220,
1441,
2779,
12543,
2733,
13,
22468,
62,
1462,
62,
2536,
7,
81,
11,
1188,
62,
4906,
8,
201,
198,
201,
198,
2,
16529,
32501,
201,
198,
2,
201,
198,
4299,
7716,
62,
403,
6933,
62,
496,
7,
1084,
62,
2100,
11,
3509,
62,
2100,
2599,
201,
198,
220,
37227,
29531,
306,
7716,
281,
2479,
1988,
357,
7783,
276,
355,
18253,
8,
1864,
284,
257,
201,
198,
220,
220,
220,
220,
8187,
6082,
1022,
262,
5288,
290,
5415,
3815,
1813,
13,
201,
198,
201,
198,
220,
220,
220,
220,
770,
2163,
318,
2829,
257,
45883,
329,
25,
201,
198,
201,
198,
220,
220,
220,
220,
220,
220,
7716,
62,
403,
6933,
62,
8367,
7,
1084,
62,
2100,
11,
3509,
62,
2100,
11,
705,
600,
11537,
201,
198,
220,
37227,
201,
198,
201,
198,
220,
6818,
949,
62,
2100,
18189,
657,
201,
198,
220,
6818,
3509,
62,
2100,
19841,
11323,
201,
198,
201,
198,
220,
1441,
7716,
62,
403,
6933,
62,
8367,
7,
1084,
62,
2100,
11,
3509,
62,
2100,
11,
705,
600,
11537,
201,
198,
201,
198,
2,
16529,
32501,
201,
198,
201,
198,
4299,
7716,
62,
11265,
62,
8367,
7,
30300,
11,
264,
13495,
11,
949,
62,
2100,
11,
3509,
62,
2100,
11,
1188,
62,
4906,
2599,
201,
198,
220,
37227,
29531,
306,
7716,
257,
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13,
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317,
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645,
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14,
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13,
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2,
38093,
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2,
1002,
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25,
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2599,
201,
198,
220,
220,
220,
708,
81,
5235,
12543,
310,
62,
6404,
3419
] | 2.595267 | 9,085 |
import logging
import os
import copy
from pathlib import Path
from mkdocs.utils import warning_filter
log = logging.getLogger(__name__)
log.addFilter(warning_filter)
INCLUDE_STATEMENT = "!tf_modules_root "
# TODO should make these config variables in mkdocs.yml
DOCUMENT_FILE_NAME = 'README.md'
NAVIGATION_KEY_NAME = 'About'
| [
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796,
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6,
628,
198
] | 2.946429 | 112 |
from coworks import TechMicroService
from coworks import entry
app = SimpleMicroService()
| [
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] | 4.086957 | 23 |
import urllib3
import csv
import os
import json
import arin
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
# TODO: Add in reverse lookup
csvfile = open('countries_by_rir.csv', 'r')
readcsv = csv.reader(csvfile, delimiter=',')
rir = {'AFRINIC': [],
'APNIC': [],
'ARIN': [],
'RIPE NCC': [],
'LACNIC': []
}
for row in readcsv:
key = row[3]
if key in rir:
rir[key].append(row[0].lower())
if __name__ == "__main__":
main()
| [
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628,
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6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
1388,
3419,
198
] | 2.116667 | 240 |
import redis
import json
import uuid
import time
HOST = 'localhost'
PORT = 6379
MSG_COUNT = 250000
r = redis.Redis (host = HOST, port = PORT)
pub = r.pubsub ()
sender_id = uuid.uuid4 ()
start_time = time.time ()
for i in range (MSG_COUNT):
data = {"sender_id": str(sender_id), "message_id": i, "body": "I am message number {}".format (i)}
r.publish ('a_topic', json.dumps (data))
end_time = time.time ()
print ("Published {} messages with sender ID {} in {} seconds".format (MSG_COUNT, sender_id, (end_time - start_time)))
| [
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4008,
198,
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62,
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796,
640,
13,
2435,
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198,
4798,
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24492,
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6218,
351,
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4522,
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1911,
18982,
357,
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38,
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62,
312,
11,
357,
437,
62,
2435,
532,
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62,
2435,
22305,
198
] | 2.622549 | 204 |
from flask import Blueprint
import json
import logging
import traceback
from datetime import datetime
from flask import Response, request, jsonify, current_app
from gevent.wsgi import WSGIServer
from flask_jwt_simple import (
JWTManager, jwt_required, create_jwt, get_jwt_identity, get_jwt
)
from backend.flask_app.models import Session, User, Wallet
from backend.flask_app.http_codes import Status
from backend.flask_app.factory import create_app, create_user
from flask_security.utils import hash_password, verify_password
wallet = Blueprint('wallet', __name__)
logger = logging.getLogger(__name__)
app = create_app()
jwt = JWTManager(app)
@wallet.route('/add', methods=['POST'])
@jwt_required
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17816,
32782,
6,
12962,
198,
31,
73,
46569,
62,
35827,
198
] | 3.18018 | 222 |
"""
PSET-1
Counting Bobs
Assume s is a string of lower case characters.
Write a program that prints the number of times the string 'bob'
occurs in s. For example, if s = 'azcbobobegghakl', then your
program should print:
Number of times bob occurs is: 2
"""
py_main()
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37811,
198,
3705,
2767,
12,
16,
198,
12332,
278,
5811,
82,
198,
198,
8021,
2454,
264,
318,
257,
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286,
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1339,
3435,
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262,
1271,
286,
1661,
262,
4731,
705,
65,
672,
6,
220,
198,
13966,
1834,
287,
264,
13,
1114,
1672,
11,
611,
264,
796,
705,
1031,
21101,
672,
672,
1533,
456,
461,
75,
3256,
788,
534,
220,
198,
23065,
815,
3601,
25,
628,
220,
7913,
286,
1661,
29202,
8833,
318,
25,
362,
198,
198,
37811,
198,
220,
220,
220,
220,
198,
9078,
62,
12417,
3419,
198
] | 2.927083 | 96 |
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