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|---|---|---|---|
from django.conf import settings
from django.db import models
import pyotp
class UserPSK(models.Model):
"""Strores custom secret key per user"""
user = models.OneToOneField(settings.AUTH_USER_MODEL, related_name='psk', on_delete=models.CASCADE)
secret_key = models.CharField(max_length=16, default=pyotp.random_base32)
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] | 2.87931 | 116 |
import sunspec2.modbus.client as client
import pytest
import socket
import sunspec2.tests.mock_socket as MockSocket
import serial
import sunspec2.tests.mock_port as MockPort
if __name__ == "__main__":
pass
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import os
import glob
import numpy as np
import networkx as nx
import peleffy
from frag_pele.constants import SCHRODINGER
from peleffy.topology import Topology
from peleffy.forcefield import OpenForceField, OPLS2005ForceField
from peleffy.topology import Molecule
from peleffy.utils import Logger
from peleffy.topology import molecule
from peleffy.utils.toolkits import RDKitToolkitWrapper
class ComputeDihedrals(object):
"""
A class to produce a library of dihedral angles.
"""
def __init__(self, pdb_file, forcefield='OPLS2005'):
"""
Initializes a ComputeDihedrals object
Parameters
----------
topology_obj : An peleffy.topology.Topology
A Topology object that contains the ligand's information
mode: str
Whether to extract all dihedrals or only those marked as flexible
"""
self._pdb_file = pdb_file
self._forcefield = forcefield
self._topology, self._molecule = self.load_molecule()
self.dihedral_library = {}
def calculate_cluster_angles(self, dihedral_list):
"""
Calculate dihedral angles from pdb
Parameters
----------
pdb_file: str
Path to the cluster representative conformation
dihedral_list: list
List of the tuples containing the atoms that form the dihedrals
match_indexes: bool
Whether to use the atom indices from the dihedral list or match to
the cluster structure before
"""
rdkit_wrapper = RDKitToolkitWrapper()
pdb_dihedrals = []
# use the input molecule as template since the cluster structures
# probably will not have proper stereochemistry
mol = molecule.Molecule(self._pdb_file, connectivity_template=self._molecule.rdkit_molecule)
# we use substructure matching to ensure that the indices in the
# clusters pdb and the input ligand are the same
for dihedral in dihedral_list:
names = [self._topology.atoms[atom].PDB_name for atom in dihedral]
angle = get_dihedral(mol, *dihedral, units="degrees")
pdb_dihedrals.append(names+[angle])
self.dihedral_library[self._pdb_file] = pdb_dihedrals
def calculate(self):
"""
Calculate dihedrals library from the bce output
"""
logger = Logger()
logger.info(' - Calculating dihedral library')
self._calculate_all_dihedrals()
def get_dihedral(mol, atom1, atom2, atom3, atom4, units="radians"):
"""
It calculates the value of the dihedral angle in the specified units
(default radians)
Parameters
----------
molecule : an offpele.topology.Molecule
The offpele's Molecule object
atom1 : int
Index of the first atom in the dihedral
atom2 : int
Index of the second atom in the dihedral
atom3 : int
Index of the third atom in the dihedral
atom4 : int
Index of the fourth atom in the dihedral
units : str
The units in which to calculate the angle (default is radians, can
be radians or degrees)
"""
from rdkit.Chem import rdMolTransforms
if units == "degrees":
angle = rdMolTransforms.GetDihedralDeg(mol.rdkit_molecule.GetConformer(), atom1, atom2, atom3, atom4)
else:
angle = rdMolTransforms.GetDihedralRad(mol.rdkit_molecule.GetConformer(), atom1, atom2, atom3, atom4)
return angle
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] | 2.516547 | 1,390 |
import re
from unittest.case import TestCase
from unittest.mock import MagicMock, patch, ANY
from samcli.lib.utils.path_observer import HandlerObserver, PathHandler, StaticFolderWrapper
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#!/usr/bin/env python3
#
# Copyright IBM Corp. 2016 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.
#
##########################################################
# #
# Disclaimer #
# #
# The script is provided here is only a sample. #
# #
# There is no official support on the script by me #
# #
# or IBM. #
# #
# Do NOT use the script in the production environment. #
# #
# You may use the script as guideline to create the #
# #
# custom action response best suited to your needs. #
# #
##########################################################
import re
import sys
import datetime
import subprocess
## ** Jump to main() function **
def updatePolicy( lstAction ):
"""
IN: list( default_ipv4,ipv4,active,1,any,tcp,22,permit,#rules_in_policy )
"""
global offence_ip, offence_port, max_rules, dry_run
epoch = datetime.datetime.now().strftime('%s')
# Add epoch timestamp to policy name.
qradar_policy_name = 'qr_{0}' . format(epoch)
policy_name,policy_type,p_active,\
rule_id,rule_ip,rule_proto,\
rule_port,rule_action,policy_rule_count = lstAction
CLONERULE = 'ipfilter --clone {0} -from {1}' . \
format(qradar_policy_name, policy_name )
DELRULE = 'ipfilter --delrule {0} -rule {1}' . \
format(qradar_policy_name, rule_id )
ADDRULE_DENY = 'ipfilter --addrule {0} -rule {1} -sip {2} -dp {3} -proto {4} -act {5}'. \
format(
qradar_policy_name,
rule_id,
offence_ip,
offence_port,
rule_proto,
'deny'
)
new_rule_id = int(max_rules) + 1
ADDRULE_PERMIT = 'ipfilter --addrule {0} -rule {1} -sip {2} -dp {3} -proto {4} -act {5}'. \
format(
qradar_policy_name,
new_rule_id,
'any',
offence_port,
rule_proto,
'permit'
)
SAVERULE = 'ipfilter --save {0}' . \
format(qradar_policy_name )
ACTIVATE = 'ipfilter --activate {0}' . \
format(qradar_policy_name )
print('{0}\n{1}\n{2}\n{3}\n{4}\n{5}'.
format( CLONERULE, DELRULE, ADDRULE_DENY, ADDRULE_PERMIT, SAVERULE, ACTIVATE )
)
print('Blocking further connections from {0} on port {1}'.
format( offence_ip, offence_port )
)
# Add ACTIVATE to following tuple
SEQ = ( CLONERULE, DELRULE, ADDRULE_DENY, ADDRULE_PERMIT, SAVERULE, ACTIVATE )
switch_cmds = ' ; ' . join( SEQ )
runCli( switch_cmds )
def loadData( switch_cmd ):
"""
This function is written to get the currently active rules
from the switch configuration.
Once the active rules are retrieved, they are flattened
in a list.
Parameters
- IN : Switch command to execute
- OUT: list in following format for all rules found
for all policies
default_ipv4,ipv4,active,1,any,tcp,22,permit
"""
global active_policies
active_policies.clear()
spaces = re.compile('\s+')
commas = re.compile(',')
lst_rules = []
# the user has specified a
# output file with switch output
# load it (simulation mode)
#f = open('switch-output', 'r')
#data = f.read()
#f.close()
data = runCli( switch_cmd )
# Process the output received from
# (ipfilter --show) switch command
# or loaded from output file
for line in data.split('\n'):
# Skip empty lines & header line
if re.search(r'^$|^Rule', line):
continue
# Extract policy_name, type and state
# from line beginning with Name
if re.search(r'^Name',line):
line2 = commas.sub('',line)
p_name = line2.split(':')[1].split(' ')[1].strip()
p_type = line2.split(':')[2].split(' ')[1].strip()
p_state = line2.split(':')[3].split(' ')[1].strip()
continue
# Consider only the active policy
if p_state == 'active' and \
p_type == ip_type :
# creating a set of policies
active_policies.add( p_name )
# convert space to commas
csv_line = spaces.sub(',',line.strip())
# create a tuple
rec = ( p_name, p_type, p_state, csv_line )
# create a comma separated values record
csv_rec = ',' . join( rec )
# store the value in a list
lst_rules.append(csv_rec)
return lst_rules
def getRuleCount( lstRules, policy_name ):
"""
This function return the rule count for a given policy
indicated by policy_name
Parameters:
- IN : 1. List containing all the rules
2. Name of the policy
- Out: # of rules in the policy.
"""
count = 0
for x in lstRules:
if x.split(',')[0] == policy_name:
count +=1
return count
def checkRules( lstRules ):
"""
IN: list ( default_ipv4,ipv4,active,1,any,tcp,22,permit )
OUT: list( default_ipv4,ipv4,active,1,any,tcp,22,permit,#rules_in_policy)
"""
global active_policies, offence_port, ip_type, max_rules
lstAction = []
ipAlreadyBlocked = False
# Iterate through the set with
# active policy name
for active_policy in active_policies:
rule_count = 0
take_action= False
# Iterate through all rules (ipv4 & ipv6)
# stored in the list
for rule in lstRules:
p_name = rule.split(',')[0]
p_type = rule.split(',')[1]
p_state = rule.split(',')[2]
rule_id = rule.split(',')[3]
rule_ip = rule.split(',')[4]
rule_proto = rule.split(',')[5]
# account for port range here
if rule.split(',')[7] == '-' :
p_begin = rule.split(',')[6]
p_end = rule.split(',')[8]
rule_port = p_begin + '-' + p_end
rule_action= rule.split(',')[9]
else:
rule_port = rule.split(',')[6]
rule_action= rule.split(',')[7]
# ip_type can be ipv4 or ipv6
# determined globally
if p_type == ip_type and \
p_name == active_policy:
# get total rules count for this policy
if rule_count == 0:
rule_count = getRuleCount( lstRules, p_name )
max_rules = rule_count
port_matched = getPortMatch( rule_port )
# check if the offensive ip is alrady blocked
# abort the further execution
if port_matched and \
rule_ip == offence_ip and \
rule_action == 'deny':
print('No policy change required')
print('IP {0} is alredy blocked for port {1}'.\
format(offence_ip,rule_port)
)
break
if port_matched and \
rule_action == 'permit' and \
rule_ip == 'any':
tup = ( rule, str(rule_count) )
action_rec = ',' . join( tup )
lstAction = list(action_rec.split(','))
# abort iner loop
take_action = True
break
# abort outer loop
if take_action:
break
return lstAction
def runCli( cli_cmd ):
"""
Purpose: Run any external command and return the output
Parameters:
- IN
1. cli command to run
- OUT
1. output of cli command
"""
global system, remote_user
lst_cmd = []
lst_cmd.append( 'ssh' )
lst_cmd.append( '-o StrictHostKeyChecking=no' )
lst_cmd.append( remote_user + "@" + system )
lst_cmd.append( cli_cmd )
print(cli_cmd)
try:
# Command execution using subprocess
stdout = subprocess.check_output( \
lst_cmd,
universal_newlines = True,
shell = False
)
if stdout != None:
return stdout
except KeyboardInterrupt:
print( "User abort ..\n" )
sys.exit( 1 )
except subprocess.CalledProcessError:
print( "Error connecting to remote host !! aborting !!! \n")
sys.exit( 1 )
if __name__ == '__main__':
argc = len(sys.argv) - 1
if argc == 0 or argc > 3 :
Usage()
system,ip_address,command = sys.argv[1:]
remote_user = 'qradaradmin'
active_policies = {'index_ignore'}
cmd_to_port_dict = { 'ssh' : 22, 'https' : 443, 'telnet' : 23, 'http' : 80 }
max_rules = 0
offence_port = cmd_to_port_dict[command]
offence_ip = ip_address
dry_run = False
if offence_ip.find('.') > 0:
ip_type = 'ipv4'
else:
ip_type = 'ipv6'
if dry_run:
print('*** Simulation only ***' )
print('Switch IP: {0}\nOffence IP:{1}\nOffence Port:{2}\n'.
format(system,ip_address,offence_port)
)
MSG = """ NOTE:
The current syslog configuration does not write protocol in
the event. The switch on the other hand has rules for
both tcp and udp protocols.
As no protocol information is sent along with the event
1st rule matching the ip / port will be chosen irrespective
of the protocol.
This may lead to unintended blocking rule.
Needs to be fixed in rsyslog event.
"""
print(MSG)
main()
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"""Tools to plot basemaps"""
import warnings
import numpy as np
from . import providers
from xyzservices import TileProvider
from .tile import bounds2img, _sm2ll, warp_tiles, _warper
from rasterio.enums import Resampling
from rasterio.warp import transform_bounds
from matplotlib import patheffects
from matplotlib.pyplot import draw
INTERPOLATION = "bilinear"
ZOOM = "auto"
ATTRIBUTION_SIZE = 8
def add_basemap(
ax,
zoom=ZOOM,
source=None,
interpolation=INTERPOLATION,
attribution=None,
attribution_size=ATTRIBUTION_SIZE,
reset_extent=True,
crs=None,
resampling=Resampling.bilinear,
**extra_imshow_args
):
"""
Add a (web/local) basemap to `ax`.
Parameters
----------
ax : AxesSubplot
Matplotlib axes object on which to add the basemap. The extent of the
axes is assumed to be in Spherical Mercator (EPSG:3857), unless the `crs`
keyword is specified.
zoom : int or 'auto'
[Optional. Default='auto'] Level of detail for the basemap. If 'auto',
it is calculated automatically. Ignored if `source` is a local file.
source : xyzservices.TileProvider object or str
[Optional. Default: Stamen Terrain web tiles]
The tile source: web tile provider or path to local file. The web tile
provider can be in the form of a :class:`xyzservices.TileProvider` object or a
URL. The placeholders for the XYZ in the URL need to be `{x}`, `{y}`,
`{z}`, respectively. For local file paths, the file is read with
`rasterio` and all bands are loaded into the basemap.
IMPORTANT: tiles are assumed to be in the Spherical Mercator
projection (EPSG:3857), unless the `crs` keyword is specified.
interpolation : str
[Optional. Default='bilinear'] Interpolation algorithm to be passed
to `imshow`. See `matplotlib.pyplot.imshow` for further details.
attribution : str
[Optional. Defaults to attribution specified by the source]
Text to be added at the bottom of the axis. This
defaults to the attribution of the provider specified
in `source` if available. Specify False to not
automatically add an attribution, or a string to pass
a custom attribution.
attribution_size : int
[Optional. Defaults to `ATTRIBUTION_SIZE`].
Font size to render attribution text with.
reset_extent : bool
[Optional. Default=True] If True, the extent of the
basemap added is reset to the original extent (xlim,
ylim) of `ax`
crs : None or str or CRS
[Optional. Default=None] coordinate reference system (CRS),
expressed in any format permitted by rasterio, to use for the
resulting basemap. If None (default), no warping is performed
and the original Spherical Mercator (EPSG:3857) is used.
resampling : <enum 'Resampling'>
[Optional. Default=Resampling.bilinear] Resampling
method for executing warping, expressed as a
`rasterio.enums.Resampling` method
**extra_imshow_args :
Other parameters to be passed to `imshow`.
Examples
--------
>>> import geopandas
>>> import contextily as ctx
>>> db = geopandas.read_file(ps.examples.get_path('virginia.shp'))
Ensure the data is in Spherical Mercator:
>>> db = db.to_crs(epsg=3857)
Add a web basemap:
>>> ax = db.plot(alpha=0.5, color='k', figsize=(6, 6))
>>> ctx.add_basemap(ax, source=url)
>>> plt.show()
Or download a basemap to a local file and then plot it:
>>> source = 'virginia.tiff'
>>> _ = ctx.bounds2raster(*db.total_bounds, zoom=6, source=source)
>>> ax = db.plot(alpha=0.5, color='k', figsize=(6, 6))
>>> ctx.add_basemap(ax, source=source)
>>> plt.show()
"""
xmin, xmax, ymin, ymax = ax.axis()
# If web source
if (
source is None
or isinstance(source, (dict, TileProvider))
or (isinstance(source, str) and source[:4] == "http")
):
# Extent
left, right, bottom, top = xmin, xmax, ymin, ymax
# Convert extent from `crs` into WM for tile query
if crs is not None:
left, right, bottom, top = _reproj_bb(
left, right, bottom, top, crs, {"init": "epsg:3857"}
)
# Download image
image, extent = bounds2img(
left, bottom, right, top, zoom=zoom, source=source, ll=False
)
# Warping
if crs is not None:
image, extent = warp_tiles(image, extent, t_crs=crs, resampling=resampling)
# Check if overlay
if _is_overlay(source) and 'zorder' not in extra_imshow_args:
# If zorder was not set then make it 9 otherwise leave it
extra_imshow_args['zorder'] = 9
# If local source
else:
import rasterio as rio
# Read file
with rio.open(source) as raster:
if reset_extent:
from rasterio.mask import mask as riomask
# Read window
if crs:
left, bottom, right, top = rio.warp.transform_bounds(
crs, raster.crs, xmin, ymin, xmax, ymax
)
else:
left, bottom, right, top = xmin, ymin, xmax, ymax
window = [
{
"type": "Polygon",
"coordinates": (
(
(left, bottom),
(right, bottom),
(right, top),
(left, top),
(left, bottom),
),
),
}
]
image, img_transform = riomask(raster, window, crop=True)
extent = left, right, bottom, top
else:
# Read full
image = np.array([band for band in raster.read()])
img_transform = raster.transform
bb = raster.bounds
extent = bb.left, bb.right, bb.bottom, bb.top
# Warp
if (crs is not None) and (raster.crs != crs):
image, bounds, _ = _warper(
image, img_transform, raster.crs, crs, resampling
)
extent = bounds.left, bounds.right, bounds.bottom, bounds.top
image = image.transpose(1, 2, 0)
# Plotting
if image.shape[2] == 1:
image = image[:, :, 0]
img = ax.imshow(
image, extent=extent, interpolation=interpolation, **extra_imshow_args
)
if reset_extent:
ax.axis((xmin, xmax, ymin, ymax))
else:
max_bounds = (
min(xmin, extent[0]),
max(xmax, extent[1]),
min(ymin, extent[2]),
max(ymax, extent[3]),
)
ax.axis(max_bounds)
# Add attribution text
if source is None:
source = providers.Stamen.Terrain
if isinstance(source, (dict, TileProvider)) and attribution is None:
attribution = source.get("attribution")
if attribution:
add_attribution(ax, attribution, font_size=attribution_size)
return
def _is_overlay(source):
"""
Check if the identified source is an overlay (partially transparent) layer.
Parameters
----------
source : dict
The tile source: web tile provider. Must be preprocessed as
into a dictionary, not just a string.
Returns
-------
bool
Notes
-----
This function is based on a very similar javascript version found in leaflet:
https://github.com/leaflet-extras/leaflet-providers/blob/9eb968f8442ea492626c9c8f0dac8ede484e6905/preview/preview.js#L56-L70
"""
if not isinstance(source, dict):
return False
if source.get('opacity', 1.0) < 1.0:
return True
overlayPatterns = [
'^(OpenWeatherMap|OpenSeaMap)',
'OpenMapSurfer.(Hybrid|AdminBounds|ContourLines|Hillshade|ElementsAtRisk)',
'Stamen.Toner(Hybrid|Lines|Labels)',
'CartoDB.(Positron|DarkMatter|Voyager)OnlyLabels',
'Hydda.RoadsAndLabels',
'^JusticeMap',
'OpenPtMap',
'OpenRailwayMap',
'OpenFireMap',
'SafeCast'
]
import re
return bool(re.match('(' + '|'.join(overlayPatterns) + ')', source.get('name', '')))
def add_attribution(ax, text, font_size=ATTRIBUTION_SIZE, **kwargs):
"""
Utility to add attribution text.
Parameters
----------
ax : AxesSubplot
Matplotlib axes object on which to add the attribution text.
text : str
Text to be added at the bottom of the axis.
font_size : int
[Optional. Defaults to 8] Font size in which to render
the attribution text.
**kwargs : Additional keywords to pass to the matplotlib `text` method.
Returns
-------
matplotlib.text.Text
Matplotlib Text object added to the plot.
"""
# Add draw() as it resizes the axis and allows the wrapping to work as
# expected. See https://github.com/darribas/contextily/issues/95 for some
# details on the issue
draw()
text_artist = ax.text(
0.005,
0.005,
text,
transform=ax.transAxes,
size=font_size,
path_effects=[patheffects.withStroke(linewidth=2, foreground="w")],
wrap=True,
**kwargs,
)
# hack to have the text wrapped in the ax extent, for some explanation see
# https://stackoverflow.com/questions/48079364/wrapping-text-not-working-in-matplotlib
wrap_width = ax.get_window_extent().width * 0.99
text_artist._get_wrap_line_width = lambda: wrap_width
return text_artist
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# -*- coding: utf-8 -*-#
'''
# Name: math_graph
# Description:
# Author: neu
# Date: 2020/7/28
'''
import numpy as np
import pandas as pd
from scipy.sparse.linalg import eigs
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh, ArpackNoConvergence
def weight_matrix(file_path, sigma2=0.1, epsilon=0.5, scaling=True):
'''
Load weight matrix function.
加载权重矩阵
:param file_path: str, the path of saved weight matrix file.
:param sigma2: float, scalar of matrix W.
:param epsilon: float, thresholds to control the sparsity of matrix W.
:param scaling: bool, whether applies numerical scaling on W.
:return: np.ndarray, [n_route, n_route].
'''
try:
W = pd.read_csv(file_path, header=None).values
except FileNotFoundError:
print(f'ERROR: input file was not found in {file_path}.')
# check whether W is a 0/1 matrix.
if set(np.unique(W)) == {0, 1}:
print('The input graph is a 0/1 matrix; set "scaling" to False.')
scaling = False
if scaling:
# 根据真实距离计算邻接矩阵
n = W.shape[0]
W = W / 10000.
W2, W_mask = W * W, np.ones([n, n]) - np.identity(n)
# refer to Eq.10
return np.exp(-W2 / sigma2) * (np.exp(-W2 / sigma2) >= epsilon) * W_mask
else:
return W
# 对邻接矩阵进行归一化处理
# 在邻接矩阵中加入自连接
# 对拉普拉斯矩阵进行归一化处理
def scaled_laplacian(W):
'''
Normalized graph Laplacian function.
归一化图拉普拉斯矩阵
:param W: np.ndarray, [n_route, n_route], weighted adjacency matrix of G.
:return: np.matrix, [n_route, n_route].
'''
# d -> diagonal degree matrix
n, d = np.shape(W)[0], np.sum(W, axis=1)
# L -> graph Laplacian
L = -W
L[np.diag_indices_from(L)] = d
for i in range(n):
for j in range(n):
if (d[i] > 0) and (d[j] > 0):
L[i, j] = L[i, j] / np.sqrt(d[i] * d[j])
# lambda_max \approx 2.0, the largest eigenvalues of L.
lambda_max = eigs(L, k=1, which='LR')[0][0].real
return np.mat(2 * L / lambda_max - np.identity(n))
# 重新调整对称归一化的图拉普拉斯矩阵，得到其简化版本
def first_approx(W, n):
'''
1st-order approximation function.
1阶近似函数
:param W: np.ndarray, [n_route, n_route], weighted adjacency matrix of G.
:param n: int, number of routes / size of graph.
:return: np.ndarray, [n_route, n_route].
'''
A = W + np.identity(n)
d = np.sum(A, axis=1)
sinvD = np.sqrt(np.mat(np.diag(d)).I)
# refer to Eq.5
return np.mat(np.identity(n) + sinvD * A * sinvD)
# 计算直到k阶的切比雪夫多项式
def chebyshev_polynomial(X, k):
# 返回一个稀疏矩阵列表
"""Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices."""
print("Calculating Chebyshev polynomials up to order {}...".format(k))
T_k = list()
T_k.append(sp.eye(X.shape[0]).tocsr()) # T0(X) = I
T_k.append(X) # T1(X) = L~
# 定义切比雪夫递归公式
def chebyshev_recurrence(T_k_minus_one, T_k_minus_two, X):
"""
:param T_k_minus_one: T(k-1)(L~)
:param T_k_minus_two: T(k-2)(L~)
:param X: L~
:return: Tk(L~)
"""
# 将输入转化为csr矩阵（压缩稀疏行矩阵）
X_ = sp.csr_matrix(X, copy=True)
# 递归公式：Tk(L~) = 2L~ * T(k-1)(L~) - T(k-2)(L~)
return 2 * X_.dot(T_k_minus_one) - T_k_minus_two
for i in range(2, k + 1):
T_k.append(chebyshev_recurrence(T_k[-1], T_k[-2], X))
# 返回切比雪夫多项式列表
return T_k
def cheb_poly_approx(L, Ks, n):
'''
Chebyshev polynomials approximation function.
切比雪夫多项式近似
:param L: np.matrix, [n_route, n_route], graph Laplacian.
:param Ks: int, kernel size of spatial convolution.
:param n: int, number of routes / size of graph.
:return: np.ndarray, [n_route, Ks*n_route].
'''
L0, L1 = np.mat(np.identity(n)), np.mat(np.copy(L))
if Ks > 1:
L_list = [np.copy(L0), np.copy(L1)]
for i in range(Ks - 2):
Ln = np.mat(2 * L * L1 - L0)
L_list.append(np.copy(Ln))
L0, L1 = np.matrix(np.copy(L1)), np.matrix(np.copy(Ln))
# L_lsit [Ks, n*n], Lk [n, Ks*n]
return np.concatenate(L_list, axis=-1)
elif Ks == 1:
return np.asarray(L0)
else:
raise ValueError(f'ERROR: the size of spatial kernel must be greater than 1, but received "{Ks}".')
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] | 1.736568 | 2,494 |
# This script illustrates the stability of learning vITL
# for emotion transfer in the presence of missing data
# Runtime ~1h on laptop
# ----------------------------------
# Imports
# ----------------------------------
import os
import torch
import matplotlib.pyplot as plt
import sys
import importlib
if importlib.util.find_spec('torch_itl') is None:
path_to_lib = os.getcwd()[:-23]
sys.path.append(path_to_lib)
from torch_itl.sampler import CircularEmoSampler
from torch_itl.model import DecomposableIdentity
from torch_itl.kernel import Gaussian
from torch_itl.estimator import EmoTransfer
from torch_itl.datasets import get_data_landmarks
# %%
# ----------------------------------
# Reading input/output data
# ----------------------------------
# Please replace those values with the right path to
# the extracted landmarks on your computer.
# See utils/README.md
path_to_rafd = '../../torch_itl/datasets/Rafd_Aligned/Rafd_LANDMARKS'
path_to_kdef = '../../torch_itl/datasets/KDEF_Aligned/KDEF_LANDMARKS'
# test of import
data_train, data_test = get_data_landmarks('KDEF', path_to_kdef)
n, m, nf = data_train.shape
print('Testing import, data dimensions:', n, m, nf)
# %%
# ----------------------------------
# Defining our model
# ----------------------------------
print('Defining the model')
# define Landmarks kernel
gamma_inp = 0.07
kernel_input = Gaussian(gamma_inp)
# define emotion kernel
gamma_out = 0.4
kernel_output = Gaussian(gamma_out)
# define functional model
model = DecomposableIdentity(kernel_input, kernel_output, nf)
# define emotion sampler
sampler = CircularEmoSampler()
# define regularization
lbda = 2e-5
# define the emotion transfer estimator
est = EmoTransfer(model, lbda, sampler, inp_emotion='joint')
#%%
# ----------------------------------
# Learning in the presence of missing data -KDEF
# ----------------------------------
print('Learning with missing data KDEF')
# number of random masks of each size
n_loops = 4
# results tensor
test_losses_kdef = torch.zeros(10, n_loops, n)
for kfold in range(10):
get_data_landmarks('KDEF', path_to_kdef, kfold=kfold)
mask_list = [torch.randperm(n * m).reshape(n, m) for j in range(n_loops)]
for j in range(n_loops):
mask_level = mask_list[j]
for i in torch.arange(n * m)[::7]:
mask = (mask_level >= i)
est.fit_partial(data_train, mask)
test_losses_kdef[kfold, j, i // 7] = est.risk(data_test)
print('done with kfold ', kfold)
# %%
#torch.save(test_losses_kdef, 'kdef_partial.pt')
# %%
# ----------------------------------
# Learning in the presence of missing data -Rafd
# ----------------------------------
print('Learning with missing data RaFD')
# number of random masks of each size
n_loops = 4
# results tensor
n = 61
test_losses_rafd = torch.zeros(10, n_loops, n)
for kfold in range(1, 11):
get_data_landmarks('RaFD', path_to_rafd, kfold=kfold)
n, m, _ = data_train.shape
mask_list = [torch.randperm(n * m).reshape(n, m) for j in range(n_loops)]
for j in range(n_loops):
mask_level = mask_list[j]
for i in torch.arange(n * m)[::7]:
mask = (mask_level >= i)
est.fit_partial(data_train, mask)
test_losses_rafd[kfold - 1, j, i // 7] = est.risk(data_test)
#%%
#torch.save(test_losses_rafd, 'rafd_partial.pt')
#%%
idx_kdef = torch.arange(test_losses_kdef.shape[2]*m)[::7].float() / test_losses_kdef.shape[2] / m
idx_rafd = torch.arange(test_losses_rafd.shape[2]*m)[::7].float() / n/m
#%%
mean_kdef = test_losses_kdef.mean(1).mean(0)
max_kdef , _ = test_losses_kdef.mean(1).max(axis=0)
min_kdef , _ = test_losses_kdef.mean(1).min(axis=0)
mean_rafd = test_losses_rafd.mean(1).mean(0)
max_rafd , _ = test_losses_rafd.mean(1).max(axis=0)
min_rafd , _ = test_losses_rafd.mean(1).min(axis=0)
#%%
plt.figure()
plt.xlabel("% of missing data")
plt.ylabel("$\log_{10}$ Test MSE")
plt.plot(idx_kdef, torch.log(mean_kdef), c='black', label='KDEF mean', marker=',')
plt.plot(idx_kdef, torch.log(min_kdef), c='black', label='KDEF min-max', linestyle='--')
plt.plot(idx_kdef, torch.log(max_kdef), c='black', linestyle='--')
plt.plot(idx_rafd, torch.log(mean_rafd), c='grey', label='RaFD mean', marker=',')
plt.plot(idx_rafd, torch.log(min_rafd), c='grey', label='RaFD min-max', linestyle='--')
plt.plot(idx_rafd, torch.log(max_rafd), c='grey', linestyle='--')
plt.legend(loc='upper left')
plt.savefig('partial_observation.pdf')
plt.show()
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] | 2.57011 | 1,733 |
# -*- coding: utf-8 -*-
# Copyright (c) 2015, Frappe Technologies and Contributors
# See license.txt
from __future__ import unicode_literals
import frappe
import unittest
# test_records = frappe.get_test_records('Integration Service')
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# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: containerd/events/content.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from containerd.vendor.gogoproto import gogo_pb2 as containerd_dot_vendor_dot_gogoproto_dot_gogo__pb2
from containerd.protobuf.plugin import fieldpath_pb2 as containerd_dot_protobuf_dot_plugin_dot_fieldpath__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name='containerd/events/content.proto',
package='containerd.events',
syntax='proto3',
serialized_options=b'Z2github.com/containerd/containerd/api/events;events\240\364\036\001',
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n\x1f\x63ontainerd/events/content.proto\x12\x11\x63ontainerd.events\x1a&containerd/vendor/gogoproto/gogo.proto\x1a*containerd/protobuf/plugin/fieldpath.proto\"S\n\rContentDelete\x12\x42\n\x06\x64igest\x18\x01 \x01(\tB2\xda\xde\x1f*github.com/opencontainers/go-digest.Digest\xc8\xde\x1f\x00\x42\x38Z2github.com/containerd/containerd/api/events;events\xa0\xf4\x1e\x01X\x00X\x01\x62\x06proto3'
,
dependencies=[containerd_dot_vendor_dot_gogoproto_dot_gogo__pb2.DESCRIPTOR,containerd_dot_protobuf_dot_plugin_dot_fieldpath__pb2.DESCRIPTOR,])
_CONTENTDELETE = _descriptor.Descriptor(
name='ContentDelete',
full_name='containerd.events.ContentDelete',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='digest', full_name='containerd.events.ContentDelete.digest', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=b'\332\336\037*github.com/opencontainers/go-digest.Digest\310\336\037\000', file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=138,
serialized_end=221,
)
DESCRIPTOR.message_types_by_name['ContentDelete'] = _CONTENTDELETE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ContentDelete = _reflection.GeneratedProtocolMessageType('ContentDelete', (_message.Message,), {
'DESCRIPTOR' : _CONTENTDELETE,
'__module__' : 'containerd.events.content_pb2'
# @@protoc_insertion_point(class_scope:containerd.events.ContentDelete)
})
_sym_db.RegisterMessage(ContentDelete)
DESCRIPTOR._options = None
_CONTENTDELETE.fields_by_name['digest']._options = None
# @@protoc_insertion_point(module_scope)
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] | 2.592561 | 1,156 |
#Embedded_ImageProcessing, Just for StyleGAN_v1 FFHQ
import numpy as np
import math
import torch
import torchvision
import model.E.E_Blur as BE
from model.stylegan1.net import Generator, Mapping #StyleGANv1
#Params
use_gpu = False
device = torch.device("cuda" if use_gpu else "cpu")
img_size = 1024
GAN_path = './checkpoint/stylegan_v1/ffhq1024/'
direction = 'eyeglasses' #smile, eyeglasses, pose, age, gender
direction_path = './latentvectors/directions/stylegan_ffhq_%s_w_boundary.npy'%direction
w_path = './latentvectors/faces/i3_cxx2.pt'
#Loading Pre-trained Model, Directions
Gs = Generator(startf=16, maxf=512, layer_count=int(math.log(img_size,2)-1), latent_size=512, channels=3)
Gs.load_state_dict(torch.load(GAN_path+'Gs_dict.pth', map_location=device))
# E = BE.BE()
# E.load_state_dict(torch.load('./checkpoint/E/styleganv1.pth',map_location=torch.device('cpu')))
direction = np.load(direction_path) #[[1, 512] interfaceGAN
direction = torch.tensor(direction).float()
direction = direction.expand(18,512)
print(direction.shape)
w = torch.load(w_path, map_location=device).clone().squeeze(0)
print(w.shape)
# discovering face semantic attribute dirrections
bonus= 70 #bonus (-10) <- (-5) <- 0 ->5 ->10
start= 0 # default 0, if not 0, will be bed performance
end= 3 # default 3 or 4. if 3, it will keep face features (glasses). if 4, it will keep dirrection features (Smile).
w[start:start+end] = (w+bonus*direction)[start:start+end]
#w = w + bonus*direction
w = w.reshape(1,18,512)
with torch.no_grad():
img = Gs.forward(w,8) # 8->1024
torchvision.utils.save_image(img*0.5+0.5, './img_bonus%d_start%d_end%d.png'%(bonus,start,end))
## end=3 人物ID的特征明显，end=4 direction的特征明显, end>4 空间纠缠严重
#smile: bonue*100, start=0, end=4(end不到4作用不大,end或bonus越大越猖狂）
#glass: bonue*200, start=0, end=4(end超过6开始崩,bonus也不宜过大)
#pose: bonue*5-10, start=0, end=3 | [
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385,
20046,
253,
38834,
22522,
250,
32573,
229,
32014,
8,
198,
2,
3455,
25,
5351,
518,
9,
20,
12,
940,
11,
923,
28,
15,
11,
886,
28,
18
] | 2.348925 | 791 |
from django.conf.urls import url
from . import views
from board.views import PostDelete
urlpatterns = [
url(r'^$', views.index, name='index'),
url(r'^login/$', views.login, name='login'),
url(r'^logout/$', views.logout, name='logout'),
url(r'^register/$', views.register, name='register'),
url(r'^delete/(?P<pk>\d+)$', PostDelete.as_view(), name='delete')
]
| [
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62,
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] | 2.590278 | 144 |
# -*- coding: utf-8 -*-
from app.common.target_urls import SHOP_GX_ID
from app.planes.jet_plane import JetPlane
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
598,
13,
11321,
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62,
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62,
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628
] | 2.511111 | 45 |
from uuid import uuid4
from ckeditor_uploader.fields import RichTextUploadingField
from django.conf import settings
from django.db import models
from question.models import *
User = settings.AUTH_USER_MODEL
| [
6738,
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] | 3.403226 | 62 |
>>> ' '.join(''.join(''.join(['' if i%3 else 'F',
'' if i%5 else 'B'])
or str('00'))
for i in range(1,16))
'00 00 F 00 B F 00 00 F B 00 F 00 00 FB'
>>> _
'00 00 F 00 B F 00 00 F B 00 F 00 00 FB'
>>> _.replace('FB','11').replace('F','01').replace('B','10').split()[::-1]
['11', '00', '00', '01', '00', '10', '01', '00', '00', '01', '10', '00', '01', '00', '00']
>>> '0b' + ''.join(_)
'0b110000010010010000011000010000'
>>> eval(_)
810092048
>>>
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3256,
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3256,
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3256,
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3256,
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15,
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6,
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28264,
8,
198,
23,
3064,
37128,
2780,
198,
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198
] | 1.866667 | 285 |
# -*- coding: utf-8 -*-
from flask import Flask, request, render_template, g, session, redirect, current_app
from flask_locale import Locale, _
app = Flask(__name__)
# DEFAULT_LOCALE is the language used for keys ins translation files:
app.config['DEFAULT_LOCALE'] = 'tr_TR'
app.config['LOCALE_PATH'] = 'translations'
app.config['SECRET_KEY'] = 'translations****'
locale = Locale(app)
@locale.localeselector
@app.route("/")
@app.route("/locale")
if __name__ == '__main__':
app.run(debug=True)
| [
2,
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31,
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13,
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361,
11593,
3672,
834,
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705,
834,
12417,
834,
10354,
198,
220,
220,
220,
598,
13,
5143,
7,
24442,
28,
17821,
8,
198
] | 2.731183 | 186 |
import os
import torch
import numpy as np
import nn.vnn as vnn
import collections
from torch import nn
from torch.nn import functional as F
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence
from model.seq2seq import Module as Base
from models.utils.metric import compute_f1, compute_exact
from nltk.translate.bleu_score import sentence_bleu
# time
import time
from collections import defaultdict
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11748,
640,
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6738,
17268,
1330,
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] | 3.417323 | 127 |
import logging
import requests
from stellar_base.builder import Builder
from stellar_base.keypair import Keypair
logger = logging.getLogger(__name__)
| [
11748,
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11187,
1362,
7,
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] | 3.627907 | 43 |
import torch
parameters = [torch.autograd.Variable(torch.FloatTensor([0, 0, 0]), requires_grad=True)]
optimizer = torch.optim.SGD(parameters, lr=0.1, momentum=0.9)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, verbose=True)
for _ in range(100):
scheduler.step(.1)
lrs = get_lrs()
lr_str = ','.join(['{:.2g}'.format(lr) for lr in lrs])
# print(lr_str)
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3808,
12962,
198,
220,
220,
220,
1303,
3601,
7,
14050,
62,
2536,
8,
198
] | 2.245714 | 175 |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, Sequence, Tuple, List
import collections
import json
import re
from fairdiplomacy.models.consts import POWERS, N_SCS
GameScores = collections.namedtuple(
"GameScores",
[
"center_ratio", # Ratio of the player's SCs to N_SCS.
"draw_score", # 1. / num alive players, but 1/0 if clear win/loss.
"square_ratio", # Ratio of the squure of the SCs to sum of squares.
"square_score", # Same as square_ratio, but 1 if is_clear_win.
"is_complete_unroll", # 0/1 whether last phase is complete.
"is_clear_win", # 0/1 whether the player has more than half SC.
"is_clear_loss", # 0/1 whether another player has more than half SC.
"is_eliminated", # 0/1 whether has 0 SC.
"is_leader", # 0/1 whether the player has at least as many SCs as anyone else.
"can_draw", # 0/1 whether the player is alive and nobody wins solo.
"num_games", # Number of games being averaged
],
)
def get_power_one(game_json_path):
"""This function is depreccated. Use fairdiplomacy.compare_agents_array."""
name = re.findall("game.*\.json", game_json_path)[0]
for power in POWERS:
if power[:3] in name:
return power
raise ValueError(f"Couldn't parse power name from {name}")
def get_game_result_from_json(game_json_path):
"""This function is depreccated. Use fairdiplomacy.compare_agents_array."""
power_one = get_power_one(game_json_path)
try:
with open(game_json_path) as f:
j = json.load(f)
except Exception as e:
print(e)
return None
rl_rewards = compute_game_scores(POWERS.index(power_one), j)
counts = {k: len(v) for k, v in j["phases"][-1]["state"]["centers"].items()}
for p in POWERS:
if p not in counts:
counts[p] = 0
powers_won = {p for p, v in counts.items() if v == max(counts.values())}
power_won = power_one if power_one in powers_won else powers_won.pop()
if counts[power_one] == 0:
return "six", power_one, power_won, rl_rewards
winner_count, winner = max([(c, p) for p, c in counts.items()])
if winner_count < 18:
return "draw", power_one, power_won, rl_rewards
if winner == power_one:
return "one", power_one, power_won, rl_rewards
else:
return "six", power_one, power_won, rl_rewards
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] | 2.473583 | 1,041 |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
bank=pd.read_csv(path)
#print(bank)
# code starts here
categorical_var=bank.select_dtypes(include = 'object')
print(categorical_var)
numerical_var=bank.select_dtypes(include = 'number')
print(numerical_var)
# code ends here
# --------------
# code starts here
#code ends here
banks=bank.drop('Loan_ID', axis=1)
#print(banks)
sums=banks.isnull().sum()
print(sums)
bank_mode=banks.mode
print(bank_mode)
banks=banks.fillna(bank_mode)
print(banks)
# --------------
# code starts here
# check the avg_loan_amount
avg_loan_amount = banks.pivot_table(index=["Gender","Married","Self_Employed"],values="LoanAmount")
print (avg_loan_amount)
# code ends here
# --------------
# code starts here
loan_approved_se=len(banks[(banks['Self_Employed'] == 'Yes') & (banks['Loan_Status']== 'Y')])
loan_approved_nse=len(banks[(banks['Self_Employed'] == 'No') & (banks['Loan_Status']== 'Y')])
# code ends here
percentage_se=(loan_approved_se/614)*100
print(percentage_se)
percentage_nse=(loan_approved_nse/614)*100
print(percentage_nse)
# --------------
# code starts here
loan_term=banks['Loan_Amount_Term'].apply(lambda x:x/12)
#print(loan_term)
big_loan_term=len(loan_term[loan_term >=25])
print(big_loan_term)
# code ends here
# --------------
# code starts here
loan_groupby =banks.groupby('Loan_Status')['ApplicantIncome', 'Credit_History']
mean_values=loan_groupby.agg(np.mean)
# code ends here
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] | 2.623509 | 587 |
import torch
import torch.nn.functional as F
from torch.utils.data import Sampler
import math
def top_filtering(logits, top_k=0, top_p=0.0, threshold=-float('Inf'), filter_value=-float('Inf')):
""" Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
Args:
logits: logits distribution shape (..., vocabulary size)
top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.
top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset
whose total probability mass is greater than or equal to the threshold top_p.
In practice, we select the highest probability tokens whose cumulative probability mass exceeds
the threshold top_p.
threshold: a minimal threshold to keep logits
"""
top_k = min(top_k, logits.size(-1))
if top_k > 0:
# Remove all tokens with a probability less than the last token in the top-k tokens
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
# Compute cumulative probabilities of sorted tokens
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probabilities > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# Back to unsorted indices and set them to -infinity
indices_to_remove = sorted_indices[sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
indices_to_remove = logits < threshold
logits[indices_to_remove] = filter_value
return logits
class SequentialDistributedSampler(Sampler):
"""
Distributed Sampler that subsamples indicies sequentially,
making it easier to collate all results at the end.
Even though we only use this sampler for eval and predict (no training),
which means that the model params won't have to be synced (i.e. will not hang
for synchronization even if varied number of forward passes), we still add extra
samples to the sampler to make it evenly divisible (like in `DistributedSampler`)
to make it easy to `gather` or `reduce` resulting tensors at the end of the loop.
""" | [
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379,
262,
886,
286,
262,
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13,
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220,
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] | 3.058612 | 836 |
if __name__ == '__main__':
# first we have to create the vertices (nodes)
node1 = Node("A")
node2 = Node("B")
node3 = Node("C")
node4 = Node("D")
node5 = Node("E")
# handle and set the neighbors accordingly
node1.adjacency_list.append(node2)
node1.adjacency_list.append(node3)
node2.adjacency_list.append(node4)
node4.adjacency_list.append(node5)
# run the DFS
depth_first_search(node1)
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198
] | 2.342105 | 190 |
#!/usr/bin/env python2.7
import sys
import json
data = load_seeds(sys.stdin)
n = len(data)
intervals = []
for (a, b, count) in make_bins(data):
intervals.append({'start': a, 'stop': b, 'prob': (100.0*count)/n})
print(json.dumps(intervals))
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] | 2.378641 | 103 |
from flask import Flask
from flask_restly import FlaskRestly
from flask_restly.decorator import resource, get
app = Flask(__name__)
# json is default serializer
# from flask_restly.serializer import json
# app.config['RESTLY_SERIALIZER'] = json
rest = FlaskRestly(app)
rest.init_app(app)
@resource(name='employees')
with app.app_context():
EmployeesResource()
if __name__ == "__main__":
app.run(host='127.0.0.1', port=5001, debug=True)
| [
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] | 2.83125 | 160 |
# -*- coding: utf-8 -*-
#
# Copyright (c) 2018~2999 - Cologler <[email protected]>
# ----------
#
# ----------
from abc import abstractmethod, ABC
from enum import Enum
from inspect import signature, Parameter
from typing import Any
from threading import RLock
import inspect
import contextlib
from .symbols import Symbols
from .utils import update_wrapper
class ServiceInfo(IServiceInfo):
'''generic `IServiceInfo`.'''
__slots__ = (
'_key', '_lifetime', '_factory',
# for not transient
'_lock',
# for singleton
'_cache_value', '_service_provider',
# options
'_options',
)
def _create(self, provider):
'''
return the finally service instance.
'''
service = self._factory(provider)
if self._options['auto_enter']:
wrapped = getattr(self._factory, '__anyioc_wrapped__', self._factory)
if isinstance(wrapped, type) and hasattr(wrapped, '__enter__') and hasattr(wrapped, '__exit__'):
service = provider.enter(service)
return service
class ProviderServiceInfo(IServiceInfo):
'''a `IServiceInfo` use for get current `ServiceProvider`.'''
__slots__ = ()
class GetAttrServiceInfo(IServiceInfo):
'''getattr from current `ServiceProvider`.'''
__slots__ = ('_attr_info')
class ValueServiceInfo(IServiceInfo):
'''a `IServiceInfo` use for get fixed value.'''
__slots__ = ('_value')
class GroupedServiceInfo(IServiceInfo):
'''a `IServiceInfo` use for get multi values as a tuple from keys list.'''
__slots__ = ('_keys')
class BindedServiceInfo(IServiceInfo):
'''a `IServiceInfo` use for get value from target key.'''
__slots__ = ('_target_key')
class CallerFrameServiceInfo(IServiceInfo):
'a `IServiceInfo` use for get caller frameinfo'
__slots__ = ()
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6,
628,
220,
220,
220,
11593,
6649,
1747,
834,
796,
7499,
198
] | 2.558743 | 732 |
from ctzzy._version import __version__ | [
6738,
269,
83,
31570,
13557,
9641,
1330,
11593,
9641,
834
] | 3.8 | 10 |
#!/usr/bin/env python
import getopt
import sys
import dpkt
import pcap
if __name__.rpartition(".")[-1] == "__main__":
main()
| [
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] | 2.37931 | 58 |
"""
Consumer factories.
"""
from microcosm.api import defaults
from microcosm.object_graph import ObjectGraph
from microcosm_sagemaker.artifact import RootInputArtifact
@defaults(
perform_load=True,
)
def load_active_bundle_and_dependencies(graph: ObjectGraph):
"""
Loads the active bundle and its dependencies immediately upon instantation.
"""
if not graph.config.load_active_bundle_and_dependencies.perform_load:
return
root_input_artifact = RootInputArtifact(graph.config.root_input_artifact_path)
graph.bundle_and_dependencies_loader(
bundle=graph.active_bundle,
root_input_artifact=root_input_artifact,
)
def configure_sagemaker(graph):
"""
Instantiates all the necessary sagemaker factories.
"""
graph.use(
"active_bundle",
"active_evaluation",
"bundle_and_dependencies_loader",
"bundle_and_dependencies_trainer",
"random",
"training_initializers",
"experiment_metrics",
)
| [
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62,
4164,
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1600,
198,
220,
220,
220,
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198
] | 2.605598 | 393 |
#!/usr/bin/python3
# Copyright (C) 2021 Sam Steele
# 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 asyncio, json, sys
from brother import Brother, SnmpError, UnsupportedModel
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close() | [
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7,
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198,
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3419
] | 3.553488 | 215 |
# -*- coding: utf-8 -*-
import unittest
import numpy as np
import pandas as pd
import random
from collections import OrderedDict
from testfixtures import TempDirectory
import os
from .. import excel
def test_suite():
"""Test suite including all test suites"""
testSuite = unittest.TestSuite()
testSuite.addTest(test_excel("test_dataframe"))
testSuite.addTest(test_excel("test_save"))
return testSuite
if __name__ == "__main__":
import sys
mysuite = test_suite()
runner = unittest.TextTestRunner()
if not runner.run(mysuite).wasSuccessful():
sys.exit(1)
| [
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220,
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13,
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7,
16,
8,
198
] | 2.700893 | 224 |
import paho.mqtt.client as mqtt
import ssl
import json
import utils
#=======================================================================
# program that replaces te physical potentiometer
# it sends a value between 0 and 255 that emulates the signal from potentiometer
# to the topic sub_topic defined in the general section of this file
# demo1.py listens on this topic and uses the sent value to control the rest of simulation
#=======================================================================
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ==================================================================
# ==================================================================
# ==================================================================
# ==================================================================
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
#####mqtt_username = "rvksim"
#####mqtt_password = "7TvpyxEQBsMHSHaF7E8m6Hd7Sux2mpQx"
#####mqtt_endpoint = "fiware.n5geh.de"
#####mqtt_endpoint = "127.0.0.1"
#####mqtt_port = 1026
#####TLS_CONNECTION = True
#####TLS_CONNECTION = False
#####ACT_AUTH = False
#####
#####sub_topic = "/rvksim/lastgang/cmd"
#####pub_topic = "/rvksim/lastgang/attrs"
#####
#####filepath = "./static_lastgang.dat"
#####
###### persistent data object
#####data = {}
if __name__ == "__main__":
main()
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198
] | 4.581769 | 373 |
"""Feature temporal aggregation functions"""
from abc import ABC
import numpy as np
class Aggregator():
"""Aggregates temporal values"""
def update_statistics(self, instances):
"""Identify statistics to standardize each feature"""
for fidx, _ in enumerate(self._aggregation_fns):
left, right = self._windows[fidx]
vec = self.operate_on_feature(fidx, instances[:, fidx, left: right + 1])
self._means[fidx] = np.mean(vec)
self._stds[fidx] = np.std(vec)
if self._stds[fidx] < 1e-10:
# FIXME: features can pass the variance test earlier but fail it here, since the samples used are different
self._stds[fidx] = 1
def operate_on_feature(self, fidx, sequences):
"""Operate on sequences for given feature"""
return (self._aggregation_fns[fidx].operate(sequences) - self._means[fidx]) / self._stds[fidx] # sequences: instances X timesteps
def operate(self, sequences):
"""Apply feature-wise operations to sequence data"""
# TODO: when perturbing a feature, other values do not need to be recomputed.
# But this seems unavoidable under the current design (analysis only calls model.predict, doesn't provide other info)
num_instances, num_features, _ = sequences.shape # sequences: instances X features X timesteps
matrix = np.zeros((num_instances, num_features))
for fidx in range(num_features):
(left, right) = self._windows[fidx]
matrix[:, fidx] = self.operate_on_feature(fidx, sequences[:, fidx, left: right + 1])
return matrix
class TabularAggregator(Aggregator):
"""Returns input as-is without aggregation (for tabular features)"""
class AggregationFunction(ABC):
"""Aggregation function base class"""
NONLINEARITY_OPERATORS = [lambda x: x, np.abs, np.square]
def operate(self, sequences):
"""Operate on sequences for given feature"""
return self._nonlinearity_operator(np.apply_along_axis(self._sequence_operator, 1, sequences)) # sequences: instances X timesteps
class Max(AggregationFunction):
"""Computes max of inputs"""
class Average(AggregationFunction):
"""Computes average of inputs"""
class MonotonicWeightedAverage(AggregationFunction):
"""Computes weighted average of inputs with monotically increasing weights"""
class RandomWeightedAverage(AggregationFunction):
"""Computes weighted average of inputs with random weights"""
AGGREGATION_OPERATORS = [Max, Average, MonotonicWeightedAverage, RandomWeightedAverage]
def get_aggregation_fn_cls(rng):
"""Sample aggregation function for feature"""
return rng.choice(AGGREGATION_OPERATORS)
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7,
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28934,
38,
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62,
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1404,
20673,
8,
198
] | 2.829897 | 970 |
#!/usr/bin/python3
import getpass
import subprocess
import glob
import time
import os
import re
import socket
from datetime import datetime
from pprint import pprint
"""
Pull Charlesreid1.com - Rojo
This script pulls the latest version of charlesreid1.com source.
"""
if __name__=="__main__":
host = socket.gethostname()
user = getpass.getuser()
if(host!="rojo"):
print("You aren't on rojo - you probably didn't mean to run this script!")
elif(user!="charles"):
print("You aren't charles - you should run this script as charles!")
else:
pull()
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25,
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220,
220,
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220,
220,
220,
220,
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3419,
628
] | 2.794393 | 214 |
a1=1
a2=1+a<caret> | [
64,
16,
28,
16,
198,
64,
17,
28,
16,
10,
64,
27,
6651,
83,
29
] | 1.2 | 15 |
#!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2010 British Broadcasting Corporation and Kamaelia Contributors(1)
#
# (1) Kamaelia Contributors are listed in the AUTHORS file and at
# http://www.kamaelia.org/AUTHORS - please extend this file,
# not this notice.
#
# 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 Kamaelia.UI.Pygame.EventHandler import EventHandler
from Axon.Component import component
import pygame
from Kamaelia.UI.Pygame.KeyEvent import KeyEvent
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2,
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13,
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2,
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628,
198,
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1689,
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13,
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13,
20519,
6057,
13,
9237,
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1330,
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261,
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25418,
13,
10080,
13,
20519,
6057,
13,
9218,
9237,
1330,
7383,
9237,
198
] | 3.684028 | 288 |
from typing import List
PRICES = {
1: 800,
2: 1520,
3: 2160,
4: 2560,
5: 3000,
}
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198
] | 1.810345 | 58 |
from FrEIA import framework as fr
from FrEIA import modules as la
import torch
import torchvision.models as models
import torch.nn as nn
import numpy as np
def get_vgg16():
"""
Get features from pretrained VGG16 model.
:return: partial VGG16 model, which takes ImageNet images and return feature0
"""
vgg = models.vgg16(pretrained=True)
vgg_feature = vgg.features[:17]
return vgg_feature
def inn_model(img_dims=4):
"""
Return INN model.
:param img_dims: size of the model input images. Default: Size of MNIST images
:return: INN model
"""
inp = fr.InputNode(img_dims, name='input')
fc1 = fr.Node([inp.out0], la.GLOWCouplingBlock, {'subnet_constructor': fc_constr, 'clamp': 2}, name='fc1')
fc2 = fr.Node([fc1.out0], la.GLOWCouplingBlock, {'subnet_constructor': fc_constr, 'clamp': 2}, name='fc2')
fc3 = fr.Node([fc2.out0], la.GLOWCouplingBlock, {'subnet_constructor': fc_constr, 'clamp': 2}, name='fc3')
outp = fr.OutputNode([fc3.out0], name='output')
nodes = [inp, outp, fc1, fc2, fc3]
model = fr.ReversibleGraphNet(nodes)
return model
fc_width = 512
conv_width = 256
n_coupling_blocks_fc = 12
n_coupling_blocks_conv_0 = 2
n_coupling_blocks_conv_1 = 2
clamp = 2.0
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] | 2.447876 | 518 |
""" Sets up reverse proxy in the application
- app's reverse proxy dynamically reroutes communication between web-server's client
and dynamic-backend services (or dyb's)
- couples director with reverse_proxy subsystems
Use case
- All requests to `/x/{serviceId}/{proxyPath}` are re-routed to a dyb service
- dy-services are managed by the director service who monitors and controls its lifetime
"""
import logging
import os
import attr
from aiohttp import web
from aiohttp.client import ClientSession
from yarl import URL
from servicelib.rest_responses import unwrap_envelope
from .director.config import APP_DIRECTOR_API_KEY
from .reverse_proxy import setup_reverse_proxy
from .reverse_proxy.abc import ServiceResolutionPolicy
THIS_CLIENT_SESSION = __name__ + ".session"
logger = logging.getLogger(__name__)
@attr.s(auto_attribs=True)
# alias
setup_app_proxy = setup
__all__ = (
'setup_app_proxy'
)
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] | 3.219178 | 292 |
import re
from therandy.utils import for_app
regex = re.compile(r'Run "(.*)" instead')
@for_app('yarn', at_least=1)
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] | 2.5 | 48 |
import torch
import functools
__all__ = [
"VisibilityFOV",
"VisibilityHeatmap"
]
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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from mwptoolkit.module.Embedder import basic_embedder,bert_embedder,position_embedder,roberta_embedder | [
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
CIE UCS Colourspace
===================
Defines the *CIE UCS* colourspace transformations:
- :func:`XYZ_to_UCS`
- :func:`UCS_to_XYZ`
- :func:`UCS_to_uv`
- :func:`UCS_uv_to_xy`
See Also
--------
`CIE UCS Colourspace IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/models/cie_ucs.ipynb>`_ # noqa
References
----------
.. [1] http://en.wikipedia.org/wiki/CIE_1960_color_space
(Last accessed 24 February 2014)
"""
from __future__ import division, unicode_literals
import numpy as np
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2014 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '[email protected]'
__status__ = 'Production'
__all__ = ['XYZ_to_UCS',
'UCS_to_XYZ',
'UCS_to_uv',
'UCS_uv_to_xy']
def XYZ_to_UCS(XYZ):
"""
Converts from *CIE XYZ* colourspace to *CIE UCS* colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
Returns
-------
ndarray, (3,)
*CIE UCS* colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 1].
- Output *CIE UCS* colourspace matrix is in domain [0, 1].
References
----------
.. [2] http://en.wikipedia.org/wiki/CIE_1960_color_space#Relation_to_CIEXYZ # noqa
(Last accessed 24 February 2014)
Examples
--------
>>> XYZ = np.array([0.1180583421, 0.1034, 0.0515089229])
>>> XYZ_to_UCS(XYZ) # doctest: +ELLIPSIS
array([ 0.0787055..., 0.1034 , 0.1218252...])
"""
X, Y, Z = np.ravel(XYZ)
return np.array([2 / 3 * X,
Y,
1 / 2 * (-X + 3 * Y + Z)])
def UCS_to_XYZ(UVW):
"""
Converts from *CIE UCS* colourspace to *CIE XYZ* colourspace.
Parameters
----------
UVW : array_like, (3,)
*CIE UCS* colourspace matrix.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Notes
-----
- Input *CIE UCS* colourspace matrix is in domain [0, 1].
- Output *CIE XYZ* colourspace matrix is in domain [0, 1].
References
----------
.. [3] http://en.wikipedia.org/wiki/CIE_1960_color_space#Relation_to_CIEXYZ # noqa
(Last accessed 24 February 2014)
Examples
--------
>>> UCS = np.array([0.07870556, 0.1034, 0.12182529])
>>> UCS_to_XYZ(UCS) # doctest: +ELLIPSIS
array([ 0.1180583..., 0.1034 , 0.0515089...])
"""
U, V, W = np.ravel(UVW)
return np.array(
[3 / 2 * U, V, 3 / 2 * U - (3 * V) + (2 * W)])
def UCS_to_uv(UVW):
"""
Returns the *uv* chromaticity coordinates from given *CIE UCS* colourspace
matrix.
Parameters
----------
UVW : array_like, (3,)
*CIE UCS* colourspace matrix.
Returns
-------
tuple
*uv* chromaticity coordinates.
Notes
-----
- Input *CIE UCS* colourspace matrix is in domain [0, 1].
- Output *uv* chromaticity coordinates are in domain [0, 1].
References
----------
.. [4] http://en.wikipedia.org/wiki/CIE_1960_color_space#Relation_to_CIEXYZ # noqa
(Last accessed 24 February 2014)
Examples
--------
>>> UCS = np.array([0.1180583421, 0.1034, 0.0515089229])
>>> UCS_to_uv(UCS) # doctest: +ELLIPSIS
(0.4324999..., 0.3788000...)
"""
U, V, W = np.ravel(UVW)
return U / (U + V + W), V / (U + V + W)
def UCS_uv_to_xy(uv):
"""
Returns the *xy* chromaticity coordinates from given *CIE UCS* colourspace
*uv* chromaticity coordinates.
Parameters
----------
uv : array_like
*CIE UCS uv* chromaticity coordinates.
Returns
-------
tuple
*xy* chromaticity coordinates.
Notes
-----
- Input *uv* chromaticity coordinates are in domain [0, 1].
- Output *xy* chromaticity coordinates are in domain [0, 1].
References
----------
.. [5] http://en.wikipedia.org/wiki/CIE_1960_color_space#Relation_to_CIEXYZ # noqa
(Last accessed 24 February 2014)
Examples
--------
>>> uv = (0.43249999995420696, 0.378800000065942)
>>> UCS_uv_to_xy(uv) # doctest: +ELLIPSIS
(0.7072386..., 0.4129510...)
"""
return (3 * uv[0] / (2 * uv[0] - 8 * uv[1] + 4),
2 * uv[1] / (2 * uv[0] - 8 * uv[1] + 4))
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] | 2.226277 | 2,055 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence
# from config import burn_in_length
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import psutil
import time
import GPUtil
import csv
import pandas as pd
# process_name = 'LiDAR360.exe'
name = psutil.Process(15500)
name_1 = psutil.Process(9996)
name_2 = psutil.Process(1476)
name_3 = psutil.Process(14976)
print(name.pid)
# print("Hello")
sleep_time = 2
mins = 60
# start = time.time()
with open('Resource_Usage3.csv', 'w') as f:
header = ['PID','Process Name', 'CPU Usage', 'GPU Usage', 'Memory Usage','IO_Usage','Timestamp']
writer = csv.writer(f)
writer.writerow(header)
lst_name = [name,name_1,name_2,name_3]
for i in range(int(mins*60/sleep_time)):
for j in range(len(lst_name)):
count = 0
cpu_total = 0
cpu_usage, process_name, memory_usage, io_usage, gpu_usage,pid = res_info(lst_name[j])
cpu_total =+cpu_usage
lst_info = [pid,process_name, cpu_usage, gpu_usage, memory_usage, io_usage,time.time()]
print("Process Name: " + str(process_name))
print("CPU Usage: " + str(cpu_usage))
print("memory_usage: " + str(memory_usage))
print("IO Usage: " + str(io_usage))
print("GPU Usage: " + str(gpu_usage))
count =+ 1
if count == 4:
print("Total CPU: "+str(cpu_total))
print("----------------------------")
# with open('Resource_Usage3.csv', 'a', newline='') as d:
# writer = csv.writer(d)
# writer.writerow(lst_info)
# # writer.writerow([])
# if count == 4:
# cpu_total = 0
# count = 0
#
# del lst_info
# time.sleep(sleep_time)
#
# df = pd.read_csv('Resource_Usage3.csv')
#
# for i in range(4):
# name = 'PID'+str(i)
# df[name]=''
#
# for i in range(4):
# if i == 1:
# df['PID1'] = df.loc[df['PID']==df['PID'][0],'CPU Usage'].sum
# for i in range(int((mins * 60) / sleep_time)):
# cpu = name.cpu_percent(interval=2) / psutil.cpu_count()
# name1 = name.name()
# memory = name.memory_percent()
#
# print("Name % = " + str(name1))
# abc = GPUtil.showUtilization(useOldCode=True)
# print(abc)
# # print(GPUtil.showUtilization())
# print("CPU % = " + str(cpu))
# print("Memory % = " + str(memory))
#
# print("----------------------")
# lst = [name1, cpu, abc, memory]
# with open('Resource_Usage.csv', 'a', newline='') as d:
# writer = csv.writer(d)
# writer.writerow(lst)
# del lst # for creating list for new row entry
# time.sleep(sleep_time)
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] | 2.039185 | 1,276 |
#!/usr/bin/env python
# Copyright 2016 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# [START imports]
import os
import urllib
from google.appengine.api import users
from google.appengine.ext import ndb
import jinja2
import webapp2
JINJA_ENVIRONMENT = jinja2.Environment(
loader=jinja2.FileSystemLoader(os.path.dirname(__file__)),
extensions=['jinja2.ext.autoescape'],
autoescape=True)
# [END imports]
DEFAULT_GENRE = 'action'
genrer = ""
genre2 = ""
# We set a parent key on the 'Greetings' to ensure that they are all
# in the same entity group. Queries across the single entity group
# will be consistent. However, the write rate should be limited to
# ~1/second.
def genre_key(genre=DEFAULT_GENRE):
"""Constructs a Datastore key for a Guestbook entity.
We use guestbook_name as the key.
"""
return ndb.Key('Guestbook', genre)
# [START Movie]
class Author(ndb.Model):
"""Sub model for representing an author."""
identity = ndb.StringProperty(indexed=False)
email = ndb.StringProperty(indexed=False)
class Movie(ndb.Model):
"""A main model for representing an individual movie entry."""
author = ndb.StructuredProperty(Author)
name = ndb.StringProperty(indexed=False)
director = ndb.StringProperty(indexed=False)
actor = ndb.StringProperty(indexed=False)
actor2 = ndb.StringProperty(indexed=False)
year = ndb.StringProperty(indexed=False)
duration = ndb.StringProperty(indexed=False)
date = ndb.DateTimeProperty(auto_now_add=True)
# [END Movie]
# [START main_page]
# [END main_page]
# [START movieinfo]
# [START app]
app = webapp2.WSGIApplication([
('/', MainPager),
('/sign', DisplayPage),('/search', SearchPage),('/enter', EnterPage),
], debug=True)
# [END app]
| [
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] | 2.966321 | 772 |
# ____ _ _
# | _ \ _ __ ___ (_) ___ ___| |_
# | |_) | '__/ _ \| |/ _ \/ __| __|
# | __/| | | (_) | | __/ (__| |_
# |_| _|_| \___// |\___|\___|\__|
# | | | | __ _ _|__/ ___| |_ __ _ ___| | __
# | |_| |/ _` | | | / __| __/ _` |/ __| |/ /
# | _ | (_| | |_| \__ \ || (_| | (__| <
# |_| |_|\__,_|\__, |___/\__\__,_|\___|_|\_\
# |___/
#
# QueryShannonEntropyBolt -
#
# Calculates the shannon entropy of the query minus any known
# domain suffix and emits a tuple of the query + shannon entropy
# value to the topology as a floating point number.
#
#
from collections import namedtuple
import logging
import math
from pyleus.storm import SimpleBolt
from haystack_topology.parse_event_bolt import Record
log = logging.getLogger('query_shannon-entropy_bolt')
QEntropy = namedtuple("QEntropy", "eventid query qentropy")
if __name__ == '__main__':
logging.basicConfig(
level=logging.DEBUG,
filename='/var/log/haystack/query_shannon-entropy_bolt.log',
format="%(message)s",
filemode='a',
)
QueryShannonEntropyBolt().run()
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] | 2.107078 | 551 |
# (C) Copyright 2017 IBM Corp.
# (C) Copyright 2017 Inova Development Inc.
# 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.
"""
Mixin class that adds methods to WBEMConnection and FakeWBEMConnection for
pywbemcli usage
This contains only methods that use the iter<...> operations but also execute
the complete iterations so that we can use these as common operations for
pywbemcli instead of having to execute an algorithm of pull vs non-pull
everywhere xa WBEMConnection possible pull operation is called.
It also adds a method to FakeWBEMConnection to build the repository.
"""
from __future__ import absolute_import, print_function
import os
import io
import errno
import glob
import hashlib
import pickle
import click
import packaging.version
import pywbem
import pywbem_mock
from .config import DEFAULT_MAXPULLCNT
from .._utils import ensure_bytes, ensure_unicode, DEFAULT_CONNECTIONS_FILE
from . import mockscripts
PYWBEM_VERSION = packaging.version.parse(pywbem.__version__)
# __all__ = ['PYWBEMCLIConnection', 'PYWBEMCLIFakedConnection']
# pylint: disable=useless-object-inheritance
class PYWBEMCLIConnectionMixin(object):
"""
Mixin class to extend WBEMConnection with a set of methods that use the
iter<...> methods as the basis for getting Instances, etc. but add the
generator processing to retrieve the instances. These can be used within
pywbemcli to allow one method call to ack as either a pull or traditional
operation pushing the differences into this mixin.
These methods do not resolve the core issues between the traditional and
pull operations such as the fact that only the pull operations pass
the FilterQuery parameter.
They are a pywbemcli convience to simplify the individual action processing
methods to a single call.
"""
def PyWbemcliEnumerateInstancePaths(self, ClassName, namespace=None,
FilterQueryLanguage=None,
FilterQuery=None,
OperationTimeout=None,
ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterEnumerateInstancePaths and retrieve the instances. Returns
the returned instances.
Uses the same parameters as the IterEnumerateInstancePaths method.
All exceptions from the underlying command are passed through this
method.
"""
result = self.IterEnumerateInstancePaths(
ClassName,
namespace=namespace,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliEnumerateInstances(self, ClassName, namespace=None,
LocalOnly=None,
DeepInheritance=None,
IncludeQualifiers=None,
IncludeClassOrigin=None, PropertyList=None,
FilterQueryLanguage=None, FilterQuery=None,
OperationTimeout=None, ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterEnumerateInstances and retrieve the instances. Returns
the returned instances.
Uses the same parameters as the IterEnumerateInstances method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterEnumerateInstances(
ClassName,
namespace=namespace,
LocalOnly=LocalOnly,
DeepInheritance=DeepInheritance,
IncludeQualifiers=IncludeQualifiers,
IncludeClassOrigin=IncludeClassOrigin,
PropertyList=PropertyList,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliReferenceInstancePaths(self, InstanceName, ResultClass=None,
Role=None,
FilterQueryLanguage=None,
FilterQuery=None,
OperationTimeout=None,
ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterReferemceInstancePaths and retrieve the instances. Returns
the paths that result from iterating the IterReferenceInstancePaths.
Uses the same parameters as the IterReferemceInstancePaths method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterReferenceInstancePaths(
InstanceName,
ResultClass=ResultClass,
Role=Role,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliReferenceInstances(self, InstanceName, ResultClass=None,
Role=None, IncludeQualifiers=None,
IncludeClassOrigin=None, PropertyList=None,
FilterQueryLanguage=None, FilterQuery=None,
OperationTimeout=None, ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterReferencesInstances and retrieve the instances. Returns
the returned instances.
Uses the same parameters as the IterReferencesInstances method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterReferenceInstances(
InstanceName,
ResultClass=ResultClass,
Role=Role,
IncludeQualifiers=IncludeQualifiers,
IncludeClassOrigin=IncludeClassOrigin,
PropertyList=PropertyList,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliAssociatorInstancePaths(self, InstanceName, AssocClass=None,
ResultClass=None,
Role=None, ResultRole=None,
FilterQueryLanguage=None,
FilterQuery=None,
OperationTimeout=None,
ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterAssociatorInstancePaths and retrieve the paths. Returns
the paths that result from iterating the IterAssociatorInstancePaths.
Uses the same parameters as the IterAssociatorInstancePaths method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterAssociatorInstancePaths(
InstanceName,
AssocClass=AssocClass,
ResultClass=ResultClass,
Role=Role,
ResultRole=ResultRole,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliAssociatorInstances(self, InstanceName, AssocClass=None,
ResultClass=None,
Role=None, ResultRole=None,
IncludeQualifiers=None,
IncludeClassOrigin=None, PropertyList=None,
FilterQueryLanguage=None, FilterQuery=None,
OperationTimeout=None,
ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterAssociatorInstances and retrieve the instances. Returns
the instances that result from iterating the IterAssociatorInstances.
Uses the same parameters as the IterAssociatorInstances method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterAssociatorInstances(
InstanceName,
AssocClass=AssocClass,
ResultClass=ResultClass,
Role=Role,
ResultRole=ResultRole,
IncludeQualifiers=IncludeQualifiers,
IncludeClassOrigin=IncludeClassOrigin,
PropertyList=PropertyList,
FilterQueryLanguage=FilterQueryLanguage,
FilterQuery=FilterQuery,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
def PyWbemcliQueryInstances(self, FilterQueryLanguage, FilterQuery,
namespace=None, ReturnQueryResultClass=None,
OperationTimeout=None, ContinueOnError=None,
MaxObjectCount=DEFAULT_MAXPULLCNT,
**extra):
# pylint: disable=unused-argument
# pylint: disable=invalid-name
"""
Execute IterQueryInstances and retrieve the instances. Returns
the instances that result from iterating the IterQueryInstances.
Uses the same parameters as the IterQueryInstances method.
All exceptions from the underlying method are passed through this
method.
"""
result = self.IterQueryInstances(
FilterQueryLanguage,
FilterQuery,
namespace=namespace,
ReturnQueryResultClass=ReturnQueryResultClass,
OperationTimeout=OperationTimeout,
ContinueOnError=ContinueOnError,
MaxObjectCount=MaxObjectCount)
return list(result)
class BuildMockenvMixin(object):
# pylint: disable=too-few-public-methods
"""
Mixin class for pywbem_mock.FakedWBEMConnection that adds the ability to
build the mock environment of a connection from a connection definition in
a connections file.
"""
def build_mockenv(self, server, file_path_list, connections_file,
connection_name, verbose):
"""
Builds the mock environment of the 'self' connection from the input
files, or from the mock cache of the connection if it is up to date.
If the mock environment was built from the input files, the mock
environment of the connection is dumped to its cache.
The input files for building the mock environment are:
* MOF files with a suffix of '.mof'.
These files are compiled into the default namespace of the connection.
* Python files with a suffix of '.py'.
These files are mock scripts that are imported and thereby executed.
The mock scripts can be used for any kind of setup of the mock
environment, for example for creating namespaces, for defining
provider classes and registering providers, or for adding CIM objects
either directly through add_cimobjects() or by compiling MOF files.
Mock scripts support two approaches for passing the connection and
server objects they should operate on:
* via a setup() function defined in the mock script. This is the
recommended approach, and it supports caching. The setup()
function has the following parameters:
conn (pywbem_mock.FakedWBEMConnection): The mock connection.
server (pywbem.WBEMServer): The server object for the mock
connection.
verbose (bool): Verbose flag from the command line.
* via global variables made available to the mock script. This
approach prevents caching. The following global variables are
made available:
CONN (pywbem_mock.FakedWBEMConnection): The mock connection.
SERVER (pywbem.WBEMServer): The server object for the mock
connection.
VERBOSE (bool): Verbose flag from the command line.
Parameters:
self (pywbem_mock.FakedWBEMConnection): The mock connection.
server (pywbem.WBEMServer): The server object for the mock connection.
file_path_list (list of string): The path names of the input files
for building the mock environment, from the connection definition.
connections_file (string): Path name of the connections file.
connection_name (string): The name of the connection definition in
the connections file.
verbose (bool): Verbose flag from the command line.
Raises:
MockFileError: Mock file does not exist.
MockMOFCompileError: Mock MOF file fails to compile.
MockScriptError: Mock script fails to execute.
SetupNotSupportedError (py<3.5): New-style setup in mock script not
supported.
"""
# Check that the input files exist. Since we loop through them multiple
# times, we check that once.
for file_path in file_path_list:
if not os.path.exists(file_path):
raise mockscripts.MockFileError(
"Mock file does not exist: {}".format(file_path))
# The connections file is set if a named connection is used, i.e.
# when specifying the -n general option. It is not set when the -s or -m
# general options were specified. When no connections file is set, no
# caching happens because there is no connection definition context
# which is required for caching.
if connections_file == DEFAULT_CONNECTIONS_FILE:
cache_rootdir = mockcache_rootdir()
if not os.path.isdir(cache_rootdir):
os.mkdir(cache_rootdir)
cache_dir = mockcache_cachedir(
cache_rootdir, connections_file, connection_name)
if not os.path.isdir(cache_dir):
os.mkdir(cache_dir)
# The mockenv pickle file contains the pickled state of the mock
# environment.
mockenv_pickle_file = os.path.join(cache_dir, 'mockenv.pkl')
# The depreg pickle file contains the provider dependents
# registry of the connection. It is used to look up the dependent
# files of a mock script. The content of these dependent files is
# also taken into account when determining whether the cache is up
# to date. This needs to go into a separate pickle file because
# it needs to be loaded and examined before the mckenv pickle
# file is loaded.
depreg_pickle_file = os.path.join(cache_dir, 'depreg.pkl')
# The md5 file contains the MD5 hash value of the content of the
# input files for the mock environment, and also taken into account
# when determining whether the cache is up to date.
md5_file = os.path.join(cache_dir, 'mockfiles.md5')
# Flag indicating that the mock environment needs to be built
# (or re-built). If False, the mock environment cache can be used.
need_rebuild = False
# Determine whether the mock environment needs to be rebuilt based
# on the (non-)existence of the cache files.
if not os.path.isfile(mockenv_pickle_file) \
or not os.path.isfile(depreg_pickle_file) \
or not os.path.isfile(md5_file):
if verbose:
click.echo("Mock environment for connection definition "
"'{}' will be built because it was not cached.".
format(connection_name))
need_rebuild = True
try:
depreg = self._load_depreg(depreg_pickle_file)
except (IOError, OSError) as exc:
if exc.errno == errno.ENOENT:
depreg = pywbem_mock.ProviderDependentRegistry()
else:
raise
# Calculate the MD5 hash value of the content of the input files
md5 = hashlib.md5()
for file_path in file_path_list:
with io.open(file_path, 'rb') as fp:
file_source = fp.read()
md5.update(file_source)
# For mock scripts, take their dependent files into account
if file_path.endswith('.py'):
dep_files = depreg.iter_dependents(file_path)
for dep_file in dep_files:
with io.open(dep_file, 'rb') as fp:
file_source = fp.read()
md5.update(file_source)
# Add the cache dir, so that manual tweaks on the cache files
# invalidates the cache.
md5.update(ensure_bytes(cache_dir))
new_md5_value = ensure_unicode(md5.hexdigest())
# Determine whether the mock environment needs to be rebuilt based
# on the MD5 hash value of the input file content.
if not need_rebuild:
with io.open(md5_file, 'r', encoding='utf-8') as fp:
cached_md5_value = fp.read()
if new_md5_value != cached_md5_value:
if verbose:
click.echo("Mock environment for connection "
"definition '{}' is cached but will be "
"rebuilt because the mock files have "
"changed.".format(connection_name))
need_rebuild = True
cache_it = True
elif connections_file:
# User-specified connections file used.
if verbose:
click.echo("Mock environment for connection definition '{}' "
"will be built because user-specified connections "
"files are not cached.".format(connection_name))
need_rebuild = True
cache_it = False
else:
# No connections file context.
if verbose:
click.echo("Mock environment for connection definition '{}' "
"will be built because no connections file is "
"known.".format(connection_name))
need_rebuild = True
cache_it = False
if need_rebuild:
try:
self._build_mockenv(server, file_path_list, verbose)
except mockscripts.NotCacheable as exc:
if verbose:
click.echo("Mock environment for connection definition "
"'{}' will be built because it is not "
"cacheable: {}.".format(connection_name, exc))
else:
if connections_file and cache_it:
self._dump_mockenv(mockenv_pickle_file)
self._dump_depreg(
self.provider_dependent_registry, depreg_pickle_file)
with io.open(md5_file, 'w', encoding='utf-8') as fp:
fp.write(new_md5_value)
if verbose:
click.echo("Mock environment for connection "
"definition '{}' has been written to "
"cache.".format(connection_name))
else:
# When no rebuild is needed, there must have been a connections
# file set.
assert connections_file
try:
self._load_mockenv(mockenv_pickle_file, file_path_list)
if verbose:
click.echo("Mock environment for connection definition "
"'{}' has been loaded from cache.".
format(connection_name))
except mockscripts.NotCacheable as exc:
if verbose:
click.echo("Mock environment for connection definition "
"'{}' will be rebuilt because it is not "
"cacheable: {}.".format(connection_name, exc))
self._build_mockenv(server, file_path_list, verbose)
def _build_mockenv(self, server, file_path_list, verbose):
"""
Build the mock environment from the input files.
Parameters:
self (pywbem_mock.FakedWBEMConnection): The mock connection.
server (pywbem.WBEMServer): The server object for the mock connection.
file_path_list (list of string): The path names of the input files
for building the mock environment, from the connection definition.
verbose (bool): Verbose flag from the command line.
Raises:
NotCacheable (py<3.5): Mock environment is not cacheable.
MockMOFCompileError: Mock MOF file fails to compile.
MockScriptError: Mock script fails to execute.
SetupNotSupportedError (py<3.5): New-style setup in mock script not
supported.
"""
for file_path in file_path_list:
ext = os.path.splitext(file_path)[1]
if ext == '.mof':
try:
# Displays any MOFParseError already
self.compile_mof_file(file_path, verbose=verbose)
except pywbem.Error as er:
# Abort the entire pywbemcli command because the
# MOF compilation might have caused inconsistencies in
# the mock repository.
if PYWBEM_VERSION.release >= (1, 0, 0):
# display just the exception.
msg = "MOF compile failed:\n{0}".format(er)
else:
# display file name. Error text displayed already.
if isinstance(er, pywbem.MOFParseError):
msg = "MOF compile failed: File: '{0}'" \
"(see above)".format(file_path)
else: # not parse error, display exception
msg = "MOF compile failed: File: {0} " \
"Error: {1}".format(file_path, er)
new_exc = mockscripts.MockMOFCompileError(msg)
new_exc.__cause__ = None
raise new_exc
else:
assert ext == '.py' # already checked
# May raise various mockscripts.MockError exceptions.
# NotCacheable will be handled by the caller by building the
# mock env.
mockscripts.setup_script(file_path, self, server, verbose)
def _dump_mockenv(self, mockenv_pickle_file):
"""
Dump the mock environment of the connection to the mockenv pickle file.
Parameters:
self (pywbem_mock.FakedWBEMConnection): The mock connection.
mockenv_pickle_file (pywbem.WBEMServer): Path name of the mockenv
pickle file.
"""
# Save the provider registry and the CIM repository
# We construct a single object, because the CIM repository is
# referenced from each provider, and pickle properly handles
# multiple references to the same object.
mockenv = dict(
cimrepository=self.cimrepository,
# pylint: disable=protected-access
provider_registry=self._provider_registry,
)
with io.open(mockenv_pickle_file, 'wb') as fp:
pickle.dump(mockenv, fp)
def _load_mockenv(self, mockenv_pickle_file, file_path_list):
"""
Load the mock environment from the mockenv pickle file.
This method also imports the Python scripts from the input files in
order to re-establish any class definitions that may be needed, for
example provider classes.
Parameters:
self (pywbem_mock.FakedWBEMConnection): The mock connection.
mockenv_pickle_file (pywbem.WBEMServer): Path name of the mockenv
pickle file.
file_path_list (list of string): The path names of the input files
for building the mock environment, from the connection definition.
Raises:
NotCacheable (py<3.5): Mock environment is not cacheable.
"""
# Restore the provider classes
for file_path in file_path_list:
ext = os.path.splitext(file_path)[1]
if ext == '.py':
# May raise mockscripts.NotCacheable which will be handled by
# the caller by building the mock env.
mockscripts.import_script(file_path)
# Restore the provider registry and the CIM repository
with io.open(mockenv_pickle_file, 'rb') as fp:
mockenv = pickle.load(fp)
# Others have references to the self._cimrepository object, so we are
# not replacing that object, but are rather replacing the state of
# that object.
cimrepository = mockenv['cimrepository']
assert isinstance(cimrepository, pywbem_mock.InMemoryRepository)
# pylint: disable=protected-access
self._cimrepository.load(cimrepository)
provider_registry = mockenv['provider_registry']
assert isinstance(provider_registry, pywbem_mock.ProviderRegistry)
# pylint: disable=protected-access
self._provider_registry.load(provider_registry)
@staticmethod
def _dump_depreg(depreg, depreg_pickle_file):
"""
Dump a provider dependent registry to a pickle file.
Parameters:
depreg (pywbem_mock.ProviderDependentRegistry): Provider dependent
registry to be dumped.
depreg_pickle_file (string): Path name of the pickle file.
"""
with io.open(depreg_pickle_file, 'wb') as fp:
pickle.dump(depreg, fp)
@staticmethod
def _load_depreg(depreg_pickle_file):
"""
Load a provider dependent registry from a pickle file and return it.
Parameters:
depreg_pickle_file (string): Path name of the pickle file to be
loaded.
Returns:
pywbem_mock.ProviderDependentRegistry: Provider dependent registry.
"""
with io.open(depreg_pickle_file, 'rb') as fp:
depreg = pickle.load(fp)
return depreg
class PYWBEMCLIConnection(pywbem.WBEMConnection, PYWBEMCLIConnectionMixin):
"""
PyWBEMCLIConnection subclass adds the methods added by
PYWBEMCLIConnectionMixin
"""
def __init__(self, *args, **kwargs):
"""
ctor passes all input parameters to superclass
"""
super(PYWBEMCLIConnection, self).__init__(*args, **kwargs)
class PYWBEMCLIFakedConnection(BuildMockenvMixin,
PYWBEMCLIConnectionMixin,
pywbem_mock.FakedWBEMConnection):
"""
PyWBEMCLIFakedConnection subclass adds the methods added by
PYWBEMCLIConnectionMixin
"""
def __init__(self, *args, **kwargs):
"""
ctor passes all input parameters to superclass
"""
super(PYWBEMCLIFakedConnection, self).__init__(*args, **kwargs)
def mockcache_rootdir():
"""
Return the directory path of the mock cache root directory.
"""
dir_path = os.path.join(os.path.expanduser('~'), '.pywbemcli_mockcache')
return dir_path
def mockcache_cachedir(rootdir, connections_file, connection_name):
"""
Return the directory path of the mock cache directory for a connection.
"""
# Construct a (reproducible) cache ID from connections file path and
# connection definition name.
# Example: 6048a3da1a34a3ec605825a1493c7bb5.simple
try:
connections_file = os.path.relpath(
connections_file, os.path.expanduser('~'))
except ValueError:
# On Windows, os.path.relpath() raises ValueError when the paths
# are on different drives
pass
md5 = hashlib.md5()
md5.update(connections_file.encode("utf-8"))
cache_id = "{}.{}".format(md5.hexdigest(), connection_name)
dir_path = os.path.join(rootdir, cache_id)
return dir_path
def delete_mock_cache(connections_file, connection_name):
"""
Delete the mock cache of the connection, if it exists.
Parameters:
self (pywbem_mock.FakedWBEMConnection): The mock connection.
connections_file (string): Path name of the connections file.
connection_name (string): The name of the connection definition in
the connections file.
Raises:
OSError: Mock cache cannot be deleted.
"""
cache_dir = mockcache_cachedir(
mockcache_rootdir(), connections_file, connection_name)
if os.path.isdir(cache_dir):
file_list = glob.glob(os.path.join(cache_dir, '*'))
for _file in file_list:
os.remove(_file)
os.rmdir(cache_dir)
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] | 2.206144 | 14,257 |
from setuptools import setup
packages = ["atop_raw", "atop_raw.headers"]
install_requires = ["numpy"]
extras_require = {"pycstruct": ["pycstruct >= 0.9"]}
package_data = {"atop_raw.headers": ["*.h"]}
setup(
name="atop_raw",
version="2",
packages=packages,
extras_require=extras_require,
package_data=package_data,
license="MIT",
description="Reader of raw files from atop",
install_requires=install_requires,
)
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] | 2.688623 | 167 |
import time
import numpy as np
from equipment.custom import mmwave_source
from equipment.srs import lockin
from equipment.hittite import signal_generator
from kid_readout.interactive import *
from kid_readout.equipment import hardware
from kid_readout.measurement import acquire
from kid_readout.roach import analog
# fg = FunctionGenerator()
hittite = signal_generator.Hittite(ipaddr='192.168.0.200')
hittite.set_power(0)
hittite.on()
lockin = lockin.Lockin(LOCKIN_SERIAL_PORT)
tic = time.time()
print lockin.identification
source = mmwave_source.MMWaveSource()
source.set_attenuator_turns(3.0,3.0)
source.multiplier_input = 'hittite'
source.waveguide_twist_angle = 45
source.ttl_modulation_source = 'roach'
ifboard = analog.HeterodyneMarkI()
setup = hardware.Hardware(source, lockin, ifboard,hittite)
setup.hittite.set_freq(148e9/12.)
ri = hardware_tools.r2_with_mk1()
ri.set_fft_gain(8)
#initial_f0s = np.load('/data/readout/resonances/2016-06-18-jpl-hex-271-32-high-qi-lo-1210-resonances.npy')
#initial_f0s = initial_f0s/1e6
initial_lo = 1210.
bbtones = np.linspace(5,220,256)
initial_f0s = np.hstack((initial_lo-bbtones-0.2,initial_lo+bbtones))
initial_f0s.sort()
nsamp = 2**15
step = 1
nstep = 24
offset_bins = np.arange(-(nstep + 1), (nstep + 1)) * step
offsets = offset_bins * 512.0 / nsamp
print (initial_f0s[1]-initial_f0s[0])*1e6, offsets.ptp()
for (lo,f0s) in [(initial_lo,initial_f0s)]:
ri.set_lo(lo)
for dac_atten in [0]:
ncf = new_nc_file(suffix='off_on_cw_%d_dB_dac' % dac_atten)
ri.set_modulation_output('high')
swpa = acquire.run_sweep(ri, tone_banks=f0s[None,:] + offsets[:,None], num_tone_samples=nsamp,
length_seconds=0.5, state=setup.state(), verbose=True,
description='source off sweep')
print "resonance sweep done", (time.time()-tic)/60.
ncf.write(swpa)
#print "sweep written", (time.time()-tic)/60.
current_f0s = []
for sidx in range(f0s.shape[0]):
swp = swpa.sweep(sidx)
res = swp.resonator
print res.f_0, res.Q, res.current_result.redchi, (f0s[sidx]*1e6-res.f_0)
if np.abs(res.f_0 - f0s[sidx]*1e6) > 0.9*(initial_f0s[1]-initial_f0s[0])*1e6:
current_f0s.append(f0s[sidx]*1e6)
print "using original frequency for ",f0s[sidx]
else:
current_f0s.append(res.f_0)
print "fits complete", (time.time()-tic)/60.
current_f0s = np.array(current_f0s)/1e6
current_f0s.sort()
if np.any(np.diff(current_f0s)<0.25):
print "problematic resonator collision:",current_f0s
print "deltas:",np.diff(current_f0s)
ri.set_tone_freqs(current_f0s,nsamp)
ri.select_fft_bins(range(f0s.shape[0]))
meas = ri.get_measurement(num_seconds=30., state=setup.state(),description='source off stream')
ncf.write(meas)
ri.set_modulation_output('low')
meas = ri.get_measurement(num_seconds=30., state=setup.state(),description='source on stream')
ncf.write(meas)
ri.set_modulation_output(7)
time.sleep(1) # wait for source modulation to stabilize
meas = ri.get_measurement(num_seconds=4., state=setup.state(),description='source modulated stream')
ncf.write(meas)
print "dac_atten %f done in %.1f minutes" % (dac_atten, (time.time()-tic)/60.)
ncf.close()
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] | 2.107795 | 1,642 |
from logging import getLogger
from re import match
from time import time
from django.conf import settings
from django.contrib.auth import get_user_model
from django.core.exceptions import ValidationError
from django.core.validators import MinValueValidator, MaxValueValidator
from django.db import models, transaction
from django.urls import reverse
from django.utils.timezone import now
from passlib.hash import md5_crypt, sha256_crypt, sha512_crypt
log = getLogger(__name__)
# Existing modeemiuserdb models that have been created manually and previously handled by the custom database router.
# Feel free to rename the models, but don't rename db_table values or field names.
# Do not change model properties unless you know what you are doing, they are used by other programs.
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7,
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8,
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198,
2,
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262,
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11,
484,
389,
973,
416,
584,
4056,
13,
628,
628,
628,
198
] | 3.728972 | 214 |
# 1. Ask for user input
# 2. Create a dynamic URL based on step 1
# 3. Fetch data from using url
# 4. Convert json to dictionary
# 5. Print Pokemon data
import requests
while True:
# getting Pokemon name from the user
user_pokemon_input = input(
'Please type name of pokemon you want to learn about? ')
# getting url with a name entered by user
pokemon_url = f'https://pokeapi.co/api/v2/pokemon/{user_pokemon_input.lower()}/'
req = requests.get(pokemon_url)
abilities = {}
ability_name = []
# checking if entered Pokemon name exists
if req.status_code != 200:
print('Incorrect Pokemon name, try again!')
continue
# if entered name exists, we get data and display some information about that Pokemon
else:
pokemon_character = req.json()
pokemon_name = pokemon_character['name'].capitalize()
print("******* DETAILS ABOUT POKEMON ******")
print(f"Entered name: {pokemon_name}")
print(f"{pokemon_name} weight: {pokemon_character['weight']}")
print(f"{pokemon_name} height: {pokemon_character['height']}")
# getting list of Pokemin abilities
for value in pokemon_character['abilities']:
# appending ability_name list
ability_name.append(value['ability']['name'])
# print(f"List of {pokemon_name} abilities: {ability_name}\n")
print(f"List of {pokemon_name} abilities:")
for index, ability in enumerate(ability_name):
print(f'{index + 1}. {ability}')
break
| [
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1391,
1799,
92,
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198,
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220,
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] | 2.697715 | 569 |
if __name__ == '__main__':
say() | [
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834,
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] | 2.111111 | 18 |
bind = "0.0.0.0:8085"
workers = 2
threads = 2
timeout = 120 | [
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import re
import nbformat
from nbconvert import PythonExporter
import warnings
warnings.filterwarnings('ignore')
def nbconverter(notebookPath,directionPath=None):
"""method name is the same as jupyter default converter name :
Nbconvert(notebookPath,directionPath)
:param notebookPath: source path of the ipynb file you want to convert
:param direction Path : direction path of the formatted .py file
"""
try:
with open(notebookPath,'r',encoding='utf-8') as fh:
nb = nbformat.reads(fh.read(), nbformat.NO_CONVERT)
except FileNotFoundError:
if '.ipynb' not in notebookPath:
notebookPath += '.ipynb'
with open(notebookPath,'r',encoding='utf-8') as fh:
nb = nbformat.reads(fh.read(), nbformat.NO_CONVERT)
if directionPath == None:
directionPath = notebookPath.replace('.ipynb','.py')
pattern_input = r'# In\[[\d\s]*\]:'
pattern_comment = '^#.+'
pattern_variables = r'^[a-zA-Z0-9_]+\[?[ |0-9]*\]? *$'
pattern_square_bracket = r'^\[.*\]$'
pattern_string = r"^[\'|\"].*[\'|\"] *$"
pattern_number = r'^\d+ *[\+|\-|\*|\/]? *\d* *$'
exporter = PythonExporter()
source, meta = exporter.from_notebook_node(nb)
source = source.split('\n')[2:]
source = [ t for t in source if t and not re.match(pattern_input,t)]
source = [ '\n' + t if re.match(pattern_comment,t) else t for t in source ]
source = [re.sub(pattern=pattern_variables,repl=print_match,string=t) for t in source]
source = [re.sub(pattern=pattern_square_bracket,repl=print_match,string=t) for t in source]
source = [re.sub(pattern=pattern_string,repl=print_match,string=t) for t in source]
source = '\n'.join(source) + '\n\n'
with open(directionPath, 'w+',encoding='utf-8') as fh:
fh.write(source)
print('{} has been saved'.format(directionPath))
nbconverter(r"C:\Users\Administrator.DG-11030335\Scripts\voc_alarm\外销意见反馈预警监控\convert2alarm.ipynb") | [
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100,
59,
1102,
1851,
17,
282,
1670,
13,
541,
2047,
65,
4943
] | 2.337821 | 817 |
# encoding: utf-8
import bisect
import collections
import json
import math
import queue
import heapq
from typing import (Any, Callable, Counter, DefaultDict, Dict, Iterable, List,
Set, Tuple)
data: Dict[str, List[Any]] = json.loads('''
{"inputs":[[7],[10],[19]],"outputs":[2,2,3]}
''')
fib = [1, 1]
while True:
x = fib[-2] + fib[-1]
if x > 0x7fffffff:
break
fib.append(x)
s = Solution()
for args, eq in zip(data['inputs'], data['outputs']):
assert_equal(s.findMinFibonacciNumbers(*args), eq)
| [
2,
21004,
25,
3384,
69,
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23,
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220,
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22,
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220,
220,
220,
220,
220,
220,
220,
2270,
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220,
220,
220,
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13,
33295,
7,
87,
8,
628,
198,
82,
796,
28186,
3419,
198,
1640,
26498,
11,
37430,
287,
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7,
7890,
17816,
15414,
82,
6,
4357,
1366,
17816,
22915,
82,
20520,
2599,
198,
220,
220,
220,
6818,
62,
40496,
7,
82,
13,
19796,
9452,
37,
571,
261,
44456,
49601,
46491,
22046,
828,
37430,
8,
198
] | 2.320513 | 234 |
import sys
n = int(sys.stdin.readline().rstrip("\n"))
distance = list(map(int,sys.stdin.readline().rstrip("\n").split(" ")))
price = list(map(int,sys.stdin.readline().rstrip("\n").split(" ")))
money = 0
curr_price = price[0]
for i in range(len(price)-1):
if curr_price > price[i]:
curr_price = price[i]
else:
price[i]= curr_price
money += price[i]*distance[i]
print(money)
| [
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62,
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220,
220,
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58,
72,
60,
9,
30246,
58,
72,
60,
198,
198,
4798,
7,
26316,
8,
198
] | 2.26257 | 179 |
# Generated by Django 3.2.12 on 2022-02-14 16:39
import cvat.apps.dataset_repo.models
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
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13,
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13,
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42625,
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13,
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13,
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13,
2934,
1616,
295,
628
] | 2.7 | 60 |
# Copyright 2018 Microsoft Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Requires Python 2.6+ and Openssl 1.0+
#
import base64
import binascii
import errno as errno
import glob
import random
import string
import subprocess
import tempfile
import uuid
import azurelinuxagent.common.conf as conf
import azurelinuxagent.common.utils.shellutil as shellutil
from azurelinuxagent.common.future import ustr
from azurelinuxagent.common.utils.cryptutil import CryptUtil
from azurelinuxagent.common.exception import CryptError
from azurelinuxagent.common.version import PY_VERSION_MAJOR
from tests.tools import *
from subprocess import CalledProcessError
if __name__ == '__main__':
unittest.main()
| [
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198,
6738,
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495,
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13,
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13,
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13,
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13,
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1330,
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198,
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13,
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13,
9641,
1330,
350,
56,
62,
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62,
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41,
1581,
198,
6738,
5254,
13,
31391,
1330,
1635,
198,
6738,
850,
14681,
1330,
34099,
18709,
12331,
198,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 3.604167 | 336 |
#!/usr/bin/env python
import sys
import math
import numpy as np
from optparse import OptionParser
def choose(n, k):
"""
A fast way to calculate binomial coefficients by Andrew Dalke (contrib).
"""
if 0 <= k <= n:
ntok = 1
ktok = 1
for t in xrange(1, min(k, n - k) + 1):
ntok *= n
ktok *= t
n -= 1
return ntok // ktok
else:
return 0
if __name__ == "__main__":
sys.exit(main())
(END)
| [
2,
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14,
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14,
24330,
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198,
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198,
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198,
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299,
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355,
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198,
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220,
220,
220,
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220,
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83,
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479,
83,
482,
198,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
657,
198,
198,
361,
11593,
3672,
834,
6624,
366,
834,
12417,
834,
1298,
198,
220,
220,
220,
25064,
13,
37023,
7,
12417,
28955,
198,
220,
220,
220,
357,
10619,
8,
198
] | 2.004098 | 244 |
#!/usr/bin/env python
# coding: utf-8
# ### 1 - SET UP THE API
# In[1]:
import requests
import sys
import os
sys.path.insert(0, "/home/admin/ansible/fwd_python_api")
sys.path.append("..")
import fwd_json
from fwd_json import fwdApi
username = os.environ['fwd_saas_user']
token = os.environ['fwd_saas_token']
network = 137407
fwd = fwdApi("https://fwd.app/api", username, token,network, {}, verify=True)
nqeUrl = "https://fwd.app/api/nqe?networkId={}".format(network)
# In[2]:
print(fwd.get_all_networks)
#start collection
fwd.start_collection(network).text
# In[3]:
#get the latest snapshot id
latest_snap = fwd.get_snapshot_latest(network).json()['id']
print(latest_snap)
# In[4]:
#basic NQE to get a report of the network
query = '''
foreach d in network.devices
select {
name: d.name,
mgmtIP: d.platform.managementIps,
model: d.platform.model,
osType: d.platform.os,
osVersion: d.platform.osVersion,
serial: (foreach c in d.platform.components
where isPresent(c.serialNumber) select c.serialNumber)
}'''
# In[5]:
fwd.post_nqe_check(query)
# ### 2 - METHOD 1 OF RUNNING NQE: define the query as a string
# In[20]:
#define a block
blockConfig='''
block=```
ip access-list standard BASELINE_ACL
10 permit 192.168.252.94/31
20 {"permit" | "deny"} host 192.168.252.220
```;
foreach d in network.devices
where isPresent(d.platform.osVersion)
where d.platform.os == OS.ARISTA_EOS
let diff = blockDiff_alpha1(d.files.config, block)
//where diff.diffCount != 0
select {
name: d.name,
model: d.platform.model,
missing_config: diff.blocks
}
'''
# In[21]:
fwd.post_nqe_check(blockConfig)
# ### 3 - METHOD 2 OF RUNNING NQE: define the API within NQE
# In[22]:
#check baseline CONFIG for all devices
config = open('baseline_acl.txt', 'r').read()
config = "test"
queryId = "Q_e6ec1965d99271ce3e3a7223897469efc253468f"
payload = {"config": config}
response = fwd.post_nqe_para_check(queryId, payload)
missingConfig = response
print(missingConfig)
# In[23]:
#check BASELINE CONFIG for subset of devices
config = open('baseline_acl.txt', 'r').read()
config = "test"
queryId = "Q_b7ed24370895b73d6ddbef0b81cffe04d22ae6f5"
#define which device to check
inputDevice = ["leaf1"]
payload = {"config": config, "deviceList": inputDevice}
response = fwd.post_nqe_para_check(queryId, payload)
missingConfig = response
print(missingConfig)
# In[26]:
#parameterized NQE for BGP neighbor
queryId = "Q_8178355cfc658ab46cac0f07f2b033f68fa92c80"
payload = {"deviceList": ["leaf4", "leaf2"]}
response = fwd.post_nqe_para_check(queryId, payload)
print(response)
# In[27]:
#parameterized NQE for interfaces that are down
queryId = "Q_37cac69e9e54556d97b175d8392fa307d7a7afc8"
payload = {"deviceList": ["leaf4"]}
response = fwd.post_nqe_para_check(queryId, payload)
print(response)
# ### 4 - PATH SEARCH API
# In[28]:
#simple path search api
srcIP = "192.168.100.1"
dstIP = "192.168.100.4"
fwd.get_path_search(latest_snap,srcIP, dstIP).json()
# In[29]:
#advanced use - define a path search and add as "Existential" intent check
sourceIp = fwd_json.gen_location(SubnetLocationFilter="192.168.100.1/32")
destIp = fwd_json.gen_location(SubnetLocationFilter="192.168.100.4/32")
fwd.post_existance_check(snapshotID=latest_snap, FROM=(sourceIp), TO=(destIp))
# In[30]:
#get results for all "Existential" intent check
result = fwd.get_intent_checks(latest_snap, "Existential").json()
print(result)
# In[ ]:
| [
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5450,
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1324,
14,
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77,
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27349,
7390,
34758,
92,
1911,
18982,
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58,
17,
5974,
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4798,
7,
69,
16993,
13,
1136,
62,
439,
62,
3262,
5225,
8,
198,
198,
2,
9688,
4947,
198,
69,
16993,
13,
9688,
62,
43681,
7,
27349,
737,
5239,
628,
198,
2,
554,
58,
18,
5974,
628,
198,
2,
1136,
262,
3452,
27479,
4686,
220,
198,
42861,
62,
45380,
796,
277,
16993,
13,
1136,
62,
45380,
9442,
62,
42861,
7,
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737,
17752,
3419,
17816,
312,
20520,
198,
4798,
7,
42861,
62,
45380,
8,
628,
198,
2,
554,
58,
19,
5974,
628,
198,
2,
35487,
399,
48,
36,
284,
651,
257,
989,
286,
262,
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] | 2.526087 | 1,380 |
hiddenimports = ['sip', 'PyQt4._qt']
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] | 2.176471 | 17 |
from configparser import ConfigParser, ExtendedInterpolation
from pathlib import Path
default_snet_folder = Path("~").expanduser().joinpath(".snet")
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] | 3.466667 | 45 |
###############################################################################
##
## Copyright (C) 2014-2016, New York University.
## Copyright (C) 2013-2014, NYU-Poly.
## All rights reserved.
## Contact: [email protected]
##
## This file is part of VisTrails.
##
## "Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are met:
##
## - Redistributions of source code must retain the above copyright notice,
## this list of conditions and the following disclaimer.
## - Redistributions in binary form must reproduce the above copyright
## notice, this list of conditions and the following disclaimer in the
## documentation and/or other materials provided with the distribution.
## - Neither the name of the New York University nor the names of its
## contributors may be used to endorse or promote products derived from
## this software without specific prior written permission.
##
## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
## AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
## THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
## PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
## EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
## PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
## OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
## WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
## OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
## ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
##
###############################################################################
from __future__ import division
import datetime
from distutils.version import LooseVersion
import re
import time
import warnings
from vistrails.core.modules.vistrails_module import Module, ModuleError
from vistrails.core.bundles import py_import
from vistrails.core.utils import VistrailsWarning
PYTZ_MIN_VER = LooseVersion('2012')
utc = UTC()
_decimal_fmt = re.compile(
r'^'
'([-+]?)' # + means we are advancing compared to UTC
'([0-9]?[0-9])' # hours
'([0-9][0-9])?$') # minutes
class TimestampsToDates(Module):
"""
Converts a List or numpy array of timestamps into datetime objects.
A UNIX timestamp represents the number of seconds since Jan 1 1970 0:00,
UTC. It represents a specific point in time that is not dependent on a
timezone.
The returned datetime objects are in the UTC timezone.
"""
_input_ports = [
('timestamps', '(org.vistrails.vistrails.basic:List)')]
_output_ports = [
('dates', '(org.vistrails.vistrails.basic:List)')]
@staticmethod
class StringsToDates(Module):
"""
Converts a List of dates (as strings) into datetime objects.
If no format is given, the dateutil.parser module will be used to guess
what each string refers to; else, it is passed to strptime() to read each
date. For example: '%Y-%m-%d %H:%M:%S'.
The 'timezone' parameter indicates which timezone these dates are expressed
in. It can be either:
* 'local', in which case each date is interpreted as being in whatever
timezone the system is set to use;
* an offset in hours/minutes from UTC, for instance '-0400' for DST
(eastern America time, when daylight saving is in effect). Note that in
this case, the same offset is used for every date, without regard for
daylight saving (if a date falls in winter, '-0500' will not be used
instead).
* if pytz is available, anything else will be passed to pytz.timezone(),
so you would be able to use strings such as 'US/Eastern' or
'Europe/Amsterdam'.
"""
_input_ports = [
('strings', '(org.vistrails.vistrails.basic:List)'),
('format', '(org.vistrails.vistrails.basic:String)',
{'optional': True, 'defaults': "['']"}),
('timezone', '(org.vistrails.vistrails.basic:String)',
{'optional': True, 'defaults': "['']"})]
_output_ports = [
('dates', '(org.vistrails.vistrails.basic:List)')]
@staticmethod
class DatesToMatplotlib(Module):
"""
Converts a List of Python's datetime objects to an array for matplotlib.
"""
_input_ports = [('datetimes', '(org.vistrails.vistrails.basic:List)')]
_output_ports = [('dates', '(org.vistrails.vistrails.basic:List)')]
@staticmethod
class TimestampsToMatplotlib(Module):
"""
Converts a List or numpy array of timestamps into an array for matplotlib.
"""
_input_ports = [
('timestamps', '(org.vistrails.vistrails.basic:List)')]
_output_ports = [
('dates', '(org.vistrails.vistrails.basic:List)')]
@staticmethod
class StringsToMatplotlib(Module):
"""
Converts a List of dates (as strings) to an array accepted by matplotlib.
"""
_input_ports = [
('strings', '(org.vistrails.vistrails.basic:List)'),
('format', '(org.vistrails.vistrails.basic:String)',
{'optional': True, 'defaults': "['']"}),
('timezone', '(org.vistrails.vistrails.basic:String)',
{'optional': True, 'defaults': "['']"})]
_output_ports = [
('dates', '(org.vistrails.vistrails.basic:List)')]
@staticmethod
_modules = {'dates': [
TimestampsToDates, StringsToDates,
DatesToMatplotlib, TimestampsToMatplotlib, StringsToMatplotlib]}
###############################################################################
import unittest
from vistrails.tests.utils import execute, intercept_result
from ..identifiers import identifier
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] | 2.824144 | 2,104 |
import sys
import plotly.graph_objects as go
import numpy as np
from plotly.subplots import make_subplots
from scipy import interpolate
sys.path += ['src/01_data_processing', 'src/02_modelling', 'src/03_modell_evaluation', 'src/00_utils']
import spectrogram as spec
import train_test_split as splt
import train_model_autoencoder as train
import naming
import eval_model_autoencoder as eval
def make_mel_trace(mel, colorbar_len=0.3, colorbar_y=1.01):
"""
Make heatmap trace of mel spectrogram
"""
mel_trace = dict(visible=True,
type='heatmap',
x=np.array(range(mel.shape[0])),
y=np.array(range(mel.shape[1])),
z=mel,
colorscale='inferno',
colorbar=dict(len=colorbar_len,
y=colorbar_y,
yanchor='top',
thickness=10))
return mel_trace
def make_invisible_error_traces(timewise_recon_error, times, thresh):
"""
Make invisible traces showing error over time and threshold.
"""
above_thresh = timewise_recon_error.copy()
below_thresh = timewise_recon_error.copy()
above_thresh[above_thresh < thresh] = np.nan
below_thresh[below_thresh > thresh] = np.nan
thresh_trace = dict(visible=False,
type='scatter',
x=[0, 10],
y=[thresh, thresh],
marker=dict(color='black'),
mode='lines',
showlegend=False)
above_trace = dict(visible=False,
type='scatter',
x=times,
y=above_thresh,
marker=dict(color='red'),
mode='lines',
showlegend=False)
below_trace = dict(visible=False,
type='scatter',
x=times,
y=below_thresh,
marker=dict(color='green'),
mode='lines',
showlegend=False)
return above_trace, below_trace, thresh_trace
def make_ref_thresh_trace_error(ref_thresh, thresh_range):
"""
Make dashed horizontal line for reference threshold
"""
assert thresh_range[0] <= ref_thresh <= thresh_range[-1], 'reference threshold outside of threshold range'
ref_step = np.abs(ref_thresh - thresh_range).argmin()
ref_thresh = thresh_range[ref_step]
ref_thresh_trace = dict(visible=True,
type='scatter',
x=[0, 10],
y=[ref_thresh, ref_thresh],
marker=dict(color='black'),
mode='lines',
line=dict(color='black', dash='dash', width=1),
showlegend=False)
return ref_thresh_trace, ref_step
def make_mean_error_trace(mean_recon_error):
"""
Sert marker for mean error of sample file
"""
mean_error_trace = dict(visible=True,
type='scatter',
x=[mean_recon_error],
y=[5],
mode='markers',
marker_symbol='x-thin',
marker=dict(size=8,
color='black',
line=dict(width=2, color='black')),
name='mean error of<br>sample + percentile',
showlegend=True)
return mean_error_trace
def make_hist_traces(reco_loss_train, ref_thresh, thresh_range):
"""
Make visible traces for histogram
"""
# Histogram trace
hist_trace = dict(visible=True,
type='histogram',
x=reco_loss_train,
marker=dict(color='green'),
histnorm='probability density',
opacity=0.3,
showlegend=False)
# Probability distribution trace
from sklearn.neighbors import KernelDensity
kde = KernelDensity(kernel='gaussian', bandwidth=0.001).fit(reco_loss_train.reshape(-1, 1))
errors = np.arange(0, 0.1, 0.0001)
prob_density = np.exp(kde.score_samples(errors.reshape(-1, 1)))
dist_trace = dict(visible=True,
type='scatter',
x=errors,
y=prob_density,
mode='lines',
line=dict(color='green', width=1),
showlegend=False)
# Ref threshold trace
assert thresh_range[0] <= ref_thresh <= thresh_range[-1], 'reference threshold outside of threshold range'
ref_step = np.abs(ref_thresh - thresh_range).argmin()
ref_thresh = thresh_range[ref_step]
ref_thresh_trace = dict(visible=True,
type='scatter',
x=[ref_thresh, ref_thresh],
y=[0, 100],
mode='lines',
line=dict(color='black', dash='dash', width=1),
name='recommended<br>threshold',
showlegend=True)
return hist_trace, dist_trace, ref_thresh_trace
def make_sliders(thresh_range, active_step, num_visible, num_invisible):
"""
Make slider to select error trace based on threshold.
"""
steps = []
imgs_per_step = int(num_invisible / len(thresh_range))
for i, thr in enumerate(thresh_range):
# import pdb; pdb.set_trace()
step = dict(label=round(thr, 2), method="update",
args=[{"visible": [True] * num_visible + [False] * num_invisible}])
for j in range(imgs_per_step):
step["args"][0]["visible"][num_visible + i * imgs_per_step + j] = True
steps.append(step)
sliders = [dict(currentvalue=dict(visible=False),
active=active_step,
steps=steps)]
return sliders
def make_figure_layout(fig, sliders, mel, thresh_range, width=600, height=1000):
"""
Make layout for figure with three subplots for mel spectrogram, error over time and training error distribution.
"""
fig.update_layout(
height=height,
width=width,
xaxis1=dict(
tickmode='array',
tickvals=np.linspace(0, mel.shape[1] - 1, 6),
ticktext=[0, 2, 4, 6, 8, 10]),
yaxis1=dict(
tickmode='array',
tickvals=np.linspace(0, mel.shape[0], 6),
ticktext=[0, 512, 1024, 2048, 4096, 8000],
title='Hz'),
yaxis2=dict(range=[0, 0.2]),
xaxis3=dict(range=[thresh_range[0], thresh_range[-1]]),
yaxis3=dict(range=[0, 100]),
sliders=sliders,
legend=dict(
traceorder='reversed',
font=dict(size=10),
yanchor="top",
y=0.275,
xanchor="right",
x=0.99))
def make_eval_visualisation(mel_file,
model,
scaler,
reco_loss_train,
dim, step,
thresh_range,
ref_thresh,
width=600,
height=1000,
status_bar_width=0.025,
as_images=True):
"""
Call functions in this module to make a visualization for a given mel spectrogram file.
"""
times, timewise_recon_error = eval.reco_loss_over_time(model=model,
scaler=scaler,
mel_file=mel_file,
dim=dim,
step=step,
as_images=as_images)
mean_recon_error = timewise_recon_error.mean()
# Interpolate linearly between point for plotting
f = interpolate.interp1d(times, timewise_recon_error)
times = np.arange(times[0], times[-1], 0.005)
timewise_recon_error = f(times)
# Generate figure with two subplots
fig = make_subplots(rows=3, cols=1, vertical_spacing=0.05,
subplot_titles=(
'spectrogram', 'reconstruction error over time', 'mean error distribution training'),
shared_xaxes=False)
########################
# VISIBLE TRACES FIRST #
########################
num_visible = 0
# First row: Spectrogram
mel = np.load(mel_file)
mel_trace = make_mel_trace(mel)
fig.add_trace(mel_trace, row=1, col=1)
num_visible += 1
# Second row: Reference threshold
ref_thresh_trace, ref_step = make_ref_thresh_trace_error(ref_thresh, thresh_range)
fig.add_trace(ref_thresh_trace, row=2, col=1)
active_step = ref_step
num_visible += 1
# Third row: mean error
mean_error_trace = make_mean_error_trace(mean_recon_error)
fig.add_trace(mean_error_trace, row=3, col=1)
num_visible += 1
# Third row: histogram and distribution
hist_traces = make_hist_traces(reco_loss_train, ref_thresh, thresh_range)
for trace in hist_traces:
fig.add_trace(trace, row=3, col=1)
num_visible += 1
# Third row: percentile label
x_range = thresh_range[-1] - thresh_range[0]
xlo = mean_recon_error - 0.055*x_range
xhi = mean_recon_error + 0.055*x_range
ylo = 9
yhi = 15
percentage_box_trace = dict(visible=True,
showlegend=False,
type='scatter',
mode='lines',
x=[xlo, xlo, xhi, xhi, xlo],
y=[ylo, yhi, yhi, ylo, ylo],
fill='toself',
fillcolor='white',
line=dict(width=0))
percentage = str(round(sum(sorted(reco_loss_train) < mean_recon_error) / len(reco_loss_train) * 100, 2))
percentage_text_trace = dict(visible=True, type='scatter',
x=[(xhi + xlo) / 2], y=[(yhi + ylo) / 2],
mode='text',
text=percentage + '%',
textposition='middle center',
showlegend=False)
fig.add_trace(percentage_box_trace, row=3, col=1)
fig.add_trace(percentage_text_trace, row=3, col=1)
num_visible += 1
# Second row: status box
xlo = 7.5
xhi = 9.5
yhi = 0.185
ylo = yhi-status_bar_width
status_box_trace = dict(visible=True,
showlegend=False,
type='scatter',
mode='lines',
x=[xlo, xlo, xhi, xhi, xlo],
y=[ylo, yhi, yhi, ylo, ylo],
fill='toself',
fillcolor='white',
line=dict(width=0))
fig.add_trace(status_box_trace, row=2, col=1)
num_visible += 1
status_text_trace = dict(visible=True,
type='scatter',
x=[xlo + 0.1 * (xhi - xlo)],
y=[ylo + 0.5 * (yhi - ylo)],
mode='text',
text='Status',
textposition='middle right',
showlegend=False)
fig.add_trace(status_text_trace, row=2, col=1)
num_visible += 2
#########################
# INVISIBLE TRACES LAST #
#########################
num_invisible = 0
# Add invsisible error traces to the second row
for thresh in thresh_range:
# Second row: colored error traces and horizontal threshold line
invisible_error_traces = make_invisible_error_traces(timewise_recon_error, times, thresh)
for trace in invisible_error_traces:
fig.add_trace(trace, row=2, col=1)
num_invisible += 1
# Second row: status
if mean_recon_error > thresh:
color = 'red'
else:
color = 'green'
status_trace = dict(visible=False,
type='scatter',
x=[xlo + 0.8 * (xhi - xlo)],
y=[ylo + 0.5 * (yhi - ylo)],
mode='markers',
marker=dict(size=18,
color=color,
line=dict(width=0)),
showlegend=False)
fig.add_trace(status_trace, row=2, col=1)
num_invisible += 1
# Third row: vertical threshold line
invisible_hist_trace = dict(visible=False, type='scatter',
x=[thresh, thresh], y=[0, 100],
mode='lines', line=dict(color='black', width=2),
name='threshold', showlegend=True)
fig.add_trace(invisible_hist_trace, row=3, col=1)
num_invisible += 1
############################
# MAKE ACTIVE STEP VISIBLE #
############################
imgs_per_step = int(num_invisible / len(thresh_range))
for data in fig.data[num_visible + imgs_per_step * active_step: num_visible + imgs_per_step * (active_step + 1)]:
data.visible = True
#################################
# SLIDERS TO CONTROL VISIBILITY #
#################################
sliders = make_sliders(thresh_range, active_step, num_visible, num_invisible)
# Make figure layout and show
make_figure_layout(fig, sliders, mel, thresh_range, width, height)
return fig
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] | 1.821193 | 7,729 |
from typing import Dict, Set
from .RF2File import RF2File
from .Transitive import Transitive
from SNOMEDCTToOWL.TransformationContext import TransformationContext
from SNOMEDCTToOWL.SNOMEDToOWLConstants import *
class Relationship:
""" A RF2 stated relationship or relationship entry
Properties:
* sourceId -- concept identifier of the subject
* typeId -- concept identifier of the predicate (if not IS a)
* destinationId -- concept identifier of the target
* relationshipGroup -- group the assertion belongs to
Filters:
* active -- only active relationships (active=='1') are included
* characteristicTypeId -- only descendants of 900000000000006009 |Defining relationship| (stated, inferred)
are included in the transformation.
* moduleId -- NOT used as a filter because fully defined definitions have to be complete to be valid
* modifierId -- only the existential modifier, 900000000000451002 |Some|, is included in the transformation
"""
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import base64
import csv
import io
from utility import Utility
class Report(object):
"""
We use this class to generate text reports.
Class based on [email protected]'s Report.py class and modified
for this purpose
"""
@classmethod
def create_csv_report(cls, vulnerable_image_check_data):
"""
Expect a dictionary object, produce text in CSV format.
Args:
- cls - reference to the current instance of hte class
- vulnerable_image_check_data (dict) - dictionary of vulnerability
data
Return:
- result (str) - base64 encoded vulnerability report
"""
# initialize the list as we will have a list of dicts
rows = []
# let's build the output for all sets in the dataset
# dataset is vulnerability info for all images in request
for set in vulnerable_image_check_data["image_issues"]:
# format the data for a cvs report
row = cls.format_vulnerable_image_data_csv(set)
# append each returned data set to the whole
rows.append(row)
# the fieldnames for the csv - DictWriter will order by these
fieldnames = \
["registry", "repository", "tag",
"package", "version", "image_digest"]
# get a stream io object
ephemeral_obj = io.BytesIO()
# write the csv data
csv_writer = csv.DictWriter(ephemeral_obj, fieldnames=fieldnames)
csv_writer.writeheader()
csv_writer.writerows(rows)
# encode to base64
result = base64.b64encode(ephemeral_obj.getvalue())
# clean up
ephemeral_obj.close()
# return report data
return result
@classmethod
def create_stdout_report(cls, vulnerable_image_check_data):
"""
Expect a dictionary object, produce text appropriate for stdout.
Args:
- cls - reference to the current instance of the class
- vulnerable_image_check_data (dict) - dictionary of vulnerability
data
Return:
- result (str) - base64 encoded vulnerability report
Format of encoded data:
Registry: DPR
Repository: bkumar89/centos
Tag: 7.1.1503
Vulnerabilities:
Package: binutils Package Version: 2.23.52.0.1-30.el7 | CVE List: cve-2014-8484 cve-2014-8485 # NOQA
"""
result = ""
# for each data set in all the data
for set in vulnerable_image_check_data["image_issues"]:
# format data as noted above
pieces = cls.format_vulnerable_image_data(set)
pieces = pieces.split('\n')
pieces = "\n".join(pieces)
# build full dataset
result += pieces
# encode data
result = base64.b64encode(result)
# return report data
return result
@classmethod
def create_slack_reports(cls, channel_reference, default_channel,
routing_rules, instances):
"""Create a plaintext report for Slack.
Args:
channel_reference(dict): Keys are channel names, values are channel
IDs.
default_channel(str): Name of default Slack channel.
routing_rules(dict): Rules for routing messages to different Slack
channels. Formatted like
{"metadata_field_name":
{"metadata_field_value_to_match": "slack_channel_name"}}
instances(dict): Instance metadata.
Returns:
dict: {"channel": "report"} where "channel" is the Slack channel
ID and "report" is the text of the report.
"""
organized = {}
# Group by target Slack channel.
for instance in instances:
channel = Utility.get_channel_for_message(channel_reference,
instance, routing_rules,
default_channel)
if channel not in organized:
organized[channel] = []
organized[channel].append(instance)
# Build report per channel, each sorted by instance ID.
report = {}
for target, content in organized.items():
x_content = {c.keys()[0]: c.values()[0] for c in content}
report[target] = cls.create_stdout_report(x_content)
return report
@classmethod
def format_vulnerable_image_data(cls, vic_data):
"""Format vulnerability data for reporting.
Args:
- cls - reference to the current instance of the class
- vic_data (dict): Formatted like this:
Registry: DPR
Repository: bkumar89/centos
Tag: 7.1.1503
Vulnerabilities:
Package: binutils Package Version: 2.23.52.0.1-30.el7 | CVE List: cve-2014-8484 cve-2014-8485 # NOQA
"""
registry = \
"\n\nRegistry: {registry}" \
"".format(registry=vic_data["image"]["registry"]["name"])
repository = \
" Repository: {repository}" \
"".format(repository=vic_data["image"]["repository"]["name"])
tags = ""
for tag in vic_data["image"]["tags"]:
tags += tag
tags += " "
tag_list = \
" Tag(s): {tag_list}".format(tag_list=tags)
vulnerabilities = " Vulnerabilities:" # NOQA
package = " Package: {package}".format(package=vic_data["name"])
# build package, package version and cve's into one line
package_version = \
" Package Version: {package_version}" \
"".format(package_version=vic_data["version"])
package += package_version
cves = ""
for cve in vic_data["cves"]:
cves += cve["name"]
cves += " "
cve_list = " | CVE List: {cve_list}".format(cve_list=cves)
package += cve_list
# order the fields and separate them by a newline
ordered_fields = [registry, repository, tag_list,
vulnerabilities, package]
# return formatted report data
return "\n".join(ordered_fields)
@classmethod
def format_vulnerable_image_data_csv(cls, vic_data):
"""
Format vulnerability data for reporting in CSV format.
Args:
vic_data (dict) - vulnerability data
Returns:
result - (dict) - vulnerability report data
"""
number_tags = len(vic_data["image"]["tags"])
counter = 0
tags = ""
increment = 1
while counter < number_tags:
tags += vic_data["image"]["tags"][counter]
tags += " "
counter = counter + increment
result = {"registry": vic_data["image"]["registry"]["name"],
"repository": vic_data["image"]["repository"]["name"],
"tag": tags,
"package": vic_data["name"],
"image_digest": vic_data["image"]["image_sha"],
"version": vic_data["version"]}
return result
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] | 2.171861 | 3,369 |
#!/usr/bin/env python3
from build import ninja_common
build = ninja_common.Build('webserver')
build.webpack('static/bundle.js', 'webpack.config.js', 'src', 'webserver/package.json')
build.install('auv-webserver', f='webserver/auv-webserver.py')
| [
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] | 2.666667 | 93 |
import sys
if __name__ == '__main__':
print(''.join(main(sys.stdin)), end='')
| [
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] | 2.351351 | 37 |
#! /usr/bin/env python
"""
Modul zawiera testy klasy AvgSizeOfHoleFeature
"""
import unittest
from bitmap.bitmap_grayscale import BitmapGrayscale
from feature.simple_features.avg_size_of_hole_feature import AvgSizeOfHoleFeature
from tests.bitmap_generator import BitmapGenerator
class TestAvgSizeOfHoleFeature(unittest.TestCase):
"""
Klasa testująca klase AvgSizeOfHoleFeature
"""
def count_feature(self, bitmap: BitmapGrayscale) -> float:
"""
Prawidłowo wylicza wartośc feature
:param bitmap: Bitmapa, dla której wyliczamy feature
:return: Wyliczony feature
"""
self.feature.prepare(bitmap)
return self.feature.calculate()
def test_reorder_calculate_prepare(self):
"""
Test sprawdza, czy wywołanie w złej kolejności metody prepare i calculate zgłaszaja wyjątek.
Oczekujemy zgłoszenia wyjątku AttributeError.
:return:
"""
with self.assertRaises(AttributeError):
self.feature.calculate()
def test_white_plain(self):
"""
Dostarczamy bitmapę wypełniona tylko białym kolorem.
Oczekujemy Liczby -1 jako informacji o braku dziur.
:return:
"""
size = 5
bitmap = BitmapGenerator.plain_white(size, size)
res = self.count_feature(bitmap)
self.assertIs(-1, res)
def test_white_plain_one_hole_of_size_1(self):
"""
Dostarczamy bitmapę wypełniona tylko białym kolorem, poza jednym pikselem.
Oczekujemy Liczby 1 jako informacji o jednej dziurze o rozmiarze 1.
:return:
"""
size = 5
bitmap = BitmapGenerator.plain_white(size, size)
bitmap.set_cell_value(1, 1, 0.0)
res = self.count_feature(bitmap)
self.assertAlmostEqual(1.0, res)
def test_white_plain_one_hole_of_size_2(self):
"""
Dostarczamy bitmapę wypełniona tylko białym kolorem, poza dwoma czarnymi pikselami.
Oczekujemy Liczby 2 jako informacji o jednej dziurze o rozmiarze 2.
:return:
"""
size = 5
bitmap = BitmapGenerator.plain_white(size, size)
bitmap.set_cell_value(1, 1, 0.0)
bitmap.set_cell_value(1, 2, 0.0)
res = self.count_feature(bitmap)
self.assertAlmostEqual(2.0, res)
def test_white_plain_two_holes_of_size_1(self):
"""
Dostarczamy bitmapę wypełniona tylko białym kolorem, poza jednym pikselem.
Oczekujemy Liczby 1 jako informacji o dwórch dziurach o rozmiarze 1.
:return:
"""
size = 5
bitmap = BitmapGenerator.plain_white(size, size)
bitmap.set_cell_value(1, 1, 0.0)
bitmap.set_cell_value(3, 3, 0.0)
res = self.count_feature(bitmap)
self.assertAlmostEqual(1.0, res)
def test_white_plain_two_holes_of_size_1_and_2(self):
"""
Dostarczamy bitmapę wypełniona tylko białym kolorem, poza dwoma dziurami o rozmiarach 1 i 2.
Oczekujemy Liczby 1.5.
:return:
"""
size = 5
bitmap = BitmapGenerator.plain_white(size, size)
bitmap.set_cell_value(1, 1, 0.0)
bitmap.set_cell_value(3, 3, 0.0)
bitmap.set_cell_value(3, 4, 0.0)
res = self.count_feature(bitmap)
self.assertAlmostEqual(1.5, res)
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] | 2.002999 | 1,667 |
def read_input_file(input_file_address) -> dict:
"""
Takes a string as input file address and returns the base strings along indices for each base string
"""
res = dict()
res['first_base_string'] = ''
res['first_base_string_indices'] = []
res['second_base_string'] = ''
res['second_base_string_indices'] = []
with open(input_file_address, 'r') as f:
input_file_lines = [line.strip() for line in f.readlines()]
is_first_base_string = True
for line in input_file_lines:
# isnumeric complexity
if not line.isnumeric() and res['first_base_string'] == '':
res['first_base_string'] = line
elif line.isnumeric() and is_first_base_string:
res['first_base_string_indices'].append(int(line))
elif not line.isnumeric() and res['first_base_string'] != '':
is_first_base_string = False
res['second_base_string'] = line
elif line.isnumeric() and not is_first_base_string:
res['second_base_string_indices'].append(int(line))
return res
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] | 2.293279 | 491 |
# -*- coding: UTF-8 -*-
# 基于 sqlite4dummy 的基本架构重新写了一个超简易版本的 sqlite 数据库管理库
# 本程序提供必要的sqlite操作方法，并对一个sqlite数据库进行初始化
# 本程序从自己启动，外部只能调用其方法
# 本程序的主入口程序为 main()
# 请填写下面几个参数：
# 第一个参数为数据库名称
DB_NAME=''
import os
import sqlite3
import sqlite4dummy
import psycopg2
class Column():
"""Represent a Column in a :class:`Table`.
Construct a Column object::
>>> from sqlite4dummy import *
>>> c = Column("employee_id", dtype.TEXT, primary_key=True)
>>> c
Column('employee_id', dtype.TEXT, nullable=True, default=None, primary_key=True)
:param column_name: the column name, alpha, digit and understore only.
Can't start with digit.
:type column_name: string
:param data_type: Data type object.
:param nullable: (default True) whether it is allow None value.
:type nullable: boolean
:param default: (default None) default value.
:type default: any Python types
:param primary_key: (default False) whether it is a primary_key.
:type primary_key: boolean
For usage example, go :mod:`unittest page<sqlite4dummy.tests.test_Column>`
and read the testcase source code.
"""
class Table(object):
"""Represent a table in a database.
Define a Table::
>>> from sqlite4dummy import *
>>> metadata = MetaData()
>>> mytable = Table("mytable", metadata,
Column("mytable_id", dtype.INTEGER, primary_key=True),
Column("value", dtype.TEXT),
)
columns can be accessed by table.c.column_name::
>>> mytable.c.mytable_id # return a Column object
_id
:param table_name: the table name, alpha, digit and understore only.
Can't start with digit.
:type table_name: string
:param metadata: Data type object.
:type metadata: :class:`MetaData`
:param args: list of Column object
:type args: :class:`Column`
For usage example, go :mod:`unittest page<sqlite4dummy.tests.test_Table>`
and read the testcase source code.
**中文文档**
:class:`sqlite4dummy.schema.Table` 是抽象数据表对象类。
定义Table的方法如下::
>>> from sqlite4dummy import *
>>> metadata = MetaData() # 定义metadata
>>> mytable = Table("mytable", metadata, # 定义表名, metadata和列
Column("mytable_id", dtype.INTEGER, primary_key=True),
Column("value", dtype.TEXT),
)
从Table中获得Column对象有如下两种方法::
>>> mytable.c._id
_id
>>> mytable.get_column("_id")
_id
"""
class CreateTable(object):
"""Generate 'CREATE TABLE' SQL statement.
Example::
CREATE TABLE table_name
(
column_name1 dtype1 CONSTRAINS,
column_name2 dtype2 CONSTRAINS,
PRIMARY KEY (column, ...),
FOREIGN KEY (table_column, ...)
)
**中文文档**
创建Table的抽象类, 用于根据Schema生成CREATE TABLE ...的SQL语句。目前不支持
FOREIGN KEY语法。
"""
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198
] | 1.941914 | 1,515 |
from .base_creator import BaseCreator | [
6738,
764,
8692,
62,
45382,
1330,
7308,
16719,
273
] | 4.111111 | 9 |
################################################################################
# Project : AuShadha
# Description : URLS for Immunisation History
# Author : Dr. Easwar T.R
# Date : 21-09-2013
# License : GNU-GPL Version 3, see AuShadha/LICENSE.txt
################################################################################
from django.conf.urls import *
from django.contrib import admin
import AuShadha.settings
from immunisation.views import *
from .dijit_widgets.pane import render_immunisation_pane
admin.autodiscover()
urlpatterns = patterns('',
url(r'json/(?P<patient_id>\d+)/$',
'immunisation.views.immunisation_json',
name='immunisation_json'
),
url(r'json/$',
'immunisation.views.immunisation_json',
name='immunisation_json_without_id'
),
url(r'pane/(?P<patient_id>\d+)/$',
render_immunisation_pane,
name='render_immunisation_pane_with_id'
),
url(r'pane/$',
render_immunisation_pane,
name='render_immunisation_pane_without_id'
),
# url(r'list/(?P<patient_id>\d+)/$',
#'immunisation.views.immunisation_list',
#name = 'immunisation_list'
#),
# url(r'list/$',
#'immunisation.views.immunisation_list',
#name = 'immunisation_list_without_id'
#),
url(r'add/(?P<patient_id>\d+)/$',
'immunisation.views.immunisation_add',
name='immunisation_add'
),
url(r'add/$',
'immunisation.views.immunisation_add',
name='immunisation_add_without_id'
),
url(r'edit/(?P<immunisation_id>\d+)/$',
'immunisation.views.immunisation_edit',
name='immunisation_edit'
),
url(r'edit/$',
'immunisation.views.immunisation_edit',
name='immunisation_edit_without_id'
),
url(r'del/(?P<immunisation_id>\d+)/$',
'immunisation.views.immunisation_del',
name='immunisation_del'
),
url(r'del/$',
'immunisation.views.immunisation_del',
name='immunisation_del_without_id'
),
)
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] | 1.712786 | 1,619 |
from dataclasses import dataclass
from typing import List, Tuple, Dict
from jsondataclass import from_json, to_json
@dataclass
movie = Movie(["comedy", "crime"], (5.6, 100), {"en": "WALL-E", "de": "WALL-E"})
json_str = to_json(movie)
print(json_str)
# {"genres": ["comedy", "crime"], "rating": [5.6, 100], "name": {"en": "WALL-E", "de": "WALL-E"}}
json_str = '{"genres": ["comedy", "crime"], "rating": [5.6, 100], "name": {"en": "WALL-E", "de": "WALL-E"}}'
movie = from_json(json_str, Movie)
print(movie)
# Movie(genres=['comedy', 'crime'], rating=(5.6, 100), name={'en': 'WALL-E', 'de': 'WALL-E'})
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2934,
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198
] | 2.359375 | 256 |
# -*- coding: utf-8 -*-
"""
@Project :
@FileName:
@Author :penghr
@Time :202x/xx/xx xx:xx
@Desc :
"""
import math
import cv2
import numpy as np
import scipy.spatial
import torch
import torch.nn as nn
import torch.nn.functional as F
def generate_point_map(kpoint, f_loc, rate=1):
'''obtain the location coordinates'''
pred_coord = np.nonzero(kpoint)
point_map = np.zeros((int(kpoint.shape[0] * rate), int(kpoint.shape[1] * rate), 3), dtype="uint8") + 255 # 22
# count = len(pred_coor[0])
coord_list = []
for i in range(0, len(pred_coord[0])):
h = int(pred_coord[0][i] * rate)
w = int(pred_coord[1][i] * rate)
coord_list.append([w, h])
cv2.circle(point_map, (w, h), 2, (0, 0, 0), -1)
for data in coord_list:
f_loc.write('{} {} '.format(math.floor(data[0]), math.floor(data[1])))
f_loc.write('\n')
return point_map
def generate_bounding_boxes(kpoint, fname, resize):
'''change the data path'''
Img_data = cv2.imread(fname)
# ori_Img_data = Img_data.copy()
Img_data = cv2.resize(Img_data, resize)
'''generate sigma'''
pts = np.array(list(zip(np.nonzero(kpoint)[1], np.nonzero(kpoint)[0])))
leafsize = 2048
# build kdtree
tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
distances, locations = tree.query(pts, k=4)
for index, pt in enumerate(pts):
pt2d = np.zeros(kpoint.shape, dtype=np.float32)
pt2d[pt[1], pt[0]] = 1.
if np.sum(kpoint) > 1:
sigma = (distances[index][1] + distances[index][2] + distances[index][3]) * 0.1
else:
sigma = np.average(np.array(kpoint.shape)) / 2. / 2. # case: 1 point
sigma = min(sigma, min(Img_data.shape[0], Img_data.shape[1]) * 0.05)
if sigma < 6:
t = 2
else:
t = 2
Img_data = cv2.rectangle(Img_data, (
int((pt[0] * Img_data.shape[1] / resize[0] - sigma)), int((pt[1] * Img_data.shape[0] / resize[1] - sigma))),
(int((pt[0] * Img_data.shape[1] / resize[0] + sigma)),
int((pt[1] * Img_data.shape[0] / resize[1] + sigma))), (0, 255, 0), t)
return Img_data
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] | 1.996419 | 1,117 |
#!/usr/bin/env python
"""
================================================================
rsfMRI: AFNI, ANTS, DicomStack, FreeSurfer, FSL, Nipy, aCompCorr
================================================================
A preprocessing workflow for Siemens resting state data.
This workflow makes use of:
- AFNI
- ANTS
- C3D_Affine_Tool
- DicomStack
- FreeSurfer
- FSL
- NiPy
For example::
python rsfmri_preprocessing.py -d /data/12345-34-1.dcm -f /data/Resting.nii
-s subj001 -n 2 --despike -o output
-p PBS --plugin_args "dict(qsub_args='-q many')"
This workflow takes resting timeseries and a Siemens dicom file corresponding
to it and preprocesses it to produce timeseries coordinates or grayordinates.
This workflow also requires 2mm subcortical atlas and templates that are
available from:
http://mindboggle.info/data.html
specifically the 2mm versions of:
- `Joint Fusion Atlas <http://mindboggle.info/data/atlases/jointfusion/OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_2mm.nii.gz>`_
- `MNI template <http://mindboggle.info/data/templates/ants/OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz>`_
The 2mm version was generated with::
>>> from nipype import freesurfer as fs
>>> rs = fs.Resample()
>>> rs.inputs.in_file = 'OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152.nii.gz'
>>> rs.inputs.resampled_file = 'OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_2mm.nii.gz'
>>> rs.inputs.voxel_size = (2., 2., 2.)
>>> rs.inputs.args = '-rt nearest -ns 1'
>>> res = rs.run()
"""
import os
from nipype.interfaces.base import CommandLine
CommandLine.set_default_terminal_output('file')
from nipype import config
config.enable_provenance()
from nipype import (ants, afni, fsl, freesurfer, nipy, Function, DataSink)
from nipype import Workflow, Node, MapNode
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.algorithms.misc import TSNR
from nipype.interfaces.fsl import EPIDeWarp
from nipype.interfaces.io import FreeSurferSource
from nipype.interfaces.c3 import C3dAffineTool
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.utils.filemanip import filename_to_list
import numpy as np
import scipy as sp
import nibabel as nb
from dcmstack.extract import default_extractor
from dicom import read_file
imports = ['import os',
'import nibabel as nb',
'import numpy as np',
'import scipy as sp',
'from nipype.utils.filemanip import filename_to_list'
]
def get_info(dicom_files):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
return (meta['RepetitionTime']/1000., meta['CsaImage.MosaicRefAcqTimes'],
meta['SpacingBetweenSlices'])
def median(in_files):
"""Computes an average of the median of each realigned timeseries
Parameters
----------
in_files: one or more realigned Nifti 4D time series
Returns
-------
out_file: a 3D Nifti file
"""
average = None
for idx, filename in enumerate(filename_to_list(in_files)):
img = nb.load(filename)
data = np.median(img.get_data(), axis=3)
if not average:
average = data
else:
average = average + data
median_img = nb.Nifti1Image(average/float(idx + 1),
img.get_affine(), img.get_header())
filename = os.path.join(os.getcwd(), 'median.nii.gz')
median_img.to_filename(filename)
return filename
def get_aparc_aseg(files):
"""Return the aparc+aseg.mgz file"""
for name in files:
if 'aparc+aseg.mgz' in name:
return name
raise ValueError('aparc+aseg.mgz not found')
def motion_regressors(motion_params, order=2, derivatives=2):
"""Compute motion regressors upto given order and derivative
motion + d(motion)/dt + d2(motion)/dt2 (linear + quadratic)
"""
out_files = []
for idx, filename in enumerate(filename_to_list(motion_params)):
params = np.genfromtxt(filename)
out_params = params
for d in range(1, derivatives + 1):
cparams = np.vstack((np.repeat(params[0, :][None, :], d, axis=0),
params))
out_params = np.hstack((out_params, np.diff(cparams, d, axis=0)))
out_params2 = out_params
for i in range(2, order + 1):
out_params2 = np.hstack((out_params2, np.power(out_params, i)))
filename = os.path.join(os.getcwd(), "motion_regressor%02d.txt" % idx)
np.savetxt(filename, out_params2, fmt="%.10f")
out_files.append(filename)
return out_files
def build_filter1(motion_params, comp_norm, outliers):
"""Builds a regressor set comparison motion parameters, composite norm and
outliers
The outliers are added as a single time point column for each outlier
Parameters
----------
motion_params: a text file containing motion parameters and its derivatives
comp_norm: a text file containing the composite norm
outliers: a text file containing 0-based outlier indices
Returns
-------
components_file: a text file containing all the regressors
"""
out_files = []
for idx, filename in enumerate(filename_to_list(motion_params)):
params = np.genfromtxt(filename)
norm_val = np.genfromtxt(filename_to_list(comp_norm)[idx])
out_params = np.hstack((params, norm_val[:, None]))
try:
outlier_val = np.genfromtxt(filename_to_list(outliers)[idx])
except IOError:
outlier_val = np.empty((0))
for index in np.atleast_1d(outlier_val):
outlier_vector = np.zeros((out_params.shape[0], 1))
outlier_vector[index] = 1
out_params = np.hstack((out_params, outlier_vector))
filename = os.path.join(os.getcwd(), "filter_regressor%02d.txt" % idx)
np.savetxt(filename, out_params, fmt="%.10f")
out_files.append(filename)
return out_files
def extract_noise_components(realigned_file, mask_file, num_components=6):
"""Derive components most reflective of physiological noise
Parameters
----------
realigned_file: a 4D Nifti file containing realigned volumes
mask_file: a 3D Nifti file containing white matter + ventricular masks
num_components: number of components to use for noise decomposition
Returns
-------
components_file: a text file containing the noise components
"""
imgseries = nb.load(realigned_file)
noise_mask = nb.load(mask_file)
voxel_timecourses = imgseries.get_data()[np.nonzero(noise_mask.get_data())]
voxel_timecourses = voxel_timecourses.byteswap().newbyteorder()
voxel_timecourses[np.isnan(np.sum(voxel_timecourses, axis=1)), :] = 0
_, _, v = sp.linalg.svd(voxel_timecourses, full_matrices=False)
components_file = os.path.join(os.getcwd(), 'noise_components.txt')
np.savetxt(components_file, v[:num_components, :].T)
return components_file
def extract_subrois(timeseries_file, label_file, indices):
"""Extract voxel time courses for each subcortical roi index
Parameters
----------
timeseries_file: a 4D Nifti file
label_file: a 3D file containing rois in the same space/size of the 4D file
indices: a list of indices for ROIs to extract.
Returns
-------
out_file: a text file containing time courses for each voxel of each roi
The first four columns are: freesurfer index, i, j, k positions in the
label file
"""
img = nb.load(timeseries_file)
data = img.get_data()
roiimg = nb.load(label_file)
rois = roiimg.get_data()
out_ts_file = os.path.join(os.getcwd(), 'subcortical_timeseries.txt')
with open(out_ts_file, 'wt') as fp:
for fsindex in indices:
ijk = np.nonzero(rois == fsindex)
ts = data[ijk]
for i0, row in enumerate(ts):
fp.write('%d,%d,%d,%d,' % (fsindex, ijk[0][i0],
ijk[1][i0], ijk[2][i0]) +
','.join(['%.10f' % val for val in row]) + '\n')
return out_ts_file
def combine_hemi(left, right):
"""Combine left and right hemisphere time series into a single text file
"""
lh_data = nb.load(left).get_data()
rh_data = nb.load(right).get_data()
indices = np.vstack((1000000 + np.arange(0, lh_data.shape[0])[:, None],
2000000 + np.arange(0, rh_data.shape[0])[:, None]))
all_data = np.hstack((indices, np.vstack((lh_data.squeeze(),
rh_data.squeeze()))))
filename = 'combined_surf.txt'
np.savetxt(filename, all_data,
fmt=','.join(['%d'] + ['%.10f'] * (all_data.shape[1] - 1)))
return os.path.abspath(filename)
"""
Creates the main preprocessing workflow
"""
"""
Creates the full workflow including getting information from dicom files
"""
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser(description=__doc__)
parser.add_argument("-d", "--dicom_file", dest="dicom_file",
help="an example dicom file from the resting series")
parser.add_argument("-f", "--files", dest="files", nargs="+",
help="4d nifti files for resting state",
required=True)
parser.add_argument("-s", "--subject_id", dest="subject_id",
help="FreeSurfer subject id", required=True)
parser.add_argument("-n", "--n_vol", dest="n_vol", default=0, type=int,
help="Volumes to skip at the beginning")
parser.add_argument("--despike", dest="despike", default=False,
action="store_true", help="Use despiked data")
parser.add_argument("--TR", dest="TR", default=None,
help="TR if dicom not provided in seconds")
parser.add_argument("--slice_times", dest="slice_times", nargs="+",
type=float, help="Slice times in seconds")
parser.add_argument("-l", "--lowpass_freq", dest="lowpass_freq",
default=-1, help="Low pass frequency (Hz)")
parser.add_argument("-u", "--highpass_freq", dest="highpass_freq",
default=-1, help="High pass frequency (Hz)")
parser.add_argument("-o", "--output_dir", dest="sink",
help="Output directory base")
parser.add_argument("-w", "--work_dir", dest="work_dir",
help="Output directory base")
parser.add_argument("-p", "--plugin", dest="plugin",
default='Linear',
help="Plugin to use")
parser.add_argument("--plugin_args", dest="plugin_args",
help="Plugin arguments")
parser.add_argument("--field_maps", dest="field_maps", nargs="+",
help="field map niftis")
parser.add_argument("--fm_echospacing", dest="echo_spacing", type=float,
help="field map echo spacing")
parser.add_argument("--fm_TE_diff", dest='TE_diff', type=float,
help="field map echo time difference")
parser.add_argument("--fm_sigma", dest='sigma', type=float,
help="field map sigma value")
args = parser.parse_args()
wf = create_resting_workflow(args)
if args.work_dir:
work_dir = os.path.abspath(args.work_dir)
else:
work_dir = os.getcwd()
wf.base_dir = work_dir
if args.plugin_args:
wf.run(args.plugin, plugin_args=eval(args.plugin_args))
else:
wf.run(args.plugin)
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] | 2.328583 | 5,122 |
import pytest
from vdirsyncer.storage.dav import _BAD_XML_CHARS
from vdirsyncer.storage.dav import _merge_xml
from vdirsyncer.storage.dav import _parse_xml
@pytest.mark.parametrize('char', range(32))
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] | 2.582278 | 79 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import math
sys.path.extend(["..","../channels/"])
from NaChannel import *
from KFastChannel import *
from KSlowChannel import *
from CaLChannel import *
from KAChannel import *
## use KCaChannel instead of KCaMPIChannel, in a non-MPI i.e. non-parallel run,
## to generate the KCaA.dat and KCaB.dat files.
#from KCaChannel import *
from KCaMPIChannel import *
## use KCaChannel_PG instead of KCaMPIChannel_PG, in a non-MPI i.e. non-parallel run,
## to generate the KCaA_PG.dat and KCaB_PG.dat files.
#from KCaChannel_PG import *
from KCaMPIChannel_PG import *
from CaPool import *
from KMChannel import *
from CaTChannel import *
from NaMitChannelMS import *
from KAChannelMS import *
from KDRChannelMS import *
import moose
from moose.neuroml import *
FARADAY = 96154.0 # Coulombs # from cadecay.mod : 1/(2*96154.0) = 5.2e-6 which is the Book of Genesis / readcell value
#FARADAY = 96485.3415 # Coulombs # from Wikipedia
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# -*- coding: utf-8 -*-
# @Time : 2021/9/10 14:23
# @Author : WuBingTai
from ztest import db
from ztest.models import Phone
from ztest.utils.common import login_required
from ztest.utils.response_code import RET
from . import api
from flask import jsonify, request, current_app
# 测试机列表
@api.route("/phone/list")
@login_required
# 新增手机
@api.route("/addPhone", methods=["POST"])
@login_required
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# Example Keplerian fit configuration file
# Required packages for setup
import os
import pandas as pd
import numpy as np
import radvel
# Define global planetary system and dataset parameters
starname = 'epic203771098'
nplanets = 2 # number of planets in the system
instnames = ['j'] # list of instrument names. Can be whatever you like (no spaces) but should match 'tel' column in the input file.
ntels = len(instnames) # number of instruments with unique velocity zero-points
fitting_basis = 'per tc secosw sesinw k' # Fitting basis, see radvel.basis.BASIS_NAMES for available basis names
bjd0 = 2454833.
planet_letters = {1: 'b', 2:'c'}
# Define prior centers (initial guesses) in a basis of your choice (need not be in the fitting basis)
anybasis_params = radvel.Parameters(nplanets,basis='per tc e w k') # initialize Parameters object
anybasis_params['per1'] = radvel.Parameter(value=20.885258) # period of 1st planet
anybasis_params['tc1'] = radvel.Parameter(value=2072.79438) # time of inferior conjunction of 1st planet
anybasis_params['e1'] = radvel.Parameter(value=0.01) # eccentricity of 'per tc secosw sesinw logk'1st planet
anybasis_params['w1'] = radvel.Parameter(value=np.pi/2.) # argument of periastron of the star's orbit for 1st planet
anybasis_params['k1'] = radvel.Parameter(value=10.0) # velocity semi-amplitude for 1st planet
anybasis_params['per2'] = radvel.Parameter(value=42.363011) # same parameters for 2nd planet ...
anybasis_params['tc2'] = radvel.Parameter(value=2082.62516)
anybasis_params['e2'] = radvel.Parameter(value=0.01)
anybasis_params['w2'] = radvel.Parameter(value=np.pi/2.)
anybasis_params['k2'] = radvel.Parameter(value=10.0)
anybasis_params['dvdt'] = radvel.Parameter(value=0.0) # slope
anybasis_params['curv'] = radvel.Parameter(value=0.0) # curvature
anybasis_params['gamma_j'] = radvel.Parameter(1.0) # velocity zero-point for hires_rj
anybasis_params['jit_j'] = radvel.Parameter(value=2.6) # jitter for hires_rj
# Convert input orbital parameters into the fitting basis
params = anybasis_params.basis.to_any_basis(anybasis_params,fitting_basis)
# Set the 'vary' attributes of each of the parameters in the fitting basis. A parameter's 'vary' attribute should
# be set to False if you wish to hold it fixed during the fitting process. By default, all 'vary' parameters
# are set to True.
params['secosw1'].vary = False
params['sesinw1'].vary = False
params['secosw2'].vary = False
params['sesinw2'].vary = False
params['tc1'].vary = False
params['per1'].vary = False
params['tc2'].vary = False
params['per2'].vary = False
# Load radial velocity data, in this example the data is contained in an hdf file,
# the resulting dataframe or must have 'time', 'mnvel', 'errvel', and 'tel' keys
# the velocities are expected to be in m/s
path = os.path.join(radvel.DATADIR, 'epic203771098.csv')
data = pd.read_csv(path)
data['time'] = data.t
data['mnvel'] = data.vel
data['errvel'] = data.errvel
data['tel'] = 'j'
# Define prior shapes and widths here.
priors = [
radvel.prior.EccentricityPrior( nplanets ), # Keeps eccentricity < 1
radvel.prior.PositiveKPrior( nplanets ), # Keeps K > 0
radvel.prior.Gaussian('tc1', params['tc1'].value, 0.01), # Gaussian prior on tc1 with center at tc1 and width 0.01 days
radvel.prior.Gaussian('per1', params['per1'].value, 0.01),
radvel.prior.Gaussian('tc2', params['tc2'].value, 0.01),
radvel.prior.Gaussian('per2', params['per2'].value, 0.01),
radvel.prior.HardBounds('jit_j', 0.0, 15.0)
]
# abscissa for slope and curvature terms (should be near mid-point of time baseline)
time_base = np.mean([np.min(data.time), np.max(data.time)])
# optional argument that can contain stellar mass in solar units (mstar) and
# uncertainty (mstar_err). If not set, mstar will be set to nan.
stellar = dict(mstar=1.12, mstar_err= 0.05)
# optional argument that can contain planet radii,
# used for computing densities. Values should be given
# in units of Earth radii
planet = dict(
rp1=5.68, rp_err1=0.56,
rp2=7.82, rp_err2=0.72,
)
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11,
374,
79,
62,
8056,
17,
28,
15,
13,
4761,
11,
198,
8,
628
] | 2.726974 | 1,520 |
from vistautils.iter_utils import only
from adam.language import TokenSequenceLinguisticDescription
from adam.learner import LearningExample, MemorizingLanguageLearner
from adam.perception import (
BagOfFeaturesPerceptualRepresentationFrame,
PerceptualRepresentation,
)
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] | 3.733333 | 75 |
print('hello')
main)
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] | 2.555556 | 9 |
# Copyright 2016, 2017 California Institute of Technology
# Users must agree to abide by the restrictions listed in the
# file "LegalStuff.txt" in the PROPER library directory.
#
# PROPER developed at Jet Propulsion Laboratory/California Inst. Technology
# Original IDL version by John Krist
# Python translation by Navtej Saini, with Luis Marchen and Nikta Amiri
def prop_get_nyquistsampling(wf, lamx = 0.0):
"""Funtion determines the Nyquist sampling interval for the current beam,
which is focal_ratio * wavelength / 2.
Parameters
----------
wf : obj
Wavefront class object
lamx : float
Wavelength to use for computing sampling. By default, the current
wavefront's wavelength is used. This parameter can be used when you
want to know the Nyquist sampling for a wavelength other than for the
current wavefront.
Returns
-------
float
Nyquist sampling interval corresponding to the current wavefront
"""
if lamx != 0.:
return wf.current_fratio * lamx / 2.
else:
return wf.current_fratio * wf.lamda / 2.
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1635,
266,
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13,
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] | 2.90201 | 398 |
from django.conf import settings
from django.core import checks
from django_google_recaptcha.constants import TEST_PRIVATE_KEY, TEST_PUBLIC_KEY
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] | 3.244444 | 45 |
# Generated by Django 3.2.7 on 2021-12-02 04:39
from django.db import migrations, models
import django.db.models.deletion
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] | 2.818182 | 44 |
# -*- coding: utf-8 -*-
from functools import wraps
try:
from dask.utils import SerializableLock as Lock
except ImportError:
from threading import Lock
def format_code(code):
"""
Formats some code with line numbers
Parameters
----------
code : str
Code
Returns
-------
str
Code prefixed with line numbers
"""
lines = ['']
lines.extend(["%-5d %s" % (i, l) for i, l
in enumerate(code.split('\n'), 1)])
return '\n'.join(lines)
class memoize_on_key(object):
"""
Memoize based on a key function supplied by the user.
The key function should return a custom key
for memoizing the decorated function, based on the arguments
passed to it.
In the following example, the arguments required to generate
the `_generate_phase_delay_kernel` function are the types of
the `lm`, `uvw` and `frequency` arrays, as well as the number
of correlations, `ncorr`.
The supplied ``key_fn`` produces a unique key based on these types
and the number of correlations, which is used to cache the
generated function.
.. code-block:: python
def key_fn(lm, uvw, frequency, ncorrs=4):
'''
Produce a unique key for the arguments of
_generate_phase_delay_kernel
'''
return (lm.dtype, uvw.dtype, frequency.dtype, ncorrs)
_code_template = jinja2.Template('''
#define ncorrs {{ncorrs}}
__global__ void phase_delay(
const {{lm_type}} * lm,
const {{uvw_type}} * uvw,
const {{freq_type}} * frequency,
{{out_type}} * out)
{
...
}
''')
_type_map = {
np.float32: 'float',
np.float64: 'double'
}
@memoize_on_key(key_fn)
def _generate_phase_delay_kernel(lm, uvw, frequency, ncorrs=4):
''' Generate the phase delay kernel '''
out_dtype = np.result_type(lm.dtype, uvw.dtype, frequency.dtype)
code = _code_template.render(lm_type=_type_map[lm.dtype],
uvw_type=_type_map[uvw.dtype],
freq_type=_type_map[frequency.dtype],
ncorrs=ncorrs)
return cp.RawKernel(code, "phase_delay")
"""
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] | 2.089721 | 1,148 |
from .Climate import Climate | [
6738,
764,
37649,
1330,
13963
] | 5.6 | 5 |
from unittest import TestCase
from glob import glob
| [
6738,
555,
715,
395,
1330,
6208,
20448,
198,
6738,
15095,
1330,
15095,
628
] | 4.076923 | 13 |
#!/usr/local/bin/python3
input = 1133
power_levels = []
for x in range(1, 301):
row = []
for y in range(1, 301):
row.append(power_level(x, y))
power_levels.append(row)
best_square = None
best_power = None
for x in range(1, 298):
for y in range(1, 298):
sum = 0
for i in range(0, 3):
for j in range(0, 3):
sum += power_levels[x + i - 1][y + j - 1]
if best_power is None or sum > best_power:
best_power = sum
best_square = [x, y]
print("best square: %d, %d with power %d" % (best_square[0], best_square[1], best_power))
| [
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62,
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58,
16,
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4008,
198
] | 2.301255 | 239 |
### Base class for Collage plugins ###
import math
import logging
import pygame
from plugin import Plugin
class Collage(Plugin):
"""
Base class for collage plugins
See simple_resize.py or recursive_split.py for example implementation of a plugin
"""
@staticmethod
def generate(self, size, wallpaper_queue):
"""
Generates the wallpaper collage
"""
raise NotImplementedError()
def _resize_wallpaper(self, wallpaper, size):
"""
Resizes wallpaper to set size, conserves aspect ratio
Returns crop co-ordinates and scaled image
"""
# find ratios
width_ratio = 1.0*size[0]/wallpaper.get_width()
height_ratio = 1.0*size[1]/wallpaper.get_height()
# resize to fit width
if width_ratio > height_ratio:
new_size = (size[0], int(math.ceil(wallpaper.get_height()*width_ratio)))
# resize to fit height
else:
new_size = (int(math.ceil(wallpaper.get_width()*height_ratio)), size[1])
# scale wallpaper according to new_size
try:
wallpaper = pygame.transform.smoothscale(wallpaper, new_size)
except ValueError:
logging.debug('bit-depth error, using crappy scaling')
wallpaper = pygame.transform.scale(wallpaper, new_size)
# Height or width might be too large
crop = (0, 0)
if wallpaper.get_width() > size[0]+1:
overflow = wallpaper.get_width() - size[0]
margin = int(overflow / 2)
crop = (margin, 0)
elif wallpaper.get_height() > size[1]+1:
overflow = wallpaper.get_height() - size[1]
margin = int(overflow / 2)
crop = (0, margin)
return crop, wallpaper
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# -*- coding: utf-8 -*-
"""
templatetricks.override_autoescaped
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Override which templates are autoescaped
http://flask.pocoo.org/snippets/41/
"""
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from flask import Flask
app = JHtmlEscapingFlask(__name__)
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# -*- coding: utf-8 -*-
import logging
import os
import pandas as pd
import shutil
import subprocess
import tempfile
from multiprocessing.pool import ThreadPool
import bg_convert
from thirdparty_xentax import phyre
if __name__ == '__main__':
# get available models
chr_path = '/media/rishin/20ACFF83ACFF5230/Users/rishin/Desktop/ffxx/ffx_data/gamedata/ps3data/chr'
bg_path = '/home/rishin/workspace/ffx-ai/assets/'
dist_path = '/home/rishin/workspace/ffx-ai/dist'
map_path = '/home/rishin/workspace/ffx-ai/enemy_map.txt'
#
angles_front = [15, 30, 45, 60, 75, 345, 330, 315, 300, 285] # [15, 30, 45, 60, 75, -15, -30, -45, -60, -75]
angles_back = [105, 120, 135, 150, 165, 255, 240, 225, 210, 195] # [105, 120, 135, 150, 165, -105, -120, -135, -150, -165]
all_angles = angles_front + angles_back
#
model_map = make_model_map(map_path)
model_filter = model_map.keys()
#print(model_filter)
#
model_data = model_gather(chr_path, ['mon'])
model_data_filtered = [m for m in model_data if m['name'] in model_filter]
tmp_dir = model_extract(model_data_filtered)
#print(model_data)
print(tmp_dir)
#
bg_tmp_dir = tempfile.mkdtemp()
bg_data = make_bgs(bg_path, bg_tmp_dir)
#print(bg_data)
print(bg_tmp_dir)
# parrallelise jobs
tp = ThreadPool(12)
# render models
xy_map = {'id': [], 'cls': []}
xxx = 0
for angle, bg, model in get_triplet(all_angles, bg_data, model_data_filtered):
model_name = model['name']
obj_path = os.path.join(tmp_dir, model_name + '.obj')
texture_path = os.path.join(tmp_dir, model_name + '.dds')
bg_alt_path = os.path.join(bg_tmp_dir, bg)
out_path = os.path.join(dist_path, '{}_{}_{}.png'.format(model_name, bg, angle))
class_suffix = '_front' if angle in angles_front else '_back'
#
xy_map['id'].append(out_path)
xy_map['cls'].append(model_map[model_name] + class_suffix)
# run subprocess
tp.apply_async(render_job, (model_name, obj_path, texture_path, bg_alt_path, out_path, str(angle)))
tp.close()
tp.join()
#
df = pd.DataFrame(xy_map)
df.to_csv(dist_path + '/img_map.csv')
# cleanup
shutil.rmtree(tmp_dir)
shutil.rmtree(bg_tmp_dir)
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from django.urls import path
from cart.views import CartItem, Carts, Checkout, ToggleCartItem
app_name = 'cart'
urlpatterns = [
path('carts/', Carts.as_view(),name='cart-list'),
path('carts/items/<slug:slug>/<size>/', CartItem.as_view(), name='add-remove-cart-item'),
path('carts/toggle/items/<slug:slug>/<size>/', ToggleCartItem.as_view(), name='toggle-cart-item'),
path('carts/checkout/', Checkout.as_view(), name='checkout'),
] | [
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] | 2.568966 | 174 |
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""
All custom model containers are listed here.
In Hummingbird we use two types of containers:
- containers for input models (e.g., `CommonONNXModelContainer`) used to represent input models in a unified way as DAG of containers
- containers for output models (e.g., `SklearnContainer`) used to surface output models as unified API format.
"""
from abc import ABC, abstractmethod
import dill
import os
import numpy as np
from onnxconverter_common.container import CommonSklearnModelContainer
import torch
from hummingbird.ml.operator_converters import constants
from hummingbird.ml._utils import onnx_runtime_installed, tvm_installed, pandas_installed, get_device, from_strings_to_ints
if pandas_installed():
from pandas import DataFrame
else:
DataFrame = None
# Input containers
class CommonONNXModelContainer(CommonSklearnModelContainer):
"""
Common container for input ONNX operators.
"""
class CommonSparkMLModelContainer(CommonSklearnModelContainer):
"""
Common container for input Spark-ML operators.
"""
# Output containers.
# Abstract containers enabling the Sklearn API.
class SklearnContainerTransformer(SklearnContainer):
"""
Abstract container mirroring Sklearn transformers API.
"""
@abstractmethod
def _transform(self, *input):
"""
This method contains container-specific implementation of transform.
"""
pass
def transform(self, *inputs):
"""
Utility functions used to emulate the behavior of the Sklearn API.
On data transformers it returns transformed output data
"""
return self._run(self._transform, *inputs)
class SklearnContainerRegression(SklearnContainer):
"""
Abstract container mirroring Sklearn regressors API.
"""
@abstractmethod
def _predict(self, *input):
"""
This method contains container-specific implementation of predict.
"""
pass
def predict(self, *inputs):
"""
Utility functions used to emulate the behavior of the Sklearn API.
On regression returns the predicted values.
On classification tasks returns the predicted class labels for the input data.
On anomaly detection (e.g. isolation forest) returns the predicted classes (-1 or 1).
"""
return self._run(self._predict, *inputs)
class SklearnContainerClassification(SklearnContainerRegression):
"""
Container mirroring Sklearn classifiers API.
"""
@abstractmethod
def _predict_proba(self, *input):
"""
This method contains container-specific implementation of predict_proba.
"""
pass
def predict_proba(self, *inputs):
"""
Utility functions used to emulate the behavior of the Sklearn API.
On classification tasks returns the probability estimates.
"""
return self._run(self._predict_proba, *inputs)
class SklearnContainerAnomalyDetection(SklearnContainerRegression):
"""
Container mirroring Sklearn anomaly detection API.
"""
@abstractmethod
def _decision_function(self, *inputs):
"""
This method contains container-specific implementation of decision_function.
"""
pass
def decision_function(self, *inputs):
"""
Utility functions used to emulate the behavior of the Sklearn API.
On anomaly detection (e.g. isolation forest) returns the decision function scores.
"""
scores = self._run(self._decision_function, *inputs)
# Backward compatibility for sklearn <= 0.21
if constants.IFOREST_THRESHOLD in self._extra_config:
scores += self._extra_config[constants.IFOREST_THRESHOLD]
return scores
def score_samples(self, *inputs):
"""
Utility functions used to emulate the behavior of the Sklearn API.
On anomaly detection (e.g. isolation forest) returns the decision_function score plus offset_
"""
return self.decision_function(*inputs) + self._extra_config[constants.OFFSET]
# PyTorch containers.
class PyTorchSklearnContainer(SklearnContainer):
"""
Base container for PyTorch models.
"""
@staticmethod
def load(location):
"""
Method used to load a container from the file system.
Args:
location: The location on the file system where to load the model.
Returns:
The loaded model.
"""
assert os.path.exists(location), "Model location {} does not exist.".format(location)
container = None
if os.path.isdir(location):
# This is a torch.jit model
model = torch.jit.load(os.path.join(location, constants.SAVE_LOAD_TORCH_JIT_PATH))
with open(os.path.join(location, "container.pkl"), "rb") as file:
container = dill.load(file)
container._model = model
else:
# This is a pytorch model
with open(location, "rb") as file:
container = dill.load(file)
# Need to set the number of threads to use as set in the original container.
if container._n_threads is not None:
if torch.get_num_interop_threads() != 1:
torch.set_num_interop_threads(1)
torch.set_num_threads(container._n_threads)
return container
class PyTorchSklearnContainerTransformer(SklearnContainerTransformer, PyTorchSklearnContainer):
"""
Container for PyTorch models mirroring Sklearn transformers API.
"""
class PyTorchSklearnContainerRegression(SklearnContainerRegression, PyTorchSklearnContainer):
"""
Container for PyTorch models mirroring Sklearn regressor API.
"""
class PyTorchSklearnContainerClassification(SklearnContainerClassification, PyTorchSklearnContainerRegression):
"""
Container for PyTorch models mirroring Sklearn classifiers API.
"""
class PyTorchSklearnContainerAnomalyDetection(PyTorchSklearnContainerRegression, SklearnContainerAnomalyDetection):
"""
Container for PyTorch models mirroning the Sklearn anomaly detection API.
"""
# TorchScript containers.
def _torchscript_wrapper(device, function, *inputs, extra_config={}):
"""
This function contains the code to enable predictions over torchscript models.
It is used to translates inputs in the proper torch format.
"""
inputs = [*inputs]
with torch.no_grad():
if type(inputs) == DataFrame and DataFrame is not None:
# Split the dataframe into column ndarrays
inputs = inputs[0]
input_names = list(inputs.columns)
splits = [inputs[input_names[idx]] for idx in range(len(input_names))]
splits = [df.to_numpy().reshape(-1, 1) for df in splits]
inputs = tuple(splits)
# Maps data inputs to the expected type and device.
for i in range(len(inputs)):
if type(inputs[i]) is list:
inputs[i] = np.array(inputs[i])
if type(inputs[i]) is np.ndarray:
# Convert string arrays into int32.
if inputs[i].dtype.kind in constants.SUPPORTED_STRING_TYPES:
assert constants.MAX_STRING_LENGTH in extra_config
inputs[i] = from_strings_to_ints(inputs[i], extra_config[constants.MAX_STRING_LENGTH])
if inputs[i].dtype == np.float64:
# We convert double precision arrays into single precision. Sklearn does the same.
inputs[i] = inputs[i].astype("float32")
inputs[i] = torch.from_numpy(inputs[i])
elif type(inputs[i]) is not torch.Tensor:
raise RuntimeError("Inputer tensor {} of not supported type {}".format(i, type(inputs[i])))
if device.type != "cpu" and device is not None:
inputs[i] = inputs[i].to(device)
return function(*inputs)
class TorchScriptSklearnContainerTransformer(PyTorchSklearnContainerTransformer):
"""
Container for TorchScript models mirroring Sklearn transformers API.
"""
class TorchScriptSklearnContainerRegression(PyTorchSklearnContainerRegression):
"""
Container for TorchScript models mirroring Sklearn regressors API.
"""
class TorchScriptSklearnContainerClassification(PyTorchSklearnContainerClassification):
"""
Container for TorchScript models mirroring Sklearn classifiers API.
"""
class TorchScriptSklearnContainerAnomalyDetection(PyTorchSklearnContainerAnomalyDetection):
"""
Container for TorchScript models mirroring Sklearn anomaly detection API.
"""
# ONNX containers.
class ONNXSklearnContainer(SklearnContainer):
"""
Base container for ONNX models.
The container allows to mirror the Sklearn API.
"""
@staticmethod
def load(location):
"""
Method used to load a container from the file system.
Args:
location: The location on the file system where to load the model.
Returns:
The loaded model.
"""
assert os.path.exists(location), "Model location {} does not exist.".format(location)
assert onnx_runtime_installed
import onnx
import onnxruntime as ort
container = None
model = onnx.load(os.path.join(location, constants.SAVE_LOAD_ONNX_PATH))
with open(os.path.join(location, constants.SAVE_LOAD_CONTAINER_PATH), "rb") as file:
container = dill.load(file)
container._model = model
sess_options = ort.SessionOptions()
if container._n_threads is not None:
# Need to set the number of threads to use as set in the original container.
sess_options.intra_op_num_threads = container._n_threads
sess_options.inter_op_num_threads = 1
sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
container._session = ort.InferenceSession(container._model.SerializeToString(), sess_options=sess_options)
return container
def _get_named_inputs(self, inputs):
"""
Retrieve the inputs names from the session object.
"""
if len(inputs) < len(self._input_names):
inputs = inputs[0]
assert len(inputs) == len(self._input_names)
named_inputs = {}
for i in range(len(inputs)):
input_ = np.array(inputs[i])
if input_.dtype.kind in constants.SUPPORTED_STRING_TYPES:
assert constants.MAX_STRING_LENGTH in self._extra_config
input_ = from_strings_to_ints(input_, self._extra_config[constants.MAX_STRING_LENGTH])
named_inputs[self._input_names[i]] = input_
return named_inputs
class ONNXSklearnContainerTransformer(ONNXSklearnContainer, SklearnContainerTransformer):
"""
Container for ONNX models mirroring Sklearn transformers API.
"""
class ONNXSklearnContainerRegression(ONNXSklearnContainer, SklearnContainerRegression):
"""
Container for ONNX models mirroring Sklearn regressors API.
"""
class ONNXSklearnContainerClassification(ONNXSklearnContainerRegression, SklearnContainerClassification):
"""
Container for ONNX models mirroring Sklearn classifiers API.
"""
class ONNXSklearnContainerAnomalyDetection(ONNXSklearnContainerRegression, SklearnContainerAnomalyDetection):
"""
Container for ONNX models mirroring Sklearn anomaly detection API.
"""
# TVM containers.
class TVMSklearnContainer(SklearnContainer):
"""
Base container for TVM models.
The container allows to mirror the Sklearn API.
The test input size must be the same as the batch size this container is created.
"""
@staticmethod
def load(location):
"""
Method used to load a container from the file system.
Args:
location: The location on the file system where to load the model.
Returns:
The loaded model.
"""
assert tvm_installed()
import tvm
from tvm.contrib import util, graph_runtime
from tvm import relay
container = None
assert os.path.exists(location), "Directory {} not found.".format(location)
path_lib = os.path.join(location, constants.SAVE_LOAD_TVM_LIB_PATH)
graph = open(os.path.join(location, constants.SAVE_LOAD_TVM_GRAPH_PATH)).read()
lib = tvm.runtime.module.load_module(path_lib)
params = relay.load_param_dict(open(os.path.join(location, constants.SAVE_LOAD_TVM_PARAMS_PATH), "rb").read())
# params = bytearray(open(os.path.join(location, "deploy_param.params"), "rb").read())
with open(os.path.join(location, constants.SAVE_LOAD_CONTAINER_PATH), "rb") as file:
container = dill.load(file)
assert container is not None, "Failed to load the model container."
ctx = tvm.cpu() if container._ctx == "cpu" else tvm.gpu
container._model = graph_runtime.create(graph, lib, ctx)
container._model.set_input(**params)
container._extra_config[constants.TVM_GRAPH] = graph
container._extra_config[constants.TVM_LIB] = lib
container._extra_config[constants.TVM_PARAMS] = params
container._extra_config[constants.TVM_CONTEXT] = ctx
container._ctx = ctx
# Need to set the number of threads to use as set in the original container.
os.environ["TVM_NUM_THREADS"] = str(container._n_threads)
return container
class TVMSklearnContainerTransformer(TVMSklearnContainer, SklearnContainerTransformer):
"""
Container for TVM models mirroring Sklearn transformers API.
"""
class TVMSklearnContainerRegression(TVMSklearnContainer, SklearnContainerRegression):
"""
Container for TVM models mirroring Sklearn regressors API.
"""
class TVMSklearnContainerClassification(TVMSklearnContainerRegression, SklearnContainerClassification):
"""
Container for TVM models mirroring Sklearn classifiers API.
"""
class TVMSklearnContainerAnomalyDetection(TVMSklearnContainerRegression, SklearnContainerAnomalyDetection):
"""
Container for TVM models mirroring Sklearn anomaly detection API.
"""
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] | 2.724543 | 5,362 |
from .options import PaymentMethod
| [
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17410,
198
] | 5 | 7 |
from django.contrib.auth.models import User
from rest_framework import authentication
from rest_framework import exceptions
from django.conf import settings
import jwt
import requests as r
import modules.keycloak_lib as keylib
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# Call easyblogger
# Client secret and authentication is already stored in ~/.easyblogger.credentials
# blogid is stored in ~/.easyblogger
import subprocess
import blogger_modifications
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# -*- coding: utf-8 -*-
from __future__ import absolute_import
import blinker
import time
from gevent import socket
try:
import simplejson as json
except ImportError:
import json # pyflakes.ignore
from . import protocol as nsq
from . import errors
from .message import Message
from .httpclient import HTTPClient
from .states import INIT, CONNECTED, DISCONNECTED
from .stream import Stream
from .decorators import cached_property
from .version import __version__
HOSTNAME = socket.gethostname()
SHORTNAME = HOSTNAME.split('.')[0]
USERAGENT = 'gnsq/%s' % __version__
class Nsqd(HTTPClient):
"""Low level object representing a TCP or HTTP connection to nsqd.
:param address: the host or ip address of the nsqd
:param tcp_port: the nsqd tcp port to connect to
:param http_port: the nsqd http port to connect to
:param timeout: the timeout for read/write operations (in seconds)
:param client_id: an identifier used to disambiguate this client (defaults
to the first part of the hostname)
:param hostname: the hostname where the client is deployed (defaults to the
clients hostname)
:param heartbeat_interval: the amount of time in seconds to negotiate with
the connected producers to send heartbeats (requires nsqd 0.2.19+)
:param output_buffer_size: size of the buffer (in bytes) used by nsqd for
buffering writes to this connection
:param output_buffer_timeout: timeout (in ms) used by nsqd before flushing
buffered writes (set to 0 to disable). Warning: configuring clients with
an extremely low (< 25ms) output_buffer_timeout has a significant effect
on nsqd CPU usage (particularly with > 50 clients connected).
:param tls_v1: enable TLS v1 encryption (requires nsqd 0.2.22+)
:param tls_options: dictionary of options to pass to `ssl.wrap_socket()
<http://docs.python.org/2/library/ssl.html#ssl.wrap_socket>`_
:param snappy: enable Snappy stream compression (requires nsqd 0.2.23+)
:param deflate: enable deflate stream compression (requires nsqd 0.2.23+)
:param deflate_level: configure the deflate compression level for this
connection (requires nsqd 0.2.23+)
:param sample_rate: take only a sample of the messages being sent to the
client. Not setting this or setting it to 0 will ensure you get all the
messages destined for the client. Sample rate can be greater than 0 or
less than 100 and the client will receive that percentage of the message
traffic. (requires nsqd 0.2.25+)
:param auth_secret: a string passed when using nsq auth (requires
nsqd 0.2.29+)
:param user_agent: a string identifying the agent for this client in the
spirit of HTTP (default: ``<client_library_name>/<version>``) (requires
nsqd 0.2.25+)
"""
@cached_property
def on_message(self):
"""Emitted when a message frame is received.
The signal sender is the connection and the `message` is sent as an
argument.
"""
return blinker.Signal(doc='Emitted when a message frame is received.')
@cached_property
def on_response(self):
"""Emitted when a response frame is received.
The signal sender is the connection and the `response` is sent as an
argument.
"""
return blinker.Signal(doc='Emitted when a response frame is received.')
@cached_property
def on_error(self):
"""Emitted when an error frame is received.
The signal sender is the connection and the `error` is sent as an
argument.
"""
return blinker.Signal(doc='Emitted when a error frame is received.')
@cached_property
def on_finish(self):
"""Emitted after :meth:`finish`.
Sent after a message owned by this connection is successfully finished.
The signal sender is the connection and the `message_id` is sent as an
argument.
"""
return blinker.Signal(doc='Emitted after the a message is finished.')
@cached_property
def on_requeue(self):
"""Emitted after :meth:`requeue`.
Sent after a message owned by this connection is requeued. The signal
sender is the connection and the `message_id`, `timeout` and `backoff`
flag are sent as arguments.
"""
return blinker.Signal(doc='Emitted after the a message is requeued.')
@cached_property
def on_auth(self):
"""Emitted after the connection is successfully authenticated.
The signal sender is the connection and the parsed `response` is sent as
arguments.
"""
return blinker.Signal(
doc='Emitted after the connection is successfully authenticated.'
)
@cached_property
def on_close(self):
"""Emitted after :meth:`close_stream`.
Sent after the connection socket has closed. The signal sender is the
connection.
"""
return blinker.Signal(doc='Emitted after the connection is closed.')
@property
def is_connected(self):
"""Check if the client is currently connected."""
return self.state == CONNECTED
@property
def is_starved(self):
"""Evaluate whether the connection is starved.
This property should be used by message handlers to reliably identify
when to process a batch of messages.
"""
return self.in_flight >= max(self.last_ready * 0.85, 1)
def connect(self):
"""Initialize connection to the nsqd."""
if self.state not in (INIT, DISCONNECTED):
return
stream = Stream(self.address, self.tcp_port, self.timeout)
stream.connect()
self.stream = stream
self.state = CONNECTED
self.send(nsq.MAGIC_V2)
def close_stream(self):
"""Close the underlying socket."""
if not self.is_connected:
return
self.stream.close()
self.state = DISCONNECTED
self.on_close.send(self)
def read_response(self):
"""Read an individual response from nsqd.
:returns: tuple of the frame type and the processed data.
"""
response = self._read_response()
frame, data = nsq.unpack_response(response)
self.last_response = time.time()
if frame not in self._frame_handlers:
raise errors.NSQFrameError('unknown frame %d' % frame)
frame_handler = self._frame_handlers[frame]
processed_data = frame_handler(data)
return frame, processed_data
def listen(self):
"""Listen to incoming responses until the connection closes."""
while self.is_connected:
self.read_response()
def identify(self):
"""Update client metadata on the server and negotiate features.
:returns: nsqd response data if there was feature negotiation,
otherwise `None`
"""
self.send(nsq.identify({
# nsqd <0.2.28
'short_id': self.client_id,
'long_id': self.hostname,
# nsqd 0.2.28+
'client_id': self.client_id,
'hostname': self.hostname,
# nsqd 0.2.19+
'feature_negotiation': True,
'heartbeat_interval': self.heartbeat_interval,
# nsqd 0.2.21+
'output_buffer_size': self.output_buffer_size,
'output_buffer_timeout': self.output_buffer_timeout,
# nsqd 0.2.22+
'tls_v1': self.tls_v1,
# nsqd 0.2.23+
'snappy': self.snappy,
'deflate': self.deflate,
'deflate_level': self.deflate_level,
# nsqd nsqd 0.2.25+
'sample_rate': self.sample_rate,
'user_agent': self.user_agent,
}))
frame, data = self.read_response()
if frame == nsq.FRAME_TYPE_ERROR:
raise data
if data == 'OK':
return
try:
data = json.loads(data)
except ValueError:
self.close_stream()
msg = 'failed to parse IDENTIFY response JSON from nsqd: %r'
raise errors.NSQException(msg % data)
self.max_ready_count = data.get('max_rdy_count', self.max_ready_count)
if self.tls_v1 and data.get('tls_v1'):
self.upgrade_to_tls()
if self.snappy and data.get('snappy'):
self.upgrade_to_snappy()
elif self.deflate and data.get('deflate'):
self.deflate_level = data.get('deflate_level', self.deflate_level)
self.upgrade_to_defalte()
if self.auth_secret and data.get('auth_required'):
self.auth()
return data
def auth(self):
"""Send authorization secret to nsqd."""
self.send(nsq.auth(self.auth_secret))
frame, data = self.read_response()
if frame == nsq.FRAME_TYPE_ERROR:
raise data
try:
response = json.loads(data)
except ValueError:
self.close_stream()
msg = 'failed to parse AUTH response JSON from nsqd: %r'
raise errors.NSQException(msg % data)
self.on_auth.send(self, response=response)
return response
def subscribe(self, topic, channel):
"""Subscribe to a nsq `topic` and `channel`."""
self.send(nsq.subscribe(topic, channel))
def publish_tcp(self, topic, data):
"""Publish a message to the given topic over tcp."""
self.send(nsq.publish(topic, data))
def multipublish_tcp(self, topic, messages):
"""Publish an iterable of messages to the given topic over tcp."""
self.send(nsq.multipublish(topic, messages))
def ready(self, count):
"""Indicate you are ready to receive `count` messages."""
self.last_ready = count
self.ready_count = count
self.send(nsq.ready(count))
def finish(self, message_id):
"""Finish a message (indicate successful processing)."""
self.send(nsq.finish(message_id))
self.finish_inflight()
self.on_finish.send(self, message_id=message_id)
def requeue(self, message_id, timeout=0, backoff=True):
"""Re-queue a message (indicate failure to process)."""
self.send(nsq.requeue(message_id, timeout))
self.finish_inflight()
self.on_requeue.send(
self,
message_id=message_id,
timeout=timeout,
backoff=backoff
)
def touch(self, message_id):
"""Reset the timeout for an in-flight message."""
self.send(nsq.touch(message_id))
def close(self):
"""Indicate no more messages should be sent."""
self.send(nsq.close())
def nop(self):
"""Send no-op to nsqd. Used to keep connection alive."""
self.send(nsq.nop())
@property
def publish_http(self, topic, data):
"""Publish a message to the given topic over http."""
nsq.assert_valid_topic_name(topic)
return self.http_post('/put', fields={'topic': topic}, body=data)
def multipublish_http(self, topic, messages):
"""Publish an iterable of messages to the given topic over http."""
nsq.assert_valid_topic_name(topic)
return self.http_post(
url='/mput',
fields={'topic': topic},
body='\n'.join(self._validate_http_mpub(m) for m in messages)
)
def create_topic(self, topic):
"""Create a topic."""
nsq.assert_valid_topic_name(topic)
return self.http_post('/create_topic', fields={'topic': topic})
def delete_topic(self, topic):
"""Delete a topic."""
nsq.assert_valid_topic_name(topic)
return self.http_post('/delete_topic', fields={'topic': topic})
def create_channel(self, topic, channel):
"""Create a channel for an existing topic."""
nsq.assert_valid_topic_name(topic)
nsq.assert_valid_channel_name(channel)
return self.http_post(
url='/create_channel',
fields={'topic': topic, 'channel': channel},
)
def delete_channel(self, topic, channel):
"""Delete an existing channel for an existing topic."""
nsq.assert_valid_topic_name(topic)
nsq.assert_valid_channel_name(channel)
return self.http_post(
url='/delete_channel',
fields={'topic': topic, 'channel': channel},
)
def empty_topic(self, topic):
"""Empty all the queued messages for an existing topic."""
nsq.assert_valid_topic_name(topic)
return self.http_post('/empty_topic', fields={'topic': topic})
def empty_channel(self, topic, channel):
"""Empty all the queued messages for an existing channel."""
nsq.assert_valid_topic_name(topic)
nsq.assert_valid_channel_name(channel)
return self.http_post(
url='/empty_channel',
fields={'topic': topic, 'channel': channel},
)
def pause_channel(self, topic, channel):
"""Pause message flow to all channels on an existing topic.
Messages will queue at topic.
"""
nsq.assert_valid_topic_name(topic)
nsq.assert_valid_channel_name(channel)
return self.http_post(
url='/pause_channel',
fields={'topic': topic, 'channel': channel},
)
def unpause_channel(self, topic, channel):
"""Resume message flow to channels of an existing, paused, topic."""
nsq.assert_valid_topic_name(topic)
nsq.assert_valid_channel_name(channel)
return self.http_post(
url='/unpause_channel',
fields={'topic': topic, 'channel': channel},
)
def stats(self):
"""Return internal instrumented statistics."""
return self.http_get('/stats', fields={'format': 'json'})
def ping(self):
"""Monitoring endpoint.
:returns: should return `"OK"`, otherwise raises an exception.
"""
return self.http_get('/ping')
def info(self):
"""Returns version information."""
return self.http_get('/info')
def publish(self, topic, data):
"""Publish a message.
If connected, the message will be sent over tcp. Otherwise it will
fall back to http.
"""
if self.is_connected:
return self.publish_tcp(topic, data)
else:
return self.publish_http(topic, data)
def multipublish(self, topic, messages):
"""Publish an iterable of messages in one roundtrip.
If connected, the messages will be sent over tcp. Otherwise it will
fall back to http.
"""
if self.is_connected:
return self.multipublish_tcp(topic, messages)
else:
return self.multipublish_http(topic, messages)
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11,
7243,
11,
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2599,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
14876,
1836,
281,
11629,
540,
286,
6218,
287,
530,
2835,
39813,
13,
628,
220,
220,
220,
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220,
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262,
6218,
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307,
1908,
625,
48265,
13,
15323,
340,
481,
198,
220,
220,
220,
220,
220,
220,
220,
2121,
736,
284,
2638,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
611,
2116,
13,
271,
62,
15236,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
16680,
541,
549,
1836,
62,
83,
13155,
7,
26652,
11,
6218,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
2116,
13,
16680,
541,
549,
1836,
62,
4023,
7,
26652,
11,
6218,
8,
198
] | 2.43127 | 6,140 |
# Generated by Django 3.2.7 on 2021-10-18 12:21
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
| [
2,
2980,
515,
416,
37770,
513,
13,
17,
13,
22,
319,
33448,
12,
940,
12,
1507,
1105,
25,
2481,
198,
198,
6738,
42625,
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13,
10414,
1330,
6460,
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42625,
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15720,
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198,
11748,
42625,
14208,
13,
9945,
13,
27530,
13,
2934,
1616,
295,
628
] | 3.019231 | 52 |
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