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|---|---|---|---|
# -*- coding: utf-8 -*-
from gevent.pool import Pool
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import argparse
import datetime
import json
import random
import time
import numpy as np
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from __future__ import absolute_import, division, print_function
from cctbx.eltbx import covalent_radii
from libtbx.test_utils import approx_equal
if (__name__ == "__main__"):
run()
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# Generated by Django 3.0.6 on 2020-05-06 02:47
from django.db import migrations, models
import uuid
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import sys, os
myPath = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, myPath + '/../')
import pytest
import investigate # use the local path, instead of what has been installed
@pytest.fixture
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#!/usr/bin/env python
import sys
import os
import shutil
import stat
programpath = os.path.abspath(sys.path[0])
sys.path.append(os.path.join(programpath,"Lib"))
from optparse import OptionParser
import PackingUnits
import SPULib
import OsLib
parser = OptionParser(usage="%prog [-f] [-q] FILENAME APPLICATIONHOME",
description="Compile, stamp and install FILENAME into the Maconomy application at APPLICATIONHOME",
version="%prog 0.1.0")
parser.add_option("-s", "--sources", dest="sourcefolder",
help="root of sources", metavar="SOURCES")
parser.add_option("--solution", dest="solution",
help="install as part of SOLUTION e.g. MCS", metavar="SOLUTION")
parser.add_option("-i", "--industryaccelerator", dest="accelerator",
help="install as part of SOLUTION e.g. MCS", metavar="IA")
parser.add_option("-q", "--quiet",
action="store_false", dest="verbose", default=True,
help="don't print status messages to stdout")
parser.print_version()
(options, args) = parser.parse_args()
if len(args)<2:
parser.error("Both FILENAME and APPLICATIONHOME must be specified")
parser.print_help()
filename=args[0]
if os.path.isabs(filename):
source=filename
else:
source=os.path.join(os.getcwd(),filename)
if not os.path.exists(source):
parser.error("%s does not exist" % source)
homefolder=args[1]
if not os.path.exists(homefolder):
parser.error("%s does not exist" % homefolder)
root = source[:source.find("CustomInstallation")+18]
if options.verbose:
print "source", source
print "filename" ,filename
print "homefolder", homefolder
print "root", root
print filename, source, homefolder
print str(options)
if options.solution:
spuSolutions = [options.solution]
else:
spuDefinition = PackingUnits.getSPUDefinition(spuDefFile=os.path.join(root, PackingUnits.getSPUDefinitionBasename()))
spuSolutions = [spdef[0] for spdef in spuDefinition]
if source.find("CustomInstallation"):
relativePath = source[source.find("CustomInstallation")+19:]
if options.solution:
destination = os.path.join(homefolder, "Solutions", options.solution, "Setup", relativePath)
else:
destination=os.path.join(homefolder, source[source.find("CustomInstallation"):])
#FIXME: Find the "Proper" solution
SPULib.standalone = True
SPULib.setFunctions()
SPULib.logfile = "d:\Temp\Temp.txt"
tempfolder=r"D:\Temp\tempsolution"
destination=os.path.join(tempfolder, "Solutions", relativePath)
os.makedirs(os.path.split(destination)[0])
shutil.copy(source, destination)
os.chmod(destination, stat.S_IWRITE)
SPULib.restructureSpuFiles(tempfolder, spuSolutions) #, fileList=[relativePath])
OsLib.move(tempfolder, homefolder, treeMove=True)
elif source.find("IA."):
destination=os.path.join(homefolder, source[source.find("IA."):])
#os.chmod(destination, stat.S_IWRITE)
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import numpy as np
from linearSearch.linearSearch import LinearSearch
class NonmonotoneGLL(LinearSearch):
"""
"""
def __init__(self, method="GLL", max_iter=100, **opt):
"""
:param method:
:param max_iter:
:param opt:
"""
super().__init__(method, max_iter, **opt)
self.name = self.__class__.__name__
self.old_value = []
if "GLL_rho" not in self.opt or "GLL_alpha" not in self.opt\
or "GLL_sigma" not in self.opt or "GLL_M" not in self.opt:
raise NameError("GLL need rho, alpha, sigma, M")
if self.opt["GLL_rho"] >= 1 or self.opt["GLL_rho"] <= 0:
raise ValueError("rho must be in (0, 1)")
else:
self.rho = self.opt["GLL_rho"]
if self.opt["GLL_alpha"] <= 0:
raise ValueError("alpha must be positive")
else:
self.alpha = self.opt["GLL_alpha"]
if self.opt["GLL_M"] <= 0:
raise ValueError("M must be positive")
else:
self.M = self.opt["GLL_M"]
if self.opt["GLL_sigma"] >= 1 or self.opt["GLL_sigma"] <= 0:
raise ValueError("sigma must be in (0, 1)")
else:
self.sigma = self.opt["GLL_sigma"]
def get_step_length(self, f, g, x, d):
"""
:param f:
:param g:
:param x:
:param d:
:return:
"""
if np.dot(g(x).T, d) > 0:
print("np.dot(g(x).T, d) :{}".format(np.dot(g(x).T, d)))
# d = -d
raise ValueError("g^T d must be negative.")
residual = self.rho * np.dot(g(x).T, d)[0][0]
self.old_value.append(f(x))
k = len(self.old_value)-1
'''
for mk in range(min([k, self.M])+1):
for t in range(1000):
if f(x+alphak*d) <= np.max(self.old_value[k-mk:]) + alphak * residual:
alphak = math.pow(self.sigma, t) * self.alpha
return np.max([alphak, 0.00001])
'''
mk = min([k, self.M])
alphak = self.alpha
for t in range(self.max_iter):
self._global_iter_increment()
try:
if f(x + alphak * d) <= np.max(self.old_value[k - mk:]) + alphak * residual:
return alphak
except:
print("x is {}, alpha is {}, d is{}".format(x, alphak, d))
alphak = alphak * self.sigma
return alphak
raise ValueError("can not find suitable alphak in GLL") | [
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] | 1.849015 | 1,371 |
"""A metric."""
from dataclasses import dataclass
from typing import Optional
from jupiter.domain.entity_name import EntityName
from jupiter.domain.metrics.metric_key import MetricKey
from jupiter.domain.metrics.metric_unit import MetricUnit
from jupiter.domain.recurring_task_gen_params import RecurringTaskGenParams
from jupiter.framework.aggregate_root import AggregateRoot
from jupiter.framework.base.entity_id import BAD_REF_ID
from jupiter.framework.base.timestamp import Timestamp
@dataclass()
class Metric(AggregateRoot):
"""A metric."""
@dataclass(frozen=True)
class Created(AggregateRoot.Created):
"""Created event."""
@dataclass(frozen=True)
class Updated(AggregateRoot.Updated):
"""Updated event."""
_key: MetricKey
_name: EntityName
_collection_params: Optional[RecurringTaskGenParams]
_metric_unit: Optional[MetricUnit]
@staticmethod
def new_metric(
key: MetricKey, name: EntityName, collection_params: Optional[RecurringTaskGenParams],
metric_unit: Optional[MetricUnit], created_time: Timestamp) -> 'Metric':
"""Create a metric."""
metric = Metric(
_ref_id=BAD_REF_ID,
_archived=False,
_created_time=created_time,
_archived_time=None,
_last_modified_time=created_time,
_events=[],
_key=key,
_name=name,
_collection_params=collection_params,
_metric_unit=metric_unit)
metric.record_event(Metric.Created.make_event_from_frame_args(created_time))
return metric
def change_name(self, name: EntityName, modification_time: Timestamp) -> 'Metric':
"""Change the name of the metric."""
if self._name == name:
return self
self._name = name
self.record_event(Metric.Updated.make_event_from_frame_args(modification_time))
return self
def change_collection_params(
self, collection_params: Optional[RecurringTaskGenParams], modification_time: Timestamp) -> 'Metric':
"""Change the collection period of the metric."""
if self._collection_params == collection_params:
return self
self._collection_params = collection_params
self.record_event(Metric.Updated.make_event_from_frame_args(modification_time))
return self
@property
def key(self) -> MetricKey:
"""The key of the metric."""
return self._key
@property
def name(self) -> EntityName:
"""The name of the metric."""
return self._name
@property
def collection_params(self) -> Optional[RecurringTaskGenParams]:
"""The collection parameters of the metric."""
return self._collection_params
@property
def metric_unit(self) -> Optional[MetricUnit]:
"""The metric unit of the metric."""
return self._metric_unit
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198
] | 2.535467 | 1,156 |
import graphene
from graphene.types.resolver import dict_resolver
from authentication import with_default_authentication
from handlers.graphql.resolvers import with_connection
from handlers.graphql.types.base.objecttype import ObjectType
from playbookloader import PlaybookLoader
from handlers.graphql.types.vm import OSVersion
import rethinkdb
from tornado.options import options as opts
@with_default_authentication
@with_connection
@with_default_authentication
@with_connection
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] | 4.016393 | 122 |
import sentencepiece as spm
import sys
path=sys.argv[1]
input=sys.argv[2]
out=sys.argv[3]
sp = spm.SentencePieceProcessor()
sp.Load(path)
with open(input) as f:
with open(out,"w") as w:
for item in f:
w.write(" ".join(sp.EncodeAsPieces(item))+"\n")
| [
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] | 2.1 | 130 |
import json
import logging
import os
import bottle
from sys import argv
from api import ping_response, start_response, move_response, end_response
from utils.arena import Arena
# Set log level
LOG_LEVEL = 'DEBUG'
if len(argv) > 1 and hasattr(logging, argv[1]):
LOG_LEVEL = argv[1]
logging.basicConfig(level=getattr(logging, LOG_LEVEL))
logger = logging.getLogger()
# Constants
COLOR = '#993333'
HEAD = 'sand-worm'
TAIL = 'pixel'
WELCOME = '''--
Welcome, contestant.
=== TA\'AURIC, ASPECT OF WAR ===
'''
# Initialize arenas
ARENAS = {}
@bottle.route('/')
@bottle.route('/static/<path:path>')
def static(path):
'''
Given a path, return the static file located relative
to the static folder.
This can be used to return the snake head URL in an API response.
'''
return bottle.static_file(path, root='static/')
@bottle.post('/ping')
def ping():
'''
A keep-alive endpoint used to prevent cloud application platforms,
such as Heroku, from sleeping the application instance.
'''
return ping_response()
@bottle.post('/start')
def start():
'''Initialize stateful data'''
logger.info(WELCOME)
logger.info(
"Initializing snake with\ncolour: %s\nhead: %s\ntail: %s",
COLOR, HEAD, TAIL)
# Initialize global arena instance (keeps state)
global ARENAS
data = bottle.request.json
game_id = data['game']['id']
b_width = data['board']['width']
b_height = data['board']['height']
ARENAS[game_id] = Arena(b_width, b_height)
return start_response(COLOR, HEAD, TAIL)
@bottle.post('/move')
def move():
'''Choose a direction to move!'''
global ARENAS
# Unpack game data
data = bottle.request.json
game_id = data['game']['id']
turn = data['turn']
name = data['you']['name']
health = data['you']['health']
logger.debug("\n===== (%s) TURN %s =====", name, turn)
# Format data for Arena
body = [(seg['x'], seg['y']) for seg in data['you']['body']]
snakes = [[(s['x'], s['y']) for s in sn['body']] for sn in data['board']['snakes']]
snakes = [seg for seg in snakes if seg not in body]
foods = [(fd['x'], fd['y']) for fd in data['board']['food']]
# Update arena
arena = ARENAS[game_id]
arena.update_heatmap(body, snakes, foods)
logger.debug("ARENA HEATMAP:\n%s", arena.arena_to_str())
# Check for self-loops
arena.check_self_loop()
# Pick best move from newly created heatmap
directions = arena.rank_moves()
if directions:
direction = directions[0]
else:
direction = 'up'
logger.debug("GUESS I'LL DIE LMAO")
logger.debug("Moving %s", direction)
return move_response(direction)
@bottle.post('/end')
def end():
'''Clean up any stateful objects'''
return end_response()
# Expose WSGI app (so gunicorn can find it)
application = bottle.default_app()
if __name__ == '__main__':
bottle.run(
application,
host=os.getenv('IP', '0.0.0.0'),
port=os.getenv('PORT', '8080'),
debug=os.getenv('DEBUG', True)
)
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13,
1136,
24330,
10786,
30531,
3256,
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8,
198,
220,
220,
220,
1267,
198
] | 2.508571 | 1,225 |
import numpy as np
from .c_spheredistrib import F2GaussianSphereDistribution
from ..core import FitFunction
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"""
Given three ints, a b c, return True if one of b or c is "close" (differing from a by at most 1), while the other is "far", differing from both other values by 2 or more. Note: abs(num) computes the absolute value of a number.
close_far(1, 2, 10) → True
close_far(1, 2, 3) → False
close_far(4, 1, 3) → True
"""
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"""
This example:
1. Connects to the current model
2. Deploys a charm and waits until it reports itself active
3. Creates an offer
4. Lists the offer
3. Destroys the unit and application
"""
import tempfile
from logging import getLogger
from juju import loop
from juju.controller import Controller
log = getLogger(__name__)
if __name__ == '__main__':
loop.run(main())
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# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'homework.ui'
#
# Created by: PyQt5 UI code generator 5.9
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
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from django.urls import path
from .views import LocationCreateView
app_name = 'campus'
urlpatterns = [
path('location/create/', LocationCreateView.as_view(), name='create_location'),
]
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# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import mock
from rally.plugins.openstack.scenarios.cinder import volume_types
from tests.unit import test
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"""Init file for the paci helpers"""
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import numpy
from omuse.units import units, constants
from omuse.community.iemic.interface import iemic
from omuse.community.iemic.implicit_utils import newton, continuation
from matplotlib import pyplot
from bstream import barotropic_streamfunction, overturning_streamfunction,z_from_cellcenterz
"""
default OCEAN parameters:
Ocean__Analyze_Jacobian: True
Ocean__Belos_Solver__FGMRES_explicit_residual_test: False
Ocean__Belos_Solver__FGMRES_iterations: 500
Ocean__Belos_Solver__FGMRES_output: 100
Ocean__Belos_Solver__FGMRES_restarts: 0
Ocean__Belos_Solver__FGMRES_tolerance: 1e-08
Ocean__Input_file: ocean_input.h5
Ocean__Load_mask: True
Ocean__Load_salinity_flux: False
Ocean__Load_state: False
Ocean__Load_temperature_flux: False
Ocean__Max_mask_fixes: 5
Ocean__Output_file: ocean_output.h5
Ocean__Save_column_integral: False
Ocean__Save_frequency: 0
Ocean__Save_mask: True
Ocean__Save_salinity_flux: True
Ocean__Save_state: True
Ocean__Save_temperature_flux: True
Ocean__THCM__Compute_salinity_integral: True
Ocean__THCM__Coriolis_Force: 1
Ocean__THCM__Coupled_Salinity: 0
Ocean__THCM__Coupled_Sea_Ice_Mask: 1
Ocean__THCM__Coupled_Temperature: 0
Ocean__THCM__Depth_hdim: 4000.0
Ocean__THCM__Fix_Pressure_Points: False
Ocean__THCM__Flat_Bottom: False
Ocean__THCM__Forcing_Type: 0
Ocean__THCM__Global_Bound_xmax: 350.0
Ocean__THCM__Global_Bound_xmin: 286.0
Ocean__THCM__Global_Bound_ymax: 74.0
Ocean__THCM__Global_Bound_ymin: 10.0
Ocean__THCM__Global_Grid_Size_l: 16
Ocean__THCM__Global_Grid_Size_m: 16
Ocean__THCM__Global_Grid_Size_n: 16
Ocean__THCM__Grid_Stretching_qz: 1.0
Ocean__THCM__Inhomogeneous_Mixing: 0
Ocean__THCM__Integral_row_coordinate_i: -1
Ocean__THCM__Integral_row_coordinate_j: -1
Ocean__THCM__Land_Mask: no_mask_specified
Ocean__THCM__Levitus_Internal_T_S: False
Ocean__THCM__Levitus_S: 1
Ocean__THCM__Levitus_T: 1
Ocean__THCM__Linear_EOS:_alpha_S: 0.00076
Ocean__THCM__Linear_EOS:_alpha_T: 0.0001
Ocean__THCM__Local_SRES_Only: False
Ocean__THCM__Mixing: 1
Ocean__THCM__Periodic: False
Ocean__THCM__Problem_Description: Unnamed
Ocean__THCM__Read_Land_Mask: False
Ocean__THCM__Read_Salinity_Perturbation_Mask: False
Ocean__THCM__Restoring_Salinity_Profile: 1
Ocean__THCM__Restoring_Temperature_Profile: 1
Ocean__THCM__Rho_Mixing: True
Ocean__THCM__Salinity_Forcing_Data: levitus/new/s00an1
Ocean__THCM__Salinity_Integral_Sign: -1
Ocean__THCM__Salinity_Perturbation_Mask: no_mask_specified
Ocean__THCM__Scaling: THCM
Ocean__THCM__Taper: 1
Ocean__THCM__Temperature_Forcing_Data: levitus/new/t00an1
Ocean__THCM__Topography: 1
Ocean__THCM__Wind_Forcing_Data: wind/trtau.dat
Ocean__THCM__Wind_Forcing_Type: 2
Ocean__Use_legacy_fort.3_output: False
Ocean__Use_legacy_fort.44_output: True
starting (derived parameters):
#~ Ocean__THCM__Starting_Parameters__ALPC: nan
#~ Ocean__THCM__Starting_Parameters__AL_T: nan
#~ Ocean__THCM__Starting_Parameters__ARCL: nan
#~ Ocean__THCM__Starting_Parameters__CMPR: nan
#~ Ocean__THCM__Starting_Parameters__CONT: nan
#~ Ocean__THCM__Starting_Parameters__Combined_Forcing: nan
#~ Ocean__THCM__Starting_Parameters__Energy: nan
#~ Ocean__THCM__Starting_Parameters__Flux_Perturbation: nan
#~ Ocean__THCM__Starting_Parameters__Horizontal_Ekman_Number: nan
#~ Ocean__THCM__Starting_Parameters__Horizontal_Peclet_Number: nan
#~ Ocean__THCM__Starting_Parameters__IFRICB: nan
#~ Ocean__THCM__Starting_Parameters__LAMB: nan
#~ Ocean__THCM__Starting_Parameters__MIXP: nan
#~ Ocean__THCM__Starting_Parameters__MKAP: nan
#~ Ocean__THCM__Starting_Parameters__NLES: nan
#~ Ocean__THCM__Starting_Parameters__Nonlinear_Factor: nan
#~ Ocean__THCM__Starting_Parameters__P_VC: nan
#~ Ocean__THCM__Starting_Parameters__RESC: nan
#~ Ocean__THCM__Starting_Parameters__Rayleigh_Number: nan
#~ Ocean__THCM__Starting_Parameters__Rossby_Number: nan
#~ Ocean__THCM__Starting_Parameters__SPL1: nan
#~ Ocean__THCM__Starting_Parameters__SPL2: nan
#~ Ocean__THCM__Starting_Parameters__Salinity_Forcing: nan
#~ Ocean__THCM__Starting_Parameters__Salinity_Homotopy: nan
#~ Ocean__THCM__Starting_Parameters__Salinity_Perturbation: nan
#~ Ocean__THCM__Starting_Parameters__Solar_Forcing: nan
#~ Ocean__THCM__Starting_Parameters__Temperature_Forcing: nan
#~ Ocean__THCM__Starting_Parameters__Vertical_Ekman_Number: nan
#~ Ocean__THCM__Starting_Parameters__Vertical_Peclet_Number: nan
#~ Ocean__THCM__Starting_Parameters__Wind_Forcing: nan
"""
if __name__=="__main__":
instance=initialize_global_iemic()
xmin=instance.parameters.Ocean__THCM__Global_Bound_xmin
xmax=instance.parameters.Ocean__THCM__Global_Bound_xmax
ymin=instance.parameters.Ocean__THCM__Global_Bound_ymin
ymax=instance.parameters.Ocean__THCM__Global_Bound_ymax
print(instance.parameters)
#~ instance.parameters.Continuation__destination_0=1.0
# Converge to an initial steady state
x = instance.get_state()
print(
instance.Ocean__THCM__Starting_Parameters)
x = newton(instance, x, 1e-10)
#~ input()
lat=instance.grid.lat
lon=instance.grid.lon
zc=instance.grid[0,0,:].z
z=z_from_cellcenterz(zc)
#~ yvar=get_yvar(instance.grid)
x = continuation(instance, x, 'Ocean->THCM->Starting Parameters->Combined Forcing', 1., 0.2, 20, tol=1.e-6)
"""
print(instance.Continuation)
instance.Continuation.destination_0=1.
instance.step_continuation()
x=instance.grid
print(x)
"""
uvel=x[:,:,:].u_velocity
vvel=x[:,:,:].v_velocity
umax=numpy.abs(uvel).max()
vmax=numpy.abs(vvel).max()
print("umax, vmax:", umax,vmax)
#~ fig, axs=pyplot.subplots(2,1,figsize=(8,8))
#~ im=axs[0].imshow(uvel.T, origin="lower", cmap="seismic", vmax=umax, vmin=-umax, extent=[xmin,xmax,ymin,ymax], interpolation="none")
#~ fig.colorbar(im,ax=axs[0],label="uvel")
#~ im=axs[1].imshow(vvel.T, origin="lower", cmap="seismic", vmax=vmax, vmin=-vmax, extent=[xmin,xmax,ymin,ymax], interpolation="none")
#~ fig.colorbar(im,ax=axs[1],label="vvel")
#~ pyplot.savefig("test.png")
#~ pyplot.show()
fig, ax=pyplot.subplots(2,1,figsize=(8,8))
dz=z[1:]-z[:-1]
dy=constants.Rearth*x[...].cellsize()[1]
psib=barotropic_streamfunction(x[...].u_velocity | 0.1*units.m/units.s,dz,dy)
vmax=numpy.abs(psib).max().value_in(units.Sv)
im=ax[0].imshow(psib.value_in(units.Sv).T, origin="lower", cmap="seismic", vmax=vmax, vmin=-vmax, extent=[xmin,xmax,ymin,ymax], interpolation="none")
fig.colorbar(im,ax=ax[0],label="psib [Sv]")
dx=constants.Rearth*x[...].cellsize()[0]*numpy.cos(x[0,:,0].lat.value_in(units.rad))
psim=overturning_streamfunction(x[...].v_velocity | 0.1*units.m/units.s,dz,dx)
instance.stop()
print("psim")
print(psim[:,-1])
vmax=numpy.abs(psim).max().number#value_in(units.Sv)
zmin=z.min().value_in(units.m)
zmax=z.max().value_in(units.m)
im=ax[1].imshow(psim.number.T, origin="lower", cmap="seismic", vmax=vmax, vmin=-vmax, extent=[ymin,ymax,zmin,zmax], interpolation="none", aspect="auto")
fig.colorbar(im,ax=ax[1],label="psim [Sv]")
pyplot.savefig("psib_psim.png")
pyplot.show()
print("done")
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3419,
628,
198,
220,
220,
220,
3601,
7203,
28060,
4943,
198
] | 2.503873 | 2,840 |
#-*- coding: utf-8 -*-
import unittest
from utils import *
from config import *
import sys
from websocket import create_connection
if __name__ == '__main__':
unittest.main()
| [
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] | 2.761194 | 67 |
from pycoin.networks.bitcoinish import create_bitcoinish_network
network = create_bitcoinish_network(
symbol="BTX", network_name="BitCore", subnet_name="mainnet",
wif_prefix_hex="80", sec_prefix="BTXSEC:", address_prefix_hex="03", pay_to_script_prefix_hex="7D",
bip32_prv_prefix_hex="0488ADE4", bip32_pub_prefix_hex="0488B21E",
magic_header_hex="F9BEB4D9", default_port=8555,
dns_bootstrap=[
"seed.bitcore.biz"
])
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] | 2.416216 | 185 |
from accounts.permissions import UserAccess
from rest_framework import mixins, status
from rest_framework.filters import SearchFilter
from rest_framework.response import Response
from rest_framework.viewsets import GenericViewSet
from ..models import FlaggedToken
from ..serializers import TokenSerializer
class TokenViewSet(mixins.ListModelMixin,
mixins.UpdateModelMixin,
mixins.DestroyModelMixin,
mixins.CreateModelMixin,
GenericViewSet):
"""
API endpoint that allows tokens to be manged by a user or staffer.
"""
serializer_class = TokenSerializer
filter_backends = [SearchFilter]
search_fields = ['user__username', 'write']
permission_classes = [UserAccess]
def get_queryset(self):
"""
Limit the queryset to the current user, except for staffers.
"""
user = self.request.user
if user.is_staff:
return FlaggedToken.objects.all()
return FlaggedToken.objects.filter(user_id=user.id)
def list(self, request, *args, **kwargs):
"""Get own tokens or all tokens if admin
"""
return Response(
self.serializer_class(
self.get_queryset(),
many=True,
read_only=True,
context={'request': request}).data)
def create(self, request):
"""Create a new authtoken for this user.
"""
serializer = self.serializer_class(data=request.data, context={'request': request})
if not serializer.is_valid():
return Response(status=status.HTTP_406_NOT_ACCEPTABLE)
data = serializer.validated_data
token = FlaggedToken.objects.create(user=request.user, write=data.get('write', False), description=data.get('description', ''))
return Response(self.serializer_class(
token,
context={'request': request}).data,
status=status.HTTP_201_CREATED)
def destroy(self, request, pk=None):
"""Delete a token
"""
try:
if request.user.is_staff:
FlaggedToken.objects.get(pk=pk).delete()
else:
FlaggedToken.objects.get(pk=pk, user=request.user).delete()
except FlaggedToken.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
return Response(status=status.HTTP_204_NO_CONTENT)
def update(self, request, pk=None):
"""Update Description oder Write Flag
"""
try:
if request.user.is_staff:
token = FlaggedToken.objects.get(pk=pk)
else:
token = FlaggedToken.objects.get(pk=pk, user=request.user)
serializer = self.serializer_class(data=request.data, context={'request': request})
if not serializer.is_valid():
return Response(status=status.HTTP_406_NOT_ACCEPTABLE)
data = serializer.validated_data
token.description = data['description']
token.write = data['write']
token.save()
except FlaggedToken.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
return Response(self.serializer_class(
token,
context={'request': request}).data,
status=status.HTTP_200_OK)
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] | 2.274681 | 1,489 |
"""
Copyright 2020 The OneFlow Authors. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from typing import Optional
import oneflow._oneflow_internal
from oneflow.compatible import single_client as flow
from oneflow.compatible.single_client.framework import id_util as id_util
from oneflow.compatible.single_client.framework import remote_blob as remote_blob_util
def categorical_ordinal_encode(
table: oneflow._oneflow_internal.BlobDesc,
size: oneflow._oneflow_internal.BlobDesc,
input_tensor: oneflow._oneflow_internal.BlobDesc,
hash_precomputed: bool = True,
name: Optional[str] = None,
) -> oneflow._oneflow_internal.BlobDesc:
"""This operator maintains a hash table to encode the categorical ordinal Blob. It converts a discrete input value into a continuous integer ID.
Args:
table (oneflow._oneflow_internal.BlobDesc): The hash table, you can assign it as a variable.
size (oneflow._oneflow_internal.BlobDesc): The size of hash table.
input_tensor (oneflow._oneflow_internal.BlobDesc): The input Blob.
hash_precomputed (bool, optional): We currently only support the 'True' mode. The internal hash value will no longer be computed. Defaults to True.
name (Optional[str], optional): The name for the operation. Defaults to None.
Returns:
oneflow._oneflow_internal.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow.compatible.single_client as flow
import numpy as np
import oneflow.compatible.single_client.typing as tp
@flow.global_function()
def categorical_ordinal_encode_Job(x: tp.Numpy.Placeholder((3, 3), dtype=flow.int32)
) -> tp.Numpy:
dtype = x.dtype
with flow.scope.namespace("categorical_ordinal_encode"):
table = flow.get_variable(
name="Table",
shape=(16,),
dtype=dtype,
initializer=flow.constant_initializer(0, dtype=dtype),
trainable=False,
reuse=False,
)
size = flow.get_variable(
name="Size",
shape=(1,),
dtype=dtype,
initializer=flow.constant_initializer(0, dtype=dtype),
trainable=False,
reuse=False,
)
return flow.categorical_ordinal_encode(
table=table, size=size, input_tensor=x, name="Encode",
)
x = np.array([[7, 0, 2],
[1, 7, 2],
[0, 1, 7]]).astype(np.int32)
out = categorical_ordinal_encode_Job(x)
# out [[1 0 2]
# [3 1 2]
# [0 3 1]]
"""
assert hash_precomputed is True
return (
flow.user_op_builder(name or id_util.UniqueStr("CategoricalOrdinalEncode_"))
.Op("CategoricalOrdinalEncode")
.Input("in", [input_tensor])
.Input("table", [table])
.Input("size", [size])
.Output("out")
.Attr("hash_precomputed", hash_precomputed)
.Build()
.InferAndTryRun()
.RemoteBlobList()[0]
)
def categorical_ordinal_encoder(
input_tensor: oneflow._oneflow_internal.BlobDesc,
capacity: int,
hash_precomputed: bool = True,
name: str = "CategoricalOrdinalEncoder",
) -> oneflow._oneflow_internal.BlobDesc:
"""This operator uses `oneflow.compatible.single_client.categorical_ordinal_encode` to encapsulate a categorical_ordinal_encoder. More details please refer to `oneflow.compatible.single_client.categorical_ordinal_encode`
Args:
input_tensor (oneflow._oneflow_internal.BlobDesc): The input Blob.
capacity (int): The capacity of hash table.
hash_precomputed (bool, optional): We currently only support the 'True' mode. The internal hash value will no longer be computed. Defaults to True.
name (str, optional): The name for the operation. Defaults to "CategoricalOrdinalEncoder".
Returns:
oneflow._oneflow_internal.BlobDesc: The result Blob.
For example:
.. code-block:: python
import oneflow.compatible.single_client as flow
import numpy as np
import oneflow.compatible.single_client.typing as tp
@flow.global_function()
def categorical_ordinal_encoder_Job(x: tp.Numpy.Placeholder((3, 3), dtype=flow.int32)
) -> tp.Numpy:
return flow.layers.categorical_ordinal_encoder(x, 16)
x = np.array([[7, 0, 2],
[1, 7, 2],
[0, 1, 7]]).astype(np.int32)
out = categorical_ordinal_encoder_Job(x)
# out [[1 0 2]
# [3 1 2]
# [0 3 1]]
"""
assert hash_precomputed is True
dtype = input_tensor.dtype
with flow.scope.namespace(name):
table = flow.get_variable(
name="Table",
shape=(capacity * 2,),
dtype=dtype,
initializer=flow.constant_initializer(0, dtype=dtype),
trainable=False,
reuse=False,
)
size = flow.get_variable(
name="Size",
shape=(1,),
dtype=dtype,
initializer=flow.constant_initializer(0, dtype=dtype),
trainable=False,
reuse=False,
)
return categorical_ordinal_encode(
table=table, size=size, input_tensor=input_tensor, name="Encode"
)
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] | 2.267342 | 2,667 |
import asyncio
from unittest import TestCase
import jsons
from jsons import InvalidDecorationError
from jsons.decorators import loaded, dumped
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"""
Airflow API (Stable)
Apache Airflow management API. # noqa: E501
The version of the OpenAPI document: 1.0.0
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
import sys
import unittest
import airflow_python_sdk
from airflow_python_sdk.model.class_reference import ClassReference
from airflow_python_sdk.model.color import Color
from airflow_python_sdk.model.task_extra_links import TaskExtraLinks
from airflow_python_sdk.model.time_delta import TimeDelta
from airflow_python_sdk.model.trigger_rule import TriggerRule
from airflow_python_sdk.model.weight_rule import WeightRule
globals()['ClassReference'] = ClassReference
globals()['Color'] = Color
globals()['TaskExtraLinks'] = TaskExtraLinks
globals()['TimeDelta'] = TimeDelta
globals()['TriggerRule'] = TriggerRule
globals()['WeightRule'] = WeightRule
from airflow_python_sdk.model.task import Task
class TestTask(unittest.TestCase):
"""Task unit test stubs"""
def testTask(self):
"""Test Task"""
# FIXME: construct object with mandatory attributes with example values
# model = Task() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
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] | 2.970516 | 407 |
import json
from sqlalchemy import Numeric
from sqlalchemy.sql.elements import BinaryExpression
from sqlalchemy_filtering.operators import FilterOperator, SQLDialect
from sqlalchemy_filtering.validators import FilterRequest, Filter, _get_numeric_types, SQLAlchemyField
def filter_apply(query, entity, obj: FilterRequest = None, dialect: SQLDialect = None):
"""
Construct filters on SQLAlchemy query
:param query: Query object of type :class:`sqlalchemy.orm.Query`.
:param entity: SQLAlchemy model class.
:param obj: :class:`FilterRequest` object.
:param dialect: :class:`SQLDialect` enum object
Example object
-- Simple request
obj = {
"filter": [
{
"field": "demographics",
"node": "age",
"operator": ">=",
"value": 20
},
{
"field": "demographics",
"node": "first_name",
"operator": "like",
"value": "%Test%"
}
],
"sort": [...]
}
--- Simple request with operators
obj = {
"filter": [
"and": [
{
"field": "demographics",
"node": "age",
"operator": ">=",
"value": 20
},
{
"field": "demographics",
"node": "first_name",
"operator": "like",
"value": "%Test%"
}
]
],
"sort": [...]
}
-- JSON request
obj =
"filter": [
{
"field": "details",
"node": "user_details",
"operator": "@>",
"valueType": "jsonb",
"value": "[{\"skill\":\"Fighting\",\"rating\":10}]"
}
],
"sort": [...]
}
:returns: Query object of type :class:`sqlalchemy.orm.Query` with applied filters.
"""
exps = []
if obj.filter is None:
return query
for key_operator in obj.filter.keys():
for f_obj in obj.filter[key_operator]:
node = f_obj.node
root_node = f_obj.field
field = f_obj.field
field_node = f_obj.field if node is None else node
values = f_obj.value
if type(values) is Filter:
new_values: Filter = values
tmp_new_node = new_values.node
new_values.node = node + '.' + new_values.field
if tmp_new_node is not None:
new_values.node = new_values.node + '.' + tmp_new_node
new_values.field = root_node
new_values.operator = new_values.operator.operator
query_obj = {"filter": [new_values.__dict__]}
query = filter_apply(query, entity, FilterRequest(query_obj), dialect=dialect)
continue
# Get model field
node_split = field_node.split('.')
if len(node_split) == 1 and type(values) is not dict:
if field == field_node:
stmt = SQLAlchemyField(entity, field).get_field()
else:
stmt = SQLAlchemyField(entity, field).get_field()[field_node]
else:
stmt = SQLAlchemyField(entity, field).get_field()
for n in field_node.split('.'):
stmt = stmt[n]
# Cast fields
stmt = _cast_statement(stmt, f_obj, dialect)
# Apply comparison operator
stmt = f_obj.operator.execute(left=stmt, right=values)
exps.append(stmt)
# Add filter to query object
query = query.filter(FilterOperator(key_operator).execute(*exps))
return query
def _cast_statement(statement, obj: Filter = None, dialect: SQLDialect = None):
"""
Cast statements to match database field types.
:param statement:
SQLAlchemy expression of types
`sqlalchemy.sql.elements.BinaryExpression` (used on simple queries)
or `sqlalchemy.orm.attributes.InstrumentedAttribute` (used on advanced JSON queries).
:param obj: :class:`Filter` object.
:param dialect: :class:`SQLDialect` enum object
:return: :class:`sqlalchemy.sql.elements.BinaryExpression` or `sqlalchemy.orm.attributes.InstrumentedAttribute`.
"""
values = obj.value
if isinstance(statement, BinaryExpression) and dialect == SQLDialect.POSTGRESQL:
value_type = type(values)
if value_type is list:
if len(values) != 0:
element = type(values[0])
statement = statement.cast(Numeric) if element in _get_numeric_types() else statement.astext
else:
return statement
elif value_type is str:
try:
json.loads(values)
return statement
except ValueError:
statement = statement.astext
elif value_type in _get_numeric_types():
statement = statement.cast(Numeric)
return statement
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7,
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220,
220,
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2643,
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] | 1.955451 | 2,761 |
from .bbox_head import BBoxHead
from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
Shared4Conv1FCBBoxHead)
from .double_bbox_head import DoubleConvFCBBoxHead
from .detr_head import DetrHead
__all__ = [
'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead',
'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead',
'DetrHead',
]
| [
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] | 2.142045 | 176 |
from pathlib import Path
from textblob import TextBlob
path = Path("src/text.txt")
with open(path) as f:
text = f.read()
blob = TextBlob(text)
for sentence in blob.sentences:
print(sentence.sentiment.polarity)
| [
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] | 2.635294 | 85 |
# coding: utf-8
__author__ = 'Alain Lichnewsky'
__license__ = 'MIT License'
__version__ = '1.0'
# (C) A.Lichnewsky, 2018, 2020
#
# My own library organization (TBD: clean up ?)
import sys
import traceback
sys.path.append("pylib")
from UnitTest import *
# Common toolkit imports
import numpy as NP
import numpy.random as RAND
import scipy.stats as STATS
from scipy import sparse
from scipy import linalg
# Using scikit-learn
import sklearn as SKL
from sklearn import linear_model, model_selection
from sklearn import ensemble, tree, discriminant_analysis, svm, naive_bayes
from sklearn import neighbors
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
# Using pandas
import pandas as PAN
# To plot pretty figures
import matplotlib as MPL
import matplotlib.pyplot as PLT
import seaborn as SNS
# Python programming
from itertools import cycle
import time as TIME
from time import time
from enum import Enum
from string import ascii_uppercase
import basicUtils as BU
from IPython.display import display
from basicDataCTE import dataModel
import basicDataCTE as BCTE
import basicUtils as BU
import lib.utilities as LIBU
#
# ----------------------------------------
# TEST FUNCTIONS
# ++++++++++++++++++++++++++++++++++++++++
#
#
#
# ----------------------------------------
# TEST of Dataframe normalization functions
# ++++++++++++++++++++++++++++++++++++++++
#
#
# ----------------------------------------
# TEST Frame
# ++++++++++++++++++++++++++++++++++++++++
#
class GraphicTest(ALTestFrameGraphics):
""" Here we perform test of Seaborn features and of functions derived from
them; many tests inspired from Seaborn manual
"""
def mkDF(addCat=None,**kwargs):
""" Make a dataframe of floats
"""
print(f'In mkDF arguments:{arguments}')
### pandas.DataFrame.apply: returns a <class 'pandas.core.series.Series'>
### w/o .info method etc
### Therefore applymap is used
GraphicTest.randseed=981 # make output deterministic
def myRandom():
"This is my deterministic random function, good enough for generating test"
GraphicTest.randseed = (GraphicTest.randseed+320)%1024
return float(GraphicTest.randseed)/512 - 1.0
od = {}
LIBU.setDefaults(od, optDict=kwargs, defaultDict={'nc' : 5, 'nl':8,
'ai':1,'brand':0.1 })
nc,nl,ai,brand = list(map(lambda x: od[x], ("nc","nl","ai","brand")))
print(f"parms = {nc,nl,ai,brand}")
print(od)
array = [ ai*i + brand * myRandom() for i in range(0,nc*nl)]
npA = NP.array(array).reshape((nl,nc))
df = PAN.DataFrame( npA,
index = [ f"row{i:03}" for i in range(1,nl+1)],
columns= [ f"col{i:03}" for i in range(1,nc+1)]
)
if addCat:
print("Miaou (Meow ! )")
if "modulo" in kwargs and kwargs["modulo"]:
imod= kwargs["modulo"]
df.loc[:,"catCol"] = [ f"Meow{(i%imod):03}" for i in range(1,nl+1)]
else:
df.loc[:,"catCol"] = [ f"Meow{i:03}" for i in range(1,nl+1)]
if "modulo" in kwargs and kwargs["modulo"]:
imod= kwargs["modulo"]
df.loc["catRow",:nc] = [ f"Miaou{(i%imod):03}" for i in range(1,nc+1)]
else:
df.loc["catRow",:nc] = [ f"Miaou{i:03}" for i in range(1,nc+1)]
df.iloc[-1,-1] = "MIA-MEOW"
return df
@unittest.expectedFailure
#
# ----------------------------------------
# LAUNCHING TESTS
# ++++++++++++++++++++++++++++++++++++++++
#
__cmdspecs__ = """"
testDataCTE : run tests under unittest environment
Usage: tesDataCTE [ <testcase> ] [ --wait=<wait> ] [ --parm=<parm>]
Options:
--parm=<parm> pass parameter
--wait=<wait> pass parameter
Here testcase is the optional testcase in the form of <class> or <class>.<method>
Please use the form --parm val and NOT --parm=val
"""
from docopt import docopt
if __name__ == '__main__':
# analyze command line args
arguments = docopt(__cmdspecs__)
ALTestFrameGraphics.processDocoptArgs(arguments)
# Now we need to remove docopt argv arguments which unittest.main() cannot handle
print ("Launching test with unittest package/framework")
r= unittest.main()
print ("RESULT=", r)
# ----------------------------------------
# Specializing tests
# ++++++++++++++++++++++++++++++++++++++++
#
# Use syntax: <python>|<script> <class>[.<method>]
# eg.
# python3 ../source/lib/testDataCTE.py GraphicTest.test_boxplot
#
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198
] | 2.112974 | 2,505 |
from funcs import *
from db import *
##
# SHOW
##
@app.route('/'+app.config['RNG_ID']+'/ips/list/all/', defaults={'pi_id': None})
@app.route('/'+app.config['RNG_ID']+'/ips/list/all/<pi_id>/')
@app.route('/'+app.config['RNG_ID']+'/ips/list/last/', defaults={'pi_id': None})
@app.route('/'+app.config['RNG_ID']+'/ips/list/last/<pi_id>/')
##
# LOG
##
@app.route('/'+app.config['RNG_ID']+'/ips/add/<pi_id>/', defaults={'int_ip': ''})
@app.route('/'+app.config['RNG_ID']+'/ips/add/<pi_id>/<int_ip>/')
#@app.route('/'+app.config['RNG_ID']+'/ips/add/<pi_id>/<pi_ip>/')
#def log_ip(pi_id, pi_ip):
#
# info = Ips(pi_id, request.remote_addr, pi_ip)
#
# db.session.add(info)
# db.session.commit()
#
# return jsonify({'pi_id': pi_id, 'external_ip': request.remote_addr, 'internal_ip': pi_ip, 'error': False}), 200
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] | 2.041363 | 411 |
import bisect
import logging
import sys
from datetime import datetime
from logging import handlers
from youtubewatched.config import MAX_TIME_DIFFERENCE
def logging_config(log_file_path: str,
file_level: int = logging.DEBUG,
console_out_level: int = logging.DEBUG,
console_err_level: int = logging.WARNING,
log_server_requests=True,
log_server_requests_to_file=False):
"""
Configures logging to file and to stdout/err
:param log_file_path: path to the log file
:param file_level: logging threshold for the file handler
:param console_out_level: logging threshold for the console std handler
:param console_err_level: logging threshold for the console err handler
:param log_server_requests: show/log werkzeug's logger messages
:param log_server_requests_to_file: show/log werkzeug's logger messages
:return:
"""
# stop non-app loggers
logging.basicConfig(level=file_level, handlers=[logging.NullHandler()])
log_format = logging.Formatter('%(asctime)s {%(name)s.%(funcName)s} '
'%(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
std_format = logging.Formatter('%(asctime)s {%(funcName)s} '
'%(levelname)s: %(message)s',
datefmt='%H:%M:%S')
file_handler = handlers.RotatingFileHandler(log_file_path, 'a',
(1024**2)*3, 5)
file_handler.setLevel(file_level)
file_handler.setFormatter(log_format)
console_out_handler = logging.StreamHandler(stream=sys.stdout)
console_out_handler.setLevel(console_out_level)
console_out_handler.setFormatter(std_format)
console_out_handler.addFilter(ConsoleOutFilter(logging.INFO))
console_err_handler = logging.StreamHandler(stream=sys.stderr)
console_err_handler.setLevel(console_err_level)
console_err_handler.setFormatter(std_format)
console_err_handler.addFilter(ConsoleOutFilter(logging.CRITICAL))
app_logger = logging.getLogger('youtubewatched')
app_logger.setLevel(file_level)
app_logger.handlers.pop() # remove the default stream handler
for handler in (file_handler, console_out_handler, console_err_handler):
app_logger.addHandler(handler)
if not log_server_requests:
logging.getLogger('werkzeug').disabled = True
if log_server_requests_to_file:
logging.getLogger('werkzeug').addHandler(file_handler)
return app_logger
def are_different_timestamps(ts1: datetime,
ts2: datetime) -> bool:
"""Since each archive could potentially have timestamps in a
different timezone, the same ones from different files could
show as multiple unique timestamps due to different day/hour
This function doesn't attempt to make timestamps accurate, and it may
block an extremely small number of legitimate ones from being
entered. Mostly, it will block the duplicates, however"""
if ts1.replace(day=1, hour=0) == ts2.replace(day=1, hour=0):
return False
return True
def remove_timestamps_from_one_list_from_another(filter_, filteree):
"""Useful for when Takeouts are added out of order and/or for
when stories show up as normal videos in newer Takeouts"""
for timestamp in filter_:
start = bisect.bisect_left(filteree,
timestamp - MAX_TIME_DIFFERENCE)
end = bisect.bisect_right(filteree,
timestamp + MAX_TIME_DIFFERENCE)
if start != end:
for potential_duplicate in range(start, end):
if not are_different_timestamps(timestamp,
filteree[potential_duplicate]):
filteree.pop(potential_duplicate)
break
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] | 2.318925 | 1,712 |
__author__ = 'Yin'
# Standard imports
import logging
from uuid import UUID
# Our imports
from emission.analysis.result.carbon import getModeCarbonFootprint, carbonFootprintForMode
from emission.core.common import Inside_polygon,berkeley_area,getConfirmationModeQuery
from emission.core.get_database import get_section_db,get_profile_db
import geojson as gj
import emission.analysis.plotting.geojson.geojson_feature_converter as gfc
import emission.core.wrapper.motionactivity as ecwm
import emission.storage.decorations.timeline as esdt
import emission.storage.decorations.local_date_queries as esdl
import emission.storage.decorations.location_queries as esdlq
import emission.core.wrapper.trip as ecwt
import emission.core.wrapper.section as ecws
import emission.storage.timeseries.geoquery as estg
import emission.storage.timeseries.timequery as estt
import emission.storage.timeseries.tcquery as esttc
import emission.storage.decorations.analysis_timeseries_queries as esda
import emission.net.usercache.abstract_usercache as enua
import emission.storage.timeseries.aggregate_timeseries as estag
import emission.storage.timeseries.cache_series as estc
MANUAL_INCIDENT_KEY = "manual/incident"
# Note that all the points here are returned in (lng, lat) format, which is the
# GeoJSON format.
def incident_heatmap(user_uuid, modes, time_query, region):
"""
Return a list of geojson points with properties for the time and the stress level
related to incidents. This should not return full entries because that can
expose the user_id in the aggregate case. Maybe it can return the data part only?
Or should we put the other entries into the properties?
:param modes: The modes that we want to query for
:param time_query: The time query, in either local date or timestamp
:param region: The region of interest
:return: list of `incident` objects, with all metadata stripped out
"""
if region is None:
geo_query = None
else:
geo_query = estg.GeoQuery(["data.loc"], region)
extra_query_list = []
if modes is not None:
mode_enum_list = [ecwm.MotionTypes[mode] for mode in modes]
extra_query_list.append(esdlq.get_mode_query(mode_enum_list))
if user_uuid is None:
incident_entry_list = esda.get_entries(MANUAL_INCIDENT_KEY, user_id=None,
time_query=time_query, geo_query=geo_query,
extra_query_list=extra_query_list)
else:
# We don't support aggregate queries on the usercache. And that is
# actually fine, because we don't expect immediate results for the
# aggregate case. We just want to query the usercache to ensure that
# the incidents don't magically disappear just because they got pushed
# to the server but are not yet processed
incident_entry_list = estc.find_entries([MANUAL_INCIDENT_KEY], time_query)
return {"incidents": [e.data for e in incident_entry_list]}
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] | 2.972441 | 1,016 |
import time
import json
import os
import unittest
from web3 import Web3
from uniswap.uniswap import UniswapV2Client, UniswapV2Utils
class UniswapV2ClientTest(BaseTest):
# FIXME add way to retrieve current liquidity balance for a par
"""def test_remove_liquidity(self):
tx = self.uniswap.remove_liquidity(
token_a=self.token_0["address"],
token_b=self.token_1["address"],
liquidity=100,
min_a=0,
min_b=0,
to=self.address,
deadline=int(time.time()) + 1000
)
receipt = self.uniswap.conn.eth.waitForTransactionReceipt(tx, timeout=2000)
self.assertIsNotNone(receipt)
self.assertTrue(receipt["status"])
def test_remove_liquidity_eth(self):
token = Web3.toChecksumAddress("0x20fe562d797a42dcb3399062ae9546cd06f63280")
liquidity = 1 * 10 ** 15
min_token = 1 * 10 ** 15
min_eth = 2 * 10 ** 13
deadline = int(time.time()) + 1000
tx = self.uniswap.remove_liquidity_eth(
token=self.token_2["address"],
liquidity=1,
min_token=0,
min_eth=0,
to=self.address,
deadline=int(time.time()) + 1000
)
receipt = self.uniswap.conn.eth.waitForTransactionReceipt(tx, timeout=2000)
self.assertIsNotNone(receipt)
self.assertTrue(receipt["status"])"""
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] | 2.056358 | 692 |
#!/usr/bin/python
#
# Script for checking global health of host running VMware ESX/ESXi
#
# Licence : GNU General Public Licence (GPL) http://www.gnu.org/
# Pre-req : pywbem
#
#@---------------------------------------------------
#@ History
#@---------------------------------------------------
#@ Date : 20080820
#@ Author : David Ligeret
#@ Reason : Initial release
#@---------------------------------------------------
#@ Date : 20080821
#@ Author : David Ligeret
#@ Reason : Add verbose mode
#@---------------------------------------------------
#@ Date :
#@ Author :
#@ Reason :
#@---------------------------------------------------
#
import sys
import time
import pywbem
NS = 'root/cimv2'
# define classes to check 'OperationStatus' instance
ClassesToCheck = [
'CIM_ComputerSystem',
'CIM_NumericSensor',
'CIM_Memory',
'CIM_Processor',
'CIM_RecordLog',
'OMC_DiscreteSensor',
'VMware_StorageExtent',
'VMware_Controller',
'VMware_StorageVolume',
'VMware_Battery',
'VMware_SASSATAPort'
]
# define exit codes
ExitOK = 0
ExitWarning = 1
ExitCritical = 2
ExitUnknown = 3
# check input arguments
if len(sys.argv) < 4:
sys.stderr.write('Usage : ' + sys.argv[0] + ' hostname user password\n')
sys.stderr.write('Example : ' + sys.argv[0] + ' https://myesxi:5989 root password\n')
sys.exit(1)
verbose = 0
if len(sys.argv) == 5 :
if sys.argv[4] == "verbose" :
verbose = 1
# connection to host
verboseoutput("Connection to "+sys.argv[1], verbose)
wbemclient = pywbem.WBEMConnection(sys.argv[1], (sys.argv[2], sys.argv[3]), NS, no_verification=True)
# run the check for each defined class
GlobalStatus = ExitOK
ExitMsg = ""
for classe in ClassesToCheck :
verboseoutput("Check classe "+classe, verbose)
instance_list = wbemclient.EnumerateInstances(classe)
for instance in instance_list :
elementName = instance['ElementName']
verboseoutput("Element Name = "+elementName, verbose)
if instance['OperationalStatus'] is not None :
elementStatus = instance['OperationalStatus'][0]
verboseoutput("Element Op Status = %d" % elementStatus, verbose)
interpretStatus = {
0 : ExitOK, # Unknown
1 : ExitCritical, # Other
2 : ExitOK, # OK
3 : ExitWarning, # Degraded
4 : ExitWarning, # Stressed
5 : ExitWarning, # Predictive Failure
6 : ExitCritical, # Error
7 : ExitCritical, # Non-Recoverable Error
8 : ExitWarning, # Starting
9 : ExitWarning, # Stopping
10 : ExitCritical, # Stopped
11 : ExitOK, # In Service
12 : ExitWarning, # No Contact
13 : ExitCritical, # Lost Communication
14 : ExitCritical, # Aborted
15 : ExitOK, # Dormant
16 : ExitCritical, # Supporting Entity in Error
17 : ExitOK, # Completed
18 : ExitOK, # Power Mode
19 : ExitOK, # DMTF Reserved
20 : ExitOK # Vendor Reserved
}[elementStatus]
if (interpretStatus == ExitCritical) :
verboseoutput("GLobal exit set to CRITICAL", verbose)
GlobalStatus = ExitCritical
ExitMsg += "CRITICAL : %s<br>" % elementName
if (interpretStatus == ExitWarning and GlobalStatus != ExitCritical) :
verboseoutput("GLobal exit set to WARNING", verbose)
GlobalStatus = ExitWarning
ExitMsg += "WARNING : %s<br>" % elementName
if GlobalStatus == 0 :
print "OK"
else :
print ExitMsg
sys.exit (GlobalStatus)
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37023,
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] | 2.791421 | 1,189 |
import datetime
import unittest
from .context import date_utilities as d_utils
###########################################################################
# Unit tests for get_datestamp()
###########################################################################
# Test names are of the format:
# test_<func>_<in_fmt>_<out_fmt>_<date>
#
# Where:
# <func> Function name being tested.
# (get_datestamp)
# <in_fmt> Date element separator format for input.
# (s=slash, d=dashed, c=contiguous, x=default)
# <out_fmt> Date element separator format for output.
# (s=slash, d=dashed, c=contiguous, x=default)
# <date> Expected return boolean value.
# (y=y2k, x=default)
###########################################################################
# Unit tests for is_date()
###########################################################################
# Test names are of the format:
# test_<func>_<standard>_<format>_<return>_{<bound>}
#
# Where:
# <func> Function name being tested.
# (is_date)
# <standard> Date standard tested.
# (i=ISO 8601, n=Non-ISO)
# <format> Date element separator format.
# (s=slash, d=dashed, c=contiguous)
# <return> Expected return boolean value.
# (t=True, f=False)
# <bound> (optional) For False returns, what offending date bound is
# being tested.
# (u=upper, l=lower)
###########################################################################
# Non-ISO slash format is_date() tests
###########################################################################
###########################################################################
# Non-ISO dashed format is_date() tests
###########################################################################
###########################################################################
# ISO 8601 dashed format is_date() tests
###########################################################################
###########################################################################
# ISO 8601 dashed format is_date() tests
###########################################################################
###########################################################################
# ISO-8601 contiguous format is_date() tests
###########################################################################
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] | 3.048698 | 883 |
from django.template.loader import render_to_string
from djaveLogin.widgets.email_base import EmailBase
from djaveLogin.models import new_sign_up_url
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] | 3.377778 | 45 |
import pygame
# The play scene function
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198
] | 3.727273 | 11 |
from django.urls import path
from . import views
urlpatterns = [
path("scripts/", views.GetAddScripts.as_view()),
path("<int:pk>/script/", views.GetUpdateDeleteScript.as_view()),
path("<int:pk>/download/", views.download),
]
| [
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] | 2.586957 | 92 |
# -*- coding: utf-8 -*-
import json
import os.path
import random
import re
from flask import Flask, send_from_directory
from flask import request, abort
from flaskrun.flaskrun import flask_run
import datab.social_database as db
app = Flask(__name__)
# Regular expression to only accept certain files
fileChecker = re.compile(r"(.*\.js|.*\.html|.*\.png|.*\.css|.*\.map)$")
numberOfAnswers = 4
random.seed(7)
@app.route('/')
@app.route('/<path:filename>')
@app.route('/register')
@app.route('/join_room')
@app.route('/answered_room')
@app.route('/get_user_id')
@app.route('/create_room')
@app.route('/get_rooms')
@app.route('/fill_room', methods=['POST'])
@app.route('/open_room')
@app.route('/close_room')
@app.route('/finish_room')
@app.route('/room_status')
@app.route('/get_room_questions')
@app.route('/post_room_answers', methods=['POST'])
@app.route('/get_quiz_question')
@app.route('/post_quiz_answer')
if __name__ == '__main__':
flask_run(app)
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198
] | 2.545918 | 392 |
#!/usr/bin/env python
# encoding: utf-8
# Taken from legacy python unittest
class WritelnDecorator:
"""Used to decorate file-like objects with a handy 'writeln' method"""
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# -*- coding: utf-8 -*-
"""
Functions to capture oscilloscope data. The curves are saved into a single
file within the 'ScopeData' directory.
Version 1.0 (2018-10-11)
Daan Wielens - PhD at ICE/QTM
University of Twente
[email protected]
"""
import visa
import numpy as np
from struct import unpack
import matplotlib.pyplot as plt
import time
import os
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# top.py - top words
'''
input: txt files from ./books folder
output: csv files into the ./data folder
This is a program that will find the top 100 words from a book discluding the stop words. These 100 top words will
be stored in a csv file in the ./data folder for later use in the set.py program.
'''
# https://towardsdatascience.com/very-simple-python-script-for-extracting-most-common-words-from-a-story-1e3570d0b9d0
import collections
import os
from os import listdir
from os.path import isfile, join
import pandas as pd
import matplotlib.pyplot as plt
import csv
# Read input file, note the encoding is specified here
# It may be different in your text file
##### CHANGE THE FILE = OPEN() LINE #####
bookPath = 'books/'
fileNames = [f for f in listdir(bookPath) if isfile(join(bookPath, f))]
fileNames = [os.path.splitext(x)[0] for x in fileNames]
for file in fileNames:
createVector(file)
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] | 3.105802 | 293 |
import numpy as np
import geopandas as geo
import pandas as pd
from skimage.segmentation import find_boundaries
from shapely.geometry import Polygon
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import connected_components
from merfishdecoder.core import zplane
from merfishdecoder.core import dataset
from cellpose import utils
from cellpose import models
def extract_polygon_per_index(
img, idx):
"""Extract features from a segmented image.
Parameters
----------
img : np.array
Segmented image.
idx : int
Index of the feature.
Returns
-------
A Polygon object.
"""
from functools import reduce
import operator
import math
(y, x) = np.where(
find_boundaries(
img == idx, mode='inner'))
points = np.array([x, y]).T
if points.shape[0] == 0:
return None
else:
hull = None
if (points[:,0].max() - points[:,0].min() > 0) & \
(points[:,1].max() - points[:,1].min() > 0):
coords = [[x, y] for (x, y) in points]
center = tuple(map(operator.truediv, reduce(lambda x, y: map(operator.add, x, y), coords), [len(coords)] * 2))
pointsOrdered = sorted(coords, key=lambda coord: (-135 - math.degrees(math.atan2(*tuple(map(operator.sub, coord, center))[::-1]))) % 360)
hull = Polygon(pointsOrdered)
return hull
def run_cell_pose(
gpu = False,
modelType = "nuclei",
images: list = None,
diameter: int = 150,
channels: list = None,
do_3D: bool = False
) -> np.ndarray:
"""Run cell pose for cell segmentation
Parameters
----------
gpu : bool
A boolen variable indicates whether to use GPU
model_type : str
Type of segmentation (nuclei or cyto)
images : np.ndarray
Input image stack for segmentation.
diameter : int
Average diameter for features
channels : list
list of channels, either of length 2 or of length number of images by 2.
First element of list is the channel to segment (0=grayscale, 1=red, 2=blue, 3=green).
Second element of list is the optional nuclear channel (0=none, 1=red, 2=blue, 3=green).
For instance, to segment grayscale images, input [0,0]. To segment images with cells
in green and nuclei in blue, input [2,3]. To segment one grayscale image and one
image with cells in green and nuclei in blue, input [[0,0], [2,3]].
do_3D: bool
set to True to run 3D segmentation on 4D image input
Returns
-------
masks: list of 2D arrays, or single 3D array (if do_3D=True)
labelled image, where 0=no masks; 1,2,...=mask labels
"""
model = models.Cellpose(
gpu = gpu,
model_type = modelType)
masks, flows, styles, diams = \
model.eval(
images,
diameter = diameter,
do_3D = do_3D,
channels= [[0, 0]] * len(images))
return masks
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] | 2.426164 | 1,246 |
"""
Implementation of bot that automates browsing
for watch live broadcasts of tvs.
"""
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.action_chains import ActionChains
TV_NONE = None
TV_FOX = 1
TV_STAR = 2
TV_KANALD = 3
TV_SHOW = 4
TV_TRT = 5
TV_ATV = 6
TV_TV2 = 7
class TVBot(object):
"""
Handles automated operations
"""
def open(self, tv_id):
"""
Opens specified tv channel to watch.
Available TV channels are listed below:
TV_FOX = 1
TV_STAR = 2
TV_KANALD = 3
TV_SHOW = 4
TV_TRT = 5
TV_ATV = 6
TV_TV2 = 7
"""
if self.closed:
self.driver = webdriver.Chrome()
self.closed = False
self.driver.maximize_window()
try:
if tv_id == TV_FOX:
self.driver.get("http://www.fox.com.tr/canli-yayin")
self.current_tv = TV_FOX
self.driver.find_element_by_css_selector("button..vjs-fullscreen-control.vjs-control.vjs-button").click()
elif tv_id == TV_STAR:
self.driver.get("https://www.youtube.com/watch?v=jWP3ntl64I4")
self.current_tv = TV_STAR
self.driver.find_element_by_css_selector("button.ytp-fullscreen-button.ytp-button").click()
elif tv_id == TV_KANALD:
self.driver.get("https://www.kanald.com.tr/canli-yayin")
self.current_tv = TV_KANALD
self.driver.find_element_by_css_selector("button.vjs-fullscreen-control.vjs-control.vjs-button").click()
elif tv_id == TV_SHOW:
self.driver.get("http://www.showtv.com.tr/canli-yayin")
self.current_tv = TV_SHOW
self.driver.find_element_by_css_selector("button.vjs-fullscreen-control.vjs-control.vjs-button").click()
elif tv_id == TV_TRT:
self.driver.get("http://www.trt.net.tr/anasayfa/canli.aspx?y=tv&k=trt1")
self.current_tv = TV_TRT
self.driver.find_element_by_css_selector('#trtnettrjwplayer').click()
self.driver.find_element_by_css_selector(".jw-icon.jw-icon-inline.jw-button-color.jw-reset.jw-icon-fullscreen").click()
elif tv_id == TV_ATV:
self.driver.get("http://www.atv.com.tr/webtv/canli-yayin")
self.current_tv = TV_ATV
self.driver.find_element_by_css_selector('div.player').click()
self.driver.find_element_by_css_selector(".jw-icon.jw-icon-inline.jw-button-color.jw-reset.jw-icon-fullscreen").click()
self.driver.find_element_by_css_selector('div.player').click()
elif tv_id == TV_TV2:
self.driver.get("http://www.teve2.com.tr/canli-yayin")
self.current_tv = TV_TV2
self.driver.find_element_by_css_selector('#player-container').click()
self.driver.find_element_by_css_selector('button.vjs-fullscreen-control.vjs-control.vjs-button').click()
except:
pass
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] | 1.917759 | 1,678 |
import numpy as np
import torch
def do_mixup(x, mixup_lambda):
"""Mixup x of even indexes (0, 2, 4, ...) with x of odd indexes
(1, 3, 5, ...).
Args:
x: (batch_size * 2, ...)
mixup_lambda: (batch_size * 2,)
Returns:
out: (batch_size, ...)
"""
out = (x[0 :: 2].transpose(0, -1) * mixup_lambda[0 :: 2] + \
x[1 :: 2].transpose(0, -1) * mixup_lambda[1 :: 2]).transpose(0, -1)
return out
def forward(model, generator, return_input=False,
return_target=False):
"""Forward data to a model.
Args:
model: object
generator: object
return_input: bool
return_target: bool
Returns:
audio_name: (audios_num,)
clipwise_output: (audios_num, classes_num)
(ifexist) segmentwise_output: (audios_num, segments_num, classes_num)
(ifexist) framewise_output: (audios_num, frames_num, classes_num)
(optional) return_input: (audios_num, segment_samples)
(optional) return_target: (audios_num, classes_num)
"""
output_dict = {}
device = next(model.parameters()).device
# Forward data to a model in mini-batches
for n, batch_data_dict in enumerate(generator):
print(n)
batch_waveform = move_data_to_device(batch_data_dict['waveform'], device)
with torch.no_grad():
model.eval()
batch_output = model(batch_waveform)
append_to_dict(output_dict, 'audio_name', batch_data_dict['audio_name'])
append_to_dict(output_dict, 'clipwise_output',
batch_output['clipwise_output'].data.cpu().numpy())
if return_input:
append_to_dict(output_dict, 'waveform', batch_data_dict['waveform'])
if return_target:
if 'target' in batch_data_dict.keys():
append_to_dict(output_dict, 'target', batch_data_dict['target'])
for key in output_dict.keys():
output_dict[key] = np.concatenate(output_dict[key], axis=0)
return output_dict
def interpolate(x, ratio):
"""Interpolate data in time domain. This is used to compensate the
resolution reduction in downsampling of a CNN.
Args:
x: (batch_size, time_steps, classes_num)
ratio: int, ratio to interpolate
Returns:
upsampled: (batch_size, time_steps * ratio, classes_num)
"""
(batch_size, time_steps, classes_num) = x.shape
upsampled = x[:, :, None, :].repeat(1, 1, ratio, 1)
upsampled = upsampled.reshape(batch_size, time_steps * ratio, classes_num)
return upsampled
def pad_framewise_output(framewise_output, frames_num):
"""Pad framewise_output to the same length as input frames. The pad value
is the same as the value of the last frame.
Args:
framewise_output: (batch_size, frames_num, classes_num)
frames_num: int, number of frames to pad
Outputs:
output: (batch_size, frames_num, classes_num)
"""
pad = framewise_output[:, -1 :, :].repeat(1, frames_num - framewise_output.shape[1], 1)
"""tensor for padding"""
output = torch.cat((framewise_output, pad), dim=1)
"""(batch_size, frames_num, classes_num)"""
return output | [
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] | 2.398324 | 1,313 |
import copy
import os
import time
import threading
import typing
import queue
from cv2 import cv2
from genicam.gentl import TimeoutException
from harvesters.core import Harvester
import numpy as np
from .._file_utils import create_output_dir
from .._image_utils import RGB8Image
from .._s3_utils import s3_upload_files, s3_bucket_exists
from .._settings import (
DEFAULT_LOCAL_DATA_DIR,
DEFAULT_S3_DATA_DIR,
DEFAULT_GENTL_PRODUCER_PATH,
IMAGE_DIR_NAME,
IMAGE_FILE_TYPE,
NETWORKS,
)
WINDOW_NAME = "Capture"
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"--gentl_producer_path",
type=str,
default=DEFAULT_GENTL_PRODUCER_PATH,
help="Path to the GenTL producer .cti file to use",
)
parser.add_argument("--s3_bucket_name", type=str)
parser.add_argument(
"--s3_data_dir",
type=str,
default=DEFAULT_S3_DATA_DIR,
help="Prefix of the s3 data objects",
)
parser.add_argument(
"--local_data_dir", type=str, default=DEFAULT_LOCAL_DATA_DIR,
)
parser.add_argument(
"--frame_rate", type=float, default=30.0,
)
parser.add_argument(
"--display_width", type=int, default=1080,
)
args = parser.parse_args()
main(args)
| [
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220,
220,
220,
220,
366,
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62,
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28,
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11,
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28,
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13,
15,
11,
198,
220,
220,
220,
1267,
198,
220,
220,
220,
30751,
13,
2860,
62,
49140,
7,
198,
220,
220,
220,
220,
220,
220,
220,
366,
438,
13812,
62,
10394,
1600,
2099,
28,
600,
11,
4277,
28,
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11,
198,
220,
220,
220,
1267,
628,
220,
220,
220,
26498,
796,
30751,
13,
29572,
62,
22046,
3419,
628,
220,
220,
220,
1388,
7,
22046,
8,
628
] | 2.320069 | 578 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
| [
2,
532,
9,
12,
19617,
25,
3384,
69,
12,
23,
532,
9,
12,
198,
6738,
11593,
37443,
834,
1330,
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62,
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42625,
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13,
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1330,
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11,
15720,
602,
628
] | 2.891892 | 37 |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2020.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
Legacy Operators (:mod:`qiskit.opflow.legacy`)
======================================================
.. currentmodule:: qiskit.opflow.legacy
These are the Operators provided by Aqua up until the 0.6 release. These are being replaced
by the operator flow function and we encourage you to use this.
Note:
At some future time this legacy operator logic will be deprecated and removed.
Legacy Operators
================
.. autosummary::
:toctree: ../stubs/
:nosignatures:
LegacyBaseOperator
WeightedPauliOperator
TPBGroupedWeightedPauliOperator
MatrixOperator
Legacy Operator support
=======================
.. autosummary::
:toctree: ../stubs/
:nosignatures:
evolution_instruction
suzuki_expansion_slice_pauli_list
pauli_measurement
measure_pauli_z
covariance
row_echelon_F2
kernel_F2
commutator
check_commutativity
PauliGraph
Z2Symmetries
"""
from .common import (evolution_instruction, suzuki_expansion_slice_pauli_list, pauli_measurement,
measure_pauli_z, covariance, row_echelon_F2,
kernel_F2, commutator, check_commutativity)
from .base_operator import LegacyBaseOperator
from .weighted_pauli_operator import WeightedPauliOperator, Z2Symmetries
from .matrix_operator import MatrixOperator
from .tpb_grouped_weighted_pauli_operator import TPBGroupedWeightedPauliOperator
from .pauli_graph import PauliGraph
__all__ = [
'evolution_instruction',
'suzuki_expansion_slice_pauli_list',
'pauli_measurement',
'measure_pauli_z',
'covariance',
'row_echelon_F2',
'kernel_F2',
'commutator',
'check_commutativity',
'PauliGraph',
'LegacyBaseOperator',
'WeightedPauliOperator',
'Z2Symmetries',
'TPBGroupedWeightedPauliOperator',
'MatrixOperator'
]
| [
2,
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62,
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220,
220,
220,
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9503,
315,
1352,
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198,
220,
220,
220,
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9122,
62,
9503,
315,
22055,
3256,
198,
220,
220,
220,
705,
12041,
72,
37065,
3256,
198,
220,
220,
220,
705,
11484,
1590,
14881,
18843,
1352,
3256,
198,
220,
220,
220,
705,
25844,
276,
12041,
72,
18843,
1352,
3256,
198,
220,
220,
220,
705,
57,
17,
13940,
3020,
316,
1678,
3256,
198,
220,
220,
220,
705,
7250,
33,
13247,
276,
25844,
276,
12041,
72,
18843,
1352,
3256,
198,
220,
220,
220,
705,
46912,
18843,
1352,
6,
198,
60,
198
] | 2.752086 | 839 |
# coding:utf-8
from __future__ import absolute_import, unicode_literals
from sanic import Sanic
from sanic.response import html, json
import os
from .api import bp
from sanic_cors import CORS
from sanic_auth import Auth, User
__author__ = "golden"
__date__ = '2018/6/1'
| [
2,
19617,
25,
40477,
12,
23,
198,
6738,
11593,
37443,
834,
1330,
4112,
62,
11748,
11,
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1098,
62,
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834,
4475,
834,
796,
705,
7908,
14,
21,
14,
16,
6,
628
] | 2.989011 | 91 |
from flask import Blueprint
bp = Blueprint("main", __name__)
from napalm_inspector.main import routes # noqa
| [
6738,
42903,
1330,
39932,
198,
198,
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796,
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11593,
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273,
13,
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220,
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198
] | 3.294118 | 34 |
peso = float(input('Qual é o seu peso: (KG) '))
altura = float(input('Qual é a sua altura: (m)'))
imc = peso / (altura ** 2)
if imc < 18.5:
print('Abaixo do peso.')
elif imc < 25:
print('Peso Ideal.')
elif imc < 30:
print('Sobrepeso')
elif imc < 40:
print('obesidade')
else:
print('Obesidade mórbida.') | [
12272,
78,
796,
12178,
7,
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78,
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198,
417,
361,
545,
66,
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25,
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220,
220,
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3601,
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672,
274,
312,
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3601,
10786,
5944,
274,
312,
671,
285,
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26145,
3755,
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8
] | 2.057325 | 157 |
from smt.surrogate_models import IDW
from .smt_model import SMTModel
class IDWModel(SMTModel):
'''Inverse distance weighting model, implemented by SMT.'''
@staticmethod
@staticmethod
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] | 2.871429 | 70 |
import os
import os.path as ospx
import torch
split = "1"
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
RESTORE_FROM_WHERE = "pretrained"
EMBEDDING = "all"
lambdaa = 0.2
#USE_CPU = True
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
BATCH_SIZE = 9
NUM_WORKERS = 3
ITER_SIZE = 1
IGNORE_LABEL = 255 # the background
INPUT_SIZE = "512,512"
LEARNING_RATE = 1e-4
MOMENTUM = 0.9
NUM_CLASSES = 21
NUM_EPOCHS = 50
POWER = 0.9
RANDOM_SEED = 1234
SAVE_NUM_IMAGES = 2
SAVE_PRED_EVERY = 500
WEIGHT_DECAY = 0.0005
LOG_DIR = "./log"
weak_size = BATCH_SIZE
weak_proportion = 0.2
DATA_PATH = "dataset/"
PRETRAINED_OUR_PATH = "model/segmentation/pretrained/our_qfsl_confidence"
SNAPSHOT_PATH = "model/segmentation/snapshots/vgg/lambda_split_single_1"
PATH = "output/"
DATA_VOC = DATA_PATH + "voc2012/"
DATA_SEM = DATA_PATH # Semantic embeddings path
SNAPSHOT_DIR = PATH + SNAPSHOT_PATH + "/" + EMBEDDING
RESULT_DIR = PATH + SNAPSHOT_PATH + "/" + "result.txt"
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name = "pyupload"
from .main import pyuploader | [
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import random
import os
import argparse
from cv2 import cv2
from model import Classifier
from matplotlib import pyplot as plt
def parse_arguments():
"""
Object for parsing command line strings into Python objects.
"""
arg = argparse.ArgumentParser()
arg.add_argument('--source', '-s', type=str, default='data/EuroSAT/2750',
help="give main source directory")
arg.add_argument('--device', '-d', default='cuda',
type=str, choices=['cuda', 'cpu'])
arg.add_argument('--model_path', '-m', type=str, default='saved_models/model_best.pth',
help="give saved model path")
arg.add_argument('--display', action='store_true')
arg.add_argument('--colab', action='store_true')
arg.add_argument('--save_path', '-sa', type=str,
default='predict_results/')
return vars(arg.parse_args())
def display(img, gt, pred, is_colab, save_path):
"""
Display the image and the prediction
"""
if gt == pred:
text = f"Correct. Pred: {pred}"
else:
text = f"Incorrect. GT: {gt}, Pred: {pred}"
if is_colab:
plt.imshow(img)
plt.title(text)
plt.savefig(f'{save_path}/{gt}.png')
else:
cv2.imshow(f'{text}', img)
cv2.waitKey(0)
if __name__ == "__main__":
kwargs = parse_arguments()
device = kwargs.pop('device')
source = kwargs.pop('source')
model_path = kwargs.pop('model_path')
is_display = kwargs.pop('display')
is_colab = kwargs.pop('colab')
save_path = kwargs.pop('save_path')
random.seed(42)
if not os.path.exists(save_path):
os.makedirs(save_path, exist_ok=True)
model = Classifier()
model = model.from_pretrained(model_path).to(device)
category_list = os.listdir(source)
for category in category_list:
category_path = os.path.join(source, category)
category_img_list = os.listdir(category_path)
random_selected = random.choice(category_img_list)
img = cv2.imread(os.path.join(
category_path, random_selected))
result = model.predict(img)
max_proba_result = max(result[0], key=result[0].get)
print("--"*20)
print(f"Ground truth: {category}")
print(f"Predicted: {max_proba_result}")
print(
f"Result: {'Correct' if category == max_proba_result else 'Incorrect'}")
if is_display:
display(img, category, max_proba_result, is_colab, save_path)
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] | 2.25 | 1,120 |
"""
Provides a class for torch model evaluation.
"""
from __future__ import absolute_import
import warnings
import torch
from .utils import common
from pdb import set_trace as st
class PyTorchModel:
""" Class for torch model evaluation.
Provide predict, intermediate_layer_outputs and adversarial_attack
methods for model evaluation. Set callback functions for each method
to process the results.
Parameters
----------
model : instance of torch.nn.Module
torch model to evaluate.
Notes
----------
All operations will be done using GPU if the environment is available
and set properly.
"""
def predict(self, dataset, callbacks, batch_size=16):
"""Predict with the model.
The method will use the model to do prediction batch by batch. For
every batch, callback functions will be invoked. Labels and predictions
will be passed to the callback functions to do further process.
Parameters
----------
dataset : instance of torch.utils.data.Dataset
Dataset from which to load the data.
callbacks : list of functions
Callback functions, each of which will be invoked when a batch is done.
batch_size : integer
Batch size for prediction
See Also
--------
:class:`metrics.accuracy.Accuracy`
"""
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
with torch.no_grad():
for data, labels in dataloader:
data = data.to(self._device)
labels = labels.to(self._device)
y_mini_batch_pred = self._model(data)
for callback in callbacks:
callback(labels, y_mini_batch_pred)
# def intermediate_layer_outputs(self, dataset, callbacks, batch_size=8):
# """Get the intermediate layer outputs of the model.
# The method will use the model to do prediction batch by batch. For
# every batch, the the intermediate layer outputs will be captured and
# callback functions will be invoked. all intermediate layer output
# will be passed to the callback functions to do further process.
# Parameters
# ----------
# dataset : instance of torch.utils.data.Dataset
# Dataset from which to load the data.
# callbacks : list of functions
# Callback functions, each of which will be invoked when a batch is done.
# batch_size : integer
# Batch size for getting intermediate layer outputs.
# See Also
# --------
# :class:`metrics.neuron_coverage.NeuronCoverage`
# """
# dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size)
# y_mini_batch_outputs = []
# hook_handles = []
# intermediate_layers = self._intermediate_layers(self._model)
# for intermediate_layer in intermediate_layers:
# def hook(module, input, output):
# y_mini_batch_outputs.append(output)
# handle = intermediate_layer.register_forward_hook(hook)
# hook_handles.append(handle)
# with torch.no_grad():
# for data in dataloader:
# if isinstance(data, list):
# data = data[0]
# y_mini_batch_outputs.clear()
# data = data.to(self._device)
# self._model(data)
# for callback in callbacks:
# callback(y_mini_batch_outputs, 0)
# for handle in hook_handles:
# handle.remove()
def _intermediate_layers(self, module, pre_name=""):
"""Get the intermediate layers of the model.
The method will get some intermediate layers of the model which might
be useful for neuron coverage computation. Some layers such as dropout
layers are excluded empirically.
Returns
-------
list of torch.nn.modules
Intermediate layers of the model.
"""
intermediate_layers = []
for name, submodule in module.named_children():
full_name = f"{pre_name}.{name}"
if len(submodule._modules) > 0:
intermediate_layers += self._intermediate_layers(submodule, full_name)
else:
# if 'Dropout' in str(submodule.type) or 'BatchNorm' in str(submodule.type) or 'ReLU' in str(submodule.type):
if 'Dropout' in str(submodule.type) or 'ReLU' in str(submodule.type) or 'Linear' in str(submodule.type) or 'Pool' in str(submodule.type):
continue
if self.intermedia_mode == "layer":
if type(self._model).__name__ == "ResNet":
if not full_name[-5:] == "1.bn2":
continue
else:
...
intermediate_layers.append(submodule)
# print(full_name, )
return intermediate_layers
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1441,
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] | 2.34663 | 2,181 |
from django.conf.urls import patterns, url
from views import index_view, search, purchase, get_balance
urlpatterns = patterns('',
url(r'^$', index_view, name='index'),
url(r'^search/(?P<sobject>[\w\-]+)/(?P<name>[\w\- ]+)$', search),
url(r'^purchase/(?P<sobject>[\w\-]+)$', purchase),
url(r'^getBalance$', get_balance),
) | [
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] | 2.421429 | 140 |
import numpy as np
from matplotlib import pyplot
import h5py
import healpy as hp
import sys
from tqdm import tqdm
from comancpipeline.Tools import Coordinates
from matplotlib.transforms import ScaledTranslation
from scipy.signal import fftconvolve
def MAD(d,axis=0):
"""
Return Median Absolute Deviation for array along one axis
"""
med_d = np.nanmedian(d,axis=axis)
rms = np.sqrt(np.nanmedian((d-med_d)**2,axis=axis))*1.48
return rms
def AutoRMS(tod):
"""
Auto-differenced RMS
"""
if len(tod.shape) == 2:
N = (tod.shape[0]//2)*2
diff = tod[1:N:2,:] - tod[:N:2,:]
rms = np.nanstd(diff,axis=0)/np.sqrt(2)
else:
N = (tod.size//2)*2
diff = tod[1:N:2] - tod[:N:2]
rms = np.nanstd(diff)/np.sqrt(2)
return rms
def TsysRMS(tod,sample_rate,bandwidth):
"""
Calculate Tsys from the RMS
"""
rms = AutoRMS(tod)
Tsys = rms*np.sqrt(bandwidth/sample_rate)
return Tsys
def weighted_mean(x,e):
"""
calculate the weighted mean
"""
return np.sum(x/e**2)/np.sum(1./e**2)
def weighted_var(x,e):
"""
calculate weighted variance
"""
m = weighted_mean(x,e)
v = np.sum((x-m)**2/e**2)/np.sum(1./e**2)
return v
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7,
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68,
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220,
220,
220,
37227,
198,
220,
220,
220,
15284,
26356,
24198,
198,
220,
220,
220,
37227,
628,
220,
220,
220,
285,
796,
26356,
62,
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7,
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11,
68,
8,
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220,
220,
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410,
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8,
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19571,
68,
1174,
17,
8,
198,
220,
220,
220,
1441,
410,
198
] | 2.091514 | 601 |
from datetime import datetime
from itemloaders.processors import Compose, MapCompose, TakeFirst
from scrapy.loader import ItemLoader
| [
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88,
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628
] | 3.805556 | 36 |
import numpy as np
from scipy.sparse import csr_matrix, lil_matrix
| [
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] | 2.68 | 25 |
from ignite.contrib.metrics.average_precision import AveragePrecision
from ignite.contrib.metrics.roc_auc import ROC_AUC
import ignite.contrib.metrics.regression
| [
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] | 3.306122 | 49 |
from functools import wraps
from typing import List
from flask_jwt_extended import jwt_required, get_jwt_identity
from cusg.db.schema import User
from cusg.utils.http import ForbiddenError
from cusg.utils.managers import UserManager
from cusg.repository.repos import UserRepository, UserGroupRepository
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70,
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13264,
628,
628
] | 3.242105 | 95 |
import logging
import os
import shutil
import numpy as np
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import csv, json
import numpy as np
from pprint import pprint
import difflib
import numeral
from scipy.stats.kde import gaussian_kde
from tqdm import tqdm
import Levenshtein
import itertools
NUMBERS = "0123456789"
DELIMITERS = "~!@#$%^*()_+`-={}|[]:<>?;',/'\\" + '"'
ROMAN = "IVXL"
CATEGORY_RATIO = 0.3
CATEGORY_NUMBER = 5
FREQUENCY_RATIO = 0.1
FREQUENCY_NUMBER = 1
DISTANCE_THRESHOLD = 1
SIMILARITY_THRESHOLD = 0.9
little_result_counter = 0
if __name__ == '__main__':
extract_data()
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# Generated by Django 2.2.2 on 2019-06-27 15:12
from django.db import migrations
import multiselectfield.db.fields
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import requests
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# -*- coding: utf-8 -*-
"""
Temporal framework doctests
"""
import doctest
import unittest
from openeo_udf.api import collection_base, feature_collection, datacube, \
machine_learn_model, spatial_extent, udf_data, structured_data
def load_tests(loader, tests, ignore):
"""Load all doctests from the base implementation as unittests"""
tests.addTests(doctest.DocTestSuite(collection_base))
tests.addTests(doctest.DocTestSuite(feature_collection))
tests.addTests(doctest.DocTestSuite(datacube))
tests.addTests(doctest.DocTestSuite(machine_learn_model))
tests.addTests(doctest.DocTestSuite(spatial_extent))
tests.addTests(doctest.DocTestSuite(structured_data))
tests.addTests(doctest.DocTestSuite(udf_data))
return tests
if __name__ == '__main__':
unittest.main()
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from pic2vec import ImageFeaturizer
import os
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--path', help='Path to the images')
parser.add_argument('--depth', help='Depth of Xception')
parser.set_defaults(depth=2)
args = parser.parse_args()
image_column_name = 'images'
my_featurizer = ImageFeaturizer(model='xception', depth=int(args.depth), autosample=True)
featurized_df = my_featurizer.featurize(image_column_name, image_path=args.path)
featurized_df.to_csv(os.path.join(args.path, 'features.csv'), index=False) | [
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# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2018-06-25 13:41
from __future__ import unicode_literals
from django.db import migrations, models
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42625,
14208,
13,
9945,
1330,
15720,
602,
11,
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628
] | 2.8 | 55 |
filename = 'risk tickers.xlsx'
xls = pd.ExcelFile(filename)
sht = xls.sheet_names
for i in sht: print i
df = pd.read_excel(filename,sheetname='Iron Steel')
#print the column names
print df.columns
#get the values for a given column
FLDS = df['Tickers'].values
for i in FLDS: print i
FLDS.shape
FLDS = FLDS.tolist()
FLDS = [x for x in FLDS if str(x) != 'nan']
len(FLDS)
setup_bbg()
start_date = '1/1/2010'
starting = time.time()
sids = mgr[FLDS]
x = sids.get_historical("PX_LAST", start_date, date.today()) #ISO FX
x.backup = x.copy
print(time.time()-starting)
# fill na
x = x.fillna(method="ffill")
#x = x.fillna(method="bfill") # hindsight biases
x.tail()
x.plot()
x.apply(normalise_max_min).plot()
#==============================================================================
# R^2 explanation
#==============================================================================
from sklearn import datasets, linear_model
regr = linear_model.LinearRegression(normalize = True, n_jobs = -1)
x.T.tail()
X = df_dropna(x.drop('ISIXSTSC Index',axis=1),axis=0)
Y = df_dropna(x['ISIXSTSC Index'].to_frame(),axis=0)
merged = pd.concat([Y,X],axis=1,join='inner') #merged = merged.dropna(axis=0)
X = merged.iloc[:,1:]
Y = merged.iloc[:,0]
# regression model outputs
regr.fit(X, Y) # scipy.linalg stored result in global envir
regr.intercept_
regr.coef_
#plot residuals
regr_res = Y - regr.predict(X)
regr_res.plot()
#merge for plotting
if len(regr.predict(X)) == Y.shape[0]:
Y1 = pd.concat([Y,pd.DataFrame(regr.predict(X),index=Y.index)],axis=1)
Y1.columns = ['Actual','Predicted']
Y1.head()
Y1.plot()
# plot regression coefficients
plot_coeff(regr.coef_)
for i in r1: print i.get_x()
#==============================================================================
# STATS TOOLS
#==============================================================================
# test stationary pairs - cointegration
import statsmodels.tsa.stattools as tsa
import statsmodels.graphics.tsaplots as tsa_plots
tsa.adfuller(regr_res)
setup_bbg()
start_date = '1/1/2010'
starting = time.time()
sids = mgr['GOOG US Equity']
x = sids.get_historical("PX_LAST", start_date, date.today()) #ISO FX
Y = x.fillna(method="ffill")
acf_ = tsa_plots.plot_acf(x=Y,alpha =.05, use_vlines=True, lags=100, unbiased=True)
acf_ = tsa_plots.plot_acf(x=log_return(Y),alpha =.05, use_vlines=True, lags=100, unbiased=True)
ret=log_return(Y)
ret.describe()
ret.plot()
#ACF
acf_, ci, Q, pvalue = tsa.acf(ret, nlags=30, alpha=.05, qstat=True) # use FFT is long ts
tsa_plots.plot_acf(x=ret,alpha =.05, use_vlines=True, lags=30, unbiased=True)
out=np.column_stack((acf_,ci))
in_cf=map(lambda x : x[0]>=x[1] and x[0]<=x[2], out)
#PACF
pacf_, ci = tsa.pacf(ret, nlags=30, alpha=.05) # use FFT is long ts
tsa_plots.plot_pacf(x=ret,alpha =.05, use_vlines=True, lags=30)
out=np.column_stack((pacf_,ci))
in_cf=map(lambda x : x[0]>=x[1] and x[0]<=x[2], out)
X
#==============================================================================
# decomposition of features - PCA with Varimax roration
#==============================================================================
from numpy import eye, asarray, dot, sum, diag
from numpy.linalg import svd
#==============================================================================
# example regression
#==============================================================================
from sklearn import datasets, linear_model
# Load the diabetes dataset
diabetes = datasets.load_diabetes()
# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis, 2]
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()
#==============================================================================
# stats model regression / ROLLING
#==============================================================================
x.shape
y=x.iloc[:,0]
X=x.iloc[:,1:]
model = pd.stats.ols.MovingOLS(y=y, x=X,
window_type='rolling', window=100, intercept=True)
#r.agg r.apply r.count r.exclusions r.max r.median r.name r.skew r.sum
#r.aggregate r.corr r.cov r.kurt r.mean r.min r.quantile r.std r.var
x.rolling(window=62,center=False).mean()
x.rolling(window=62,center=False).apply(normalise_max_min,axis=0)
R = x.rolling(window=62,min_periods=20,center=False)
R.mean()
R.agg({'result1' : np.sum,'result2' : np.mean})
R.apply(lambda x: np.std(x))
C=R.corr(x[x.columns.values[0]],pairwise=True)
C.columns
R.apply(corr,x[x.columns.values[0]],pairwise=True)
R.corr(pairwise=True)
import inspect
print inspect.getsource(clustered_corr)
print inspect.getsource(R.corr)
R.corr??
R.apply??
C.columns
C.tail(2)
C.iloc[:,1].plot()
x.shape
# 2 arguments mapper with lambda
f = lambda (x, y): (x+y, x-y)
t1=x.iloc[:,0]
t2=x.iloc[:,1]
from itertools import repeat
zz=map(f, zip(t1,t2))
h=pd.DataFrame()
h['a'],h['b']=zip(*zz)
h.tail()
type(h)
#lambda (x,y) for correlation
from scipy.stats.stats import pearsonr
pearsonr(t1,t2,)
f = lambda (x, y): (x+y, x-y)
t1=x.iloc[:,0]
t2=x.iloc[:,1]
from itertools import repeat
zz=map(f, zip(t1,t2))
zz=x.iloc[:,:2].apply(tuple,axis=1)
h=pd.DataFrame()
h['a'],h['b']=zip(*zz)
h.tail()
type(h)
plot_pdf_level_one(C,nrow=3,ncol=3,total=C.shape[1],sz=(10,8),filename="rolling_corr.pdf")
#==============================================================================
# ROLLING OLS REGRESSION PAIRWISE R^2 take TOP 3
#==============================================================================
y = x['ISIXSTSC Index'].to_frame()
x = x.drop(['ISIXSTSC Index'],axis=1)
y.shape
x.shape
pairwise_R2(y,x,start_date='2015',lookback=21,min_pd=5,sz=(15,15),filename='pairwise_rollingR2.pdf')
#==============================================================================
# MANUAL PCA
#==============================================================================
X = df_dropna(x)
eigenvals, components = np.linalg.eig(np.cov(X.transpose()))
vr_components = pretty_matrix(mat=varimax(components[:,:3]),digits=7)
pd.DataFrame(vr_components,index=mgr[X.columns].NAME)
# find index of coeff != 0 in components
ID_tup = zip(*np.where(vr_components != 0))
ID = pd.DataFrame(ID_tup).iloc[:,1].values
mgr[X.columns[ID]].NAME
#==============================================================================
# AUTO PCA
#==============================================================================
pca = PCA(n_components=3) # tol for singular SVD
X = df_dropna(x)
fit = pca.fit(X)
trans_X = pca.transform(X)
trans_X = pd.DataFrame(trans_X, index = X.index,
columns=["PC"+str(i+1) for i in xrange(trans_X.shape[1])])
trans_X.plot() # plot first 3 components
trans_X.corr()
trans_X.shape
trans_X.describe()
trans_X.head()
y.head()
# summarize components
print("Explained Variance: %s") % fit.explained_variance_ratio_
var1=np.cumsum(np.round(fit.explained_variance_ratio_, decimals=4)*100)
var1
plt.plot(var1)
#np.set_printoptions(threshold=np.inf)
print(fit.components_.T)
fit.n_features_
fit.n_components_
pairwise_R2(y,trans_X,start_date='2015',
lookback=21,min_pd=5,sz=(15,15),filename='pairwise_rollingR2 PCA.pdf')
#==============================================================================
# regress 1
#==============================================================================
from sklearn import datasets, linear_model
# Load the diabetes dataset
diabetes = datasets.load_boston()
type(diabetes)
diabetes.keys()
diabetes.data.shape
type(diabetes_X)
type(diabetes.target)
# Use only one feature
diabetes_X = diabetes.data[:, ]
len(diabetes_X)
# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]
# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
regr.score(diabetes_X_train, diabetes_y_train)
regr.intercept_
diabetes.data
regr.columns
# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print("Mean squared error: %.2f"
% np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()
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] | 2.633919 | 3,644 |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 11 20:36:35 2019
@author: Stuart
"""
import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
import neural_layers as nl #
from transf_decoder import Transformer
import utils
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import argparse
"""class Seq2SeqModel:
def __init__(self, output_lang, VOCAB_SIZE):
hidden_size = 256
#self.encoder = nl.BERTEncoder.from_pretrained('bert-base-multilingual-cased', num_labels=hidden_size ).to(nl.device)
#self.encoder = nl.BERTEncoder.from_pretrained('bert-base-uncased', num_labels=hidden_size ).to(nl.device)
self.encoder = nl.BERTEncoder.from_pretrained('bert-large-cased', num_labels=hidden_size ).to(nl.device)
self.encoder.get_embedding(VOCAB_SIZE)
#self.decoder = nl.DecoderRNN(hidden_size = 256, output_size = output_lang.n_words).to(nl.device)
self.decoder = nl.AttnDecoderRNN(hidden_size = 256, output_size = output_lang.n_words).to(nl.device)
def trainItersBert(self, training_pairs, eval_pairs, input_lang, output_lang):
nl.trainItersBert(self.encoder, self.decoder, 75000, training_pairs, eval_pairs, input_lang, output_lang, print_every=500, learning_rate=0.01, mom=0.001)
def translate(self, sentence, training_ans, eval_pairs, input_lang, output_lang):
output_words, attentions = nl.evaluate(self.encoder, self.decoder, sentence, training_ans, input_lang, output_lang )
plt.matshow(attentions.numpy())
return output_words, attentions
def load_model(self, load_model_name ):
enc_path, dec_path = "./model/qald-test/%(model_name)s/%(model_name)s.encoder"%{"model_name":load_model_name} , "./model/qald-test/%(model_name)s/%(model_name)s.decoder"%{"model_name":load_model_name}
self.encoder.load_state_dict(torch.load(enc_path))
self.encoder.eval()
self.decoder.load_state_dict(torch.load(dec_path))
self.decoder.eval()
print("\n ...model loaded.") """
"""class Transformer(nn.Module):
def __init__(self,
src_vocab_size,
src_max_len,
tgt_vocab_size,
tgt_max_len,
num_layers=6,
model_dim=512,
num_heads=8,
ffn_dim=2048,
dropout=0.2):
super(Transformer, self).__init__()
self.encoder = nl.BERTEncoder.from_pretrained('bert-large-cased', num_labels=model_dim ).to(nl.device)
# Encoder(src_vocab_size, src_max_len, num_layers, model_dim, num_heads, ffn_dim, dropout)
self.decoder = Decoder(tgt_vocab_size, tgt_max_len, num_layers, model_dim,
num_heads, ffn_dim, dropout)
self.linear = nn.Linear(model_dim, tgt_vocab_size, bias=False)
self.softmax = nn.Softmax(dim=2)
def forward(self, src_seq, src_len, tgt_seq, tgt_len):
context_attn_mask = padding_mask(tgt_seq, src_seq)
output, enc_self_attn = self.encoder(src_seq, src_len)
output, dec_self_attn, ctx_attn = self.decoder(
tgt_seq, tgt_len, output, context_attn_mask)
output = self.linear(output)
output = self.softmax(output)
return output, enc_self_attn, dec_self_attn, ctx_attn """
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=" Neural Knowledge-graph QA Model Based On Pretrained BERT & Reinforcement Learning ")
parser.add_argument('--train_dataset', type=str, default="./data/qald9/dataset.txt")
parser.add_argument('--eval_dataset', type=str, default="./data/qald9/train.txt")
parser.add_argument('--train', type=str, default = None)
parser.add_argument('--pretrain', type=str, default = None)
parser.add_argument('--translate', type=str, default = None)
parser.add_argument('--model_name', type=str, default = None)
parser.add_argument('--batch_size', type=int, default = 62 )
args = parser.parse_args()
training_fp = args.train_dataset
eval_fp = args.eval_dataset
train = args.train
load_model_name = args.pretrain
sentence = args.translate
batch_size = args.batch_size
model_name = args.model_name if args.model_name is not None else str().join(str(training_fp.split("/")[-1]).split(".")[:-1] )
input_lang, output_lang, training_pairs, eval_pairs, VOCAB_SIZE = utils.prepareData(training_fp, eval_fp, False)
'''
eval_tensors = [utils.tensorsFromPair(pair, input_lang, output_lang, nl.device) for pair in eval_pairs ]
eval_inputs = [ tensors[0] for tensors in eval_tensors ]
eval_targets = [ tensors[1] for tensors in eval_tensors ]
eval_inputs = rnn_utils.pad_sequence(eval_inputs, batch_first=True, padding_value=0)
eval_targets = rnn_utils.pad_sequence(eval_targets, batch_first=True, padding_value=0)
torch.save(eval_inputs, "./model/eval_inputs.pt")
torch.save(eval_targets, "./model/eval_targets.pt")'''
seq2seq = Transformer(input_lang, output_lang)#Seq2SeqModel(output_lang, VOCAB_SIZE)
if load_model_name is not None:
seq2seq.load_model(load_model_name) ;
if train is not None:
#seq2seq.trainItersBert(training_pairs, eval_pairs, input_lang, output_lang)
nl.trainItersBert(model=seq2seq, n_iters=75000, training_pairs=training_pairs, eval_pairs=eval_pairs, input_lang=input_lang, output_lang=output_lang, batch_size=batch_size, model_name="q-dev")
#if train or load is not None:
# seq2seq.translate(sentence, input_lang, output_lang)
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] | 2.247721 | 2,523 |
import argparse
if __name__ == '__main__':
args = parse_args()
if args.mode == 'sensitivity':
organize_accuracy(args.input, args.output)
elif args.mode == 'unaligned':
organize_unaligned(args.input, args.output)
elif args.mode == 'num_incorrect':
organize_numincorrect(args.input, args.output)
else:
print ('invalid mode:', args.mode)
exit()
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] | 2.369942 | 173 |
# Copyright 2021 The Brax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for the URDF converter."""
from absl.testing import absltest
from brax.tools import urdf
_TEST_XML = """
<robot name="test robot">
<joint name="test_joint" type="revolute">
<parent link="parent_link" />
<child link="child_link" />
<dynamics damping="1.0" friction="0.0001" />
<origin rpy="1.57080 0.0 1.57080" xyz="0.1 0.2 -0.3" />
<axis xyz="1.00000 0.00000 0.00000" />
</joint>
<link name="parent_link">
<inertial>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000" />
<mass value="1.00000" />
<inertia ixx="0.00100" ixy="0" ixz="0" iyy="0.00100" iyz="0" izz="0.00100" />
</inertial>
<visual>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.00000 0.00000 0.00000" />
<geometry>
<sphere radius="0.05000" />
</geometry>
</visual>
</link>
<link name="child_link">
<inertial>
<origin rpy="0.00000 -0.00000 0.00000" xyz="0.0 0.0 -0.0" />
<mass value="2.0" />
<inertia ixx="0.1" ixy="0" ixz="0" iyy="0.1" iyz="0" izz="0.1" />
</inertial>
<visual>
<origin rpy="0.0 0.0 0.0" xyz="0.0 0.0 0.0" />
<geometry>
<cylinder length="0.5" radius="0.1" />
</geometry>
</visual>
<collision>
<origin rpy="0.0 0.0 0.0" xyz="0.0 0.0 0.0" />
<geometry>
<cylinder length="0.5" radius="0.1" />
</geometry>
</collision>
</link>
</robot>
"""
if __name__ == '__main__':
absltest.main()
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] | 2.16565 | 984 |
# Generated by Django 1.11.15 on 2018-11-25 08:34
import django.contrib.auth.validators
from django.db import migrations, models
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] | 2.977273 | 44 |
import sys,os
import collections
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),'..')))
from work import Ohio
import unittest
Ohio = Ohio()
# #need to delete all files from testing folders
if __name__=='__main__':
unittest.main( )
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] | 2.68 | 100 |
from ecoreleve_server.Models import Base,DBSession
from sqlalchemy import Column, DateTime, Float, ForeignKey, Index, Integer, Numeric, String, Text, Unicode, text,Sequence,orm,and_,text
from sqlalchemy.dialects.mssql.base import BIT
from sqlalchemy.orm import relationship
FieldSizeToClass = {0:'col-md-3',1:'col-md-6',2:'col-md-12'}
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] | 2.888889 | 117 |
#
# File:
# scatter2.py
#
# Synopsis:
# Draws random markers using user-defined markers.
#
# Category:
# XY plots
# polymarkers
#
# Author:
# Mary Haley
#
# Date of initial publication:
# December, 2005
#
# Description:
# This example generates some random data and plots the data as an
# XY plot using markers created by the Ngl.new_marker function.
#
# Effects illustrated:
# o Defining your own markers.
# o Usage of the the Python "random" module.
#
# Output:
# A single visualization is produced showing the random markers
# on an XY plot.
#
# Notes:
#
#
# Import numpy and random.
#
from __future__ import print_function
import numpy
import random
#
# Import Ngl support functions.
#
import Ngl
random.seed(10) # set a seed for the random number generator
#
# Generate some dummy data.
#
y = numpy.zeros([3,100],'f')
for i in range(100):
y[0,i] = 90.*random.random()+105.
y[1,i] = 90.*random.random()+105.
y[2,i] = 90.*random.random()+105.
wks_type = "png"
wks = Ngl.open_wks(wks_type,"scatter2")
#
# Set up parameters for creating some new markers.
#
mrk_indices = numpy.zeros(3,'i')
mstrings = ["u","z","y"] # triangle, star, sqaure
fontnums = [34,35,35]
yoffsets = [0.4, 0.0, 0.0]
sizes = [2.0, 1.5, 1.0]
mrk_indices[0] = Ngl.new_marker(wks, mstrings[0], fontnums[0], 0, \
yoffsets[0], 1, sizes[0], 15.)
mrk_indices[1] = Ngl.new_marker(wks, mstrings[1], fontnums[1], 0, \
yoffsets[1], 1, sizes[1], 0.)
mrk_indices[2] = Ngl.new_marker(wks, mstrings[2], fontnums[2], 0, \
yoffsets[2], 1, sizes[2], 0.)
#
# Set up resource list for XY plot.
#
res = Ngl.Resources()
res.xyMarkLineMode = "Markers" # Default is to draw lines.
res.xyMonoMarkLineMode = True # Default is only one marker style.
res.xyMarkers = mrk_indices # Set new markers
res.xyMarkerColors = ["red","green","blue"]
res.tiMainString = "Scatter plot with user-defined markers"
plot = Ngl.y(wks,y,res) # Draw the plot.
Ngl.end()
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] | 2.190863 | 985 |
#!/usr/bin/env python3
from base64 import b64encode, b64decode
from sys import byteorder
| [
2,
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"""TileSet class.
TiledImageVisual uses this class to track the tiles it's drawing.
"""
from typing import Dict, List, NamedTuple, Set
from ...layers.image.experimental import OctreeChunk, OctreeChunkKey
from .texture_atlas import AtlasTile
class TileData(NamedTuple):
"""TileSet stores one TileData per tile.
Attributes
----------
octree_chunk : OctreeChunk
The chunk that created the tile.
atlas_tile : AtlasTile
The tile that was created from the chunk.
"""
octree_chunk: OctreeChunk
atlas_tile: AtlasTile
class TileSet:
"""The tiles we are drawing.
Fast test for membership in both directions: dict and a set.
Attributes
----------
_tiles : Dict[int, TileData]
Maps tile_index to the the TileData we have for that tile.
_chunks : Set[OctreeChunkKey]
The chunks we have in the set, for fast membership tests.
"""
def __len__(self) -> int:
"""Return the number of tiles in the set.
Return
------
int
The number of tiles in the set.
"""
return len(self._tiles)
def clear(self) -> None:
"""Clear out all our tiles and chunks. Forget everything."""
self._tiles.clear()
self._chunks.clear()
def add(self, octree_chunk: OctreeChunk, atlas_tile: AtlasTile) -> None:
"""Add this TiledData to the set.
Parameters
----------
octree_chunk : OctreeChunk
The chunk we are adding to the tile set.
atlas_tile : AtlasTile
The atlas tile that was created for this chunks.
"""
tile_index = atlas_tile.index
self._tiles[tile_index] = TileData(octree_chunk, atlas_tile)
self._chunks.add(octree_chunk.key)
def remove(self, tile_index: int) -> None:
"""Remove the TileData at this index from the set.
tile_index : int
Remove the TileData at this index.
"""
octree_chunk = self._tiles[tile_index].octree_chunk
self._chunks.remove(octree_chunk.key)
del self._tiles[tile_index]
@property
def chunk_set(self) -> Set[OctreeChunkKey]:
"""Return the set of chunks we drawing.
Return
------
Set[OctreeChunkKey]
The set of chunks we are drawing.
"""
return self._chunks
@property
def chunks(self) -> List[OctreeChunk]:
"""Return all the chunks we are tracking.
Return
------
List[OctreeChunk]
All the chunks in the set.
"""
return [tile_data.octree_chunk for tile_data in self._tiles.values()]
@property
def tile_data(self) -> List[TileData]:
"""Return the data for all tiles in the set, unsorted.
Return
------
List[TileData]
Data for all the tiles in the set sorted back to front.
"""
return self._tiles.values()
@property
def tile_data_sorted(self) -> List[TileData]:
"""Return the data for all tiles in the set, sorted back to front.
We return tiles from higher octree levels first. These are the
larger coarser tiles. These are "the background" while smaller
higher resolution tiles are drawn in front. So we show the "best
available" data in all locations.
Return
------
List[TileData]
Data for all the tiles in the set sorted back to front.
"""
return sorted(
self._tiles.values(),
key=lambda x: x.octree_chunk.location.level_index,
reverse=True,
)
def contains_octree_chunk(self, octree_chunk: OctreeChunk) -> bool:
"""Return True if the set contains this chunk.
Parameters
----------
octree_chunk : OctreeChunk
Check if this chunk is in the set.
Return
------
bool
True if the set contains this chunk data.
"""
return octree_chunk.key in self._chunks
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] | 2.380645 | 1,705 |
from __future__ import annotations
import logging
import datetime
from fastapi import APIRouter, Depends, params
from typing import List, Optional
from app.core.auth import get_current_user
from app.ratelimit.time_bucketed import rate_limit
from app.routes.businesses.service.business_metrics.business_metric_service import BusinessMetricService
from app.exceptions.application_exception import exception
from app.routes.businesses.service.business_metrics.dto.media_topics_output import MediaTopicsOutput
from app.routes.businesses.service.business_metrics.dto.keywords_output import KeywordsOutput
from app.routes.businesses.service.business_metrics.dto.redflag_output import RedFlagOutput
from app.routes.businesses.service.business_metrics.dto.public_perception_output import PublicPerceptionOutput
from app.routes.businesses.service.business_metrics.dto.entity_details_output import EntityDetailsOutput
from app.routes.businesses.service.business_metrics.dto.sentiment_distribution_output import SentimentDistributionOutput
from app.routes.businesses.service.business_metrics.dto.average_senetiment_output import AverageSenetimentOutput
from app.routes.businesses.service.business_metrics.dto.platform_index_output import PlatformIndexOutput
from app.routes.businesses.service.business_metrics.dto.news_output import NewsOutput
from app.routes.businesses.service.business_metrics.dto.watch_level_metric_output import WatchLevelOutput
from app.routes.businesses.service.business_metrics.dto.bsi_score_metric_output import BsiScoreMetricOutput
router = APIRouter(prefix="/business-metric")
CALLS = 900
PERIOD = 900
start = datetime.date.today().isoformat()
@router.get("/bsi-score", tags=["Business Metrics"], response_model=List[BsiScoreMetricOutput])
async def bsi_score(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the media presence of the company on a given date. This is calculated using the ‘Business Sentiment Index’ formula.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date,
}
return BusinessMetricService.get_bsi_score(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/watch-level", tags=["Business Metrics"], response_model=WatchLevelOutput)
async def watch_level(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the watch level for the company on a given date. Watch Levels can be categorized into different types based on ‘Business Sentiment Index’.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date,
'account_id': int(auth.account_id)
}
return BusinessMetricService.get_watch_level(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/credit-bureau-score", tags=["Business Metrics"], include_in_schema=False)
@router.get("/recent-news", tags=["Business Metrics"], response_model=List[NewsOutput])
async def recent_news(
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Lists the most negative business sentiment articles in the given month, in the order of date published.
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'date': date,
'account_id': int(auth.account_id)
}
return BusinessMetricService.get_recent_news(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/top-news", tags=["Business Metrics"], response_model=List[NewsOutput])
async def top_news(auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Lists the most negative business sentiment articles in the past 3 months, in the descending order of sentiment value.
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'account_id': int(auth.account_id)
}
return BusinessMetricService.get_top_news(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/current-platform-index", tags=["Business Metrics"], response_model=List[PlatformIndexOutput])
async def current_platform_index(
business_id: int,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the media presence of the company (Platform level) on a given date. This is calculated using the ‘Business Sentiment Index’ formula.
- **business_id**: unique id of the business
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id}
return BusinessMetricService.get_current_platform_index(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/average-sentiment", tags=["Business Metrics"], response_model=List[AverageSenetimentOutput])
async def avg_sentiment(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the monthly average sentiment for the company on the given date.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_avg_sentiment(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/sentiment-distribution", tags=["Business Metrics"], response_model=List[SentimentDistributionOutput])
async def sentiment_distribution(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the monthly sentiment distribution for the company on the given date. Sentiment are categorized into positive, neutral and negative.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_sentiment_distribution(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/entity-details", tags=["Business Metrics"], response_model=List[EntityDetailsOutput])
async def entity_details(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the entity details of the given company.
- **business_id**: unique id of the business
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_entity_details(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/public-perception", tags=["Business Metrics"], response_model=PublicPerceptionOutput)
async def public_perception(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the emotions expressed by customers in their reviews. The average values for the sentiment data are calculated using the proprietary formula. The data is collected on the selected month.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_public_perception(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/redflags", tags=["Business Metrics"], response_model=List[RedFlagOutput])
async def redflags(
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the name of certain flagged phrases in the company’s media and news feeds on the give month.
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'date': date,
'account_id': int(auth.account_id)
}
return BusinessMetricService.get_redflags(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/keywords", tags=["Business Metrics"], response_model=List[KeywordsOutput])
async def keywords(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows the various topics in the reviews mentioned by the customers in the news and media feed on the given month.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_keywords(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
@router.get("/media_topics", tags=["Business Metrics"], response_model=List[MediaTopicsOutput])
async def media_topics(
business_id: int,
date: Optional[datetime.date] = start,
auth: Depends = Depends(get_current_user),
) -> dict[str, int]:
"""
Shows a representation of the various topics in the reviews mentioned by the customers in the news and media feed on the given date.
- **business_id**: unique id of the business
- **date**: any date. If not given, current date will be taken
\f
:param item: User input.
"""
try:
rate_limit(auth.client_id, CALLS, PERIOD)
params = {
'business_id': business_id,
'date': date
}
return BusinessMetricService.get_media_topics(params)
except Exception as error:
logging.error(error)
raise exception.internal_server_error()
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] | 2.687643 | 4,370 |
from django.contrib import admin
from .models import CarMake, CarModel
# Car Model Inline
# Car Make Inline
# Car Make Admin
# Car Model Admin
admin.site.register(CarMake)
admin.site.register(CarModel)
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] | 3.212121 | 66 |
# System
import unittest
import json
import logging
import pprint
try:
exePath=os.path.dirname(os.path.abspath(__file__))
parentPath,childDir=os.path.split(exePath)
sys.path.insert(1,os.path.join(parentPath,"lib"))
except:
print "Unable to load local library paths"
sys.exit(1)
# Local
import NDE
import setup_logging
if __name__ == '__main__':
unittest.main()
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9058,
62,
6404,
2667,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
198,
220,
198,
361,
11593,
3672,
834,
6624,
705,
834,
12417,
834,
10354,
198,
220,
220,
220,
555,
715,
395,
13,
12417,
3419,
198
] | 2.418182 | 165 |
"""ZCM type definitions
This file automatically generated by zcm.
DO NOT MODIFY BY HAND!!!!
"""
try:
import cStringIO.StringIO as BytesIO
except ImportError:
from io import BytesIO
import struct
| [
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'''
Module of Android API for plyer.devicename.
'''
from jnius import autoclass
from plyer.facades import DeviceName
Build = autoclass('android.os.Build')
class AndroidDeviceName(DeviceName):
'''
Implementation of Android devicename API.
'''
def _get_device_name(self):
"""
Method to get the device name aka model in an android environment.
Changed the implementation from 'android.provider.Settings.Global' to
'android.os.Build' because 'android.provider.Settings.Global' was
introduced in API 17 whereas 'android.os.Build' is present since API 1
Thereby making this method more backward compatible.
"""
return Build.MODEL
def instance():
'''
Instance for facade proxy.
'''
return AndroidDeviceName()
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220,
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220,
220,
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220,
220,
1318,
1525,
1642,
428,
2446,
517,
19528,
11670,
13,
198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
10934,
13,
33365,
3698,
628,
198,
4299,
4554,
33529,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
2262,
590,
329,
43562,
15741,
13,
198,
220,
220,
220,
705,
7061,
198,
220,
220,
220,
1441,
5565,
24728,
5376,
3419,
198
] | 2.871886 | 281 |
"""
"ReTry" (c) by Ignacio Slater M.
"ReTry" is licensed under a
Creative Commons Attribution 4.0 International License.
You should have received a copy of the license along with this
work. If not, see <https://creativecommons.org/licenses/by/4.0/>.
"""
from retry.geometry import Point
from retry.tree.rtree import RTree
if __name__ == '__main__':
import random
rng = random.Random()
tree = RTree(2, 5)
for _ in range(0, 100):
tree.insert(Point(rng.random() * 100, rng.random() * 100))
tree.draw(100, 100)
| [
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7,
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220,
220,
220,
220,
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220,
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5509,
13,
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7,
12727,
7,
81,
782,
13,
25120,
3419,
1635,
1802,
11,
374,
782,
13,
25120,
3419,
1635,
1802,
4008,
198,
220,
220,
220,
5509,
13,
19334,
7,
3064,
11,
1802,
8,
198
] | 2.744898 | 196 |
# Copyright 2021 cms.rendner (Daniel Schmidt)
#
# 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 plugin_code.apply_fallback_patch import ApplyFallbackPatch
from plugin_code.apply_map_fallback_patch import ApplyMapFallbackPatch
from plugin_code.background_gradient_patch import BackgroundGradientPatch
from plugin_code.base_apply_map_patcher import BaseApplyMapPatcher
from plugin_code.base_apply_patcher import BaseApplyPatcher
from plugin_code.exported_style import ExportedStyle
from plugin_code.highlight_extrema_patch import HighlightExtremaPatch
from plugin_code.table_structure import TableStructure
# == copy after here ==
import inspect
import numpy as np
from pandas import DataFrame
from pandas.io.formats.style import Styler
from typing import Callable, List, Tuple, Union
| [
2,
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62,
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62,
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21466,
198,
6738,
13877,
62,
8189,
13,
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62,
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260,
2611,
62,
17147,
1330,
3334,
2971,
11627,
260,
2611,
33952,
198,
6738,
13877,
62,
8189,
13,
11487,
62,
301,
5620,
1330,
8655,
1273,
5620,
198,
198,
2,
6624,
4866,
706,
994,
6624,
198,
11748,
10104,
198,
11748,
299,
32152,
355,
45941,
198,
6738,
19798,
292,
1330,
6060,
19778,
198,
6738,
19798,
292,
13,
952,
13,
687,
1381,
13,
7635,
1330,
42378,
1754,
198,
6738,
19720,
1330,
4889,
540,
11,
7343,
11,
309,
29291,
11,
4479,
628
] | 3.650704 | 355 |
from flask import Flask
from .proxy import create_proxy
from . import stats
def create_app(config):
"""Create flask app"""
app = Flask(__name__)
routes = config["routes"]
@app.route("/")
for route in routes:
create_proxy(app, route["path_prefix"], route["backend"], config["backends"])
@app.errorhandler(404)
@app.route("/stats")
return app
| [
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7203,
14,
34242,
4943,
628,
220,
220,
220,
1441,
598,
198
] | 2.739437 | 142 |
import numpy as np
from scipy.ndimage import shift
import random
from targets import one_hot
| [
11748,
299,
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355,
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198,
6738,
629,
541,
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4738,
198,
6738,
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1330,
530,
62,
8940,
628,
628,
198
] | 3.464286 | 28 |
# Modify the program to show the numbers from 1 to 100.
x=1
while x<=100:
print(x)
x=x+1 | [
2,
3401,
1958,
262,
1430,
284,
905,
262,
3146,
422,
352,
284,
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7,
87,
8,
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220,
220,
220,
2124,
28,
87,
10,
16
] | 2.285714 | 42 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author Eric Bullen <[email protected]>
@application jtune.py
@version 4.0.1
@abstract This tool will give detailed information about the running
JVM in real-time. It produces useful information that can
further assist the user in debugging and optimization.
@license Copyright 2015 LinkedIn Corp. 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.
"""
import atexit
import datetime
import getpass
import locale
import logging
import math
import os
import re
import resource
import shlex
import socket
import subprocess as sp
import sys
import textwrap
import time
from decimal import Decimal
from itertools import zip_longest, count
import argparse
import multiprocessing as mp
try:
locale.setlocale(locale.LC_ALL, 'en_US')
except locale.Error:
# Try UTF8 variant before failing
locale.setlocale(locale.LC_ALL, 'en_US.utf8')
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s: "%(name)s" (line: %(lineno)d) - %(levelname)s: %(message)s'))
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logger.addHandler(handler)
# For me to use in PyCharm to read flight recorder files
DEBUG = False
class GCRecord(object):
"""Object definition for a single gc record."""
_version = "1.0"
def __repr__(self):
"""This prints out the gc record so that it looks as though it came straight from
the logs."""
output = list()
output.append("{0} Runtime: {1} GC Type: {2}".format(self.record_timestamp, self.jvm_running_time, self.gc_type))
output.append("Desired Survivor Size: {0}, Curr Threshold: {1} (Max: {2})".format(self.desired_survivor_size, self.curr_threshold, self.max_threshold))
for age in self.ages:
if age[1] > -1 or age[2] > -1:
output.append("- Age {0}: {1:>10} bytes, {2:>10} total".format(age[0], age[1], age[2]))
output.append("YG Before GC: {0}K, YG After GC: {1}K (Total: {2}K), {3} secs".format(self.young_size_before_gc, self.young_size_after_gc, self.young_size_total, self.young_gc_time))
output.append("Total Heap Before GC: {0}K, Total Heap After GC: {1}K (Total: {2}K), {3} secs".format(self.total_heap_before_gc, self.total_heap_after_gc, self.total_heap, self.total_gc_time))
return "\n".join(output)
def _parse_record(self):
"""This loops through record_array to set the class variables that make up the record."""
self.record_timestamp, record_array = self.raw_gc_record
#############################################################
# Capture STW (Full GC, remarks, etc.). Yeah, I could combine
# these three, but this is good enough for now.
if any("CMS Initial Mark" in line for line in record_array):
match = re.search(r", ([\d\.]+) secs\] ", record_array[-1])
if match:
self.gc_type = "CMS-STW"
self.is_stw_gc = True
self.valid_record = True
self.stw_time += float(match.group(1))
if any("CMS Final Remark" in line for line in record_array):
match = re.search(r", ([\d\.]+) secs\] ", record_array[-1])
if match:
self.gc_type = "CMS-STW"
self.is_stw_gc = True
self.valid_record = True
self.stw_time += float(match.group(1))
if any("Full GC" in line for line in record_array):
match = re.search(r", ([\d\.]+) secs\] ", record_array[-1])
if match:
self.gc_type = "FULL"
self.is_stw_gc = True
self.valid_record = True
self.stw_time += float(match.group(1))
if not self.is_stw_gc:
for line in record_array:
if "CMS-concurrent-sweep: " in line:
match = re.match(r"^\d+-\d+-\d+T\d+:\d+:[\d\.]+[+-]\d+: ([\d\.]+): \[CMS-concurrent-sweep: [\d\.]+/([\d\.]+) secs", line)
if match:
self.is_cms_gc = True
self.valid_record = True
self.gc_type = "CMS"
self.jvm_running_time = float(match.group(1))
self.cms_sweep_time = float(match.group(2))
break
if not (self.jvm_running_time or self.gc_type):
match = re.match(r"^\d+-\d+-\d+T\d+:\d+:[\d\.]+[+-]\d+: ([\d\.]+): .*\[(\S+)", line)
if match:
self.jvm_running_time = float(match.group(1))
self.gc_type = match.group(2)
if not (self.desired_survivor_size or self.curr_threshold or self.max_threshold):
match = re.match(r"^Desired survivor size (\d+) bytes, new threshold (\d+) \(max (\d+)\)", line)
if match:
self.valid_record = True
self.desired_survivor_size = int(match.group(1))
self.curr_threshold = int(match.group(2))
self.max_threshold = int(match.group(3))
# Here I set the survivor size beforehand, for any that
# may be missing as I want all the ages even if they aren't
# being used for comparison between GCs
for age in range(1, self.max_threshold + 1):
self.ages.append((age, -1, -1))
continue
################################################
# Skipping records when the JVM has been running
# for less than 300 seconds
if self.jvm_running_time < 300:
self.valid_record = False
break
#############################
# Capture survivor ages, etc.
match = re.match(r"^- age\s+(\d+):\s+(\d+) bytes,\s+(\d+) total", line)
if match:
############################################################
# This while logic block catches any ages that were
# fully reaped, and fills them with zeros. This is important
# as the analytics needs to know this to determine survivor
# death rates/ratios
age = int(match.group(1))
curr_size = int(match.group(2))
max_size = int(match.group(3))
self.ages[age - 1] = (age, curr_size, max_size)
continue
###############################
# Capture gc reallocation stats
match = re.match(r"^: (\d+)\w->(\d+)\w\((\d+)\w\), ([\d\.]+) secs\] (\d+)\w->(\d+)\w\((\d+)\w\), ([\d\.]+) secs\]", line)
if match:
self.young_size_before_gc = int(match.group(1)) * 1024
self.young_size_after_gc = int(match.group(2)) * 1024
self.young_size_total = int(match.group(3)) * 1024
self.young_gc_time = Decimal(match.group(4))
self.total_heap_before_gc = int(match.group(5)) * 1024
self.total_heap_after_gc = int(match.group(6)) * 1024
self.total_heap = int(match.group(7)) * 1024
self.total_gc_time = Decimal(match.group(8))
self.og_used = self.total_heap_after_gc - self.young_size_after_gc
def liverun(cmd=None):
"""Run cmd, and return an iterator of said cmd.
Keyword arguments:
cmd -- the command to run
"""
global subproc
env = dict(os.environ)
# Combining stdout and stderr. I can't find a way to keep both separate
# while getting the data 'live'. itertools.izip_longest seemed like it'd
# almost do it, but it caches the results before sending it out...
subproc = sp.Popen(shlex.split(cmd), stdout=sp.PIPE, stderr=sp.STDOUT, env=env)
return iter(subproc.stdout.readline, b'')
def reduce_seconds(secs=None):
"""Return a compressed representation of time in seconds
Keyword arguments:
secs -- a float/int representing the seconds to be 'compressed'
"""
# The nested if statements keep it from being too long,
# by lopping off the non significant values
retval = ""
secs = int(float(secs))
mins, secs = divmod(secs, 60)
hours, mins = divmod(mins, 60)
days, hours = divmod(hours, 24)
secs = int("{0:0.0f}".format(secs))
if days:
retval += "{0}d".format(days)
if hours:
retval += "{0}h".format(hours)
if days > 0:
return retval
if mins:
retval += "{0}m".format(mins)
if hours or days:
return retval
if secs:
retval += "{0:}s".format(secs)
return retval
def sec_diff(first_time=None, second_time=None):
"""Return the number of seconds between two datetime objects
Keyword arguments:
first_time -- The (typically) older time of the two
second_time -- The (typically) newer time of the two
"""
time_delta = second_time - first_time
return time_delta.days * 86400 + time_delta.seconds + Decimal(str(time_delta.microseconds / float(1000000)))
def _min(values=None):
"""A wrapper around the min() function so that it does not error on an empty list"""
try:
return min(values)
except ValueError:
return 0
def _max(values=None):
"""A wrapper around the max() function so that it does not error on an empty list"""
try:
return max(values)
except ValueError:
return 0
def median(values=None):
"""Return the median of 'values'
Keyword arguments:
values -- the list of numbers
"""
sorts = sorted(values)
length = len(sorts)
result = None
if not values:
result = 0
# raise ValueError, "I can't find the median of an empty list."
elif not length % 2:
result = (sorts[(length // 2)] + sorts[(length // 2) - 1]) / 2.0
else:
result = sorts[length // 2]
return result
def mean(values=None, _length=None):
"""Return the mean of 'values'
Keyword arguments:
values -- the list of numbers
_length -- mostly not usable for end-users, needed by the stdev function
"""
result = None
if not _length:
_length = len(values)
if _length > 0:
result = Decimal(str(sum(values))) / _length
else:
result = 0
return result
def stdev(values=None):
"""Return the standard deviation of values
Keyword arguments:
values -- The poorly named argument that contains the list of numbers
"""
values_mean = mean(values)
variance = [math.pow(Decimal(str(x)) - values_mean, 2) for x in values]
return math.sqrt(mean(variance, len(variance) - 1))
def percentile(values=None, pct=None):
"""Return the percentile of a given values
Keyword arguments:
values -- The list of numbers to be analyzed
pct -- The percentile (can be a float) to be used (100 == 100%, not 1 = 100%, etc.)
"""
watermark_index = int(round((float(pct) / 100) * len(values) + .5))
watermark = sorted(values)[watermark_index - 1]
return [element for element in values if element <= watermark]
def reduce_k(size=None, precision=2, short_form=True, _place_holder=0):
"""Return a compressed representation of a given number of bytes
Keyword arguments:
size -- the size in bytes
precision -- what precision should be used (places to the right of the decimal)
short_form -- (true/false). Use 'K' instead of 'KiB', etc.
"""
if not isinstance(size, Decimal):
size = Decimal(str(size))
# You know.. just in case we ever get to a yottabyte
if short_form:
iec_scale = ['K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y']
else:
iec_scale = ['KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB', 'ZiB', 'YiB']
if not isinstance(size, Decimal):
size = Decimal(str(size))
if abs(size) >= 1024:
_place_holder += 1
return reduce_k(size / Decimal("1024.0"), precision=precision, short_form=short_form, _place_holder=_place_holder)
else:
value = Decimal("{0:.{1}f}".format(size, precision))
if Decimal(str(int(value))) == value:
value = int(value)
if short_form:
return "{0}{1}".format(value, iec_scale[_place_holder])
else:
return "{0} {1}".format(value, iec_scale[_place_holder])
def _run_analysis(gc_data=None, jmap_data=None, jstat_data=None, proc_details=None, optimized_for_ygcs_rate=None):
"""The meat-and-potatoes of this tool. This takes in numerous data structures,
and prints out a report of the analysis of them."""
# Formulas to get the JVM configuration just from JMap:
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# eden_size == (newsize * survivor_ratio)/(2 + survivor_ratio)
# survivor_size == eden_size * (1/survivor_ratio)
# og_size == max_heap_size - eden_size - survivor_size
# og_used == heap_used - eden_used
if not gc_data:
logger.error("I can't do any analysis for this sample period because there wasn't enough data in the GC log. Exiting.")
sys.exit(1)
############################################################
# Get some summary data that doesn't require GC log analysis
# Loop through the GC data array to find all CMS events, and capture
# how long they took.
cms_times = [record.cms_sweep_time for record in gc_data if record.is_cms_gc]
display.render("\n")
display.render("Meta:\n")
display.render("~~~~~\n")
sample_time_secs = sec_diff(gc_data[0].record_timestamp, gc_data[-1].record_timestamp)
if sample_time_secs < 60:
display.render("GC Sample Time: {0} seconds\n".format(sample_time_secs))
else:
display.render("GC Sample Time: {0} ({1} seconds)\n".format(reduce_seconds(sample_time_secs), sample_time_secs))
display.render("GC Sample Time from {0} to {1}\n".format(gc_data[0].record_timestamp, gc_data[-1].record_timestamp))
if proc_details:
cpu_count = mp.cpu_count()
cpu_uptime = cpu_count * proc_details['sys_uptime_seconds']
proc_utime_pct = proc_details['proc_utime_seconds'] / cpu_uptime
proc_stime_pct = proc_details['proc_stime_seconds'] / cpu_uptime
display.render("System Uptime: {0}\n".format(reduce_seconds(proc_details['sys_uptime_seconds'])))
display.render("Proc Uptime: {0}\n".format(reduce_seconds(proc_details['proc_uptime_seconds'])))
display.render("Proc Usertime: {0} ({1:0.2%})\n".format(reduce_seconds(proc_details['proc_utime_seconds']), proc_utime_pct))
display.render("Proc Systime: {0} ({1:0.2%})\n".format(reduce_seconds(proc_details['proc_stime_seconds']), proc_stime_pct))
display.render("Proc RSS: {0}\n".format(reduce_k(proc_details['proc_rss_bytes'] / 1024)))
display.render("Proc VSize: {0}\n".format(reduce_k(proc_details['proc_vsize_bytes'] / 1024)))
display.render("Proc # Threads: {0}\n".format(proc_details['num_threads']))
display.render("\n")
# Exit out as I don't have enough gc_data to do any analysis on
if len(gc_data) < 2:
display.render("\n")
display.render("* NOTE: There wasn't enough data to do any analysis. Please let the tool\n")
display.render(" gather at least 2 complete gc.log records (found {0}).\n".format(len(gc_data)))
return False
survivor_info = dict()
young_gc_count_delta = len([record.is_stw_gc for record in gc_data if not record.is_stw_gc])
full_gc_count_delta = len([record.is_stw_gc for record in gc_data if record.is_stw_gc])
sample_gc_time = sum(record.total_gc_time for record in gc_data)
sample_gc_load = (sample_gc_time / Decimal(str(sample_time_secs))) * 100
#######################################################
# Get young gen allocation rates over the sample period
yg_rates = list()
for first_gc, second_gc in zip(gc_data, gc_data[1:]):
if any([second_gc.is_stw_gc, first_gc.is_stw_gc, first_gc.is_cms_gc, second_gc.is_cms_gc]):
continue
# Iterate over the gc logs 2 at a time
# [1, 2, 3, 4] ->
# [(1, 2), (2, 3), (3, 4)]
#
time_delta = sec_diff(first_gc.record_timestamp, second_gc.record_timestamp)
try:
yg_size_delta = second_gc.young_size_before_gc - first_gc.young_size_after_gc
yg_growth_delta = second_gc.young_size_after_gc - first_gc.young_size_after_gc
except TypeError:
display.render("\n".join(textwrap.wrap("Warning: Something is really wrong with this JVM; I couldn't get correct GC data for it.", display.textwrap_offset)))
display.render("")
yg_size_delta = 0
yg_growth_delta = 0
# These are in KiB/s
yg_alloc_rate = yg_size_delta / time_delta
yg_growth_rate = yg_growth_delta / time_delta
yg_rates.append((yg_alloc_rate, yg_growth_rate))
#####################################################
# Get old gen promotion rates over the sample period
og_rates = list()
for first_gc, second_gc in zip(gc_data, gc_data[1:]):
if any([second_gc.is_stw_gc, first_gc.is_stw_gc, first_gc.is_cms_gc, second_gc.is_cms_gc]):
continue
time_delta = sec_diff(first_gc.record_timestamp, second_gc.record_timestamp)
# These are in KiB/s
og_allocation_delta = (second_gc.og_used - first_gc.og_used) / Decimal("1024")
og_allocation_rate = og_allocation_delta / time_delta
############################################################################
# I only want when the old gen is growing. If it's decreasing, it's probably
# b/c there was a FGC, and space is being reclaimed.
if og_allocation_delta > 0:
# This is in KiB/s
og_rates.append(og_allocation_rate)
############################
# Calc survivor death ratios
gc_survivor_death_rates = list()
for first_gc, second_gc in zip(gc_data, gc_data[1:]):
if any([second_gc.is_stw_gc, first_gc.is_stw_gc, first_gc.is_cms_gc, second_gc.is_cms_gc]):
continue
survivor_death_rates = list()
for first_age, second_age in zip(first_gc.ages, second_gc.ages[1:]):
# The second age CAN be bigger than the first age. I verified
# this in the gc.logs (still not sure how/why)
# ID 0 is the age number
# ID 1 is bytes in that age
# ID 2 is the total bytes for that age
if second_age[1] == -1:
# I don't think I want to capture any changes if
# the survivor space didn't exist (-1 as a default value- see above)
continue
# survivor_death_rates.append(Decimal(0))
else:
survivor_death_rates.append(1 - (Decimal(second_age[1]) / first_age[1]))
gc_survivor_death_rates.append(survivor_death_rates)
#################################################################################
# Since I have 2 in-scope valid GCs, I'm going to calculate some needed JVM sizes
# the sizes will be fixed if I have a fixed heap size (which we do in prod)
jvm_mem_cfg = dict()
try:
jvm_mem_cfg["og_size"] = (first_gc.total_heap - first_gc.young_size_total) * 1024
except TypeError:
display.render("\n".join(textwrap.wrap("Error: I could not find a non CMS/FGC GC record for analysis. Exiting.", display.textwrap_offset)))
display.render("")
sys.exit(1)
jvm_mem_cfg["survivor_size"] = (first_gc.desired_survivor_size * 2)
jvm_mem_cfg["eden_size"] = (first_gc.young_size_total * 1024) - jvm_mem_cfg["survivor_size"]
jvm_mem_cfg["total_heap"] = (first_gc.total_heap * 1024) + jvm_mem_cfg["survivor_size"]
jvm_mem_cfg["new_size"] = (jvm_mem_cfg["eden_size"] + (jvm_mem_cfg["survivor_size"] * 2))
#########################################################
# Now that I have a crap-ton of curated data, report out.
# This grabs the first part of the tuple (which is
# the total allocation for that gc (not growth!)
yg_alloc_rates = [entry[0] for entry in yg_rates]
min_yg_rate, mean_yg_rate, max_yg_rate = _min(yg_alloc_rates), mean(yg_alloc_rates), _max(yg_alloc_rates)
display.render("YG Allocation Rates*:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~\n")
display.render("per sec (min/mean/max): {0:>13} {1:>13} {2:>13}\n".format(reduce_k(min_yg_rate) + "/s", reduce_k(mean_yg_rate) + "/s", reduce_k(max_yg_rate) + "/s"))
display.render("per hr (min/mean/max): {0:>13} {1:>13} {2:>13}\n".format(reduce_k(min_yg_rate * 3600) + "/h", reduce_k(mean_yg_rate * 3600) + "/h", reduce_k(max_yg_rate * 3600) + "/h"))
display.render("\n")
# This grabs the second part of the tuple (which is
# the total growth for that gc (not allocation rate!)
min_og_rate, mean_og_rate, max_og_rate = _min(og_rates), mean(og_rates), _max(og_rates)
display.render("OG Promotion Rates:\n")
display.render("~~~~~~~~~~~~~~~~~~~\n")
display.render("per sec (min/mean/max): {0:>13} {1:>13} {2:>13}\n".format(reduce_k(min_og_rate) + "/s", reduce_k(mean_og_rate) + "/s", reduce_k(max_og_rate) + "/s"))
display.render("per hr (min/mean/max): {0:>13} {1:>13} {2:>13}\n".format(reduce_k(min_og_rate * 3600) + "/h", reduce_k(mean_og_rate * 3600) + "/h", reduce_k(max_og_rate * 3600) + "/h"))
display.render("\n")
################################################
# Survivor Lengths- wanted to make a nested list
# comprehension, but I suppose that's a bit ugly
# to debug/read
display.render("Survivor Death Rates:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~\n")
survivor_lengths = list()
for sub_arr in gc_survivor_death_rates:
survivor_lengths.append(len([elem for elem in sub_arr if elem > 0]))
display.render("Lengths (min/mean/max): {0}/{1:0.1f}/{2}\n".format(_min(survivor_lengths), mean(survivor_lengths), _max(survivor_lengths)))
display.render("Death Rate Breakdown:\n")
cuml_pct = 1
death_ages = list()
for survivor_num, pct_list in enumerate(zip_longest(*gc_survivor_death_rates, fillvalue=0), 1):
min_pct = min(pct_list)
mean_pct = mean(pct_list)
max_pct = max(pct_list)
cuml_pct *= 1 - mean_pct
death_ages.append(mean_pct)
survivor_info[survivor_num] = min_pct, mean_pct, max_pct
display.render(" Age {0}: {1:>5} / {2:>5} / {3:>5} / {4:>5} (min/mean/max/cuml alive %)\n".format(survivor_num, "{0:0.1%}".format(min_pct), "{0:0.1%}".format(mean_pct), "{0:0.1%}".format(max_pct),
"{0:0.1%}".format(cuml_pct)))
##################################
# GC Times
young_gc_times = [record.young_gc_time * 1000 for record in gc_data if not record.is_stw_gc]
full_gc_times = [record.stw_time * 1000 for record in gc_data if record.is_stw_gc]
if sample_time_secs:
if young_gc_count_delta:
ygc_rate = (young_gc_count_delta / sample_time_secs) * 60
else:
ygc_rate = 0
if full_gc_count_delta:
fgc_rate = (full_gc_count_delta / sample_time_secs) * 60
else:
fgc_rate = 0
display.render("\n")
display.render("GC Information:\n")
display.render("~~~~~~~~~~~~~~~\n")
display.render("YGC/FGC Count: {0}/{1} (Rate: {2:0.2f}/min, {3:0.2f}/min)\n".format(young_gc_count_delta, full_gc_count_delta, ygc_rate, fgc_rate))
display.render("\n")
display.render("Sample Period GC Load: {0:0.2f}%\n".format(sample_gc_load))
display.render("")
display.render("CMS Sweep Times: {0:0.3f}s / {1:0.3f}s / {2:0.3f}s / {3:0.2f} (min/mean/max/stdev)\n".format(_min(cms_times), mean(cms_times), _max(cms_times), stdev(cms_times)))
display.render("YGC Times: {0:0.0f}ms / {1:0.0f}ms / {2:0.0f}ms / {3:0.2f} (min/mean/max/stdev)\n".format(_min(young_gc_times), mean(young_gc_times), _max(young_gc_times), stdev(young_gc_times)))
display.render("FGC Times: {0:0.0f}ms / {1:0.0f}ms / {2:0.0f}ms / {3:0.2f} (min/mean/max/stdev)\n".format(_min(full_gc_times), mean(full_gc_times), _max(full_gc_times), stdev(full_gc_times)))
agg_ygc_time = sum(young_gc_times)
agg_fgc_time = sum(full_gc_times)
display.render("Agg. YGC Time: {0:0.0f}ms\n".format(agg_ygc_time))
display.render("Agg. FGC Time: {0:0.0f}ms\n".format(agg_fgc_time))
display.render("\n")
if og_rates:
display.render(
"Est. Time Between FGCs (min/mean/max): {0:>10} {1:>10} {2:>10}\n".format(reduce_seconds(jvm_mem_cfg["og_size"] / min_og_rate), reduce_seconds(jvm_mem_cfg["og_size"] / mean_og_rate),
reduce_seconds(jvm_mem_cfg["og_size"] / max_og_rate)))
else:
display.render("Est. Time Between FGCs (min/mean/max): {0:>10} {1:>10} {2:>10}\n".format("n/a", "n/a", "n/a"))
display.render("Est. OG Size for 1 FGC/hr (min/mean/max): {0:>10} {1:>10} {2:>10}\n".format(reduce_k(min_og_rate * 3600), reduce_k(mean_og_rate * 3600), reduce_k(max_og_rate * 3600)))
display.render("\n")
display.render("Overall JVM Efficiency Score*: {0:0.3f}%\n".format(100 - sample_gc_load))
display.render("\n")
###################################
# JMap Data
display.render("Current JVM Mem Configuration:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
if jmap_data:
for k, v in jmap_data.items():
if "Size" in k:
v = reduce_k(v / 1024)
display.render("{0:>17}: {1}\n".format(k, v))
else:
for k, v in jvm_mem_cfg.items():
display.render("{0:>17}: {1}\n".format(k, reduce_k(v / 1024)))
display.render("\n")
######################
# Show recommendations
_show_recommendations(death_ages, young_gc_times, full_gc_times, fgc_rate, ygc_rate, yg_alloc_rates, og_rates, jvm_mem_cfg, jmap_data, jstat_data, gc_data, cms_times, survivor_info,
optimized_for_ygcs_rate, proc_details)
display.render("~~~\n")
display.render("* The allocation rate is the increase in usage before a GC done. Growth rate\n")
display.render(" is the increase in usage after a GC is done.\n")
display.render("\n")
display.render("* The JVM efficiency score is a convenient way to quantify how efficient the\n")
display.render(" JVM is. The most efficient JVM is 100% (pretty much impossible to obtain).\n")
if full_gc_count_delta == 0:
display.render("\n")
display.render("* There were no full GCs during this sample period. This reporting will\n")
display.render(" be less useful/accurate as a result.\n")
display.render("\n")
display.render("* A copy of the critical data used to generate this report is stored\n")
display.render(" in /tmp/jtune_data-{0}.bin.bz2. Please copy this to your homedir if you\n".format(user))
display.render(" want to save/analyze this further.\n")
def _get_survivor_info(death_ages=None, survivor_info=None, gc_data=None, survivor_problem_pct=None, curr_ng_size=None, adj_ng_size=None):
"""This looks at the survivor info data structure, and will return the max
tenuring size, and max tenuring age that it feels is needed."""
# This is roughly how much larger the survivor space should be to counteract the increase
# in the frequency of ygcs caused from the smaller NG size as it pushes data into the
# survivor space more often. I don't need to change the MaxTenuringThreshold as that is
# mostly constant depending on how data ages.
#
# I'm adjusting the size of the survivor space based on the eden change. It MAY be better
# adjusting this based on time of how frequent the ygcs are happening.
ng_size_delta = curr_ng_size - adj_ng_size
# Going to use this to change the maxtenuringtrheshold parameter. The reason is that
# ygcs will happen less/more often if I change the ng size, and I'll need to counter
# that by increasing/decreasing the tenuring threshold to keep things in balance.
ng_size_delta_pct = adj_ng_size / curr_ng_size
# Changing the 'survivor_problem_pct' which is the watermark
# for objects still alive. If it's over that amount, then the
# tenuring threshold needs to be increased, if it's less, then
# the age is good. HOWEVER, I use death rate, so[-1] a 85% death
# rate is a 15% survivor rate.
survivor_watermark = 100 - survivor_problem_pct
# Get the max survivor age allowed per the jvm configuration
max_survivor_age = gc_data[0].max_threshold
# The survivor_info structure is the decrease in size for that
# age going into the next, so if the max here is 6, the actual max
# survivor size used is 7.
longest_used_ratio = len(survivor_info) + 1
# Survivor percentage of surviving objects
age_objects_still_alive = list()
current_percentage = 100
for key in sorted(survivor_info):
# [1] is the average, [2] is the max
mean_death_rate_pct = survivor_info[key][1]
current_percentage *= 1 - mean_death_rate_pct
age_objects_still_alive.append(current_percentage)
error_msg = None
if max_survivor_age < 15:
if longest_used_ratio == max_survivor_age:
if age_objects_still_alive[-1] > ((100 - survivor_watermark) / 100.0):
error_msg = "The survivor ratio of {0} is too small as {1:0.1f}% of the objects are still alive. Try increasing the MaxTenuringThreshold (Max: 15) parameter, and running this analysis again.".format(
longest_used_ratio, age_objects_still_alive[-1])
elif not survivor_info:
error_msg = "For the examined sample period, I could not retrieve any meaningful survivor statistics from the gc.log. This JVM is either sick, or the sample period was too short."
elif not survivor_info:
error_msg = "For the examined sample period, I could not retrieve any meaningful survivor statistics from the gc.log. This JVM is either sick, or the sample period was too short."
elif not survivor_info:
error_msg = "For the examined sample period, I could not retrieve any meaningful survivor statistics from the gc.log. This JVM is either sick, or the sample period was too short."
if error_msg:
raise ValueError(error_msg)
###########################################################
# Don't confuse the 'min()' with the 'max' variable. I want
# the first age where it's less than survivor_problem_pct
try:
max_tenuring_age = min([k for k, v in enumerate(age_objects_still_alive, 1) if v <= survivor_problem_pct])
except ValueError:
max_tenuring_age = 0
if not max_tenuring_age:
# Not sure if I like this algorithm, but it seems close enough
below_threshold_ct = len([death_pct for death_pct in death_ages if death_pct <= Decimal(".04")])
below_threshold_pct = below_threshold_ct / float(len(death_ages))
# If more than 33% of the ages are at or below 4%, make a note of it.
if below_threshold_pct > .33:
# It's speculative that I should add to the heap any objects that aren't reaped
# after cutting off the MaxTenuringThrehold, but since it's not getting reaped anyway,
# it may not change anything, so not adjusting for the time being.
# We're using all the available ages, but objects are still alive...
if max_survivor_age == len(death_ages):
display.render("\n".join(textwrap.wrap(
"* Warning: It looks like your tenuring threshold is too high - {0:0.0%} of your ages are reaping at or below 4% of the objects. We could make it easier for the JVM if we reduce your MaxTenuringThreshold by {1} to {2} instead of {3}.".format(
below_threshold_pct, below_threshold_ct, len(death_ages) - below_threshold_ct, max_survivor_age))))
else:
display.render("\n".join(textwrap.wrap(
"* Warning: It looks like your tenuring threshold is too high - {0:0.0%} of your ages are reaping at or below 4% of the objects. We could make it easier for the JVM if we reduce your MaxTenuringThreshold by {1} to {2} instead of {3}. BE CAREFUL - your max *used* age in the gc.logs of {4} is less than the configured max age of {3} - make sure that you used a large enough sample size, and let the JVM go through 3 FGCs (option: '-s 3') and is being checked during peak traffic.".format(
below_threshold_pct, below_threshold_ct, len(death_ages) - below_threshold_ct, max_survivor_age, len(death_ages)))))
max_tenuring_age = len(death_ages) - below_threshold_ct
else:
display.render("\n".join(textwrap.wrap(
"* Warning: Your survivor age is too short, your last age of {0} has {1:0.2f}% of its objects still alive. Because of this, I'm unable to reliably determine how your objects are aging. Unset or increase the MaxTenuringThreshold (max: 15) to mitigate this problem.".format(
len(age_objects_still_alive), age_objects_still_alive[-1]))))
tenure_sizes = list()
for gc_record in gc_data:
try:
tenure_sizes.append(gc_record.ages[max_tenuring_age - 1][2])
except IndexError:
# I saw a gc record that doesn't have that age
# level, so skip it.
pass
# It's recommended to have the tenuring size 2x the max tenure size, I then
# add in the change in newgen (ng_size_delta) to offset the decrease/increase
# in newgen as calculated in this parent's function. The 'ng_size_delta / 2' is
# such that I increase the whole max_tenuring_size by ng_size_delta, but since
# there are two survivor spaces, I need to split the ng_size_delta by 2 for each
# survivor space
max_tenuring_size = (max(tenure_sizes) * 2) + (ng_size_delta / 2)
survivor_ratio = adj_ng_size / max_tenuring_size
# Checking if survivor space is LARGER than the newgen size
if survivor_ratio < 1:
display.render("\n".join(textwrap.wrap(
"* Warning: The calculated recommended survivor ratio of {0:0.2f} is less than 1. This is not possible, so I increased the size of newgen by {1}, and set the survivor ratio to 1. Try the tuning suggestions, and watch closely.\n".format(
survivor_ratio, reduce_k((max_tenuring_size - adj_ng_size) / 1024)), display.textwrap_offset)) + "\n\n")
# This is close, but still wrong. If I run into this condition, then I need to
# also fix the newgen size b/c the tenured size is based off of the newgen
# size before I knew there was an issue. I think this is probably close enough
# for now.
survivor_ratio = 1
adj_ng_size = max_tenuring_size
else:
adj_ng_size += max_tenuring_size
# Now, change the max tenuring age/threshold
max_tenuring_age *= (1 / ng_size_delta_pct)
return adj_ng_size, survivor_ratio, max_tenuring_size, max_tenuring_age
def _show_recommendations(death_ages=None, young_gc_times=None, full_gc_times=None, fgc_rate=None, ygc_rate=None, yg_alloc_rates=None, og_rates=None, jvm_mem_cfg=None, jmap_data=None, jstat_data=None,
gc_data=None, cms_times=None, survivor_info=None, optimized_for_ygcs_rate=None, proc_details=None):
"""This is where any jvm tuning recommendations happens."""
###########################################################################
# The basis of these recommendations are as follows:
#
# 1) More frequent YGCs which take less time is almost always better
# than less frequent YGCs, but taking longer; consistently slow is
# better than periodically slower
# 2) YGC times should have a low standard deviation(<= 5)
# 3) YGC times should be low (<= 50ms, ideally)
display.render("Recommendation Summary:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~~~\n")
# This is how many ygcs/sec should be happening, if the mean ygc
# times are higher than desired
ygc_time_goal_ms = 50
ygc_stdev_goal = 5
# YGC mean ms percentile - lop off the worst offenders
# I am changing it instead of a mean of the 99p, doing a
# max of the 75p; may be better
ygc_pctile = 75
# This is just for analysis purposes; need a decent sample set count
ygc_count_goal = 10
fgc_count_goal = 3
# Marker for indicating if current config is good for
# the Java G1 garbage collector
ready_for_g1 = False
survivor_problem_pct = 10
ygc_stdev = stdev(percentile(young_gc_times, ygc_pctile))
ygc_mean_ms = float(max(percentile(young_gc_times, ygc_pctile)))
if jmap_data:
curr_ng_size = jmap_data['NewSize']
curr_og_size = jmap_data['OldSize']
# Not using b/c this data is not in the GC logs (and
# really doesn't need to be tuned...
# if "PermSize" in jmap_data:
# curr_pg_ms_size = jmap_data['PermSize']
# else:
# curr_pg_ms_size = jmap_data['MetaspaceSize']
max_heap_size = jmap_data['MaxHeapSize']
else:
curr_ng_size = jvm_mem_cfg["new_size"]
curr_og_size = jvm_mem_cfg["og_size"]
max_heap_size = jvm_mem_cfg["total_heap"]
adj_ng_size = curr_ng_size
#########################################################################################################
# This is an estimate. Because we use CMS for FGCs, it's an iterative process, and while the CMS reset is
# happening, more objects are being tenured into OG. The best we can do (I think) is to find the minimum
# size of OU, and go from there. This is why it's super important to have more than 2 FGCs to look at.
#
# This is tricky. I need to find the first record where the previous og size is bigger than
# the current. This identifies when the first CMS runs, and from there, I can find the minimum
normal_gc_data = [x for x in gc_data if x.og_used > 0]
try:
record_num = [record_num for record_num, first_gc, second_gc in zip(count(), normal_gc_data, normal_gc_data[1:]) if first_gc.og_used > second_gc.og_used][0]
except IndexError:
live_data_size_bytes = None
else:
live_data_size_bytes = _min(record.og_used for record in normal_gc_data[record_num:])
if proc_details and proc_details['proc_uptime_seconds'] < 300:
display.render("\n".join(textwrap.wrap(
"Warning: The process I'm doing the analysis on has been up for {0}, and may not be in a steady-state. It's best to let it be up for more than 5 minutes to get more realistic results.\n".format(
reduce_seconds(proc_details['proc_uptime_seconds'])))) + "\n\n")
#################################################
# Find the recommended NewGen size
if len(young_gc_times) < ygc_count_goal:
display.render("\n".join(
textwrap.wrap("Warning: There were only {0} YGC entries to do the analysis on. It's better to have > {1} to get more realistic results.\n".format(len(young_gc_times), ygc_count_goal),
display.textwrap_offset)) + "\n\n")
if ygc_stdev > ygc_stdev_goal * 4:
comment = "VERY inconsistent"
elif ygc_stdev > ygc_stdev_goal * 2:
comment = "pretty inconsistent"
elif ygc_stdev > ygc_stdev_goal:
comment = "somewhat consistent"
ready_for_g1 = True
else:
comment = "very consistent"
ready_for_g1 = True
messages = list()
# This logic block goes through different optimizaion scenarios that it
# uses to find an optimal setting.
# messages.append("- The mean YGC rate is {0:0.2f}/min, and the max {1} percentile YGC time is {2:0.0f}ms (stdev of {3:0.2f} which is {4}). It's best to have the mean YGC time be at or below {5}ms, and the YGC stdev at or below {6} if possible.".format(ygc_rate, ord_num(ygc_pctile), ygc_mean_ms, ygc_stdev, comment, ygc_time_goal_ms, ygc_stdev_goal))
# TODO: Too much repetition in this code block
if (optimized_for_ygcs_rate > ygc_rate) and (ygc_stdev > ygc_stdev_goal or ygc_mean_ms > ygc_time_goal_ms):
adj_ng_size = curr_ng_size * (ygc_rate / optimized_for_ygcs_rate)
######################################################################
# Figure out Tenuring Threshold & size for the survivor spaces, basing
# it on the last age where below 10% still live
try:
new_adj_ng_size, survivor_ratio, max_tenuring_size, max_tenuring_age = _get_survivor_info(death_ages, survivor_info, gc_data, survivor_problem_pct, curr_ng_size, adj_ng_size)
# Go ahead and set it regardless
adj_ng_size = new_adj_ng_size
except ValueError as msg:
display.render("\n" + "\n".join(textwrap.wrap("* Error: {0}".format(msg), display.textwrap_offset)) + "\n\n")
display.render("")
return False
messages.append(
"- With a mean YGC time goal of {0:0.0f}ms, the suggested (optimized for a YGC rate of {1:0.2f}/min) size of NewGen (including adjusting for calculated max tenuring size) considering the above criteria should be {2:0.0f} MiB (currently: {3:0.0f} MiB).".format(
ygc_time_goal_ms, optimized_for_ygcs_rate, float(adj_ng_size) / 1024.0 / 1024.0, float(curr_ng_size) / 1024.0 / 1024.0))
if new_adj_ng_size < curr_ng_size:
messages.append(
"- Because we're decreasing the size of NewGen, it can have an impact on system load due to increased memory management requirements. There's not an easy way to predict the impact to the application, so watch this after it's tuned.")
elif ygc_mean_ms > ygc_time_goal_ms:
adj_ng_size = curr_ng_size * (ygc_time_goal_ms / ygc_mean_ms)
######################################################################
# Figure out Tenuring Threshold & size for the survivor spaces, basing
# it on the last age where below 10% still live
try:
new_adj_ng_size, survivor_ratio, max_tenuring_size, max_tenuring_age = _get_survivor_info(death_ages, survivor_info, gc_data, survivor_problem_pct, curr_ng_size, adj_ng_size)
# Go ahead and set it regardless
adj_ng_size = new_adj_ng_size
except ValueError as msg:
display.render("\n" + "\n".join(textwrap.wrap("* Error: {0}".format(msg), display.textwrap_offset)) + "\n\n")
display.render("")
return False
messages.append(
"- With a mean YGC time goal of {0:0.0f}ms, the suggested (optimized for YGC time) size of NewGen (including adjusting for calculated max tenuring size) considering the above criteria should be {1:0.0f} MiB (currently: {2:0.0f} MiB).".format(
ygc_time_goal_ms, float(adj_ng_size) / 1024.0 / 1024.0, float(curr_ng_size) / 1024.0 / 1024.0))
if new_adj_ng_size < curr_ng_size:
messages.append(
"- Because we're decreasing the size of NewGen, it can have an impact on system load due to increased memory management requirements. There's not an easy way to predict the impact to the application, so watch this after it's tuned.")
else:
adj_ng_size = curr_ng_size
######################################################################
# Figure out Tenuring Threshold & size for the survivor spaces, basing
# it on the last age where below 10% still alive
try:
new_adj_ng_size, survivor_ratio, max_tenuring_size, max_tenuring_age = _get_survivor_info(death_ages, survivor_info, gc_data, survivor_problem_pct, curr_ng_size, adj_ng_size)
# Go ahead and set it regardless
adj_ng_size = new_adj_ng_size
except ValueError as msg:
display.render("\n" + "\n".join(textwrap.wrap("* Error: {0}".format(msg), display.textwrap_offset)) + "\n\n")
display.render("")
return False
messages.append("- The mean YGC rate is {0:0.2f}/min, and the mean YGC time is {1:0.0f}ms (stdev of {2:0.2f} which is {3}).".format(ygc_rate, ygc_mean_ms, ygc_stdev, comment))
for message in messages:
display.render("\n".join(textwrap.wrap(message)) + "\n")
#################################################
# Find the recommended PermGen size
#
# Removing this block b/c permgen/metaspace usage isn't in the gc.logs
#
############################################
# Find out what the survivor ratio should be
display.render("\n".join(
textwrap.wrap("- Looking at the worst (max) survivor percentages for all the ages, it looks like a TenuringThreshold of {0:0.0f} is ideal.".format(max_tenuring_age), display.textwrap_offset)) + "\n")
display.render("\n".join(textwrap.wrap(
"- The survivor size should be 2x the max size for tenuring threshold of {0:0.0f} given above. Given this, the survivor size of {1:0.0f}M is ideal.".format(max_tenuring_age,
max_tenuring_size / 1024 / 1024), display.textwrap_offset)) + "\n")
display.render("\n".join(textwrap.wrap("- To ensure enough survivor space is allocated, a survivor ratio of {0:0.0f} should be used.".format(survivor_ratio), display.textwrap_offset)) + "\n")
#################################################
# Find the recommended max heap size
if len(full_gc_times) < fgc_count_goal:
display.render("\n" + "\n".join(textwrap.wrap(
"* Error: You really need to have at least {0} (preferably more) FGCs happen (I found {1}) before doing any OG size recommendation analysis. Stopping any further analysis.\n".format(
fgc_count_goal, len(full_gc_times)), display.textwrap_offset)) + "\n\n")
display.render("\n")
return False
recommended_max_heap_size = 3.5 * float(live_data_size_bytes) + float(max_tenuring_size + adj_ng_size)
if max_heap_size != recommended_max_heap_size:
display.render("\n".join(textwrap.wrap(
"- It's recommended to have the max heap size 3-4x the size of the live data size (OldGen + PermGen), and adjusted to include the recommended survivor and newgen size. New recommended size is {0:0.0f}MiB (currently: {1:0.0f}MiB).".format(
float(recommended_max_heap_size) / 1024.0 / 1024.0, float(max_heap_size) / 1024.0 / 1024.0), display.textwrap_offset)) + "\n")
#################################################
# Figure out the occupancy fraction
max_cms_time = float(_max(cms_times))
# Not doing the MAX, but a max of a percentile of the og rates- I think that's better
# maybe doing a mean of a percentile?
pct_number = 99
# KiB -> B
max_og_rate = float(_max(percentile(og_rates, pct_number))) * 1024
oldgen_offset = curr_og_size - (float(_max(yg_alloc_rates) / 1024) * max_cms_time) - (max_cms_time * max_og_rate)
occ_fraction = math.floor((float(oldgen_offset) / curr_og_size) * 100)
display.render("\n".join(textwrap.wrap(
"- With a max {0} percentile OG promotion rate of {1}/s, and the max CMS sweep time of {2}s, you should not have a occupancy fraction any higher than {3:0.0f}.".format(ord_num(pct_number),
reduce_k(Decimal(str(
max_og_rate / 1024.0))),
max_cms_time,
occ_fraction),
display.textwrap_offset)) + "\n")
# Java 7 G1 Stuff
display.render("\n")
display.render("Java G1 Settings:\n")
display.render("~~~~~~~~~~~~~~~~~~~\n")
if ready_for_g1:
display.render("\n".join(textwrap.wrap(
"- With a max ygc stdev of {0:0.2f}, and a {1} percentile ygc mean ms of {2:0.0f}ms, your config is good enough to move to the G1 garbage collector.".format(ygc_stdev, ord_num(pct_number),
ygc_mean_ms),
display.textwrap_offset)) + "\n")
display.render("\n".join(textwrap.wrap("- Since G1 uses one space for everything, the consolidated heap size should be {0:0.0f}MiB.".format(float(recommended_max_heap_size) / 1024.0 / 1024.0),
display.textwrap_offset)) + "\n")
else:
display.render("\n".join(textwrap.wrap(
"- With a max ygc stdev of {0:0.2f}, and a {1} percentile ygc mean ms of {2:0.0f}ms, your config is probably not ready to move to the G1 garbage collector. Try tuning the JVM, and see if that improves things first.".format(
ygc_stdev, ord_num(pct_number), ygc_mean_ms), display.textwrap_offset)) + "\n")
display.render("\n")
display.render("The JVM arguments from the above recommendations:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n")
display.render("\n".join(textwrap.wrap("-Xmx{0:0.0f}m -Xms{0:0.0f}m -Xmn{1:0.0f}m -XX:SurvivorRatio={2:0.0f} -XX:MaxTenuringThreshold={3:0.0f} -XX:CMSInitiatingOccupancyFraction={4:0.0f}".format(recommended_max_heap_size / 1024.0 / 1024.0, float(adj_ng_size) / 1024.0 / 1024.0, survivor_ratio, max_tenuring_age, occ_fraction), display.textwrap_offset)) + "\n")
if ready_for_g1:
display.render("\n")
display.render("The JVM arguments for G1:\n")
display.render("~~~~~~~~~~~~~~~~~~~~~~~~~\n")
display.render("\n".join(textwrap.wrap("-XX:+UseG1GC -XX:MaxGCPauseMillis={0:0.0f} -Xms{1:0.0f}m -Xmx{1:0.0f}m ".format(ygc_mean_ms, recommended_max_heap_size / 1024.0 / 1024.0), display.textwrap_offset)) + "\n")
def get_proc_info(pid=None):
"""Return a data structure with details of the given process id
Keyword arguments:
pid -- the process id of the process to be checked
"""
details = dict()
try:
cpu_ticks_per_sec = int(os.sysconf(os.sysconf_names['SC_CLK_TCK']))
bytes_per_page = resource.getpagesize()
details['gc_file_rotation'] = False
for line in liverun("readlink /proc/{0}/cwd".format(pid)):
line = line.decode()
details['proc_cwd'] = line.strip()
with open("/proc/{0}/cmdline".format(pid), "r") as _file:
for blob in _file:
for line in blob.split("\0"):
if "-Xloggc" in line:
gc_path = line.split(":", 1)[1]
if gc_path.startswith("/"):
details['gc_log_path'] = gc_path
else:
details['gc_log_path'] = details['proc_cwd'] + "/" + gc_path
elif "/bin/java" in line:
details['java_path'] = os.path.dirname(line)
elif "-XX:+UseGCLogFileRotation" in line:
details['gc_file_rotation'] = True
elif "-Xms" in line:
details['min_heap_size'] = line.split("ms")[1]
elif "-Xmx" in line:
details['max_heap_size'] = line.split("mx")[1]
elif "-XX:+PrintGCDateStamps" in line:
details['print_gc_date_stamps'] = True
elif "-XX:+PrintGCDetails" in line:
details['print_gc_details'] = True
elif "-XX:+PrintTenuringDistribution" in line:
details['print_tenuring_distribution'] = True
elif "-XX:SurvivorRatio=" in line:
details['survivor_ratio'] = line.split("SurvivorRatio=")[1]
elif "-XX:+UseConcMarkSweepGC" in line:
details['use_cms'] = True
elif "-XX:+UseParNewGC" in line:
details['use_parnew'] = True
if 'java_path' not in details:
details['java_path'] = ''.join(liverun("which java")).strip().replace("/java", "")
with open("/proc/uptime", "r") as _file:
for line in _file:
details['sys_uptime_seconds'] = Decimal(line.split()[0])
break
with open("/proc/{0}/stat".format(pid), "r") as _file:
for line in _file:
field = line.split()
utime_ticks = int(field[13])
stime_ticks = int(field[14])
num_threads = int(field[19])
uptime_ticks = int(field[21])
vsize_bytes = int(field[22])
rss_bytes = int(field[23]) * bytes_per_page
details['proc_uptime_seconds'] = (details['sys_uptime_seconds']) - Decimal(str(uptime_ticks / float(cpu_ticks_per_sec)))
details['proc_utime_seconds'] = utime_ticks / Decimal(cpu_ticks_per_sec)
details['proc_stime_seconds'] = stime_ticks / Decimal(cpu_ticks_per_sec)
details['proc_rss_bytes'] = rss_bytes
details['proc_vsize_bytes'] = vsize_bytes
details['num_threads'] = num_threads
break
for line in liverun("{0}/java -version".format(details['java_path'])):
line = line.decode()
if "java version" in line:
line = line.strip().replace("\"", "")
fields = line.split()
details['java_build_version'] = fields[-1]
match = re.match(r"^(\d+)\.(\d+)\.(\d+)", details['java_build_version'])
details['java_ver_int'] = match.group(2)
break
except IOError:
# The data structure will be empty, and I'll catch it when
# I get a key error on accessing it
pass
return details
def process_gclog(log_file=None, log_file_pos=0):
"""Pretty basic function that iterates through a gc log, and returns a data structure
of the log data.
Keyword arguments:
log_file -- the gc log file to be read
log_file_pos -- the offset of the log file from whence to start (as bytes)
"""
gc_log_queue = list()
try:
line_num = 0
print()
print("* Reading gc.log file...", end=" ")
current_size = os.stat(log_file).st_size
if current_size < log_file_pos:
print("log file was truncated/rotated; reading from the start", end=" ")
log_file_pos = 0
start_time = datetime.datetime.now()
with open(log_file, "r") as _file:
_file.seek(log_file_pos)
for line in _file:
gc_log_queue.append(line)
line_num += 1
elapsed_time = sec_diff(start_time, datetime.datetime.now())
print("done. Scanned {0} lines in {1:0.4f} seconds.".format(line_num, elapsed_time))
except IOError:
# I don't want/need to check the exception. If it fails, it fails.
pass
else:
gc_log_queue.append("END_OF_FILE")
return gc_log_queue
def _run_jmap(pid=None, procdetails=None):
"""Rung jmap for the given process id, and java path, returning
a data structure with the information"""
jmap_data = dict()
java_path = procdetails['java_path']
try:
for line in liverun("{0}/jmap -J-Xmx128M -heap {1}".format(java_path, pid)):
line = line.decode()
field = line.split()
if "MinHeapFreeRatio" in line:
jmap_data['MinHeapFreeRatio'] = int(field[2])
elif "MaxHeapFreeRatio" in line:
jmap_data['MaxHeapFreeRatio'] = int(field[2])
elif "MaxHeapSize" in line:
jmap_data['MaxHeapSize'] = int(field[2])
elif "NewSize" in line:
jmap_data['NewSize'] = int(field[2])
elif "MaxNewSize" in line:
jmap_data['MaxNewSize'] = int(field[2])
elif "OldSize" in line:
# JMap seems to be scaled wrong. Comparing it to jstat, it shows that
# it's off by about 1000 (1024). There's a bug in Java6 where this is in KB
# not bytes like the others. Appears to be fixed in Java8 (maybe Java7, too)
java_int = int(procdetails['java_ver_int'])
if java_int < 8:
jmap_data['OldSize'] = int(field[2]) * 1024
else:
jmap_data['OldSize'] = int(field[2])
elif "NewRatio" in line:
jmap_data['NewRatio'] = int(field[2])
elif "SurvivorRatio" in line:
jmap_data['SurvivorRatio'] = int(field[2])
elif "PermSize" in line:
jmap_data['PermSize'] = int(field[2])
elif "MaxPermSize" in line:
jmap_data['MaxPermSize'] = int(field[2])
elif "MaxMetaspaceSize" in line:
if "MB" in line:
jmap_data['MaxMetaspaceSize'] = int(field[2]) * 1024 * 1024
else:
jmap_data['MaxMetaspaceSize'] = int(field[2])
elif "MetaspaceSize" in line:
jmap_data['MetaspaceSize'] = int(field[2])
except (IOError, KeyboardInterrupt):
pass
return jmap_data
def run_jstat(pid=None, java_path=None, no_jstat_output=None, fgc_stop_count=None, max_count=None, ygc_stop_count=None):
"""Rung jstat, and outputs the data in a nice column and aligned layout.
Keyword arguments:
pid -- the process pid to run jstat against
java_path -- the path to use to run jstat
no_jstat_output -- true/false that tells this function to not output any data
fgc_stop_count -- the integer value that tells this function to stop at this number of full (cms) gcs
max_count -- the max number of lines the function should display
ygc_stop_count -- the integer value that tells this function to stop at this number of young gcs
"""
global subproc
jstat_data = dict()
jstat_data['TIME_STAMP'] = list()
# This is how the columns will be displayed in order.
ordered_fields = ["EC", "EP", "EU", "S0C/S1C", "S0C", "S1C", "S0U", "S1U", "OC", "OP", "OU", "MC", "MU", "PC", "PU", "YGC", "YGCD", "FGC", "FGCD"]
displayed_output = False
combined_survivors = False
field_map = dict()
line_num = 0
field_widths = dict()
first_fgc_ct = None
prev_fgc_ct = None
last_fgc_ct = None
total_fgcs = None
total_ygcs = None
short_fields = True
# Being able to use python3's print function that I could override would
# work much better here; instead I have to do this ghetto way...
display.render("#" * 5 + "\n")
display.render("# Start Time: {0} GMT\n".format(datetime.datetime.now()))
display.render("# Host: {0}\n".format(socket.getfqdn()))
display.render("#" * 5 + "\n")
if max_count > 0:
cmd = "{0}/jstat -J-Xmx128M -gc {1} 1000 {2}".format(java_path, pid, max_count)
else:
cmd = "{0}/jstat -J-Xmx128M -gc {1} 1000".format(java_path, pid)
try:
for line in liverun(cmd):
line = line.decode()
timestamp = datetime.datetime.now()
line = line.strip()
#######################################################################
# Print the header, and first two lines should be printed. After that,
# the logic block at the end (to see if there's been a fgc or not)
# takes over, and prints the line conditionally with decoration
field_num = 0
for field in line.split():
if line_num == 0:
jstat_data[field] = list()
field_map[field_num] = field
else:
field_name = field_map[field_num]
if field_name in ['YGCT', 'FGCT', 'GCT']:
jstat_data[field_name].append(Decimal(field))
else:
# Minding sigfigs- no decimal needed for large numbers; that's
# just silly
jstat_data[field_name].append(Decimal("{0:0.0f}".format(Decimal(field))))
field_num += 1
if jstat_data['OC'] and jstat_data['OU']:
# Better to handle the percentage-awareness here instead
# of making a unique conditional later on
if "OP" not in jstat_data:
jstat_data['OP'] = list()
jstat_data['OP'].append("{0:0.1%}".format(jstat_data['OU'][-1] / jstat_data['OC'][-1]))
if jstat_data['EC'] and jstat_data['EU']:
# Better to handle the percentage-awareness here instead
# of making a unique conditional later on
if "EP" not in jstat_data:
jstat_data['EP'] = list()
jstat_data['EP'].append("{0:0.1%}".format(jstat_data['EU'][-1] / jstat_data['EC'][-1]))
if jstat_data['GCT']:
if "YGCD" not in jstat_data:
jstat_data['YGCD'] = list()
if "FGCD" not in jstat_data:
jstat_data['FGCD'] = list()
# Young gc count delta
try:
if jstat_data['YGC'][-1] > jstat_data['YGC'][-2]:
delta = "+" + str(jstat_data['YGC'][-1] - jstat_data['YGC'][-2])
else:
delta = "-"
except IndexError:
delta = "-"
jstat_data['YGCD'].append(delta)
# full gc count delta
try:
if jstat_data['FGC'][-1] > jstat_data['FGC'][-2]:
delta = "+" + str(jstat_data['FGC'][-1] - jstat_data['FGC'][-2])
else:
delta = "-"
except IndexError:
delta = "-"
jstat_data['FGCD'].append(delta)
##################################
# I need at least two lines to get
# historical data
if line_num >= 2:
# Keep a timestamp for each record (to get sub-second granularity)
first_fgc_ct = jstat_data['FGC'][0]
first_ygc_ct = jstat_data['YGC'][0]
prev_fgc_ct = jstat_data['FGC'][-2]
last_fgc_ct = jstat_data['FGC'][-1]
last_ygc_ct = jstat_data['YGC'][-1]
total_fgcs = last_fgc_ct - first_fgc_ct
total_ygcs = last_ygc_ct - first_ygc_ct
#############################################
# line 1 is actual data, 0 is just the header
if line_num > 0:
jstat_data['TIME_STAMP'].append(timestamp)
####################################################
# See if I can combine the S0C/S1C fields (probably)
if jstat_data['S0C'][-1] == jstat_data['S1C'][-1]:
if "S0C/S1C" not in jstat_data:
jstat_data['S0C/S1C'] = list()
jstat_data['S0C/S1C'].append(jstat_data['S0C'][-1])
combined_survivors = True
else:
# This is redundant as I catch it earlier. Leaving it here for now.
logger.error("Looks like you're not running with the CMS garbage collector. You can enable this option by setting your JVM arguments to use '-XX:+UseConcMarkSweepGC'.")
sys.exit(1)
if not field_widths:
field_widths = _get_widths(jstat_data, short_fields)
if not displayed_output:
displayed_output = True
#############################################
# Don't display any output, just continue to
# the next iteration. Ick, double-negative..
if no_jstat_output:
continue
# Print the column header
display.render(" ", keep_newline=False)
for field in ordered_fields:
if combined_survivors and field != "S0C" and field != "S1C":
if field in field_widths:
width = field_widths[field]
display.render("{0:>{1}}".format(field, width + 1), keep_newline=False)
display.render("\n")
# Print a nice line spacer all even-like
display.render(" ", keep_newline=False)
for field in ordered_fields:
if combined_survivors and field != "S0C" and field != "S1C":
if field in field_widths:
width = field_widths[field]
display.render("{0:>{1}}".format("~" * width, width + 1), keep_newline=False)
display.render("\n")
# Print the first row of data that was cached so it can
# be used to determine field widths
display.render(" ", keep_newline=False)
for field in ordered_fields:
if field in field_widths:
width = field_widths[field]
# Get the last value
if combined_survivors and field != "S0C" and field != "S1C":
value = jstat_data[field][0]
if short_fields and field not in ['EP', 'OP', 'YGC', 'YGCT', 'FGC', 'FGCT', 'GCT', 'FGCD', 'YGCD']:
value = reduce_k(value, precision=1)
display.render("{0:>{1}}".format(value, width + 1), keep_newline=False)
display.render("\n")
else:
#################################
# Don't display any output, just
# continue to the next iteration.
if no_jstat_output:
if last_fgc_ct > prev_fgc_ct:
display.render("* ", keep_newline=False)
else:
display.render(" ", keep_newline=False)
# Now print the actual numbers
for field in ordered_fields:
if field in field_widths:
width = field_widths[field]
# Get the last value
if combined_survivors and field != "S0C" and field != "S1C":
value = jstat_data[field][-1]
if short_fields and field not in ['EP', 'OP', 'YGC', 'YGCT', 'FGC', 'FGCT', 'GCT', 'FGCD', 'YGCD']:
value = reduce_k(value, precision=1)
display.render("{0:>{1}}".format(value, width + 1), keep_newline=False)
display.render("\n")
else:
if last_fgc_ct > prev_fgc_ct:
display.render("* ", keep_newline=False)
else:
display.render(" ", keep_newline=False)
# Now print the actual numbers
for field in ordered_fields:
if field in field_widths:
width = field_widths[field]
# Get the last value
if combined_survivors and field != "S0C" and field != "S1C":
value = jstat_data[field][-1]
if short_fields and field not in ['EP', 'OP', 'YGC', 'YGCT', 'FGC', 'FGCT', 'GCT', 'FGCD', 'YGCD']:
value = reduce_k(value, precision=1)
display.render("{0:>{1}}".format(value, width + 1), keep_newline=False)
display.render("\n")
if 0 < fgc_stop_count <= total_fgcs:
break
if 0 < ygc_stop_count <= total_ygcs:
break
line_num += 1
except (IOError, KeyboardInterrupt):
# This triggers if I exit the 'liverun'
pass
finally:
if subproc and subproc.poll() is None:
# The process hasn't terminated
subproc.terminate()
return jstat_data
def _get_widths(jstat_data=None, short_fields=False):
"""Function that returns the recommended field widths of the jstat output"""
widths = dict()
for field in jstat_data:
max_width = max(list(map(len, list(map(str, jstat_data[field])))))
field_width = len(field)
if field_width > max_width:
widths[field] = field_width
else:
widths[field] = max_width
##################################################################
# Special handling for survivor spaces (S0C, S1C, S0U, S1U) should
# all be the same width, and b/c S{01}U alternate, it's better to
# set the width from S{01}C
if short_fields:
# The '5' accounts for 'x.xxN' (3.23K/M/G), etc.
survivor_max = 6
newgen_max = 6
oldgen_max = 6
else:
survivor_max = max(widths['S0C'], widths['S1C'], widths['S0U'], widths['S1U'])
newgen_max = max(widths['EC'], widths['EU'])
oldgen_max = max(widths['OC'], widths['OU'])
widths['OC'] = oldgen_max
widths['OU'] = oldgen_max
widths['EC'] = newgen_max
widths['EU'] = newgen_max
widths['S0C'] = survivor_max
widths['S1C'] = survivor_max
widths['S0U'] = survivor_max
widths['S1U'] = survivor_max
widths['EP'] = 6
widths['OP'] = 6
return widths
def _at_exit(raw_gc_log=None, jmap_data=None, jstat_data=None, proc_details=None, optimized_for_ygcs_rate=None):
"""The exit function that is called when the user presses ctrl-c, or when it exits after X number
of jstat iterations. It calls various functions to display useful information to the end-user."""
gc_data = list()
in_stanza = False
date_time = None
entry = list()
# I don't know if I like this, but I wouldn't get to
# this point unless I asked for GC data from stdin...
if not raw_gc_log:
raw_gc_log = sys.stdin
for line in raw_gc_log:
#############################################################################
# Since I'm using the timestamp as the record stanza delimiter, I may as well
# convert it to a datetime object here instead of doing it later.
match = re.match(r"^(\d+)-(\d+)-(\d+)T(\d+):(\d+):([\d\.]+)[+-]\d+: ([\d\.]+):", line)
if match:
in_stanza = True
# If I'm at the start of a new block, save the previous block
if date_time and entry:
gc_record = GCRecord((date_time, entry))
if gc_data:
prev_gc_record = gc_data[-1]
if gc_record.jvm_running_time and prev_gc_record.jvm_running_time > gc_record.jvm_running_time:
logger.warning("The JVM restarted at {0}. Re-initing the internal datastructures.".format(gc_record.record_timestamp))
gc_data = list()
if gc_record.valid_record:
gc_data.append(gc_record)
entry = list()
year = int(match.group(1))
month = int(match.group(2))
day = int(match.group(3))
hour = int(match.group(4))
minute = int(match.group(5))
second = Decimal(match.group(6))
# up_time = Decimal(match.group(7))
date_time = datetime.datetime.strptime("{0}-{1}-{2} {3}:{4}:{5}".format(year, month, day, hour, minute, second), "%Y-%m-%d %H:%M:%S.%f")
if in_stanza:
entry.append(line)
_run_analysis(gc_data, jmap_data, jstat_data, proc_details, optimized_for_ygcs_rate)
def get_rotated_log_file(gc_log_file):
"""Function will scan existing log files to determine latest rotated log, if none found will return
non rotated file name.
"""
log_number = 0
while os.path.isfile("{0}.{1}".format(gc_log_file, log_number)):
log_number += 1
if log_number:
gc_log_file = "{0}.{1}".format(gc_log_file, (log_number - 1))
else:
logger.debug("\n".join(
textwrap.wrap(
"Was not able to find a rotated GC log for this process, defaulting to gc log from process.",
display.textwrap_offset)))
return gc_log_file
def get_jmap_data(pid=None, procdetails=None):
"""Function that runs jmap, only needed b/c jmap may not start, and this retries on failure."""
jmap_data = None
for seconds in [x * 2 for x in range(1, 8)]:
jmap_data = _run_jmap(pid, procdetails)
if "NewSize" in jmap_data:
break
else:
logger.warning("Couldn't connect to jvm via jmap to get valid data. Sleeping {0:0.0f} seconds, and trying again.".format(seconds))
time.sleep(seconds)
return jmap_data
################################################################
# Main
user = os.environ.get("SUDO_USER", None)
if not user:
user = getpass.getuser()
subproc = None
display = Display()
if __name__ == '__main__':
main()
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220,
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220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
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90,
15,
25,
29,
90,
16,
11709,
1911,
18982,
7203,
93,
1,
1635,
9647,
11,
9647,
1343,
352,
828,
1394,
62,
3605,
1370,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
59,
77,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
12578,
262,
717,
5752,
286,
1366,
326,
373,
39986,
523,
340,
460,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
307,
973,
284,
5004,
2214,
9647,
82,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
220,
33172,
1394,
62,
3605,
1370,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2214,
287,
6149,
62,
25747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2214,
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2214,
62,
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82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9647,
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2214,
62,
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82,
58,
3245,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3497,
262,
938,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5929,
62,
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452,
669,
290,
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366,
50,
15,
34,
1,
290,
2214,
14512,
366,
50,
16,
34,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
474,
14269,
62,
7890,
58,
3245,
7131,
15,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1790,
62,
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290,
2214,
407,
287,
37250,
8905,
3256,
705,
3185,
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705,
56,
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705,
56,
38,
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3256,
705,
37,
15916,
3256,
705,
30386,
4177,
3256,
705,
38,
4177,
3256,
705,
30386,
8610,
3256,
705,
56,
38,
8610,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
4646,
62,
74,
7,
8367,
11,
15440,
28,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
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90,
15,
25,
29,
90,
16,
11709,
1911,
18982,
7,
8367,
11,
9647,
1343,
352,
828,
1394,
62,
3605,
1370,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
59,
77,
4943,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
29113,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2094,
470,
3359,
597,
5072,
11,
655,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2555,
284,
262,
1306,
24415,
13,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
645,
62,
73,
14269,
62,
22915,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
938,
62,
69,
36484,
62,
310,
1875,
8654,
62,
69,
36484,
62,
310,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
9,
33172,
1394,
62,
3605,
1370,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
220,
33172,
1394,
62,
3605,
1370,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2735,
3601,
262,
4036,
3146,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2214,
287,
6149,
62,
25747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2214,
287,
2214,
62,
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82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9647,
796,
2214,
62,
10394,
82,
58,
3245,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3497,
262,
938,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5929,
62,
48846,
452,
669,
290,
2214,
14512,
366,
50,
15,
34,
1,
290,
2214,
14512,
366,
50,
16,
34,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
474,
14269,
62,
7890,
58,
3245,
7131,
12,
16,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1790,
62,
25747,
290,
2214,
407,
287,
37250,
8905,
3256,
705,
3185,
3256,
705,
56,
15916,
3256,
705,
56,
38,
4177,
3256,
705,
37,
15916,
3256,
705,
30386,
4177,
3256,
705,
38,
4177,
3256,
705,
30386,
8610,
3256,
705,
56,
38,
8610,
6,
5974,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
4646,
62,
74,
7,
8367,
11,
15440,
28,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
90,
15,
25,
29,
90,
16,
11709,
1911,
18982,
7,
8367,
11,
9647,
1343,
352,
828,
1394,
62,
3605,
1370,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
59,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
938,
62,
69,
36484,
62,
310,
1875,
8654,
62,
69,
36484,
62,
310,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
9,
33172,
1394,
62,
3605,
1370,
28,
25101,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3359,
13,
13287,
7203,
220,
33172,
1394,
62,
3605,
1370,
28,
25101,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2735,
3601,
262,
4036,
3146,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
2214,
287,
6149,
62,
25747,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
2214,
287,
2214,
62,
10394,
82,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
9647,
796,
2214,
62,
10394,
82,
58,
3245,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
3497,
262,
938,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
5929,
62,
48846,
452,
669,
290,
2214,
14512,
366,
50,
15,
34,
1,
290,
2214,
14512,
366,
50,
16,
34,
1298,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
474,
14269,
62,
7890,
58,
3245,
7131,
12,
16,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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] | 2.208069 | 34,229 |
"""LightningFlower Model"""
import flwr as fl
import pytorch_lightning as pl
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#coding:utf-8
from flask import *
import pymysql
import db,modules
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#
# The sphinx-jinja extension is available from https://github.com/tardyp/sphinx-jinja,
# licensed under the MIT License.
#
#
# The MIT License
#
# Copyright (c) 2016-2019 Pierre Tardy
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import codecs
import os
import sys
import urllib
from docutils import nodes
from docutils.parsers.rst import Directive
from docutils.parsers.rst import directives
from docutils.statemachine import StringList
from jinja2 import FileSystemLoader, Environment
import sphinx.util
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628,
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] | 3.672289 | 415 |
import identifiers_api
from .identifier_object_builder import set_identifier_object
def manage_ids(jsons_data, **metadata_properties):
"""
:param jsons_data:
:param kwargs:
:return:
"""
organism = metadata_properties.get("organism", None)
for dataset in jsons_data:
collection_name = dataset.get("collectionName", None)
collection_data = dataset.get("collectionData", None)
ontology_name = dataset.get("ontologyName", None)
metadata_properties["classAcronym"] = dataset.get("classAcronym", None)
metadata_properties["subClassAcronym"] = dataset.get("subClassAcronym", None)
metadata_properties["ontologyName"] = ontology_name
# Trying to obtain identifiers from the collection that is been
# processed, in order to check if the pre-identifier that is been
# processed is going to be updated or created
collection_identifiers = identifiers_api.regulondbmultigenomic.get_identifiers_by(type=collection_name, ontology_name=ontology_name, organism=organism)
for json_object in collection_data:
identifier_object = set_identifier_object(json_object, collection_name, **metadata_properties)
handle_id(identifier_object, collection_identifiers) | [
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7,
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7483,
62,
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11,
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8
] | 2.898649 | 444 |
from .preprocessing.filter_genes import filter_genes
from .preprocessing.normalize import normalize_total
from .preprocessing.log_scale import log1p
from .preprocessing.log_scale import scale
from .preprocessing.graph import neighbors
from .image_preprocessing.image_tiling import tiling
from .image_preprocessing.feature_extractor import extract_feature
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] | 3.858696 | 92 |
# copyright (c) 2018 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
from __future__ import print_function
import os
import numpy as np
import random
import unittest
import logging
import warnings
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from paddle.fluid.optimizer import AdamOptimizer
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
from paddle.fluid.dygraph.container import Sequential
from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
from paddle.nn.layer import ReLU, LeakyReLU, Sigmoid, Softmax, PReLU
from paddle.nn import Linear, Conv2D, Softmax, BatchNorm2D, MaxPool2D
from paddle.fluid.log_helper import get_logger
from paddle.fluid.dygraph import nn
from imperative_test_utils import fix_model_dict, train_lenet
paddle.enable_static()
os.environ["CPU_NUM"] = "1"
if core.is_compiled_with_cuda():
fluid.set_flags({"FLAGS_cudnn_deterministic": True})
_logger = get_logger(
__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
if __name__ == '__main__':
unittest.main()
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] | 3.194444 | 540 |
# Generated by Django 2.2.3 on 2019-08-08 02:52
from django.db import migrations, models
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] | 2.84375 | 32 |
from Tkinter import *
from pytesser import image_to_string
from PIL import Image, ImageFilter, ImageEnhance, ImageTk
import picamera
from datetime import datetime
from pic_window import pic_window
##TODO: implement all these functions
##from parnter implementations
| [
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nums = [4, 6, 2, 6, 7, 2, 1] | [
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#!/usr/bin/env python
# coding: utf-8
# In this Kernel, I'd like to show you a very basic segmentation technique whihc only applies pure computer vision techniques. Nothing fancy.
#
# At first, I'll show the step-by-step processing and after that I will create the submission for the competition.
#
# With this kernel, I could reach *0.229 LB* which is not very nice but I am sure that with a few tweaks we could get better score. And consider that **we don't even use the train data**! which is pretty awesome in my opinion.
# In[ ]:
import numpy as np
import pandas as pd
import os
from os.path import join
import glob
import cv2
import matplotlib.pyplot as plt
# In[ ]:
TRAIN_PATH = "../input/stage1_train/"
TEST_PATH = "../input/stage1_test/"
# In[ ]:
train_ids = os.listdir(TRAIN_PATH)
test_ids = os.listdir(TEST_PATH)
# In[ ]:
test_image_paths = [
glob.glob(join(TEST_PATH, test_id, "images", "*"))[0] for test_id in test_ids
]
# # Step-by-step processing
# In[ ]:
tmp_image_path = np.random.choice(test_image_paths)
tmp_image = cv2.imread(tmp_image_path, cv2.IMREAD_GRAYSCALE)
# In[ ]:
plt.imshow(tmp_image)
# Now comes the crucial part of the processing. First we would like to create a binary matrix from the original image. The ones in the matrix are considered to be objects and the zeros are the background. So If we don't do this correctly we're going to loose a lot of inforamtion.
# In[ ]:
ret, thresh = cv2.threshold(tmp_image, 100, 255, cv2.THRESH_OTSU)
# In[ ]:
fig, axs = plt.subplots(1, 2, figsize=(10, 10))
axs[0].imshow(tmp_image)
axs[1].imshow(thresh)
# There are images where the thresholding does not help because the ones will be the background and the zeros the objects. This happend when the background is more brighter than the objects.
#
# But how we detect this?
#
# We just have to find the contours of the objects. Than calculate the area of the contour and if it is above some threshold value than we will just invert the image.
# In[ ]:
_, cnts, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
# In[ ]:
max_cnt_area = cv2.contourArea(cnts[0])
# In[ ]:
print("The area of the largest object is: {0}".format(max_cnt_area))
# This is the part where we invert the threshold image if we are not satisfied with the area of the largest contour
# In[ ]:
if max_cnt_area > 50000:
ret, thresh = cv2.threshold(
tmp_image, 100, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV
)
# And here comes the *morphology*.
#
# We will use:
# - Dilation (read more: https://homepages.inf.ed.ac.uk/rbf/HIPR2/dilate.htm)
# - Erosion (read more: https://homepages.inf.ed.ac.uk/rbf/HIPR2/erode.htm)
# In[ ]:
mask = cv2.dilate(thresh, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))
mask = cv2.erode(mask, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)))
# In[ ]:
fig, axs = plt.subplots(1, 4, figsize=(30, 30))
axs[0].imshow(tmp_image)
axs[1].imshow(thresh)
axs[2].imshow(mask)
axs[3].imshow(cv2.bitwise_and(tmp_image, tmp_image, mask=mask))
# # Process the test images for submission
# I separated the logic into 2 funcrtions so it is easier to use it.
# In[ ]:
# Now we only have to create the mask images from the test images
# In[ ]:
segmented = []
for test_image_path in test_image_paths:
tmp_image = cv2.imread(test_image_path, cv2.IMREAD_GRAYSCALE)
thresh = threshold(tmp_image)
mask = apply_morphology(thresh)
segmented.append(mask)
# In[ ]:
# Run length Encoding from https://www.kaggle.com/rakhlin/fast-run-length-encoding-python
from skimage.morphology import label
# In[ ]:
new_test_ids = []
rles = []
for n, id_ in enumerate(test_ids):
rle = list(prob_to_rles(segmented[n]))
rles.extend(rle)
new_test_ids.extend([id_] * len(rle))
# In[ ]:
submission_df = pd.DataFrame()
submission_df["ImageId"] = new_test_ids
submission_df["EncodedPixels"] = pd.Series(rles).apply(
lambda x: " ".join(str(y) for y in x)
)
# In[ ]:
submission_df.sample(3)
# In[ ]:
if not len(np.unique(submission_df["ImageId"])) == len(test_ids):
print("Submission is not complete")
print(
"Missing test ids: {0}".format(
set(test_ids).difference(set(np.unique(submission_df["ImageId"])))
)
)
else:
print("Submission is complete")
# In[ ]:
submission_df.to_csv("submission_pure_cv.csv", index=False)
# In[ ]:
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554,
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198
] | 2.562607 | 1,749 |
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