content
stringlengths 1
1.04M
| input_ids
sequencelengths 1
774k
| ratio_char_token
float64 0.38
22.9
| token_count
int64 1
774k
|
|---|---|---|---|
from collections import List | [
6738,
17268,
1330,
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] | 7 | 4 |
import numpy as np
# core functions
totalreturn = lambda x: (x[-1]/x[0])-1
finalreturn = lambda x: x[-1]
sharpe = lambda x: np.sqrt(12) * (np.mean(x) / np.std(x))
ann_vol = lambda x: np.sqrt(12) * np.std(x)
cagr = lambda x: ((((x[-1]) / x[0])) ** (12.0/(x.count()-1))) - 1
cagr2 = lambda x: ((np.mean(x)+1) ** 12) -1
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import sys # NOTE: for exiting
import requests
import datetime
import pprint
import ujson as json # NOTE: faster json
API_KEY = '' # TODO: input api key here!!!
if not API_KEY:
sys.exit('Please insert your Decisive API key')
print
print 'Creating session to always add API key...'
# NOTE: you can also use decisive.DecisiveApiClient
session = requests.Session()
session.auth = (API_KEY,'')
API_HOST = 'https://ads.decisive.is'
print
print 'Selecting ads...'
ads = get('ads', offset=1, limit=5, approved='true')
ad_ids = [a['ad_id'] for a in ads]
print 'selected', ad_ids
print
print 'Creating retargeting campaign...'
to_retargeting_id = lambda a: 'clicks_{}'.format(ad['ad_id'])
# TODO: fill in your own campaign details
new_ad = {'url':'http://google.com',
'name':'example ad name',
'budget':1984,
'bidmode':'Manual',
'cpm_bid':3.1415,
'creative_urls':['https://www.google.com.au/images/srpr/logo11w.png'],
'start_date':datetime.datetime.now().isoformat(),
'end_date':datetime.datetime.now().isoformat(),
'blacklist':{'country':['Canada','France'],'site': ['tmz.com', 'dogecoin.com']}
}
ad['targeting'] = {'device_groups':map(to_retargeting_id, ad_ids)} # NOTE: retargeting
print post(ad, 'ads')
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commands = CommandsRegistry()
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from typing import Tuple, List
import numpy as np
import tensorflow as tf
from webdnn.frontend.tensorflow.converter import TensorFlowConverter
from webdnn.frontend.tensorflow.util import unary_op_handler, check_data_format, convert_odd_padding_to_concat, parse_padding
from webdnn.graph.axis import Axis
from webdnn.graph.operators.average_pooling_2d import AveragePooling2D
from webdnn.graph.operators.clipped_relu import ClippedRelu
from webdnn.graph.operators.concat import Concat
from webdnn.graph.operators.convolution2d import Convolution2D
from webdnn.graph.operators.deconvolution2d import Deconvolution2D
from webdnn.graph.operators.elu import Elu
from webdnn.graph.operators.max_pooling_2d import MaxPooling2D
from webdnn.graph.operators.relu import Relu
from webdnn.graph.operators.softmax import Softmax
from webdnn.graph.operators.softplus import Softplus
from webdnn.graph.operators.softsign import Softsign
from webdnn.graph.order import Order
from webdnn.graph.variables.constant_variable import ConstantVariable
from webdnn.util import console
@TensorFlowConverter.register_handler("AvgPool")
@TensorFlowConverter.register_handler("AvgPool3D")
@TensorFlowConverter.register_handler("AvgPool3DGrad")
@TensorFlowConverter.register_handler("AvgPoolGrad")
@TensorFlowConverter.register_handler("BatchNormWithGlobalNormalization")
@TensorFlowConverter.register_handler("BatchNormWithGlobalNormalizationGrad")
@TensorFlowConverter.register_handler("BiasAdd")
@TensorFlowConverter.register_handler("BiasAddGrad")
@TensorFlowConverter.register_handler("BiasAddV1")
@TensorFlowConverter.register_handler("Conv2D")
@TensorFlowConverter.register_handler("Conv2DBackpropFilter")
@TensorFlowConverter.register_handler("Conv2DBackpropInput")
@TensorFlowConverter.register_handler("Conv3D")
@TensorFlowConverter.register_handler("Conv3DBackpropFilter")
@TensorFlowConverter.register_handler("Conv3DBackpropFilterV2")
@TensorFlowConverter.register_handler("Conv3DBackpropInput")
@TensorFlowConverter.register_handler("Conv3DBackpropInputV2")
@TensorFlowConverter.register_handler("DepthwiseConv2dNative")
@TensorFlowConverter.register_handler("DepthwiseConv2dNativeBackpropFilter")
@TensorFlowConverter.register_handler("DepthwiseConv2dNativeBackpropInput")
@TensorFlowConverter.register_handler("Dilation2D")
@TensorFlowConverter.register_handler("Dilation2DBackpropFilter")
@TensorFlowConverter.register_handler("Dilation2DBackpropInput")
TensorFlowConverter.register_handler("Elu")(unary_op_handler(Elu))
@TensorFlowConverter.register_handler("EluGrad")
@TensorFlowConverter.register_handler("FractionalAvgPoolGrad")
@TensorFlowConverter.register_handler("FractionalMaxPoolGrad")
@TensorFlowConverter.register_handler("FusedBatchNorm")
@TensorFlowConverter.register_handler("FusedPadConv2D")
@TensorFlowConverter.register_handler("FusedResizeAndPadConv2D")
@TensorFlowConverter.register_handler("InTopK")
@TensorFlowConverter.register_handler("InTopKV2")
@TensorFlowConverter.register_handler("L2Loss")
@TensorFlowConverter.register_handler("LRN")
@TensorFlowConverter.register_handler("LRNGrad")
@TensorFlowConverter.register_handler("LogSoftmax")
@TensorFlowConverter.register_handler("MaxPool")
@TensorFlowConverter.register_handler("MaxPool3D")
@TensorFlowConverter.register_handler("MaxPool3DGrad")
@TensorFlowConverter.register_handler("MaxPool3DGradGrad")
@TensorFlowConverter.register_handler("MaxPoolGrad")
@TensorFlowConverter.register_handler("MaxPoolGradGrad")
@TensorFlowConverter.register_handler("MaxPoolGradGradWithArgmax")
@TensorFlowConverter.register_handler("MaxPoolGradWithArgmax")
@TensorFlowConverter.register_handler("MaxPoolWithArgmax")
@TensorFlowConverter.register_handler("QuantizedAvgPool")
@TensorFlowConverter.register_handler("QuantizedBatchNormWithGlobalNormalization")
@TensorFlowConverter.register_handler("QuantizedBiasAdd")
@TensorFlowConverter.register_handler("QuantizedConv2D")
@TensorFlowConverter.register_handler("QuantizedMaxPool")
@TensorFlowConverter.register_handler("QuantizedRelu")
@TensorFlowConverter.register_handler("QuantizedRelu6")
@TensorFlowConverter.register_handler("QuantizedReluX")
TensorFlowConverter.register_handler("Relu")(unary_op_handler(Relu))
@TensorFlowConverter.register_handler("Relu6")
@TensorFlowConverter.register_handler("Relu6Grad")
@TensorFlowConverter.register_handler("ReluGrad")
@TensorFlowConverter.register_handler("Softmax")
@TensorFlowConverter.register_handler("SoftmaxCrossEntropyWithLogits")
@TensorFlowConverter.register_handler("Softplus")
@TensorFlowConverter.register_handler("SoftplusGrad")
TensorFlowConverter.register_handler("Softsign")(unary_op_handler(Softsign))
@TensorFlowConverter.register_handler("SoftsignGrad")
@TensorFlowConverter.register_handler("SparseSoftmaxCrossEntropyWithLogits")
@TensorFlowConverter.register_handler("TopK")
@TensorFlowConverter.register_handler("TopKV2")
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198
] | 2.960519 | 1,697 |
from numbers import Number
one = One()
zero = 0
| [
6738,
3146,
1330,
7913,
198,
198,
505,
796,
1881,
3419,
198,
22570,
796,
657,
198
] | 3.266667 | 15 |
from creevey.ops.image import centercrop
import numpy as np
from PIL import Image
import torch.nn as nn
def center_crop_pil_image(img):
"""
Helper function to crop the center out of images.
Utilizes the centercrop function from `creevey`
Parameters
----------
img: array
PIL image array
Returns
-------
PIL.Image: Slice of input image corresponding to a cropped area around the center
"""
img = np.array(img)
cropped_img = centercrop(img, reduction_factor=.4)
return Image.fromarray(cropped_img)
class _Identity(nn.Module):
"""
Used to pass penultimate layer features to the the ensemble
Motivation for this is that the features from the penultimate layer
are likely more informative than the 1000 way softmax that was used
in the multi_output_model_v2.
"""
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85,
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] | 2.985965 | 285 |
"""
Class to help create and manage data schema and to validate json files.
"""
from jsonschema import Draft7Validator
from copy import deepcopy
from genson import SchemaBuilder
from .dictSchema import DictSchema
from cornflow_client.core.tools import load_json, save_json
class SchemaManager:
"""
A schema manager between json-schema, dict-schema and marshmallow
"""
def __init__(self, schema, validator=Draft7Validator):
"""
Class to help create and manage data schema.
Once a schema is loaded, allow the validation of data.
:param schema: a json schema
"""
self.validator = validator
self.jsonschema = schema
@classmethod
def from_filepath(cls, path):
"""
Load a json schema from a json file.
:param path the file path
return The SchemaManager instance
"""
schema = cls.load_json(path)
return cls(schema)
def get_jsonschema(self):
"""
Return a copy of the stored jsonschema.
"""
return deepcopy(self.jsonschema)
def get_validation_errors(self, data):
"""
Validate json data according to the loaded jsonschema and return a list of errors.
Return an empty list if data is valid.
:param dict data: data to validate.
:return: A list of validation errors.
For more details about the error format, see:
https://python-jsonschema.readthedocs.io/en/latest/errors/#jsonschema.exceptions.ValidationError
"""
v = self.validator(self.get_jsonschema())
if not v.is_valid(data):
error_list = [e for e in v.iter_errors(data)]
return error_list
return []
def validate_data(self, data, print_errors=False):
"""
Validate json data according to the loaded jsonschema.
:param dict data: the data to validate.
:param bool print_errors: If true, will print the errors.
:return: True if data format is valid, else False.
"""
errors_list = self.get_validation_errors(data)
if print_errors:
for e in errors_list:
print(e)
return len(errors_list) == 0
def get_file_errors(self, path):
"""
Get json file errors according to the loaded jsonschema.
:param path the file path
:return: A list of validation errors.
For more details about the error format, see:
https://python-jsonschema.readthedocs.io/en/latest/errors/#jsonschema.exceptions.ValidationError
"""
data = self.load_json(path)
return self.get_validation_errors(data)
def validate_file(self, path, print_errors=False):
"""
Validate a json file according to the loaded jsonschema.
:param path the file path
:param print_errors: If true, will print the errors.
:return: True if the data is valid and False if it is not.
"""
data = self.load_json(path)
return self.validate_data(data, print_errors=print_errors)
def to_dict_schema(self):
"""
Transform a jsonschema into a dictionary format
:return: The schema dictionary
"""
return self.to_schema_dict_obj().get_schema()
def to_schema_dict_obj(self):
"""
Returns an DictSchema object equivalent of the jsonschema
"""
return DictSchema(self.get_jsonschema())
@property
def to_marshmallow(self):
"""
Create marshmallow schemas
:return: a dict containing the flask marshmallow schemas
:rtype: Schema()
"""
return self.to_schema_dict_obj().to_marshmallow()
def export_schema_dict(self, path):
"""
Print the schema_dict in a json file.
:param path: the path where to save the dict.format
:return: nothing
"""
self.save_json(self.to_dict_schema(), path)
def draft_schema_from(self, path, save_path=None):
"""
Create a draft jsonschema from a json file of data.
:param path: path to the json file.
:param save_path: path where to save the generated schema.
:return: the generated schema.
"""
file = self.load_json(path)
builder = SchemaBuilder()
builder.add_schema({"type": "object", "properties": {}})
builder.add_object(file)
draft_schema = builder.to_json()
if save_path is not None:
with open(save_path, "w") as outfile:
outfile.write(draft_schema)
return draft_schema
def to_template(self):
"""
This function assumes certain structure for the jsonschema.
For now, three types of tables exist: array of objects, arrays and objects.
{
table1: [{col1: a, col2: b}, {col1: aa, col2: bb}, ...],
table2: [1, 2, 3, ],
table3: {config1: a, config2: b},
}
"""
master_table_name = "_README"
type_table_name = "_TYPES"
tables = {master_table_name: [], type_table_name: []}
# we update the master table of tables:
real_props = [
(k, v)
for (k, v) in self.jsonschema["properties"].items()
if not k.startswith("$")
]
for key, value in real_props:
tables[master_table_name].append(
dict(table=key, description=value.get("description", ""))
)
# then we get each table
for key, value in real_props:
tables[key] = self._get_table(value)
# then we get column types
example_inv = {1: "integer", "string": "string"}
for key, value in real_props:
rows = []
if len(tables[key]) > 1:
rows = [
dict(table=key, column=v, type="string") for v in ["key", "value"]
]
if len(tables[key]) == 1:
row1 = tables[key][0]
rows = [
dict(table=key, column=k, type=example_inv[v])
for k, v in row1.items()
]
tables[type_table_name].extend(rows)
return tables
@staticmethod
@staticmethod
def load_json(path):
"""
Load a json file
:param path: the path of the json file.json
return the json content.
"""
return load_json(path)
@staticmethod
"""
Aliases:
"""
dict_to_flask = to_marshmallow
load_schema = from_filepath
jsonschema_to_flask = to_marshmallow
jsonschema_to_dict = to_dict_schema
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1462,
62,
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796,
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62,
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62,
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2611,
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] | 2.247398 | 2,979 |
from src import log
from src.summary.base import BaseSummary
if __name__ == "__main__":
import json
with open('json/C_TemplateMatchingSummary.json') as f:
_dict = json.load(f)
dicts = _dict['metadata'][0]['value']
summary = TriggerSummary()
should_be_reset = summary.should_be_reset(dicts)
log.print(should_be_reset)
if should_be_reset:
summary.reset()
summary.set(dicts)
trigger = summary.get_trigger()
log.print(trigger)
end = summary.get_end()
log.print(end)
metadata = summary.get_metadata()
log.print(metadata)
summary.stack()
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220,
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13,
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3419,
198
] | 2.549587 | 242 |
#LICENCE : http://www.apache.org/licenses/LICENSE-2.0
#CREATOR BY : PRANKBOT
#MOD BY ACIL
__all__ = ['fastbinary', 'TBase', 'TBinaryProtocol', 'TCompactProtocol', 'TJSONProtocol', 'TProtocol']
| [
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] | 2.468354 | 79 |
#!/usr/bin/env python
# md5: a2cb6a826f222fa843ab488ae8a2de22
# coding: utf-8
import json
| [
2,
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69,
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] | 2 | 46 |
import turtle
from turtle import Turtle, Screen
a = turtle.Turtle()
screen = Screen()
a.shape('turtle')
print('Welcome to sketch n draw')
print('select your color 1 and 2')
color_1 = (input('color 1: '))
color_2 = (input('color 2: '))
print('ok! your colors are ' + color_1 + (' and ') + color_2)
print("changed pad's color according to the requirments")
a.color(color_1, color_2)
print('So now you can proceed to draw')
while 1 == 1:
x = (input('enter command: '))
if x == ('For()'):
y = int(input('enter the distance: '))
a.forward(y)
if x == ('size-pen()'):
z = int(input('enter new pensize: '))
a.pensize(z)
if x == ('Bac()'):
o = int(input('select distance: '))
a.backward(o)
if x == ('dir;dig()'):
ol = int(input('enter degrees: '))
olv = input('enter direction: ')
if olv == ('right'):
a.right(ol)
if olv == ('left'):
a.left(ol)
if x == ('set-cor()'):
color_1 = (input('color 1: '))
color_2 = (input('color 2: '))
a.color(color_1, color_2)
if x == ('"'):
import sys
sys.exit()
if x == ('on-mouse'):
print('sketch with mouse enabled, now you can not use any other function')
main()
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] | 2.144481 | 616 |
import torch.distributed as dist
from torch.utils.data import DataLoader
from .data.transforms import make_albumentations
from .factory import make_model
from .hooks import DistSamplerSeedHook, IterTimerHook, LogBufferHook
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import pytest
import pybackend.urilib as U
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#! /usr/bin/env python
# coding=utf-8
"""
Example from the paper Rüde/Waluga/Wohlmuth 2013
"""
__author__ = "Christian Waluga ([email protected])"
__copyright__ = "Copyright (c) 2013 %s" % __author__
from dolfin import *
from energy_correction.correction import *
from energy_correction.meshtools import *
from energy_correction.singular import *
from energy_correction.extrapolate import *
import math
set_log_level(ERROR)
# convergence rates
# boundary definition for error
if __name__ == '__main__':
# main program
parameters["allow_extrapolation"] = True
maxlevel = 7 # maximum level (this is where 'exact' solution is computed)
compute = False # does the 'exact' solution need to be computed?
gzip = True # gzip the 'exact' solution once computed and saved?
correct = False # do we want energy-correction
mesh_filename = 'meshes/slit-channel-crossed.xml.gz'
cachedgamma = False # use cached values of gamma or recompute with method specified below?
method = 'one-level-inexact'
#method = 'two-level-inexact'
# beyond the L-shape, we have to symmetrize if we want to use only one correction per corner.
# (set to 2pi if the mesh is already symmetric at the corners)
symmetrization_threshold = 2.0*pi
output_weight = False # output the weighting function to VTK?
# find reentrant corners
mesh, corners, angles, corner_meshes \
= generate_corner_info(Mesh(mesh_filename), symmetrization_threshold)
weight = WeightingFunction(corners, angles, 4.0)
#plot(mesh); interactive()
gammas_cached = [0.27914627419934601, 0.27914440403310525, 0.27916755164797358, 0.27912214602624991, 0.27908848023107002, 0.27915828177500074, 0.2791861111329178]
if correct:
if cachedgamma:
print 'using cached gammas'
gammas = gammas_cached
else:
print 'computing gammas'
funcs = [ math.cos for c in corners ] # all Neumann
gammas = extrapolate_gammas(corners, angles, corner_meshes, method = method, \
start_at = maxlevel, extrapolation = 'richardson', \
maxlevel = maxlevel+2, funcs = funcs)[0]
else:
gammas = [0.0 for i in range(len(corners))]
print 'gammas =', gammas
# generate series of refined meshes
print 'generating meshes'
meshes = [ mesh ]
for i in xrange(maxlevel):
meshes.append(refine(meshes[-1]))
if output_weight:
File('output/weight.pvd') << interpolate(weight, FunctionSpace(meshes[3], 'Lagrange', 1))
filename = 'output/uh_fine_{0}'.format(maxlevel)
if compute:
print 'computing fine solution'
if not correct: print 'warning: computing fine solution without correction'
uh_fine = solve_problem(meshes[-1], corners, gammas)
File(filename + '.xml') << uh_fine
File(filename + '.pvd') << uh_fine
if gzip:
from subprocess import call
call(['gzip', '-f', filename + '.xml'])
else:
print 'loading fine solution'
V_fine = FunctionSpace(meshes[-1], 'Lagrange', 1)
uh_fine = Function(V_fine, filename + '.xml.gz' if gzip else '')
errors = []
print 'solving'
# perform a convergence study
for i, mesh in enumerate(meshes[:-2]):
uh = solve_problem(mesh, corners, gammas)
intorder, intmesh = 1, meshes[i+2]
U = project(uh_fine, FunctionSpace(intmesh, 'Lagrange', intorder))
File('output/uh-{0}.pvd'.format(i)) << uh
right_boundary = RightBoundary()
boundaries = FacetFunction("size_t", intmesh)
boundaries.set_all(0)
right_boundary.mark(boundaries, 1)
dG = Measure("ds")[boundaries]
#h = CellSize(mesh)
h = Constant(mesh.hmin())
# compute errors between current level and highest level solutions
err1 = sqrt(assemble((uh - U)**2*dx, mesh = intmesh))
err2 = sqrt(assemble(weight*(uh - U)**2*dx, mesh = intmesh))
#err2 = sqrt(assemble(h*(Dn(uh) - Dn(U))**2*dG(1), mesh = intmesh))
errors.append((err1, err2))
print_rates(errors)
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] | 2.629458 | 1,514 |
from utils import CanadianScraper, CanadianPerson as Person
import re
COUNCIL_PAGE = 'http://www.wellesley.ca/council/councillors/?q=council/councillors'
| [
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"""
ParallelCluster
ParallelCluster API # noqa: E501
The version of the OpenAPI document: 3.0.0
Generated by: https://openapi-generator.tech
"""
import sys
import unittest
import pcluster.client
from pcluster.client.model.change import Change
from pcluster.client.model.config_validation_message import ConfigValidationMessage
from pcluster.client.model.update_error import UpdateError
globals()['Change'] = Change
globals()['ConfigValidationMessage'] = ConfigValidationMessage
globals()['UpdateError'] = UpdateError
from pcluster.client.model.update_cluster_bad_request_exception_response_content import UpdateClusterBadRequestExceptionResponseContent
class TestUpdateClusterBadRequestExceptionResponseContent(unittest.TestCase):
"""UpdateClusterBadRequestExceptionResponseContent unit test stubs"""
def testUpdateClusterBadRequestExceptionResponseContent(self):
"""Test UpdateClusterBadRequestExceptionResponseContent"""
# FIXME: construct object with mandatory attributes with example values
# model = UpdateClusterBadRequestExceptionResponseContent() # noqa: E501
pass
if __name__ == '__main__':
unittest.main()
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] | 3.384615 | 351 |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2019, 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.
# This file is part of QuTiP: Quantum Toolbox in Python.
#
# Copyright (c) 2011 and later, Paul D. Nation and Robert J. Johansson.
# All rights reserved.
# pylint: disable=no-value-for-parameter, invalid-name, import-error
"""Pulse DE solver for problems in qutip format."""
import numpy as np
from scipy.integrate import ode
from scipy.integrate._ode import zvode
# pylint: disable=no-name-in-module
from .pulse_utils import td_ode_rhs_static
def construct_pulse_zvode_solver(exp, op_system):
""" Constructs a scipy ODE solver for a given exp and op_system
Parameters:
exp (dict): dict containing experimental
op_system (PulseSimDescription): container for simulation information
Returns:
ode: scipy ode
"""
# extract relevant data from op_system
global_data = op_system.global_data
ode_options = op_system.ode_options
channels = dict(op_system.channels)
# Init register
register = np.ones(global_data['n_registers'], dtype=np.uint8)
ODE = ode(td_ode_rhs_static)
ODE.set_f_params(global_data, exp, op_system.system, channels, register)
ODE._integrator = qiskit_zvode(method=ode_options.method,
order=ode_options.order,
atol=ode_options.atol,
rtol=ode_options.rtol,
nsteps=ode_options.nsteps,
first_step=ode_options.first_step,
min_step=ode_options.min_step,
max_step=ode_options.max_step
)
# Forces complex ODE solving
if not ODE._y:
ODE.t = 0.0
ODE._y = np.array([0.0], complex)
ODE._integrator.reset(len(ODE._y), ODE.jac is not None)
# Since all experiments are defined to start at zero time.
ODE.set_initial_value(global_data['initial_state'], 0)
return ODE
class qiskit_zvode(zvode):
"""Modifies the stepper for ZVODE so that
it always stops at a given time in tlist;
by default, it over shoots the time.
"""
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] | 2.34331 | 1,136 |
from RPLCD import CharLCD
import RPi.GPIO as GPIO
lcd = CharLCD(numbering_mode=GPIO.BOARD, cols=16, rows=2, pin_rs=37, pin_e=35, pins_data=[33, 31, 29, 23])
lcd.write_string(u'Hello World')
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] | 2.289157 | 83 |
"""Tests for the IPTT Report Data Indicators (TP/TVA) to ensure their query counts stay O(n) and not O(n^2)
- api_report_data takes program_pk and frequency, calls IPTT<TVA/TP>ReportIndicatorSerializer.load_report
- IPTT<TVA/TP>ReportIndicatorSerializer.load_report takes program_pk and frequency
- queries for program data (start/end dates)
- queries for disaggregations data ??
- calls IPTTIndicator.tva/timperiods
- IPTTIndicator
- get queryset adds prefetch
- with_annotations adds lop_target lop_actual, lop_percent_met, and old_level if necessary
- with_disaggaggregation_annotations takes disaggregation_category_pks and adds lop_actual for each
- with_frequency_annotations takes freq, start, end, and disaggregation_category_pks and adds
frequency_disaggregation_actual for each disaggregation and overall frequency actual
(if TVA also adds overall frequency target and percent met)
"""
from django import test
from indicators.models import Indicator, PeriodicTarget
from indicators.queries.iptt_queries import IPTTIndicator
from factories import (
indicators_models as i_factories,
workflow_models as w_factories
)
QUERIES_PREFETCH = 8
QUERIES_FREQUENCIES = QUERIES_PREFETCH + 0
QUERIES_DISAGG_FREQUENCIES = QUERIES_FREQUENCIES + 0
TP_QUERYSET = IPTTIndicator.timeperiods
TVA_QUERYSET = IPTTIndicator.tva
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] | 2.833002 | 503 |
from django.db import models
from django.db.models.signals import post_save
from django.contrib.auth.models import User
from django.core.validators import MaxValueValidator
from imagekit.models import ProcessedImageField
from imagekit.processors import ResizeToFill
from home.models import ModelAbstract, Song
class Profile(ModelAbstract):
"""
A user's profile. Data like profile picture/banner is stored here,
but also anything else that should probably be extending the
default User model.
It takes hard work to set a custom User model once you've already
started a project, and most of the things I want would
fit nicely in a model like this anyways.
"""
user = models.OneToOneField(User, on_delete=models.CASCADE)
avatar = ProcessedImageField(
upload_to='avatars', default='avatars/placeholder.png',
processors=[ResizeToFill(256, 256)],
format="PNG"
)
banner = ProcessedImageField(
upload_to='banners', default='banners/placeholder.png',
processors=[ResizeToFill(1300, 400)],
format="PNG"
)
# def get_recent_ratings(self, limit=20):
# """
# Get the user's recently rated songs.
# @limit: How many songs to get
# """
# ratings = self.user.songlist.songrating_set.all()
# songs = Song.objects.filter(songrating__in=ratings).values('video_link', 'anime__cover', 'pk').order_by(
# '-songrating__last_modified')[:limit]
# return songs
def get_top_ratings(self, limit=20):
"""
Get the user's recently rated songs.
Gets very little details, mainly just used for carousels.
@limit: How many songs to get (default 20)
"""
# Grab all the user's ratings
ratings = self.user.songlist.songrating_set.all()
# Filter through the highest rated, and then most recently rated songs rated by the user.
songs = Song.objects.filter(songrating__in=ratings).values('video_link', 'anime__cover', 'pk').order_by(
'-songrating__rating', 'songrating__last_modified')[:limit]
return songs
def get_rated_songs(self, limit=None):
"""
Grab all songs that a User has rated.
Used for the user's list.
@limit: How many songs to get
"""
# Grab all the user's SongRatings, along with details about the song.
ratings = SongRating.objects.filter(parent_list=self.user.songlist).values(
'song__anime__cover', 'song__anime__english_name',
'song__song_type', 'song__number', 'rating', 'song__video_link', 'song__pk').order_by(
'-rating', '-last_modified'
)
return ratings
# On User creation, make a profile as well
post_save.connect(create_profile, sender=User)
class SongList(ModelAbstract):
"""
A user's list of songs
"""
user = models.OneToOneField(User, on_delete=models.CASCADE)
# On User creation, make a profile as well
post_save.connect(create_songlist, sender=User)
class SongRating(ModelAbstract):
"""
How a user has rated a song in their list
"""
song = models.ForeignKey('home.Song', on_delete=models.CASCADE)
parent_list = models.ForeignKey(SongList, on_delete=models.CASCADE)
rating = models.PositiveIntegerField(null=True, blank=True, validators=[MaxValueValidator(10)])
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] | 2.669014 | 1,278 |
from motor.motor_asyncio import AsyncIOMotorClient
db = Database() | [
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] | 3.045455 | 22 |
from shrinky.glsl_block import GlslBlock
from shrinky.glsl_block import extract_tokens
from shrinky.glsl_block_member import glsl_parse_member_list
########################################
# GlslBlockStruct ######################
########################################
class GlslBlockStruct(GlslBlock):
"""Struct declaration."""
def __init__(self, type_name, members, name=None, size=0):
"""Constructor."""
GlslBlock.__init__(self)
self.__type_name = type_name
self.__members = members
self.__name = name
self.__size = size
self.__member_accesses = []
# Hierarchy.
name.setType(type_name)
self.addNamesDeclared(name)
self.addNamesUsed(name)
def format(self, force):
"""Return formatted output."""
lst = "".join([x.format(force) for x in self.__members])
ret = ("struct %s{%s}" % (self.__type_name.format(force), lst, self.__name.format(force)))
if self.__name:
ret += self.__name.format(force)
if self.__size:
ret += "[%s]" % (self.__size.format(force))
return ret + ";"
def getMembers(self):
"""Accessor."""
return self.__members
def getMemberAccesses(self):
"""Accessor."""
return self.__member_accesses
def getName(self):
"""Accessor."""
return self.__name
def getTypeName(self):
"""Accessor."""
return self.__type_name
def setMemberAccesses(self, lst):
"""Set collected member accesses."""
self.__member_accesses = lst
def __str__(self):
"""String representation."""
return "Struct(%i)" % (len(self.__content))
########################################
# Functions ############################
########################################
def glsl_parse_struct(source):
"""Parse struct block."""
(type_name, scope, content) = extract_tokens(source, ("struct", "?n", "?{"))
if not type_name:
return (None, source)
# Get potential name and size.
(name, size, remaining) = extract_tokens(content, ("?n", "[", "?i", "]", ";"))
if not name:
size = None
(name, remaining) = extract_tokens(content, ("?n", ";"))
if not name:
name = None
(terminator, remaining) = extract_tokens(content, ("?;",))
if not terminator:
return (None, source)
# Parse members
members = glsl_parse_member_list(scope)
if not members:
raise RuntimeError("empty member list for struct")
return (GlslBlockStruct(type_name, members, name, size), remaining)
def is_glsl_block_struct(op):
"""Tell if given object is GlslBlockInout."""
return isinstance(op, GlslBlockStruct)
| [
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7,
11299,
11,
5855,
30,
26,
1600,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
407,
5651,
1352,
25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
357,
14202,
11,
2723,
8,
198,
220,
220,
220,
1303,
2547,
325,
1866,
198,
220,
220,
220,
1866,
796,
1278,
6649,
62,
29572,
62,
19522,
62,
4868,
7,
29982,
8,
198,
220,
220,
220,
611,
407,
1866,
25,
198,
220,
220,
220,
220,
220,
220,
220,
5298,
43160,
12331,
7203,
28920,
2888,
1351,
329,
2878,
4943,
198,
220,
220,
220,
1441,
357,
9861,
6649,
12235,
44909,
7,
4906,
62,
3672,
11,
1866,
11,
1438,
11,
2546,
828,
5637,
8,
628,
198,
4299,
318,
62,
4743,
6649,
62,
9967,
62,
7249,
7,
404,
2599,
198,
220,
220,
220,
37227,
24446,
611,
1813,
2134,
318,
2671,
6649,
12235,
818,
448,
526,
15931,
198,
220,
220,
220,
1441,
318,
39098,
7,
404,
11,
2671,
6649,
12235,
44909,
8,
198
] | 2.471162 | 1,127 |
import pandas as pd
import numpy as np
from typing import List
user_ID = 78
query = 'userID == ' + str(user_ID)
# head = my_join()
head = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'IMAX', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Short', 'Thriller', 'War', 'Western']
joined = pd.read_csv('/home/kwitnoncy/Documents/politechnika/wti/wtiproj03/data/joined.dat', sep='\t')
joined = joined.query(query).to_numpy()
print([np.nanmean([(row[2] * row[genre + 9]) if row[genre + 9] != 0 else np.nan for row in joined]) for genre in range(len(head))])
| [
11748,
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7,
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62,
77,
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3419,
198,
198,
4798,
26933,
37659,
13,
12647,
32604,
26933,
7,
808,
58,
17,
60,
1635,
5752,
58,
35850,
1343,
860,
12962,
611,
5752,
58,
35850,
1343,
860,
60,
14512,
657,
2073,
45941,
13,
12647,
329,
5752,
287,
5399,
12962,
329,
12121,
287,
2837,
7,
11925,
7,
2256,
4008,
12962,
628
] | 2.647303 | 241 |
from machine import Pin, RTC
from time import sleep, sleep_ms, sleep_us
from json import load as jload
from ntptime import settime
ADDR_DELAY_US = 100
GOTO_DELAY_MS = 20
HOUR_LUT = jload(open("hour_lut.json"))
MINUTE_LUT = jload(open("minute_lut.json"))
TIME_ZONE = +2
encoder_pins = [Pin(i, Pin.IN, Pin.PULL_UP) for i in [0, 2, 4, 5, 12, 13]]
run = Pin(16, Pin.OUT)
run.off()
adc_hour = Pin(14, Pin.OUT)
adc_hour.off()
adc_minute = Pin(15, Pin.OUT)
adc_minute.off()
try:
settime()
except:
pass
rtc = RTC()
while True:
update_time()
sleep(5)
| [
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11,
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62,
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83,
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8,
198,
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3419,
198,
324,
66,
62,
9769,
796,
13727,
7,
1415,
11,
13727,
13,
12425,
8,
198,
324,
66,
62,
9769,
13,
2364,
3419,
198,
324,
66,
62,
11374,
796,
13727,
7,
1314,
11,
13727,
13,
12425,
8,
198,
324,
66,
62,
11374,
13,
2364,
3419,
198,
198,
28311,
25,
198,
220,
220,
220,
900,
2435,
3419,
198,
16341,
25,
198,
220,
220,
220,
1208,
198,
17034,
66,
796,
371,
4825,
3419,
198,
198,
4514,
6407,
25,
198,
220,
220,
220,
4296,
62,
2435,
3419,
198,
220,
220,
220,
3993,
7,
20,
8,
198
] | 2.216535 | 254 |
import pytest
import retro.data
inttypes = {
'exp': retro.data.Integrations.EXPERIMENTAL_ONLY,
'contrib': retro.data.Integrations.CONTRIB_ONLY,
}
| [
11748,
12972,
9288,
198,
11748,
12175,
13,
7890,
198,
198,
600,
19199,
796,
1391,
198,
220,
220,
220,
705,
11201,
10354,
12175,
13,
7890,
13,
34500,
9143,
13,
6369,
18973,
3955,
3525,
1847,
62,
1340,
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11,
198,
220,
220,
220,
705,
3642,
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12175,
13,
7890,
13,
34500,
9143,
13,
10943,
5446,
9865,
62,
1340,
11319,
11,
198,
92,
628
] | 2.516129 | 62 |
#!/usr/bin/env python
from binance.futures import Futures as Client
import logging
from binance.lib.utils import config_logging
config_logging(logging, logging.DEBUG)
futures_client = Client()
logging.info(futures_client.funding_rate("BTCUSDT",**{'limit':100}))
| [
2,
48443,
14629,
14,
8800,
14,
24330,
21015,
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24544,
1600,
1174,
90,
6,
32374,
10354,
3064,
92,
4008,
628,
198
] | 2.923077 | 91 |
# Copyright 2019 PerfKitBenchmarker 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.
"""CapacityReservation for AWS virtual machines.
AWS EC2 has the concept of capacity reservations which allow the
user to request a reservation for a given number of VMs of a
specified shape (machine type and os type) in a given zone, for
an optionally-supplied duration. This module implements this functionaly.
A useful feature of using AwsCapacityReservation is that it allows the
user to specify a region instead of a zone, and this module will automatically
pick a zone that has capacity, and the VM(s) will then be launched in that zone.
AwsCapacityReservation modifies all the VMs in a given vm_group in the
following way:
1. The capacity_reservation_id attribute on the VM is set after the
reservation is created. The VM needs to reference this id during
creation.
2. If the user supplied a region instead of zone, then this module
will update the zone attribute on the VM, as well as the zone
attribute on the VM's network instance.
A run of PKB may have several capacity reservations; there is a 1:1 mapping
from AWS vm_groups to AwsCapacityReservation instances. This is because all
VMs in a VM group share the same shape and zone.
"""
import datetime
import json
import logging
from absl import flags
from perfkitbenchmarker import capacity_reservation
from perfkitbenchmarker import errors
from perfkitbenchmarker import os_types
from perfkitbenchmarker import vm_util
from perfkitbenchmarker.providers import aws
from perfkitbenchmarker.providers.aws import util
FLAGS = flags.FLAGS
_INSUFFICIENT_CAPACITY = 'InsufficientInstanceCapacity'
class AwsCapacityReservation(capacity_reservation.BaseCapacityReservation):
"""An object representing an AWS EC2 CapacityReservation."""
CLOUD = aws.CLOUD
def _Create(self):
"""Creates the AWS CapacaityReservation.
A reservation will be created given the VM shape in self.vm_groups.
Count is determined by the number of VMs in said group. The reservation
will have a lifetime determined by the general PKB concept of
timeout_minutes. If the reservation exceeds this timeout, AWS will
cancel it automatically. The VMs in the reservation will not be deleted.
Note that an empty capacity reservation will encur costs for the
VM shape / count, even if no VMs are using it.
After the reservation is created, this method updates all the VMs
in self.vm_groups by setting the capacity_reservation_id, as well
as the zone attributes on the VM, and the VM's network instance.
Raises:
UnsupportedOsTypeError: If creating a capacity reservation for the
given os type is not supported.
CreationError: If a capacity reservation cannot be created in the
region (typically indicates a stockout).
"""
if self.os_type in os_types.LINUX_OS_TYPES:
instance_platform = 'Linux/UNIX'
elif self.os_type in os_types.WINDOWS_OS_TYPES:
instance_platform = 'Windows'
else:
raise UnsupportedOsTypeError(
'Unsupported os_type for AWS CapacityReservation: %s.'
% self.os_type)
# If the user did not specify an AZ, we need to try to create the
# CapacityReservation in a specifc AZ until it succeeds.
# Then update the zone attribute on all the VMs in the group,
# as well as the zone attribute on the VMs' network instance.
if util.IsRegion(self.zone_or_region):
zones_to_try = util.GetZonesInRegion(self.region)
else:
zones_to_try = [self.zone_or_region]
end_date = (
datetime.datetime.utcnow() +
datetime.timedelta(minutes=FLAGS.timeout_minutes))
for zone in zones_to_try:
cmd = util.AWS_PREFIX + [
'ec2',
'create-capacity-reservation',
'--instance-type=%s' % self.machine_type,
'--instance-platform=%s' % instance_platform,
'--availability-zone=%s' % zone,
'--instance-count=%s' % self.vm_count,
'--instance-match-criteria=targeted',
'--region=%s' % self.region,
'--end-date-type=limited',
'--end-date=%s' % end_date.isoformat(),
]
stdout, stderr, retcode = vm_util.IssueCommand(cmd,
raise_on_failure=False)
if retcode:
logging.info('Unable to create CapacityReservation in %s. '
'This may be retried. Details: %s', zone, stderr)
if _INSUFFICIENT_CAPACITY in stderr:
logging.error(util.STOCKOUT_MESSAGE)
raise errors.Benchmarks.InsufficientCapacityCloudFailure(
util.STOCKOUT_MESSAGE + ' CapacityReservation in ' + zone)
continue
json_output = json.loads(stdout)
self.capacity_reservation_id = (
json_output['CapacityReservation']['CapacityReservationId'])
self._UpdateVmsInGroup(self.capacity_reservation_id, zone)
return
raise CreationError('Unable to create CapacityReservation in any of the '
'following zones: %s.' % zones_to_try)
def _Delete(self):
"""Deletes the capacity reservation."""
cmd = util.AWS_PREFIX + [
'ec2',
'cancel-capacity-reservation',
'--capacity-reservation-id=%s' % self.capacity_reservation_id,
'--region=%s' % self.region,
]
vm_util.IssueCommand(cmd, raise_on_failure=False)
def _Exists(self):
"""Returns true if the underlying reservation exists and is active."""
cmd = util.AWS_PREFIX + [
'ec2',
'describe-capacity-reservations',
'--capacity-reservation-id=%s' % self.capacity_reservation_id,
'--region=%s' % self.region,
]
stdout, _, retcode = vm_util.IssueCommand(cmd, raise_on_failure=False)
if retcode != 0:
return False
json_output = json.loads(stdout)
return json_output['CapacityReservations'][0]['State'] == 'active'
def _UpdateVmsInGroup(self, capacity_reservation_id, zone):
"""Updates the VMs in a group with necessary reservation details.
AWS virtual machines need to reference the capacity reservation id
during creation, so it is set on all VMs in the group. Additionally,
this class may determine which zone to run in, so that needs to be
updated too (on the VM, and the VM's network instance).
Args:
capacity_reservation_id: ID of the reservation created by this instance.
zone: Zone chosen by this class, or if it was supplied, the zone
provided by the user. In the latter case, setting the zone is equivalent
to a no-op.
"""
for vm in self.vm_group:
vm.capacity_reservation_id = capacity_reservation_id
vm.zone = zone
vm.network.zone = zone
| [
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318,
326,
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262,
198,
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284,
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257,
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2427,
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481,
6338,
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257,
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326,
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262,
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7,
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220,
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220,
220,
220,
45887,
13,
27349,
13,
11340,
796,
6516,
198
] | 2.854902 | 2,550 |
from __future__ import absolute_import
from .tfidf import TFIDF
from .textrank import TextRank
try:
from .analyzer import ChineseAnalyzer
except ImportError:
pass
default_tfidf = TFIDF()
default_textrank = TextRank()
extract_tags = tfidf = default_tfidf.extract_tags
set_idf_path = default_tfidf.set_idf_path
textrank = default_textrank.extract_tags
| [
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43027,
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974,
62,
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201,
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] | 2.652482 | 141 |
__doc__ = """ Factory function to allocate variables for Cosserat Rod"""
__all__ = ["allocate"]
import typing
from typing import Optional, Tuple
import warnings
import logging
import numpy as np
from numpy.testing import assert_allclose
from elastica.utils import MaxDimension, Tolerance
from elastica._linalg import _batch_cross, _batch_norm, _batch_dot
def _position_validity_checker(position, start, n_elements):
"""Checker on user-defined position validity"""
_assert_shape(position, (MaxDimension.value(), n_elements + 1), "position")
# Check if the start position of the rod and first entry of position array are the same
assert_allclose(
position[..., 0],
start,
atol=Tolerance.atol(),
err_msg=str(
"First entry of position" + " (" + str(position[..., 0]) + " ) "
" is different than start " + " (" + str(start) + " ) "
),
)
def _directors_validity_checker(directors, tangents, n_elements):
"""Checker on user-defined directors validity"""
_assert_shape(
directors, (MaxDimension.value(), MaxDimension.value(), n_elements), "directors"
)
# Check if d1, d2, d3 are unit vectors
d1 = directors[0, ...]
d2 = directors[1, ...]
d3 = directors[2, ...]
assert_allclose(
_batch_norm(d1),
np.ones((n_elements)),
atol=Tolerance.atol(),
err_msg=(" d1 vector of input director matrix is not unit vector "),
)
assert_allclose(
_batch_norm(d2),
np.ones((n_elements)),
atol=Tolerance.atol(),
err_msg=(" d2 vector of input director matrix is not unit vector "),
)
assert_allclose(
_batch_norm(d3),
np.ones((n_elements)),
atol=Tolerance.atol(),
err_msg=(" d3 vector of input director matrix is not unit vector "),
)
# Check if d3xd1 = d2
assert_allclose(
_batch_cross(d3, d1),
d2,
atol=Tolerance.atol(),
err_msg=(" d3 x d1 != d2 of input director matrix"),
)
# Check if computed tangents from position is the same with d3
assert_allclose(
tangents,
d3,
atol=Tolerance.atol(),
err_msg=" Tangent vector computed using node positions is different than d3 vector of input directors",
)
| [
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286,
5128,
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1600,
198,
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] | 2.463753 | 938 |
from env_wrapper import wzq_env
from policy.rand_policy import rand_agent
from policy.algo.ppo import PPO
from policy.net.net_v0 import policy_net
from policy.net.net_v0 import value_net
from policy.rl_policy import ppo_agent
import time
import numpy as np
agent0 = ppo_agent(size=15)
agent1 = rand_agent(size=15)
env = wzq_env([agent0, agent1])
env.run() | [
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25781,
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] | 2.80315 | 127 |
from dataclasses import dataclass, field
from typing import Optional
from bindings.gmd.abstract_object_type import AbstractObjectType
from bindings.gmd.binary_property_type import BinaryPropertyType
from bindings.gmd.character_string_property_type import CharacterStringPropertyType
from bindings.gmd.ci_citation_type import CiCitationPropertyType
__NAMESPACE__ = "http://www.isotc211.org/2005/gmd"
@dataclass
class MdApplicationSchemaInformationType(AbstractObjectType):
"""
Information about the application schema used to build the dataset.
"""
name: Optional[CiCitationPropertyType] = field(
default=None,
metadata={
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
"required": True,
},
)
schema_language: Optional[CharacterStringPropertyType] = field(
default=None,
metadata={
"name": "schemaLanguage",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
"required": True,
},
)
constraint_language: Optional[CharacterStringPropertyType] = field(
default=None,
metadata={
"name": "constraintLanguage",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
"required": True,
},
)
schema_ascii: Optional[CharacterStringPropertyType] = field(
default=None,
metadata={
"name": "schemaAscii",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
},
)
graphics_file: Optional[BinaryPropertyType] = field(
default=None,
metadata={
"name": "graphicsFile",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
},
)
software_development_file: Optional[BinaryPropertyType] = field(
default=None,
metadata={
"name": "softwareDevelopmentFile",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
},
)
software_development_file_format: Optional[CharacterStringPropertyType] = field(
default=None,
metadata={
"name": "softwareDevelopmentFileFormat",
"type": "Element",
"namespace": "http://www.isotc211.org/2005/gmd",
},
)
| [
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] | 2.253953 | 1,075 |
import pickle
import matplotlib.pyplot as plt
import matplotlib.patches
import matplotlib as mpl
import numpy as np
import sys, argparse
sys.path.append("../")
import Plotting
Names = {
'mini_gb2': 'VC-GB2',
'mini_gb5': 'VC-GB5',
'mini_lin': 'VC-Lin',
'epsall_gb2': '$\epsilon$-GB2',
'epsall_gb5': '$\epsilon$-GB5',
'epsall_lin': '$\epsilon$-Lin',
'lin': 'LinUCB'
}
Styles = {
'mini_gb2': ['k', 'solid'],
'mini_gb5': ['r', 'solid'],
'mini_lin': ['g', 'solid'],
'epsall_gb2': ['k', 'dashed'],
'epsall_gb5': ['r', 'dashed'],
'epsall_lin': ['g', 'dashed'],
'lin': ['b', 'solid']
}
parser = argparse.ArgumentParser()
parser.add_argument('--save', dest='save', action='store_true')
Args = parser.parse_args(sys.argv[1:])
D1 = Plotting.read_dir("../results/mslr30k_T=36000_L=3_e=0.1/")
D2 = Plotting.read_dir("../results/yahoo_T=40000_L=2_e=0.5/")
print(mpl.rcParams['figure.figsize'])
fig = plt.figure(figsize=(mpl.rcParams['figure.figsize'][0]*2, mpl.rcParams['figure.figsize'][1]-1))
ax = fig.add_subplot(111,frameon=False)
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')
std = True
legendHandles = []
keys = ['epsall_lin', 'mini_lin', 'epsall_gb2', 'mini_gb2', 'epsall_gb5', 'mini_gb5', 'lin']
for k in keys:
params = []
mus = []
stds = []
for (k1,v1) in D1[0].items():
if k1.find(k) == 0 and len(D1[0][k1]) != 0:
x = np.arange(100, 10*len(D1[0][k1][0])+1, 100)
mus.append(np.mean(D1[0][k1],axis=0)[9::10]/x)
stds.append(2/np.sqrt(len(D1[0][k1]))*(np.std(D1[0][k1],axis=0)[9::10]/x))
params.append(k1.split("_")[-1])
if len(mus) == 0:
continue
A = np.vstack(mus)
B = np.vstack(stds)
ids = np.argmax(A, axis=0)
mu = np.array([A[ids[i], i] for i in range(len(ids))])
stdev = np.array([B[ids[i], i] for i in range(len(ids))])
if k == 'mini_gb5':
mu = np.mean(D1[0]['mini_gb5_0.008'], axis=0)[9::10]/x
stdev = 2/np.sqrt(len(D1[0]['mini_gb5_0.008']))*(np.std(D1[0]['mini_gb5_0.008'], axis=0)[9::10]/x)
l1 = ax1.plot(x,mu,rasterized=True, linewidth=2.0, label=Names[k], color=Styles[k][0], linestyle=Styles[k][1])
legendHandles.append((matplotlib.patches.Patch(color=l1[0].get_color(), label=Names[k]), Names[k]))
if std and k=='mini_gb5' or k=='lin':
ax1.fill_between(x,
mu - stdev,
mu + stdev,
color = l1[0].get_color(), alpha=0.2, rasterized = True)
for k in keys:
params = []
mus = []
stds = []
for (k1,v1) in D2[0].items():
if k1.find(k) == 0 and len(D2[0][k1]) != 0:
x = np.arange(100, 10*len(D2[0][k1][0])+1, 100)
mus.append(np.mean(D2[0][k1],axis=0)[9::10]/x)
stds.append(2/np.sqrt(len(D2[0][k1]))*(np.std(D2[0][k1],axis=0)[9::10]/x))
params.append(k1.split("_")[-1])
if len(mus) == 0:
continue
A = np.vstack(mus)
B = np.vstack(stds)
ids = np.argmax(A, axis=0)
mu = np.array([A[ids[i], i] for i in range(len(ids))])
stdev = np.array([B[ids[i], i] for i in range(len(ids))])
if k == 'mini_gb5':
mu = np.mean(D2[0]['mini_gb5_0.008'], axis=0)[9::10]/x
stdev = 2/np.sqrt(len(D2[0]['mini_gb5_0.008']))*(np.std(D2[0]['mini_gb5_0.008'], axis=0)[9::10]/x)
l1 = ax2.plot(x,mu,rasterized=True, linewidth=2.0, label=Names[k], color=Styles[k][0], linestyle=Styles[k][1])
if std and k=='mini_gb5' or k=='lin':
ax2.fill_between(x,
mu - stdev,
mu + stdev,
color = l1[0].get_color(), alpha=0.2, rasterized = True)
plt.rc('font', size=18)
plt.rcParams['text.usetex'] = True
plt.rc('font', family='sans-serif')
## Ax1 is MSLR
ticks=ax1.get_yticks()
print(ticks)
ax1.set_ylim(2.15, 2.35)
print("Setting ylim to %0.2f, %0.2f" % (ticks[3], ticks[len(ticks)-2]))
ticks = ax1.get_yticks()
print(ticks)
ticks = ["", "", "2.2", "", "2.3", ""]
ax1.set_yticklabels(ticks,size=20)
ticks = ['', '', '10000', '', '20000', '', '30000']
ax1.set_xlim(1000, 31000)
ax1.set_xticklabels(ticks,size=20)
# Ax2 is Yahoo!
ticks=ax2.get_yticks()
print(ticks)
ax2.set_ylim(2.90,3.12)
print("Setting ylim to %0.2f, %0.2f" % (ticks[3], 3.15))
ticks=ax2.get_yticks()
print(ticks)
ticks = ["", "2.9", "", "3.0", "", "3.1"]
ax2.set_yticklabels(ticks,size=20)
ticks = ['', '', '10000', '', '20000', '', '30000']
ax2.set_xlim(1000, 32000)
ax2.set_xticklabels(ticks,size=20)
plt.sca(ax)
plt.ylabel('Average reward')
plt.xlabel('Number of interactions (T)')
leg = ax2.legend([x[1] for x in legendHandles], loc='upper center', bbox_to_anchor=(-0.1, -0.15), fancybox=False, shadow=False, ncol=7, frameon=False,fontsize=18)
for legobj in leg.legendHandles:
legobj.set_linewidth(4.0)
plt.sca(ax1)
tt1 = plt.title('Dataset: MSLR',fontsize=18)
tt1.set_position([0.5, 1.02])
plt.sca(ax2)
tt2 = plt.title('Dataset: Yahoo!',fontsize=18)
tt2.set_position([0.5, 1.02])
plt.gcf().subplots_adjust(bottom=0.25)
if Args.save:
plt.savefig("../figs/plots_grouped.png", format='png', dpi=100, bbox_inches='tight')
plt.savefig("../figs/plots_grouped.pdf", format='pdf', dpi=100, bbox_inches='tight')
else:
plt.show()
## (DONE) No band
## (DONE) markers + update legend
## (DONE) No legend frame
## (DONE) font is too big
## space between title and plot
## space between ylabel and yticks
## Get P-values (paired ttest and regular ttest)
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] | 1.948012 | 2,943 |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Lucas Sinclair.
MIT Licensed.
Contact at www.sinclair.bio
"""
# Built-in modules #
import os
# First party modules #
from fasta import FASTQ
from plumbing.check_cmd_found import check_cmd
from plumbing.cache import property_cached
from autopaths.tmp_path import new_temp_dir, new_temp_path
# Third party modules #
import sh
###############################################################################
class MothurUchime:
"""
Takes care of detecting and removing chimeric reads, by calling
`mothur.uchime` as seen in:
https://github.com/novigit/broCode/blob/master/pbamp/readCurationPipeline.sh#L103
mothur "#chimera.uchime(fasta=roi.$sample.rhq.trim.fwdrev.pol.fasta,
reference=self, chunks=16, abskew=1, chimealns=T)"
mothur "#remove.seqs(fasta=roi.$sample.rhq.trim.fwdrev.pol.fasta,
accnos=roi.$sample.rhq.trim.fwdrev.pol.denovo.uchime.accnos)"
In case of missing output see:
https://forum.mothur.org/t/chimera-uchime-does-not-produce-any-output/20621
"""
#------------------------------ Running ----------------------------------#
#------------------------------- Results ---------------------------------#
def __bool__(self):
"""
Return True if the chimeras software was run already and the results are
stored on the filesystem. Return False if it was not yet run.
"""
return self.dest.exists
@property_cached
###############################################################################
class ChimerasResults(FASTQ):
"""A file with the results from chimeras."""
pass | [
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] | 2.778675 | 619 |
'''
Read and write Olympia state files.
'''
import os
import os.path
import sys
from contextlib import redirect_stdout
from .oid import to_oid
from .formatters import print_one_thing, read_oly_file
def fixup_ms(data):
'''
For whatever reason, the value in IM/ms needs to have a trailing space
'''
for box in data:
if 'IM' in data[box]:
if 'ms' in data[box]['IM']:
value = data[box]['IM']['ms']
value[0] = value[0].strip() + ' '
data[box]['IM']['ms'] = value
def write_oly_file(data, kind=False, verbose=False):
'''
The main function that drives outputting a file
'''
fixup_ms(data)
order = sorted([int(box) for box in data.keys()])
count = 0
for box in order:
box = str(box)
if kind:
if ' '+kind+' ' not in data[box].get('firstline', '')[0]:
continue
print_one_thing(data[box])
del data[box]
count += 1
if verbose:
print('wrote', count, verbose, 'boxes.', file=sys.stderr)
def read_players(dir, verbose=False):
'''
read every fie in dir whose name is an integer
'''
ret = {}
files = os.listdir(dir)
for name in files:
if name.isdigit():
data = read_oly_file(os.path.join(dir, name), verbose='player ' + name)
ret.update(data)
return ret
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1005,
13,
19119,
7,
7890,
8,
198,
220,
220,
220,
1441,
1005,
628,
628,
198
] | 2.196875 | 640 |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
| [
2,
532,
9,
12,
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25,
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] | 2.891892 | 37 |
from L1TriggerConfig.CSCTFConfigProducers.CSCTFConfigOnline_cfi import *
#from L1TriggerConfig.CSCTFConfigProducers.CSCTFAlignmentOnline_cfi import *
from L1TriggerConfig.CSCTFConfigProducers.L1MuCSCPtLutConfigOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTEtaPatternLutOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTExtLutOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTPhiLutOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTPtaLutOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTQualPatternLutOnline_cfi import *
from L1TriggerConfig.DTTrackFinder.L1MuDTTFParametersOnline_cfi import *
from L1TriggerConfig.RPCTriggerConfig.L1RPCConfigOnline_cfi import *
from L1TriggerConfig.RPCTriggerConfig.L1RPCConeDefinitionOnline_cfi import *
from L1TriggerConfig.RPCTriggerConfig.L1RPCBxOrConfigOnline_cfi import *
from L1TriggerConfig.RPCTriggerConfig.L1RPCHsbConfigOnline_cfi import *
from L1TriggerConfig.GMTConfigProducers.L1MuGMTParametersOnlineProducer_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1MuTriggerPtScaleOnlineProducer_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1MuTriggerScalesOnlineProducer_cfi import *
L1MuGMTParametersOnlineProducer.ignoreVersionMismatch = True
from L1TriggerConfig.RCTConfigProducers.L1RCTParametersOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1EmEtScaleConfigOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1CaloEcalScaleConfigOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1CaloHcalScaleConfigOnline_cfi import *
from L1TriggerConfig.GctConfigProducers.L1GctJetFinderParamsOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1HtMissScaleOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1HfRingEtScaleOnline_cfi import *
from L1TriggerConfig.L1ScalesProducers.L1JetEtScaleOnline_cfi import *
from L1TriggerConfig.L1GtConfigProducers.l1GtParametersOnline_cfi import *
from L1TriggerConfig.L1GtConfigProducers.l1GtPsbSetupOnline_cfi import *
from L1TriggerConfig.L1GtConfigProducers.l1GtTriggerMenuOnline_cfi import *
| [
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38,
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48344,
23381,
14439,
62,
66,
12463,
1330,
1635,
198
] | 2.841463 | 738 |
from .base import db
| [
6738,
764,
8692,
1330,
20613,
628
] | 3.666667 | 6 |
import pulsar as psr
| [
11748,
22271,
283,
355,
26692,
81,
198
] | 3 | 7 |
# -*- coding: utf-8 -*-
"""
This package includes the Flask blueprint and all related functionality for displaying system
information.
"""
from .blueprint import blueprint
from .status_item import StatusItem
from .status_group import StatusGroup
import orchard.system_status.views # NOQA
__all__ = ['blueprint', 'StatusGroup', 'StatusItem']
| [
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] | 3.470588 | 102 |
# embedded validation code, run from C
# input vars: PRODUCT, QUANTITY, BUYER
# output vars: ERRORS, WARNINGS
import string # all python tools are available to embedded code
import inventory # plus C extensions, Python modules, classes,..
msgs, errs = [], [] # warning, error message lists
first, last = BUYER[0], BUYER[1:] # code is changeable on-site:
if first not in string.uppercase: # this file is run as one long
errs.append('buyer-name:' + first) # code-string, with input and
if BUYER not in inventory.buyers(): # output vars used by the C app
msgs.append('new-buyer-added')
inventory.add_buyer(BUYER)
validate_order()
ERRORS = ' '.join(errs) # add a space between messages
WARNINGS = ' '.join(msgs) # pass out as strings: "" == none
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220,
220,
796,
705,
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22179,
7,
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8,
220,
220,
220,
220,
220,
1303,
751,
257,
2272,
1022,
6218,
201,
198,
31502,
50,
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705,
45302,
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7,
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8,
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1303,
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503,
355,
13042,
25,
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6624,
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201,
198,
201,
198
] | 2.484058 | 345 |
from typing import Any, Dict, Iterable, List, Text
from ..common import FeatureSet
class SessionFeatureSet(FeatureSet):
"""
This class represents a session's set of features.
"""
def __init__(self, features: Dict[Text, Any], aura_id_name="AURA_ID", aura_id_global_name="AURA_ID_GLOBAL",
session_id_name="SESSION_ID"):
"""
:param features: dictionary of features.
:param aura_id_name: the name of the aura_id feature.
:param aura_id_global_name: the name of the aura_id_global feature.
:param session_id_name: the name of the session_id feature.
"""
super().__init__(features)
self.aura_id_name = aura_id_name
self.aura_id_global_name = aura_id_global_name
self.session_id_name = session_id_name
@property
@property
@property
@classmethod
def build_from_row(cls, values: List[Any], names: List[Text], aura_id_name="AURA_ID",
aura_id_global_name="AURA_ID_GLOBAL"):
"""
Build a SessionFeaturesSet from the contents of a row (composed of a list of values and corresponding names).
:param values: list of feature values.
:param names: list of the corresponding names.
:param aura_id_name: the name of the aura_id feature.
:param aura_id_global_name: the name of the aura_id_global feature.
:param session_id_name: the name of the session_id feature.
"""
return SessionFeatureSet(cls.build_features(values, names), aura_id_name=aura_id_name,
aura_id_global_name=aura_id_global_name, session_id_name=aura_id_global_name)
class SessionClusterModel(object):
"""
This class represents a clustering model for sessions.
"""
def predict(self, X: Iterable[SessionFeatureSet], **kwargs) -> List[int]:
"""
Predict the fittest cluster for a list of sessions.
:param X: List of sessions to cluster.
:return: List with the ids of the fittest clusters.
"""
raise NotImplementedError("{} is an abstract class and cannot be directly instantiated. "
"The method predict must be implemented!".format(self.__class__))
def fit(self, X: Iterable[SessionFeatureSet], **kwargs):
"""
:param X: List of sessions to cluster.
:param kwargs:
"""
raise NotImplementedError("{} is an abstract class and cannot be directly instantiated. "
"The method fit must be implemented!".format(self.__class__))
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] | 2.46736 | 1,057 |
# -*- coding: utf-8 -*-
# This file is part of bw.
# Distributed under the terms of the last AGPL License.
__author__ = 'Jean Chassoul'
import arrow
import ujson as json
import logging
import uuid
from tornado import gen
def validate_uuid4(uuid_string):
'''
Validate that a UUID string is in
fact a valid uuid4.
Happily, the uuid module does the actual
checking for us.
'''
try:
val = uuid.UUID(uuid_string, version=4)
except ValueError:
# If it's a value error, then the string
# is not a valid hex code for a UUID.
return False
return str(val) == uuid_string
def get_average(total, marks):
'''
Get average from signals
'''
return float(total) / len(marks)
def get_percentage(part, whole):
'''
Get percentage of part and whole.
'''
return "{0:.0f}%".format(float(part)/whole * 100)
@gen.coroutine
def check_json(struct):
'''
Check for malformed JSON
'''
try:
message = json.loads(struct)
except Exception as error:
message = json.dumps({'error': 400})
raise error
return message
@gen.coroutine
def check_times(start, end):
'''
Check times
'''
try:
start = (arrow.get(start) if start
else arrow.get(arrow.utcnow().date()))
end = (arrow.get(end) if end else start.replace(days=+1))
message = {'start': start.timestamp, 'end': end.timestamp}
except Exception as error:
logging.exception(error)
raise error
return message
@gen.coroutine
def check_times_get_timestamp(start, end):
'''
Check times get timestamp
'''
try:
start = (arrow.get(start) if start
else arrow.get(arrow.utcnow().date()))
end = (arrow.get(end) if end else start.replace(days=+1))
message = {'start': start.timestamp, 'end': end.timestamp}
except Exception as error:
logging.exception(error)
raise error
return message
@gen.coroutine
def check_times_get_datetime(start, end):
'''
Check times get datetime
'''
try:
start = (arrow.get(start) if start
else arrow.get(arrow.utcnow().date()))
end = (arrow.get(end) if end else start.replace(days=+1))
message = {'start': start.naive, 'end': end.naive}
except Exception as error:
logging.exception(error)
raise error
return message
def clean_message(struct):
'''
clean message
'''
struct = struct.to_native()
struct = {
key: struct[key] for key in struct if struct[key] is not None
}
return struct
def clean_structure(struct):
'''
clean structure
'''
struct = struct.to_primitive()
struct = {
key: struct[key] for key in struct if struct[key] is not None
}
return struct
def clean_results(results):
'''
clean results
'''
results = results.to_primitive()
results = results.get('results')
results = [
{
key: dic[key] for key in dic if dic[key] is not None
} for dic in results
]
return {'results': results}
def str2bool(boo):
'''
String to boolean
'''
return boo.lower() in ('yes', 'true', 't', '1')
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220,
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1441,
1391,
6,
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17,
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7,
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8505,
3256,
705,
7942,
3256,
705,
83,
3256,
705,
16,
11537,
198
] | 2.318498 | 1,438 |
from pyox.client import Client, ServiceError
| [
198,
6738,
12972,
1140,
13,
16366,
1330,
20985,
11,
4809,
12331,
198
] | 3.833333 | 12 |
from os import system
# 重启电脑
| [
6738,
28686,
1330,
1080,
628,
198,
2,
16268,
229,
235,
28938,
107,
18796,
113,
164,
226,
239,
628
] | 1.777778 | 18 |
from flask import Flask, request, render_template, jsonify, make_response, redirect, flash, url_for
from functools import wraps
import re
import pyctrl
from pyctrl.block import Logger
import warnings
import importlib
import traceback, sys, io
from pyctrl.flask import JSONEncoder, JSONDecoder
encoder = JSONEncoder(sort_keys = True, indent = 4)
decoder = JSONDecoder()
# decorators
# decode
# decode_kwargs_aux
# decode_kwargs
# json_response
# Server class
if __name__ == "__main__":
try:
import os
os.environ['RCPY_NO_HANDLERS'] = 't'
from pyctrl.rc import Controller
debug = False
RCPY = True
except:
from pyctrl.timer import Controller
debug = True
RCPY = False
try:
app = Server(__name__)
app.config['SECRET_KEY'] = 'secret!'
# initialize controller
app.set_controller(controller = Controller(period = .01))
# run app
app.run(host='0.0.0.0',
debug = debug)
except:
pass
finally:
sys.exit(0)
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13,
15,
13,
15,
13,
15,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14257,
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14257,
8,
628,
220,
220,
220,
2845,
25,
198,
220,
220,
220,
220,
220,
220,
220,
1208,
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220,
220,
3443,
25,
198,
220,
220,
220,
220,
220,
220,
220,
25064,
13,
37023,
7,
15,
8,
198
] | 2.30625 | 480 |
from django.db.models import Q
from rest_framework.filters import SearchFilter, OrderingFilter
from rest_framework.generics import ListAPIView, CreateAPIView, RetrieveAPIView, RetrieveUpdateAPIView, DestroyAPIView
from rest_framework.permissions import IsAuthenticated, IsAuthenticatedOrReadOnly
from posts.models import Post
from .pagination import PostPageNumberPagination
from .permissions import IsOwnerOrReadOnly
from .serializers import PostCreateUpdateSerializer, PostDetailSerializer, PostListSerializer | [
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11,
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11242,
603,
32634,
7509,
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] | 3.849624 | 133 |
# by ARTRoyale (A. Lebedev) for ZapRoyale
import socket
import threading
import struct
import os
import uuid
import random
# начинаем дебаг
global debugmode
debugmode = True
global gl_server_address
gl_server_address = ('***.***.*.**', 9339)
if __name__ == "__main__":
port_num = 9339
print('[INFO] Proksi podkluchaetsa k portu', port_num)
ThreadedServer('0.0.0.0',port_num).listen()
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3256,
634,
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] | 2.320225 | 178 |
from pydantic import BaseModel
# class MetricsOutput(BaseModel):
# name: str
# metrics: dict
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from quart import request, jsonify
import time
from api.models import User
from .. import bp
import utils
request: utils.Request
@bp.route("/<int:user_id>/roles", methods=["GET"])
@utils.auth_required
async def fetch_user_roles(user_id: int):
"""Fetch the specific users roles"""
query = """
SELECT json_agg(json_build_object(
'name', r.name,
'base', r.base,
'id', r.id::TEXT,
'color', r.color,
'position', r.position,
'permissions', r.permissions::TEXT
))
FROM roles r WHERE r.id IN (
SELECT ur.role_id FROM userroles WHERE ur.user_id = $1
)
"""
record = await User.pool.fetchval(query, user_id)
return jsonify(roles=record)
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] | 2.290123 | 324 |
import os
import torch
import argparse
import numpy as np
import matplotlib.pylab as plt
from text import text_to_sequence
from model.model import Tacotron2
from hparams import hparams as hps
from utils.util import mode, to_var, to_arr
from utils.audio import load_wav, save_wav, melspectrogram
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--ckpt_pth', type = str, default = '',
required = True, help = 'path to load checkpoints')
parser.add_argument('-n', '--npy_pth', type = str, default = 'dump',
help = 'path to save mels')
args = parser.parse_args()
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
model = load_model(args.ckpt_pth)
flist = files_to_list()
for x in flist:
ret = infer(x[0], x[1], model)
name = x[0].split('/')[-1].split('.wav')[0]
if args.npy_pth != '':
save_mel(ret, args.npy_pth, name)
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11,
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13,
77,
9078,
62,
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400,
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8,
198
] | 2.315789 | 437 |
from typing import Union, Tuple
import pandas as pd
from sklearn.utils import shuffle
from doors_detector.dataset.torch_dataset import TRAIN_SET, TEST_SET, SET
from generic_dataset.dataset_manager import DatasetManager
from doors_detector.dataset.dataset_doors_final.door_sample import DoorSample, DOOR_LABELS
from sklearn.model_selection import train_test_split
from doors_detector.dataset.dataset_doors_final.final_doors_dataset import DatasetDoorsFinal
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] | 3.06 | 150 |
from collections import defaultdict
from bisect import bisect
# Your TimeMap object will be instantiated and called as such:
# obj = TimeMap()
# obj.set(key,value,timestamp)
# param_2 = obj.get(key,timestamp)
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] | 3.261538 | 65 |
import fileinput
import sys
import math
import time
import os
import click
# prevent pygame from printing their welcome message
os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide"
import pygame
# Define some basic colors for easy use
white = (255, 255, 255)
red = (255, 0, 0)
black = (0, 0, 0)
green = (0, 255, 0)
blue = (0, 0, 255)
# Screen resolution to use
window_dimensions = (1200, 800)
# Higher frequency is less updates, lower frequency is more updates (it's a x % frequency == 0)
UPDATE_FREQUENCY = 1000
# Only updates every nth triangle, increases clarity in high density datasets
# Can also put this to 1 and make the scaling factor larger
THINNING_FACTOR = 1
pygame.init()
screen = pygame.display.set_mode(window_dimensions)
screen.fill(white)
font = pygame.font.SysFont("Arial", 12)
# TODO: Split label and value for each statistics field
time_taken = font.render("time:", True, white, blue)
tt_rect = time_taken.get_rect(bottomright=(80, window_dimensions[1] - 65))
screen.blit(time_taken, tt_rect)
time_taken_val = font.render(" ", True, white, blue)
tt_rect2 = time_taken_val.get_rect(bottomleft=(80, window_dimensions[1] - 65))
screen.blit(time_taken_val, tt_rect2)
points_per_second = font.render("avg #pts/s:", True, white, blue)
pps_rect = points_per_second.get_rect(bottomright=(80, window_dimensions[1] - 45))
screen.blit(points_per_second, pps_rect)
points_per_second_val = font.render(" ", True, white, blue)
pps_rect2 = points_per_second_val.get_rect(bottomleft=(80, window_dimensions[1] - 45))
screen.blit(points_per_second_val, pps_rect2)
# points_last_minute = font.render(" # pts last minute:", True, white, blue)
# plm_rect = points_last_minute.get_rect(bottomright=(80, window_dimensions[1] - 95))
# screen.blit(points_last_minute, plm_rect)
# points_last_minute_val = font.render(" ", True, white, blue)
# plm_rect2 = points_last_minute_val.get_rect(bottomleft=(80, window_dimensions[1] - 95))
# screen.blit(points_last_minute_val, plm_rect2)
total_points = font.render("# pts:", True, white, blue)
tp_rect = total_points.get_rect(bottomright=(80, window_dimensions[1] - 25))
screen.blit(total_points, tp_rect)
total_points_val = font.render(" ", True, white, blue)
tp_rect2 = total_points_val.get_rect(bottomleft=(80, window_dimensions[1] - 25))
screen.blit(total_points_val, tp_rect2)
total_triangles = font.render("# triangles:", True, white, blue)
ttr_rect = total_triangles.get_rect(bottomright=(80, window_dimensions[1] - 5))
screen.blit(total_triangles, ttr_rect)
total_triangles_val = font.render(" ", True, white, blue)
ttr_rect2 = total_triangles_val.get_rect(bottomleft=(80, window_dimensions[1] - 5))
screen.blit(total_triangles_val, ttr_rect2)
pygame.display.set_caption('sstvis')
pygame.display.flip()
@click.command()
@click.option('--thinning', default=THINNING_FACTOR, help='thinning factor (1 = no thinning)')
@click.option('--frequency', default=UPDATE_FREQUENCY, help='Higher frequency is less updates, lower frequency is more updates')
if __name__ == "__main__":
main() | [
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] | 2.775658 | 1,101 |
# Copyright (c) "Neo4j"
# Neo4j Sweden AB [http://neo4j.com]
#
# This file is part of Neo4j.
#
# 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 abc
import asyncio
from collections import deque
from logging import getLogger
from time import perf_counter
from ..._async_compat.network import AsyncBoltSocket
from ..._async_compat.util import AsyncUtil
from ..._exceptions import (
BoltError,
BoltHandshakeError,
SocketDeadlineExceeded,
)
from ...addressing import Address
from ...api import (
ServerInfo,
Version,
)
from ...conf import PoolConfig
from ...exceptions import (
AuthError,
DriverError,
IncompleteCommit,
ServiceUnavailable,
SessionExpired,
)
from ...meta import get_user_agent
from ...packstream import (
Packer,
Unpacker,
)
from ._common import (
AsyncInbox,
CommitResponse,
Outbox,
)
# Set up logger
log = getLogger("neo4j")
class AsyncBolt:
""" Server connection for Bolt protocol.
A :class:`.Bolt` should be constructed following a
successful .open()
Bolt handshake and takes the socket over which
the handshake was carried out.
"""
MAGIC_PREAMBLE = b"\x60\x60\xB0\x17"
PROTOCOL_VERSION = None
# flag if connection needs RESET to go back to READY state
is_reset = False
# The socket
in_use = False
# When the connection was last put back into the pool
idle_since = float("-inf")
# The socket
_closing = False
_closed = False
# The socket
_defunct = False
#: The pool of which this connection is a member
pool = None
# Store the id of the most recent ran query to be able to reduce sent bits by
# using the default (-1) to refer to the most recent query when pulling
# results for it.
most_recent_qid = None
@property
@abc.abstractmethod
def supports_multiple_results(self):
""" Boolean flag to indicate if the connection version supports multiple
queries to be buffered on the server side (True) or if all results need
to be eagerly pulled before sending the next RUN (False).
"""
pass
@property
@abc.abstractmethod
def supports_multiple_databases(self):
""" Boolean flag to indicate if the connection version supports multiple
databases.
"""
pass
@classmethod
def protocol_handlers(cls, protocol_version=None):
""" Return a dictionary of available Bolt protocol handlers,
keyed by version tuple. If an explicit protocol version is
provided, the dictionary will contain either zero or one items,
depending on whether that version is supported. If no protocol
version is provided, all available versions will be returned.
:param protocol_version: tuple identifying a specific protocol
version (e.g. (3, 5)) or None
:return: dictionary of version tuple to handler class for all
relevant and supported protocol versions
:raise TypeError: if protocol version is not passed in a tuple
"""
# Carry out Bolt subclass imports locally to avoid circular dependency issues.
from ._bolt3 import AsyncBolt3
from ._bolt4 import (
AsyncBolt4x1,
AsyncBolt4x2,
AsyncBolt4x3,
AsyncBolt4x4,
)
from ._bolt5 import AsyncBolt5x0
handlers = {
AsyncBolt3.PROTOCOL_VERSION: AsyncBolt3,
# 4.0 unsupported because no space left in the handshake
AsyncBolt4x1.PROTOCOL_VERSION: AsyncBolt4x1,
AsyncBolt4x2.PROTOCOL_VERSION: AsyncBolt4x2,
AsyncBolt4x3.PROTOCOL_VERSION: AsyncBolt4x3,
AsyncBolt4x4.PROTOCOL_VERSION: AsyncBolt4x4,
AsyncBolt5x0.PROTOCOL_VERSION: AsyncBolt5x0,
}
if protocol_version is None:
return handlers
if not isinstance(protocol_version, tuple):
raise TypeError("Protocol version must be specified as a tuple")
if protocol_version in handlers:
return {protocol_version: handlers[protocol_version]}
return {}
@classmethod
def version_list(cls, versions, limit=4):
""" Return a list of supported protocol versions in order of
preference. The number of protocol versions (or ranges)
returned is limited to four.
"""
# In fact, 4.3 is the fist version to support ranges. However, the
# range support got backported to 4.2. But even if the server is too
# old to have the backport, negotiating BOLT 4.1 is no problem as it's
# equivalent to 4.2
first_with_range_support = Version(4, 2)
result = []
for version in versions:
if (result
and version >= first_with_range_support
and result[-1][0] == version[0]
and result[-1][1][1] == version[1] + 1):
# can use range to encompass this version
result[-1][1][1] = version[1]
continue
result.append(Version(version[0], [version[1], version[1]]))
if len(result) == 4:
break
return result
@classmethod
def get_handshake(cls):
""" Return the supported Bolt versions as bytes.
The length is 16 bytes as specified in the Bolt version negotiation.
:return: bytes
"""
supported_versions = sorted(cls.protocol_handlers().keys(), reverse=True)
offered_versions = cls.version_list(supported_versions)
return b"".join(version.to_bytes() for version in offered_versions).ljust(16, b"\x00")
@classmethod
async def ping(cls, address, *, timeout=None, **config):
""" Attempt to establish a Bolt connection, returning the
agreed Bolt protocol version if successful.
"""
config = PoolConfig.consume(config)
try:
s, protocol_version, handshake, data = \
await AsyncBoltSocket.connect(
address,
timeout=timeout,
custom_resolver=config.resolver,
ssl_context=config.get_ssl_context(),
keep_alive=config.keep_alive,
)
except (ServiceUnavailable, SessionExpired, BoltHandshakeError):
return None
else:
AsyncBoltSocket.close_socket(s)
return protocol_version
@classmethod
async def open(
cls, address, *, auth=None, timeout=None, routing_context=None,
**pool_config
):
"""Open a new Bolt connection to a given server address.
:param address:
:param auth:
:param timeout: the connection timeout in seconds
:param routing_context: dict containing routing context
:param pool_config:
:return: connected AsyncBolt instance
:raise BoltHandshakeError:
raised if the Bolt Protocol can not negotiate a protocol version.
:raise ServiceUnavailable: raised if there was a connection issue.
"""
t0 = perf_counter()
pool_config = PoolConfig.consume(pool_config)
socket_connection_timeout = pool_config.connection_timeout
if socket_connection_timeout is None:
socket_connection_timeout = time_remaining()
elif timeout is not None:
socket_connection_timeout = min(pool_config.connection_timeout,
time_remaining())
s, pool_config.protocol_version, handshake, data = \
await AsyncBoltSocket.connect(
address,
timeout=socket_connection_timeout,
custom_resolver=pool_config.resolver,
ssl_context=pool_config.get_ssl_context(),
keep_alive=pool_config.keep_alive,
)
# Carry out Bolt subclass imports locally to avoid circular dependency
# issues.
if pool_config.protocol_version == (3, 0):
from ._bolt3 import AsyncBolt3
bolt_cls = AsyncBolt3
# Implementation for 4.0 exists, but there was no space left in the
# handshake to offer this version to the server. Hence, the server
# should never request us to speak bolt 4.0.
# elif pool_config.protocol_version == (4, 0):
# from ._bolt4 import AsyncBolt4x0
# bolt_cls = AsyncBolt4x0
elif pool_config.protocol_version == (4, 1):
from ._bolt4 import AsyncBolt4x1
bolt_cls = AsyncBolt4x1
elif pool_config.protocol_version == (4, 2):
from ._bolt4 import AsyncBolt4x2
bolt_cls = AsyncBolt4x2
elif pool_config.protocol_version == (4, 3):
from ._bolt4 import AsyncBolt4x3
bolt_cls = AsyncBolt4x3
elif pool_config.protocol_version == (4, 4):
from ._bolt4 import AsyncBolt4x4
bolt_cls = AsyncBolt4x4
elif pool_config.protocol_version == (5, 0):
from ._bolt5 import AsyncBolt5x0
bolt_cls = AsyncBolt5x0
else:
log.debug("[#%04X] S: <CLOSE>", s.getsockname()[1])
AsyncBoltSocket.close_socket(s)
supported_versions = cls.protocol_handlers().keys()
raise BoltHandshakeError(
"The Neo4J server does not support communication with this "
"driver. This driver has support for Bolt protocols "
"{}".format(tuple(map(str, supported_versions))),
address=address, request_data=handshake, response_data=data
)
connection = bolt_cls(
address, s, pool_config.max_connection_lifetime, auth=auth,
user_agent=pool_config.user_agent, routing_context=routing_context
)
try:
connection.socket.set_deadline(time_remaining())
try:
await connection.hello()
except SocketDeadlineExceeded as e:
# connection._defunct = True
raise ServiceUnavailable(
"Timeout during initial handshake occurred"
) from e
finally:
connection.socket.set_deadline(None)
except Exception:
await connection.close_non_blocking()
raise
return connection
@property
@abc.abstractmethod
@property
@abc.abstractmethod
@property
@abc.abstractmethod
@abc.abstractmethod
async def hello(self):
""" Appends a HELLO message to the outgoing queue, sends it and consumes
all remaining messages.
"""
pass
@abc.abstractmethod
async def route(self, database=None, imp_user=None, bookmarks=None):
""" Fetch a routing table from the server for the given
`database`. For Bolt 4.3 and above, this appends a ROUTE
message; for earlier versions, a procedure call is made via
the regular Cypher execution mechanism. In all cases, this is
sent to the network, and a response is fetched.
:param database: database for which to fetch a routing table
Requires Bolt 4.0+.
:param imp_user: the user to impersonate
Requires Bolt 4.4+.
:param bookmarks: iterable of bookmark values after which this
transaction should begin
:return: dictionary of raw routing data
"""
pass
@abc.abstractmethod
def run(self, query, parameters=None, mode=None, bookmarks=None,
metadata=None, timeout=None, db=None, imp_user=None, **handlers):
""" Appends a RUN message to the output queue.
:param query: Cypher query string
:param parameters: dictionary of Cypher parameters
:param mode: access mode for routing - "READ" or "WRITE" (default)
:param bookmarks: iterable of bookmark values after which this transaction should begin
:param metadata: custom metadata dictionary to attach to the transaction
:param timeout: timeout for transaction execution (seconds)
:param db: name of the database against which to begin the transaction
Requires Bolt 4.0+.
:param imp_user: the user to impersonate
Requires Bolt 4.4+.
:param handlers: handler functions passed into the returned Response object
:return: Response object
"""
pass
@abc.abstractmethod
def discard(self, n=-1, qid=-1, **handlers):
""" Appends a DISCARD message to the output queue.
:param n: number of records to discard, default = -1 (ALL)
:param qid: query ID to discard for, default = -1 (last query)
:param handlers: handler functions passed into the returned Response object
:return: Response object
"""
pass
@abc.abstractmethod
def pull(self, n=-1, qid=-1, **handlers):
""" Appends a PULL message to the output queue.
:param n: number of records to pull, default = -1 (ALL)
:param qid: query ID to pull for, default = -1 (last query)
:param handlers: handler functions passed into the returned Response object
:return: Response object
"""
pass
@abc.abstractmethod
def begin(self, mode=None, bookmarks=None, metadata=None, timeout=None,
db=None, imp_user=None, **handlers):
""" Appends a BEGIN message to the output queue.
:param mode: access mode for routing - "READ" or "WRITE" (default)
:param bookmarks: iterable of bookmark values after which this transaction should begin
:param metadata: custom metadata dictionary to attach to the transaction
:param timeout: timeout for transaction execution (seconds)
:param db: name of the database against which to begin the transaction
Requires Bolt 4.0+.
:param imp_user: the user to impersonate
Requires Bolt 4.4+
:param handlers: handler functions passed into the returned Response object
:return: Response object
"""
pass
@abc.abstractmethod
def commit(self, **handlers):
""" Appends a COMMIT message to the output queue."""
pass
@abc.abstractmethod
def rollback(self, **handlers):
""" Appends a ROLLBACK message to the output queue."""
pass
@abc.abstractmethod
async def reset(self):
""" Appends a RESET message to the outgoing queue, sends it and consumes
all remaining messages.
"""
pass
@abc.abstractmethod
def goodbye(self):
"""Append a GOODBYE message to the outgoing queued."""
pass
def _append(self, signature, fields=(), response=None):
""" Appends a message to the outgoing queue.
:param signature: the signature of the message
:param fields: the fields of the message as a tuple
:param response: a response object to handle callbacks
"""
with self.outbox.tmp_buffer():
self.packer.pack_struct(signature, fields)
self.outbox.wrap_message()
self.responses.append(response)
async def send_all(self):
""" Send all queued messages to the server.
"""
if self.closed():
raise ServiceUnavailable(
"Failed to write to closed connection {!r} ({!r})".format(
self.unresolved_address, self.server_info.address
)
)
if self.defunct():
raise ServiceUnavailable(
"Failed to write to defunct connection {!r} ({!r})".format(
self.unresolved_address, self.server_info.address
)
)
await self._send_all()
@abc.abstractmethod
async def _process_message(self, details, summary_signature,
summary_metadata):
""" Receive at most one message from the server, if available.
:return: 2-tuple of number of detail messages and number of summary
messages fetched
"""
pass
async def fetch_all(self):
""" Fetch all outstanding messages.
:return: 2-tuple of number of detail messages and number of summary
messages fetched
"""
detail_count = summary_count = 0
while self.responses:
response = self.responses[0]
while not response.complete:
detail_delta, summary_delta = await self.fetch_message()
detail_count += detail_delta
summary_count += summary_delta
return detail_count, summary_count
_stale = False
async def close(self):
"""Close the connection."""
if self._closed or self._closing:
return
self._closing = True
if not self._defunct:
self.goodbye()
try:
await self._send_all()
except (OSError, BoltError, DriverError):
pass
log.debug("[#%04X] C: <CLOSE>", self.local_port)
try:
self.socket.close()
except OSError:
pass
finally:
self._closed = True
async def close_non_blocking(self):
"""Set the socket to non-blocking and close it.
This will try to send the `GOODBYE` message (given the socket is not
marked as defunct). However, should the write operation require
blocking (e.g., a full network buffer), then the socket will be closed
immediately (without `GOODBYE` message).
"""
if self._closed or self._closing:
return
self.socket.settimeout(0)
await self.close()
def is_idle_for(self, timeout):
"""Check if connection has been idle for at least the given timeout.
:param timeout: timeout in seconds
:type timeout: float
:rtype: bool
"""
return perf_counter() - self.idle_since > timeout
AsyncBoltSocket.Bolt = AsyncBolt
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220,
220,
220,
220,
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198,
220,
220,
220,
220,
220,
220,
220,
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20225,
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25,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
1058,
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25,
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198,
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220,
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220,
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220,
220,
1058,
81,
4906,
25,
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198,
220,
220,
220,
220,
220,
220,
220,
37227,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
23035,
62,
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3419,
532,
2116,
13,
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62,
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1875,
26827,
628,
198,
42367,
33,
5978,
39105,
13,
33,
5978,
796,
1081,
13361,
33,
5978,
198
] | 2.41644 | 7,725 |
#!/usr/bin/env python3
"""
process data generated by PageBuilder
"""
import json
INFN = "document.json"
OUTFN = "_DATA.json"
print ("PROCESSING ===========================================================")
########################################################################
## READING THE INPUT FILE
print ("reading", INFN)
with open(INFN, "r") as f: document_json = f.read()
print ("parsing", INFN)
document_data = json.loads(document_json)
print ("analysing {} ({} records)".format(INFN, len(document_data)))
data = []
FIELDS = ["_filename", "data"]
for r in document_data:
data.append({ k: r.get(k, None) for k in FIELDS})
print ("extracted {} records".format(len(data)))
print ("EXTRACTED DATA:", data)
########################################################################
## PROCESSING
out_sums = {}
for i in range(len(data)):
d = data[i]
sdata = d['data'].split(",")
sdata = map(int, sdata)
out_sums[d["_filename"]] = {"sum": sum(sdata)}
########################################################################
## WRITING THE OUTPUT FILE
out = {
"_select": out_sums,
# the key `_select` is special; it MUST contain a dict where the
# dict keys are the filename (from the `_filename` field); when
# a specific file `filename` is processed, the content of
# out["_select"][filename] (which must be a dict) is added to
# the environment, and can be added in the template
#"sums": 1,
}
with open(OUTFN, "w") as f: f.write(json.dumps(out))
print("OUT:", out)
print ("END PROCESSING =======================================================")
| [
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220,
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220,
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220,
220,
220,
220,
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262,
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62,
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63,
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8,
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220,
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220,
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1,
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7,
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8,
628,
198,
4798,
5855,
10619,
41755,
7597,
2751,
46111,
4770,
50155,
4943,
198
] | 3.134357 | 521 |
from cyder.api.v1.endpoints.dhcp.vrf import api
| [
6738,
3075,
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13,
85,
16,
13,
437,
13033,
13,
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13155,
13,
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69,
1330,
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198
] | 2.4 | 20 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
| [
2,
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69,
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532,
9,
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628
] | 2.043478 | 23 |
import os
from sys import intern
from typing import Iterable, FrozenSet, Optional
from pyramids.categorization import Category
from pyramids.rules.leaf import LeafRule
from pyramids.word_sets import WordSetUtils
| [
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4487,
628
] | 3.962963 | 54 |
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2011 Nicira Networks, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
Routines for configuring Neutron
"""
import os
from oslo.config import cfg
from paste import deploy
from neutron.api.v2 import attributes
from neutron.common import utils
from neutron.openstack.common.db.sqlalchemy import session as db_session
from neutron.openstack.common import log as logging
from neutron.openstack.common import rpc
from neutron.version import version_info as neutron_version
LOG = logging.getLogger(__name__)
core_opts = [
cfg.StrOpt('bind_host', default='0.0.0.0',
help=_("The host IP to bind to")),
cfg.IntOpt('bind_port', default=9696,
help=_("The port to bind to")),
cfg.StrOpt('api_paste_config', default="api-paste.ini",
help=_("The API paste config file to use")),
cfg.StrOpt('api_extensions_path', default="",
help=_("The path for API extensions")),
cfg.StrOpt('policy_file', default="policy.json",
help=_("The policy file to use")),
cfg.StrOpt('auth_strategy', default='keystone',
help=_("The type of authentication to use")),
cfg.StrOpt('core_plugin',
help=_("The core plugin Neutron will use")),
cfg.ListOpt('service_plugins', default=[],
help=_("The service plugins Neutron will use")),
cfg.StrOpt('base_mac', default="fa:16:3e:00:00:00",
help=_("The base MAC address Neutron will use for VIFs")),
cfg.IntOpt('mac_generation_retries', default=16,
help=_("How many times Neutron will retry MAC generation")),
cfg.BoolOpt('allow_bulk', default=True,
help=_("Allow the usage of the bulk API")),
cfg.BoolOpt('allow_pagination', default=False,
help=_("Allow the usage of the pagination")),
cfg.BoolOpt('allow_sorting', default=False,
help=_("Allow the usage of the sorting")),
cfg.StrOpt('pagination_max_limit', default="-1",
help=_("The maximum number of items returned in a single "
"response, value was 'infinite' or negative integer "
"means no limit")),
cfg.IntOpt('max_dns_nameservers', default=5,
help=_("Maximum number of DNS nameservers")),
cfg.IntOpt('max_subnet_host_routes', default=20,
help=_("Maximum number of host routes per subnet")),
cfg.IntOpt('max_fixed_ips_per_port', default=5,
help=_("Maximum number of fixed ips per port")),
cfg.IntOpt('dhcp_lease_duration', default=86400,
deprecated_name='dhcp_lease_time',
help=_("DHCP lease duration")),
cfg.BoolOpt('dhcp_agent_notification', default=True,
help=_("Allow sending resource operation"
" notification to DHCP agent")),
cfg.BoolOpt('allow_overlapping_ips', default=False,
help=_("Allow overlapping IP support in Neutron")),
cfg.StrOpt('host', default=utils.get_hostname(),
help=_("The hostname Neutron is running on")),
cfg.BoolOpt('force_gateway_on_subnet', default=False,
help=_("Ensure that configured gateway is on subnet")),
]
core_cli_opts = [
cfg.StrOpt('state_path',
default='/var/lib/neutron',
help=_("Where to store Neutron state files. "
"This directory must be writable by the agent.")),
]
# Register the configuration options
cfg.CONF.register_opts(core_opts)
cfg.CONF.register_cli_opts(core_cli_opts)
# Ensure that the control exchange is set correctly
rpc.set_defaults(control_exchange='neutron')
_SQL_CONNECTION_DEFAULT = 'sqlite://'
# Update the default QueuePool parameters. These can be tweaked by the
# configuration variables - max_pool_size, max_overflow and pool_timeout
db_session.set_defaults(sql_connection=_SQL_CONNECTION_DEFAULT,
sqlite_db='', max_pool_size=10,
max_overflow=20, pool_timeout=10)
def setup_logging(conf):
"""Sets up the logging options for a log with supplied name.
:param conf: a cfg.ConfOpts object
"""
product_name = "neutron"
logging.setup(product_name)
LOG.info(_("Logging enabled!"))
def load_paste_app(app_name):
"""Builds and returns a WSGI app from a paste config file.
:param app_name: Name of the application to load
:raises ConfigFilesNotFoundError when config file cannot be located
:raises RuntimeError when application cannot be loaded from config file
"""
config_path = cfg.CONF.find_file(cfg.CONF.api_paste_config)
if not config_path:
raise cfg.ConfigFilesNotFoundError(
config_files=[cfg.CONF.api_paste_config])
config_path = os.path.abspath(config_path)
LOG.info(_("Config paste file: %s"), config_path)
try:
app = deploy.loadapp("config:%s" % config_path, name=app_name)
except (LookupError, ImportError):
msg = (_("Unable to load %(app_name)s from "
"configuration file %(config_path)s.") %
{'app_name': app_name,
'config_path': config_path})
LOG.exception(msg)
raise RuntimeError(msg)
return app
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] | 2.448117 | 2,390 |
import unittest
from test import support
support.import_module('_testcapi')
from _testcapi import _test_structmembersType, CHAR_MAX, CHAR_MIN, UCHAR_MAX, SHRT_MAX, SHRT_MIN, USHRT_MAX, INT_MAX, INT_MIN, UINT_MAX, LONG_MAX, LONG_MIN, ULONG_MAX, LLONG_MAX, LLONG_MIN, ULLONG_MAX, PY_SSIZE_T_MAX, PY_SSIZE_T_MIN
ts = _test_structmembersType(False, 1, 2, 3, 4, 5, 6, 7, 8, 23, 9.99999,
10.101010101, 'hi')
if __name__ == '__main__':
unittest.main()
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] | 2.301508 | 199 |
#!/usr/bin/env python3
import argparse
import copy
import logging
import sys
import warnings
import numpy as np
import rasterio as rio
import torch
import torch.hub
import tqdm
from rasterio.windows import Window
BACKBONES = [
'vgg16', 'densenet161', 'shufflenet_v2_x1_0', 'mobilenet_v2',
'mobilenet_v3_large', 'mobilenet_v3_small', 'resnet18', 'resnet34',
'resnet50', 'resnet101', 'resnet152', 'efficientnet_b0', 'efficientnet_b1',
'efficientnet_b2', 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5',
'efficientnet_b6', 'efficientnet_b7', 'fpn_resnet18', 'fpn_resnet34',
'fpn_resnet50'
]
if __name__ == '__main__':
warnings.filterwarnings('ignore')
args = cli_parser().parse_args()
logging.basicConfig(stream=sys.stderr, level=logging.INFO, format='%(asctime)-15s %(message)s')
log = logging.getLogger()
n = args.window_size
device = torch.device(args.device)
model = torch.hub.load('jamesmcclain/algae-classifier:730726f5bccc679fa334da91fe4dc4cb71a35208',
'make_algae_model',
in_channels=[4, 12, 224],
prescale=args.prescale,
backbone_str=args.backbone,
pretrained=False)
model.load_state_dict(torch.load(args.pth_load))
model.to(device)
model.eval()
if args.outfile is None:
model_name = args.pth_load.split('/')[-1].split('.')[0]
args.outfile = [transmute(f) for f in args.infile]
for (infile, outfile) in zip(args.infile, args.outfile):
log.info(outfile)
with rio.open(infile, 'r') as infile_ds, torch.no_grad():
out_raw_profile = copy.deepcopy(infile_ds.profile)
out_raw_profile.update({
'compress': 'lzw',
'dtype': np.float32,
'count': 1,
'bigtiff': 'yes',
'sparse_ok': 'yes',
'tiled': 'yes',
})
width = infile_ds.width
height = infile_ds.height
bandcount = infile_ds.count
ar_out = torch.zeros((1, height, width), dtype=torch.float32).to(device)
pixel_hits = torch.zeros((1, height, width), dtype=torch.uint8).to(device)
if bandcount == 224:
indexes = list(range(1, 224 + 1))
elif bandcount in {12, 13}:
indexes = list(range(1, 12 + 1))
# NOTE: 13 bands does not indicate L1C support, this is
# for Franklin COGs that have an extra band.
bandcount = 12
elif bandcount == 4:
indexes = list(range(1, 4 + 1))
elif bandcount == 5:
indexes = [1, 2, 3, 5]
bandcount = 4
else:
raise Exception(f'bands={bandcount}')
# gather up batches
batches = []
for i in range(0, width - n, args.stride):
for j in range(0, height - n, args.stride):
batches.append((i, j))
batches = [batches[i:i + args.chunksize] for i in range(0, len(batches), args.chunksize)]
for batch in tqdm.tqdm(batches):
windows = [infile_ds.read(indexes, window=Window(i, j, n, n)) for (i, j) in batch]
windows = [w.astype(np.float32) for w in windows]
if args.ndwi_mask:
windows = [w * (((w[2] - w[7]) / (w[2] + w[7])) > 0.0) for w in windows]
try:
windows = np.stack(windows, axis=0)
except:
continue
windows = torch.from_numpy(windows).to(dtype=torch.float32, device=device)
prob = model(windows)
for k, (i, j) in enumerate(batch):
if 'seg' in prob:
_prob = torch.sigmoid(prob.get('seg')[k, 1]) - torch.sigmoid(prob.get('seg')[k, 0])
ar_out[0, j:(j + n), i:(i + n)] += _prob
else:
ar_out[0, j:(j + n), i:(i + n)] += torch.sigmoid(prob.get('class')[k, 0])
pixel_hits[0, j:(j + n), i:(i + n)] += 1
# Bring results back to CPU
ar_out /= pixel_hits
ar_out = ar_out.cpu().numpy()
# Write results to file
with rio.open(outfile, 'w', **out_raw_profile) as outfile_raw_ds:
outfile_raw_ds.write(ar_out[0], indexes=1)
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] | 1.878181 | 2,397 |
# -*- coding: utf-8 -*-
from django.conf import settings
# How often to check
TZ_DETECT_PERIOD = getattr(settings, 'TZ_DETECT_PERIOD', 3*3600)
# Version of moment and moment-timezone to load
TZ_DETECT_SCRIPTS = getattr(settings, 'TZ_DETECT_SCRIPTS', [
'<script src="https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.24.0/moment.min.js" integrity="sha256-4iQZ6BVL4qNKlQ27TExEhBN1HFPvAvAMbFavKKosSWQ=" crossorigin="anonymous"></script>',
'<script src="https://cdnjs.cloudflare.com/ajax/libs/moment-timezone/0.5.28/moment-timezone-with-data-10-year-range.min.js" integrity="sha256-HS6OzSyhM0rDG0PhZGwf/FvptBzIJnv4MgL2pe87xgg=" crossorigin="anonymous"></script>'
]) | [
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] | 2.23 | 300 |
import random
from cs.structures import Edge, Graph, Node, V
def kargers_min_cut(orig_graph: Graph[V]) -> set[Edge[V]]:
"""
Partitions a graph using Karger's Algorithm. Works on directed and undirected
graphs, but involves random choices, so it does not give consistent outputs.
Args:
graph: A dictionary containing adacency lists for the graph.
Nodes must be strings.
Returns:
The cutset of the cut found by Karger's Algorithm.
"""
graph: Graph[Node[tuple[V, ...]]] = Graph.from_graph(
orig_graph, node_fn=lambda x: Node((x,))
)
while len(graph) > 2:
edge = random.choice(tuple(graph.edges))
# Contract edge (u, v) to new node uv
uv = Node(edge.start.data + edge.end.data)
uv_neighbors = graph[edge.start] | graph[edge.end]
del uv_neighbors[edge.start]
del uv_neighbors[edge.end]
graph.add_node(uv)
for neighbor in uv_neighbors:
graph.add_edge(uv, neighbor)
if graph.is_directed:
graph.add_edge(neighbor, uv)
# Remove nodes u and v.
graph.remove_node(edge.start)
graph.remove_node(edge.end)
# Find cutset.
group1, group2 = graph.nodes
result_set = set()
for subnode in group1.data:
for subneighbor in group2.data:
if subneighbor in orig_graph[subnode] or subnode in orig_graph[subneighbor]:
result_set.add(orig_graph[subnode][subneighbor])
return result_set
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] | 2.269345 | 672 |
#!/bin/python3
import os
# Complete the findDigits function below.
if __name__ == '__main__':
fptr = open(os.environ['OUTPUT_PATH'], 'w')
t = int(input())
for t_itr in range(t):
n = int(input())
result = findDigits(n)
fptr.write(str(result) + '\n')
fptr.close()
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] | 2.166667 | 144 |
from django.urls import path
from dfirtrack_artifacts.creator import artifact_creator
from dfirtrack_artifacts.exporter.spreadsheet import xls
from dfirtrack_artifacts.views import (
artifact_view,
artifactpriority_view,
artifactstatus_view,
artifacttype_view,
)
urlpatterns = (
# urls for Artifact
path(
r'artifact/',
artifact_view.ArtifactListView.as_view(),
name='artifacts_artifact_list',
),
path(
r'artifact/closed/',
artifact_view.ArtifactClosedView.as_view(),
name='artifacts_artifact_closed',
),
path(
r'artifact/all/',
artifact_view.ArtifactAllView.as_view(),
name='artifacts_artifact_all',
),
path(
r'artifact/create/',
artifact_view.ArtifactCreateView.as_view(),
name='artifacts_artifact_create',
),
path(
r'artifact/detail/<int:pk>/',
artifact_view.ArtifactDetailView.as_view(),
name='artifacts_artifact_detail',
),
path(
r'artifact/update/<int:pk>/',
artifact_view.ArtifactUpdateView.as_view(),
name='artifacts_artifact_update',
),
path(
r'artifact/<int:pk>/set_user/',
artifact_view.ArtifactSetUser.as_view(),
name='artifact_set_user',
),
path(
r'artifact/<int:pk>/unset_user/',
artifact_view.ArtifactUnsetUser.as_view(),
name='artifact_unset_user',
),
path(
r'artifact/creator/', artifact_creator.artifact_creator, name='artifact_creator'
),
path(
r'artifact/exporter/spreadsheet/xls/artifact/',
xls.artifact,
name='artifact_exporter_spreadsheet_xls',
),
path(
r'artifact/exporter/spreadsheet/xls/artifact/cron/',
xls.artifact_create_cron,
name='artifact_exporter_spreadsheet_xls_cron',
),
)
urlpatterns += (
# urls for Artifactpriority
path(
r'artifactpriority/',
artifactpriority_view.ArtifactpriorityListView.as_view(),
name='artifacts_artifactpriority_list',
),
path(
r'artifactpriority/detail/<int:pk>/',
artifactpriority_view.ArtifactpriorityDetailView.as_view(),
name='artifacts_artifactpriority_detail',
),
)
urlpatterns += (
# urls for Artifactstatus
path(
r'artifactstatus/',
artifactstatus_view.ArtifactstatusListView.as_view(),
name='artifacts_artifactstatus_list',
),
path(
r'artifactstatus/detail/<int:pk>/',
artifactstatus_view.ArtifactstatusDetailView.as_view(),
name='artifacts_artifactstatus_detail',
),
)
urlpatterns += (
# urls for Artifacttype
path(
r'artifacttype/',
artifacttype_view.ArtifacttypeListView.as_view(),
name='artifacts_artifacttype_list',
),
path(
r'artifacttype/create/',
artifacttype_view.ArtifacttypeCreateView.as_view(),
name='artifacts_artifacttype_create',
),
path(
r'artifacttype/add_popup/',
artifacttype_view.ArtifacttypeCreatePopup.as_view(),
name='artifacttype_add_popup',
),
path(
r'artifacttype/detail/<int:pk>/',
artifacttype_view.ArtifacttypeDetailView.as_view(),
name='artifacts_artifacttype_detail',
),
path(
r'artifacttype/update/<int:pk>/',
artifacttype_view.ArtifacttypeUpdateView.as_view(),
name='artifacts_artifacttype_update',
),
)
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] | 2.211097 | 1,568 |
from mayan.apps.appearance.classes import Icon
icon_user_locale_profile_detail = Icon(
driver_name='fontawesome', symbol='globe'
)
icon_user_locale_profile_edit = Icon(
driver_name='fontawesome-dual', primary_symbol='globe',
secondary_symbol='pencil-alt'
)
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] | 2.7 | 100 |
import os
import sys
import pdb
import re
from copy import deepcopy
from operator import itemgetter
import json
import pandas as pd
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(0, dir_path + '/../..')
from plastering.metadata_interface import *
from plastering.evaluator import *
target_building = 'sdh'
currfile = __file__
base_dir = os.path.dirname(currfile)
target_dir = base_dir + '/' + target_building
orig_cluster_sizes = {}
total_names = []
for filename in os.listdir(target_dir):
if not re.match('{0}-ORIGINAL-METADATA-\\d+$'.format(target_building.upper()),
filename):
continue
cid = get_number(filename)
with open(target_dir + '/' + filename, 'r') as fp:
names = fp.readlines()
orig_cluster_sizes[cid] = len(names)
total_names += names
total_names = list(set(total_names))
total_srcids = [get_srcid(name) for name in total_names]
curr_cluster_sizes = deepcopy(orig_cluster_sizes)
true_tagsets = {srcid: LabeledMetadata.objects(srcid=srcid).first().tagsets
for srcid in total_srcids}
true_points = {srcid: LabeledMetadata.objects(srcid=srcid).first().point_tagset
for srcid in total_srcids}
qualified_examples_nums = {}
for filename in os.listdir(target_dir):
if not re.match('l-ex-\\d+-out$', filename):
continue
cid = get_number(filename)
df = pd.read_csv(target_dir + '/' + filename)
df.columns = df.columns.str.strip()
coverages = df['Num Examples Thought to be fully qualified'].tolist()
qualified_examples_nums[cid] = coverages
inferred_points_dict = {i: {} for i in curr_cluster_sizes.keys()}
for filename in os.listdir(target_dir):
if not re.match('l-ex-\\d+-out-points-qualified$', filename):
continue
cid = get_number(filename)
with open(target_dir + '/' + filename, 'r') as fp:
lines = fp.readlines()
for line in lines:
ex_id = int(line.split(' ')[0])
if "'" not in line:
items = []
else:
items = line.split('[')[-1].split(']')[0][1:-1].split("', '")
inferred_points_dict[cid][ex_id] = items
pred = {}
curr_eids = {i: 0 for i in curr_cluster_sizes.keys()}
total_num = sum(orig_cluster_sizes.values())
pred_names = set()
cnt = 0
accs = []
f1s = []
mf1s = []
anymf1s = []
srcids = []
pred = {srcid: [] for srcid in total_srcids}
point_pred = {srcid: [] for srcid in total_srcids}
res = []
while not is_finished():
# select cluster
#max_cid = max(curr_cluster_sizes.items(), key=itemgetter(1))[0]
cnt += 1
max_cid = select_next_cid()
curr_eids[max_cid] += 1
curr_eid = curr_eids[max_cid]
found_names = set(inferred_points_dict[max_cid][curr_eid])
new_names = found_names - pred_names
new_srcids = [get_srcid(name) for name in new_names]
pred_names = pred_names.union(new_names)
curr_cluster_sizes[max_cid] = orig_cluster_sizes[max_cid] - len(found_names)
acc = len(pred_names) / total_num
print('{0}\tacc: {1}'.format(cnt, acc))
pred.update({srcid: LabeledMetadata.objects(srcid=srcid).first().tagsets
for srcid in new_srcids})
point_pred.update({
srcid: LabeledMetadata.objects(srcid=srcid).first().point_tagset
for srcid in new_srcids})
anymf1 = get_macro_f1(true_tagsets, pred)
mf1 = get_macro_f1(true_points, point_pred)
f1 = get_micro_f1(true_points, point_pred)
#mf1s.append(mf1)
#f1s.append(f1)
#anymf1s.append(anymf1)
#accs.append(acc)
#srcids.append(len(pred_names))
row = {
'metrics': {
'f1': f1,
'macrof1': mf1,
'accuracy': acc,
'macrof1-all': anymf1
},
'learning_srcids': cnt
}
res.append(row)
with open('result/pointonly_notransfer_arka_{0}_0.json'.format(target_building),
'w') as fp:
json.dump(res, fp)
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7,
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198
] | 2.213477 | 1,766 |
import pandas as pd
import warnings
from learntools.core import *
Label = MultipartProblem(LabelA, LabelB)
Cardinality = MultipartProblem(CardinalityA, CardinalityB)
qvars = bind_exercises(globals(), [
Drop,
Label,
Cardinality,
OneHot
],
var_format='step_{n}',
)
__all__ = list(qvars)
| [
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] | 2.484375 | 128 |
# _*_ coding: utf-8 _*_
# Copyright (c) Nikita Kovaliov, maizy.ru, 2013
# See LICENSE.txt for details.
from tornado.web import asynchronous
from vnc_me.controllers import HttpHandler
from vnc_me.vnc_client import VncClient
| [
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] | 2.825 | 80 |
import pyknotid.spacecurves.spacecurve as sp
import pyknotid.make as mk
from functools import wraps
import os
from os import path
import numpy as np
import pytest
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
@pass_trefoil
| [
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] | 2.23913 | 184 |
#Author: Miguel Molero <[email protected]>
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
from pcloudpy.gui.graphics.QVTKWidget import QVTKWidget
if __name__ == "__main__":
from vtk import vtkConeSource
from vtk import vtkPolyDataMapper, vtkActor
app = QApplication(['QVTKWindow'])
win = QVTKMainWindow()
cone = vtkConeSource()
cone.SetResolution(8)
coneMapper = vtkPolyDataMapper()
coneMapper.SetInput(cone.GetOutput())
coneActor = vtkActor()
coneActor.SetMapper(coneMapper)
win.vtkWidget.renderer.AddActor(coneActor)
# show the widget
win.show()
# start event processing
app.exec_() | [
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198,
220,
220,
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598,
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] | 2.45583 | 283 |
# Copyright (c) 2015 - 2021, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# * Neither the name of Intel Corporation nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY LOG OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
import sphinx_rtd_theme
sys.path.insert(0, os.path.abspath('../..'))
# -- Project information -----------------------------------------------------
project = 'GEOPM Service'
copyright = '2021, Intel (R) Corporation'
author = 'Intel (R) Corporation'
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.napoleon',
'sphinx_rtd_theme',
]
napoleon_google_docstring = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_logo = 'https://geopm.github.io/images/geopm-logo-clear.png'
logo_only = True
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import functools
import logging
from flask_restful import Resource
from flask import request
import json
| [
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from ._tabular import fetch_crime_data, fetch_traffic_data
from ._vector import (fetch_beach_access_data,
fetch_crime_shp_data,
fetch_family_resource_centers_data,
fetch_shipping_lanes_data) | [
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#!/usr/bin/python3
import cv2
import numpy as np
import os
import process_img
import pickle
from shutil import copyfile
# model = cv2.ml.KNearest_create()
# model.load('model.xml')
model = cv2.ml.KNearest_load('model.xml')
img_area = 40 * 40
# 将序列化的内容加载到内存中
f = open('id_label_map.txt', 'rb')
try:
id_label_map = pickle.load(f)
except EOFError:
pass
f.close()
filenames = os.listdir('img')
for filename in filenames:
filelist = [ f for f in os.listdir('predict')]
for f in filelist:
os.remove(os.path.join('predict', f))
copyfile(os.path.join('img', filename), os.path.join('predict', filename))
img_captcha = cv2.imread(os.path.join('predict', filename))
process_img.run('predict', 'result', 1)
predict = sorted(os.listdir('result'))
r = []
for p in predict:
img = cv2.imread(os.path.join('result', p), cv2.IMREAD_GRAYSCALE)
sample = img.reshape((1, img_area)).astype(np.float32)
ret, results, neighbours, distances = model.findNearest(sample, k = 3)
label_id = int(results[0, 0])
label = id_label_map[label_id]
r.append(label)
print(' '.join(r))
cv2.imshow('image', img_captcha)
key = cv2.waitKey(0)
if key == 27:
exit()
else :
cv2.destroyAllWindows() | [
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] | 2.191201 | 591 |
#https://programmers.co.kr/learn/courses/30/lessons/12934 | [
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import csv
import logging
import neo4j
import os
import uuid
from concurrent.futures import ThreadPoolExecutor
from pyopenie import OpenIE5
from queue import Empty, Queue
from spacy.lang.en import English
from time import sleep
from typing import List
ENCODING = "utf-8"
DATA_DIRECTORY = "./data"
CACHE_DIRECTORY = "cache/"
CACHED_CONNECTIONS_FILE = "entity_connections.cache"
CACHED_FILTERED_CONNECTIONS_FILE = "entity_connections_filtered.cache"
QUEUE_WAIT_TIMEOUT = 5
CONNECTION_BUILDER_THREADS = 5
RELATIONSHIP_EXTRACTION_SERVICE_RETRIES = 5
RELATIONSHIP_EXTRACTION_SERVICE_TIMEOUT = 3
RELATIONSHIP_EXTRACTION_SERVICE_URL = 'http://localhost:8000'
NEO4J_URL = "bolt://localhost:7687"
NEO4J_CREDENTIALS_FILE = ".credentials"
GRAPH_LOADER_THREADS = 1
nlp:English = None
extractor:OpenIE5 = None
sentence_queue:Queue = None
connection_list:List[EntityConnection] = None
query_queue:Queue = None
loader:Loader = None
connection_cache_source:int = 0
if __name__ == "__main__":
main() | [
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366,
834,
12417,
834,
1298,
198,
220,
220,
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] | 2.8017 | 353 |
import pandas as pd
df = pd.read_excel('C:/Users/gkdud/PycharmProjects/TeamProject/Scraping/files/fashion_scraping.xlsx')
import sqlite3
connect = sqlite3.connect('./wadizdb.sqlite3')
df.to_sql('table_fashion', connect, if_exists='append', index=False)
connect.close() | [
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import os
from os.path import getatime, getctime, getmtime
import errno
from django.core.exceptions import ImproperlyConfigured
from . import compressors
__all__ = ["CompressMixin"]
DEFAULT_METHODS = ["gz", "br"]
METHOD_MAPPING = {
"gz": compressors.ZopfliCompressor,
"br": compressors.BrotliCompressor,
"gz+zlib": compressors.ZlibCompressor,
# gz+zlib and gz cannot be used at the same time, because they produce the same file extension.
}
| [
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] | 2.840491 | 163 |
from ..models import RunwayPoint, Runway
| [
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###########################
# Project Euler Problem 9
# Special Pythagorean triplet
#
# Code by Kevin Marciniak
###########################
total = 1000
product = 0
for c in range(1, 1000):
for b in range(1, c):
for a in range(1, b):
if (a + b + c) == 1000:
if ((a * a) + (b * b)) == (c * c):
product = a * b * c
print(product)
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] | 2.601504 | 133 |
import logging
from six import int2byte
from binascii import unhexlify
from twisted.internet import defer
from .resolve import Resolver
from lbryschema.error import URIParseError
from lbryschema.uri import parse_lbry_uri
from torba.baseledger import BaseLedger
from .account import Account
from .network import Network
from .database import WalletDatabase
from .transaction import Transaction
from .header import Headers, UnvalidatedHeaders
log = logging.getLogger(__name__)
| [
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'''
Created on 01.05.2017
@author: mario
Emontranslator
Receive messages from serial/uart
Generate JSON Emon Input Messages
Insert via EMON API / APIKEY to emoncms on locahost (running on pi)
'''
import serial
import httplib
import time
domain = "localhost"
emoncmspath = "emoncms"
apikey = "2eba96e51f6b41534f52110ad063b0c8"
nodeid = 10
conn = httplib.HTTPConnection(domain)
# Set this to the serial port of your emontx and baud rate, 9600 is standard emontx baud rate
ser = serial.Serial('/dev/ttyS0', 9600)
while 1:
try:
# Read in line of readings from serial / uart
linestr = ser.readline()
linestr = linestr.rstrip()
#print linestr
nodeid,temp,humid,voltage=parseLine(linestr)
if nodeid:
params = ("{temp:%.2f,humid:%.2f,voltage:%.2f}"%(temp,humid,voltage))
print params
print "nodeid:"+str(nodeid)
# Send to emoncms
conn.connect()
conn.request("GET", "/"+emoncmspath+"/input/post.json?&node="+str(nodeid)+"&json="+params+"&apikey="+apikey)
response = conn.getresponse()
print response.read()
except KeyboardInterrupt:
raise
except Exception as e:
print e.__doc__
print e.message
pass
time.sleep(1)
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220,
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220,
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220,
220,
220,
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304,
25,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
304,
13,
834,
15390,
834,
198,
220,
220,
220,
220,
220,
220,
220,
3601,
304,
13,
20500,
198,
220,
220,
220,
220,
220,
220,
220,
1208,
198,
220,
220,
220,
220,
220,
220,
220,
220,
198,
220,
220,
220,
640,
13,
42832,
7,
16,
8,
198
] | 2.185738 | 603 |
import logging
import asyncio
import binascii
import time
import struct
from ipaddress import ip_network, ip_address
import protocol
import const
PUFFIN_SUB = ["107.178.32.0/20", "45.33.128.0/20", "101.127.206.0/23",
"101.127.208.0/23"]
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] | 2.419048 | 105 |
from django.utils.translation import ugettext_lazy as _
from mapentity.filters import MapEntityFilterSet
from geotrek.core.filters import TopologyFilter
from .models import Trek, POI, Service
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# 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 copy
import re
import sys
import mock
import netaddr
import webob.exc
from apic_ml2.neutron.db import port_ha_ipaddress_binding as ha_ip_db
from apic_ml2.neutron.tests.unit.ml2.drivers.cisco.apic import (
test_cisco_apic_common as mocked)
from apicapi import apic_mapper
from neutron.agent import securitygroups_rpc as sg_cfg
from neutron.common import rpc as n_rpc
from neutron import context
from neutron.db import api as db_api
from neutron.db import db_base_plugin_v2 as n_db
from neutron.db import model_base
from neutron.extensions import portbindings
from neutron import manager
from opflexagent import constants as ocst
from oslo_config import cfg
from oslo_serialization import jsonutils
from gbpservice.neutron.plugins.ml2.drivers.grouppolicy.apic import driver
from gbpservice.neutron.services.grouppolicy import (
group_policy_context as p_context)
from gbpservice.neutron.services.grouppolicy import config
from gbpservice.neutron.services.grouppolicy.drivers.cisco.apic import (
apic_mapping as amap)
from gbpservice.neutron.services.l3_router import l3_apic
from gbpservice.neutron.tests.unit.services.grouppolicy import (
test_resource_mapping as test_rmd)
APIC_L2_POLICY = 'l2_policy'
APIC_L3_POLICY = 'l3_policy'
APIC_POLICY_RULE_SET = 'policy_rule_set'
APIC_POLICY_TARGET_GROUP = 'policy_target_group'
APIC_POLICY_RULE = 'policy_rule'
APIC_EXTERNAL_RID = '1.0.0.1'
APIC_EXTERNAL_EPG = 'ext-epg'
APIC_PRE_L3OUT_TENANT = 'common'
APIC_PRE_VRF_TENANT = APIC_PRE_L3OUT_TENANT
APIC_PRE_VRF = 'pre-vrf'
AGENT_TYPE = ocst.AGENT_TYPE_OPFLEX_OVS
AGENT_CONF = {'alive': True, 'binary': 'somebinary',
'topic': 'sometopic', 'agent_type': AGENT_TYPE,
'configurations': {'opflex_networks': None,
'bridge_mappings': {'physnet1': 'br-eth1'}}}
AGENT_TYPE_DVS = driver.AGENT_TYPE_DVS
AGENT_CONF_DVS = {'alive': True, 'binary': 'somebinary',
'topic': 'sometopic', 'agent_type': AGENT_TYPE_DVS,
'configurations': {'opflex_networks': None}}
BOOKED_PORT_VALUE = 'myBookedPort'
class FakeNetworkContext(object):
"""To generate network context for testing purposes only."""
@property
@property
class FakePortContext(object):
"""To generate port context for testing purposes only."""
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# TODO(ivar): verify rule intersection with hierarchical PRS happens
# on APIC
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
# Although the naming convention used here has been chosen poorly,
# I'm separating the tests in order to get the mock re-set.
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302,
12,
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13,
628,
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220,
220,
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262,
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9831,
973,
994,
468,
587,
7147,
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11,
198,
220,
220,
220,
1303,
314,
1101,
27259,
262,
5254,
287,
1502,
284,
651,
262,
15290,
302,
12,
2617,
13,
628,
628,
628
] | 2.939148 | 1,479 |
import psutil
import pandas as pd
from datetime import datetime
from termcolor import colored
import GetInfo
import Notify
import time
import os
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-c", "--columns", default="name,cpu_usage,memory_usage,read_bytes,write_bytes,status,create_time,n_threads")
parser.add_argument("-s", "--sort-by", dest="sort_by",default="memory_usage")
parser.add_argument("--ascending", action="store_true")
parser.add_argument("-n", default=20)
parser.add_argument("-u", "--live-update", action="store_true")
parser.add_argument("--kill", dest="process_to_close")
parser.add_argument("--after", dest="duration", default=0)
args = parser.parse_args()
columns = args.columns
sort_by = args.sort_by
descending = args.ascending
n = int(args.n)
live_update = args.live_update
kill = args.process_to_close
duration = int(args.duration)
# Fix terminal size
if 'nt' in os.name:
while(1):
(width, height) = os.get_terminal_size()
if(int(height) < 33 or int(width)<120):
print(colored("Terminal size too small. Resize the terminal", 'red', attrs=['bold']))
else:
break
time.sleep(0.5)
os.system("cls")
else:
while(1):
height, width = os.popen('stty size', 'r').read().split()
if(int(height) < 33 or int(width)<120):
print(colored("Terminal size too small. Resize the terminal", 'red', attrs=['bold']))
else:
break
time.sleep(0.5)
os.system("clear")
processes = GetInfo.get_processes_info()
df = construct_dataframe(processes)
print_header()
if n == 0:
print(df.to_string())
elif n > 0:
print(df.head(n).to_string())
draw_graph()
while live_update:
processes = GetInfo.get_processes_info()
df = construct_dataframe(processes)
os.system("cls") if "nt" in os.name else os.system("clear")
print_header()
if n == 0:
print(colored(df.to_string(), 'red','on_white'))
elif n > 0:
print(colored(df.head(n).to_string(), 'red','on_white'))
draw_graph()
time.sleep(1)
if(kill):
kill_process(df.head(n).to_string(), kill, duration*60)
| [
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62,
8841,
22784,
1494,
11,
9478,
9,
1899,
8,
201,
198
] | 2.141638 | 1,172 |
# encoding=utf8
import os
import socket
from sync_binlog.output_log import logger as loging
import time
import sys
from sync_binlog.update_post import update_datetime
try:
import psutil
except ImportError:
print("psutil 模块不存在,请使用 pip install psutil 安装")
sys.exit(0)
# Shutdown complete
if __name__ == "__main__":
print("%sStarting shutdown..." % update_datetime())
shutdown_program()
time.sleep(3)
process_id = judgeprocess('startup.py')
if process_id is not False:
psutil.Process(process_id).kill()
print("%sShutdown complete" % update_datetime())
loging.info("Shutdown complete")
else:
print("%s程序自动关闭，请手工检查" % update_datetime())
loging.info("程序自动关闭，请手工检查")
| [
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] | 2.156977 | 344 |
import turtle
turtle.mode("logo")
turtle.shape("turtle")
turtle.bgcolor("black")
turtle.hideturtle()
turtle.pensize(12)
turtle.colormode(255)
s = 50
a = 0
for i in range(10):
turtle.pencolor(200-a, a, 100)
turtle.pu()
turtle.goto(25*i, 0)
turtle.pd()
turtle.forward(s)
a = a + 20
s = s + 10
turtle.done()
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264,
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264,
1343,
838,
628,
198,
83,
17964,
13,
28060,
3419,
198
] | 2.119497 | 159 |
#!/usr/bin/env python3
from hpecp import ContainerPlatformClient, APIException
from hpecp.k8s_cluster import K8sClusterHostConfig
import textwrap
client = ContainerPlatformClient(username='admin',
password='admin123',
api_host='127.0.0.1',
api_port=8080,
use_ssl=True,
verify_ssl='/certs/hpecp-ca-cert.pem')
client.create_session()
print( client.k8s_worker.get_k8shosts().tabulate() )
try:
k8shosts_config=[
K8sClusterHostConfig(4, 'worker'),
K8sClusterHostConfig(5, 'master')
]
k8s_cluster_id = client.k8s_cluster.create(name='def', description='my cluster', k8s_version='1.17.0', k8shosts_config=k8shosts_config)
print('creating cluster id: ' + k8s_cluster_id)
except APIException as e:
text = """APIException(
Backend API Response -> {}
HTTP Method -> {}
Request URL -> {}
Request Data -> [{}]
)"""
print( textwrap.dedent(text).format(e.message, e.request_method, e.request_url, e.request_data) ) | [
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13,
25927,
62,
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62,
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8,
1267
] | 2.028369 | 564 |
import os
import shutil
# wbg_pth='/datadrive/Reflection/training_data/wbg'
# img_pth='/datadrive/Reflection/training_data/images'
# dst_pth='/datadrive/Reflection/training_data/valB'
# move_data(wbg_pth,img_pth,dst_pth)
wbg_pth='/datadrive/Reflection/training_data/wbg'
trainA_pth='/datadrive/pytorch-CycleGAN-and-pix2pix/datasets/cars/trainA'
imgs_pth='/datadrive/Reflection/training_data/images'
trainB_pth='/datadrive/pytorch-CycleGAN-and-pix2pix/datasets/cars/trainB'
move_data(wbg_pth,trainA_pth,imgs_pth,trainB_pth) | [
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82,
62,
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11,
27432,
33,
62,
79,
400,
8
] | 2.150407 | 246 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
"""A class for storing the field information."""
class _FieldInfo(object):
"""A class for storing the field information."""
def __init__(self, field_type, documentation, list_element_type=None, user_keys=False, serialized_name=None,
exclude_if_none=False):
"""Class FieldInfo constructor.
:param field_type: The data type of field.
:type field_type: object
:param documentation: The field information
:type documentation: str
:param list_element_type: The type of list element.
:type list_element_type: object
:param user_keys: user_keys=True, if keys in the value of the field are user keys.
user keys are not case normalized.
:type user_keys: bool
:param serialized_name:
:type serialized_name: str
:param exclude_if_none: Exclude from serialized output if value is None.
:type exclude_if_none: bool
"""
self._field_type = field_type
self._documentation = documentation
self._list_element_type = list_element_type
self._user_keys = user_keys
self._serialized_name = serialized_name
self._exclude_if_none = exclude_if_none
@property
def field_type(self):
"""Get field type.
:return: Returns the field type.
:rtype: object
"""
return self._field_type
@property
def documentation(self):
"""Return documentation.
:return: Returns the documentation.
:rtype: str
"""
return self._documentation
@property
def list_element_type(self):
"""Get list element type.
:return: Returns the list element type.
:rtype: object
"""
return self._list_element_type
@property
def user_keys(self):
"""Get user keys setting.
:return: Returns the user keys setting.
:rtype: bool
"""
return self._user_keys
@property
def serialized_name(self):
"""Get serialized name.
:return: Returns the serialized name.
:rtype: str
"""
return self._serialized_name
@property
def exclude_if_none(self):
"""Get whether to exclude None from serialized output.
:return: Returns whether to exclude None form serialized output.
:rtype: bool
"""
return self._exclude_if_none
| [
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] | 2.405501 | 1,127 |
# Copyright 2016 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
"""Cryptography helpers for verifying and signing messages.
The simplest way to verify signatures is using :func:`verify_signature`::
cert = open('certs.pem').read()
valid = crypt.verify_signature(message, signature, cert)
If you're going to verify many messages with the same certificate, you can use
:class:`RSAVerifier`::
cert = open('certs.pem').read()
verifier = crypt.RSAVerifier.from_string(cert)
valid = verifier.verify(message, signature)
To sign messages use :class:`RSASigner` with a private key::
private_key = open('private_key.pem').read()
signer = crypt.RSASigner.from_string(private_key)
signature = signer.sign(message)
The code above also works for :class:`ES256Signer` and :class:`ES256Verifier`.
Note that these two classes are only available if your `cryptography` dependency
version is at least 1.4.0.
"""
import six
from google.auth.crypt import base
from google.auth.crypt import rsa
try:
from google.auth.crypt import es256
except ImportError: # pragma: NO COVER
es256 = None
if es256 is not None: # pragma: NO COVER
__all__ = [
"ES256Signer",
"ES256Verifier",
"RSASigner",
"RSAVerifier",
"Signer",
"Verifier",
]
else: # pragma: NO COVER
__all__ = ["RSASigner", "RSAVerifier", "Signer", "Verifier"]
# Aliases to maintain the v1.0.0 interface, as the crypt module was split
# into submodules.
Signer = base.Signer
Verifier = base.Verifier
RSASigner = rsa.RSASigner
RSAVerifier = rsa.RSAVerifier
if es256 is not None: # pragma: NO COVER
ES256Signer = es256.ES256Signer
ES256Verifier = es256.ES256Verifier
def verify_signature(message, signature, certs, verifier_cls=rsa.RSAVerifier):
"""Verify an RSA or ECDSA cryptographic signature.
Checks that the provided ``signature`` was generated from ``bytes`` using
the private key associated with the ``cert``.
Args:
message (Union[str, bytes]): The plaintext message.
signature (Union[str, bytes]): The cryptographic signature to check.
certs (Union[Sequence, str, bytes]): The certificate or certificates
to use to check the signature.
verifier_cls (Optional[~google.auth.crypt.base.Signer]): Which verifier
class to use for verification. This can be used to select different
algorithms, such as RSA or ECDSA. Default value is :class:`RSAVerifier`.
Returns:
bool: True if the signature is valid, otherwise False.
"""
if isinstance(certs, (six.text_type, six.binary_type)):
certs = [certs]
for cert in certs:
verifier = verifier_cls.from_string(cert)
if verifier.verify(message, signature):
return True
return False
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3326,
7483,
796,
3326,
7483,
62,
565,
82,
13,
6738,
62,
8841,
7,
22583,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
611,
3326,
7483,
13,
332,
1958,
7,
20500,
11,
9877,
2599,
201,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
6407,
201,
198,
220,
220,
220,
1441,
10352,
201,
198
] | 2.710796 | 1,269 |
eggs = 'global'
spam() | [
198,
33856,
82,
796,
705,
20541,
6,
198,
2777,
321,
3419
] | 2.090909 | 11 |
from github.Issue import Issue
from github.Repository import Repository
import logging
from typing import List
REQUEST_REPO = 'Azure/sdk-release-request'
REST_REPO = 'Azure/azure-rest-api-specs'
AUTO_ASSIGN_LABEL = 'assigned'
AUTO_PARSE_LABEL = 'auto-link'
_LOG = logging.getLogger(__name__)
| [
6738,
33084,
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13,
1136,
11187,
1362,
7,
834,
3672,
834,
8,
628,
198
] | 2.715596 | 109 |
import argparse
import os
import random
import sys
import time
import requests
parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, help='Path to text files with bills', required=True)
parser.add_argument('--count', type=int, help='How much files process', required=False, default=20)
args = parser.parse_args()
url = 'http://ws.clarin-pl.eu/nlprest2'
parsed = {}
id2file = {}
already_parsed = os.listdir(args.path + 'ner/')
count = min(args.count, 100 - len(already_parsed))
directory_contents = random.sample(list(filter(lambda entry: os.path.isfile(args.path + entry)
and entry not in already_parsed,
os.listdir(args.path))),
k=count)
for filename in directory_contents:
with open(args.path + filename, encoding='utf-8') as file:
response = requests.post(url=url + '/base/startTask',
json={'text': file.read(), 'lpmn': 'any2txt|wcrft2|liner2({"model":"n82"})',
'user': ''})
response_string = str(response.content).replace("b\'", "").replace("\'", "")
id2file[response_string] = filename
parsed[response_string] = {"value": None, "status": None}
print("{} read and sent".format(filename))
id_list = list(parsed.keys())
print("Finished reading files")
counter = 0
while len(id_list) > 0:
for id in id_list:
parsed[id] = requests.get(url=url + '/base/getStatus/' + str(id)).json()
if parsed[id]['status'] == 'DONE':
counter += 1
with open(args.path + 'ner/' + id2file[id], 'wb') as file:
for element in parsed[id]['value']:
# print(requests.get(url=url + '/base/download' + element['fileID']).content)
# file.write(str(requests.get(url=url + '/base/download' + element['fileID']).content)[2:-1])
file.write(requests.get(url=url + '/base/download' + element['fileID']).content)
id_list.remove(id)
print("{} finished".format(counter))
elif parsed[id]['status'] == 'ERROR':
print(parsed[id]['value'], file=sys.stderr)
exit(-1)
time.sleep(2)
print('{} docs left'.format(len(id_list)))
| [
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8,
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8420,
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7,
17,
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34165,
1364,
4458,
18982,
7,
11925,
7,
312,
62,
4868,
22305,
198
] | 2.142206 | 1,097 |
from fastapi import APIRouter
from src.utils.events import Events
from src.schemas.schema import x_schedule_header
from src.controllers.faculties_controller import get_all_faculties, is_faculty_exists
from src.utils.tracking import track
tag = "Faculties"
router = APIRouter()
@router.get("", tags=[tag])
@track(fmt="", event=Events.GET_ALL_FACULTIES)
@router.get("/exists", tags=[tag])
@track(fmt="query={query}", event=Events.IS_FACULTY_EXISTS)
| [
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62,
37,
2246,
6239,
9936,
62,
6369,
1797,
4694,
8,
198
] | 2.739394 | 165 |
# -*- coding: utf-8 -*-
import ckan.plugins as p
#from ckan.lib.base import BaseController, config
import ckan.lib.base as base
import ckan.lib.helpers as h
import ckan.model as model
import ckan.logic as logic
import ckan.logic.schema as schema
import ckan.new_authz as new_authz
import ckan.lib.captcha as captcha
import ckan.lib.navl.dictization_functions as dictization_functions
import functools
import requests
from sqlalchemy import text
import logging
from pylons import config
from ckan.common import _, c, g, request, response
c = base.c
request = base.request
log = logging.getLogger(__name__)
| [
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] | 3.029851 | 201 |
""" Module for loading PypeIt files
"""
import os
import warnings
import numpy as np
from astropy import units
from astropy.time import Time
from astropy.io import fits
from astropy.table import Table
from linetools.spectra.xspectrum1d import XSpectrum1D
from linetools.spectra.utils import collate
import linetools.utils
from pypeit import msgs
from IPython import embed
from pypeit.core import parse
# TODO I don't think we need this routine
def load_ext_to_array(hdulist, ext_id, ex_value='OPT', flux_value=True, nmaskedge=None):
'''
It will be called by load_1dspec_to_array.
Load one-d spectra from ext_id in the hdulist
Args:
hdulist: FITS HDU list
ext_id: extension name, i.e., 'SPAT1073-SLIT0001-DET03', 'OBJID0001-ORDER0003', 'OBJID0001-ORDER0002-DET01'
ex_value: 'OPT' or 'BOX'
flux_value: if True load fluxed data, else load unfluxed data
Returns:
tuple: Returns wave, flux, ivar, mask
'''
if (ex_value != 'OPT') and (ex_value != 'BOX'):
msgs.error('{:} is not recognized. Please change to either BOX or OPT.'.format(ex_value))
# get the order/slit information
ntrace0 = np.size(hdulist)-1
idx_names = []
for ii in range(ntrace0):
idx_names.append(hdulist[ii+1].name) # idx name
# Initialize ext
ext = None
for indx in (idx_names):
if ext_id in indx:
ext = indx
if ext is None:
msgs.error('Can not find extension {:}.'.format(ext_id))
else:
hdu_iexp = hdulist[ext]
wave = hdu_iexp.data['{:}_WAVE'.format(ex_value)]
mask = hdu_iexp.data['{:}_MASK'.format(ex_value)]
# Mask Edges
if nmaskedge is not None:
mask[:int(nmaskedge)] = False
mask[-int(nmaskedge):] = False
if flux_value:
flux = hdu_iexp.data['{:}_FLAM'.format(ex_value)]
ivar = hdu_iexp.data['{:}_FLAM_IVAR'.format(ex_value)]
else:
msgs.warn('Loading unfluxed spectra')
flux = hdu_iexp.data['{:}_COUNTS'.format(ex_value)]
ivar = hdu_iexp.data['{:}_COUNTS_IVAR'.format(ex_value)]
return wave, flux, ivar, mask
# TODO merge this with unpack orders
def load_1dspec_to_array(fnames, gdobj=None, order=None, ex_value='OPT', flux_value=True, nmaskedge=None):
'''
Load the spectra from the 1d fits file into arrays.
If Echelle, you need to specify which order you want to load.
It can NOT load all orders for Echelle data.
Args:
fnames (list): 1D spectra fits file(s)
gdobj (list): extension name (longslit/multislit) or objID (Echelle)
order (None or int): order number
ex_value (str): 'OPT' or 'BOX'
flux_value (bool): if True it will load fluxed spectra, otherwise load counts
Returns:
tuple: Returns the following:
- waves (ndarray): wavelength array of your spectra, see
below for the shape information of this array.
- fluxes (ndarray): flux array of your spectra
- ivars (ndarray): ivars of your spectra
- masks (ndarray, bool): mask array of your spectra
The shapes of all returns are exactly the same.
- Case 1: np.size(fnames)=np.size(gdobj)=1, order=None for
Longslit or order=N (an int number) for Echelle
Longslit/single order for a single fits file, they are 1D
arrays with the size equal to Nspec
- Case 2: np.size(fnames)=np.size(gdobj)>1, order=None for
Longslit or order=N (an int number) for Echelle
Longslit/single order for a list of fits files, 2D array,
the shapes are Nspec by Nexp
- Case 3: np.size(fnames)=np.size(gdobj)=1, order=None All
Echelle orders for a single fits file, 2D array, the
shapes are Nspec by Norders
- Case 4: np.size(fnames)=np.size(gdobj)>1, order=None All
Echelle orders for a list of fits files, 3D array, the
shapres are Nspec by Norders by Nexp
'''
# read in the first fits file
if isinstance(fnames, (list, np.ndarray)):
nexp = np.size(fnames)
fname0 = fnames[0]
elif isinstance(fnames, str):
nexp = 1
fname0 = fnames
hdulist = fits.open(fname0)
header = hdulist[0].header
npix = header['NPIX']
pypeline = header['PYPELINE']
# get the order/slit information
ntrace0 = np.size(hdulist)-1
idx_orders = []
for ii in range(ntrace0):
idx_orders.append(int(hdulist[ii+1].name.split('-')[1][5:])) # slit ID or order ID
if pypeline == "Echelle":
## np.unique automatically sort the returned array which is not what I want!!!
## order_vec = np.unique(idx_orders)
dum, order_vec_idx = np.unique(idx_orders, return_index=True)
order_vec = np.array(idx_orders)[np.sort(order_vec_idx)]
norder = np.size(order_vec)
else:
norder = 1
#TODO This is unneccessarily complicated. The nexp=1 case does the same operations as the nexp > 1 case. Refactor
# this so that it just does the same set of operations once and then reshapes the array at the end to give you what
# you want. Let's merge this with unpack orders
## Loading data from a single fits file
if nexp == 1:
# initialize arrays
if (order is None) and (pypeline == "Echelle"):
waves = np.zeros((npix, norder,nexp))
fluxes = np.zeros_like(waves)
ivars = np.zeros_like(waves)
masks = np.zeros_like(waves, dtype=bool)
for ii, iord in enumerate(order_vec):
ext_id = gdobj[0]+'-ORDER{:04d}'.format(iord)
wave_iord, flux_iord, ivar_iord, mask_iord = load_ext_to_array(hdulist, ext_id, ex_value=ex_value,
flux_value=flux_value, nmaskedge=nmaskedge)
waves[:,ii,0] = wave_iord
fluxes[:,ii,0] = flux_iord
ivars[:,ii,0] = ivar_iord
masks[:,ii,0] = mask_iord
else:
if pypeline == "Echelle":
ext_id = gdobj[0]+'-ORDER{:04d}'.format(order)
else:
ext_id = gdobj[0]
waves, fluxes, ivars, masks = load_ext_to_array(hdulist, ext_id, ex_value=ex_value, flux_value=flux_value,
nmaskedge=nmaskedge)
## Loading data from a list of fits files
else:
# initialize arrays
if (order is None) and (pypeline == "Echelle"):
# store all orders into one single array
waves = np.zeros((npix, norder, nexp))
else:
# store a specific order or longslit
waves = np.zeros((npix, nexp))
fluxes = np.zeros_like(waves)
ivars = np.zeros_like(waves)
masks = np.zeros_like(waves,dtype=bool)
for iexp in range(nexp):
hdulist_iexp = fits.open(fnames[iexp])
# ToDo: The following part can be removed if all data are reduced using the leatest pipeline
if pypeline == "Echelle":
ntrace = np.size(hdulist_iexp) - 1
idx_orders = []
for ii in range(ntrace):
idx_orders.append(int(hdulist_iexp[ii + 1].name.split('-')[1][5:])) # slit ID or order ID
dum, order_vec_idx = np.unique(idx_orders, return_index=True)
order_vec = np.array(idx_orders)[np.sort(order_vec_idx)]
# ToDo: The above part can be removed if all data are reduced using the leatest pipeline
if (order is None) and (pypeline == "Echelle"):
for ii, iord in enumerate(order_vec):
ext_id = gdobj[iexp]+'-ORDER{:04d}'.format(iord)
wave_iord, flux_iord, ivar_iord, mask_iord = load_ext_to_array(hdulist_iexp, ext_id, ex_value=ex_value,
nmaskedge = nmaskedge, flux_value=flux_value)
waves[:,ii,iexp] = wave_iord
fluxes[:,ii,iexp] = flux_iord
ivars[:,ii,iexp] = ivar_iord
masks[:,ii,iexp] = mask_iord
else:
if pypeline == "Echelle":
ext_id = gdobj[iexp]+'-ORDER{:04d}'.format(order)
else:
ext_id = gdobj[iexp]
wave, flux, ivar, mask = load_ext_to_array(hdulist_iexp, ext_id, ex_value=ex_value, flux_value=flux_value,
nmaskedge=nmaskedge)
waves[:, iexp] = wave
fluxes[:, iexp] = flux
ivars[:, iexp] = ivar
masks[:, iexp] = mask
return waves, fluxes, ivars, masks, header
def load_spec_order(fname,norder, objid=None, order=None, extract='OPT', flux=True):
"""
Loading single order spectrum from a PypeIt 1D specctrum fits file.
it will be called by ech_load_spec
Args:
fname (str) : The file name of your spec1d file
objid (str) : The id of the object you want to load. (default is the first object)
order (int) : which order you want to load (default is None, loading all orders)
extract (str) : 'OPT' or 'BOX'
flux (bool) : default is True, loading fluxed spectra
Returns:
XSpectrum1D: spectrum_out
"""
if objid is None:
objid = 0
if order is None:
msgs.error('Please specify which order you want to load')
# read extension name into a list
primary_header = fits.getheader(fname, 0)
nspec = primary_header['NSPEC']
extnames = [primary_header['EXT0001']] * nspec
for kk in range(nspec):
extnames[kk] = primary_header['EXT' + '{0:04}'.format(kk + 1)]
# Figure out which extension is the required data
extnames_array = np.reshape(np.array(extnames),(norder,int(nspec/norder)))
extnames_good = extnames_array[:,int(objid[3:])-1]
extname = extnames_good[order]
try:
exten = extnames.index(extname) + 1
msgs.info("Loading extension {:s} of spectrum {:s}".format(extname, fname))
except:
msgs.error("Spectrum {:s} does not contain {:s} extension".format(fname, extname))
spectrum = load_1dspec(fname, exten=exten, extract=extract, flux=flux)
# Polish a bit -- Deal with NAN, inf, and *very* large values that will exceed
# the floating point precision of float32 for var which is sig**2 (i.e. 1e38)
bad_flux = np.any([np.isnan(spectrum.flux), np.isinf(spectrum.flux),
np.abs(spectrum.flux) > 1e30,
spectrum.sig ** 2 > 1e10,
], axis=0)
# Sometimes Echelle spectra have zero wavelength
bad_wave = spectrum.wavelength < 1000.0*units.AA
bad_all = bad_flux + bad_wave
## trim bad part
wave_out,flux_out,sig_out = spectrum.wavelength[~bad_all],spectrum.flux[~bad_all],spectrum.sig[~bad_all]
spectrum_out = XSpectrum1D.from_tuple((wave_out,flux_out,sig_out), verbose=False)
#if np.sum(bad_flux):
# msgs.warn("There are some bad flux values in this spectrum. Will zero them out and mask them (not ideal)")
# spectrum.data['flux'][spectrum.select][bad_flux] = 0.
# spectrum.data['sig'][spectrum.select][bad_flux] = 0.
return spectrum_out
def ech_load_spec(files,objid=None,order=None,extract='OPT',flux=True):
"""
Loading Echelle spectra from a list of PypeIt 1D spectrum fits files
Args:
files (str) : The list of file names of your spec1d file
objid (str) : The id (one per fits file) of the object you want to load. (default is the first object)
order (int) : which order you want to load (default is None, loading all orders)
extract (str) : 'OPT' or 'BOX'
flux (bool) : default is True, loading fluxed spectra
Returns:
XSpectrum1D: spectrum_out
"""
nfiles = len(files)
if objid is None:
objid = ['OBJ0000'] * nfiles
elif len(objid) == 1:
objid = objid * nfiles
elif len(objid) != nfiles:
msgs.error('The length of objid should be either 1 or equal to the number of spectra files.')
fname = files[0]
ext_first = fits.getheader(fname, 1)
ext_final = fits.getheader(fname, -1)
norder = abs(ext_final['ECHORDER'] - ext_first['ECHORDER']) + 1
msgs.info('spectrum {:s} has {:d} orders'.format(fname, norder))
if norder <= 1:
msgs.error('The number of orders have to be greater than one for echelle. Longslit data?')
# Load spectra
spectra_list = []
for ii, fname in enumerate(files):
if order is None:
msgs.info('Loading all orders into a gaint spectra')
for iord in range(norder):
spectrum = load_spec_order(fname, norder, objid=objid[ii],order=iord,extract=extract,flux=flux)
# Append
spectra_list.append(spectrum)
elif order >= norder:
msgs.error('order number cannot greater than the total number of orders')
else:
spectrum = load_spec_order(fname,norder, objid=objid[ii], order=order, extract=extract, flux=flux)
# Append
spectra_list.append(spectrum)
# Join into one XSpectrum1D object
spectra = collate(spectra_list)
# Return
return spectra
def load_sens_dict(filename):
"""
Load a full (all slit) wv_calib dict
Includes converting the JSON lists of particular items into ndarray
Fills self.wv_calib and self.par
Args:
filename (str): Master file
Returns:
dict or None: self.wv_calib
"""
# Does the master file exist?
if not os.path.isfile(filename):
msgs.warn("No sensfunc file found with filename {:s}".format(filename))
return None
else:
msgs.info("Loading sensfunc from file {:s}".format(filename))
sens_dict = linetools.utils.loadjson(filename)
# Recast a few items as arrays
for key in sens_dict.keys():
try:
int(key)
except ValueError:
continue
else:
for tkey in sens_dict[key].keys():
if isinstance(sens_dict[key][tkey], list):
sens_dict[key][tkey] = np.array(sens_dict[key][tkey])
return sens_dict
def load_multiext_fits(filename, ext):
"""
Load data and primary header from a multi-extension FITS file
Args:
filename (:obj:`str`):
Name of the file.
ext (:obj:`str`, :obj:`int`, :obj:`list`):
One or more file extensions with data to return. The
extension can be designated by its 0-indexed integer
number or its name.
Returns:
tuple: Returns the image data from each provided extension.
If return_header is true, the primary header is also
returned.
"""
# Format the input and set the tuple for an empty return
_ext = ext if isinstance(ext, list) else [ext]
n_ext = len(_ext)
# Open the file
hdu = fits.open(filename)
head0 = hdu[0].header
# Only one extension
if n_ext == 1:
data = hdu[_ext[0]].data.astype(np.float)
return data, head0
# Multiple extensions
data = tuple([None if hdu[k].data is None else hdu[k].data.astype(np.float) for k in _ext])
# Return
return data+(head0,)
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628
] | 2.162529 | 7,211 |
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