content
stringlengths 0
894k
| type
stringclasses 2
values |
|---|---|
num = str(input())
[int(i) for i in str(num)]
n = sorted(num, reverse=True)
print(n)
if n[0] > n[1]:
print(n[1])
else:
buf = 0
for j in n:
if n[buf] < n[0]:
print(n[buf])
break
else:
buf += 1
| python |
import opensim
import math
import numpy as np
import os
from .utils.mygym import convert_to_gym
import gym
class Osim(object):
# Initialize simulation
model = None
state = None
state0 = None
joints = []
bodies = []
brain = None
maxforces = []
curforces = []
def __init__(self, model_path, visualize):
self.model = opensim.Model(model_path)
self.model.initSystem()
self.brain = opensim.PrescribedController()
# Enable the visualizer
self.model.setUseVisualizer(visualize)
self.muscleSet = self.model.getMuscles()
self.forceSet = self.model.getForceSet()
self.bodySet = self.model.getBodySet()
self.jointSet = self.model.getJointSet()
self.contactGeometrySet = self.model.getContactGeometrySet()
for j in range(self.muscleSet.getSize()):
func = opensim.Constant(1.0)
self.brain.addActuator(self.muscleSet.get(j))
self.brain.prescribeControlForActuator(j, func)
self.maxforces.append(self.muscleSet.get(j).getMaxIsometricForce())
self.curforces.append(1.0)
self.model.addController(self.brain)
def set_strength(self, strength):
self.curforces = strength
for i in range(len(self.curforces)):
self.muscleSet.get(i).setMaxIsometricForce(self.curforces[i] * self.maxforces[i])
def get_body(self, name):
return self.bodySet.get(name)
def get_joint(self, name):
return self.jointSet.get(name)
def get_muscle(self, name):
return self.muscleSet.get(name)
def get_contact_geometry(self, name):
return self.contactGeometrySet.get(name)
def get_force(self, name):
return self.forceSet.get(name)
def initializeState(self):
self.state = self.model.initializeState()
class Spec(object):
def __init__(self, *args, **kwargs):
self.id = 0
self.timestep_limit = 1000
class OsimEnv(gym.Env):
stepsize = 0.01
integration_accuracy = 1e-3
timestep_limit = 1000
test = False
action_space = None
observation_space = None
osim_model = None
istep = 0
model_path = ""
visualize = False
ninput = 0
noutput = 0
last_action = None
spec = None
metadata = {
'render.modes': ['human'],
'video.frames_per_second' : 50
}
def __getstate__(self):
state = self.__dict__.copy()
del state['osim_model']
print ("HERE1")
return state
def __setstate__(self, newstate):
self.__dict__.update(newstate)
self.osim_model = Osim(self.model_path, True)
self.configure()
def angular_dist(self, t,s):
x = (t-s) % (2*math.pi)
return min(x, 2*math.pi-x)
def compute_reward(self):
return 0.0
def is_done(self):
return False
def terminate(self):
pass
def __init__(self, visualize = True, noutput = None):
self.visualize = visualize
self.osim_model = Osim(self.model_path, self.visualize)
self.noutput = noutput
if not noutput:
self.noutput = self.osim_model.muscleSet.getSize()
if not self.action_space:
self.action_space = ( [0.0] * self.noutput, [1.0] * self.noutput )
if not self.observation_space:
self.observation_space = ( [-math.pi] * self.ninput, [math.pi] * self.ninput )
self.action_space = convert_to_gym(self.action_space)
self.observation_space = convert_to_gym(self.observation_space)
self.spec = Spec()
self.horizon = self.spec.timestep_limit
self.configure()
# self.reset()
def configure(self):
pass
def _reset(self):
self.istep = 0
self.osim_model.initializeState()
return self.get_observation()
def sanitify(self, x):
if math.isnan(x):
return 0.0
BOUND = 1000.0
if x > BOUND:
x = BOUND
if x < -BOUND:
x = -BOUND
return x
def activate_muscles(self, action):
if np.any(np.isnan(action)):
raise ValueError("NaN passed in the activation vector. Values in [0,1] interval are required.")
brain = opensim.PrescribedController.safeDownCast(self.osim_model.model.getControllerSet().get(0))
functionSet = brain.get_ControlFunctions()
for j in range(functionSet.getSize()):
func = opensim.Constant.safeDownCast(functionSet.get(j))
func.setValue( float(action[j]) )
def _step(self, action):
self.last_action = action
self.activate_muscles(action)
# Integrate one step
manager = opensim.Manager(self.osim_model.model)
manager.setInitialTime(self.stepsize * self.istep)
manager.setFinalTime(self.stepsize * (self.istep + 1))
try:
manager.integrate(self.osim_model.state)
except Exception as e:
print (e)
return self.get_observation(), -500, True, {}
self.istep = self.istep + 1
res = [ self.get_observation(), self.compute_reward(), self.is_done(), {} ]
return res
def _render(self, mode='human', close=False):
return
| python |
import sys
import os
import numpy as np
import math
from oct2py import octave
from extract_feature import get_sequence, calc_z_curve, z_curve_fft
if __name__=='__main__':
taxonomy= sys.argv[1]
fft_length= int(sys.argv[2])
time_length= int(sys.argv[3])
file_list= list(filter(lambda x: 'fna' == x[-3:], os.listdir(taxonomy)))
for seq_file in file_list:
print(seq_file)
seqs= get_sequence(taxonomy+'/'+seq_file, 1000)#1000 is not very meaningfull.
if len(seqs)==0:
continue
feature_idx= 1
seqs= list(filter(lambda x: len(x) > fft_length* time_length,
seqs))
for seq in seqs:
for sub_seq_idx in range(int(len(seq)/(fft_length*time_length))):
cur_seqs= seq[sub_seq_idx*fft_length*time_length: (sub_seq_idx+1)*fft_length*time_length]
cur_seqs= np.reshape(list(cur_seqs), (time_length, fft_length)).tolist()
cur_ffts=[]
for cur_seq in cur_seqs:
z_curve= calc_z_curve(cur_seq)
fft_result= z_curve_fft(z_curve)
cur_ffts.append(fft_result)
print(seq_file+"_"+str(feature_idx))
np.save(taxonomy+'/'+seq_file+'_'+str(feature_idx)+'_'+str(fft_length)+'_'+str(time_length), np.array(cur_ffts, dtype='f'))
feature_idx+= 1
| python |
from .gpib_bus_server import GPIBBusServer
from .gpib_device_manager import GPIBDeviceManager | python |
import numpy as np
class Convolution():
def initalizeParams(self):
self.W = np.random.randn(self.shape[0],self.shape[1],self.shape[2],self.shape[3])
self.b = np.zeros([1,self.ksize])
# 初始化一个 w shape的矩阵，在convAdd中使用
# self.wConvAdd = np.zeros(self.windowWidth,self.windowHeight,self.ksize)
# for i in range(self.windowWidth):
# for j in range(self.windowHeight):
# self.wConvAdd[i,j,:] = 1
def __init__(self,ids,shape,ifOutput,preLayer):
self.name = 'convolution'
self.firstLayer = False
self.ids = ids
self.shape = shape
# self.ksize = ksize
self.samples = preLayer.A.shape[0]
self.ifOutput = ifOutput
self.preLayer = preLayer
self.inputWidth = self.preLayer.A.shape[1]
self.inputHeight = self.preLayer.A.shape[2]
self.windowWidth = self.shape[0]
self.windowHeight = self.shape[1]
self.outputWidth = self.inputWidth - self.windowWidth + 1
self.outputHeight = self.inputHeight - self.windowHeight + 1
self.ksize = self.shape[3]
# print ("input dx,dy:(%d,%d),output dx,dy:(%d,%d),kenerl size:%d"%(self.inputWidth,
# self.inputHeight,
# self.outputWidth,
# self.outputHeight,
# self.ksize))
self.initalizeParams()
def convAdd(self,sameMatrix):
# 对于同维度小矩阵和权值矩阵进行向量叠加
result = 0.0
for i in range(self.windowWidth):
for j in range(self.windowHeight):
result += sameMatrix[:,i,j,i,j,:]
# print("result" + str(result.shape))
return result
def computeForward(self,model):
# print("begin")
temp_x = np.dot(self.preLayer.A,self.W) + self.b
# print('temo_x:' + str(temp_x.shape))
self.A = np.zeros([self.samples,self.outputWidth, self.outputHeight, self.ksize])
for i in range(self.inputWidth - self.windowWidth + 1):
for j in range(self.inputHeight-self.windowHeight + 1):
sameMatrix = temp_x[:,i:i + self.windowWidth,j:j + self.windowHeight,:,:,:]
self.A[:,i,j] = self.convAdd(sameMatrix=sameMatrix)
# print(self.A)
# print('forward done!')
del(temp_x)
def computeBackward(self,model):
def computeDWAndDXAndDb():
dW = np.zeros_like(self.W)
dX = np.zeros_like(self.preLayer.A)
db = np.zeros_like(self.b)
# 遍历整个dZ 依次累加 dW dX
for i in range(dZ.shape[1]):
for j in range(dZ.shape[2]):
dz = dZ[:,i,j,:] # 8 x 10
for m in range(self.windowWidth):
for n in range(self.windowHeight):
dW[m,n,:,:] += np.dot(self.preLayer.A[:,i+m,j+n,:].T,dz) # 100 x10 = 100 x 8 x 8 x 10
dX[:,i+m,j+n,:] += np.dot(dz,self.W[m,n,:,:].T) # 8 x 100 = 8 x 10 x 10 x 100
db += np.dot(np.ones([1,self.samples]),dz)
return dW,dX,db
dZ = model.layers[self.ids+1].dX
self.dW,self.dX,self.db = computeDWAndDXAndDb()
def update(self,lr):
self.W -= lr * self.dW
self.b -= lr * self.db
| python |
"""
Owner: Noctsol
Contributors: N/A
Date Created: 2021-10-24
Summary:
Just here for messing around.
"""
# import os
# DATA_DIR = "src/data/"
# with open(os.path.join(DATA_DIR, "VERSION"), "w", encoding="utf-8") as fh:
# fh.write(f"2.8.8\n")
| python |
"""
Test No Operation Operator
"""
import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from gva.flows.operators import NoOpOperator
try:
from rich import traceback
traceback.install()
except ImportError:
pass
def test_noop_operator():
in_d = {'a':1}
in_c = {'b':2}
n = NoOpOperator(print_message=True)
d, c = n.execute(in_d,in_c)
assert d == in_d
assert c == in_c
if __name__ == "__main__":
test_noop_operator()
print('okay')
| python |
# -*- coding: utf-8 -*-
"""
This is the config-loading and json-loading module which loads and parses the
config file as well as the json file.
It handles the [General]-Section of the config.
All object-getters create deepcopies.
"""
import logging
from copy import deepcopy
import hjson
try:
import ConfigParser
except ImportError:
import configparser as ConfigParser
from ast import literal_eval
from scrapy.utils.log import configure_logging
import os
class CrawlerConfig(object):
"""
The actual class. First parameter: config-file.
This class is a singleton-class,
Usage:
First creation and loading of the config-file:
c = CrawlerConfig.get_instance()
c.setup(<config_file>)
Further using:
c = CrawlerConfig.get_instance()
"""
# singleton-helper-class
# Source: http://code.activestate.com/recipes/52558-the-singleton-pattern-implemented-with-python/#c4
class SingletonHelper(object):
"""The singleton-helper-class"""
# https://pythontips.com/2013/08/04/args-and-kwargs-in-python-explained/
def __call__(self, *args, **kw):
if CrawlerConfig.instance is None:
CrawlerConfig.instance = CrawlerConfig()
return CrawlerConfig.instance
# singleton-helper-variable + function
get_instance = SingletonHelper()
instance = None
# Here starts the actual class
log = None
log_output = []
sections = None
parser = None
__current_section = None
__scrapy_options = None
__config = None
def __init__(self):
"""
The constructor
(keep in mind: this is a singleton, so just called once)
"""
if CrawlerConfig.instance is not None:
self.log_output.append(
{"level": "error",
"msg": "Multiple instances of singleton-class"})
raise RuntimeError('Multiple instances of singleton-class')
def setup(self, filepath):
"""
Setup the actual class.
:param str filepath: path to the config-file (including file-name)
"""
if self.log is not None:
self.log.warning("Disallowed multiple setup of config.")
return
self.log = logging.getLogger(__name__)
self.parser = ConfigParser.RawConfigParser()
self.parser.read(filepath)
self.sections = self.parser.sections()
self.log_output.append(
{"level": "info", "msg": "Loading config-file (%s)" % filepath})
self.load_config()
self.handle_logging()
def load_config(self):
"""
Loads the config-file
"""
self.__config = {}
# Parse sections, its options and put it in self.config.
for section in self.sections:
self.__config[section] = {}
options = self.parser.options(section)
# Parse options of each section
for option in options:
try:
opt = self.parser \
.get(section, option)
try:
self.__config[section][option] = literal_eval(opt)
except (SyntaxError, ValueError):
self.__config[section][option] = opt
self.log_output.append(
{"level": "debug",
"msg": "Option not literal_eval-parsable"
" (maybe string): [{0}] {1}"
.format(section, option)})
if self.__config[section][option] == -1:
self.log_output.append(
{"level": "debug",
"msg": "Skipping: [%s] %s" % (section, option)}
)
except ConfigParser.NoOptionError as exc:
self.log_output.append(
{"level": "error",
"msg": "Exception on [%s] %s: %s"
% (section, option, exc)}
)
self.__config[section][option] = None
def get_scrapy_options(self):
"""
:return: all options listed in the config section 'Scrapy'
"""
if self.__scrapy_options is None:
self.__scrapy_options = {}
options = self.section("Scrapy")
for key, value in options.items():
self.__scrapy_options[key.upper()] = value
return self.__scrapy_options
def handle_logging(self):
"""
To allow devs to log as early as possible, logging will already be
handled here
"""
configure_logging(self.get_scrapy_options())
# Disable duplicates
self.__scrapy_options["LOG_ENABLED"] = False
# Now, after log-level is correctly set, lets log them.
for msg in self.log_output:
if msg["level"] is "error":
self.log.error(msg["msg"])
elif msg["level"] is "info":
self.log.info(msg["msg"])
elif msg["level"] is "debug":
self.log.debug(msg["msg"])
def config(self):
"""
Get the whole config as a dict.
:returns: The whole config as dict[section][option] (all lowercase)
:rtype: dict
"""
return deepcopy(self.__config)
def section(self, section):
"""
Get the whole section of a the config.
:param section (string): The section to get all the options from.
:return dict[option] (all lowercase)
"""
return deepcopy(self.__config[section])
def set_section(self, section):
"""
Sets the current section to get the options from.
:param section (string)
"""
self.__current_section = section
def option(self, option):
"""
Gets the option, set_section needs to be set before.
:param option (string): The option to get.
:return mixed: The option from from the config.
"""
if self.__current_section is None:
raise RuntimeError('No section set in option-getting')
return self.__config[self.__current_section][option]
def get_working_path(self):
"""
Gets the working path. If the path starts with a ~, this will be replaced by the current user's home path.
:return:
"""
self.set_section('Files')
raw_path = self.option("working_path")
if raw_path.startswith('~'):
raw_path = os.path.expanduser('~') + raw_path[1:]
return raw_path
class JsonConfig(object):
"""
The actual class. First parameter: config-file.
This class is a singleton-class,
Usage:
First creation and loading of the config-file:
c = JsonConfig.get_instance()
c.setup(<config_file>)
Further using:
c = JsonConfig.get_instance()
"""
# singleton-helper-class
# Source: http://code.activestate.com/recipes/52558-the-singleton-pattern-implemented-with-python/#c4
class SingletonHelper(object):
"""The singleton-helper-class"""
def __call__(self, *args, **kw):
if JsonConfig.instance is None:
JsonConfig.instance = JsonConfig()
return JsonConfig.instance
# singleton-helper-variable + function
get_instance = SingletonHelper()
instance = None
# Here starts the actual class!
log = None
__json_object = None
def __init__(self):
"""
The constructor
(keep in mind: this is a singleton, so just called once)
"""
self.log = logging.getLogger(__name__)
if JsonConfig.instance is not None:
self.log.error('Multiple instances of singleton-class')
raise RuntimeError('Multiple instances of singleton-class')
def setup(self, filepath):
"""
Setup the actual class.
:param str filepath: path to the config-file (including file-name)
"""
self.log.debug("Loading JSON-file (%s)", filepath)
self.load_json(filepath)
def load_json(self, filepath):
"""
Loads the JSON-file from the filepath.
:param filepath (string): The location of the JSON-file.
"""
self.__json_object = hjson.load(open(filepath, 'r'))
def config(self):
"""
Get the whole JSON as a dict.
:return dict
"""
return deepcopy(self.__json_object)
def get_site_objects(self):
"""
Get the object containing all sites.
:return sites (dict): The sites from the JSON-file
"""
return deepcopy(self.__json_object["base_urls"])
def get_url_array(self):
"""
Get all url-objects in an array
:return sites (array): The sites from the JSON-file
"""
urlarray = []
for urlobjects in self.__json_object["base_urls"]:
urlarray.append(urlobjects["url"])
return urlarray
| python |
import itertools
import collections
from pyclts import CLTS
from pycldf import Sources
from clldutils.misc import nfilter, slug
from clldutils.color import qualitative_colors
from clld.cliutil import Data, bibtex2source
from clld.db.meta import DBSession
from clld.db.models import common
from clld.lib import bibtex
from nameparser import HumanName
import tppsr
from tppsr import models
def iteritems(cldf, t, *cols): # pragma: no cover
cmap = {cldf[t, col].name: col for col in cols}
for item in cldf[t]:
for k, v in cmap.items():
item[v] = item[k]
yield item
def main(args): # pragma: no cover
data = Data()
clts = CLTS(input('Path to cldf-clts/clts:') or '../../cldf-clts/clts')
ds = data.add(
common.Dataset,
tppsr.__name__,
id=tppsr.__name__,
name='Tableaux phonétiques des patois suisses romands Online',
domain='tppsr.clld.org',
contact="[email protected]",
publisher_name="Max Planck Institute for Evolutionary Anthropology",
publisher_place="Leipzig",
publisher_url="https://www.eva.mpg.de",
license="https://creativecommons.org/licenses/by/4.0/",
jsondata={
'license_icon': 'cc-by.png',
'license_name': 'Creative Commons Attribution 4.0 International License'},
)
for i, name in enumerate(['Hans Geisler', 'Robert Forkel', 'Johann-Mattis List']):
common.Editor(
dataset=ds,
ord=i,
contributor=common.Contributor(id=slug(HumanName(name).last), name=name)
)
contrib = data.add(
common.Contribution,
None,
id='cldf',
name=args.cldf.properties.get('dc:title'),
description=args.cldf.properties.get('dc:bibliographicCitation'),
)
for lang in iteritems(args.cldf, 'LanguageTable', 'id', 'name', 'latitude', 'longitude'):
data.add(
models.Variety,
lang['id'],
id=lang['Number'],
name=lang['name'],
description=lang['FullName'],
latitude=lang['latitude'],
longitude=lang['longitude'],
canton=lang['Canton'],
group=lang['DialectGroup'],
recorded=lang['DateOfRecording'],
population=int(lang['Population']) if lang['Population'] else None,
speaker_age=int(lang['SpeakerAge']) if lang['SpeakerAge'] else None,
speaker_proficiency=lang['SpeakerProficiency'],
speaker_language_use=lang['SpeakerLanguageUse'],
speaker_gender=lang['SpeakerGender'],
investigators=lang['Investigators'],
)
colors = qualitative_colors(len(set(l.canton for l in data['Variety'].values())), set='tol')
for i, (_, langs) in enumerate(itertools.groupby(
sorted(data['Variety'].values(), key=lambda l: l.canton),
lambda l: l.canton,
)):
for lang in langs:
lang.update_jsondata(color=colors[i])
for rec in bibtex.Database.from_file(args.cldf.bibpath, lowercase=True):
data.add(common.Source, rec.id, _obj=bibtex2source(rec))
refs = collections.defaultdict(list)
for param in iteritems(args.cldf, 'ParameterTable', 'id', 'concepticonReference', 'name'):
data.add(
models.Concept,
param['id'],
id=param['Number'],
number=int(param['Number']),
name='{} [{}]'.format(param['name'], param['Number']),
latin_gloss=param['Latin_Gloss'],
french_gloss=param['French_Gloss'],
concepticon_id=param['concepticonReference'],
concepticon_gloss=param['Concepticon_Gloss'],
concepticon_concept_id=param['id'].split('_')[0],
)
inventories = collections.defaultdict(set)
scan_url_template = args.cldf['FormTable', 'Scan'].valueUrl
for form in iteritems(args.cldf, 'FormTable', 'id', 'value', 'form', 'languageReference', 'parameterReference', 'source'):
if not form['form']:
continue
inventories[form['languageReference']] = inventories[form['languageReference']].union(form['Segments'])
vsid = (form['languageReference'], form['parameterReference'])
vs = data['ValueSet'].get(vsid)
if not vs:
vs = data.add(
common.ValueSet,
vsid,
id='-'.join(vsid),
language=data['Variety'][form['languageReference']],
parameter=data['Concept'][form['parameterReference']],
contribution=contrib,
)
for ref in form.get('source', []):
sid, pages = Sources.parse(ref)
refs[(vsid, sid)].append(pages)
f = data.add(
models.Form,
form['id'], # Gauchat-1925-480-1_
id=form['id'],
name=form['form'].replace('+', ' '),
description=form['value'],
segments=' '.join(form['Segments']),
valueset=vs,
scan=scan_url_template.expand(**form),
prosodic_structure=form['ProsodicStructure'],
)
for example in args.cldf['ExampleTable']:
sentence = models.Phrase(
id=example['ID'],
language=data['Variety'][example['Language_ID']],
name=example['Primary_Text'],
description=example['Translated_Text'],
original_script=example['Alt_Transcription'],
)
for cid in example['Concept_ID']:
DBSession.add(models.ConceptSentence(concept=data['Concept'][cid], sentence=sentence))
for fid in example['Form_ID']:
DBSession.add(common.ValueSentence(value=data['Form'][fid], sentence=sentence))
for lid, inv in inventories.items():
inv = [clts.bipa[c] for c in inv]
data['Variety'][lid].update_jsondata(
inventory=[(str(c), c.name) for c in inv if hasattr(c, 'name')])
for (vsid, sid), pages in refs.items():
DBSession.add(common.ValueSetReference(
valueset=data['ValueSet'][vsid],
source=data['Source'][sid],
description='; '.join(nfilter(pages))
))
def prime_cache(args):
"""If data needs to be denormalized for lookup, do that here.
This procedure should be separate from the db initialization, because
it will have to be run periodically whenever data has been updated.
"""
| python |
# coding: utf-8
from .mecab_read import read_mecab_data
from collections import defaultdict
def Q_036():
""" 36. 単語の出現頻度
文章中に出現する単語とその出現頻度を求め，出現頻度の高い順に並べよ．
"""
data = read_mecab_data('data/neko.txt.mecab')
noun_phrase_set = defaultdict(lambda: 0)
for sent in data:
for word in sent:
noun_phrase_set[word['surface']] += 1
return [(k, v) for k, v in sorted(noun_phrase_set.items(), key=lambda x:x[1], reverse=True)]
| python |
# -*- coding: utf-8 -*-
import os
import shutil
import yaml
# logging related packages
import logging
from logging.handlers import RotatingFileHandler
PROJECT_DIR = os.path.dirname(os.path.realpath(__file__))
DebugConf = True
#DebugConf = False
model_logger = logging.getLogger('bart-web')
formatter = logging.Formatter('[%(asctime)s][pid:%(process)s-tid:%(thread)s] %(module)s.%(funcName)s: %(levelname)s: %(message)s')
# StreamHandler for print log to console
hdr = logging.StreamHandler()
hdr.setFormatter(formatter)
hdr.setLevel(logging.DEBUG) #level at debug, which output debug info and error information to screen according to level of information
# RotatingFileHandler
## Set log dir
abs_path = os.path.dirname(os.path.abspath(__file__))
log_dir_path = abs_path + '/usercase/log'
if not os.path.exists(log_dir_path):
os.makedirs(log_dir_path)
## Specific file handler
fhr_model = RotatingFileHandler('%s/bartweb_backend.log'%(log_dir_path), maxBytes=10*1024*1024, backupCount=3)
fhr_model.setFormatter(formatter)
fhr_model.setLevel(logging.DEBUG) #level at debug, which output debug info and error information to screen according to level of information
model_logger.addHandler(fhr_model)
if DebugConf:
model_logger.addHandler(hdr)
model_logger.setLevel(logging.DEBUG)
else:
model_logger.setLevel(logging.ERROR)
if __name__ == '__main__':
'''
Usage:
from tools.log_tools import data_process_logger as logger
logger.debug('debug debug')
'''
model_logger.info('Ohhh model')
model_logger.error('error model')
| python |
from sanic.app import Sanic
from sanic.blueprints import Blueprint
__version__ = "19.6.0"
__all__ = ["Sanic", "Blueprint"]
| python |
from django.contrib.auth.mixins import LoginRequiredMixin,UserPassesTestMixin
from django.contrib.auth.models import User
from django.views.generic import ListView,DetailView
from .models import Rating,Post
from .forms import PostForm,RatingForm
from django.contrib.auth.decorators import login_required
from django.shortcuts import render, redirect, get_object_or_404
from django.http import HttpResponseRedirect
from users.models import Profile
from django.http import JsonResponse
# Create your views here.
class PostListView(LoginRequiredMixin,ListView):
model=Post
context_object_name = 'projects'
ordering = ['-date_posted']
class UserPostListView(ListView,LoginRequiredMixin):
model = Post
# <app>/<model>_<viewtype>.html image_list.html
context_object_name = 'images'
paginate_by = 7
def get_queryset(self):
user = get_object_or_404(User, username=self.kwargs.get('username'))
return Image.objects.filter(author=user).order_by('-date_posted')
@login_required(login_url='login')
def project(request,project_id):
current_user = request.user
try:
project = Post.objects.get(id=project_id)
except Project.DoesNotExist:
raise ObjectDoesNotExist()
total_design = 0
total_usability = 0
total_creativity = 0
total_content = 0
overall_score = 0
ratings = Rating.objects.filter(project=project_id)
if len(ratings) > 0:
users = len(ratings)
else:
users = 1
design = list(Rating.objects.filter(project=project_id).values_list('design',flat=True))
usability = list(Rating.objects.filter(project=project_id).values_list('usability',flat=True))
creativity = list(Rating.objects.filter(project=project_id).values_list('creativity',flat=True))
content = list(Rating.objects.filter(project=project_id).values_list('content',flat=True))
total_design=sum(design)/users
total_usability=sum(usability)/users
total_creativity=sum(creativity)/users
total_content=sum(content)/users
overall_score=(total_design+total_content+total_usability+total_creativity)/4
project.design = total_design
project.usability = total_usability
project.creativity = total_creativity
project.content = total_content
project.overall = overall_score
project.save()
if request.method == 'POST':
form = RatingForm(request.POST, request.FILES)
if form.is_valid():
rating = form.save(commit=False)
rating.project= project
if not Rating.objects.filter( project=project).exists():
rating.overall_score = (rating.design+rating.usability+rating.creativity+rating.content)/4
rating.save()
else:
form = RatingForm()
return render(request, "awward/post_detail.html",{"project":project,
"ratings":ratings,"form":form, 'total_design':total_design, 'total_usability':total_usability,
'total_creativity':total_creativity, 'total_content':total_content})
@login_required(login_url='login')
def search_project(request):
if request.method == 'GET':
title = request.GET.get("title")
results = Post.objects.filter(title__icontains=title).all()
message = f'name'
params = {
'results': results,
'message': message
}
return render(request, 'awward/results.html', params)
else:
message = "You haven't searched for any image category"
return render(request, 'awward/results.html', {'message': message})
@login_required(login_url='login')
def upload(request):
current_user = request.user
profile =Profile.objects.get(user=current_user)
if request.method == 'POST':
form = PostForm(request.POST, request.FILES)
if form.is_valid():
image = form.save(commit=False)
image.author = current_user
image.save()
return redirect('projects')
else:
form = PostForm()
return render(request, 'awward/post_form.html', {'form': form,'profile':profile})
| python |
from libfmp.b import plot_matrix
import numpy as np
from numba import jit
import matplotlib.pyplot as plt
from synctoolbox.feature.filterbank import FS_PITCH, generate_list_of_downsampled_audio, get_fs_index, filtfilt_matlab,\
generate_filterbank
PITCH_NAME_LABELS = [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C0 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C1 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C2 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C3 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C4 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C5 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C6 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C7 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C8 ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ',
'C9 ']
def audio_to_pitch_features(f_audio: np.ndarray,
Fs: float = 22050,
feature_rate: int = 50,
midi_min: int = 21,
midi_max: int = 108,
tuning_offset: int = 0,
verbose: bool = False) -> np.ndarray:
"""Computes pitch-based features via an IIR filterbank aggregated as STMSP
(short-time mean-square power). The signal is decomposed into subbands that
correspond to MIDI pitches between midi_min and midi_max.
In the output array, each row corresponds to one MIDI pitch. Per convention,
the output has size 128xN. Only the rows between ``midi_min`` and ``midi_max``
are filled, the rest contains zeros.
Parameters
----------
f_audio : np.ndarray
One dimensional audio array (mono)
Fs : float
Sampling rate of ``f_audio`` (in Hz)
feature_rate: int
Features per second
midi_min : int
Minimum MIDI index (indices below ``midi_min`` are filled with zero in the output)
midi_max : int
Maximum MIDI index (indices above ``midi_max`` are filled with zero in the output)
tuning_offset : int
Tuning offset used to shift the filterbank (in cents)
verbose : bool
Set `True` to activate the visualization of features
Returns
-------
f_pitch : np.ndarray [shape=(128, N)]
Matrix containing the extracted pitch-based features
"""
if verbose:
print("Generating filterbank...")
h = generate_filterbank(semitone_offset_cents=tuning_offset)
if verbose:
print("Downsampling signal...")
wav_ds = generate_list_of_downsampled_audio(f_audio)
# Compute features for all pitches
wav_size = f_audio.size
win_len_STMSP = Fs / feature_rate * 2
step_size = int(win_len_STMSP / 2)
group_delay = np.round(win_len_STMSP / 2)
# Group delay is adjusted
seg_wav_start = np.concatenate([np.ones(1), np.arange(1, wav_size+1, step_size)]).astype(np.float64)
seg_wav_stop = np.minimum(seg_wav_start + win_len_STMSP, wav_size)
seg_wav_stop[0] = np.minimum(group_delay, wav_size)
seg_wav_num = seg_wav_start.size
f_pitch = np.zeros((128, seg_wav_num))
if verbose:
print("Processing midi pitches", midi_min, "to", midi_max)
for midi_pitch in range(midi_min, midi_max + 1):
if verbose and midi_pitch % 10 == 0:
print(midi_pitch, end="")
else:
print(".", end="")
index = get_fs_index(midi_pitch)
b = h[midi_pitch]['b']
a = h[midi_pitch]['a']
f_filtfilt = filtfilt_matlab(x=wav_ds[index], b=b, a=a)
f_square = f_filtfilt ** 2
start = np.floor(seg_wav_start / Fs * FS_PITCH[index]).astype(np.int) # floor due to indexing
stop = np.floor(seg_wav_stop / Fs * FS_PITCH[index]).astype(np.int)
factor = Fs / FS_PITCH[index]
__window_and_sum(f_pitch, f_square, midi_pitch, seg_wav_num, start, stop, factor)
if verbose:
print("")
__visualize_pitch(f_pitch, feature_rate=feature_rate)
plt.show()
return f_pitch
@jit(nopython=True)
def __window_and_sum(f_pitch, f_square, midi_pitch, seg_wav_num, start, stop, factor):
for k in range(seg_wav_num): # TODO this is extremely inefficient, can we use better numpy indexing to improve this? np.convolve?
f_pitch[midi_pitch, k] = np.sum(f_square[start[k]:stop[k]]) * factor
def __visualize_pitch(f_pitch: np.ndarray,
midi_min: int = 21,
midi_max: int = 108,
feature_rate: float = 0,
use_pitch_name_labels: bool = False,
y_tick: np.ndarray = np.array([21, 30, 40, 50, 60, 70, 80, 90, 100], np.int)):
f_image = f_pitch[midi_min:midi_max + 1, :]
fig, ax, im = plot_matrix(X=f_image, extent=[0, f_pitch.shape[1]/feature_rate, midi_min, midi_max+1],
title='Pitch Features', ylabel='MIDI Pitch', figsize=(9, 9),
colorbar_aspect=50)
pitchscale = np.arange(midi_min, midi_max + 1)
ax[0].set_yticks(pitchscale[::2])
if use_pitch_name_labels:
ax[0].set_yticks(np.arange(midi_min, midi_max + 1))
ax[0].set_yticklabels(PITCH_NAME_LABELS[midi_min-1:midi_max], fontsize=12)
else:
ax[0].set_yticks(pitchscale[::2])
ax[0].set_yticklabels(pitchscale[::2], fontsize=10)
| python |
"""Allows light-weight profiling of code execution."""
import time
class Profiler:
"""Collects messages with timestamps so you can profile your code."""
def __init__(self):
self.clear()
def add_event(self, message):
milliseconds = int(round(time.time() * 1000))
self._profile_events.append((message[0:30], milliseconds))
def clear(self):
self._profile_events = []
def __str__(self):
return self._get_profile()
def _get_profile(self):
output = [
"",
"Message Run Time Total time",
"---------------------------------------------------",
]
rows = []
i = 0
previous_time = None
net_time = 0
for message, time in self._profile_events:
if i is not 0:
t = time - previous_time
net_time += t
rows[i - 1][1] = t
previous_time = time
rows.append([message, 0, net_time])
i += 1
for row in rows:
output.append('%-30s %-8s %10s' % (row[0], row[1], row[2]))
return "\n".join(output)
| python |
#!/usr/bin/env python
"""
Code for Harris corner detection.
"""
import cv2
import numpy as np
def interactive_harris(title, img):
cv2.imshow(title, img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def update_harris(pos):
bs_i = cv2.getTrackbarPos('bs', title)
ks_i = cv2.getTrackbarPos('ks', title)
k_i = cv2.getTrackbarPos('k', title)
odds = [2*x+1 for x in range(100)]
bs = odds[bs_i]
ks = odds[ks_i]
k = k_i
harris = cv2.cornerHarris(gray, blockSize=bs, ksize=ks, k=k)
harris = cv2.normalize(harris, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX).astype(np.uint8)
print "%s :: bs=%d, ks=%d, k=%d" % (title, bs, ks, k)
cv2.imshow(title, np.vstack((harris,gray)))
cv2.createTrackbar('bs', title, 0, 20, update_harris)
cv2.createTrackbar('ks', title, 0, 15, update_harris)
cv2.createTrackbar('k', title, 0, 100, update_harris)
update_harris(None)
if __name__ == '__main__':
digits = cv2.imread('../images/digits.png')
interactive_harris('digits', digits)
symbols = cv2.imread('../images/symbols.png')
interactive_harris('symbols', symbols)
print "Done. Press enter."
cv2.waitKey()
| python |
import logging
logging.basicConfig(level=logging.DEBUG)
from experiments_seminar_2 import ptl_wandb_run_builder
if __name__ == "__main__":
"""
Best fit with multiple orders
"""
config_dict = {
"env": {
"num_dcs": 3,
"num_customers": 10,
"num_commodities": 5,
"orders_per_day": 4, # start with one, and then play with this.
"dcs_per_customer": 2,
"demand_mean": 500,
"demand_var": 150,
"num_steps": 30, # steps per episode
"big_m_factor": 10000, # how many times the customer cost is the big m.
# New parameters 2021
"version": "v2",
"order_generator": "biased",
"reward_function": "negative_cost", # big_m_diff
},
"hps": {
"env": "shipping-v0", # openai env ID.
"episode_length": 150, # todo isn't this an env thing?
"max_episodes": 10, # to do is this num episodes, is it being used?
# "batch_size": 30,
# "sync_rate": 2, # Rate to sync the target and learning network, not used with this agent
"lr": 1e-3,
"discount": 0.8,
"epsilon": 0.01,
"init_state_value": 0.001,
},
"seed": 0,
# "agent": "lookahead"
# "agent": "tree_search"
"agent": "best_fit"
# "agent": "random_valid",
}
trainer, model = ptl_wandb_run_builder.create_ptl_shipping_allocation_rl_runner(
config_dict,
# run_mode="local_debug",
run_mode="debug",
experiment_name=f"{config_dict['agent']}_multicommodity_multiorder",
project_name="rl_warehouse_assignment",
)
trainer.fit(model)
| python |
# coding: utf-8
# # Tutorial 2 - MicaSense library
#
# This tutorial assumes you have gone through the [basic setup](./Micasense Image Processing Setup.html) and builds on the basic radiance, irradiance, and reflectance concepts and code covered in the [first tutorial](./MicaSense Image Processing Tutorial 1.html).
#
# In this tutorial, we will cover usage of the MicaSense python library to access images and groups of images. Most of the processing details are hidden away in the library, but the library code is open and available in the git repository.
#
# # Library Components
#
# In the first tutorial, we introduced `micasense.utils` which provided some helper functions for single image manipulation, and `micasense.plotutils` which provided some plotting helpers.
#
# For this second tutorial, we are going to introduce the usage of the included micasense libraries for opening, converting, and displaying images. This will allow us to discuss and visualize results at a high level, while the underlying source code is available for those interested in the implementation details. In some cases, the libraries themselves may be enough to implement a custom workflow without the need to re-implement or translate the code to another system or language.
#
# The library code provides some basic classes to manage image data. At the highest level is the `ImageSet`, which is able to load a list of files or recursively search a whole directory into data structures which are easy to access and manipulate. `ImageSet`s are made up of `Capture`s, which hold the set of (usually 5) images as they are simultaneously gathered by the RedEdge camera. Within `Capture`s are `Image`s, which hold a single image file and allow easy access to the image metadata. The `Image` class also provides the ability to extract metadata from individual images and to convert individual images in similar ways to those described in the first tutorial.
#
# For the rest of this article, we will look at each of the objects available starting with the single `Image` object, and work our way up to the whole `ImageSet`. Each section in this article is standalone, and can be copied into another workbook or edited in place to explore more of the functions associated with that object.
# ## micasense.Image
#
# An image is the lowest level object. It represents the data in a single tiff file as taken by the camera. `Image` objects expose a set of data retrieval methods which provide access to raw, radiance, and reflectance corrected images, and to undistort any of those images. Note that when retrieving image data from an `Image` object, the data is stored internally in the object, increasing the object's memory footprint. If operating on a large number of images, it may be necessary to release this data memory after each image is processed to limit the program memory footprint. This can be done by calling the `Image.clear_image_data()` method.
# In[ ]:
import os
import micasense.image as image
get_ipython().run_line_magic('matplotlib', 'inline')
image_path = os.path.join('.','data','0000SET','000','IMG_0000_1.tif')
img = image.Image(image_path)
img.plot_raw();
# ### Accessing `Image` Metadata
#
# Metadata for each image is available in the `Image.meta` parameter. This object is a `micasense.Metadata` object and can be accessed directly for image specific metadata extraction. Below, we print the same metadata values as we did in Tutorial #1, but using direct access to the `Metadata` object parameters.
#
# A notebook for experimenting with the `Image` class can be found [here](Images.html).
# In[ ]:
print('{0} {1} firmware version: {2}'.format(img.meta.camera_make(),
img.meta.camera_model(),
img.meta.firmware_version()))
print('Exposure Time: {0} seconds'.format(img.meta.exposure()))
print('Imager Gain: {0}'.format(img.meta.gain()))
print('Size: {0}x{1} pixels'.format(img.meta.image_size()[0],
img.meta.image_size()[1]))
print('Band Name: {0}'.format(img.meta.band_name()))
print('Center Wavelength: {0} nm'.format(img.meta.center_wavelength()))
print('Bandwidth: {0} nm'.format(img.meta.bandwidth()))
print('Capture ID: {0}'.format(img.meta.capture_id()))
print('Flight ID: {0}'.format(img.meta.flight_id()))
# ## micasense.Capture
#
# The `Capture` class is a container for `Image`s which allows access to metadata common to the group of images. The internal `Image` objects are accessible via the `capture.images` properties, and images in this list are kept sorted by the `band` property. Data which is different for each image can be accessed through composite methods, such as the `capture.dls_irradiance()` method, which returns a list of irradiances in band order.
# In[ ]:
import os, glob
import micasense.capture as capture
images_path = os.path.join('.','data','0000SET','000')
image_names = glob.glob(os.path.join(images_path,'IMG_0000_*.tif'))
cap = capture.Capture.from_filelist(image_names)
cap.plot_radiance();
# ### Acessing `Capture` metadata
#
# Metadata which is common to all captures can be accessed via methods on the `Capture` object. Metadata which varies between the images of the capture, such as DLS information, is available as lists accessed from the capture object.
#
# <div class="alert alert-info">
# <strong>Note:</strong> The lists returned from metadata access on the `Capture` object are returned in `band_index` order. All images within a capture are sorted by the image `band_index`, and all lists adhere to this ordering. This ordering is consistent with the number at the end of each filename of a RedEdge image.
# </div>
#
# Below we plot the raw and tilt compensated DLS irradiance by center wavelength and by band name.
#
# In[ ]:
import matplotlib.pyplot as plt
print(cap.band_names())
fig = plt.figure(figsize=(14,6))
plt.subplot(1,2,1)
plt.scatter(cap.center_wavelengths(), cap.dls_irradiance())
plt.ylabel('Irradiance $(W/m^2/nm)$')
plt.xlabel('Center Wavelength (nm)')
plt.subplot(1,2,2)
plt.scatter(cap.band_names(), [img.meta.exposure() for img in cap.images])
plt.xlabel('Band Names')
plt.ylim([0,2.5e-3])
plt.ylabel('Exposure Time (s)')
plt.show()
# A notebook for experimenting with the `Capture` class can be found [here](Captures.html).
# ## micasense.Panel
#
# The `Panel` class is a helper class which can automatically extract panel information from MicaSense calibrated reflectance panels by finding the QR code within an image and using the QR Code location and orientation information to find the lambertian panel area. The class then allows extraction of statistics from the panel area such as mean raw values, mean radiance, standard deviation, and the number of saturated pixels in the panel region. The panel object can be included standalone, or used within the context of a `Capture` object.
#
# <div class="alert alert-info">
# <strong>Note:</strong> For the automatic panel QR code finding functions of the library to work, zbar and it's python bindings must be installed. We have made every effort to ensure this fails gracefully if zbar isn't available. Unfortunately zbar is only available using Python 2.7, not Python 3. If you're using Python 3.x, the code available in '/micasense/panel.py' shows how to find QR codes in images and to find the panel area from the QR location. We're currently looking for Python QR code finding options that work across platforms and Python versions, let us know if you have one that supports location!
# </div>
# In[ ]:
import os, glob
import micasense.image as image
import micasense.panel as panel
image_path = os.path.join('.','data','0000SET','000','IMG_0000_1.tif')
img = image.Image(image_path)
# panelCorners - if we dont have zbar installed to scan the QR codes, detect panel manually and
panelCorners = [[[809,613],[648,615],[646,454],[808,452]],
[[772,623],[613,625],[610,464],[770,462]],
[[771,651],[611,653],[610,492],[770,490]],
[[829,658],[668,659],[668,496],[829,496]],
[[807,632],[648,634],[645,473],[805,471]]]
pnl = panel.Panel(img,panelCorners = panelCorners[0])
print("Panel found: {}".format(pnl.panel_detected()))
print("Panel serial: {}".format(pnl.serial))
print("QR Code Corners:\n{}".format(pnl.qr_corners()))
mean, std, count, saturated_count = pnl.raw()
print("Panel mean raw pixel value: {}".format(mean))
print("Panel raw pixel standard deviation: {}".format(std))
print("Panel region pixel count: {}".format(count))
print("Panel region saturated pixel count: {}".format(count))
pnl.plot();
# A notebook for experimenting with the `Panel` class can be found [here](Panels.html)
# ## micasense.ImageSet
#
# An `ImageSet` contains a group of `Capture`s. The captures can be loaded from image object, from a list of files, or by recursively searching a directory for images.
#
# Loading an `ImageSet` can be a time consuming process. It uses python multithreading under the hood to maximize cpu usage on multi-core machines.
# In[ ]:
from ipywidgets import FloatProgress
from IPython.display import display
f = FloatProgress(min=0, max=1)
display(f)
def update_f(val):
f.value=val
import micasense.imageset as imageset
import os
images_dir = os.path.join('.','data','0000SET')
imgset = imageset.ImageSet.from_directory(images_dir, progress_callback=update_f)
for cap in imgset.captures:
print ("Opened Capture {} with bands {}".format(cap.uuid,[str(band) for band in cap.band_names()]))
# ## Extended ImageSet examples
#
# A large group of images captured over a central California orchard are available for [download here](https://s3-us-west-2.amazonaws.com/sample.micasense.com/imageprocessing/RedEdgeImageSet.zip).
#
# With this set extracted to a working folder, the [extended ImageSet example](./ImageSets.html) notebook provides more usages of ImageSet data.
#
# # Conclusion
#
# In this tutorial, we have introduced the MicaSense library and provided some examples of opening Images, Captures, and ImageSets, as well as detecting and extracting panel information from images.
#
# The next tutorial covers basic usage of DLS information, and is available [here](./MicaSense%20Image%20Processing%20Tutorial%203.html)
# ---
# Copyright (c) 2017-2018 MicaSense, Inc. For licensing information see the [project git repository](https://github.com/micasense/imageprocessing)
| python |
"""
For each Results/Final/LargeSet_20180106/ subfolder:
alpha
maxiter
lsiterations
population
eliteprop
mutantprop
generations
inheritance
create list of results
ex: alpha_results = {
'paramval': get from file,
'objfunc': [],
'objfunc_avg': value
}
for each file inside
read and add data series
alpha_results_x = "alpha param value"
-> get from file name
alpha_results_y = objective function
append alpha_results_y to objfunc
compute average and save in objfunc_avg
plot and save plot to Results/Final/LargeSet_20180106/Plots
alpha_plot.png
"""
import json
import matplotlib.pyplot as plt
import sys
import os
from matplotlib.backends.backend_pdf import PdfPages
from random import randrange
import re
import traceback
from datetime import datetime
import argparse
import operator
import shutil
import pprint
pp = pprint.PrettyPrinter(indent=2)
def chartPlot(plotname, savefilename, x, y, axisnames, labels):
fig, ax = plt.subplots()
xs = range(len(y))
x0 = xs[0]
x1 = xs[-1]
y1 = y[0]
for ys in y:
if ys < y1:
y1 = ys
ax.plot([x0, x1], [y1, y1], 'k-', c='r')
plt.plot(range(len(y)),y, marker='.', color='b', ls='', label=labels[0])
plt.xticks(range(len(x)),x)
plt.xlabel(axisnames[0])
plt.ylabel(axisnames[1])
ax.legend(loc='upper right', fontsize='small')
#fig.subplots_adjust(bottom=0.9)
fig.tight_layout()
#plt.axis([0, len(results), 0, max(y)])
plt.savefig(os.path.join('..','LargeSet_graphs',savefilename + '.png'))
plt.show()
plt.close()
# copy to Documentation folder
savename2 = 'best-' + savefilename[18:]
shutil.copy(os.path.join('..','LargeSet_graphs',savefilename + '.png'), os.path.join('..','..','..','Documentation','img',savename2 + '.png'))
def buildCharts(parameters_list, name):
"""
parameters_list=[{
'name': parameter,
'results': [{
'paramval': paramval,
'objfunc': [objfunc],
'objfunc_avg': objfunc
},
{
'paramval': paramval,
'objfunc': [objfunc],
'objfunc_avg': objfunc
},
...
]
},
{
'name': parameter,
'results': [{
'paramval': paramval,
'objfunc': [objfunc],
'objfunc_avg': objfunc
},
{
'paramval': paramval,
'objfunc': [objfunc],
'objfunc_avg': objfunc
},
...
]
},
]
"""
best_values = {}
for elem in parameters_list:
print(elem["name"])
if elem["name"] == "generation":
pp.pprint(elem)
elem["results"] = sorted(elem["results"], key=lambda k: k['paramval'])
bestvalue = elem["results"][0]["paramval"]
min_objfunc_avg = elem["results"][0]["objfunc_avg"]
# if elem["name"] == "generation":
# pp.pprint(elem)
for paramval in elem["results"]:
#print(paramval["paramval"])
#print(paramval["objfunc_avg"])
#avg verification
thesum = 0
for objfs in paramval["objfunc"]:
thesum += objfs
theavg = thesum / len(paramval["objfunc"])
if round(theavg,2) != round(paramval["objfunc_avg"],2):
print("----->Avg error!")
print(theavg)
print(paramval["objfunc_avg"])
print()
if paramval["objfunc_avg"] < min_objfunc_avg:
min_objfunc_avg = paramval["objfunc_avg"]
bestvalue = paramval["paramval"]
best_values[elem["name"]] = bestvalue
# plotname
# plotfilename
# x
x_ = [ x["paramval"] for x in elem["results"]]
# y
y_ = [ y["objfunc_avg"] for y in elem["results"]]
# legend
# axes
thelabel = elem["name"]
if elem["name"] == "lsiteration":
thelabel = "failedIterations"
elif elem["name"] == "maxIter":
thelabel = "maxIterations"
elif elem["name"] == "generation":
thelabel = "generations"
chartPlot(
plotname=elem["name"],
savefilename=name + "-" + elem["name"],
x=x_,
y=y_,
axisnames=["Parameter values", "Average objective function"],
labels=[thelabel])
print(best_values)
with open(os.path.join('..','LargeSet_graphs','BestValues-') + name, 'w+' ) as fout:
fout.write(json.dumps(best_values))
def extractParameterValue(parameter, filename):
prefixes = {
'alpha': {'prefix': '-a', 'type': 'float'},
'maxiter': {'prefix': '-i', 'type': 'int'},
'lsiteration': {'prefix': '-lsit', 'type': 'int'},
'generation': {'prefix': '-g', 'type': 'int'},
'population': {'prefix': '-p', 'type': 'int'},
'inheritance': {'prefix': '-i', 'type': 'float'},
'eliteprop': {'prefix': '-e', 'type': 'float'},
'mutantprop': {'prefix': '-m', 'type': 'float'},
}
prefix = prefixes[parameter]["prefix"]
i0 = filename.find('-i-ng')
if i0 == -1:
i0 = 0
else:
i0 += len('-i-ng')
i1 = filename[i0:].find(prefix)
i2 = i0 + i1 + len(prefix)
i3 = filename[i2:].find('-')
if i3 == -1:
i3 = filename[i2:].find('.json')
value = filename[i2:i2 + i3]
if prefixes[parameter]["type"] == "float":
try:
value = float(value)
except:
print(parameter)
print(prefix)
print(i0)
print(i1)
print(i2)
print(i2 + i3)
print(filename)
print(value)
exit()
else:
value = int(value)
return value
def parsefile(fileobject, parameters_list, parameter, filename):
paramval = 0
objfunc = 0
# get param value from filename
paramval = extractParameterValue(parameter, filename)
# if parameter == "generation" and not (paramval in [5,10 ,15 ,20 ]):
# return
# extract objective function
results = json.load(fileobject)
for elem in results:
for k,v in elem.items():
if k == 'end':
continue
# get objective function
objfunc = int(v['ObjectiveFunction'])
break
# add new result to parameters_list
for elem in parameters_list:
if elem["name"] == parameter:
param_results = elem["results"]
found = False
for res in param_results:
if res["paramval"] == paramval:
found = True
res["objfunc"].append(objfunc)
l = len(res["objfunc"])
res["objfunc_avg"] = (res["objfunc_avg"] * (l - 1) + objfunc ) / l
break
if not found:
param_results.append({
'paramval': paramval,
'objfunc': [objfunc],
'objfunc_avg': objfunc
})
break
return
if __name__ == '__main__':
results_folder = os.path.join('..','..','Results','Final','LargeSet_20180106')
parser = argparse.ArgumentParser()
parser.add_argument("--folder",help="folder where to read results from")
args = parser.parse_args()
if args.folder:
results_folder = os.path.join(args.folder,'data')
os.chdir(results_folder)
parameters_list = []
for root, dirs, files in os.walk("."):
for folder in dirs:
print(folder)
parameter = folder
parameter_results = {
'name': parameter,
'results': []
}
parameters_list.append(parameter_results)
for result in files:
parameter = root.split('/')[-1]
if not result.endswith(".json"):
continue
filepath = os.path.join(root,result)
with open(filepath,'r+') as f:
try:
#print(os.path.join(root,result))
parsefile(f, parameters_list, parameter, result)
except Exception:
print()
print("Exception in " + result)
print("-"*60)
traceback.print_exc(file=sys.stdout)
print("-"*60)
#print(parameters_list)
buildCharts(parameters_list, '{0:%Y%m%d_%H-%M-%S}'.format(datetime.now()) )
| python |
"""
Problem:
You come across a dictionary of sorted words in a language you've never seen before.
Write a program that returns the correct order of letters in this language.
For example, given ['xww', 'wxyz', 'wxyw', 'ywx', 'ywz'], you should return
['x', 'z', 'w', 'y'].
"""
from typing import Dict, List, Optional, Set
def update_letter_order(sorted_words: List[str], letters: Dict[str, Set[str]]) -> None:
order = []
new_words = {}
prev_char = None
for word in sorted_words:
if word:
char = word[0]
if char != prev_char:
order.append(char)
if char not in new_words:
new_words[char] = list()
new_words[char].append(word[1:])
prev_char = char
for index, char in enumerate(order):
letters[char] = letters[char] | set(order[index + 1 :])
for char in new_words:
update_letter_order(new_words[char], letters)
def find_path(
letters: Dict[str, Set[str]], start: str, path: List[str], length: int
) -> Optional[List[str]]:
if len(path) == length:
return path
if not letters[start]:
return None
for next_start in letters[start]:
new_path = find_path(letters, next_start, path + [next_start], length)
if new_path:
return new_path
def get_letter_order(sorted_words: List[str]):
letters = {}
for word in sorted_words:
for letter in word:
if letter not in letters:
letters[letter] = set()
update_letter_order(sorted_words, letters)
max_children = max([len(x) for x in letters.values()])
potential_heads = [x for x in letters if len(letters[x]) == max_children]
path = None
for head in potential_heads:
path = find_path(letters, head, path=[head], length=len(letters))
if path:
break
return path
if __name__ == "__main__":
print(get_letter_order(["xww", "wxyz", "wxyw", "ywx", "ywz"]))
"""
SPECS:
TIME COMPLEXITY: O(words x letters + words ^ 2 + letters ^ 2)
SPACE COMPLEXITY: O(words x letters)
"""
| python |
# -*- coding: utf-8 -*-
from typing import List
from decibel.tab_chord_parser.segment import Segment
from decibel.tab_chord_parser.line_type import LineType
from decibel.tab_chord_parser.line import Line
from decibel.tab_chord_parser.system import System
def find_systems(segment: Segment):
system_nr = 0
system_line_nr = 0
while system_line_nr < len(segment.lines):
line = segment.lines[system_line_nr]
if line.line_type == LineType.ChordsAndLyrics:
segment.add_system(System(system_nr))
segment.systems[system_nr].add_chords_and_lyrics_line(line)
system_line_nr += 1
system_nr += 1
elif line.line_type == LineType.Chords:
system = System(system_nr)
segment.add_system(system)
system.add_chords_line(line)
system_line_nr += 1
if system_line_nr == len(segment.lines):
break
if segment.is_start_of_tab_block(system_line_nr):
# Here is a tab block, but we ignore it as we already know the chords
system_line_nr += 6
# If the tab block is followed by max. 3 subsequent lyrics lines, add the lyrics to the system
nr_of_subsequent_lyrics_lines = segment.length_of_lyrics_block(system_line_nr)
for subsequent_lyric_i in range(0, nr_of_subsequent_lyrics_lines):
system.add_lyrics_line(segment.lines[system_line_nr + subsequent_lyric_i])
system_line_nr += nr_of_subsequent_lyrics_lines
system_nr += 1
elif segment.is_start_of_tab_block(system_line_nr):
# Add new system
system = System(system_nr)
segment.systems.append(system)
tab_block_str = [block_line.content for block_line in segment.lines[system_line_nr:system_line_nr + 6]]
system.add_tab_block(tab_block_str)
system_line_nr += 6
# If the tab block is followed by max. 3 subsequent lyrics lines, add the lyrics to the system
nr_of_subsequent_lyrics_lines = segment.length_of_lyrics_block(system_line_nr)
for subsequent_lyric_i in range(0, nr_of_subsequent_lyrics_lines):
system.add_lyrics_line(segment.lines[system_line_nr + subsequent_lyric_i])
system_line_nr += nr_of_subsequent_lyrics_lines
system_nr += 1
else:
system_line_nr += 1
def segment_line_list(line_list: List[Line]) -> List[Segment]:
"""
Takes a list of Lines and divides them into Segments, based on Empty LineTypes. Returns a list of them.
:param line_list: List of Lines from a tab file
:return: List of segments from a tab file
"""
result = []
segment_nr = 0
new_segment = True
for line in line_list:
if line.line_type == LineType.Empty:
if not new_segment:
new_segment = True
segment_nr += 1
else:
if new_segment:
result.append(Segment(segment_nr))
new_segment = False
result[segment_nr].add_line(line)
for segment in result:
find_systems(segment)
return result
| python |
import argparse
import asyncio
import getpass
import logging
import os
import sys
import traceback
import yaml
import pkg_resources
from aiohttp import web
from colorlog import ColoredFormatter
from pathlib import Path
from rest_api.intkey_client import IntkeyClient
from rest_api.exceptions import IntKeyCliException
from rest_api.exceptions import IntkeyClientException
from rest_api.route_handler import RouteHandler
import config
from zmq.asyncio import ZMQEventLoop
from sawtooth_signing import create_context
from sawtooth_signing.secp256k1 import Secp256k1PublicKey
LOGGER = logging.getLogger(__file__)
DISTRIBUTION_NAME = 'sawtooth-intkey'
DEFAULT_URL = 'http://127.0.0.1:8008'
ARGS = ""
def create_console_handler(verbose_level):
clog = logging.StreamHandler()
formatter = ColoredFormatter(
"%(log_color)s[%(asctime)s %(levelname)-8s%(module)s]%(reset)s "
"%(white)s%(message)s",
datefmt="%H:%M:%S",
reset=True,
log_colors={
'DEBUG': 'cyan',
'INFO': 'green',
'WARNING': 'yellow',
'ERROR': 'red',
'CRITICAL': 'red',
})
clog.setFormatter(formatter)
if verbose_level == 0:
clog.setLevel(logging.WARN)
elif verbose_level == 1:
clog.setLevel(logging.INFO)
else:
clog.setLevel(logging.DEBUG)
return clog
def setup_loggers(verbose_level):
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
logger.addHandler(create_console_handler(verbose_level))
def create_parent_parser(prog_name):
parent_parser = argparse.ArgumentParser(prog=prog_name, add_help=False)
parent_parser.add_argument(
'-v', '--verbose',
action='count',
help='enable more verbose output')
# add bind port to run server
parent_parser.add_argument(
'-b', '--bind',
help='identify host and port for api to run on',
default='txt-rest-api:8000')
parent_parser.add_argument(
'--url',
type=str,
help='specify URL of REST API')
parent_parser.add_argument(
'--keyfile',
type=str,
help="identify file containing user's private key")
parent_parser.add_argument(
'--wait',
nargs='?',
const=sys.maxsize,
type=int,
help='set time, in seconds, to wait for transaction to commit')
try:
version = pkg_resources.get_distribution(DISTRIBUTION_NAME).version
except pkg_resources.DistributionNotFound:
version = 'UNKNOWN'
parent_parser.add_argument(
'-V', '--version',
action='version',
version=(DISTRIBUTION_NAME + ' (Hyperledger Sawtooth) version {}')
.format(version),
help='display version information')
return parent_parser
def generate_private_key():
context = create_context('secp256k1')
private_key = context.new_random_private_key()
private_key_hex = Secp256k1PublicKey.as_hex(private_key)
real_user = getpass.getuser()
home = os.path.expanduser("~")
key_dir = os.path.join(home, ".sawtooth", "keys")
path = key_dir + "/" + real_user + ".priv"
Path(key_dir).mkdir(parents=True, exist_ok=True)
f = open(path, "w")
f.write(private_key_hex)
f.close()
def start_rest_api(host, port):
loop = asyncio.get_event_loop()
app = web.Application(loop=loop)
client = _get_client(ARGS)
handler = RouteHandler(loop, client)
app.router.add_post('/invoke', handler.invoke_function)
LOGGER.warning('Starting REST API on %s:%s', host, port)
web.run_app(
app,
host=host,
port=port,
access_log=LOGGER)
def _get_client(args, read_key_file=True):
return IntkeyClient(
url=DEFAULT_URL if args.url is None else args.url,
keyfile=_get_keyfile(args) if read_key_file else None)
def _get_keyfile(args):
try:
if args.keyfile is not None:
return args.keyfile
except AttributeError:
return None
real_user = getpass.getuser()
home = os.path.expanduser("~")
key_dir = os.path.join(home, ".sawtooth", "keys")
return '{}/{}.priv'.format(key_dir, real_user)
def main(prog_name=os.path.basename(sys.argv[0]), args=None):
if args is None:
args = sys.argv[1:]
parser = create_parent_parser(prog_name)
args = parser.parse_args(args)
global ARGS
ARGS = args
if args.verbose is None:
verbose_level = 0
else:
verbose_level = args.verbose
setup_loggers(verbose_level=verbose_level)
loop = ZMQEventLoop()
asyncio.set_event_loop(loop)
try:
host, port = args.bind.split(":")
port = int(port)
except ValueError:
print("Unable to parse binding {}: Must be in the format"
" host:port".format(args.bind))
sys.exit(1)
config.init_config()
start_rest_api(host, port)
def main_wrapper():
# pylint: disable=bare-except
try:
generate_private_key()
main()
except (IntKeyCliException, IntkeyClientException) as err:
print("Error: {}".format(err), file=sys.stderr)
sys.exit(1)
except KeyboardInterrupt:
pass
except SystemExit as e:
raise e
except:
traceback.print_exc(file=sys.stderr)
sys.exit(1)
main_wrapper()
| python |
# -*- coding: utf-8 -*-
def str_dict(str_headers):
di = []
try:
for i in str_headers.split("\n"):
he = i.split(": ", 1)
if he != [""]:
di.append(he)
return dict(di)
except ValueError as error:
print("请把请求类型一行去掉：POST /xxx/xxx/xxx HTTP/1.1" + "\n" + "错误为：%s" % error)
| python |
#!/usr/local/bin/python3
import torch
# Element-wise , componenet-wise, point-wise
# If the two tensors have the same shape, we can perform element wise
# operations on them. +-*/ are all element wise operations.
# Returns a tensor filled with random numbers from a uniform
# distribution on the interval [0,1)
t1 = torch.rand((5, 5))
print(t1.shape)
t2 = torch.rand((5, 5))
print(t2.shape)
result = t1 + t2
print(result)
# Broadcasting works however, just like numpy.
result = result - 1
# A filter matrix
filterMatrix = result > 0
print("Filter Matrix is:\n", filterMatrix)
# Will print the values that were bigger than 0.
print("Values bigger than 0:\n", result[filterMatrix])
# For element wise multiplication use *. For casting use result.int(), or double() etc.
print("Values bigger than 0 in their place as a matrix.\n",
result * filterMatrix.int()
)
# We can get the same results using the in-built functions
# Greater equals
print("Greater equals to 0:\n", result, "\n", result.ge(0))
# Greater
print("Greater than 0:\n", result.gt(0))
t3 = torch.rand((5, 1))
# Because of Broadcasting, we can do the following operation.
print("Broadcasting works:\n", t3 + t1)
| python |
import tensorflow.contrib.learn as skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier_model = skflow.LinearClassifier(feature_columns=[tf.contrib.layers.real_valued_column("", dimension=iris.data.shape[1])],
n_classes=3)
classifier_model.fit(iris.data, iris.target)
score = metrics.accuracy_score(iris.target,classifier_model.predict(iris.data))
print("Accuracy: %f" % score) | python |
'''Author: Sourabh Bajaj'''
import ez_setup
ez_setup.use_setuptools()
from setuptools import setup, find_packages
setup(
name='QSTK',
version='0.2.8.2',
author='Sourabh Bajaj',
packages=find_packages(),
namespace_packages=['QSTK'],
include_package_data=True,
long_description=open('README.md').read(),
author_email='[email protected]',
url='https://github.com/tucker777/QuantSoftwareToolkit',
license=open('LICENSE.txt').read(),
description='QuantSoftware Toolkit',
install_requires=[
"numpy >= 1.6.1",
"scipy >= 0.9.0",
"matplotlib >= 1.1.0",
"pandas >= 0.7.3",
"python-dateutil == 1.5",
"cvxopt>=1.1.8",
"scikit-learn >= 0.11",
"pandas-datareader>=0.4.0"
],
classifiers=[
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'Intended Audience :: Developers',
'Intended Audience :: Education',
'Intended Audience :: Science/Research',
'Intended Audience :: Financial and Insurance Industry',
'License :: OSI Approved :: BSD License',
'Natural Language :: English',
'Operating System :: MacOS :: MacOS X',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX',
'Programming Language :: Python :: 2.7',
'Topic :: Utilities',
],
)
| python |
from typing import Any
__all__ = ["AttrDict"]
class AttrDict(dict):
"""
Wrapper of dict class, to allow usage of attribute notation (instance.key)
in place of index notation (instance["key"]).
Can be used as a mixin for Mappings.
"""
def __getattr__(self, item: str) -> Any:
if item in self:
return self[item]
return getattr(super(), item)
| python |
from django.conf.urls import url
from zebra import views
urlpatterns = [
url(r'webhooks/$', views.webhooks, name='webhooks'),
url(r'webhooks/v2/$', views.webhooks_v2, name='webhooks_v2'),
]
| python |
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pytest
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore import amp
from mindspore.nn import Dense
from mindspore.nn import TrainOneStepCell, WithLossCell
from mindspore.nn.cell import Cell
from mindspore.nn.layer.basic import Flatten
from mindspore.nn.layer.conv import Conv2d
from mindspore.nn.layer.normalization import BatchNorm2d
from mindspore.nn.layer.pooling import MaxPool2d
from mindspore.nn.optim import Momentum
from mindspore.ops import operations as P
from mindspore.ops.operations import TensorAdd
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
def random_normal_init(shape, mean=0.0, stddev=0.01, seed=None):
init_value = np.ones(shape).astype(np.float32) * 0.01
return Tensor(init_value)
def variance_scaling_raw(shape):
variance_scaling_value = np.ones(shape).astype(np.float32) * 0.01
return Tensor(variance_scaling_value)
def weight_variable_0(shape):
zeros = np.zeros(shape).astype(np.float32)
return Tensor(zeros)
def weight_variable_1(shape):
ones = np.ones(shape).astype(np.float32)
return Tensor(ones)
def conv3x3(in_channels, out_channels, stride=1, padding=1):
"""3x3 convolution """
weight_shape = (out_channels, in_channels, 3, 3)
weight = variance_scaling_raw(weight_shape)
return Conv2d(in_channels, out_channels,
kernel_size=3, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
def conv1x1(in_channels, out_channels, stride=1, padding=0):
"""1x1 convolution"""
weight_shape = (out_channels, in_channels, 1, 1)
weight = variance_scaling_raw(weight_shape)
return Conv2d(in_channels, out_channels,
kernel_size=1, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
def conv7x7(in_channels, out_channels, stride=1, padding=0):
"""1x1 convolution"""
weight_shape = (out_channels, in_channels, 7, 7)
weight = variance_scaling_raw(weight_shape)
return Conv2d(in_channels, out_channels,
kernel_size=7, stride=stride, weight_init=weight, has_bias=False, pad_mode="same")
def bn_with_initialize(out_channels):
shape = (out_channels)
mean = weight_variable_0(shape)
var = weight_variable_1(shape)
beta = weight_variable_0(shape)
gamma = weight_variable_1(shape)
bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
return bn
def bn_with_initialize_last(out_channels):
shape = (out_channels)
mean = weight_variable_0(shape)
var = weight_variable_1(shape)
beta = weight_variable_0(shape)
gamma = weight_variable_0(shape)
bn = BatchNorm2d(out_channels, momentum=0.1, eps=0.0001, gamma_init=gamma,
beta_init=beta, moving_mean_init=mean, moving_var_init=var)
return bn
def fc_with_initialize(input_channels, out_channels):
weight_shape = (out_channels, input_channels)
bias_shape = (out_channels)
weight = random_normal_init(weight_shape)
bias = weight_variable_0(bias_shape)
return Dense(input_channels, out_channels, weight, bias)
class ResidualBlock(Cell):
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1,
down_sample=False):
super(ResidualBlock, self).__init__()
out_chls = out_channels // self.expansion
self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
self.bn1 = bn_with_initialize(out_chls)
self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
self.bn2 = bn_with_initialize(out_chls)
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
self.bn3 = bn_with_initialize_last(out_channels)
self.relu = P.ReLU()
self.add = TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.add(out, identity)
out = self.relu(out)
return out
class ResidualBlockWithDown(Cell):
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1,
down_sample=False):
super(ResidualBlockWithDown, self).__init__()
out_chls = out_channels // self.expansion
self.conv1 = conv1x1(in_channels, out_chls, stride=1, padding=0)
self.bn1 = bn_with_initialize(out_chls)
self.conv2 = conv3x3(out_chls, out_chls, stride=stride, padding=1)
self.bn2 = bn_with_initialize(out_chls)
self.conv3 = conv1x1(out_chls, out_channels, stride=1, padding=0)
self.bn3 = bn_with_initialize_last(out_channels)
self.relu = P.ReLU()
self.downSample = down_sample
self.conv_down_sample = conv1x1(
in_channels, out_channels, stride=stride, padding=0)
self.bn_down_sample = bn_with_initialize(out_channels)
self.add = TensorAdd()
def construct(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
identity = self.conv_down_sample(identity)
identity = self.bn_down_sample(identity)
out = self.add(out, identity)
out = self.relu(out)
return out
class MakeLayer0(Cell):
def __init__(self, block, layer_num, in_channels, out_channels, stride):
super(MakeLayer0, self).__init__()
self.a = ResidualBlockWithDown(
in_channels, out_channels, stride=1, down_sample=True)
self.b = block(out_channels, out_channels, stride=stride)
self.c = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
return x
class MakeLayer1(Cell):
def __init__(self, block, layer_num, in_channels, out_channels, stride):
super(MakeLayer1, self).__init__()
self.a = ResidualBlockWithDown(
in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
self.d = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
x = self.d(x)
return x
class MakeLayer2(Cell):
def __init__(self, block, layer_num, in_channels, out_channels, stride):
super(MakeLayer2, self).__init__()
self.a = ResidualBlockWithDown(
in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
self.d = block(out_channels, out_channels, stride=1)
self.e = block(out_channels, out_channels, stride=1)
self.f = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
x = self.d(x)
x = self.e(x)
x = self.f(x)
return x
class MakeLayer3(Cell):
def __init__(self, block, layer_num, in_channels, out_channels, stride):
super(MakeLayer3, self).__init__()
self.a = ResidualBlockWithDown(
in_channels, out_channels, stride=stride, down_sample=True)
self.b = block(out_channels, out_channels, stride=1)
self.c = block(out_channels, out_channels, stride=1)
def construct(self, x):
x = self.a(x)
x = self.b(x)
x = self.c(x)
return x
class ResNet(Cell):
def __init__(self, block, layer_num, num_classes=100):
super(ResNet, self).__init__()
self.conv1 = conv7x7(3, 64, stride=2, padding=3)
self.bn1 = bn_with_initialize(64)
self.relu = P.ReLU()
self.maxpool = MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
self.layer1 = MakeLayer0(
block, layer_num[0], in_channels=64, out_channels=256, stride=1)
self.layer2 = MakeLayer1(
block, layer_num[1], in_channels=256, out_channels=512, stride=2)
self.layer3 = MakeLayer2(
block, layer_num[2], in_channels=512, out_channels=1024, stride=2)
self.layer4 = MakeLayer3(
block, layer_num[3], in_channels=1024, out_channels=2048, stride=2)
self.pool = nn.AvgPool2d(7, 1)
self.fc = fc_with_initialize(512 * block.expansion, num_classes)
self.flatten = Flatten()
def construct(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.pool(x)
x = self.flatten(x)
x = self.fc(x)
return x
def resnet50(num_classes):
return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_trainTensor(num_classes=10, epoch=8, batch_size=1):
net = resnet50(num_classes)
lr = 0.1
momentum = 0.9
optimizer = Momentum(filter(lambda x: x.requires_grad,
net.get_parameters()), lr, momentum)
criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(
net_with_criterion, optimizer) # optimizer
train_network.set_train()
losses = []
for i in range(0, epoch):
data = Tensor(np.ones([batch_size, 3, 224, 224]
).astype(np.float32) * 0.01)
label = Tensor(np.ones([batch_size]).astype(np.int32))
loss = train_network(data, label)
losses.append(loss)
assert (losses[-1].asnumpy() < 1)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_trainTensor_big_batchSize(num_classes=10, epoch=8, batch_size=170):
net = resnet50(num_classes)
lr = 0.1
momentum = 0.9
optimizer = Momentum(filter(lambda x: x.requires_grad,
net.get_parameters()), lr, momentum)
criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
net_with_criterion = WithLossCell(net, criterion)
train_network = TrainOneStepCell(
net_with_criterion, optimizer) # optimizer
train_network.set_train()
losses = []
for i in range(0, epoch):
data = Tensor(np.ones([batch_size, 3, 224, 224]
).astype(np.float32) * 0.01)
label = Tensor(np.ones([batch_size]).astype(np.int32))
loss = train_network(data, label)
losses.append(loss)
assert (losses[-1].asnumpy() < 1)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_trainTensor_amp(num_classes=10, epoch=18, batch_size=16):
net = resnet50(num_classes)
lr = 0.1
momentum = 0.9
optimizer = Momentum(filter(lambda x: x.requires_grad,
net.get_parameters()), lr, momentum)
criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
train_network = amp.build_train_network(
net, optimizer, criterion, level="O2")
train_network.set_train()
losses = []
for i in range(0, epoch):
data = Tensor(np.ones([batch_size, 3, 224, 224]
).astype(np.float32) * 0.01)
label = Tensor(np.ones([batch_size]).astype(np.int32))
loss = train_network(data, label)
losses.append(loss)
assert (losses[-1][0].asnumpy() < 1)
assert (losses[-1][1].asnumpy() == False)
assert (losses[-1][2].asnumpy() > 1)
| python |
import pandas as pd
import time
#------------------------------------
#loading dataset
begin = time.time()
df = pd.read_csv("adult.data"
, names=["age", "workclass", "fnlwgt", "education", "education-num", "marital-status", "occupation", "relationship", "race", "sex", "capital-gain", "capital-loss", "hours-per-week", "native-country", "earning"])
print("dataset loaded in ",time.time()-begin," seconds")
#------------------------------------
rows = df.shape[0] - 1
columns = df.shape[1]
"""
#dataset summary
for i in range(0, columns):
if df[df.columns[i]].dtypes != "int64":
print(df.columns[i],": ",df[df.columns[i]].unique()," (",len(df[df.columns[i]].unique())," classes)")
else:
print(df.columns[i])
"""
#------------------------------------
f = open('one-hot-encoded.txt', 'w')
#dump header
header = ""
for i in range(0, columns):
if i == 0:
seperator = ""
else:
seperator = ","
if df[df.columns[i]].dtypes != "int64":
for k in range(0, len(df[df.columns[i]].unique())):
header += seperator + df[df.columns[i]].unique()[k]
else:
header += seperator + df.columns[i]
header += "\n"
#print(header)
f.write(header)
#------------------------------------
#iterate on rows
for index, row in df.iterrows():
new_line = ""
#iterate on columns
for i in range(0, columns):
if i == 0:
seperator = ""
else:
seperator = ","
column_name = df.columns[i]
if df[df.columns[i]].dtypes == "int64":
new_line = new_line + seperator + str(row[column_name])
else: #class
num_hot_encoded_classes = len(df[df.columns[i]].unique())
for k in range(0, num_hot_encoded_classes):
if df[df.columns[i]].unique()[k] == row[column_name]:
new_line = new_line + seperator + "1"
else:
new_line = new_line + seperator + "0"
new_line += "\n"
#print(new_line)
f.write(new_line)
#------------------------------------
f.close()
print("converted to one-hot-encoded dataset in ",time.time()-begin," seconds") | python |
#Author: Zhicheng Zhu
#Email: [email protected], [email protected]
#copyright @ 2018: Zhicheng Zhu. All right reserved.
#Info:
#main file to solve multi-stage DEF of CBM model by using linearization and solver
#
#Last update: 10/18/2018
#!/usr/bin/python
from __future__ import print_function
import sys
import cplex
import itertools
import time
from scipy.stats import gamma
#####################################
#class info
#####################################
class component_info():
def transProb(self, stateFrom, stateTo, inspItvl):
if stateFrom > stateTo:
return 0;
stepSize = self.failTsh/(self.nStates - 1); #step size for normal states
degFrom = stateFrom * stepSize; #degradation lower bound of the state
degToU = (stateTo + 1) * stepSize; #degradation upper bound of the state
degToL = stateTo * stepSize; #degradation lower bound of the state
if stateTo >= self.nStates - 1:
deltaDeg = self.failTsh - degFrom;
prob = 1 - gamma.cdf(deltaDeg, self.gammaAlpha*inspItvl, scale=self.gammaBeta);
else:
deltaDeg1 = degToU - degFrom;
prob1 = gamma.cdf(deltaDeg1, self.gammaAlpha*inspItvl, scale=self.gammaBeta);
deltaDeg2 = degToL - degFrom;
prob2 = gamma.cdf(deltaDeg2, self.gammaAlpha*inspItvl, scale=self.gammaBeta);
prob = prob1 - prob2;
return prob;
'''
def state2lv():
crtState = self.crtState;
bound = [];
bound.append(0);#put it here for now..
bound.append(1);
return bound;
'''
def __init__(self, idx, gam_a, gam_b, states, S, \
initState,cCM, cPM):
self.idx = idx;
self.gammaAlpha = gam_a;
self.gammaBeta = gam_b;
self.nStates = states; # 0 ... nStates - 1. nStates - 1 is failure states.
self.failTsh = S; #failure threshold
self.initState = initState;
#self.crtState = initState;
#self.crtDgLvRange = self.state2lv();
self.cCM = cCM;
self.cPM = cPM;
#system information
#parameters
class system_info():
def add_com(self, comInfo):
self.comInfoAll.append(comInfo);
def __init__(self, N, T, inspInterval, cS, cInsp):
self.nComponents = N;
self.nStages = T;
self.inspItvl = inspInterval;
self.cS = cS;
self.cInsp = cInsp;
self.comInfoAll = [];
def get_child_nodes(node, sysInfo):
#find/generate child nodes
#(t, 0), (t, 1), ..., (t, m**tn)
m = sysInfo.comInfoAll[0].nStates;
n = sysInfo.nComponents;
numOutcome = m**n;
start = node*numOutcome;
childNodes = list(range(start, start + numOutcome));
#we only return next stage nodes index.
return childNodes;
def node_2_outcome(node, sysInfo):
#translate a node to outcome:
m = sysInfo.comInfoAll[0].nStates;
n = sysInfo.nComponents;
numOutcome = m**n;
outcome = node % numOutcome;
return outcome;
#######################################
#1. initialization, START FROM HERE!!!.
#######################################
#init system parameter
start_time = time.clock();
#init system parameter
nComponents = 2;
nStates = 4; #number of states for components, 0 - (m-1);
nStages = 6;
initState = [3,2];
inspInterval = 10;
cS = 20; #setup cost
cInsp = 1;
sysInfo = system_info(nComponents, nStages, inspInterval, cS, cInsp);
nOrder = 0; #order of approximation
#init component parameter
#gamma distribution is assumed.
gam_a = [1]*nComponents;
gam_b = [5]*nComponents;
S = [60]*nComponents; #failure threshold
cCM = [20]*nComponents;
cPM = [5]*nComponents;
for i in range(nComponents):
comInfo = component_info(i, gam_a[i], gam_b[i], nStates,\
S[i], initState[i], cCM[i], cPM[i]);
sysInfo.add_com(comInfo);
########################################
#2. build multi-stage DEF model and run
########################################
start_time = time.clock();
#2.1
# (1) get scenario combinations
omega = [];
for i in itertools.product(list(range(nStates)), repeat = sysInfo.nComponents):
omega.append(list(i));
'''
#no set j when order = 0;
# (2) get subsets which cardinality = j
setS = []; #start from j = 2
for j in range(2, sysInfo.nComponents + 1): #sysInfo.nComponents >=2;
setSj = [];
for i in itertools.combinations(list(range(sysInfo.nComponents)), j):
setSj.append(list(i));
setS.append(setSj);
'''
# get coeA and coeB
# no coeA when order = 0
#coeA = []; #scen*n*scen
coeB = []; #scen*n*scen
#coeAInit = []; #n*scen, store init coeA
coeBInit = []; #n*scen, store init coeB
for w1 in range(len(omega)):
stateFrom = omega[w1];
#coeAW1 = [];
coeBW1 = [];
for i in range(sysInfo.nComponents):
biw = [];
#aiw = [];
for w2 in range(len(omega)):
comStatesTo = omega[w2];
comIFrom = stateFrom[i];
comITo = comStatesTo[i];
tmp = sysInfo.comInfoAll[i].transProb(comIFrom, comITo, sysInfo.inspItvl);
biw.append(tmp);
#aiw.append(sysInfo.comInfoAll[i].transProb(0, comITo, sysInfo.inspItvl) - tmp);
#coeAW1.append(aiw);
coeBW1.append(biw);
if stateFrom == initState:
#coeAInit = coeAW1;
coeBInit = coeBW1;
#coeA.append(coeAW1);
coeB.append(coeBW1);
cpx = cplex.Cplex(); #init solver
cpx.objective.set_sense(cpx.objective.sense.minimize);
#2.2 add decision variables
#add X
varX = [];
dictX = {};
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates ** (stageIdx * sysInfo.nComponents);
for node in range(nodeNum): #nodes in current stage
for i in range(sysInfo.nComponents):
scripts = str(i) + str(stageIdx) + str(node);
nameTmp = "x"+scripts;
dictX[scripts] = nameTmp;
varX.append(cpx.variables.get_num());
objCoe = 0;
if stageIdx == 0:
objCoe = sysInfo.comInfoAll[i].cPM;
cpx.variables.add(obj = [objCoe], lb = [0.0], ub=[1.0], types=["B"], names=[nameTmp]);
#add Y
varY = [];
dictY = {};
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates ** (stageIdx * sysInfo.nComponents);
for node in range(nodeNum): #nodes in current stage
for i in range(sysInfo.nComponents):
scripts = str(i)+str(stageIdx)+str(node);
nameTmp = "y" + scripts;
dictY[scripts] = nameTmp;
varY.append(cpx.variables.get_num());
objCoe = 0;
if stageIdx == 0:
objCoe = sysInfo.comInfoAll[i].cCM - sysInfo.comInfoAll[i].cPM;
cpx.variables.add(obj = [objCoe], lb = [0.0], ub=[1.0], types=["B"], names=[nameTmp]);
#add Z
varZ = [];
dictZ = {};
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates ** (stageIdx * sysInfo.nComponents);
for node in range(nodeNum): #nodes in current stage
scripts = str(stageIdx) + str(node);
nameTmp = "z" + scripts;
dictZ[scripts] = nameTmp;
varZ.append(cpx.variables.get_num());
objCoe = 0;
if stageIdx == 0:
objCoe = sysInfo.cS;
cpx.variables.add(obj = [objCoe], lb = [0.0], ub=[1.0], types=["B"], names=[nameTmp]);
#add Theta
varTheta = [];
dictTheta = {};
for stageIdx in range(1, nStages):
nodeNum = sysInfo.comInfoAll[0].nStates ** (stageIdx * sysInfo.nComponents);
for node in range(nodeNum): #nodes in current stage
coeTmp = 0;
if stageIdx == 1:
coeTmp = 1;
for i in range(sysInfo.nComponents):
coeTmp = coeTmp * coeBInit[i][node];
#print ("ThetacoeTmp=" + str(coeTmp));
scripts = str(stageIdx) + str(node);
nameTmp = "th" + scripts;
dictTheta[scripts] = nameTmp;
varTheta.append(cpx.variables.get_num());
cpx.variables.add(obj = [coeTmp], lb = [0.0], ub=[cplex.infinity], types=["C"], names=[nameTmp])
'''
#no V & W & U when order = 0
#add V
varV = [];
dictV= {};
for stageIdx in range(nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates ** (stageIdx * sysInfo.nComponents);
for curNode in range(nodeNum):
childNodes = get_child_nodes(curNode, sysInfo);
for chNode in childNodes:
for i in range(sysInfo.nComponents):
#v corresponds to cardinality set when cardinality j = 1.
if stageIdx != 0:
coeTmp = 0;
else:
coeTmp = coeAInit[i][chNode];
for r in range(sysInfo.nComponents):
if r != i:
coeTmp = coeTmp * coeBInit[r][chNode];
#print ("VcoeTmp=" + str(coeTmp));
scripts = str(i) + str(stageIdx) + str(curNode) + str(chNode);
nameTmp = "v" + scripts;
dictV[scripts] = nameTmp;
varV.append(cpx.variables.get_num());
#continuous variable
cpx.variables.add(obj = [coeTmp], lb = [0.0], ub=[cplex.infinity], types=["C"], names=[nameTmp]);
#add W
varW = [];
dictW = {};
for stageIdx in range(nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for curNode in range(nodeNum):
childNodes = get_child_nodes(curNode, sysInfo);
for chNode in childNodes:
for j in range(2, sysInfo.nComponents+1):
#cardinality starts from 2 to n.
setSj = setS[j-2];
for k in range(len(setSj)):
if stageIdx != 0:
coeTmp = 0;
else:
setSjk = setSj[k];
coeTmp = 1;
for i in range(sysInfo.nComponents):
if i in setSjk:
coeTmp = coeTmp*coeAInit[i][chNode];
else:
coeTmp = coeTmp*coeBInit[i][chNode];
#print ("WcoeTmp=" + str(coeTmp));
scripts = str(j) + str(k) + str(stageIdx) + str(curNode) + str(chNode);
nameTmp = "w" + scripts;
dictW[scripts] = nameTmp;
varW.append(cpx.variables.get_num());
#continuous variable
cpx.variables.add(obj = [coeTmp], lb = [0.0], ub=[cplex.infinity], types=["C"], names=[nameTmp]);
#add U: auxilary variable that used in w
varU = [];
dictU = {};
for stageIdx in range(nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum): #nodes in current stage
for j in range(2, sysInfo.nComponents+1):
#cardinality starts from 2 to n.
setSj = setS[j-2];
for k in range(len(setSj)):
scripts = str(j) + str(k) + str(stageIdx) + str(node);
nameTmp = "u" + scripts;
dictU[scripts] = nameTmp;
varU.append(cpx.variables.get_num());
cpx.variables.add(obj = [0], lb = [0.0], ub=[1.0], types=["B"], names=[nameTmp]);
'''
## 2.2 add constraints
# 1
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
coefNameZ = dictZ[str(stageIdx) + str(node)];
for i in range(sysInfo.nComponents):
coefNameX = dictX[str(i) + str(stageIdx) + str(node)];
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([coefNameX, coefNameZ], [1, -1])], senses=["L"], range_values=[0.0], rhs=[0]);
# 2 & 3
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
curOutcome = 0; #distinct outcome index.
for node in range(nodeNum):
coefValueVec = [];
coefNameVec = [];
if stageIdx == 0:
curStates = initState;
else:
curStates = omega[curOutcome];
curOutcome += 1;
if curOutcome == len(omega):
curOutcome = 0;
for i in range(sysInfo.nComponents):
# 2
curStatesI = curStates[i];
coefNameY = dictY[str(i) + str(stageIdx) + str(node)];
coefValueY = curStatesI;
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([coefNameY],[-coefValueY])], senses=["L"], range_values=[0.0], rhs=[sysInfo.comInfoAll[i].nStates-2-curStatesI]);
# 3
nameIdxScriptX = str(i) + str(stageIdx) + str(node);
coefNameX = dictX[nameIdxScriptX];
coefValueX = -1;
coefValueY = 1; #value changed here for 3rd constraint
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([coefNameY, coefNameX],[coefValueY, coefValueX])], senses=["L"], range_values=[0.0], rhs=[0.0]);
# 4: tooooo complex:
# in 4, theta starts from stage 1 to nStages - 2.
for stageIdx in range(1, nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
# do the first part
coefNameVec = [];
coefValueVec = [];
nameTmp = dictTheta[str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(-1);
for i in range(sysInfo.nComponents):
#add x
nameTmp = dictX[str(i) +str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.comInfoAll[i].cPM);
#add y
nameTmp = dictY[str(i) +str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.comInfoAll[i].cCM - sysInfo.comInfoAll[i].cPM);
#add z
nameTmp = dictZ[str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.cS);
#do the second part
childNodes = get_child_nodes(node, sysInfo);
for chNode in childNodes:
#within the second part...
#part 1
nameTmp = dictTheta[str(stageIdx+1) + str(chNode)];
stateFromIdx = node_2_outcome(node, sysInfo);
stateFrom = omega[stateFromIdx];
stateToIdx = node_2_outcome(chNode, sysInfo);
stateTo = omega[stateToIdx];
valueTmp = 1;
for i in range(sysInfo.nComponents):
valueTmp = valueTmp * coeB[stateFromIdx][i][stateToIdx];
if valueTmp == 0:
break; #make it faster;
coefNameVec.append(nameTmp);
coefValueVec.append(valueTmp);
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair(coefNameVec,coefValueVec)], senses=["E"], range_values=[0.0], rhs=[0.0]);
'''
# only have the constant term in zero-order approximation
#print (valueTmp);
#part 2
for i in range(sysInfo.nComponents):
nameTmp = dictV[str(i) + str(stageIdx) + str(node) + str(chNode)];
valueTmp = coeA[stateFromIdx][i][stateToIdx];
for r in range(sysInfo.nComponents):
if r != i:
valueTmp = valueTmp * coeB[stateFromIdx][r][stateToIdx];
if valueTmp == 0:
break; #make it faster
coefNameVec.append(nameTmp);
coefValueVec.append(valueTmp);
#part 3:
for j in range(2, sysInfo.nComponents + 1):
setSj = setS[j - 2]; #setS starts from 2
for k in range(len(setSj)):
nameTmp = dictW[str(j) + str(k) + str(stageIdx) + str(node) + str(chNode)];
valueTmp = 1;
setSjk = setSj[k];
for i in range(sysInfo.nComponents):
if i in setSjk:
valueTmp = valueTmp * coeA[stateFromIdx][i][stateToIdx];
else:
valueTmp = valueTmp * coeB[stateFromIdx][i][stateToIdx];
if valueTmp == 0:
break; #make it faster
coefNameVec.append(nameTmp);
coefValueVec.append(valueTmp);
#theta is stage * node
'''
# 5: theta at last stage
stageIdx = nStages - 1;
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
coefNameVec = [];
coefValueVec = [];
nameTmp = dictTheta[str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(-1);
for i in range(sysInfo.nComponents):
#add x
nameTmp = dictX[str(i) +str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.comInfoAll[i].cPM);
#add y
nameTmp = dictY[str(i) +str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.comInfoAll[i].cCM - sysInfo.comInfoAll[i].cPM);
#add z
nameTmp = dictZ[str(stageIdx) + str(node)];
coefNameVec.append(nameTmp);
coefValueVec.append(sysInfo.cS);
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair(coefNameVec,coefValueVec)], senses=["E"], range_values=[0.0], rhs=[0.0]);
'''
# 6: add linearization of V:
# There are 4 parts in this section:
upperM = 10000; #upper bound of theta
for stageIdx in range(0, nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
childNodes = get_child_nodes(node, sysInfo);
for i in range(sysInfo.nComponents):
nameTmpX = dictX[str(i) + str(stageIdx) + str(node)];
valueTmpX = -upperM;
for chNode in childNodes:
nameTmpV = dictV[str(i) + str(stageIdx) + str(node) + str(chNode)];
valueTmpV = 1;
# part 1
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpX, nameTmpV],[valueTmpX, valueTmpV])], senses=["L"], range_values=[0.0], rhs=[0.0]);
# part 2
nameTmpTheta = dictTheta[str(stageIdx + 1) + str(chNode)];
valueTmpTheta = -1;
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpTheta, nameTmpV],[valueTmpTheta, valueTmpV])], senses=["L"], range_values=[0.0], rhs=[0.0]);
#part 3
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpV, nameTmpTheta, nameTmpX],[valueTmpV, valueTmpTheta, valueTmpX])], senses=["G"], range_values=[0.0], rhs=[valueTmpX]);
# part 4 is added when adding variable V
# 7: add linearization of W:
# There are 4 parts of W
for stageIdx in range(0, nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
childNodes = get_child_nodes(node, sysInfo);
for chNode in childNodes:
for j in range(2, sysInfo.nComponents + 1):
setSj = setS[j - 2];
for k in range(len(setSj)):
nameTmpW = dictW[str(j) + str(k) + str(stageIdx) + str(node) + str(chNode)];
valueTmpW = 1;
nameTmpU = dictU[str(j) + str(k) + str(stageIdx) + str(node)];
valueTmpU = -upperM;
# part 1
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpW, nameTmpU],[valueTmpW, valueTmpU])], senses=["L"], range_values=[0.0], rhs=[0.0]);
# part 2
nameTmpTheta = dictTheta[str(stageIdx + 1) + str(chNode)];
valueTmpTheta = -1;
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpW, nameTmpTheta],[valueTmpW, valueTmpTheta])], senses=["L"], range_values=[0.0], rhs=[0.0]);
# part 3
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpW, nameTmpTheta, nameTmpU],[valueTmpW, valueTmpTheta, valueTmpU])], senses=["G"], range_values=[0.0], rhs=[valueTmpU]);
# part 4 is added when adding variable W
# 8: add linearization of U:
# There are 3 parts of U
for stageIdx in range(nStages - 1):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
for j in range(2, sysInfo.nComponents + 1):
setSj = setS[j - 2];
for k in range(len(setSj)):
setSjk = setSj[k];
nameTmpU = dictU[str(j) + str(k) + str(stageIdx) + str(node)];
valueTmpU = 1;
namePart2 = [];
valuePart2 = [];
namePart2.append(nameTmpU);
valuePart2.append(valueTmpU);
for i in setSjk:
nameTmpX = dictX[str(i) + str(stageIdx) + str(node)];
valueTmpX = -1;
#part 1:
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair([nameTmpU, nameTmpX],[valueTmpU, valueTmpX])], senses=["L"], range_values=[0.0], rhs=[0.0]);
#prepare for part 2:
namePart2.append(nameTmpX);
valuePart2.append(valueTmpX);
#part 2
cpx.linear_constraints.add(lin_expr=[cplex.SparsePair(namePart2, valuePart2)], senses=["G"], range_values=[0.0], rhs=[-j + 1]); # -(j - 1)
# part 3 is added when adding variable U
'''
########################################
#3. solve and result handling
########################################
end_time = time.clock();
time_elapsed0 = end_time - start_time;
start_time = time.clock();
cpx.solve();
solution = cpx.solution;
#obj value
objValues = solution.get_objective_value();
#get solutions
solutionAll = solution.get_values();
#get X
minTmp = varX[0];
maxTmp = varX[-1] + 1;
solutionX = solutionAll[minTmp:maxTmp];
#get Y
minTmp = varY[0];
maxTmp = varY[-1] + 1;
solutionY = solutionAll[minTmp:maxTmp];
#get Z
minTmp = varZ[0];
maxTmp = varZ[-1] + 1;
solutionZ = solutionAll[minTmp:maxTmp];
#get theta
minTmp = varTheta[0];
maxTmp = varTheta[-1] + 1;
solutionTheta = solutionAll[minTmp:maxTmp];
'''
#get V
minTmp = varV[0];
maxTmp = varV[-1] + 1;
solutionV = solutionAll[minTmp:maxTmp];
#get W
minTmp = varW[0];
maxTmp = varW[-1] + 1;
solutionW = solutionAll[minTmp:maxTmp];
#get U
minTmp = varU[0];
maxTmp = varU[-1] + 1;
solutionU = solutionAll[minTmp:maxTmp];
'''
end_time = time.clock();
time_elapsed = end_time - start_time;
f = open("log3.txt", "w");
old = sys.stdout;
sys.stdout = f;
print ("\n===============================main_multi_DEF_solver_0, (m, n, t)=(%d,%d,%d)============"
%(nStates, sysInfo.nComponents, nStages));
print ("loading time is %f" %time_elapsed0);
print ("calculation time is %f" %time_elapsed);
print ("objValues:");
print (objValues);
countX = 0;
countY = 0;
countZ = 0;
countV = 0;
countW = 0;
countU = 0;
countTheta = 0;
for stageIdx in range(nStages):
nodeNum = sysInfo.comInfoAll[0].nStates**(stageIdx*sysInfo.nComponents);
for node in range(nodeNum):
print ("=======(stage, scen) = (%d, %d)========" %(stageIdx,node));
#get X Y Z theta
solX = [];
solY = [];
solZ = solutionZ[countZ];
countZ += 1;
solTheta = [];
if stageIdx != 0:
solTheta = solutionTheta[countTheta];
countTheta += 1;
for i in range(sysInfo.nComponents):
solX.append(solutionX[countX]);
countX += 1;
solY.append(solutionY[countY]);
countY += 1;
print ("solutionX:");
print (solX);
print ("solutionY:");
print (solY);
print ("solutionZ:");
print (solZ);
print ("solutionTheta:");
print (solTheta);
'''
#get U
if stageIdx == nStages - 1: #last stage, no U V W
continue;
solU = [];
for j in range(2, sysInfo.nComponents + 1):
setSj = setS[j - 2];
for k in range(len(setSj)):
solU.append(solutionU[countU]);
countU += 1;
print ("solutionU:");
print (solU);
#get v and w
childNodes = get_child_nodes(node, sysInfo);
solV = [];
solW = [];
for chNode in childNodes:
#get V
solVTmp = [];
for i in range(sysInfo.nComponents):
solVTmp.append(solutionV[countV]);
countV += 1;
solV.append(solVTmp);
#get W
solWTmp = [];
for j in range(2, sysInfo.nComponents + 1):
setSj = setS[j - 2];
for k in range(len(setSj)):
solWTmp.append(solutionW[countW]);
countW += 1;
solW.append(solWTmp);
print ("solutionV:");
print (solV);
print ("solutionW:");
print (solW);
print ("===================\n");
'''
'''
print ("=======coeA======");
print (coeA);
print ("=======coeB======");
print (coeB);
print ("=======coeU======");
print (coeU);
print ("=======coeX======");
print (coeX);
print ("=======costTerm======");
print (consTerm);
'''
## 4. end of file
sys.stdout = old;
f.close();
| python |
"""
██████╗██╗██████╗ ██╗ ██╗███████╗██╗ ██╗
██╔════╝██║██╔══██╗██║ ██║██╔════╝╚██╗ ██╔╝
██║ ██║██████╔╝███████║█████╗ ╚████╔╝
██║ ██║██╔═══╝ ██╔══██║██╔══╝ ╚██╔╝
╚██████╗██║██║ ██║ ██║███████╗ ██║
© Brandon Skerritt
Github: brandonskerritt
"""
from copy import copy
from distutils import util
from typing import Optional, Dict, Union, Set, List
import re
from loguru import logger
import ciphey
import cipheycore
from ciphey.iface import ParamSpec, Cracker, CrackResult, T, CrackInfo, registry
from ciphey.common import fix_case
@registry.register
class Vigenere(ciphey.iface.Cracker[str]):
def getInfo(self, ctext: str) -> CrackInfo:
if self.keysize is not None:
analysis = self.cache.get_or_update(
ctext,
f"vigenere::{self.keysize}",
lambda: cipheycore.analyse_string(ctext.lower(), self.keysize, self.group),
)
val = cipheycore.vigenere_detect(analysis, self.expected)
logger.debug(f"Vigenere has likelihood {val}")
return CrackInfo(
success_likelihood=val,
# TODO: actually calculate runtimes
success_runtime=1e-3,
failure_runtime=1e-2,
)
likely_lens = self.cache.get_or_update(
ctext,
f"vigenere::likely_lens",
lambda: cipheycore.vigenere_likely_key_lens(ctext.lower(), self.expected, self.group, self.detect_p_value),
)
likely_lens_cpy = likely_lens
# Filter out the lens that make no sense
likely_lens = [i for i in likely_lens if i.len <= self.MAX_KEY_LENGTH]
for keysize in likely_lens:
# Store the analysis
analysis = self.cache.get_or_update(
ctext, f"vigenere::{keysize.len}", lambda: keysize.tab
)
if len(likely_lens) == 0:
return CrackInfo(
success_likelihood=0,
# TODO: actually calculate runtimes
success_runtime=2e-3,
failure_runtime=2e-2,
)
logger.debug(f"Vigenere has likelihood {likely_lens[0].p_value} with lens {[i.len for i in likely_lens]}")
return CrackInfo(
success_likelihood=likely_lens[0].p_value,
# TODO: actually calculate runtimes
success_runtime=2e-4,
failure_runtime=2e-4,
)
@staticmethod
def getTarget() -> str:
return "vigenere"
def crackOne(
self, ctext: str, analysis: cipheycore.windowed_analysis_res, real_ctext: str
) -> List[CrackResult]:
possible_keys = cipheycore.vigenere_crack(
analysis, self.expected, self.group, self.p_value
)
if len(possible_keys) > self.clamp:
possible_keys = possible_keys[:self.clamp]
logger.trace(
f"Vigenere crack got keys: {[[i for i in candidate.key] for candidate in possible_keys]}"
)
return [
CrackResult(
value=fix_case(cipheycore.vigenere_decrypt(ctext, candidate.key, self.group), real_ctext),
key_info="".join([self.group[i] for i in candidate.key]),
misc_info=f"p-value was {candidate.p_value}"
)
for candidate in possible_keys[: min(len(possible_keys), 10)]
]
def attemptCrack(self, ctext: str) -> List[CrackResult]:
logger.debug("Trying vigenere cipher")
# Convert it to lower case
if self.lower:
message = ctext.lower()
else:
message = ctext
# Analysis must be done here, where we know the case for the cache
if self.keysize is not None:
return self.crackOne(
message,
self.cache.get_or_update(
ctext,
f"vigenere::{self.keysize}",
lambda: cipheycore.analyse_string(message, self.keysize, self.group),
),
ctext
)
else:
arrs = []
likely_lens = self.cache.get_or_update(
ctext,
f"vigenere::likely_lens",
lambda: cipheycore.vigenere_likely_key_lens(message, self.expected, self.group),
)
possible_lens = [i for i in likely_lens]
possible_lens.sort(key=lambda i: i.p_value)
logger.trace(f"Got possible lengths {[i.len for i in likely_lens]}")
# TODO: work out length
for i in possible_lens:
arrs.extend(
self.crackOne(
message,
self.cache.get_or_update(
ctext,
f"vigenere::{i.len}",
lambda: cipheycore.analyse_string(message, i.len, self.group),
),
ctext
)
)
logger.debug(f"Vigenere returned {len(arrs)} candidates")
return arrs
@staticmethod
def getParams() -> Optional[Dict[str, ParamSpec]]:
return {
"expected": ciphey.iface.ParamSpec(
desc="The expected distribution of the plaintext",
req=False,
config_ref=["default_dist"],
),
"group": ciphey.iface.ParamSpec(
desc="An ordered sequence of chars that make up the caesar cipher alphabet",
req=False,
default="abcdefghijklmnopqrstuvwxyz",
),
"lower": ciphey.iface.ParamSpec(
desc="Whether or not the ciphertext should be converted to lowercase first",
req=False,
default=True,
),
"keysize": ciphey.iface.ParamSpec(
desc="A key size that should be used. If not given, will attempt to work it out",
req=False,
),
"p_value": ciphey.iface.ParamSpec(
desc="The p-value to use for windowed frequency analysis",
req=False,
default=0.5,
),
"detect_p_value": ciphey.iface.ParamSpec(
desc="The p-value to use for the detection of Vigenere length",
req=False,
default=0.01,
),
"clamp": ciphey.iface.ParamSpec(
desc="The maximum number of candidates that can be returned per key len",
req=False,
default=10,
),
}
def __init__(self, config: ciphey.iface.Config):
super().__init__(config)
self.lower: Union[str, bool] = self._params()["lower"]
if type(self.lower) != bool:
self.lower = util.strtobool(self.lower)
self.group = list(self._params()["group"])
self.expected = config.get_resource(self._params()["expected"])
self.cache = config.cache
self.keysize = self._params().get("keysize")
if self.keysize is not None:
self.keysize = int(self.keysize)
self.p_value = float(self._params()["p_value"])
self.detect_p_value = float(self._params()["detect_p_value"])
self.clamp = int(self._params()["clamp"])
self.MAX_KEY_LENGTH = 16
| python |
from django.contrib import admin
from .models import Coach, Comment
class CoachAdmin(admin.ModelAdmin):
list_display = (
'id',
'first_name',
'last_name',
'email',
'phone_number',
'image',
)
ordering = ('first_name',)
class CommentAdmin(admin.ModelAdmin):
list_display = (
'coach',
'stars',
'comment',
'author',
)
ordering = ('coach',)
admin.site.register(Coach, CoachAdmin)
admin.site.register(Comment, CommentAdmin)
| python |
# Generated by Django 3.0 on 2020-12-03 14:37
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('manager', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='activity',
name='amount',
field=models.PositiveIntegerField(),
),
]
| python |
from enum import Enum, IntEnum
from pathlib import Path
from typing import List, Literal, Optional, Union
from pydantic import BaseModel
import tomlkit # type: ignore (no stub)
from .iec_62056_protocol.obis_data_set import (
ObisFloatDataSet,
ObisId,
ObisIntegerDataSet,
ObisStringDataSet,
)
def load_default_configuration():
return PyPowerMeterMonitorConfig()
def load_configuration_from_file_path(config_file_path: Path):
if not config_file_path.is_file():
return load_default_configuration()
return load_configuration_from_text(config_file_path.read_text())
def load_configuration_from_text(config_file_text: str) -> "PyPowerMeterMonitorConfig":
return PyPowerMeterMonitorConfig.parse_obj(dict(tomlkit.parse(config_file_text)))
class LoggingLevel(IntEnum):
critical = 50
error = 40
warning = 30
info = 20
debug = 10
class LoggingConfig(BaseModel):
level: LoggingLevel = LoggingLevel.error
class SerialPortParity(Enum):
NONE = "N"
EVEN = "E"
ODD = "O"
MARK = "M"
SPACE = "S"
class SerialPortStopBits(Enum):
ONE = 1
ONE_POINT_FIVE = 1.5
TWO = 2
class SerialPortConfig(BaseModel):
port_url: str = "/dev/ttyUSB0"
baud_rate: int = 300
byte_size: int = 7
parity: SerialPortParity = SerialPortParity.EVEN
stop_bits: SerialPortStopBits = SerialPortStopBits.ONE
polling_delay: float = 30.0
response_delay: float = 0.3
read_timeout: float = 30.0
write_timeout: float = 10.0
class Config:
allow_mutation = False
class MqttBrokerConfig(BaseModel):
hostname: str = "localhost"
port: int = 1883
username: Optional[str] = None
password: Optional[str] = None
class MqttDeviceConfig(BaseModel):
id: str = "power-meter-0"
name: str = "Power Meter 0"
manufacturer: str = "Unknown Manufacturer"
model: str = "Unknown Model"
class MqttConfig(BaseModel):
enabled: bool = True
configuration_topic_template: str = "homeassistant/sensor/{entity_id}/config"
state_topic_template: str = "homeassistant/sensor/{entity_id}/state"
broker: MqttBrokerConfig = MqttBrokerConfig()
device: MqttDeviceConfig = MqttDeviceConfig()
class ObisBaseDataSetConfig(BaseModel):
id: ObisId
name: str
class ObisIntegerDataSetConfig(ObisBaseDataSetConfig):
value_type: Literal["integer"]
@property
def obis_data_set_type(self):
return ObisIntegerDataSet
class ObisFloatDataSetConfig(ObisBaseDataSetConfig):
value_type: Literal["float"]
@property
def obis_data_set_type(self):
return ObisFloatDataSet
class ObisStringDataSetConfig(ObisBaseDataSetConfig):
value_type: Literal["string"]
@property
def obis_data_set_type(self):
return ObisStringDataSet
ObisDataSetConfig = Union[
ObisIntegerDataSetConfig, ObisFloatDataSetConfig, ObisStringDataSetConfig
]
class ObisConfig(BaseModel):
data_sets: List[ObisDataSetConfig] = []
class PyPowerMeterMonitorConfig(BaseModel):
logging: LoggingConfig = LoggingConfig()
serial_port: SerialPortConfig = SerialPortConfig()
mqtt: MqttConfig = MqttConfig()
obis: ObisConfig = ObisConfig()
class Config:
allow_mutation = False
| python |
from telegram.ext import Dispatcher,CommandHandler,CallbackQueryHandler
from telegram import InlineKeyboardMarkup,InlineKeyboardButton, BotCommand
import random
def whoAreYou(update,context):
msg = [
"""You can call me Operation Lune 9000, I'm actually just a random reply AI(not really)""",
"""Bro, I'm Operation Lune 9000, I am an emotional AI with supercalifregeristicexpialidocious brain!""",
"""I'm gonna be your first personal AI, you can call me Operation Lune 9000!""",
"""I am a random Sentence AI Operation Lune 9000, you can ask me anything!""",
]
update.message.reply_text("Hey, it seems that you are understanding who I am, let me tell you more :D \n%s"%(random.choice(msg)))
def add_handler(dp:Dispatcher):
About_handler = CommandHandler('About', whoAreYou)
dp.add_handler(About_handler) | python |
import datetime, pytz
from dateutil.tz import tzlocal
log_dir = None
verbose = False
def log(message):
ts = pytz.utc.localize(datetime.datetime.now()).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
if verbose:
print(f'{ts} {message}')
if log_dir is not None:
print(f'{ts} {message}', file=open(log_dir, 'a'))
| python |
from WMCore.WMException import WMException
class WMSpecFactoryException(WMException):
"""
_WMSpecFactoryException_
This exception will be raised by validation functions if
the code fails validation. It will then be changed into
a proper HTTPError in the ReqMgr, with the message you enter
used as the message for farther up the line.
"""
pass
| python |
#! python3
from __future__ import print_function
import SimpleITK as sitk
import numpy as np
import sys
import os
#
def LocalFusionWithLocalSimilarity(targetImage, registeredAtlases, outputPath, debug):
"""" Fuses the labels from a set of registered atlases using local similarity metrics.
Arguments:
targetImage: image being segmented:
registeredAtlases: dictionary with a set of atlases having the fields intensityImage and labels
"""
# Generate a new image:
fusedLabels = sitk.Image(targetImage.GetSize(), sitk.sitkUInt8)
fusedLabels.SetSpacing(targetImage.GetSpacing())
fusedLabels.SetOrigin(targetImage.GetOrigin())
fusedLabels.SetDirection(targetImage.GetDirection())
# We need to evaluate the similarity between the target image and each atlas for each voxel.
# The atlas to be propagated depends on every voxel, so I need to go through them:
for i in range(0, targetImage.GetWidth()):
for j in range(0, targetImage.GetHeight()):
for k in range(0, targetImage.GetDepth()):
for atlas in registeredAtlases:
LocalNormalizedCrossCorrelation(targetImage, registeredAtlases[""], i, j, k)
return fusedLabels
def LocalNormalizedCrossCorrelation(image1, image2, r, c, z, kernelRadius):
lncc = 0
patchImage1 = image1[r-kernelRadius:r+kernelRadius, c-kernelRadius:c+kernelRadius, z-kernelRadius:z+kernelRadius]
patchImage2 = image2[r - kernelRadius:r + kernelRadius, c - kernelRadius:c + kernelRadius,
z - kernelRadius:z + kernelRadius]
lncc = np.cov(patchImage1, patchImage2)/(np.std(patchImage1)*np.std(patchImage2))
return lncc | python |
def create_adjacency_list(num_nodes, edges):
graph = [set() for _ in range(num_nodes)]
for index, edge in enumerate(edges):
v_1, v_2 = edge[0], edge[1]
graph[v_1].add(v_2)
graph[v_2].add(v_1)
return graph
| python |
"""
Copyright (c) 2015-2020 Raj Patel([email protected]), StopStalk
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
from gluon import current
kind_mapping = {
"just_count": ["total"],
"success_failure": ["success", "failure"],
"average": ["list"]
}
# ==============================================================================
def get_redis_int_value(key_name):
value = current.REDIS_CLIENT.get(key_name)
return 0 if value is None else int(value)
# ==============================================================================
class MetricHandler(object):
# --------------------------------------------------------------------------
def __init__(self, genre, kind, site, log_to_redis):
"""
Constructor for a specific MetricHandler
@param genre (String): Metric identifier
@param kind (String): Metric type ("just_count" or "success_failure")
@param site (String): Metric handler is for which site
@param log_to_redis (Boolean): If need to add it to redis
"""
self.redis_client = current.REDIS_CLIENT
# Kind of tracking that we need to do
self.genre = genre
# The label to print in the health report
self.label = " ".join([x.capitalize() for x in self.genre.split("_")])
# Just count or percentage
self.kind = kind
# Submission site
self.site = site
# If there metrics need to be persisted in redis
self.log_to_redis = log_to_redis
# The redis keys which will be used
self.redis_keys = {}
for type_of_key in kind_mapping[self.kind]:
self.redis_keys[type_of_key] = "health_metrics:%s__%s__%s" % (self.genre,
self.site,
type_of_key)
# --------------------------------------------------------------------------
def flush_keys(self):
"""
Remove all the keys for this MetricHandler from redis
"""
if self.log_to_redis is False:
return
[self.redis_client.delete(key) for key in self.redis_keys.values()]
# --------------------------------------------------------------------------
def increment_count(self, type_of_key, increment_amount=1):
"""
Increment count of a metric given success key or failure key
@param type_of_key (String): "success" or "failure"
@param increment_amount (Number): Amount by which the redis key
should be incremented
"""
if self.log_to_redis is False:
return
redis_key = self.redis_keys[type_of_key]
value = self.redis_client.get(redis_key)
if value is None:
value = 0
else:
value = int(value)
self.redis_client.set(redis_key, value + increment_amount)
# --------------------------------------------------------------------------
def add_to_list(self, type_of_key, value):
"""
Add a value to the list for computing average later
@param value (Decimal): A decimal to be added to the list
@param type_of_key (String): At present just "list"
"""
if self.log_to_redis is False:
return
self.redis_client.lpush(self.redis_keys[type_of_key], value)
# --------------------------------------------------------------------------
def _get_average_string(self):
all_values = self.redis_client.lrange(self.redis_keys["list"], 0, -1)
return_str = None
if len(all_values):
all_values = [float(x) for x in all_values]
average = sum(all_values) * 1.0 / len(all_values)
return_str = str(average)
else:
return_str = "-"
return return_str
# --------------------------------------------------------------------------
def get_html(self):
html_body = "<tr><td style='background-color: lavender;'><b>%s</b></td>" % self.label
if self.kind == "just_count":
html_body += "<td colspan='3'>Total: %d</td>" % get_redis_int_value(self.redis_keys["total"])
elif self.kind == "success_failure":
success = get_redis_int_value(self.redis_keys["success"])
failure = get_redis_int_value(self.redis_keys["failure"])
if failure > 0:
failure_percentage = str(failure * 100.0 / (failure + success))
else:
failure_percentage = "-"
html_body += """
<td>Success: %d</td><td>Failure: %d</td><td>Failure per: %s</td>
""" % (success,
failure,
failure_percentage)
elif self.kind == "average":
html_body += "<td colspan='3'>Average: %s</td>" % self._get_average_string()
else:
html_body += "<td colspane='3'>Unknown kind</td>"
html_body += "</tr>"
return html_body
# --------------------------------------------------------------------------
def __str__(self):
"""
Representation of the MetricHandler
"""
return_str = self.label + ": "
if self.kind == "just_count":
return_str += str(get_redis_int_value(self.redis_keys["total"]))
elif self.kind == "success_failure":
return_str += str(get_redis_int_value(self.redis_keys["success"])) + " " + \
str(get_redis_int_value(self.redis_keys["failure"]))
elif self.kind == "average":
return_str += self._get_average_string()
return return_str
| python |
import glob
import pandas as pd
from pathlib import Path
import re
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
import os
def transformCordinates(coordinates, wmax, hmax):
maxis = coordinates[0]
minaxis = coordinates[1]
angle = coordinates[2]
xcoor = coordinates[3]
ycoor = coordinates[4]
maxis = float(maxis)
minaxis = float(minaxis)
angle = float(angle)
xcoor = float(xcoor)
ycoor = float(ycoor)
w = 2*(np.sqrt((maxis*np.cos(angle))**2 + (minaxis*np.sin(angle))**2))
h = 2*(np.sqrt((maxis*np.sin(angle))**2 + (minaxis*np.cos(angle))**2))
xmax = xcoor-w/2
ymax = ycoor-h/2
return(xmax,ymax,w,h)
def generateArray(file):
with open(file, "r") as f:
arr = f.read().splitlines()
arr_len = len(arr)
i = 0
rg = re.compile("(\d)*_(\d)*_(\d)*_big")
arr_temp = []
while i != arr_len:
val = arr[i]
mtch = rg.match(val)
if mtch:
try:
my_dict = dict()
val = "{}.jpg".format(val)
my_dict["name"] = val
#matplotlib
img = mpimg.imread(os.path.join("dataset", val))
fig, ax = plt.subplots(1)
ax.imshow(img)
(h, w, _) = img.shape
s = int(arr[i+1])
for j in range(0, s):
coord = arr[i + 2 + j]
trans = transformCordinates(coord.split(" "),h,w)
# print(trans)
#print(trans)
newf = patches.Rectangle(
(trans[0], trans[1]), trans[2], trans[3],
linewidth=1,
edgecolor = 'b',
facecolor ='none')
ax.add_patch(newf)
plt.show()
my_dict["annotations"] = arr_temp
i = i+1+s
except:
print("{}not found...".format(val))
i+=1
else:
i+=1
def returnEllipseListFiles(path):
return [ str(f) for f in Path(path).glob("**/*-ellipseList.txt") ]
folder = glob.glob("dataset/*.jpg")
folder = pd.Series(folder)
files = returnEllipseListFiles("labels")
print(folder)
print(files)
d = generateArray(files[0])
print(d)
| python |
"""
Setup to install the 'factorymind' Python package
"""
import os
from setuptools import find_packages, setup
def read(file_name: str):
"""Utility function to read the README file.
Used for the long_description. It's nice, because now
1) we have a top level README file and
2) it's easier to type in the README file than to put a raw
string in belows 'setup()' config
Args:
file_name (str): Path to file
"""
return open(os.path.join(os.path.dirname(__file__), file_name)).read()
install_requires = ["numpy", "pandas", "pytest"]
setup_requirements = ["pytest-runner", "better-setuptools-git-version"]
test_requirements = ["pytest", "nbformat"]
setup(
author="FactoryMind AS",
author_email="[email protected]",
classifiers=[
"Development Status :: 3 - Alpha",
"Topic :: Utilities",
"License :: OSI Approved :: MIT License",
],
name="factorymind",
version="0.1.3",
# version_config={"version_format": "{tag}.dev{sha}", "starting_version": "0.0.1"},
description="Python module `factorymind` for the FactoryMind platform",
long_description=open("README.md").read(),
packages=find_packages("src"),
package_dir={"": "src"},
setup_requires=setup_requirements,
test_suite="tests",
tests_require=test_requirements,
install_requires=install_requires,
)
| python |
from dataclasses import dataclass, field
from enum import Enum
from typing import Optional
__NAMESPACE__ = "NISTSchema-SV-IV-list-negativeInteger-enumeration-1-NS"
class NistschemaSvIvListNegativeIntegerEnumeration1Type(Enum):
VALUE_17702143_68213_73070785813457_55650_85440493680_6799621_74925_12_72537592001056039 = (
-17702143,
-68213,
-73070785813457,
-55650,
-85440493680,
-6799621,
-74925,
-12,
-72537592001056039,
)
VALUE_26245_7189050820_38959743015554837_343346_3844467_100883_9141710_7583 = (
-26245,
-7189050820,
-38959743015554837,
-343346,
-3844467,
-100883,
-9141710,
-7583,
)
VALUE_98937535565323_54852263_56348773_97523843296749_777_588340914_5277957_838038027052 = (
-98937535565323,
-54852263,
-56348773,
-97523843296749,
-777,
-588340914,
-5277957,
-838038027052,
)
VALUE_81203437_48_202_57278_5095_786160081_93919465439172544_975282546950578033 = (
-81203437,
-48,
-202,
-57278,
-5095,
-786160081,
-93919465439172544,
-975282546950578033,
)
VALUE_208126785236_890121210854_63897214775493060_6698254859648_491278952624_90261_93114747005637_462457_91376823432390_68 = (
-208126785236,
-890121210854,
-63897214775493060,
-6698254859648,
-491278952624,
-90261,
-93114747005637,
-462457,
-91376823432390,
-68,
)
VALUE_355_80669246608_1445178596306_679353181481903_49652061562_533421508 = (
-355,
-80669246608,
-1445178596306,
-679353181481903,
-49652061562,
-533421508,
)
VALUE_58848_29022908056015_35829309187105862_25293146353_75728153211129700_70406362_42467387928552_2736381_8869532336 = (
-58848,
-29022908056015,
-35829309187105862,
-25293146353,
-75728153211129700,
-70406362,
-42467387928552,
-2736381,
-8869532336,
)
VALUE_4044_23904266024445_16124907064250493_345_668380045472_7602241 = (
-4044,
-23904266024445,
-16124907064250493,
-345,
-668380045472,
-7602241,
)
VALUE_83357543849_27799953103921681_47075936_933435736058_81852_85553_37083595_212426303157_64 = (
-83357543849,
-27799953103921681,
-47075936,
-933435736058,
-81852,
-85553,
-37083595,
-212426303157,
-64,
)
@dataclass
class NistschemaSvIvListNegativeIntegerEnumeration1:
class Meta:
name = "NISTSchema-SV-IV-list-negativeInteger-enumeration-1"
namespace = "NISTSchema-SV-IV-list-negativeInteger-enumeration-1-NS"
value: Optional[NistschemaSvIvListNegativeIntegerEnumeration1Type] = field(
default=None,
metadata={
"required": True,
}
)
| python |
"""
The go starter template.
Author: Tom Fleet
Created: 24/06/2021
"""
import shutil
import subprocess
from pathlib import Path
from typing import List, Optional
from pytoil.exceptions import GoNotInstalledError
from pytoil.starters.base import BaseStarter
class GoStarter(BaseStarter):
"""
The go starter template class.
"""
def __init__(self, path: Path, name: str) -> None:
"""
The pytoil go starter template.
Args:
path (Path): Root path under which to generate the
project from this template.
name (str): The name of the project to be created.
"""
self._path = path
self._name = name
self._files = ["README.md", "main.go"]
def __repr__(self) -> str:
return self.__class__.__qualname__ + f"(path={self.path!r}, name={self.name!r})"
@property
def path(self) -> Path:
return self._path
@property
def name(self) -> str:
return self._name
@property
def root(self) -> Path:
return self._path.joinpath(self._name)
@property
def files(self) -> List[Path]:
return [self.root.joinpath(filename) for filename in self._files]
def raise_for_go(self) -> None:
"""
Raises an error if the user doesn't have go installed.
"""
if not bool(shutil.which("go")):
raise GoNotInstalledError("Go not found on $PATH.")
def generate(self, username: Optional[str] = None) -> None:
"""
Generate a new go starter template.
This is a mix of creating files in python, and invoking
`go mod init` in a subprocess to initialise the go
modules file.
"""
# Must have go installed to run go mod init
self.raise_for_go()
# Make the parent directory
self.root.mkdir(parents=True)
for file in self.files:
file.touch()
# Put the header in the readme
readme = self.root.joinpath("README.md")
readme.write_text(f"# {self.name}\n", encoding="utf-8")
# Populate the main.go file
go_file = self.root.joinpath("main.go")
go_text = 'package main\n\nimport "fmt"\n\nfunc main() {\n\tfmt.Println("Hello World")\n}\n' # noqa: E501
go_file.write_text(go_text, encoding="utf-8")
# Invoke go mod init
_ = subprocess.run(
["go", "mod", "init", f"github.com/{username}/{self.name}"],
check=True,
cwd=self.root,
capture_output=True,
)
| python |
# Generated by Django 3.0.7 on 2020-10-30 16:41
from django.db import migrations
import inclusive_django_range_fields.fields
class Migration(migrations.Migration):
dependencies = [
('jobsapp', '0011_auto_20201030_1636'),
]
operations = [
migrations.AddField(
model_name='job',
name='salary',
field=inclusive_django_range_fields.fields.InclusiveIntegerRangeField(help_text='Minimum and maximum annual salary for this job.', null=True, verbose_name='Salary'),
),
]
| python |
from operator import eq, ge
from functools import partial
import pandas as pd
from microsetta_public_api.resources import resources
ops = {
'equal': eq,
'greater_or_equal': ge,
}
conditions = {
"AND": partial(pd.DataFrame.all, axis=1),
"OR": partial(pd.DataFrame.any, axis=1)
}
def _is_rule(node):
rule_fields = ["id", "operator", "value"]
for field in rule_fields:
if field not in node:
return False
op = node["operator"]
if op not in ops:
raise ValueError(f"Only operators in {ops} are supported. "
f"Got {op}")
return True
class MetadataRepo:
def __init__(self, metadata=None):
if metadata is not None:
self._metadata = metadata
else:
self._metadata = resources.get('metadata', pd.DataFrame())
@property
def metadata(self):
return self._metadata
@property
def categories(self):
return list(self._metadata.columns)
@property
def samples(self):
return list(self._metadata.index)
def category_values(self, category, exclude_na=True):
"""
Parameters
----------
category : str
Metadata category to return the values of
exclude_na : bool
If True, not a number (na) values will be dropped from the
category values
Returns
-------
list
Contains the unique values in the metadata category
Raises
------
ValueError
If `category` is not an existing category in the metadata
"""
if category not in self._metadata.columns:
raise ValueError(f'No category with name `{category}`')
category_values = self._metadata[category].unique()
if exclude_na:
category_values = category_values[~pd.isnull(category_values)]
return list(category_values)
def has_category(self, category):
if isinstance(category, str):
return category in self._metadata.columns
else:
cols = set(self._metadata.columns)
return [cat in cols for cat in category]
def has_sample_id(self, sample_id):
if isinstance(sample_id, str):
return sample_id in self._metadata.index
else:
index = set(self._metadata.index)
return [id_ in index for id_ in sample_id]
def get_metadata(self, categories, sample_ids=None, fillna=None):
md = self._metadata[categories]
if sample_ids is not None:
md = md.reindex(sample_ids, fill_value=None)
md = md.astype('object')
md[pd.isna(md)] = fillna
return md
def sample_id_matches(self, query):
"""
Parameters
----------
query : dict
Expects a jquerybuilder formatted query
Returns
-------
list
The sample IDs that match the given `query`
"""
slice_ = self._process_query(query)
return list(self._metadata.index[slice_])
def _process_query(self, query):
group_fields = ["condition", "rules"]
if _is_rule(query):
category, op, value = query['id'], query['operator'], \
query['value']
return ops[op](self._metadata[category], value)
else:
for field in group_fields:
if field not in query:
raise ValueError(f"query=`{query}` does not appear to be "
f"a rule or a group.")
if query['condition'] not in conditions:
raise ValueError(f"Only conditions in {conditions} are "
f"supported. Got {query['condition']}.")
else:
condition = conditions[query['condition']]
return condition(self._safe_concat([self._process_query(rule) for
rule in query['rules']],
axis=1))
def _safe_concat(self, list_of_df, **concat_kwargs):
if len(list_of_df) > 0:
return pd.concat(list_of_df, **concat_kwargs)
return pd.DataFrame(pd.Series(True, index=self._metadata.index))
| python |
# Copyright (C) 2019 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Auto-generated file for MCP4725 v0.1.0.
# Generated from peripherals/MCP4725.yaml using Cyanobyte Codegen v0.1.0
from i2cdevice import Device, Register, BitField
I2C_ADDR = 98
EEPROM = Register('EEPROM', 96, fields=(
BitField('digitalOut', 0b0001111111111111, bitwidth=13, values_in=_byte_swap, values_out=_byte_swap, values_map={
GND: 0,
VCC: 4095
})
), read_only=False, bitwidth=12)
VOUT = Register('VOUT', 64, read_only=False, bitwidth=12)
mcp4725 = Device(I2C_ADDR, registers=(
EEPROM,
VOUT
)) | python |
from unittest.mock import patch
import pytest
from telegram.ext import CommandHandler
from autonomia.features import dublin_bike
@pytest.mark.vcr
def test_cmd_dublin_bike(update, context):
with patch.object(update.message, "reply_text") as m:
context.args = ["89"]
dublin_bike.cmd_dublin_bike(update, context)
m.assert_called_with(
"Dublin bike station 89:\n"
" Bikes 4\n"
" Free spaces 36\n"
" Location FITZWILLIAM SQUARE EAST\n"
)
def test_get_bike_station_info_with_invalid_station():
msg = dublin_bike._get_bike_station_info("80000")
assert msg == "deu merda!"
def test_cmd_dublin_bike_without_bike_stop(update, context):
with patch.object(update.message, "reply_text") as m:
context.args = []
dublin_bike.cmd_dublin_bike(update, context)
m.assert_called_with("Use: /bike <bike station number>")
@patch("urllib.request.urlopen")
def test_cmd_dublin_bike_on_error(urlopen_mock, update, context):
urlopen_mock.site_effect = ValueError()
with patch.object(update.message, "reply_text") as m:
context.args = ["200"]
dublin_bike.cmd_dublin_bike(update, context)
m.assert_called_with("Oops deu merda!")
def test_dublin_bike_factory():
handler = dublin_bike.dublin_bike_factory()
assert isinstance(handler, CommandHandler)
assert handler.callback == dublin_bike.cmd_dublin_bike
assert handler.command == ["bike"]
assert handler.pass_args
| python |
# All content Copyright (C) 2018 Genomics plc
from wecall.bamutils.read_sequence import HIGH_QUALITY
from wecall.bamutils.sequence_builder import sequence_builder
class SequenceBank(object):
"""
A container to hold annotated DNA sequences in relation to a reference sequence.
"""
def __init__(self, reference):
self.reference = reference
self._read_sequences_with_coverage = []
def __getitem__(self, item):
return self._read_sequences_with_coverage[item]
def __len__(self):
return len(self._read_sequences_with_coverage)
@property
def chrom(self):
return self.reference.chrom
@property
def variants(self):
variants = set()
for sequence in self._read_sequences_with_coverage:
variants.update(sequence.read_sequence.variants)
return variants
def add_sequence(
self,
seq_string,
quality_string=None,
n_fwd=None,
n_rev=None,
mapping_quality=HIGH_QUALITY,
insert_size=None,
read_id=None,
read_flags=None,
cigar_string=None,
read_start=None,
read_mate_start=None
):
self._read_sequences_with_coverage.extend(
sequence_builder(
self.reference,
seq_string,
quality_string,
n_fwd,
n_rev,
mapping_quality,
insert_size,
read_id,
read_flags,
cigar_string,
read_start,
read_mate_start
)
)
return self
def build_reads(self, chrom_id, read_tags):
for read_seq_with_coverage in self._read_sequences_with_coverage:
for read in read_seq_with_coverage.build_reads(
chrom_id, read_tags):
yield read
class AsciiVariantGenerator(object):
def __init__(self, reference):
self.reference = reference
def get_variants(self, ascii_haplotypes):
seq_bank = SequenceBank(self.reference)
for candidate_ascii_haplotype in ascii_haplotypes:
seq_bank.add_sequence(candidate_ascii_haplotype)
return seq_bank.variants
| python |
#
# PySNMP MIB module ZHONE-GEN-INTERFACE-CONFIG-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ZHONE-GEN-INTERFACE-CONFIG-MIB
# Produced by pysmi-0.3.4 at Wed May 1 15:47:34 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueSizeConstraint, ConstraintsUnion, ValueRangeConstraint, SingleValueConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "ConstraintsUnion", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsIntersection")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
ModuleIdentity, iso, Bits, Counter32, NotificationType, Integer32, Unsigned32, TimeTicks, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, Counter64, ObjectIdentity, Gauge32, IpAddress = mibBuilder.importSymbols("SNMPv2-SMI", "ModuleIdentity", "iso", "Bits", "Counter32", "NotificationType", "Integer32", "Unsigned32", "TimeTicks", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter64", "ObjectIdentity", "Gauge32", "IpAddress")
DisplayString, TruthValue, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TruthValue", "TextualConvention")
zhoneModules, zhoneInterfaceConfig = mibBuilder.importSymbols("Zhone", "zhoneModules", "zhoneInterfaceConfig")
ZhoneAlarmSeverity, ZhoneRowStatus = mibBuilder.importSymbols("Zhone-TC", "ZhoneAlarmSeverity", "ZhoneRowStatus")
alarmConfigMib = ModuleIdentity((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1))
alarmConfigMib.setRevisions(('2010-12-07 02:37', '2008-02-26 06:25',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: alarmConfigMib.setRevisionsDescriptions(('V01.00.02 - Added alarmSeverity', 'V01.00.01 - adding alarmConfigTraps',))
if mibBuilder.loadTexts: alarmConfigMib.setLastUpdated('201012071714Z')
if mibBuilder.loadTexts: alarmConfigMib.setOrganization('Organization.')
if mibBuilder.loadTexts: alarmConfigMib.setContactInfo('Contact-info.')
if mibBuilder.loadTexts: alarmConfigMib.setDescription('Description.')
alarmConfigTable = MibTable((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1), )
if mibBuilder.loadTexts: alarmConfigTable.setStatus('current')
if mibBuilder.loadTexts: alarmConfigTable.setDescription('The alarm configuration table')
alarmConfigEntry = MibTableRow((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: alarmConfigEntry.setStatus('current')
if mibBuilder.loadTexts: alarmConfigEntry.setDescription('An entry in the alarm configuration table.')
alarmConfigBitRateThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 1), TruthValue()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigBitRateThreshold.setStatus('current')
if mibBuilder.loadTexts: alarmConfigBitRateThreshold.setDescription('This field describes the enable status of the Bit Rate Threshold Alarm. If this field is true (1) then the Bit Rate Threshold alarm is enabled. If this field is false (2) then the Bit Rate Threshold Alarm is disabled.')
alarmConfigBitRateThresholdValue = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 2), Integer32()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigBitRateThresholdValue.setStatus('current')
if mibBuilder.loadTexts: alarmConfigBitRateThresholdValue.setDescription('This field indicates the Bit Rate Threshold Value which will generate an alarm if the Bit Rate Threshold Alarm is enabled and the Bit Rate of this ifIndex drops below this value.')
alarmConfigBitRateThresholdHoldtime = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 3), Integer32()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigBitRateThresholdHoldtime.setStatus('current')
if mibBuilder.loadTexts: alarmConfigBitRateThresholdHoldtime.setDescription('This field indicates the Bit Rate Threshold Holdtime in seconds for which the Bit Rate of the ifIndex must remain below the Bit Rate Threshold Value before an alarm will be generated if the Bit Rate Threshold Alarm is enabled. ')
alarmConfigStatusTrap = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 4), TruthValue()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigStatusTrap.setStatus('current')
if mibBuilder.loadTexts: alarmConfigStatusTrap.setDescription('This field describes the enable status of the Status Trap Alarm. If this field is true (1) then the Status Trap alarm is enabled. If this field is false (2) then the Status Trap Alarm is disabled.')
alarmConfigAdminUp = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 5), TruthValue()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigAdminUp.setStatus('current')
if mibBuilder.loadTexts: alarmConfigAdminUp.setDescription('This field describes the enable status of the Admin Up Alarm. If this field is true (1) then the Admin Up alarm is enabled. If this field is false (2) then the Admin Up Alarm is disabled.')
alarmConfigAlarmSeverity = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 6), ZhoneAlarmSeverity()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigAlarmSeverity.setStatus('current')
if mibBuilder.loadTexts: alarmConfigAlarmSeverity.setDescription("This object is used to override Trap, Central Alarm Manager and CLI 'LineAlarm' severity levels for the specified interface. ")
alarmConfigRowStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 1, 1, 7), ZhoneRowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: alarmConfigRowStatus.setStatus('current')
if mibBuilder.loadTexts: alarmConfigRowStatus.setDescription('This object is used to create, delete or modify a row in this table. ')
alarmConfigTraps = MibIdentifier((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 2))
alarmConfigTrapPrefix = ObjectIdentity((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 2, 0))
if mibBuilder.loadTexts: alarmConfigTrapPrefix.setStatus('current')
if mibBuilder.loadTexts: alarmConfigTrapPrefix.setDescription('Description.')
zhoneAlarmConfigThresholdTrap = NotificationType((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 2, 0, 1))
if mibBuilder.loadTexts: zhoneAlarmConfigThresholdTrap.setStatus('current')
if mibBuilder.loadTexts: zhoneAlarmConfigThresholdTrap.setDescription('This Trap is generated when this ifIndex bandwidth drops below the alarmConfigBitRateThresholdValue for the time specified in alarmConfigBitRateThesholdHoldtime.')
zhoneAlarmConfigThresholdClearTrap = NotificationType((1, 3, 6, 1, 4, 1, 5504, 3, 13, 1, 2, 0, 2))
if mibBuilder.loadTexts: zhoneAlarmConfigThresholdClearTrap.setStatus('current')
if mibBuilder.loadTexts: zhoneAlarmConfigThresholdClearTrap.setDescription('This trap is generated when this ifIndex bandwidth goes above the alarmConfigBitRateThresholdValue for the time specified in alarmConfigBitRateThresholdHoldtime.')
mibBuilder.exportSymbols("ZHONE-GEN-INTERFACE-CONFIG-MIB", alarmConfigRowStatus=alarmConfigRowStatus, alarmConfigMib=alarmConfigMib, zhoneAlarmConfigThresholdTrap=zhoneAlarmConfigThresholdTrap, alarmConfigBitRateThresholdHoldtime=alarmConfigBitRateThresholdHoldtime, alarmConfigStatusTrap=alarmConfigStatusTrap, alarmConfigAdminUp=alarmConfigAdminUp, alarmConfigBitRateThreshold=alarmConfigBitRateThreshold, alarmConfigEntry=alarmConfigEntry, alarmConfigBitRateThresholdValue=alarmConfigBitRateThresholdValue, alarmConfigTraps=alarmConfigTraps, alarmConfigAlarmSeverity=alarmConfigAlarmSeverity, alarmConfigTrapPrefix=alarmConfigTrapPrefix, PYSNMP_MODULE_ID=alarmConfigMib, zhoneAlarmConfigThresholdClearTrap=zhoneAlarmConfigThresholdClearTrap, alarmConfigTable=alarmConfigTable)
| python |
from typing import Any
from rpg.items import Equippable
class Armor(Equippable):
config_filename = "armor.yaml"
__slots__ = ("type",)
def __init__(self, **kwargs: Any):
self.type: str = kwargs.pop("type")
super().__init__(**kwargs)
def __repr__(self) -> str:
return f"<{self.__class__.__name__} id={self.id} name={self.name} type={self.type} modifiers={self.modifiers}>"
| python |
from .dual_network import DualNetBounds, robust_loss, robust_loss_parallel, DualNetwork
from .dual_layers import DualLinear, DualReLU
from .dual_inputs import select_input, InfBallBoxBounds
from .utils import DenseSequential, Dense, epsilon_from_model | python |
import a1 #$ use=moduleImport("a1")
x = a1.blah1 #$ use=moduleImport("a1").getMember("blah1")
import a2 as m2 #$ use=moduleImport("a2")
x2 = m2.blah2 #$ use=moduleImport("a2").getMember("blah2")
import a3.b3 as m3 #$ use=moduleImport("a3").getMember("b3")
x3 = m3.blah3 #$ use=moduleImport("a3").getMember("b3").getMember("blah3")
from a4.b4 import c4 as m4 #$ use=moduleImport("a4").getMember("b4").getMember("c4")
x4 = m4.blah4 #$ use=moduleImport("a4").getMember("b4").getMember("c4").getMember("blah4")
import a.b.c.d #$ use=moduleImport("a")
ab = a.b #$ use=moduleImport("a").getMember("b")
abc = ab.c #$ use=moduleImport("a").getMember("b").getMember("c")
abcd = abc.d #$ use=moduleImport("a").getMember("b").getMember("c").getMember("d")
x5 = abcd.method() #$ use=moduleImport("a").getMember("b").getMember("c").getMember("d").getMember("method").getReturn()
from a6 import m6 #$ use=moduleImport("a6").getMember("m6")
x6 = m6().foo().bar() #$ use=moduleImport("a6").getMember("m6").getReturn().getMember("foo").getReturn().getMember("bar").getReturn()
import foo.baz.baz as fbb #$ use=moduleImport("foo").getMember("baz").getMember("baz")
from foo.bar.baz import quux as fbbq #$ use=moduleImport("foo").getMember("bar").getMember("baz").getMember("quux")
from ham.bar.eggs import spam as hbes #$ use=moduleImport("ham").getMember("bar").getMember("eggs").getMember("spam")
fbb.quux #$ use=moduleImport("foo").getMember("baz").getMember("baz").getMember("quux")
fbbq #$ use=moduleImport("foo").getMember("bar").getMember("baz").getMember("quux")
hbes #$ use=moduleImport("ham").getMember("bar").getMember("eggs").getMember("spam")
import foo.bar.baz #$ use=moduleImport("foo")
# Relative imports. These are ignored
from .foo import bar
from ..foobar import baz
# Use of imports across scopes
def use_m4():
x = m4.blah4 #$ use=moduleImport("a4").getMember("b4").getMember("c4").getMember("blah4")
def local_import_use():
from foo import bar #$ use=moduleImport("foo").getMember("bar")
x = bar() #$ use=moduleImport("foo").getMember("bar").getReturn()
from eggs import ham as spam #$ use=moduleImport("eggs").getMember("ham")
def bbb():
f = spam #$ use=moduleImport("eggs").getMember("ham")
from danger import SOURCE #$ use=moduleImport("danger").getMember("SOURCE")
foo = SOURCE #$ use=moduleImport("danger").getMember("SOURCE")
def change_foo():
global foo
foo = SOURCE #$ use=moduleImport("danger").getMember("SOURCE")
def f():
global foo
sink(foo) #$ use=moduleImport("danger").getMember("SOURCE")
foo = NONSOURCE
change_foo()
sink(foo) #$ use=moduleImport("danger").getMember("SOURCE")
# Built-ins
def use_of_builtins():
for x in range(5): #$ use=moduleImport("builtins").getMember("range").getReturn()
if x < len([]): #$ use=moduleImport("builtins").getMember("len").getReturn()
print("Hello") #$ use=moduleImport("builtins").getMember("print").getReturn()
raise Exception("Farewell") #$ use=moduleImport("builtins").getMember("Exception").getReturn()
def imported_builtins():
import builtins #$ use=moduleImport("builtins")
def open(f):
return builtins.open(f) #$ MISSING: use=moduleImport("builtins").getMember("open").getReturn()
def redefine_print():
def my_print(x):
import builtins #$ use=moduleImport("builtins")
builtins.print("I'm printing", x) #$ use=moduleImport("builtins").getMember("print").getReturn()
print = my_print
print("these words")
def local_redefine_chr():
chr = 5
return chr
def global_redefine_chr():
global chr
chr = 6
return chr
def what_is_chr_now():
# If global_redefine_chr has been run, then the following is _not_ a reference to the built-in chr
return chr(123) #$ MISSING: use=moduleImport("builtins").getMember("chr").getReturn()
def obscured_print():
p = print #$ use=moduleImport("builtins").getMember("print")
p("Can you see me?") #$ use=moduleImport("builtins").getMember("print").getReturn()
def python2_style():
# In Python 3, `__builtin__` has no special meaning.
from __builtin__ import open #$ use=moduleImport("__builtin__").getMember("open")
open("hello.txt") #$ use=moduleImport("__builtin__").getMember("open").getReturn()
| python |
from .users import * # importamos todas las clases del archivo circle. | python |
# -*- coding: utf-8 -*-
# Copyright 2014 Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import tempfile
from oslo_concurrency import processutils
from os_net_config import impl_eni
from os_net_config import objects
from os_net_config.tests import base
from os_net_config import utils
_AUTO = "auto eth0\n"
_v4_IFACE_NO_IP = _AUTO + "iface eth0 inet manual\n"
_V4_IFACE_STATIC_IP = _AUTO + """iface eth0 inet static
address 192.168.1.2
netmask 255.255.255.0
"""
_V6_IFACE_STATIC_IP = _AUTO + """iface eth0 inet6 static
address fe80::2677:3ff:fe7d:4c
netmask ffff:ffff:ffff:ffff:ffff:ffff:ffff:ffff
"""
_IFACE_DHCP = _AUTO + "iface eth0 inet dhcp\n"
_OVS_PORT_BASE = _AUTO + "allow-br0 eth0\n"
_OVS_PORT_IFACE = _OVS_PORT_BASE + """iface eth0 inet manual
ovs_bridge br0
ovs_type OVSPort
"""
_OVS_BRIDGE_DHCP = """auto br0
allow-ovs br0
iface br0 inet dhcp
ovs_type OVSBridge
ovs_ports eth0
pre-up ip addr flush dev eth0
"""
_OVS_BRIDGE_DHCP_PRIMARY_INTERFACE = _OVS_BRIDGE_DHCP + \
" ovs_extra set bridge br0 other-config:hwaddr=a1:b2:c3:d4:e5\n"
_OVS_BRIDGE_DHCP_OVS_EXTRA = _OVS_BRIDGE_DHCP + \
" ovs_extra set bridge br0 other-config:hwaddr=a1:b2:c3:d4:e5" + \
" -- br-set-external-id br-ctlplane bridge-id br-ctlplane\n"
_VLAN_NO_IP = """auto vlan5
iface vlan5 inet manual
vlan-raw-device eth0
"""
_VLAN_OVS_PORT = """auto vlan5
allow-br0 vlan5
iface vlan5 inet manual
ovs_bridge br0
ovs_type OVSIntPort
ovs_options tag=5
"""
_RTS = """up route add -net 172.19.0.0 netmask 255.255.255.0 gw 192.168.1.1
down route del -net 172.19.0.0 netmask 255.255.255.0 gw 192.168.1.1
"""
class TestENINetConfig(base.TestCase):
def setUp(self):
super(TestENINetConfig, self).setUp()
self.provider = impl_eni.ENINetConfig()
self.if_name = 'eth0'
def tearDown(self):
super(TestENINetConfig, self).tearDown()
def get_interface_config(self, name="eth0"):
return self.provider.interfaces[name]
def get_route_config(self):
return self.provider.routes[self.if_name]
def _default_interface(self, addr=[], rts=[]):
return objects.Interface(self.if_name, addresses=addr, routes=rts)
def test_interface_no_ip(self):
interface = self._default_interface()
self.provider.add_interface(interface)
self.assertEqual(_v4_IFACE_NO_IP, self.get_interface_config())
def test_add_interface_with_v4(self):
v4_addr = objects.Address('192.168.1.2/24')
interface = self._default_interface([v4_addr])
self.provider.add_interface(interface)
self.assertEqual(_V4_IFACE_STATIC_IP, self.get_interface_config())
def test_add_interface_with_v6(self):
v6_addr = objects.Address('fe80::2677:3ff:fe7d:4c')
interface = self._default_interface([v6_addr])
self.provider.add_interface(interface)
self.assertEqual(_V6_IFACE_STATIC_IP, self.get_interface_config())
def test_add_interface_dhcp(self):
interface = self._default_interface()
interface.use_dhcp = True
self.provider.add_interface(interface)
self.assertEqual(_IFACE_DHCP, self.get_interface_config())
def test_add_interface_with_both_v4_and_v6(self):
v4_addr = objects.Address('192.168.1.2/24')
v6_addr = objects.Address('fe80::2677:3ff:fe7d:4c')
interface = self._default_interface([v4_addr, v6_addr])
self.provider.add_interface(interface)
self.assertEqual(_V4_IFACE_STATIC_IP + _V6_IFACE_STATIC_IP,
self.get_interface_config())
def test_add_ovs_port_interface(self):
interface = self._default_interface()
interface.ovs_port = True
interface.bridge_name = 'br0'
self.provider.add_interface(interface)
self.assertEqual(_OVS_PORT_IFACE, self.get_interface_config())
def test_network_with_routes(self):
route1 = objects.Route('192.168.1.1', '172.19.0.0/24')
v4_addr = objects.Address('192.168.1.2/24')
interface = self._default_interface([v4_addr], [route1])
self.provider.add_interface(interface)
self.assertEqual(_V4_IFACE_STATIC_IP, self.get_interface_config())
self.assertEqual(_RTS, self.get_route_config())
def test_network_ovs_bridge_with_dhcp(self):
interface = self._default_interface()
bridge = objects.OvsBridge('br0', use_dhcp=True,
members=[interface])
self.provider.add_bridge(bridge)
self.provider.add_interface(interface)
self.assertEqual(_OVS_PORT_IFACE, self.get_interface_config())
self.assertEqual(_OVS_BRIDGE_DHCP, self.provider.bridges['br0'])
def test_network_ovs_bridge_with_dhcp_and_primary_interface(self):
def test_interface_mac(name):
return "a1:b2:c3:d4:e5"
self.stubs.Set(utils, 'interface_mac', test_interface_mac)
interface = objects.Interface(self.if_name, primary=True)
bridge = objects.OvsBridge('br0', use_dhcp=True,
members=[interface])
self.provider.add_bridge(bridge)
self.provider.add_interface(interface)
self.assertEqual(_OVS_PORT_IFACE, self.get_interface_config())
self.assertEqual(_OVS_BRIDGE_DHCP_PRIMARY_INTERFACE,
self.provider.bridges['br0'])
def test_network_ovs_bridge_with_dhcp_and_primary_with_ovs_extra(self):
def test_interface_mac(name):
return "a1:b2:c3:d4:e5"
self.stubs.Set(utils, 'interface_mac', test_interface_mac)
interface = objects.Interface(self.if_name, primary=True)
ovs_extra = "br-set-external-id br-ctlplane bridge-id br-ctlplane"
bridge = objects.OvsBridge('br0', use_dhcp=True,
members=[interface],
ovs_extra=[ovs_extra])
self.provider.add_bridge(bridge)
self.provider.add_interface(interface)
self.assertEqual(_OVS_PORT_IFACE, self.get_interface_config())
self.assertEqual(_OVS_BRIDGE_DHCP_OVS_EXTRA,
self.provider.bridges['br0'])
def test_vlan(self):
vlan = objects.Vlan('eth0', 5)
self.provider.add_vlan(vlan)
self.assertEqual(_VLAN_NO_IP, self.get_interface_config('vlan5'))
def test_vlan_ovs_bridge_int_port(self):
vlan = objects.Vlan('eth0', 5)
bridge = objects.OvsBridge('br0', use_dhcp=True,
members=[vlan])
self.provider.add_bridge(bridge)
self.provider.add_vlan(vlan)
self.assertEqual(_VLAN_OVS_PORT, self.get_interface_config('vlan5'))
class TestENINetConfigApply(base.TestCase):
def setUp(self):
super(TestENINetConfigApply, self).setUp()
self.temp_config_file = tempfile.NamedTemporaryFile()
self.ifup_interface_names = []
def test_config_path():
return self.temp_config_file.name
self.stubs.Set(impl_eni, '_network_config_path', test_config_path)
def test_execute(*args, **kwargs):
if args[0] == '/sbin/ifup':
self.ifup_interface_names.append(args[1])
pass
self.stubs.Set(processutils, 'execute', test_execute)
self.provider = impl_eni.ENINetConfig()
def tearDown(self):
self.temp_config_file.close()
super(TestENINetConfigApply, self).tearDown()
def test_network_apply(self):
route = objects.Route('192.168.1.1', '172.19.0.0/24')
v4_addr = objects.Address('192.168.1.2/24')
interface = objects.Interface('eth0', addresses=[v4_addr],
routes=[route])
self.provider.add_interface(interface)
self.provider.apply()
iface_data = utils.get_file_data(self.temp_config_file.name)
self.assertEqual((_V4_IFACE_STATIC_IP + _RTS), iface_data)
self.assertIn('eth0', self.ifup_interface_names)
def test_apply_noactivate(self):
route = objects.Route('192.168.1.1', '172.19.0.0/24')
v4_addr = objects.Address('192.168.1.2/24')
interface = objects.Interface('eth0', addresses=[v4_addr],
routes=[route])
self.provider.add_interface(interface)
self.provider.apply(activate=False)
iface_data = utils.get_file_data(self.temp_config_file.name)
self.assertEqual((_V4_IFACE_STATIC_IP + _RTS), iface_data)
self.assertEqual([], self.ifup_interface_names)
def test_dhcp_ovs_bridge_network_apply(self):
interface = objects.Interface('eth0')
bridge = objects.OvsBridge('br0', use_dhcp=True,
members=[interface])
self.provider.add_interface(interface)
self.provider.add_bridge(bridge)
self.provider.apply()
iface_data = utils.get_file_data(self.temp_config_file.name)
self.assertEqual((_OVS_BRIDGE_DHCP + _OVS_PORT_IFACE), iface_data)
self.assertIn('eth0', self.ifup_interface_names)
self.assertIn('br0', self.ifup_interface_names)
| python |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from typing import Dict, Iterable, Optional
import torch
import torch.nn as nn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer,
build_runner)
from mmcv.utils import ConfigDict, build_from_cfg
from mmdet.core import DistEvalHook, EvalHook
from mmfewshot.detection.core import (QuerySupportDistEvalHook,
QuerySupportEvalHook)
from mmfewshot.detection.datasets import (build_dataloader, build_dataset,
get_copy_dataset_type)
from mmfewshot.utils import compat_cfg, get_root_logger
def train_detector(model: nn.Module,
dataset: Iterable,
cfg: ConfigDict,
distributed: bool = False,
validate: bool = False,
timestamp: Optional[str] = None,
meta: Optional[Dict] = None) -> None:
cfg = compat_cfg(cfg)
logger = get_root_logger(log_level=cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
train_dataloader_default_args = dict(
samples_per_gpu=2,
workers_per_gpu=2,
# `num_gpus` will be ignored if distributed
num_gpus=len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed,
data_cfg=copy.deepcopy(cfg.data),
use_infinite_sampler=cfg.use_infinite_sampler,
persistent_workers=False)
train_loader_cfg = {
**train_dataloader_default_args,
**cfg.data.get('train_dataloader', {})
}
data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
# Please use MMCV >= 1.4.4 for CPU training!
model = MMDataParallel(model, device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
# Infinite sampler will return a infinite stream of index. It can NOT
# be used in `EpochBasedRunner`, because the `EpochBasedRunner` will
# enumerate the dataloader forever. Thus, `InfiniteEpochBasedRunner`
# is designed to handle dataloader with infinite sampler.
if cfg.use_infinite_sampler and cfg.runner['type'] == 'EpochBasedRunner':
cfg.runner['type'] = 'InfiniteEpochBasedRunner'
runner = build_runner(
cfg.runner,
default_args=dict(
model=model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta))
# an ugly workaround to make .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=distributed)
elif distributed and 'type' not in cfg.optimizer_config:
optimizer_config = OptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = cfg.optimizer_config
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
if distributed:
if isinstance(runner, EpochBasedRunner):
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
# currently only support single images testing
val_dataloader_default_args = dict(
samples_per_gpu=1,
workers_per_gpu=2,
dist=distributed,
shuffle=False,
persistent_workers=False)
val_dataloader_args = {
**val_dataloader_default_args,
**cfg.data.get('val_dataloader', {})
}
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
assert val_dataloader_args['samples_per_gpu'] == 1, \
'currently only support single images testing'
val_dataloader = build_dataloader(val_dataset, **val_dataloader_args)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
# Prepare `model_init` dataset for model initialization. In most cases,
# the `model_init` dataset contains support images and few shot
# annotations. The meta-learning based detectors will extract the
# features from images and save them as part of model parameters.
# The `model_init` dataset can be mutually configured or
# randomly selected during runtime.
if cfg.data.get('model_init', None) is not None:
# The randomly selected few shot support during runtime can not be
# configured offline. In such case, the copy datasets are designed
# to directly copy the randomly generated support set for model
# initialization. The copy datasets copy the `data_infos` by
# passing it as argument and other arguments can be different
# from training dataset.
if cfg.data.model_init.pop('copy_from_train_dataset', False):
if cfg.data.model_init.ann_cfg is not None:
warnings.warn(
'model_init dataset will copy support '
'dataset used for training and original '
'ann_cfg will be discarded', UserWarning)
# modify dataset type to support copying data_infos operation
cfg.data.model_init.type = \
get_copy_dataset_type(cfg.data.model_init.type)
if not hasattr(dataset[0], 'get_support_data_infos'):
raise NotImplementedError(
f'`get_support_data_infos` is not implemented '
f'in {dataset[0].__class__.__name__}.')
cfg.data.model_init.ann_cfg = [
dict(data_infos=dataset[0].get_support_data_infos())
]
# The `model_init` dataset will be saved into checkpoint, which
# allows model to be initialized with these data as default, if
# the config of data is not be overwritten during testing.
cfg.checkpoint_config.meta['model_init_ann_cfg'] = \
cfg.data.model_init.ann_cfg
samples_per_gpu = cfg.data.model_init.pop('samples_per_gpu', 1)
workers_per_gpu = cfg.data.model_init.pop('workers_per_gpu', 1)
model_init_dataset = build_dataset(cfg.data.model_init)
# Noted that `dist` should be FALSE to make all the models on
# different gpus get same data results in same initialized models.
model_init_dataloader = build_dataloader(
model_init_dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=workers_per_gpu,
dist=False,
shuffle=False)
# eval hook for meta-learning based query-support detector, it
# supports model initialization before regular evaluation.
eval_hook = QuerySupportDistEvalHook \
if distributed else QuerySupportEvalHook
runner.register_hook(
eval_hook(model_init_dataloader, val_dataloader, **eval_cfg),
priority='LOW')
else:
# for the fine-tuned based methods, the evaluation is the
# same as mmdet.
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(
eval_hook(val_dataloader, **eval_cfg), priority='LOW')
# user-defined hooks
if cfg.get('custom_hooks', None):
custom_hooks = cfg.custom_hooks
assert isinstance(
custom_hooks, list
), f'custom_hooks expect list type, but got {type(custom_hooks)}'
for hook_cfg in cfg.custom_hooks:
assert isinstance(
hook_cfg, dict
), f'Each item in custom_hooks expects dict type, but ' \
f'got {type(hook_cfg)}'
hook_cfg = hook_cfg.copy()
priority = hook_cfg.pop('priority', 'NORMAL')
hook = build_from_cfg(hook_cfg, HOOKS)
runner.register_hook(hook, priority=priority)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow)
| python |
import argparse
import gym
from gym import wrappers
import os.path as osp
import random
import numpy as np
import tensorflow as tf
import tensorflow.contrib.layers as layers
import dqn
from dqn_utils import *
from atari_wrappers import *
def cartpole_model(img_in, num_actions, scope, reuse=False):
# as described in https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
with tf.variable_scope(scope, reuse=reuse):
# out = tf.ones(tf.shape(img_in))
out = img_in
out = layers.flatten(out)
with tf.variable_scope("action_value"):
out = layers.fully_connected(out, num_outputs=16,
activation_fn=tf.nn.relu, scope='fc_input')
out = layers.fully_connected(out, num_outputs=num_actions,
activation_fn=None, scope='fc_head')
return out
def cartpole_learn(env, session, num_timesteps):
# This is just a rough estimate
num_iterations = float(num_timesteps) / 4.0
# lr_multiplier = 1.0
# lr_multiplier = 0.1
# lr_schedule = PiecewiseSchedule([
# (0, 1e-4 * lr_multiplier),
# (num_iterations / 2, 1e-5 * lr_multiplier),
# ],
# outside_value=5e-5 * lr_multiplier)
lr_schedule = InverseSchedule(initial_p=0.1, gamma=0.6)
optimizer = dqn.OptimizerSpec(
constructor=tf.train.GradientDescentOptimizer,
# constructor=tf.train.AdamOptimizer,
# kwargs=dict(epsilon=1e-4),
kwargs=dict(),
# constructor=tf.train.RMSPropOptimizer,
# kwargs=dict(epsilon=1e-1),
lr_schedule=lr_schedule
)
def stopping_criterion(env, t):
# notice that here t is the number of steps of the wrapped env,
# which is different from the number of steps in the underlying env
return get_wrapper_by_name(env, "Monitor").get_total_steps() >= num_timesteps
exploration_schedule = PiecewiseSchedule(
[
(0, 1.0),
# (0.2 * num_timesteps, 0.9),
# (0.5 * num_timesteps, 0.5),
(0.1 * num_timesteps, 0.1),
], outside_value=0.01
)
dqn.learn(
env,
q_func=cartpole_model,
optimizer_spec=optimizer,
session=session,
exploration=exploration_schedule,
stopping_criterion=stopping_criterion,
replay_buffer_size=100000,
batch_size=256,
gamma=0.99,
learning_starts=2000,
learning_freq=1,
frame_history_len=4,
target_update_freq=1000,
grad_norm_clipping=1000,
)
env.close()
def get_available_gpus():
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
return [x.physical_device_desc for x in local_device_protos if x.device_type == 'GPU']
def set_global_seeds(i):
try:
import tensorflow as tf
except ImportError:
pass
else:
tf.set_random_seed(i)
np.random.seed(i)
random.seed(i)
def get_session():
tf.reset_default_graph()
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1)
session = tf.Session(config=tf_config)
print("AVAILABLE GPUS: ", get_available_gpus())
return session
def get_env(task, seed):
env_id = task.env_id
env = gym.make(env_id)
set_global_seeds(seed)
env.seed(seed)
expt_dir = '/tmp/hw3_vid_dir2/'
env = wrappers.Monitor(env, osp.join(expt_dir, "gym"), force=True)
env = wrap_deepmind(env)
return env
def main():
# Run training
max_timesteps = 100000
seed = 0 # Use a seed of zero (you may want to randomize the seed!)
env = gym.make("CartPole-v0")
env.seed(seed)
set_global_seeds(seed)
env = wrappers.Monitor(env, '/tmp/cartpole-experiment-1', force=True)
session = get_session()
cartpole_learn(env, session, num_timesteps=max_timesteps)
if __name__ == "__main__":
main()
| python |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# 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.
"""
Composite StateTomography and ProcessTomography experiment tests
"""
from test.base import QiskitExperimentsTestCase
from qiskit import QuantumCircuit
import qiskit.quantum_info as qi
from qiskit.providers.aer import AerSimulator
from qiskit_experiments.framework import BatchExperiment, ParallelExperiment
from qiskit_experiments.library import StateTomography, ProcessTomography
from .tomo_utils import filter_results
class TestCompositeTomography(QiskitExperimentsTestCase):
"""Test composite tomography experiments"""
def test_batch_qst_exp(self):
"""Test batch state tomography experiment with measurement_qubits kwarg"""
# Subsystem unitaries
seed = 1111
nq = 3
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Preparation circuit
circuit = QuantumCircuit(nq)
for i, op in enumerate(ops):
circuit.append(op, [i])
# Component experiments
exps = []
targets = []
for i in range(nq):
targets.append(qi.Statevector(ops[i].to_instruction()))
exps.append(StateTomography(circuit, measurement_qubits=[i]))
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
batch_exp = BatchExperiment(exps)
batch_data = batch_exp.run(backend)
self.assertExperimentDone(batch_data)
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(batch_exp.num_experiments):
results = batch_data.child_data(i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(
isinstance(state, qi.DensityMatrix), msg="fitted state is not density matrix"
)
# Check fit state fidelity
fid = filter_results(results, "state_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.state_fidelity(state, targets[i], validate=False)
self.assertAlmostEqual(fid, target_fid, places=6, msg="result fidelity is incorrect")
def test_parallel_qst_exp(self):
"""Test parallel state tomography experiment"""
# Subsystem unitaries
seed = 1221
nq = 4
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Component experiments
exps = []
targets = []
for i in range(nq):
exps.append(StateTomography(ops[i], qubits=[i]))
targets.append(qi.Statevector(ops[i].to_instruction()))
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
par_exp = ParallelExperiment(exps)
par_data = par_exp.run(backend)
self.assertExperimentDone(par_data)
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(par_exp.num_experiments):
results = par_data.child_data(i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(
isinstance(state, qi.DensityMatrix), msg="fitted state is not density matrix"
)
# Check fit state fidelity
fid = filter_results(results, "state_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.state_fidelity(state, targets[i], validate=False)
self.assertAlmostEqual(fid, target_fid, places=6, msg="result fidelity is incorrect")
def test_batch_qpt_exp_with_measurement_qubits(self):
"""Test batch process tomography experiment with kwargs"""
seed = 1111
nq = 3
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Preparation circuit
circuit = QuantumCircuit(nq)
for i, op in enumerate(ops):
circuit.append(op, [i])
# Component experiments
exps = []
targets = []
for i in range(nq):
targets.append(ops[i])
exps.append(ProcessTomography(circuit, measurement_qubits=[i], preparation_qubits=[i]))
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
batch_exp = BatchExperiment(exps)
batch_data = batch_exp.run(backend)
self.assertExperimentDone(batch_data)
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(batch_exp.num_experiments):
results = batch_data.child_data(i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi), msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state, targets[i], require_tp=False, require_cp=False)
self.assertAlmostEqual(fid, target_fid, places=6, msg="result fidelity is incorrect")
def test_parallel_qpt_exp(self):
"""Test parallel process tomography experiment"""
# Subsystem unitaries
seed = 1221
nq = 4
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Component experiments
exps = []
targets = []
for i in range(nq):
exps.append(ProcessTomography(ops[i], qubits=[i]))
targets.append(ops[i])
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
par_exp = ParallelExperiment(exps)
par_data = par_exp.run(backend)
self.assertExperimentDone(par_data)
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(par_exp.num_experiments):
results = par_data.child_data(i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi), msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state, targets[i], require_tp=False, require_cp=False)
self.assertAlmostEqual(fid, target_fid, places=6, msg="result fidelity is incorrect")
def test_mixed_batch_exp(self):
"""Test batch state and process tomography experiment"""
# Subsystem unitaries
state_op = qi.random_unitary(2, seed=321)
chan_op = qi.random_unitary(2, seed=123)
state_target = qi.Statevector(state_op.to_instruction())
chan_target = qi.Choi(chan_op.to_instruction())
state_exp = StateTomography(state_op)
chan_exp = ProcessTomography(chan_op)
batch_exp = BatchExperiment([state_exp, chan_exp])
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
par_data = batch_exp.run(backend)
self.assertExperimentDone(par_data)
f_threshold = 0.95
# Check state tomo results
state_results = par_data.child_data(0).analysis_results()
state = filter_results(state_results, "state").value
# Check fit state fidelity
state_fid = filter_results(state_results, "state_fidelity").value
self.assertGreater(state_fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.state_fidelity(state, state_target, validate=False)
self.assertAlmostEqual(state_fid, target_fid, places=6, msg="result fidelity is incorrect")
# Check process tomo results
chan_results = par_data.child_data(1).analysis_results()
chan = filter_results(chan_results, "state").value
# Check fit process fidelity
chan_fid = filter_results(chan_results, "process_fidelity").value
self.assertGreater(chan_fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(chan, chan_target, require_cp=False, require_tp=False)
self.assertAlmostEqual(chan_fid, target_fid, places=6, msg="result fidelity is incorrect")
| python |
# %%
consumer_key = "idBfc3mYzrfBPxRM1z5AhXxAA"
consumer_secret = "K50925I1FObqf6LA8MwiUyCBWlOxtrXXpi0aUAFD0wNCFBPQ3j"
access_token = "1245495541330579457-6EBT7O9j98LgAt3dXxzsTK5FFAA2Lg"
access_secret = "jUP2N1nHeC6nzD30F4forjx7WxoOI603b4CqHdUnA6wqL"
# %%
import tweepy
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_secret)
# %%
api = tweepy.API(auth)
# %%
public_tweets = api.home_timeline()
for tweet in public_tweets:
print(tweet.text)
# %%
api.me().screen_name
# %%
| python |
"""
Copyright 2011 Lars Kruse <[email protected]>
This file is part of PyCAM.
PyCAM is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PyCAM is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PyCAM. If not, see <http://www.gnu.org/licenses/>.
"""
import imp
import inspect
import os
import uuid
from pycam.Utils import get_non_conflicting_name
from pycam.Utils.events import get_event_handler
import pycam.Utils.log
import pycam.Utils.locations
_log = pycam.Utils.log.get_logger()
def _get_plugin_imports():
# We want to import all relevant GUI modules into the namespace of each plugin.
# We do this once for all - in order to centralize and minimize error handling.
result = {key: None for key in ("gtk", "gdk", "gdkpixbuf", "gdkobject", "gio", "glib",
"GL", "GLU", "GLUT")}
# By default, Gdk loads the OpenGL 3.2 Core profile. However, PyCAM's rendering
# code uses the fixed function pipeline, which was removed in the Core profile.
# So we have to resort to this semi-public API to ask Gdk to use a Compatibility
# profile instead.
os.environ['GDK_GL'] = 'legacy'
try:
import gi
gi.require_version('Gtk', '3.0')
from gi.repository import Gtk
from gi.repository import Gdk
from gi.repository import GdkPixbuf
from gi.repository import Gio
from gi.repository import GLib
from gi.repository import GObject
result["gtk"] = Gtk
result["gdk"] = Gdk
result["gdkpixbuf"] = GdkPixbuf
result["gio"] = Gio
result["glib"] = GLib
result["gobject"] = GObject
except ImportError:
_log.warning("Failed to import GTK3 module. Maybe you want to install 'python3-gi' "
"for pycam's graphical user interface.")
if result["gtk"]:
try:
import OpenGL.GL
import OpenGL.GLU
import OpenGL.GLUT
result["GL"] = OpenGL.GL
result["GLU"] = OpenGL.GLU
result["GLUT"] = OpenGL.GLUT
except ImportError:
# OpenGL-related plugins will complain later about the missing dependency
_log.warning("Failed to import OpenGL module. Maybe you want to install "
"'python3-opengl' for the 3D visualization.")
return result
class PluginBase:
UI_FILE = None
DEPENDS = []
CATEGORIES = []
ICONS = {}
ICON_SIZE = 23
_imports = _get_plugin_imports()
def __init__(self, core, name):
self.enabled = True
self.name = name
self.core = core
self.gui = None
self.log = _log
# convenience imports for GUI modules (self._gtk, self._gdk, self._GL, ...)
for key, value in self._imports.items():
setattr(self, "_" + key, value)
if self.UI_FILE and self._gtk:
gtk_build_file = pycam.Utils.locations.get_ui_file_location(self.UI_FILE)
if gtk_build_file:
self.gui = self._gtk.Builder()
try:
self.gui.add_from_file(gtk_build_file)
except RuntimeError as err_msg:
self.log.info("Failed to import UI file (%s): %s", gtk_build_file, err_msg)
self.gui = None
else:
# All windows should share the same accel group (for
# keyboard shortcuts).
try:
common_accel_group = self.core["gtk-accel-group"]
except KeyError:
self.log.info("Failed to connect to a common GTK accelerator group")
common_accel_group = None
if common_accel_group:
for obj in self.gui.get_objects():
if isinstance(obj, self._gtk.Window):
obj.add_accel_group(common_accel_group)
if self._gtk:
for key in self.ICONS:
icon_location = pycam.Utils.locations.get_ui_file_location(self.ICONS[key])
if icon_location:
try:
self.ICONS[key] = self._gdkpixbuf.Pixbuf.new_from_file_at_size(
icon_location, self.ICON_SIZE, self.ICON_SIZE)
except self._gobject.GError:
self.log.info("Failed to load icon: %s", self.ICONS[key])
self.ICONS[key] = None
else:
self.log.debug("Failed to locate icon: %s", self.ICONS[key])
self.ICONS[key] = None
self._func_cache = {}
self._gtk_handler_id_cache = []
self.enabled = True
self._state_items = []
def register_state_item(self, path, get_func, set_func=None):
group = (path, get_func, set_func)
if group in self._state_items:
self.log.debug("Trying to register a state item twice: %s", path)
else:
self._state_items.append(group)
def clear_state_items(self):
self._state_items = []
def unregister_state_item(self, path, get_func, set_func=None):
group = (path, get_func, set_func)
if group in self._state_items:
self._state_items.remove(group)
else:
self.log.debug("Trying to unregister an unknown state item: %s", path)
def dump_state(self, result):
for path, get_func, set_func in self._state_items:
if callable(get_func):
value = get_func()
else:
value = get_func
result.append((path, value))
def __get_handler_func(self, func, params=None):
if params is None:
params = []
params = tuple(params)
try:
key = (hash(func), repr(params))
except TypeError:
key = (id(func), repr(params))
if key not in self._func_cache:
if callable(func):
if not params:
result = func
else:
result = lambda *args, **kwargs: func(*(args + params), **kwargs)
else:
# it is the name of a signal
result = lambda *args: self.core.emit_event(func, *params)
self._func_cache[key] = result
return self._func_cache[key]
def register_event_handlers(self, event_handlers):
for name, target in event_handlers:
self.core.register_event(name, self.__get_handler_func(target))
def register_gtk_handlers(self, gtk_widget_handlers):
for data in gtk_widget_handlers:
obj, signal, func = data[:3]
params = data[3:] if len(data) > 3 else []
handler_id = obj.connect(signal, self.__get_handler_func(func, params))
self._gtk_handler_id_cache.append((obj, handler_id))
def unregister_event_handlers(self, event_handlers):
for name, target in event_handlers:
self.core.unregister_event(name, self.__get_handler_func(target))
def unregister_gtk_handlers(self, gtk_widget_handlers):
while self._gtk_handler_id_cache:
obj, handler_id = self._gtk_handler_id_cache.pop()
obj.disconnect(handler_id)
def setup(self):
raise NotImplementedError("Module %s (%s) does not implement 'setup'"
% (self.name, __file__))
def teardown(self):
raise NotImplementedError("Module %s (%s) does not implement 'teardown'"
% (self.name, __file__))
def _get_gtk_action_group_by_name(self, group_name, create_if_missing=False):
ui_manager = self.core.get("gtk-uimanager")
# find the action group of the given name or create a new one
for action_group in ui_manager.get_action_groups():
if action_group.get_name() == group_name:
return action_group
else:
if create_if_missing:
action_group = self._gtk.ActionGroup(name=group_name)
ui_manager.insert_action_group(action_group)
return action_group
else:
return None
def register_gtk_accelerator(self, groupname, action, accel_string, accel_name):
actiongroup = self._get_gtk_action_group_by_name(groupname, create_if_missing=True)
accel_path = "<pycam>/%s" % accel_name
action.set_accel_path(accel_path)
# it is a bit pointless, but we allow an empty accel_string anyway ...
if accel_string:
key, mod = self._gtk.accelerator_parse(accel_string)
self._gtk.AccelMap.change_entry(accel_path, key, mod, True)
actiongroup.add_action(action)
def unregister_gtk_accelerator(self, groupname, action):
actiongroup = self._get_gtk_action_group_by_name(groupname)
if actiongroup is None:
self.log.warning("Failed to unregister unknown GTK Action Group: %s", groupname)
actiongroup.remove_action(action)
# remove the connected action group, if it is empty (no more actions assigned)
ui_manager = self.core.get("gtk-uimanager")
if ui_manager and (len(actiongroup.list_actions()) == 0):
ui_manager.remove_action_group(actiongroup)
class PluginManager:
def __init__(self, core):
self.core = core
self.modules = {}
self.core.set("plugin-manager", self)
def import_plugins(self, directory=None, ignore_names=None):
if ignore_names is None:
ignore_names = []
if directory is None:
directory = os.path.dirname(__file__)
try:
files = os.listdir(directory)
except OSError:
return
plugins = []
for filename in files:
if (filename.endswith(".py")
and (filename.lower() != "__init__.py")
and os.path.isfile(os.path.join(directory, filename))):
mod_name = filename[0:-(len(".py"))]
if mod_name in ignore_names:
_log.info("Skipping plugin %s (marked as 'ignore')", mod_name)
continue
try:
mod_file, mod_filename, mod_desc = imp.find_module(mod_name, [directory])
full_mod_name = "pycam.Plugins.%s" % mod_name
mod = imp.load_module(full_mod_name, mod_file, mod_filename, mod_desc)
except ImportError as exc:
_log.info("Skipping plugin %s: %s", os.path.join(directory, filename), exc)
continue
for attr in dir(mod):
item = getattr(mod, attr)
if inspect.isclass(item) and issubclass(item, PluginBase):
plugins.append((item, mod_filename, attr))
try_again = True
while try_again:
try_again = False
postponed_plugins = []
for plugin, filename, name in plugins:
for dep in plugin.DEPENDS:
if dep not in self.modules:
# dependency not loaded, yet
postponed_plugins.append((plugin, filename, name))
break
else:
self._load_plugin(plugin, filename, name)
try_again = True
plugins = postponed_plugins
for plugin, filename, name in plugins:
# module failed to load due to missing dependencies
missing = []
for depend in plugin.DEPENDS:
try:
# check if this dependency is available
self.get_plugin(depend)
except KeyError:
missing.append(depend)
_log.info("Skipping plugin '%s' due to missing dependencies: %s",
name, ", ".join(missing))
def _load_plugin(self, obj, filename, plugin_name):
if plugin_name in self.modules:
_log.debug("Cleaning up module %s", plugin_name)
self.modules[plugin_name].teardown()
_log.debug("Initializing module %s (%s)", plugin_name, filename)
new_plugin = obj(self.core, plugin_name)
try:
if not new_plugin.setup():
_log.info("Failed to setup plugin '%s'", str(plugin_name))
else:
self.modules[plugin_name] = new_plugin
self.core.emit_event("plugin-list-changed")
except NotImplementedError as err_msg:
_log.info("Skipping incomplete plugin '%s': %s", plugin_name, err_msg)
def disable_all_plugins(self):
_log.info("Disabling all plugins")
for plugin_name in self.modules:
if self.get_plugin_state(plugin_name):
self.disable_plugin(plugin_name, recursively=True)
def get_plugin(self, name):
if name in self.modules:
return self.modules[name]
else:
raise KeyError("Plugin '%s' is not available" % name)
def enable_plugin(self, name):
plugin = self.get_plugin(name)
if plugin.enabled:
_log.debug("Refused to enable an active plugin: %s" % name)
return
else:
plugin.enabled = plugin.setup()
def disable_plugin(self, name, recursively=False):
plugin = self.get_plugin(name)
if not plugin.enabled:
_log.debug("Refused to disable an disabled plugin: %s" % name)
return
else:
if recursively and self.is_plugin_required(name):
for dep_name in self.get_dependent_plugins(name):
if self.get_plugin_state(dep_name):
self.disable_plugin(dep_name, recursively=True)
if self.is_plugin_required(name):
_log.warning("Refusing to disable plugin: %s (dependent plugins: %s)",
name, " ".join(self.get_dependent_plugins(name)))
else:
_log.debug("Disabling plugin: %s", name)
plugin.teardown()
plugin.enabled = False
def get_plugin_state(self, name):
plugin = self.get_plugin(name)
return plugin.enabled
def get_plugins(self):
return list(self.modules.values())
def get_plugin_names(self):
names = self.modules.keys()
return sorted(names)
def get_dependent_plugins(self, name):
return {plugin.name for plugin in self.modules.values()
if plugin.enabled and (name in plugin.DEPENDS)}
def is_plugin_required(self, name):
return len(self.get_dependent_plugins(name)) > 0
def get_plugin_missing_dependencies(self, name):
plugin = self.get_plugin(name)
missing = []
for depend in plugin.DEPENDS:
if (depend in self.modules) and self.modules[depend].enabled:
continue
else:
missing.append(depend)
return missing
class ListPluginBase(PluginBase):
ACTION_UP, ACTION_DOWN, ACTION_DELETE, ACTION_CLEAR = range(4)
COLLECTION_ITEM_TYPE = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._update_model_funcs = []
self._gtk_modelview = None
get_event_handler().register_event(self.COLLECTION_ITEM_TYPE.list_changed_event,
self._update_model)
def __del__(self):
try:
unregister = get_event_handler().unregister_event
except AttributeError:
pass
unregister(self.COLLECTION_ITEM_TYPE.list_changed_event, self._update_model)
def get_all(self):
return tuple(self.get_collection())
def clear(self):
self.get_collection().clear()
def get_selected(self, **kwargs):
if self._gtk_modelview:
return self._get_gtk_selected(**kwargs)
else:
return None
def _get_gtk_selected(self, index=False, force_list=False):
modelview = self._gtk_modelview
if hasattr(modelview, "get_selection"):
# a treeview selection
selection = modelview.get_selection()
if selection is None:
# probably we are just shutting down right now
selection_mode = None
paths = []
else:
selection_mode = selection.get_mode()
paths = selection.get_selected_rows()[1]
elif hasattr(modelview, "get_active"):
# combobox
selection_mode = self._gtk.SELECTION_SINGLE
active = modelview.get_active()
if active < 0:
paths = []
else:
paths = [[active]]
else:
# an iconview
selection_mode = modelview.get_selection_mode()
paths = modelview.get_selected_items()
if index:
get_result = lambda path: path[0]
else:
get_result = self.get_by_path
if (selection_mode == self._gtk.SelectionMode.MULTIPLE) or force_list:
result = []
for path in paths:
result.append(get_result(path))
else:
if not paths:
return None
else:
result = get_result(paths[0])
return result
def select(self, selected):
if not isinstance(selected, (list, tuple)):
selected = [selected]
if self._gtk_modelview:
self._select_gtk(selected)
def _select_gtk(self, selected_objs):
selection = self._gtk_modelview.get_selection()
selected_uuids = [item.get_id() for item in selected_objs]
for index, item in enumerate(self.get_collection()):
path = self._gtk.TreePath.new_from_indices((index, ))
if item.get_id() in selected_uuids:
selection.select_path(path)
else:
selection.unselect_path(path)
def set_gtk_modelview(self, modelview):
self._gtk_modelview = modelview
def force_gtk_modelview_refresh(self):
# force a table update by simulating a change of the list store
model = self._gtk_modelview.get_model()
if model is not None:
model.prepend(None)
model.remove(model.get_iter_first())
def _update_gtk_treemodel(self):
if not self._gtk_modelview:
return
treemodel = self._gtk_modelview.get_model()
if treemodel is None:
# this my happen during shutdown
return
previous_count = len(treemodel)
current_uuids = [item.get_id() for item in self.get_collection()]
# remove all superfluous rows from "treemodel"
removal_indices = [index for index, item in enumerate(treemodel)
if item[0] not in current_uuids]
removal_indices.reverse()
for index in removal_indices:
treemodel.remove(treemodel.get_iter((index, )))
# add all missing items to "treemodel"
model_uuids = [row[0] for row in treemodel]
for this_uuid in current_uuids:
if this_uuid not in model_uuids:
treemodel.append((this_uuid, ))
# reorder the treemodel according to the current list
sorted_indices = [current_uuids.index(row[0]) for row in treemodel]
if sorted_indices:
treemodel.reorder(sorted_indices)
# Explicitly select the first item - otherwise the pre-filled defaults do not cause a
# selection. This would be annoying for the ExportSettings, since the Toolpath view uses
# the first selected set of settings (but would fail by default).
if (previous_count == 0) and current_uuids:
self.select(self.get_collection()[0])
def get_by_path(self, path):
if not self._gtk_modelview:
return None
this_uuid = self._gtk_modelview.get_model()[int(path[0])][0]
return self.get_collection()[this_uuid]
def _update_model(self):
self._update_gtk_treemodel()
for update_func in self._update_model_funcs:
update_func()
def register_model_update(self, func):
self._update_model_funcs.append(func)
def unregister_model_update(self, func):
if func in self._update_model_funcs:
self._update_model_funcs.remove(func)
def _list_action(self, *args):
# the second-to-last parameter should be the model view
modelview = args[-2]
# the last parameter should be the action (ACTION_UP|DOWN|DELETE|CLEAR)
action = args[-1]
if action not in (self.ACTION_UP, self.ACTION_DOWN, self.ACTION_DELETE, self.ACTION_CLEAR):
self.log.info("Invalid action for ListPluginBase.list_action: %s", str(action))
return
selected_items = self.get_selected(index=True, force_list=True)
selected_items.sort()
if action in (self.ACTION_DOWN, self.ACTION_DELETE):
selected_items.sort(reverse=True)
collection = self.get_collection()
new_selection = []
if action == self.ACTION_CLEAR:
collection.clear()
else:
for index in selected_items:
if action == self.ACTION_UP:
if index > 0:
collection.swap_by_index(index, index - 1)
new_selection.append(index - 1)
elif action == self.ACTION_DOWN:
if index < len(self.get_collection()) - 1:
collection.swap_by_index(index, index + 1)
new_selection.append(index + 1)
elif action == self.ACTION_DELETE:
del collection[index]
if collection:
new_selection.append(min(index, len(collection) - 1))
else:
pass
self._update_model()
if hasattr(modelview, "get_selection"):
selection = modelview.get_selection()
else:
selection = modelview
selection.unselect_all()
for index in new_selection:
path = self._gtk.TreePath.new_from_indices((index, ))
selection.select_path(path)
def get_collection(self):
return self.COLLECTION_ITEM_TYPE.get_collection()
def _update_list_action_button_state(self, *args):
modelview = args[-3] # noqa F841 - maybe we need it later
action = args[-2]
button = args[-1]
paths = self.get_selected(index=True, force_list=True)
if action == self.ACTION_CLEAR:
button.set_sensitive(len(self.get_collection()) > 0)
elif not paths:
button.set_sensitive(False)
else:
if action == self.ACTION_UP:
button.set_sensitive(0 not in paths)
elif action == self.ACTION_DOWN:
button.set_sensitive((len(self.get_collection()) - 1) not in paths)
else:
button.set_sensitive(True)
def register_list_action_button(self, action, button):
modelview = self._gtk_modelview
if hasattr(modelview, "get_selection"):
# a treeview
selection = modelview.get_selection()
selection.connect("changed", self._update_list_action_button_state, modelview, action,
button)
else:
modelview.connect("selection-changed", self._update_list_action_button_state,
modelview, action, button)
model = modelview.get_model()
for signal in ("row-changed", "row-deleted", "row-has-child-toggled", "row-inserted",
"rows-reordered"):
model.connect(signal, self._update_list_action_button_state, modelview, action, button)
button.connect("clicked", self._list_action, modelview, action)
# initialize the state of the button
self._update_list_action_button_state(modelview, action, button)
def get_visible(self):
return [item for item in self.get_all() if item.get_application_value("visible", True)]
def edit_item_name(self, cell, path, new_text):
item = self.get_by_path(path)
if item and (new_text != item.get_application_value("name")) and new_text:
item.set_application_value("name", new_text)
def render_item_name(self, column, cell, model, m_iter, data):
item = self.get_by_path(model.get_path(m_iter))
if item:
cell.set_property("text", item.get_application_value("name", "No Name"))
def render_item_visible_state(self, column, cell, model, m_iter, data):
item = self.get_by_path(model.get_path(m_iter))
if item.get_application_value("visible", True):
cell.set_property("pixbuf", self.ICONS["visible"])
else:
cell.set_property("pixbuf", self.ICONS["hidden"])
return item, cell
def toggle_item_visibility(self, treeview, path, column):
item = self.get_by_path(path)
if item:
item.set_application_value("visible", not item.get_application_value("visible"))
self.core.emit_event("visual-item-updated")
def get_non_conflicting_name(self, name_template):
return get_non_conflicting_name(
name_template, [item.get_application_value("name") for item in self.get_all()])
class ObjectWithAttributes(dict):
def __init__(self, node_key=None, attributes=None, **kwargs):
super().__init__(**kwargs)
if attributes is not None:
self.update(attributes)
self["uuid"] = str(uuid.uuid4())
self.node_key = node_key
def filter_list(items, *args, **kwargs):
if len(args) > 1:
_log.info("This filter accepts only a single unnamed parameter: index(es), but %d "
"parameters were given", len(args))
return []
elif len(args) == 1:
try:
items = [items[index] for index in args[0]]
except TypeError:
# not iterable
try:
items = [items[args[0]]]
except (IndexError, TypeError):
_log.info("Invalid index requested in filter: %s", str(args[0]))
return []
else:
pass
result = []
for item in items:
for filter_key in kwargs:
try:
if not item[filter_key] == kwargs[filter_key]:
break
except KeyError:
_log.info("Tried to filter an unknown attribute: %s", str(filter_key))
break
else:
# all keys are matching
result.append(item)
return result
def get_filter(items):
return lambda *args, **kwargs: filter_list(items, *args, **kwargs)
| python |
# SPDX-License-Identifier: BSD-3-Clause
# Depthcharge: <https://github.com/nccgroup/depthcharge>
"""
U-Boot environment variable parsing and handling functionality
"""
import copy
import os
import re
from zlib import crc32
from .. import log
from ..arch import Architecture
# This is a bit bonkers because U-Boot let's you run pretty wild with
# your variable naming...
#
# Here's a few examples to ruin your day:
# setenv ' ' true
# setenv '' :)
# setenv '\$ foo' 'bar ${ }'
# setenv '\$\{bar\} ' 'run echo ${\$ foo}'
# setenv '\$omg \$stahp\}' \#cursed
# setenv \{test\$\{test 42
#
# See U-Boot's lib/hashtable.c for name handling.
_VAR_NAME_RE = re.compile(r"""
\$\{(.*?)\}| # ${foo}
\$([^\$\s\{][^\$\s]*) # $bar
""", re.VERBOSE)
# Representation of an environment variable stored in NV memory
_ENV_VAR_PAT = b'(?P<name>[\x20-\x3c\x3d-\x7f]+)=(?P<value>[\x20-\x7f]+)\x00'
def raw_regex(min_entries: int = 5, max_entries: int = None):
"""
Return a compiled regular expression for locating a U-Boot environment
in a binary. This does not include ``env_t`` metadata, such as the
environment's CRC32 word and optional flags byte.
The *min_entries* and *max_entries* parameters can be used to bound
the size (in number of entries) of the environment to be matched.
If you haven't already, consider using :py:class:`~depthcharge.hunter.EnvironmentHunter`
instead, as this may already do everything you're looking to implement.
"""
min_entries = min_entries or ''
max_entries = max_entries or ''
pattern = b'(' + _ENV_VAR_PAT + b'){'
pattern += str(min_entries).encode('ascii')
pattern += b','
pattern += str(max_entries).encode('ascii')
pattern += b'}'
return re.compile(pattern)
def raw_var_regex():
"""
Return a compiled regular expression that can be used to match an
environment variable definition in a binary.
If you haven't already, consider using :py:class:`~depthcharge.hunter.EnvironmentHunter`
instead, as this may already do everything you're looking to implement.
"""
return re.compile(b'(?P<name>[\x20-\x3c\x3e-\x7f]+)=(?P<value>[\x09\x0a\x0d\x20-\x7f]+)\x00')
def parse(text: str) -> dict:
"""
Parse the contents of the environment contained in the provided *text*
(e.g. obtained through the console interface) and return the environment as
a dictionary.
A :py:exc:`ValueError` is raised if no environment variables are found.
"""
results = {}
prev_name = None
expect_continuation = False
for line in text.splitlines():
if expect_continuation:
results[prev_name] += os.linesep + line
expect_continuation = line.endswith('\\')
else:
if not line or line.startswith('Environment size: '):
continue
try:
delim_idx = line.index('=')
except ValueError:
# Try to be resilient and ignore bizzare or malformed lines...
continue
name = line[:delim_idx]
value = line[delim_idx+1:]
results[name] = value
prev_name = name
expect_continuation = value.endswith('\\')
if not results:
raise ValueError('No environment variables found')
return results
def expand_variable(env: dict, to_expand: str, **kwargs) -> str:
"""
Return the environment variable named *to_expand* with all variable definitions
contained within it fully expanded.
A :py:exc:`KeyError` is raised if *to_expand* is not present in the provided
*env* dictionary.
**Optional Keyword Arguments**:
*limit* - Maximum expansion iterations to peform. Default: ``100``
*warn_only* - Print a warning, but do not raise an exception, if the variable definition
cannot be fully expended due to an undefined environment variable. This situtaion is
possibly indicative of an issue with the U-Boot environment itself, rather than Depthcharge
or anything the user has done incorrectly; it may be the case that some incomplete
development cruft or reference design vestiges are present in the environment. If this
occurs and this setting is set to ``False``, a :py:exc:`ValueError` will be raised.
Default: ``True``
*quiet* - Suppress the above warning. (Requires *warn_only=True*.)
"""
result = None
limit = kwargs.get('limit', 100)
warn_only = kwargs.get('warn_only', True)
quiet = kwargs.get('quiet', False)
value = env[to_expand]
for _ in range(0, limit):
prev = value
for match in _VAR_NAME_RE.finditer(value):
var_name = match.group(1) or match.group(2)
if var_name in env:
expansion = env[var_name]
if match.group(1):
value = value.replace('${' + var_name + '}', expansion)
else:
value = value.replace('$' + var_name, expansion)
if prev == value:
result = value
break
if result is None:
raise ValueError('Expansion iteration limit reached')
# Are there any unexpanded definitions remaining?
match = _VAR_NAME_RE.search(value)
if match:
var_name = match.group(1) or match.group(2)
msg = 'No definition for environment variable "{:s}" found when expanding "{:s}"'
msg = msg.format(var_name, to_expand)
if warn_only:
if not quiet:
log.warning(msg)
else:
raise ValueError(msg)
return result
def expand(env: dict, **kwargs) -> dict:
"""
Return a copy of the provided U-Boot environment variable dictionary with all
variable definitions fully resolved.
This function supports the same keyword arguments as :py:func:`expand_variable()`.
"""
ret = copy.deepcopy(env)
for var in env:
ret[var] = expand_variable(env, var, **kwargs)
return ret
def parse_raw(data: bytes) -> dict:
"""
Parse the contents of an environment retrieved from flash or memory
and provide an equivalent dictionary.
The provided *data* should being at the start of the variable definitions.
It **must not** contain the ``env_t`` metadata, such as the CRC32 word
and the ``flags`` value (only present when compiled with
"``CONFIG_SYS_REDUNDAND_ENVIRONMENT``".
A :py:exc:`ValueError` is raised if no environment variables are found.
"""
results = {}
regex = raw_var_regex()
for match in regex.finditer(data):
name = match.group('name').decode('ascii')
value = match.group('value').decode('ascii')
results[name] = value
if not results:
raise ValueError('No environment variables found')
return results
def load(filename: str) -> dict:
"""
Load a U-Boot environment from a text file and return it as a dictionary.
The text file is expected to be in the same format as that used by U-Boot's
``printenv`` command output.
A :py:exc:`ValueError` is raised if no environment variables are found.
"""
with open(filename, 'r') as infile:
text = infile.read()
return parse(text)
def load_raw(filename: str, arch: str, has_crc=True, has_flags=False) -> tuple:
"""
Load an environment previously carved from a binary or saved with
:py:func:`save_raw()`. It is returned as a tuple: ``(env: dict, metadata: dict)``
This function expects the environment (metadata) to begin at offset 0 in
the opened file. The name of the target architecture (*arch*) must be
provided.
The *has_crc* and *has_flags* boolean parameters should be used to
specify whether the file contains a U-Boot env_t header.
"""
with open(filename, 'rb') as infile:
data = infile.read()
metadata = {}
start = 0
if has_crc:
arch = Architecture.get(arch)
crc = int.from_bytes(data[0:4], arch.endianness)
start += 4
metadata['crc'] = crc
if has_flags:
metadata['flags'] = data[start]
start += 1
data = data[start:]
metadata['actual_crc'] = crc32(data)
metadata['size'] = len(data)
env = parse_raw(data)
return (env, metadata)
def save(filename: str, env: dict):
"""
Write the contents of an environment to a text file that can later
be loaded via :py:func:load()`.
"""
with open(filename, 'w') as outfile:
for name in sorted(env.keys()):
value = env[name]
outfile.write(name + '=' + value + os.linesep)
def save_raw(filename: str, env: dict, size: int, arch: str, flags: int = None, no_header=False):
"""
Convert the environment information stored in *env* and save it to *filename*.
Refer to :py:func:`create_raw_environment` for more information about this function's arguments.
"""
env_data = create_raw(env, size, arch, flags, no_header)
with open(filename, 'wb') as outfile:
outfile.write(env_data)
def create_raw(env: dict, size: int, arch: str, flags: int = None, no_header=False) -> bytes:
"""
Convert the environment contained the *env* dictionary to the binary format that can be used to
replace an environment in non-volatile storage.
The *size* parameter must match the target's compile-time ``CONFIG_ENV_SIZE`` definition.
The environment is zero-padded to this length prior to the computation of its CRC32
checksum. If you don't know this value and can extract flash contents, you can use
:py:class:`~depthcharge.hunter.EnvironmentHunter` to locate environment instances. The ``src_size``
entry in the results for of :py:meth:`~depthcharge.hunter.EnvironmentHunter.find()`
and :py:meth:`~depthcharge.hunter.EnvironmentHunter.finditer()` correspond to this size.
The *arch* parameter must name the target architecture that will be processing the environment.
Finally, an optional *flags* value can be provided. This is an ``env_t``
structure field present only when U-Boot is compiled with the
`CONFIG_SYS_REDUNDAND_ENV <https://gitlab.denx.de/u-boot/u-boot/-/blob/v2020.04/env/Kconfig#L394>`_
(sic) option. This option enables the use of two environment copies, should
one become corrupted during the programming operation (e.g. via unexpected power-loss).
Although called "flags", it's basically a monotonic modulo-256 counter that's incremented
by one at each write to denote the freshest copy. (See `env/common.c
<https://gitlab.denx.de/u-boot/u-boot/-/blob/v2020.04/env/common.c#L181>`_)
If you are replacing an environment that uses this feature, be sure to provide either the same
*flags* value or a greater value.
Setting *no_header=True* will create the environment contents without any header metadata
(i.e., no CRC word, no flags).
"""
ret = bytearray()
endianness = Architecture.get(arch).endianness
env_bin = b''
for name in sorted(env.keys()):
env_bin += name.encode('ascii')
env_bin += b'='
env_bin += env[name].encode('ascii')
env_bin += b'\x00'
padding = size - len(env_bin)
if no_header is False:
padding -= 4 # CRC word
if flags is not None:
padding -= 1
if padding < 0:
msg = 'Environment contents ({:d} bytes) exceed storage size ({:d} bytes)'
raise ValueError(msg.format(len(env_bin) - padding, size))
env_bin += b'\x00' * padding
crc_bytes = crc32(env_bin).to_bytes(4, endianness)
if no_header is not True:
ret += crc_bytes
if flags is not None:
ret += flags.to_bytes(1, 'big')
ret += env_bin
return bytes(ret)
| python |
# Function to add looted inventory to player inventory
def addToInventory(inventory, addedItems):
for loot in addedItems:
if loot in inventory:
inventory[loot] = inventory[loot] + 1
else:
inventory.setdefault(loot, 1)
return inventory
# Function to display inventory
def displayInventory(inventory):
print('Inventory:')
itemTotal = 0
for item in inventory:
itemTotal = itemTotal + inventory[item]
print(inventory[item], end=' ')
print(item)
print('Total number of items: ' + str(itemTotal))
# Player's inventory
playerInventory = {'gold coin' : 42,
'rope' : 1}
# Dragon's inventory
dragonLoot = ['gold coin', 'dagger', 'gold coin', 'gold coin', 'ruby']
# Call functions
playerInventory = addToInventory(playerInventory, dragonLoot)
displayInventory(playerInventory) | python |
import requests
url = 'https://images-api.nasa.gov/search?q=Ilan%20Ramon'
image_metadata_url= 'https://images-assets.nasa.gov/image/{0}/metadata.json' #KSC-03pd2975/metadata.json'
# params = dict(
# origin='Chicago,IL',
# destination='Los+Angeles,CA',
# waypoints='Joplin,MO|Oklahoma+City,OK',
# sensor='false'
# )
resp = requests.get(url=url)
data = resp.json()
for item in data['collection']['items']:
item_nasa_id = item['data'][0]['nasa_id']
item_href = item['links'][0]['href']
image_metadata = requests.get(url=image_metadata_url.format(item['data'][0]['nasa_id']))
file_type = str(image_metadata.json()['File:FileSize']).split(' ')[1]
if file_type != "MB": #kb
file_zise = int(str(image_metadata.json()['File:FileSize']).split(' ')[0])
if file_zise > 1000:
print(file_zise)
print(item_nasa_id)
print(item_href) | python |
import multiprocessing
import os
from argparse import ArgumentParser
from pathlib import Path
import torch
from nflows import distributions, transforms
from pyprojroot import here
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.loggers import WandbLogger
from torch.utils.data import DataLoader, TensorDataset
from src.data.toy import get_bivariate_data
from src.models.dists import get_base_dist
from src.models.flows import Gaussianization2D
from src.models.gaussianization import (get_marginalization_transform,
get_rotation)
from src.viz.bivariate import plot_2d_joint, plot_2d_joint_probs
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
root = here(project_files=[".here"])
home = str(Path.home())
save_path = Path(root).joinpath("reports/figures/experiments/bivariate")
def main(args):
# =======================
# Initialize Logger
# =======================
wandb_logger = WandbLogger(project=args.wandb_project, entity=args.wandb_entity)
wandb_logger.experiment.config.update(args)
seed_everything(args.seed)
X_data = get_bivariate_data(
dataset=args.dataset, n_samples=args.n_train, noise=args.noise, seed=args.seed
)
X_val = get_bivariate_data(
dataset=args.dataset,
n_samples=args.n_valid,
noise=args.noise,
seed=args.seed + 1,
)
n_features = 2
# plot data samples
plot_2d_joint(
X_data,
color="blue",
label="Real Data",
wandb_logger=wandb_logger.experiment,
log_name="samples_real",
# save=str(save_path.joinpath(f"{args.dataset}_samples_real.png")),
)
# get number of layers
layers = []
if args.init_rot:
# initialize with rotation layer
layers.append(
get_rotation(
n_features=n_features,
num_householder=args.num_householder,
identity_init=args.identity,
rotation=args.rotation,
)
)
# loop through layers
for _ in range(args.n_layers):
# marginal transform
layers.append(
get_marginalization_transform(
n_features=n_features,
squash=args.squash,
num_bins=args.n_bins,
tails=args.tails,
tail_bound=args.tail_bound,
identity_init=args.identity,
)
)
# rotation
layers.append(
get_rotation(
n_features=n_features,
num_householder=args.num_householder,
identity_init=args.identity,
rotation=args.rotation,
)
)
# get marginal transformation
gauss_flows = transforms.CompositeTransform(layers)
# createval_loader
# initialize NF trainer
gf_model = Gaussianization2D(
gauss_flows, base_distribution=get_base_dist(n_features), hparams=args
)
# plot initial latent space
with torch.no_grad():
z = gf_model.model.transform_to_noise(torch.Tensor(X_data))
plot_2d_joint(
z.numpy(),
color="green",
label="Latent Space",
wandb_logger=wandb_logger.experiment,
log_name="latent_init",
# save=str(save_path.joinpath(f"{args.dataset}_samples_real.png")),
)
# ====================================
# DATA
# ====================================
X_data, X_val = torch.FloatTensor(X_data), torch.FloatTensor(X_val)
train_dataset, val_dataset = TensorDataset(X_data), TensorDataset(X_val)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=multiprocessing.cpu_count(),
pin_memory=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=multiprocessing.cpu_count(),
pin_memory=True,
)
# ====================================
# TRAINING
# ====================================
trainer = Trainer(max_epochs=args.n_epochs, gpus=1, logger=wandb_logger)
trainer.fit(gf_model, train_loader, val_loader)
# ====================================
# PLOTS
# ====================================
with torch.no_grad():
# LATENT SPACE
z = gf_model.model.transform_to_noise(X_data)
plot_2d_joint(
z.detach().numpy(),
color="green",
label="Latent Space",
wandb_logger=wandb_logger.experiment,
log_name="latent_trained",
# save=str(save_path.joinpath("latent_trained.png")),
)
# PROBABILITIES
X_logprob = gf_model.model.log_prob(X_data)
plot_2d_joint_probs(
X_data.detach().numpy(),
probs=X_logprob.numpy(),
wandb_logger=wandb_logger.experiment,
log_name="log_probs",
# save=str(save_path.joinpath("latent_trained.png")),
)
plot_2d_joint_probs(
X_data.detach().numpy(),
probs=X_logprob.exp().numpy(),
wandb_logger=wandb_logger.experiment,
log_name="probs",
# save=str(save_path.joinpath("latent_trained.png")),
)
# SAMPLING
with torch.no_grad():
X_approx = gf_model.model.sample(args.n_samples)
plot_2d_joint(
X_approx.numpy(),
color="red",
label="Gen. Samples",
wandb_logger=wandb_logger.experiment,
log_name="samples_gen",
# save=str(save_path.joinpath("samples_gen.png")),
)
if __name__ == "__main__":
parser = ArgumentParser(add_help=False)
# ======================
# Data parameters
# ======================
parser.add_argument(
"--dataset",
type=str,
default="rbig",
help="2D Dataset",
)
parser.add_argument(
"--n-train",
type=int,
default=5_000,
help="Number of training samples",
)
parser.add_argument(
"--n-valid",
type=int,
default=500,
help="Number of validation samples",
)
parser.add_argument(
"--noise",
type=float,
default=0.10,
help="Noise level",
)
# ======================
# Transform Params
# ======================
parser.add_argument(
"--init-rot",
type=int,
default=1,
help="Init rotation",
)
parser.add_argument(
"--n-layers",
type=int,
default=3,
help="Number of layers",
)
parser.add_argument(
"--squash",
type=int,
default=0,
help="Number of bins for spline transformation",
)
parser.add_argument(
"--n-bins",
type=int,
default=10,
help="Number of bins for spline transformation",
)
parser.add_argument(
"--tail-bound",
type=float,
default=10.0,
help="Number of bins for spline transformation",
)
parser.add_argument(
"--tails",
type=str,
default="linear",
help="tails",
)
parser.add_argument(
"--identity",
type=int,
default=1,
help="Initialize with identity",
)
parser.add_argument(
"--rotation",
type=str,
default="pca",
help="Rotation layer",
)
parser.add_argument(
"--num-householder",
type=int,
default=2,
help="Number of householder matrices",
)
# ======================
# Training Params
# ======================
parser.add_argument(
"--lr",
type=float,
default=1e-2,
help="Learning Rate",
)
parser.add_argument(
"--batch-size",
type=int,
default=50,
help="Batch size",
)
parser.add_argument(
"--n-epochs",
type=int,
default=100,
help="Number of epochs for training",
)
# ======================
# VIZ Params
# ======================
parser.add_argument(
"--n-samples",
type=int,
default=5_000,
help="Number of samples",
)
# ======================
# Testing
# ======================
parser.add_argument(
"-sm",
"--smoke-test",
action="store_true",
help="to do a smoke test without logging",
)
parser.add_argument(
"--seed",
type=int,
default=123,
help="Seed for project",
)
# ======================
# Logger Parameters
# ======================
parser.add_argument("--wandb-entity", type=str, default="emanjohnson91")
parser.add_argument("--wandb-project", type=str, default="rbig20-2d")
args = parser.parse_args()
if args.smoke_test:
os.environ["WANDB_MODE"] = "dryrun"
args.n_epochs = 5
args.n_train = 100
main(args)
| python |
# Generated by Django 2.2.5 on 2019-09-07 04:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('bhs', '0002_group_pos'),
]
operations = [
migrations.AlterField(
model_name='group',
name='chapters',
field=models.CharField(blank=True, editable=False, help_text='\n The denormalized chapter group.', max_length=255),
),
migrations.AlterField(
model_name='group',
name='participants',
field=models.CharField(blank=True, default='', editable=False, help_text='Director(s) or Members (listed TLBB)', max_length=255),
),
migrations.AlterField(
model_name='group',
name='pos',
field=models.IntegerField(blank=True, editable=False, help_text='\n The number of active performers.', null=True),
),
]
| python |
# Authors:
# Loic Gouarin <[email protected]>
# Benjamin Graille <[email protected]>
# Thibaut Van Hoof <[email protected]>
#
# License: BSD 3 clause
from .model import ModelWidget
from .test_case import TestCaseWidget
from .lb_scheme import LBSchemeWidget
from .stability import StabilityWidget
from .simulation import SimulationWidget
from .parametric_study import ParametricStudyWidget
from .post_treatment import PostTreatmentWidget
from .pylbmwidget import out
from .debug import debug, debug_func
from . import responses | python |
"""
Пул воркеров. Полное управление и контроль воркерами.
"""
import logging
import threading
from functools import partial
from multiprocessing import Pool, cpu_count, Queue, Process
logger = logging.getLogger(__name__)
class Worker(Process):
"""
Свой процесс. Тут мы вызываем команду.
"""
def __init__(self, queue, pk, *args, **kwargs):
super(Worker, self).__init__(*args, **kwargs)
self.queue = queue
self.pk = pk
def run(self):
logger.info('Worker `{}` started.'.format(self.pk))
for data in iter(self.queue.get, None):
try:
command, args, kwargs = data.get('command', None), data.get('args', []), data.get('kwargs', {})
if command:
logger.info('Worker `{}`. Start task:`{}`, args: `{}`, kwargs: `{}`.'.format(
self.pk, command, args, kwargs
))
result = command(*args, **kwargs)
logger.info('Worker `{}`. End task:`{}`, args: `{}`, kwargs: `{}, result: `{}`.'.format(
self.pk, command, args, kwargs, result
))
else:
logger.error('Worker `{}` error. Command not found in `{}`.'.format(self.pk, data))
except:
logger.error(
'Exception for worker `{}` command: `{}`. More information: '.format(self.pk, data), exc_info=True
)
logger.info('Worker `{}` finished.'.format(self.pk))
class NewPoolWorkers(object):
"""
Ручной пул воркеров.
Простая общая очередь на несколько воркеров.
"""
def __init__(self, count=None):
self.__count_workers = count if count else cpu_count() - 1
self.__queue = Queue()
self.__workers = {
key + 1: Worker(self.__queue, key + 1) for key in range(count)
}
for key, val in self.__workers.items():
val.start()
def apple_async(self, target, *args, **kwargs):
"""
Добавление задачи для асинхронного выполнения.
:param target: Зачада.
:type target: function
"""
logger.info('Add task for pool. Task: `{}`, args: `{}`, kwargs: `{}`.'.format(target, args, kwargs))
self.__queue.put({"command": target, "args": args, "kwargs": kwargs})
def close(self):
"""
Убиваем все воркеры и сам пул. Предварительно ждем завершения задачи.
"""
logger.info('Poll workers shutdown started.')
for _ in self.__workers.keys():
self.__queue.put(None)
for key, val in self.__workers.items():
val.join()
val.terminate()
logger.info('Pool workers shutdown finished.')
class RenewableWorker(Process):
"""
Одноразовый воркер.
"""
def __init__(self, pk, end_callback=None, error_callback=None, *args, **kwargs):
"""
Одноразовый воркер.
:param int pk: ID воркера.
:param function end_callback: Функция, выполняющаяся после успешного выполнения задачи.
:param function error_callback: Функция, выполняющаяся после ошибки в задаче.
"""
super(RenewableWorker, self).__init__(*args, **kwargs)
self.pk = pk
self.end_callback = end_callback
self.error_callback = error_callback
def run(self):
logger.info('Worker `{}` start.'.format(self.pk))
try:
result = super(RenewableWorker, self).run()
except Exception as e:
logger.error('Worker `{}` exception.'.format(self.pk), exc_info=True)
return self.error_callback(e) if self.error_callback else None
logger.info('Worker `{}` end.'.format(self.pk))
return self.end_callback(result) if self.end_callback else result
class PollRenewableWorkers(object):
"""
Пул возобновляемых воркеров.
На каждую задачу создается процесс, после выполнения задачи процесс грохается.
"""
def __init__(self, count=None):
self.__count_workers = count if count else cpu_count() - 1
self.__workers = {}
def apple_async(self, target, end_callback=None, error_callback=None, *args, **kwargs):
"""
Добавление задачи для асинхронного выполнения.
:param target: Зачада.
:param end_callback: Функция, которая выполнится после успешного завершения задачи.
:param error_callback: Функция, которая выполнится после ошибки во время задачи.
:type target: function
:type end_callback: function
:type error_callback: function
"""
process = RenewableWorker(
self.__create_pk(),
end_callback=end_callback,
error_callback=error_callback,
target=target,
args=args,
kwargs=kwargs
)
self.__workers[process.pk] = process
self.__workers[process.pk].start()
def close(self):
"""
Завершает все процессы безопасно.
"""
for key, val in self.__workers.items():
logger.info("Worker `{}` served his own. It's time to retire.".format(key))
val.terminate()
val.join()
logger.info("Worker `{}` retired. Bye Bye.".format(key))
def __create_pk(self):
"""
Формирует и возвращает PK воркера.
:return: PK для нового воркера.
:rtype: int
"""
pks = sorted(list(self.__workers.keys()), reverse=True)
return pks[0] + 1 if pks else 1
class PoolWorkers(object):
"""
Пул воркеров, с которым работаем.
Стандартный пул, без наработок.
"""
def __init__(self, count=None):
self.__count_workers = count if count else cpu_count() - 1
self.__pool = Pool(self.__count_workers)
@property
def state(self):
try:
return self.__pool._state
except Exception as e:
logger.error(e, exc_info=True)
return None
def apple_async(self, target, end_callback=None, error_callback=None, *args, **kwargs):
"""
Добавление задачи для асинхронного выполнения.
:param target: Зачада.
:param end_callback: Функция, которая выполнится после успешного завершения задачи.
:param error_callback: Функция, которая выполнится после ошибки во время задачи.
:type target: function
:type end_callback: function
:type error_callback: function
"""
self.__pool.apply_async(target, args=args, kwds=kwargs, callback=end_callback, error_callback=error_callback)
def join(self):
"""
Ждем выполнения всех воркеров.
"""
return self.__pool.join()
def close(self):
"""
Убиваем все воркеры и сам пул. Предварительно ждем завершения задачи.
"""
self.__pool.close()
class Timer(threading.Thread):
"""
Свой таймер, отдельным потоком. Несмотря на GIL, должно работать.
"""
def __init__(self, handler, args=None, kwargs=None, interval=20 * 60, deffer=False):
"""
Делаем атрибуты для таймера.
:param handler: Функция, которую надо вызывать каждые interval секунд.
:param args: Позиционные аргументы для функции.
:param kwargs: Именованные аргументы для функции.
:param interval: Интервал, через который надо вызывать handler.
:param deffer: Отложенный запуск.
:type handler: func
:type args: tuple
:type kwargs: dict
:type interval: int
:type deffer: bool
"""
threading.Thread.__init__(self)
self.__finished = threading.Event()
self.__interval = float(interval)
args, kwargs = args if args else (), kwargs if kwargs else {}
self.__handler = partial(handler, *args, **kwargs)
self.__deffer = deffer
def set_interval(self, interval):
"""
Изменить интервал, на который будет засыпать поток.
"""
self.__interval = interval
def shutdown(self):
"""
Останавливаем поток.
"""
self.__finished.set()
def __run_deffer(self):
"""
Запускает отложенный таймер. Т.е. первый раз функция выполнится через interval секунд.
"""
while True:
if self.__finished.isSet():
return
# Спим, пока не пройдет интервал или сдохнет процесс.
self.__finished.wait(self.__interval)
self.__handler()
def __run_normal(self):
"""
Запускает нормальный таймер. Т.е. первый раз функция выполнится сразу.
"""
while True:
if self.__finished.isSet():
return
self.__handler()
# Спим, пока не пройдет интервал или сдохнет процесс.
self.__finished.wait(self.__interval)
def run(self):
"""
Сам запуск задачи.
"""
logger.info('Start timer target `{}` interval `{}`'.format(self.__handler, self.__interval))
while True:
try:
if self.__deffer:
self.__run_deffer()
else:
self.__run_normal()
except:
logger.error(
'In timer exception target `{}` interval `{}`.'.format(self.__handler, self.__interval),
exc_info=True
)
if self.__finished.isSet():
break
if self.__finished.isSet():
break
logger.info('Timer target `{}` interval `{}` rerun.'.format(self.__handler, self.__interval))
logger.info('End timer target `{}` interval `{}`.'.format(self.__handler, self.__interval))
| python |
import sys
import tempfile
from textwrap import dedent
import _pytest
import pytest
import yaml
from mock import Mock
from mock import patch
from tavern.core import run
from tavern.schemas.extensions import validate_file_spec
from tavern.testutils.helpers import validate_pykwalify
from tavern.testutils.helpers import validate_regex, validate_content
from tavern.testutils.pytesthook.item import YamlItem
from tavern.util import exceptions
from tavern.util.dict_util import _check_parsed_values, format_keys
class FakeResponse:
def __init__(self, text):
self.text = text
self.headers = dict(test_header=text)
class TestRegex:
def test_regex_match(self):
response = FakeResponse("abchelloabc")
matched = validate_regex(response, "(?P<greeting>hello)")
assert "greeting" in matched["regex"]
def test_regex_no_match(self):
response = FakeResponse("abchelloabc")
with pytest.raises(AssertionError):
validate_regex(response, "(?P<greeting>hola)")
def test_regex_match_header(self):
response = FakeResponse("abchelloabc")
matched = validate_regex(response, "(?P<greeting>hello)", "test_header")
assert "greeting" in matched["regex"]
def test_regex_no_match_header(self):
response = FakeResponse("abchelloabc")
with pytest.raises(AssertionError):
validate_regex(response, "(?P<greeting>hola)", "test_header")
class TestRunAlone:
def test_run_calls_pytest(self):
"""This should just return from pytest.main()"""
with patch("tavern.core.pytest.main") as pmock:
run("abc")
assert pmock.called
def test_normal_args(self):
with patch("tavern.core.pytest.main") as pmock:
run(
**{
"tavern_global_cfg": None,
"in_file": "kfdoskdof",
"tavern_http_backend": "requests",
"tavern_mqtt_backend": "paho-mqtt",
"tavern_strict": True,
}
)
assert pmock.called
def test_extra_args(self):
with pytest.warns(FutureWarning):
with patch("tavern.core.pytest.main") as pmock:
run(
**{
"tavern_global_cfg": None,
"in_file": "kfdoskdof",
"tavern_http_backend": "requests",
"tavern_mqtt_backend": "paho-mqtt",
"tavern_strict": True,
"gfg": "2efsf",
}
)
assert pmock.called
class TestTavernRepr:
@pytest.fixture(name="fake_item")
def fix_fake_item(self, request):
item = YamlItem(
name="Fake Test Item", parent=request.node, spec={}, path="/tmp/hello"
)
return item
def _make_fake_exc_info(self, exc_type):
# Copied from pytest tests
class FakeExcinfo(_pytest._code.ExceptionInfo):
pass
try:
raise exc_type
except exc_type:
excinfo = FakeExcinfo(sys.exc_info())
return excinfo
def test_not_called_for_normal_exception(self, fake_item):
"""Should call normal pytest repr_info"""
fake_info = self._make_fake_exc_info(RuntimeError)
with patch("tavern.testutils.pytesthook.item.ReprdError") as rmock:
fake_item.repr_failure(fake_info)
assert not rmock.called
def test_not_called_if_flag_not_enabled(self, fake_item):
"""Not called by default for tavern exceptions"""
fake_info = self._make_fake_exc_info(exceptions.BadSchemaError)
with patch("tavern.testutils.pytesthook.item.ReprdError") as rmock:
fake_item.repr_failure(fake_info)
assert not rmock.called
def test_not_called_for_badschema_tavern_exception_(self, fake_item):
"""Enable ini flag, should be called"""
fake_info = self._make_fake_exc_info(exceptions.BadSchemaError)
with patch.object(fake_item.config, "getini", return_value=True):
with patch("tavern.testutils.pytesthook.item.ReprdError") as rmock:
fake_item.repr_failure(fake_info)
assert not rmock.called
def test_called_for_tavern_exception_ini(self, fake_item):
"""Enable ini flag, should be called"""
fake_info = self._make_fake_exc_info(exceptions.InvalidSettingsError)
with patch.object(fake_item.config, "getini", return_value=True):
with patch("tavern.testutils.pytesthook.item.ReprdError") as rmock:
fake_item.repr_failure(fake_info)
assert rmock.called
def test_called_for_tavern_exception_cli(self, fake_item):
"""Enable cli flag, should be called"""
fake_info = self._make_fake_exc_info(exceptions.InvalidSettingsError)
with patch.object(fake_item.config, "getoption", return_value=True):
with patch("tavern.testutils.pytesthook.item.ReprdError") as rmock:
fake_item.repr_failure(fake_info)
assert rmock.called
@pytest.fixture(name="nested_response")
def fix_nested_response():
class response_content(object):
content = {
"top": {
"Thing": "value",
"float": 0.1,
"nested": {"doubly": {"inner_value": "value", "inner_list": [1, 2, 3]}},
},
"an_integer": 123,
"a_string": "abc",
"a_bool": True,
}
def json(self):
return self.content
return response_content()
class TestContent:
def test_correct_jmes_path(self, nested_response):
comparisons = [
{"jmespath": "top.Thing", "operator": "eq", "expected": "value"},
{"jmespath": "an_integer", "operator": "eq", "expected": 123},
{
"jmespath": "top.nested.doubly.inner_list",
"operator": "type",
"expected": "list",
},
]
validate_content(nested_response, comparisons)
assert True
def test_incorrect_jmes_path(self, nested_response):
comparisons = [{"jmespath": "userId", "operator": "eq", "expected": 1}]
with pytest.raises(exceptions.JMESError):
validate_content(nested_response, comparisons)
def test_incorrect_value(self, nested_response):
comparisons = [{"jmespath": "a_bool", "operator": "eq", "expected": False}]
with pytest.raises(exceptions.JMESError):
validate_content(nested_response, comparisons)
class TestPykwalifyExtension:
def test_validate_schema_correct(self, nested_response):
correct_schema = dedent(
"""
type: map
required: true
mapping:
top:
type: map
required: true
mapping:
Thing:
type: str
float:
type: float
nested:
type: any
an_integer:
type: int
a_string:
type: str
a_bool:
type: bool
"""
)
validate_pykwalify(
nested_response, yaml.load(correct_schema, Loader=yaml.SafeLoader)
)
def test_validate_schema_incorrect(self, nested_response):
correct_schema = dedent(
"""
type: seq
required: true
sequence:
- type: str
"""
)
with pytest.raises(exceptions.BadSchemaError):
validate_pykwalify(
nested_response, yaml.load(correct_schema, Loader=yaml.SafeLoader)
)
class TestCheckParseValues(object):
@pytest.mark.parametrize(
"item", [[134], {"a": 2}, yaml, yaml.load, yaml.SafeLoader]
)
def test_warns_bad_type(self, item):
with patch("tavern.util.dict_util.logger.warning") as wmock:
_check_parsed_values("{fd}", {"fd": item})
assert wmock.called_with(
"Formatting 'fd' will result in it being coerced to a string (it is a {})".format(
type(item)
)
)
@pytest.mark.parametrize("item", [1, "a", 1.3, format_keys("{s}", dict(s=2))])
def test_no_warn_good_type(self, item):
with patch("tavern.util.dict_util.logger.warning") as wmock:
_check_parsed_values("{fd}", {"fd": item})
assert not wmock.called
class TestCheckFileSpec(object):
def _wrap_test_block(self, dowith):
validate_file_spec({"files": dowith}, Mock(), Mock())
def test_string_valid(self):
with tempfile.NamedTemporaryFile() as tfile:
self._wrap_test_block(tfile.name)
def test_dict_valid(self):
with tempfile.NamedTemporaryFile() as tfile:
self._wrap_test_block({"file_path": tfile.name})
def test_nonexistsnt_string(self):
with pytest.raises(exceptions.BadSchemaError):
self._wrap_test_block("kdsfofs")
def nonexistent_dict(self):
with pytest.raises(exceptions.BadSchemaError):
self._wrap_test_block({"file_path": "gogfgl"})
def extra_keys_dict(self):
with pytest.raises(exceptions.BadSchemaError):
self._wrap_test_block({"file_path": "gogfgl", "blop": 123})
| python |
import copy
import os
import json
from hpbandster.core.base_iteration import Datum
class Run(object):
"""
Not a proper class, more a 'struct' to bundle important
information about a particular run
"""
def __init__(self, config_id, budget, loss, info, time_stamps, error_logs):
self.config_id = config_id
self.budget = budget
self.error_logs = error_logs
self.loss = loss
self.info = info
self.time_stamps = time_stamps
def __repr__(self):
return(\
"config_id: %s\t"%(self.config_id,) + \
"budget: %f\t"%self.budget + \
"loss: %s\n"%self.loss + \
"time_stamps: {submitted} (submitted), {started} (started), {finished} (finished)\n".format(**self.time_stamps) + \
"info: %s\n"%self.info
)
def __getitem__ (self, k):
"""
in case somebody wants to use it like a dictionary
"""
return(getattr(self, k))
def extract_HB_learning_curves(runs):
"""
function to get the hyperband learning curves
This is an example function showing the interface to use the
HB_result.get_learning_curves method.
Parameters:
-----------
runs: list of HB_result.run objects
the performed runs for an unspecified config
Returns:
--------
list of learning curves: list of lists of tuples
An individual learning curve is a list of (t, x_t) tuples.
This function must return a list of these. One could think
of cases where one could extract multiple learning curves
from these runs, e.g. if each run is an independent training
run of a neural network on the data.
"""
sr = sorted(runs, key=lambda r: r.budget)
return([[(r.budget, r.loss) for r in sr],])
class json_result_logger(object):
"""
convenience logger for 'semi-live-results'
Logger that writes job results into two files (configs.json and results.json).
Both files contain propper json objects in each line.
This version (v1) opens and closes the files for each result.
This might be very slow if individual runs are fast and the
filesystem is rather slow (e.g. a NFS).
"""
def __init__(self, directory, overwrite=False):
"""
Parameters:
-----------
directory: string
the directory where the two files 'configs.json' and
'results.json' are stored
overwrite: bool
In case the files already exist, this flag controls the
behavior:
> True: The existing files will be overwritten.
Potential risk of deleting previous results
> False: A FileEvistsError is raised and the files are
not modified.
"""
os.makedirs(directory, exist_ok=True)
self.config_fn = os.path.join(directory, 'configs.json')
self.results_fn = os.path.join(directory, 'results.json')
try:
with open(self.config_fn, 'x') as fh: pass
except FileExistsError:
if overwrite:
with open(self.config_fn, 'w') as fh: pass
else:
raise FileExistsError('The file %s already exists.'%self.config_fn)
except:
raise
try:
with open(self.results_fn, 'x') as fh: pass
except FileExistsError:
if overwrite:
with open(self.results_fn, 'w') as fh: pass
else:
raise FileExistsError('The file %s already exists.'%self.config_fn)
except:
raise
self.config_ids = set()
def new_config(self, config_id, config, config_info):
if not config_id in self.config_ids:
self.config_ids.add(config_id)
with open(self.config_fn, 'a') as fh:
fh.write(json.dumps([config_id, config, config_info]))
fh.write('\n')
def __call__(self, job):
if not job.id in self.config_ids:
#should never happen! TODO: log warning here!
self.config_ids.add(job.id)
with open(self.config_fn, 'a') as fh:
fh.write(json.dumps([job.id, job.kwargs['config'], {}]))
fh.write('\n')
with open(self.results_fn, 'a') as fh:
fh.write(json.dumps([job.id, job.kwargs['budget'], job.timestamps, job.result, job.exception]))
fh.write("\n")
def logged_results_to_HB_result(directory):
"""
function to import logged 'live-results' and return a HB_result object
You can load live run results with this function and the returned
HB_result object gives you access to the results the same way
a finished run would.
"""
data = {}
time_ref = float('inf')
budget_set = set()
with open(os.path.join(directory, 'configs.json')) as fh:
for line in fh:
line = json.loads(line)
if len(line) == 3:
config_id, config, config_info = line
if len(line) == 2:
config_id, config, = line
config_info = 'N/A'
data[tuple(config_id)] = Datum(config=config, config_info=config_info)
with open(os.path.join(directory, 'results.json')) as fh:
for line in fh:
config_id, budget,time_stamps, result, exception = json.loads(line)
id = tuple(config_id)
data[id].time_stamps[budget] = time_stamps
data[id].results[budget] = result
data[id].exceptions[budget] = exception
budget_set.add(budget)
time_ref = min(time_ref, time_stamps['submitted'])
# infer the hyperband configuration from the data
budget_list = sorted(list(budget_set))
HB_config = {
'eta' : None if len(budget_list) < 2 else budget_list[1]/budget_list[0],
'min_budget' : min(budget_set),
'max_budget' : max(budget_set),
'budgets' : budget_list,
'max_SH_iter': len(budget_set),
'time_ref' : time_ref
}
return(Result([data], HB_config))
class Result(object):
"""
Object returned by the HB_master.run function
This class offers a simple API to access the information from
a Hyperband run.
"""
def __init__ (self, HB_iteration_data, HB_config):
self.data = HB_iteration_data
self.HB_config = HB_config
self._merge_results()
def __getitem__(self, k):
return(self.data[k])
def get_incumbent_id(self):
"""
Find the config_id of the incumbent.
The incumbent here is the configuration with the smallest loss
among all runs on the maximum budget! If no run finishes on the
maximum budget, None is returned!
"""
tmp_list = []
for k,v in self.data.items():
try:
# only things run for the max budget are considered
res = v.results[self.HB_config['max_budget']]
if not res is None:
tmp_list.append((res['loss'], k))
except KeyError as e:
pass
except:
raise
if len(tmp_list) > 0:
return(min(tmp_list)[1])
return(None)
def get_incumbent_trajectory(self, all_budgets=True):
"""
Returns the best configurations over time
Parameters:
-----------
all_budgets: bool
If set to true all runs (even those not with the largest budget) can be the incumbent.
Otherwise, only full budget runs are considered
Returns:
--------
dict:
dictionary with all the config IDs, the times the runs
finished, their respective budgets, and corresponding losses
"""
all_runs = self.get_all_runs(only_largest_budget = not all_budgets)
if not all_budgets:
all_runs = list(filter(lambda r: r.budget==res.HB_config['max_budget'], all_runs))
all_runs.sort(key=lambda r: r.time_stamps['finished'])
return_dict = { 'config_ids' : [],
'times_finished': [],
'budgets' : [],
'losses' : [],
}
current_incumbent = float('inf')
incumbent_budget = -float('inf')
for r in all_runs:
if r.loss is None: continue
if ((r.budget == incumbent_budget and r.loss < current_incumbent) or \
(r.budget > incumbent_budget)):
current_incumbent = r.loss
incumbent_budget = r.budget
return_dict['config_ids'].append(r.config_id)
return_dict['times_finished'].append(r.time_stamps['finished'])
return_dict['budgets'].append(r.budget)
return_dict['losses'].append(r.info['test_error'][0])
if current_incumbent != r.loss:
r = all_runs[-1]
return_dict['config_ids'].append(return_dict['config_ids'][-1])
return_dict['times_finished'].append(r.time_stamps['finished'])
return_dict['budgets'].append(return_dict['budgets'][-1])
return_dict['losses'].append(return_dict['losses'][-1])
return (return_dict)
def get_runs_by_id(self, config_id):
"""
returns a list of runs for a given config id
The runs are sorted by ascending budget, so '-1' will give
the longest run for this config.
"""
d = self.data[config_id]
runs = []
for b in d.results.keys():
try:
err_logs = d.exceptions.get(b, None)
if d.results[b] is None:
r = Run(config_id, b, None, None , d.time_stamps[b], err_logs)
else:
r = Run(config_id, b, d.results[b]['loss'], d.results[b]['info'] , d.time_stamps[b], err_logs)
runs.append(r)
except:
raise
runs.sort(key=lambda r: r.budget)
return(runs)
def get_learning_curves(self, lc_extractor=extract_HB_learning_curves, config_ids=None):
"""
extracts all learning curves from all run configurations
Parameters:
-----------
lc_extractor: callable
a function to return a list of learning_curves.
defaults to hpbanster.HB_result.extract_HP_learning_curves
config_ids: list of valid config ids
if only a subset of the config ids is wanted
Returns:
--------
dict
a dictionary with the config_ids as keys and the
learning curves as values
"""
config_ids = self.data.keys() if config_ids is None else config_ids
lc_dict = {}
for id in config_ids:
runs = self.get_runs_by_id(id)
lc_dict[id] = lc_extractor(runs)
return(lc_dict)
def get_all_runs(self, only_largest_budget=False):
"""
returns all runs performed
Parameters:
-----------
only_largest_budget: boolean
if True, only the largest budget for each configuration
is returned. This makes sense if the runs are continued
across budgets and the info field contains the information
you care about. If False, all runs of a configuration
are returned
"""
all_runs = []
for k in self.data.keys():
runs = self.get_runs_by_id(k)
if len(runs) > 0:
if only_largest_budget:
all_runs.append(runs[-1])
else:
all_runs.extend(runs)
return(all_runs)
def get_id2config_mapping(self):
"""
returns a dict where the keys are the config_ids and the values
are the actual configurations
"""
new_dict = {}
for k, v in self.data.items():
new_dict[k] = {}
new_dict[k]['config'] = copy.deepcopy(v.config)
try:
new_dict[k]['config_info'] = copy.deepcopy(v.config_info)
except:
pass
return(new_dict)
def _merge_results(self):
"""
hidden function to merge the list of results into one
dictionary and 'normalize' the time stamps
"""
new_dict = {}
for it in self.data:
new_dict.update(it)
for k,v in new_dict.items():
for kk, vv in v.time_stamps.items():
for kkk,vvv in vv.items():
new_dict[k].time_stamps[kk][kkk] = vvv - self.HB_config['time_ref']
self.data = new_dict
def num_iterations(self):
return(max([k[0] for k in self.data.keys()]) + 1)
def get_fANOVA_data(self, config_space, budgets=None):
import numpy as np
import ConfigSpace as CS
id2conf = self.get_id2config_mapping()
if budgets is None:
budgets = self.HB_config['budgets']
if len(budgets)>1:
config_space.add_hyperparameter(CS.UniformFloatHyperparameter('budget', min(budgets), max(budgets), log=True))
hp_names = list(map( lambda hp: hp.name, config_space.get_hyperparameters()))
all_runs = self.get_all_runs(only_largest_budget=False)
all_runs=list(filter( lambda r: r.budget in budgets, all_runs))
X = []
y = []
for r in all_runs:
if r.loss is None: continue
config = id2conf[r.config_id]['config']
if len(budgets)>1:
config['budget'] = r.budget
config = CS.Configuration(config_space, config)
X.append([config[n] for n in hp_names])
y.append(r.loss)
return(np.array(X), np.array(y), config_space)
| python |
userColors = []
| python |
class AbstractRequest(object):
opcode = -1
class AbstractRequestCodec(object):
@staticmethod
def decode(payload):
raise NotImplementedError
@staticmethod
def encode(request):
raise NotImplementedError
| python |
import cairo
import math
import random
import sys
import os
sys.path.append(os.path.abspath('..'))
from lib import palettes
from lib import colors
# Final image dimensions
IMG_HEIGHT = 2000
IMG_WIDTH = int(IMG_HEIGHT * (16/9))
SPACING = 2
def line(ctx, y, line_interval, color, x_increment=(IMG_WIDTH // 40)):
line_width = line_interval // 20
x = 0
ctx.move_to(x, y)
nodes = []
while x < IMG_WIDTH:
x += random.randint(x_increment // 2, x_increment)
y_offset = random.randint(0, line_interval // 2 - SPACING)
y_offset = y_offset if random.random() < 0.5 else -1 * y_offset
nodes.append((x, y + y_offset))
ctx.line_to(x, y + y_offset)
ctx.set_source_rgb(*color)
ctx.set_line_width(line_width)
ctx.stroke()
for node in nodes:
(node_x, node_y) = node
r = random.randint(line_width * 2, line_width * 4)
ctx.arc(node_x, node_y, r, 0, 2 * math.pi)
ctx.set_source_rgb(*color)
ctx.fill()
# Ring around the node
ctx.arc(node_x, node_y, r, 0, 2 * math.pi)
ctx.set_source_rgb(*random.choice(colors.shades(color, 5)))
ctx.set_line_width(line_width)
ctx.stroke()
def main(filename="output.png", palette=random.choice(palettes.PALETTES), lines=20):
ims = cairo.ImageSurface(cairo.FORMAT_ARGB32, IMG_WIDTH, IMG_HEIGHT)
ims.set_fallback_resolution(300.0, 300.0)
ctx = cairo.Context(ims)
# Background
ctx.rectangle(0, 0, IMG_WIDTH, IMG_HEIGHT)
ctx.set_source_rgb(*palettes.hex_to_tuple(palette['background']))
ctx.fill()
line_interval = IMG_HEIGHT // lines
for y in range(line_interval, IMG_HEIGHT, line_interval):
color = palettes.hex_to_tuple(random.choice(palette['colors']))
line(ctx, y, line_interval, color)
ims.write_to_png(filename)
if __name__ == "__main__":
for idx, l in enumerate([5, 10, 15, 20, 40]):
main(filename="output-{}.png".format(idx), palette=random.choice(palettes.PALETTES), lines=l)
| python |
from typing import Callable, Sequence, Union, TYPE_CHECKING
import io
from enum import Enum
if TYPE_CHECKING:
from .expressions import (
ReadSubstitute,
WriteSubstitute,
)
from .arguments import UncompiledArgument
PublicArgument = Union[
str,
int,
float,
'ReadSubstitute',
'WriteSubstitute',
]
PublicKeywordArgument = Union[
bool,
str,
int,
float,
'ReadSubstitute',
'WriteSubstitute',
]
InternalArgument = Union[str, 'UncompiledArgument']
ShalchemyFile = Union[
str,
io.IOBase,
]
ShalchemyOutputStream = Union[
io.IOBase,
int,
]
KeywordArgumentRenderer = Callable[[str, PublicKeywordArgument], Sequence[str]]
class ParenthesisKind(Enum):
NEVER = 1
ALWAYS = 2
COMPOUND_ONLY = 3
| python |
from main.game.ConvertStringArray import historyToArray
from main.game.verifyCheck import verificarCheck
def especialMove(allpieces,piece,history):
history = historyToArray(history)
if history != ['']:
if piece[0] == 'p':
return EnPassant(piece,history)
elif piece[0] == 'k':
return Castles(allpieces,piece,history)
def Castles(allPieces,piece,history):
rookRightMoved = False
rookLeftMoved = False
isMoved = False
#checa se o rei já se mexeu
for moved in history:
if moved:
if piece[0] == moved[0]:
if piece[1] == moved[1]:
isMoved = True
#checa se as torres já se mexeram
if isMoved == False:
for rookMoved in history:
if rookMoved:
if rookMoved[0] == 'r':
if rookMoved[1] == piece[1]:
if rookMoved[3]=='7':
rookRightMoved = True
elif rookMoved[3] == '0':
if rookMoved[1] == piece[1]:
rookLeftMoved = True
myPossibleCastles = ''
if isMoved == False:
if rookRightMoved == False:
if allPieces[int(piece[2])][int(piece[3])+1] == '----':
if allPieces[int(piece[2])][int(piece[3])+2] == '----':
moves = [piece]
moves.append(piece[0]+piece[1]+piece[2]+str(int(piece[3])+1))
moves.append(piece[0]+piece[1]+piece[2]+str(int(piece[3])+2))
realMoves = [piece,moves]
movimentosSemCheck = verificarCheck(allPieces,realMoves)
if realMoves[1] == movimentosSemCheck[slice(1,4)]:
myPossibleCastles = piece[0]+piece[1]+piece[2]+str(int(piece[3])+2)
if rookLeftMoved == False:
if allPieces[int(piece[2])][int(piece[3])-1] == '----':
if allPieces[int(piece[2])][int(piece[3])-2] == '----':
if allPieces[int(piece[2])][int(piece[3])-3] == '----':
moves = [piece]
moves.append(piece[0]+piece[1]+piece[2]+str(int(piece[3])-1))
moves.append(piece[0]+piece[1]+piece[2]+str(int(piece[3])-2))
realMoves = [piece,moves]
movimentosSemCheck = verificarCheck(allPieces,realMoves)
if realMoves[1] == movimentosSemCheck[slice(1,4)]:
myPossibleCastles += piece[0]+piece[1]+piece[2]+str(int(piece[3])-2)
return myPossibleCastles
def EnPassant(piece,history):
lastMove = history[len(history) -2] #exemplo de resultado pb64pb44
#checa se o último movimento foi de um peão
if lastMove[4] == 'p':
#checa se o peão na casa 6 agora está na casa 4. No caso checa se o peão ao se mover ele mexeu dois quadrados, indicando que era o primeiro movimento dele no jogo. Exemplo pb66 pb46
if int(lastMove[2]) == int(lastMove[6])+2 or int(lastMove[2]) == int(lastMove[6])-2:
color = piece[1]
if color == 'w':
if piece[2] == '4':
return(piece[0]+piece[1]+str(int(lastMove[6])+1)+lastMove[7])
if color =='b':
if piece[2] == '3':
return(piece[0]+piece[1]+str(int(lastMove[6])-1)+lastMove[7])
| python |
eps = 10e-7
| python |
from ..geometry import np
import math
class Quaternion(object):
def __init__(self, coeffs=[0., 0., 0., 1.]):
self._coeffs = np.array(coeffs)
def vec(self):
return self._coeffs[0:3]
def coeffs(self):
return self._coeffs
def normalize(self):
norm = np.linalg.norm(self._coeffs)
self._coeffs = self._coeffs/norm
def normalized(self):
norm = np.linalg.norm(self._coeffs)
coeffs = self._coeffs/norm
return Quaternion(coeffs)
@property
def w(self):
return self._coeffs[3]
@w.setter
def w(self, value):
self._coeffs[3] = value
@property
def x(self):
return self._coeffs[0]
@x.setter
def x(self, value):
self._coeffs[0] = value
@property
def y(self):
return self._coeffs[1]
@y.setter
def y(self, value):
self._coeffs[1] = value
@property
def z(self):
return self._coeffs[2]
@z.setter
def z(self, value):
self._coeffs[2] = value
def conjugate(self):
return Quaternion([-self.x(), -self.y(), -self.z(), self.w()])
def to_rotation_matrix(self):
qx, qy, qz, qw = self._coeffs
sqw = qw * qw
sqx = qx * qx
sqy = qy * qy
sqz = qz * qz
invs = 1. / (sqx + sqy + sqz + sqw)
m00 = ( sqx - sqy - sqz + sqw) * invs
m11 = (-sqx + sqy - sqz + sqw) * invs
m22 = (-sqx - sqy + sqz + sqw) * invs
qxy = qx * qy
qzw = qw * qz
m10 = 2. * (qxy + qzw) * invs
m01 = 2. * (qxy - qzw) * invs
qxz = qx * qz
qyw = qy * qw
m20 = 2.0 * (qxz - qyw) * invs
m02 = 2.0 * (qxz + qyw) * invs
qyz = qy * qz
qxw = qx * qw
m21 = 2. * (qyz + qxw) * invs
m12 = 2. * (qyz - qxw) * invs
return np.array([[m00, m01, m02], [m10, m11, m12], [m20, m21, m22]])
@staticmethod
def from_rotation_matrix(rotmat):
m00, m01, m02 = rotmat[0]
m10, m11, m12 = rotmat[1]
m20, m21, m22 = rotmat[2]
trace = m00 + m11 + m22
if (trace > 0.):
S = math.sqrt(trace + 1.0) * 2.
qw = 0.25 * S
qx = (m21 - m12) / S
qy = (m02 - m20) / S
qz = (m10 - m01) / S
return Quaternion([qx, qy, qz, qw])
elif (m00 > m11 and m00 > m22):
S = math.sqrt(1. + m00 - m11 - m22) * 2
qw = (m21 - m12) / S
qx = 0.25 * S
qy = (m01 + m10) / S
qz = (m02 + m20) / S
return Quaternion([qx, qy, qz, qw])
elif (m11 > m22):
S = math.sqrt(1.0 + m11 - m00 - m22) * 2
qw = (m10 - m20) / S
qx = (m01 + m10) / S
qy = 0.25 * S
qz = (m12 + m21) / S
return Quaternion([qx, qy, qz, qw])
else:
S = math.sqrt(1.0 + m22 - m00 - m11) * 2
qw = (m10 - m01) / S
qx = (m02 + m20) / S
qy = (m12 + m21) / S
qz = 0.25 * S
return Quaternion([qx, qy, qz, qw])
| python |
from django.http import HttpResponse
from django.shortcuts import render
from webcam_manager import *
import time
webcam_manager = WebcamManager()
encryption_manager = EncryptionManager()
webcam_manager.start()
def make_aes_response(response_data):
response = encryption_manager.get_aes_packet(response_data)
if response == None:
return HttpResponse(status=500)
else:
return HttpResponse(response, content_type='application/octet-stream')
def index(request):
try:
image_data = webcam_manager.get(0) # get most recent image
return make_aes_response(image_data)
except:
return HttpResponse(status=500)
def get_zip(request):
try:
zip_data, zip_name = webcam_manager.get_zip_of_all_files()
return make_aes_response(zip_data)
except:
return HttpResponse(status=500)
| python |
import json
import numpy as np
import os
from env_rl import EnvRL
from pathlib import Path
def score_rl_solution(submission_filepath='example_output_rl.json', final_submission=False):
base_path = Path(__file__).parent.absolute()
test_data_instance_path = base_path.joinpath('data/valid/instances')
test_data_adj_path = base_path.joinpath('data/valid/adjs')
f = open(submission_filepath)
submission = json.load(f)
scores = []
rewardss = []
pens = []
n_feas_sols = 0
for instance_name in submission.keys():
x_path = os.path.join(test_data_instance_path, instance_name + '.csv')
adj_path = os.path.join(test_data_adj_path, 'adj-' + instance_name + '.csv')
seed = submission[instance_name]['seed']
env = EnvRL(from_file=True, seed=seed, x_path=x_path, adj_path=adj_path)
instance = submission[instance_name]
if final_submission:
n_tours = len(instance['tours'].keys())
assert n_tours == 100, f'each instance must have 100 tours, but found {n_tours} in {instance_name}'
for tour_name in instance['tours'].keys():
sol = instance['tours'][tour_name]
for node in sol[1:]:
env.step(node)
rewards = env.get_collected_rewards()
pen = env.get_incurred_penalties()
feas = env.get_feasibility()
assert tour_name == env.get_sim_name(), f'submission {tour_name} in {instance_name} is in the wrong order.'
score = rewards + pen
n_feas_sols += float(feas)
scores.append(score)
rewardss.append(rewards)
pens.append(pen)
env.reset()
avg_score = np.mean(scores)
avg_rewards = np.mean(rewardss)
avg_pen = np.mean(pens)
print (avg_score, avg_rewards, avg_pen)
return np.round(avg_score, 5)
if __name__ == '__main__':
print(f'Your submission scored {score_rl_solution():.05f}')
| python |
# Reference: https://leetcode.com/problems/number-of-islands/
# Approach:
# 1. Get a list of all locations that have 1
# 2. Iterate through this list and call DFS for every unmarked / unvisited 1 and mark all it's reachable locations with the current_island_count
# 3. The final value of current_island_count is the answer
# Status: basic test case working.
# Issue with:
# obj.numIslands([["1","1","0","0","0"],["1","1","0","0","0"],["0","0","1","0","0"],["0","0","0","1","1"]]) # o: 3
# When submitting, leetcode shows my solution as 2, whereas execution in jupyter or even leetcode interactive shows answer as 3?
# Strange! post on forums and understand
# optimization: land_locations can be a dictionary instead of a list and then you use the sane for visited_dict functionality and find and remove keys (land_locations) whenever they are marked. So you do not have to go through the whole thing of retrieving next land_location and checking whether it's visited. You can be sure that land_locations at all times maintains the unvisited locations due to the removal procedure of newighbours
class Solution(object):
grid = []
visited_dict = {}
rowLen = 0
colLen = 0
islandCount = 0
def numIslands(self, grid):
"""
:type grid: List[List[str]]
:rtype: int
"""
self.grid = grid
# islandCount = 0
land_locations = []
self.rowLen = len(grid)
self.colLen = len(grid[0]) # considering symmetric matrix. Also add defensive condition on index 0 access
# TODO: can be wrapped in a separate function called get land locations
for row in range(self.rowLen):
for col in range(self.colLen):
if grid[row][col ] == "1":
land_locations.append((row, col))
for (row, col) in land_locations:
# if already visited, do not recurse on marking
if (row, col) not in self.visited_dict:
self.islandCount += 1
# mark self and neighbours
self.markNeighbours(row, col, self.islandCount)
return self.islandCount
def markNeighbours(self, row, col, islandCountMarker):
# first mark self
self.visited_dict[(row, col)] = islandCountMarker
# check whether neighbours exist and mark if it's a piece of connected land
# TODO: shouldn't this include top & left too? -- mostly it should. try to add and submit on leetcode
# bottom
if row + 1 < self.rowLen and self.grid[row + 1][col] == "1":
self.markNeighbours(row + 1, col, islandCountMarker)
# right
if col + 1 < self.colLen and self.grid[row][col + 1] == "1":
self.markNeighbours(row, col + 1, islandCountMarker)
obj = Solution()
# obj.numIslands([["1","1","1","1","0"],["1","1","0","1","0"],["1","1","0","0","0"],["0","0","0","0","0"]]) # Output: 1
# obj.numIslands([["1","1","0","0","0"],["1","1","0","0","0"],["0","0","1","0","0"],["0","0","0","1","1"]]) # Output: 3
# obj.numIslands([["1","0"],["0", "1"]]) # o: 2
# obj.numIslands([["1","1"],["1", "1"]]) # o: 1
# obj.numIslands([["1","0","0"],["1","0","0"],["1","1","1"]]) # o: 1
# obj.numIslands([["1","0","1"],["1","0","0"],["0","1","1"]]) # o: 3
# obj.numIslands([["1","0","1"],["1","0","1"],["1","1","0"]]) # o: 2
obj.numIslands([["1","1","0","0","0"],["1","1","0","0","0"],["0","0","1","0","0"],["0","0","0","1","1"]]) # o: 3
# Interesting Pointer:
# When local variable islandCount was used, Test Case 2 had issues. Possibly because the local parameter was colliding. Strange but true.
# Whereas, when class variable used, it worked perfectly. REM: When you have one value being updated by multiple objects or function calls (here recursive calls), use class variable
# (switching between 2 & 3. Error only happened when string "1" used)
# Possibly due to the way the for loop and recursive calls are interlocked.
| python |
from socket import * | python |
from __future__ import unicode_literals
import frappe
import json
from toolz.curried import compose, merge, map, filter
@frappe.whitelist()
def query(doctype, txt, searchfield, start, page_len, filters):
station = filters.get("station")
cond = (
" OR ".join(
[
"so.initial_station = %(station)s",
"so.final_station = %(station)s",
"sots.station = %(station)s",
]
)
if station
else "1 = 1"
)
return frappe.db.sql(
"""
SELECT DISTINCT so.name, so.vehicle, so.driver_name, so.driver
FROM `tabShipping Order` AS so
LEFT JOIN `tabShipping Order Transit Station` AS sots
ON sots.parent = so.name
WHERE ({cond}) AND (
so.docstatus = 1 AND
so.name LIKE %(txt)s
) LIMIT %(start)s, %(page_len)s
""".format(
cond=cond,
),
values={
"station": station,
"txt": "%%%s%%" % txt,
"start": start,
"page_len": page_len,
},
)
@frappe.whitelist()
def get_history(name):
logs = frappe.db.sql(
"""
SELECT
sl.posting_datetime,
sl.station,
sl.activity,
lo.on_load_no_of_packages,
lo.off_load_no_of_packages
FROM `tabShipping Log` AS sl
LEFT JOIN `tabLoading Operation` AS lo ON
lo.name = sl.loading_operation
WHERE sl.shipping_order = %(shipping_order)s
ORDER BY sl.posting_datetime
""",
values={"shipping_order": name},
as_dict=1,
)
def get_message(log):
activity = log.get("activity")
if activity == "Operation":
on_load = log.get("on_load_no_of_packages")
off_load = log.get("off_load_no_of_packages")
msg = (
" and ".join(
filter(
None,
[
on_load and "Loaded {} packages".format(on_load),
off_load and "Unloaded {} packages".format(off_load),
],
)
)
or "Operation"
)
return "{} at {}".format(msg, log.get("station"),)
if activity == "Stopped":
return "Stopped at {}".format(log.get("station"))
if activity == "Moving":
return "Moving to {}".format(log.get("station"))
return activity
def get_link(log):
if log.get("loading_operation"):
"#Form/Loading Operation/{}".format(log.get("loading_operation"))
return ""
def get_event(log):
return {
"datetime": log.get("posting_datetime"),
"status": log.get("activity"),
"message": get_message(log),
"link": get_link(log),
}
return [get_event(x) for x in logs]
def get_manifest_rows(shipping_order):
return frappe.db.sql(
"""
SELECT
lobo.booking_order,
lobo.loading_unit,
lobo.qty,
SUM(lobo.no_of_packages) AS cur_no_of_packages,
SUM(lobo.weight_actual) AS cur_weight_actual,
GROUP_CONCAT(bofd.item_description SEPARATOR ', ') AS item_description,
bo.destination_station,
bo.consignor_name,
bo.consignee_name,
bo.no_of_packages,
bo.weight_actual
FROM `tabLoading Operation Booking Order` AS lobo
LEFT JOIN `tabLoading Operation` AS lo ON
lo.name = lobo.parent
LEFT JOIN `tabBooking Order` AS bo ON
bo.name = lobo.booking_order
LEFT JOIN `tabBooking Order Freight Detail` AS bofd ON
bofd.name = lobo.bo_detail
WHERE
lo.docstatus = 1 AND
lobo.parentfield = 'on_loads' AND
lo.shipping_order = %(shipping_order)s
GROUP BY lobo.booking_order
ORDER BY lo.name, lobo.idx
""",
values={"shipping_order": shipping_order},
as_dict=1,
)
def get_freight_summary_rows(shipping_order):
def get_amount(row):
rate = row.get("rate") or 0
if row.get("based_on") == "Packages":
return (row.get("cur_no_of_packages") or 0) * rate
if row.get("based_on") == "Weight":
return (row.get("cur_weight_actual") or 0) * rate
return row.get("amount") or 0
freight_rows = frappe.db.sql(
"""
SELECT
bo.name AS booking_order,
bo.consignor_name,
bo.consignee_name,
bofd.item_description,
SUM(lobo.no_of_packages) AS cur_no_of_packages,
SUM(lobo.weight_actual) AS cur_weight_actual,
bofd.based_on,
bofd.rate
FROM `tabLoading Operation Booking Order` AS lobo
LEFT JOIN `tabLoading Operation` AS lo ON
lo.name = lobo.parent
LEFT JOIN `tabBooking Order` AS bo ON
bo.name = lobo.booking_order
LEFT JOIN `tabBooking Order Freight Detail` AS bofd ON
bofd.name = lobo.bo_detail
WHERE
lo.docstatus = 1 AND
lobo.parentfield = 'on_loads' AND
lo.shipping_order = %(shipping_order)s
GROUP BY lobo.name
ORDER BY lo.name, lobo.idx
""",
values={"shipping_order": shipping_order},
as_dict=1,
)
booking_orders = set([x.get("booking_order") for x in freight_rows])
get_first_loaded_booking_orders = compose(
list, map(lambda x: x.get("booking_order")), frappe.db.sql,
)
first_loaded_booking_orders = (
get_first_loaded_booking_orders(
"""
SELECT
lobo.booking_order,
lo.shipping_order
FROM `tabLoading Operation Booking Order` AS lobo
LEFT JOIN `tabLoading Operation` AS lo ON
lo.name = lobo.parent
LEFT JOIN `tabBooking Order Charge` AS boc ON
boc.parent = lobo.booking_order
WHERE
lo.docstatus = 1 AND
lobo.parentfield = 'on_loads' AND
lobo.booking_order IN %(booking_orders)s
GROUP by lobo.booking_order
HAVING lo.shipping_order = %(shipping_order)s
ORDER BY lo.posting_datetime
""",
values={"booking_orders": booking_orders, "shipping_order": shipping_order},
as_dict=1,
)
if booking_orders
else []
)
charges_rows = (
frappe.db.sql(
"""
SELECT
bo.name AS booking_order,
bo.consignor_name,
bo.consignee_name,
GROUP_CONCAT(boc.charge_type SEPARATOR ', ') AS item_description,
0 AS cur_no_of_packages,
0 AS cur_weight_actual,
'' AS based_on,
0 AS rate,
SUM(boc.charge_amount) AS amount
FROM `tabBooking Order` AS bo
LEFT JOIN `tabBooking Order Charge` AS boc ON
boc.parent = bo.name
WHERE
bo.name IN %(booking_orders)s AND
boc.charge_amount > 0
GROUP BY bo.name
""",
values={"booking_orders": first_loaded_booking_orders},
as_dict=1,
)
if first_loaded_booking_orders
else []
)
return sorted(
[merge(x, {"amount": get_amount(x)}) for x in freight_rows + charges_rows],
key=lambda x: x.get("booking_order"),
)
| python |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE.txt file in the root directory of this source tree.
import argparse
import json
import os
import os.path
import random
from typing import Counter, DefaultDict, Dict, List, Optional, Tuple
import h5py
import numpy as np
from torchbiggraph.config import (
ConfigSchema,
EntitySchema,
RelationSchema,
get_config_dict_from_module,
)
from torchbiggraph.converters.dictionary import Dictionary
def collect_relation_types(
relation_configs: List[RelationSchema],
edge_paths: List[str],
dynamic_relations: bool,
rel_col: Optional[int],
relation_type_min_count: int,
) -> Dictionary:
if dynamic_relations:
if rel_col is None:
raise RuntimeError("Need to specify rel_col in dynamic mode.")
print("Looking up relation types in the edge files...")
counter: Counter[str] = Counter()
for edgepath in edge_paths:
with open(edgepath, "rt") as tf:
for line_num, line in enumerate(tf, start=1):
words = line.split()
try:
rel_word = words[rel_col]
except IndexError:
raise RuntimeError(
"Line %d of %s has only %d words"
% (line_num, edgepath, len(words))) from None
counter[rel_word] += 1
print("- Found %d relation types" % len(counter))
if relation_type_min_count > 0:
print("- Removing the ones with fewer than %d occurrences..."
% relation_type_min_count)
counter = Counter({k: c for k, c in counter.items()
if c >= relation_type_min_count})
print("- Left with %d relation types" % len(counter))
print("- Shuffling them...")
names = list(counter.keys())
random.shuffle(names)
else:
names = [rconfig.name for rconfig in relation_configs]
print("Using the %d relation types given in the config" % len(names))
return Dictionary(names)
def collect_entities_by_type(
relation_types: Dictionary,
entity_configs: Dict[str, EntitySchema],
relation_configs: List[RelationSchema],
edge_paths: List[str],
dynamic_relations: bool,
lhs_col: int,
rhs_col: int,
rel_col: Optional[int],
entity_min_count: int,
) -> Dict[str, Dictionary]:
counters: Dict[str, Counter[str]] = {}
for entity_name in entity_configs.keys():
counters[entity_name] = Counter()
print("Searching for the entities in the edge files...")
for edgepath in edge_paths:
with open(edgepath, "rt") as tf:
for line_num, line in enumerate(tf, start=1):
words = line.split()
try:
lhs_word = words[lhs_col]
rhs_word = words[rhs_col]
rel_word = words[rel_col] if rel_col is not None else None
except IndexError:
raise RuntimeError(
"Line %d of %s has only %d words"
% (line_num, edgepath, len(words))) from None
if dynamic_relations or rel_col is None:
rel_id = 0
else:
try:
rel_id = relation_types.get_id(rel_word)
except KeyError:
raise RuntimeError("Could not find relation type in config")
counters[relation_configs[rel_id].lhs][lhs_word] += 1
counters[relation_configs[rel_id].rhs][rhs_word] += 1
entities_by_type: Dict[str, Dictionary] = {}
for entity_name, counter in counters.items():
print("Entity type %s:" % entity_name)
print("- Found %d entities" % len(counter))
if entity_min_count > 0:
print("- Removing the ones with fewer than %d occurrences..."
% entity_min_count)
counter = Counter({k: c for k, c in counter.items()
if c >= entity_min_count})
print("- Left with %d entities" % len(counter))
print("- Shuffling them...")
names = list(counter.keys())
random.shuffle(names)
entities_by_type[entity_name] = Dictionary(
names, num_parts=entity_configs[entity_name].num_partitions)
return entities_by_type
def generate_entity_path_files(
entity_path: str,
entities_by_type: Dict[str, Dictionary],
relation_types: Dictionary,
dynamic_relations: bool,
) -> None:
print("Preparing entity path %s:" % entity_path)
for entity_name, entities in entities_by_type.items():
for part in range(entities.num_parts):
print("- Writing count of entity type %s and partition %d"
% (entity_name, part))
with open(os.path.join(
entity_path, "entity_count_%s_%d.txt" % (entity_name, part)
), "wt") as tf:
tf.write("%d" % entities.part_size(part))
if dynamic_relations:
print("- Writing count of dynamic relations")
with open(os.path.join(entity_path, "dynamic_rel_count.txt"), "wt") as tf:
tf.write("%d" % relation_types.size())
def generate_edge_path_files(
edge_file_in: str,
entities_by_type: Dict[str, Dictionary],
relation_types: Dictionary,
relation_configs: List[RelationSchema],
dynamic_relations: bool,
lhs_col: int,
rhs_col: int,
rel_col: Optional[int],
) -> None:
basename, _ = os.path.splitext(edge_file_in)
edge_path_out = basename + '_partitioned'
print("Preparing edge path %s, out of the edges found in %s"
% (edge_path_out, edge_file_in))
os.makedirs(edge_path_out, exist_ok=True)
num_lhs_parts = max(entities_by_type[rconfig.lhs].num_parts
for rconfig in relation_configs)
num_rhs_parts = max(entities_by_type[rconfig.rhs].num_parts
for rconfig in relation_configs)
print("- Edges will be partitioned in %d x %d buckets."
% (num_lhs_parts, num_rhs_parts))
buckets: DefaultDict[Tuple[int, int], List[Tuple[int, int, int]]] = \
DefaultDict(list)
processed = 0
skipped = 0
with open(edge_file_in, "rt") as tf:
for line_num, line in enumerate(tf, start=1):
words = line.split()
try:
lhs_word = words[lhs_col]
rhs_word = words[rhs_col]
rel_word = words[rel_col] if rel_col is not None else None
except IndexError:
raise RuntimeError(
"Line %d of %s has only %d words"
% (line_num, edge_file_in, len(words))) from None
if rel_col is None:
rel_id = 0
else:
try:
rel_id = relation_types.get_id(rel_word)
except KeyError:
# Ignore edges whose relation type is not known.
skipped += 1
continue
if dynamic_relations:
lhs_type = relation_configs[0].lhs
rhs_type = relation_configs[0].rhs
else:
lhs_type = relation_configs[rel_id].lhs
rhs_type = relation_configs[rel_id].rhs
try:
lhs_part, lhs_offset = \
entities_by_type[lhs_type].get_partition(lhs_word)
rhs_part, rhs_offset = \
entities_by_type[rhs_type].get_partition(rhs_word)
except KeyError:
# Ignore edges whose entities are not known.
skipped += 1
continue
buckets[lhs_part, rhs_part].append((lhs_offset, rhs_offset, rel_id))
processed = processed + 1
if processed % 100000 == 0:
print("- Processed %d edges so far..." % processed)
print("- Processed %d edges in total" % processed)
if skipped > 0:
print("- Skipped %d edges because their relation type or entities were "
"unknown (either not given in the config or filtered out as too "
"rare)." % skipped)
for i in range(num_lhs_parts):
for j in range(num_rhs_parts):
print("- Writing bucket (%d, %d), containing %d edges..."
% (i, j, len(buckets[i, j])))
edges = np.asarray(buckets[i, j])
with h5py.File(os.path.join(
edge_path_out, "edges_%d_%d.h5" % (i, j)
), "w") as hf:
hf.attrs["format_version"] = 1
hf.create_dataset("lhs", data=edges[:, 0])
hf.create_dataset("rhs", data=edges[:, 1])
hf.create_dataset("rel", data=edges[:, 2])
def convert_input_data(
config: str,
edge_paths: List[str],
lhs_col: int,
rhs_col: int,
rel_col: Optional[int] = None,
entity_min_count: int = 1,
relation_type_min_count: int = 1,
) -> None:
entity_configs, relation_configs, entity_path, dynamic_relations = \
validate_config(config)
some_output_paths = []
some_output_paths.append(os.path.join(entity_path, "dictionary.json"))
some_output_paths.extend(
os.path.join(entity_path, "entity_count_%s_0.txt" % entity_name)
for entity_name in entity_configs.keys())
if dynamic_relations:
some_output_paths.append(os.path.join(entity_path, "dynamic_rel_count.txt"))
some_output_paths.extend(
os.path.join(os.path.splitext(edge_file)[0] + "_partitioned", "edges_0_0.h5")
for edge_file in edge_paths)
if all(os.path.exists(path) for path in some_output_paths):
print("Found some files that indicate that the input data "
"has already been preprocessed, not doing it again.")
print("These files are: %s" % ", ".join(some_output_paths))
return
os.makedirs(entity_path, exist_ok=True)
relation_types = collect_relation_types(
relation_configs,
edge_paths,
dynamic_relations,
rel_col,
relation_type_min_count,
)
entities_by_type = collect_entities_by_type(
relation_types,
entity_configs,
relation_configs,
edge_paths,
dynamic_relations,
lhs_col,
rhs_col,
rel_col,
entity_min_count,
)
dump = {
"relations": relation_types.get_list(),
"entities": {k: v.get_list() for k, v in entities_by_type.items()},
}
with open(os.path.join(entity_path, "dictionary.json"), "wt") as tf:
json.dump(dump, tf, indent=4)
generate_entity_path_files(
entity_path,
entities_by_type,
relation_types,
dynamic_relations,
)
for edge_path in edge_paths:
generate_edge_path_files(
edge_path,
entities_by_type,
relation_types,
relation_configs,
dynamic_relations,
lhs_col,
rhs_col,
rel_col,
)
def validate_config(
config: str,
) -> Tuple[Dict[str, EntitySchema], List[RelationSchema], str, bool]:
user_config = get_config_dict_from_module(config)
# validate entites and relations config
entities_config = user_config.get("entities")
relations_config = user_config.get("relations")
entity_path = user_config.get("entity_path")
dynamic_relations = user_config.get("dynamic_relations", False)
if not isinstance(entities_config, dict):
raise TypeError("Config entities is not of type dict")
if not isinstance(relations_config, list):
raise TypeError("Config relations is not of type list")
if not isinstance(entity_path, str):
raise TypeError("Config entity_path is not of type str")
if not isinstance(dynamic_relations, bool):
raise TypeError("Config dynamic_relations is not of type bool")
entities = {}
relations = []
for entity, entity_config in entities_config.items():
entities[entity] = EntitySchema.from_dict(entity_config)
for relation in relations_config:
relations.append(RelationSchema.from_dict(relation))
return entities, relations, entity_path, dynamic_relations
def main():
config_help = '\n\nConfig parameters:\n\n' + '\n'.join(ConfigSchema.help())
parser = argparse.ArgumentParser(
epilog=config_help,
# Needed to preserve line wraps in epilog.
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument('config', help='Path to config file')
parser.add_argument('edge_paths', nargs='*', help='Input file paths')
parser.add_argument('-l', '--lhs-col', type=int, required=True,
help='Column index for source entity')
parser.add_argument('-r', '--rhs-col', type=int, required=True,
help='Column index for target entity')
parser.add_argument('--rel-col', type=int,
help='Column index for relation entity')
parser.add_argument('--relation-type-min-count', type=int, default=1,
help='Min count for relation types')
parser.add_argument('--entity-min-count', type=int, default=1,
help='Min count for entities')
opt = parser.parse_args()
convert_input_data(
opt.config,
opt.edge_paths,
opt.lhs_col,
opt.rhs_col,
opt.rel_col,
opt.entity_min_count,
opt.relation_type_min_count,
)
if __name__ == "__main__":
main()
| python |
__author__ = 'rogerjiang'
'''
Purpose:
1. Data augmentation, including:
1.1 random translation in horizontal and vertical directions
1.2 horizontal and vertical flipping
1.3 random rotation
'''
'''
Class blancing:
Each class is trained using a different model, weights should be applied
to the true and false labels if imbalanced.
Cross validation can be performed at angles different from the training images.
Loss options:
1. Jaccard loss
2. Cross entropy
Optimizer options:
1. Adam (learning rate drop at around 0.2 of the initial rate for every
30 epochs)
2. NAdam (no improvement over Adam) (50 epochs with a learning rate
of 1e-3 and additional 50 epochs with a learning rate of 1e-4. Each epoch
was trained on 400 batches, each batch containing 128 image patches (112x112).)
Ensembling:
1. Arithmetic averaging over different angles
Special treatment:
1. Waterways using NDWI and CCCI).
'''
import pandas as pd
import os
import utils.data_utils as data_utils
import numpy as np
import cv2
import sys
import gc
# data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
data_dir = "E:\\workspace\\mystoreroom\\dstl_unet-master"
CLASSES = {
1: 'Bldg',
2: 'Struct',
3: 'Road',
4: 'Track',
5: 'Trees',
6: 'Crops',
7: 'Fast H20',
8: 'Slow H20',
9: 'Truck',
10: 'Car',
}
# train_wkt_v4 = pd.read_csv(os.path.join(data_dir, 'data/train_wkt_v4.csv'))
train_wkt_v4 = pd.read_csv(os.path.join(data_dir, 'data\\train_wkt_v4.csv'))
# grid_sizes = pd.read_csv(os.path.join(data_dir, 'data/grid_sizes.csv'),
# skiprows = 1, names = ['ImageId', 'Xmax', 'Ymin'])
grid_sizes = pd.read_csv(os.path.join(data_dir, 'data\\grid_sizes.csv'),
skiprows = 1, names = ['ImageId', 'Xmax', 'Ymin'])
x_crop = 3345
y_crop = 3338
test_names = ['6110_1_2', '6110_3_1', '6100_1_3', '6120_2_2']
#train_names = list(set(data_utils.all_train_names) - set(test_names))
train_names = data_utils.all_train_names
test_ids = [data_utils.train_IDs_dict_r[name] for name in test_names]
train_ids = [data_utils.train_IDs_dict_r[name] for name in train_names]
# no_train_img = len(train_names)
# no_test_img = len(test_names)
def generate_train_ids(cl):
'''
Create train ids, and exclude the images with no true labels
:param cl:
:return:
'''
df = data_utils.collect_stats()
df = df.pivot(index = 'ImageId', columns = 'Class', values = 'TotalArea')
df = df.fillna(0)
df = df[df[data_utils.CLASSES[cl + 1]] != 0]
train_names = sorted(list(df.index.get_values()))
return [data_utils.train_IDs_dict_r[name] for name in train_names]
def get_all_data(img_ids, train = True):
'''
Load all the training feature and label into memory. This requires 35 GB
memory on Mac and takes a few minutes to finish.
:return:
'''
image_feature = []
image_label = []
no_img = len(img_ids)
phase = ['validation', 'training'][train]
for i in range(no_img):
id = img_ids[i]
image_data = data_utils.ImageData(id)
image_data.create_train_feature()
image_data.create_label()
image_feature.append(image_data.train_feature[: x_crop, : y_crop, :])
image_label.append(image_data.label[: x_crop, : y_crop, :])
sys.stdout.write('\rLoading {} data: [{}{}] {}%\n'.\
format(phase,
'=' * i,
' ' * (no_img - i - 1),
100 * i / (no_img - 1)))
sys.stdout.flush()
# del image_data
# gc.collect()
sys.stdout.write('\n')
image_feature = np.stack(image_feature, -1)
image_label = np.stack(image_label, -1)
sys.stdout.write('Labels are{}valid.\n'.format(
' ' if np.isfinite(image_label).all() and \
(image_label >= 0).all() and (image_label <= 1).all() else ' not '))
sys.stdout.write('Image features are{}valid.\n'.format(
' ' if np.isfinite(image_feature).all() and \
(image_feature >= -5000).all() and (image_feature <= 5000).all() \
else ' not '))
sys.stdout.write('\n')
sys.stdout.flush()
return np.rollaxis(image_feature, 3, 0), np.rollaxis(image_label, 3, 0)
def input_data(crop_size, class_id = 0, crop_per_img = 1,
reflection = True, rotation = 8, train = True, verbose = False):
'''
Returns the training images (feature) and the corresponding labels
:param crop_size:
:param class_id:
:param crop_per_img:
:param reflection:
:param rotation:
:param train:
:return:
'''
# img_ids = generate_train_ids(class_id) if train else test_ids
img_ids = train_ids if train else test_ids
no_img = len(img_ids)
image_feature, image_label = get_all_data(img_ids, train = train)
while True:
images = []
labels = []
# Rotation angle is assumed to be the same, so that the
# transformation only needs to be calculated once.
if not rotation or rotation == 1:
crop_diff = 0
crop_size_new = crop_size
else:
angle = 360. * np.random.randint(0, rotation) / rotation
radian = 2. * np.pi * angle / 360.
if verbose:
print ('Rotation angle : {0}(degree), {1: 0.2f}(radian)'.\
format(int(angle), radian))
crop_size_new = int(
np.ceil(float(crop_size) * (abs(np.sin(radian)) +
abs(np.cos(radian)))))
rot_mat = cv2.getRotationMatrix2D((float(crop_size_new) / 2.,
float(crop_size_new) / 2.),
angle, 1.)
crop_diff = int((crop_size_new - crop_size) / 2.)
np.random.shuffle(img_ids)
for i in range(no_img):
id = img_ids[i]
for _ in range(crop_per_img):
x_base = np.random.randint(0, x_crop - crop_size_new)
y_base = np.random.randint(0, y_crop - crop_size_new)
if verbose:
print ('x_base {} for No. {} image'.format(x_base, id))
print ('y_base {} for No. {} image'.format(y_base, id))
img_crop = np.squeeze(image_feature[i, x_base: x_base + crop_size_new,
y_base: y_base + crop_size_new, :])
label_crop = np.squeeze(image_label[i, x_base: x_base + crop_size_new,
y_base: y_base + crop_size_new, class_id])
if not rotation or rotation == 1:
img_rot = img_crop
label_rot = label_crop
else:
img_rot = cv2.warpAffine(img_crop, rot_mat,
(crop_size_new, crop_size_new))
label_rot = cv2.warpAffine(label_crop, rot_mat,
(crop_size_new, crop_size_new))
x_step = 1 if not reflection else \
[-1, 1][np.random.randint(0, 2)]
y_step = 1 if not reflection else \
[-1, 1][np.random.randint(0, 2)]
images.append(img_rot[crop_diff: crop_diff + crop_size:,
crop_diff: crop_diff + crop_size, :]\
[:: x_step, :: y_step, :])
labels.append(label_rot[crop_diff: crop_diff + crop_size,
crop_diff: crop_diff + crop_size]\
[:: x_step, :: y_step])
yield np.stack(images, 0), np.stack(labels, 0)
| python |
# Tests should generate (and then clean up) any files they need for testing. No
# binary files should be included in the repository.
import json
import event_model
from suitcase.mongo_embedded import Serializer
import pytest
def test_export(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with default parameters.
"""
permanent_db = db_factory()
serializer = Serializer(permanent_db)
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def test_multithread(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with multiple worker threads.
"""
permanent_db = db_factory()
serializer = Serializer(permanent_db, num_threads=5)
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def test_smallbuffer(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with a small buffer.
"""
permanent_db = db_factory()
serializer = Serializer(permanent_db, embedder_size=3000)
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def test_smallqueue(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with a small buffer.
"""
permanent_db = db_factory()
serializer = Serializer(permanent_db, queue_size=1)
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def test_smallpage(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with a small mongo page saize.
"""
permanent_db = db_factory()
serializer = Serializer(permanent_db, page_size=10000)
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def test_evil_db(db_factory, example_data):
"""
Test suitcase-mongo-embedded serializer with a db that raises an exception
on bulk_write.
"""
def evil_func(*args, **kwargs):
raise RuntimeError
permanent_db = db_factory()
serializer = Serializer(permanent_db)
serializer._bulkwrite_event = evil_func
serializer._bulkwrite_datum = evil_func
with pytest.raises(RuntimeError):
run(example_data, serializer, permanent_db)
if not serializer._frozen:
serializer.close()
def run(example_data, serializer, permanent_db):
"""
Testbench for suitcase-mongo-embedded serializer.
This stores all documents that are going to the serializer into a
dictionary. After the run completes, it then queries the permanent
mongo database, and reads the documents to a separate dictionary. The two
dictionaries are checked to see if they match.
"""
run_dict = {'start': {}, 'stop': {}, 'descriptor': [],
'resource': [], 'event': [], 'datum': []}
documents = example_data()
mongo_serializer = serializer
for item in documents:
# Fix formatting for JSON.
item = event_model.sanitize_doc(item)
# Send the bluesky doc to the serializer
mongo_serializer(*item)
# Bulk_event/datum need to be converted to a list of events/datum
# before inserting in the run_dict.
if item[0] in {'bulk_events', 'bulk_datum'}:
pages = bulk_to_pages(*item)
doc_list = pages_to_list(pages)
for doc in doc_list:
run_dict[doc[0]].append(doc[1])
else:
if item[0] in {'event_page', 'datum_page'}:
doc_list = page_to_list(*item)
for doc in doc_list:
run_dict[doc[0]].append(doc[1])
else:
if type(run_dict.get(item[0])) == list:
run_dict[item[0]].append(item[1])
else:
run_dict[item[0]] = item[1]
# Read the run from the mongo database and store in a dict.
frozen_run_dict = run_list_to_dict(get_embedded_run(
permanent_db, run_dict['start']['uid']))
# Sort the event field of each dictionary. With multiple streams, the
# documents that don't go through the serializer don't appear to be sorted
# correctly.
if len(run_dict['event']):
run_dict['event'] = sorted(run_dict['event'],
key=lambda x: x['descriptor'])
frozen_run_dict['event'] = sorted(frozen_run_dict['event'],
key=lambda x: x['descriptor'])
# Compare the two dictionaries.
assert (json.loads(json.dumps(run_dict, sort_keys=True))
== json.loads(json.dumps(frozen_run_dict, sort_keys=True)))
def run_list_to_dict(embedded_run_list):
"""
Converts a run from the mongo database to a dictionary.
"""
run_dict = {'start': {},
'stop': {},
'descriptor': [],
'resource': [],
'event': [],
'datum': []}
header = embedded_run_list[0][1]
run_dict['start'] = header['start'][0]
run_dict['stop'] = header['stop'][0]
run_dict['descriptor'] = header.get('descriptors', [])
run_dict['resource'] = header.get('resources', [])
for name, doc in embedded_run_list[1:]:
if name == 'event':
run_dict['event'] += list(event_model.unpack_event_page(doc))
elif name == 'datum':
run_dict['datum'] += list(event_model.unpack_datum_page(doc))
return run_dict
def get_embedded_run(db, run_uid):
"""
Gets a run from a database. Returns a list of the run's documents.
"""
run = list()
# Get the header.
header = db.header.find_one({'run_id': run_uid}, {'_id': False})
if header is None:
raise RuntimeError(f"Run not found {run_uid}")
run.append(('header', header))
# Get the events.
if 'descriptors' in header.keys():
for descriptor in header['descriptors']:
run += [('event', doc) for doc in
db.event.find({'descriptor': descriptor['uid']},
{'_id': False})]
# Get the datum.
if 'resources' in header.keys():
for resource in header['resources']:
run += [('datum', doc) for doc in
db.datum.find({'resource': resource['uid']},
{'_id': False})]
return run
def bulk_to_pages(name, doc):
"""
Converts bulk_events/datum to event/datum_page.
"""
key_map = {'bulk_events': 'event_page', 'bulk_datum': 'datum_page'}
if name == 'bulk_events':
doc = event_model.bulk_events_to_event_pages(doc)
elif name == 'bulk_datum':
doc = event_model.bulk_datum_to_datum_pages(doc)
page_list = [[key_map[name], item] for item in doc]
return page_list
def pages_to_list(pages):
"""
Converts event/datum_page to event/datum lists.
"""
doc_list = []
for page in pages:
if page[0] == 'event_page':
doc_list.extend([['event', event] for event
in event_model.unpack_event_page(page[1])])
if page[0] == 'datum_page':
doc_list.extend([['datum', datum] for datum
in event_model.unpack_datum_page(page[1])])
return doc_list
def page_to_list(name, page):
"""
Converts event/datum_page to event/datum lists.
"""
doc_list = []
if name == 'event_page':
doc_list.extend([['event', event] for event
in event_model.unpack_event_page(page)])
if name == 'datum_page':
doc_list.extend([['datum', datum] for datum
in event_model.unpack_datum_page(page)])
return doc_list
| python |
#!/usr/bin/env python
import json
import os
import logging
from ruv_dl.constants import CACHE_LOCATION, CACHE_VERSION, CACHE_VERSION_KEY
logger = logging.getLogger(__name__)
class CacheVersionException(Exception):
pass
class DiskCache:
def __init__(self, program_id):
self.location = os.path.join(CACHE_LOCATION, f'{program_id}.json')
try:
with open(self.location, 'r') as f:
self._data = json.loads(f.read())
SAVED_CACHE_VERSION = self._data.get(CACHE_VERSION_KEY)
if SAVED_CACHE_VERSION != CACHE_VERSION:
logger.info(
f'Have cache version "{SAVED_CACHE_VERSION}" but '
f'want {CACHE_VERSION}. Starting with empty cache.'
)
raise CacheVersionException()
logger.debug('Cache version OK.')
except (FileNotFoundError, CacheVersionException):
self._data = {
CACHE_VERSION_KEY: CACHE_VERSION,
}
def get(self, key):
return self._data[key]
def set(self, key, data):
self._data[key] = data
def has(self, key):
return key in self._data
def remove(self, key):
del self._data[key]
def write(self):
with open(self.location, 'w') as f:
f.write(json.dumps(self._data))
| python |
import socket, time, signal
def resolves(domain, timeout):
try:
socket.gethostbyname(domain)
return True
except socket.gaierror:
return False
| python |
from __future__ import annotations
from typing import Union, List, Set, FrozenSet, Optional, Dict, IO, Callable
from pathlib import Path
from gd2c.project import Project
from gd2c.target import Target
from gd2c.gdscriptclass import GDScriptClass, GDScriptFunction, GDScriptMember, GDScriptGlobal
from gd2c.targets._gdnative.context import GlobalContext, ClassContext, FunctionContext
from gd2c.variant import VariantType
from gd2c import controlflow
import gd2c.targets._gdnative.transform as transform
import gd2c.targets._gdnative.class_codegen as class_codegen
import gd2c.targets._gdnative.function_codegen as function_codegen
class CPPNativeTarget(Target):
project: Project
def __init__(self, project: Project):
self.project = project
def transform(self) -> None:
for cls in self.project.classes():
for func in cls.functions():
func.cfg = controlflow.build_control_flow_graph(func)
func.cfg.live_variable_analysis()
transform.insert_initializers_transformation(func)
transform.insert_parameter_copies(func)
#transform.replace_init_calls_with_noop_transformation(func)
transform.insert_destructors_transformation(func)
def emit(self, output_path: str) -> None:
gen = CPPNativeCodeGen(self.project, output_path)
gen.transpile()
class CPPNativeCodeGen:
def __init__(self, project: Project, output_path: Union[str, Path]):
self.project = project
self.global_context = GlobalContext()
self.class_contexts: Dict[int, ClassContext] = {}
self.output_path = Path(output_path)
@property
def output_path(self) -> Path:
return self._output_path
@output_path.setter
def output_path(self, value: str):
p = Path(value)
assert p.is_dir(), "output_path must be a directory"
assert not str(p.resolve()).startswith(str(Path(self.project.root).resolve()))
self._output_path = p
def transpile(self):
self._initialize_contexts()
self._transpile_header_file()
self._transpile_c_file()
def _initialize_contexts(self):
self.class_contexts = {}
for cls in self.project.iter_classes_in_dependency_order():
context = ClassContext(cls, self.global_context, self.class_contexts.get(cls.base.type_id, None) if cls.base else None)
self.class_contexts[cls.type_id] = context
self.global_context.initialize_globals(next(iter(self.class_contexts.values())).cls.globals)
def _transpile_header_file(self):
p = Path(self._output_path, "godotproject.h")
with p.open(mode="w") as header:
header.write(f"""\
#ifndef __GD2C_GODOTPROJECT__
#define __GD2C_GODOTPROJECT__
#include "gd2c.h"
""")
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
class_codegen.transpile_struct(class_context, header)
class_codegen.transpile_constant_declarations(class_context, header)
for func in cls.functions():
if func.has_constants:
func_context = class_context.get_function_context(func)
if len(func.global_names) > 0:
header.write(f"""\
godot_string_name {func_context.global_names_identifier}[{len(func.global_names)}];
godot_string {func_context.global_strings_identifier}[{len(func.global_names)}];
""")
if func.len_constants:
header.write(f"""godot_variant {func_context.local_constants_array_identifier}[{func.len_constants}];\n""")
header.write(f"""int {func_context.initialized_local_constants_array_identifier} = 0;\n""")
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
class_codegen.transpile_property_signatures(class_context, header)
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
class_codegen.transpile_ctor_signature(class_context, header)
header.write(";\n")
class_codegen.transpile_dtor_signature(class_context, header)
header.write(";\n")
for func_context in class_context.function_contexts.values():
function_codegen.transpile_signature(func_context, header)
header.write(f"""\
#endif
""")
def _transpile_c_file(self):
p = Path(self._output_path, "godotproject.cpp")
with p.open(mode="w") as writer:
writer.write(f"""\
#include "gd2c.h"
#include "godotproject.h"
#include "math.h"
{self.global_context.define()}
""")
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
class_codegen.transpile_ctor(class_context, writer)
class_codegen.transpile_dtor(class_context, writer)
class_codegen.transpile_property_implementations(class_context, writer)
for func_context in class_context.function_contexts.values():
function_codegen.transpile_function(func_context, writer)
class_codegen.transpile_vtable(class_context, writer)
self._transpile_gdnative_init(writer)
self._transpile_gdnative_terminate(writer)
self._transpile_nativescript_init(writer)
def _transpile_gdnative_init(self, writer: IO):
writer.write(f"""\
void GDN_EXPORT {self.project.export_prefix}_gdnative_init(godot_gdnative_init_options *p_options) {{
//printf("Enter: {self.project.export_prefix}_gdnative_init\\n");
api10 = p_options->api_struct;
const godot_gdnative_api_struct *extension = api10->next;
while (extension) {{
if (extension->version.major == 1 && extension->version.minor == 1) {{
//printf(" Found api11\\n");
api11 = (const godot_gdnative_core_1_1_api_struct*)extension;
}}
if (extension == extension->next) break;
extension = extension->next;
}}
for (int i = 0; i < api10->num_extensions; ++i) {{
switch (api10->extensions[i]->type) {{
case GDNATIVE_EXT_NATIVESCRIPT: {{
extension = api10->extensions[i];
nativescript10 = (godot_gdnative_ext_nativescript_api_struct*)extension;
while (extension) {{
if (extension->version.major == 1 && extension->version.minor == 1) {{
//printf(" Found nativescript11\\n");
nativescript11 = (const godot_gdnative_ext_nativescript_1_1_api_struct*)extension;
}}
if (extension == extension->next) break;
extension = extension->next;
}}
}}; break;
default:
break;
}}
}}
gd2c_api_initialize();
vtable_init_base();
api10->godot_variant_new_nil(&__nil);
//printf("Exit: {self.project.export_prefix}_gdnative_init\\n");
}}
""")
def _transpile_gdnative_terminate(self, writer: IO):
writer.write(f"""\
void GDN_EXPORT {self.project.export_prefix}_gdnative_terminate(godot_gdnative_terminate_options *p_options) {{
//printf("Enter: {self.project.export_prefix}_gdnative_terminate\\n");
api10->godot_variant_destroy(&__nil);
""")
self._transpile_class_constants_destruction(writer)
self._transpile_global_constants_array_destruction(writer)
writer.write(f"""}}\n""")
def _transpile_nativescript_init(self, writer: IO):
writer.write(f"""\
void GDN_EXPORT {self.project.export_prefix}_nativescript_init(void *p_handle) {{
//printf("Enter: {self.project.export_prefix}_nativescript_init\\n");
""")
def visitor(cls: GDScriptClass, depth: int):
class_context = self.class_contexts[cls.type_id]
writer.write(f"""\
{{
//printf(" Register class: {cls.name}\\n");
godot_instance_create_func create = {{ NULL, NULL, NULL }};
create.create_func = {class_context.ctor_identifier};
godot_instance_destroy_func destroy = {{ NULL, NULL, NULL }};
destroy.destroy_func = {class_context.dtor_identifier};
nativescript10->godot_nativescript_register_class(p_handle, "{cls.name}", "{cls.built_in_type}", create, destroy);
}}
""")
writer.write(f"""\
{{
//printf(" Register method: __gd2c_is_class_instanceof\\n");
godot_instance_method method = {{ NULL, NULL, NULL }};
method.method = &__gd2c_is_class_instanceof;
godot_method_attributes attributes = {{ GODOT_METHOD_RPC_MODE_DISABLED }};
nativescript10->godot_nativescript_register_method(p_handle, "{cls.name}", "__gd2c_is_class_instanceof", attributes, method);
}}
""")
for entry in class_context.vtable_entries:
writer.write(f"""\
{{
//printf(" Register method: {entry.func_context.function_identifier}\\n");
godot_instance_method method = {{ NULL, NULL, NULL }};
method.method = &{entry.func_context.function_identifier};
godot_method_attributes attributes = {{ GODOT_METHOD_RPC_MODE_DISABLED }};
nativescript10->godot_nativescript_register_method(p_handle, "{cls.name}", "{entry.func_context.func.name}", attributes, method);
}}
""")
for signal in cls.signals():
writer.write(f"""\
{{
//printf(" Register signal: {signal}\\n");
godot_string name = api10->godot_string_chars_to_utf8("{signal}");
godot_signal signal = {{ name, 0, NULL, 0, NULL }};
nativescript10->godot_nativescript_register_signal(p_handle, "{signal}", &signal);
}}
""")
for member_context in class_context.member_contexts.values():
writer.write(f"""\
{{
//printf(" Register member: {member_context.member_identifier}\\n");
godot_property_set_func setter = {{ NULL, NULL, NULL }};
setter.set_func = &{member_context.setter_identifier};
godot_property_get_func getter = {{ NULL, NULL, NULL }};
getter.get_func = &{member_context.getter_identifier};
godot_property_attributes attributes = {{ GODOT_METHOD_RPC_MODE_DISABLED }};
nativescript10->godot_nativescript_register_property(p_handle, "{class_context.cls.name}", "{member_context.path}", &attributes, setter, getter);
}}
""")
writer.write(f"""\
{class_context.vtable_init_function_identifier}();
""")
self.project.visit_classes_in_dependency_order(visitor)
self._transpile_global_constants_array_initialization(writer)
self._transpile_class_constants_initialization(writer)
writer.write(f"""\
//printf("Exit: {self.project.export_prefix}_nativescript_init\\n");
}}
""")
def _transpile_global_constants_array_initialization(self, writer: IO) -> None:
for i in range(0, len(self.global_context.globals) + 1):
if i in self.global_context.globals:
cnst = self.global_context.globals[i]
if cnst.source in (GDScriptGlobal.SOURCE_CONSTANT, GDScriptGlobal.SOURCE_HARDCODED):
if cnst.vtype == VariantType.INT:
writer.write(f"api10->godot_variant_new_int({self.global_context.address_of_expression(cnst.index)}, {cnst.value});\n")
elif cnst.vtype == VariantType.REAL:
literal = cnst.value
if cnst.value == "inf":
literal = "INFINITY"
elif cnst.value == "nan":
literal = "NAN"
writer.write(f"api10->godot_variant_new_real({self.global_context.address_of_expression(cnst.index)}, {literal});\n")
elif cnst.source == GDScriptGlobal.SOURCE_SINGLETON:
writer.write(f"""\
{{
godot_object *singleton = api10->godot_global_get_singleton("{cnst.original_name}");
api10->godot_variant_new_object({self.global_context.address_of_expression(cnst.index)}, singleton);
}}
""")
elif cnst.source == GDScriptGlobal.SOURCE_CLASSDB:
utf8 = bytes(cnst.original_name, "UTF-8")
writer.write(f"""\
{{
// {cnst.original_name}
char data[] = {{ {','.join(map(lambda b: str(b), utf8))} }};
register_classdb_global(\
{self.global_context.address_of_expression(cnst.index)}, \
(const char *)data, \
{len(utf8)});
}}
""")
def _transpile_global_constants_array_destruction(self, writer: IO) -> None:
for i in range(0, len(self.global_context.globals) + 1):
if i in self.global_context.globals:
cnst = self.global_context.globals[i]
if cnst.source in (GDScriptGlobal.SOURCE_CONSTANT, GDScriptGlobal.SOURCE_HARDCODED):
if cnst.vtype == VariantType.INT:
writer.write(f"api10->godot_variant_destroy({self.global_context.address_of_expression(cnst.index)});\n")
elif cnst.vtype == VariantType.REAL:
writer.write(f"api10->godot_variant_destroy({self.global_context.address_of_expression(cnst.index)});\n")
elif cnst.source == GDScriptGlobal.SOURCE_SINGLETON:
writer.write(f"api10->godot_variant_destroy({self.global_context.address_of_expression(cnst.index)});\n")
elif cnst.source == GDScriptGlobal.SOURCE_CLASSDB:
writer.write(f"api10->godot_variant_destroy({self.global_context.address_of_expression(cnst.index)});\n")
def _transpile_class_constants_initialization(self, writer: IO) -> None:
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
for cc in class_context.constant_contexts.values():
writer.write(f"""\
{{
uint8_t data[] = {{ {','.join(map(lambda b: str(b), cc.constant.data))} }};
int bytesRead;
gd2c10->variant_decode(&{class_context.constants_array_identifier}[{cc.index}], data, {len(cc.constant.data)}, &bytesRead, true);
}}
""")
def _transpile_class_constants_destruction(self, writer: IO) -> None:
for cls in self.project.iter_classes_in_dependency_order():
class_context = self.class_contexts[cls.type_id]
for cc in class_context.constant_contexts.values():
writer.write(f"""\
api10->godot_variant_destroy(&{class_context.constants_array_identifier}[{cc.index}]);
""")
for func in class_context.cls.functions():
function_context = class_context.get_function_context(func.name)
assert function_context
if function_context.func.len_constants:
writer.write(f"""if (0 != {function_context.initialized_local_constants_array_identifier}) {{\n""")
for i in range(function_context.func.len_constants):
writer.write(f"""api10->godot_variant_destroy(&{function_context.local_constants_array_identifier}[{i}]);\n""")
writer.write(f"""}}\n""")
| python |
from django import template
from django.conf import settings
from django.urls import reverse
from django.utils.html import format_html
from django_gravatar.helpers import get_gravatar_url
register = template.Library()
@register.simple_tag
def user_link(user):
gravatar_url = get_gravatar_url(user.email, size=16)
profile_url = reverse('user_profile', args=[user.username])
return format_html("""<a href="{0}"><img class="gravatar-small" src="{1}"/>{2}</a>""", profile_url, gravatar_url, user.get_full_name())
@register.inclusion_tag('assets/asset_title.html')
def asset_title(asset, as_link):
return {'asset': asset, 'as_link': as_link}
@register.inclusion_tag('assets/asset_common.html')
def asset_common(user, asset, verbose):
return {'user': user, 'asset': asset, 'verbose': verbose}
@register.inclusion_tag('assets/asset_thumbnail.html')
def asset_thumbnail(asset, as_link=True):
return {'asset': asset, 'as_link': as_link}
| python |
# -*- coding: utf-8 -*-
"""
Test of the non-stationary poisson process sampling func.
"""
import numpy as np
import simpy
from forecast_ed.sampling import nspp
fname = 'data/arrivals.csv'
data = np.genfromtxt(fname, delimiter=',', skip_header=1)
arrivals = []
def generate(env):
a = nspp(data)
for time in a:
iat = time - env.now
arrivals.append(time)
print("Now: {0}; IAT: {1}; Next: {2}".format(env.now, iat, env.now+iat))
yield env.timeout(iat)
run_time = 1440*5
time = 0
env = simpy.Environment()
env.process(generate(env))
env.run(until=run_time)
np.savetxt('data.csv', np.array(arrivals), delimiter=',')
| python |
from dotenv import load_dotenv
import os
import requests
load_dotenv()
import json
API_URL=os.getenv("shopify_product_url")
url=API_URL+'?limit=250'
products=[]
headers={'Content-Type': 'application/json'}
r=requests.get(url,headers=headers)
products=products+r.json()['products']
header_link=r.headers['Link']
header_link_arr=header_link.split(',')
print(header_link_arr)
while not(header_link.find('rel="next"')==-1):
# if(len(header_link_arr)==2):
# print(header_link_arr[0])
# print(header_link_arr[1])
# break
# print(page_rel)
if(len(header_link_arr)==2):
page_rel=header_link_arr[1]
page_rel=page_rel[page_rel.find('&')+1:]
else:
page_rel=header_link_arr[0]
page_rel=page_rel[page_rel.find('&')+1:]
next_page_rel=page_rel[page_rel.find('=')+1:page_rel.find('>')]
url=API_URL+'?limit=250&page_info='+next_page_rel
r=requests.get(url,headers=headers)
products=products+r.json()['products']
header_link=r.headers['Link']
header_link_arr=header_link.split(',')
print(header_link_arr)
# if not(page_rel.find('rel="next"')==-1):
# next_page_rel=page_rel[page_rel.find('=')+1:page_rel.find('>')]
# print(next_page_rel)
with open('products.json', 'w') as fout:
json.dump(products , fout) | python |
from .base_state import *
from .channel_state import *
from .emoji_state import *
from .guild_state import *
from .message_state import *
from .role_state import *
from .user_state import *
| python |
from django.urls import re_path
from .views import SettingsView, UpdateSettingsView
app_name = "baserow.api.settings"
urlpatterns = [
re_path(r"^update/$", UpdateSettingsView.as_view(), name="update"),
re_path(r"^$", SettingsView.as_view(), name="get"),
]
| python |
# coding: utf-8
"""Test device 1."""
from . import release
from .TestDevice1 import TestDevice1
from .TestDevice2 import TestDevice2
__version__ = release.__version__
__version_info__ = release.__version_info__
| python |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import socket
import threading
import sys
import time
from filesocket import filesocket
'''path to temporary directory used for file sockets'''
SOCKSER_DIR =''
'''SOCKS5 RFC described connection methods'''
CONNECT = 1
BIND = 2
UDP_ASSOCIATE = 3
'''SOCKS5 RFC described supported address types'''
IPV4 = 1
DOMAINNAME = 3
IPV6 = 4
'''ERROR messages'''
CONNECT_SUCCESS = 0
ERROR_ATYPE = "[-] Client address error!"
ERROR_VERSION = "[-] Client version error!"
ERROR_METHOD = "[-] Client method error!"
ERROR_RSV = "[-] Client Reserved byte error!"
ERROR_CMD = "[-] Command not implemented by server error!"
''' Reserver byte '''
RSV = 0
''' '''
BNDADDR = "\x00" * 4
BNDPORT = "\x00" * 2
'''SOCKS VERSION (used in initial negotiation)'''
SOCKS_VERSION = 5
# ALLOWED_METHOD = [0, 2]
ALLOWED_METHOD = [0]
def main():
global SOCKSER_DIR
if len(sys.argv) != 4:
print "Usage : "
print "\tpython %s [L_HOST] [L_PORT] [SOCKSER_TMP_DIRECTORY]" % (sys.argv[0])
print "Example : "
print "\tpython %s 127.0.0.1 1080 /tmp/sockser/" % (sys.argv[0])
exit(1)
LOCAL_HOST = sys.argv[1]
LOCAL_PORT = int(sys.argv[2])
MAX_CONNECTION = 0x100
SOCKSER_DIR = sys.argv[3]
if SOCKSER_DIR[-1] != '/':
SOCKSER_DIR += '/'
print "Sockser dir :" + SOCKSER_DIR
server(LOCAL_HOST, LOCAL_PORT, MAX_CONNECTION)
def server(local_host, local_port, max_connection):
try:
server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
server_socket.bind((local_host, local_port))
server_socket.listen(max_connection)
print '[+] Server started [%s:%d]' % (local_host, local_port)
while True:
local_socket, local_address = server_socket.accept()
print '[+] Detect connection from [%s:%s]' % (local_address[0], local_address[1])
result = socks_selection(local_socket)
if not result[0]:
print "[-] socks selection error!"
break
result = socks_request(result[1])
if not result[0]:
print "[-] socks request error!"
break
local_socket, remote_socket = result[1]
# TODO : loop all socket to close...
print "[+] Releasing resources..."
local_socket.close()
print "[+] Closing server..."
server_socket.close()
print "[+] Server shuted down!"
except KeyboardInterrupt:
print ' Ctl-C stop server'
try:
remote_socket.close()
except:
pass
try:
local_socket.close()
except:
pass
try:
server_socket.close()
except:
pass
return
def socks_selection(socket):
'''Parses first request and retrieves client info (host,port,socks version and method)'''
''' retrieves client supported version number'''
client_version = ord(socket.recv(1))
print "[+] client version : %d" % client_version
''' checks if client supported version is supported by server'''
if not client_version == SOCKS_VERSION:
socket.shutdown(socket.SHUT_RDWR)
socket.close()
return False, ERROR_VERSION
''' retrieves client supported connection methods'''
support_method_number = ord(socket.recv(1))
print "[+] Client Supported method number : %d" % support_method_number
''' creates supported methods list'''
support_methods = []
for i in range(support_method_number):
method = ord(socket.recv(1))
print "[+] Client Method : %d" % method
support_methods.append(method)
''' chooses method from those supported'''
selected_method = None
for method in ALLOWED_METHOD:
if method in support_methods:
selected_method = 0
''' checks if method was chosen '''
if selected_method is None:
socket.shutdown(socket.SHUT_RDWR)
socket.close()
return False, ERROR_METHOD
''' sends chosen method to client '''
print "[+] Server select method : %d" % selected_method
response = chr(SOCKS_VERSION) + chr(selected_method)
socket.send(response)
''' returns socket if everything went well'''
return True, socket
def socks_request(local_socket):
# start SOCKS negotiation
client_version = ord(local_socket.recv(1))
print "[+] client version : %d" % client_version
if not client_version == SOCKS_VERSION:
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return False, ERROR_VERSION
cmd = ord(local_socket.recv(1))
if cmd == CONNECT:
print "[+] CONNECT request from client"
rsv = ord(local_socket.recv(1))
if rsv != 0:
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return False, ERROR_RSV
atype = ord(local_socket.recv(1))
if atype == IPV4:
dst_address = ("".join(["%d." % (ord(i)) for i in local_socket.recv(4)]))[0:-1]
print "[+] IPv4 : %s" % dst_address
dst_port = ord(local_socket.recv(1)) * 0x100 + ord(local_socket.recv(1))
print "[+] Port : %s" % dst_port
''' setting up filesocket '''
remote_socket = filesocket.filesocket(socket_dir = SOCKSER_DIR)
try:
print "[+] Fake connecting : %s:%s" % (dst_address, dst_port)
timestamp = str(int(time.time()))
remote_socket.connect((dst_address, dst_port),timestamp)
response = ""
response += chr(SOCKS_VERSION)
response += chr(CONNECT_SUCCESS)
response += chr(RSV)
response += chr(IPV4)
response += BNDADDR
response += BNDPORT
local_socket.send(response)
print "[+] Tunnel connected! Transferring data..."
r = threading.Thread(target=transfer_in, args=(
local_socket, remote_socket))
r.start()
s = threading.Thread(target=transfer_out, args=(
remote_socket, local_socket))
s.start()
return True, (local_socket, remote_socket)
except socket.error as e:
print e
remote_socket.shutdown(socket.SHUT_RDWR)
remote_socket.close()
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
elif atype == DOMAINNAME:
domainname_length = ord(local_socket.recv(1))
domainname = ""
for i in range(domainname_length):
domainname += (local_socket.recv(1))
print "[+] Domain name : %s" % (domainname)
dst_port = ord(local_socket.recv(1)) * 0x100 + ord(local_socket.recv(1))
print "[+] Port : %s" % (dst_port)
# SETTING UP FILENAME instead of preparing socket
remote_socket = filesocket.filesocket(socket_dir = SOCKSER_DIR)
try:
print "[+] Fake connecting : %s:%s" % (domainname, dst_port)
timestamp = str(int(time.time()))
remote_socket.connect((domainname, dst_port),timestamp)
response = ""
response += chr(SOCKS_VERSION)
response += chr(CONNECT_SUCCESS)
response += chr(RSV)
response += chr(IPV4)
response += BNDADDR
response += BNDPORT
local_socket.send(response)
print "[+] Tunnel connected! Transferring data..."
r = threading.Thread(target=transfer_in, args=(
local_socket, remote_socket))
r.start()
s = threading.Thread(target=transfer_out, args=(
remote_socket, local_socket))
s.start()
return (True, (local_socket, remote_socket))
except socket.error as e:
print e
remote_socket.shutdown(socket.SHUT_RDWR)
remote_socket.close()
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
elif atype == IPV6:
#TODO
dst_address = int(local_socket.recv(4).encode("hex"), 16)
print "[+] IPv6 : %x" % (dst_address)
dst_port = ord(local_socket.recv(1)) * 0x100 + ord(local_socket.recv(1))
print "[+] Port : %s" % (dst_port)
# TODO IPv6 under constrution
print "IPv6 support under constrution"
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return (False, ERROR_ATYPE)
else:
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return (False, ERROR_ATYPE)
elif cmd == BIND:
# TODO
print "socks5 BIND command is not supported for now."
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return (False, ERROR_CMD)
elif cmd == UDP_ASSOCIATE:
# TODO
print "socks5 UDP_ASSOCIATE command is not supported for now."
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return (False, ERROR_CMD)
else:
local_socket.shutdown(socket.SHUT_RDWR)
local_socket.close()
return (False, ERROR_CMD)
return (True, local_socket)
def transfer_in(local_socket, remote_socket):
''' local_socket - local socket '''
''' remote_socket - fileSocket '''
local_socket_name = local_socket.getpeername()
local_socket_address = local_socket_name[0]
local_socket_port = local_socket_name[1]
remote_socket_address = remote_socket.getHostname()
remote_socket_port = str(remote_socket.getPort())
print "[+] Starting transfer [%s:%s] => [%s:%s]" % (local_socket_address, local_socket_port, remote_socket_address, remote_socket_port)
while True:
''' receive from local socket'''
buff = local_socket.recv(0x1000)
''' if buffer not empty send to filesocket'''
if buff:
#remote_socket.send(handle(buff))
remote_socket.send(buff)
''' if socket broke break '''
if not buff or remote_socket.is_out_closed():
print "[-] No data received from NETWORK! Breaking filesocket and remote connection..."
remote_socket.close_in()
print "[+] Closing connections! [%s:%s]" % (local_socket_address, local_socket_port)
local_socket.close()
break
print "[+] %s:%d => %s:%s [%s]" % (local_socket_address, local_socket_port, remote_socket_address, remote_socket_port, repr(buff))
print "[+] %s:%s => %s:%s => Length : [%d]" % (local_socket_address, local_socket_port, remote_socket_address, remote_socket_port, len(buff))
def transfer_out(remote_socket, local_socket):
''' Description : this function reads in all the data from the *.out file and closes it when all is read then sends data to local socket'''
''' remote_socket - the file socket '''
''' local_socket - local socket '''
remote_socket_address = remote_socket.getHostname()
remote_socket_port = remote_socket.getPort()
local_socket_name = local_socket.getpeername()
local_socket_address = local_socket_name[0]
local_socket_port = local_socket_name[1]
print "[+] Starting transfer [%s:%s] => [%s:%s]" % (remote_socket_address, remote_socket_port, local_socket_address, local_socket_port)
while True:
''' receive from file socket'''
buff = remote_socket.recv()
''' if buffer not empty send to local socket'''
if buff:
'''
NOTE : this try except block is present only in transfer_out
since a socket.error occurs on send to dead socket
on recv the buffer is just empty but no error is triggered
'''
try:
#local_socket.send(handle(buff))
local_socket.send(buff)
except socket.error as e:
''' if socket is closed we close our input too '''
print "[-] socket error in transfer_out"
print "[-] No data could be sent to socket"
print "[-] Closing in connection on FILESOCKET "
remote_socket.close_in()
''' if socket broke, break '''
if (not buff) or remote_socket.is_in_closed():
print "[-] No data received from FILESOCKET! Closing out connection on filesocket and breaking connection!"
remote_socket.close_out()
print "[+] Closing connection! [%s:%s]" % (local_socket, local_socket)
local_socket.close()
break
def handle(buffer):
return buffer
if __name__ == "__main__":
main()
| python |
from django.urls import path
from . import views
urlpatterns = [
path('friendrequest', views.send_friend_request,
name="send_friend_request"),
path('friendrequest/handle', views.handle_friend_request,
name="handle_friend_request"),
path('friendrequest/<slug:author_id>/', views.retrieve_friend_request_of_author_id,
name="retrieve_friend_request_of_author_id")
]
| python |
import itertools
import sys
import os
from rdkit import Chem
from rdkit.Chem import rdMolTransforms, rdMolAlign
import openbabel
from qmconftool import QMMol
def find_dihedral_idx(mol,smarts_patt):
patt_mol = Chem.MolFromSmarts(smarts_patt)
matches = mol.GetSubstructMatches(patt_mol)
unique_match = list()
match_list = list()
for m in matches:
if m[:3] not in match_list:
unique_match.append(m)
match_list.append(m[:3])
if len(unique_match) != 2:
print("more than two dihedrals in " + filename)
quit()
return unique_match
def changeAndOpt(rdkit, theta):
Chem.SanitizeMol(rdkit)
initconf = rdkit.GetConformer()
# set outer most dihedral to 180 degrees.
smarts_patt = "C-S-C-[C,Si,Ge;H0]"
outer_dihedral_idx = find_dihedral_idx(rdkit, smarts_patt)
for k, i, j, l in outer_dihedral_idx:
rdMolTransforms.SetDihedralDeg(initconf, k,i,j,l, 180.0)
# change second outmost dihedral with +-120 degrees.
patt = "S-C-[C,Si,Ge;H0]-[C,Si,Ge]"
dihedral_idx = find_dihedral_idx(rdkit, patt)
new_angles = list()
for k, i, j, l in dihedral_idx:
init_dihedral_angle = rdMolTransforms.GetDihedralDeg(initconf, k,i,j,l)
new_angles.append([init_dihedral_angle + x*theta for x in range(int(360./theta))])
angle_combinations = list(itertools.product(*new_angles)) # all combinations.
for dihedrals in angle_combinations:
for (k,i,j,l), angle in zip(dihedral_idx, dihedrals):
rdMolTransforms.SetDihedralDeg(initconf, k,i,j,l, angle )
rdkit.AddConformer(initconf, assignId=True)
rdMolAlign.AlignMolConformers(rdkit)
mol_list = list()
for idx, conf in enumerate(rdkit.GetConformers()):
if idx == 0:
continue
sdf_txt = Chem.SDWriter.GetText(rdkit, conf.GetId())
m = Chem.MolFromMolBlock(sdf_txt, removeHs=False)
conf_name = m.GetProp("_Name") + "-" + str(idx-1)
m.SetProp("_Name", conf_name)
mol_list.append(m)
# Optimize structures with new dihedrals.
confqmmol = QMMol(mol_list, fmt="mol_list", charge=0, multi=1, charged_fragments=True)
confqmmol.optimize(program="xtb", method="opt", cpus=24, babelAC=True)
# Write xyz files of conformers
for newConf in confqmmol.GetConformers():
obConversion = openbabel.OBConversion()
obConversion.SetInAndOutFormats("sdf", "xyz")
newConfm = openbabel.OBMol()
obConversion.ReadString(newConfm, Chem.MolToMolBlock(newConf))
new_xyz = obConversion.WriteString(newConfm)
with open(newConf.GetProp("_Name") + ".xyz", 'w') as f:
f.write(new_xyz)
if __name__ == "__main__":
mols = list()
for fname in os.listdir('.'):
if fname.endswith("sdf"):
m = Chem.MolFromMolFile(fname, removeHs=False)
m.SetProp("_Name", fname.split('.')[0])
mols.append(m)
# optimize mol with xTB.
qmmol = QMMol(mols, fmt="mol_list", charge=0, multi=1, charged_fragments=True)
qmmol.optimize(program="xtb", method="opt", cpus=47, babelAC=True)
theta_change = 120.
# Change dihedrals
for c in qmmol.GetConformers():
changeAndOpt(c, theta_change)
| python |
"""Root of podpointclient"""
| python |
import unittest
from cpuinfo import *
import helpers
class MockDataSource_enforcing(object):
@staticmethod
def has_sestatus():
return True
@staticmethod
def sestatus_b():
returncode = 0
output = r'''
SELinux status: enabled
SELinuxfs mount: /sys/fs/selinux
SELinux root directory: /etc/selinux
Loaded policy name: targeted
Current mode: enforcing
Mode from config file: enforcing
Policy MLS status: enabled
Policy deny_unknown status: allowed
Memory protection checking: actual (secure)
Max kernel policy version: 31
'''
return returncode, output
class MockDataSource_not_enforcing(object):
@staticmethod
def has_sestatus():
return True
@staticmethod
def sestatus_b():
returncode = 0
output = r'''
SELinux status: enabled
SELinuxfs mount: /sys/fs/selinux
SELinux root directory: /etc/selinux
Loaded policy name: targeted
Current mode: eating
Mode from config file: enforcing
Policy MLS status: enabled
Policy deny_unknown status: allowed
Memory protection checking: actual (secure)
Max kernel policy version: 31
'''
return returncode, output
class MockDataSource_exec_mem_and_heap(object):
@staticmethod
def has_sestatus():
return True
@staticmethod
def sestatus_b():
returncode = 0
output = r'''
allow_execheap on
allow_execmem on
'''
return returncode, output
class MockDataSource_no_exec_mem_and_heap(object):
@staticmethod
def has_sestatus():
return True
@staticmethod
def sestatus_b():
returncode = 0
output = r'''
allow_execheap off
allow_execmem off
'''
return returncode, output
class TestSELinux(unittest.TestCase):
def setUp(self):
helpers.backup_data_source(cpuinfo)
self.trace = Trace(False, False)
def tearDown(self):
helpers.restore_data_source(cpuinfo)
def test_enforcing(self):
helpers.monkey_patch_data_source(cpuinfo, MockDataSource_enforcing)
self.assertEqual(True, cpuinfo._is_selinux_enforcing(self.trace))
def test_not_enforcing(self):
helpers.monkey_patch_data_source(cpuinfo, MockDataSource_not_enforcing)
self.assertEqual(False, cpuinfo._is_selinux_enforcing(self.trace))
def test_exec_mem_and_heap(self):
helpers.monkey_patch_data_source(cpuinfo, MockDataSource_exec_mem_and_heap)
self.assertEqual(False, cpuinfo._is_selinux_enforcing(self.trace))
def test_no_exec_mem_and_heap(self):
helpers.monkey_patch_data_source(cpuinfo, MockDataSource_no_exec_mem_and_heap)
self.assertEqual(True, cpuinfo._is_selinux_enforcing(self.trace))
| python |
from .context_processors import *
from .middleware import *
from .templatetags import *
from .http_client import *
| python |