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fastGraph/__main__.py | AngusKung/fastGraph | 0 | 62246 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import random
import psutil
import logging
import pandas as pd
import numpy as np
from io import open
from collections import Counter
from multiprocessing import cpu_count
from concurrent.futures import ProcessPoolExecutor
from scipy.sparse import csr_matrix, save_npz, load_npz, lil_matrix
from argparse import ArgumentParser, FileType, ArgumentDefaultsHelpFormatter
from gensim.models import Word2Vec
from gensim.models.word2vec import LineSentence
from six import text_type as unicode
from six import iteritems
from six.moves import range
from fastGraph.graph import Graph
from fastGraph import ngram
import pdb
p = psutil.Process(os.getpid())
try:
p.cpu_affinity(list(range(cpu_count())))
except AttributeError:
pass
LOGFORMAT = "%(asctime).19s %(levelname)s %(filename)s Line %(lineno)s: %(message)s"
logging.basicConfig(format=LOGFORMAT)
logger = logging.getLogger("fastGraph")
logger.setLevel(logging.INFO)
DTYPE = np.float64
def debug(type_, value, tb):
if hasattr(sys, 'ps1') or not sys.stderr.isatty():
sys.__excepthook__(type_, value, tb)
else:
import traceback
import pdb
traceback.print_exception(type_, value, tb)
print(u"\n")
pdb.pm()
def load_matrix(args):
logger.info("Reading from "+str(args.input))
if "wiki-Vote.csv" in args.input:
df = pd.read_csv(args.input, sep=',', comment='#')
max_node = max(max(df['FromNodeId'].unique()), max(df['ToNodeId'].unique()))
total_len = max_node + 1
matrix = lil_matrix(np.zeros((total_len, total_len), dtype=DTYPE))
for row in df.itertuples():
matrix[row.FromNodeId, row.ToNodeId] = matrix[row.FromNodeId, row.ToNodeId] + 1 # Each edge is binary
return csr_matrix(matrix)
elif "weighted_directed.csv" in args.input:
df = pd.read_csv(args.input, sep=',', comment='#')
max_node = max(max(df['SOURCE'].unique()), max(df['TARGET'].unique()))
total_len = max_node + 1
matrix = lil_matrix(np.zeros((total_len, total_len), dtype=DTYPE))
for row in df.itertuples():
matrix[row.SOURCE, row.TARGET] = matrix[row.SOURCE, row.TARGET] + row.RATING # Each edge has different weights
return csr_matrix(matrix)
elif ".npz" in args.input or ".npy" in args.input:
logger.info("Load matrix directly")
matrix = np.load(args.input)
return csr_matrix(matrix)
else:
# Implement parsing here to transform into matrix form.
raise NotImplementedError("Implement customized parsing here.")
def fastGraph_flow(args):
# Read and process different input
matrix = load_matrix(args)
logger.info("Matrix loaded.")
graph = Graph()
graph.build_graph_from_matrix(matrix, is_directed=True, remove_self_loops=False,
normalized_edge=True, outward_prob_check=True)
# Generate walks, select which walk to use by de-comment
if args.walk_type == "likely":
# walks = graph.build_likely_walk_corpus_multiprocess(args.number_paths, args.path_length,
# rand=random.Random(0), shuffle=True, deduplicate=False)
walks = graph.build_likely_walk_corpus(args.number_paths, args.path_length, rand=random.Random(0),
shuffle=True, deduplicate=False)
elif args.walk_type == "node2vec":
graph.preprocess_node2vec_walk(args.p, args.q)
walks = graph.build_node2vec_walk_corpus(args.number_paths, args.path_length, rand=random.Random(0),
shuffle=True, deduplicate=False)
elif args.walk_type == "deep":
walks = graph.build_deepwalk_corpus(args.number_paths, args.path_length, rand=random.Random(0),
shuffle=True, deduplicate=False)
else:
raise ValueError("--walk-type must be either 'likely', 'node2vec' or 'deep'.")
# Save walks to storage, enabling gensim's iterator ability.
walks_file = ''.join(str(args.input).split('.')[:-1])+'.walks'
with open(walks_file, 'w') as fout:
for walk in walks:
fout.write(' '.join(walk)+'\n')
logger.info("Walks saved to "+walks_file)
walks = LineSentence(args.input)
# Phrases
if args.ngram > 1:
logger.info("Building n-gram with n="+str(args.ngram)+"...")
walks, ngram_phrasers = ngram.build_ngram(walks, args.ngram)
# Word2Vec
logger.info("Training ...")
w2v = Word2Vec(walks, size=args.embed_size, window=args.window_size, min_count=0,
sg=1, hs=0, negative=10, workers=args.workers)
# Save model
w2v.save(args.output)
def main():
parser = ArgumentParser("fastGraph", formatter_class=ArgumentDefaultsHelpFormatter, conflict_handler='resolve')
parser.add_argument("-l", "--log", dest="log", default="INFO",
help="log verbosity level")
parser.add_argument('--input', nargs='?', required=True,
help='Input matrix')
parser.add_argument('--max-memory-data-size', default=1000000000, type=int,
help='Size to start dumping walks to disk, instead of keeping them in memory.')
parser.add_argument('--number-paths', default=5, type=int,
help='Number of random walks to start at each node')
parser.add_argument('--output', required=True,
help='Output representation file')
parser.add_argument('--embed-size', default=64, type=int,
help='Dimension of the latent vector as embedding.')
parser.add_argument('--seed', default=0, type=int,
help='Seed for random walk generator.')
parser.add_argument('--directed', default=True, type=bool,
help='Treat the graph as directed.')
parser.add_argument('--path-length', default=40, type=int,
help='Length of the random walk started at each node')
parser.add_argument('--window-size', default=5, type=int,
help='Window size of skipgram model.')
parser.add_argument('--walk-type', default="likely", type=str,
help='Which walk method to use: likely, random, node2vec.')
parser.add_argument('--p', default=5, type=int,
help="p value, refer to original paper: https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf ")
parser.add_argument('--q', default=3, type=int,
help="q value, refer to original paper: https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf ")
parser.add_argument('--workers', default=cpu_count(), type=int,
help='Number of parallel processes.')
parser.add_argument('--ngram', default=1, type=int,
help='N of n-grams, e.g.: set 2 for bigrams, 3 for trigrams, etc.')
args = parser.parse_args()
numeric_level = getattr(logging, args.log.upper(), None)
logging.basicConfig(format=LOGFORMAT)
logger.setLevel(numeric_level)
fastGraph_flow(args)
if __name__ == "__main":
sys.exit(main()) | [
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2
] |
CodeChef/CENS20D.py | PROxZIMA/Competitive-Coding | 1 | 82881 | # https://www.codechef.com/viewsolution/36973732
for _ in range(int(input())):
n = int(input())
a = list(map(int, input().split()))
count = 0
for i in range(n):
for j in range(i + 1, n):
if a[i] & a[j] == a[i]:
count += 1
print(count) | [
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] |
mycode/test03.py | JamesWang007/kitti_object_vis | 0 | 191401 | import torch
import numpy as np
import _init_path
import numpy as np
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_sched
from torch.nn.utils import clip_grad_norm_
from torch.utils.data import DataLoader
import tensorboard_logger as tb_log
from dataset import KittiDataset
import argparse
import importlib
from pointnet2_msg import Pointnet2MSG as pointnet2_msg
import kitti_utils
import mayavi.mlab as mlab
# pts_mode='sphere'
def draw_lidar(
pc,
color=None,
fig=None,
bgcolor=(0, 0, 0),
pts_scale=0.3,
pts_mode="sphere",
pts_color=None,
color_by_intensity=False,
pc_label=False,
):
""" Draw lidar points
Args:
pc: numpy array (n,3) of XYZ
color: numpy array (n) of intensity or whatever
fig: mayavi figure handler, if None create new one otherwise will use it
Returns:
fig: created or used fig
"""
# ind = (pc[:,2]< -1.65)
# pc = pc[ind]
pts_mode = "point"
print("====================", pc.shape)
if fig is None:
fig = mlab.figure(
figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(1600, 1000)
)
if color is None:
color = pc[:, 2]
if pc_label:
color = pc[:, 4]
if color_by_intensity:
color = pc[:, 2]
mlab.points3d(
pc[:, 0],
pc[:, 1],
pc[:, 2],
color,
color=pts_color,
mode=pts_mode,
colormap="gnuplot",
scale_factor=pts_scale,
figure=fig,
)
# draw origin
mlab.points3d(0, 0, 0, color=(1, 1, 1), mode="sphere", scale_factor=0.2)
# draw axis
axes = np.array(
[[2.0, 0.0, 0.0, 0.0], [0.0, 2.0, 0.0, 0.0], [0.0, 0.0, 2.0, 0.0]],
dtype=np.float64,
)
mlab.plot3d(
[0, axes[0, 0]],
[0, axes[0, 1]],
[0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig,
)
mlab.plot3d(
[0, axes[1, 0]],
[0, axes[1, 1]],
[0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig,
)
mlab.plot3d(
[0, axes[2, 0]],
[0, axes[2, 1]],
[0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig,
)
# draw fov (todo: update to real sensor spec.)
fov = np.array(
[[20.0, 20.0, 0.0, 0.0], [20.0, -20.0, 0.0, 0.0]], dtype=np.float64 # 45 degree
)
mlab.plot3d(
[0, fov[0, 0]],
[0, fov[0, 1]],
[0, fov[0, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig,
)
mlab.plot3d(
[0, fov[1, 0]],
[0, fov[1, 1]],
[0, fov[1, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig,
)
# draw square region
TOP_Y_MIN = -20
TOP_Y_MAX = 20
TOP_X_MIN = 0
TOP_X_MAX = 40
TOP_Z_MIN = -2.0
TOP_Z_MAX = 0.4
x1 = TOP_X_MIN
x2 = TOP_X_MAX
y1 = TOP_Y_MIN
y2 = TOP_Y_MAX
mlab.plot3d(
[x1, x1],
[y1, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x2, x2],
[y1, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x1, x2],
[y1, y1],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
mlab.plot3d(
[x1, x2],
[y2, y2],
[0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig,
)
# mlab.orientation_axes()
mlab.view(
azimuth=180,
elevation=70,
focalpoint=[12.0909996, -1.04700089, -2.03249991],
distance=62.0,
figure=fig,
)
return fig
def load_res(filename):
res = np.fromfile(filename, dtype=np.float32, sep=' ')
return res.reshape(-1,4).view()
def main():
# load data
pc = np.fromfile('../data/save//train_03.bin', dtype=np.float32).reshape(-1,4)
res = np.fromfile('res_03.txt', dtype=np.float32).reshape(-1,4)
fig = draw_lidar(pc)
if __name__ == "__main__":
main()
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] |
displ/queue/internal.py | tflovorn/displ | 4 | 1604844 | <filename>displ/queue/internal.py<gh_stars>1-10
import os
import subprocess
from displ.queue.queuefile import get_qf_path
from displ.queue.queue_util import global_config
def enqueue(config):
# Assumes appropriate queufile already exists.
# (allow for queuefile to be written and examined before enqueuing)
machine = config["machine"]
gconf = global_config()
if machine == "ls5" or machine == "stampede2":
_enqueue_tacc(config)
else:
raise ValueError("Unrecognized config['machine'] value")
def _enqueue_tacc(config):
qf_path = get_qf_path(config)
cwd = os.getcwd()
if config["calc"] == "wan_setup":
qf_dir = os.path.join(config["base_path"], config["prefix"], "wannier")
os.chdir(qf_dir)
subprocess.call(["sbatch", qf_path])
os.chdir(cwd)
elif config["calc"] == "wan_setup_group":
_run_sbatch_group(config, cwd, "wan_setup")
elif config["calc"] == "pw_post_group":
_run_sbatch_group(config, cwd, "pw_post")
elif config["calc"] == "bands_only_group":
_run_sbatch_group(config, cwd, "bands_only")
else:
raise ValueError("unsupported config['calc'] for enqueue")
def _run_sbatch_group(config, cwd, calc_name):
os.chdir(config["base_path"])
qf = "{}_{}_{}".format(config["global_prefix"], calc_name, config["prefix"])
qf_path = os.path.join(config["base_path"], qf)
subprocess.call(["sbatch", qf_path])
os.chdir(cwd)
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] |
hyperparam_optim_mask_rcnn.py | branislav1991/MicroscopyUNet | 1 | 53324 | <gh_stars>1-10
import os
from sys import float_info
from hyperopt import fmin, tpe, hp
from train_mask_rcnn import train_mask_rcnn, CHECKPOINT_DIR
import pickle
import math
from keras import backend as K
train_path="./data/stage1_train/"
val_path="./data/stage1_val/"
train_ids = next(os.walk(train_path))
train_ids = [[train_ids[0] + d,d] for d in train_ids[1]]
val_ids = next(os.walk(val_path))
val_ids = [[val_ids[0] + d,d] for d in val_ids[1]]
def objective(args):
lr_heads = args["lr_heads"]
lr_all = args["lr_all"]
histories = train_mask_rcnn(train_ids, val_ids, init_with="coco", checkpoint_dir=CHECKPOINT_DIR,
procedures=[{"layers": "heads", "learning_rate": lr_heads, "epochs": 5},
{"layers": "all", "learning_rate": lr_all, "epochs": 5}])
if "val_loss" in histories[0].history:
h = histories[0].history["val_loss"][-1].flat[0]
if math.isfinite(h) is not True:
h = float_info.max
else:
h = float_info.max
K.clear_session()
return h
space = {"lr_heads": hp.loguniform('lr_heads', math.log(0.0001), math.log(0.1)),
"lr_all": hp.loguniform('lr_all', math.log(0.0001), math.log(0.1))}
best = fmin(objective, space, algo=tpe.suggest, max_evals=15)
print(best)
# save best hyperparameters to file
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BOT-DEMO/MoLFI_demo.py | Pad0y/logparser | 0 | 67246 | <reponame>Pad0y/logparser
#!/usr/bin/env python
import sys
sys.path.append("../")
from logparser import MoLFI
import pandas as pd
input_dir = "../logs/HDFS/" # The input directory of log file
output_dir = "MoLFI_result/" # The output directory of parsing results
log_file = "HDFS_2k.log" # The input log file name
log_format = "<Date> <Time> <Pid> <Level> <Component>: <Content>" # HDFS log format
regex = [r"blk_-?\d+", r"(\d+\.){3}\d+(:\d+)?"] # Regular expression list for optional preprocessing (default: [])
parser = MoLFI.LogParser(input_dir, output_dir, log_format, rex=regex)
parser.parse(log_file)
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13,
2
] |
mysign_app/migrations/0002_add_is_admin.py | mindhashnl/roomsignage | 0 | 162951 | # Generated by Django 2.2.5 on 2019-09-24 09:16
import django.db.models.deletion
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('mysign_app', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='user',
name='is_admin',
field=models.BooleanField(default=False),
preserve_default=False,
),
migrations.AlterField(
model_name='doordevice',
name='company',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='mysign_app.Company'),
),
]
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Beta/Longest Subarray with Maximum Average Value.py | mwk0408/codewars_solutions | 6 | 165524 | <reponame>mwk0408/codewars_solutions
def solve(arr, k):
cur=float("-inf")
ans=tuple()
total=sum(arr)
for i in range(len(arr), k-1, -1):
temp=compute(arr, total, i)
if temp[0]>cur:
cur=temp[0]
ans=(temp[1], i)
total-=arr[i-1]
return ans
def compute(arr, total, k):
cur=total
index=0
avg=cur/k
for i in range(k, len(arr)):
cur+=arr[i]-arr[i-k]
if (cur/k)>=avg:
avg=cur/k
index=i-k+1
return (avg, index) | [
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2
] |
networking/connection/stun_client.py | bcgrendel/python_networking | 0 | 8551 | <reponame>bcgrendel/python_networking
import socket
import sys
import traceback
import struct
import threading;
from threading import Thread;
import time;
import datetime;
import json
#import buffered_message;
import hashlib
from Crypto.PublicKey import RSA
from connection_state import ConnectionState
# publickey = RSA.importKey(key_string)
import tcp;
import udp;
# *************
# EXAMPLE USAGE
# *************
'''
import socket
import tcp
import udp
import stun_client
import time
start_listening = True
local_ip = socket.gethostbyname(socket.gethostname())
local_port = 30779
server_ip = socket.gethostbyname(socket.gethostname())
server_port = 30788
socket_timeout = 3.0
peer_block_manager = None
client = stun_client.STUN_Client(start_listening, local_ip, local_port, server_ip, server_port, socket_timeout, peer_block_manager)
# Set your available listening port ranges
client.available_ports = [[35000, 35100], [36500, 36700],]
# Register a user acccount with the stun server.
class RegisterCallback:
def __init__(self):
self.error_message = ""
self.success = None
def handle_timeout(self, params=None):
self.success = False
self.error_message = "Registration request to server has timed-out."
def complete_registration(self, success, error_message=""):
self.success = success
self.error_message = error_message
username = "test_user"
password = "<PASSWORD>"
profile_map = {}
callback_object = RegisterCallback()
registration_type = "permanent"
client.register(username, password, profile_map, callback_object, registration_type)
response_check_interval = 0.5;
while callback_object.success == None:
time.sleep(response_check_interval)
if not callback_object.success:
print "Error: %s" % callback_object.error_message
exit()
# Login with username and password.
class AuthCallback:
def __init__(self):
self.error_message = ""
self.success = None
def handle_timeout(self, params=None):
self.success = False
self.error_message = "Authentication request to server has timed-out."
def complete_authentication(self, success, error_message=""):
self.success = success
self.error_message = error_message
callback_object = AuthCallback()
login = True # this authentication is to login. It'd be False if we wanted to log out.
client.authenticate(username, password, callback_object, login)
while callback_object.success == None:
time.sleep(response_check_interval)
if not callback_object.success:
print "Error: %s" % callback_object.error_message
exit()
# Now we can access the list of peers connected to the server.
# Alternatively, assign a function reference to client.peer_map_callback (argument will be a reference to client.peer_map) to be notified of peer list updates as they are received.
#
# sample peer_map:
# ["test_user":["test_user", None], "another_user":["another_user", None],]
# Get a peer from the list.
peer_username = None;
for _username, data in client.peer_map.iteritems():
if username != _username:
peer_username = _username
break
# Connect to that peer (hole-punch)
class ConnectionCallback:
def __init__(self):
self.error_message = ""
self.success = None
self.client_key = None
def handle_timeout(self, params=None):
self.success = False
self.error_message = "Connection request to server has timed-out."
def complete_connection(self, peer_username, success, error_message=""):
self.success = success
if success:
self.client_key = error_message
else:
self.error_message = error_message
buffer_size = 128
callback_object = ConnectionCallback()
client.connect_to_peer(peer_username, buffer_size, callback_object)
while callback_object.success == None:
time.sleep(response_check_interval)
if not callback_object.success:
print "Error: %s" % callback_object.error_message
exit()
client_key = callback_object.client_key
udp_client = client.client_map[client_key]
# Now you can communicate with that peer.
udp_client.send_message("Greetings!")
udp_client.pop_all_messages()
'''
class STUN_Client:
def __init__(self,
start_listen_thread=False,
local_ip=socket.gethostbyname(socket.gethostname()),
local_port=30779,
server_ip=socket.gethostbyname(socket.gethostname()),
server_port=30788,
socket_timeout=3.0,
peer_block_manager=None):
self.local_ip = local_ip;
self.local_port = local_port;
self.socket_timeout = socket_timeout;
self.peer_block_manager = peer_block_manager;
self.thread_sleep_duration = 0.1;
self.error_log = [];
self.username = None;
self.password = <PASSWORD>;
self.profile_map = {};
self.authenticated = False;
self.auth_callback = None;
self.auth_keys = None;
self.auth_timeout = 15; # 15 seconds is the limit for authentication requests. It's just a magic number like many of these timeout values.
self.last_auth = None;
self.login_expiration = 20; # login will expire after this many seconds passes without successful keep-alive authentication
self.auth_keep_alive_interval = 5;
self.auth_keep_alive_multiplier = 1; # Avoid hammering the server if it's down. Will increment every time re-auth fails, returns to 1 upon successful authentication.
self.re_auth_ready = None;
self.master_log = []; # all messages recieved
self.message_log_map = {}; # log per message type.
# this will handle callbacks for keeping track of whether the user's authentication expires (namely from losing connection to the server.)
self.authentication_monitor_object = None;
self.hole_punch_timeout = 20;
self.hole_punch_max_attempts = 20;
self.server_response_timeout = 20;
# Server response flags. Set to None when sending a request; they are flipped to True upon receiving a response. Used for determining response time-out.
self._auth_status = None;
self._registration_status = None; # Private. Internal use only.
self._holepunch_status = {};
self.available_ports = [[34000, 34100],] # list of ranges, e.g. ports 34000 - 34100
self.used_ports = [];
self.registration_key = None;
self.udp_client_keep_alive_timeout = 30;
# dictionary of active udp connections (hole-punched)
self.client_map = {};
self.callback_map = {};
self.send_queue = [];
self.connection_state = ConnectionState(False);
# Initialize TCP client.
self.init_tcp_client(server_ip, server_port);
self.peer_map = {};
# Start listening to the stun server.
self.init_stun_listener();
self.keep_alive_monitor = KeepAliveMonitor(self);
self.peer_map_callback = None;
def shutdown(self, stun_only=True):
self.authenticated = False;
self.connection_state.active = False; # kills main thread, making the logout auth sequence impossible in its current implementation (get salt/key, then perform request) which needs the main loop.
self.stun_client.disconnect();
if not stun_only:
# disconnect all udp clients...
for key, client in self.client_map.iteritems():
client.disconnect();
self.client_map.clear();
self.peer_map.clear();
del self.used_ports[:]
def restart(self, stun_only=True):
self.shutdown(stun_only);
self.init_tcp_client(self.server_ip, self.server_port);
self.init_stun_listener();
def log_error(self, error_message, extra=None):
err_msg = "[STUN_Server] Line #%s: %s\n\n%s" % (str(traceback.tb_lineno(sys.exc_traceback)), traceback.format_exc(), sys.exc_info());
timestamp = time.time();
date_string = datetime.datetime.fromtimestamp(timestamp).strftime('(%Y-%m-%d) %H:%M:%S')
self.error_log.append((timestamp, date_string, err_msg, extra));
def monitor_response(self, target_object, target_key=None, timeout=20, callback=None, callback_params=None, timeout_callback=None, timeout_callback_params=None):
"""Waits until target is no longer null or timeout occurs. Timeout is in seconds. target_object and target_key should be strings.
If target key is not null, then target_object will be treated as a dictionary (using target_key for the index).
This function is best utilized on its own separate thread."""
# Wait until salt and key have been retrieved or timeout occurs.
time_elapsed = 0;
start_time = time.time();
target_attribute = getattr(self, target_object);
target = None;
connection_state = self.connection_state
#print "Monitoring for %s" % target_object;
# Behold, python lambda expressions in the wild!
if target_key == None:
target = lambda parent: getattr(parent, target_object);
else:
target = lambda parent: getattr(parent, target_object)[target_key];
while time_elapsed < timeout:
time_elapsed = time.time() - start_time;
# check for shutdown.
if not connection_state.active:
return;
# check for target condition
if target(self) != None:
break;
time.sleep(self.thread_sleep_duration);
# Check for timeout.
if target(self) == None:
#print "Timeout on %s" % target_object;
has_timeout_callback = timeout_callback != None;
if has_timeout_callback:
if timeout_callback_params != None:
timeout_callback(timeout_callback_params);
else:
timeout_callback();
return;
#else:
# print "No timeout on %s" % target_object;
# Success, run the callback if one was provided (maybe not if one is only concerned with the timeout event).
if callback != None:
if callback_params != None:
callback(target_object, target_key, callback_params);
else:
callback(target_object, target_key);
def authenticate_thread(self, username, password, callback_object=None, login=True):
# callback_object should have a complete_authentication(success, error_message) method.
self.username = username;
self.password = password;
self.auth_callback = callback_object;
timeout_handler = None;
has_timeout_handler = ((callback_object != None) and (hasattr(callback_object, "handle_timeout")))
if has_timeout_handler:
timeout_handler = callback_object.handle_timeout
# Send salt and dynamic key retrieval request.
self.auth_keys = None;
message = "auth_salt_request %s" % username;
if not self.stun_send_message(message):
#callback_object.complete_authentication(False, "Failed to connect to the server.");
if timeout_handler != None:
timeout_handler("Failed to connect to the server.");
return;
# Wait until salt and key have been retrieved or timeout occurs.
self.monitor_response("auth_keys", None, self.server_response_timeout, self.authenticate_send_credentials, [login, callback_object], timeout_handler, "Server failed to respond.");
def authenticate_send_credentials(self, target_object=None, target_key=None, params=None):
callback_object = None;
if params != None:
callback_object = params[1];
login = params[0]
# hash the password
salt, dynamic_key = self.auth_keys;
if not salt:
if callback_object != None:
callback_object.complete_authentication(False, "Failed to connect to the server.");
return;
salted_password = <PASSWORD>" % (salt, self.password)
hashed_salted_password = hashlib.<PASSWORD>4(<PASSWORD>).hex<PASSWORD>();
#print "hash1: %s\n" % hashed_salted_password;
key_and_hash = "%s%s" % (dynamic_key, hashed_salted_password)
hashed_password = <PASSWORD>(key_and_hash).<PASSWORD>();
#print "hash2: %s" % hashed_password;
self._auth_status = None;
# Send authentication request.
message = "authenticate %s" % json.dumps([self.username, hashed_password, login, json.dumps(self.available_ports), json.dumps(self.used_ports)]);
if not self.stun_send_message(message):
if callback_object != None:
callback_object.complete_authentication(False, "Failed to connect to the server.");
return;
timeout_handler = None;
has_timeout_handler = ((callback_object != None) and (hasattr(callback_object, "handle_timeout")))
if has_timeout_handler:
timeout_handler = callback_object.handle_timeout
self.monitor_response("_auth_status", None, self.server_response_timeout, None, None, timeout_handler);
def registration_completion_handler(self, target_object, target_key, params):
callback_object = params;
registration_handler = None;
has_registration_handler = ((callback_object != None) and (hasattr(callback_object, "complete_registration")))
if has_registration_handler:
callback_object.complete_registration(True, "");
def send_encrypted_registration_request(self, target_object=None, target_key=None, params=None):
username, password, profile_map, callback_object, registration_type = params;
self._registration_status = None;
# Construct the message.
message = "%s" % json.dumps([username, password, profile_map, registration_type]);
# Encrypt the message.
public_key = RSA.importKey(self.registration_key)
message = public_key.encrypt(message, 32);
# Tack on the username in plain text and json_encode again. The STUN Server needs to username to determine which private key to use to decrypt the message.
message = "register %s %s" % (username, message[0]);
if not self.stun_send_message(message):
callback_object.complete_registration(False, "Failed to connect to the server.");
return;
timeout_handler = None;
has_timeout_handler = ((callback_object != None) and (hasattr(callback_object, "handle_timeout")))
if has_timeout_handler:
timeout_handler = callback_object.handle_timeout
# Wait until salt and key have been retrieved or timeout occurs.
self.monitor_response("_registration_status", None, self.server_response_timeout, self.registration_completion_handler, callback_object, timeout_handler);
def register_thread(self, username, password, profile_map, callback_object=None, registration_type="permanent"):
# callback_object should have a complete_registration(success, error_message) method.
self.username = username;
self.password = password;
self.profile_map = profile_map;
self.register_callback = callback_object;
self.registration_key = None;
message = "register_key %s" % username;
if not self.stun_send_message(message):
callback_object.complete_registration(False, "Failed to connect to the server.");
return;
timeout_handler = None;
has_timeout_handler = ((callback_object != None) and (hasattr(callback_object, "handle_timeout")))
if has_timeout_handler:
timeout_handler = callback_object.handle_timeout
params = [username, password, profile_map, callback_object, registration_type];
self.monitor_response("registration_key", None, self.server_response_timeout, self.send_encrypted_registration_request, params, timeout_handler);
def authenticate(self, username, password, callback_object=None, login=True):
"""Non-blocking. Sends a user authentication request."""
# Spawn a separate thread to perform authentication. This is to keep from blocking the caller, since a callback is expected to handle results.
Thread(target=self.authenticate_thread, args=(username, password, callback_object, login)).start();
def maintain_authentication(self, callback_object=None):
#self.authentication_monitor_object
username = self.username
password = <PASSWORD>
last_auth = self.last_auth
self.re_auth_ready = True;
while self.authenticated:
last_reauth = self.keep_alive_monitor.last_reauth_attempt;
now = time.time();
ready_time = last_reauth + (self.auth_keep_alive_multiplier * self.auth_keep_alive_interval);
time_for_another_reauth_attempt = now >= ready_time;
# By re_auth_ready, I'm saying a re-authentication attempt isn't currently in progress. Yes, it's a poorly named variable.
# I'll need to rename it something better. Maybe later (trademark).
if self.re_auth_ready and time_for_another_reauth_attempt:
self.re_auth_ready = False;
self.authenticate(self.username, self.password, self.keep_alive_monitor);
time.sleep(self.thread_sleep_duration);
def logout(self):
self.authenticated = False;
self.authenticate(self.username, self.password, self.keep_alive_monitor, False);
def register(self, username, password, profile_map, callback_object=None, registration_type="permanent"):
"""Non-blocking. Sends a user registration request.
Only type of registration available for now is 'permanent'. Temporary to come later, maybe (for guests/'unregistered' users).
Note that profile_map should be a json-encoded string (you can store arbitrary data here)."""
# Spawn a separate thread to perform registration. This is to keep from blocking the caller, since a callback is expected to handle results.
Thread(target=self.register_thread, args=(username, password, profile_map, callback_object, registration_type)).start();
def init_tcp_client(self, server_ip, server_port, buffer_size=1024):
self.server_ip = server_ip;
self.server_port = server_port;
self.stun_client = tcp.TCP_Client(server_ip, server_port, buffer_size);
def init_stun_listener(self):
self.connection_state = ConnectionState(True);
Thread(target=self.stun_listen_loop).start();
def stun_send_message(self, message, json_encode=False, prepare=True):
try:
self.stun_client.send_message(message, json_encode, prepare);
return True;
except:
return False;
def stun_listen_loop(self):
connection_state = self.connection_state
message_object = None
while self.connection_state.active:
try:
message_object = self.stun_client.pop_message();
is_valid_message = ((message_object != None) and (len(message_object) > 2));
self.master_log.append(message_object);
if is_valid_message:
message = message_object[2];
message_type, message_body = message.split(" ",1);
if message_type not in self.message_log_map:
self.message_log_map[message_type] = [];
self.message_log_map[message_type].append(message_object);
#print "MESSAGE: %s\n" % message_object;
if(message_type == "peer_map"):
# peer data should be [[peer_username, public_profile_map], ...]
message_data = json.loads(message_body);
self.update_peer_map(message_data);
if self.peer_map_callback != None:
self.peer_map_callback(self.peer_map);
elif(message_type == "hole_punch"):
peer_allowed = True;
# message body should be [listen_ip, listen_port, peer_ip, peer_port, peer_username, buffer_size]
message_data = json.loads(message_body);
listen_ip, listen_port, peer_ip, peer_port, peer_username, buffer_size = message_data
port_in_use = False;
# Ensure port isn't already in use.
if listen_port in self.used_ports:
port_in_use = True;
self.stun_send_message("hole_punch_reject %s" % json.dumps([listen_ip, listen_port, self.username, peer_ip, peer_port, peer_username, buffer_size, port_in_use]));
continue;
message_body = json.dumps([listen_ip, listen_port, self.username, peer_ip, peer_port, peer_username, buffer_size, port_in_use]);
if(self.peer_block_manager != None):
peer_allowed = self.peer_block_manager.is_peer_allowed(message_data);
if(peer_allowed):
self.stun_send_message("hole_punch_ack %s" % message_body);
else:
self.stun_send_message("hole_punch_reject %s" % message_body);
elif(message_type == "hole_punch_request_rejected"):
# Deals with requests that fail due to lack of authentication (this client or the target client) or target client doesn't exist.
# message_body should be [listen_ip, listen_port, self.username, target_ip, target_port, username, buffer_size]
fail_type, target_username, error_message = json.loads(message_body);
if target_username in self.callback_map:
callback_object = self.callback_map[target_username];
callback_object.complete_connection(target_username, False, error_message);
del self.callback_map[target_username];
elif(message_type == "hole_punch_rejected"):
# message_body should be [listen_ip, listen_port, self.username, target_ip, target_port, username, buffer_size]
message_data = json.loads(message_body);
listen_ip, listen_port, self.username, target_ip, target_port, username, buffer_size = message_data
client_key = "%s-%s-%s" % (target_ip, target_port, username);
callback_object = None;
if client_key in self.callback_map:
callback_object = self.callback_map[client_key]
if callback_object != None:
callback_object.complete_connection(client_key, False, "Peer rejected the connection request.");
del self.callback_map[client_key];
elif(message_type == "init_hole_punch"):
try:
listen_ip, listen_port, peer_ip, peer_port, peer_username, buffer_size = json.loads(message_body);
if listen_port not in self.used_ports:
self.used_ports.append(listen_port);
# No else. We're just going to hope there's no way for that if to not run, and that we're just being half-assed at feeling paranoid.
# My mind is feeling like it's been twisted into a few knots at this point, to be honest.
Thread(target=self.connect_to_remote_peer, args=(listen_ip, listen_port, peer_ip, peer_port, buffer_size, peer_username)).start();
client_key = "%s_%s_%s" % (peer_ip, peer_port, peer_username)
if peer_username in self._holepunch_status:
self._holepunch_status[peer_username] = True;
if peer_username in self.callback_map:
self.callback_map[client_key] = self.callback_map[peer_username];
del self.callback_map[peer_username]
except Exception as e:
self.log_error(e);
elif(message_type == "auth_keys"):
# message body should be [salt, dynamic_key]
self.auth_keys = json.loads(message_body);
elif(message_type == "auth_response"):
# message body should be [success, username, profile_map, login, error_message]
success, username, profile_map, login, error_message = json.loads(message_body);
self._auth_status = True;
new_auth = not self.authenticated;
if success:
if login:
self.authenticated = True;
self.auth_keep_alive_multiplier = 1;
self.last_auth = time.time();
self.username = username;
self.profile_map = profile_map;
if new_auth:
Thread(target=self.maintain_authentication).start();
else:
self.authenticated = False;
self.auth_keep_alive_multiplier = 1;
self.last_auth = time.time();
self.username = username;
self.profile_map = profile_map;
if self.auth_callback != None:
self.auth_callback.complete_authentication(success, error_message);
elif(message_type == "registration_key"):
# message body should be "public_key"
self.registration_key = message_body;
elif(message_type == "registration_response"):
# message body should be [success, username, profile_map, error_message]
success, username, profile_map, error_message = json.loads(message_body);
if success:
self.username = username;
self.profile_map = profile_map;
self._registration_status = True;
if self.registration_callback != None:
self.register_callback.complete_registration(success, error_message);
except Exception as exc:
self.log_error(exc, message_object);
time.sleep(self.thread_sleep_duration);
def update_peer_map(self, packet):
username_list = [];
current_username_list = self.peer_map.keys();
for user_block in packet:
peer_username, profile_map = user_block;
valid_username = ((peer_username != None) and (peer_username.replace(" ","").replace("\t","").replace("\n","").replace("\r","") != ""));
if valid_username:
username_list.append(peer_username);
self.peer_map[peer_username] = user_block;
remove_username_list = [];
for username in current_username_list:
if username not in username_list:
remove_username_list.append(username);
for username in remove_username_list:
del self.peer_map[username];
def auto_select_local_endpoint(self):
listen_ip = self.local_ip;
range_count = len(self.available_ports);
for i in range(0, range_count):
x = range_count - (1 + i)
port_range = self.available_ports[x]
port_count = port_range[1] - port_range[0]
for j in range(0, port_count):
port = port_range[1] - j;
if port not in self.used_ports:
return (listen_ip, port);
return None;
def connect_to_peer(self, target_username, buffer_size, callback_object=None, listen_ip = None, listen_port = None):
""" callback_object should have a complete_connection(target, success, error_message) method where success is True or False.
Extract info with:
ip, port, username = target.split("-",2)
Returns False if it fails to send request message (e.g. peer is blocked or connection to server failed.).
"""
local_endpoint_not_specified = ((listen_ip == None) or (listen_port == None))
if local_endpoint_not_specified:
try:
listen_ip, listen_port = self.auto_select_local_endpoint();
except:
callback_object.complete_connection(client_key, False, "All available allowed local ports are already in use. Cannot initiate connection to peer.");
return False;
# Disallow connecting to yourself. What are you trying to pull?
if self.username == target_username:
callback_object.complete_connection(client_key, False, "You cannot connect to yourself.");
return False;
# disallow connecting to blocked peers.
if(self.peer_block_manager != None):
peer_allowed = self.peer_block_manager.is_peer_allowed([target_username, buffer_size]);
if not peer_allowed:
callback_object.complete_connection(client_key, False, "This peer has been blocked.");
return False;
client_key = target_username;
self.callback_map[client_key] = callback_object;
self._holepunch_status[client_key] = None;
# Start hole_punch process.
message = "request_hole_punch %s" % json.dumps([listen_ip, listen_port, self.username, target_username, buffer_size])
if not self.stun_send_message(message):
callback_object.complete_connection(client_key, False, "Failed to connect to the server.");
del self.callback_map[client_key];
return False;
timeout_handler = None;
has_timeout_handler = ((callback_object != None) and (hasattr(callback_object, "handle_timeout")))
if has_timeout_handler:
timeout_handler = callback_object.handle_timeout
# Wait until salt and key have been retrieved or timeout occurs.
Thread(target=self.monitor_response, args=("_holepunch_status", client_key, self.server_response_timeout, None, None, timeout_handler)).start();
return True;
def connect_to_remote_peer(self, local_ip, local_port, target_ip, target_port, buffer_size, username):
"""Warning: Internal use only!"""
print "Connecting to remote peer."
udp_client = udp.UDP_Client(True, local_ip, local_port, target_ip, target_port, buffer_size, True);
client_key = "%s_%s_%s" % (target_ip, target_port, username)
callback_object = None;
if client_key in self.callback_map:
callback_object = self.callback_map[client_key]
if self.hole_punch(udp_client, self.hole_punch_max_attempts, self.hole_punch_timeout):
print "Hole-punch succeeded."
if callback_object != None:
callback_object.complete_connection(username, True, client_key);
self.client_map[client_key] = udp_client; # success, add it to the map.
else:
print "Hole-punch failed."
# remove that port from the used ports list.
port_count = len(self.used_ports);
for i in range(0, port_count):
if self.used_ports[i] == local_port:
del self.used_ports[i]
break;
# run the callback, if there is one.
if callback_object != None:
callback_object.complete_connection(client_key, False, "Failed to connect to peer.");
def hole_punch_send_loop(self, udp_client, maximum_retries=20, delay=0.5):
for i in range(0, maximum_retries):
udp_client.send_message("syn", False, False);
time.sleep(delay);
# Create and return a udp socket that has established connection with the target peer, or None if it fails.
def hole_punch(self, udp_client, maximum_retries=20, timeout=20):
print "Performing hole-punch."
delay = 0.5
result = False;
connection_state = self.connection_state
Thread(target=self.hole_punch_send_loop, args=(udp_client, maximum_retries, delay)).start();
start_time = time.time();
for i in range(0, maximum_retries):
time.sleep(delay)
if not connection_state.active:
# give up and close it out.
udp_client.disconnect();
print "Fail 1";
return False;
packet = "";
try:
packet = udp_client.pop_message();
except:
pass;
if packet != None:
print "hole_punch_response: " + str(packet);
if len(packet) >= 3:
# check the packet.
if(packet[2] == "syn"):
udp_client.send_message("ack", False, False); # send acknowledge
elif(packet[2] == "ack"):
udp_client.send_message("ack2", False, False); # send ack ack and return socket.
result = True;
print "Success 1";
break;
elif(packet[2] == "ack2"):
result = True; # ack ack received, return socket.
print "Success 2";
break;
# check for timeout
time_elapsed = time.time() - start_time;
if(time_elapsed >= timeout):
print "Fail 2";
break;
return result;
class KeepAliveMonitor:
def __init__(self, parent):
self.parent = parent;
self.last_reauth_attempt = time.time();
def complete_authentication(self, success, error_message=""):
self.parent.re_auth_ready = True;
self.last_reauth_attempt = time.time();
if not success:
self.parent.auth_keep_alive_multiplier += 1;
def handle_timeout(self, params=None):
self.last_reauth_attempt = time.time();
self.parent.re_auth_ready = True;
self.parent.auth_keep_alive_multiplier += 1;
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297,
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603,
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11815,
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5974,
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338,
297,
6923,
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3318,
322,
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29918,
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367,
6031,
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3318,
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14914,
408,
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313,
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29918,
1989,
363,
278,
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467,
13,
12,
12,
13,
12,
12,
4013,
740,
338,
1900,
3667,
1891,
373,
967,
1914,
5004,
3244,
1213,
15945,
13,
12,
12,
29937,
20340,
2745,
15795,
322,
1820,
505,
1063,
27387,
470,
11815,
10008,
29889,
13,
12,
12,
2230,
29918,
295,
28170,
353,
29871,
29900,
29936,
13,
12,
12,
2962,
29918,
2230,
353,
931,
29889,
2230,
890,
13,
12,
12,
5182,
29918,
12715,
353,
679,
5552,
29898,
1311,
29892,
3646,
29918,
3318,
416,
13,
12,
12,
5182,
353,
6213,
29936,
13,
12,
12,
13,
12,
12,
9965,
29918,
3859,
353,
1583,
29889,
9965,
29918,
3859,
13,
12,
12,
13,
12,
12,
29937,
2158,
376,
7185,
2105,
292,
363,
1273,
29879,
29908,
1273,
3646,
29918,
3318,
29936,
13,
12,
12,
13,
12,
12,
29937,
1522,
8948,
29892,
3017,
14013,
12241,
297,
278,
8775,
29991,
13,
12,
12,
361,
3646,
29918,
1989,
1275,
6213,
29901,
13,
12,
12,
12,
5182,
353,
14013,
3847,
29901,
679,
5552,
29898,
3560,
29892,
3646,
29918,
3318,
416,
13,
12,
12,
2870,
29901,
13,
12,
12,
12,
5182,
353,
14013,
3847,
29901,
679,
5552,
29898,
3560,
29892,
3646,
29918,
3318,
9601,
5182,
29918,
1989,
1385,
13,
12,
12,
13,
12,
12,
8000,
931,
29918,
295,
28170,
529,
11815,
29901,
13,
12,
12,
12,
2230,
29918,
295,
28170,
353,
931,
29889,
2230,
580,
448,
1369,
29918,
2230,
29936,
13,
12,
12,
12,
29937,
1423,
363,
12522,
3204,
29889,
13,
12,
12,
12,
361,
451,
3957,
29918,
3859,
29889,
4925,
29901,
13,
12,
12,
12,
12,
2457,
29936,
13,
12,
12,
12,
13,
12,
12,
12,
29937,
1423,
363,
3646,
4195,
13,
12,
12,
12,
361,
3646,
29898,
1311,
29897,
2804,
6213,
29901,
13,
12,
12,
12,
12,
8690,
29936,
13,
12,
12,
12,
2230,
29889,
17059,
29898,
1311,
29889,
7097,
29918,
17059,
29918,
19708,
416,
13,
12,
12,
13,
12,
12,
29937,
5399,
363,
11815,
29889,
13,
12,
12,
361,
3646,
29898,
1311,
29897,
1275,
6213,
29901,
13,
12,
12,
12,
29937,
2158,
376,
10851,
373,
1273,
29879,
29908,
1273,
3646,
29918,
3318,
29936,
13,
12,
12,
12,
5349,
29918,
15619,
29918,
14035,
353,
11815,
29918,
14035,
2804,
6213,
29936,
13,
12,
12,
12,
361,
756,
29918,
15619,
29918,
14035,
29901,
13,
12,
12,
12,
12,
361,
11815,
29918,
14035,
29918,
7529,
2804,
6213,
29901,
13,
12,
12,
12,
12,
12,
15619,
29918,
14035,
29898,
15619,
29918,
14035,
29918,
7529,
416,
13,
12,
12,
12,
12,
2870,
29901,
13,
12,
12,
12,
12,
12,
15619,
29918,
14035,
890,
13,
12,
12,
12,
2457,
29936,
13,
12,
12,
29937,
2870,
29901,
13,
12,
12,
29937,
12,
2158,
376,
3782,
11815,
373,
1273,
29879,
29908,
1273,
3646,
29918,
3318,
29936,
13,
12,
12,
13,
12,
12,
29937,
21397,
29892,
1065,
278,
6939,
565,
697,
471,
4944,
313,
26026,
451,
565,
697,
338,
871,
15041,
411,
278,
11815,
1741,
467,
13,
12,
12,
361,
6939,
2804,
6213,
29901,
13,
12,
12,
12,
361,
6939,
29918,
7529,
2804,
6213,
29901,
13,
12,
12,
12,
12,
14035,
29898,
5182,
29918,
3318,
29892,
3646,
29918,
1989,
29892,
6939,
29918,
7529,
416,
13,
12,
12,
12,
2870,
29901,
13,
12,
12,
12,
12,
14035,
29898,
5182,
29918,
3318,
29892,
3646,
29918,
1989,
416,
13,
12,
13,
12,
1753,
15585,
403,
29918,
7097,
29898,
1311,
29892,
8952,
29892,
4800,
29892,
6939,
29918,
3318,
29922,
8516,
29892,
6464,
29922,
5574,
1125,
13,
12,
12,
29937,
6939,
29918,
3318,
881,
505,
263,
4866,
29918,
23055,
29898,
8698,
29892,
1059,
29918,
4906,
29897,
1158,
29889,
13,
12,
12,
1311,
29889,
6786,
353,
8952,
29936,
13,
12,
12,
1311,
29889,
5630,
353,
4800,
29936,
13,
12,
12,
1311,
29889,
5150,
29918,
14035,
353,
6939,
29918,
3318,
29936,
13,
12,
12,
13,
12,
12,
15619,
29918,
13789,
353,
6213,
29936,
13,
12,
12,
5349,
29918,
15619,
29918,
13789,
353,
5135,
14035,
29918,
3318,
2804,
6213,
29897,
322,
313,
5349,
5552,
29898,
14035,
29918,
3318,
29892,
376,
8411,
29918,
15619,
29908,
4961,
13,
12,
12,
361,
756,
29918,
15619,
29918,
13789,
29901,
13,
12,
12,
12,
15619,
29918,
13789,
353,
6939,
29918,
3318,
29889,
8411,
29918,
15619,
13,
12,
12,
13,
12,
12,
29937,
15076,
15795,
322,
7343,
1820,
5663,
16837,
2009,
29889,
13,
12,
12,
1311,
29889,
5150,
29918,
8149,
353,
6213,
29936,
13,
12,
12,
4906,
353,
376,
5150,
29918,
29879,
1997,
29918,
3827,
1273,
29879,
29908,
1273,
8952,
29936,
13,
12,
12,
361,
451,
1583,
29889,
303,
348,
29918,
6717,
29918,
4906,
29898,
4906,
1125,
13,
12,
12,
12,
29937,
14035,
29918,
3318,
29889,
8835,
29918,
23055,
29898,
8824,
29892,
376,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
361,
11815,
29918,
13789,
2804,
6213,
29901,
13,
12,
12,
12,
12,
15619,
29918,
13789,
703,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
2457,
29936,
13,
12,
12,
13,
12,
12,
29937,
20340,
2745,
15795,
322,
1820,
505,
1063,
27387,
470,
11815,
10008,
29889,
13,
12,
12,
1311,
29889,
3712,
2105,
29918,
5327,
703,
5150,
29918,
8149,
613,
6213,
29892,
1583,
29889,
2974,
29918,
5327,
29918,
15619,
29892,
1583,
29889,
27218,
403,
29918,
6717,
29918,
11944,
9409,
29892,
518,
7507,
29892,
6939,
29918,
3318,
1402,
11815,
29918,
13789,
29892,
376,
6004,
5229,
304,
10049,
18327,
13,
12,
12,
13,
12,
13,
12,
1753,
15585,
403,
29918,
6717,
29918,
11944,
9409,
29898,
1311,
29892,
3646,
29918,
3318,
29922,
8516,
29892,
3646,
29918,
1989,
29922,
8516,
29892,
8636,
29922,
8516,
1125,
13,
12,
12,
14035,
29918,
3318,
353,
6213,
29936,
13,
12,
12,
361,
8636,
2804,
6213,
29901,
13,
12,
12,
12,
14035,
29918,
3318,
353,
8636,
29961,
29896,
1385,
13,
12,
12,
7507,
353,
8636,
29961,
29900,
29962,
13,
12,
12,
29937,
6608,
278,
4800,
13,
12,
12,
29879,
1997,
29892,
7343,
29918,
1989,
353,
1583,
29889,
5150,
29918,
8149,
29936,
13,
13,
12,
12,
361,
451,
15795,
29901,
13,
12,
12,
12,
361,
6939,
29918,
3318,
2804,
6213,
29901,
13,
12,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
23055,
29898,
8824,
29892,
376,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
12,
2457,
29936,
13,
12,
12,
13,
12,
12,
29879,
1997,
287,
29918,
5630,
353,
529,
25711,
17013,
11903,
1273,
313,
29879,
1997,
29892,
1583,
29889,
5630,
29897,
13,
12,
12,
8568,
287,
29918,
29879,
1997,
287,
29918,
5630,
353,
6608,
1982,
19423,
25711,
17013,
29958,
29946,
29898,
29966,
25711,
17013,
29958,
467,
20970,
29966,
25711,
17013,
8295,
13,
12,
12,
29937,
2158,
376,
8568,
29896,
29901,
1273,
29879,
29905,
29876,
29908,
1273,
6608,
287,
29918,
29879,
1997,
287,
29918,
5630,
29936,
13,
12,
12,
1989,
29918,
392,
29918,
8568,
353,
11860,
29879,
29995,
29879,
29908,
1273,
313,
16626,
29918,
1989,
29892,
6608,
287,
29918,
29879,
1997,
287,
29918,
5630,
29897,
13,
12,
12,
8568,
287,
29918,
5630,
353,
529,
25711,
17013,
5961,
1989,
29918,
392,
29918,
8568,
467,
29966,
25711,
17013,
8295,
13,
12,
12,
29937,
2158,
376,
8568,
29906,
29901,
1273,
29879,
29908,
1273,
6608,
287,
29918,
5630,
29936,
13,
12,
12,
13,
12,
12,
1311,
3032,
5150,
29918,
4882,
353,
6213,
29936,
13,
12,
12,
29937,
15076,
10760,
2009,
29889,
13,
12,
12,
4906,
353,
376,
27218,
403,
1273,
29879,
29908,
1273,
4390,
29889,
29881,
17204,
4197,
1311,
29889,
6786,
29892,
6608,
287,
29918,
5630,
29892,
6464,
29892,
4390,
29889,
29881,
17204,
29898,
1311,
29889,
16515,
29918,
4011,
511,
4390,
29889,
29881,
17204,
29898,
1311,
29889,
3880,
29918,
4011,
4638,
416,
13,
12,
12,
361,
451,
1583,
29889,
303,
348,
29918,
6717,
29918,
4906,
29898,
4906,
1125,
13,
12,
12,
12,
361,
6939,
29918,
3318,
2804,
6213,
29901,
13,
12,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
23055,
29898,
8824,
29892,
376,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
12,
2457,
29936,
13,
12,
12,
13,
12,
12,
15619,
29918,
13789,
353,
6213,
29936,
13,
12,
12,
5349,
29918,
15619,
29918,
13789,
353,
5135,
14035,
29918,
3318,
2804,
6213,
29897,
322,
313,
5349,
5552,
29898,
14035,
29918,
3318,
29892,
376,
8411,
29918,
15619,
29908,
4961,
13,
12,
12,
361,
756,
29918,
15619,
29918,
13789,
29901,
13,
12,
12,
12,
15619,
29918,
13789,
353,
6939,
29918,
3318,
29889,
8411,
29918,
15619,
13,
12,
12,
13,
12,
12,
1311,
29889,
3712,
2105,
29918,
5327,
703,
29918,
5150,
29918,
4882,
613,
6213,
29892,
1583,
29889,
2974,
29918,
5327,
29918,
15619,
29892,
6213,
29892,
6213,
29892,
11815,
29918,
13789,
416,
13,
12,
13,
12,
1753,
22583,
29918,
5729,
12757,
29918,
13789,
29898,
1311,
29892,
3646,
29918,
3318,
29892,
3646,
29918,
1989,
29892,
8636,
1125,
13,
12,
12,
14035,
29918,
3318,
353,
8636,
29936,
13,
12,
12,
1727,
8306,
29918,
13789,
353,
6213,
29936,
13,
12,
12,
5349,
29918,
1727,
8306,
29918,
13789,
353,
5135,
14035,
29918,
3318,
2804,
6213,
29897,
322,
313,
5349,
5552,
29898,
14035,
29918,
3318,
29892,
376,
8835,
29918,
1727,
8306,
29908,
4961,
13,
12,
12,
361,
756,
29918,
1727,
8306,
29918,
13789,
29901,
13,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
1727,
8306,
29898,
5574,
29892,
376,
1496,
13,
13,
12,
1753,
3638,
29918,
3977,
14740,
29918,
1727,
8306,
29918,
3827,
29898,
1311,
29892,
3646,
29918,
3318,
29922,
8516,
29892,
3646,
29918,
1989,
29922,
8516,
29892,
8636,
29922,
8516,
1125,
13,
12,
12,
6786,
29892,
4800,
29892,
8722,
29918,
1958,
29892,
6939,
29918,
3318,
29892,
22583,
29918,
1853,
353,
8636,
29936,
13,
12,
12,
13,
12,
12,
1311,
3032,
1727,
8306,
29918,
4882,
353,
6213,
29936,
13,
12,
12,
13,
12,
12,
29937,
1281,
4984,
278,
2643,
29889,
13,
12,
12,
4906,
353,
11860,
29879,
29908,
1273,
4390,
29889,
29881,
17204,
4197,
6786,
29892,
4800,
29892,
8722,
29918,
1958,
29892,
22583,
29918,
1853,
5691,
13,
12,
12,
13,
12,
12,
29937,
11346,
4641,
278,
2643,
29889,
13,
12,
12,
3597,
29918,
1989,
353,
390,
8132,
29889,
5215,
2558,
29898,
1311,
29889,
1727,
8306,
29918,
1989,
29897,
13,
12,
12,
4906,
353,
970,
29918,
1989,
29889,
3977,
4641,
29898,
4906,
29892,
29871,
29941,
29906,
416,
13,
12,
12,
13,
12,
12,
29937,
323,
547,
373,
278,
8952,
297,
8656,
1426,
322,
4390,
29918,
12508,
1449,
29889,
450,
6850,
3904,
5656,
4225,
304,
8952,
304,
8161,
607,
2024,
1820,
304,
671,
304,
1602,
4641,
278,
2643,
29889,
13,
12,
12,
4906,
353,
376,
9573,
1273,
29879,
1273,
29879,
29908,
1273,
313,
6786,
29892,
2643,
29961,
29900,
5691,
13,
12,
12,
13,
12,
12,
361,
451,
1583,
29889,
303,
348,
29918,
6717,
29918,
4906,
29898,
4906,
1125,
13,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
1727,
8306,
29898,
8824,
29892,
376,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
2457,
29936,
13,
12,
12,
13,
12,
12,
15619,
29918,
13789,
353,
6213,
29936,
13,
12,
12,
5349,
29918,
15619,
29918,
13789,
353,
5135,
14035,
29918,
3318,
2804,
6213,
29897,
322,
313,
5349,
5552,
29898,
14035,
29918,
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12,
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13,
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12,
12,
12,
13,
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12,
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7700,
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376,
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261,
22225,
278,
3957,
2009,
18327,
13,
12,
12,
12,
12,
12,
12,
12,
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13,
12,
12,
12,
12,
12,
12,
13,
12,
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12,
12,
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637,
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12,
12,
12,
12,
12,
12,
12,
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3880,
29918,
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20631,
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637,
416,
13,
12,
12,
12,
12,
12,
12,
12,
29937,
1939,
1683,
29889,
1334,
29915,
276,
925,
2675,
304,
4966,
727,
29915,
29879,
694,
982,
363,
393,
565,
304,
451,
1065,
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322,
393,
591,
29915,
276,
925,
1641,
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29899,
465,
287,
472,
11223,
610,
1562,
333,
29889,
13,
12,
12,
12,
12,
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12,
12,
29937,
1619,
3458,
338,
11223,
763,
372,
29915,
29879,
1063,
3252,
12652,
964,
263,
2846,
889,
1862,
472,
445,
1298,
29892,
304,
367,
15993,
29889,
13,
12,
12,
12,
12,
12,
12,
12,
4899,
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5182,
29922,
1311,
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6915,
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517,
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16674,
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412,
261,
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666,
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637,
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2311,
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23533,
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6786,
8106,
2962,
890,
13,
12,
12,
12,
12,
12,
12,
12,
4645,
29918,
1989,
353,
11860,
29879,
29918,
29995,
29879,
29918,
29995,
29879,
29908,
1273,
313,
412,
261,
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666,
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23533,
29918,
637,
29892,
23533,
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13,
12,
12,
12,
12,
12,
12,
12,
361,
23533,
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6786,
297,
1583,
3032,
29716,
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3322,
29918,
4882,
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13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
3032,
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4882,
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412,
261,
29918,
6786,
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353,
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13,
12,
12,
12,
12,
12,
12,
12,
361,
23533,
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6786,
297,
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14035,
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1958,
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13,
12,
12,
12,
12,
12,
12,
12,
12,
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29889,
14035,
29918,
1958,
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353,
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29889,
14035,
29918,
1958,
29961,
412,
261,
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6786,
1385,
13,
12,
12,
12,
12,
12,
12,
12,
12,
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14035,
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1958,
29961,
412,
261,
29918,
6786,
29962,
13,
12,
12,
12,
12,
12,
12,
19499,
8960,
408,
321,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
1188,
29918,
2704,
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29872,
416,
13,
12,
12,
12,
12,
12,
13,
12,
12,
12,
12,
12,
23681,
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4906,
29918,
1853,
1275,
376,
5150,
29918,
8149,
29908,
1125,
13,
12,
12,
12,
12,
12,
12,
29937,
2643,
3573,
881,
367,
518,
29879,
1997,
29892,
7343,
29918,
1989,
29962,
13,
12,
12,
12,
12,
12,
12,
1311,
29889,
5150,
29918,
8149,
353,
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18132,
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2587,
416,
13,
12,
12,
12,
12,
12,
13,
12,
12,
12,
12,
12,
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29918,
1853,
1275,
376,
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29918,
5327,
29908,
1125,
13,
12,
12,
12,
12,
12,
12,
29937,
2643,
3573,
881,
367,
518,
8698,
29892,
8952,
29892,
8722,
29918,
1958,
29892,
6464,
29892,
1059,
29918,
4906,
29962,
13,
12,
12,
12,
12,
12,
12,
8698,
29892,
8952,
29892,
8722,
29918,
1958,
29892,
6464,
29892,
1059,
29918,
4906,
353,
4390,
29889,
18132,
29898,
4906,
29918,
2587,
416,
13,
12,
12,
12,
12,
12,
12,
1311,
3032,
5150,
29918,
4882,
353,
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29936,
13,
12,
12,
12,
12,
12,
12,
1482,
29918,
5150,
353,
451,
1583,
29889,
27218,
630,
29936,
13,
12,
12,
12,
12,
12,
12,
361,
2551,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
361,
6464,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
27218,
630,
353,
5852,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
5150,
29918,
17462,
29918,
284,
573,
29918,
18056,
4926,
353,
29871,
29896,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
4230,
29918,
5150,
353,
931,
29889,
2230,
890,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
6786,
353,
8952,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
10185,
29918,
1958,
353,
8722,
29918,
1958,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
361,
716,
29918,
5150,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
12,
12,
4899,
29898,
5182,
29922,
1311,
29889,
29885,
2365,
475,
29918,
23055,
467,
2962,
890,
13,
12,
12,
12,
12,
12,
12,
12,
2870,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
27218,
630,
353,
7700,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
5150,
29918,
17462,
29918,
284,
573,
29918,
18056,
4926,
353,
29871,
29896,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
4230,
29918,
5150,
353,
931,
29889,
2230,
890,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
6786,
353,
8952,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
10185,
29918,
1958,
353,
8722,
29918,
1958,
29936,
13,
12,
12,
12,
12,
12,
12,
361,
1583,
29889,
5150,
29918,
14035,
2804,
6213,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
5150,
29918,
14035,
29889,
8835,
29918,
23055,
29898,
8698,
29892,
1059,
29918,
4906,
416,
13,
12,
12,
12,
12,
12,
13,
12,
12,
12,
12,
12,
23681,
29898,
4906,
29918,
1853,
1275,
376,
1727,
8306,
29918,
1989,
29908,
1125,
13,
12,
12,
12,
12,
12,
12,
29937,
2643,
3573,
881,
367,
376,
3597,
29918,
1989,
29908,
13,
12,
12,
12,
12,
12,
12,
1311,
29889,
1727,
8306,
29918,
1989,
353,
2643,
29918,
2587,
29936,
13,
12,
12,
12,
12,
12,
13,
12,
12,
12,
12,
12,
23681,
29898,
4906,
29918,
1853,
1275,
376,
1727,
8306,
29918,
5327,
29908,
1125,
13,
12,
12,
12,
12,
12,
12,
29937,
2643,
3573,
881,
367,
518,
8698,
29892,
8952,
29892,
8722,
29918,
1958,
29892,
1059,
29918,
4906,
29962,
13,
12,
12,
12,
12,
12,
12,
8698,
29892,
8952,
29892,
8722,
29918,
1958,
29892,
1059,
29918,
4906,
353,
4390,
29889,
18132,
29898,
4906,
29918,
2587,
416,
13,
12,
12,
12,
12,
12,
12,
361,
2551,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
6786,
353,
8952,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
10185,
29918,
1958,
353,
8722,
29918,
1958,
29936,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
3032,
1727,
8306,
29918,
4882,
353,
5852,
29936,
13,
12,
12,
12,
12,
12,
12,
361,
1583,
29889,
1727,
8306,
29918,
14035,
2804,
6213,
29901,
13,
12,
12,
12,
12,
12,
12,
12,
1311,
29889,
9573,
29918,
14035,
29889,
8835,
29918,
1727,
8306,
29898,
8698,
29892,
1059,
29918,
4906,
416,
13,
12,
12,
12,
12,
12,
13,
12,
12,
12,
19499,
8960,
408,
5566,
29901,
13,
12,
12,
12,
12,
1311,
29889,
1188,
29918,
2704,
29898,
735,
29883,
29892,
2643,
29918,
3318,
416,
13,
12,
12,
12,
13,
12,
12,
12,
2230,
29889,
17059,
29898,
1311,
29889,
7097,
29918,
17059,
29918,
19708,
416,
13,
13,
12,
1753,
2767,
29918,
412,
261,
29918,
1958,
29898,
1311,
29892,
18203,
1125,
13,
12,
12,
6786,
29918,
1761,
353,
13769,
13,
12,
12,
3784,
29918,
6786,
29918,
1761,
353,
1583,
29889,
412,
261,
29918,
1958,
29889,
8149,
890,
13,
12,
12,
1454,
1404,
29918,
1271,
297,
18203,
29901,
13,
12,
12,
12,
412,
261,
29918,
6786,
29892,
8722,
29918,
1958,
353,
1404,
29918,
1271,
29936,
13,
12,
12,
12,
3084,
29918,
6786,
353,
5135,
412,
261,
29918,
6786,
2804,
6213,
29897,
322,
313,
412,
261,
29918,
6786,
29889,
6506,
703,
28796,
2564,
6506,
14182,
29873,
3284,
2564,
6506,
14182,
29876,
3284,
2564,
6506,
14182,
29878,
3284,
1159,
2804,
376,
8983,
13,
12,
12,
12,
361,
2854,
29918,
6786,
29901,
13,
12,
12,
12,
12,
6786,
29918,
1761,
29889,
4397,
29898,
412,
261,
29918,
6786,
416,
13,
12,
12,
12,
12,
1311,
29889,
412,
261,
29918,
1958,
29961,
412,
261,
29918,
6786,
29962,
353,
1404,
29918,
1271,
29936,
13,
12,
12,
5992,
29918,
6786,
29918,
1761,
353,
13769,
13,
12,
12,
1454,
8952,
297,
1857,
29918,
6786,
29918,
1761,
29901,
13,
12,
12,
12,
361,
8952,
451,
297,
8952,
29918,
1761,
29901,
13,
12,
12,
12,
12,
5992,
29918,
6786,
29918,
1761,
29889,
4397,
29898,
6786,
416,
13,
13,
12,
12,
1454,
8952,
297,
3349,
29918,
6786,
29918,
1761,
29901,
13,
12,
12,
12,
6144,
1583,
29889,
412,
261,
29918,
1958,
29961,
6786,
1385,
13,
12,
13,
12,
1753,
4469,
29918,
2622,
29918,
2997,
29918,
29734,
29898,
1311,
1125,
13,
12,
12,
20631,
29918,
666,
353,
1583,
29889,
2997,
29918,
666,
29936,
13,
12,
12,
3881,
29918,
2798,
353,
7431,
29898,
1311,
29889,
16515,
29918,
4011,
416,
13,
12,
12,
1454,
474,
297,
3464,
29898,
29900,
29892,
3464,
29918,
2798,
1125,
13,
12,
12,
12,
29916,
353,
3464,
29918,
2798,
448,
313,
29896,
718,
474,
29897,
13,
12,
12,
12,
637,
29918,
3881,
353,
1583,
29889,
16515,
29918,
4011,
29961,
29916,
29962,
13,
12,
12,
12,
637,
29918,
2798,
353,
2011,
29918,
3881,
29961,
29896,
29962,
448,
2011,
29918,
3881,
29961,
29900,
29962,
13,
12,
12,
12,
1454,
432,
297,
3464,
29898,
29900,
29892,
2011,
29918,
2798,
1125,
13,
12,
12,
12,
12,
637,
353,
2011,
29918,
3881,
29961,
29896,
29962,
448,
432,
29936,
13,
12,
12,
12,
12,
361,
2011,
451,
297,
1583,
29889,
3880,
29918,
4011,
29901,
13,
12,
12,
12,
12,
12,
2457,
313,
20631,
29918,
666,
29892,
2011,
416,
13,
12,
12,
2457,
6213,
29936,
13,
13,
12,
1753,
4511,
29918,
517,
29918,
412,
261,
29898,
1311,
29892,
3646,
29918,
6786,
29892,
6835,
29918,
2311,
29892,
6939,
29918,
3318,
29922,
8516,
29892,
11621,
29918,
666,
353,
6213,
29892,
11621,
29918,
637,
353,
6213,
1125,
13,
12,
12,
15945,
29908,
6939,
29918,
3318,
881,
505,
263,
4866,
29918,
9965,
29898,
5182,
29892,
2551,
29892,
1059,
29918,
4906,
29897,
1158,
988,
2551,
338,
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470,
7700,
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13,
12,
12,
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1461,
5235,
411,
29901,
29871,
13,
12,
12,
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12,
666,
29892,
2011,
29892,
8952,
353,
3646,
29889,
5451,
703,
29899,
613,
29906,
29897,
13,
12,
12,
11609,
29879,
7700,
565,
372,
8465,
304,
3638,
2009,
2643,
313,
29872,
29889,
29887,
29889,
23533,
338,
24370,
470,
3957,
304,
1923,
5229,
6250,
29871,
13,
12,
12,
15945,
29908,
13,
12,
12,
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29918,
29734,
29918,
1333,
29918,
6550,
2164,
353,
5135,
20631,
29918,
666,
1275,
6213,
29897,
470,
313,
20631,
29918,
637,
1275,
6213,
876,
13,
12,
12,
361,
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29918,
29734,
29918,
1333,
29918,
6550,
2164,
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13,
12,
12,
12,
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13,
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12,
12,
12,
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29918,
666,
29892,
11621,
29918,
637,
353,
1583,
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29918,
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29918,
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890,
13,
12,
12,
12,
19499,
29901,
13,
12,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
9965,
29898,
4645,
29918,
1989,
29892,
7700,
29892,
376,
3596,
3625,
6068,
1887,
16169,
526,
2307,
297,
671,
29889,
15808,
14511,
403,
3957,
304,
23533,
18327,
13,
12,
12,
12,
12,
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7700,
29936,
13,
12,
12,
13,
12,
12,
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9536,
16791,
304,
7535,
29889,
1724,
526,
366,
1811,
304,
8206,
29973,
13,
12,
12,
361,
1583,
29889,
6786,
1275,
3646,
29918,
6786,
29901,
13,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
9965,
29898,
4645,
29918,
1989,
29892,
7700,
29892,
376,
3492,
2609,
4511,
304,
7535,
18327,
13,
12,
12,
12,
2457,
7700,
29936,
13,
12,
12,
13,
12,
12,
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9536,
16791,
304,
24370,
1236,
414,
29889,
13,
12,
12,
361,
29898,
1311,
29889,
412,
261,
29918,
1271,
29918,
12847,
2804,
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1125,
13,
12,
12,
12,
412,
261,
29918,
24622,
353,
1583,
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412,
261,
29918,
1271,
29918,
12847,
29889,
275,
29918,
412,
261,
29918,
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5182,
29918,
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29892,
6835,
29918,
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5691,
13,
12,
12,
12,
361,
451,
23533,
29918,
24622,
29901,
13,
12,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
9965,
29898,
4645,
29918,
1989,
29892,
7700,
29892,
376,
4013,
23533,
756,
1063,
24370,
18327,
13,
12,
12,
12,
12,
2457,
7700,
29936,
13,
12,
12,
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29918,
1989,
353,
3646,
29918,
6786,
29936,
13,
12,
12,
1311,
29889,
14035,
29918,
1958,
29961,
4645,
29918,
1989,
29962,
353,
6939,
29918,
3318,
29936,
13,
12,
12,
13,
12,
12,
1311,
3032,
29716,
29886,
3322,
29918,
4882,
29961,
4645,
29918,
1989,
29962,
353,
6213,
29936,
13,
12,
12,
13,
12,
12,
29937,
7370,
16188,
29918,
29886,
3322,
1889,
29889,
13,
12,
12,
4906,
353,
376,
3827,
29918,
29716,
29918,
29886,
3322,
1273,
29879,
29908,
1273,
4390,
29889,
29881,
17204,
4197,
20631,
29918,
666,
29892,
11621,
29918,
637,
29892,
1583,
29889,
6786,
29892,
3646,
29918,
6786,
29892,
6835,
29918,
2311,
2314,
13,
12,
12,
361,
451,
1583,
29889,
303,
348,
29918,
6717,
29918,
4906,
29898,
4906,
1125,
13,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
9965,
29898,
4645,
29918,
1989,
29892,
7700,
29892,
376,
17776,
304,
4511,
304,
278,
1923,
18327,
13,
12,
12,
12,
6144,
1583,
29889,
14035,
29918,
1958,
29961,
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29918,
1989,
1385,
13,
12,
12,
12,
2457,
7700,
29936,
13,
12,
12,
13,
12,
12,
15619,
29918,
13789,
353,
6213,
29936,
13,
12,
12,
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29918,
15619,
29918,
13789,
353,
5135,
14035,
29918,
3318,
2804,
6213,
29897,
322,
313,
5349,
5552,
29898,
14035,
29918,
3318,
29892,
376,
8411,
29918,
15619,
29908,
4961,
13,
12,
12,
361,
756,
29918,
15619,
29918,
13789,
29901,
13,
12,
12,
12,
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29918,
13789,
353,
6939,
29918,
3318,
29889,
8411,
29918,
15619,
13,
12,
12,
29937,
20340,
2745,
15795,
322,
1820,
505,
1063,
27387,
470,
11815,
10008,
29889,
13,
12,
12,
4899,
29898,
5182,
29922,
1311,
29889,
3712,
2105,
29918,
5327,
29892,
6389,
29922,
703,
29918,
29716,
29886,
3322,
29918,
4882,
613,
3132,
29918,
1989,
29892,
1583,
29889,
2974,
29918,
5327,
29918,
15619,
29892,
6213,
29892,
6213,
29892,
11815,
29918,
13789,
8106,
2962,
890,
13,
12,
12,
13,
12,
12,
2457,
5852,
29936,
13,
13,
12,
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4511,
29918,
517,
29918,
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29918,
412,
261,
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1311,
29892,
1887,
29918,
666,
29892,
1887,
29918,
637,
29892,
3646,
29918,
666,
29892,
3646,
29918,
637,
29892,
6835,
29918,
2311,
29892,
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1125,
13,
12,
12,
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29908,
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29901,
512,
1890,
671,
871,
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13,
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12,
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376,
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292,
304,
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13,
12,
12,
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29918,
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353,
318,
6099,
29889,
29965,
11191,
29918,
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29892,
1887,
29918,
666,
29892,
1887,
29918,
637,
29892,
3646,
29918,
666,
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3646,
29918,
637,
29892,
6835,
29918,
2311,
29892,
5852,
416,
13,
12,
12,
4645,
29918,
1989,
353,
11860,
29879,
29918,
29995,
29879,
29918,
29995,
29879,
29908,
1273,
313,
5182,
29918,
666,
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3646,
29918,
637,
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13,
12,
12,
13,
12,
12,
14035,
29918,
3318,
353,
6213,
29936,
13,
12,
12,
361,
3132,
29918,
1989,
297,
1583,
29889,
14035,
29918,
1958,
29901,
13,
12,
12,
12,
14035,
29918,
3318,
353,
1583,
29889,
14035,
29918,
1958,
29961,
4645,
29918,
1989,
29962,
13,
12,
12,
13,
12,
12,
361,
1583,
29889,
29716,
29918,
29886,
3322,
29898,
566,
29886,
29918,
4645,
29892,
1583,
29889,
29716,
29918,
29886,
3322,
29918,
3317,
29918,
1131,
3456,
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29716,
29918,
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3322,
29918,
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1125,
13,
12,
12,
12,
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376,
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29899,
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3322,
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13,
12,
12,
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361,
6939,
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2804,
6213,
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12,
12,
12,
12,
14035,
29918,
3318,
29889,
8835,
29918,
9965,
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29892,
5852,
29892,
3132,
29918,
1989,
416,
13,
12,
12,
12,
1311,
29889,
4645,
29918,
1958,
29961,
4645,
29918,
1989,
29962,
353,
318,
6099,
29918,
4645,
29936,
396,
2551,
29892,
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372,
304,
278,
2910,
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13,
12,
12,
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29901,
13,
12,
12,
12,
2158,
376,
29950,
1772,
29899,
29886,
3322,
5229,
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13,
12,
12,
12,
29937,
3349,
393,
2011,
515,
278,
1304,
16169,
1051,
29889,
13,
12,
12,
12,
637,
29918,
2798,
353,
7431,
29898,
1311,
29889,
3880,
29918,
4011,
416,
13,
12,
12,
12,
1454,
474,
297,
3464,
29898,
29900,
29892,
2011,
29918,
2798,
1125,
13,
12,
12,
12,
12,
361,
1583,
29889,
3880,
29918,
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29961,
29875,
29962,
1275,
1887,
29918,
637,
29901,
13,
12,
12,
12,
12,
12,
6144,
1583,
29889,
3880,
29918,
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29961,
29875,
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13,
12,
12,
12,
12,
12,
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29936,
13,
12,
12,
12,
13,
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src/lamdas/segment-delete/segment_delete.py | aws-samples/amazon-pinpoint-segment-campaign-automation | 3 | 99282 | import logging
import os
import time
import boto3
client = boto3.client("pinpoint")
def lambda_handler(event, context):
log_level = str(os.environ.get("LOG_LEVEL")).upper()
if log_level not in ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]:
log_level = "ERROR"
logging.getLogger().setLevel(log_level)
logging.info(event)
response = client.delete_segment(
ApplicationId=os.environ.get("PINPOINT_PROJECT_ID"),
SegmentId=event["SegmentId"],
)
logging.info(response)
db_logging_status = "CAMPAIGN_FAILED"
return {
"SegmentId": event["SegmentId"],
"interest": event["interest"],
"CampaignStatus": event["CampaignStatus"],
"product_name": event["product_name"],
"product_link": event["product_link"],
"db_logging_status": db_logging_status,
}
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] |
ConfManager/models/interfaces.py | cygerior/LabManager | 0 | 144115 | <filename>ConfManager/models/interfaces.py
from django.db import models
from polymorphic.models import PolymorphicModel
class Interface(PolymorphicModel):
name = models.CharField(max_length=30, unique=True)
def __str__(self):
return self.name
class NetworkInterface(Interface):
mac_address = models.CharField(max_length=23)
address = models.CharField(max_length=30)
class BackplaneNetworkInterface(Interface):
pass
class UartInterface(Interface):
uri = models.CharField(max_length=30)
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] |
galaxydb/__init__.py | alantelles/galaxydb | 2 | 33394 | <gh_stars>1-10
from galaxydb.column import Column
from galaxydb.scheme import Scheme
from galaxydb.logic import Logic
from galaxydb.table import Table
from galaxydb.constants import *
from galaxydb.statics import *
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1,
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] |
pravash/createincident.py | amvasudeva/rapidata | 0 | 140792 | #! /usr/bin/python
#Need to install requests package for python
#sudo easy_install requests
import requests
# Set the request parameters
url = 'https://wiprodemo.service-now.com/api/now/table/incident'
user = 'Default'
pwd = '<PASSWORD>'
# Set proper headers
headers = {"Content-Type":"application/json","Accept":"application/json"}
# Do the HTTP request
response = requests.post(url, auth=(user, pwd), headers=headers ,data="{'short_description':'Jenkins: Build Failed'}")
# Check for HTTP codes other than 200
if response.status_code != 201:
print('Status:', response.status_code, 'Headers:', response.headers, 'Error Response:',response.json())
exit()
# Decode the JSON response into a dictionary and use the data
#print('Status:',response.status_code,'Headers:',response.headers,'Response:',response.json())
json_data = response.json()
print json_data['result']['number']
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] |
scraper/functions/progress_bar.py | cordeirossauro/iw-scraper | 0 | 138976 | <reponame>cordeirossauro/iw-scraper
import math
class ProgressBar:
def __init__(self, total_steps, bar_size, message, current_step = 1):
self.total_steps = total_steps
self.current_step = current_step
self.bar_size = bar_size
self.message = message
def print_bar(self):
steps_done = math.floor(
self.current_step / self.total_steps * self.bar_size
)
bar = (
" |"
+ ("█" * steps_done)
+ (" " * (self.bar_size - 1 - steps_done))
+ "|"
)
progress = f" [{self.current_step}/{self.total_steps}]"
print(self.message + progress + bar, end="\r")
def update_bar(self, current_step):
self.current_step = current_step
self.print_bar()
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backend/src/baserow/contrib/database/migrations/0007_datefield.py | cjh0613/baserow | 839 | 156947 | # Generated by Django 2.2.11 on 2020-05-26 10:23
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('database', '0006_auto_20200522_1329'),
]
operations = [
migrations.CreateModel(
name='DateField',
fields=[
('field_ptr', models.OneToOneField(
auto_created=True,
on_delete=django.db.models.deletion.CASCADE,
parent_link=True,
primary_key=True,
serialize=False,
to='database.Field'
)),
('date_format', models.CharField(
choices=[('EU', 'European (D/M/Y)'),
('US', 'US (M/D/Y)'),
('ISO', 'ISO (Y-M-D)')],
default=('EU', 'European (D/M/Y)'),
max_length=32
)),
('date_include_time', models.BooleanField(default=False)),
('date_time_format', models.CharField(
choices=[('24', '24 hour'),
('12', '12 hour')],
default=('24', '24 hour'),
max_length=32
)),
],
bases=('database.field',),
),
]
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open_spiel/python/algorithms/psro_v2/rl_policy.py | julianstastny/open_spiel | 0 | 76210 | # Copyright 2019 DeepMind Technologies Ltd. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python3
"""DQN as a policy.
Treating RL Oracles as policies allows us to streamline their use with tabular
policies and other policies in OpenSpiel, and freely mix populations using
different types of oracles.
"""
from open_spiel.python import policy
from open_spiel.python import rl_environment
from open_spiel.python.algorithms import dqn
from open_spiel.python.algorithms import policy_gradient_pytorch as policy_gradient
def rl_policy_factory(rl_class):
"""Transforms an RL Agent into an OpenSpiel policy.
Args:
rl_class: An OpenSpiel class inheriting from 'rl_agent.AbstractAgent' such
as policy_gradient.PolicyGradient or dqn.DQN.
Returns:
An RLPolicy class that wraps around an instance of rl_class to transform it
into a policy.
"""
class RLPolicy(policy.Policy):
"""A 'policy.Policy' wrapper around an 'rl_agent.AbstractAgent' instance."""
def __init__(self, env, player_id, **kwargs):
"""Constructs an RL Policy.
Args:
env: An OpenSpiel RL Environment instance.
player_id: The ID of the DQN policy's player.
**kwargs: Various kwargs used to initialize rl_class.
"""
game = env.game
super(RLPolicy, self).__init__(game, player_id)
self._policy = rl_class(**{"player_id": player_id, **kwargs})
self._frozen = False
self._rl_class = rl_class
self._env = env
self._obs = {
"info_state": [None] * self.game.num_players(),
"legal_actions": [None] * self.game.num_players()
}
def get_time_step(self):
time_step = self._env.get_time_step()
return time_step
def action_probabilities(self, state, player_id=None):
cur_player = state.current_player() if player_id is None else player_id
legal_actions = state.legal_actions(cur_player)
step_type = rl_environment.StepType.LAST if state.is_terminal(
) else rl_environment.StepType.MID
self._obs["current_player"] = cur_player
self._obs["info_state"][cur_player] = (
state.information_state_tensor(cur_player))
self._obs["legal_actions"][cur_player] = legal_actions
# pylint: disable=protected-access
rewards = state.rewards()
if rewards:
time_step = rl_environment.TimeStep(
observations=self._obs, rewards=rewards,
discounts=self._env._discounts, step_type=step_type)
else:
rewards = [0] * self._num_players
time_step = rl_environment.TimeStep(
observations=self._obs, rewards=rewards,
discounts=self._env._discounts,
step_type=rl_environment.StepType.FIRST)
# pylint: enable=protected-access
p = self._policy.step(time_step, is_evaluation=True).probs
prob_dict = {action: p[action] for action in legal_actions}
return prob_dict
def step(self, time_step, is_evaluation=False):
# The _frozen attribute freezes the weights of the current policy. This
# effect is achieved by considering that we always are evaluating when the
# current policy's weights are frozen. For more details, see the freeze()
# method.
is_evaluation = (is_evaluation) or (self._frozen)
return self._policy.step(time_step, is_evaluation)
def freeze(self):
"""This method freezes the policy's weights.
The weight freezing effect is implemented by preventing any training to
take place through calls to the step function. The weights are therefore
not effectively frozen, and unconventional calls may trigger weights
training.
The weight-freezing effect is especially needed in PSRO, where all
policies that aren't being trained by the oracle must be static. Freezing
trained policies permitted us not to change how 'step' was called when
introducing self-play (By not changing 'is_evaluation' depending on the
current player).
"""
self._frozen = True
def unfreeze(self):
self._frozen = False
def is_frozen(self):
return self._frozen
def get_weights(self):
return self._policy.get_weights()
def copy_with_noise(self, sigma=0.0):
copied_object = RLPolicy.__new__(RLPolicy)
super(RLPolicy, copied_object).__init__(self.game, self.player_ids)
setattr(copied_object, "_rl_class", self._rl_class)
setattr(copied_object, "_obs", self._obs)
setattr(copied_object, "_policy",
self._policy.copy_with_noise(sigma=sigma))
setattr(copied_object, "_env", self._env)
copied_object.unfreeze()
return copied_object
return RLPolicy
# Generating policy classes for Policy Gradient and DQN
# pylint: disable=invalid-name
PGPolicy = rl_policy_factory(policy_gradient.PolicyGradient)
DQNPolicy = rl_policy_factory(dqn.DQN)
# pylint: enable=invalid-name
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] |
optimetry/zoo/ggt.py | optimetry/optimetry | 2 | 119856 | <reponame>optimetry/optimetry<filename>optimetry/zoo/ggt.py
"""GGT optimizer: an efficient full-matrix adaptive gradient method.
"""
import math
import torch
class GGT(torch.optim.Optimizer):
"""GGT full-matrix adaptive optimizer. Uses GPU-friendly operations
for applying pseudoinverse powers of a low-rank Gram matrix. By
default, this implementation matches AdaGrad/RMSprop in its other
features; Adam is possible, but beware the beta2 hyperparameter.
Paper: <NAME>, <NAME>, <NAME>, <NAME>,
<NAME>, <NAME>, <NAME>.
Efficient Full-Matrix Adaptive Regularization.
https://arxiv.org/abs/1806.02958
Arguments:
params (iterable): iterable of parameters to optimize or dicts
defining parameter groups
lr (float, optional): global lr multiplier (default: 1e-3)
window_size (int, optional): length of history (default: 100)
betas (Tuple[float, float], optional): coefficients used for
computing running averages of gradient and its square
(default: (0.9, 0.999))
eps (float, optional): truncation threshold for SVD
(default: 1e-4)
weight_decay (float, optional): weight decay (L2 penalty)
(default: 0)
adam_scale (bool, optional): use Adam scaling rule and bias
correction (default: True)
"""
# pylint: disable-msg=too-many-arguments
def __init__(self, params, lr=1e-3, window_size=100, betas=(0.9, 1.), eps=1e-4,
weight_decay=0, adam_scale=False, decouple_weight_decay=False):
defaults = dict(lr=lr, window_size=window_size, betas=betas, eps=eps,
weight_decay=weight_decay, adam_scale=adam_scale,
decouple_weight_decay=decouple_weight_decay)
super(GGT, self).__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
state = self.state[p]
if len(state) == 0:
# initialize state
state['step'] = 0
state['m1'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['Gt'] = torch.zeros([group['window_size'], p.numel()],
dtype=p.dtype, device=p.device)
state['GtG'] = torch.zeros([group['window_size'], group['window_size']],
dtype=p.dtype, device=p.device)
beta1, beta2 = group['betas']
adam_scale = group['adam_scale']
t, w = state['step'], group['window_size']
weight_decay = group['weight_decay']
lr = group['lr']
Gt, GtG = state['Gt'], state['GtG']
m1 = state['m1']
if weight_decay != 0:
if group['decouple_weight_decay']:
p.mul_(1 - lr*weight_decay)
else:
p.grad.add_(p, alpha=weight_decay)
m1 *= beta1
if adam_scale:
m1.add_(p.grad, alpha=1-beta1)
else:
m1.add_(p.grad)
# overwrite oldest grad in window buffer
idx = t % w
Gt[idx, :].copy_(p.grad.view(-1))
# update new row and column of small Gram matrix
row = Gt @ p.grad
if adam_scale:
row *= 1 - beta2
GtG *= beta2
GtG[idx, :] = GtG[:, idx] = row
# get eigendecomposition of small Gram matrix
eigs, V = torch.symeig(GtG, eigenvectors=True)
precond_eigs = torch.where(eigs >= group['eps'],
eigs.pow(-1.5), torch.zeros_like(eigs))
# update by GGT-trick-preconditioned step
precond_grad = Gt.t() @ (V @ (precond_eigs[:, None] * V.t() @ (Gt @ m1)))
p.sub_(precond_grad,
alpha=lr * math.sqrt(1 - beta2**(t+1)) / (1 - beta1**(t+1))
if adam_scale else lr)
state['step'] += 1
return loss
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client/data/email_builder.py | jurandirjdsilva/pycryptomail | 1 | 141702 | <reponame>jurandirjdsilva/pycryptomail<filename>client/data/email_builder.py
class EmailBuilder:
def __init__(self):
self.message = {}
def set_from_email(self, email):
self.message['from'] = email
return self
def set_receiver_email(self, email):
self.message['receiver'] = email
def set_cc_emails(self, emails):
self.message['cc'] = emails
return self
def set_attachaments(self, attachments):
self.message['attachments'] = attachments
return self
def set_subject(self, subject):
self.message['subject'] = subject
return self
def set_msg(self, message):
self.message['message'] = message
return self
def set_priority(self, priority):
self.message['priority'] = priority
return self
def build(self):
return self.message
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hmmlearn/tests/test_hmm.py | paschalidoud/hmmlearn | 0 | 133809 | from __future__ import print_function
from unittest import TestCase
import numpy as np
from nose import SkipTest
from numpy.testing import assert_array_equal, assert_array_almost_equal
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn.mixture import GMM
from sklearn.utils import check_random_state
from hmmlearn import hmm
from hmmlearn.utils import normalize, assert_raises
np.seterr(all='warn')
def fit_hmm_and_monitor_log_likelihood(h, X, lengths=None, n_iter=1):
h.n_iter = 1 # make sure we do a single iteration at a time
h.init_params = '' # and don't re-init params
loglikelihoods = np.empty(n_iter, dtype=float)
for i in range(n_iter):
h.fit(X, lengths=lengths)
loglikelihoods[i] = h.score(X, lengths=lengths)
return loglikelihoods
class GaussianHMMTestMixin(object):
covariance_type = None # set by subclasses
def setUp(self):
self.prng = prng = np.random.RandomState(10)
self.n_components = n_components = 3
self.n_features = n_features = 3
self.startprob = prng.rand(n_components)
self.startprob = self.startprob / self.startprob.sum()
self.transmat = prng.rand(n_components, n_components)
self.transmat /= np.tile(self.transmat.sum(axis=1)[:, np.newaxis],
(1, n_components))
self.means = prng.randint(-20, 20, (n_components, n_features))
self.covars = {
'spherical': (1.0 + 2 * np.dot(prng.rand(n_components, 1),
np.ones((1, n_features)))) ** 2,
'tied': (make_spd_matrix(n_features, random_state=0)
+ np.eye(n_features)),
'diag': (1.0 + 2 * prng.rand(n_components, n_features)) ** 2,
'full': np.array([make_spd_matrix(n_features, random_state=0)
+ np.eye(n_features)
for x in range(n_components)]),
}
self.expanded_covars = {
'spherical': [np.eye(n_features) * cov
for cov in self.covars['spherical']],
'diag': [np.diag(cov) for cov in self.covars['diag']],
'tied': [self.covars['tied']] * n_components,
'full': self.covars['full'],
}
def test_bad_covariance_type(self):
with assert_raises(ValueError):
h = hmm.GaussianHMM(20, covariance_type='badcovariance_type')
h.means_ = self.means
h.covars_ = []
h.startprob_ = self.startprob
h.transmat_ = self.transmat
h._check()
def test_score_samples_and_decode(self):
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.means_ = self.means
h.covars_ = self.covars[self.covariance_type]
# Make sure the means are far apart so posteriors.argmax()
# picks the actual component used to generate the observations.
h.means_ = 20 * h.means_
gaussidx = np.repeat(np.arange(self.n_components), 5)
n_samples = len(gaussidx)
X = self.prng.randn(n_samples, self.n_features) + h.means_[gaussidx]
h._init(X, params="st")
ll, posteriors = h.score_samples(X)
self.assertEqual(posteriors.shape, (n_samples, self.n_components))
assert_array_almost_equal(posteriors.sum(axis=1), np.ones(n_samples))
viterbi_ll, stateseq = h.decode(X)
assert_array_equal(stateseq, gaussidx)
def test_sample(self, n=1000):
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.transmat_ = self.transmat
# Make sure the means are far apart so posteriors.argmax()
# picks the actual component used to generate the observations.
h.means_ = 20 * self.means
h.covars_ = np.maximum(self.covars[self.covariance_type], 0.1)
X, state_sequence = h.sample(n, random_state=self.prng)
self.assertEqual(X.shape, (n, self.n_features))
self.assertEqual(len(state_sequence), n)
def test_fit(self, params='stmc', n_iter=5, **kwargs):
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.transmat_ = normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.means_ = 20 * self.means
h.covars_ = self.covars[self.covariance_type]
lengths = [10] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(X, lengths=lengths)
trainll = fit_hmm_and_monitor_log_likelihood(
h, X, lengths=lengths, n_iter=n_iter)
# Check that the log-likelihood is always increasing during training.
diff = np.diff(trainll)
message = ("Decreasing log-likelihood for {0} covariance: {1}"
.format(self.covariance_type, diff))
self.assertTrue(np.all(diff >= -1e-6), message)
def test_fit_works_on_sequences_of_different_length(self):
lengths = [3, 4, 5]
X = self.prng.rand(sum(lengths), self.n_features)
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
# This shouldn't raise
# ValueError: setting an array element with a sequence.
h.fit(X, lengths=lengths)
def test_fit_with_length_one_signal(self):
lengths = [10, 8, 1]
X = self.prng.rand(sum(lengths), self.n_features)
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
# This shouldn't raise
# ValueError: zero-size array to reduction operation maximum which
# has no identity
h.fit(X, lengths=lengths)
def test_fit_with_priors(self, params='stmc', n_iter=5):
startprob_prior = 10 * self.startprob + 2.0
transmat_prior = 10 * self.transmat + 2.0
means_prior = self.means
means_weight = 2.0
covars_weight = 2.0
if self.covariance_type in ('full', 'tied'):
covars_weight += self.n_features
covars_prior = self.covars[self.covariance_type]
h = hmm.GaussianHMM(self.n_components, self.covariance_type)
h.startprob_ = self.startprob
h.startprob_prior = startprob_prior
h.transmat_ = normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.transmat_prior = transmat_prior
h.means_ = 20 * self.means
h.means_prior = means_prior
h.means_weight = means_weight
h.covars_ = self.covars[self.covariance_type]
h.covars_prior = covars_prior
h.covars_weight = covars_weight
lengths = [100] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
# Re-initialize the parameters and check that we can converge to the
# original parameter values.
h_learn = hmm.GaussianHMM(self.n_components, self.covariance_type,
params=params)
h_learn.n_iter = 0
h_learn.fit(X, lengths=lengths)
fit_hmm_and_monitor_log_likelihood(
h_learn, X, lengths=lengths, n_iter=n_iter)
# Make sure we've converged to the right parameters.
# a) means
self.assertTrue(np.allclose(sorted(h.means_.tolist()),
sorted(h_learn.means_.tolist()),
0.01))
# b) covars are hard to estimate precisely from a relatively small
# sample, thus the large threshold
self.assertTrue(np.allclose(sorted(h._covars_.tolist()),
sorted(h_learn._covars_.tolist()),
10))
def test_fit_non_ergodic_transmat(self):
h = hmm.GaussianHMM(n_components=5, covariance_type='full',
n_iter=100, init_params='st')
h.startprob_ = np.array([1, 0, 0, 0, 0])
h.transmat_ = np.array([[0.9, 0.1, 0, 0, 0],
[0, 0.9, 0.1, 0, 0],
[0, 0, 0.9, 0.1, 0],
[0, 0, 0, 0.9, 0.1],
[0, 0, 0, 0, 1.0]])
h.means_ = np.zeros((5, 10))
h.covars_ = np.tile(np.identity(10), (5, 1, 1))
lengths = [10] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
h.fit(X, lengths=lengths)
# TODO: write the actual test
class TestGaussianHMMWithSphericalCovars(GaussianHMMTestMixin, TestCase):
covariance_type = 'spherical'
def test_fit_startprob_and_transmat(self):
self.test_fit('st')
class TestGaussianHMMWithDiagonalCovars(GaussianHMMTestMixin, TestCase):
covariance_type = 'diag'
def test_covar_is_writeable(self):
h = hmm.GaussianHMM(n_components=1, covariance_type='diag')
X = np.random.normal(size=(1000, 5))
h._init(X, params="c")
# np.diag returns a read-only view of the array in NumPy 1.9.X.
# Make sure this doesn't prevent us from fitting an HMM with
# diagonal covariance matrix. See PR#44 on GitHub for details
# and discussion.
assert h._covars_.flags["WRITEABLE"]
class TestGaussianHMMWithTiedCovars(GaussianHMMTestMixin, TestCase):
covariance_type = 'tied'
class TestGaussianHMMWithFullCovars(GaussianHMMTestMixin, TestCase):
covariance_type = 'full'
class MultinomialHMMTestCase(TestCase):
"""Using examples from Wikipedia
- http://en.wikipedia.org/wiki/Hidden_Markov_model
- http://en.wikipedia.org/wiki/Viterbi_algorithm
"""
def setUp(self):
self.prng = np.random.RandomState(9)
self.n_components = 2 # ('Rainy', 'Sunny')
self.n_features = 3 # ('walk', 'shop', 'clean')
self.emissionprob = np.array([[0.1, 0.4, 0.5], [0.6, 0.3, 0.1]])
self.startprob = np.array([0.6, 0.4])
self.transmat = np.array([[0.7, 0.3], [0.4, 0.6]])
self.h = hmm.MultinomialHMM(self.n_components)
self.h.startprob_ = self.startprob
self.h.transmat_ = self.transmat
self.h.emissionprob_ = self.emissionprob
def test_set_emissionprob(self):
h = hmm.MultinomialHMM(self.n_components)
emissionprob = np.array([[0.8, 0.2, 0.0], [0.7, 0.2, 1.0]])
h.emissionprob = emissionprob
assert np.allclose(emissionprob, h.emissionprob)
def test_wikipedia_viterbi_example(self):
# From http://en.wikipedia.org/wiki/Viterbi_algorithm:
# "This reveals that the observations ['walk', 'shop', 'clean']
# were most likely generated by states ['Sunny', 'Rainy',
# 'Rainy'], with probability 0.01344."
X = [[0], [1], [2]]
logprob, state_sequence = self.h.decode(X)
self.assertAlmostEqual(np.exp(logprob), 0.01344)
assert_array_equal(state_sequence, [1, 0, 0])
def test_decode_map_algorithm(self):
X = [[0], [1], [2]]
h = hmm.MultinomialHMM(self.n_components, algorithm="map")
h.startprob_ = self.startprob
h.transmat_ = self.transmat
h.emissionprob_ = self.emissionprob
_logprob, state_sequence = h.decode(X)
assert_array_equal(state_sequence, [1, 0, 0])
def test_predict(self):
X = [[0], [1], [2]]
state_sequence = self.h.predict(X)
posteriors = self.h.predict_proba(X)
assert_array_equal(state_sequence, [1, 0, 0])
assert_array_almost_equal(posteriors, [
[0.23170303, 0.76829697],
[0.62406281, 0.37593719],
[0.86397706, 0.13602294],
])
def test_attributes(self):
h = hmm.MultinomialHMM(self.n_components)
self.assertEqual(h.n_components, self.n_components)
h.startprob_ = self.startprob
h.transmat_ = self.transmat
h.emissionprob_ = self.emissionprob
assert_array_almost_equal(h.emissionprob_, self.emissionprob)
with assert_raises(ValueError):
h.emissionprob_ = []
h._check()
with assert_raises(ValueError):
h.emissionprob_ = np.zeros((self.n_components - 2,
self.n_features))
h._check()
def test_score_samples(self):
idx = np.repeat(np.arange(self.n_components), 10)
n_samples = len(idx)
X = np.atleast_2d(
(self.prng.rand(n_samples) * self.n_features).astype(int)).T
ll, posteriors = self.h.score_samples(X)
self.assertEqual(posteriors.shape, (n_samples, self.n_components))
assert_array_almost_equal(posteriors.sum(axis=1), np.ones(n_samples))
def test_sample(self, n=1000):
X, state_sequence = self.h.sample(n, random_state=self.prng)
self.assertEqual(X.ndim, 2)
self.assertEqual(len(X), n)
self.assertEqual(len(state_sequence), n)
self.assertEqual(len(np.unique(X)), self.n_features)
def test_fit(self, params='ste', n_iter=5, **kwargs):
h = self.h
h.params = params
lengths = np.array([10] * 10)
X, _state_sequence = h.sample(lengths.sum(), random_state=self.prng)
# Mess up the parameters and see if we can re-learn them.
h.startprob_ = normalize(self.prng.rand(self.n_components))
h.transmat_ = normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.emissionprob_ = normalize(
self.prng.rand(self.n_components, self.n_features), axis=1)
trainll = fit_hmm_and_monitor_log_likelihood(
h, X, lengths=lengths, n_iter=n_iter)
# Check that the log-likelihood is always increasing during training.
diff = np.diff(trainll)
self.assertTrue(np.all(diff >= -1e-6),
"Decreasing log-likelihood: {0}" .format(diff))
def test_fit_emissionprob(self):
self.test_fit('e')
def test_fit_with_init(self, params='ste', n_iter=5, verbose=False,
**kwargs):
h = self.h
learner = hmm.MultinomialHMM(self.n_components)
lengths = [10] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
# use init_function to initialize paramerters
learner._init(X, lengths=lengths, params=params)
trainll = fit_hmm_and_monitor_log_likelihood(learner, X, n_iter=n_iter)
# Check that the loglik is always increasing during training
if not np.all(np.diff(trainll) > 0) and verbose:
print()
print('Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll)))
self.assertTrue(np.all(np.diff(trainll) > -1.e-3))
def test__check_input_symbols(self):
self.assertTrue(self.h._check_input_symbols([[0, 0, 2, 1, 3, 1, 1]]))
self.assertFalse(self.h._check_input_symbols([[0, 0, 3, 5, 10]]))
self.assertFalse(self.h._check_input_symbols([[0]]))
self.assertFalse(self.h._check_input_symbols([[0., 2., 1., 3.]]))
self.assertFalse(self.h._check_input_symbols([[0, 0, -2, 1, 3, 1, 1]]))
def create_random_gmm(n_mix, n_features, covariance_type, prng=0):
prng = check_random_state(prng)
g = GMM(n_mix, covariance_type=covariance_type)
g.means_ = prng.randint(-20, 20, (n_mix, n_features))
mincv = 0.1
g.covars_ = {
'spherical': (mincv + mincv * np.dot(prng.rand(n_mix, 1),
np.ones((1, n_features)))) ** 2,
'tied': (make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features)),
'diag': (mincv + mincv * prng.rand(n_mix, n_features)) ** 2,
'full': np.array(
[make_spd_matrix(n_features, random_state=prng)
+ mincv * np.eye(n_features) for x in range(n_mix)])
}[covariance_type]
g.weights_ = normalize(prng.rand(n_mix))
return g
class GMMHMMTestMixin(object):
def setUp(self):
self.prng = np.random.RandomState(9)
self.n_components = 3
self.n_mix = 2
self.n_features = 2
self.covariance_type = 'diag'
self.startprob = self.prng.rand(self.n_components)
self.startprob = self.startprob / self.startprob.sum()
self.transmat = self.prng.rand(self.n_components, self.n_components)
self.transmat /= np.tile(self.transmat.sum(axis=1)[:, np.newaxis],
(1, self.n_components))
self.gmms = []
for state in range(self.n_components):
self.gmms.append(create_random_gmm(
self.n_mix, self.n_features, self.covariance_type,
prng=self.prng))
def test_score_samples_and_decode(self):
h = hmm.GMMHMM(self.n_components)
h.startprob_ = self.startprob
h.transmat_ = self.transmat
h.gmms_ = self.gmms
# Make sure the means are far apart so posteriors.argmax()
# picks the actual component used to generate the observations.
for g in h.gmms_:
g.means_ *= 20
refstateseq = np.repeat(np.arange(self.n_components), 5)
n_samples = len(refstateseq)
X = [h.gmms_[x].sample(1, random_state=self.prng).flatten()
for x in refstateseq]
_ll, posteriors = h.score_samples(X)
self.assertEqual(posteriors.shape, (n_samples, self.n_components))
assert_array_almost_equal(posteriors.sum(axis=1), np.ones(n_samples))
_logprob, stateseq = h.decode(X)
assert_array_equal(stateseq, refstateseq)
def test_sample(self, n=1000):
h = hmm.GMMHMM(self.n_components, covariance_type=self.covariance_type)
h.startprob_ = self.startprob
h.transmat_ = self.transmat
h.gmms_ = self.gmms
X, state_sequence = h.sample(n, random_state=self.prng)
self.assertEqual(X.shape, (n, self.n_features))
self.assertEqual(len(state_sequence), n)
def test_fit(self, params='stmwc', n_iter=5, verbose=False, **kwargs):
h = hmm.GMMHMM(self.n_components, covars_prior=1.0)
h.startprob_ = self.startprob
h.transmat_ = normalize(
self.transmat + np.diag(self.prng.rand(self.n_components)), 1)
h.gmms_ = self.gmms
lengths = [10] * 10
X, _state_sequence = h.sample(sum(lengths), random_state=self.prng)
# Mess up the parameters and see if we can re-learn them.
h.n_iter = 0
h.fit(X, lengths=lengths)
h.transmat_ = normalize(self.prng.rand(self.n_components,
self.n_components), axis=1)
h.startprob_ = normalize(self.prng.rand(self.n_components))
trainll = fit_hmm_and_monitor_log_likelihood(
h, X, lengths=lengths, n_iter=n_iter)
if not np.all(np.diff(trainll) > 0) and verbose:
print('Test train: (%s)\n %s\n %s' % (params, trainll,
np.diff(trainll)))
# XXX: this test appears to check that training log likelihood should
# never be decreasing (up to a tolerance of 0.5, why?) but this is not
# the case when the seed changes.
raise SkipTest("Unstable test: trainll is not always increasing "
"depending on seed")
self.assertTrue(np.all(np.diff(trainll) > -0.5))
def test_fit_works_on_sequences_of_different_length(self):
lengths = [3, 4, 5]
X = self.prng.rand(sum(lengths), self.n_features)
h = hmm.GMMHMM(self.n_components, covariance_type=self.covariance_type)
# This shouldn't raise
# ValueError: setting an array element with a sequence.
h.fit(X, lengths=lengths)
class TestGMMHMMWithDiagCovars(GMMHMMTestMixin, TestCase):
covariance_type = 'diag'
def test_fit_startprob_and_transmat(self):
self.test_fit('st')
def test_fit_means(self):
self.test_fit('m')
class TestGMMHMMWithTiedCovars(GMMHMMTestMixin, TestCase):
covariance_type = 'tied'
class TestGMMHMMWithFullCovars(GMMHMMTestMixin, TestCase):
covariance_type = 'full'
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classifier/vec/get_embedding_matrix.py | banboooo044/natural-language-sentiment-anaysis | 0 | 186126 | <reponame>banboooo044/natural-language-sentiment-anaysis
# Word2vec, Fasttext の単語分散表現から文章分散表現を取得
## 学習済みモデルをそのまま使う場合:
## word2vec : python3 get_embedding_matrix word2vec
## fasttext : python3 get_embedding_matrix fasttext
## fasttext+neologd : python3 get_embedding_matrix fasttext-neologd
## 自分で学習した [filename].model を使う場合
## python3 get_embedding_matrix filename
## modelファイルの形式によっては, 関数load_model をいじらないとダメかもしれない.
import sys, re
import numpy as np
import pandas as pd
import logging
from gensim.models import Word2Vec,KeyedVectors
from sklearn.preprocessing import normalize
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
"""
This is a model which is described in "Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms"
See for details https://arxiv.org/pdf/1805.09843.pdf
"""
# SWEM - hier
def hier(vecs, n=2):
sz, k = vecs.shape
ret = np.ones((1, k)) * (-np.inf)
if sz == 1:
vecs = np.vstack((vecs, np.zeros((2, k))))
elif sz == 2:
vecs = np.vstack((vecs, np.zeros(k)))
sz, k = vecs.shape
for i in range(sz - n + 1):
ret = np.max(np.vstack((ret, np.mean(vecs[i:i+n], axis=0))), axis=0)
return ret
def load_model(path):
model = KeyedVectors.load(f'./{path}.model')
return model
# ベクトルを作る
def createVector(df, model, num_features):
print("create vector ... ")
x_mean = np.zeros((len(df), num_features))
x_max = np.zeros((len(df), num_features))
x_hier = np.zeros((len(df), num_features))
for idx, (text, label, _) in df.iterrows():
tmp = np.array([ np.array(model.wv[word]) if word in model.wv.vocab else np.random.randn(num_features) for word in text.split(",") ])
if len(tmp) != 0:
# SWEM-aver
x_mean[idx] = np.mean(tmp, axis=0).reshape(1, num_features)
# SWEM-max
x_max[idx] = np.max(tmp, axis=0).reshape(1, num_features)
# SWEM-hier
x_hier[idx] = hier(tmp, n=2)
return x_mean, x_max, x_hier
if __name__ == "__main__":
PATH = "../data/corpus-wakati-juman.tsv"
df = pd.read_table(PATH, index_col=0)
df = df[~pd.isnull(df["text"])]
token = df["text"].apply(lambda x: x.split(","))
args = sys.argv
# Word2vec
if args[1] == "word2vec":
model = KeyedVectors.load_word2vec_format('./word2vec_pre/entity_vector.model.bin', binary=True)
OUTPUT_FILENAME = "word2vec"
# Fasttext
elif args[1] == "fasttext":
model = KeyedVectors.load_word2vec_format('./fasttext_pre/fasttext.vec', binary=False)
OUTPUT_FILENAME = "fasttext"
# Fasttext + neologd辞書
elif args[1] == "fasttext-neologd":
model = KeyedVectors.load_word2vec_format('./fasttext_pre/fasttext-neologd.vec', binary=False)
OUTPUT_FILENAME = "fasttext-neologd"
# その他
else:
model = load_model(args[1])
OUTPUT_FILENAME = input("[filename].model")
x_mean, x_max, x_hier = createVector(df, model, num_features=model.vector_size)
np.save(f"./{OUTPUT_FILENAME}_aver.npy", x_mean)
np.save(f"./{OUTPUT_FILENAME}_max.npy", x_max)
np.save(f"./{OUTPUT_FILENAME}_hier.npy", x_hier) | [
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] |
one_point_stats/stats/noise.py | piyanatk/one_point_stats | 0 | 165997 | import numpy as np
from scipy.stats import skew, kurtosis
__all__ = ['sky_noise_error', 'propagate_noise_error', 'mcnoise']
def sky_noise_error(nu_obs, nu_emit, nu_ch_bw, tint, a_eff, n_station, bmax):
"""Calculate instrument noise error of an interferometer.
This assume that Tsys is dominated by Tsky.
(see Furlanetto et al. (2006) section 9)
Parameters
----------
nu_obs : float or array-like
Observing frequency in [MHz].
Can be array-like to compute noise at multiple frequencies.
nu_emit : float
Emitted frequency of the observed spectral line in [MHz].
nu_ch_bw : float
Observed frequency channel bandwidth in [MHz].
tint : float, optional
Integration time. Default is 1000. [hours]
a_eff : float
Effective area of a station in [m**2].
n_station : integer
Number of antennas (or stations in case of a phase-array).
bmax : float
Maximum baseline length of the array in [wavelength].
Returns
-------
Noise error (standard deviation) in [mK] in the same format as nu_obs.
"""
nu_obs = np.asarray(nu_obs)
a_tot = a_eff * n_station
z = (nu_emit / nu_obs) - 1.
theta = 1.22 / bmax * 60 * 180 / np.pi
err = 2.9 * (1.e5 / a_tot) * (10. / theta) ** 2 * \
((1 + z) / 10.0) ** 4.6 * np.sqrt(100. / (nu_ch_bw * tint))
return err
def propagate_noise_error(noise_err, m2, m3, m4, m6, npix):
"""Analytically propagate error to variance and skewness.
Based on error propagation described in the appendix of
Watkinson & Pritchard (2014)
Parameters
----------
noise_err : float or array-like
Noise error.
m2 : float
2nd moment of the data
m3 : float
3rd moment of the data
m4 : float
4th moment of the data
m6 : float
6th moment of the data
npix : int
Number of pixels in the data
Returns
-------
Error of 2nd moment, 3rd moment and skewness.
"""
noise_var = np.asarray(noise_err) ** 2
m2_var = (2. / npix) * (2 * m2 * noise_var + noise_var ** 2)
m3_var = (3. / npix) * (3 * noise_var * m4 + 12 * m2 * noise_var ** 2 +
5 * noise_var ** 3)
m4_var = (8. / npix) * (2 * m6 * noise_var + 21 * m4 * noise_var ** 2 +
48 * m2 * noise_var ** 3 + 12 * noise_var ** 4)
m2m3_cov = (6 / npix) * m3 * noise_var
m2m4_cov = (4. / npix) * (2 * m4 * noise_var + 9 * m2 * noise_var ** 2 +
3 * noise_var ** 3)
skew_var = (m3_var / (m2 ** 3)) + \
((9 * m3 ** 2 * m2_var) / (4 * m2 ** 5)) - \
(3 * m3 * m2m3_cov / (m2 ** 4))
kurt_var = (1. / m2 ** 4) * m4_var + 4 * (m4 ** 2 / m2 ** 6) * m2_var - \
4 * (m4 / m2 ** 5) * m2m4_cov
return np.sqrt(m2_var), np.sqrt(skew_var), np.sqrt(kurt_var)
def mcnoise(data, noise_std, n, noise_scaling=1.):
"""
Parameters
----------
data : ndarray
Array of data.
noise_std : float
Standard deviation of the noise
n : int
Number of repetition
noise_scaling: float
Scaling factor for noise
Returns
-------
variance, variance error, skewness, skewness error, kurtosis, kurtosis error
"""
noise_arr = np.random.normal(0, noise_std, (n, data.size)) * noise_scaling
var_sample = np.var(data + noise_arr, axis=1)
skew_sample = skew(data + noise_arr, axis=1)
kurt_sample = kurtosis(data + noise_arr, axis=1)
var_val = np.mean(var_sample)
skew_val = np.mean(skew_sample)
kurt_val = np.mean(kurt_sample)
var_err = np.std(var_sample)
skew_err = np.std(skew_sample)
kurt_err = np.std(kurt_sample)
return var_val, var_err, skew_val, skew_err, kurt_val, kurt_err
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data/2019-11-28/openml-datasets-single-column/plot_features_versus_target.py | VIDA-NYU/prida | 1 | 1604003 | '''
This script gets a file with metadata, learning features, and target
values for each line, and plots different features against the target.
'''
FEATURE_NAMES = ['query_num_of_columns','query_num_of_rows','query_row_column_ratio','query_max_mean','query_max_outlier_percentage','query_max_skewness','query_max_kurtosis','query_max_unique','candidate_num_of_columns','candidate_num_rows','candidate_row_column_ratio','candidate_max_mean','candidate_max_outlier_percentage','candidate_max_skewness','candidate_max_kurtosis','candidate_max_unique','query_target_max_pearson','query_target_max_spearman','query_target_max_covariance','query_target_max_mutual_info','candidate_target_max_pearson','candidate_target_max_spearman','candidate_target_max_covariance','candidate_target_max_mutual_info','max_pearson_difference', 'containment_fraction']
TARGET_GAIN_IN_R2_SCORE_ID = -1
FIRST_FEATURE_ID = 3
FIRST_TARGET_ID = -4
OUTLIER_THRESHOLD_MAD = 2
OUTLIER_THRESHOLD_ZSCORES = 3
import numpy as np
import sys
import matplotlib.pyplot as plt
from scipy.stats import median_absolute_deviation
def normalize(data):
max_ = max(data)
return [i/max_ for i in data]
def remove_outliers_based_on_zscores(x_data, y_data):
mean_x = np.mean(x_data)
std_x = np.std(x_data)
mean_y = np.mean(y_data)
std_y = np.std(y_data)
filtered_x = []
filtered_y = []
for x, y in zip(x_data, y_data):
if np.fabs((x - mean_x)/std_x) < OUTLIER_THRESHOLD_ZSCORES and np.fabs((y - mean_y)/std_y) < OUTLIER_THRESHOLD_ZSCORES:
filtered_x.append(x)
filtered_y.append(y)
return filtered_x, filtered_y
def remove_outliers_based_on_mad(x_data, y_data):
mad_x = median_absolute_deviation(x_data)
median_x = np.median(x_data)
mad_y = median_absolute_deviation(y_data)
median_y = np.median(y_data)
filtered_x = []
filtered_y = []
for x, y in zip(x_data, y_data):
if np.fabs((x - median_x)/mad_x) < OUTLIER_THRESHOLD_MAD and np.fabs((y - median_y)/mad_y) < OUTLIER_THRESHOLD_MAD:
filtered_x.append(x)
filtered_y.append(y)
return filtered_x, filtered_y
def plot_scatterplot(feature_data, target_data, image_name, xlabel, ylabel):
feature_data, target_data = remove_outliers_based_on_zscores(feature_data, target_data)
if not feature_data or not target_data:
return
#plt.scatter(normalize(feature_data), normalize(target_data), alpha=0.5)
plt.scatter(feature_data, target_data, alpha=0.5)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.tight_layout()
plt.savefig(image_name, dpi=300)
plt.close()
if __name__ == '__main__':
filename = sys.argv[1]
lines = open(sys.argv[1]).readlines()
features = []
target = []
for line in lines:
fields = line.strip().split(',')
features.append([float(i) for i in fields[FIRST_FEATURE_ID:FIRST_TARGET_ID]])
target.append(float(fields[TARGET_GAIN_IN_R2_SCORE_ID]))
features = np.array(features)
target = np.array(target)
num_features = features.shape[1]
for i in range(num_features):
plot_scatterplot(features[:,i], target, FEATURE_NAMES[i] + '_vs_gain_in_r2_score.png', FEATURE_NAMES[i], 'gain_in_r2_score')
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algorithms/aiAlgorithms/cnn/cnn_facial_recognition.py | bigfoolliu/liu_aistuff | 1 | 46137 | <reponame>bigfoolliu/liu_aistuff<filename>algorithms/aiAlgorithms/cnn/cnn_facial_recognition.py<gh_stars>1-10
#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# author: bigfoolliu
# TODO: not finished.
"""
fer2013.csv文件内容
- emotion,pixels,Usage
- pixels为48*48的像素值
"""
import string, os, sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import random
import tensorflow as tf
import pandas as pd
import datetime
# 文件
FILE_PATH = "../dataset/fer2013/fer2013.csv"
# 训练和测试
TRAIN_NUM = 30000 # 训练数据不超过35887
TEST_NUM = 5000 # 测试数据不超过5000
BATCH_SIZE = 50 # 训练批次的数量
TRAIN_EPOCH = 100
LEARNING_RATE = 0.001
train_batch_num = TRAIN_NUM / BATCH_SIZE
test_batch_num = TEST_NUM / BATCH_SIZE
# 神经网络
N_INPUT = 2304 # data input (img shape: 48*48)
N_CLASSES = 7 # total classes
DROPOUT = 0.5 # Dropout, probability to keep units
def show_time():
"""显示当前时间"""
return datetime.datetime.now().isoformat()
def read_file(file_path):
"""读取csv文件"""
print("[INFO]{}:Begin to read csv file: {}".format(show_time(), file_path))
data = pd.read_csv(file_path, dtype='a')
label = np.array(data['emotion'])
img_data = np.array(data['pixels'])
N_sample = label.size
print("[INFO]{}:The size of the sample: {}".format(show_time(), N_sample))
Face_data = np.zeros((N_sample, 48 * 48)) # N_sample * (48*48)的数组
Face_label = np.zeros((N_sample, 7), dtype=int)
temp = np.zeros((7), dtype=int)
# 遍历读取样本的像素值，因为是以字符串的形式保存的，所以要转换格式为float
for i in range(N_sample):
x = img_data[i]
x = np.fromstring(x, dtype=float, sep=" ") # 文本字符串转换为float，分隔符为空格
x_max = x.max()
x = x / (x_max + 0.0001) # 归一化处理，将所有数据缩小至(0, 1)之间
Face_data[i] = x
Face_label[i, int(label[i])] = 1 # 对应的表情列标1，其余的为0
print("[INFO]{}:Sample {}, {}\t".format(show_time(), i, Face_label[i]))
train_x = Face_data[0: TRAIN_NUM, :]
train_y = Face_label[0: TRAIN_NUM, :]
test_x = Face_data[TRAIN_NUM: TRAIN_NUM + TEST_NUM, :]
test_y = Face_label[TRAIN_NUM: TRAIN_NUM + TEST_NUM, :]
print("[INFO]{}:train_x:{}\ntrain_y:{}\ntest_x:{}\ntest_y:{}".format(show_time(), train_x, train_y, test_x, test_y))
print("[INFO]{}:Read the file {} complete.".format(show_time(), file_path))
read_file(FILE_PATH)
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
"""Conv2D wrapper, with bias and relu activation"""
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
"""MaxPool2D wrapper"""
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='VALID')
def conv_net(x, weights, biases, dropout):
"""卷积神经网络模型"""
x = tf.reshape(x, shape=[-1, 48, 48, 1]) # 改变输入的图片的形状
conv1 = conv2d(x, weights['wc1'], biases['bc1']) # 卷积层
conv1 = maxpool2d(conv1, k=2) # 池化
conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) # 卷积层２
conv2 = maxpool2d(conv2, k=2) # 池化
conv3 = conv2d(conv2, weights['wc3'], biases['bc3']) # 卷积层3
conv3 = maxpool2d(conv3, k=2) # 池化
# 全连接层，Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
# Output, class prediction
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
def run():
"""初始化weights和biases"""
weights = {
'wc1': tf.Variable(tf.random_normal([3, 3, 1, 128])), # 3 * 3的卷积，1个输入,128输出
'wc2': tf.Variable(tf.random_normal([3, 3, 128, 64])),
'wc3': tf.Variable(tf.random_normal([3, 3, 64, 32])),
'wd1': tf.Variable(tf.random_normal([6 * 6 * 32, 200])), # 全连接
'out': tf.Variable(tf.random_normal([200, n_classes])) # 1024输入，10输出，(class prediction)
}
biases = {
'bc1': tf.Variable(tf.random_normal([128])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([32])),
'bd1': tf.Variable(tf.random_normal([200])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
# 构建模型
pred = conv_net(x, weights, biases, keep_prob)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
Train_ind = np.arange(train_num)
Test_ind = np.arange(test_num)
with tf.Session() as sess:
sess.run(init)
for epoch in range(0, train_epoch):
Total_test_loss = 0
Total_test_acc = 0
for train_batch in range(0, int(train_batch_num)):
sample_ind = Train_ind[train_batch * batch_size:(train_batch + 1) * batch_size]
batch_x = train_x[sample_ind, :]
batch_y = train_y[sample_ind, :]
# Run optimization op (backprop)
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if train_batch % batch_size == 0:
# Calculate loss and accuracy
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
print("Epoch: " + str(epoch + 1) + ", Batch: " + str(train_batch) + ", Loss= " + "{:.3f}".format(
loss) + ", Training Accuracy= " + "{:.3f}".format(acc))
# Calculate test loss and test accuracy
for test_batch in range(0, int(test_batch_num)):
sample_ind = Test_ind[test_batch * batch_size:(test_batch + 1) * batch_size]
batch_x = test_x[sample_ind, :]
batch_y = test_y[sample_ind, :]
test_loss, test_acc = sess.run([cost, accuracy],
feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
Total_test_lost = Total_test_loss + test_loss
Total_test_acc = Total_test_acc + test_acc
Total_test_acc = Total_test_acc / test_batch_num
Total_test_loss = Total_test_lost / test_batch_num
print("Epoch: " + str(epoch + 1) + ", Test Loss= " + "{:.3f}".format(
Total_test_loss) + ", Test Accuracy= " + "{:.3f}".format(Total_test_acc))
plt.subplot(2, 1, 1)
plt.ylabel('Test loss')
plt.plot(Total_test_loss, 'r')
plt.subplot(2, 1, 2)
plt.ylabel('Test Accuracy')
plt.plot(Total_test_acc, 'r')
print("All is well")
plt.show()
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Reconocimiento/Prueba1lidarchidovibr.py | larj3852/Lidar_TT | 0 | 58320 | # -*- coding: utf-8 -*-
"""
...
"""
import LibraryTT.txt2array as conversion
import numpy as np
from numpy import sqrt
import pandas as pd
import matplotlib.pyplot as plt
import random
import math
from mpl_toolkits.mplot3d import Axes3D
# import open3d as o3d
# %matplotlib inline
D = conversion.txt2array()
DD = np.copy(D) # Creamos copia de datos para no afectar a los originales
Epsilon = 30
MinPts = 75 #78
# result = DBSCAN(DD,Epsilon,MinPts)
chch = conversion.RObjetos(DD,Epsilon,MinPts)
TN = conversion.usar(chch)
# Graficar un dato
conversion.imprimir3D(D)
# conversion.imprimir3D(DD)
# Imprimir sin ruido--- graficar
conversion.imprimirObjetos(TN,chch,0,0)
# Graficar con ruido
conversion.imprimirObjetos(TN,chch,1,0)
# conversion.imprimirObjetos(TN,chch,2,1)
# (Objetos,tamañoobjetos,2,cualObjeto)
# el ransac
# vectores para guardar datos.
abcd = np.array([[0,0,0,0]])
ldps = np.array([])
gplns = np.array([])
abcd,ldps,gplns = conversion.rnsc(TN,chch,abcd,ldps,gplns)
abcd = np.delete(abcd,0,axis=0)
# BUSCAR centros de planos aunque debiera buscar algo más con planos pequeños.
cplns = 0 # va pasando por cada valor abcd para hacer la prueba
# p1 = 0
# sc = 0
# p2 = gplns[sc]
cplanos = np.array([[0,0,0]])
Dists = np.array([])
cplanos,Dists = conversion.centros(cplanos,Dists,TN,ldps,gplns)
dext = 100
dint = 50
tvdext = np.array([])
tvdint = np.array([])
# Para checar que objetos andan dentro del rango int y ext
# np.append(datosx,[[xmin,xmax]],axis=0)
# Se guardan las posiciones
for ima in range(0,len(Dists)):
if (Dists[ima] <= dext):
tvdext = np.append(tvdext,ima)
# print("hay un obstaculo en zona alejada")
if (Dists[ima] <= dint):
tvdint = np.append(tvdint,ima)
# print("Hay obstaculo cercano, detener y cambiar posicion")
# Para conocer mejor cuales son int mas que ext porque son mas importantes
if (len(tvdext) > 0) and (len(tvdint) > 0):
for ixt in range(0,len(tvdint)):
for ixtt in range(0,len(tvdext)):
if (tvdint[ixt] == tvdext[ixtt]):
tvdext = np.delete(tvdext[ixtt])
if (len(tvdext) <= 0):
break
prac = 0
if (len(tvdext) > 0) or (len(tvdint) > 0):
if (len(tvdint)>0):
for din in range(0,len(tvdint)):
xd = cplanos[int(tvdint[din]),0]
yd = cplanos[int(tvdint[din]),1]
angulo = math.atan2(xd,yd)
angulo = math.degrees(angulo)
# En cada uno encender vibrador
if (angulo >= 120):
print("rapido dar un paso a la derecha")
prac += 1
if (angulo <= 60):
print("rapido dar un paso a la izquierda")
prac += 1
if ((angulo > 60)and(angulo < 120)):
print("Deten tu carruaje")
prac += 1
# Aqui apagara los vibradores
if (prac == 0) and (len(tvdext)>0):
for din in range(0,len(tvdext)):
xd = cplanos[int(tvdext[din]),0]
yd = cplanos[int(tvdext[din]),1]
angulo = math.atan2(xd,yd)
angulo = math.degrees(angulo)
# En cada uno encender vibrador
if (angulo >= 120):
print("dar un paso a la derecha")
if (angulo <= 60):
print("dar un paso a la izquierda")
if ((angulo > 60)and(angulo < 120)):
print("Abra algo")
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] |
resource/pypi/cryptography-1.7.1/tests/hazmat/primitives/test_keywrap.py | hipnusleo/Laserjet | 0 | 196972 | # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
from __future__ import absolute_import, division, print_function
import binascii
import os
import pytest
from cryptography.hazmat.backends.interfaces import CipherBackend
from cryptography.hazmat.primitives import keywrap
from cryptography.hazmat.primitives.ciphers import algorithms, modes
from .utils import _load_all_params
from ...utils import load_nist_vectors
@pytest.mark.requires_backend_interface(interface=CipherBackend)
class TestAESKeyWrap(object):
@pytest.mark.parametrize(
"params",
_load_all_params(
os.path.join("keywrap", "kwtestvectors"),
["KW_AE_128.txt", "KW_AE_192.txt", "KW_AE_256.txt"],
load_nist_vectors
)
)
@pytest.mark.supported(
only_if=lambda backend: backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.ECB()
),
skip_message="Does not support AES key wrap (RFC 3394) because AES-ECB"
" is unsupported",
)
def test_wrap(self, backend, params):
wrapping_key = binascii.unhexlify(params["k"])
key_to_wrap = binascii.unhexlify(params["p"])
wrapped_key = keywrap.aes_key_wrap(wrapping_key, key_to_wrap, backend)
assert params["c"] == binascii.hexlify(wrapped_key)
@pytest.mark.parametrize(
"params",
_load_all_params(
os.path.join("keywrap", "kwtestvectors"),
["KW_AD_128.txt", "KW_AD_192.txt", "KW_AD_256.txt"],
load_nist_vectors
)
)
@pytest.mark.supported(
only_if=lambda backend: backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.ECB()
),
skip_message="Does not support AES key wrap (RFC 3394) because AES-ECB"
" is unsupported",
)
def test_unwrap(self, backend, params):
wrapping_key = binascii.unhexlify(params["k"])
wrapped_key = binascii.unhexlify(params["c"])
if params.get("fail") is True:
with pytest.raises(keywrap.InvalidUnwrap):
keywrap.aes_key_unwrap(wrapping_key, wrapped_key, backend)
else:
unwrapped_key = keywrap.aes_key_unwrap(
wrapping_key, wrapped_key, backend
)
assert params["p"] == binascii.hexlify(unwrapped_key)
@pytest.mark.supported(
only_if=lambda backend: backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.ECB()
),
skip_message="Does not support AES key wrap (RFC 3394) because AES-ECB"
" is unsupported",
)
def test_wrap_invalid_key_length(self, backend):
# The wrapping key must be of length [16, 24, 32]
with pytest.raises(ValueError):
keywrap.aes_key_wrap(b"badkey", b"sixteen_byte_key", backend)
@pytest.mark.supported(
only_if=lambda backend: backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.ECB()
),
skip_message="Does not support AES key wrap (RFC 3394) because AES-ECB"
" is unsupported",
)
def test_unwrap_invalid_key_length(self, backend):
with pytest.raises(ValueError):
keywrap.aes_key_unwrap(b"badkey", b"\x00" * 24, backend)
@pytest.mark.supported(
only_if=lambda backend: backend.cipher_supported(
algorithms.AES(b"\x00" * 16), modes.ECB()
),
skip_message="Does not support AES key wrap (RFC 3394) because AES-ECB"
" is unsupported",
)
def test_wrap_invalid_key_to_wrap_length(self, backend):
# Keys to wrap must be at least 16 bytes long
with pytest.raises(ValueError):
keywrap.aes_key_wrap(b"sixteen_byte_key", b"\x00" * 15, backend)
# Keys to wrap must be a multiple of 8 bytes
with pytest.raises(ValueError):
keywrap.aes_key_wrap(b"sixteen_byte_key", b"\x00" * 23, backend)
def test_unwrap_invalid_wrapped_key_length(self, backend):
# Keys to unwrap must be at least 24 bytes
with pytest.raises(ValueError):
keywrap.aes_key_unwrap(b"sixteen_byte_key", b"\x00" * 16, backend)
# Keys to unwrap must be a multiple of 8 bytes
with pytest.raises(ValueError):
keywrap.aes_key_unwrap(b"sixteen_byte_key", b"\x00" * 27, backend)
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] |
contact_PPO.py | ZhaomingXie/RLAlg | 0 | 1609445 | import argparse
import os
import sys
import random
import numpy as np
import scipy
import torch
import torch.optim as optim
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.utils.data
from params import Params
import pickle
import time as t
from model import ActorCriticNet, Shared_obs_stats, ActorCriticNetWithContact
import statistics
import matplotlib.pyplot as plt
from operator import add, sub
import pickle
import threading
import torch.multiprocessing as mp
import queue
from utils import TrafficLight
from utils import Counter
from radam import RAdam
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# try:
# mp.set_start_method('spawn')
# except RuntimeError:
# pass
import sys
sys.path.append('/home/zhaoming/Documents/dev/gym/gym/envs/mujoco')
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.sample_index = 0
def push(self, events):
for event in zip(*events):
self.memory.append(event)
if len(self.memory)>self.capacity:
del self.memory[0]
def push_half(self, events):
temp_memory = []
for event in zip(*events):
temp_memory.append(event)
self.memory = self.memory + temp_memory[len(temp_memory)//4:3*len(temp_memory)//2]
while len(self.memory)>self.capacity:
del self.memory[0]
def push_half(self, events):
temp_memory = []
for event in zip(*events):
temp_memory.append(event)
self.memory = self.memory + temp_memory[2*len(temp_memory)//4:len(temp_memory)]
while len(self.memory)>self.capacity:
del self.memory[0]
def clear(self):
self.memory = []
self.sample_index = 0
def sample(self, batch_size):
#print(len(self.memory), batch_size)
samples = zip(*random.sample(self.memory, batch_size))
return map(lambda x: np.concatenate(x, 0), samples)
def clean_memory(self):
while len(self.memory) > self.capacity:
del self.memory[0]
def shuffle(self):
random.shuffle(self.memory)
def sample_one_at_a_time(self):
samples = zip(*self.memory[self.sample_index:self.sample_index+1])
self.sample_index += 1
return map(lambda x: np.concatenate(x, 0), samples)
def normal(x, mu, log_std):
a = (x - mu)/(log_std.exp())
a = -0.5 * a.pow(2)
a = torch.sum(a, dim=1)
b = torch.sum(log_std, dim=1)
#print(a-b)
return a-b
class RL(object):
def __init__(self, env, hidden_layer=[64, 64], contact=False):
self.env = env
#self.env.env.disableViewer = False
self.num_inputs = env.observation_space.shape[0]
self.num_outputs = env.action_space.shape[0]
self.hidden_layer = hidden_layer
self.num_contact = 2
self.params = Params()
if contact:
self.Net = ActorCriticNetWithContact
else:
self.Net = ActorCriticNet
self.model = self.Net(self.num_inputs, self.num_outputs, self.hidden_layer, num_contact=self.num_contact)
self.model.share_memory()
self.shared_obs_stats = Shared_obs_stats(self.num_inputs)
self.memory = ReplayMemory(10000000)
self.value_memory = ReplayMemory(10000000)
self.test_mean = []
self.test_std = []
self.noisy_test_mean = []
self.noisy_test_std = []
self.fig = plt.figure()
#self.fig2 = plt.figure()
self.lr = self.params.lr
plt.show(block=False)
self.test_list = []
self.noisy_test_list = []
self.queue = mp.Queue()
self.value_queue = mp.Queue()
self.mpdone = [mp.Event(), mp.Event(), mp.Event(), mp.Event()]
self.process = []
self.traffic_light = TrafficLight()
self.counter = Counter()
self.best_trajectory = ReplayMemory(5000)
self.best_score_queue = mp.Queue()
self.best_score = mp.Value("f", 0)
self.max_reward = mp.Value("f", 1)
self.expert_trajectory = ReplayMemory(1e7)
self.validation_trajectory = ReplayMemory(6000*9)
self.best_validation = 1.0
self.current_best_validation = 1.0
self.return_obs_stats = Shared_obs_stats(1)
self.gpu_model = self.Net(self.num_inputs, self.num_outputs,self.hidden_layer, num_contact=self.num_contact)
self.base_controller = None
def normalize_data(self, num_iter=1000, file='shared_obs_stats.pkl'):
state = self.env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
#model_old = ActorCriticNet(self.num_inputs, self.num_outputs,self.hidden_layer)
#model_old.load_state_dict(self.model.state_dict())
for i in range(num_iter):
print(i)
self.shared_obs_stats.observes(state)
state = self.shared_obs_stats.normalize(state)#.to(device)
#mu = self.model.sample_actions(state)
#action = mu#(mu + log_std.exp()*Variable(eps))
#env_action = action.cpu().data.squeeze().numpy()
env_action = np.random.randn(self.num_outputs)
state, reward, done, _ = self.env.step(env_action*0)
if done:
state = self.env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
with open(file, 'wb') as output:
pickle.dump(self.shared_obs_stats, output, pickle.HIGHEST_PROTOCOL)
def run_test(self, num_test=1):
state = self.env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
ave_test_reward = 0
total_rewards = []
for i in range(num_test):
total_reward = 0
while True:
state = self.shared_obs_stats.normalize(state)
mu = self.model.sample_best_actions(state)
action = mu.cpu().data.squeeze().numpy()
if self.base_controller is not None:
base_action = self.base_controller.sample_best_actions(state)
action += base_action.cpu().data.squeeze().numpy()
state, reward, done, _ = self.env.step(action)
total_reward += reward
#print(state)
#print("done", done, "state", state)
if done:
state = self.env.reset()
#print(self.env.position)
#print(self.env.time)
state = Variable(torch.Tensor(state).unsqueeze(0))
ave_test_reward += total_reward / num_test
total_rewards.append(total_reward)
break
state = Variable(torch.Tensor(state).unsqueeze(0))
#print("avg test reward is", ave_test_reward)
reward_mean = statistics.mean(total_rewards)
reward_std = statistics.stdev(total_rewards)
self.test_mean.append(reward_mean)
self.test_std.append(reward_std)
self.test_list.append((reward_mean, reward_std))
#print(self.model.state_dict())
def run_test_with_noise(self, num_test=10):
state = self.env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
ave_test_reward = 0
total_rewards = []
for i in range(num_test):
total_reward = 0
while True:
state = self.shared_obs_stats.normalize(state)
mu = self.model.sample_actions(state)
eps = torch.randn(mu.size())
action = (mu + 0.0*Variable(eps))
action = action.cpu().data.squeeze().numpy()
if self.base_controller is not None:
base_action = self.base_controller.sample_best_actions(state)
action += base_action.cpu().data.squeeze().numpy()
state, reward, done, _ = self.env.step(action)
total_reward += reward
if done:
state = self.env.reset()
state = Variable(torch.Tensor(state).unsqueeze(0))
ave_test_reward += total_reward / num_test
total_rewards.append(total_reward)
break
state = Variable(torch.Tensor(state).unsqueeze(0))
#print("avg test reward is", ave_test_reward)
reward_mean = statistics.mean(total_rewards)
reward_std = statistics.stdev(total_rewards)
self.noisy_test_mean.append(reward_mean)
self.noisy_test_std.append(reward_std)
self.noisy_test_list.append((reward_mean, reward_std))
def plot_statistics(self):
ax = self.fig.add_subplot(121)
ax2 = self.fig.add_subplot(122)
low = []
high = []
index = []
noisy_low = []
noisy_high = []
for i in range(len(self.test_mean)):
low.append(self.test_mean[i] - self.test_std[i])
high.append(self.test_mean[i] + self.test_std[i])
noisy_low.append(self.noisy_test_mean[i]-self.noisy_test_std[i])
noisy_high.append(self.noisy_test_mean[i]+self.noisy_test_std[i])
index.append(i)
plt.xlabel('iterations')
plt.ylabel('average rewards')
ax.plot(self.test_mean, 'b')
ax2.plot(self.noisy_test_mean, 'g')
ax.fill_between(index, low, high, color='cyan')
ax2.fill_between(index, noisy_low, noisy_high, color='r')
#ax.plot(map(sub, test_mean, test_std))
self.fig.canvas.draw()
def collect_samples(self, num_samples, start_state=None, noise=-2.0, env_index=0, random_seed=1):
random.seed(random_seed)
torch.manual_seed(random_seed+1)
np.random.seed(random_seed+2)
env.seed(random_seed + 3)
#env.seed(random_seed+3)
#print(noise)
if start_state == None:
start_state = self.env.reset()
samples = 0
done = False
states = []
next_states = []
actions = []
rewards = []
values = []
q_values = []
real_rewards = []
log_probs = []
noise = self.base_noise * self.explore_noise.value
self.model.set_noise(noise)
state = start_state
state = Variable(torch.Tensor(state).unsqueeze(0))
total_reward = 0
#q_value = Variable(torch.zeros(1, 1))
while True:
noise = self.base_noise * self.explore_noise.value
self.model.set_noise(noise)
#print("local", self.model.p_fcs[1].bias.data[0])
#self.model.load_state_dict(torch.load(self.model_name))
signal_init = self.traffic_light.get()
score = 0
while samples < num_samples and not done:
#self.shared_obs_stats.observes(state)
states.append(state.cpu().data.numpy())
#self.shared_obs_stats.observes(state)
#print("samples", samples)
state = self.shared_obs_stats.normalize(state)
action = self.model.sample_actions(state)
log_prob = self.model.calculate_prob(state, action)
actions.append(action.cpu().data.numpy())
log_probs.append(log_prob.data.numpy())
env_action = action.data.squeeze().numpy()
if self.base_controller is not None:
base_action = self.base_controller.sample_best_actions(state)
env_action += base_action.cpu().data.squeeze().numpy()
state, reward, done, _ = self.env.step(env_action)
score += reward
if reward > self.max_reward.value:
self.max_reward.value = reward
if self.max_reward.value > 50:
self.max_reward.value = 50
#print(self.max_reward.value)
#reward *= 0.3
rewards.append(Variable(reward * torch.ones(1)).data.numpy())
real_rewards.append(Variable(reward * torch.ones(1)).data.numpy())
state = Variable(torch.Tensor(state).unsqueeze(0))
next_states.append(state.cpu().data.numpy())
next_state = self.shared_obs_stats.normalize(state)
samples += 1
state = self.shared_obs_stats.normalize(state)
v = (self.model.get_value(state))*self.max_reward.value# / self.return_obs_stats.std) + self.return_obs_stats.mean
if self.base_controller is not None:
v += self.base_controller.get_value(state)*self.max_reward.value
if done:
R = torch.zeros(1, 1)
else:
R = v.data
R = Variable(R)
for i in reversed(range(len(real_rewards))):
reward = Variable(torch.from_numpy(real_rewards[i]).unsqueeze(0))
R = self.params.gamma * R + reward#self.return_obs_stats.normalize(reward)# Variable(torch.from_numpy(real_rewards[i]))
q_values.insert(0, R.cpu().data.numpy())
#self.return_obs_stats.observes(R)
#mirror
# mirror_states = np.array(states)
# mirror_actions = np.array(actions)
# (
# negation_obs_indices,
# right_obs_indices,
# left_obs_indices,
# negation_action_indices,
# right_action_indices,
# left_action_indices,
# ) = self.env.get_mirror_indices()
# mirror_states[:, :, negation_obs_indices] *= -1
# rl = np.concatenate((right_obs_indices, left_obs_indices))
# lr = np.concatenate((left_obs_indices, right_obs_indices))
# mirror_states[:, :, rl] = mirror_states[:, :,lr]
# #mirror_actions = self.model.sample_best_actions(batch_states)
# mirror_actions[:, :, negation_action_indices] = mirror_actions[:, :, negation_action_indices] * -1
# rl = np.concatenate((right_action_indices, left_action_indices))
# lr = np.concatenate((left_action_indices, right_action_indices))
# mirror_actions[:, :, rl] = mirror_actions[:, :, lr]
# mirror_states = list(mirror_states)
# mirror_actions = list(mirror_actions)
# #self.queue.put([mirror_states, mirror_actions, np.copy(next_states), np.copy(rewards), np.copy(q_values), np.copy(log_probs)])
# value_states = states + mirror_states
# value_actions = actions + mirror_actions
# value_next_states = next_states + next_states
# value_rewards = rewards + rewards
# value_q_values = q_values + q_values
# value_log_probs = log_probs + log_probs
self.queue.put([states, actions, next_states, rewards, q_values, log_probs])
#self.value_queue.put([value_states, value_actions, value_next_states, value_rewards, value_q_values, value_log_probs])
self.counter.increment()
self.env.reset()
while self.traffic_light.get() == signal_init:
pass
start_state = self.env.reset()
state = start_state
state = Variable(torch.Tensor(state).unsqueeze(0))
total_reward = 0
samples = 0
done = False
states = []
next_states = []
actions = []
rewards = []
values = []
q_values = []
real_rewards = []
log_probs = []
#print("child", self.model.noise)
#if self.model.noise[0] > -2:
# self.model.noise *= 1.001
def collect_expert_samples(self, num_samples, filename, noise=-2.0,validation=False, difficulty=[0, 0]):
import gym
expert_env = gym.make("mocca_envs:Walker3DStepperEnv-v0")
expert_env.set_difficulty(difficulty)
start_state = expert_env.reset()
samples = 0
done = False
states = []
next_states = []
actions = []
rewards = []
q_values = []
model_expert = self.Net(self.num_inputs, self.num_outputs,self.hidden_layer)
model_expert.load_state_dict(torch.load(filename))
policy_noise = noise * np.ones(self.num_outputs)
model_expert.set_noise(policy_noise)
state = start_state
state = Variable(torch.Tensor(state).unsqueeze(0))
total_reward = 0
total_sample = 0
#q_value = Variable(torch.zeros(1, 1))
if validation:
max_sample = 300
else:
max_sample = 50000
while total_sample < max_sample:
score = 0
while samples < num_samples and not done:
state = self.shared_obs_stats.normalize(state)
states.append(state.data.numpy())
mu = model_expert.sample_best_actions(state)
actions.append(mu.data.numpy())
eps = torch.randn(mu.size())
if validation:
weight = 0.1
else:
weight = 0.1
env_action = model_expert.sample_actions(state)
env_action = env_action.data.squeeze().numpy()
state, reward, done, _ = expert_env.step(env_action)
reward = 1
rewards.append(Variable(reward * torch.ones(1)).data.numpy())
state = Variable(torch.Tensor(state).unsqueeze(0))
next_state = self.shared_obs_stats.normalize(state)
next_states.append(next_state.data.numpy())
samples += 1
#total_sample += 1
score += reward
print("expert score", score)
state = self.shared_obs_stats.normalize(state)
#print(state)
v = model_expert.get_value(state)
if done:
R = torch.zeros(1, 1)
else:
R = v.data
R = torch.ones(1, 1) * 100
R = Variable(R)
for i in reversed(range(len(rewards))):
R = self.params.gamma * R + Variable(torch.from_numpy(rewards[i]))
q_values.insert(0, R.data.numpy())
if not validation and score >= num_samples:
self.expert_trajectory.push([states, actions, next_states, rewards, q_values])
total_sample += num_samples
elif score >= num_samples:
self.validation_trajectory.push([states, actions, next_states, rewards, q_values])
start_state = expert_env.reset()
state = start_state
state = Variable(torch.Tensor(state).unsqueeze(0))
total_reward = 0
samples = 0
done = False
states = []
next_states = []
actions = []
rewards = []
q_values = []
def normalize(self):
for i in range(len(self.memory.memory)):
batch_states, _, _, _, _ = self.memory.sample_one_at_a_time()
batch_states = Variable(torch.Tensor(batch_states))
self.shared_obs_stats.observes(batch_states)
def update_critic(self, batch_size, num_epoch):
self.gpu_model.train()
optimizer = optim.Adam(self.gpu_model.parameters(), lr=10*self.lr)
#optimizer = RAdam(self.model.parameters(), lr=self.lr*10)
for k in range(num_epoch):
batch_states, batch_actions, batch_next_states, batch_rewards, batch_q_values, _ = self.memory.sample(batch_size)
batch_states = self.shared_obs_stats.normalize(Variable(torch.Tensor(batch_states))).to(device)
batch_q_values = Variable(torch.Tensor(batch_q_values)).to(device) / self.max_reward.value
v_pred = self.gpu_model.get_value(batch_states, device=device)
if self.base_controller is not None:
v_pred = self.base_controller.get_value(batch_states) + v_pred
loss_value = (v_pred - batch_q_values)**2
loss_value = 0.5*torch.mean(loss_value)
optimizer.zero_grad()
loss_value.backward(retain_graph=True)
optimizer.step()
#print(loss_value)
def update_actor(self, batch_size, num_epoch, supervised=False):
model_old = self.Net(self.num_inputs, self.num_outputs, self.hidden_layer, num_contact=self.num_contact).to(device)
model_old.load_state_dict(self.gpu_model.state_dict())
model_old.set_noise(self.model.noise)
self.gpu_model.train()
optimizer = optim.Adam(self.gpu_model.parameters(), lr=self.lr)
#optimizer = RAdam(self.model.parameters(), lr=self.lr)
for k in range(num_epoch):
batch_states, batch_actions, _, _, batch_q_values, batch_log_probs = self.memory.sample(batch_size)
#mirror
batch_mirror_states = np.copy(batch_states)
batch_states = self.shared_obs_stats.normalize(Variable(torch.Tensor(batch_states))).to(device)
batch_q_values = Variable(torch.Tensor(batch_q_values)).to(device) / self.max_reward.value
#batch_q_values = self.return_obs_stats.normalize(Variable(torch.Tensor(batch_q_values)))
batch_actions = Variable(torch.Tensor(batch_actions)).to(device)
v_pred_old = model_old.get_value(batch_states, device=device)
if self.base_controller is not None:
v_pred_old += self.base_controller.get_value(batch_states)
batch_advantages = (batch_q_values - v_pred_old)
probs = self.gpu_model.calculate_prob_gpu(batch_states, batch_actions)
probs_old = Variable(torch.Tensor(batch_log_probs)).to(device)#model_old.calculate_prob_gpu(batch_states, batch_actions)
ratio = (probs - (probs_old)).exp()
ratio = ratio.unsqueeze(1)
#print("ratio", ratio)
#print(probs, probs_old)
surr1 = ratio * batch_advantages
surr2 = ratio.clamp(1-self.params.clip, 1+self.params.clip) * batch_advantages
loss_clip = -torch.mean(torch.min(surr1, surr2))
#expert loss
if supervised is True:
if k % 1000 == 999:
batch_expert_states, batch_expert_actions, _, _, _ = self.expert_trajectory.sample(len(self.expert_trajectory.memory))
else:
batch_expert_states, batch_expert_actions, _, _, _ = self.expert_trajectory.sample(min(batch_size, len(self.expert_trajectory.memory)))
batch_expert_states = Variable(torch.Tensor(batch_expert_states)).to(device)
batch_expert_actions = Variable(torch.Tensor(batch_expert_actions)).to(device)
mu_expert = self.gpu_model.sample_best_actions(batch_expert_states)
loss_expert = torch.mean((batch_expert_actions-mu_expert)**2)
print(loss_expert)
else:
loss_expert = 0
#mirror loss
# (
# negation_obs_indices,
# right_obs_indices,
# left_obs_indices,
# negation_action_indices,
# right_action_indices,
# left_action_indices,
# ) = self.env.get_mirror_indices()
# batch_mirror_states[:, negation_obs_indices] *= -1
# rl = np.concatenate((right_obs_indices, left_obs_indices))
# lr = np.concatenate((left_obs_indices, right_obs_indices))
# batch_mirror_states[:, rl] = batch_mirror_states[:, lr]
# #with torch.no_grad():
# batch_mirror_actions = self.gpu_model.sample_best_actions(batch_states)
# if self.base_controller is not None:
# batch_mirror_actions = self.base_controller.sample_best_actions(batch_states) + batch_mirror_actions
# batch_mirror_actions_clone = batch_mirror_actions.clone()
# batch_mirror_actions_clone[:, negation_action_indices] = batch_mirror_actions[:, negation_action_indices] * -1
# rl = np.concatenate((right_action_indices, left_action_indices))
# lr = np.concatenate((left_action_indices, right_action_indices))
# batch_mirror_actions_clone[:, rl] = batch_mirror_actions[:, lr]
# batch_mirror_states = Variable(torch.Tensor(batch_mirror_states)).to(device)
# mirror_mu = self.gpu_model.sample_best_actions(batch_mirror_states)
# if self.base_controller is not None:
# mirror_mu = self.base_controller.sample_best_actions(batch_mirror_states) + mirror_mu
# mirror_loss = torch.mean((mirror_mu - batch_mirror_actions_clone)**2)
loss_w = 0#torch.mean(batch_w**2)
entropy_loss = -self.gpu_model.log_std.mean()
if supervised:
total_loss = 1.0*loss_expert
else:
total_loss = loss_clip
#print(total_loss)
#print("mirror_loss", mirror_loss)
#print(k, loss_w)
optimizer.zero_grad()
total_loss.backward(retain_graph=True)
#print(torch.nn.utils.clip_grad_norm(self.model.parameters(),1))
optimizer.step()
#print(self.shared_obs_stats.mean.data)
if self.lr > 1e-5:
self.lr *= 0.99
else:
self.lr = 1e-5
if self.weight > 10:
self.weight *= 0.99
if self.weight < 10:
self.weight = 10.0
def validation(self):
batch_states, batch_actions, batch_next_states, batch_rewards, batch_q_values = self.validation_trajectory.sample(300)
model_old = ActorCriticNet(self.num_inputs, self.num_outputs, self.hidden_layer)
model_old.load_state_dict(self.model.state_dict())
batch_states = Variable(torch.Tensor(batch_states))
batch_q_values = Variable(torch.Tensor(batch_q_values))
batch_actions = Variable(torch.Tensor(batch_actions))
mu_old, log_std_old, v_pred_old = model_old(batch_states)
loss = torch.mean((batch_actions-mu_old)**2)
if loss.data < self.current_best_validation:
self.current_best_validation = loss.data
print("validation error", self.current_best_validation)
def clear_memory(self):
self.memory.clear()
self.value_memory.clear()
def save_model(self, filename):
torch.save(self.model.state_dict(), filename)
def save_shared_obs_stas(self, filename):
with open(filename, 'wb') as output:
pickle.dump(self.shared_obs_stats, output, pickle.HIGHEST_PROTOCOL)
def save_statistics(self, filename):
statistics = [self.time_passed, self.num_samples, self.test_mean, self.test_std, self.noisy_test_mean, self.noisy_test_std]
with open(filename, 'wb') as output:
pickle.dump(statistics, output, pickle.HIGHEST_PROTOCOL)
def collect_samples_multithread(self):
#queue = Queue.Queue()
import time
self.start = time.time()
self.lr = 1e-4
self.weight = 10
num_threads = 50
self.num_samples = 0
self.time_passed = 0
score_counter = 0
total_thread = 0
max_samples = 25000
seeds = [
i * 100 for i in range(num_threads)
]
self.explore_noise = mp.Value("f", -1.5)
#self.base_noise = np.array([2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2])
self.base_noise = np.ones(self.num_outputs)
noise = self.base_noise * self.explore_noise.value
#noise[[0, 1, 5, 6]] = -3
ts = [
mp.Process(target=self.collect_samples,args=(500,), kwargs={'noise':noise, 'random_seed':seed})
for seed in seeds
]
for t in ts:
t.start()
#print("started")
self.model.set_noise(noise)
self.gpu_model.set_noise(noise)
while score_counter < 100:
if len(self.noisy_test_mean) % 100 == 1:
self.save_statistics("stats/walker2d_contact_seed16_Iter%d.stat"%(len(self.noisy_test_mean)))
#print(self.traffic_light.val.value)
#if len(self.test_mean) % 100 == 1 and self.test_mean[len(self.test_mean)-1] > 300:
# self.save_model("torch_model/multiskill/v4_cassie3dMirrorIter%d.pt"%(len(self.test_mean),))
# while len(self.memory.memory) < 50000:
# if self.counter.get() == num_threads:
# for i in range(num_threads):
# self.memory.push(self.queue.get())
# self.counter.increment()
# if len(self.memory.memory) < 50000 and self.counter.get() == num_threads + 1:
# self.counter.reset()
# self.traffic_light.switch()
self.save_model(self.model_name)
while len(self.memory.memory) < max_samples:
#print(self.counter.get())
if self.counter.get() == num_threads:
for i in range(num_threads):
#if random.randint(0, 1) == 0:
self.memory.push(self.queue.get())
#self.value_memory.push(self.value_queue.get())
total_thread += num_threads
# else:
# self.memory.push_half(self.queue.get())
self.counter.increment()
if self.counter.get() == num_threads + 1 and len(self.memory.memory) < max_samples:
self.traffic_light.switch()
self.counter.reset()
self.num_samples += len(self.memory.memory)
#while not self.best_score_queue.empty():
# self.best_trajectory.push_half(self.best_score_queue.get())
#self.normalize()
#self.model.to(device)
self.gpu_model.load_state_dict(self.model.state_dict())
self.gpu_model.to(device)
self.gpu_model.set_noise(self.model.noise)
if self.base_controller is not None:
self.base_controller.to(device)
self.update_critic(min(128, len(self.memory.memory)), (len(self.memory.memory)//3000 + 1)*64)
self.update_actor(min(128, len(self.memory.memory)), (len(self.memory.memory)//3000 + 1)*64, supervised=False)
#self.update_critic(128, 2560)
#self.update_actor(128, 2560, supervised=False)
self.gpu_model.to("cpu")
if self.base_controller is not None:
self.base_controller.to("cpu")
self.model.load_state_dict(self.gpu_model.state_dict())
self.clear_memory()
self.run_test(num_test=2)
self.run_test_with_noise(num_test=2)
print(self.num_samples, self.noisy_test_mean[-1])
if self.noisy_test_mean[-1] > 3500:
score_counter += 1
else:
score_counter = 0
if self.explore_noise.value > -1.5:
print("main", self.model.noise)
self.explore_noise.value *= 1.001
self.model.noise = self.base_noise * self.explore_noise.value
print(self.max_reward.value)
self.plot_statistics()
self.time_passed = time.time() - self.start
total_thread = 0
#print("main", self.model.p_fcs[1].bias.data[0])
self.traffic_light.switch()
self.counter.reset()
def add_env(self, env):
self.env_list.append(env)
def mkdir(base, name):
path = os.path.join(base, name)
if not os.path.exists(path):
os.makedirs(path)
return path
if __name__ == '__main__':
seed = 16
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
torch.set_num_threads(1)
import gym
env = gym.make("Walker2d-v2")
env.set_contact(1)
ppo = RL(env, [256, 256], contact=True)
#ppo.base_controller = ActorCriticNet(ppo.num_inputs, ppo.num_outputs, hidden_layer=[256, 256, 256, 256, 256], num_contact=2)
#ppo.base_controller.load_state_dict(torch.load("torch_model/StepperOct06.pt"))
ppo.model_name = "torch_model/walker2d_contact_seed16.pt"
#ppo.model.load_state_dict(torch.load("torch_model/Stepper256X5_65_10_seed8.pt"))
#ppo.env.set_difficulty([0.65, 0.65, 20, 20])
#ppo.max_reward.value = 50
#with open('torch_model/cassie3dMirror2kHz_shared_obs_stats.pkl', 'rb') as input:
# shared_obs_stats = pickle.load(input)
#ppo.normalize_data()
#ppo.save_shared_obs_stas("torch_model/cassie_terrain_obs_stats.pkl")
# ppo.collect_expert_samples(500, "torch_model/Stepper256X5_65_00_seed8.pt", noise=-2.0, difficulty = [0.65, 0])
# ppo.collect_expert_samples(500, "torch_model/Stepper256X5_75_00_seed8.pt", noise=-2.0, difficulty = [0.75, 0])
# ppo.collect_expert_samples(500, "torch_model/Stepper256X5_85_00_seed8.pt", noise=-2.0, difficulty = [0.85, 0])
# ppo.collect_expert_samples(500, "torch_model/Stepper256X5_65_10_seed8.pt", noise=-2.0, difficulty = [0.65, 10])
#ppo.save_model(ppo.model_name)
ppo.collect_samples_multithread()
#ppo.start = t.time() | [
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tests/filterbanks/stft_test.py | michelolzam/asteroid | 1 | 141575 | <reponame>michelolzam/asteroid
import pytest
import torch
from torch import testing
import numpy as np
from scipy.signal import get_window
from asteroid.filterbanks import Encoder, Decoder, STFTFB
from asteroid.filterbanks import make_enc_dec, griffin_lim, misi
from asteroid.filterbanks.stft_fb import perfect_synthesis_window
from asteroid.filterbanks import transforms
def fb_config_list():
keys = ['n_filters', 'kernel_size', 'stride']
param_list = [
[256, 256, 128], # Usual STFT, 50% overlap
[256, 256, 64], # Usual STFT, 25% overlap
[512, 32, 16], # Overcomplete STFT, 50% overlap
]
return [dict(zip(keys, values)) for values in param_list]
@pytest.mark.parametrize("fb_config", fb_config_list())
def test_stft_def(fb_config):
""" Check consistency between two calls."""
fb = STFTFB(**fb_config)
enc = Encoder(fb)
dec = Decoder(fb)
enc2, dec2 = make_enc_dec('stft', **fb_config)
testing.assert_allclose(enc.filterbank.filters, enc2.filterbank.filters)
testing.assert_allclose(dec.filterbank.filters, dec2.filterbank.filters)
@pytest.mark.parametrize("fb_config", fb_config_list())
def test_stft_windows(fb_config):
n_filters, kernel_size = fb_config["n_filters"], fb_config["kernel_size"]
win = np.hanning(kernel_size)
fb = STFTFB(**fb_config, window=win)
with pytest.raises(AssertionError):
win = np.hanning(kernel_size + 1)
fb = STFTFB(**fb_config, window=win)
@pytest.mark.parametrize("fb_config", fb_config_list())
def test_filter_shape(fb_config):
# Instantiate STFT
fb = STFTFB(**fb_config)
# Check filter shape.
assert fb.filters.shape == (fb_config['n_filters'] + 2, 1,
fb_config['kernel_size'])
@pytest.mark.parametrize("fb_config", fb_config_list())
def test_perfect_istft_default_parameters(fb_config):
""" Unit test perfect reconstruction with default values. """
kernel_size = fb_config['kernel_size']
enc, dec = make_enc_dec('stft', **fb_config)
inp_wav = torch.randn(2, 1, 32000)
out_wav = dec(enc(inp_wav))[:, :, kernel_size: -kernel_size]
inp_test = inp_wav[:, :, kernel_size: -kernel_size]
testing.assert_allclose(inp_test, out_wav)
@pytest.mark.parametrize("fb_config", fb_config_list())
@pytest.mark.parametrize("analysis_window_name", [
'blackman', 'hamming', 'hann', 'bartlett', 'boxcar'
])
def test_perfect_resyn_window(fb_config, analysis_window_name):
""" Unit test perfect reconstruction """
kernel_size = fb_config['kernel_size']
window = get_window(analysis_window_name, kernel_size)
enc = Encoder(STFTFB(**fb_config, window=window))
# Compute window for perfect resynthesis
synthesis_window = perfect_synthesis_window(enc.filterbank.window,
enc.stride)
dec = Decoder(STFTFB(**fb_config, window=synthesis_window))
inp_wav = torch.ones(1, 1, 32000)
out_wav = dec(enc(inp_wav))[:, :, kernel_size: -kernel_size]
inp_test = inp_wav[:, :, kernel_size: -kernel_size]
testing.assert_allclose(inp_test,
out_wav)
@pytest.mark.parametrize("fb_config", fb_config_list())
@pytest.mark.parametrize("feed_istft", [True, False])
@pytest.mark.parametrize("feed_angle", [True, False])
def test_griffinlim(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
wav = torch.randn(2, 1, 8000)
spec = stft(wav)
tf_mask = torch.sigmoid(torch.randn_like(spec))
masked_spec = spec * tf_mask
mag = transforms.take_mag(masked_spec, -2)
angles = None if not feed_angle else transforms.angle(masked_spec, -2)
griffin_lim(mag, stft, angles=angles, istft_dec=istft, n_iter=3)
@pytest.mark.parametrize("fb_config", fb_config_list())
@pytest.mark.parametrize("feed_istft", [True, False])
@pytest.mark.parametrize("feed_angle", [True, False])
def test_misi(fb_config, feed_istft, feed_angle):
stft = Encoder(STFTFB(**fb_config))
istft = None if not feed_istft else Decoder(STFTFB(**fb_config))
n_src = 3
# Create mixture
wav = torch.randn(2, 1, 8000)
spec = stft(wav).unsqueeze(1)
# Create n_src masks on mixture spec and apply them
shape = list(spec.shape)
shape[1] *= n_src
tf_mask = torch.sigmoid(torch.randn(*shape))
masked_specs = spec * tf_mask
# Separate mag and angle.
mag = transforms.take_mag(masked_specs, -2)
angles = None if not feed_angle else transforms.angle(masked_specs, -2)
est_wavs = misi(wav, mag, stft, angles=angles, istft_dec=istft, n_iter=2)
# We actually don't know the last dim because ISTFT(STFT()) cuts the end
assert est_wavs.shape[:-1] == (2, n_src)
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] |
WORC/plotting/plot_barchart.py | MStarmans91/WORC | 47 | 104525 | #!/usr/bin/env python
# Copyright 2016-2019 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# 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 matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import tikzplotlib
import numpy as np
import pandas as pd
from collections import Counter
import argparse
def plot_barchart(prediction, estimators=10, label_type=None, output_tex=None,
output_png=None):
'''
Make a barchart of the top X hyperparameters settings of the ranked
estimators in all cross validation iterations.
Parameters
----------
prediction: filepath, mandatory
Path pointing to the .hdf5 file which was is the output of the
trainclassifier function.
estimators: integer, default 10
Number of hyperparameter settings/estimators used in each cross
validation. The settings are ranked, so when supplying e.g. 10,
the best 10 settings in each cross validation setting will be used.
label_type: string, default None
The name of the label predicted by the estimator. If None,
the first label from the prediction file will be used.
output_tex: filepath, optional
If given, the barchart will be written to this tex file.
output_png: filepath, optional
If given, the barchart will be written to this png file.
Returns
----------
fig: matplotlib figure
The figure in which the barchart is plotted.
'''
# Load input prediction
prediction = pd.read_hdf(prediction)
# Determine for which label we extract the estimator
keys = prediction.keys()
if label_type is None:
label_type = keys[0]
try:
prediction = prediction[label_type]
except KeyError:
# Multiclass reroute
prediction = prediction[keys[0]]
# Extract the parameter settings:
parameters = dict()
for n_crossval, est in enumerate(prediction.classifiers):
for n_setting in range(0, estimators):
# Extract parameter settings of nth estimator
parameters_all = est.cv_results_['params'][n_setting]
# Stack settings in parameters dictionary
for k in parameters_all.keys():
if k not in parameters.keys():
parameters[k] = list()
parameters[k].append(parameters_all[k])
# Count for every parameter how many times a setting occurs
counts = count_parameters(parameters)
# Normalize the values
normalization_factor = len(prediction.classifiers) * estimators
# Make the barplot
fig = plot_bars(counts, normalization_factor)
# Try making it fullscreen
# Save the output
if output_tex is not None:
print(f'Saving barchart to {output_tex}.')
tikzplotlib.save(output_tex)
if output_png is not None:
print(f'Saving barchart to {output_png}.')
fig.savefig(output_png, bbox_inches='tight', pad_inches=0, dpi=500)
def plot_bars(params, normalization_factor=None, figwidth=40, fontsize=30,
spacing=2):
# Fixing random state for reproducibility
np.random.seed(19680801)
# Count how often feature groups are used
ntimes_groups = list()
groups = list()
for key in params.keys():
# Check if parameter is a boolean
if 'True' in params[key].keys() or 'False' in params[key].keys():
if 'True' in params[key].keys():
ntimes_groups.append(params[key]['True'])
groups.append(key)
else:
# Only False
ntimes_groups.append(0)
groups.append(key)
# Normalize the values in order to not make figure to large
if normalization_factor is None:
normalization_factor = max(ntimes_groups)
normalization_factor = float(normalization_factor) # Needed for percentages
ntimes_groups = [x / normalization_factor for x in ntimes_groups]
# Create the figure for the barchart
plt.rcdefaults()
fig, ax = plt.subplots()
fig.set_figwidth(figwidth)
fig.set_figheight(figwidth)
ax.set_xlim(0, 1)
# Determine positions of all the labels
y_pos = np.arange(len(groups) * spacing)
ntimes_groups_plot = list()
groups_plot = list()
num = 0
for i in range(len(groups) * spacing):
if i % spacing == 0:
ntimes_groups_plot.append(ntimes_groups[num])
groups_plot.append(groups[num])
num += 1
else:
# empty entry to fill up spacing
ntimes_groups_plot.append(0.0)
groups_plot.append('')
# Normal features
colors = ['steelblue', 'lightskyblue']
ax.barh(y_pos, ntimes_groups_plot, align='center',
color=colors[0], ecolor='black')
ax.set_yticks(y_pos)
ax.set_yticklabels(groups_plot)
ax.tick_params(axis='both', labelsize=fontsize)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Percentage', fontsize=fontsize)
return fig
def count_parameters(parameters):
# Count for every parameter how many times a setting occurs
output = dict()
for setting, values in parameters.items():
output[setting] = dict()
try:
c = Counter(values)
for k, v in zip(c.keys(), c.values()):
output[setting][k] = v
except TypeError:
# Not possible to count parameters, remove
del output[setting]
return output
def paracheck(parameters):
# NOTE: Deprecated
output = dict()
# print parameters
f = parameters['semantic_features']
total = float(len(f))
count_semantic = sum([i == 'True' for i in f])
ratio_semantic = count_semantic/total
print("Semantic: " + str(ratio_semantic))
output['semantic_features'] = ratio_semantic
f = parameters['patient_features']
count_patient = sum([i == 'True' for i in f])
ratio_patient = count_patient/total
print("patient: " + str(ratio_patient))
output['patient_features'] = ratio_patient
f = parameters['orientation_features']
count_orientation = sum([i == 'True' for i in f])
ratio_orientation = count_orientation/total
print("orientation: " + str(ratio_orientation))
output['orientation_features'] = ratio_orientation
f = parameters['histogram_features']
count_histogram = sum([i == 'True' for i in f])
ratio_histogram = count_histogram/total
print("histogram: " + str(ratio_histogram))
output['histogram_features'] = ratio_histogram
f = parameters['shape_features']
count_shape = sum([i == 'True' for i in f])
ratio_shape = count_shape/total
print("shape: " + str(ratio_shape))
output['shape_features'] = ratio_shape
if 'coliage_features' in parameters.keys():
f = parameters['coliage_features']
count_coliage = sum([i == 'True' for i in f])
ratio_coliage = count_coliage/total
print("coliage: " + str(ratio_coliage))
output['coliage_features'] = ratio_coliage
if 'phase_features' in parameters.keys():
f = parameters['phase_features']
count_phase = sum([i == 'True' for i in f])
ratio_phase = count_phase/total
print("phase: " + str(ratio_phase))
output['phase_features'] = ratio_phase
if 'vessel_features' in parameters.keys():
f = parameters['vessel_features']
count_vessel = sum([i == 'True' for i in f])
ratio_vessel = count_vessel/total
print("vessel: " + str(ratio_vessel))
output['vessel_features'] = ratio_vessel
if 'log_features' in parameters.keys():
f = parameters['log_features']
count_log = sum([i == 'True' for i in f])
ratio_log = count_log/total
print("log: " + str(ratio_log))
output['log_features'] = ratio_log
f = parameters['texture_features']
count_texture_all = sum([i == 'True' for i in f])
ratio_texture_all = count_texture_all/total
print("texture_all: " + str(ratio_texture_all))
output['texture_all_features'] = ratio_texture_all
count_texture_no = sum([i == 'False' for i in f])
ratio_texture_no = count_texture_no/total
print("texture_no: " + str(ratio_texture_no))
output['texture_no_features'] = ratio_texture_no
count_texture_Gabor = sum([i == 'Gabor' for i in f])
ratio_texture_Gabor = count_texture_Gabor/total
print("texture_Gabor: " + str(ratio_texture_Gabor))
output['texture_Gabor_features'] = ratio_texture_Gabor
count_texture_LBP = sum([i == 'LBP' for i in f])
ratio_texture_LBP = count_texture_LBP/total
print("texture_LBP: " + str(ratio_texture_LBP))
output['texture_LBP_features'] = ratio_texture_LBP
count_texture_GLCM = sum([i == 'GLCM' for i in f])
ratio_texture_GLCM = count_texture_GLCM/total
print("texture_GLCM: " + str(ratio_texture_GLCM))
output['texture_GLCM_features'] = ratio_texture_GLCM
count_texture_GLRLM = sum([i == 'GLRLM' for i in f])
ratio_texture_GLRLM = count_texture_GLRLM/total
print("texture_GLRLM: " + str(ratio_texture_GLRLM))
output['texture_GLRLM_features'] = ratio_texture_GLRLM
count_texture_GLSZM = sum([i == 'GLSZM' for i in f])
ratio_texture_GLSZM = count_texture_GLSZM/total
print("texture_GLSZM: " + str(ratio_texture_GLSZM))
output['texture_GLSZM_features'] = ratio_texture_GLSZM
count_texture_NGTDM = sum([i == 'NGTDM' for i in f])
ratio_texture_NGTDM = count_texture_NGTDM/total
print("texture_NGTDM: " + str(ratio_texture_NGTDM))
output['texture_NGTDM_features'] = ratio_texture_NGTDM
if 'degree' in parameters.keys():
f = parameters['degree']
print("Polynomial Degree: " + str(np.mean(f)))
output['polynomial_degree'] = np.mean(f)
return output
def main():
parser = argparse.ArgumentParser(description='Plot a Barchart.')
parser.add_argument('-prediction', '--prediction', metavar='prediction',
nargs='+', dest='prediction', type=str, required=True,
help='Prediction file (HDF)')
parser.add_argument('-estimators', '--estimators', metavar='estimator',
nargs='+', dest='estimators', type=str, required=False,
help='Number of estimators to evaluate in each cross validation.')
parser.add_argument('-label_type', '--label_type', metavar='label_type',
nargs='+', dest='label_type', type=str, required=False,
help='Key of the label which was predicted.')
parser.add_argument('-output_tex', '--output_tex', metavar='output_tex',
nargs='+', dest='output_tex', type=str, required=True,
help='Output file path (.tex)')
parser.add_argument('-output_png', '--output_png', metavar='output_png',
nargs='+', dest='output_png', type=str, required=True,
help='Output file path (.png)')
args = parser.parse_args()
# Convert the inputs to the correct format
if type(args.prediction) is list:
args.prediction = ''.join(args.prediction)
if type(args.output) is list:
args.output = ''.join(args.output)
if type(args.estimators) is list:
args.estimators = int(args.estimators[0])
if type(args.label_type) is list:
args.label_type = ''.join(args.label_type)
plot_barchart(prediction=args.prediction,
estimators=args.estimators,
label_type=args.label_type,
output_tex=args.output_tex,
output_png=args.output_png)
if __name__ == '__main__':
main()
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sshpipe/sshpipe/bin/sshpipe_socket_handler.py | Acrisel/sshpipe | 0 | 117576 | #!/usr/bin/env python3
'''
Created on Oct 18, 2017
@author: arnon
'''
from sshpipe import SSHPipeHandler
import logging
from logging.handlers import SocketHandler
import pickle
import struct
mlogger = logging.getLogger(__file__)
class SSHPipeSocketHandler(SSHPipeHandler, SocketHandler):
''' SSHPipeSocketHandler modeled over logging.handlers.SocketHandler
'''
def __init__(self, port, host='172.16.58.3', *args, **kwargs):
SSHPipeHandler.__init__(self, *args, **kwargs)
SocketHandler.__init__(self, host, port)
if port is None:
self.address = host
else:
self.address = (host, port)
self.mlogger.debug("Server socket address: {}.".format(self.address))
def makePickle(self, record):
"""
Pickles the record in binary format with a length prefix, and
returns it ready for transmission across the socket.
"""
if isinstance(record, str):
s = pickle.dumps(record)
else: # object
d = dict(record.__dict__)
s = pickle.dumps(d, 1)
slen = struct.pack(">L", len(s))
return slen + s
def atstart(self, receieved):
# file = "{}{}".format(__file__, ".remote.log")
# self.mlogger.debug("Opening file: {}.".format(file))
# self.file = open(file, 'w')
# call create socket to prevent it being called at first handle.
self.createSocket()
def atexit(self, received):
# if self.file is not None:
# self.file.close()
SSHPipeHandler.atexit(self, received)
SocketHandler.close(self)
self.mlogger.debug("Closed handlers.")
def handle(self, received):
"""
Send a pickled string to the socket.
This function allows for partial sends which can happen when the
network is busy.
"""
# self.file.write(str(received))
self.emit(received)
self.mlogger.debug("Emitted record to socket: {}.".format(received))
def cmdargs():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--host', type=str, default='127.0.0.1', required=False, help='')
parser.add_argument('--port', type=int, required=False, help='')
parser.add_argument("--id", type=str, required=False, default=1, dest='handler_id')
args = parser.parse_args()
return vars(args)
if __name__ == '__main__':
args = cmdargs()
# TODO: add command line options
client = SSHPipeSocketHandler(**args)
client.service_loop()
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3132,
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29918,
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580,
13,
2
] |
app/schemas/card_serealize.py | Suspir0n/Care-API | 1 | 90834 | from ..settings.config import ma
class CardSchema(ma.Schema):
class Meta:
fields = ('uid', 'active', 'deleted', 'createAt', 'updateAt', 'num_card', 'name', 'date_valid', 'cod_security')
card_schema = CardSchema()
cards_schema = CardSchema(many=True) | [
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asq/test/test_to_set.py | sixty-north/asq | 175 | 89007 | import unittest
from asq.queryables import Queryable
__author__ = "<NAME>"
class TestToSet(unittest.TestCase):
def test_to_set(self):
a = [1, 2, 4, 8, 16, 32]
b = Queryable(a).to_set()
c = set([1, 2, 4, 8, 16, 32])
self.assertEqual(b, c)
def test_to_set_closed(self):
a = [1, 2, 4, 8, 16, 32]
b = Queryable(a)
b.close()
self.assertRaises(ValueError, lambda: b.to_set())
def test_to_set_duplicates(self):
a = [1, 2, 4, 8, 8, 16, 32]
b = Queryable(a)
self.assertRaises(ValueError, lambda: b.to_set())
| [
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axonius_api_client/cli/grp_system/__init__.py | rwils83/axonius_api_client | 0 | 165934 | # -*- coding: utf-8 -*-
"""Command line interface for Axonius API Client."""
import click
from ..context import AliasedGroup
from . import (
grp_central_core,
grp_discover,
grp_meta,
grp_nodes,
grp_roles,
grp_settings,
grp_users,
)
@click.group(cls=AliasedGroup)
def system():
"""Group: System control commands."""
system.add_command(grp_meta.meta)
system.add_command(grp_nodes.instances)
system.add_command(grp_central_core.central_core)
system.add_command(grp_roles.roles)
system.add_command(grp_settings.settings_lifecycle)
system.add_command(grp_settings.settings_gui)
system.add_command(grp_settings.settings_core)
system.add_command(grp_users.users)
system.add_command(grp_discover.discover)
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] |
examples/widget_demo.py | PBLab/magicgui | 0 | 137704 | """Widget demonstration of magicgui."""
import datetime
from enum import Enum
from pathlib import Path
from magicgui import magicgui
class Medium(Enum):
"""Enum for various media and their refractive indices."""
Glass = 1.520
Oil = 1.515
Water = 1.333
Air = 1.0003
@magicgui(
main_window=True,
call_button="Calculate",
layout="vertical",
result_widget=True,
slider_float={"widget_type": "FloatSlider", "max": 100},
slider_int={"widget_type": "Slider", "readout": False},
radio_option={
"widget_type": "RadioButtons",
"orientation": "horizontal",
"choices": [("first option", 1), ("second option", 2)],
},
filename={"label": "Pick a file:"},
)
def widget_demo(
boolean=True,
integer=1,
spin_float=3.14159,
slider_float=43.5,
slider_int=550,
string="Text goes here",
dropdown=Medium.Glass,
radio_option=2,
date=datetime.date(1999, 12, 31),
time=datetime.time(1, 30, 20),
datetime=datetime.datetime.now(),
filename=Path.home(),
):
"""We can use numpy docstrings to provide tooltips.
Parameters
----------
boolean : bool, optional
A checkbox for booleans, by default True
integer : int, optional
Some integer, by default 1
spin_float : float, optional
This one is a float, by default "pi"
slider_float : float, optional
Hey look! I'm a slider, by default 43.5
slider_int : float, optional
I only take integers, and I've hidden my readout, by default 550
string : str, optional
We'll use this string carefully, by default "Text goes here"
dropdown : Enum, optional
Pick a medium, by default Medium.Glass
radio_option : int
A set of radio buttons.
date : datetime.date, optional
Your birthday, by default datetime.date(1999, 12, 31)
time : datetime.time, optional
Some time, by default datetime.time(1, 30, 20)
datetime : datetime.datetime, optional
A very specific time and date, by default ``datetime.datetime.now()``
filename : str, optional
Pick a path, by default Path.home()
"""
return locals().values()
widget_demo.changed.connect(lambda event: print(widget_demo))
widget_demo.show(run=True)
| [
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] |
python/lib/path/dijkstra_dense.py | KATO-Hiro/atcoder-1 | 0 | 1600733 | """<https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm>"""
import sys
import numba as nb
import numpy as np
input = sys.stdin.readline
# Dijkstra algorithm without priority queue (this is slow for sparse graphs)
@nb.njit("i8[:](i8,i8[:,:],i8,i8)", cache=True)
def dijkstra(V, G, s, INF):
# Shortest path from vertex s
dist = np.full(shape=V, fill_value=INF, dtype=np.int64)
dist[s] = 0
unvisited = [True] * V
while len(unvisited) != 0:
min_dist = INF
u = -1
for v in range(V):
if unvisited[v] and dist[v] < min_dist:
min_dist = dist[v]
u = v
# when planning a complete traversal; occurs when there is no connection
# between the initial node and remaining unvisited nodes
if min_dist == INF:
break
unvisited[u] = False
for v in range(V):
if unvisited[v] and G[u][v] != INF:
alt = dist[u] + G[u][v]
if alt < dist[v]:
dist[v] = alt
return dist
def main():
N = int(input())
INF = 1 << 30
G = np.full(shape=(N, N), fill_value=INF, dtype=np.int64)
for _ in range(N):
u, k, *vc = map(int, input().split())
for i in range(k):
v = vc[2 * i]
c = vc[2 * i + 1]
G[u][v] = c
for s in range(N):
print(s, dijkstra(N, G, s, INF))
if __name__ == "__main__":
main()
"""
<https://onlinejudge.u-aizu.ac.jp/courses/lesson/1/ALDS1/12/ALDS1_12_B>
Example for input
5
0 3 2 3 3 1 1 2
1 2 0 2 3 4
2 3 0 3 3 1 4 1
3 4 2 1 0 1 1 4 4 3
4 2 2 1 3 3
0 [0 2 2 1 3]
1 [2 0 4 3 5]
2 [2 4 0 1 1]
3 [1 3 1 0 2]
4 [3 5 1 2 0]
"""
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13,
2
] |
exercicios/Lista1/Q27.py | AlexandrePeBrito/CursoUdemyPython | 0 | 1601996 | <gh_stars>0
#Leia um valor de area em hectares e apresente-o convertido em metros quadrados.
#a formula de conversao eh: M=H*10000
#sendo M a area em metros quadrado e H area em hectaries
h=float(input("Informe a area em hectares: "))
m=h*10000
print(f"A area convertida em metros quadrados eh {round(m,2)}") | [
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lyrics_crawler_single/lyric_spider.py | Tauranis/lyrics-crawler | 0 | 86147 | <gh_stars>0
import scrapy
import logging
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
try:
from lyrics_crawler_single.constants import (
OUTPUT_FILE,
)
except:
from constants import OUTPUT_FILE
class LyricsSpider(scrapy.Spider):
name = "lyrics"
start_urls = open(OUTPUT_FILE).readlines()
def parse(self, response):
for lyric in response.css("article"):
lyric_title = lyric.css("h1::text").get()
lyric_author = lyric.css("span::text").get()
# logger.info(f"lyric_title {lyric_title}")
# logger.info(f"lyric_author {lyric_author}")
lyric_text = ""
for paragraph in lyric.css("p::text"):
lyric_text += " " + paragraph.get().replace("<br>", " ")
yield {"author": lyric_author, "title": lyric_title, "text": lyric_text}
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2
] |
silx/gui/plot/actions/control.py | linupi/silx | 2 | 1608287 | <gh_stars>1-10
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2004-2019 European Synchrotron Radiation Facility
#
# 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.
#
# ###########################################################################*/
"""
:mod:`silx.gui.plot.actions.control` provides a set of QAction relative to control
of a :class:`.PlotWidget`.
The following QAction are available:
- :class:`ColormapAction`
- :class:`CrosshairAction`
- :class:`CurveStyleAction`
- :class:`GridAction`
- :class:`KeepAspectRatioAction`
- :class:`PanWithArrowKeysAction`
- :class:`ResetZoomAction`
- :class:`ShowAxisAction`
- :class:`XAxisLogarithmicAction`
- :class:`XAxisAutoScaleAction`
- :class:`YAxisInvertedAction`
- :class:`YAxisLogarithmicAction`
- :class:`YAxisAutoScaleAction`
- :class:`ZoomBackAction`
- :class:`ZoomInAction`
- :class:`ZoomOutAction`
"""
from __future__ import division
__authors__ = ["<NAME>", "<NAME>", "<NAME>"]
__license__ = "MIT"
__date__ = "24/04/2018"
from . import PlotAction
import logging
from silx.gui.plot import items
from silx.gui.plot._utils import applyZoomToPlot as _applyZoomToPlot
from silx.gui import qt
from silx.gui import icons
_logger = logging.getLogger(__name__)
class ResetZoomAction(PlotAction):
"""QAction controlling reset zoom on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(ResetZoomAction, self).__init__(
plot, icon='zoom-original', text='Reset Zoom',
tooltip='Auto-scale the graph',
triggered=self._actionTriggered,
checkable=False, parent=parent)
self._autoscaleChanged(True)
plot.getXAxis().sigAutoScaleChanged.connect(self._autoscaleChanged)
plot.getYAxis().sigAutoScaleChanged.connect(self._autoscaleChanged)
def _autoscaleChanged(self, enabled):
xAxis = self.plot.getXAxis()
yAxis = self.plot.getYAxis()
self.setEnabled(xAxis.isAutoScale() or yAxis.isAutoScale())
if xAxis.isAutoScale() and yAxis.isAutoScale():
tooltip = 'Auto-scale the graph'
elif xAxis.isAutoScale(): # And not Y axis
tooltip = 'Auto-scale the x-axis of the graph only'
elif yAxis.isAutoScale(): # And not X axis
tooltip = 'Auto-scale the y-axis of the graph only'
else: # no axis in autoscale
tooltip = 'Auto-scale the graph'
self.setToolTip(tooltip)
def _actionTriggered(self, checked=False):
self.plot.resetZoom()
class ZoomBackAction(PlotAction):
"""QAction performing a zoom-back in :class:`.PlotWidget` limits history.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(ZoomBackAction, self).__init__(
plot, icon='zoom-back', text='Zoom Back',
tooltip='Zoom back the plot',
triggered=self._actionTriggered,
checkable=False, parent=parent)
self.setShortcutContext(qt.Qt.WidgetShortcut)
def _actionTriggered(self, checked=False):
self.plot.getLimitsHistory().pop()
class ZoomInAction(PlotAction):
"""QAction performing a zoom-in on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(ZoomInAction, self).__init__(
plot, icon='zoom-in', text='Zoom In',
tooltip='Zoom in the plot',
triggered=self._actionTriggered,
checkable=False, parent=parent)
self.setShortcut(qt.QKeySequence.ZoomIn)
self.setShortcutContext(qt.Qt.WidgetShortcut)
def _actionTriggered(self, checked=False):
_applyZoomToPlot(self.plot, 1.1)
class ZoomOutAction(PlotAction):
"""QAction performing a zoom-out on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(ZoomOutAction, self).__init__(
plot, icon='zoom-out', text='Zoom Out',
tooltip='Zoom out the plot',
triggered=self._actionTriggered,
checkable=False, parent=parent)
self.setShortcut(qt.QKeySequence.ZoomOut)
self.setShortcutContext(qt.Qt.WidgetShortcut)
def _actionTriggered(self, checked=False):
_applyZoomToPlot(self.plot, 1. / 1.1)
class XAxisAutoScaleAction(PlotAction):
"""QAction controlling X axis autoscale on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(XAxisAutoScaleAction, self).__init__(
plot, icon='plot-xauto', text='X Autoscale',
tooltip='Enable x-axis auto-scale when checked.\n'
'If unchecked, x-axis does not change when reseting zoom.',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.setChecked(plot.getXAxis().isAutoScale())
plot.getXAxis().sigAutoScaleChanged.connect(self.setChecked)
def _actionTriggered(self, checked=False):
self.plot.getXAxis().setAutoScale(checked)
if checked:
self.plot.resetZoom()
class YAxisAutoScaleAction(PlotAction):
"""QAction controlling Y axis autoscale on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(YAxisAutoScaleAction, self).__init__(
plot, icon='plot-yauto', text='Y Autoscale',
tooltip='Enable y-axis auto-scale when checked.\n'
'If unchecked, y-axis does not change when reseting zoom.',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.setChecked(plot.getYAxis().isAutoScale())
plot.getYAxis().sigAutoScaleChanged.connect(self.setChecked)
def _actionTriggered(self, checked=False):
self.plot.getYAxis().setAutoScale(checked)
if checked:
self.plot.resetZoom()
class XAxisLogarithmicAction(PlotAction):
"""QAction controlling X axis log scale on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(XAxisLogarithmicAction, self).__init__(
plot, icon='plot-xlog', text='X Log. scale',
tooltip='Logarithmic x-axis when checked',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.axis = plot.getXAxis()
self.setChecked(self.axis.getScale() == self.axis.LOGARITHMIC)
self.axis.sigScaleChanged.connect(self._setCheckedIfLogScale)
def _setCheckedIfLogScale(self, scale):
self.setChecked(scale == self.axis.LOGARITHMIC)
def _actionTriggered(self, checked=False):
scale = self.axis.LOGARITHMIC if checked else self.axis.LINEAR
self.axis.setScale(scale)
class YAxisLogarithmicAction(PlotAction):
"""QAction controlling Y axis log scale on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(YAxisLogarithmicAction, self).__init__(
plot, icon='plot-ylog', text='Y Log. scale',
tooltip='Logarithmic y-axis when checked',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.axis = plot.getYAxis()
self.setChecked(self.axis.getScale() == self.axis.LOGARITHMIC)
self.axis.sigScaleChanged.connect(self._setCheckedIfLogScale)
def _setCheckedIfLogScale(self, scale):
self.setChecked(scale == self.axis.LOGARITHMIC)
def _actionTriggered(self, checked=False):
scale = self.axis.LOGARITHMIC if checked else self.axis.LINEAR
self.axis.setScale(scale)
class GridAction(PlotAction):
"""QAction controlling grid mode on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param str gridMode: The grid mode to use in 'both', 'major'.
See :meth:`.PlotWidget.setGraphGrid`
:param parent: See :class:`QAction`
"""
def __init__(self, plot, gridMode='both', parent=None):
assert gridMode in ('both', 'major')
self._gridMode = gridMode
super(GridAction, self).__init__(
plot, icon='plot-grid', text='Grid',
tooltip='Toggle grid (on/off)',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.setChecked(plot.getGraphGrid() is not None)
plot.sigSetGraphGrid.connect(self._gridChanged)
def _gridChanged(self, which):
"""Slot listening for PlotWidget grid mode change."""
self.setChecked(which != 'None')
def _actionTriggered(self, checked=False):
self.plot.setGraphGrid(self._gridMode if checked else None)
class CurveStyleAction(PlotAction):
"""QAction controlling curve style on a :class:`.PlotWidget`.
It changes the default line and markers style which updates all
curves on the plot.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(CurveStyleAction, self).__init__(
plot, icon='plot-toggle-points', text='Curve style',
tooltip='Change curve line and markers style',
triggered=self._actionTriggered,
checkable=False, parent=parent)
def _actionTriggered(self, checked=False):
currentState = (self.plot.isDefaultPlotLines(),
self.plot.isDefaultPlotPoints())
if currentState == (False, False):
newState = True, False
else:
# line only, line and symbol, symbol only
states = (True, False), (True, True), (False, True)
newState = states[(states.index(currentState) + 1) % 3]
self.plot.setDefaultPlotLines(newState[0])
self.plot.setDefaultPlotPoints(newState[1])
class ColormapAction(PlotAction):
"""QAction opening a ColormapDialog to update the colormap.
Both the active image colormap and the default colormap are updated.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
self._dialog = None # To store an instance of ColormapDialog
super(ColormapAction, self).__init__(
plot, icon='colormap', text='Colormap',
tooltip="Change colormap",
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.plot.sigActiveImageChanged.connect(self._updateColormap)
self.plot.sigActiveScatterChanged.connect(self._updateColormap)
def setColorDialog(self, colorDialog):
"""Set a specific color dialog instead of using the default dialog."""
assert(colorDialog is not None)
assert(self._dialog is None)
self._dialog = colorDialog
self._dialog.visibleChanged.connect(self._dialogVisibleChanged)
self.setChecked(self._dialog.isVisible())
@staticmethod
def _createDialog(parent):
"""Create the dialog if not already existing
:parent QWidget parent: Parent of the new colormap
:rtype: ColormapDialog
"""
from silx.gui.dialog.ColormapDialog import ColormapDialog
dialog = ColormapDialog(parent=parent)
dialog.setModal(False)
return dialog
def _actionTriggered(self, checked=False):
"""Create a cmap dialog and update active image and default cmap."""
if self._dialog is None:
self._dialog = self._createDialog(self.plot)
self._dialog.visibleChanged.connect(self._dialogVisibleChanged)
# Run the dialog listening to colormap change
if checked is True:
self._updateColormap()
self._dialog.show()
else:
self._dialog.hide()
def _dialogVisibleChanged(self, isVisible):
self.setChecked(isVisible)
def _updateColormap(self):
if self._dialog is None:
return
image = self.plot.getActiveImage()
if isinstance(image, items.ImageComplexData):
# Specific init for complex images
colormap = image.getColormap()
mode = image.getComplexMode()
if mode in (items.ImageComplexData.ComplexMode.AMPLITUDE_PHASE,
items.ImageComplexData.ComplexMode.LOG10_AMPLITUDE_PHASE):
data = image.getData(
copy=False, mode=items.ImageComplexData.ComplexMode.PHASE)
else:
data = image.getData(copy=False)
# Set histogram and range if any
self._dialog.setData(data)
elif isinstance(image, items.ColormapMixIn):
# Set dialog from active image
colormap = image.getColormap()
# Set histogram and range if any
self._dialog.setItem(image)
else:
# No active image or active image is RGBA,
# Check for active scatter plot
scatter = self.plot._getActiveItem(kind='scatter')
if scatter is not None:
colormap = scatter.getColormap()
self._dialog.setItem(scatter)
else:
# No active data image nor scatter,
# set dialog from default info
colormap = self.plot.getDefaultColormap()
# Reset histogram and range if any
self._dialog.setData(None)
self._dialog.setColormap(colormap)
class ColorBarAction(PlotAction):
"""QAction opening the ColorBarWidget of the specified plot.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
self._dialog = None # To store an instance of ColorBar
super(ColorBarAction, self).__init__(
plot, icon='colorbar', text='Colorbar',
tooltip="Show/Hide the colorbar",
triggered=self._actionTriggered,
checkable=True, parent=parent)
colorBarWidget = self.plot.getColorBarWidget()
old = self.blockSignals(True)
self.setChecked(colorBarWidget.isVisibleTo(self.plot))
self.blockSignals(old)
colorBarWidget.sigVisibleChanged.connect(self._widgetVisibleChanged)
def _widgetVisibleChanged(self, isVisible):
"""Callback when the colorbar `visible` property change."""
if self.isChecked() == isVisible:
return
self.setChecked(isVisible)
def _actionTriggered(self, checked=False):
"""Create a cmap dialog and update active image and default cmap."""
colorBarWidget = self.plot.getColorBarWidget()
if not colorBarWidget.isHidden() == checked:
return
self.plot.getColorBarWidget().setVisible(checked)
class KeepAspectRatioAction(PlotAction):
"""QAction controlling aspect ratio on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
# Uses two images for checked/unchecked states
self._states = {
False: (icons.getQIcon('shape-circle-solid'),
"Keep data aspect ratio"),
True: (icons.getQIcon('shape-ellipse-solid'),
"Do no keep data aspect ratio")
}
icon, tooltip = self._states[plot.isKeepDataAspectRatio()]
super(KeepAspectRatioAction, self).__init__(
plot,
icon=icon,
text='Toggle keep aspect ratio',
tooltip=tooltip,
triggered=self._actionTriggered,
checkable=False,
parent=parent)
plot.sigSetKeepDataAspectRatio.connect(
self._keepDataAspectRatioChanged)
def _keepDataAspectRatioChanged(self, aspectRatio):
"""Handle Plot set keep aspect ratio signal"""
icon, tooltip = self._states[aspectRatio]
self.setIcon(icon)
self.setToolTip(tooltip)
def _actionTriggered(self, checked=False):
# This will trigger _keepDataAspectRatioChanged
self.plot.setKeepDataAspectRatio(not self.plot.isKeepDataAspectRatio())
class YAxisInvertedAction(PlotAction):
"""QAction controlling Y orientation on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
# Uses two images for checked/unchecked states
self._states = {
False: (icons.getQIcon('plot-ydown'),
"Orient Y axis downward"),
True: (icons.getQIcon('plot-yup'),
"Orient Y axis upward"),
}
icon, tooltip = self._states[plot.getYAxis().isInverted()]
super(YAxisInvertedAction, self).__init__(
plot,
icon=icon,
text='Invert Y Axis',
tooltip=tooltip,
triggered=self._actionTriggered,
checkable=False,
parent=parent)
plot.getYAxis().sigInvertedChanged.connect(self._yAxisInvertedChanged)
def _yAxisInvertedChanged(self, inverted):
"""Handle Plot set y axis inverted signal"""
icon, tooltip = self._states[inverted]
self.setIcon(icon)
self.setToolTip(tooltip)
def _actionTriggered(self, checked=False):
# This will trigger _yAxisInvertedChanged
yAxis = self.plot.getYAxis()
yAxis.setInverted(not yAxis.isInverted())
class CrosshairAction(PlotAction):
"""QAction toggling crosshair cursor on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param str color: Color to use to draw the crosshair
:param int linewidth: Width of the crosshair cursor
:param str linestyle: Style of line. See :meth:`.Plot.setGraphCursor`
:param parent: See :class:`QAction`
"""
def __init__(self, plot, color='black', linewidth=1, linestyle='-',
parent=None):
self.color = color
"""Color used to draw the crosshair (str)."""
self.linewidth = linewidth
"""Width of the crosshair cursor (int)."""
self.linestyle = linestyle
"""Style of line of the cursor (str)."""
super(CrosshairAction, self).__init__(
plot, icon='crosshair', text='Crosshair Cursor',
tooltip='Enable crosshair cursor when checked',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.setChecked(plot.getGraphCursor() is not None)
plot.sigSetGraphCursor.connect(self.setChecked)
def _actionTriggered(self, checked=False):
self.plot.setGraphCursor(checked,
color=self.color,
linestyle=self.linestyle,
linewidth=self.linewidth)
class PanWithArrowKeysAction(PlotAction):
"""QAction toggling pan with arrow keys on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
super(PanWithArrowKeysAction, self).__init__(
plot, icon='arrow-keys', text='Pan with arrow keys',
tooltip='Enable pan with arrow keys when checked',
triggered=self._actionTriggered,
checkable=True, parent=parent)
self.setChecked(plot.isPanWithArrowKeys())
plot.sigSetPanWithArrowKeys.connect(self.setChecked)
def _actionTriggered(self, checked=False):
self.plot.setPanWithArrowKeys(checked)
class ShowAxisAction(PlotAction):
"""QAction controlling axis visibility on a :class:`.PlotWidget`.
:param plot: :class:`.PlotWidget` instance on which to operate
:param parent: See :class:`QAction`
"""
def __init__(self, plot, parent=None):
tooltip = 'Show plot axis when checked, otherwise hide them'
PlotAction.__init__(self,
plot,
icon='axis',
text='show axis',
tooltip=tooltip,
triggered=self._actionTriggered,
checkable=True,
parent=parent)
self.setChecked(self.plot._backend.isAxesDisplayed())
plot._sigAxesVisibilityChanged.connect(self.setChecked)
def _actionTriggered(self, checked=False):
self.plot.setAxesDisplayed(checked)
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15945,
29908,
13,
13,
3166,
4770,
29888,
9130,
1649,
1053,
8542,
13,
13,
1649,
5150,
943,
1649,
353,
6796,
29966,
5813,
28341,
9872,
5813,
28341,
9872,
5813,
29958,
3108,
13,
1649,
506,
1947,
1649,
353,
376,
26349,
29908,
13,
1649,
1256,
1649,
353,
376,
29906,
29946,
29914,
29900,
29946,
29914,
29906,
29900,
29896,
29947,
29908,
13,
13,
3166,
869,
1053,
18399,
4276,
13,
5215,
12183,
13,
3166,
4047,
29916,
29889,
23569,
29889,
5317,
1053,
4452,
13,
3166,
4047,
29916,
29889,
23569,
29889,
5317,
3032,
13239,
1053,
3394,
29999,
29667,
1762,
20867,
408,
903,
7302,
29999,
29667,
1762,
20867,
13,
3166,
4047,
29916,
29889,
23569,
1053,
3855,
29873,
13,
3166,
4047,
29916,
29889,
23569,
1053,
27673,
13,
13,
29918,
21707,
353,
12183,
29889,
657,
16363,
22168,
978,
1649,
29897,
13,
13,
13,
1990,
2538,
300,
29999,
29667,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
10092,
19342,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
27175,
29999,
29667,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
2502,
290,
29899,
13492,
742,
1426,
2433,
27175,
17421,
290,
742,
13,
9651,
5780,
12632,
2433,
12300,
29899,
7052,
278,
3983,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
3032,
1300,
14174,
744,
7590,
29898,
5574,
29897,
13,
4706,
6492,
29889,
657,
29990,
16070,
2141,
18816,
12300,
17185,
7590,
29889,
6915,
29898,
1311,
3032,
1300,
14174,
744,
7590,
29897,
13,
4706,
6492,
29889,
657,
29979,
16070,
2141,
18816,
12300,
17185,
7590,
29889,
6915,
29898,
1311,
3032,
1300,
14174,
744,
7590,
29897,
13,
13,
1678,
822,
903,
1300,
14174,
744,
7590,
29898,
1311,
29892,
9615,
1125,
13,
4706,
921,
16070,
353,
1583,
29889,
5317,
29889,
657,
29990,
16070,
580,
13,
4706,
343,
16070,
353,
1583,
29889,
5317,
29889,
657,
29979,
16070,
580,
13,
4706,
1583,
29889,
842,
10861,
29898,
29916,
16070,
29889,
275,
12300,
17185,
580,
470,
343,
16070,
29889,
275,
12300,
17185,
3101,
13,
13,
4706,
565,
921,
16070,
29889,
275,
12300,
17185,
580,
322,
343,
16070,
29889,
275,
12300,
17185,
7295,
13,
9651,
5780,
12632,
353,
525,
12300,
29899,
7052,
278,
3983,
29915,
13,
4706,
25342,
921,
16070,
29889,
275,
12300,
17185,
7295,
29871,
396,
1126,
451,
612,
9685,
13,
9651,
5780,
12632,
353,
525,
12300,
29899,
7052,
278,
921,
29899,
8990,
310,
278,
3983,
871,
29915,
13,
4706,
25342,
343,
16070,
29889,
275,
12300,
17185,
7295,
29871,
396,
1126,
451,
1060,
9685,
13,
9651,
5780,
12632,
353,
525,
12300,
29899,
7052,
278,
343,
29899,
8990,
310,
278,
3983,
871,
29915,
13,
4706,
1683,
29901,
29871,
396,
694,
9685,
297,
1120,
14174,
744,
13,
9651,
5780,
12632,
353,
525,
12300,
29899,
7052,
278,
3983,
29915,
13,
4706,
1583,
29889,
842,
12229,
29911,
666,
29898,
10154,
12632,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1583,
29889,
5317,
29889,
12071,
29999,
29667,
580,
13,
13,
13,
1990,
17421,
290,
5841,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
15859,
263,
19342,
29899,
1627,
297,
584,
1990,
29901,
1412,
20867,
8801,
29952,
13071,
4955,
29889,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29999,
29667,
5841,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
2502,
290,
29899,
1627,
742,
1426,
2433,
29999,
29667,
7437,
742,
13,
9651,
5780,
12632,
2433,
29999,
29667,
1250,
278,
6492,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
21322,
7582,
2677,
29898,
17915,
29889,
17303,
29889,
8801,
21322,
7582,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1583,
29889,
5317,
29889,
657,
29931,
326,
1169,
20570,
2141,
7323,
580,
13,
13,
13,
1990,
17421,
290,
797,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
15859,
263,
19342,
29899,
262,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29999,
29667,
797,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
2502,
290,
29899,
262,
742,
1426,
2433,
29999,
29667,
512,
742,
13,
9651,
5780,
12632,
2433,
29999,
29667,
297,
278,
6492,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
21322,
7582,
29898,
17915,
29889,
29984,
2558,
20529,
29889,
29999,
29667,
797,
29897,
13,
4706,
1583,
29889,
842,
21322,
7582,
2677,
29898,
17915,
29889,
17303,
29889,
8801,
21322,
7582,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
903,
7302,
29999,
29667,
1762,
20867,
29898,
1311,
29889,
5317,
29892,
29871,
29896,
29889,
29896,
29897,
13,
13,
13,
1990,
17421,
290,
3744,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
15859,
263,
19342,
29899,
449,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29999,
29667,
3744,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
2502,
290,
29899,
449,
742,
1426,
2433,
29999,
29667,
4451,
742,
13,
9651,
5780,
12632,
2433,
29999,
29667,
714,
278,
6492,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
21322,
7582,
29898,
17915,
29889,
29984,
2558,
20529,
29889,
29999,
29667,
3744,
29897,
13,
4706,
1583,
29889,
842,
21322,
7582,
2677,
29898,
17915,
29889,
17303,
29889,
8801,
21322,
7582,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
903,
7302,
29999,
29667,
1762,
20867,
29898,
1311,
29889,
5317,
29892,
29871,
29896,
29889,
847,
29871,
29896,
29889,
29896,
29897,
13,
13,
13,
1990,
1060,
16070,
12300,
17185,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
1060,
9685,
1120,
14174,
744,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29990,
16070,
12300,
17185,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
29916,
6921,
742,
1426,
2433,
29990,
5202,
14174,
744,
742,
13,
9651,
5780,
12632,
2433,
20701,
921,
29899,
8990,
4469,
29899,
7052,
746,
7120,
7790,
29876,
29915,
13,
462,
1678,
525,
3644,
443,
11238,
29892,
921,
29899,
8990,
947,
451,
1735,
746,
10092,
292,
19342,
29889,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
17817,
29898,
5317,
29889,
657,
29990,
16070,
2141,
275,
12300,
17185,
3101,
13,
4706,
6492,
29889,
657,
29990,
16070,
2141,
18816,
12300,
17185,
7590,
29889,
6915,
29898,
1311,
29889,
842,
17817,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1583,
29889,
5317,
29889,
657,
29990,
16070,
2141,
842,
12300,
17185,
29898,
11238,
29897,
13,
4706,
565,
7120,
29901,
13,
9651,
1583,
29889,
5317,
29889,
12071,
29999,
29667,
580,
13,
13,
13,
1990,
612,
16070,
12300,
17185,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
612,
9685,
1120,
14174,
744,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29979,
16070,
12300,
17185,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
29891,
6921,
742,
1426,
2433,
29979,
5202,
14174,
744,
742,
13,
9651,
5780,
12632,
2433,
20701,
343,
29899,
8990,
4469,
29899,
7052,
746,
7120,
7790,
29876,
29915,
13,
462,
1678,
525,
3644,
443,
11238,
29892,
343,
29899,
8990,
947,
451,
1735,
746,
10092,
292,
19342,
29889,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
17817,
29898,
5317,
29889,
657,
29979,
16070,
2141,
275,
12300,
17185,
3101,
13,
4706,
6492,
29889,
657,
29979,
16070,
2141,
18816,
12300,
17185,
7590,
29889,
6915,
29898,
1311,
29889,
842,
17817,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1583,
29889,
5317,
29889,
657,
29979,
16070,
2141,
842,
12300,
17185,
29898,
11238,
29897,
13,
4706,
565,
7120,
29901,
13,
9651,
1583,
29889,
5317,
29889,
12071,
29999,
29667,
580,
13,
13,
13,
1990,
1060,
16070,
3403,
23830,
13076,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
1060,
9685,
1480,
6287,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29990,
16070,
3403,
23830,
13076,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
29916,
1188,
742,
1426,
2433,
29990,
4522,
29889,
6287,
742,
13,
9651,
5780,
12632,
2433,
3403,
23830,
13076,
921,
29899,
8990,
746,
7120,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
8990,
353,
6492,
29889,
657,
29990,
16070,
580,
13,
4706,
1583,
29889,
842,
17817,
29898,
1311,
29889,
8990,
29889,
657,
17185,
580,
1275,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
29897,
13,
4706,
1583,
29889,
8990,
29889,
18816,
17185,
7590,
29889,
6915,
29898,
1311,
3032,
842,
17817,
3644,
3403,
17185,
29897,
13,
13,
1678,
822,
903,
842,
17817,
3644,
3403,
17185,
29898,
1311,
29892,
6287,
1125,
13,
4706,
1583,
29889,
842,
17817,
29898,
7052,
1275,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
6287,
353,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
565,
7120,
1683,
1583,
29889,
8990,
29889,
18521,
1718,
13,
4706,
1583,
29889,
8990,
29889,
842,
17185,
29898,
7052,
29897,
13,
13,
13,
1990,
612,
16070,
3403,
23830,
13076,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
612,
9685,
1480,
6287,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
29979,
16070,
3403,
23830,
13076,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
29891,
1188,
742,
1426,
2433,
29979,
4522,
29889,
6287,
742,
13,
9651,
5780,
12632,
2433,
3403,
23830,
13076,
343,
29899,
8990,
746,
7120,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
8990,
353,
6492,
29889,
657,
29979,
16070,
580,
13,
4706,
1583,
29889,
842,
17817,
29898,
1311,
29889,
8990,
29889,
657,
17185,
580,
1275,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
29897,
13,
4706,
1583,
29889,
8990,
29889,
18816,
17185,
7590,
29889,
6915,
29898,
1311,
3032,
842,
17817,
3644,
3403,
17185,
29897,
13,
13,
1678,
822,
903,
842,
17817,
3644,
3403,
17185,
29898,
1311,
29892,
6287,
1125,
13,
4706,
1583,
29889,
842,
17817,
29898,
7052,
1275,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
6287,
353,
1583,
29889,
8990,
29889,
14480,
1718,
13054,
29924,
2965,
565,
7120,
1683,
1583,
29889,
8990,
29889,
18521,
1718,
13,
4706,
1583,
29889,
8990,
29889,
842,
17185,
29898,
7052,
29897,
13,
13,
13,
1990,
11657,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
6856,
4464,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
851,
6856,
6818,
29901,
450,
6856,
4464,
304,
671,
297,
525,
20313,
742,
525,
21355,
4286,
13,
462,
308,
2823,
584,
29885,
621,
29901,
1412,
20867,
8801,
29889,
842,
9527,
5756,
29952,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
6856,
6818,
2433,
20313,
742,
3847,
29922,
8516,
1125,
13,
4706,
4974,
6856,
6818,
297,
6702,
20313,
742,
525,
21355,
1495,
13,
4706,
1583,
3032,
7720,
6818,
353,
6856,
6818,
13,
13,
4706,
2428,
29898,
5756,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
7720,
742,
1426,
2433,
5756,
742,
13,
9651,
5780,
12632,
2433,
27199,
6856,
313,
265,
29914,
2696,
29897,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
842,
17817,
29898,
5317,
29889,
657,
9527,
5756,
580,
338,
451,
6213,
29897,
13,
4706,
6492,
29889,
18816,
2697,
9527,
5756,
29889,
6915,
29898,
1311,
3032,
7720,
7590,
29897,
13,
13,
1678,
822,
903,
7720,
7590,
29898,
1311,
29892,
607,
1125,
13,
4706,
9995,
29903,
8276,
19866,
363,
18399,
8801,
6856,
4464,
1735,
1213,
15945,
13,
4706,
1583,
29889,
842,
17817,
29898,
4716,
2804,
525,
8516,
1495,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1583,
29889,
5317,
29889,
842,
9527,
5756,
29898,
1311,
3032,
7720,
6818,
565,
7120,
1683,
6213,
29897,
13,
13,
13,
1990,
10837,
345,
5568,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
11672,
3114,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
739,
3620,
278,
2322,
1196,
322,
29320,
3114,
607,
11217,
599,
13,
1678,
19684,
373,
278,
6492,
29889,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
2428,
29898,
23902,
345,
5568,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
5317,
29899,
13270,
29899,
9748,
742,
1426,
2433,
23902,
345,
3114,
742,
13,
9651,
5780,
12632,
2433,
7277,
11672,
1196,
322,
29320,
3114,
742,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
3847,
29922,
3560,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
1857,
2792,
353,
313,
1311,
29889,
5317,
29889,
275,
4592,
20867,
20261,
3285,
13,
462,
4706,
1583,
29889,
5317,
29889,
275,
4592,
20867,
20325,
3101,
13,
13,
4706,
565,
1857,
2792,
1275,
313,
8824,
29892,
7700,
1125,
13,
9651,
716,
2792,
353,
5852,
29892,
7700,
13,
4706,
1683,
29901,
13,
9651,
396,
1196,
871,
29892,
1196,
322,
5829,
29892,
5829,
871,
13,
9651,
5922,
353,
313,
5574,
29892,
7700,
511,
313,
5574,
29892,
5852,
511,
313,
8824,
29892,
5852,
29897,
13,
9651,
716,
2792,
353,
5922,
15625,
28631,
29889,
2248,
29898,
3784,
2792,
29897,
718,
29871,
29896,
29897,
1273,
29871,
29941,
29962,
13,
13,
4706,
1583,
29889,
5317,
29889,
842,
4592,
20867,
20261,
29898,
1482,
2792,
29961,
29900,
2314,
13,
4706,
1583,
29889,
5317,
29889,
842,
4592,
20867,
20325,
29898,
1482,
2792,
29961,
29896,
2314,
13,
13,
13,
1990,
1530,
555,
481,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
8718,
263,
1530,
555,
481,
7647,
304,
2767,
278,
784,
555,
481,
29889,
13,
13,
1678,
9134,
278,
6136,
1967,
784,
555,
481,
322,
278,
2322,
784,
555,
481,
526,
4784,
29889,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
1583,
3032,
15901,
353,
6213,
29871,
396,
1763,
3787,
385,
2777,
310,
1530,
555,
481,
7647,
13,
4706,
2428,
29898,
1625,
555,
481,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
1054,
555,
481,
742,
1426,
2433,
1625,
555,
481,
742,
13,
9651,
5780,
12632,
543,
7277,
784,
555,
481,
613,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
1583,
29889,
5317,
29889,
18816,
9966,
2940,
7590,
29889,
6915,
29898,
1311,
3032,
5504,
1625,
555,
481,
29897,
13,
4706,
1583,
29889,
5317,
29889,
18816,
9966,
4421,
2620,
7590,
29889,
6915,
29898,
1311,
3032,
5504,
1625,
555,
481,
29897,
13,
13,
1678,
822,
731,
3306,
7647,
29898,
1311,
29892,
2927,
7647,
1125,
13,
4706,
9995,
2697,
263,
2702,
2927,
7928,
2012,
310,
773,
278,
2322,
7928,
1213,
15945,
13,
4706,
4974,
29898,
2780,
7647,
338,
451,
6213,
29897,
13,
4706,
4974,
29898,
1311,
3032,
15901,
338,
6213,
29897,
13,
4706,
1583,
3032,
15901,
353,
2927,
7647,
13,
4706,
1583,
3032,
15901,
29889,
12872,
7590,
29889,
6915,
29898,
1311,
3032,
15901,
12911,
7590,
29897,
13,
4706,
1583,
29889,
842,
17817,
29898,
1311,
3032,
15901,
29889,
275,
12911,
3101,
13,
13,
1678,
732,
7959,
5696,
13,
1678,
822,
903,
3258,
7647,
29898,
3560,
1125,
13,
4706,
9995,
4391,
278,
7928,
565,
451,
2307,
5923,
13,
13,
4706,
584,
3560,
660,
8801,
3847,
29901,
22280,
310,
278,
716,
784,
555,
481,
13,
4706,
584,
29878,
1853,
29901,
1530,
555,
481,
7647,
13,
4706,
9995,
13,
4706,
515,
4047,
29916,
29889,
23569,
29889,
15901,
29889,
1625,
555,
481,
7647,
1053,
1530,
555,
481,
7647,
13,
4706,
7928,
353,
1530,
555,
481,
7647,
29898,
3560,
29922,
3560,
29897,
13,
4706,
7928,
29889,
842,
19751,
29898,
8824,
29897,
13,
4706,
736,
7928,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
9995,
4391,
263,
274,
1958,
7928,
322,
2767,
6136,
1967,
322,
2322,
274,
1958,
1213,
15945,
13,
4706,
565,
1583,
3032,
15901,
338,
6213,
29901,
13,
9651,
1583,
3032,
15901,
353,
1583,
3032,
3258,
7647,
29898,
1311,
29889,
5317,
29897,
13,
9651,
1583,
3032,
15901,
29889,
12872,
7590,
29889,
6915,
29898,
1311,
3032,
15901,
12911,
7590,
29897,
13,
13,
4706,
396,
7525,
278,
7928,
19866,
304,
784,
555,
481,
1735,
13,
4706,
565,
7120,
338,
5852,
29901,
13,
9651,
1583,
3032,
5504,
1625,
555,
481,
580,
13,
9651,
1583,
3032,
15901,
29889,
4294,
580,
13,
4706,
1683,
29901,
13,
9651,
1583,
3032,
15901,
29889,
11458,
580,
13,
13,
1678,
822,
903,
15901,
12911,
7590,
29898,
1311,
29892,
338,
12911,
1125,
13,
4706,
1583,
29889,
842,
17817,
29898,
275,
12911,
29897,
13,
13,
1678,
822,
903,
5504,
1625,
555,
481,
29898,
1311,
1125,
13,
4706,
565,
1583,
3032,
15901,
338,
6213,
29901,
13,
9651,
736,
13,
4706,
1967,
353,
1583,
29889,
5317,
29889,
657,
9966,
2940,
580,
13,
13,
4706,
565,
338,
8758,
29898,
3027,
29892,
4452,
29889,
2940,
8909,
29916,
1469,
1125,
13,
9651,
396,
21220,
2069,
363,
4280,
4558,
13,
9651,
784,
555,
481,
353,
1967,
29889,
657,
1625,
555,
481,
580,
13,
13,
9651,
4464,
353,
1967,
29889,
657,
8909,
29916,
6818,
580,
13,
9651,
565,
4464,
297,
313,
7076,
29889,
2940,
8909,
29916,
1469,
29889,
8909,
29916,
6818,
29889,
19297,
29931,
1806,
29965,
2287,
29918,
19689,
8127,
29892,
13,
462,
4706,
4452,
29889,
2940,
8909,
29916,
1469,
29889,
8909,
29916,
6818,
29889,
14480,
29896,
29900,
29918,
19297,
29931,
1806,
29965,
2287,
29918,
19689,
8127,
1125,
13,
18884,
848,
353,
1967,
29889,
657,
1469,
29898,
13,
462,
1678,
3509,
29922,
8824,
29892,
4464,
29922,
7076,
29889,
2940,
8909,
29916,
1469,
29889,
8909,
29916,
6818,
29889,
19689,
8127,
29897,
13,
9651,
1683,
29901,
13,
18884,
848,
353,
1967,
29889,
657,
1469,
29898,
8552,
29922,
8824,
29897,
13,
13,
9651,
396,
3789,
9825,
13342,
322,
3464,
565,
738,
13,
9651,
1583,
3032,
15901,
29889,
842,
1469,
29898,
1272,
29897,
13,
13,
4706,
25342,
338,
8758,
29898,
3027,
29892,
4452,
29889,
1625,
555,
481,
29924,
861,
797,
1125,
13,
9651,
396,
3789,
7928,
515,
6136,
1967,
13,
9651,
784,
555,
481,
353,
1967,
29889,
657,
1625,
555,
481,
580,
13,
9651,
396,
3789,
9825,
13342,
322,
3464,
565,
738,
13,
9651,
1583,
3032,
15901,
29889,
842,
2001,
29898,
3027,
29897,
13,
13,
4706,
1683,
29901,
13,
9651,
396,
1939,
6136,
1967,
470,
6136,
1967,
338,
390,
29954,
5688,
29892,
13,
9651,
396,
5399,
363,
6136,
14801,
6492,
13,
9651,
14801,
353,
1583,
29889,
5317,
3032,
657,
9966,
2001,
29898,
14380,
2433,
1557,
2620,
1495,
13,
9651,
565,
14801,
338,
451,
6213,
29901,
13,
18884,
784,
555,
481,
353,
14801,
29889,
657,
1625,
555,
481,
580,
13,
18884,
1583,
3032,
15901,
29889,
842,
2001,
29898,
1557,
2620,
29897,
13,
13,
9651,
1683,
29901,
13,
18884,
396,
1939,
6136,
848,
1967,
3643,
14801,
29892,
13,
18884,
396,
731,
7928,
515,
2322,
5235,
13,
18884,
784,
555,
481,
353,
1583,
29889,
5317,
29889,
657,
4592,
1625,
555,
481,
580,
13,
18884,
396,
2538,
300,
9825,
13342,
322,
3464,
565,
738,
13,
18884,
1583,
3032,
15901,
29889,
842,
1469,
29898,
8516,
29897,
13,
13,
4706,
1583,
3032,
15901,
29889,
842,
1625,
555,
481,
29898,
1054,
555,
481,
29897,
13,
13,
13,
1990,
9159,
4297,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
8718,
278,
9159,
4297,
8801,
310,
278,
6790,
6492,
29889,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
1583,
3032,
15901,
353,
6213,
29871,
396,
1763,
3787,
385,
2777,
310,
9159,
4297,
13,
4706,
2428,
29898,
3306,
4297,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
9849,
2433,
2780,
1646,
742,
1426,
2433,
3306,
1646,
742,
13,
9651,
5780,
12632,
543,
8964,
29914,
29950,
680,
278,
2927,
1646,
613,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
5574,
29892,
3847,
29922,
3560,
29897,
13,
4706,
2927,
4297,
8801,
353,
1583,
29889,
5317,
29889,
657,
3306,
4297,
8801,
580,
13,
4706,
2030,
353,
1583,
29889,
1271,
10140,
1338,
29898,
5574,
29897,
13,
4706,
1583,
29889,
842,
17817,
29898,
2780,
4297,
8801,
29889,
275,
12911,
1762,
29898,
1311,
29889,
5317,
876,
13,
4706,
1583,
29889,
1271,
10140,
1338,
29898,
1025,
29897,
13,
4706,
2927,
4297,
8801,
29889,
18816,
12911,
7590,
29889,
6915,
29898,
1311,
3032,
8030,
12911,
7590,
29897,
13,
13,
1678,
822,
903,
8030,
12911,
7590,
29898,
1311,
29892,
338,
12911,
1125,
13,
4706,
9995,
10717,
746,
278,
2927,
1646,
421,
12872,
29952,
2875,
1735,
1213,
15945,
13,
4706,
565,
1583,
29889,
275,
17817,
580,
1275,
338,
12911,
29901,
13,
9651,
736,
13,
4706,
1583,
29889,
842,
17817,
29898,
275,
12911,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
9995,
4391,
263,
274,
1958,
7928,
322,
2767,
6136,
1967,
322,
2322,
274,
1958,
1213,
15945,
13,
4706,
2927,
4297,
8801,
353,
1583,
29889,
5317,
29889,
657,
3306,
4297,
8801,
580,
13,
4706,
565,
451,
2927,
4297,
8801,
29889,
275,
25108,
580,
1275,
7120,
29901,
13,
9651,
736,
13,
4706,
1583,
29889,
5317,
29889,
657,
3306,
4297,
8801,
2141,
842,
12911,
29898,
11238,
29897,
13,
13,
13,
1990,
19152,
2887,
1103,
29934,
20819,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
9565,
11959,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
396,
10783,
267,
1023,
4558,
363,
7120,
29914,
348,
11238,
5922,
13,
4706,
1583,
3032,
28631,
353,
426,
13,
9651,
7700,
29901,
313,
27078,
29889,
657,
29984,
12492,
877,
12181,
29899,
16622,
29899,
2929,
333,
5477,
13,
462,
1678,
376,
9598,
1022,
848,
9565,
11959,
4968,
13,
9651,
5852,
29901,
313,
27078,
29889,
657,
29984,
12492,
877,
12181,
29899,
295,
5843,
29899,
2929,
333,
5477,
13,
462,
259,
376,
6132,
694,
3013,
848,
9565,
11959,
1159,
13,
4706,
500,
13,
13,
4706,
9849,
29892,
5780,
12632,
353,
1583,
3032,
28631,
29961,
5317,
29889,
275,
9598,
1022,
1469,
2887,
1103,
29934,
20819,
580,
29962,
13,
4706,
2428,
29898,
9598,
1022,
2887,
1103,
29934,
20819,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
13,
9651,
9849,
29922,
4144,
29892,
13,
9651,
1426,
2433,
27199,
3013,
9565,
11959,
742,
13,
9651,
5780,
12632,
29922,
10154,
12632,
29892,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
13,
9651,
3847,
29922,
3560,
29897,
13,
4706,
6492,
29889,
18816,
2697,
9598,
1022,
1469,
2887,
1103,
29934,
20819,
29889,
6915,
29898,
13,
9651,
1583,
3032,
17462,
1469,
2887,
1103,
29934,
20819,
7590,
29897,
13,
13,
1678,
822,
903,
17462,
1469,
2887,
1103,
29934,
20819,
7590,
29898,
1311,
29892,
9565,
29934,
20819,
1125,
13,
4706,
9995,
13554,
18399,
731,
3013,
9565,
11959,
7182,
15945,
29908,
13,
4706,
9849,
29892,
5780,
12632,
353,
1583,
3032,
28631,
29961,
294,
1103,
29934,
20819,
29962,
13,
4706,
1583,
29889,
842,
12492,
29898,
4144,
29897,
13,
4706,
1583,
29889,
842,
12229,
29911,
666,
29898,
10154,
12632,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
396,
910,
674,
7135,
903,
17462,
1469,
2887,
1103,
29934,
20819,
7590,
13,
4706,
1583,
29889,
5317,
29889,
842,
9598,
1022,
1469,
2887,
1103,
29934,
20819,
29898,
1333,
1583,
29889,
5317,
29889,
275,
9598,
1022,
1469,
2887,
1103,
29934,
20819,
3101,
13,
13,
13,
1990,
612,
16070,
797,
1765,
287,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
640,
22155,
612,
19843,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
6492,
29892,
3847,
29922,
8516,
1125,
13,
4706,
396,
10783,
267,
1023,
4558,
363,
7120,
29914,
348,
11238,
5922,
13,
4706,
1583,
3032,
28631,
353,
426,
13,
9651,
7700,
29901,
313,
27078,
29889,
657,
29984,
12492,
877,
5317,
29899,
2941,
776,
5477,
13,
462,
1678,
376,
15988,
296,
612,
9685,
1623,
1328,
4968,
13,
9651,
5852,
29901,
313,
27078,
29889,
657,
29984,
12492,
877,
5317,
29899,
29891,
786,
5477,
13,
462,
259,
376,
15988,
296,
612,
9685,
701,
1328,
4968,
13,
4706,
500,
13,
13,
4706,
9849,
29892,
5780,
12632,
353,
1583,
3032,
28631,
29961,
5317,
29889,
657,
29979,
16070,
2141,
275,
797,
1765,
287,
580,
29962,
13,
4706,
2428,
29898,
29979,
16070,
797,
1765,
287,
4276,
29892,
1583,
467,
1649,
2344,
12035,
13,
9651,
6492,
29892,
13,
9651,
9849,
29922,
4144,
29892,
13,
9651,
1426,
2433,
797,
1765,
612,
319,
11497,
742,
13,
9651,
5780,
12632,
29922,
10154,
12632,
29892,
13,
9651,
19799,
29922,
1311,
3032,
2467,
20211,
287,
29892,
13,
9651,
1423,
519,
29922,
8824,
29892,
13,
9651,
3847,
29922,
3560,
29897,
13,
4706,
6492,
29889,
657,
29979,
16070,
2141,
18816,
797,
1765,
287,
7590,
29889,
6915,
29898,
1311,
3032,
29891,
16070,
797,
1765,
287,
7590,
29897,
13,
13,
1678,
822,
903,
29891,
16070,
797,
1765,
287,
7590,
29898,
1311,
29892,
21292,
287,
1125,
13,
4706,
9995,
13554,
18399,
731,
343,
9685,
21292,
287,
7182,
15945,
29908,
13,
4706,
9849,
29892,
5780,
12632,
353,
1583,
3032,
28631,
29961,
262,
1765,
287,
29962,
13,
4706,
1583,
29889,
842,
12492,
29898,
4144,
29897,
13,
4706,
1583,
29889,
842,
12229,
29911,
666,
29898,
10154,
12632,
29897,
13,
13,
1678,
822,
903,
2467,
20211,
287,
29898,
1311,
29892,
7120,
29922,
8824,
1125,
13,
4706,
396,
910,
674,
7135,
903,
29891,
16070,
797,
1765,
287,
7590,
13,
4706,
343,
16070,
353,
1583,
29889,
5317,
29889,
657,
29979,
16070,
580,
13,
4706,
343,
16070,
29889,
842,
797,
1765,
287,
29898,
1333,
343,
16070,
29889,
275,
797,
1765,
287,
3101,
13,
13,
13,
1990,
315,
1883,
845,
1466,
4276,
29898,
20867,
4276,
1125,
13,
1678,
9995,
29984,
4276,
17304,
1847,
274,
1883,
845,
1466,
10677,
373,
263,
584,
1990,
29901,
1412,
20867,
8801,
1412,
13,
13,
1678,
584,
3207,
6492,
29901,
584,
1990,
29901,
1412,
20867,
8801,
29952,
2777,
373,
607,
304,
21994,
13,
1678,
584,
3207,
851,
2927,
29901,
9159,
304,
671,
304,
4216,
278,
274,
1883,
845,
1466,
13,
1678,
584,
3207,
938,
1196,
2103,
29901,
21485,
310,
278,
274,
1883,
845,
1466,
10677,
13,
1678,
584,
3207,
851,
6276,
342,
1508,
29901,
22135,
310,
1196,
29889,
2823,
584,
29885,
621,
29901,
1412,
20867,
29889,
842,
9527,
19890,
29952,
13,
1678,
584,
3207,
3847,
29901,
2823,
584,
1990,
18078,
29984,
4276,
29952,
13,
1678,
9995,
13,
13,
1678,
822,
4770,
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] |
Configuration/StandardSequences/python/RawToDigi_Repacked_cff.py | nistefan/cmssw | 0 | 165674 | import FWCore.ParameterSet.Config as cms
from Configuration.StandardSequences.RawToDigi_cff import *
scalersRawToDigi.scalersInputTag = 'rawDataRepacker'
csctfDigis.producer = 'rawDataRepacker'
dttfDigis.DTTF_FED_Source = 'rawDataRepacker'
gctDigis.inputLabel = 'rawDataRepacker'
gtDigis.DaqGtInputTag = 'rawDataRepacker'
gtEvmDigis.EvmGtInputTag = 'rawDataRepacker'
siPixelDigis.InputLabel = 'rawDataRepacker'
siStripDigis.ProductLabel = 'rawDataRepacker'
#False by default ecalDigis.DoRegional = False
ecalDigis.InputLabel = 'rawDataRepacker'
ecalPreshowerDigis.sourceTag = 'rawDataRepacker'
hcalDigis.InputLabel = 'rawDataRepacker'
muonCSCDigis.InputObjects = 'rawDataRepacker'
muonDTDigis.inputLabel = 'rawDataRepacker'
muonRPCDigis.InputLabel = 'rawDataRepacker'
castorDigis.InputLabel = 'rawDataRepacker'
RawToDigi = cms.Sequence(csctfDigis+dttfDigis+gctDigis+gtDigis+gtEvmDigis+siPixelDigis+siStripDigis+ecalDigis+ecalPreshowerDigis+hcalDigis+muonCSCDigis+muonDTDigis+muonRPCDigis+castorDigis+scalersRawToDigi)
RawToDigi_woGCT = cms.Sequence(csctfDigis+dttfDigis+gtDigis+gtEvmDigis+siPixelDigis+siStripDigis+ecalDigis+ecalPreshowerDigis+hcalDigis+muonCSCDigis+muonDTDigis+muonRPCDigis+castorDigis+scalersRawToDigi)
siStripVRDigis = siStripDigis.clone(ProductLabel = 'virginRawDataRepacker')
RawToDigi_withVR = cms.Sequence(RawToDigi + siStripVRDigis)
| [
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] |
boards/apollo2_evb/examples/multi_boot_secure_sample/generate_secureboot_assets.py | wher0001/AmbiqSuiteSDK | 25 | 97958 | #!/usr/bin/env python3
import argparse
import sys
import os
# This key table has to match the one in bootloader
keyTbl = [0xDEADBEEF, 0xAAAAAAAA, 0x11111111, 0x00000000, 0xFFFFFFFF, 0x55555555, 0xA5A5A5A5, 0x66666666]
#******************************************************************************
#
# Main function
#
#******************************************************************************
def main():
# Read the binary file from the command line.
with open(args.binfile, mode='rb') as binfile:
clear_application= binfile.read()
print('Loading Clear application {} bytes from {}...'.format(len(clear_application), args.binfile), flush=True)
plaintext = pad_to_block_size(clear_application, 4)
ivVal = word_from_bytes(os.urandom(4), 0)
print("Initialization Vector")
print(hex(ivVal))
application = encrypt_app(args.keyidxVal, plaintext, ivVal)
trailer = sec_trailer(args.keyidxVal, plaintext, ivVal, int(args.protectionVal, 0))
print('Saving encrypted image {} bytes to {}...'.format(len(application), args.encimagefile), flush=True)
with open(args.encimagefile, mode='wb') as encimagefile:
encimagebytearray = bytearray(application)
encimagefile.write(encimagebytearray)
print('Saving security trailer {} bytes to {}...'.format(len(trailer), args.sectrailerfile), flush=True)
with open(args.sectrailerfile, mode='wb') as sectrailerfile:
trailerbytearray = bytearray(trailer)
sectrailerfile.write(trailerbytearray)
print('Done.')
#******************************************************************************
#
# Turn a 32-bit number into a series of bytes for transmission.
#
# This command will split a 32-bit integer into an array of bytes, ordered
# LSB-first for transmission over the UART.
#
#******************************************************************************
def int_to_bytes(n):
A = [n & 0xFF,
(n >> 8) & 0xFF,
(n >> 16) & 0xFF,
(n >> 24) & 0xFF]
return A
#******************************************************************************
#
# Extract a word from a byte array
#
#******************************************************************************
def word_from_bytes(B, n):
return (B[n] + (B[n + 1] << 8) + (B[n + 2] << 16) + (B[n + 3] << 24))
#******************************************************************************
#
# CRC function that matches the CRC used by the Apollo bootloader.
#
#******************************************************************************
poly32 = 0x1EDC6F41
def crc32(L):
rem = 0
for b in L:
rem = rem ^ (b << 24)
for i in range(8):
if rem & 0x80000000:
rem = ((rem << 1) ^ poly32)
else:
rem = (rem << 1)
rem = rem & 0xFFFFFFFF
return rem
def pad_to_block_size(text, block_size):
text_length = len(text)
amount_to_pad = block_size - (text_length % block_size)
if amount_to_pad == 0:
amount_to_pad = block_size
for i in range(0, amount_to_pad, 1):
text += bytes(chr(amount_to_pad), 'ascii')
return text
def encrypt_app(keyidx, clear_app, iv):
key32 = keyTbl[keyidx]
applen = len(clear_app)
enc_app = []
for i in range(0, applen, 4):
word = word_from_bytes(clear_app, i)
word = (word ^ iv) ^ key32
iv = word
enc_app.extend(int_to_bytes(word))
return enc_app
def sec_trailer(keyidx, clear_app, iv, protection):
key32 = keyTbl[keyidx]
secTrailer = []
secTrailer.extend(int_to_bytes(keyidx))
secTrailer.extend(int_to_bytes(protection))
applen = len(clear_app)
secTrailer.extend(int_to_bytes(applen))
crc = crc32(clear_app)
sig = key32 ^ crc
secTrailer.extend(int_to_bytes(sig))
secTrailer.extend(int_to_bytes(iv))
# Trailer Signature
secTrailerSig = crc32(secTrailer) ^ key32
secTrailer.extend(int_to_bytes(secTrailerSig))
return secTrailer
#******************************************************************************
#
# Main program flow
#
#******************************************************************************
if __name__ == '__main__':
parser = argparse.ArgumentParser(description =
'Secure Image generation utility for Apollo or Apollo2')
parser.add_argument('binfile',
help = 'Binary file to program into the target device')
parser.add_argument('keyidxVal', default=0, type=int, help = 'encryption key index')
parser.add_argument('protectionVal', default=0, help = 'Image Protection Value (hex)')
parser.add_argument('encimagefile', help = 'Destination file for Encrypted image')
parser.add_argument('sectrailerfile', help = 'Destination file for security trailer')
args = parser.parse_args()
main()
| [
1,
18787,
4855,
29914,
2109,
29914,
6272,
3017,
29941,
13,
13,
5215,
1852,
5510,
13,
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10876,
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] |
riseofsavior.py | aimogue/Adventure-game---Rise-of-Savior | 2 | 127973 | import sys
import os
import time
import random
import keyboard
from console.screen import sc
from rich.console import Console
mythical_creature_list = None
male_name_list = None
both_sex_name_list = None
name_of_queen = None
weapon_type_list = None
your_weapon_type = None
name_of_trainer = None
def print_delay(message):
console = Console()
i = 0.05
for c in '\n' + message:
with sc.location(0, 0):
console.print('Press [Backspace] to skip conversations', style='reverse')
if keyboard.is_pressed('backspace'):
i = 0
sys.stdout.write(c)
sys.stdout.flush() # defeat buffering
time.sleep(random.random() * i)
def print_delay_dialogue(name, message):
print_delay(f"{name}: \"{message}\"")
def binary_decision(request1, request2):
print("\nEnter 1 to" + request1)
print("Enter 2 to" + request2)
print("What would you like to do?")
answer = input("(Please Enter 1 or 2)\n")
while answer != "1" and answer != "2":
answer = binary_decision(request1, request2)
return answer
def binary_outcome(choice, path1, path2):
if choice == "1":
path1()
elif choice == "2":
path2()
def retry_path():
print_delay("Let's run this back.")
start()
def exit_path():
print_delay("Goodbye!")
quit()
def prompt_try_again(outcome_boolean):
if outcome_boolean:
print_delay("CONGRULATION, YOU WIN!")
else:
print_delay("Clearly, you lose …")
print_delay("GAME OVER!")
choice_try_again = binary_decision(" try again", " quit.")
binary_outcome(choice_try_again, retry_path, exit_path)
def start():
os.system("title Rise of Savior")
os.system('cls')
print_delay("You are having a hard time falling asleep,"
" so you decided to go for a stroll in your neighborhood.")
print_delay("As you are walking, you notice a large vintage-looking"
" leather book laying on a pile of garbage.")
print_delay("Despite the distasteful smell, you decide to pick up the"
" book and you read the cover title:")
print_delay("\n-------------- REPRISE OF THE HOLY HERO --------------")
print_delay("You open the book and it only has one page with writing."
" It reads as follow:")
print_delay("A legendary hero is needed to save our world."
" Will you be our savior?")
choice1 = binary_decision(" be a savior.", " be a bystander.")
binary_outcome(choice1, choice1_path1, choice1_path2)
def choice1_path2():
print_delay("You have chosen to be a bystander and you are privileged"
" enough to witness something.")
print_delay("Kick back. Grab some popcorn and watch the destruction of"
" their world as well as yours!")
print_delay("In a flash, the book burns and the whole area is engulfed"
" in complete calamity.")
prompt_try_again(False)
def queen_weapon_comment(weapon):
if weapon == "sword":
return "This sword will allow you to slice through your enemies."
elif weapon == "axe":
return "You have received a weapon with great"\
"brut force. Use it wisely!"
elif weapon == "gun":
return "With this weapon, you will become a"\
"master of long-range combat."
elif weapon == "shield":
return "You're ability to defend and counter will be unmatched"\
"with this holy shield."
elif weapon == "spear":
return "You have received a very versatile weapon."
def choice1_path1():
global mythical_creature_list
print_delay("As you finished reading the prompt and ponder on the idea"
" of being a hero, the book heats up and a huge flash of"
" light stuns you.")
print_delay("Suddenly …")
color_list = ['blue', 'red', 'yellow', 'green', 'orange', 'purple',
'brown', 'black', 'white', 'grey']
color_of_cloak = random.choice(color_list)
print_delay("You wake up and find yourself surrounded by individuals"
f"in {color_of_cloak} cloaks.")
mythical_creature_list = ['Elf', 'Titan', 'Dwarf', 'Fairy', 'Ogre',
'Werewolf', 'Slim', 'Chimera', 'Ghost',
'Dragon']
female_name_list = ['Ruth', 'Eleanor', 'Prudence', 'Sylvia', 'Wihelmina',
'Deborah', 'Hazel', 'Adeline', 'Geraldine', 'Zenobia']
global male_name_list
male_name_list = ['Furman', 'Huxley', 'Alaric', 'Geoffrey', 'Jasper',
'Edison', 'Gert', 'Lamont', 'Manfred', 'Ragnar']
global both_sex_name_list
both_sex_name_list = female_name_list + male_name_list
global name_of_queen
name_of_queen = random.choice(female_name_list)
female_name_list.remove(name_of_queen)
tribe_of_queen = random.choice(mythical_creature_list)
timeline_before_battle_list = [3, 6, 9, 12, 24, 48]
timeline_before_battle = random.choice(timeline_before_battle_list)
print_delay(f"She then says: \"My name is {name_of_queen}, I am the"
f" leader of the {tribe_of_queen} tribe and the"
" representative of all the tribes in our world.")
print_delay_dialogue(name_of_queen, "We have summoned you from your"
" world through great sacrifice to defeat an evil"
" malice that will threaten the very existence of"
" both our worlds.")
print_delay_dialogue(name_of_queen, "We need your strength and courage to"
" defeat this foe. This foe will invade our worlds in"
f" {timeline_before_battle} months.")
print_delay("You tell them that in your world that humans have nuclear"
" weapons, heavy artillery, robotics, biological weapons and"
" possibly a billion soldiers.")
print_delay("However, they inform you that only one person can travel"
" between worlds every 2000 years and both parallel worlds"
" are linked via Muller High-Dimensional Strings.")
print_delay("As a result, if this parallel world is destroyed, so shall"
" Earth suffer the same fate.")
print_delay(f"The reality sinks in but {name_of_queen} says that she has"
" something to give you.")
print_delay_dialogue(name_of_queen, "In our world, they are 5 holy weapon"
" types in our world: sword, axe, gun, shield and"
" spear.")
print_delay_dialogue(name_of_queen, "Every individual in our world"
" including you has a weapon-type.")
print_delay_dialogue(name_of_queen, "We shall give you a vessel"
" weapon that will transform into either of those"
" 5 types upon contact.")
global weapon_type_list
weapon_type_list = ["sword", "axe", "gun", "shield", "spear"]
global your_weapon_type
your_weapon_type = random.choice(weapon_type_list)
print_delay("You grab the vessel weapon that feels like"
f" clay and it looks like it's a {your_weapon_type}.")
print_delay_dialogue(name_of_queen, "Congragulation, you have acquired"
f" a holy {your_weapon_type}.")
print_delay_dialogue(name_of_queen, queen_weapon_comment(your_weapon_type))
print_delay_dialogue(name_of_queen, "We shall also provide you with only"
" 4 support-warriors for battle due to the"
" constraints of an evil barrier"
" protecting its realm.")
print_delay_dialogue(name_of_queen, "You will get to build your team"
" to invade the evil realm very soon!")
global name_of_trainer
name_of_trainer = random.choice(male_name_list)
male_name_list.remove(name_of_trainer)
trainer_tribe = random.choice(mythical_creature_list)
print_delay("She then provides you with exquisite armor, money,"
" accommodations as well as a trainer"
f" named: {name_of_trainer} of the {trainer_tribe} tribe.")
print_delay("He marchs towards you.")
print_delay_dialogue(name_of_trainer, "Will you accept me as"
" your trainer?")
choice2 = binary_decision(f" accept {name_of_trainer}'s offer.",
f" reject {name_of_trainer}'s offer.")
binary_outcome(choice2, choice2path1, choice2path2)
def choice2path2():
print_delay("You tell him that you are more of a lone wolf"
" and that you would like to figure things out"
" on your own but …")
print_delay_dialogue(name_of_trainer, f"I understand but are"
" you sure that I cannot assist you or"
" provide any help?")
choice2_1 = binary_decision(f" accept {name_of_trainer}'s"
" final offer.", " reject"
f" {name_of_trainer}'s final offer.")
binary_outcome(choice2_1, choice2_1path2, choice2_2path2)
def choice2_2path2():
print_delay_dialogue(name_of_trainer, "Very well, I will"
" respect your wishes.")
solo_path3()
def choice2_1path2():
print_delay_dialogue(name_of_trainer, "I am glad that you"
" reco1nsidered my offer, let's start"
" right away our training.")
trainer_path3()
def choice2path1():
print_delay_dialogue(name_of_trainer, "Thank you for accepting"
" me as your trainer, we shall start your"
" training immediately.")
trainer_path3()
def solo_path3():
print_delay("You’re training vigorously by yourself"
" in the outskirt of the home base.")
print_delay("You are using a trial-and-error approach"
" to figure out how to use your weapon.")
print_delay("You're inexperience with your weapon"
" leads to a fatal injury.")
print_delay("No one was there to save you and the"
" worlds are left without their faithful hero.")
prompt_try_again(False)
def trainer_path3():
print_delay("Your training is fierce, and you are dedicated to the cause.")
team_selection_path4()
def warrior_template_maker(name_list, race_list, weapon_type_list):
name = random.choice(name_list)
race = random.choice(race_list)
weapon_type = random.choice(weapon_type_list)
warrior_template = [name, race, weapon_type]
return warrior_template
def warrior_list(team_size, name_list, race_list, weapon_type_list):
index = 0
warrior_list = []
while index < team_size:
warrior_template = warrior_template_maker(name_list,
race_list,
weapon_type_list)
name_list.remove(warrior_template[0])
warrior_list.append(warrior_template)
index += 1
return warrior_list
def warrior_list_print(warrior, index):
print(f"\nIndividual #{index+1}")
print(f"Name: {warrior[0]}")
print(f"Race: {warrior[1]}")
print(f"Weapon-type: {warrior[2]}")
def warrior_selection_print(warrior_list, round, round_boolean):
if round_boolean:
print(f"\n-------------- ROUND #{round} --------------")
index = 0
for warriors in range(len(warrior_list)):
warrior_list_print(warrior_list[index], index)
index += 1
def team_size_list(choice, warrior_list_round):
if choice == '1':
return warrior_list_round[0]
elif choice == '2':
return warrior_list_round[1]
def warrior_selection_process():
round = 1
round_boolean = True
team_list = []
while round < 5:
warrior_list_round = warrior_list(2, both_sex_name_list,
mythical_creature_list,
weapon_type_list)
warrior_selection_print(warrior_list_round, round, round_boolean)
choice3 = binary_decision(" select individual #1.",
" select individual #2.")
warrior_selected = team_size_list(choice3, warrior_list_round)
team_list.append(warrior_selected)
round += 1
your_warrior_template = ["Yourself", "Human", your_weapon_type]
team_list.append(your_warrior_template)
return team_list
def team_selection_path4():
print_delay("Suddenly...")
print_delay("A white-bearded fellow approaches you.")
name_of_scout = random.choice(male_name_list)
male_name_list.remove(name_of_scout)
scout_tribe = random.choice(mythical_creature_list)
print_delay_dialogue("White-bearded fellow", "Dear hero, my name is"
f" {name_of_scout} of the"
f" {scout_tribe} tribe and"
" I am the weapon designer for our faction.")
print_delay_dialogue(name_of_scout, "We shall go through 4 selection"
" rounds and you shall choose one warrior among"
" the pairs that are presented to you.")
print_delay_dialogue(name_of_scout, "Please remember creating a balanced"
" team is key! All warriors will be presented based"
" on their names, race and most importantly"
" weapon-type.")
print_delay_dialogue(name_of_scout, "Let’s begin: ")
teammates_list = warrior_selection_process()
print_delay_dialogue(name_of_scout, "You have now selected your team;"
" you may proceed to train together.")
print_delay("You train with your team for months and"
" the time has finally come to go to the evil realm.")
print_delay_dialogue(name_of_queen, "You are guide by"
f" {name_of_queen} to the barrier.")
print_delay_dialogue(name_of_queen, "Good luck my heroes!")
barrier_battle_path5(teammates_list)
def warrior_binary_list_round(warrior_list):
warrior_binary_list = []
index_list = range(len(warrior_list))
for warriors in range(2):
index = random.choice(index_list)
warrior_binary_list.append(warrior_list[index])
return warrior_binary_list
def warrior_binary_options_print(warrior_binary_list, evil_leader_name,
evil_team_boolean):
if warrior_binary_list[0] == warrior_binary_list[1]:
if evil_team_boolean and warrior_binary_list[0][0] == evil_leader_name:
print_delay_dialogue(evil_leader_name, "It seems the dice has"
" chosen myself as the contestant"
" for this round.")
elif evil_team_boolean and \
warrior_binary_list[0][0] != evil_leader_name:
print_delay_dialogue(evil_leader_name, "It seems the dice has"
" chosen one of my fateful warriors.")
elif not evil_team_boolean and warrior_binary_list[0][0] == "Yourself":
print_delay("You will be the fighter for this round.")
elif not evil_team_boolean and warrior_binary_list[0][0] != "Yourself":
print_delay("Your dice role has yielded two identical names.")
else:
if evil_team_boolean:
print_delay_dialogue(evil_leader_name, f"I will choose between"
" two of my fateful warriors for"
" this fight.")
elif not evil_team_boolean:
print_delay("You must choose between your two provided options.")
def round_introduction_print(round):
print_delay(f"THIS IS ROUND #{round}.")
print_delay("It is time to roll the dices.")
print_delay("Here are the results for the dice roll.")
def input_dictionnary_check(keys, values, weapon):
if weapon in keys:
dictionnary = dict(zip(keys, values))
return dictionnary
else:
print("Wrong weapon was passed.")
def battle_result(weapon_avenger, weapon_enemy):
weapons_list = ["axe", "gun", "shield", "spear", "sword"]
results_lists = [{0: 0, 1: -1, 2: 1, 3: 1, 4: -1},
{0: 1, 1: 0, 2: -1, 3: -1, 4: 1},
{0: -1, 1: 1, 2: 0, 3: 1, 4: -1},
{0: -1, 1: 1, 2: -1, 3: 0, 4: 1},
{0: 1, 1: -1, 2: 1, 3: -1, 4: 0}]
integer_list = [0, 1, 2, 3, 4]
converter_dictionnary = input_dictionnary_check(weapons_list,
integer_list,
weapon_enemy)
results_nested_dictionnary = input_dictionnary_check(weapons_list,
results_lists,
weapon_avenger)
interger_weapon_enemy = converter_dictionnary.get(weapon_enemy)
avenger_match_result = results_nested_dictionnary[weapon_avenger][
interger_weapon_enemy]
return avenger_match_result
def score_updater(value, your_team_total_score, enemy_team_total_score):
team_score_list = [your_team_total_score, enemy_team_total_score]
if value == 1:
print_delay("You win this round.")
team_score_list[0] += 1
print_delay("The total score on your party is"
f" {team_score_list[0]} and the enemy's"
f" is {team_score_list[1]}.")
return team_score_list
elif value == -1:
print_delay("You lose this round.")
team_score_list[1] += 1
print_delay("The total score on your party is"
f" {team_score_list[0]} and the enemy's is"
f" {team_score_list[1]}.")
return team_score_list
elif value == 0:
print_delay("It's a tie for this round")
print_delay("The total score on your party is"
f" {team_score_list[0]} and the enemy's is"
f" {team_score_list[1]}.")
return team_score_list
def game_outcome(your_team_total_score, enemy_team_total_score,
evil_leader_name):
if your_team_total_score > enemy_team_total_score:
print_delay("It seems you have won the fiend's game.")
print_delay_dialogue(evil_leader_name, "Congrats on your victory"
" as promised we will surrender.")
print_delay_dialogue(evil_leader_name, "Farewell!")
print_delay("The evil creatures start to fadeaway"
" and so does the barrier.")
prompt_try_again(True)
elif enemy_team_total_score > your_team_total_score:
print_delay_dialogue(evil_leader_name, "I guess we are victorious.")
print_delay_dialogue(evil_leader_name, "Enjoy oblivion!")
prompt_try_again(False)
def warrior_games(team_list, evil_warriors_list, evil_leader_name):
round = 1
your_team_total_score = 0
enemy_team_total_score = 0
while your_team_total_score < 4 and enemy_team_total_score < 3:
your_team_options = warrior_binary_list_round(team_list)
enemy_team_options = warrior_binary_list_round(evil_warriors_list)
round_introduction_print(round)
print_delay("ENEMY TEAM:")
warrior_selection_print(enemy_team_options, round, False)
print_delay("Your TEAM:")
warrior_selection_print(your_team_options, round, False)
warrior_binary_options_print(enemy_team_options,
evil_leader_name, True)
warrior_binary_options_print(your_team_options,
evil_leader_name, False)
choice4 = binary_decision(" select individual #1.",
" select individual #2.")
your_warrior_selected = team_size_list(choice4, your_team_options)
enemy_warrior_selected = random.choice(enemy_team_options)
weapon_of_your_warrior = your_warrior_selected[2]
weapon_of_your_enemy = enemy_warrior_selected[2]
value_round = battle_result(weapon_of_your_warrior,
weapon_of_your_enemy)
score_list = score_updater(value_round, your_team_total_score,
enemy_team_total_score)
your_team_total_score = score_list[0]
enemy_team_total_score = score_list[1]
round += 1
game_outcome(your_team_total_score, enemy_team_total_score,
evil_leader_name)
def barrier_battle_path5(team_list):
villain_name_list = ['Frieza', 'Voldermort', 'Moro', 'Scar', 'Madara',
'Loki', '<NAME>', '<NAME>', 'Envy', 'Dante']
evil_creature_list = ['Devil', 'Goblin', 'Satan Moth', 'Dark Sorcerer',
'Dark Elf', 'Vampire', 'Draugr', 'Arachne',
'Cerberus', 'Ammit']
evil_leader_tribe = random.choice(evil_creature_list)
evil_leader_name = random.choice(villain_name_list)
villain_name_list.remove(evil_leader_name)
print_delay("You penetrate the barrier with your team, and"
" you are greeted by a fiend.")
print_delay("As it approaches you, your whole team including"
" yourself is unable to move.")
print_delay_dialogue("Fiend", f"My name is {evil_leader_name} of the"
f" {evil_leader_tribe} tribe.")
print_delay_dialogue(evil_leader_name, "I am the leader of the wonderful"
" movement that will reshape our worlds.")
print_delay_dialogue(evil_leader_name, "Don’t worry I will not hurt you"
" yet!")
print_delay_dialogue(evil_leader_name, "Let’s play a game to decide"
" the fate of our worlds.")
evil_warriors_list = warrior_list(4, villain_name_list, evil_creature_list,
weapon_type_list)
evil_leader_weapon = random.choice(weapon_type_list)
evil_leader_warrior_template = [evil_leader_name, evil_leader_tribe,
evil_leader_weapon]
evil_warriors_list.append(evil_leader_warrior_template)
print_delay_dialogue(evil_leader_name, "I have 4 supporting teammates so"
" both parties have 5 members in total including the"
" leaders of course.")
print_delay_dialogue(evil_leader_name, "We shall have 1-on-1 fights where"
" a contestant will come from each party.")
print_delay_dialogue(evil_leader_name, "A victory in a fight will result"
" in that party receiving a point.")
print_delay_dialogue(evil_leader_name, "First team to win 3 rounds wins"
" the game.")
print_delay_dialogue(evil_leader_name, "You win and we will surrender."
" On the other hand, if you lose hahahaah…")
print_delay_dialogue(evil_leader_name, "I don’t have to tell you what will"
" happen.")
print_delay_dialogue(evil_leader_name, "Each team will be handed two"
" five-faced dices with each face will have the name"
" of the contestant.")
print_delay_dialogue(evil_leader_name, "We both roll our dices and"
" based on the names presented on the dice; each"
" team shall decide whom from the two names presented"
" shall represent their team for that round.")
print_delay_dialogue(evil_leader_name, "This decision of each team"
" will only be revealed right before a round"
" must begin.")
print_delay_dialogue(evil_leader_name, "If both dices result in the"
" same name that contestant must represent the"
" team for that round.")
print_delay_dialogue(evil_leader_name, "A contestant can participate"
" in more than one round.")
print_delay("You notice the enemies have similar weapons as yours.")
print_delay("You remember that each weapon-type can beat two types,"
" lose to two other types and come in a stalemate"
" in a mirror match.")
print_delay("unfortunately, you don't remember the"
" advantageous matchups but suddenly ...")
print_delay_dialogue(evil_leader_name, "Let's begin the games!")
warrior_games(team_list, evil_warriors_list, evil_leader_name)
if __name__ == "__main__":
start()
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qiskit/transpiler/passes/optimization/optimize_1q_decomposition.py | rao107/qiskit-terra | 0 | 196132 | # This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""Optimize chains of single-qubit gates using Euler 1q decomposer"""
import logging
import numpy as np
from qiskit.quantum_info import Operator
from qiskit.transpiler.basepasses import TransformationPass
from qiskit.quantum_info.synthesis import one_qubit_decompose
from qiskit.converters import circuit_to_dag
LOG = logging.getLogger(__name__)
class Optimize1qGatesDecomposition(TransformationPass):
"""Optimize chains of single-qubit gates by combining them into a single gate."""
def __init__(self, basis=None):
"""Optimize1qGatesDecomposition initializer.
Args:
basis (list[str]): Basis gates to consider, e.g. `['u3', 'cx']`. For the effects
of this pass, the basis is the set intersection between the `basis` parameter
and the Euler basis.
"""
super().__init__()
self.basis = None
if basis:
self.basis = []
basis_set = set(basis)
for basis_name, gates in one_qubit_decompose.ONE_QUBIT_EULER_BASIS_GATES.items():
if set(gates).issubset(basis_set):
self.basis.append(one_qubit_decompose.OneQubitEulerDecomposer(basis_name))
def run(self, dag):
"""Run the Optimize1qGatesDecomposition pass on `dag`.
Args:
dag (DAGCircuit): the DAG to be optimized.
Returns:
DAGCircuit: the optimized DAG.
"""
if not self.basis:
LOG.info("Skipping pass because no basis is set")
return dag
runs = dag.collect_1q_runs()
identity_matrix = np.eye(2)
for run in runs:
# Don't try to optimize a single 1q gate
if len(run) <= 1:
params = run[0].op.params
# Remove single identity gates
if len(params) > 0 and np.array_equal(run[0].op.to_matrix(),
identity_matrix):
dag.remove_op_node(run[0])
continue
new_circs = []
operator = Operator(run[0].op)
for gate in run[1:]:
operator = operator.compose(gate.op)
for decomposer in self.basis:
new_circs.append(decomposer(operator))
if new_circs:
new_circ = min(new_circs, key=len)
if len(run) > len(new_circ):
new_dag = circuit_to_dag(new_circ)
dag.substitute_node_with_dag(run[0], new_dag)
# Delete the other nodes in the run
for current_node in run[1:]:
dag.remove_op_node(current_node)
return dag
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sdk/python/pulumi_gcp/compute/region_instance_group_manager.py | sisisin/pulumi-gcp | 121 | 42004 | <filename>sdk/python/pulumi_gcp/compute/region_instance_group_manager.py<gh_stars>100-1000
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
from . import outputs
from ._inputs import *
__all__ = ['RegionInstanceGroupManagerArgs', 'RegionInstanceGroupManager']
@pulumi.input_type
class RegionInstanceGroupManagerArgs:
def __init__(__self__, *,
base_instance_name: pulumi.Input[str],
versions: pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]],
auto_healing_policies: Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']] = None,
description: Optional[pulumi.Input[str]] = None,
distribution_policy_target_shape: Optional[pulumi.Input[str]] = None,
distribution_policy_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
named_ports: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
stateful_disks: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]] = None,
target_pools: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
target_size: Optional[pulumi.Input[int]] = None,
update_policy: Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']] = None,
wait_for_instances: Optional[pulumi.Input[bool]] = None,
wait_for_instances_status: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a RegionInstanceGroupManager resource.
:param pulumi.Input[str] base_instance_name: The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]] versions: Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
:param pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs'] auto_healing_policies: The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
:param pulumi.Input[str] description: An optional textual description of the instance
group manager.
:param pulumi.Input[str] distribution_policy_target_shape: The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
:param pulumi.Input[Sequence[pulumi.Input[str]]] distribution_policy_zones: The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
:param pulumi.Input[str] name: - Version name.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]] named_ports: The named port configuration. See the section below
for details on configuration.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
:param pulumi.Input[str] region: The region where the managed instance group resides. If not provided, the provider region is used.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]] stateful_disks: Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
:param pulumi.Input[Sequence[pulumi.Input[str]]] target_pools: The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
:param pulumi.Input[int] target_size: - The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
:param pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs'] update_policy: The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
:param pulumi.Input[bool] wait_for_instances: Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
:param pulumi.Input[str] wait_for_instances_status: When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
pulumi.set(__self__, "base_instance_name", base_instance_name)
pulumi.set(__self__, "versions", versions)
if auto_healing_policies is not None:
pulumi.set(__self__, "auto_healing_policies", auto_healing_policies)
if description is not None:
pulumi.set(__self__, "description", description)
if distribution_policy_target_shape is not None:
pulumi.set(__self__, "distribution_policy_target_shape", distribution_policy_target_shape)
if distribution_policy_zones is not None:
pulumi.set(__self__, "distribution_policy_zones", distribution_policy_zones)
if name is not None:
pulumi.set(__self__, "name", name)
if named_ports is not None:
pulumi.set(__self__, "named_ports", named_ports)
if project is not None:
pulumi.set(__self__, "project", project)
if region is not None:
pulumi.set(__self__, "region", region)
if stateful_disks is not None:
pulumi.set(__self__, "stateful_disks", stateful_disks)
if target_pools is not None:
pulumi.set(__self__, "target_pools", target_pools)
if target_size is not None:
pulumi.set(__self__, "target_size", target_size)
if update_policy is not None:
pulumi.set(__self__, "update_policy", update_policy)
if wait_for_instances is not None:
pulumi.set(__self__, "wait_for_instances", wait_for_instances)
if wait_for_instances_status is not None:
pulumi.set(__self__, "wait_for_instances_status", wait_for_instances_status)
@property
@pulumi.getter(name="baseInstanceName")
def base_instance_name(self) -> pulumi.Input[str]:
"""
The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
"""
return pulumi.get(self, "base_instance_name")
@base_instance_name.setter
def base_instance_name(self, value: pulumi.Input[str]):
pulumi.set(self, "base_instance_name", value)
@property
@pulumi.getter
def versions(self) -> pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]]:
"""
Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
"""
return pulumi.get(self, "versions")
@versions.setter
def versions(self, value: pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]]):
pulumi.set(self, "versions", value)
@property
@pulumi.getter(name="autoHealingPolicies")
def auto_healing_policies(self) -> Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]:
"""
The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
"""
return pulumi.get(self, "auto_healing_policies")
@auto_healing_policies.setter
def auto_healing_policies(self, value: Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]):
pulumi.set(self, "auto_healing_policies", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
An optional textual description of the instance
group manager.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="distributionPolicyTargetShape")
def distribution_policy_target_shape(self) -> Optional[pulumi.Input[str]]:
"""
The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
"""
return pulumi.get(self, "distribution_policy_target_shape")
@distribution_policy_target_shape.setter
def distribution_policy_target_shape(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "distribution_policy_target_shape", value)
@property
@pulumi.getter(name="distributionPolicyZones")
def distribution_policy_zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
"""
return pulumi.get(self, "distribution_policy_zones")
@distribution_policy_zones.setter
def distribution_policy_zones(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "distribution_policy_zones", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
- Version name.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="namedPorts")
def named_ports(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]]:
"""
The named port configuration. See the section below
for details on configuration.
"""
return pulumi.get(self, "named_ports")
@named_ports.setter
def named_ports(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]]):
pulumi.set(self, "named_ports", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
The region where the managed instance group resides. If not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="statefulDisks")
def stateful_disks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]]:
"""
Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
"""
return pulumi.get(self, "stateful_disks")
@stateful_disks.setter
def stateful_disks(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]]):
pulumi.set(self, "stateful_disks", value)
@property
@pulumi.getter(name="targetPools")
def target_pools(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
"""
return pulumi.get(self, "target_pools")
@target_pools.setter
def target_pools(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "target_pools", value)
@property
@pulumi.getter(name="targetSize")
def target_size(self) -> Optional[pulumi.Input[int]]:
"""
- The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
"""
return pulumi.get(self, "target_size")
@target_size.setter
def target_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "target_size", value)
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']]:
"""
The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
"""
return pulumi.get(self, "update_policy")
@update_policy.setter
def update_policy(self, value: Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']]):
pulumi.set(self, "update_policy", value)
@property
@pulumi.getter(name="waitForInstances")
def wait_for_instances(self) -> Optional[pulumi.Input[bool]]:
"""
Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
"""
return pulumi.get(self, "wait_for_instances")
@wait_for_instances.setter
def wait_for_instances(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "wait_for_instances", value)
@property
@pulumi.getter(name="waitForInstancesStatus")
def wait_for_instances_status(self) -> Optional[pulumi.Input[str]]:
"""
When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
return pulumi.get(self, "wait_for_instances_status")
@wait_for_instances_status.setter
def wait_for_instances_status(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "wait_for_instances_status", value)
@pulumi.input_type
class _RegionInstanceGroupManagerState:
def __init__(__self__, *,
auto_healing_policies: Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']] = None,
base_instance_name: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
distribution_policy_target_shape: Optional[pulumi.Input[str]] = None,
distribution_policy_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
fingerprint: Optional[pulumi.Input[str]] = None,
instance_group: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
named_ports: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
self_link: Optional[pulumi.Input[str]] = None,
stateful_disks: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]] = None,
statuses: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatusArgs']]]] = None,
target_pools: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
target_size: Optional[pulumi.Input[int]] = None,
update_policy: Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']] = None,
versions: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]]] = None,
wait_for_instances: Optional[pulumi.Input[bool]] = None,
wait_for_instances_status: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering RegionInstanceGroupManager resources.
:param pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs'] auto_healing_policies: The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
:param pulumi.Input[str] base_instance_name: The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
:param pulumi.Input[str] description: An optional textual description of the instance
group manager.
:param pulumi.Input[str] distribution_policy_target_shape: The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
:param pulumi.Input[Sequence[pulumi.Input[str]]] distribution_policy_zones: The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
:param pulumi.Input[str] fingerprint: The fingerprint of the instance group manager.
:param pulumi.Input[str] instance_group: The full URL of the instance group created by the manager.
:param pulumi.Input[str] name: - Version name.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]] named_ports: The named port configuration. See the section below
for details on configuration.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
:param pulumi.Input[str] region: The region where the managed instance group resides. If not provided, the provider region is used.
:param pulumi.Input[str] self_link: The URL of the created resource.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]] stateful_disks: Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatusArgs']]] statuses: The status of this managed instance group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] target_pools: The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
:param pulumi.Input[int] target_size: - The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
:param pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs'] update_policy: The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
:param pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]] versions: Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
:param pulumi.Input[bool] wait_for_instances: Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
:param pulumi.Input[str] wait_for_instances_status: When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
if auto_healing_policies is not None:
pulumi.set(__self__, "auto_healing_policies", auto_healing_policies)
if base_instance_name is not None:
pulumi.set(__self__, "base_instance_name", base_instance_name)
if description is not None:
pulumi.set(__self__, "description", description)
if distribution_policy_target_shape is not None:
pulumi.set(__self__, "distribution_policy_target_shape", distribution_policy_target_shape)
if distribution_policy_zones is not None:
pulumi.set(__self__, "distribution_policy_zones", distribution_policy_zones)
if fingerprint is not None:
pulumi.set(__self__, "fingerprint", fingerprint)
if instance_group is not None:
pulumi.set(__self__, "instance_group", instance_group)
if name is not None:
pulumi.set(__self__, "name", name)
if named_ports is not None:
pulumi.set(__self__, "named_ports", named_ports)
if project is not None:
pulumi.set(__self__, "project", project)
if region is not None:
pulumi.set(__self__, "region", region)
if self_link is not None:
pulumi.set(__self__, "self_link", self_link)
if stateful_disks is not None:
pulumi.set(__self__, "stateful_disks", stateful_disks)
if statuses is not None:
pulumi.set(__self__, "statuses", statuses)
if target_pools is not None:
pulumi.set(__self__, "target_pools", target_pools)
if target_size is not None:
pulumi.set(__self__, "target_size", target_size)
if update_policy is not None:
pulumi.set(__self__, "update_policy", update_policy)
if versions is not None:
pulumi.set(__self__, "versions", versions)
if wait_for_instances is not None:
pulumi.set(__self__, "wait_for_instances", wait_for_instances)
if wait_for_instances_status is not None:
pulumi.set(__self__, "wait_for_instances_status", wait_for_instances_status)
@property
@pulumi.getter(name="autoHealingPolicies")
def auto_healing_policies(self) -> Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]:
"""
The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
"""
return pulumi.get(self, "auto_healing_policies")
@auto_healing_policies.setter
def auto_healing_policies(self, value: Optional[pulumi.Input['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]):
pulumi.set(self, "auto_healing_policies", value)
@property
@pulumi.getter(name="baseInstanceName")
def base_instance_name(self) -> Optional[pulumi.Input[str]]:
"""
The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
"""
return pulumi.get(self, "base_instance_name")
@base_instance_name.setter
def base_instance_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "base_instance_name", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
An optional textual description of the instance
group manager.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="distributionPolicyTargetShape")
def distribution_policy_target_shape(self) -> Optional[pulumi.Input[str]]:
"""
The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
"""
return pulumi.get(self, "distribution_policy_target_shape")
@distribution_policy_target_shape.setter
def distribution_policy_target_shape(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "distribution_policy_target_shape", value)
@property
@pulumi.getter(name="distributionPolicyZones")
def distribution_policy_zones(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
"""
return pulumi.get(self, "distribution_policy_zones")
@distribution_policy_zones.setter
def distribution_policy_zones(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "distribution_policy_zones", value)
@property
@pulumi.getter
def fingerprint(self) -> Optional[pulumi.Input[str]]:
"""
The fingerprint of the instance group manager.
"""
return pulumi.get(self, "fingerprint")
@fingerprint.setter
def fingerprint(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "fingerprint", value)
@property
@pulumi.getter(name="instanceGroup")
def instance_group(self) -> Optional[pulumi.Input[str]]:
"""
The full URL of the instance group created by the manager.
"""
return pulumi.get(self, "instance_group")
@instance_group.setter
def instance_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "instance_group", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
- Version name.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="namedPorts")
def named_ports(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]]:
"""
The named port configuration. See the section below
for details on configuration.
"""
return pulumi.get(self, "named_ports")
@named_ports.setter
def named_ports(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerNamedPortArgs']]]]):
pulumi.set(self, "named_ports", value)
@property
@pulumi.getter
def project(self) -> Optional[pulumi.Input[str]]:
"""
The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@project.setter
def project(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "project", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[str]]:
"""
The region where the managed instance group resides. If not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> Optional[pulumi.Input[str]]:
"""
The URL of the created resource.
"""
return pulumi.get(self, "self_link")
@self_link.setter
def self_link(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "self_link", value)
@property
@pulumi.getter(name="statefulDisks")
def stateful_disks(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]]:
"""
Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
"""
return pulumi.get(self, "stateful_disks")
@stateful_disks.setter
def stateful_disks(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatefulDiskArgs']]]]):
pulumi.set(self, "stateful_disks", value)
@property
@pulumi.getter
def statuses(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatusArgs']]]]:
"""
The status of this managed instance group.
"""
return pulumi.get(self, "statuses")
@statuses.setter
def statuses(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerStatusArgs']]]]):
pulumi.set(self, "statuses", value)
@property
@pulumi.getter(name="targetPools")
def target_pools(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
"""
return pulumi.get(self, "target_pools")
@target_pools.setter
def target_pools(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "target_pools", value)
@property
@pulumi.getter(name="targetSize")
def target_size(self) -> Optional[pulumi.Input[int]]:
"""
- The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
"""
return pulumi.get(self, "target_size")
@target_size.setter
def target_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "target_size", value)
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']]:
"""
The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
"""
return pulumi.get(self, "update_policy")
@update_policy.setter
def update_policy(self, value: Optional[pulumi.Input['RegionInstanceGroupManagerUpdatePolicyArgs']]):
pulumi.set(self, "update_policy", value)
@property
@pulumi.getter
def versions(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]]]:
"""
Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
"""
return pulumi.get(self, "versions")
@versions.setter
def versions(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegionInstanceGroupManagerVersionArgs']]]]):
pulumi.set(self, "versions", value)
@property
@pulumi.getter(name="waitForInstances")
def wait_for_instances(self) -> Optional[pulumi.Input[bool]]:
"""
Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
"""
return pulumi.get(self, "wait_for_instances")
@wait_for_instances.setter
def wait_for_instances(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "wait_for_instances", value)
@property
@pulumi.getter(name="waitForInstancesStatus")
def wait_for_instances_status(self) -> Optional[pulumi.Input[str]]:
"""
When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
return pulumi.get(self, "wait_for_instances_status")
@wait_for_instances_status.setter
def wait_for_instances_status(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "wait_for_instances_status", value)
class RegionInstanceGroupManager(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
auto_healing_policies: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]] = None,
base_instance_name: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
distribution_policy_target_shape: Optional[pulumi.Input[str]] = None,
distribution_policy_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
named_ports: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
stateful_disks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]]] = None,
target_pools: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
target_size: Optional[pulumi.Input[int]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']]] = None,
versions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]]] = None,
wait_for_instances: Optional[pulumi.Input[bool]] = None,
wait_for_instances_status: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
The Google Compute Engine Regional Instance Group Manager API creates and manages pools
of homogeneous Compute Engine virtual machine instances from a common instance
template.
To get more information about regionInstanceGroupManagers, see:
* [API documentation](https://cloud.google.com/compute/docs/reference/latest/regionInstanceGroupManagers)
* How-to Guides
* [Regional Instance Groups Guide](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups)
> **Note:** Use [compute.InstanceGroupManager](https://www.terraform.io/docs/providers/google/r/compute_instance_group_manager.html) to create a zonal instance group manager.
## Example Usage
### With Top Level Instance Template (`Google` Provider)
```python
import pulumi
import pulumi_gcp as gcp
autohealing = gcp.compute.HealthCheck("autohealing",
check_interval_sec=5,
timeout_sec=5,
healthy_threshold=2,
unhealthy_threshold=10,
http_health_check=gcp.compute.HealthCheckHttpHealthCheckArgs(
request_path="/healthz",
port=8080,
))
appserver = gcp.compute.RegionInstanceGroupManager("appserver",
base_instance_name="app",
region="us-central1",
distribution_policy_zones=[
"us-central1-a",
"us-central1-f",
],
versions=[gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver"]["id"],
)],
target_pools=[google_compute_target_pool["appserver"]["id"]],
target_size=2,
named_ports=[gcp.compute.RegionInstanceGroupManagerNamedPortArgs(
name="custom",
port=8888,
)],
auto_healing_policies=gcp.compute.RegionInstanceGroupManagerAutoHealingPoliciesArgs(
health_check=autohealing.id,
initial_delay_sec=300,
))
```
### With Multiple Versions
```python
import pulumi
import pulumi_gcp as gcp
appserver = gcp.compute.RegionInstanceGroupManager("appserver",
base_instance_name="app",
region="us-central1",
target_size=5,
versions=[
gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver"]["id"],
),
gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver-canary"]["id"],
target_size=gcp.compute.RegionInstanceGroupManagerVersionTargetSizeArgs(
fixed=1,
),
),
])
```
## Import
Instance group managers can be imported using the `name`, e.g.
```sh
$ pulumi import gcp:compute/regionInstanceGroupManager:RegionInstanceGroupManager appserver appserver-igm
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']] auto_healing_policies: The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
:param pulumi.Input[str] base_instance_name: The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
:param pulumi.Input[str] description: An optional textual description of the instance
group manager.
:param pulumi.Input[str] distribution_policy_target_shape: The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
:param pulumi.Input[Sequence[pulumi.Input[str]]] distribution_policy_zones: The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
:param pulumi.Input[str] name: - Version name.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]] named_ports: The named port configuration. See the section below
for details on configuration.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
:param pulumi.Input[str] region: The region where the managed instance group resides. If not provided, the provider region is used.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]] stateful_disks: Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
:param pulumi.Input[Sequence[pulumi.Input[str]]] target_pools: The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
:param pulumi.Input[int] target_size: - The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
:param pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']] update_policy: The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]] versions: Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
:param pulumi.Input[bool] wait_for_instances: Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
:param pulumi.Input[str] wait_for_instances_status: When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: RegionInstanceGroupManagerArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
The Google Compute Engine Regional Instance Group Manager API creates and manages pools
of homogeneous Compute Engine virtual machine instances from a common instance
template.
To get more information about regionInstanceGroupManagers, see:
* [API documentation](https://cloud.google.com/compute/docs/reference/latest/regionInstanceGroupManagers)
* How-to Guides
* [Regional Instance Groups Guide](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups)
> **Note:** Use [compute.InstanceGroupManager](https://www.terraform.io/docs/providers/google/r/compute_instance_group_manager.html) to create a zonal instance group manager.
## Example Usage
### With Top Level Instance Template (`Google` Provider)
```python
import pulumi
import pulumi_gcp as gcp
autohealing = gcp.compute.HealthCheck("autohealing",
check_interval_sec=5,
timeout_sec=5,
healthy_threshold=2,
unhealthy_threshold=10,
http_health_check=gcp.compute.HealthCheckHttpHealthCheckArgs(
request_path="/healthz",
port=8080,
))
appserver = gcp.compute.RegionInstanceGroupManager("appserver",
base_instance_name="app",
region="us-central1",
distribution_policy_zones=[
"us-central1-a",
"us-central1-f",
],
versions=[gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver"]["id"],
)],
target_pools=[google_compute_target_pool["appserver"]["id"]],
target_size=2,
named_ports=[gcp.compute.RegionInstanceGroupManagerNamedPortArgs(
name="custom",
port=8888,
)],
auto_healing_policies=gcp.compute.RegionInstanceGroupManagerAutoHealingPoliciesArgs(
health_check=autohealing.id,
initial_delay_sec=300,
))
```
### With Multiple Versions
```python
import pulumi
import pulumi_gcp as gcp
appserver = gcp.compute.RegionInstanceGroupManager("appserver",
base_instance_name="app",
region="us-central1",
target_size=5,
versions=[
gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver"]["id"],
),
gcp.compute.RegionInstanceGroupManagerVersionArgs(
instance_template=google_compute_instance_template["appserver-canary"]["id"],
target_size=gcp.compute.RegionInstanceGroupManagerVersionTargetSizeArgs(
fixed=1,
),
),
])
```
## Import
Instance group managers can be imported using the `name`, e.g.
```sh
$ pulumi import gcp:compute/regionInstanceGroupManager:RegionInstanceGroupManager appserver appserver-igm
```
:param str resource_name: The name of the resource.
:param RegionInstanceGroupManagerArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(RegionInstanceGroupManagerArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
auto_healing_policies: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]] = None,
base_instance_name: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
distribution_policy_target_shape: Optional[pulumi.Input[str]] = None,
distribution_policy_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
name: Optional[pulumi.Input[str]] = None,
named_ports: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
stateful_disks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]]] = None,
target_pools: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
target_size: Optional[pulumi.Input[int]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']]] = None,
versions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]]] = None,
wait_for_instances: Optional[pulumi.Input[bool]] = None,
wait_for_instances_status: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = RegionInstanceGroupManagerArgs.__new__(RegionInstanceGroupManagerArgs)
__props__.__dict__["auto_healing_policies"] = auto_healing_policies
if base_instance_name is None and not opts.urn:
raise TypeError("Missing required property 'base_instance_name'")
__props__.__dict__["base_instance_name"] = base_instance_name
__props__.__dict__["description"] = description
__props__.__dict__["distribution_policy_target_shape"] = distribution_policy_target_shape
__props__.__dict__["distribution_policy_zones"] = distribution_policy_zones
__props__.__dict__["name"] = name
__props__.__dict__["named_ports"] = named_ports
__props__.__dict__["project"] = project
__props__.__dict__["region"] = region
__props__.__dict__["stateful_disks"] = stateful_disks
__props__.__dict__["target_pools"] = target_pools
__props__.__dict__["target_size"] = target_size
__props__.__dict__["update_policy"] = update_policy
if versions is None and not opts.urn:
raise TypeError("Missing required property 'versions'")
__props__.__dict__["versions"] = versions
__props__.__dict__["wait_for_instances"] = wait_for_instances
__props__.__dict__["wait_for_instances_status"] = wait_for_instances_status
__props__.__dict__["fingerprint"] = None
__props__.__dict__["instance_group"] = None
__props__.__dict__["self_link"] = None
__props__.__dict__["statuses"] = None
super(RegionInstanceGroupManager, __self__).__init__(
'gcp:compute/regionInstanceGroupManager:RegionInstanceGroupManager',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
auto_healing_policies: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']]] = None,
base_instance_name: Optional[pulumi.Input[str]] = None,
description: Optional[pulumi.Input[str]] = None,
distribution_policy_target_shape: Optional[pulumi.Input[str]] = None,
distribution_policy_zones: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
fingerprint: Optional[pulumi.Input[str]] = None,
instance_group: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
named_ports: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]]] = None,
project: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[str]] = None,
self_link: Optional[pulumi.Input[str]] = None,
stateful_disks: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]]] = None,
statuses: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatusArgs']]]]] = None,
target_pools: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
target_size: Optional[pulumi.Input[int]] = None,
update_policy: Optional[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']]] = None,
versions: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]]] = None,
wait_for_instances: Optional[pulumi.Input[bool]] = None,
wait_for_instances_status: Optional[pulumi.Input[str]] = None) -> 'RegionInstanceGroupManager':
"""
Get an existing RegionInstanceGroupManager resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerAutoHealingPoliciesArgs']] auto_healing_policies: The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
:param pulumi.Input[str] base_instance_name: The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
:param pulumi.Input[str] description: An optional textual description of the instance
group manager.
:param pulumi.Input[str] distribution_policy_target_shape: The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
:param pulumi.Input[Sequence[pulumi.Input[str]]] distribution_policy_zones: The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
:param pulumi.Input[str] fingerprint: The fingerprint of the instance group manager.
:param pulumi.Input[str] instance_group: The full URL of the instance group created by the manager.
:param pulumi.Input[str] name: - Version name.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerNamedPortArgs']]]] named_ports: The named port configuration. See the section below
for details on configuration.
:param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
:param pulumi.Input[str] region: The region where the managed instance group resides. If not provided, the provider region is used.
:param pulumi.Input[str] self_link: The URL of the created resource.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatefulDiskArgs']]]] stateful_disks: Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerStatusArgs']]]] statuses: The status of this managed instance group.
:param pulumi.Input[Sequence[pulumi.Input[str]]] target_pools: The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
:param pulumi.Input[int] target_size: - The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
:param pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerUpdatePolicyArgs']] update_policy: The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegionInstanceGroupManagerVersionArgs']]]] versions: Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
:param pulumi.Input[bool] wait_for_instances: Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
:param pulumi.Input[str] wait_for_instances_status: When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _RegionInstanceGroupManagerState.__new__(_RegionInstanceGroupManagerState)
__props__.__dict__["auto_healing_policies"] = auto_healing_policies
__props__.__dict__["base_instance_name"] = base_instance_name
__props__.__dict__["description"] = description
__props__.__dict__["distribution_policy_target_shape"] = distribution_policy_target_shape
__props__.__dict__["distribution_policy_zones"] = distribution_policy_zones
__props__.__dict__["fingerprint"] = fingerprint
__props__.__dict__["instance_group"] = instance_group
__props__.__dict__["name"] = name
__props__.__dict__["named_ports"] = named_ports
__props__.__dict__["project"] = project
__props__.__dict__["region"] = region
__props__.__dict__["self_link"] = self_link
__props__.__dict__["stateful_disks"] = stateful_disks
__props__.__dict__["statuses"] = statuses
__props__.__dict__["target_pools"] = target_pools
__props__.__dict__["target_size"] = target_size
__props__.__dict__["update_policy"] = update_policy
__props__.__dict__["versions"] = versions
__props__.__dict__["wait_for_instances"] = wait_for_instances
__props__.__dict__["wait_for_instances_status"] = wait_for_instances_status
return RegionInstanceGroupManager(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="autoHealingPolicies")
def auto_healing_policies(self) -> pulumi.Output[Optional['outputs.RegionInstanceGroupManagerAutoHealingPolicies']]:
"""
The autohealing policies for this managed instance
group. You can specify only one value. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/creating-groups-of-managed-instances#monitoring_groups).
"""
return pulumi.get(self, "auto_healing_policies")
@property
@pulumi.getter(name="baseInstanceName")
def base_instance_name(self) -> pulumi.Output[str]:
"""
The base instance name to use for
instances in this group. The value must be a valid
[RFC1035](https://www.ietf.org/rfc/rfc1035.txt) name. Supported characters
are lowercase letters, numbers, and hyphens (-). Instances are named by
appending a hyphen and a random four-character string to the base instance
name.
"""
return pulumi.get(self, "base_instance_name")
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
"""
An optional textual description of the instance
group manager.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter(name="distributionPolicyTargetShape")
def distribution_policy_target_shape(self) -> pulumi.Output[str]:
"""
The shape to which the group converges either proactively or on resize events (depending on the value set in update_policy.0.instance_redistribution_type). For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/regional-mig-distribution-shape).
"""
return pulumi.get(self, "distribution_policy_target_shape")
@property
@pulumi.getter(name="distributionPolicyZones")
def distribution_policy_zones(self) -> pulumi.Output[Sequence[str]]:
"""
The distribution policy for this managed instance
group. You can specify one or more values. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/distributing-instances-with-regional-instance-groups#selectingzones).
"""
return pulumi.get(self, "distribution_policy_zones")
@property
@pulumi.getter
def fingerprint(self) -> pulumi.Output[str]:
"""
The fingerprint of the instance group manager.
"""
return pulumi.get(self, "fingerprint")
@property
@pulumi.getter(name="instanceGroup")
def instance_group(self) -> pulumi.Output[str]:
"""
The full URL of the instance group created by the manager.
"""
return pulumi.get(self, "instance_group")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
- Version name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="namedPorts")
def named_ports(self) -> pulumi.Output[Optional[Sequence['outputs.RegionInstanceGroupManagerNamedPort']]]:
"""
The named port configuration. See the section below
for details on configuration.
"""
return pulumi.get(self, "named_ports")
@property
@pulumi.getter
def project(self) -> pulumi.Output[str]:
"""
The ID of the project in which the resource belongs. If it
is not provided, the provider project is used.
"""
return pulumi.get(self, "project")
@property
@pulumi.getter
def region(self) -> pulumi.Output[str]:
"""
The region where the managed instance group resides. If not provided, the provider region is used.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter(name="selfLink")
def self_link(self) -> pulumi.Output[str]:
"""
The URL of the created resource.
"""
return pulumi.get(self, "self_link")
@property
@pulumi.getter(name="statefulDisks")
def stateful_disks(self) -> pulumi.Output[Optional[Sequence['outputs.RegionInstanceGroupManagerStatefulDisk']]]:
"""
Disks created on the instances that will be preserved on instance delete, update, etc. Structure is documented below. For more information see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/configuring-stateful-disks-in-migs). Proactive cross zone instance redistribution must be disabled before you can update stateful disks on existing instance group managers. This can be controlled via the `update_policy`.
"""
return pulumi.get(self, "stateful_disks")
@property
@pulumi.getter
def statuses(self) -> pulumi.Output[Sequence['outputs.RegionInstanceGroupManagerStatus']]:
"""
The status of this managed instance group.
"""
return pulumi.get(self, "statuses")
@property
@pulumi.getter(name="targetPools")
def target_pools(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
The full URL of all target pools to which new
instances in the group are added. Updating the target pools attribute does
not affect existing instances.
"""
return pulumi.get(self, "target_pools")
@property
@pulumi.getter(name="targetSize")
def target_size(self) -> pulumi.Output[int]:
"""
- The number of instances calculated as a fixed number or a percentage depending on the settings. Structure is documented below.
"""
return pulumi.get(self, "target_size")
@property
@pulumi.getter(name="updatePolicy")
def update_policy(self) -> pulumi.Output['outputs.RegionInstanceGroupManagerUpdatePolicy']:
"""
The update policy for this managed instance group. Structure is documented below. For more information, see the [official documentation](https://cloud.google.com/compute/docs/instance-groups/updating-managed-instance-groups) and [API](https://cloud.google.com/compute/docs/reference/rest/beta/regionInstanceGroupManagers/patch)
"""
return pulumi.get(self, "update_policy")
@property
@pulumi.getter
def versions(self) -> pulumi.Output[Sequence['outputs.RegionInstanceGroupManagerVersion']]:
"""
Application versions managed by this instance group. Each
version deals with a specific instance template, allowing canary release scenarios.
Structure is documented below.
"""
return pulumi.get(self, "versions")
@property
@pulumi.getter(name="waitForInstances")
def wait_for_instances(self) -> pulumi.Output[Optional[bool]]:
"""
Whether to wait for all instances to be created/updated before
returning. Note that if this is set to true and the operation does not succeed, the provider will
continue trying until it times out.
"""
return pulumi.get(self, "wait_for_instances")
@property
@pulumi.getter(name="waitForInstancesStatus")
def wait_for_instances_status(self) -> pulumi.Output[Optional[str]]:
"""
When used with `wait_for_instances` it specifies the status to wait for.
When `STABLE` is specified this resource will wait until the instances are stable before returning. When `UPDATED` is
set, it will wait for the version target to be reached and any per instance configs to be effective as well as all
instances to be stable before returning. The possible values are `STABLE` and `UPDATED`
"""
return pulumi.get(self, "wait_for_instances_status")
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resource/pypi/cffi-1.9.1/demo/api.py | hipnusleo/Laserjet | 0 | 67486 | <gh_stars>0
import cffi
from cffi import FFI
class PythonFFI(FFI):
def __init__(self, backend=None):
FFI.__init__(self, backend=backend)
self._pyexports = {}
def pyexport(self, signature):
tp = self._typeof(signature, consider_function_as_funcptr=True)
def decorator(func):
name = func.__name__
if name in self._pyexports:
raise cffi.CDefError("duplicate pyexport'ed function %r"
% (name,))
callback_var = self.getctype(tp, name)
self.cdef("%s;" % callback_var)
self._pyexports[name] = _PyExport(tp, func)
return decorator
def verify(self, source='', **kwargs):
extras = []
pyexports = sorted(self._pyexports.items())
for name, export in pyexports:
callback_var = self.getctype(export.tp, name)
extras.append("%s;" % callback_var)
extras.append(source)
source = '\n'.join(extras)
lib = FFI.verify(self, source, **kwargs)
for name, export in pyexports:
cb = self.callback(export.tp, export.func)
export.cb = cb
setattr(lib, name, cb)
return lib
class _PyExport(object):
def __init__(self, tp, func):
self.tp = tp
self.func = func
if __name__ == '__main__':
ffi = PythonFFI()
@ffi.pyexport("int(int)")
def add1(n):
print n
return n + 1
ffi.cdef("""
int f(int);
""")
lib = ffi.verify("""
int f(int x) {
return add1(add1(x));
}
""")
assert lib.f(5) == 7
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29888,
29898,
29945,
29897,
1275,
29871,
29955,
30004,
13,
2
] |
ReLERNN/imports.py | peterdfields/ReLERNN | 14 | 40829 | import glob
import pickle
import sys
import msprime as msp
import numpy as np
import os
import multiprocessing as mp
import shutil
import random
import copy
import argparse
import h5py
import allel
import time
from sklearn.neighbors import NearestNeighbors
from sklearn.utils import resample
import matplotlib as mpl
mpl.use('pdf')
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.models import Model, model_from_json
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TerminateOnNaN
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] |
get_episodes.py | sam-drew/random-episodes | 0 | 157092 | <gh_stars>0
from selenium import webdriver
import time
def log_id(ID):
f = open("IDs.txt", "a")
f.write((str((ID + "\n"))))
f.close()
print("Logging ID: " + ID)
def strip_id(url):
splitString = url.rsplit("?", 1)
splitString = splitString[0].rsplit("/")
ep_id = splitString[len(splitString) - 1]
return(ep_id)
driver = webdriver.Chrome('/home/user/Downloads/chromedriver')
last_id = "80006978"
driver.get("https://www.netflix.com/watch/80006978")
# Really really hacky way of getting credentials set. User has 42 seconds to
# log in and return to ep1 URL.
time.sleep(40)
print("I AM AWAKE.\n2 seconds remaining.\nMake sure you're on ep1 page.")
time.sleep(2)
# User manualy clicks through the episodes until reaches S9E24. Logs as url
# changes.
while last_id != "80144357":
if strip_id(driver.current_url) != last_id:
log_id(strip_id(driver.current_url))
last_id = strip_id(driver.current_url)
driver.quit()
| [
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] |
lesson7/exercise1b.py | mfeindt0705/pynetmf | 0 | 186628 | #!/usr/bin/nv python3
from lxml import etree
f = open("show_security_zones.xml")
lines = f.read()
my_xml = etree.fromstring(lines)
bstring = etree.tostring(my_xml)
print(bstring.decode())
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] |
flaskbb/utils/helpers.py | konstantin1985/forum | 0 | 114305 | <filename>flaskbb/utils/helpers.py
# -*- coding: utf-8 -*-
"""
flaskbb.utils.helpers
~~~~~~~~~~~~~~~~~~~~
A few helpers that are used by flaskbb
:copyright: (c) 2014 by the FlaskBB Team.
:license: BSD, see LICENSE for more details.
"""
import re
import time
import itertools
import operator
import struct
from io import BytesIO
from datetime import datetime, timedelta
import requests
import unidecode
from flask import session, url_for, flash
from jinja2 import Markup
from babel.dates import format_timedelta
from flask_babelplus import lazy_gettext as _
from flask_themes2 import render_theme_template
from flask_login import current_user
from flaskbb._compat import range_method, text_type
from flaskbb.extensions import redis_store
from flaskbb.utils.settings import flaskbb_config
from flaskbb.utils.markup import markdown
from flask_allows import Permission
_punct_re = re.compile(r'[\t !"#$%&\'()*\-/<=>?@\[\\\]^_`{|},.]+')
def slugify(text, delim=u'-'):
"""Generates an slightly worse ASCII-only slug.
Taken from the Flask Snippets page.
:param text: The text which should be slugified
:param delim: Default "-". The delimeter for whitespace
"""
text = unidecode.unidecode(text)
result = []
for word in _punct_re.split(text.lower()):
if word:
result.append(word)
return text_type(delim.join(result))
def render_template(template, **context): # pragma: no cover
"""A helper function that uses the `render_theme_template` function
without needing to edit all the views
"""
if current_user.is_authenticated and current_user.theme:
theme = current_user.theme
else:
theme = session.get('theme', flaskbb_config['DEFAULT_THEME'])
return render_theme_template(theme, template, **context)
def do_topic_action(topics, user, action, reverse):
"""Executes a specific action for topics. Returns a list with the modified
topic objects.
:param topics: A iterable with ``Topic`` objects.
:param user: The user object which wants to perform the action.
:param action: One of the following actions: locked, important and delete.
:param reverse: If the action should be done in a reversed way.
For example, to unlock a topic, ``reverse`` should be
set to ``True``.
"""
from flaskbb.utils.requirements import IsAtleastModeratorInForum, CanDeleteTopic
from flaskbb.user.models import User
from flaskbb.forum.models import Post
if not Permission(IsAtleastModeratorInForum(forum=topics[0].forum)):
flash(_("You do not have the permissions to execute this "
"action."), "danger")
return False
modified_topics = 0
if action != "delete":
for topic in topics:
if getattr(topic, action) and not reverse:
continue
setattr(topic, action, not reverse)
modified_topics += 1
topic.save()
elif action == "delete":
for topic in topics:
if not Permission(CanDeleteTopic):
flash(_("You do not have the permissions to delete this "
"topic."), "danger")
return False
involved_users = User.query.filter(Post.topic_id == topic.id,
User.id == Post.user_id).all()
modified_topics += 1
topic.delete(involved_users)
return modified_topics
def get_categories_and_forums(query_result, user):
"""Returns a list with categories. Every category has a list for all
their associated forums.
The structure looks like this::
[(<Category 1>,
[(<Forum 1>, None),
(<Forum 2>, <flaskbb.forum.models.ForumsRead at 0x38fdb50>)]),
(<Category 2>,
[(<Forum 3>, None),
(<Forum 4>, None)])]
and to unpack the values you can do this::
In [110]: for category, forums in x:
.....: print category
.....: for forum, forumsread in forums:
.....: print "\t", forum, forumsread
This will print something like this:
<Category 1>
<Forum 1> None
<Forum 2> <flaskbb.forum.models.ForumsRead object at 0x38fdb50>
<Category 2>
<Forum 3> None
<Forum 4> None
:param query_result: A tuple (KeyedTuple) with all categories and forums
:param user: The user object is needed because a signed out user does not
have the ForumsRead relation joined.
"""
it = itertools.groupby(query_result, operator.itemgetter(0))
forums = []
if user.is_authenticated:
for key, value in it:
forums.append((key, [(item[1], item[2]) for item in value]))
else:
for key, value in it:
forums.append((key, [(item[1], None) for item in value]))
return forums
def get_forums(query_result, user):
"""Returns a tuple which contains the category and the forums as list.
This is the counterpart for get_categories_and_forums and especially
usefull when you just need the forums for one category.
For example::
(<Category 2>,
[(<Forum 3>, None),
(<Forum 4>, None)])
:param query_result: A tuple (KeyedTuple) with all categories and forums
:param user: The user object is needed because a signed out user does not
have the ForumsRead relation joined.
"""
it = itertools.groupby(query_result, operator.itemgetter(0))
if user.is_authenticated:
for key, value in it:
forums = key, [(item[1], item[2]) for item in value]
else:
for key, value in it:
forums = key, [(item[1], None) for item in value]
return forums
def forum_is_unread(forum, forumsread, user):
"""Checks if a forum is unread
:param forum: The forum that should be checked if it is unread
:param forumsread: The forumsread object for the forum
:param user: The user who should be checked if he has read the forum
"""
# If the user is not signed in, every forum is marked as read
if not user.is_authenticated:
return False
read_cutoff = datetime.utcnow() - timedelta(
days=flaskbb_config["TRACKER_LENGTH"])
# disable tracker if TRACKER_LENGTH is set to 0
if flaskbb_config["TRACKER_LENGTH"] == 0:
return False
# If there are no topics in the forum, mark it as read
if forum and forum.topic_count == 0:
return False
# If the user hasn't visited a topic in the forum - therefore,
# forumsread is None and we need to check if it is still unread
if forum and not forumsread:
return forum.last_post_created > read_cutoff
try:
# check if the forum has been cleared and if there is a new post
# since it have been cleared
if forum.last_post_created > forumsread.cleared:
if forum.last_post_created < forumsread.last_read:
return False
except TypeError:
pass
# else just check if the user has read the last post
return forum.last_post_created > forumsread.last_read
def topic_is_unread(topic, topicsread, user, forumsread=None):
"""Checks if a topic is unread.
:param topic: The topic that should be checked if it is unread
:param topicsread: The topicsread object for the topic
:param user: The user who should be checked if he has read the last post
in the topic
:param forumsread: The forumsread object in which the topic is. If you
also want to check if the user has marked the forum as
read, than you will also need to pass an forumsread
object.
"""
if not user.is_authenticated:
return False
read_cutoff = datetime.utcnow() - timedelta(
days=flaskbb_config["TRACKER_LENGTH"])
# disable tracker if read_cutoff is set to 0
if flaskbb_config["TRACKER_LENGTH"] == 0:
return False
# check read_cutoff
if topic.last_post.date_created < read_cutoff:
return False
# topicsread is none if the user has marked the forum as read
# or if he hasn't visited yet
if topicsread is None:
# user has cleared the forum sometime ago - check if there is a new post
if forumsread and forumsread.cleared is not None:
return forumsread.cleared < topic.last_post.date_created
# user hasn't read the topic yet, or there is a new post since the user
# has marked the forum as read
return True
# check if there is a new post since the user's last topic visit
return topicsread.last_read < topic.last_post.date_created
def mark_online(user_id, guest=False): # pragma: no cover
"""Marks a user as online
:param user_id: The id from the user who should be marked as online
:param guest: If set to True, it will add the user to the guest activity
instead of the user activity.
Ref: http://flask.pocoo.org/snippets/71/
"""
now = int(time.time())
expires = now + (flaskbb_config['ONLINE_LAST_MINUTES'] * 60) + 10
if guest:
all_users_key = 'online-guests/%d' % (now // 60)
user_key = 'guest-activity/%s' % user_id
else:
all_users_key = 'online-users/%d' % (now // 60)
user_key = 'user-activity/%s' % user_id
p = redis_store.pipeline()
p.sadd(all_users_key, user_id)
p.set(user_key, now)
p.expireat(all_users_key, expires)
p.expireat(user_key, expires)
p.execute()
def get_online_users(guest=False): # pragma: no cover
"""Returns all online users within a specified time range
:param guest: If True, it will return the online guests
"""
current = int(time.time()) // 60
minutes = range_method(flaskbb_config['ONLINE_LAST_MINUTES'])
if guest:
return redis_store.sunion(['online-guests/%d' % (current - x)
for x in minutes])
return redis_store.sunion(['online-users/%d' % (current - x)
for x in minutes])
def crop_title(title, length=None, suffix="..."):
"""Crops the title to a specified length
:param title: The title that should be cropped
:param suffix: The suffix which should be appended at the
end of the title.
"""
length = flaskbb_config['TITLE_LENGTH'] if length is None else length
if len(title) <= length:
return title
return title[:length].rsplit(' ', 1)[0] + suffix
def render_markup(text):
"""Renders the given text as markdown
:param text: The text that should be rendered as markdown
"""
return Markup(markdown.render(text))
def is_online(user):
"""A simple check to see if the user was online within a specified
time range
:param user: The user who needs to be checked
"""
return user.lastseen >= time_diff()
def time_diff():
"""Calculates the time difference between now and the ONLINE_LAST_MINUTES
variable from the configuration.
"""
now = datetime.utcnow()
diff = now - timedelta(minutes=flaskbb_config['ONLINE_LAST_MINUTES'])
return diff
def format_date(value, format='%Y-%m-%d'):
"""Returns a formatted time string
:param value: The datetime object that should be formatted
:param format: How the result should look like. A full list of available
directives is here: http://goo.gl/gNxMHE
"""
return value.strftime(format)
def time_since(time): # pragma: no cover
"""Returns a string representing time since e.g.
3 days ago, 5 hours ago.
:param time: A datetime object
"""
delta = time - datetime.utcnow()
locale = "en"
if current_user.is_authenticated and current_user.language is not None:
locale = current_user.language
return format_timedelta(delta, add_direction=True, locale=locale)
def format_quote(username, content):
"""Returns a formatted quote depending on the markup language.
:param username: The username of a user.
:param content: The content of the quote
"""
profile_url = url_for('user.profile', username=username)
content = "\n> ".join(content.strip().split('\n'))
quote = u"**[{username}]({profile_url}) wrote:**\n> {content}\n".\
format(username=username, profile_url=profile_url, content=content)
return quote
def get_image_info(url):
"""Returns the content-type, image size (kb), height and width of a image
without fully downloading it. It will just download the first 1024 bytes.
LICENSE: New BSD License (taken from the start page of the repository)
https://code.google.com/p/bfg-pages/source/browse/trunk/pages/getimageinfo.py
"""
r = requests.get(url, stream=True)
image_size = r.headers.get("content-length")
image_size = float(image_size) / 1000 # in kilobyte
data = r.raw.read(1024)
size = len(data)
height = -1
width = -1
content_type = ''
if size:
size = int(size)
# handle GIFs
if (size >= 10) and data[:6] in (b'GIF87a', b'GIF89a'):
# Check to see if content_type is correct
content_type = 'image/gif'
w, h = struct.unpack(b'<HH', data[6:10])
width = int(w)
height = int(h)
# See PNG 2. Edition spec (http://www.w3.org/TR/PNG/)
# Bytes 0-7 are below, 4-byte chunk length, then 'IHDR'
# and finally the 4-byte width, height
elif ((size >= 24) and data.startswith(b'\211PNG\r\n\032\n') and
(data[12:16] == b'IHDR')):
content_type = 'image/png'
w, h = struct.unpack(b">LL", data[16:24])
width = int(w)
height = int(h)
# Maybe this is for an older PNG version.
elif (size >= 16) and data.startswith(b'\211PNG\r\n\032\n'):
# Check to see if we have the right content type
content_type = 'image/png'
w, h = struct.unpack(b">LL", data[8:16])
width = int(w)
height = int(h)
# handle JPEGs
elif (size >= 2) and data.startswith(b'\377\330'):
content_type = 'image/jpeg'
jpeg = BytesIO(data)
jpeg.read(2)
b = jpeg.read(1)
try:
while (b and ord(b) != 0xDA):
while (ord(b) != 0xFF):
b = jpeg.read(1)
while (ord(b) == 0xFF):
b = jpeg.read(1)
if (ord(b) >= 0xC0 and ord(b) <= 0xC3):
jpeg.read(3)
h, w = struct.unpack(b">HH", jpeg.read(4))
break
else:
jpeg.read(int(struct.unpack(b">H", jpeg.read(2))[0])-2)
b = jpeg.read(1)
width = int(w)
height = int(h)
except struct.error:
pass
except ValueError:
pass
return {"content-type": content_type, "size": image_size,
"width": width, "height": height}
def check_image(url):
"""A little wrapper for the :func:`get_image_info` function.
If the image doesn't match the ``flaskbb_config`` settings it will
return a tuple with a the first value is the custom error message and
the second value ``False`` for not passing the check.
If the check is successful, it will return ``None`` for the error message
and ``True`` for the passed check.
:param url: The image url to be checked.
"""
img_info = get_image_info(url)
error = None
if not img_info["content-type"] in flaskbb_config["AVATAR_TYPES"]:
error = "Image type is not allowed. Allowed types are: {}".format(
", ".join(flaskbb_config["AVATAR_TYPES"])
)
return error, False
if img_info["width"] > flaskbb_config["AVATAR_WIDTH"]:
error = "Image is too wide! {}px width is allowed.".format(
flaskbb_config["AVATAR_WIDTH"]
)
return error, False
if img_info["height"] > flaskbb_config["AVATAR_HEIGHT"]:
error = "Image is too high! {}px height is allowed.".format(
flaskbb_config["AVATAR_HEIGHT"]
)
return error, False
if img_info["size"] > flaskbb_config["AVATAR_SIZE"]:
error = "Image is too big! {}kb are allowed.".format(
flaskbb_config["AVATAR_SIZE"]
)
return error, False
return error, True
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6493,
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13,
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13,
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14627,
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333,
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17838,
29922,
8824,
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396,
282,
23929,
29901,
694,
4612,
13,
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9995,
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263,
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13,
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1989,
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1273,
313,
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333,
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313,
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5570,
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13,
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1202,
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497,
29918,
7193,
29918,
1989,
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1404,
29918,
333,
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13,
1678,
282,
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students/k3343/laboratory_works/Berezhnova_Marina/laboratory_work_1/django_project_flights/flights_app/models.py | TonikX/ITMO_ICT_-WebProgramming_2020 | 10 | 25183 | <gh_stars>1-10
from django.db import models
from django.contrib.auth.models import User
# Create your models here.
class Companies(models.Model):
name = models.CharField(max_length=30)
def __str__(self):
return "{}".format(self.name)
class Gates(models.Model):
name = models.CharField(max_length=30)
def __str__(self):
return "{}".format(self.name)
class Flights(models.Model):
company = models.ForeignKey(Companies, on_delete=models.CASCADE)
gate = models.ForeignKey(Gates, on_delete=models.CASCADE)
def __str__(self):
return "Company: {} | Gate: {}".format(self.company, self.gate)
class FlightActivities(models.Model):
ACTIVITY = [
('0', 'arrival'),
('1', 'departure')
]
flight = models.ForeignKey(Flights, on_delete=models.CASCADE)
activity = models.CharField(choices=ACTIVITY, default='0', max_length=1)
time = models.DateField()
def __str__(self):
return "{} | Arrival/departure: {} | Date {}".format(self.flight, self.get_activity_display(), self.time)
class FlightComments(models.Model):
flight = models.ForeignKey(FlightActivities, on_delete=models.CASCADE)
COMMENT_TYPE = [
('0', 'Gate changing'),
('1', 'Lateness'),
('2', 'Other')
]
com_type = models.CharField(choices=COMMENT_TYPE, default='0', max_length=1)
com_text = models.CharField(max_length=1024)
author = models.ForeignKey(User, on_delete=models.CASCADE)
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flextensor/testing/others/hand-craft/tune_conv2d_NCHWc.py | jcf94/FlexTensor | 4 | 98761 | <gh_stars>1-10
from __future__ import absolute_import
import time
import json
import tvm
from flextensor.task import register_task, Task
from flextensor.measure import _evaluate
from flextensor.nn import conv2d_nchwc
from flextensor.configs.conv2d_config import yolo_shapes_b1
from flextensor.scheduler import schedule, schedule_with_config
from flextensor.utils import RpcInfo
def conv2d_nchwc_compute_avx2(N, C, H, W, K, k=3, use_bias=False, st=1, pad=0, dilation=1, group=1, vlen1=8, vlen2=8):
inputs = tvm.placeholder([N, C // vlen1 // group, H, W, vlen1], dtype="float32")
weight = tvm.placeholder([K // vlen2, C // vlen1 // group, k, k, vlen1, vlen2], dtype="float32")
if use_bias:
bias = tvm.placeholder([K // vlen2, vlen2], dtype="float32")
else:
bias = None
output = conv2d_nchwc(inputs, weight, bias, stride=st, padding=pad, dilation=dilation, groups=group)
if use_bias:
return output, [inputs, weight, bias, output]
else:
return [output.op], [inputs, weight, output]
if __name__ == "__main__":
N, C, H, W, K, _, k, _, _, st, pad, dilation, group = yolo_shapes_b1[5]
use_bias = False
vlen = 8
target = "llvm"
dev_id = 0
trials = 100
timeout = 10
parallel = 20
method = "searching"
force_inline = True
use_model = False
logfile = open("tmp.log", "w")
rpc_info = RpcInfo("0.0.0.0", 9090, target_host="llvm")
args = (N, C, H, W, K, k, use_bias, st, pad, dilation, group)
task = Task("conv2d_nchwc", "yolo_conv6", conv2d_nchwc_compute_avx2, args, target, dev_id=dev_id)
register_task(task, override=False)
beg = time.time()
s, bufs, configs = schedule(
task.key,
op_trial=trials,
timeout=timeout,
op_stop=30,
parallel=parallel,
method=method,
use_model=use_model,
trials=[trials//10, trials],
force_inline=force_inline,
rpc_info=rpc_info,
slevel=2,
rlevel=2
)
end = time.time()
print("######################################")
print("op schedules:")
for config in configs.op_config_lst:
print("----------------------------------")
for name, value in config.items():
if value:
print(name, value)
print("graph schedules:")
for name, value in configs.graph_config.items():
if value:
print(name, value)
string = json.dumps(configs)
line = task.key + ":" + string
print(line, file=logfile, flush=True)
s, bufs = schedule_with_config(task.key, configs)
time_cost = _evaluate(s, bufs, target, dev_id, 10)
print("Use", time_cost, "ms", "throughput: %f GFLOPS" % (N * C * H * W * K * k * k / st / st / group / 1e6 / time_cost))
print("Cost", end - beg, "s")
logfile.close()
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] |
tests/pytests/test_tags.py | wayn111/RediSearch | 0 | 2914 | <filename>tests/pytests/test_tags.py
# -*- coding: utf-8 -*-
from includes import *
from common import *
def search(env, r, *args):
return r.execute_command('ft.search', *args)
def testTagIndex(env):
r = env
env.expect('ft.create', 'idx', 'ON', 'HASH','schema', 'title', 'text', 'tags', 'tag').ok()
N = 10
for n in range(N):
env.expect('ft.add', 'idx', 'doc%d' % n, 1.0, 'fields',
'title', 'hello world term%d' % n, 'tags', 'foo bar,xxx,tag %d' % n).ok()
for _ in r.retry_with_rdb_reload():
waitForIndex(r, 'idx')
res = env.cmd('ft.search', 'idx', 'hello world')
env.assertEqual(10, res[0])
res = env.cmd('ft.search', 'idx', 'foo bar')
env.assertEqual(0, res[0])
res = env.cmd('ft.search', 'idx', '@tags:{foo bar}')
env.assertEqual(N, res[0])
# inorder should not affect tags
res = env.cmd(
'ft.search', 'idx', '@tags:{tag 1} @tags:{foo bar}', 'slop', '0', 'inorder')
env.assertEqual(1, res[0])
for n in range(N - 1):
res = env.cmd(
'ft.search', 'idx', '@tags:{tag %d}' % n, 'nocontent')
env.assertEqual(1, res[0])
env.assertEqual('doc%d' % n, res[1])
res = env.cmd(
'ft.search', 'idx', '@tags:{tag\\ %d}' % n, 'nocontent')
env.assertEqual(1, res[0])
res = env.cmd(
'ft.search', 'idx', 'hello world @tags:{tag\\ %d|tag %d}' % (n, n + 1), 'nocontent')
env.assertEqual(2, res[0])
res = py2sorted(res[1:])
env.assertEqual('doc%d' % n, res[0])
env.assertEqual('doc%d' % (n + 1), res[1])
res = env.cmd(
'ft.search', 'idx', 'term%d @tags:{tag %d}' % (n, n), 'nocontent')
env.assertEqual(1, res[0])
env.assertEqual('doc%d' % n, res[1])
def testSeparator(env):
r = env
env.expect(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'separator', ':').ok()
env.expect('ft.add', 'idx', 'doc1', 1.0, 'fields',
'title', 'hello world', 'tags', 'x:hello world: fooz bar:foo,bar:BOO FAR').ok()
for _ in r.retry_with_rdb_reload():
waitForIndex(r, 'idx')
for q in ('@tags:{hello world}', '@tags:{fooz bar}', '@tags:{foo\\,bar}', '@tags:{boo\\ far}', '@tags:{x}'):
res = env.cmd('ft.search', 'idx', q)
env.assertEqual(1, res[0])
def testTagPrefix(env):
env.skipOnCluster()
r = env
env.expect(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'separator', ',').ok()
env.expect('ft.add', 'idx', 'doc1', 1.0, 'fields', 'title', 'hello world',
'tags', 'hello world,hello-world,hell,jell').ok()
env.expect('FT.DEBUG', 'dump_tagidx', 'idx', 'tags') \
.equal([['hell', [1]], ['hello world', [1]], ['hello-world', [1]], ['jell', [1]]])
for _ in r.retry_with_rdb_reload():
waitForIndex(r, 'idx')
for q in ('@tags:{hello world}', '@tags:{hel*}', '@tags:{hello\\-*}', '@tags:{he*}'):
res = env.cmd('ft.search', 'idx', q)
env.assertEqual(res[0], 1)
def testTagFieldCase(env):
r = env
env.expect(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'TAgs', 'tag').ok()
env.expect('ft.add', 'idx', 'doc1', 1.0, 'fields',
'title', 'hello world', 'TAgs', 'HELLO WORLD,FOO BAR').ok()
for _ in r.retry_with_rdb_reload():
waitForIndex(r, 'idx')
env.assertListEqual([0], r.execute_command(
'FT.SEARCH', 'idx', '@tags:{HELLO WORLD}'))
env.assertListEqual([1, 'doc1'], r.execute_command(
'FT.SEARCH', 'idx', '@TAgs:{HELLO WORLD}', 'NOCONTENT'))
env.assertListEqual([1, 'doc1'], r.execute_command(
'FT.SEARCH', 'idx', '@TAgs:{foo bar}', 'NOCONTENT'))
env.assertListEqual([0], r.execute_command(
'FT.SEARCH', 'idx', '@TAGS:{foo bar}', 'NOCONTENT'))
def testInvalidSyntax(env):
r = env
# invalid syntax
with env.assertResponseError():
r.execute_command(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'separator')
with env.assertResponseError():
r.execute_command(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'separator', "foo")
with env.assertResponseError():
r.execute_command(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'separator', "")
def testTagVals(env):
r = env
r.execute_command(
'ft.create', 'idx', 'ON', 'HASH',
'schema', 'title', 'text', 'tags', 'tag', 'othertags', 'tag')
N = 100
alltags = set()
for n in range(N):
tags = ('foo %d' % n, 'bar %d' % n, 'x')
alltags.add(tags[0])
alltags.add(tags[1])
alltags.add(tags[2])
env.assertOk(r.execute_command('ft.add', 'idx', 'doc%d' % n, 1.0, 'fields',
'tags', ','.join(tags), 'othertags', 'baz %d' % int(n // 2)))
for _ in r.retry_with_rdb_reload():
waitForIndex(r, 'idx')
res = r.execute_command('ft.tagvals', 'idx', 'tags')
env.assertEqual(N * 2 + 1, len(res))
env.assertEqual(alltags, set(res))
res = r.execute_command('ft.tagvals', 'idx', 'othertags')
env.assertEqual(N / 2, len(res))
env.expect('ft.tagvals', 'idx').raiseError()
env.expect('ft.tagvals', 'idx', 'idx', 'idx').raiseError()
env.expect('ft.tagvals', 'fake_idx', 'tags').raiseError()
env.expect('ft.tagvals', 'idx', 'fake_tags').raiseError()
env.expect('ft.tagvals', 'idx', 'title').raiseError()
def testSearchNotExistsTagValue(env):
# this test basically make sure we are not leaking
env.expect('FT.CREATE idx ON HASH SCHEMA t TAG SORTABLE').ok()
env.expect('FT.SEARCH idx @t:{val}').equal([0])
def testIssue1305(env):
env.expect('FT.CREATE myIdx ON HASH SCHEMA title TAG').ok()
env.expect('FT.ADD myIdx doc2 1.0 FIELDS title "work"').ok()
env.expect('FT.ADD myIdx doc2 1.0 FIELDS title "hello"').error()
env.expect('FT.ADD myIdx doc3 1.0 FIELDS title "hello"').ok()
env.expect('FT.ADD myIdx doc1 1.0 FIELDS title "hello,work"').ok()
expectedRes = {'doc1' : ['inf', ['title', '"hello,work"']], 'doc3' : ['inf', ['title', '"hello"']], 'doc2' : ['inf', ['title', '"work"']]}
res = env.cmd('ft.search', 'myIdx', '~@title:{wor} ~@title:{hell}', 'WITHSCORES')[1:]
res = {res[i]:res[i + 1: i + 3] for i in range(0, len(res), 3)}
env.assertEqual(res, expectedRes)
def testTagCaseSensitive(env):
conn = getConnectionByEnv(env)
env.expect('FT.CREATE idx1 SCHEMA t TAG').ok()
env.expect('FT.CREATE idx2 SCHEMA t TAG CASESENSITIVE').ok()
env.expect('FT.CREATE idx3 SCHEMA t TAG SEPARATOR .').ok()
env.expect('FT.CREATE idx4 SCHEMA t TAG SEPARATOR . CASESENSITIVE').ok()
env.expect('FT.CREATE idx5 SCHEMA t TAG CASESENSITIVE SEPARATOR .').ok()
conn.execute_command('HSET', 'doc1', 't', 'foo,FOO')
conn.execute_command('HSET', 'doc2', 't', 'FOO')
conn.execute_command('HSET', 'doc3', 't', 'foo')
if not env.is_cluster():
conn.execute_command('FT.CONFIG', 'SET', 'FORK_GC_CLEAN_THRESHOLD', '0')
env.expect('FT.DEBUG', 'dump_tagidx', 'idx1', 't').equal([['foo', [1, 2, 3]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx2', 't').equal([['foo', [1, 3]], ['FOO', [1, 2]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx3', 't').equal([['foo', [2, 3]], ['foo,foo', [1]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx4', 't').equal([['foo', [3]], ['foo,FOO', [1]], ['FOO', [2]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx5', 't').equal([['foo', [3]], ['foo,FOO', [1]], ['FOO', [2]]])
env.expect('FT.SEARCH', 'idx1', '@t:{FOO}') \
.equal([3, 'doc1', ['t', 'foo,FOO'], 'doc2', ['t', 'FOO'], 'doc3', ['t', 'foo']])
env.expect('FT.SEARCH', 'idx1', '@t:{foo}') \
.equal([3, 'doc1', ['t', 'foo,FOO'], 'doc2', ['t', 'FOO'], 'doc3', ['t', 'foo']])
env.expect('FT.SEARCH', 'idx2', '@t:{FOO}') \
.equal([2, 'doc1', ['t', 'foo,FOO'], 'doc2', ['t', 'FOO']])
env.expect('FT.SEARCH', 'idx2', '@t:{foo}') \
.equal([2, 'doc1', ['t', 'foo,FOO'], 'doc3', ['t', 'foo']])
conn.execute_command('HSET', 'doc1', 't', 'f o,F O')
conn.execute_command('HSET', 'doc2', 't', 'F O')
conn.execute_command('HSET', 'doc3', 't', 'f o')
if not env.is_cluster():
forceInvokeGC(env, 'idx1')
forceInvokeGC(env, 'idx2')
forceInvokeGC(env, 'idx3')
forceInvokeGC(env, 'idx4')
forceInvokeGC(env, 'idx5')
env.expect('FT.DEBUG', 'dump_tagidx', 'idx1', 't').equal([['f o', [4, 5, 6]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx2', 't').equal([['f o', [4, 6]], ['F O', [4, 5]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx3', 't').equal([['f o', [5, 6]], ['f o,f o', [4]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx4', 't').equal([['f o', [6]], ['f o,F O', [4]], ['F O', [5]]])
env.expect('FT.DEBUG', 'dump_tagidx', 'idx5', 't').equal([['f o', [6]], ['f o,F O', [4]], ['F O', [5]]])
# not casesensitive
env.expect('FT.SEARCH', 'idx1', '@t:{F\\ O}') \
.equal([3, 'doc1', ['t', 'f o,F O'], 'doc2', ['t', 'F O'], 'doc3', ['t', 'f o']])
env.expect('FT.SEARCH', 'idx1', '@t:{f\\ o}') \
.equal([3, 'doc1', ['t', 'f o,F O'], 'doc2', ['t', 'F O'], 'doc3', ['t', 'f o']])
# casesensitive
env.expect('FT.SEARCH', 'idx2', '@t:{F\\ O}') \
.equal([2, 'doc1', ['t', 'f o,F O'], 'doc2', ['t', 'F O']])
env.expect('FT.SEARCH', 'idx2', '@t:{f\\ o}') \
.equal([2, 'doc1', ['t', 'f o,F O'], 'doc3', ['t', 'f o']])
# not casesensitive
env.expect('FT.SEARCH', 'idx3', '@t:{f\\ o\\,f\\ o}') \
.equal([1, 'doc1', ['t', 'f o,F O']])
env.expect('FT.SEARCH', 'idx3', '@t:{f\\ o\\,F\\ O}') \
.equal([1, 'doc1', ['t', 'f o,F O']])
env.expect('FT.SEARCH', 'idx3', '@t:{F\\ O\\,F\\ O}') \
.equal([1, 'doc1', ['t', 'f o,F O']])
env.expect('FT.SEARCH', 'idx3', '@t:{F\\ O}') \
.equal([2, 'doc2', ['t', 'F O'], 'doc3', ['t', 'f o']])
env.expect('FT.SEARCH', 'idx3', '@t:{f\\ o}') \
.equal([2, 'doc2', ['t', 'F O'], 'doc3', ['t', 'f o']])
# casesensitive
env.expect('FT.SEARCH', 'idx4', '@t:{f\\ o\\,f\\ o}') \
.equal([0])
env.expect('FT.SEARCH', 'idx4', '@t:{f\\ o\\,F\\ O}') \
.equal([1, 'doc1', ['t', 'f o,F O']])
env.expect('FT.SEARCH', 'idx4', '@t:{F\\ O\\,F\\ O}') \
.equal([0])
env.expect('FT.SEARCH', 'idx4', '@t:{F\\ O}') \
.equal([1, 'doc2', ['t', 'F O']])
env.expect('FT.SEARCH', 'idx4', '@t:{f\\ o}') \
.equal([1, 'doc3', ['t', 'f o']])
def testTagGCClearEmpty(env):
env.skipOnCluster()
conn = getConnectionByEnv(env)
conn.execute_command('FT.CONFIG', 'SET', 'FORK_GC_CLEAN_THRESHOLD', '0')
conn.execute_command('FT.CREATE', 'idx', 'SCHEMA', 't', 'TAG')
conn.execute_command('HSET', 'doc1', 't', 'foo')
conn.execute_command('HSET', 'doc2', 't', 'bar')
conn.execute_command('HSET', 'doc3', 't', 'baz')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([['foo', [1]], ['bar', [2]], ['baz', [3]]])
env.expect('FT.SEARCH', 'idx', '@t:{foo}').equal([1, 'doc1', ['t', 'foo']])
# delete two tags
conn.execute_command('DEL', 'doc1')
conn.execute_command('DEL', 'doc2')
forceInvokeGC(env, 'idx')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([['baz', [3]]])
env.expect('FT.SEARCH', 'idx', '@t:{foo}').equal([0])
# delete last tag
conn.execute_command('DEL', 'doc3')
forceInvokeGC(env, 'idx')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([])
# check term can be used after being empty
conn.execute_command('HSET', 'doc4', 't', 'foo')
conn.execute_command('HSET', 'doc5', 't', 'foo')
env.expect('FT.SEARCH', 'idx', '@t:{foo}') \
.equal([2, 'doc4', ['t', 'foo'], 'doc5', ['t', 'foo']])
def testTagGCClearEmptyWithCursor(env):
env.skipOnCluster()
conn = getConnectionByEnv(env)
conn.execute_command('FT.CONFIG', 'SET', 'FORK_GC_CLEAN_THRESHOLD', '0')
conn.execute_command('FT.CREATE', 'idx', 'SCHEMA', 't', 'TAG')
conn.execute_command('HSET', 'doc1', 't', 'foo')
conn.execute_command('HSET', 'doc2', 't', 'foo')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([['foo', [1, 2]]])
res, cursor = env.cmd('FT.AGGREGATE', 'idx', '@t:{foo}', 'WITHCURSOR', 'COUNT', '1')
env.assertEqual(res, [1, []])
# delete both documents and run the GC to clean 'foo' inverted index
env.expect('DEL', 'doc1').equal(1)
env.expect('DEL', 'doc2').equal(1)
forceInvokeGC(env, 'idx')
# make sure the inverted index was cleaned
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([])
# read from the cursor
res, cursor = env.cmd('FT.CURSOR', 'READ', 'idx', cursor)
env.assertEqual(res, [0])
env.assertEqual(cursor, 0)
def testTagGCClearEmptyWithCursorAndMoreData(env):
env.skipOnCluster()
conn = getConnectionByEnv(env)
conn.execute_command('FT.CONFIG', 'SET', 'FORK_GC_CLEAN_THRESHOLD', '0')
conn.execute_command('FT.CREATE', 'idx', 'SCHEMA', 't', 'TAG')
conn.execute_command('HSET', 'doc1', 't', 'foo')
conn.execute_command('HSET', 'doc2', 't', 'foo')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([['foo', [1, 2]]])
res, cursor = env.cmd('FT.AGGREGATE', 'idx', '@t:{foo}', 'WITHCURSOR', 'COUNT', '1')
env.assertEqual(res, [1, []])
# delete both documents and run the GC to clean 'foo' inverted index
env.expect('DEL', 'doc1').equal(1)
env.expect('DEL', 'doc2').equal(1)
forceInvokeGC(env, 'idx')
# make sure the inverted index was cleaned
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([])
# add data
conn.execute_command('HSET', 'doc3', 't', 'foo')
conn.execute_command('HSET', 'doc4', 't', 'foo')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([['foo', [3, 4]]])
# read from the cursor
res, cursor = conn.execute_command('FT.CURSOR', 'READ', 'idx', cursor)
env.assertEqual(res, [0])
env.assertEqual(cursor, 0)
# ensure later documents with same tag are read
res = conn.execute_command('FT.AGGREGATE', 'idx', '@t:{foo}')
env.assertEqual(res, [1, [], []])
@unstable
def testEmptyTagLeak(env):
env.skipOnCluster()
cycles = 1
tags = 30
conn = getConnectionByEnv(env)
conn.execute_command('FT.CONFIG', 'SET', 'FORK_GC_CLEAN_THRESHOLD', '0')
conn.execute_command('FT.CREATE', 'idx', 'SCHEMA', 't', 'TAG')
pl = conn.pipeline()
for i in range(cycles):
for j in range(tags):
x = j + i * tags
pl.execute_command('HSET', 'doc{}'.format(x), 't', 'tag{}'.format(x))
pl.execute()
for j in range(tags):
pl.execute_command('DEL', 'doc{}'.format(j + i * tags))
pl.execute()
forceInvokeGC(env, 'idx')
env.expect('FT.DEBUG', 'DUMP_TAGIDX', 'idx', 't').equal([])
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] |
scrips/rename.py | YingqiLiulll/scrips_for_SR | 0 | 94939 |
import numpy as np
import os
from skimage import io
path = r'/data1/zyli/benchmark/Set5/LR_bicubic/X4/'
img_path = sorted([os.path.join(path, name) for name in os.listdir(path) if name.endswith('.png')])###这里的'.tif'可以换成任意的文件后缀
for i in range(len(img_path)):
img_name = img_path[i]
print(img_name)
name = img_name.split('x4')[0]
ext = img_name.split('x4')[1]
print(name+ext)
os.rename(img_name, name+ext)
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monitor_sdk/api/translate/translate_client.py | easyopsapis/easyops-api-python | 5 | 130973 | # -*- coding: utf-8 -*-
import os
import sys
import monitor_sdk.api.translate.list_translate_data_pb2
import monitor_sdk.api.translate.list_translate_rule_pb2
import monitor_sdk.utils.http_util
import google.protobuf.json_format
class TranslateClient(object):
def __init__(self, server_ip="", server_port=0, service_name="", host=""):
"""
初始化client
:param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由
:param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由
:param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和service_name同时设置，server_ip优先级更高
:param host: 指定sdk请求服务的host名称, 如cmdb.easyops-only.com
"""
if server_ip == "" and server_port != 0 or server_ip != "" and server_port == 0:
raise Exception("server_ip和server_port必须同时指定")
self._server_ip = server_ip
self._server_port = server_port
self._service_name = service_name
self._host = host
def list_translate_data(self, request, org, user, timeout=10):
# type: (monitor_sdk.api.translate.list_translate_data_pb2.ListTranslateDataRequest, int, str, int) -> monitor_sdk.api.translate.list_translate_data_pb2.ListTranslateDataResponse
"""
获取翻译规则对应的翻译数据
:param request: list_translate_data请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: monitor_sdk.api.translate.list_translate_data_pb2.ListTranslateDataResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.monitor.translate.ListTranslateData"
uri = "/api/v2/translate/storm/data"
requestParam = request
rsp_obj = monitor_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.monitor_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = monitor_sdk.api.translate.list_translate_data_pb2.ListTranslateDataResponse()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def list_translate_rule(self, request, org, user, timeout=10):
# type: (monitor_sdk.api.translate.list_translate_rule_pb2.ListTranslateRuleRequest, int, str, int) -> monitor_sdk.api.translate.list_translate_rule_pb2.ListTranslateRuleResponse
"""
获取翻译规则列表
:param request: list_translate_rule请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: monitor_sdk.api.translate.list_translate_rule_pb2.ListTranslateRuleResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.monitor.translate.ListTranslateRule"
uri = "/api/v1/translate/rule"
requestParam = request
rsp_obj = monitor_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.monitor_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = monitor_sdk.api.translate.list_translate_rule_pb2.ListTranslateRuleResponse()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
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problem_49/test_prime_permutations.py | plilja/project-euler | 0 | 34913 | <filename>problem_49/test_prime_permutations.py
import unittest
from prime_permutations import *
class PrimePermutationsTest(unittest.TestCase):
def test_prime_permutations(self):
self.assertEqual(sorted(prime_permutations()), sorted([2969, 6299, 9629]))
if __name__ == '__main__':
unittest.main()
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djangular_serve/__init__.py | forafekt/djangular-serve | 0 | 188261 | <reponame>forafekt/djangular-serve
from .serve import main
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test/test_dgcspn.py | fedelux3/deeprob-kit | 0 | 113191 | <reponame>fedelux3/deeprob-kit
import unittest
import torch
import numpy as np
from deeprob.spn.layers.dgcspn import SpatialGaussianLayer, SpatialProductLayer, SpatialSumLayer, SpatialRootLayer
class TestDgcSpn(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestDgcSpn, self).__init__(*args, **kwargs)
@classmethod
def setUpClass(cls):
torch.manual_seed(42)
torch.set_grad_enabled(False)
cls.in_features = (3, 32, 32)
cls.in_channels, cls.in_height, cls.in_width = cls.in_features
cls.data = torch.ones([8, *cls.in_features])
def test_gaussian_layer(self):
gaussian = SpatialGaussianLayer(
self.in_features, out_channels=16,
optimize_scale=False, uniform_loc=(-1.0, 1.0)
)
self.assertEqual(gaussian.out_features, (16, self.in_height, self.in_width))
self.assertTrue(torch.all(gaussian.loc >= -1.0) and torch.all(gaussian.loc <= 1.0))
self.assertTrue(torch.all(gaussian.scale == 1.0))
self.assertFalse(gaussian.scale.requires_grad)
gaussian = SpatialGaussianLayer(
self.in_features, out_channels=16,
optimize_scale=True
)
self.assertTrue(torch.all(gaussian.scale > 0.0))
self.assertTrue(gaussian.scale.requires_grad)
self.assertRaises(
ValueError, SpatialGaussianLayer, self.in_features, 16,
quantiles_loc=np.zeros([16, *self.in_features]), uniform_loc=(-1.0, 1.0)
)
def test_depthwise_product_layer(self):
product = SpatialProductLayer(
self.in_features, kernel_size=2, padding='full',
stride=1, dilation=4, depthwise=True
)
self.assertEqual(product.pad, [4, 4, 4, 4])
self.assertTrue(torch.all(product.weight == torch.ones(self.in_channels, 1, 2, 2)))
self.assertEqual(product.out_features, (self.in_channels, self.in_height + 4, self.in_width + 4))
self.assertTrue(torch.allclose(product(self.data)[:, :, 4:-4, 4:-4], torch.tensor(4.0)))
product = SpatialProductLayer(
self.in_features, kernel_size=2, padding='valid',
stride=2, dilation=1, depthwise=True
)
self.assertEqual(product.pad, [0, 0, 0, 0])
self.assertTrue(torch.all(product.weight == torch.ones(self.in_channels, 1, 2, 2)))
self.assertEqual(product.out_features, (self.in_channels, self.in_height // 2, self.in_width // 2))
self.assertTrue(torch.allclose(product(self.data), torch.tensor(4.0)))
def test_non_depthwise_product_layer(self):
product = SpatialProductLayer(
self.in_features, kernel_size=2, padding='full',
stride=1, dilation=8, depthwise=False
)
self.assertEqual(product.pad, [8, 8, 8, 8])
self.assertEqual(tuple(product.weight.shape), (self.in_channels ** 4, self.in_channels, 2, 2))
self.assertTrue(torch.allclose(torch.sum(product.weight, dim=1), torch.tensor(1.0)))
self.assertEqual(product.out_features, (self.in_channels ** 4, self.in_height + 8, self.in_width + 8))
self.assertTrue(torch.allclose(product(self.data)[:, :, 8:-8, 8:-8], torch.tensor(4.0)))
def test_sum_layer(self):
sum = SpatialSumLayer(self.in_features, out_channels=8)
self.assertEqual(sum.out_features, (8, self.in_height, self.in_width))
def test_root_layer(self):
root = SpatialRootLayer(self.in_features, out_channels=8)
self.assertEqual(root.out_channels, 8)
self.assertEqual(tuple(root.weight.shape), (8, np.prod(self.in_features)))
if __name__ == '__main__':
unittest.main()
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] |
app/editor/new_game_dialog.py | zerorock1312/lt-maker-master | 0 | 141937 | from PyQt5.QtWidgets import QVBoxLayout, QDialog, QLineEdit, QDialogButtonBox
from app.extensions.custom_gui import PropertyBox, Dialog
class NewGameDialog(Dialog):
def __init__(self, parent=None):
super().__init__(parent)
self.setWindowTitle("New Game")
self.window = parent
layout = QVBoxLayout()
self.setLayout(layout)
self.game_box = PropertyBox('Unique Game ID', QLineEdit, self)
self.game_box.edit.setPlaceholderText("Unique Game ID")
self.game_box.edit.textChanged.connect(self.text_changed)
layout.addWidget(self.game_box)
self.game_title_box = PropertyBox("Game Title", QLineEdit, self)
self.game_title_box.edit.setPlaceholderText("Game Title")
self.game_title_box.edit.textChanged.connect(self.text_changed)
layout.addWidget(self.game_title_box)
layout.addWidget(self.buttonbox)
self.buttonbox.button(QDialogButtonBox.Ok).setEnabled(False)
def text_changed(self, text):
if (self.game_title_box.edit.text() or self.game_box.edit.text()):
self.buttonbox.button(QDialogButtonBox.Ok).setEnabled(True)
else:
self.buttonbox.button(QDialogButtonBox.Ok).setEnabled(False)
@classmethod
def get(cls, parent=None):
dialog = cls(parent)
result = dialog.exec_()
if result == QDialog.Accepted:
return (dialog.game_box.edit.text(), dialog.game_title_box.edit.text())
else:
return None
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] |
cassie/misc/plotting_ex.py | WooQi57/cassie-run | 36 | 77487 | import numpy as np
import time
import math
# from cassie_env import CassieEnv
from cassiemujoco import *
from trajectory.trajectory import CassieTrajectory
import matplotlib.pyplot as plt
from matplotlib import style
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
from IPython import display
def visualise_sim_graph(file_path, freq_of_sim):
traj = np.load(file_path)
# env = CassieEnv("walking")
# csim = CassieSim("./cassie/cassiemujoco/cassie.xml")
# vis = CassieVis(csim, "./cassie/cassiemujoco/cassie.xml")
u = pd_in_t()
# pelvisXYZ = traj.f.qpos_replay[:, 0:3]
# render_state = vis.draw(csim)
# saved_time = traj.f.time[:]
#################Graphing###########
log_time = traj.f.time[:]
y_val = traj.f.qpos_replay[:,2] #z - height
x_data= log_time
y_data = y_val
delt_x = (x_data[1] - x_data[0]) * 1000 #convert seconds to ms
num_frames = math.ceil(len(x_data) / 10)
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
output = plt.plot([])
plt.close()
print(output[0])
x = np.linspace(0,2*np.pi, 100)
fig = plt.figure()
lines = plt.plot([])
line = lines[0]
#other setup //set x and y lims
plt.xlim(x_data.min(), x_data.max())
plt.ylim(y_data.min(), y_data.max())
def animate(frame):
#update
x = x_data[:frame*10]
y = y_data[:frame*10]
# y = np.sin(x + 2*np.pi * frame/100)
line.set_data((x,y))
anim = FuncAnimation(fig, animate, frames=num_frames, interval=(1/freq_of_sim * 1000 + (10 * delt_x))) #20 is 50 fps
anim.save('lines.mp4', writer=writer)
# html = display.HTML(video)
# display.display(html)
plt.close()
visualise_sim_graph("./outfile8.npz", 30) | [
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] |
cli.py | superiorbocal/AnPy | 1 | 195983 | <reponame>superiorbocal/AnPy
import argparse
import sqlite3
from tabulate import tabulate
from anpy_lib import file_management
from anpy_lib import table_generator
from anpy_lib.data_handling import SQLDataHandler
def set_up():
file_management.create_anpy_dir_if_not_exist()
handler = SQLDataHandler(sqlite3.Connection(file_management.DATABASE_PATH))
return handler
def create_categories(args):
handler = set_up()
print('Creating...')
for category in args.categories:
try:
handler.new_category(category)
except ValueError:
print('{} is invalid... skipping.'.format(category))
def start(args):
requested_category = args.category
handler = set_up()
if handler.is_active_session():
print('An active session is running!')
return
if args.create:
try:
handler.new_category(requested_category)
category = requested_category
except ValueError:
print('{} is invalid (bad name or already exists)... cancelling.'
.format(requested_category))
return
elif requested_category in handler.active_categories:
category = requested_category
else:
categories = handler.active_categories
potential_matches = list(
filter(lambda x: x.startswith(requested_category), categories))
if not potential_matches:
print('{} does not match any category names.'
.format(requested_category))
return
elif len(potential_matches) > 1:
print('"{}" is ambiguous: could be {}'
.format(requested_category, potential_matches))
return
category = potential_matches[0]
assert category
handler.start(category)
def end(_):
handler = set_up()
if handler.is_active_session():
handler.complete()
else:
print("There's no session running!")
def cancel(_):
handler = set_up()
if handler.is_active_session():
handler.cancel()
else:
print('Cannot cancel: No active session.')
def status(_):
handler = set_up()
table, headers = table_generator.create_table_iterable_and_headers(
data_handler=handler)
print(tabulate(table, headers=headers))
print()
print('Active categories: {}'.format(', '.join(handler.active_categories)))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
create_parser = subparsers.add_parser('create',
help='Create new categories')
create_parser.add_argument('categories', metavar='C', nargs='+',
help='Categories to create. Active categories'
'that already exist will be ignored.')
create_parser.set_defaults(func=create_categories)
start_subparser = subparsers.add_parser('start',
help='Start a time-tracking'
'session with the given'
'category.')
start_subparser.add_argument('category', metavar='C',
help='Category under which timer is started. '
'Abbreviated category name is supported '
'as long as unambiugous (e.g. "w" for '
'work')
start_subparser.add_argument('-c', '--create', action='store_true',
help='If flag is set, create the category '
'if it does not already exist. If the '
'flag is not set (default), then '
'nothing will happen.')
start_subparser.set_defaults(func=start)
status_subparser = subparsers.add_parser('status',
help='Displays the progress of '
'the previous 7 days and '
'displays the active '
'categories.')
status_subparser.set_defaults(func=status)
end_subparser = subparsers.add_parser('end', help='Completes the current '
'time-tracking session.')
end_subparser.set_defaults(func=end)
cancel_subparser = subparsers.add_parser('cancel',
help='Cancels the current time-'
'tracking session.')
cancel_subparser.set_defaults(func=cancel)
parser.add_argument('-i', '--interactive', action='store_true')
# TODO: implement interactive
args = parser.parse_args()
args.func(args)
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] |
congd/split_MFSK_features.py | dailai/ConGD-Baseline-Method | 8 | 1604212 | <reponame>dailai/ConGD-Baseline-Method<gh_stars>1-10
import copy
import os
import shutil
def check_create_par_dir(file_path):
dir_ = os.path.dirname(file_path)
if not os.path.exists(dir_):
os.makedirs(dir_)
def split_feat(feats, seg_points, save_path):
feats_copy = copy.copy(feats)
check_create_par_dir(save_path)
out_f = open(save_path, 'w')
for line in feats_copy:
frame = int(line.split(' ', 1)[0])
s_min, s_max = seg_points[0], seg_points[1]
if s_min <= frame <= s_max:
out_f.write(line)
feats.remove(line)
elif frame > s_max:
break
out_f.close()
def split(MFSK_src_dir, split_dir, input_list_file, output_list_file):
with open(input_list_file) as input:
input_list = input.readlines()
output = open(output_list_file, 'w')
for i, line in enumerate(input_list):
video_name, segments_info = line.split(' ', 1)
print 'Splitting', video_name, '%d/%d' % (i, len(input_list))
segments = segments_info.split()
features_file = os.path.join(MFSK_src_dir, video_name+'.mfsk')
with open(features_file) as f_file:
feats = f_file.readlines()
if len(segments) == 1:
seg = segments[0]
points, label = seg.split(':')
s, e = points.split(',')
save_path = os.path.join(split_dir, video_name, '%s-%s.mfsk' % (s, e))
check_create_par_dir(save_path)
shutil.copy(features_file, save_path)
output.write('%s %s\n' % (save_path, label))
else:
for seg in segments:
points, label = seg.split(':')
s, e = points.split(',')
save_path = os.path.join(split_dir, video_name, '%s-%s.mfsk' % (s, e))
split_feat(feats, (int(s), int(e)), save_path)
output.write('%s %s\n' % (save_path, label))
if __name__ == '__main__':
split(MFSK_src_dir='/data2/szhou/gesture/baseline/MFSK_features_CON/train',
split_dir='/data2/szhou/gesture/baseline/MFSK_features_CON_splited/train',
input_list_file='con_list/train.txt',
output_list_file='con_list/train_split.list')
split(MFSK_src_dir='/data2/szhou/gesture/baseline/MFSK_features_CON/valid',
split_dir='/data2/szhou/gesture/baseline/MFSK_features_CON_splited/valid',
input_list_file='con_list/valid_segmented.txt',
output_list_file='con_list/valid_split.list')
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batuta/base.py | makecodes/batuta | 0 | 28294 | from dynaconf import FlaskDynaconf
from flask import Flask
def create_app(**config):
app = Flask(__name__)
FlaskDynaconf(app) # config managed by Dynaconf
app.config.load_extensions('EXTENSIONS') # Load extensions from settings.toml
app.config.update(config) # Override with passed config
return app
def create_app_wsgi():
# workaround for Flask issue
# that doesn't allow **config
# to be passed to create_app
# https://github.com/pallets/flask/issues/4170
app = create_app()
return app
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tests/test_pllcalcs.py | bobbyjsmith11/pll | 0 | 185264 | <filename>tests/test_pllcalcs.py
from unittest import TestCase
from pll.pll_calcs import *
class TestGeneralFunctions(TestCase):
def test_interp_linear_1(self):
""" test the linear interpolator with a value within the x array
"""
test_var = interp_linear([10,20], [1,2], 12)
self.assertAlmostEqual(1.2, test_var[1])
def test_interp_linear_2(self):
""" test the linear interpolator with a value below the x array
"""
test_var = interp_linear([1,2,3], [1,0,3], 1.5)
self.assertAlmostEqual(0.5, test_var[1])
def test_interp_linear_2(self):
""" test the linear interpolator with a value above the x array
"""
test_var = interp_linear([1,2,3], [1,2,3], 3.5)
self.assertAlmostEqual(3.5, test_var[1])
def test_freq_points_per_decade(self):
""" tests that the get_freq_points_per_decade() function returns
the correct array
"""
f_good = list(range(10,100,10))
f_good.extend(range(100,1000,100))
f_good.extend(range(1000,11000,1000))
[float(i) for i in f_good]
f_test = get_freq_points_per_decade(10,10000,10)
self.assertEqual( set(f_good), set(f_test))
class Test2ndOrderPassive(TestCase):
""" The only real function of the class is to provide component values.
Testing this function will indirectly test all underlying functions
of the class.
"""
def test_2nd_order_passive_phase_margin(self):
""" Tests full operation of PllSecondOrderPassive.
Instantiate the class with some hard-coded values. Simulate the PLL
by calling simulatePll function. Test that the phase margin (pm) and
cutoff frequency (fc) are equal to the hard-coded values.
"""
fc = 100e3
pm = 45.0
gamma = 1.024
kphi = 4.69e-3
kvco = 10e6
fstart = 1
fstop = 100e6
ptsPerDec = 100
N = 200
R = 4
pll = PllSecondOrderPassive( fc,
pm,
kphi,
kvco,
N,
gamma=gamma )
d_test = pll.calc_components()
pm_test, fc_test = get_pm_fc_from_actual_filter_components(d_test, fstart, fstop, ptsPerDec, kphi, kvco, N, R)
self.assertAlmostEqual(pm,pm_test)
def test_2nd_order_passive_loop_bandwidth(self):
""" Tests full operation of PllSecondOrderPassive.
Instantiate the class with some hard-coded values. Simulate the PLL
by calling simulatePll function. Test that the phase margin (pm) and
cutoff frequency (fc) are equal to the hard-coded values.
"""
fc = 100e3
pm = 45.0
gamma = 1.024
kphi = 4.69e-3
kvco = 10e6
fstart = 1
fstop = 100e6
ptsPerDec = 100
N = 200
R = 4
pll = PllSecondOrderPassive( fc,
pm,
kphi,
kvco,
N,
gamma=gamma )
d_test = pll.calc_components()
pm_test, fc_test = get_pm_fc_from_actual_filter_components(d_test, fstart, fstop, ptsPerDec, kphi, kvco, N, R)
self.assertAlmostEqual(fc,fc_test)
class Test3rdOrderPassive(TestCase):
""" The only real function of the class is to provide component values.
Testing this function will indirectly test all underlying functions
of the class.
"""
def test_3rd_order_passive_phase_margin(self):
""" Tests full operation of PllThirdOrderPassive.
Instantiate the class with some hard-coded values. Simulate the PLL
by calling simulatePll function. Test that the phase margin (pm) and
cutoff frequency (fc) are equal to the hard-coded values.
"""
fc = 100e3
pm = 45.0
kphi = 5e-3
kvco = 10e6
N = 200
fstart = 1
fstop = 100e6
ptsPerDec = 100
R = 1
pll = PllThirdOrderPassive(fc,
pm,
kphi,
kvco,
N,
gamma=1.024,
t31=0.6)
d_test = pll.calc_components()
pm_test, fc_test = get_pm_fc_from_actual_filter_components(d_test, fstart, fstop, ptsPerDec, kphi, kvco, N, R)
self.assertAlmostEqual(pm, pm_test)
def test_3rd_order_passive_loop_bandwidth(self):
""" Tests full operation of PllThirdOrderPassive.
Instantiate the class with some hard-coded values. Simulate the PLL
by calling simulatePll function. Test that the phase margin (pm) and
cutoff frequency (fc) are equal to the hard-coded values.
"""
fc = 100e3
pm = 45.0
kphi = 5e-3
kvco = 10e6
N = 200
fstart = 1
fstop = 100e6
ptsPerDec = 100
R = 1
pll = PllThirdOrderPassive( fc,
pm,
kphi,
kvco,
N,
gamma=1.024,
t31=0.6)
d_test = pll.calc_components()
pm_test, fc_test = get_pm_fc_from_actual_filter_components(d_test, fstart, fstop, ptsPerDec, kphi, kvco, N, R)
self.assertAlmostEqual(fc,fc_test)
############ Helper functions ############333
def get_pm_fc_from_actual_filter_components(d, fstart, fstop, ptsPerDec, kphi, kvco, N, R):
""" return pm and fc from simulating actual filter components
Parameters
d (dict) - returned from a call to calc_components in a pll class
Returns
tuple(pm (float), fc (float))
"""
flt = {
'c1':d['c1'],
'c2':d['c2'],
'c3':d['c3'],
'c4':d['c4'],
'r2':d['r2'],
'r3':d['r3'],
'r4':d['r4'],
'flt_type':"passive"
}
f,g,p,fz,pz,ref_cl,vco_cl = simulatePll( fstart,
fstop,
ptsPerDec,
kphi,
kvco,
N,
R,
filt=flt)
return pz, fz
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] |
scripts/courtlistener/tokenizeText.py | sandeepsoni/semantic-progressiveness | 2 | 135136 | """ Tokenize and tag documents from a .jsonl file """
import spacy
import ujson
import plac
import os
import sys
import logging
if "../../" not in sys.path:
sys.path.append ("../../")
from modules import constants
TAGGED_EXT=".tokenized"
MAX_LENGTH = 10000000
NLP = spacy.load ("en_core_web_sm")
NLP.max_length = MAX_LENGTH
logging.basicConfig (format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO)
def tokenizeText (jurname, filesdir):
with open (os.path.join (filesdir, jurname + constants.JSONL_EXT)) as fin, open (os.path.join (filesdir, jurname + TAGGED_EXT + constants.JSONL_EXT), "w") as fout:
for i, line in enumerate (fin):
js = ujson.loads (line)
doc = NLP (js["text"], disable=["tagger", "parser", "ner"])
tokens = [token.text.lower() for token in doc]
js["tokens"] = tokens
fout.write (ujson.dumps (js) + constants.NL)
if (i+1) % 1000 == 0:
logging.info ("{0} lines processed for {1}".format (i+1, jurname))
@plac.annotations (
filesdir = ("directory with input files and output files", "positional"),
jurname=("the jurisdiction name", "positional")
)
def main (filesdir, jurname):
tokenizeText (jurname, filesdir)
if __name__ == "__main__":
plac.call (main)
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] |
evaluate/evaluate_debug.py | goodgodgd/vode-2020 | 4 | 22984 | <filename>evaluate/evaluate_debug.py
import os
import os.path as op
import numpy as np
import pandas as pd
import cv2
import tensorflow as tf
import settings
from config import opts
from tfrecords.tfrecord_reader import TfrecordReader
import utils.util_funcs as uf
import utils.convert_pose as cp
from model.synthesize.synthesize_base import SynthesizeMultiScale
from model.train_val import ModelValidater, merge_results
from model.model_main import set_configs, get_dataset, create_training_parts
from model.model_util.logger import stack_reconstruction_images
import model.loss_and_metric.losses as lm
def inspect_results():
set_configs()
dataset_val, tfr_config, val_steps = get_dataset("kitti_raw", "val", False)
model, augmenter, loss_object, optimizer = \
create_training_parts(initial_epoch=1, tfr_config=tfr_config, learning_rate=0.001,
loss_weights=opts.LOSS_RIGID_T1, net_names=opts.RIGID_NET, weight_suffix='ep15')
validater = ModelValidaterInspect(model, loss_object, val_steps, True)
validater.run_an_epoch(dataset_val)
class ModelValidaterInspect(ModelValidater):
def run_an_epoch(self, dataset):
results = []
for step, features in enumerate(dataset):
preds, loss, loss_by_type = self.run_a_batch(features)
batch_result, log_msg = merge_results(features, preds, loss, loss_by_type, self.stereo)
self.print_result(batch_result, step, log_msg, features, preds)
results.append(batch_result)
self.show_images(features, preds)
results = pd.DataFrame(results)
return results
def print_result(self, batch_result, step, log_msg, features, predictions):
print(f"{step}/{self.steps_per_epoch} steps, {log_msg}")
msg = " "
for i, (key, val) in enumerate(batch_result.items()):
msg += f"{key}={val:1.5f}, "
print(msg)
if "pose_gt" in features:
pose_gt_vec = cp.pose_matr2rvec_batch(features["pose_gt"]).numpy()
pose_pr_vec = predictions["pose"].numpy()
xyz_true = pose_gt_vec[:, :, :3]
xyz_pred = pose_pr_vec[:, :, :3]
scale = np.sum(xyz_true * xyz_pred, axis=2) / np.sum(xyz_pred ** 2, axis=2)
print(" pose gt:", pose_gt_vec[0, 0])
print(" pose pr:", pose_pr_vec[0, 0])
print(f" pose scale, diff: {scale[0, 0]:1.4f}", np.abs(pose_gt_vec[0, 0] - pose_pr_vec[0, 0]))
if "depth_gt" in features:
print(f" depth scale, gt depth, pr depth: {batch_result['gtdepth']/batch_result['prdepth']:1.4f}",
batch_result["gtdepth"], batch_result["prdepth"])
def show_images(self, features, predictions):
total_loss = lm.TotalLoss()
scaleidx, batchidx, srcidx = 0, 0, 0
view1 = stack_reconstruction_images(total_loss, features, predictions, (scaleidx, batchidx, srcidx))
cv2.imshow("recon", view1)
if "pose_gt" in features:
pose_gt_vec = cp.pose_matr2rvec_batch(features["pose_gt"])
predictions["pose"] = pose_gt_vec
view2 = stack_reconstruction_images(total_loss, features, predictions, (scaleidx, batchidx, srcidx))
cv2.imshow("recon_by_gtpose", view2)
cv2.waitKey()
def evaluate_for_debug(data_dir_name, model_name):
"""
function to check if learning process is going right
to evaluate current model, save losses and error metrics to csv files and save debugging images
- debug_depth.csv: 타겟 프레임별로 predicted depth의 error와 smootheness loss 저장
- debug_pose.csv: 소스 프레임별로 photometric loss, trajectory error, rotation error 저장
- trajectory.csv: 소스 프레임별로 gt trajectory, pred trajectory 저장
- debug_imgs(directory): loss와 metric 별로 가장 성능이 안좋은 프레임들을 모아서 inspection view 이미지로 저장
1) target image
2) reconstructed target from gt
3) reconstructed target from pred
4) source image
5) predicted target depth
"""
if not uf.check_tfrecord_including(op.join(opts.DATAPATH_TFR, data_dir_name), ["pose_gt", "depth_gt"]):
print("Evaluation is NOT possible without pose_gt and depth_gt")
return
set_configs(model_name)
model = create_model()
model = try_load_weights(model, model_name)
model.compile(optimizer="sgd", loss="mean_absolute_error")
dataset = TfrecordReader(op.join(opts.DATAPATH_TFR, data_dir_name), batch_size=1).get_dataset()
depth_result = []
pose_result = []
trajectory = []
steps_per_epoch = uf.count_steps(data_dir_name, 1)
for i, x in enumerate(dataset):
uf.print_numeric_progress(i, steps_per_epoch)
depth_res, pose_res, traj = evaluate_batch(i, x, model)
depth_result.append(depth_res)
pose_result.append(pose_res)
trajectory.append(traj)
print("")
depth_result = save_depth_result_and_get_df(depth_result, model_name)
pose_result = save_pose_result_and_get_df(pose_result, model_name)
save_trajectories(trajectory, model_name)
depth_sample_inds = find_worst_depth_samples(depth_result, 5)
print("worst depth sample indices\n", depth_sample_inds[0])
pose_sample_inds = find_worst_pose_samples(pose_result, 5)
print("worst pose sample indices\n", pose_sample_inds[0])
worst_sample_inds = depth_sample_inds + pose_sample_inds
pathname = op.join(opts.DATAPATH_EVL, model_name, 'debug_imgs')
os.makedirs(pathname, exist_ok=True)
for i, x in enumerate(dataset):
uf.print_numeric_progress(i, steps_per_epoch)
for sample_inds in worst_sample_inds:
# sample_inds: df['frame', 'srcidx', metric or loss]
save_worst_views(i, x, model, sample_inds, pathname)
def evaluate_batch(index, x, model):
numsrc = opts.SNIPPET_LEN - 1
stacked_image = x['image']
intrinsic = x['intrinsic']
depth_true = x['depth_gt']
pose_true_mat = x['pose_gt']
source_image, target_image = uf.split_into_source_and_target(stacked_image)
predictions = model(x['image'])
disp_pred_ms = predictions['disp_ms']
pose_pred = predictions['pose']
depth_pred_ms = uf.safe_reciprocal_number_ms(disp_pred_ms)
# evaluate depth from numpy arrays and take only 'abs_rel' metric
depth_err, scale = compute_depth_error(depth_pred_ms[0].numpy()[0], depth_true.numpy()[0])
smooth_loss = compute_smooth_loss(disp_pred_ms[0], target_image)
pose_pred_mat = cp.pose_rvec2matr_batch_tf(pose_pred)
# pose error output: [batch, numsrc]
trj_err, trj_len = compute_trajectory_error(pose_pred_mat, pose_true_mat, scale)
rot_err = ef.calc_rotational_error_tensor(pose_pred_mat, pose_true_mat)
# compute photometric loss: [batch, numsrc]
photo_loss = compute_photo_loss(target_image, source_image, intrinsic, depth_pred_ms, pose_pred)
depth_res = [index, smooth_loss, depth_err]
# pose_res: [numsrc, -1]
pose_res = np.stack([np.array([index] * 4), np.arange(numsrc), photo_loss.numpy().reshape(-1),
trj_err.numpy().reshape(-1), trj_len.numpy().reshape(-1),
rot_err.numpy().reshape(-1)], axis=1)
# to collect trajectory
trajectory = np.concatenate([np.array([index] * 4)[:, np.newaxis], np.arange(numsrc)[:, np.newaxis],
pose_true_mat.numpy()[:, :, :3, 3].reshape((-1, 3)),
pose_pred_mat.numpy()[:, :, :3, 3].reshape((-1, 3))*scale], axis=1)
return depth_res, pose_res, trajectory
def compute_photo_loss(target_true, source_image, intrinsic, depth_pred_ms, pose_pred):
# synthesize target image
synth_target_ms = SynthesizeMultiScale()(source_image, intrinsic, depth_pred_ms, pose_pred)
losses = []
target_pred = synth_target_ms[0]
# photometric loss: [batch, numsrc]
loss = photometric_loss(target_pred, target_true)
return loss
def compute_smooth_loss(disparity, target_image):
# [batch]
loss = smootheness_loss(disparity, target_image)
# return scalar
return loss.numpy()[0]
def compute_trajectory_error(pose_pred_mat, pose_true_mat, scale):
"""
:param pose_pred_mat: predicted snippet pose matrices, [batch, numsrc, 4, 4]
:param pose_true_mat: ground truth snippet pose matrices, [batch, numsrc, 4, 4]
:param scale: scale for pose_pred to have real scale
:return: trajectory error in meter [batch, numsrc]
"""
xyz_pred = pose_pred_mat[:, :, :3, 3]
xyz_true = pose_true_mat[:, :, :3, 3]
# adjust the trajectory scaling due to ignorance of abolute scale
# scale = tf.reduce_sum(xyz_true * xyz_pred, axis=2) / tf.reduce_sum(xyz_pred ** 2, axis=2)
# scale = tf.expand_dims(scale, -1)
traj_error = xyz_true - xyz_pred * tf.constant([[[scale]]])
traj_error = tf.sqrt(tf.reduce_sum(traj_error ** 2, axis=2))
traj_len = tf.sqrt(tf.reduce_sum(xyz_true ** 2, axis=2))
return traj_error, traj_len
def compute_depth_error(depth_pred, depth_true):
mask = np.logical_and(depth_true > opts.MIN_DEPTH, depth_true < opts.MAX_DEPTH)
# crop used by Garg ECCV16 to reprocude Eigen NIPS14 results
# if used on gt_size 370x1224 produces a crop of [-218, -3, 44, 1180]
gt_height, gt_width, _ = depth_true.shape
crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
# scale matching
scaler = np.median(depth_true[mask]) / np.median(depth_pred[mask])
depth_pred[mask] *= scaler
# clip prediction and compute error metrics
depth_pred = np.clip(depth_pred, opts.MIN_DEPTH, opts.MAX_DEPTH)
metrics = ef.compute_depth_metrics(depth_pred[mask], depth_true[mask])
# return only abs rel
return metrics[0], scaler
def save_depth_result_and_get_df(depth_result, model_name):
depth_result = np.array(depth_result)
depth_result = pd.DataFrame(data=depth_result, columns=['frame', 'smooth_loss', 'depth_err'])
depth_result['frame'] = depth_result['frame'].astype(int)
filename = op.join(opts.DATAPATH_EVL, model_name, 'debug_depth.csv')
depth_result.to_csv(filename, encoding='utf-8', index=False, float_format='%.4f')
return depth_result
def save_pose_result_and_get_df(pose_result, model_name):
pose_result = np.concatenate(pose_result, axis=0)
columns = ['frame', 'srcidx', 'photo_loss', 'trj_err', 'distance', 'rot_err']
pose_result = pd.DataFrame(data=pose_result, columns=columns)
pose_result['frame'] = pose_result['frame'].astype(int)
pose_result['srcidx'] = pose_result['srcidx'].astype(int)
filename = op.join(opts.DATAPATH_EVL, model_name, 'debug_pose.csv')
pose_result.to_csv(filename, encoding='utf-8', index=False, float_format='%.4f')
return pose_result
def save_trajectories(trajectory, model_name):
trajectory = np.concatenate(trajectory, axis=0)
trajectory = pd.DataFrame(data=trajectory, columns=['frame', 'srcidx', 'tx', 'ty', 'tz', 'px', 'py', 'pz'])
trajectory['frame'] = trajectory['frame'].astype(int)
trajectory['srcidx'] = trajectory['srcidx'].astype(int)
filename = op.join(opts.DATAPATH_EVL, model_name, 'trajectory.csv')
trajectory.to_csv(filename, encoding='utf-8', index=False, float_format='%.4f')
def find_worst_depth_samples(depth_result, num_samples):
dfcols = list(depth_result)
sample_inds = []
for colname in ['depth_err']:
sorted_result = depth_result[dfcols[:1] + [colname]].sort_values(by=[colname], ascending=False)
sorted_result = sorted_result.reset_index(drop=True).head(num_samples)
sorted_result['srcidx'] = 0
sorted_result = sorted_result[['frame', 'srcidx', colname]]
sample_inds.append(sorted_result)
return sample_inds
def find_worst_pose_samples(pose_result, num_samples):
dfcols = list(pose_result)
sample_inds = []
for colname in ['photo_loss', 'trj_err']:
sorted_result = pose_result[dfcols[:2] + [colname]].sort_values(by=[colname], ascending=False)
sorted_result = sorted_result.reset_index(drop=True).head(num_samples)
sample_inds.append(sorted_result)
return sample_inds
def save_worst_views(frame, x, model, sample_inds, save_path, scale=1):
if frame not in sample_inds['frame'].tolist():
return
colname = list(sample_inds)[-1]
indices = sample_inds.loc[sample_inds['frame'] == frame, :].index.tolist()
stacked_image = x['image']
intrinsic = x['intrinsic']
depth_gt = x['depth_gt']
pose_gt = x['pose_gt']
pose_gt = cp.pose_matr2rvec_batch(pose_gt)
depth_gt_ms = uf.multi_scale_depths(depth_gt, [1, 2, 4, 8])
source_image, target_image = uf.split_into_source_and_target(stacked_image)
predictions = model(x['image'])
disp_pred_ms = predictions['disp_ms']
pose_pred = predictions['pose']
depth_pred_ms = uf.safe_reciprocal_number_ms(disp_pred_ms)
depth_pred_ms = [depth*scale for depth in depth_pred_ms]
synthesizer = SynthesizeMultiScale()
synth_target_pred_ms = synthesizer(source_image, intrinsic, depth_pred_ms, pose_pred)
synth_target_gt_ms = synthesizer(source_image, intrinsic, depth_gt_ms, pose_gt)
for ind in indices:
srcidx = sample_inds.loc[ind, 'srcidx']
view_imgs = {"target": target_image, "synthesized": synth_target_pred_ms[0][0, srcidx],
"depth": depth_pred_ms[0][0, srcidx], "synth_by_gt": synth_target_gt_ms[0][0, srcidx]}
view = uf.stack_titled_images(view_imgs)
filename = op.join(save_path, f"{colname[:3]}_{frame:04d}_{srcidx}.png")
print("save file:", filename)
cv2.imwrite(filename, view)
if __name__ == "__main__":
np.set_printoptions(precision=3, suppress=True, linewidth=100)
inspect_results()
# evaluate_for_debug('kitti_raw_test', 'vode1')
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app/utilities/__init__.py | Maxyme/movie-review | 0 | 108869 | <filename>app/utilities/__init__.py
"""app utilities"""
| [
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src/data/batchloader.py | jiahui890/chexpert-aml | 0 | 51247 | <reponame>jiahui890/chexpert-aml<gh_stars>0
import pandas as pd
import numpy as np
class BatchLoader:
def __init__(self, dataset, batch_size, return_labels=None, without_image=False, return_X_y=True):
self.dataset = dataset
self.batch_size = batch_size
self.return_labels = return_labels
self.data_size = len(dataset)
self.start = 0
self.without_image = without_image
self.return_X_y = return_X_y
def __iter__(self):
self.start = 0
return self
def __next__(self):
if self.start >= self.data_size:
raise StopIteration
start = self.start
end = min(start + self.batch_size, self.data_size)
all_features = []
image_features = []
all_labels = []
if self.without_image:
featrue_columns = ['Path'] + self.dataset._feature_header.tolist()
if self.return_labels:
labels_columns = self.return_labels
else:
labels_columns = self.dataset._label_header.tolist()
if self.return_X_y:
features = self.dataset.df.iloc[start:end][featrue_columns]
labels = self.dataset.df.iloc[start:end][labels_columns]
self.start = end
return features, labels
else:
columns = featrue_columns + labels_columns
data = self.dataset.df.iloc[start:end][columns]
self.start = end
return data
else:
for i in range(start, end):
features, image_feature, labels = self.dataset[i]
all_features.append(features)
image_features.append(image_feature)
all_labels.append(labels)
x_features, x_image = pd.DataFrame(all_features, columns=self.dataset._feature_header), pd.DataFrame(image_features)
y = pd.DataFrame(all_labels, columns=self.dataset._label_header)
if self.return_labels:
if len(self.return_labels) > 1:
y = y[self.return_labels]
else:
y = y[self.return_labels[0]]
self.start = end
return x_features, x_image, y
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] |
src/data/prepare_train_data.py | ds-praveenkumar/m5-accuracy-prediction | 0 | 30966 | # github link: https://github.com/ds-praveenkumar/kaggle
# Author: ds-praveenkumar
# file: forcasting/prepare_train_data.py/
# Created by ds-praveenkumar at 13-06-2020 15 34
# feature:
import os
import pandas as pd
import numpy as np
import click
from src.utility.timeit import timeit
root = os.path.dirname(os.getcwd())
train_data_path = os.path.join(root, 'data', 'training')
preprocess_data_path = os.path.join(root, 'data', 'preprocess')
@timeit
def prepare_train_data(prep_path, train_path):
prep_df = pd.DataFrame(np.load(os.path.join(prep_path, 'sales_mat.npy')))
prep_df = prep_df.T
prep_df['ds'] = pd.date_range(end='2016-06-19',periods=1913).values
for column in prep_df.iloc[:,:30489]:
train_items = prep_df[['ds',column]][-365:]
train_items.rename(columns={column:'y'},inplace=True)
save_at = os.path.join(train_path,f"{column}.csv")
train_items.to_csv(save_at,index=False)
print(f"file saved at {save_at}")
@click.command()
@click.argument('preprocess_data_path', type=click.Path(exists=True))
@click.argument('train_data_path', type=click.Path())
def main(preprocess_data_path, train_data_path):
prepare_train_data(preprocess_data_path, train_data_path)
if __name__=='__main__':
main() | [
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] |
basic-programs/tensor_autograd.py | lcskrishna/my-pytorch-experiments | 0 | 191531 | import torch
x = torch.ones(2,2, requires_grad = True)
print (x)
y = x + 2
print (y)
print (y.grad_fn)
z = y * y * 3
out = z.mean()
print (z)
print (out)
a = torch.randn(2,2)
a = ((a * 3)/ (a - 1))
print (a.requires_grad)
a.requires_grad_(True)
print (a.requires_grad)
b = (a * a).sum()
print (b.grad_fn)
out.backward()
print (x.grad)
x = torch.randn(3, requires_grad = True)
y = x * 2
while y.data.norm() < 1000:
y = y * 2
print (y)
gradients = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(gradients)
print (x.grad)
print (x.requires_grad)
print ((x ** 2).requires_grad)
with torch.no_grad():
print ((x ** 2).requires_grad)
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] |
examples/topic_with_admin_key.py | wensheng/hedera-sdk-py | 9 | 1602489 | from hedera import (
PrivateKey,
KeyList,
TopicCreateTransaction,
TopicUpdateTransaction,
TopicInfoQuery,
)
from get_client import client
initialAdminKeys = [PrivateKey.generate() for i in range(3)]
thresholdKey = KeyList.of(initialAdminKeys[0], initialAdminKeys[1])
# Create topic
tran = TopicCreateTransaction(
).setTopicMemo("demo topic"
).setAdminKey(thresholdKey
).freezeWith(client
).sign(initialAdminKeys[0]
).sign(initialAdminKeys[1])
resp = tran.execute(client)
topicId = resp.getReceipt(client).topicId
print("Created new topic ", topicId.toString(), " with 2-of-3 threshold key as adminKey.")
newAdminKeys = [PrivateKey.generate() for i in range(4)]
thresholdKey = KeyList.of(newAdminKeys[0], newAdminKeys[1], newAdminKeys[2])
# Sign with the initial adminKey. 2 of the 3 keys already part of the topic's adminKey.
# Sign with the new adminKey. 3 of 4 keys already part of the topic's adminKey.
tran = TopicUpdateTransaction(
).setTopicId(topicId
).setTopicMemo("updated demo topic"
).setAdminKey(thresholdKey
).freezeWith(client
).sign(initialAdminKeys[0]
).sign(initialAdminKeys[1]
).sign(newAdminKeys[0]
).sign(newAdminKeys[1]
).sign(newAdminKeys[2])
resp = tran.execute(client)
receipt = resp.getReceipt(client)
print("Updated topic ", topicId.toString(), " with 3-of-4 threshold key as adminKey.")
topicInfo = TopicInfoQuery().setTopicId(topicId).execute(client)
print(topicInfo.toString())
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] |
lingvo/tasks/asr/tools/simple_wer_v2.py | allenwang28/lingvo | 2,611 | 132399 | # Lint as: python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The new version script to evalute the word error rate (WER) for ASR tasks.
Tensorflow and Lingvo are not required to run this script.
Example of Usage:
a) `python simple_wer_v2.py file_hypothesis file_reference`
b) `python simple_wer_v2.py file_hypothesis file_reference file_keyphrases`
where `file_hypothesis` is the filename for hypothesis text,
`file_reference` is the filename for reference text, and
`file_keyphrases` is the optional filename for important phrases
(one phrase per line).
Note that the program will also generate a html to diagnose the errors,
and the html filename is `{$file_hypothesis}_diagnois.html`.
Another way is to use this file as a stand-alone library, by calling class
SimpleWER with the following member functions:
- AddHypRef(hyp, ref): Updates the evaluation for each (hyp,ref) pair.
- GetWER(): Computes word error rate (WER) for all the added hyp-ref pairs.
- GetSummaries(): Generates strings to summarize word and key phrase errors.
- GetKeyPhraseStats(): Measures stats for key phrases.
Stats include:
(1) Jaccard similarity: https://en.wikipedia.org/wiki/Jaccard_index.
(2) F1 score: https://en.wikipedia.org/wiki/Precision_and_recall.
"""
import re
import sys
def TxtPreprocess(txt):
"""Preprocess text before WER caculation."""
# Lowercase, remove \t and new line.
txt = re.sub(r'[\t\n]', ' ', txt.lower())
# Remove punctuation before space.
txt = re.sub(r'[,.\?!]+ ', ' ', txt)
# Remove punctuation before end.
txt = re.sub(r'[,.\?!]+$', ' ', txt)
# Remove punctuation after space.
txt = re.sub(r' [,.\?!]+', ' ', txt)
# Remove quotes, [, ], ( and ).
txt = re.sub(r'["\(\)\[\]]', '', txt)
# Remove extra space.
txt = re.sub(' +', ' ', txt.strip())
return txt
def RemoveCommentTxtPreprocess(txt):
"""Preprocess text and remove comments in the brancket, such as [comments]."""
# Remove comments surrounded by box brackets:
txt = re.sub(r'\[\w+\]', '', txt)
return TxtPreprocess(txt)
def HighlightAlignedHtml(hyp, ref, err_type):
"""Generate a html element to highlight the difference between hyp and ref.
Args:
hyp: Hypothesis string.
ref: Reference string.
err_type: one of 'none', 'sub', 'del', 'ins'.
Returns:
a html string where disagreements are highlighted.
Note `hyp` is highlighted in green, and marked with <del> </del>
`ref` is highlighted in yellow. If you want html with nother styles,
consider to write your own function.
Raises:
ValueError: if err_type is not among ['none', 'sub', 'del', 'ins'].
or if when err_type == 'none', hyp != ref
"""
highlighted_html = ''
if err_type == 'none':
if hyp != ref:
raise ValueError('hyp (%s) does not match ref (%s) for none error' %
(hyp, ref))
highlighted_html += '%s ' % hyp
elif err_type == 'sub':
highlighted_html += """<span style="background-color: yellow">
<del>%s</del></span><span style="background-color: yellow">
%s </span> """ % (hyp, ref)
elif err_type == 'del':
highlighted_html += """<span style="background-color: red">
%s </span> """ % (
ref)
elif err_type == 'ins':
highlighted_html += """<span style="background-color: green">
<del>%s</del> </span> """ % (
hyp)
else:
raise ValueError('unknown err_type ' + err_type)
return highlighted_html
def ComputeEditDistanceMatrix(hyp_words, ref_words):
"""Compute edit distance between two list of strings.
Args:
hyp_words: the list of words in the hypothesis sentence
ref_words: the list of words in the reference sentence
Returns:
Edit distance matrix (in the format of list of lists), where the first
index is the reference and the second index is the hypothesis.
"""
reference_length_plus = len(ref_words) + 1
hypothesis_length_plus = len(hyp_words) + 1
edit_dist_mat = [[]] * reference_length_plus
# Initialization.
for i in range(reference_length_plus):
edit_dist_mat[i] = [0] * hypothesis_length_plus
for j in range(hypothesis_length_plus):
if i == 0:
edit_dist_mat[0][j] = j
elif j == 0:
edit_dist_mat[i][0] = i
# Do dynamic programming.
for i in range(1, reference_length_plus):
for j in range(1, hypothesis_length_plus):
if ref_words[i - 1] == hyp_words[j - 1]:
edit_dist_mat[i][j] = edit_dist_mat[i - 1][j - 1]
else:
tmp0 = edit_dist_mat[i - 1][j - 1] + 1
tmp1 = edit_dist_mat[i][j - 1] + 1
tmp2 = edit_dist_mat[i - 1][j] + 1
edit_dist_mat[i][j] = min(tmp0, tmp1, tmp2)
return edit_dist_mat
class SimpleWER:
"""Compute word error rates after the alignment.
Attributes:
key_phrases: list of important phrases.
aligned_htmls: list of diagnois htmls, each of which corresponding to a pair
of hypothesis and reference.
hyp_keyphrase_counts: dict. `hyp_keyphrase_counts[w]` counts how often a key
phrases `w` appear in the hypotheses.
ref_keyphrase_counts: dict. `ref_keyphrase_counts[w]` counts how often a key
phrases `w` appear in the references.
matched_keyphrase_counts: dict. `matched_keyphrase_counts[w]` counts how
often a key phrase `w` appear in the aligned transcripts when the
reference and hyp_keyphrase match.
wer_info: dict with four keys: 'sub' (substitution error), 'ins' (insersion
error), 'del' (deletion error), 'nw' (number of words). We can use
wer_info to compute word error rate (WER) as
(wer_info['sub']+wer_info['ins']+wer_info['del'])*100.0/wer_info['nw']
"""
def __init__(self,
key_phrases=None,
html_handler=HighlightAlignedHtml,
preprocess_handler=RemoveCommentTxtPreprocess):
"""Initialize SimpleWER object.
Args:
key_phrases: list of strings as important phrases. If key_phrases is
None, no key_phrases related metric will be computed.
html_handler: function to generate a string with html tags.
preprocess_handler: function to preprocess text before computing WER.
"""
self._preprocess_handler = preprocess_handler
self._html_handler = html_handler
self.key_phrases = key_phrases
self.aligned_htmls = []
self.wer_info = {'sub': 0, 'ins': 0, 'del': 0, 'nw': 0}
if key_phrases:
# Pre-process key_phrase list
if self._preprocess_handler:
self.key_phrases = \
[self._preprocess_handler(k) for k in self.key_phrases]
# Init keyphrase_counts for every key phrase
self.ref_keyphrase_counts = {}
self.hyp_keyphrase_counts = {}
self.matched_keyphrase_counts = {}
for k in self.key_phrases:
self.ref_keyphrase_counts[k] = 0
self.hyp_keyphrase_counts[k] = 0
self.matched_keyphrase_counts[k] = 0
else:
self.ref_keyphrase_counts = None
self.hyp_keyphrase_counts = None
self.matched_keyphrase_counts = None
def AddHypRef(self, hypothesis, reference):
"""Update WER when adding one pair of strings: (hypothesis, reference).
Args:
hypothesis: Hypothesis string.
reference: Reference string.
Raises:
ValueError: when the program fails to parse edit distance matrix.
"""
if self._preprocess_handler:
hypothesis = self._preprocess_handler(hypothesis)
reference = self._preprocess_handler(reference)
# Compute edit distance.
hyp_words = hypothesis.split()
ref_words = reference.split()
distmat = ComputeEditDistanceMatrix(hyp_words, ref_words)
# Back trace, to distinguish different errors: ins, del, sub.
pos_hyp, pos_ref = len(hyp_words), len(ref_words)
wer_info = {'sub': 0, 'ins': 0, 'del': 0, 'nw': len(ref_words)}
aligned_html = ''
matched_ref = ''
while pos_hyp > 0 or pos_ref > 0:
err_type = ''
# Distinguish error type by back tracking
if pos_ref == 0:
err_type = 'ins'
elif pos_hyp == 0:
err_type = 'del'
else:
if hyp_words[pos_hyp - 1] == ref_words[pos_ref - 1]:
err_type = 'none' # correct error
elif distmat[pos_ref][pos_hyp] == distmat[pos_ref - 1][pos_hyp - 1] + 1:
err_type = 'sub' # substitute error
elif distmat[pos_ref][pos_hyp] == distmat[pos_ref - 1][pos_hyp] + 1:
err_type = 'del' # deletion error
elif distmat[pos_ref][pos_hyp] == distmat[pos_ref][pos_hyp - 1] + 1:
err_type = 'ins' # insersion error
else:
raise ValueError('fail to parse edit distance matrix.')
# Generate aligned_html
if self._html_handler:
if pos_hyp == 0 or not hyp_words:
tmph = ' '
else:
tmph = hyp_words[pos_hyp - 1]
if pos_ref == 0 or not ref_words:
tmpr = ' '
else:
tmpr = ref_words[pos_ref - 1]
aligned_html = self._html_handler(tmph, tmpr, err_type) + aligned_html
# If no error, go to previous ref and hyp.
if err_type == 'none':
matched_ref = hyp_words[pos_hyp - 1] + ' ' + matched_ref
pos_hyp, pos_ref = pos_hyp - 1, pos_ref - 1
continue
# Update error.
wer_info[err_type] += 1
# Adjust position of ref and hyp.
if err_type == 'del':
pos_ref = pos_ref - 1
elif err_type == 'ins':
pos_hyp = pos_hyp - 1
else: # err_type == 'sub'
pos_hyp, pos_ref = pos_hyp - 1, pos_ref - 1
# Verify the computation of edit distance finishes
assert distmat[-1][-1] == wer_info['ins'] + \
wer_info['del'] + wer_info['sub']
# Accumulate err_info before the next (hyp, ref).
for k in wer_info:
self.wer_info[k] += wer_info[k]
# Collect aligned_htmls.
if self._html_handler:
self.aligned_htmls += [aligned_html]
# Update key phrase info.
if self.key_phrases:
for w in self.key_phrases:
self.ref_keyphrase_counts[w] += reference.count(w)
self.hyp_keyphrase_counts[w] += hypothesis.count(w)
self.matched_keyphrase_counts[w] += matched_ref.count(w)
def GetWER(self):
"""Compute Word Error Rate (WER).
Note WER can be larger than 100.0, esp when there are many insertion errors.
Returns:
WER as percentage number, usually between 0.0 to 100.0
"""
nref = self.wer_info['nw']
nref = max(1, nref) # non_zero value for division
total_error = self.wer_info['ins'] \
+ self.wer_info['del'] + self.wer_info['sub']
return total_error * 100.0 / nref
def GetBreakdownWER(self):
"""Compute breakdown WER.
Returns:
A dictionary with del/ins/sub as key, and the error rates in percentage
number as value.
"""
nref = self.wer_info['nw']
nref = max(1, nref) # non_zero value for division
wer_breakdown = dict()
wer_breakdown['ins'] = self.wer_info['ins'] * 100.0 / nref
wer_breakdown['del'] = self.wer_info['del'] * 100.0 / nref
wer_breakdown['sub'] = self.wer_info['sub'] * 100.0 / nref
return wer_breakdown
def GetKeyPhraseStats(self):
"""Measure the Jaccard similarity of key phrases between hyps and refs.
Returns:
jaccard_similarity: jaccard similarity, between 0.0 and 1.0
F1_keyphrase: F1 score (=2/(1/prec + 1/recall)), between 0.0 and 1.0
matched_keyphrases: num of matched key phrases.
ref_keyphrases: num of key phrases in the reference strings.
hyp_keyphrases: num of key phrases in the hypothesis strings.
"""
matched_k = sum(self.matched_keyphrase_counts.values())
ref_k = sum(self.ref_keyphrase_counts.values())
hyp_k = sum(self.hyp_keyphrase_counts.values())
joined_k = ref_k + hyp_k - matched_k
joined_k = max(1, joined_k) # non_zero value for division
jaccard_similarity = matched_k * 1.0 / joined_k
f1_k = 2.0 * matched_k / max(ref_k + hyp_k, 1.0)
return (jaccard_similarity, f1_k, matched_k, ref_k, hyp_k)
def GetSummaries(self):
"""Generate strings to summarize word errors and key phrase errors.
Returns:
str_sum: string summarizing total error, total word and WER.
str_details: string breaking down three error types: del, ins, sub.
str_str_keyphrases_info: string summarizing kerphrase information.
"""
nref = self.wer_info['nw']
total_error = self.wer_info['ins'] \
+ self.wer_info['del'] + self.wer_info['sub']
str_sum = 'total WER = %d, total word = %d, wer = %.2f%%' % (
total_error, nref, self.GetWER())
str_details = 'Error breakdown: del = %.2f%%, ins=%.2f%%, sub=%.2f%%' % (
self.wer_info['del'] * 100.0 / nref, self.wer_info['ins'] * 100.0 /
nref, self.wer_info['sub'] * 100.0 / nref)
str_keyphrases_info = ''
if self.key_phrases:
jaccard_p, f1_p, matched_p, ref_p, hyp_p = self.GetKeyPhraseStats()
str_keyphrases_info = ('matched %d key phrases (%d in ref, %d in hyp), '
'jaccard similarity=%.2f, F1=%.2f') % \
(matched_p, ref_p, hyp_p, jaccard_p, f1_p)
return str_sum, str_details, str_keyphrases_info
def main(argv):
hypothesis = open(argv[1], 'r').read()
reference = open(argv[2], 'r').read()
if len(argv) == 4:
phrase_lines = open(argv[3]).readlines()
keyphrases = [line.strip() for line in phrase_lines]
else:
keyphrases = None
wer_obj = SimpleWER(
key_phrases=keyphrases,
html_handler=HighlightAlignedHtml,
preprocess_handler=RemoveCommentTxtPreprocess)
wer_obj.AddHypRef(hypothesis, reference)
str_summary, str_details, str_keyphrases_info = wer_obj.GetSummaries()
print(str_summary)
print(str_details)
print(str_keyphrases_info)
try:
fn_output = argv[1] + '_diagnosis.html'
aligned_html = '<br>'.join(wer_obj.aligned_htmls)
with open(fn_output, 'wt') as fp:
fp.write('<body><html>')
fp.write('<div>%s</div>' % aligned_html)
fp.write('</body></html>')
except IOError:
print('failed to write diagnosis html')
if __name__ == '__main__':
if len(sys.argv) < 3 or len(sys.argv) > 4:
print("""
Example of Usage:
python simple_wer_v2.py file_hypothesis file_reference
or
python simple_wer_v2.py file_hypothesis file_reference file_keyphrases
where file_hypothesis is the file name for hypothesis text
file_reference is the file name for reference text.
file_keyphrases (optional) is the filename of key phrases over which
you want to measure accuracy.
Or you can use this file as a library, and call class SimpleWER
.AddHypRef(hyp, ref): add one pair of hypothesis/reference. You can call this
function multiple times.
.GetWER(): get the Word Error Rate (WER).
.GetBreakdownWER(): get the del/ins/sub breakdown WER.
.GetKeyPhraseStats(): get stats for key phrases. The first value is Jaccard
Similarity of key phrases.
.GetSummaries(): generate strings to summarize word error and
key phrase errors.
""")
sys.exit(1)
main(sys.argv)
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13,
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390,
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267,
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13,
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1420,
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6629,
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543,
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2654,
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13,
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262,
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310,
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310,
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13,
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3863,
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5721,
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2922,
353,
518,
2636,
29962,
334,
3407,
29918,
2848,
29918,
11242,
13,
13,
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396,
17250,
2133,
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13,
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363,
474,
297,
3464,
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2848,
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11242,
1125,
13,
1678,
3863,
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5721,
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2922,
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353,
518,
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334,
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2848,
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11242,
13,
1678,
363,
432,
297,
3464,
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1478,
720,
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29918,
2848,
29918,
11242,
1125,
13,
418,
565,
474,
1275,
29871,
29900,
29901,
13,
4706,
3863,
29918,
5721,
29918,
2922,
29961,
29900,
3816,
29926,
29962,
353,
432,
13,
418,
25342,
432,
1275,
29871,
29900,
29901,
13,
4706,
3863,
29918,
5721,
29918,
2922,
29961,
29875,
3816,
29900,
29962,
353,
474,
13,
13,
29871,
396,
1938,
7343,
8720,
29889,
13,
29871,
363,
474,
297,
3464,
29898,
29896,
29892,
3407,
29918,
2848,
29918,
11242,
1125,
13,
1678,
363,
432,
297,
3464,
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29896,
29892,
20051,
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2848,
29918,
11242,
1125,
13,
418,
565,
2143,
29918,
9303,
29961,
29875,
448,
29871,
29896,
29962,
1275,
10163,
29918,
9303,
29961,
29926,
448,
29871,
29896,
5387,
13,
4706,
3863,
29918,
5721,
29918,
2922,
29961,
29875,
3816,
29926,
29962,
353,
3863,
29918,
5721,
29918,
2922,
29961,
29875,
448,
29871,
29896,
3816,
29926,
448,
29871,
29896,
29962,
13,
418,
1683,
29901,
13,
4706,
13128,
29900,
353,
3863,
29918,
5721,
29918,
2922,
29961,
29875,
448,
29871,
29896,
3816,
29926,
448,
29871,
29896,
29962,
718,
29871,
29896,
13,
4706,
13128,
29896,
353,
3863,
29918,
5721,
29918,
2922,
29961,
29875,
3816,
29926,
448,
29871,
29896,
29962,
718,
29871,
29896,
13,
4706,
13128,
29906,
353,
3863,
29918,
5721,
29918,
2922,
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448,
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3816,
29926,
29962,
718,
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29896,
13,
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3863,
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5721,
29918,
2922,
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29875,
3816,
29926,
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353,
1375,
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7050,
29900,
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13128,
29896,
29892,
13128,
29906,
29897,
13,
13,
29871,
736,
3863,
29918,
5721,
29918,
2922,
13,
13,
13,
1990,
12545,
29956,
1001,
29901,
13,
29871,
9995,
20606,
29872,
1734,
1059,
19257,
1156,
278,
22239,
29889,
13,
13,
29871,
6212,
5026,
29901,
13,
1678,
1820,
29918,
24588,
2129,
29901,
1051,
310,
4100,
12216,
2129,
29889,
13,
1678,
26118,
29918,
1420,
29879,
29901,
1051,
310,
7936,
1217,
275,
3472,
29879,
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1269,
310,
607,
6590,
304,
263,
5101,
13,
418,
310,
20051,
322,
3407,
29889,
13,
1678,
10163,
29918,
1989,
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559,
29918,
2798,
29879,
29901,
9657,
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421,
29882,
1478,
29918,
1989,
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559,
29918,
2798,
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29961,
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7961,
18139,
920,
4049,
263,
1820,
13,
418,
12216,
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421,
29893,
29952,
2615,
297,
278,
13752,
21523,
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13,
1678,
2143,
29918,
1989,
24588,
559,
29918,
2798,
29879,
29901,
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29889,
421,
999,
29918,
1989,
24588,
559,
29918,
2798,
29879,
29961,
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920,
4049,
263,
1820,
13,
418,
12216,
2129,
421,
29893,
29952,
2615,
297,
278,
9282,
29889,
13,
1678,
19228,
29918,
1989,
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559,
29918,
2798,
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29901,
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4352,
287,
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1989,
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559,
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2798,
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29961,
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920,
13,
418,
4049,
263,
1820,
16549,
421,
29893,
29952,
2615,
297,
278,
26118,
1301,
924,
29879,
746,
278,
13,
418,
3407,
322,
10163,
29918,
1989,
24588,
559,
1993,
29889,
13,
1678,
2949,
29918,
3888,
29901,
9657,
411,
3023,
6611,
29901,
525,
1491,
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313,
22492,
5008,
1059,
511,
525,
1144,
29915,
313,
1144,
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13,
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313,
311,
1026,
291,
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511,
525,
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29915,
313,
4537,
310,
3838,
467,
1334,
508,
671,
13,
418,
2949,
29918,
3888,
304,
10272,
1734,
1059,
6554,
313,
29956,
1001,
29897,
408,
13,
418,
313,
556,
29918,
3888,
1839,
1491,
2033,
29974,
556,
29918,
3888,
1839,
1144,
2033,
29974,
556,
29918,
3888,
1839,
6144,
11287,
29930,
29896,
29900,
29900,
29889,
29900,
29914,
556,
29918,
3888,
1839,
29876,
29893,
2033,
13,
29871,
9995,
13,
13,
29871,
822,
4770,
2344,
12035,
1311,
29892,
13,
1669,
1820,
29918,
24588,
2129,
29922,
8516,
29892,
13,
1669,
3472,
29918,
13789,
29922,
16382,
4366,
2499,
12961,
10922,
29892,
13,
1669,
758,
5014,
29918,
13789,
29922,
15941,
20001,
29911,
486,
6572,
5014,
1125,
13,
1678,
9995,
6644,
6646,
12545,
29956,
1001,
1203,
29889,
13,
13,
1678,
826,
3174,
29901,
13,
418,
1820,
29918,
24588,
2129,
29901,
29871,
1051,
310,
6031,
408,
4100,
12216,
2129,
29889,
960,
1820,
29918,
24588,
2129,
338,
13,
4706,
6213,
29892,
694,
1820,
29918,
24588,
2129,
4475,
12714,
674,
367,
15712,
29889,
13,
418,
3472,
29918,
13789,
29901,
740,
304,
5706,
263,
1347,
411,
3472,
8282,
29889,
13,
418,
758,
5014,
29918,
13789,
29901,
740,
304,
758,
5014,
1426,
1434,
20602,
399,
1001,
29889,
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graph_analysis/deprecated/gensim_embedding.py | roy860328/VSGM | 6 | 1607747 | # https://stackoverflow.com/questions/35747245/bigram-to-a-vector
# Bigram to a vector
from gensim.models import word2vec, Phrases
from glove_embedding import load_object
import gensim.downloader as api
def five():
word_vectors = api.load("glove-wiki-gigaword-100")
word_vectors["apple"].shape
five()
def bigram2vec(unigrams, bigram_to_search):
bigrams = Phrases(unigrams)
model = word2vec.Word2Vec(bigrams[unigrams])
if bigram_to_search in model.vocab.keys():
return model[bigram_to_search]
else:
return None
# docs = ['new york is is united states', 'new york is most populated city in the world','i love to stay in new york']
# token_ = [doc.split(" ") for doc in docs]
# bigram2vec(token_, "new_york")
# https://stackoverflow.com/questions/51426107/how-to-build-a-gensim-dictionary-that-includes-bigrams
def two():
from gensim.models import Phrases
from gensim.models.phrases import Phraser
from gensim import models
docs = ['new york is is united states', 'new york is most populated city in the world','i love to stay in new york']
token_ = [doc.split(" ") for doc in docs]
bigram = Phrases(token_, min_count=1, threshold=2, delimiter=b' ')
bigram_phraser = Phraser(bigram)
bigram_token = []
for sent in token_:
bigram_token.append(bigram_phraser[sent])
print(bigram_token)
def three():
docs = ['new york is is united states', 'new york is most populated city in the world','i love to stay in new york']
sentence_stream = [doc.split(" ") for doc in docs]
# sentence_stream=brown_raw[0:10]
bigram = Phrases(sentence_stream, min_count=1, threshold=2, delimiter=b' ')
trigram = Phrases(bigram[sentence_stream], min_count=1, delimiter=b' ')
print(bigram)
for sent in sentence_stream:
bigrams_ = [b for b in bigram[sent] if b.count(" ") == 1]
trigrams_ = [t for t in trigram[bigram[sent]] if t.count(" ")==2]
print(bigrams_)
print(trigrams_)
def four():
from gensim.models import KeyedVectors
model = KeyedVectors.load_word2vec_format('data/GoogleGoogleNews-vectors-negative300.bin', binary=True)
vector = model['easy']
vector.shape
docs = ['new york is is united states', 'new york is most populated city in the world','i love to stay in new york']
sentence_stream = [doc.split(" ") for doc in docs]
model["new_york"].shape
# four()
# if __name__ == '__main__':
# objects = load_object("./data/objects.txt")
# vector = bigram2vec(objects[0], "alarm_clock")
# print(vector) | [
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29937,
268,
1596,
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8111,
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2
] |
src/tasks.py | accraze/stream-processor | 0 | 139377 | import time
from src.celery import app
@app.task(bind=True, default_retry_delay=10)
def process_event(self, event):
process_change(event['wait'])
return event
def process_change(wait_secs):
start = time.time()
while time.time() - start < wait_secs:
time.sleep(0.001)
| [
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2
] |
data/vispapers/preprocess.py | sonne-academic/solr-utils | 0 | 106721 | from data.vispapers import read_all, DATA_FOLDER
import json
import gzip
def preprocess():
with gzip.open(DATA_FOLDER / 'vispapers.jsonl.gz', 'wt', encoding='utf-8') as file:
for document in read_all():
file.write(json.dumps(document)+'\n')
if __name__ == '__main__':
preprocess() | [
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] |
restore-firm0firm1.py | ihaveamac/hardmod-b9s-installer | 13 | 1604545 | <gh_stars>10-100
#!/usr/bin/env python3
import os
import sys
import time
def doexit(msg, errcode=0):
print(msg)
input('Press Enter to continue...')
sys.exit(errcode)
def doexit(msg, errcode=0):
print(msg)
input('Press Enter to continue...')
sys.exit(errcode)
if not os.path.isfile('NAND-patched.bin'):
doexit('NAND-patched.bin not found.', errcode=1)
use_separate = True # use separate firm(0/1)_enc.bak
if os.path.isfile('firm0firm1.bak'):
print('Using firm0firm1.bak.')
elif os.path.isfile('firm0_enc.bak') and os.path.isfile('firm1_enc.bak'):
print('Using firm0_enc.bak and firm1_enc.bak')
use_separate = True
else:
doexit('FIRM backup was not found.\n'
'This can be in a single "firm0firm1.bak" file, or\n'
'two "firm0_enc.bak" and "firm1_enc.bak" files.', errcode=1)
if os.path.isfile('NAND-unpatched.bin'):
doexit('NAND-unpatched.bin was found.\n'
'In order to prevent writing a good backup with a bad one, the '
'install has stopped. Please move or delete the old file if you '
'are sure you want to continue.', errcode=1)
readsize = 0x100000 # must be divisible by 0x3AF00000 and 0x4D800000
print('Trying to open NAND-patched.bin...')
with open('NAND-patched.bin', 'rb+') as nand:
print('Restoring FIRM0FIRM1.')
nand.seek(0xB130000)
if use_separate:
with open('firm0_enc.bak', 'rb') as f:
firm_final = f.read(0x400000).ljust(0x400000, b'\0')
with open('firm1_enc.bak', 'rb') as f:
firm_final += f.read(0x400000).ljust(0x400000, b'\0')
else:
with open('firm0firm1.bak', 'rb') as f:
firm_final = f.read(0x800000)
start_time = time.time()
for curr in range(0x800000 // readsize):
print('Writing {:06X} ({:>5.1f}%)'.format((curr + 1) * readsize,
(((curr + 1) * readsize) / 0x800000) * 100), end='\r')
nand.write(bytes(firm_final[curr * readsize:(curr + 1) * readsize]))
os.rename('NAND-patched.bin', 'NAND-unpatched.bin')
doexit('\nWriting finished in {:>.2f} seconds.'.format(
time.time() - start_time))
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pyscript/apps/temp/old/load_optimizer.py | janiversen/ha_config | 0 | 43893 | <gh_stars>0
@state_trigger("sensor.power_meter != '9999'")
def load_optimizer(value=None):
pass
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iocage/cli/stop.py | project-fifo/iocage | 0 | 199178 | <gh_stars>0
"""stop module for the cli."""
import logging
from collections import OrderedDict
from operator import itemgetter
import click
from iocage.lib.ioc_json import IOCJson
from iocage.lib.ioc_list import IOCList
from iocage.lib.ioc_stop import IOCStop
__cmdname__ = "stop_cmd"
__rootcmd__ = True
@click.command(name="stop", help="Stops the specified jails or ALL.")
@click.option("--rc", default=False, is_flag=True,
help="Will stop all jails with boot=on, in the specified"
" order with higher value for priority stopping first.")
@click.argument("jails", nargs=-1)
def stop_cmd(rc, jails):
"""
Looks for the jail supplied and passes the uuid, path and configuration
location to stop_jail.
"""
lgr = logging.getLogger('ioc_cli_stop')
_jails, paths = IOCList("uuid").list_datasets()
jail_order = {}
boot_order = {}
for j in _jails:
path = paths[j]
conf = IOCJson(path).json_load()
boot = conf["boot"]
priority = conf["priority"]
jail_order[j] = int(priority)
# This removes having to grab all the JSON again later.
if boot == "on":
boot_order[j] = int(priority)
jail_order = OrderedDict(sorted(jail_order.items(),
key=itemgetter(1), reverse=True) )
boot_order = OrderedDict(sorted(boot_order.items(),
key=itemgetter(1), reverse=True))
if rc:
for j in boot_order.keys():
uuid = _jails[j]
path = paths[j]
conf = IOCJson(path).json_load()
status, _ = IOCList().list_get_jid(uuid)
if status:
lgr.info(" Stopping {} ({})".format(uuid, j))
IOCStop(uuid, j, path, conf, silent=True)
else:
lgr.info("{} ({}) is not running!".format(uuid, j))
exit()
if len(jails) >= 1 and jails[0] == "ALL":
if len(_jails) < 1:
raise RuntimeError("No jails exist to stop!")
for j in jail_order:
uuid = _jails[j]
path = paths[j]
conf = IOCJson(path).json_load()
IOCStop(uuid, j, path, conf)
else:
if len(jails) < 1:
raise RuntimeError("Please specify either one or more jails or "
"ALL!")
for jail in jails:
_jail = {tag: uuid for (tag, uuid) in _jails.items() if
uuid.startswith(jail) or tag == jail}
if len(_jail) == 1:
tag, uuid = next(iter(_jail.items()))
path = paths[tag]
elif len(_jail) > 1:
lgr.error("Multiple jails found for"
" {}:".format(jail))
for t, u in sorted(_jail.items()):
lgr.error(" {} ({})".format(u, t))
raise RuntimeError()
else:
raise RuntimeError("{} not found!".format(jail))
conf = IOCJson(path).json_load()
IOCStop(uuid, tag, path, conf)
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] |
data_split.py | TalSchuster/FewRel | 0 | 15771 | <reponame>TalSchuster/FewRel<filename>data_split.py
import os
import random
from shutil import copyfile
import json
random.seed(123)
ROOT_PATH = './data/'
k = 5
target_path = './data/wiki_5_splits/'
'''
Splits the training set to 5 folds.
In each split, the held out set is used for test.
'''
path = os.path.join(ROOT_PATH, 'train_wiki' + '.json')
data = json.load(open(path, 'r'))
relations = list(data.keys())
num_relations = len(relations)
rels_per_split = round(num_relations / k)
random.shuffle(relations)
for i in range(k):
split_val_rels = relations[i*rels_per_split: (i+1)*rels_per_split]
split_train = {}
split_val = {}
for rel, examples in data.items():
if rel in split_val_rels:
split_val[rel] = examples
else:
split_train[rel] = examples
print(f"split {i}: train: {len(split_val.keys())}, test: {len(split_train.keys())}")
os.makedirs(os.path.join(target_path, str(i)), exist_ok=True)
with open(os.path.join(target_path, str(i), 'train.json'), 'w') as f:
json.dump(split_train, f)
with open(os.path.join(target_path, str(i), 'val.json'), 'w') as f:
json.dump(split_val, f)
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] |
server/core/views/api/admin_jenkins_job.py | FranciscoShi/jenkins-gitlab-integrator | 2 | 153247 | import json
from functools import partial
from aiohttp import web
from aiohttp.web_exceptions import HTTPConflict
from server.core.common import LoggingMixin
from server.core.json_encoders import CustomJSONEncoder
from server.core.models import RecordNotFound
from server.core.models.jenkins_jobs import JenkinsJob
from server.core.security.policy import require_permission, Permission
from server.core.views.api.mixins import WebHookApiMixin
from server.core.views import create_jenkins_group_manager, create_jenkins_job_manager, set_log_marker, \
create_gitlab_client
class AdminApiV1JenkinsJobListView(web.View, LoggingMixin):
"""
Admin API for find Jenkins Job by group
"""
@set_log_marker
@create_jenkins_job_manager
@require_permission(Permission.ADMIN_UI)
async def get(self):
group_id = self.request.match_info['group_id']
jobs = await self.jenkins_job_manager.find_by_group_id(int(group_id))
return web.json_response(jobs, dumps=partial(json.dumps, cls=CustomJSONEncoder))
class AdminApiV1JenkinsJobView(web.View, LoggingMixin, WebHookApiMixin):
"""
Admin API for mangmanet Jenkins Job
"""
@set_log_marker
@create_jenkins_job_manager
@require_permission(Permission.ADMIN_UI)
async def get(self):
group_id = self.request.match_info['group_id']
job_id = self.request.match_info['id']
job = await self.jenkins_job_manager.get(job_id)
return web.json_response(job, dumps=partial(json.dumps, cls=CustomJSONEncoder))
@set_log_marker
@create_jenkins_job_manager
@require_permission(Permission.ADMIN_UI)
async def post(self):
group_id = self.request.match_info['group_id']
json_data = await self.request.json()
self._logging_debug(json_data)
obj = JenkinsJob()
obj.jenkins_group_id = group_id
obj.name = json_data['name']
try:
obj.jenkins_job_perent_id = int(json_data['jenkins_job_perent_id'])
except Exception as e:
obj.jenkins_job_perent_id = None
obj.gitlab_project_id = int(json_data['gitlab_project_id'])
try:
first_job = await self.jenkins_job_manager.find_first_by_group_id(group_id)
if first_job and obj.jenkins_job_perent_id is None:
raise HTTPConflict(reason="only one job can be first")
except RecordNotFound:
pass
job = await self.jenkins_job_manager.create(obj)
return web.json_response(job, dumps=partial(json.dumps, cls=CustomJSONEncoder))
@set_log_marker
@create_jenkins_job_manager
@require_permission(Permission.ADMIN_UI)
async def put(self):
group_id = int(self.request.match_info['group_id'])
job_id = int(self.request.match_info['id'])
json_data = await self.request.json()
self._logging_debug(json_data)
obj = JenkinsJob()
obj.id = job_id
obj.jenkins_group_id = group_id
obj.name = json_data['name']
try:
obj.jenkins_job_perent_id = int(json_data['jenkins_job_perent_id'])
except Exception as e:
obj.jenkins_job_perent_id = None
obj.gitlab_project_id = int(json_data['gitlab_project_id'])
first_job = await self.jenkins_job_manager.find_first_by_group_id(group_id)
if not first_job.id == job_id:
raise HTTPConflict(reason="only one job can be first")
self._logging_debug(obj.values)
job = await self.jenkins_job_manager.update(obj)
return web.json_response(job, dumps=partial(json.dumps, cls=CustomJSONEncoder))
@set_log_marker
@create_jenkins_job_manager
@create_jenkins_group_manager
@create_gitlab_client
@require_permission(Permission.ADMIN_UI)
async def delete(self):
#delete webhook
job = await self.jenkins_job_manager.get(self.request.match_info['id'])
group = await self.jenkins_group_manager.get(job.jenkins_group_id)
await self._delete_job_webhook(group, job, ignore_errors=True)
#delete job
job = await self.jenkins_job_manager.delete(self.request.match_info['id'])
return web.json_response({})
class AdminApiV1JenkinsJobGitLabWebHookView(web.View, LoggingMixin, WebHookApiMixin):
@set_log_marker
@create_jenkins_job_manager
@create_jenkins_group_manager
@create_gitlab_client
@require_permission(Permission.ADMIN_UI)
async def delete(self):
#delete webhook
job = await self.jenkins_job_manager.get(self.request.match_info['id'])
group = await self.jenkins_group_manager.get(job.jenkins_group_id)
await self._delete_job_webhook(group, job, ignore_errors=True)
return web.json_response({})
@set_log_marker
@create_jenkins_job_manager
@create_jenkins_group_manager
@create_gitlab_client
@require_permission(Permission.ADMIN_UI)
async def put(self):
#delete webhook
job = await self.jenkins_job_manager.get(self.request.match_info['id'])
group = await self.jenkins_group_manager.get(job.jenkins_group_id)
await self._delete_job_webhook(group, job, ignore_errors=True)
await self._create_job_webhook(group, job)
return web.json_response({})
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] |
pyfarm/models/job.py | guidow/pyfarm-master | 0 | 186500 | <filename>pyfarm/models/job.py
# No shebang line, this module is meant to be imported
#
# Copyright 2013 <NAME>
#
# 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.
"""
Job Models
==========
Models and interface classes related to jobs.
"""
from datetime import datetime
try:
import pwd
except ImportError: # pragma: no cover
pwd = None
from sys import maxsize
from sqlalchemy import event, distinct, or_, and_
from sqlalchemy.orm import validates
from pyfarm.core.logger import getLogger
from pyfarm.core.enums import WorkState, DBWorkState, _WorkState, AgentState
from pyfarm.master.application import db
from pyfarm.master.config import config
from pyfarm.models.core.functions import work_columns
from pyfarm.models.core.types import JSONDict, IDTypeWork
from pyfarm.models.core.mixins import (
ValidatePriorityMixin, WorkStateChangedMixin, ReprMixin,
ValidateWorkStateMixin, UtilityMixins)
from pyfarm.models.statistics.task_event_count import TaskEventCount
from pyfarm.models.jobtype import JobType, JobTypeVersion
from pyfarm.models.task import Task
try:
# pylint: disable=undefined-variable
range_ = xrange
except NameError:
range_ = range
__all__ = ("Job", )
logger = getLogger("models.job")
JobTagAssociation = db.Table(
config.get("table_job_tag_assoc"),
db.metadata,
db.Column(
"job_id",
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job")),
primary_key=True,
doc="The id of the job associated with this task"),
db.Column(
"tag_id",
db.Integer,
db.ForeignKey("%s.id" % config.get("table_tag")),
primary_key=True,
doc="The id of the tag being associated with the job")
)
JobDependency = db.Table(
config.get("table_job_dependency"), db.metadata,
db.Column(
"parentid",
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job")),
primary_key=True,
doc="The parent job id of the job dependency"),
db.Column(
"childid",
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job")),
primary_key=True,
doc="The child job id of the job dependency")
)
class JobNotifiedUser(db.Model):
"""
Defines the table containing users to be notified of certain
events pertaining to jobs.
"""
__tablename__ = config.get("table_job_notified_users")
user_id = db.Column(
db.Integer,
db.ForeignKey("%s.id" % config.get("table_user")),
primary_key=True,
doc="The id of the user to be notified")
job_id = db.Column(
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job")),
primary_key=True,
doc="The id of the associated job")
on_success = db.Column(
db.Boolean,
nullable=False, default=True,
doc="True if a user should be notified on successful "
"completion of a job")
on_failure = db.Column(
db.Boolean,
nullable=False, default=True,
doc="True if a user should be notified of a job's failure")
on_deletion = db.Column(
db.Boolean,
nullable=False, default=False,
doc="True if a user should be notified on deletion of "
"a job")
user = db.relationship(
"User",
backref=db.backref("subscribed_jobs", lazy="dynamic"))
class Job(db.Model, ValidatePriorityMixin, ValidateWorkStateMixin,
WorkStateChangedMixin, ReprMixin, UtilityMixins):
"""
Defines the attributes and environment for a job. Individual commands
are kept track of by :class:`Task`
"""
__tablename__ = config.get("table_job")
REPR_COLUMNS = ("id", "state", "project")
REPR_CONVERT_COLUMN = {"state": repr}
STATE_ENUM = list(WorkState) + [None]
# shared work columns
id, state, priority, time_submitted, time_started, time_finished = \
work_columns(None, "job.priority")
jobtype_version_id = db.Column(
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job_type_version")),
nullable=False,
doc="The foreign key which stores :class:`JobTypeVersion.id`")
job_queue_id = db.Column(
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job_queue")),
nullable=True,
doc="The foreign key which stores :class:`JobQueue.id`")
job_group_id = db.Column(
IDTypeWork,
db.ForeignKey("%s.id" % config.get("table_job_group")),
nullable=True,
doc="The foreign key which stores:class:`JobGroup.id`")
user_id = db.Column(
db.Integer,
db.ForeignKey("%s.id" % config.get("table_user")),
doc="The id of the user who owns this job")
minimum_agents = db.Column(
db.Integer,
nullable=True,
doc="The scheduler will try to assign at least this number "
"of agents to this job as long as it can use them, "
"before any other considerations.")
maximum_agents = db.Column(
db.Integer,
nullable=True,
doc="The scheduler will never assign more than this number"
"of agents to this job.")
weight = db.Column(
db.Integer,
nullable=False,
default=config.get("queue_default_weight"),
doc="The weight of this job. The scheduler will distribute "
"available agents between jobs and job queues in the "
"same queue in proportion to their weights.")
title = db.Column(
db.String(config.get("jobtitle_max_length")),
nullable=False,
doc="The title of this job")
notes = db.Column(
db.Text,
default="",
doc="Notes that are provided on submission or added after "
"the fact. This column is only provided for human "
"consumption, is not scanned, indexed, or used when "
"searching")
output_link = db.Column(
db.Text,
nullable=True,
doc="An optional link to a URI where this job's output can "
"be viewed.")
# task data
by = db.Column(
db.Numeric(10, 4),
default=1,
doc="The number of frames to count by between `start` and "
"`end`. This column may also sometimes be referred to "
"as 'step' by other software.")
num_tiles = db.Column(
db.Integer,
nullable=True,
doc="How many regions to split frames into for rendering.")
batch = db.Column(
db.Integer,
default=config.get("job_default_batch"),
doc="Number of tasks to run on a single agent at once. Depending "
"on the capabilities of the software being run this will "
"either cause a single process to execute on the agent "
"or multiple processes one after the other.")
requeue = db.Column(
db.Integer,
default=config.get("job_requeue_default"),
doc="Number of times to requeue failed tasks "
""
".. csv-table:: **Special Values**"
" :header: Value, Result"
" :widths: 10, 50"
""
" 0, never requeue failed tasks"
" -1, requeue failed tasks indefinitely")
cpus = db.Column(
db.Integer,
default=config.get("job_default_cpus"),
doc="Number of cpus or threads each task should consume on"
"each agent. Depending on the job type being executed "
"this may result in additional cpu consumption, longer "
"wait times in the queue (2 cpus means 2 'fewer' cpus on "
"an agent), or all of the above."
""
".. csv-table:: **Special Values**"
" :header: Value, Result"
" :widths: 10, 50"
""
" 0, minimum number of cpu resources not required "
" -1, agent cpu is exclusive for a task from this job")
ram = db.Column(
db.Integer,
default=config.get("job_default_ram"),
doc="Amount of ram a task from this job will require to be "
"free in order to run. A task exceeding this value will "
"not result in any special behavior."
""
".. csv-table:: **Special Values**"
" :header: Value, Result"
" :widths: 10, 50"
""
"0, minimum amount of free ram not required"
"-1, agent ram is exclusive for a task from this job")
ram_warning = db.Column(
db.Integer,
nullable=True,
doc="Amount of ram used by a task before a warning raised. "
"A task exceeding this value will not cause any work "
"stopping behavior.")
ram_max = db.Column(
db.Integer,
nullable=True,
doc="Maximum amount of ram a task is allowed to consume on "
"an agent."
""
".. warning:: "
" If set, the task will be **terminated** if the ram in "
" use by the process exceeds this value.")
hidden = db.Column(
db.Boolean,
default=False, nullable=False,
doc="If True, keep the job hidden from the queue and web "
"ui. This is typically set to True if you either want "
"to save a job for later viewing or if the jobs data "
"is being populated in a deferred manner.")
environ = db.Column(
JSONDict,
doc="Dictionary containing information about the environment "
"in which the job will execute. "
""
".. note::"
" Changes made directly to this object are **not** "
" applied to the session.")
data = db.Column(
JSONDict,
doc="Json blob containing additional data for a job "
""
".. note:: "
" Changes made directly to this object are **not** "
" applied to the session.")
to_be_deleted = db.Column(
db.Boolean,
nullable=False, default=False,
doc="If true, the master will stop all running tasks for "
"this job and then delete it.")
completion_notify_sent = db.Column(
db.Boolean,
nullable=False, default=False,
doc="Whether or not the finish notification mail has already "
"been sent out.")
autodelete_time = db.Column(
db.Integer,
nullable=True, default=None,
doc="If not None, this job will be automatically deleted this "
"number of seconds after it finishes.")
#
# Relationships
#
queue = db.relationship(
"JobQueue",
backref=db.backref("jobs", lazy="dynamic"),
doc="The queue for this job")
group = db.relationship(
"JobGroup",
backref=db.backref("jobs", lazy="dynamic"),
doc="The job group this job belongs to")
user = db.relationship(
"User",
backref=db.backref("jobs", lazy="dynamic"),
doc="The owner of this job")
# self-referential many-to-many relationship
parents = db.relationship(
"Job",
secondary=JobDependency,
primaryjoin=id==JobDependency.c.childid,
secondaryjoin=id==JobDependency.c.parentid,
backref="children")
notified_users = db.relationship(
"JobNotifiedUser",
lazy="dynamic",
backref=db.backref("job"),
cascade="all,delete")
tasks_queued = db.relationship(
"Task",
lazy="dynamic",
primaryjoin="(Task.state == None) & "
"(Task.job_id == Job.id)",
doc="Relationship between this job and any :class:`Task` "
"objects which are queued.")
tasks_running = db.relationship(
"Task",
lazy="dynamic",
primaryjoin="(Task.state == %s) & "
"(Task.job_id == Job.id)" % DBWorkState.RUNNING,
doc="Relationship between this job and any :class:`Task` "
"objects which are running.")
tasks_done = db.relationship("Task", lazy="dynamic",
primaryjoin="(Task.state == %s) & "
"(Task.job_id == Job.id)" % DBWorkState.DONE,
doc="Relationship between this job and any :class:`Task` objects "
"which are done.")
tasks_failed = db.relationship("Task", lazy="dynamic",
primaryjoin="(Task.state == %s) & "
"(Task.job_id == Job.id)" % DBWorkState.FAILED,
doc="Relationship between this job and any :class:`Task` objects "
"which have failed.")
# resource relationships
tags = db.relationship(
"Tag",
backref="jobs", lazy="dynamic",
secondary=JobTagAssociation,
doc="Relationship between this job and :class:`.Tag` objects")
def paused(self):
return self.state == WorkState.PAUSED
def update_state(self):
# Import here instead of at the top of the file to avoid a circular
# import
from pyfarm.scheduler.tasks import send_job_completion_mail
from pyfarm.models.agent import Agent
num_active_tasks = db.session.query(Task).\
filter(Task.job == self,
or_(Task.state == None, and_(
Task.state != WorkState.DONE,
Task.state != WorkState.FAILED))).count()
if num_active_tasks == 0:
num_failed_tasks = db.session.query(Task).filter(
Task.job == self,
Task.state == WorkState.FAILED).count()
if num_failed_tasks == 0:
if self.state != _WorkState.DONE:
logger.info("Job %r (id %s): state transition %r -> 'done'",
self.title, self.id, self.state)
self.state = WorkState.DONE
send_job_completion_mail.apply_async(args=[self.id, True],
countdown=5)
else:
if self.state != _WorkState.FAILED:
logger.info("Job %r (id %s): state transition %r -> "
"'failed'",
self.title, self.id, self.state)
self.state = WorkState.FAILED
send_job_completion_mail.apply_async(args=[self.id, False],
countdown=5)
db.session.add(self)
elif self.state != _WorkState.PAUSED:
num_running_tasks = db.session.query(Task).\
filter(Task.job == self,
Task.agent_id != None,
Task.agent.has(and_(Agent.state != AgentState.OFFLINE,
Agent.state != AgentState.DISABLED)),
or_(
Task.state == WorkState.RUNNING,
Task.state == None)).count()
if num_running_tasks == 0:
logger.debug("No running tasks in job %s (id %s), setting it "
"to queued", self.title, self.id)
self.state = None
db.session.add(self)
elif self.state != _WorkState.RUNNING:
self.state = WorkState.RUNNING
# Methods used by the scheduler
def num_assigned_agents(self):
# Import here instead of at the top of the file to avoid circular import
from pyfarm.models.agent import Agent
# Optimization: Blindly assume that we have no agents assigned if not
# running
if self.state != _WorkState.RUNNING:
return 0
try:
return self.assigned_agents_count
except AttributeError:
self.assigned_agents_count =\
db.session.query(distinct(Task.agent_id)).\
filter(Task.job == self,
Task.agent_id != None,
or_(Task.state == None,
Task.state == WorkState.RUNNING),
Task.agent.has(
and_(Agent.state != AgentState.OFFLINE,
Agent.state != AgentState.DISABLED)))\
.count()
return self.assigned_agents_count
def clear_assigned_counts(self):
try:
del self.assigned_agents_count
except AttributeError:
pass
if self.queue:
self.queue.clear_assigned_counts()
def can_use_more_agents(self):
# Import here instead of at the top of the file to avoid circular import
from pyfarm.models.agent import Agent
unassigned_tasks = Task.query.filter(
Task.job == self,
or_(Task.state == None,
~Task.state.in_([WorkState.DONE, WorkState.FAILED])),
or_(Task.agent == None,
Task.agent.has(Agent.state.in_(
[AgentState.OFFLINE, AgentState.DISABLED])))).count()
return unassigned_tasks > 0
def get_batch(self, agent):
# Import here instead of at the top of the file to avoid circular import
from pyfarm.models.agent import Agent
tasks_query = Task.query.filter(
Task.job == self,
~Task.failed_in_agents.any(id=agent.id),
or_(Task.state == None,
~Task.state.in_([WorkState.DONE, WorkState.FAILED])),
or_(Task.agent == None,
Task.agent.has(Agent.state.in_(
[AgentState.OFFLINE, AgentState.DISABLED])))).\
order_by("frame asc, tile asc")
batch = []
for task in tasks_query:
if (len(batch) < self.batch and
len(batch) < (self.jobtype_version.max_batch or maxsize) and
(not self.jobtype_version.batch_contiguous or
(len(batch) == 0 or
batch[-1].frame + self.by == task.frame))):
batch.append(task)
return batch
def alter_frame_range(self, start, end, by):
# We have to import this down here instead of at the top to break a
# circular dependency between the modules
from pyfarm.scheduler.tasks import delete_task
if end < start:
raise ValueError("`end` must be greater than or equal to `start`")
self.by = by
required_frames = []
current_frame = start
while current_frame <= end:
required_frames.append(current_frame)
current_frame += by
existing_tasks = Task.query.filter_by(job=self).all()
frames_to_create = required_frames
num_created = 0
for task in existing_tasks:
if task.frame not in required_frames:
delete_task.delay(task.id)
else:
frames_to_create.remove(task.frame)
for frame in frames_to_create:
if self.num_tiles:
for tile in range_(self.num_tiles - 1):
num_created += 1
task = Task()
task.job = self
task.frame = frame
task.tile = tile
task.priority = self.priority
db.session.add(task)
else:
num_created += 1
task = Task()
task.job = self
task.frame = frame
task.priority = self.priority
db.session.add(task)
if frames_to_create:
if self.state != WorkState.RUNNING:
self.state = None
if config.get("enable_statistics"):
task_event_count = TaskEventCount(num_new=num_created,
job_queue_id=self.job_queue_id)
task_event_count.time_start = datetime.utcnow()
task_event_count.time_end = datetime.utcnow()
db.session.add(task_event_count)
def rerun(self):
"""
Makes this job rerun all its task. Tasks that are currently running are
left untouched.
"""
num_restarted = 0
for task in self.tasks:
if task.state != _WorkState.RUNNING and task.state is not None:
task.state = None
task.agent = None
task.failures = 0
db.session.add(task)
num_restarted += 1
self.completion_notify_sent = False
self.update_state()
db.session.add(self)
if config.get("enable_statistics"):
task_event_count = TaskEventCount(job_queue_id=self.job_queue_id,
num_restarted=num_restarted)
task_event_count.time_start = datetime.utcnow()
task_event_count.time_end = datetime.utcnow()
db.session.add(task_event_count)
db.session.commit()
for child in self.children:
child.rerun()
def rerun_failed(self):
"""
Makes this job rerun all its failed tasks. Tasks that are done or are
currently running are left untouched
"""
num_restarted = 0
for task in self.tasks:
if task.state == _WorkState.FAILED:
task.state = None
task.agent = None
task.failures = 0
db.session.add(task)
num_restarted += 1
self.completion_notify_sent = False
self.update_state()
db.session.add(self)
if config.get("enable_statistics"):
task_event_count = TaskEventCount(job_queue_id=self.job_queue_id,
num_restarted=num_restarted)
task_event_count.time_start = datetime.utcnow()
task_event_count.time_end = datetime.utcnow()
db.session.commit()
for child in self.children:
child.rerun_failed()
@validates("ram", "cpus")
def validate_resource(self, key, value):
"""
Validation that ensures that the value provided for either
:attr:`.ram` or :attr:`.cpus` is a valid value with a given range
"""
assert isinstance(value, int), "%s must be an integer" % key
min_value = config.get("agent_min_%s" % key)
max_value = config.get("agent_max_%s" % key)
# check the provided input
if min_value > value or value > max_value:
msg = "value for `%s` must be between " % key
msg += "%s and %s" % (min_value, max_value)
raise ValueError(msg)
return value
@validates("progress")
def validate_progress(self, key, value):
if value < 0.0 or value > 1.0:
raise ValueError("Progress must be between 0.0 and 1.0")
event.listen(Job.state, "set", Job.state_changed)
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tests/processors.py | rysdyk/avocado | 0 | 124603 | <reponame>rysdyk/avocado
from avocado.models import DataContext
from avocado.query.pipeline import QueryProcessor
class ManagerQueryProcessor(QueryProcessor):
def __init__(self, *args, **kwargs):
kwargs['context'] = DataContext(json={
'field': 'tests.employee.is_manager',
'operator': 'exact',
'value': True,
})
super(ManagerQueryProcessor, self).__init__(*args, **kwargs)
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formatting.py | yvesdubief/biofeedback | 0 | 184986 | %matplotlib inline
# plots graphs within the notebook
%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format
from IPython.display import display,Image, Latex
from __future__ import division
from sympy.interactive import printing
printing.init_printing(use_latex='mathjax')
from IPython.display import clear_output
import time
from IPython.display import display,Image, Latex
from IPython.display import clear_output
class PDF(object):
def __init__(self, pdf, size=(200,200)):
self.pdf = pdf
self.size = size
def _repr_html_(self):
return '<iframe src={0} width={1[0]} height={1[1]}></iframe>'.format(self.pdf, self.size)
def _repr_latex_(self):
return r'\includegraphics[width=1.0\textwidth]{{{0}}}'.format(self.pdf)
class ListTable(list):
""" Overridden list class which takes a 2-dimensional list of
the form [[1,2,3],[4,5,6]], and renders an HTML Table in
IPython Notebook. """
def _repr_html_(self):
html = ["<table>"]
for row in self:
html.append("<tr>")
for col in row:
html.append("<td>{0}</td>".format(col))
html.append("</tr>")
html.append("</table>")
return ''.join(html)
font = {'family' : 'serif',
#'color' : 'black',
'weight' : 'normal',
'size' : 16,
}
fontlabel = {'family' : 'serif',
#'color' : 'black',
'weight' : 'normal',
'size' : 16,
}
from matplotlib.ticker import FormatStrFormatter
plt.rc('font', **font)
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jenkins_job_wrecker/__init__.py | chenhuahuan/jenkins-job-wrecker | 210 | 141507 | __version__ = '1.7.1'
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python/dgllife/model/pretrain/__init__.py | VIGNESHinZONE/dgl-lifesci | 0 | 15229 | # -*- coding: utf-8 -*-
#
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
#
# pylint: disable= no-member, arguments-differ, invalid-name
#
# Utilities for using pre-trained models.
import torch
from dgl.data.utils import _get_dgl_url, download
from .moleculenet import *
from .generative_models import *
from .property_prediction import *
from .reaction import *
__all__ = ['load_pretrained']
url = {**moleculenet_url, **generative_url, **property_url, **reaction_url}
def download_and_load_checkpoint(model_name, model, model_postfix,
local_pretrained_path='pre_trained.pth', log=True):
"""Download pretrained model checkpoint
The model will be loaded to CPU.
Parameters
----------
model_name : str
Name of the model
model : nn.Module
Instantiated model instance
model_postfix : str
Postfix for pretrained model checkpoint
local_pretrained_path : str
Local name for the downloaded model checkpoint
log : bool
Whether to print progress for model loading
Returns
-------
model : nn.Module
Pretrained model
"""
url_to_pretrained = _get_dgl_url(model_postfix)
local_pretrained_path = '_'.join([model_name, local_pretrained_path])
download(url_to_pretrained, path=local_pretrained_path, log=log)
checkpoint = torch.load(local_pretrained_path, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
if log:
print('Pretrained model loaded')
return model
# pylint: disable=I1101
def load_pretrained(model_name, log=True):
"""Load a pretrained model
Parameters
----------
model_name : str
Currently supported options include
* ``'GCN_Tox21'``: A GCN-based model for molecular property prediction on Tox21
* ``'GAT_Tox21'``: A GAT-based model for molecular property prediction on Tox21
* ``'Weave_Tox21'``: A Weave model for molecular property prediction on Tox21
* ``'AttentiveFP_Aromaticity'``: An AttentiveFP model for predicting number of
aromatic atoms on a subset of Pubmed
* ``'DGMG_ChEMBL_canonical'``: A DGMG model trained on ChEMBL with a canonical
atom order
* ``'DGMG_ChEMBL_random'``: A DGMG model trained on ChEMBL for molecule generation
with a random atom order
* ``'DGMG_ZINC_canonical'``: A DGMG model trained on ZINC for molecule generation
with a canonical atom order
* ``'DGMG_ZINC_random'``: A DGMG model pre-trained on ZINC for molecule generation
with a random atom order
* ``'JTNN_ZINC'``: A JTNN model pre-trained on ZINC for molecule generation
* ``'wln_center_uspto'``: A WLN model pre-trained on USPTO for reaction prediction
* ``'wln_rank_uspto'``: A WLN model pre-trained on USPTO for candidate product ranking
* ``'gin_supervised_contextpred'``: A GIN model pre-trained with supervised learning
and context prediction
* ``'gin_supervised_infomax'``: A GIN model pre-trained with supervised learning
and deep graph infomax
* ``'gin_supervised_edgepred'``: A GIN model pre-trained with supervised learning
and edge prediction
* ``'gin_supervised_masking'``: A GIN model pre-trained with supervised learning
and attribute masking
* ``'GCN_canonical_BACE'``: A GCN model trained on BACE with canonical
featurization for atoms
* ``'GCN_attentivefp_BACE'``: A GCN model trained on BACE with attentivefp
featurization for atoms
* ``'GAT_canonical_BACE'``: A GAT model trained on BACE with canonical
featurization for atoms
* ``'GAT_attentivefp_BACE'``: A GAT model trained on BACE with attentivefp
featurization for atoms
* ``'Weave_canonical_BACE'``: A Weave model trained on BACE with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_BACE'``: A Weave model trained on BACE with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_BACE'``: An MPNN model trained on BACE with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_BACE'``: An MPNN model trained on BACE with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_BACE'``: An AttentiveFP model trained on BACE with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_BACE'``: An AttentiveFP model trained on BACE with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_BACE'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on BACE
* ``'gin_supervised_infomax_BACE'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on BACE
* ``'gin_supervised_edgepred_BACE'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on BACE
* ``'gin_supervised_masking_BACE'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on BACE
* ``'NF_canonical_BACE'``: An NF model trained on BACE with canonical
featurization for atoms
* ``'GCN_canonical_BBBP'``: A GCN model trained on BBBP with canonical
featurization for atoms
* ``'GCN_attentivefp_BBBP'``: A GCN model trained on BBBP with attentivefp
featurization for atoms
* ``'GAT_canonical_BBBP'``: A GAT model trained on BBBP with canonical
featurization for atoms
* ``'GAT_attentivefp_BBBP'``: A GAT model trained on BBBP with attentivefp
featurization for atoms
* ``'Weave_canonical_BBBP'``: A Weave model trained on BBBP with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_BBBP'``: A Weave model trained on BBBP with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_BBBP'``: An MPNN model trained on BBBP with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_BBBP'``: An MPNN model trained on BBBP with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_BBBP'``: An AttentiveFP model trained on BBBP with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_BBBP'``: An AttentiveFP model trained on BBBP with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_BBBP'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on BBBP
* ``'gin_supervised_infomax_BBBP'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on BBBP
* ``'gin_supervised_edgepred_BBBP'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on BBBP
* ``'gin_supervised_masking_BBBP'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on BBBP
* ``'NF_canonical_BBBP'``: An NF model pre-trained on BBBP with canonical
featurization for atoms
* ``'GCN_canonical_ClinTox'``: A GCN model trained on ClinTox with canonical
featurization for atoms
* ``'GCN_attentivefp_ClinTox'``: A GCN model trained on ClinTox with attentivefp
featurization for atoms
* ``'GAT_canonical_ClinTox'``: A GAT model trained on ClinTox with canonical
featurization for atoms
* ``'GAT_attentivefp_ClinTox'``: A GAT model trained on ClinTox with attentivefp
featurization for atoms
* ``'Weave_canonical_ClinTox'``: A Weave model trained on ClinTox with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_ClinTox'``: A Weave model trained on ClinTox with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_ClinTox'``: An MPNN model trained on ClinTox with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_ClinTox'``: An MPNN model trained on ClinTox with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_ClinTox'``: An AttentiveFP model trained on ClinTox with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_BACE'``: An AttentiveFP model trained on ClinTox with
attentivefp featurization for atoms and bonds
* ``'GCN_canonical_ESOL'``: A GCN model trained on ESOL with canonical
featurization for atoms
* ``'GCN_attentivefp_ESOL'``: A GCN model trained on ESOL with attentivefp
featurization for atoms
* ``'GAT_canonical_ESOL'``: A GAT model trained on ESOL with canonical
featurization for atoms
* ``'GAT_attentivefp_ESOL'``: A GAT model trained on ESOL with attentivefp
featurization for atoms
* ``'Weave_canonical_ESOL'``: A Weave model trained on ESOL with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_ESOL'``: A Weave model trained on ESOL with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_ESOL'``: An MPNN model trained on ESOL with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_ESOL'``: An MPNN model trained on ESOL with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_ESOL'``: An AttentiveFP model trained on ESOL with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_ESOL'``: An AttentiveFP model trained on ESOL with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_ESOL'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on ESOL
* ``'gin_supervised_infomax_ESOL'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on ESOL
* ``'gin_supervised_edgepred_ESOL'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on ESOL
* ``'gin_supervised_masking_ESOL'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on ESOL
* ``'GCN_canonical_FreeSolv'``: A GCN model trained on FreeSolv with canonical
featurization for atoms
* ``'GCN_attentivefp_FreeSolv'``: A GCN model trained on FreeSolv with attentivefp
featurization for atoms
* ``'GAT_canonical_FreeSolv'``: A GAT model trained on FreeSolv with canonical
featurization for atoms
* ``'GAT_attentivefp_FreeSolv'``: A GAT model trained on FreeSolv with attentivefp
featurization for atoms
* ``'Weave_canonical_FreeSolv'``: A Weave model trained on FreeSolv with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_FreeSolv'``: A Weave model trained on FreeSolv with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_FreeSolv'``: An MPNN model trained on FreeSolv with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_FreeSolv'``: An MPNN model trained on FreeSolv with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_FreeSolv'``: An AttentiveFP model trained on FreeSolv with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_FreeSolv'``: An AttentiveFP model trained on FreeSolv with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_FreeSolv'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on FreeSolv
* ``'gin_supervised_infomax_FreeSolv'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on FreeSolv
* ``'gin_supervised_edgepred_FreeSolv'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on FreeSolv
* ``'gin_supervised_masking_FreeSolv'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on FreeSolv
* ``'GCN_canonical_HIV'``: A GCN model trained on HIV with canonical
featurization for atoms
* ``'GCN_attentivefp_HIV'``: A GCN model trained on HIV with attentivefp
featurization for atoms
* ``'GAT_canonical_HIV'``: A GAT model trained on BACE with canonical
featurization for atoms
* ``'GAT_attentivefp_HIV'``: A GAT model trained on BACE with attentivefp
featurization for atoms
* ``'Weave_canonical_HIV'``: A Weave model trained on HIV with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_HIV'``: A Weave model trained on HIV with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_HIV'``: An MPNN model trained on HIV with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_HIV'``: An MPNN model trained on HIV with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_HIV'``: An AttentiveFP model trained on HIV with canonical
featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_HIV'``: An AttentiveFP model trained on HIV with attentivefp
featurization for atoms and bonds
* ``'gin_supervised_contextpred_HIV'``: A GIN model pre-trained with supervised learning
and context prediction, and fine-tuned on HIV
* ``'gin_supervised_infomax_HIV'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on HIV
* ``'gin_supervised_edgepred_HIV'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on HIV
* ``'gin_supervised_masking_HIV'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on HIV
* ``'NF_canonical_HIV'``: An NF model trained on HIV with canonical
featurization for atoms
* ``'GCN_canonical_Lipophilicity'``: A GCN model trained on Lipophilicity with canonical
featurization for atoms
* ``'GCN_attentivefp_Lipophilicity'``: A GCN model trained on Lipophilicity with
attentivefp featurization for atoms
* ``'GAT_canonical_Lipophilicity'``: A GAT model trained on Lipophilicity with canonical
featurization for atoms
* ``'GAT_attentivefp_Lipophilicity'``: A GAT model trained on Lipophilicity with
attentivefp featurization for atoms
* ``'Weave_canonical_Lipophilicity'``: A Weave model trained on Lipophilicity with
canonical featurization for atoms and bonds
* ``'Weave_attentivefp_Lipophilicity'``: A Weave model trained on Lipophilicity with
attentivefp featurization for atoms and bonds
* ``'MPNN_canonical_Lipophilicity'``: An MPNN model trained on Lipophilicity with
canonical featurization for atoms and bonds
* ``'MPNN_attentivefp_Lipophilicity'``: An MPNN model trained on Lipophilicity with
attentivefp featurization for atoms and bonds
* ``'AttentiveFP_canonical_Lipophilicity'``: An AttentiveFP model trained on
Lipophilicity with canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_Lipophilicity'``: An AttentiveFP model trained on
Lipophilicity with attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_Lipophilicity'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on Lipophilicity
* ``'gin_supervised_infomax_Lipophilicity'``: A GIN model pre-trained with supervised
learning and infomax, and fine-tuned on Lipophilicity
* ``'gin_supervised_edgepred_Lipophilicity'``: A GIN model pre-trained with supervised
learning and edge prediction, and fine-tuned on Lipophilicity
* ``'gin_supervised_masking_Lipophilicity'``: A GIN model pre-trained with supervised
learning and masking, and fine-tuned on Lipophilicity
* ``'GCN_canonical_MUV'``: A GCN model trained on MUV with canonical
featurization for atoms
* ``'GCN_attentivefp_MUV'``: A GCN model trained on MUV with attentivefp
featurization for atoms
* ``'GAT_canonical_MUV'``: A GAT model trained on MUV with canonical
featurization for atoms
* ``'GAT_attentivefp_MUV'``: A GAT model trained on MUV with attentivefp
featurization for atoms
* ``'Weave_canonical_MUV'``: A Weave model trained on MUV with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_MUV'``: A Weave model trained on MUV with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_MUV'``: An MPNN model trained on MUV with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_MUV'``: An MPNN model trained on MUV with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_MUV'``: An AttentiveFP model trained on MUV with canonical
featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_MUV'``: An AttentiveFP model trained on MUV with attentivefp
featurization for atoms and bonds
* ``'gin_supervised_contextpred_MUV'``: A GIN model pre-trained with supervised learning
and context prediction, and fine-tuned on MUV
* ``'gin_supervised_infomax_MUV'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on MUV
* ``'gin_supervised_edgepred_MUV'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on MUV
* ``'gin_supervised_masking_MUV'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on MUV
* ``'GCN_canonical_PCBA'``: A GCN model trained on PCBA with canonical
featurization for atoms
* ``'GCN_attentivefp_PCBA'``: A GCN model trained on PCBA with attentivefp
featurization for atoms
* ``'GAT_canonical_PCBA'``: A GAT model trained on PCBA with canonical
featurization for atoms
* ``'GAT_attentivefp_PCBA'``: A GAT model trained on PCBA with attentivefp
featurization for atoms
* ``'Weave_canonical_PCBA'``: A Weave model trained on PCBA with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_PCBA'``: A Weave model trained on PCBA with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_PCBA'``: An MPNN model trained on PCBA with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_PCBA'``: An MPNN model trained on PCBA with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_PCBA'``: An AttentiveFP model trained on PCBA with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_PCBA'``: An AttentiveFP model trained on PCBA with
attentivefp featurization for atoms and bonds
* ``'GCN_canonical_SIDER'``: A GCN model trained on SIDER with canonical
featurization for atoms
* ``'GCN_attentivefp_SIDER'``: A GCN model trained on SIDER with attentivefp
featurization for atoms
* ``'GAT_canonical_SIDER'``: A GAT model trained on SIDER with canonical
featurization for atoms
* ``'GAT_attentivefp_SIDER'``: A GAT model trained on SIDER with attentivefp
featurization for atoms
* ``'Weave_canonical_SIDER'``: A Weave model trained on SIDER with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_SIDER'``: A Weave model trained on SIDER with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_SIDER'``: An MPNN model trained on SIDER with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_SIDER'``: An MPNN model trained on SIDER with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_SIDER'``: An AttentiveFP model trained on SIDER with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_SIDER'``: An AttentiveFP model trained on SIDER with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_SIDER'``: A GIN model pre-trained with supervised learning
and context prediction, and fine-tuned on SIDER
* ``'gin_supervised_infomax_SIDER'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on SIDER
* ``'gin_supervised_edgepred_SIDER'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on SIDER
* ``'gin_supervised_masking_SIDER'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on SIDER
* ``'NF_canonical_SIDER'``: An NF model trained on SIDER with canonical
featurization for atoms
* ``'GCN_canonical_Tox21'``: A GCN model trained on Tox21 with canonical
featurization for atoms
* ``'GCN_attentivefp_Tox21'``: A GCN model trained on Tox21 with attentivefp
featurization for atoms
* ``'GAT_canonical_Tox21'``: A GAT model trained on Tox21 with canonical
featurization for atoms
* ``'GAT_attentivefp_Tox21'``: A GAT model trained on Tox21 with attentivefp
featurization for atoms
* ``'Weave_canonical_Tox21'``: A Weave model trained on Tox21 with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_Tox21'``: A Weave model trained on Tox21 with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_Tox21'``: An MPNN model trained on Tox21 with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_Tox21'``: An MPNN model trained on Tox21 with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_Tox21'``: An AttentiveFP model trained on Tox21 with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_Tox21'``: An AttentiveFP model trained on Tox21 with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_Tox21'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on Tox21
* ``'gin_supervised_infomax_Tox21'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on Tox21
* ``'gin_supervised_edgepred_Tox21'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on Tox21
* ``'gin_supervised_masking_Tox21'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on Tox21
* ``'NF_canonical_Tox21'``: An NF model trained on Tox21 with canonical
featurization for atoms
* ``'GCN_canonical_ToxCast'``: A GCN model trained on ToxCast with canonical
featurization for atoms
* ``'GCN_attentivefp_ToxCast'``: A GCN model trained on ToxCast with attentivefp
featurization for atoms
* ``'GAT_canonical_ToxCast'``: A GAT model trained on ToxCast with canonical
featurization for atoms
* ``'GAT_attentivefp_ToxCast'``: A GAT model trained on ToxCast with attentivefp
featurization for atoms
* ``'Weave_canonical_ToxCast'``: A Weave model trained on ToxCast with canonical
featurization for atoms and bonds
* ``'Weave_attentivefp_ToxCast'``: A Weave model trained on ToxCast with attentivefp
featurization for atoms and bonds
* ``'MPNN_canonical_ToxCast'``: An MPNN model trained on ToxCast with canonical
featurization for atoms and bonds
* ``'MPNN_attentivefp_ToxCast'``: An MPNN model trained on ToxCast with attentivefp
featurization for atoms and bonds
* ``'AttentiveFP_canonical_ToxCast'``: An AttentiveFP model trained on ToxCast with
canonical featurization for atoms and bonds
* ``'AttentiveFP_attentivefp_ToxCast'``: An AttentiveFP model trained on ToxCast with
attentivefp featurization for atoms and bonds
* ``'gin_supervised_contextpred_ToxCast'``: A GIN model pre-trained with supervised
learning and context prediction, and fine-tuned on ToxCast
* ``'gin_supervised_infomax_ToxCast'``: A GIN model pre-trained with supervised learning
and infomax, and fine-tuned on ToxCast
* ``'gin_supervised_edgepred_ToxCast'``: A GIN model pre-trained with supervised learning
and edge prediction, and fine-tuned on ToxCast
* ``'gin_supervised_masking_ToxCast'``: A GIN model pre-trained with supervised learning
and masking, and fine-tuned on ToxCast
* ``'NF_canonical_ToxCast'``: An NF model trained on ToxCast with canonical
featurization for atoms and bonds
log : bool
Whether to print progress for model loading
Returns
-------
model
"""
if model_name not in url:
raise RuntimeError("Cannot find a pretrained model with name {}".format(model_name))
for func in [create_moleculenet_model, create_generative_model,
create_property_model, create_reaction_model]:
model = func(model_name)
if model is not None:
break
return download_and_load_checkpoint(model_name, model, url[model_name], log=log)
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] |
ptmv/snd.py | ryw89/ptmv | 0 | 46975 | import os
import subprocess
import tempfile
import time
import wave
import simpleaudio
def extract(file):
ptmv_tempdir = os.path.join(tempfile.gettempdir(), "ptmv")
if not os.path.exists(ptmv_tempdir): os.makedirs(ptmv_tempdir)
snd_file = ptmv_tempdir + str(int(time.time())) + ".wav"
command = "ffmpeg -i " + file + " -b:a 48k -ac 1 " + snd_file
subprocess.run(command.split(), stdout = subprocess.DEVNULL, stderr = subprocess.DEVNULL)
return snd_file
def play(file, start_time):
if not os.path.exists(file): return
wave_raw = wave.open(file)
frame_rate = wave_raw.getframerate()
wave_raw.setpos(int(frame_rate * start_time))
return simpleaudio.WaveObject.from_wave_read(wave_raw).play()
def stop(play_obj): play_obj.stop() | [
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wsgi/openshift/manage.py | chiora93/WavelenBlog | 0 | 181173 | #!/usr/bin/env python
import settings.development as settings
import os, sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "settings.development")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv) | [
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Tutorials/10 Days of Statistics/Day 5 - Poisson Distribution I/solution.py | abhinavgunwant/hackerrank-solutions | 1 | 158924 | #!/bin/python3
# Calculates factorial
def fact(n):
f = 1
while n > 0:
f *= n
n -= 1
return f
# Calculates Poisson Distribution
def poissonDist(k, l):
return (l**k*(2.71828**(-l)))/fact(k)
if __name__ == '__main__':
x = float(input())
y = float(input())
print(round(poissonDist(y, x), 3)) | [
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] |
manyfaced/common/arguments.py | Zloool/mlgwebhoneypot | 8 | 1607948 | import argparse
import sys
import settings
"""
usage: mfh.py [-h] [-c [PORT]] [-s [PORT]] [-u] [-v]
Serve some sweet honey to the ubiquitous bots!
optional arguments:
-h, --help show this help message and exit
-c [PORT] port to start a CLIENT on
-s [PORT] port to start a SERVER on
-u enable self updating
-v, --verbose increase output verbosity
And that`s how you`d detect a sneaky chinese bot.
"""
def parse():
parser = argparse.ArgumentParser(
description='Serve some sweet honey to the ubiquitous bots!',
epilog='And that`s how you`d detect a sneaky chinese bot.',
prog='mfh.py',
)
parser.add_argument(
'-c',
const=settings.HONEYPORT,
dest='client',
help='port to start a CLIENT on',
metavar='PORT',
nargs='?',
type=int,
)
parser.add_argument(
'-s',
const=settings.HIVEPORT,
dest='server',
help='port to start a SERVER on',
metavar='PORT',
nargs='?',
type=int,
)
parser.add_argument(
'-u',
action='store_true',
dest='updater',
help='enable self updating',
)
parser.add_argument(
'-v',
'--verbose',
action='store_true',
help='increase output verbosity',
)
parser.add_argument(
'-p',
'--proxy',
action='store_true',
help='set this argument if you want to put honeypot behind proxy',
)
if len(sys.argv[1:]) == 0:
parser.print_usage()
parser.exit()
return parser.parse_args()
| [
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2
] |
setup.py | edwardchalstrey1/mapreader-plant-scivision | 0 | 94654 | #!/usr/bin/env python
from setuptools import find_packages, setup
requirements = []
with open("requirements.txt") as f:
for line in f:
stripped = line.split("#")[0].strip()
if len(stripped) > 0:
requirements.append(stripped)
setup(
name="mapreader-plant-scivision",
version="0.0.1",
description="scivision test plugin, using mapreader",
author="",
author_email="",
url="https://github.com/alan-turing-institute/mapreader-plant-scivision",
packages=find_packages(),
install_requires=requirements,
python_requires=">=3.7",
)
| [
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] |
cairis/gui/DictionaryListCtrl.py | RachelLar/cairis_update | 0 | 12246 | # 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 wx
from cairis.core.armid import *
from DictionaryEntryDialog import DictionaryEntryDialog
class DictionaryListCtrl(wx.ListCtrl):
def __init__(self,parent):
wx.ListCtrl.__init__(self,parent,PROJECTSETTINGS_LISTDICTIONARY_ID,size=wx.DefaultSize,style=wx.LC_REPORT | wx.LC_SORT_ASCENDING)
self.keys = []
self.InsertColumn(0,'Name')
self.SetColumnWidth(0,150)
self.InsertColumn(1,'Definition')
self.SetColumnWidth(1,300)
self.theSelectedIdx = -1
self.theMenu = wx.Menu()
self.theMenu.Append(DICTIONARYLISTCTRL_MENUADD_ID,'Add')
self.theMenu.Append(DICTIONARYLISTCTRL_MENUDELETE_ID,'Delete')
self.Bind(wx.EVT_LIST_ITEM_RIGHT_CLICK,self.OnRightDown)
self.Bind(wx.EVT_LIST_ITEM_SELECTED,self.OnItemSelected)
self.Bind(wx.EVT_LIST_ITEM_DESELECTED,self.OnItemDeselected)
self.Bind(wx.EVT_LIST_ITEM_ACTIVATED,self.onEntryActivated)
wx.EVT_MENU(self.theMenu,DICTIONARYLISTCTRL_MENUADD_ID,self.onAddEntry)
wx.EVT_MENU(self.theMenu,DICTIONARYLISTCTRL_MENUDELETE_ID,self.onDeleteEntry)
def OnItemSelected(self,evt):
self.theSelectedIdx = evt.GetIndex()
def OnItemDeselected(self,evt):
self.theSelectedIdx = -1
def OnRightDown(self,evt):
self.PopupMenu(self.theMenu)
def onAddEntry(self,evt):
dlg = DictionaryEntryDialog(self)
if (dlg.ShowModal() == DICTIONARYENTRY_BUTTONCOMMIT_ID):
name = dlg.name()
definition = dlg.definition()
idx = self.GetItemCount()
self.InsertStringItem(idx,name)
self.SetStringItem(idx,1,definition)
def onDeleteEntry(self,evt):
if (self.theSelectedIdx == -1):
errorText = 'No entry selected'
errorLabel = 'Delete definition'
dlg = wx.MessageDialog(self,errorText,errorLabel,wx.OK)
dlg.ShowModal()
dlg.Destroy()
else:
selectedValue = self.GetItemText(self.theSelectedIdx)
self.DeleteItem(self.theSelectedIdx)
def onEntryActivated(self,evt):
self.theSelectedIdx = evt.GetIndex()
name = self.GetItemText(self.theSelectedIdx)
definition = self.GetItem(self.theSelectedIdx,1)
dlg = DictionaryEntryDialog(self,name,definition.GetText())
if (dlg.ShowModal() == DICTIONARYENTRY_BUTTONCOMMIT_ID):
self.SetStringItem(self.theSelectedIdx,0,dlg.name())
self.SetStringItem(self.theSelectedIdx,1,dlg.definition())
def load(self,entries):
self.keys = entries.keys()
self.keys.sort()
for name in self.keys:
idx = self.GetItemCount()
self.InsertStringItem(idx,name)
self.SetStringItem(idx,1,entries[name])
def dimensions(self):
entries = []
for x in range(self.GetItemCount()):
name = self.GetItemText(x)
definition = self.GetItem(x,1)
entries.append((name,definition.GetText()))
return entries
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] |
girder/girder_large_image/rest/large_image_resource.py | girder/large_image | 85 | 196616 | ##############################################################################
# Copyright Kitware Inc.
#
# Licensed under the Apache License, Version 2.0 ( the "License" );
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import concurrent.futures
import datetime
import json
import re
import sys
import time
import psutil
from girder_jobs.constants import JobStatus
from girder_jobs.models.job import Job
from girder import logger
from girder.api import access
from girder.api.describe import Description, describeRoute
from girder.api.rest import Resource
from girder.exceptions import RestException
from girder.models.file import File
from girder.models.item import Item
from girder.models.setting import Setting
from large_image import cache_util
from large_image.exceptions import TileGeneralError
from .. import constants, girder_tilesource
from ..models.image_item import ImageItem
def createThumbnailsJobTask(item, spec):
"""
For an individual item, check or create all of the appropriate thumbnails.
:param item: the image item.
:param spec: a list of thumbnail specifications.
:returns: a dictionary with the total status of the thumbnail job.
"""
status = {'checked': 0, 'created': 0, 'failed': 0}
for entry in spec:
try:
if entry.get('imageKey'):
result = ImageItem().getAssociatedImage(item, checkAndCreate=True, **entry)
else:
result = ImageItem().getThumbnail(item, checkAndCreate=True, **entry)
status['checked' if result is True else 'created'] += 1
except TileGeneralError as exc:
status['failed'] += 1
status['lastFailed'] = str(item['_id'])
logger.info('Failed to get thumbnail for item %s: %r' % (item['_id'], exc))
except AttributeError:
raise
except Exception:
status['failed'] += 1
status['lastFailed'] = str(item['_id'])
logger.exception(
'Unexpected exception when trying to create a thumbnail for item %s' %
item['_id'])
return status
def createThumbnailsJobLog(job, info, prefix='', status=None):
"""
Log information aboyt the create thumbnails job.
:param job: the job object.
:param info: a dictionary with the number of thumbnails checked, created,
and failed.
:param prefix: a string to place in front of the log message.
:param status: if not None, a new status for the job.
"""
msg = prefix + 'Checked %d, created %d thumbnail files' % (
info['checked'], info['created'])
if prefix == '' and info.get('items', 0) * info.get('specs', 0):
done = info['checked'] + info['created'] + info['failed']
if done < info['items'] * info['specs']:
msg += ' (estimated %4.2f%% done)' % (
100.0 * done / (info['items'] * info['specs']))
msg += '\n'
if info['failed']:
msg += 'Failed on %d thumbnail file%s (last failure on item %s)\n' % (
info['failed'],
's' if info['failed'] != 1 else '', info['lastFailed'])
job = Job().updateJob(job, log=msg, status=status)
return job, msg
def cursorNextOrNone(cursor):
"""
Given a Mongo cursor, return the next value if there is one. If not,
return None.
:param cursor: a cursor to get a value from.
:returns: the next value or None.
"""
try:
return cursor.next() # noqa - B305
except StopIteration:
return None
def createThumbnailsJob(job):
"""
Create thumbnails for all of the large image items.
The job object contains::
- spec: an array, each entry of which is the parameter dictionary
for the model getThumbnail function.
- logInterval: the time in seconds between log messages. This
also controls the granularity of cancelling the job.
- concurrent: the number of threads to use. 0 for the number of
cpus.
:param job: the job object including kwargs.
"""
job = Job().updateJob(
job, log='Started creating large image thumbnails\n',
status=JobStatus.RUNNING)
concurrency = int(job['kwargs'].get('concurrent', 0))
concurrency = psutil.cpu_count(logical=True) if concurrency < 1 else concurrency
status = {
'checked': 0,
'created': 0,
'failed': 0,
}
spec = job['kwargs']['spec']
logInterval = float(job['kwargs'].get('logInterval', 10))
job = Job().updateJob(job, log='Creating thumbnails (%d concurrent)\n' % concurrency)
nextLogTime = time.time() + logInterval
tasks = []
# This could be switched from ThreadPoolExecutor to ProcessPoolExecutor
# without any other changes. Doing so would probably improve parallel
# performance, but may not work reliably under Python 2.x.
pool = concurrent.futures.ThreadPoolExecutor(max_workers=concurrency)
try:
# Get a cursor with the list of images
items = Item().find({'largeImage.fileId': {'$exists': True}})
if hasattr(items, 'count'):
status['items'] = items.count()
status['specs'] = len(spec)
nextitem = cursorNextOrNone(items)
while len(tasks) or nextitem is not None:
# Create more tasks than we strictly need so if one finishes before
# we check another will be ready. This is balanced with not
# creating too many to avoid excessive memory use. As such, we
# can't do a simple iteration over the database cursor, as it will
# be exhausted before we are done.
while len(tasks) < concurrency * 4 and nextitem is not None:
tasks.append(pool.submit(createThumbnailsJobTask, nextitem, spec))
nextitem = cursorNextOrNone(items)
# Wait a short time or until the oldest task is complete
try:
tasks[0].result(0.1)
except concurrent.futures.TimeoutError:
pass
# Remove completed tasks from our list, adding their results to the
# status.
for pos in range(len(tasks) - 1, -1, -1):
if tasks[pos].done():
r = tasks[pos].result()
status['created'] += r['created']
status['checked'] += r['checked']
status['failed'] += r['failed']
status['lastFailed'] = r.get('lastFailed', status.get('lastFailed'))
tasks[pos:pos + 1] = []
# Periodically, log the state of the job and check if it was
# deleted or canceled.
if time.time() > nextLogTime:
job, msg = createThumbnailsJobLog(job, status)
# Check if the job was deleted or canceled; if so, quit
job = Job().load(id=job['_id'], force=True)
if not job or job['status'] in (JobStatus.CANCELED, JobStatus.ERROR):
cause = {
None: 'deleted',
JobStatus.CANCELED: 'canceled',
JobStatus.ERROR: 'stopped due to error',
}[None if not job else job.get('status')]
msg = 'Large image thumbnails job %s' % cause
logger.info(msg)
# Cancel any outstanding tasks. If they haven't started,
# they are discarded. Those that have started will still
# run, though.
for task in tasks:
task.cancel()
return
nextLogTime = time.time() + logInterval
except Exception:
logger.exception('Error with large image create thumbnails job')
Job().updateJob(
job, log='Error creating large image thumbnails\n',
status=JobStatus.ERROR)
return
finally:
# Clean up the task pool asynchronously
pool.shutdown(False)
job, msg = createThumbnailsJobLog(job, status, 'Finished: ', JobStatus.SUCCESS)
logger.info(msg)
class LargeImageResource(Resource):
def __init__(self):
super().__init__()
self.resourceName = 'large_image'
self.route('GET', ('cache', ), self.cacheInfo)
self.route('PUT', ('cache', 'clear'), self.cacheClear)
self.route('GET', ('settings',), self.getPublicSettings)
self.route('GET', ('sources',), self.listSources)
self.route('GET', ('thumbnails',), self.countThumbnails)
self.route('PUT', ('thumbnails',), self.createThumbnails)
self.route('DELETE', ('thumbnails',), self.deleteThumbnails)
self.route('GET', ('associated_images',), self.countAssociatedImages)
self.route('DELETE', ('associated_images',), self.deleteAssociatedImages)
self.route('GET', ('histograms',), self.countHistograms)
self.route('DELETE', ('histograms',), self.deleteHistograms)
self.route('DELETE', ('tiles', 'incomplete'), self.deleteIncompleteTiles)
@describeRoute(
Description('Clear tile source caches to release resources and file handles.')
)
@access.admin
def cacheClear(self, params):
before = cache_util.cachesInfo()
cache_util.cachesClear()
after = cache_util.cachesInfo()
# Add a small delay to give the memcached time to clear
stoptime = time.time() + 5
while time.time() < stoptime and any(after[key]['used'] for key in after):
time.sleep(0.1)
after = cache_util.cachesInfo()
return {'cacheCleared': datetime.datetime.utcnow(), 'before': before, 'after': after}
@describeRoute(
Description('Get information on caches.')
)
@access.admin
def cacheInfo(self, params):
return cache_util.cachesInfo()
@describeRoute(
Description('Get public settings for large image display.')
)
@access.public
def getPublicSettings(self, params):
keys = [getattr(constants.PluginSettings, key)
for key in dir(constants.PluginSettings)
if key.startswith('LARGE_IMAGE_')]
return {k: Setting().get(k) for k in keys}
@describeRoute(
Description('Count the number of cached thumbnail files for '
'large_image items.')
.param('spec', 'A JSON list of thumbnail specifications to count. '
'If empty, all cached thumbnails are counted. The '
'specifications typically include width, height, encoding, and '
'encoding options.', required=False)
)
@access.admin
def countThumbnails(self, params):
return self._countCachedImages(params.get('spec'))
@describeRoute(
Description('Count the number of cached associated image files for '
'large_image items.')
.param('imageKey', 'If specific, only include images with the '
'specified key', required=False)
)
@access.admin
def countAssociatedImages(self, params):
return self._countCachedImages(
None, associatedImages=True, imageKey=params.get('imageKey'))
def _countCachedImages(self, spec, associatedImages=False, imageKey=None):
if spec is not None:
try:
spec = json.loads(spec)
if not isinstance(spec, list):
raise ValueError()
except ValueError:
raise RestException('The spec parameter must be a JSON list.')
spec = [json.dumps(entry, sort_keys=True, separators=(',', ':'))
for entry in spec]
else:
spec = [None]
count = 0
for entry in spec:
query = {'isLargeImageThumbnail': True, 'attachedToType': 'item'}
if entry is not None:
query['thumbnailKey'] = entry
elif associatedImages:
if imageKey and re.match(r'^[0-9A-Za-z].*$', imageKey):
query['thumbnailKey'] = {'$regex': '"imageKey":"%s"' % imageKey}
else:
query['thumbnailKey'] = {'$regex': '"imageKey":'}
count += File().find(query).count()
return count
@describeRoute(
Description('Create cached thumbnail files from large_image items.')
.notes('This creates a local job that processes all large_image items.')
.param('spec', 'A JSON list of thumbnail specifications to create. '
'The specifications typically include width, height, encoding, '
'and encoding options.')
.param('logInterval', 'The number of seconds between log messages. '
'This also determines how often the creation job is checked if '
'it has been canceled or deleted. A value of 0 will log after '
'each thumbnail is checked or created.', required=False,
dataType='float')
.param('concurrent', 'The number of concurrent threads to use when '
'making thumbnails. 0 or unspecified to base this on the '
'number of reported cpus.', required=False, dataType='int')
)
@access.admin
def createThumbnails(self, params):
self.requireParams(['spec'], params)
try:
spec = json.loads(params['spec'])
if not isinstance(spec, list):
raise ValueError()
except ValueError:
raise RestException('The spec parameter must be a JSON list.')
maxThumbnailFiles = int(Setting().get(
constants.PluginSettings.LARGE_IMAGE_MAX_THUMBNAIL_FILES))
if maxThumbnailFiles <= 0:
raise RestException('Thumbnail files are not enabled.')
jobKwargs = {'spec': spec}
if params.get('logInterval') is not None:
jobKwargs['logInterval'] = float(params['logInterval'])
if params.get('concurrent') is not None:
jobKwargs['concurrent'] = float(params['concurrent'])
job = Job().createLocalJob(
module='girder_large_image.rest.large_image_resource',
function='createThumbnailsJob',
kwargs=jobKwargs,
title='Create large image thumbnail files.',
type='large_image_create_thumbnails',
user=self.getCurrentUser(),
public=True,
asynchronous=True,
)
Job().scheduleJob(job)
return job
@describeRoute(
Description('Delete cached thumbnail files from large_image items.')
.param('spec', 'A JSON list of thumbnail specifications to delete. '
'If empty, all cached thumbnails are deleted. The '
'specifications typically include width, height, encoding, and '
'encoding options.', required=False)
)
@access.admin
def deleteThumbnails(self, params):
return self._deleteCachedImages(params.get('spec'))
@describeRoute(
Description('Delete cached associated image files from large_image items.')
.param('imageKey', 'If specific, only include images with the '
'specified key', required=False)
)
@access.admin
def deleteAssociatedImages(self, params):
return self._deleteCachedImages(
None, associatedImages=True, imageKey=params.get('imageKey'))
def _deleteCachedImages(self, spec, associatedImages=False, imageKey=None):
if spec is not None:
try:
spec = json.loads(spec)
if not isinstance(spec, list):
raise ValueError()
except ValueError:
raise RestException('The spec parameter must be a JSON list.')
spec = [json.dumps(entry, sort_keys=True, separators=(',', ':'))
for entry in spec]
else:
spec = [None]
removed = 0
for entry in spec:
query = {'isLargeImageThumbnail': True, 'attachedToType': 'item'}
if entry is not None:
query['thumbnailKey'] = entry
elif associatedImages:
if imageKey and re.match(r'^[0-9A-Za-z].*$', imageKey):
query['thumbnailKey'] = {'$regex': '"imageKey":"%s"' % imageKey}
else:
query['thumbnailKey'] = {'$regex': '"imageKey":'}
for file in File().find(query):
File().remove(file)
removed += 1
return removed
@describeRoute(
Description('Remove large images from items where the large image job '
'incomplete.')
.notes('This is used to clean up all large image conversion jobs that '
'have failed to complete. If a job is in progress, it will be '
'cancelled. The return value is the number of items that were '
'adjusted.')
)
@access.admin
def deleteIncompleteTiles(self, params):
result = {'removed': 0}
while True:
item = Item().findOne({'largeImage.expected': True})
if not item:
break
job = Job().load(item['largeImage']['jobId'], force=True)
if job and job.get('status') in (
JobStatus.QUEUED, JobStatus.RUNNING):
job = Job().cancelJob(job)
if job and job.get('status') in (
JobStatus.QUEUED, JobStatus.RUNNING):
result['message'] = ('The job for item %s could not be '
'canceled' % (str(item['_id'])))
break
ImageItem().delete(item)
result['removed'] += 1
return result
@describeRoute(
Description('List all Girder tile sources with associated extensions, '
'mime types, and versions. Lower values indicate a '
'higher priority for an extension of mime type with that '
'source.')
)
@access.public
def listSources(self, params):
results = {}
for key, source in girder_tilesource.AvailableGirderTileSources.items():
results[key] = {
'extensions': {
k or 'default': v for k, v in source.extensions.items()},
'mimeTypes': {
k or 'default': v for k, v in source.mimeTypes.items()},
}
for cls in source.__mro__:
try:
if sys.modules[cls.__module__].__version__:
results[key]['version'] = sys.modules[cls.__module__].__version__
break
except Exception:
pass
return results
@describeRoute(
Description('Count the number of cached histograms for large_image items.')
)
@access.admin
def countHistograms(self, params):
query = {
'isLargeImageData': True,
'attachedToType': 'item',
'thumbnailKey': {'$regex': '"imageKey":"histogram"'},
}
count = File().find(query).count()
return count
@describeRoute(
Description('Delete cached histograms from large_image items.')
)
@access.admin
def deleteHistograms(self, params):
query = {
'isLargeImageData': True,
'attachedToType': 'item',
'thumbnailKey': {'$regex': '"imageKey":"histogram"'},
}
removed = 0
for file in File().find(query):
File().remove(file)
removed += 1
return removed
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dante.py | jbudis/dante | 4 | 164322 | <filename>dante.py
import matplotlib
matplotlib.use('Agg')
import os
import cProfile
import pstats
import multiprocess
import sys
from datetime import datetime
import traceback
import logging
import arguments
from parser import ReadFile
import prefiltering
import templates
import report
import annotation
import postfilter
from annotation import Annotator
import all_call
def run_annot_iter(reader, annotators, filters, threads, annotated_read_prev, filtered_reads_prev, filter_on=True, report_every=100000):
"""
Runs the annotator iteratively and in parallel on all reads in reader.
:param reader: iterator - iterative reader
:param annotators: list(Annotator) - annotator classes for annotating reads
:param filters: list(filters) - filters for pre-filtering reads
:param threads: int - number of threads to use
:param annotated_read_prev: list(int) - previously annotated reads
:param filtered_reads_prev: int - previously filtered reads
:param filter_on: bool - whether the filtering in subprocesses is on
:param report_every: int - report progress every this number of reads
:return: list(2D), int, int - list of annotations for filtered reads, number of annotated reads, number of filtered reads
"""
# construct shared value and lock
annotated_reads = multiprocess.Array('i', annotated_read_prev)
filtered_reads = multiprocess.Value('i', filtered_reads_prev)
lock = multiprocess.Lock()
def init_pool(l, ar, fr, an, flt, f_on, report_num):
"""
Initializes a pool with variables
:param l: Lock - shared lock object for all processes
:param ar: Array - shared Array for all processes
:param fr: Array - shared Array for all processes
:param an: Annotators - shared Annotators object for all processes
:param flt: filter functions - filter functions for pre-filtering reads
:param f_on: bool - filter is on?
:param report_num: int - how often to report progress
:return: None
"""
global lock
global annotated_reads
global filtered_reads
global annotators
global filters
global filter_on
global report_every
lock = l
annotated_reads = ar
filtered_reads = fr
annotators = an
filters = flt
filter_on = f_on
report_every = report_num
if PROFILE:
global prof
prof = cProfile.Profile()
def fin():
try:
with open('%s/profile-%s.out' % (config['general']['output_dir'], multiprocess.current_process().pid), "w") as f:
pstats.Stats(prof, stream=f).strip_dirs().sort_stats("time").print_stats()
except TypeError: # empty prof
pass
multiprocess.util.Finalize(None, fin, exitpriority=1)
def annotate_read_sequentially(read, annotated_reads, filtered_reads, annotators, filters, filter_on, report_every, lock=None):
"""
Annotate a single read with global Annotator object.
:param read: triple - read to be annotated
:param l: Lock - shared lock object for all processes
:param annotated_reads: Array - shared Array for all processes
:param filtered_reads: Array - shared Array for all processes
:param annotators: Annotators - shared Annotators object for all processes
:param filters: filter functions - filter functions for pre-filtering reads
:param filter_on: bool - filter is on?
:param report_every: int - how often to report progress
:param lock: lock object - if specified then use lock for printing
:return: tuple - annotated read (output of Annotator), read
"""
# filter and annotate a read
annotation = [None for _ in range(len(annotators))]
for i, (annotator, filter) in enumerate(zip(annotators, filters)):
annot = None
if filter.filter_read(read):
annot = annotator.annotate(read)
annotation[i] = annot
annotated_reads[i] += 1
if filter_on and not annotation[i].is_annotated_right():
annotation[i] = None
# was this read correctly annotated?
if config['general']['verbosity'] > 1:
if lock is not None:
lock.acquire()
report.log_str('Read %15s annotated with Annotator %s Filter %s - %s' % (
str(read), annotator, filter, 'OK' if annotation[i] is not None else ('FLT_FAIL' if annot is None else 'ANN_FAIL')))
if lock is not None:
lock.release()
# write down progress
filtered_reads.value += 1
# print progress
if report_every > 0 and filtered_reads.value % report_every == 0:
if lock is not None:
lock.acquire()
s = "\r processed: %8d (passed filtering: %s) %s" % (filtered_reads.value, " ".join(map(str, annotated_reads)), '{duration}'.format(duration=datetime.now() - start_time))
report.log_str(s, stdout_too=False, priority=logging.DEBUG, flush=True)
sys.stdout.write(s)
sys.stdout.flush()
if lock is not None:
lock.release()
# return result
return annotation
def annotate_read(read):
"""
Annotate a single read with global Annotator object.
:param read: triple - read to be annotated
:return: tuple - annotated read (output of Annotator), read
"""
# start profiler
if PROFILE:
global prof
prof.enable()
global lock
global annotated_reads
global filtered_reads
global annotators
global filters
global filter_on
global report_every
try:
annotation = annotate_read_sequentially(read, annotated_reads, filtered_reads, annotators, filters, filter_on, report_every, lock)
except Exception as e:
print("Exception in child process:")
traceback.print_exc()
raise e
# stop profiler.
if PROFILE:
prof.disable()
# return result
return annotation
# crate pool and initialize it with init_pool
res = [[] for _ in range(len(annotators))]
pool = None
if threads > 1 or config['general'].get('force_parallel', False):
# print('Running in parallel ({threads} cores)'.format(threads=threads))
pool = multiprocess.Pool(threads, initializer=init_pool, initargs=(lock, annotated_reads, filtered_reads, annotators, filters, filter_on, report_every))
results = pool.map(annotate_read, reader, chunksize=100)
else:
# print('Running sequentially')
results = (annotate_read_sequentially(read, annotated_reads, filtered_reads, annotators, filters, filter_on, report_every) for read in reader)
report.log_str("Threads initialized, processing reads...")
# go through all results
for partial_res in results:
for i, pr in enumerate(partial_res):
if pr is not None:
res[i].append(pr)
if pool is not None:
pool.close()
pool.join()
if report_every > 0:
print('')
return res, filtered_reads.value, annotated_reads
def construct_annotator(motif):
"""
Construct Annotator and prefilter based on motif.
:param motif: dict - motif specification
:return: Annotator, *Filter - annotator and prefilter constructed
"""
sequence = ','.join([module_dict['seq'] for module_dict in motif['modules']])
motif_tuple = report.seq_into_tuple(sequence)
# initialize annotator
if not config['general']['quiet_mode']:
report.log_str("Motif %12s: Constructing annotator for sequence %s" % (motif['full_name'], sequence))
annotator = Annotator(motif_tuple, config['annotation']['delete_prob'], config['annotation']['insert_prob'],
config['annotation']['max_delete_skips'], config['annotation']['motif_frequency'],
config['annotation']['snp_chance'])
# initialize filter
read_filter = prefiltering.create_filter(motif['prefilter'], motif_tuple)
if not config['general']['quiet_mode']:
report.log_str("Motif %12s: Filter constructed: %s" % (motif['full_name'], str(read_filter)))
return annotator, read_filter
"""
Start of real code: whole dante annotation algorithm.
"""
if __name__ == "__main__":
# print time of the start:
start_time = datetime.now()
print(templates.START_TIME.format(start=start_time))
# load arguments
config = arguments.load_arguments()
# start profiling if needed
prog_prof = None
PROFILE = config['general']['profiler']
if PROFILE:
prog_prof = cProfile.Profile()
prog_prof.enable()
# initialize logging module:
report.configure_logger("%s/dante.log" % config['general']['output_dir'])
report.log_str(templates.START_TIME.format(start=start_time), stdout_too=False)
# print arguments (too clunky)
# report.log_str(arguments.save_arguments(config))
#if not config['skip_annotation']:
# deduplicated reads
dedup_ap = [[] for _ in range(len(config['motifs']))]
# go through all motifs and construct annotators and filters:
annotators = []
filters = []
# create annotator generator
if len(config['motifs']) > 100 and config['general']['cpu'] > 1:
report.log_str('Annotators processing in parallel on {cpu} cores.'.format(cpu=config['general']['cpu']))
pool = multiprocess.Pool(config['general']['cpu'])
results = pool.imap(construct_annotator, config['motifs'])
else:
report.log_str('Annotators processing sequentially.')
results = (construct_annotator(motif) for motif in config['motifs'])
# fill annotators
for i, (annotator, read_filter) in enumerate(results):
if config['general']['quiet_mode'] and i % 100 == 0:
report.log_str('Constructing annotators: {i:6d}/{cnt:6d} {date:%Y-%m-%d %H:%M:%S}'.format(i=i, cnt=len(config['motifs']), date=datetime.now()))
annotators.append(annotator)
filters.append(read_filter)
# main annotation
all_reads = 0
annotations = [[] for _ in range(len(annotators))]
annotated_reads = [0] * len(annotations)
readers = []
# read reads from specific region in bam?, read unmapped reads?
bam = True
include_unmapped = False
for motif in config['motifs']:
if 'chromosome' not in motif:
bam = False
include_unmapped = False
break
elif 'include_unmapped' in motif and motif['include_unmapped']:
include_unmapped = True
for input_file in config['inputs']:
# initialize reader
read_filename = input_file['path']
file_type = None if input_file['filetype'] == 'infer' else input_file['filetype']
max_reads = None if input_file['max_reads'] == 'all' else input_file['max_reads']
if bam:
# annotate specified reads for every motif
report.log_str(f"Parsing file {read_filename} (bam type)")
for i, motif in enumerate(config['motifs']):
if config['general']['quiet_mode'] and i % 100 == 0:
report.log_str('Running annotators: {i:6d}/{cnt:6d} {date:%Y-%m-%d %H:%M:%S}'.format(i=i, cnt=len(config['motifs']), date=datetime.now()))
min_mapq = None if 'min_mapq' not in motif else motif['min_mapq']
read_file = ReadFile(read_filename, config['general']['stranded'], max_reads, file_type, config['general']['verbosity'], motif['chromosome'], motif['ref_start'], motif['ref_end'], min_mapq=min_mapq)
# initialize annotator and filter for current motif
annot = [annotators[i]]
filt = [filters[i]]
areads = [annotated_reads[i]]
readers.append(read_file)
# run annotator for current motif
report.log_str(f"Running annotator on {config['general']['cpu']} process(es) for file {read_filename} and motif {motif['description']}",
stdout_too=not config['general']['quiet_mode'])
next_annotations, cur_reads, areads = run_annot_iter(read_file, annot, filt, 1, areads, all_reads,
not config['general']['output_all'], 0 if config['general']['quiet_mode'] else config['general']['report_every'])
new_reads = cur_reads - all_reads
all_reads += new_reads
annotated_reads[i] = areads[0]
annotations[i].extend(next_annotations[0])
# write stats for current file and motif
report.log_str("Reads: %10d, annotated: %s" % (new_reads, ' '.join(map(str, map(len, next_annotations)))), stdout_too=not config['general']['quiet_mode'])
if not bam or include_unmapped:
if read_filename == 'sys.stdin':
read_file = ReadFile(None, config['general']['stranded'], max_reads, file_type, verbosity=config['general']['verbosity'], unmapped=include_unmapped)
else:
read_file = ReadFile(read_filename, config['general']['stranded'], max_reads, file_type, verbosity=config['general']['verbosity'], unmapped=include_unmapped)
report.log_str("Parsing file %s (%s type) with parser %s %s " % (read_filename, read_file.file_type, read_file.__class__.__name__, read_file.reader.__name__))
# run annotators
report.log_str("Running %d annotator(s) on %2d process(es) for file %s" % (len(annotators), config['general']['cpu'], read_filename))
readers.append(read_file)
next_annotations, cur_reads, annotated_reads = run_annot_iter(read_file, annotators, filters, config['general']['cpu'], annotated_reads, all_reads,
not config['general']['output_all'], 0 if config['general']['quiet_mode'] else config['general']['report_every'])
new_reads = cur_reads - all_reads
all_reads += new_reads
for i, pr in enumerate(next_annotations):
annotations[i].extend(pr)
# write stats for current file
if not config['general']['quiet_mode']:
report.log_str("Reads: %10d, annotated: %s" % (new_reads, ' '.join(map(str, map(len, next_annotations)))))
# write read distribution
report.write_read_distribution('%s/read_distr.npy' % config['general']['output_dir'], readers)
# write stats
for i, (motif, annot) in enumerate(zip(config['motifs'], annotations)):
report.log_str('Motif %12s: Kept %8d/%8d reads' % (motif['full_name'], len(annot), all_reads), stdout_too=not config['general']['quiet_mode'])
# setup motif sequences and dir
motif_dir = '%s/%s' % (config['general']['output_dir'], motif['full_name'])
sequence = ','.join([module_dict['seq'] for module_dict in motif['modules']])
motif_tuple = report.seq_into_tuple(sequence)
# convert to annotation pairs, deduplicate and convert back
annotation_pairs = annotation.annotations_to_pairs(annot)
dedup_ap[i], duplicates = annotation.remove_pcr_duplicates(annotation_pairs)
# report them
if not config['general']['quiet_mode']:
if not os.path.exists(motif_dir):
os.makedirs(motif_dir)
report.write_annotation_pairs('%s/annotation_pairs.txt' % motif_dir, dedup_ap[i])
report.write_annotation_pairs('%s/annotation_pairs_duplicates.txt' % motif_dir, duplicates)
# log it
report.log_str('Motif %12s: %8d reads -- %8d pairs (%8d deduplicated + %8d PCR duplicates)' % (
motif['full_name'], len(annot), len(annotation_pairs), len(dedup_ap[i]), len(duplicates)), stdout_too=not config['general']['quiet_mode'])
# go through all motifs
for j, pstf in enumerate(motif['postfilter']):
# setup post filtering - no primers, insufficient quality, ...
postfilter_class = postfilter.Postfilter(pstf, motif_tuple)
# get indices
index_rep, index_rep2 = postfilter_class.get_indexes()
# write index into config back:
config['motifs'][i]['postfilter'][j]['index_rep'] = index_rep
# deduplicated annotations (for each pair we keep only one):
dedup_annot = annotation.pairs_to_annotations_pick(dedup_ap[i], index_rep - 1)
# log it
if not config['general']['quiet_mode']:
report.log_str('Motif %12s: Extracted %8d reads from %8d pairs' % (motif['full_name'], len(dedup_annot), len(dedup_ap[i])), stdout_too=not config['general']['quiet_mode'])
# get filtered stuff
report.log_str("Motif %12s: Running post-filtering for %s required repetitions and %s required bases" % (motif['full_name'], pstf['repetitions'], pstf['bases']),
stdout_too=not config['general']['quiet_mode'])
qual_annot, primer_annot, filt_annot = postfilter_class.get_filtered(dedup_annot)
report.log_str("Motif %12s: Post-filtered %5d (at least one primer %5d), remained %5d" % (motif['full_name'], len(filt_annot), len(primer_annot), len(qual_annot)),
stdout_too=not config['general']['quiet_mode'])
# write it to files
report.log_str("Motif %12s: Generating output files into %s" % (motif['full_name'], motif_dir), stdout_too=not config['general']['quiet_mode'])
report.write_all(qual_annot, primer_annot, filt_annot, dedup_ap[i], all_reads, motif_dir, motif['modules'], index_rep, index_rep2, j, config['general']['quiet_mode'])
# -------- All_Call part of DANTE
# run all_call
for i, motif in enumerate(config['motifs']):
sequence = ','.join([module_dict['seq'] for module_dict in motif['modules']])
motif_tuple = report.seq_into_tuple(sequence)
motif_dir = '%s/%s' % (config['general']['output_dir'], motif['full_name'])
for j, pstf in enumerate(motif['postfilter']):
# setup post filtering - no primers, insufficient quality, ...
postfilter_class = postfilter.Postfilter(pstf, motif_tuple)
# get indices
index_rep, index_rep2 = postfilter_class.get_indexes()
# write index into config back:
config['motifs'][i]['postfilter'][j]['index_rep'] = index_rep
# and strings
rep_seq = motif['modules'][index_rep - 1]['seq']
len_str = len(rep_seq.split('-')[1])
# is it normal or 2-index?
if index_rep2 is not None:
rep_seq = motif['modules'][index_rep2 - 1]['seq']
# len_str2 = len(rep_seq.split('-')[1])
continue # don't do a thing for now
# deduplicated annotations (for each pair we keep only one):
dedup_annot = annotation.pairs_to_annotations_pick(dedup_ap[i], index_rep - 1)
# get filtered stuff
qual_annot, primer_annot, _ = postfilter_class.get_filtered(dedup_annot)
postfilter_bases = postfilter_class.get_filtering_bases()
# load read distribution
read_distribution = report.load_read_distribution('%s/read_distr.npy' % config['general']['output_dir'])
# run inference
inference = all_call.Inference(read_distribution, config['allcall']['param_file'], str_rep=len_str,
minl_primer1=postfilter_bases[index_rep - 2], minl_primer2=postfilter_bases[index_rep], minl_str=postfilter_bases[index_rep - 1])
file_pcolor = '%s/pcolor_%d' % (motif_dir, j + 1)
if config['general']['quiet_mode']:
file_pcolor = None
file_output = '%s/allcall_%d.txt' % (motif_dir, j + 1)
inference.all_call(qual_annot, primer_annot, index_rep - 1, file_pcolor, file_output, motif['full_name'])
# write the report
confidence = report.read_all_call('%s/allcall_%d.txt' % (motif_dir, j + 1))
if confidence is not None:
conf, a1, a2, c1, c2, _, _, _, _ = confidence
if not config['general']['quiet_mode']:
if isinstance(a1, int) and a1 > 0:
report.write_alignment('%s/alignment_%d_a%d.fasta' % (motif_dir, j + 1, a1), qual_annot, index_rep - 1, allele=a1)
if isinstance(a2, int) and a2 != a1 and a2 != 0:
report.write_alignment('%s/alignment_%d_a%d.fasta' % (motif_dir, j + 1, a2), qual_annot, index_rep - 1, allele=a2)
# -------- generation of reports and finalizing
# generate report and output files for whole run
report.log_str('Generating final report')
report.write_report(config['general']['output_dir'], config['motifs'], config['general']['output_dir'], config['general']['quiet_mode'])
# print the time of the end:
end_time = datetime.now()
report.log_str('DANTE Stopping : {finish:%Y-%m-%d %H:%M:%S}'.format(finish=end_time))
report.log_str('Total time of run : {duration}'.format(duration=end_time - start_time))
# stop profiler:
if PROFILE:
prog_prof.disable()
with open('%s/profile-main.out' % config['general']['output_dir'], "w") as f:
pstats.Stats(prog_prof, stream=f).strip_dirs().sort_stats("time").print_stats()
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QuillSourceProcessor.py | Sturtuk/IND_langTras | 78 | 149853 | <reponame>Sturtuk/IND_langTras<filename>QuillSourceProcessor.py
# -*- coding: utf-8 -*-
# @Date : Jul 13, 2016
# @Author : <NAME>, <NAME>
# @Version : 1
import QuillLanguage as qlang
import QuillEngXlit as xlit
import re
import const
import primaryHelper
class QuillSourceProcessor(object):
def __init__(self):
useCCart=True
bengaliDefFile='Bengali_Vrinda.xml'
bengaliKnowledgeInput='bengali'
gujaratiDefFile='Gujarati_Shruti.xml'
gujaratiKnowledgeInput='gujarati'
hindiDefFile='Hindi_Mangal.xml'
hindiKnowledgeInput='hindi'
hindiMobileDefFile='Hindi_Mangal_Mobile.xml'
hindiMobileKnowledgeInput='hindiMobile'
kannadaDefFile='Kannada_Tunga.xml'
kannadaKnowledgeInput='kannada'
kannadaMobileDefFile='Kannada_Tunga_Mobile.xml'
kannadaMobileKnowledgeInput='kannada_list_mobile.txt'
malayalamDefFile='Malayalam_Kartika.xml'
malayalamKnowledgeInput='malayalam'
malayalamMobileDefFile='Malayalam_Kartika_Mobile.xml'
malayalamMobileKnowledgeInput='malayalam_list_mobile.txt'
marathiDefFile='Marathi_Mangal.xml'
marathiKnowledgeInput='marathi'
marathiMobileDefFile='Marathi_Mangal_Mobile.xml'
marathiMobileKnowledgeInput='marathi_list_mobile.txt'
nepaliDefFile='Nepali_Mangal.xml'
nepaliKnowledgeInput='nepali'
punjabiDefFile='Punjabi_Raavi.xml'
punjabiKnowledgeInput='punjabi'
tamilDefFile='Tamil_Latha.xml'
tamilKnowledgeInput='tamil'
tamilMobileDefFile='Tamil_Latha_Mobile.xml'
tamilMobileKnowledgeInput='tamil_list_mobile.txt'
teluguDefFile='Telugu_Raavi.xml'
teluguKnowledgeInput='telugu'
teluguMobileDefFile='Telugu_Raavi_Mobile.xml'
teluguMobileKnowledgeInput='telugu_list_mobile.txt'
self.scriptEngines = {'english':None,
'bengali':qlang.QuillLanguage(bengaliDefFile,bengaliKnowledgeInput,useCCart),
#'gujarati':qlang.QuillLanguage(gujaratiDefFile,gujaratiKnowledgeInput,useCCart),
#'hindi':qlang.QuillLanguage(hindiDefFile,hindiKnowledgeInput,useCCart),
#'hindiMobile':qlang.QuillLanguage(hindiMobileDefFile,hindiMobileKnowledgeInput,useCCart),
#'kannada':qlang.QuillLanguage(kannadaDefFile,kannadaKnowledgeInput,useCCart),
#'kannadaMobile':qlang.QuillLanguage(kannadaMobileDefFile,kannadaMobileKnowledgeInput,useCCart),
#'malayalam':qlang.QuillLanguage(malayalamDefFile,malayalamKnowledgeInput,useCCart),
#'malayalamMobile':qlang.QuillLanguage(malayalamMobileDefFile,malayalamMobileKnowledgeInput,useCCart),
#'marathi':qlang.QuillLanguage(marathiDefFile,marathiKnowledgeInput,useCCart),
#'marathiMobile':qlang.QuillLanguage(marathiMobileDefFile,marathiMobileKnowledgeInput,useCCart),
#'nepali':qlang.QuillLanguage(nepaliDefFile,nepaliKnowledgeInput,useCCart),
#'punjabi':qlang.QuillLanguage(punjabiDefFile,punjabiKnowledgeInput,useCCart),
#'tamil':qlang.QuillLanguage(tamilDefFile,tamilKnowledgeInput,useCCart),
#'tamilMobile':qlang.QuillLanguage(tamilMobileDefFile,tamilMobileKnowledgeInput,useCCart),
#'telugu':qlang.QuillLanguage(teluguDefFile,teluguKnowledgeInput,useCCart),
#'teluguMobile':qlang.QuillLanguage(teluguMobileDefFile,teluguMobileKnowledgeInput,useCCart)
}
self.xlitEngines = {
'kannada': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Kannada_Xlit.xml'),
'bengali': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Bengali_Xlit.xml'),
'gujarati': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Gujarati_Xlit.xml'),
'hindi': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Hindi_Xlit.xml'),
'marathi': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Marathi_Xlit.xml'),
'nepali': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Nepali_Xlit.xml'),
'punjabi': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Punjabi_Xlit.xml'),
'telugu': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Telugu_Xlit.xml'),
'tamil': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Tamil_Xlit.xml'),
'malayalam': xlit.QuillEngXliterator('EnglishPronouncingTrees','IndianPronouncingTrees','Malayalam_Xlit.xml')
}
self.clashMaps = {
'bengali': self.makeClashMap('bengaliClashList.txt'),
'gujarati': self.makeClashMap('gujaratiClash.txt'),
'hindi': self.makeClashMap('hindiClash.txt'),
'kannada': self.makeClashMap('kannadaClash.txt'),
'tamil': self.makeClashMap('tamilClash.txt'),
'marathi': self.makeClashMap('marathiClash.txt'),
'nepali': self.makeClashMap('nepaliClash.txt'),
'punjabi': self.makeClashMap('punjabiClash.txt'),
'telugu': self.makeClashMap('teluguClash.txt'),
'malayalam': self.makeClashMap('malayalamClash.txt')
}
self.modeTypes = ['predictive','xliterate','itrans']
self.inputBuffer =''
self.outputBuffer=''
self.scriptCommandRE = r"(?<!\\)\\(english|bengali|gujarati|hindi|hindiMobile|kannada|kannadaMobile|malayalam|malayalamMobile|marathi|marathiMobile|nepali|punjabi|tamil|tamilMobile|telugu|teluguMobile)" #starts with alpha followed alpha-numerics
self.modeCommandRE = r"(?<!\\)\\(predictive|xliterate|itrans){((?:\\{|[^{}\\]|\\}|\\)*)}"
self.compSC = re.compile(self.scriptCommandRE)
self.compMC = re.compile(self.modeCommandRE)
self.currLanguage = 'english'
self.currMode = 'predictive'
self.engine = None
self.loadEnglishDict('dict.txt')
def loadEnglishDict(self, fname):
words = open(fname).read().split()
self.engWords = dict([(w, None) for w in words])
"Loaded english dictionary from...", fname
def makeClashMap(self, fname):
words = open(fname).read().split()
return dict([(w, None) for w in words])
def processText(self,inString, onlyFirstOptions=False):
self.inputBuffer = inString
self.outputBuffer = ''
index = 0
langText=''
while index < len(self.inputBuffer):
scriptCmdMatch = self.compSC.match(self.inputBuffer,index)
modeCmdMatch = self.compMC.match(self.inputBuffer,index)
if scriptCmdMatch != None:
self.outputBuffer += self.renderText(langText)
langText = ''
self.currLanguage = scriptCmdMatch.group(1)
self.switchLanguage(self.currLanguage)
index = scriptCmdMatch.end()
elif modeCmdMatch != None and self.currLanguage != 'english':
self.outputBuffer += self.renderText(langText)
langText = ''
mode = modeCmdMatch.group(1)
text = modeCmdMatch.group(2)
self.switchMode(mode)
self.outputBuffer += self.renderText(text)
self.switchMode('predictive')
index = modeCmdMatch.end()
else:
langText += self.inputBuffer[index]
index +=1
self.outputBuffer += self.renderText(langText, onlyFirstOptions)
return self.outputBuffer
def switchMode(self,mode):
self.currMode = mode
def renderText(self,langText, onlyFirstOptions=False):
index = 0
insideWord = False
renderedText = ''
currWord = ''
if self.engine == None:
return langText
if self.currMode == 'predictive' and (not onlyFirstOptions):
convertedList = self.engine.convert(langText,"predictive", True)
if len(convertedList) == 1:
onlyTuple = convertedList[0]
if type(onlyTuple[0]) == str:
renderedText = onlyTuple[0]
else:
renderedText = const.optionSeperator.join(onlyTuple[0])
else :
renderedText += '----multiple----\n'
for (ustr, count) in convertedList:
if type(ustr) == str:
#some char like ,.-' etc..
renderedText += str(ustr) + "\n"
else:
renderedText += const.langWordMark + str(const.optionSeperator).join(ustr) + "\n";
elif self.currMode == 'predictive' and onlyFirstOptions:
convertedList = self.engine.convert(langText,"predictive", True)
for (ustr, count) in convertedList:
if type(ustr) == str:
renderedText += str(ustr)
else:
renderedText += ustr[0]
elif self.currMode == 'itrans':
convertedList = self.engine.convert(langText,"primary")
for (uStr,count) in convertedList:
for s in uStr :
renderedText += s
elif self.currMode == 'xliterate':
renderedText = langText
return renderedText
def switchLanguage(self,script):
if self.scriptEngines.has_key(script):
self.engine = self.scriptEngines[script]
else:
self.engine = None
def xlit(self, inString, lang):
if lang in self.xlitEngines:
inString = inString.lower()
engine = self.xlitEngines[lang]
return {'xlitWords': engine.xliterate(inString)}
else:
return {'xlitWords': [inString]}
def processString(self, inString, lang):
def transliterate(word):
if re.search("[a-zA-Z]+", word):
return self.processWord(word, lang)["twords"][0]["options"][0]
return word
words = map(lambda x: x[0], re.findall("(([a-zA-Z]+)|([^a-zA-Z])+)", inString))
return "".join(map(transliterate, words))
def processReverseWord(self, uStr, lang):
if self.scriptEngines.has_key(lang):
engine = self.scriptEngines[lang]
trainTuples = engine.getTrainingTuples(uStr)
literals = [''.join(lit) for (lit,c,flags) in trainTuples]
return literals
else:
return []
def processWord(self, inString, lang):
response = {"inString": inString, "twords": []}
inString = inString.lower()
if self.scriptEngines.has_key(lang):
engine = self.scriptEngines[lang]
else:
# We don't support the language
response["twords"].append({
"word": True,
"options": [inString],
"optmap": {inString: inString.split()}
})
return
convertedList, numOptions = engine.literalToUnicode(inString,
"predictive", True)
options = ["".join(litList) for litList in convertedList]
def dictSort(dlang, arr):
a1 = []
a2 = []
for i in arr:
if primaryHelper.isDictWord(dlang, i):
a1.append(i)
else:
a2.append(i)
return a1 + a2
if (lang=="hindiMobile") or (lang=="hindi"):
options = dictSort("hindi", options)
else :
options = dictSort(lang, options)
def isNotITRANS(word):
for i in word:
if i in ".~^/":
return False
return True
def isNotDigit(word):
for i in word:
if i in "0123456789":
return False
return True
if lang in self.xlitEngines and isNotITRANS(inString) and isNotDigit(inString):
xlitWords = self.xlitEngines[lang].xliterate(inString)
if len(xlitWords) > 0 and len(xlitWords[0]) > 0:
xlitWord = xlitWords[0]
if inString in self.engWords:
if inString in self.clashMaps[lang]:
if xlitWord not in options[:4]:
options = options[:1] + [xlitWord] + options[1:]
else:
if xlitWord in options:
options.remove(xlitWord)
options = [xlitWord] + options
else:
if xlitWord not in options[:4]:
options = options[:3] + [xlitWord] + options[3:]
response["twords"].append({
"word": True,
"options": options,
"optmap": dict(map(lambda x: ("".join(x), x), convertedList))
})
return response
def getCorrections(self, lang, currWord, userInput, pos):
if self.scriptEngines.has_key(lang):
engine = self.scriptEngines[lang]
else:
return ["".join(currWord)]
return engine.getCorrections(currWord, userInput, pos)
def getCorrectionsStr(self, lang, currWord, userInput, pos):
if self.scriptEngines.has_key(lang):
engine = self.scriptEngines[lang]
else:
return currWord
return engine.getCorrectionsStr(currWord, userInput, pos)
if __name__ == '__main__':
inString = "raja-deepthi"
proc = QuillSourceProcessor()
proc.switchLanguage("hindi")
out = proc.processText(inString);
f = open('out.txt','w')
utext= out.encode('utf-8')
f.write(utext)
f.close()
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python/simplepvr/simple_pvr/pvr_initializer.py | olefriis/simplepvr | 5 | 77280 | # -*- coding: <utf-8> -*-
from .config import config
from datetime import timedelta
import os
from time import sleep
from .master_import import HDHomeRun
from .master_import import RecordingManager
from .master_import import Scheduler
from .server import app
_config_recordings_path = config.get('SimplePVR', 'recordings_path')
__hdhomerun = HDHomeRun()
recordings_path = _config_recordings_path
__recording_manager = RecordingManager(recordings_directory=recordings_path)
__scheduler = Scheduler()
def scheduler():
return __scheduler
def recording_manager():
return __recording_manager
def hdhomerun():
return __hdhomerun
def setup():
from .master_import import Channel
__scheduler.setDaemon(True)
__scheduler.start()
if not Channel.query.all():
__hdhomerun.scan_for_channels()
def sleep_forever():
forever = timedelta(days=6000)
sleep(forever) | [
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src/opendr/control/mobile_manipulation/mobileRL/evaluation.py | makistsantekidis/opendr | 3 | 161601 | <reponame>makistsantekidis/opendr<gh_stars>1-10
# Copyright 2020-2021 OpenDR European Project
#
# 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 numpy as np
import rospy
import time
import torch
from matplotlib import pyplot as plt
from opendr.control.mobile_manipulation.mobileRL.envs.env_utils import calc_disc_return
from opendr.control.mobile_manipulation.mobileRL.utils import create_env
def episode_is_success(nr_kin_fails: int, nr_collisions: int, goal_reached: bool) -> bool:
return (nr_kin_fails == 0) and (nr_collisions == 0) and goal_reached
def evaluation_rollout(policy, env, num_eval_episodes: int, global_step: int, verbose: bool = True,
name_prefix: str = ''):
name_prefix = f"{name_prefix + '_' if name_prefix else ''}{env.loggingname}"
episode_rewards, episode_lengths, episode_returns, episode_successes, fails_per_episode, goal_reached, vel_norms = [
[] for _ in range(7)]
max_len = 100_000
with torch.no_grad():
for i in range(num_eval_episodes):
t = time.time()
done = False
obs = env.reset()
rewards, infos, actions = [], [], []
while not done:
action, state = policy.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
rewards.append(reward)
infos.append(info)
episode_length = len(rewards)
actions.append(action)
if episode_length > max_len:
assert episode_length < max_len, f"EPISODE OF {episode_length} STEPS!"
episode_rewards.append(np.sum(rewards))
return_disc = calc_disc_return(rewards, gamma=policy.gamma)
episode_returns.append(return_disc)
episode_lengths.append(episode_length)
goal_reached.append(infos[-1]['ee_done'])
# kin fails are cumulative
fails_per_episode.append(infos[-1]['nr_kin_failures'])
episode_successes.append(
episode_is_success(nr_kin_fails=fails_per_episode[-1], nr_collisions=0, goal_reached=goal_reached[-1]))
unscaled_actions = [env._convert_policy_to_env_actions(a) for a in actions]
vel_norms.append(np.mean([a[0] for a in unscaled_actions]))
if (verbose > 1) or (env.get_world() != "sim"):
rospy.loginfo(
f"{name_prefix}: Eval ep {i}: {(time.time() - t) / 60:.2f} minutes, {episode_length} steps. "
f"Ik failures: {fails_per_episode[-1]}. "
f"{sum(episode_successes)}/{i + 1} full success.")
log_dict = {}
if env._learn_vel_norm != -1:
log_dict['vel_norm'] = np.mean(vel_norms)
ik_fail_thresh = env._ik_fail_thresh
fails_per_episode = np.array(fails_per_episode)
metrics = {'return_undisc': np.mean(episode_rewards),
'return_disc': np.mean(episode_returns),
'epoch_len': np.mean(episode_lengths),
f'ik_b{ik_fail_thresh}': np.mean(fails_per_episode <= ik_fail_thresh),
'ik_b11': np.mean(fails_per_episode < 11),
'ik_zero_fail': np.mean(fails_per_episode == 0),
'ik_fails': np.mean(fails_per_episode),
'goal_reached': np.mean(goal_reached),
'success': np.mean(episode_successes),
'global_step': global_step,
'timesteps_total': global_step}
rospy.loginfo("---------------------------------------")
rospy.loginfo(f"T {global_step}, {name_prefix:} evaluation over {num_eval_episodes:.0f} episodes: "
f"Avg. return (undisc) {metrics[f'return_undisc']:.2f}, (disc) {metrics[f'return_disc']:.2f}, "
f"Avg failures {metrics[f'ik_fails']:.2f}, Avg success: {metrics['success']:.2f}")
rospy.loginfo(
f"IK fails: {metrics[f'ik_b{ik_fail_thresh}']:.2f}p < {ik_fail_thresh}, {metrics[f'ik_b11']:.2f}p < 11, "
f"{metrics[f'ik_zero_fail']:.2f}p < 1")
rospy.loginfo("---------------------------------------")
log_dict.update({(f'{name_prefix}/{k}' if ('step' not in k) else k): v for k, v in metrics.items()})
for k, v in log_dict.items():
policy.logger.record(k, v)
plt.close('all')
return episode_rewards, episode_lengths, metrics, name_prefix
def evaluate_on_task(config, agent, eval_env_config, task: str, world_type: str):
eval_env_config = eval_env_config.copy()
eval_env_config['world_type'] = world_type
env = create_env(eval_env_config,
task=task,
node_handle=eval_env_config["node_handle"],
flatten_obs=True)
rospy.loginfo(f"Evaluating on task {env.taskname()} with {world_type} execution.")
prefix = ''
if world_type != 'sim':
prefix += f'ts{config["time_step"]}_slow{config["slow_down_real_exec"]}'
agent.eval(env, nr_evaluations=config["nr_evaluations"], name_prefix=prefix)
env.clear()
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code/lip_tracking/speech_feature_extraction/speechpy/feature.py | hengxyz/Talking-head-Audio-Visual-Matching | 1 | 157818 | <filename>code/lip_tracking/speech_feature_extraction/speechpy/feature.py<gh_stars>1-10
from __future__ import division
import numpy as np
from . import processing
from scipy.fftpack import dct
import math
from . import functions
def filterbanks(num_filter, fftpoints, sampling_freq, low_freq=None, high_freq=None):
"""Compute the Mel-filterbanks. Each filter will be stored in one rows. The columns correspond to fft bins.
Args:
num_filter (int): the number of filters in the filterbank, default 20.
fftpoints (int): the FFT size. Default is 512.
sampling_freq (float): the samplerate of the signal we are working with. Affects mel spacing.
low_freq (float): lowest band edge of mel filters, default 0 Hz
high_freq (float): highest band edge of mel filters, default samplerate/2
Returns:
array: A numpy array of size num_filter x (fftpoints//2 + 1) which are filterbank
"""
high_freq = high_freq or sampling_freq / 2
low_freq = low_freq or 300
assert high_freq <= sampling_freq / 2, "High frequency cannot be greater than half of the sampling frequency!"
assert low_freq >= 0, "low frequency cannot be less than zero!"
######################################################
########### Computing the Mel filterbank #############
######################################################
# converting the upper and lower frequencies to Mels.
# num_filter + 2 is because for num_filter filterbanks we need num_filter+2 point.
mels = np.linspace(functions.frequency_to_mel(low_freq), functions.frequency_to_mel(high_freq), num_filter + 2)
# we should convert Mels back to Hertz because the start and end-points should be at the desired frequencies.
hertz = functions.mel_to_frequency(mels)
# The frequency resolution required to put filters at the
# exact points calculated above should be extracted.
# So we should round those frequencies to the closest FFT bin.
freq_index = (np.floor((fftpoints + 1) * hertz / sampling_freq)).astype(int)
# Initial definition
filterbank = np.zeros([num_filter, fftpoints])
# The triangular function for each filter
for i in range(0, num_filter):
left = int(freq_index[i])
middle = int(freq_index[i + 1])
right = int(freq_index[i + 2])
z = np.linspace(left, right, num=right - left + 1)
filterbank[i, left:right + 1] = functions.triangle(z, left=left, middle=middle, right=right)
return filterbank
def mfcc(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,num_cepstral =13,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None, dc_elimination=True):
"""Compute MFCC features from an audio signal.
Args:
signal (array): the audio signal from which to compute features. Should be an N x 1 array
sampling_frequency (int): the sampling frequency of the signal we are working with.
frame_length (float): the length of each frame in seconds. Default is 0.020s
frame_stride (float): the step between successive frames in seconds. Default is 0.02s (means no overlap)
num_filters (int): the number of filters in the filterbank, default 40.
fft_length (int): number of FFT points. Default is 512.
low_frequency (float): lowest band edge of mel filters. In Hz, default is 0.
high_frequency (float): highest band edge of mel filters. In Hz, default is samplerate/2
num_cepstral (int): Number of cepstral coefficients.
dc_elimination (bool): hIf the first dc component should be eliminated or not.
Returns:
array: A numpy array of size (num_frames x num_cepstral) containing mfcc features.
"""
feature, energy = mfe(signal, sampling_frequency=sampling_frequency, frame_length=frame_length, frame_stride=frame_stride,
num_filters=num_filters, fft_length=fft_length, low_frequency=low_frequency, high_frequency=high_frequency)
if len(feature) == 0:
return np.empty((0, num_cepstral))
feature = np.log(feature)
feature = dct(feature, type=2, axis=-1, norm='ortho')[:, :num_cepstral]
# replace first cepstral coefficient with log of frame energy for DC elimination.
if dc_elimination:
feature[:, 0] = np.log(energy)
return feature
def mfe(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None):
"""Compute Mel-filterbank energy features from an audio signal.
signal (array): the audio signal from which to compute features. Should be an N x 1 array
sampling_frequency (int): the sampling frequency of the signal we are working with.
frame_length (float): the length of each frame in seconds. Default is 0.020s
frame_stride (float): the step between successive frames in seconds. Default is 0.02s (means no overlap)
num_filters (int): the number of filters in the filterbank, default 40.
fft_length (int): number of FFT points. Default is 512.
low_frequency (float): lowest band edge of mel filters. In Hz, default is 0.
high_frequency (float): highest band edge of mel filters. In Hz, default is samplerate/2
Returns:
array: features - the energy of fiterbank: num_frames x num_filters frame_energies.
The energy of each frame: num_frames x 1
"""
# Convert to float
signal = signal.astype(float)
# Stack frames
frames = processing.stack_frames(signal, sampling_frequency=sampling_frequency, frame_length=frame_length,
frame_stride=frame_stride,
filter=lambda x: np.ones((x,)),
zero_padding=False)
# getting the high frequency
high_frequency = high_frequency or sampling_frequency / 2
# calculation of the power sprectum
power_spectrum = processing.power_spectrum(frames, fft_length)
number_fft_coefficients = power_spectrum.shape[1]
frame_energies = np.sum(power_spectrum, 1) # this stores the total energy in each frame
# Handling zero enegies.
frame_energies = functions.zero_handling(frame_energies)
# Extracting the filterbank
filter_banks = filterbanks(num_filters, number_fft_coefficients, sampling_frequency, low_frequency, high_frequency)
# Filterbank energies
features = np.dot(power_spectrum, filter_banks.T)
features = functions.zero_handling(features)
return features, frame_energies
def lmfe(signal, sampling_frequency, frame_length=0.020, frame_stride=0.01,
num_filters=40, fft_length=512, low_frequency=0, high_frequency=None):
"""Compute log Mel-filterbank energy features from an audio signal.
Args:
signal (array): the audio signal from which to compute features. Should be an N x 1 array
sampling_frequency (int): the sampling frequency of the signal we are working with.
frame_length (float): the length of each frame in seconds. Default is 0.020s
frame_stride (float): the step between successive frames in seconds. Default is 0.02s (means no overlap)
num_filters (int): the number of filters in the filterbank, default 40.
fft_length (int): number of FFT points. Default is 512.
low_frequency (float): lowest band edge of mel filters. In Hz, default is 0.
high_frequency (float): highest band edge of mel filters. In Hz, default is samplerate/2
Returns:
array: Features - The energy of fiterbank: num_frames x num_filters
frame_log_energies. The log energy of each frame: num_frames x 1
"""
feature, frame_energies = mfe(signal, sampling_frequency=sampling_frequency, frame_length=frame_length,
frame_stride=frame_stride,
num_filters=num_filters, fft_length=fft_length, low_frequency=low_frequency,
high_frequency=high_frequency)
feature = np.log(feature)
return feature
def extract_derivative_feature(feature):
"""
This function extracts temporal derivative features which are first and second derivatives.
Args:
feature (array): The feature vector which its size is: N x M
Return:
array: The feature cube vector which contains the static, first and second derivative features of size: N x M x 3
"""
first_derivative_feature = processing.derivative_extraction(feature, DeltaWindows=2)
second_derivative_feature = processing.derivative_extraction(first_derivative_feature, DeltaWindows=2)
# Creating the future cube for each file
feature_cube = np.concatenate(
(feature[:, :, None], first_derivative_feature[:, :, None],
second_derivative_feature[:, :, None]),
axis=2)
return feature_cube
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democracy/signals.py | shawnadelic/django-democracy | 0 | 94905 | from django.contrib.contenttypes.models import ContentType
from django.db.models.signals import post_save
from django.dispatch import receiver
from .models import (PollBase, PollChoiceBase, PollChoiceModelMixin,
PollModelMixin)
@receiver(post_save)
def create_poll_choice(sender, instance, created, **kwargs):
if not isinstance(instance, PollChoiceModelMixin) and created:
return
if created:
choice_content_type = ContentType.objects.get_for_model(sender)
PollChoiceBase.objects.create(
poll_base=instance.poll.poll_base,
content_type=choice_content_type, object_id=instance.id
)
@receiver(post_save)
def create_poll(sender, instance, created, **kwargs):
if not isinstance(instance, PollModelMixin):
return
if created:
poll_content_type = ContentType.objects.get_for_model(sender)
PollBase.objects.create(
content_type=poll_content_type, object_id=instance.id
)
else:
pass
# Edit a poll, maybe sync choices?
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2
] |
examples/class_a.py | maxschommer/pcbdl | 117 | 111737 | #!/usr/bin/env python3
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://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.
"""
Simple Class A amplifier example.
https://www.electronics-tutorials.ws/amplifier/amp_5.html
"""
from pcbdl import *
ac_coupling_value = "1000u"
vcc, gnd = Net("vcc"), Net("gnd")
q = BJT("2n3904")
C = C_POL
q.BASE << (
C(ac_coupling_value, to=Net("vin")),
R("1k", to=vcc),
R("1k", to=gnd),
)
q.COLLECTOR << (
C(ac_coupling_value, to=Net("vout")),
R("100", to=vcc),
)
q.EMITTER << (
R("100", "Rc", to=gnd),
C("1u", "C10", to=gnd),
)
| [
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] |
gaphor/C4Model/toolbox.py | mrmonkington/gaphor | 42 | 134550 | """The action definition for the C4 model toolbox."""
from gaphor.C4Model import c4model, diagramitems
from gaphor.core import gettext
from gaphor.diagram.diagramtoolbox import (
ToolboxDefinition,
ToolDef,
ToolSection,
default_namespace,
general_tools,
namespace_config,
)
from gaphor.diagram.diagramtools import new_item_factory
from gaphor.UML import diagramitems as uml_items
from gaphor.UML.actions.actionstoolbox import actions
from gaphor.UML.classes.classestoolbox import classes
from gaphor.UML.interactions.interactionstoolbox import interactions
from gaphor.UML.states.statestoolbox import states
def software_system_config(new_item):
default_namespace(new_item)
subject = new_item.subject
subject.type = "Software System"
subject.name = "NewSoftwareSystem"
def container_config(new_item):
default_namespace(new_item)
subject = new_item.subject
subject.type = "Container"
subject.name = "NewContainer"
def container_database_config(new_item):
default_namespace(new_item)
subject = new_item.subject
subject.type = "Container"
subject.technology = "Database"
subject.name = "NewDatabase"
def component_config(new_item):
default_namespace(new_item)
subject = new_item.subject
subject.type = "Component"
subject.name = "NewComponent"
c4 = ToolSection(
gettext("C4 Model"),
(
ToolDef(
"c4-person",
gettext("Person"),
"gaphor-c4-person-symbolic",
"P",
new_item_factory(
diagramitems.C4PersonItem,
c4model.C4Person,
config_func=namespace_config,
),
),
ToolDef(
"c4-software-system",
gettext("Software System"),
"gaphor-c4-software-system-symbolic",
"<Shift>S",
new_item_factory(
diagramitems.C4ContainerItem,
c4model.C4Container,
config_func=software_system_config,
),
),
ToolDef(
"c4-container",
gettext("Container"),
"gaphor-c4-container-symbolic",
"o",
new_item_factory(
diagramitems.C4ContainerItem,
c4model.C4Container,
config_func=container_config,
),
),
ToolDef(
"c4-container-database",
gettext("Container: Database"),
"gaphor-c4-database-symbolic",
"<Shift>B",
new_item_factory(
diagramitems.C4DatabaseItem,
c4model.C4Database,
config_func=container_database_config,
),
),
ToolDef(
"c4-component",
gettext("Component"),
"gaphor-c4-component-symbolic",
"<Shift>X",
new_item_factory(
diagramitems.C4ContainerItem,
c4model.C4Container,
config_func=component_config,
),
),
ToolDef(
"c4-dependency",
gettext("Dependency"),
"gaphor-dependency-symbolic",
"d",
new_item_factory(uml_items.DependencyItem),
),
),
)
c4model_toolbox_actions: ToolboxDefinition = (
general_tools,
c4,
classes,
actions,
interactions,
states,
)
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22060,
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29892,
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13,
2
] |
Sixt_Day/Seventh_Day_Templet1.py | vijayingale/Adv_Python_Trainig | 0 | 170333 | from jinja2 import Template
class Persone:
def __init__(self, name ,age):
self.name = name
self.age = age
def getAge(self):
return self.age
def getName(self):
return self.name
person = Persone('peter',23)
tn = Template("my name is {{ per.getName()}} and I am {{ per.getAge() }} ")
msg = tn.render(per=person)
print(msg) | [
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downloads/managers.py | akondasif/Clone-test-repo | 0 | 176135 | <filename>downloads/managers.py
from django.db.models import Manager
from django.db.models.query import QuerySet
class ReleaseQuerySet(QuerySet):
def published(self):
return self.filter(is_published=True)
def draft(self):
return self.filter(is_published=False)
def downloads(self):
""" For the main downloads landing page """
return self.select_related('release_page').filter(
is_published=True,
show_on_download_page=True,
).order_by('-release_date')
def python2(self):
return self.filter(version=2, is_published=True)
def python3(self):
return self.filter(version=3, is_published=True)
def latest_python2(self):
return self.python2().filter(is_latest=True)
def latest_python3(self):
return self.python3().filter(is_latest=True)
def pre_release(self):
return self.filter(pre_release=True)
def released(self):
return self.filter(is_published=True, pre_release=False)
class ReleaseManager(Manager):
def get_queryset(self):
return ReleaseQuerySet(self.model, using=self._db)
def published(self):
return self.get_queryset().published()
def draft(self):
return self.get_queryset().draft()
def downloads(self):
""" For the main downloads landing page """
return self.get_queryset().downloads()
def python2(self):
return self.get_queryset().python2()
def python3(self):
return self.get_queryset().python3()
def latest_python2(self):
qs = self.get_queryset().latest_python2()
if qs:
return qs[0]
else:
return None
def latest_python3(self):
qs = self.get_queryset().latest_python3()
if qs:
return qs[0]
else:
return None
def pre_release(self):
return self.get_queryset().pre_release()
def released(self):
return self.get_queryset().released()
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mojoco trivial/mujocoSim/UR5/simple_example/Mujoco_py_example.py | garlicbutter/Jonathan-Tom | 2 | 8335 | <filename>mojoco trivial/mujocoSim/UR5/simple_example/Mujoco_py_example.py
import numpy as np
import mujoco_py as mj
from mujoco_py_renderer import SimulationError, XMLError, MujocoPyRenderer
from mujoco_py import (MjSim, load_model_from_xml,functions,
load_model_from_path, MjSimState,
ignore_mujoco_warnings,
load_model_from_mjb)
from matplotlib import pyplot as plt
import time
xml = """
<mujoco model="example">
<compiler coordinate="global"/>
<default>
<geom rgba=".8 .6 .4 1"/>
</default>
<asset>
<texture type="skybox" builtin="gradient" rgb1="1 1 1" rgb2=".6 .8 1"
width="256" height="256"/>
</asset>
<worldbody>
<light pos="0 1 1" dir="0 -1 -1" diffuse="1 1 1"/>
<geom name="floor" pos="0 0 0" rgba="0.8 0.9 0.8 1" size="10 10 10" type="plane"/>
<body>
<site name="world" size="0.1" pos="0 0 0" />
<geom name="first_pole" type="capsule" fromto="0 0 0 0 0 0.5" size="0.04"/>
<joint name='a' type="hinge" pos="0 0 0" axis="0 0 1" />
<body name="second_pole">
<inertial pos="0 0 0" mass="0.00000001" diaginertia="1e-008 1e-008 1e-008" />
<geom type="capsule" fromto="0 0 0.5 0.5 0 0.5" size="0.04" name="second_pole"/>
<joint name='b' type="hinge" pos="0 0 0.5" axis="0 1 0"/>
<body name='third_pole'>
<inertial pos="0 0 0" mass="0.00000001" diaginertia="1e-008 1e-008 1e-008" />
<geom type="capsule" fromto="0.5 0 0.5 1 0 0.5" size="0.04" name="third_pole"/>
<joint name='c' type="hinge" pos="0.5 0 0.5" axis="0 1 0"/>
<site name="target" size="0.1" pos="1 0 0.5" />
<body name="mass">
<inertial pos="1 0 0.5" mass="1e-2" diaginertia="1e-008 1e-008 1e-008" />
<geom type="sphere" pos="1 0 0.5" size="0.2" name="mass"/>
</body>
</body>
</body>
</body>
</worldbody>
<actuator>
<motor joint="a"/>
<motor joint="b"/>
<motor joint="c"/>
</actuator>
</mujoco>
"""
model = load_model_from_xml(xml)
sim = MjSim(model)
viewer = MujocoPyRenderer(sim)
sim.reset()
# After reset jacobians are all zeros
sim.forward()
target_jacp = np.zeros(3 * sim.model.nv)
target_jacr= np.zeros(3 * sim.model.nv)
F=np.array([0,0,-9.81*1e-2,0,0,0]).T
#np.testing.assert_allclose(target_jacp, np.zeros(3 * sim.model.nv))
# After first forward, jacobians are real
#sim.forward()
K_diag=2000
C_diag=100
A_diag=1e-3
K=np.identity(3)*K_diag
C=np.identity(3)*C_diag
A=np.identity(3)*A_diag
#K_diag=0.3
#C_diag=0.05
for i in range(3):
K[i, i]=K_diag
C[i,i]=C_diag
A[i, i] = A_diag
x_intial=sim.data.site_xpos[1]
print(x_intial)
x_desired=np.array([0,1,0.3])
v_intial=sim.data.site_xvelp[1]
v_desired=np.array([0,0,0])
a_desired=np.array([0,0,0])
a_intial=np.array([0,0,0])
dt=sim.model.opt.timestep
#sim.data.get_site_jacp('target', jacp=target_jacp)
# Should be unchanged after steps (zero action)
graph=[]
for _ in range(100000):
F[:3]=np.dot(K,x_desired-x_intial)+np.dot(C,v_desired-v_intial)+np.dot(A,a_desired-a_intial)
H = np.zeros(sim.model.nv* sim.model.nv)
functions.mj_fullM(sim.model, H, sim.data.qM)
sim.data.get_site_jacp('target', jacp=target_jacp)
sim.data.get_site_jacr('target', jacr=target_jacr)
J_L = target_jacp.reshape((3, sim.model.nv))
J_A = target_jacr.reshape((3, sim.model.nv))
J = np.concatenate((J_L, J_A), axis=0)
H_L =np.dot(np.linalg.pinv(J_L.T),np.dot(H.reshape(sim.model.nv, sim.model.nv), np.linalg.pinv(J_L)))
H_all=np.dot(np.linalg.pinv(J.T),np.dot(H.reshape(sim.model.nv, sim.model.nv), np.linalg.pinv(J)))
#F_a=np.dot(A,0.3-sim.data.qacc)
#action = np.dot(J_L.T, np.dot(H_L, F[:3]))+sim.data.qfrc_bias
action = sim.data.qfrc_bias+np.dot(H.reshape(3,3),np.dot(J_L.T,F[:3]))
#print(action)
#action = np.dot(J.T, F)
sim.data.ctrl[:] = action
sim.step()
sim.forward()
#print(np.max(action))
#print(sim.data.qacc)
viewer.render()
x_intial = sim.data.site_xpos[1]
a_intial=(v_intial-sim.data.site_xvelp[1])/dt
print(a_intial)
v_intial = sim.data.site_xvelp[1]
normal=np.linalg.norm(x_intial-x_desired)
#print(normal)
if normal<0.1:
print("in")
if x_desired[0]==0:
x_desired = np.array([-1, 0, 0.5])
elif x_desired[0]==1:
x_desired = np.array([0, 1, 0.3])
elif x_desired[0] == -1:
x_desired = np.array([1, 0, 0.5])
graph.append(np.abs(x_intial-x_desired))
# sim.forward()
print("the desired is {} and the intial is{}".format(x_desired,x_intial))
plt.plot(graph)
plt.show() | [
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1275,
448,
29896,
29901,
13,
9651,
921,
29918,
2783,
2859,
353,
7442,
29889,
2378,
4197,
29896,
29892,
29871,
29900,
29892,
29871,
29900,
29889,
29945,
2314,
13,
13,
13,
1678,
3983,
29889,
4397,
29898,
9302,
29889,
6897,
29898,
29916,
29918,
524,
616,
29899,
29916,
29918,
2783,
2859,
876,
13,
396,
259,
1027,
29889,
11333,
580,
13,
13,
13,
2158,
703,
1552,
7429,
338,
6571,
322,
278,
938,
616,
338,
8875,
1642,
4830,
29898,
29916,
29918,
2783,
2859,
29892,
29916,
29918,
524,
616,
876,
13,
572,
29873,
29889,
5317,
29898,
4262,
29897,
13,
572,
29873,
29889,
4294,
580,
2
] |
Subsets and Splits