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pymunk-pymunk-4.0.0/examples/pyglet_util_demo.py | sneharavi12/DeepLearningFinals | 9 | 172493 | """Showcase what the output of pymunk.pyglet_util draw methods will look like.
See pygame_util_demo.py for a comparison to pygame.
"""
__version__ = "$Id:$"
__docformat__ = "reStructuredText"
import sys
import pyglet
import pymunk
from pymunk.vec2d import Vec2d
import pymunk.pyglet_util
from shapes_for_draw_demos import add_objects
window = pyglet.window.Window(600, 600)
space = pymunk.Space()
add_objects(space)
textbatch = pyglet.graphics.Batch()
pyglet.text.Label('Demo example of shapes drawn by pyglet_util.draw()',
x=5, y=5, batch=textbatch, color=(200,200,200,200))
pyglet.text.Label('Static shapes', x=50, y=570, batch=textbatch)
pyglet.text.Label('Dynamic shapes', x=250, y=570, batch=textbatch)
pyglet.text.Label('Constraints', x=450, y=570, batch=textbatch)
pyglet.text.Label('Other', x=450, y=290, batch=textbatch)
batch = pyglet.graphics.Batch()
@window.event
def on_draw():
pyglet.gl.glClearColor(0,0,0,1)
window.clear()
pymunk.pyglet_util.draw(space)
textbatch.draw()
@window.event
def on_key_press(symbol, modifiers):
if symbol == pyglet.window.key.P:
pyglet.image.get_buffer_manager().get_color_buffer().save("pyglet_util_demo.png")
pyglet.app.run() | [
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researchutils/chainer/training/__init__.py | keio-ytlab/researchutils | 1 | 101885 | from researchutils.chainer.training import extensions | [
1,
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29889,
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pydocteur/__init__.py | awecx/PyDocTeur | 4 | 185003 | <filename>pydocteur/__init__.py
from pydocteur.app import application
__all__ = ["application"]
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examples/bintree.py | jdrprod/Pym-s | 3 | 32847 | class binary_tree:
def __init__(self):
pass
class Node(binary_tree):
__field_0 : int
__field_1 : binary_tree
__field_2 : binary_tree
def __init__(self, arg_0 : int , arg_1 : binary_tree , arg_2 : binary_tree ):
self.__field_0 = arg_0
self.__field_1 = arg_1
self.__field_2 = arg_2
def __iter__(self):
yield self.__field_0
yield self.__field_1
yield self.__field_2
class Leaf(binary_tree):
__field_0 : int
def __init__(self, arg_0 : int ):
self.__field_0 = arg_0
def __iter__(self):
yield self.__field_0
| [
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updatesproducer/tests/mock_updates_provider.py | AppleteeYT/Iris | 0 | 159303 | from datetime import datetime
from updatesproducer.iupdates_provider import IUpdatesProvider
from updatesproducer.updateapi.update import Update
class MockUpdatesProvider(IUpdatesProvider):
def get_updates(self, user_id: str):
return [
Update(
content='Mock updateapi',
author_id='Mock author',
creation_date=datetime.now(),
url='mockurl://updateapi.com',
media=[],
repost=False,
should_redownload_video=False)
] | [
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PyOffice/OpenFile.py | AMoazeni/PyOffice | 0 | 152205 | <filename>PyOffice/OpenFile.py
from time import time as t
from urllib.request import urlopen as get
def OpenFile(*args, **kwargs):
''' OpenFile(str 'file_path') requires an 'str' PATH or URL input,
returns a Python Dictionary with the file data.
'''
# Initialize
func_name = 'OpenFile'
print(func_name, '[START]')
if args: print(func_name, 'Path:', args[0])
time_start = t()
error = 0
error_msg = ''
path = ''
data = {}
data_len = 0
# OpenFile Function Block
try:
path = str(args[0])
data['Data'] = []
# PATH is a URL.
if 'http' in path.lower():
page = get(path)
page_code = page.getcode()
data['ResponseCode'] = page_code
page_headers = page.getheaders()
data['Headers'] = page_headers
for line in page.readlines():
data['Data'].append(line.decode('utf-8'))
data_len = data_len + len(str(line))
# PATH is a local file.
else:
with open(path, 'r', encoding="utf-8") as file:
for line in file:
data['Data'].append(line)
data_len = data_len + len(line)
data['TimeStart'] = float(round(time_start, 4))
data['TimeEx'] = float(round(t() - time_start, 4))
data['Path'] = path
data['Name'] = path.split('/')[-1]
data['Extension'] = path.split('.')[-1]
data['Lines'] = len(data['Data'])
data['DataLength'] = data_len
return data
# Error Handler
except BaseException as e:
error = 1
error_msg = str(e)
# Clean up block
finally:
if error:
print(func_name, '[ERROR]:', error_msg)
else:
print(func_name, '[OK] {0:.3f}s\n'.format(float(t() - time_start)))
pass
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python-checker/Examples/FULL/cob_command_gui/src/command_gui_buttons/__init__.py | andersfischernielsen/ROS-dependency-checker | 0 | 171309 | from command_gui_buttons import *
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convertJSON.py | Grupo-3-PEI/website | 0 | 191660 | <filename>convertJSON.py
import json
def convert(jsonfile):
return json.dumps({ "type": "FeatureCollection",
"features": [
{"type": "Feature",
"geometry": { "type": "Point",
"coordinates": [ feature['lon'],
feature['lat']]},
"properties": { key: value
for key, value in feature.jsonfile()
if key not in ('lat', 'lon') }
}
for feature in json.loads(jsonfile)
]
}) | [
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FreeBSD/bsd_netstats_poller.py | failedrequest/telegraf-plugins | 0 | 19639 | <filename>FreeBSD/bsd_netstats_poller.py
#!/usr/bin/env python3
# 3/21/2021
# Updated for python3
# A Simple sysctl to telegraf plugin for freebsd's netstats ip info
from freebsd_sysctl import Sysctl as sysctl
import subprocess as sp
import re
import json
import sys
import pprint as pp
hostname = sysctl("kern.hostname").value
netstat_data = {}
points_netstat = {}
netstat_output = sp.check_output(["netstat", "-s", "-p", "ip", "--libxo", "json", "/dev/null"],universal_newlines=True)
netstat_data = json.loads(netstat_output)
for x in netstat_data["statistics"]:
for k,v in netstat_data["statistics"][x].items():
points_netstat[k] = v
def points_to_influx(points):
field_tags= ",".join(["{k}={v}".format(k=str(x[0]), v=x[1]) for x in list(points_netstat.items())])
print(("bsd_netstat,type=netstat {}").format(field_tags))
points_to_influx(points_netstat)
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] |
utils/convert.py | apozanco/icaps2022.github.io | 0 | 126263 | <filename>utils/convert.py
import sys
def convert(in_file, out_file):
with open(in_file, 'r') as f:
lines = [l.strip() for l in f.readlines()]
new_lines = []
# First, combine all lines that are not empty
for line in lines:
if (line == '') or (line[0] in ['-', '*']):
new_lines.append(line)
else:
new_lines[-1] += ' ' + line
# Convert the rest
for i in range(len(new_lines)):
line = new_lines[i].replace('\t', ' ').replace('\r', '').replace('\n', '').strip()
if line == '':
new_lines[i] = ''
elif line[:2] == '# ':
new_lines[i] = '<h3>' + line[2:] + '</h3>'
elif line[0] in ['-', '*']:
new_lines[i] = '<li>' + line[1:] + '</li>'
elif line[:3] in [f'{i}. ' for i in range(1,10)]:
new_lines[i] = '<li>' + line[3:] + '</li>'
else:
new_lines[i] = '<p>' + line + '</p>'
with open(out_file, 'w') as f:
for line in new_lines:
f.write(line + '\n')
if __name__ == '__main__':
if len(sys.argv) != 3:
print('Usage: convert.py <input_file> <output_file>')
sys.exit(1)
input_file = sys.argv[1]
output_file = sys.argv[2]
convert(input_file, output_file)
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ggcontable/production.py | jmjacquet/IronWeb | 0 | 53515 | <reponame>jmjacquet/IronWeb
# Django settings for sistema_bomberos project.
from .settings import *
from decouple import config
DEBUG = False
STATIC_URL = '/staticfiles/'
DB_USER = config('DB_USER')
DB_PASS = config('DB_PASS')
DB_HOST = config('DB_HOST')
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.
'NAME': ENTIDAD_DB, # Or path to database file if using sqlite3.
'USER': DB_USER,
'PASSWORD': <PASSWORD>, # Not used with sqlite3.
'HOST': DB_HOST, # Set to empty string for localhost. Not used with sqlite3.
'PORT': '',
},
}
TEMPLATE_DEBUG = DEBUG
STATIC_ROOT = os.path.join(SITE_ROOT, 'static')
STATICFILES_DIRS = (
os.path.join(SITE_ROOT, "staticfiles"),
# os.path.join(SITE_ROOT, "dist"),
) | [
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29918,
21289,
29892,
376,
5721,
4968,
259,
13,
29897,
2
] |
gpenkf/experiments/data_provider.py | danilkuzin/GP-EnKF | 12 | 156235 | <reponame>danilkuzin/GP-EnKF
class DataProvider(object):
pass | [
1,
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Codility/PassingCars.py | batumoglu/Python_Algorithms | 0 | 121578 | # you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
def prefix(A):
pref = [0] * (len(A)+1)
for i in range(1, len(A)+1):
pref[i] = pref[i-1] + A[i-1]
return pref
def countTotal(P, left, right):
return P[right] - P[left]
def solution(A):
pref = prefix(A)
n = len(A)
pairs = 0
for i in range(len(A)):
if A[i] == 0:
pairs += countTotal(pref, i, n)
if pairs > 1000000000:
return -1
return pairs
if __name__ == '__main__':
#print(solution([0,0,0,0,1,1,1,0,1,0,1,0,1,0]))
print(solution([0,1,0,1,1])) | [
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] |
parakeet/models/fastspeech/length_regulator.py | xbsdsongnan/Parakeet | 1 | 133923 | <gh_stars>1-10
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import math
import parakeet.models.fastspeech.utils
import paddle.fluid.dygraph as dg
import paddle.fluid.layers as layers
import paddle.fluid as fluid
from parakeet.modules.customized import Conv1D
class LengthRegulator(dg.Layer):
def __init__(self, input_size, out_channels, filter_size, dropout=0.1):
"""Length Regulator block in FastSpeech.
Args:
input_size (int): the channel number of input.
out_channels (int): the output channel number.
filter_size (int): the filter size of duration predictor.
dropout (float, optional): dropout probability. Defaults to 0.1.
"""
super(LengthRegulator, self).__init__()
self.duration_predictor = DurationPredictor(
input_size=input_size,
out_channels=out_channels,
filter_size=filter_size,
dropout=dropout)
def LR(self, x, duration_predictor_output):
output = []
batch_size = x.shape[0]
for i in range(batch_size):
output.append(
self.expand(x[i:i + 1], duration_predictor_output[i:i + 1]))
output = self.pad(output)
return output
def pad(self, input_ele):
max_len = max([input_ele[i].shape[0] for i in range(len(input_ele))])
out_list = []
for i in range(len(input_ele)):
pad_len = max_len - input_ele[i].shape[0]
one_batch_padded = layers.pad(input_ele[i], [0, pad_len, 0, 0],
pad_value=0.0)
out_list.append(one_batch_padded)
out_padded = layers.stack(out_list)
return out_padded
def expand(self, batch, predicted):
out = []
time_steps = batch.shape[1]
fertilities = predicted.numpy()
batch = layers.squeeze(batch, [0])
for i in range(time_steps):
if fertilities[0, i] == 0:
continue
out.append(
layers.expand(batch[i:i + 1, :], [int(fertilities[0, i]), 1]))
out = layers.concat(out, axis=0)
return out
def forward(self, x, alpha=1.0, target=None):
"""
Compute length of mel from encoder output use TransformerTTS attention
Args:
x (Variable): shape(B, T, C), dtype float32, the encoder output.
alpha (float32, optional): the hyperparameter to determine the length of
the expanded sequence mel, thereby controlling the voice speed. Defaults to 1.0.
target (Variable, optional): shape(B, T_text), dtype int64, the duration of phoneme compute from pretrained transformerTTS.
Defaults to None.
Returns:
output (Variable): shape(B, T, C), the output after exppand.
duration_predictor_output (Variable): shape(B, T, C), the output of duration predictor.
"""
duration_predictor_output = self.duration_predictor(x)
if fluid.framework._dygraph_tracer()._train_mode:
output = self.LR(x, target)
return output, duration_predictor_output
else:
duration_predictor_output = duration_predictor_output * alpha
duration_predictor_output = layers.ceil(duration_predictor_output)
output = self.LR(x, duration_predictor_output)
mel_pos = dg.to_variable(np.arange(1, output.shape[1] + 1)).astype(
np.int64)
mel_pos = layers.unsqueeze(mel_pos, [0])
return output, mel_pos
class DurationPredictor(dg.Layer):
def __init__(self, input_size, out_channels, filter_size, dropout=0.1):
"""Duration Predictor block in FastSpeech.
Args:
input_size (int): the channel number of input.
out_channels (int): the output channel number.
filter_size (int): the filter size.
dropout (float, optional): dropout probability. Defaults to 0.1.
"""
super(DurationPredictor, self).__init__()
self.input_size = input_size
self.out_channels = out_channels
self.filter_size = filter_size
self.dropout = dropout
k = math.sqrt(1.0 / self.input_size)
self.conv1 = Conv1D(
num_channels=self.input_size,
num_filters=self.out_channels,
filter_size=self.filter_size,
padding=1,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.XavierInitializer()),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-k, high=k)))
#data_format='NTC')
k = math.sqrt(1.0 / self.out_channels)
self.conv2 = Conv1D(
num_channels=self.out_channels,
num_filters=self.out_channels,
filter_size=self.filter_size,
padding=1,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.XavierInitializer()),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-k, high=k)))
#data_format='NTC')
self.layer_norm1 = dg.LayerNorm(self.out_channels)
self.layer_norm2 = dg.LayerNorm(self.out_channels)
self.weight = fluid.ParamAttr(
initializer=fluid.initializer.XavierInitializer())
k = math.sqrt(1.0 / self.out_channels)
self.bias = fluid.ParamAttr(initializer=fluid.initializer.Uniform(
low=-k, high=k))
self.linear = dg.Linear(
self.out_channels, 1, param_attr=self.weight, bias_attr=self.bias)
def forward(self, encoder_output):
"""
Predict the duration of each character.
Args:
encoder_output (Variable): shape(B, T, C), dtype float32, the encoder output.
Returns:
out (Variable): shape(B, T, C), the output of duration predictor.
"""
# encoder_output.shape(N, T, C)
out = layers.transpose(encoder_output, [0, 2, 1])
out = self.conv1(out)
out = layers.transpose(out, [0, 2, 1])
out = layers.dropout(
layers.relu(self.layer_norm1(out)),
self.dropout,
dropout_implementation='upscale_in_train')
out = layers.transpose(out, [0, 2, 1])
out = self.conv2(out)
out = layers.transpose(out, [0, 2, 1])
out = layers.dropout(
layers.relu(self.layer_norm2(out)),
self.dropout,
dropout_implementation='upscale_in_train')
out = layers.relu(self.linear(out))
out = layers.squeeze(out, axes=[-1])
return out
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keras_textclassification/keras_layers/attention_dot.py | wyq0706/Keras-TextClassification | 1 | 139680 | <filename>keras_textclassification/keras_layers/attention_dot.py
# !/usr/bin/python
# -*- coding: utf-8 -*-
# @time : 2020/3/31 19:10
# @author : Mo
# @function:
from keras.regularizers import L1L2, Regularizer
# from keras.engine.topology import Layer
from keras.layers import Layer
from keras import backend as K
import tensorflow as tf
class AttentionDot(Layer):
def __init__(self, **kwargs):
super().__init__(** kwargs)
def build(self, input_shape):
self.W = self.add_weight(name='Attention_Dot_Weight',
shape=(input_shape[1], input_shape[1]),
regularizer=L1L2(0.0000032),
initializer='uniform',
trainable=True)
self.b = self.add_weight(name='Attention_Dot_Bias',
regularizer=L1L2(0.00032),
shape=(input_shape[1],),
initializer='uniform',
trainable=True)
super().build(input_shape)
def call(self, input):
x_transpose = K.permute_dimensions(input, (0, 2, 1))
x_tanh_softmax = K.softmax(K.tanh(K.dot(x_transpose, self.W) + self.b))
outputs = K.permute_dimensions(x_tanh_softmax * x_transpose, (0, 2, 1))
# outputs = K.sum(outputs, axis=1)
return outputs
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[1], input_shape[2]
class CVG_Layer(Layer):
def __init__(self, embed_size, filter, label, **kwargs):
self.embed_size = embed_size
self.filter = filter
self.label = label
super().__init__(** kwargs)
def build(self, input_shape):
self._filter = self.add_weight(name=f'filter_{self.filter}',
shape=(self.filter, self.label, 1, 1),
regularizer=L1L2(0.00032),
initializer='uniform',
trainable=True)
self.class_w = self.add_weight(name='class_w',
shape=(self.label, self.embed_size),
regularizer=L1L2(0.0000032),
initializer='uniform',
trainable=True)
self.b = self.add_weight(name='bias',
shape=(1,),
regularizer=L1L2(0.00032),
initializer='uniform',
trainable=True)
super().build(input_shape)
def call(self, input):
# C * V / G
# l2_normalize of x, y
input_norm = tf.nn.l2_normalize(input) # b * s * e
class_w_relu = tf.nn.relu(self.class_w) # c * e
label_embedding_reshape = tf.transpose(class_w_relu, [1, 0]) # e * c
label_embedding_reshape_norm = tf.nn.l2_normalize(label_embedding_reshape) # e * c
# C * V
G = tf.contrib.keras.backend.dot(input_norm, label_embedding_reshape_norm) # b * s * c
G_transpose = tf.transpose(G, [0, 2, 1]) # b * c * s
G_expand = tf.expand_dims(G_transpose, axis=-1) # b * c * s * 1
# text_cnn
conv = tf.nn.conv2d(name='conv', input=G_expand, filter=self._filter,
strides=[1, 1, 1, 1], padding='SAME')
pool = tf.nn.relu(name='relu', features=tf.nn.bias_add(conv, self.b)) # b * c * s * 1
# pool = tf.nn.max_pool(name='pool', value=h, ksize=[1, int((self.filters[0]-1)/2), 1, 1],
# strides=[1, 1, 1, 1], padding='SAME')
# max_pool
pool_squeeze = tf.squeeze(pool, axis=-1) # b * c * s
pool_squeeze_transpose = tf.transpose(pool_squeeze, [0, 2, 1]) # b * s * c
G_max_squeeze = tf.reduce_max(input_tensor=pool_squeeze_transpose, axis=-1, keepdims=True) # b * s * 1
# divide of softmax
exp_logits = tf.exp(G_max_squeeze)
exp_logits_sum = tf.reduce_sum(exp_logits, axis=1, keepdims=True)
att_v_max = tf.div(exp_logits, exp_logits_sum)
# β * V
x_att = tf.multiply(input, att_v_max)
x_att_sum = tf.reduce_sum(x_att, axis=1)
return x_att_sum
def compute_output_shape(self, input_shape):
return None, K.int_shape(self.class_w)[1]
if __name__=="__main__":
att = AttentionDot()
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] |
templates/setup/setup.py | 0-complexity/kubernetes | 0 | 190190 |
from js9 import j
from zerorobot.template.base import TemplateBase
from zerorobot.template.state import StateCheckError
class Setup(TemplateBase):
version = '0.0.1'
template_name = "setup"
SSHKEY_TEMPLATE = 'github.com/openvcloud/0-templates/sshkey/0.0.1'
OVC_TEMPLATE = 'github.com/openvcloud/0-templates/openvcloud/0.0.1'
ACCOUNT_TEMPLATE = 'github.com/openvcloud/0-templates/account/0.0.1'
VDC_TEMPLATE = 'github.com/openvcloud/0-templates/vdc/0.0.1'
NODE_TEMPLATE = 'github.com/openvcloud/0-templates/node/0.0.1'
ZROBOT_TEMPLATE = 'github.com/openvcloud/0-templates/zrobot/0.0.1'
K8S_TEMPLATE = 'github.com/openvcloud/kubernetes/kubernetes/0.0.1'
def __init__(self, name, guid=None, data=None):
super().__init__(name=name, guid=guid, data=data)
self._config = None
def validate(self):
for key in ['vdc', 'workers', 'sshKey']:
value = self.data[key]
if not value:
raise ValueError('"%s" is required' % key)
@property
def config(self):
'''
returns an object with names of vdc, account, and ovc
'''
if self._config is not None:
return self._config
config = {
'vdc': self.data['vdc'],
}
# traverse the tree up words so we have all info we need to return, connection and
# account
matches = self.api.services.find(template_uid=self.VDC_TEMPLATE, name=config['vdc'])
if len(matches) != 1:
raise RuntimeError('found %d vdcs with name "%s"' % (len(matches), config['vdc']))
vdc = matches[0]
self._vdc = vdc
task = vdc.schedule_action('get_account')
task.wait()
config['account'] = task.result
matches = self.api.services.find(template_uid=self.ACCOUNT_TEMPLATE, name=config['account'])
if len(matches) != 1:
raise ValueError('found %s accounts with name "%s"' % (len(matches), config['account']))
account = matches[0]
# get connection
task = account.schedule_action('get_openvcloud')
task.wait()
config['ovc'] = task.result
self._config = config
return self._config
def _find_or_create(self, zrobot, template_uid, service_name, data):
found = zrobot.services.find(
template_uid=template_uid,
name=service_name
)
if len(found) != 0:
return found[0]
return zrobot.services.create(
template_uid=template_uid,
service_name=service_name,
data=data
)
def _ensure_helper(self):
name = '%s-little-helper' % self.name
node = self._find_or_create(
self.api,
template_uid=self.NODE_TEMPLATE,
service_name=name,
data={
'vdc': self.data['vdc'],
'sshKey': self.data['sshKey'],
'sizeId': 2,
}
)
task = node.schedule_action('install')
task.wait()
if task.state == 'error':
raise task.eco
return node
def _ensure_zrobot(self, helper):
name = '%s-little-bot' % self.name
bot = self._find_or_create(
self.api,
template_uid=self.ZROBOT_TEMPLATE,
service_name=name,
data={
'node': helper.name,
'port': 6600,
'templates': [
'https://github.com/openvcloud/0-templates.git',
'https://github.com/openvcloud/kubernetes.git',
],
},
)
# update data in the disk service
task = bot.schedule_action('install')
task.wait()
if task.state == 'error':
raise task.eco
return bot
def _mirror_services(self, zrobot):
config = self.config
ovc = j.clients.openvcloud.get(config['ovc'])
self._find_or_create(
zrobot,
template_uid=self.SSHKEY_TEMPLATE,
service_name='%s-ssh' % self.name,
data={
'passphrase': <PASSWORD>wd(20, 'ABCDEF<PASSWORD>'),
}
)
self._find_or_create(
zrobot,
template_uid=self.OVC_TEMPLATE,
service_name=config['ovc'],
data={
'address': ovc.config.data['address'],
'port': ovc.config.data['port'],
'location': ovc.config.data['location'],
'token': ovc.config.data['jwt_'],
}
)
account = self._find_or_create(
zrobot,
template_uid=self.ACCOUNT_TEMPLATE,
service_name=config['account'],
data={
'openvcloud': config['ovc'],
'create': False,
}
)
vdc = self._find_or_create(
zrobot,
template_uid=self.VDC_TEMPLATE,
service_name=config['vdc'],
data={
'account': config['account'],
'create': False,
}
)
# make sure they are installed
for instance in [account, vdc]:
task = instance.schedule_action('install')
task.wait()
if task.state == 'error':
raise task.eco
def _deply_k8s(self, zrobot):
k8s = self._find_or_create(
zrobot,
template_uid=self.K8S_TEMPLATE,
service_name=self.name,
data={
'workersCount': self.data['workers'],
'sizeId': self.data['sizeId'],
'dataDiskSize': self.data['dataDiskSize'],
'sshKey': '%s-ssh' % self.name,
'vdc': self.data['vdc']
}
)
task = k8s.schedule_action('install')
task.wait()
if task.state == 'error':
raise task.eco
return task.result
def install(self):
try:
self.state.check('actions', 'install', 'ok')
return
except StateCheckError:
pass
helper = self._ensure_helper()
bot = self._ensure_zrobot(helper)
zrobot = self.api.robots[bot.name]
self._mirror_services(zrobot)
self.data['credentials'] = self._deply_k8s(zrobot)
# next step, make a deployment
self.state.set('actions', 'install', 'ok')
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] |
tests/test_dialect.py | zzzeek/calchipan | 1 | 137716 | <reponame>zzzeek/calchipan
from sqlalchemy.testing.fixtures import TestBase
import pandas as pd
from sqlalchemy import create_engine, MetaData, Table, Column, \
String, Integer, ForeignKey, select, exc, func
from . import eq_, assert_raises_message
class DialectTest(TestBase):
def _emp_d_fixture(self, id_cols=True):
if id_cols:
emp_df = pd.DataFrame([
{"emp_id": 1, "name": "ed", "fullname": "<NAME>",
"dep_id": 1},
{"emp_id": 2, "name": "wendy", "fullname": "<NAME>",
"dep_id": 1},
{"emp_id": 3, "name": "jack", "fullname": "<NAME>",
"dep_id": 2},
])
dept_df = pd.DataFrame([
{"dep_id": 1, "name": "Engineering"},
{"dep_id": 2, "name": "Accounting"},
{"dep_id": 3, "name": "Sales"},
])
else:
emp_df = pd.DataFrame([
{"name": "ed", "fullname": "<NAME>",
"dep_id": 0},
{"name": "wendy", "fullname": "<NAME>",
"dep_id": 0},
{"name": "jack", "fullname": "<NAME>",
"dep_id": 1},
])
dept_df = pd.DataFrame([
{"name": "Engineering"},
{"name": "Accounting"},
{"name": "Sales"},
])
return {"employee": emp_df, "department": dept_df}
def _md_fixture(self, pandas_index_pk=False):
m = MetaData()
emp = Table('employee', m,
Column('emp_id', Integer, primary_key=True),
Column('name', String),
Column('fullname', String),
Column('dep_id', Integer, ForeignKey('department.dep_id')),
pandas_index_pk=pandas_index_pk
)
dep = Table('department', m,
Column('dep_id', Integer, primary_key=True),
Column('name', String),
pandas_index_pk=pandas_index_pk
)
return emp, dep
def test_has_table(self):
eng = create_engine("pandas+calchipan://",
namespace=self._emp_d_fixture())
assert eng.has_table('employee')
assert not eng.has_table('fake')
def test_execute_compiled(self):
eng = create_engine("pandas+calchipan://",
namespace=self._emp_d_fixture())
emp, dep = self._md_fixture()
result = eng.execute(select([emp]))
eq_(
list(result),
[(1, 'ed', '<NAME>', 1),
(2, 'wendy', '<NAME>', 1),
(3, 'jack', '<NAME>', 2)]
)
def test_execute_standalone_fn(self):
eng = create_engine("pandas+calchipan://",
namespace=self._emp_d_fixture())
emp, dep = self._md_fixture()
from calchipan import non_aggregate_fn
@non_aggregate_fn()
def myfunc():
return "hi"
result = eng.execute(myfunc())
eq_(
list(result),
[('hi',)]
)
def test_inserted_primary_key_autoinc(self):
eng = create_engine("pandas+calchipan://",
namespace={"employee":
pd.DataFrame(columns=["name", "dep_id"])})
emp, dep = self._md_fixture(pandas_index_pk=True)
r = eng.execute(emp.insert().values(name="ed", dep_id=5))
eq_(r.inserted_primary_key, [0])
r = eng.execute(emp.insert().values(name="jack", dep_id=12))
eq_(r.inserted_primary_key, [1])
def test_inserted_primary_key_no_autoinc(self):
eng = create_engine("pandas+calchipan://",
namespace={"employee":
pd.DataFrame(columns=["name", "dep_id"])})
emp, dep = self._md_fixture(pandas_index_pk=False)
r = eng.execute(emp.insert().values(name="ed", dep_id=5))
eq_(r.inserted_primary_key, [None])
r = eng.execute(emp.insert().values(name="jack", dep_id=12))
eq_(r.inserted_primary_key, [None])
def test_inserted_primary_key_manualinc(self):
eng = create_engine("pandas+calchipan://",
namespace={"employee":
pd.DataFrame(columns=["emp_id", "name", "dep_id"])})
emp, dep = self._md_fixture()
r = eng.execute(emp.insert().values(emp_id=3, name="ed", dep_id=5))
eq_(r.inserted_primary_key, [3])
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www/purple_admin/urls.py | SubminO/vas | 0 | 23537 | from django.urls import path
from purple_admin import views
urlpatterns = [
path('', views.cabinet, name='admin_panel_cabinet'),
# Адмника Наименований маршрутов
path('route_list', views.cabinet_list, {'type': 'route'}, name='admin_panel_route_list'),
path('route_add', views.cabinet_add, {'type': 'route'}, name='admin_panel_route_add'),
path('route_edit/<int:pk>/', views.cabinet_edit, {'type': 'route'}, name='admin_panel_route_edit'),
path('route_delete/<int:pk>/', views.cabinet_delete, {'type': 'route'}, name='admin_panel_route_delete'),
# Адмника наименований остановок
path('route_platform_list', views.cabinet_list, {'type': 'route_platform'}, name='admin_panel_route_platform_list'),
path('route_platform_add', views.cabinet_add, {'type': 'route_platform'}, name='admin_panel_route_platform_add'),
path('route_platform_edit/<int:pk>/', views.cabinet_edit, {'type': 'route_platform'},
name='admin_panel_route_platform_edit'),
path('route_platform_delete/<int:pk>/', views.cabinet_delete, {'type': 'route_platform'},
name='admin_panel_route_platform_delete'),
path('route_relation_add_ajax', views.cabinet_add, {'type': 'route_platform_type'},
name='admin_panel_route_platform_type_relation_ajax_add'),
# Админка ТС
path('ts_list', views.cabinet_list, {'type': 'ts'}, name='admin_panel_ts_list'),
path('ts_add', views.cabinet_add, {'type': 'ts'}, name='admin_panel_ts_add'),
path('ts_edit/<int:pk>/', views.cabinet_edit, {'type': 'ts'}, name='admin_panel_ts_edit'),
path('ts_delete/<int:pk>/', views.cabinet_delete, {'type': 'ts'}, name='admin_panel_ts_delete'),
# Адмника Создания маршрута на карте
path('map_route_editor_add', views.mapped_route_add, name='admin_panel_mapped_route_add'),
]
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] |
gtm/GTM.py | TimurGimadiev/GTM | 0 | 176024 | <filename>gtm/GTM.py
import os
import sys
import math
import torch
import torch.nn as nn
class GTMEstimator(nn.Module):
"""
Generative Topographic Mapping implementation with Gaussian Noise manifold model.
Source: https://www.microsoft.com/en-us/research/wp-content/uploads/1998/01/bishop-gtm-ncomp-98.pdf
"""
def __init__(self, in_features=None, out_features=None, n_x_points=100, verbose=False, y=None, method="mean",
lmbd=0, cuda=False):
"""
Initialisation:
Creates Class instance.
:param in_features: Size of latent space
:param out_features: Size of variable space
:param n_x_points: Size of basis grid
:param verbose: If true, the training process will output intermediate information
:param y: Mapping function, has to be differentiable by pytorch.
"""
super(GTMEstimator, self).__init__()
assert not (isinstance(in_features, type(None)) or isinstance(out_features, type(None))), \
'You must provide sizes for hidden and variable dimension'
if cuda:
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
self.in_features = in_features
self.out_features = out_features
self.lmbd = lmbd
if isinstance(y, type(None)):
self.y = nn.Linear(in_features, out_features)
self.y = y
self.betta = torch.rand(1, requires_grad=True, device=self.device)
self.norm = nn.BatchNorm1d(out_features, momentum=0.5)
self.grid = self.get_grid(n_x_points)
self.verbose = verbose
self.betta_opt = torch.optim.Adam([self.betta])
self.y_opt = torch.optim.Adam(self.y.parameters())
self.norm_opt = torch.optim.Adam(self.norm.parameters())
self.method = method
self.mean = 0
self.std = 1
def get_grid(self, n_points):
"""
Initializing basis grid for likelihood estimation
:param n_points: size of the grid in the latent space
:return: grid in the form of flattened torch tensor
"""
grid = torch.meshgrid(*[torch.linspace(0, 1, n_points) for _ in range(self.in_features)])
grid = torch.stack([t.flatten() for t in grid]).T.to(self.device)
return grid
def train_epoch(self, X, batch_size=256):
"""
Training function, to estimate the distributions sigma and mapping weights.
:param X: Data from variable space
:param batch_size: size of training batches
:return: history of losses
"""
l_h = [] # Loss history
n_x_variable, D = X.size()
for i in range(math.ceil(n_x_variable / batch_size)):
self.y.zero_grad()
self.norm.zero_grad()
self.betta_opt.zero_grad()
self.y_opt.zero_grad()
self.norm_opt.zero_grad()
batch = self.norm(X[i * batch_size:(i + 1) * batch_size])
size, _ = batch.size()
dist = (-self.betta / 2) * torch.pow(torch.cdist(self.y(self.grid), batch), 2)
exp = torch.exp(dist)
p = torch.pow(self.betta / (2 * math.pi), D / 2) * exp
p_x = torch.clip(torch.mean(p, dim=0), min=1e-20)
loss = -1 * torch.log(p_x).sum(dim=0)
if self.lmbd:
for params in self.y.parameters():
if len(params.size()) == 2:
D1, D2 = params.size()
M = D1 * D2
else:
D1, = params.size()
M = D1
reg = torch.sum(torch.pow(params, 2))
exp_reg = torch.exp((-self.lmbd / 2) * reg)
p_w = exp_reg * (self.lmbd / (2 * math.pi)) ** (M / 2)
loss += -1 * p_w
loss.backward()
self.betta_opt.step()
self.y_opt.step()
self.norm_opt.step()
l_h.append(loss.item() / size)
return l_h
def train_epochs(self, X, n_epochs=1, batch_size=256):
"""
Training function, to estimate the distributions sigma and mapping weights.
Runs one epoch procedure `n_epochs` times.
:param X: Data from variable space
:param n_epochs: number of epochs to run
:param batch_size: size of training batches
:return: history of losses
"""
l_h = []
self.train()
self.mean = X.mean(dim=0)
self.std = X.std(dim=0)
for i in range(n_epochs):
l_h.extend(self.train_epoch(X, batch_size))
if self.verbose:
print('epoch #{}: likelihood: {:.3f} betta: {:.3f}'.format(i + 1, l_h[-1], self.betta.item()))
return l_h
def transform(self, X, batch_size=256):
"""
Performs mapping from variable space to latent space.
:param X: Data from variable space
:param batch_size: Size of the batch to use
:return: Data in latent space
"""
assert self.method in ('mean', 'mode'), "Mode can be either mean or mode."
self.eval()
with torch.no_grad():
tensor_list = []
n_x_variable, D = X.size()
for i in range(math.ceil(n_x_variable / batch_size)):
dist = (-self.betta / 2) * torch.pow(torch.cdist(self.y(self.grid),
self.norm(X[i*batch_size:(i+1)*batch_size]), 2))
exp = torch.exp(dist)
p = torch.pow(self.betta / (2 * math.pi), D / 2) * exp
p_x = p / p.sum(dim=0)
if self.method == 'mean':
tensor_list.append((self.grid.T @ p_x).T)
elif self.method == 'mode':
tensor_list.append(self.grid[p_x.argmax(dim=0), :] )
return torch.cat(tensor_list, dim=0)
def inverse_transform(self, H, batch_size=256):
"""
Performs mapping from latent space to variable space.
:param H: Data from latent space
:param batch_size: Size of the batch to use
:return: Data in variable space
"""
assert self.method in ('mean', 'mode'), "Mode can be either mean or mode."
self.eval()
with torch.no_grad():
tensor_list = []
n_x_variable, D = H.size()
for i in range(math.ceil(n_x_variable / batch_size)):
tensor_list.append(self.y(self.norm(H[batch_size*i: batch_size*(i+1)])))
return torch.cat(tensor_list, dim=0)
if __name__ == "__main__":
in_features = 2
hidden_features = 12
out_features = 4
y = nn.Sequential(
nn.Linear(in_features, hidden_features),
nn.ReLU(),
nn.Linear(hidden_features, hidden_features),
nn.ReLU(),
nn.Linear(hidden_features, out_features))
est = GTMEstimator(in_features=in_features, out_features=out_features, n_x_points=3, verbose=True, y=y)
Df = torch.rand(1024, 4)
est.train_epochs(Df, 1000)
output = (est.transform(Df)).numpy()
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module2-sql-for-analysis/assignment_2_postgresql.py | ash827/DS-Unit-3-Sprint-2-SQL-and-Databases | 0 | 142367 | <filename>module2-sql-for-analysis/assignment_2_postgresql.py<gh_stars>0
# -*- coding: utf-8 -*-
"""Assignment 2 PostgreSQL.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1RaFLSTAwoZ8IeLg27YQqhnMe-g3Vtt4m
POSTGRES DEMO
"""
!pip install psycopg2-binary
import psycopg2
import os
from dotenv import load_dotenv
load_dotenv()
"""1. Establish connection
2. Cursor
3. Execute query
4. Get RESULTS!
"""
DB_HOST = os.getenv("DB_HOST", default="OOPS")
DB_NAME = os.getenv("DB_NAME", default="OOPS")
DB_USER = os.getenv("DB_USER", default="OOPS")
DB_PASSWORD = os.getenv("DB_PASSWORD", default="<PASSWORD>")
connection = psycopg2.connect(
dbname=DB_NAME,
user=DB_USER,
password=<PASSWORD>,
host=DB_HOST
)
cursor = connection.cursor()
cursor.execute('SELECT * from playground;')
results = cursor.fetchall()
print(results)
"""##Working with the rpg database"""
import sqlite3
import pandas as pd
conn = sqlite3.connect('rpg_db (1).sqlite3')
sl_cursor = conn.cursor()
count = sl_cursor.execute('SELECT COUNT(*) FROM charactercreator_character;').fetchall()
count
characters = sl_cursor.execute('SELECT * FROM charactercreator_character').fetchall()
characters
#This shows us the schema
sl_cursor.execute('PRAGMA table_info(charactercreator_character);').fetchall()
create_character_table = '''
CREATE TABLE character_table (
character_id SERIAL PRIMARY KEY,
name VARCHAR(100),
level INT,
exp INT,
hp INT,
strength INT,
intelligence INT,
dexterity INT,
wisdom INT
);
'''
pg_cursor.execute(create_character_table)
pg_cursor.execute("INSERT INTO character_table VALUES (1, 'Aliquid iste optio reiciendi, 0, 0, 10, 1, 1, 1, 1');")
for character in characters:
print("INSERT INTO character_table VALUES " + str(character) + '.')
"""#Titanic Data for Assignment"""
!wget 'https://raw.githubusercontent.com/ash827/DS-Unit-3-Sprint-2-SQL-and-Databases/master/module2-sql-for-analysis/titanic.csv'
import pandas as pd
df_titanic = pd.read_csv('https://raw.githubusercontent.com/ash827/DS-Unit-3-Sprint-2-SQL-and-Databases/master/module2-sql-for-analysis/titanic.csv')
df_titanic.shape
df_titanic.columns.tolist()
df_titanic = df_titanic.reindex(columns = ['Name', 'Age', 'Sex', 'Pclass', 'Fare', 'Siblings/Spouses Aboard', 'Parents/Children Aboard', 'Survived'])
#The columns are now in a nice order :)
"""##Using sqlite3 with the Titanic data
#Having trouble getting the titanic information to load as a db?
"""
conn = sqlite3.connect('titanic.csv')
ti_cursor = conn.cursor()
count = ti_cursor.execute('SELECT COUNT(*) FROM Name;').fetchall()
count
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YouTube-PyQt5/09_radio_button.py | dloperab/Python-GUI | 2 | 39693 | import sys
from PyQt5 import QtGui, QtCore
from PyQt5.QtWidgets import QApplication, QDialog, QGroupBox, QVBoxLayout, QHBoxLayout, QRadioButton, QLabel
class MainWindow(QDialog):
def __init__(self):
super().__init__()
self.title = "PyQt5 Radio Button"
self.top = 400
self.left = 200
self.width = 400
self.height = 150
self.icon_name = "images/home.png"
self._init_window()
def _init_window(self):
self.setWindowIcon(QtGui.QIcon(self.icon_name))
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
self._create_ui_components()
vbox = QVBoxLayout()
vbox.addWidget(self.group_box)
self.lbl_Info = QLabel(self)
self.lbl_Info.setFont(QtGui.QFont("Sanserif", 15))
vbox.addWidget(self.lbl_Info)
self.setLayout(vbox)
self.show()
def _create_ui_components(self):
self.group_box = QGroupBox("What is your favorite sport?")
self.group_box.setFont(QtGui.QFont("Sanserif", 13))
hbox_layout = QHBoxLayout()
self.rdbtn_soccer = QRadioButton("Soccer")
self.rdbtn_soccer.setIcon(QtGui.QIcon("images/soccer.png"))
self.rdbtn_soccer.setIconSize(QtCore.QSize(30, 30))
self.rdbtn_soccer.setFont(QtGui.QFont("Sanserif", 13))
self.rdbtn_soccer.toggled.connect(self._on_radiobutton_checked)
hbox_layout.addWidget(self.rdbtn_soccer)
self.rdbtn_basketball = QRadioButton("Basketball")
self.rdbtn_basketball.setIcon(QtGui.QIcon("images/basketball.png"))
self.rdbtn_basketball.setIconSize(QtCore.QSize(30, 30))
self.rdbtn_basketball.setFont(QtGui.QFont("Sanserif", 13))
self.rdbtn_basketball.toggled.connect(self._on_radiobutton_checked)
hbox_layout.addWidget(self.rdbtn_basketball)
self.rdbtn_tennis = QRadioButton("Tennis")
self.rdbtn_tennis.setIcon(QtGui.QIcon("images/tennis.png"))
self.rdbtn_tennis.setIconSize(QtCore.QSize(30, 30))
self.rdbtn_tennis.setFont(QtGui.QFont("Sanserif", 13))
self.rdbtn_tennis.toggled.connect(self._on_radiobutton_checked)
hbox_layout.addWidget(self.rdbtn_tennis)
self.group_box.setLayout(hbox_layout)
def _on_radiobutton_checked(self):
rdbtn = self.sender()
if rdbtn.isChecked():
self.lbl_Info.setText(f"Sport selected: {rdbtn.text()}")
if __name__ == "__main__":
app = QApplication(sys.argv)
window = MainWindow()
sys.exit(app.exec()) | [
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Backend_folder/models/socials.py | Miguel22247/MatchGame | 0 | 123893 | <reponame>Miguel22247/MatchGame<gh_stars>0
#!/usr/bin/python3.6
"""Script to create an instance of the class Social"""
from sqlalchemy import Column, String
from models.base_model import Base, BaseModel
class Social(BaseModel, Base):
"""Class to store the social data"""
__tablename__ = "social"
name = Column(String(20), nullable=False)
| [
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storage.py | eybisi/theFS | 0 | 139105 | import rpyc
import uuid
import os
from rpyc.utils.server import ThreadedServer
DATA_DIR = "/tmp/storage/"
class StorageService(rpyc.Service):
# exposed classes & methods
ALIAES = ["storage"]
def on_connect(self,conn):
print("[+] Client Connected {}".format(conn._channel.stream.sock.getpeername()))
def on_disconnect(self,conn):
print("[+] Client Disconnected {}".format(conn._channel.stream.sock.getpeername()))
class exposed_Storage():
blocks= {}
def exposed_pull(self,uuid):
data_addr = DATA_DIR+str(uuid)
if not os.path.isfile(data_addr):
print("[-] Block not found {}".format(uuid))
return None
with open(data_addr) as f:
return f.read()
def exposed_push(self,data,storages,uuid):
with open(DATA_DIR+str(uuid),'w') as f:
print("[+] Writing data to {}".format(DATA_DIR+str(uuid)))
f.write(data)
if len(storages)>0:
print("[~] Forwarding to other storages")
self.forward(data,storages,uuid)
def exposed_delete(self, uuid):
if os.path.isfile(DATA_DIR+str(uuid)):
print("[+] Removing {}".format(DATA_DIR+str(uuid)))
os.remove(DATA_DIR+str(uuid))
def forward(self,data,storages,uuid):
print "8888: forwarding to:"
print uuid, storages
storage = storages[0]
storages = storages[1:]
host,port = storage
conn = rpyc.connect(host,port=port)
storage = conn.root.Storage()
storage.push(data,storages,uuid)
if __name__ == "__main__":
if not os.path.isdir(DATA_DIR): os.mkdir(DATA_DIR)
t = ThreadedServer(StorageService, port = 8888)
t.start()
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] |
app/controllers/web/admin/component.py | arxcdr/silverback | 0 | 145068 | """
Component Web Controller
"""
# Standard Library
import os
# Third Party Library
from django.views import View
from django.http import Http404
from django.shortcuts import render
from django.utils.translation import gettext as _
# Local Library
from app.modules.core.context import Context
from app.modules.core.decorators import login_if_not_authenticated
from app.modules.core.component import Component as ComponentModule
class ComponentList(View):
template_name = 'templates/admin/component/list.html'
__context = Context()
__component = ComponentModule()
__correlation_id = None
@login_if_not_authenticated
def get(self, request):
self.__correlation_id = request.META["X-Correlation-ID"] if "X-Correlation-ID" in request.META else ""
self.__context.autoload_options()
self.__context.autoload_user(request.user.id if request.user.is_authenticated else None)
self.__context.push({
"page_title": _("Components · %s") % self.__context.get("app_name", os.getenv("APP_NAME", "Silverback"))
})
return render(request, self.template_name, self.__context.get())
class ComponentAdd(View):
template_name = 'templates/admin/component/add.html'
__context = Context()
__component = ComponentModule()
__correlation_id = None
@login_if_not_authenticated
def get(self, request):
self.__correlation_id = request.META["X-Correlation-ID"] if "X-Correlation-ID" in request.META else ""
self.__context.autoload_options()
self.__context.autoload_user(request.user.id if request.user.is_authenticated else None)
self.__context.push({
"page_title": _("Add a Component · %s") % self.__context.get("app_name", os.getenv("APP_NAME", "Silverback")),
"groups": self.__component.get_all_groups()
})
return render(request, self.template_name, self.__context.get())
class ComponentEdit(View):
template_name = 'templates/admin/component/edit.html'
__context = Context()
__component = ComponentModule()
__correlation_id = None
@login_if_not_authenticated
def get(self, request, component_id):
self.__correlation_id = request.META["X-Correlation-ID"] if "X-Correlation-ID" in request.META else ""
component = self.__component.get_one_by_id(component_id)
if not component:
raise Http404("Component not found.")
self.__context.autoload_options()
self.__context.autoload_user(request.user.id if request.user.is_authenticated else None)
self.__context.push({
"page_title": _("Edit Component · %s") % self.__context.get("app_name", os.getenv("APP_NAME", "Silverback")),
"component": component,
"groups": self.__component.get_all_groups()
})
return render(request, self.template_name, self.__context.get())
| [
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4703,
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3101,
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] |
pyPowerCLI/__init__.py | arielsafari/pyPowerCLI | 0 | 120604 |
from .install import is_installed
from .install import install_powercli
from .install import uninstall_powercli
from .powershell import Powershell
from .powershell import PowershellException
__version__ = "0.0.1" | [
1,
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src/lib/utils/matching.py | jfparentledartech/DEFT | 0 | 172598 | <filename>src/lib/utils/matching.py<gh_stars>0
import lap
import numpy as np
import scipy
from cython_bbox import bbox_overlaps as bbox_ious
from scipy.spatial.distance import cdist
from lib.utils.tracking_utils import kalman_filter
import os.path, copy
from scipy.spatial import ConvexHull
# This function is taken from : https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
def merge_matches(m1, m2, shape):
O, P, Q = shape
m1 = np.asarray(m1)
m2 = np.asarray(m2)
M1 = scipy.sparse.coo_matrix((np.ones(len(m1)), (m1[:, 0], m1[:, 1])), shape=(O, P))
M2 = scipy.sparse.coo_matrix((np.ones(len(m2)), (m2[:, 0], m2[:, 1])), shape=(P, Q))
mask = M1 * M2
match = mask.nonzero()
match = list(zip(match[0], match[1]))
unmatched_O = tuple(set(range(O)) - set([i for i, j in match]))
unmatched_Q = tuple(set(range(Q)) - set([j for i, j in match]))
return match, unmatched_O, unmatched_Q
def _indices_to_matches(cost_matrix, indices, thresh):
matched_cost = cost_matrix[tuple(zip(*indices))]
matched_mask = matched_cost <= thresh
matches = indices[matched_mask]
unmatched_a = tuple(set(range(cost_matrix.shape[0])) - set(matches[:, 0]))
unmatched_b = tuple(set(range(cost_matrix.shape[1])) - set(matches[:, 1]))
return matches, unmatched_a, unmatched_b
# This function is taken from : https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
def linear_assignment(cost_matrix, thresh):
if cost_matrix.size == 0:
return (
np.empty((0, 2), dtype=int),
tuple(range(cost_matrix.shape[0])),
tuple(range(cost_matrix.shape[1])),
)
matches, unmatched_a, unmatched_b = [], [], []
cost, x, y = lap.lapjv(cost_matrix, extend_cost=True, cost_limit=thresh)
for ix, mx in enumerate(x):
if mx >= 0:
matches.append([ix, mx])
unmatched_a = np.where(x < 0)[0]
unmatched_b = np.where(y < 0)[0]
matches = np.asarray(matches)
return matches, unmatched_a, unmatched_b
# This function is taken from : https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
def ious(atlbrs, btlbrs):
"""
Compute cost based on IoU
:type atlbrs: list[tlbr] | np.ndarray
:type atlbrs: list[tlbr] | np.ndarray
:rtype ious np.ndarray
"""
ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
if ious.size == 0:
return ious
ious = bbox_ious(
np.ascontiguousarray(atlbrs, dtype=np.float),
np.ascontiguousarray(btlbrs, dtype=np.float),
)
return ious
# Part of this function is taken from : https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
def iou_distance(atracks, btracks, frame_id=0, use_prediction=True):
"""
Compute cost based on IoU
:type atracks: list[STrack]
:type btracks: list[STrack]
:rtype cost_matrix np.ndarray
"""
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
):
atlbrs = atracks
btlbrs = btracks
else:
if use_prediction:
atlbrs = [track.prediction_at_frame_tlbr(frame_id) for track in atracks]
else:
atlbrs = [track.tlbr for track in atracks]
btlbrs = [track.tlbr for track in btracks]
_ious = ious(atlbrs, btlbrs)
cost_matrix = 1 - _ious
return cost_matrix
def iou_ddd_distance(atracks, btracks, frame_id=0, use_prediction=True):
"""
Compute cost based on IoU
:type atracks: list[STrack]
:type btracks: list[STrack]
:rtype cost_matrix np.ndarray
"""
if (len(atracks) > 0 and isinstance(atracks[0], np.ndarray)) or (
len(btracks) > 0 and isinstance(btracks[0], np.ndarray)
):
atlbrs = atracks
btlbrs = btracks
else:
atlbrs = [convert_3dbox_to_8corner(track.ddd_bbox) for track in atracks]
btlbrs = [convert_3dbox_to_8corner(track.ddd_bbox) for track in btracks]
iou_matrix = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
if iou_matrix.size == 0:
return iou_matrix
for d, det in enumerate(btlbrs):
for t, trk in enumerate(atlbrs):
iou_matrix[t, d] = iou3d(det, trk)[0] # det: 8 x 3, trk: 8 x 3
iou_matrix = 1 - iou_matrix
return iou_matrix
def poly_area(x, y):
return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
def box3d_vol(corners):
""" corners: (8,3) no assumption on axis direction """
a = np.sqrt(np.sum((corners[0, :] - corners[1, :]) ** 2))
b = np.sqrt(np.sum((corners[1, :] - corners[2, :]) ** 2))
c = np.sqrt(np.sum((corners[0, :] - corners[4, :]) ** 2))
return a * b * c
def convex_hull_intersection(p1, p2):
""" Compute area of two convex hull's intersection area.
p1,p2 are a list of (x,y) tuples of hull vertices.
return a list of (x,y) for the intersection and its volume
"""
inter_p = polygon_clip(p1, p2)
if inter_p is not None:
hull_inter = ConvexHull(inter_p)
return inter_p, hull_inter.volume
else:
return None, 0.0
def polygon_clip(subjectPolygon, clipPolygon):
""" Clip a polygon with another polygon.
Args:
subjectPolygon: a list of (x,y) 2d points, any polygon.
clipPolygon: a list of (x,y) 2d points, has to be *convex*
Note:
**points have to be counter-clockwise ordered**
Return:
a list of (x,y) vertex point for the intersection polygon.
"""
def inside(p):
return (cp2[0] - cp1[0]) * (p[1] - cp1[1]) > (cp2[1] - cp1[1]) * (p[0] - cp1[0])
def computeIntersection():
dc = [cp1[0] - cp2[0], cp1[1] - cp2[1]]
dp = [s[0] - e[0], s[1] - e[1]]
n1 = cp1[0] * cp2[1] - cp1[1] * cp2[0]
n2 = s[0] * e[1] - s[1] * e[0]
n3 = 1.0 / (dc[0] * dp[1] - dc[1] * dp[0])
return [(n1 * dp[0] - n2 * dc[0]) * n3, (n1 * dp[1] - n2 * dc[1]) * n3]
outputList = subjectPolygon
cp1 = clipPolygon[-1]
for clipVertex in clipPolygon:
cp2 = clipVertex
inputList = outputList
outputList = []
s = inputList[-1]
for subjectVertex in inputList:
e = subjectVertex
if inside(e):
if not inside(s):
outputList.append(computeIntersection())
outputList.append(e)
elif inside(s):
outputList.append(computeIntersection())
s = e
cp1 = cp2
if len(outputList) == 0:
return None
return outputList
def convert_3dbox_to_8corner(bbox3d_input):
# compute rotational matrix around yaw axis
bbox3d = copy.copy(bbox3d_input)
# transform to kitti format first
bbox3d_nuscenes = copy.copy(bbox3d)
# kitti: [x, y, z, a, l, w, h]
bbox3d[0] = bbox3d_nuscenes[3]
bbox3d[1] = bbox3d_nuscenes[4]
bbox3d[2] = bbox3d_nuscenes[5]
bbox3d[3] = bbox3d_nuscenes[-1]
bbox3d[4] = bbox3d_nuscenes[2]
bbox3d[5] = bbox3d_nuscenes[1]
bbox3d[6] = bbox3d_nuscenes[0]
R = roty(bbox3d[3])
# 3d bounding box dimensions
l = bbox3d[4]
w = bbox3d[5]
h = bbox3d[6]
# 3d bounding box corners
x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]
y_corners = [0, 0, 0, 0, -h, -h, -h, -h]
z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]
# rotate and translate 3d bounding box
corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners]))
corners_3d[0, :] = corners_3d[0, :] + bbox3d[0]
corners_3d[1, :] = corners_3d[1, :] + bbox3d[1]
corners_3d[2, :] = corners_3d[2, :] + bbox3d[2]
return np.transpose(corners_3d)
def roty(t):
""" Rotation about the y-axis. """
c = np.cos(t)
s = np.sin(t)
return np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]])
def iou3d(corners1, corners2):
""" Compute 3D bounding box IoU.
Input:
corners1: numpy array (8,3), assume up direction is negative Y
corners2: numpy array (8,3), assume up direction is negative Y
Output:
iou: 3D bounding box IoU
iou_2d: bird's eye view 2D bounding box IoU
"""
# corner points are in counter clockwise order
rect1 = [(corners1[i, 0], corners1[i, 2]) for i in range(3, -1, -1)]
rect2 = [(corners2[i, 0], corners2[i, 2]) for i in range(3, -1, -1)]
area1 = poly_area(np.array(rect1)[:, 0], np.array(rect1)[:, 1])
area2 = poly_area(np.array(rect2)[:, 0], np.array(rect2)[:, 1])
inter, inter_area = convex_hull_intersection(rect1, rect2)
iou_2d = inter_area / (area1 + area2 - inter_area)
ymax = min(corners1[0, 1], corners2[0, 1])
ymin = max(corners1[4, 1], corners2[4, 1])
inter_vol = inter_area * max(0.0, ymax - ymin)
vol1 = box3d_vol(corners1)
vol2 = box3d_vol(corners2)
iou = inter_vol / (vol1 + vol2 - inter_vol)
return iou, iou_2d
def embedding_distance(tracks, detections, metric="cosine"):
"""
:param tracks: list[STrack]
:param detections: list[BaseTrack]
:param metric:
:return: cost_matrix np.ndarray
"""
cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
if cost_matrix.size == 0:
return cost_matrix
det_features = np.asarray([track.curr_feat for track in detections], dtype=np.float)
track_features = np.asarray([track.smooth_feat for track in tracks], dtype=np.float)
cost_matrix = np.maximum(
0.0, cdist(track_features, det_features, metric)
) # Nomalized features
return cost_matrix
def gate_cost_matrix(kf, cost_matrix, tracks, detections, only_position=True):
if cost_matrix.size == 0:
return cost_matrix
gating_dim = 2 if only_position else 4
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([det.to_xyah() for det in detections])
for row, track in enumerate(tracks):
gating_distance = kf.gating_distance(
track.mean, track.covariance, measurements, only_position
)
cost_matrix[row, gating_distance > (gating_threshold + 10)] = np.inf
return cost_matrix
# Part of this function is taken from : https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
def fuse_motion(
kf,
cost_matrix,
tracks,
detections,
frame_id,
use_lstm=True,
only_position=True,
lambda_=0.9,
):
if cost_matrix.size == 0:
return cost_matrix
gating_dim = 2 if only_position else 4
gating_threshold = kalman_filter.chi2inv95[gating_dim]
if not use_lstm:
measurements = np.asarray([det.to_xyah() for det in detections])
for row, track in enumerate(tracks):
gating_distance = kf.gating_distance(
track.mean, track.covariance, measurements, only_position, metric="maha"
)
cost_matrix[row, gating_distance > 5.0 * gating_threshold] = np.inf
cost_matrix[row] = (
lambda_ * cost_matrix[row] + 0.05 * (1 - lambda_) * gating_distance
)
else:
measurements = np.asarray([det.to_xyah() for det in detections])
for row, track in enumerate(tracks):
if len(track.observations) >= 300:
gating_distance = kf.gating_distance(
track.prediction_at_frame(frame_id),
track.covariance,
measurements,
only_position,
metric="maha",
)
cost_matrix[row, gating_distance > 5.0 * gating_threshold] = np.inf
cost_matrix[row] = (
lambda_ * cost_matrix[row] + 0.05 * (1 - lambda_) * gating_distance
)
else:
gating_distance = kf.gating_distance(
track.prediction_at_frame(frame_id),
track.covariance,
measurements,
only_position,
metric="gaussian",
)
cost_matrix[row, gating_distance > 50] = np.inf
cost_matrix[row] = (
lambda_ * cost_matrix[row]
+ 0.0005 * (1 - lambda_) * gating_distance
)
return cost_matrix
def fuse_motion_ddd(
kf,
cost_matrix,
tracks,
detections,
frame_id,
use_lstm=True,
only_position=False,
lambda_=0.9,
use_prediction=False,
classe_name=None,
):
if cost_matrix.size == 0:
return cost_matrix
gating_dim = 7
gating_threshold = kalman_filter.chi2inv95[gating_dim]
measurements = np.asarray([det.ddd_bbox for det in detections])
for row, track in enumerate(tracks):
if use_prediction:
gating_distance = kf.gating_distance(
track.ddd_prediction_at_frame(frame_id),
track.covariance,
measurements,
only_position,
metric="gaussian",
)
else:
gating_distance = kf.gating_distance(
track.ddd_bbox,
track.covariance,
measurements,
only_position,
metric="gaussian",
)
thr = 0.2 * track.depth
if classe_name == "pedestrian":
if thr < 5:
thr = 5
else:
if thr < 10:
thr = 10
cost_matrix[row, gating_distance > thr] = np.inf
cost_matrix[row] = lambda_ * cost_matrix[row] + 0.001 * gating_distance
return cost_matrix
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29889,
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386,
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29962,
353,
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29889,
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29961,
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29962,
353,
313,
13,
462,
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334,
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29900,
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330,
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330,
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29918,
19244,
353,
413,
29888,
29889,
29887,
1218,
29918,
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29898,
13,
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271,
29918,
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29898,
2557,
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333,
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462,
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29889,
24542,
279,
8837,
29892,
13,
462,
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20398,
29892,
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462,
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3283,
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13,
13,
1753,
285,
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29892,
13,
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3515,
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333,
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29929,
29945,
29961,
29887,
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353,
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294,
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297,
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671,
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11965,
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13,
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413,
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13,
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29918,
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271,
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333,
511,
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24542,
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29918,
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29881,
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29890,
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12714,
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565,
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529,
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29945,
13,
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13,
13,
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565,
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529,
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13,
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353,
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798,
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330,
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29900,
29896,
334,
330,
1218,
29918,
19244,
13,
13,
1678,
736,
3438,
29918,
5344,
13,
2
] |
test1.py | redeye999/pyneta | 0 | 198265 | x = { 1, 2, 3, 4, 5 }
for i in x:
print i
| [
1,
921,
353,
426,
29871,
29896,
29892,
29871,
29906,
29892,
29871,
29941,
29892,
29871,
29946,
29892,
29871,
29945,
500,
13,
1454,
474,
297,
921,
29901,
13,
1678,
1596,
474,
13,
13,
2
] |
reminder.py | starhound/Parts_Bot | 2 | 118677 | <filename>reminder.py<gh_stars>1-10
import main
import parts_history
import json
import datetime
import threading
import logger
PARTS_CHAT = 1
def report_action_card(message):
message = message["msg"]
content = message.split(':')
reminder = "Parts order for " + content + " has been unclaimed for three or more days!"
main.bot.api.chat_post_message(text=reminder, channel=PARTS_CHAT)
logger.log_info_event(reminder)
def check_messages():
message_history = parts_history.history()
messages = json.loads(message_history)
dt = datetime.datetime.today()
date = dt.day
for message in messages['messages']:
time = message["ts"]
message_date = time.split('-')[2]
if message_date < int(date) - 3:
report_action_card(message)
def start_reminders():
threading.Timer(86400.0, check_messages).start()
| [
1,
529,
9507,
29958,
1745,
4995,
29889,
2272,
29966,
12443,
29918,
303,
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29896,
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29900,
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5215,
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13,
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353,
931,
29889,
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877,
29899,
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29962,
13,
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565,
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1256,
529,
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448,
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29941,
29901,
13,
9651,
3461,
29918,
2467,
29918,
7543,
29898,
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29897,
13,
13,
13,
1753,
1369,
29918,
1745,
513,
414,
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13,
1678,
3244,
292,
29889,
14745,
29898,
29947,
29953,
29946,
29900,
29900,
29889,
29900,
29892,
1423,
29918,
19158,
467,
2962,
580,
13,
2
] |
bin/generateLogTenX.py | ErikMeinders/cirrus | 0 | 98480 | <filename>bin/generateLogTenX.py<gh_stars>0
#
# Copyright 2010-2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# This file is licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
#
from __future__ import print_function # Python 2/3 compatibility
import boto3
import json
import decimal
from boto3.dynamodb.conditions import Key, Attr
def atoi(s):
n, notnegative = 0, 1
if s[0]=="-":
notnegative = -1
s = s[1:]
for i in s:
n = n*10 + ord(i)-ord("0")
return notnegative*n
import sys
def printf(format, *args):
sys.stdout.write(format % args)
# Helper class to convert a DynamoDB item to JSON.
class DecimalEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, decimal.Decimal):
return str(o)
return super(DecimalEncoder, self).default(o)
dynamodb = boto3.resource('dynamodb', region_name='eu-central-1')
table = dynamodb.Table('avidyneFlightLogs')
empty_package={
"metadata": {
"application" : "ErixLogTenUpdater",
"version" : "1.0",
"dateFormat" : "MM/dd/yy",
"dateAndTimeFormat" : "MM/dd/yy%20HH:mm:ss",
"timesAreZulu" : "true",
"shouldApplyAutoFillTimes" : "true"
},
"entities": []
}
logtenline='logtenprox://v2/addEntities?package='
response = table.scan( )
entries=[]
def writeFlight(flight):
try:
datum = flight['flightID'].split(' ')[0]
[ mo, da, yr ] = datum.split('/')
# Only consider flights after june 1 2018
if ( 10000 * atoi(yr) + 100 * atoi(mo) + atoi(da) ) <= 190216:
return
if not 'icaoFrom' in flight:
flight['icaoFrom']='ABCD'
if not 'icaoTo' in flight:
flight['icaoTo']='DCBA'
printf ("%s/%02d/%02d: ", yr, int(mo), int(da))
entry={
"entity_name" : "Flight",
"flight_flightDate" : datum,
"flight_key" : flight['flightID'],
"flight_from" : flight['icaoFrom'],
"flight_to" : flight['icaoTo'],
"flight_distance" : str(flight['Traveled']),
"flight_actualDepartureTime" : datum+' '+flight['OffBlock'],
"flight_actualArrivalTime" : datum+' '+flight['OnBlocks'],
"flight_takeoffTime" : datum+' '+flight['TakeOff'],
"flight_landingTime" : datum+' '+flight['Landing'],
"flight_totalTime" : flight['PICTime'],
# "flight_dayLandings" : "1",
# "flight_dayTakeoffs" : "1",
"flight_selectedAircraftType": "SR-20",
"flight_selectedAircraftID" : "N808KB",
"flight_selectedCrewPIC" : "<NAME>"
}
entries.append(entry)
package=empty_package
package['entities'] = [ entry ]
line=logtenline+json.dumps(package)
printf ("<a href='%s'>Form %4s to %4s</a> Key: %s<br>\n",
line,
entry['flight_from'],
entry['flight_to'],
entry['flight_actualDepartureTime'])
except Exception as e:
printf ("Exception discovered %s\n", e)
# main
for i in response[u'Items']:
writeFlight(i)
# write sum
package=empty_package
package['entities']=entries
line=logtenline+json.dumps(package)
printf ("<br>Alle vluchten <a href='%s'>na 1 juni 2018</a>\n", line)
####
sample = {
"lonEnd": "6.8838",
"flightID": "4/9/17 09:26:47",
"lonStart": "5.5261",
"AvgSpeed": "100.416255672",
"Flying": "0:38:36",
"latStart": "52.4580",
"Landing": "10:10:42",
"Traveled": "64.6011244822",
"icaoTo": "EHTW",
"gcDist": "50.9485443277",
"OnBlocks": "10:11:36",
"TakeOff": "09:32:06",
"icaoFrom": "EHLE",
"Ground": "0:05:06",
"PICTime": "0:43:42",
"OffBlock": "09:27:54",
"latEnd": "52.2767"
}
| [
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264,
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realtime_kiawase/realtime_kiawase.py | actionpterygii/arishihiNoDetectMultiScale | 0 | 87038 | # -*- coding: utf-8 -*-
"""
Created on Fri Dec 09 13:52:37 2016
@author: _
"""
import cv, cv2, numpy as np
if __name__ == '__main__':
ESC_KEY = 27
INTERVAL= 33
FRAME_RATE = 30
DEVICE_ID = 0
cap = cv2.VideoCapture(DEVICE_ID)
end_flag, c_frame = cap.read()
cascade = cv2.CascadeClassifier('scascade_shog.xml')
rt,org_im = cap.read()
while end_flag == True:
rt,org_im = cap.read()
height,width,chanels = org_im.shape
ork_im = org_im[50:height,150:470]
hsv_im = cv2.cvtColor(ork_im, cv2.COLOR_BGR2HSV)
hsvbun = cv2.split(hsv_im)
cv2.equalizeHist(hsvbun[2],hsvbun[2])
skk_im = cv2.merge((hsvbun[0],hsvbun[1],hsvbun[2]))
si_im = cv2.cvtColor(skk_im, cv2.COLOR_HSV2BGR)
fushiii = cascade.detectMultiScale(ork_im, 1.1, 3)
i = 1
for (x, y, w, h) in fushiii:
print("%d,%d,%d,%d,%d" %(i, x, y, w, h))
center = (int(x+w/2), int(y+h/2))
radius = int(w/2+5)
cv2.circle(ork_im, center, radius, cv.RGB(255, 255, 255), thickness=3)
i += 1
cv2.imshow('SKK', ork_im)
key = cv2.waitKey(INTERVAL)
if key == ESC_KEY:
break
c_frame = cap.read()
cv2.destroyAllWindows()
cap.release()
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] |
esphomeyaml/helpers.py | TheJulianJES/esphomeyaml | 0 | 80825 | <filename>esphomeyaml/helpers.py
from __future__ import print_function
import errno
import logging
import os
import socket
import subprocess
from esphomeyaml.py_compat import text_type, IS_PY2
_LOGGER = logging.getLogger(__name__)
def ensure_unique_string(preferred_string, current_strings):
test_string = preferred_string
current_strings_set = set(current_strings)
tries = 1
while test_string in current_strings_set:
tries += 1
test_string = u"{}_{}".format(preferred_string, tries)
return test_string
def indent_all_but_first_and_last(text, padding=u' '):
lines = text.splitlines(True)
if len(lines) <= 2:
return text
return lines[0] + u''.join(padding + line for line in lines[1:-1]) + lines[-1]
def indent_list(text, padding=u' '):
return [padding + line for line in text.splitlines()]
def indent(text, padding=u' '):
return u'\n'.join(indent_list(text, padding))
# From https://stackoverflow.com/a/14945195/8924614
def cpp_string_escape(string, encoding='utf-8'):
if isinstance(string, text_type):
string = string.encode(encoding)
result = ''
for character in string:
if IS_PY2:
character = ord(character)
if not (32 <= character < 127) or character in ('\\', '"'):
result += '\\%03o' % character
else:
result += chr(character)
return '"' + result + '"'
def color(the_color, message=''):
from colorlog.escape_codes import escape_codes, parse_colors
if not message:
res = parse_colors(the_color)
else:
res = parse_colors(the_color) + message + escape_codes['reset']
return res
def run_system_command(*args):
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout, stderr = p.communicate()
rc = p.returncode
return rc, stdout, stderr
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def is_ip_address(host):
parts = host.split('.')
if len(parts) != 4:
return False
try:
for p in parts:
int(p)
return True
except ValueError:
return False
def resolve_ip_address(host):
try:
ip = socket.gethostbyname(host)
except socket.error as err:
from esphomeyaml.core import EsphomeyamlError
raise EsphomeyamlError("Error resolving IP address: {}".format(err))
return ip
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] |
run_vn_baselines.py | netlab-lcy/CMIX | 1 | 58237 | <gh_stars>1-10
'''
argv[1]: log_dir path
argv[2]: the VNEnv pickle instance path
'''
from baselines.algorithm import *
from envs.vn_env import VNEnv
from utils.utils import cleanup_dir
import sys
import pickle
import time
scheme_list = ["no_relay", "max_SINR", "DCRA", "min_latency", "greedy"]
log_dir = sys.argv[1]
cleanup_dir(log_dir)
load_env_file = open(sys.argv[2], "rb")
env = pickle.load(load_env_file)
load_env_file.close()
env.reset()
env.set_scheme("basic")
store_env_file = open("%s/env.pickle" % (log_dir), "wb")
pickle.dump(env, store_env_file)
store_env_file.close()
file_out = open("%s/result.log" % (log_dir), "w")
# Average q value not applied in this work
print("Average data rate, Global utility, Jains index, Average latency, Peak violation, Average constraint q value, Geo mean, Number of vehicles, Running time", file = file_out)
for scheme in scheme_list:
if scheme == "no_relay":
t0 = time.time()
ave_rate, utility_sum, Jains_index, ave_latency_whole, peak_violation_sum, q_ave_val, geo_mean, n_vehicle_net = no_relay(env)
t1 = time.time()
print("no_relay:", round(ave_rate, 2), round(utility_sum, 2), round(Jains_index, 4), round(ave_latency_whole, 2), peak_violation_sum, round(q_ave_val, 2), round(geo_mean, 3), n_vehicle_net, t1-t0, file = file_out)
elif scheme == "max_SINR":
t0 = time.time()
ave_rate, utility_sum, Jains_index, ave_latency_whole, peak_violation_sum, q_ave_val, geo_mean, n_vehicle_net = max_SINR(env)
t1 = time.time()
print("max_SINR:", round(ave_rate, 2), round(utility_sum, 2), round(Jains_index, 4), round(ave_latency_whole, 2), peak_violation_sum, round(q_ave_val, 2), round(geo_mean, 3), n_vehicle_net, t1-t0, file = file_out)
elif scheme == "DCRA":
t0 = time.time()
ave_rate, utility_sum, Jains_index, ave_latency_whole, peak_violation_sum, q_ave_val, geo_mean, n_vehicle_net = DCRA(env)
t1 = time.time()
print("DCRA:", round(ave_rate, 2), round(utility_sum, 2), round(Jains_index, 4), round(ave_latency_whole, 2), peak_violation_sum, round(q_ave_val, 2), round(geo_mean, 3), n_vehicle_net, t1-t0, file = file_out)
elif scheme == "greedy":
t0 = time.time()
ave_rate, utility_sum, Jains_index, ave_latency_whole, peak_violation_sum, q_ave_val, geo_mean, n_vehicle_net = greedy(env)
t1 = time.time()
print("greedy:", round(ave_rate, 2), round(utility_sum, 2), round(Jains_index, 4), round(ave_latency_whole, 2), peak_violation_sum, round(q_ave_val, 2), round(geo_mean, 3), n_vehicle_net, t1-t0, file = file_out)
elif scheme == "min_latency":
t0 = time.time()
ave_rate, utility_sum, Jains_index, ave_latency_whole, peak_violation_sum, q_ave_val, geo_mean, n_vehicle_net = min_latency(env)
t1 = time.time()
print("min_latency:", round(ave_rate, 2), round(utility_sum, 2), round(Jains_index, 4), round(ave_latency_whole, 2), peak_violation_sum, round(q_ave_val, 2), round(geo_mean, 3), n_vehicle_net, t1-t0, file = file_out)
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] |
wandb/sklearn/calculate/outlier_candidates.py | soumik12345/client | 0 | 135718 | <reponame>soumik12345/client
from warnings import simplefilter
import numpy as np
import wandb
from wandb.sklearn import utils
# ignore all future warnings
simplefilter(action="ignore", category=FutureWarning)
def outlier_candidates(regressor, X, y):
# Fit a linear model to X and y to compute MSE
regressor.fit(X, y)
# Leverage is computed as the diagonal of the projection matrix of X
leverage = (X * np.linalg.pinv(X).T).sum(1)
# Compute the rank and the degrees of freedom of the OLS model
rank = np.linalg.matrix_rank(X)
df = X.shape[0] - rank
# Compute the MSE from the residuals
residuals = y - regressor.predict(X)
mse = np.dot(residuals, residuals) / df
# Compute Cook's distance
residuals_studentized = residuals / np.sqrt(mse) / np.sqrt(1 - leverage)
distance_ = residuals_studentized**2 / X.shape[1]
distance_ *= leverage / (1 - leverage)
# Compute the influence threshold rule of thumb
influence_threshold_ = 4 / X.shape[0]
outlier_percentage_ = sum(distance_ >= influence_threshold_) / X.shape[0]
outlier_percentage_ *= 100.0
distance_dict, count = [], 0
for d in distance_:
distance_dict.append(d)
count += 1
if utils.check_against_limit(
count,
"outlier_candidates",
utils.chart_limit,
):
break
table = make_table(distance_dict, outlier_percentage_, influence_threshold_)
chart = wandb.visualize("wandb/outliers/v1", table)
return chart
def make_table(distance, outlier_percentage, influence_threshold):
columns = [
"distance",
"instance_indicies",
"outlier_percentage",
"influence_threshold",
]
data = [
[distance[i], i, utils.round_3(outlier_percentage), influence_threshold]
for i in range(len(distance))
]
table = wandb.Table(columns=columns, data=data)
return table
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setup.py | marchete/django-adldap-sync | 5 | 194545 | #!/usr/bin/env python
from setuptools import find_packages
from setuptools import setup
from adldap_sync import __version__ as version
with open('README.md') as f:
readme = f.read()
setup(
name='django-adldap-sync',
version=version,
description='A Django application for synchronizing LDAP users, groups and group memberships',
long_description=readme,
license='BSD',
author='Marchete',
author_email='<EMAIL>',
url='https://github.com/marchete/django-adldap-sync',
download_url='https://github.com/marchete/django-adldap-sync',
packages=find_packages(),
include_package_data=True,
install_requires=['pyldap>=2.4.25.1'],
classifiers=[
'Development Status :: 3 - Alpha',
'Environment :: Web Environment',
'Programming Language :: Python',
'Framework :: Django',
'Intended Audience :: Developers',
'License :: OSI Approved :: BSD License',
'Operating System :: OS Independent',
'Topic :: Software Development :: Libraries :: Python Modules',
'Topic :: System :: Systems Administration :: Authentication/Directory',
],
keywords=['django', 'ldap', 'active directory', 'synchronize', 'sync'],
)
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pysal/spatial_dynamics/tests/test_directional.py | cubensys/pysal | 0 | 78939 | <reponame>cubensys/pysal
import unittest
import pysal
from pysal.spatial_dynamics import directional
import numpy as np
class Rose_Tester(unittest.TestCase):
def setUp(self):
f = open(pysal.examples.get_path("spi_download.csv"), 'r')
lines = f.readlines()
f.close()
lines = [line.strip().split(",") for line in lines]
names = [line[2] for line in lines[1:-5]]
data = np.array([map(int, line[3:]) for line in lines[1:-5]])
sids = range(60)
out = ['"United States 3/"',
'"Alaska 3/"',
'"District of Columbia"',
'"Hawaii 3/"',
'"New England"',
'"Mideast"',
'"Great Lakes"',
'"Plains"',
'"Southeast"',
'"Southwest"',
'"Rocky Mountain"',
'"Far West 3/"']
snames = [name for name in names if name not in out]
sids = [names.index(name) for name in snames]
states = data[sids, :]
us = data[0]
years = np.arange(1969, 2009)
rel = states / (us * 1.)
gal = pysal.open(pysal.examples.get_path('states48.gal'))
self.w = gal.read()
self.w.transform = 'r'
self.Y = rel[:, [0, -1]]
def test_rose(self):
k = 4
np.random.seed(100)
r4 = directional.rose(self.Y, self.w, k, permutations=999)
exp = [0., 1.57079633, 3.14159265, 4.71238898, 6.28318531]
obs = list(r4['cuts'])
for i in range(k + 1):
self.assertAlmostEqual(exp[i], obs[i])
self.assertEquals(list(r4['counts']), [32, 5, 9, 2])
exp = [0.02, 0.001, 0.001, 0.001]
obs = list(r4['pvalues'])
for i in range(k):
self.assertAlmostEqual(exp[i], obs[i])
suite = unittest.TestSuite()
test_classes = [Rose_Tester]
for i in test_classes:
a = unittest.TestLoader().loadTestsFromTestCase(i)
suite.addTest(a)
if __name__ == '__main__':
runner = unittest.TextTestRunner()
runner.run(suite)
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nicos_mlz/delab/setups/qmesydaq.py | mlz-ictrl/nicos | 12 | 163508 | description = 'testing qmesydaq'
group = 'optional'
excludes = []
sysconfig = dict(
datasinks = ['LiveViewSink'],
)
tango_base = 'tango://localhost:10000/test/qmesydaq/'
devices = dict(
# RAWFileSaver = device('nicos.devices.datasinks.RawImageSink',
# description = 'Saves image data in RAW format',
# filenametemplate = [
# '%(proposal)s_%(pointcounter)06d.raw',
# '%(proposal)s_%(scancounter)s_'
# '%(pointcounter)s_%(pointnumber)s.raw'],
# subdir = 'QMesyDAQ2',
# lowlevel = True,
# ),
LiveViewSink = device('nicos.devices.datasinks.LiveViewSink',
description = 'Sends image data to LiveViewWidget',
),
qm_ctr0 = device('nicos.devices.entangle.CounterChannel',
description = 'QMesyDAQ Counter0',
tangodevice = tango_base + 'counter0',
type = 'counter',
lowlevel = True,
),
qm_ctr1 = device('nicos.devices.entangle.CounterChannel',
description = 'QMesyDAQ Counter1',
tangodevice = tango_base + 'counter1',
type = 'counter',
lowlevel = True,
),
qm_ctr2 = device('nicos.devices.entangle.CounterChannel',
description = 'QMesyDAQ Counter2',
tangodevice = tango_base + 'counter2',
type = 'monitor',
lowlevel = True,
),
qm_ctr3 = device('nicos.devices.entangle.CounterChannel',
description = 'QMesyDAQ Counter3',
tangodevice = tango_base + 'counter3',
type = 'monitor',
lowlevel = True,
),
qm_ev = device('nicos.devices.entangle.CounterChannel',
description = 'QMesyDAQ Events channel',
tangodevice = tango_base + 'events',
type = 'counter',
lowlevel = True,
),
qm_timer = device('nicos.devices.entangle.TimerChannel',
description = 'QMesyDAQ Timer',
tangodevice = tango_base + 'timer',
),
mesytec = device('nicos.devices.generic.Detector',
description = 'QMesyDAQ Image type Detector',
timers = ['qm_timer'],
counters = [
'qm_ev',
# 'qm_ctr0', 'qm_ctr2'
],
monitors = [
#'qm_ctr1', 'qm_ctr3'
],
others = [],
),
)
startupcode = '''
SetDetectors(mesytec)
'''
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XGBoosting/code.py | Aditya1231/ga-learner-dsmp-repo | 0 | 136324 | <reponame>Aditya1231/ga-learner-dsmp-repo
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
df = pd.read_csv(path)
X = df.drop(['customerID','Churn'],axis=1)
y = df['Churn'].copy()
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3,random_state=0)
# Code starts here
# --------------
import numpy as np
from sklearn.preprocessing import LabelEncoder
X_train['TotalCharges'].replace(' ',np.NaN, inplace=True)
X_test['TotalCharges'].replace(' ',np.NaN, inplace=True)
X_train['TotalCharges'] = X_train['TotalCharges'].astype(float)
X_train['TotalCharges'] = X_train['TotalCharges'].astype(float)
X_test['TotalCharges'] = X_test['TotalCharges'].astype(float)
X_train['TotalCharges'].fillna(X_train['TotalCharges'].mean(),inplace=True)
X_test['TotalCharges'].fillna(X_test['TotalCharges'].mean(),inplace=True)
print(X_train.isnull().sum())
cat_cols = X_train.select_dtypes(include='O').columns.tolist()
for x in cat_cols:
le=LabelEncoder()
X_train[x] = le.fit_transform(X_train[x])
for x in cat_cols:
le = LabelEncoder()
X_test[x] = le.fit_transform(X_test[x])
dict1 = {'No':0, 'Yes':1}
y_train = y_train.replace(dict1)
y_test = y_test.replace(dict1)
# Code starts here
# --------------
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score,classification_report,confusion_matrix
# Code starts here
print(X_train,X_test,y_train,y_test)
ada_model = AdaBoostClassifier(random_state=0)
ada_model.fit(X_train,y_train)
y_pred = ada_model.predict(X_test)
ada_score = accuracy_score(y_test,y_pred)
ada_cm = confusion_matrix(y_test,y_pred)
ada_cr = classification_report(y_test,y_pred)
# --------------
from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV
#Parameter list
parameters={'learning_rate':[0.1,0.15,0.2,0.25,0.3],
'max_depth':range(1,3)}
xgb_model = XGBClassifier(random_state=0)
xgb_model.fit(X_train,y_train)
y_pred = xgb_model.predict(X_test)
xgb_score = accuracy_score(y_test,y_pred)
print(xgb_score)
xgb_cm = confusion_matrix(y_test,y_pred)
print(xgb_cm)
xgb_cr = classification_report(y_test,y_pred)
print(xgb_cr)
clf_model = GridSearchCV(estimator=xgb_model,param_grid=parameters)
clf_model.fit(X_train,y_train)
y_pred = clf_model.predict(X_test)
clf_score = accuracy_score(y_test,y_pred)
print(clf_score)
clf_cm = confusion_matrix(y_test,y_pred)
print(clf_cm)
clf_cr = classification_report(y_test,y_pred)
print(clf_cr)
# Code starts here
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] |
retro/models.py | WSMathias/Retro360-API | 0 | 182680 | <reponame>WSMathias/Retro360-API<filename>retro/models.py
from django.db import models
from django.contrib.auth.models import User
from django.urls import reverse
from django.http import HttpResponseRedirect
# class RetroUser(models.Model):
# user = models.OneToOneField(User,on_delete=models.CASCADE,default='0')
# user_type = models.CharField(max_length= 5,default='user')
# def __str__(self):
# return "{} {}".format(self.user.username)
# def get_absolute_url(self):
# return HttpResponseRedirect(reverse('retro:list'))
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] |
Sergeant-RANK/PRACTICE/1420A.py | rohansaini886/Peer-Programming-Hub-CP-Winter_Camp | 2 | 155677 | <reponame>rohansaini886/Peer-Programming-Hub-CP-Winter_Camp<filename>Sergeant-RANK/PRACTICE/1420A.py
for _ in range(int(input())):
n = int(input())
l = list(map(int, input().split(" ")))
is_true = False
for i in range(1, n):
if l[i] >= l[i-1]:
is_true = True
break
if is_true:
print("YES")
else:
print("NO")
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] |
source/setups/euler/3d/bonnor-ebert/generate.py | jmark/nemo | 0 | 63384 | #!/usr/bin/env python
import sys,os
root = os.environ['NEMO_ROOT']
site = os.environ['NEMO_SITE']
sys.path.append(os.path.join(root,'scripts'))
from nemo import Makefile
mk = Makefile(
root = root,
sites = site,
flags = dict(PP_N_NODES = 4, PP_KERNEL_DGFV_QMAX = 2, PP_SPLIT_FORM = 0, PP_MESH_PERIODIC = 0),
)
sourcedir = root + '/source'
SETUPDIR = sourcedir + '/setups/euler/3d/self-gravity'
hydro = root + '/source/flux/euler/dgfv/3d'
#hydro = root + '/source/flux/euler/dgfv/3d/multilevel-lobatto'
#hydro = root + '/source/flux/euler/dgfv/3d/multilevel'
#hydro = root + '/source/flux/euler/dgfv/3d/singlelevel'
#hydro = root + '/source/flux/euler/dgfv/3d/singlelevel-multiflux'
kernel = root + '/source/kernel/dgfv/3d'
#timedisc = root + '/source/timedisc/rungekutta/5-4'
timedisc = root + '/source/timedisc/rungekutta/ssp/5-4'
equations = root + '/source/equations/euler/polytropic/3d'
riemann = equations + '/riemann/rusanov'
#riemann = equations + '/riemann/hllc'
#riemann = hydro + '/riemann/standard/roe'
#riemann = hydro + '/riemann/standard/roe-entropy-fix'
#riemann = hydro + '/riemann/standard/hlle+roe-entropy-fix'
#riemann = hydro + '/riemann/standard/hll'
#riemann = hydro + '/riemann/standard/hllc'
#riemann = hydro + '/riemann/standard/hllm'
#riemann = hydro + '/riemann/standard/entropy-stable'
#riemann = hydro + '/riemann/multidim/rusanov'
#riemann = hydro + '/riemann/multidim/chandrashekar'
#twopoint = hydro + '/equations/polytropic/two_point_flux/standard'
#twopoint = hydro + '/equations/polytropic/two_point_flux/kennedy-gruber'
#twopoint = hydro + '/equations/polytropic/two_point_flux/chandrashekar'
#twopoint = hydro + '/equations/polytropic/two_point_flux/schnuecke'
#mk.add(twopoint + '/two_point_flux_mod.f90')
mk.add(root + '/source/share_mod.f90')
mk.add(root + '/source/driver_mod.f90')
mk.add(root + '/source/runtime_mod.f90')
mk.add(root + '/source/common/*.f90')
mk.add(root + '/source/mesh/*.f90')
mk.add(root + '/source/mesh/bindings/*.f90')
mk.add(root + '/source/checkpoint/base/*.f90')
mk.add(root + '/source/checkpoint/hdf5/p4est/*.f90')
mk.add(riemann + '/*.f90')
mk.add(equations + '/*.f90')
mk.add(kernel,'*.f90')
mk.add(timedisc,'*.f90')
mk.add(SETUPDIR + '/modules/*_mod.f90')
# mk.add(SETUPDIR + '/modules/setup_amr_mod.f90')
# mk.add(SETUPDIR + '/modules/setup_amr_sensor_mod.f90')
# mk.add(SETUPDIR + '/modules/setup_amr_types_mod.f90')
mk.generate()
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tkinter/GroupBox/GroupBox.py | Python-clique/Examples_Python | 1 | 177459 | <reponame>Python-clique/Examples_Python<filename>tkinter/GroupBox/GroupBox.py<gh_stars>1-10
#!/usr/bin/env python3
# -*-coding:utf-8 -*
import tkinter
import tkinter.messagebox
import tkinter.ttk
class Form1(tkinter.Tk):
def __init__(self):
super().__init__()
self.frame = tkinter.ttk.Frame(self)
self.frame.pack(fill = tkinter.BOTH, expand=1)
self.groupBox1 = tkinter.ttk.LabelFrame(self.frame, text="GroupBox 1", height=460, width=305)
self.groupBox1.place(x=10, y=10)
self.groupBox2 = tkinter.ttk.LabelFrame(self.frame, height=460, width=305)
self.groupBox2.place(x=325, y=10)
self.geometry("640x480+200+100")
self.title("GroupBox example")
def main(self=None):
form = Form1()
form.mainloop()
if __name__ == '__main__':
Form1.main()
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basic/007_Reverse_Int.py | Dynamic-Ruo/Leetcode-Practice-with-Python | 1 | 83789 | class Solution:
def reverse(self, x: int) -> int:
# get the sign of x
if x == 0:
return x
sign = abs(x) / x
x = abs(x)
# reverse number
num = 0
# reverse process
while x != 0:
temp = x % 10
num = num * 10 + temp
x = x // 10
num = int(sign * num)
return num if -2**31 < num < 2**31 - 1 else 0 | [
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indico/modules/events/util.py | javfg/indico | 1 | 72875 | # This file is part of Indico.
# Copyright (C) 2002 - 2021 CERN
#
# Indico is free software; you can redistribute it and/or
# modify it under the terms of the MIT License; see the
# LICENSE file for more details.
import json
import os
import random
import re
import warnings
from collections import defaultdict
from contextlib import contextmanager
from copy import deepcopy
from mimetypes import guess_extension
from tempfile import NamedTemporaryFile
from zipfile import ZipFile
from flask import current_app, flash, g, redirect, request, session
from sqlalchemy import inspect
from sqlalchemy.orm import load_only, noload
from werkzeug.exceptions import BadRequest, Forbidden
from werkzeug.urls import url_parse
from indico.core import signals
from indico.core.config import config
from indico.core.errors import NoReportError, UserValueError
from indico.core.permissions import FULL_ACCESS_PERMISSION, READ_ACCESS_PERMISSION
from indico.modules.categories.models.roles import CategoryRole
from indico.modules.events import Event
from indico.modules.events.contributions import contribution_settings
from indico.modules.events.contributions.models.contributions import Contribution
from indico.modules.events.contributions.models.subcontributions import SubContribution
from indico.modules.events.layout import theme_settings
from indico.modules.events.models.events import EventType
from indico.modules.events.models.persons import EventPerson
from indico.modules.events.models.principals import EventPrincipal
from indico.modules.events.models.roles import EventRole
from indico.modules.events.models.static_list_links import StaticListLink
from indico.modules.events.registration.models.forms import RegistrationForm
from indico.modules.events.sessions.models.sessions import Session
from indico.modules.events.timetable.models.breaks import Break
from indico.modules.events.timetable.models.entries import TimetableEntry
from indico.modules.networks import IPNetworkGroup
from indico.modules.users import User
from indico.util.caching import memoize_request
from indico.util.fs import chmod_umask, secure_filename
from indico.util.i18n import _
from indico.util.string import strip_tags
from indico.util.user import principal_from_identifier
from indico.web.flask.util import send_file, url_for
from indico.web.forms.colors import get_colors
def check_event_locked(rh, event, force=False):
if (not getattr(rh, 'ALLOW_LOCKED', False) or force) and event.is_locked and request.method not in ('GET', 'HEAD'):
raise NoReportError.wrap_exc(Forbidden(_('This event has been locked so no modifications are possible.')))
def get_object_from_args(args=None):
"""Retrieve an event object from request arguments.
This utility is meant to be used in cases where the same controller
can deal with objects attached to various parts of an event which
use different URLs to indicate which object to use.
:param args: The request arguments. If unspecified,
``request.view_args`` is used.
:return: An ``(object_type, event, object)`` tuple. The event is
always the :class:`Event` associated with the object.
The object may be an `Event`, `Session`, `Contribution`
or `SubContribution`. If the object does not exist,
``(object_type, None, None)`` is returned.
"""
if args is None:
args = request.view_args
object_type = args['object_type']
event = Event.get(args['event_id'], is_deleted=False)
if event is None:
obj = None
elif object_type == 'event':
obj = event
elif object_type == 'session':
obj = Session.query.with_parent(event).filter_by(id=args['session_id']).first()
elif object_type == 'contribution':
obj = Contribution.query.with_parent(event).filter_by(id=args['contrib_id']).first()
elif object_type == 'subcontribution':
obj = (SubContribution.query
.filter(SubContribution.id == args['subcontrib_id'],
~SubContribution.is_deleted,
SubContribution.contribution.has(event=event, id=args['contrib_id'], is_deleted=False))
.first())
else:
raise ValueError(f'Unexpected object type: {object_type}')
if obj is not None:
return object_type, event, obj
else:
return object_type, None, None
def get_theme(event, override_theme_id=None):
"""Get the theme ID and whether it's an override.
This is useful for places where a user may specify a different
timetable theme. If the override theme is not valid for the
event, a message is flashed and an exception redirecting the user
to the main event page is raised.
:raise BadRequest: if the override theme id is not valid
:return: a ``(theme_id, is_override)`` tuple
"""
if not override_theme_id:
return event.theme, False
elif override_theme_id == 'default':
theme = theme_settings.defaults[event.type]
return theme, theme != event.theme
elif override_theme_id in theme_settings.get_themes_for(event.type_.name):
return override_theme_id, override_theme_id != event.theme
else:
if override_theme_id in theme_settings.themes:
flash(_("The theme '{}' is not valid for this event.").format(override_theme_id))
else:
flash(_("The theme '{}' does not exist.").format(override_theme_id))
raise BadRequest(response=redirect(event.url))
def get_events_managed_by(user, dt=None):
"""Get the IDs of events where the user has management privs.
:param user: A `User`
:param dt: Only include events taking place on/after that date
:return: A set of event ids
"""
query = (user.in_event_acls
.join(Event)
.options(noload('user'), noload('local_group'), load_only('event_id'))
.filter(~Event.is_deleted, Event.ends_after(dt))
.filter(EventPrincipal.has_management_permission('ANY')))
return {principal.event_id for principal in query}
def get_events_created_by(user, dt=None):
"""Get the IDs of events created by the user.
:param user: A `User`
:param dt: Only include events taking place on/after that date
:return: A set of event ids
"""
query = (user.created_events
.filter(~Event.is_deleted, Event.ends_after(dt))
.options(load_only('id')))
return {event.id for event in query}
def get_events_with_linked_event_persons(user, dt=None):
"""
Return a dict containing the event ids and role for all events
where the user is a chairperson or (in case of a lecture) speaker.
:param user: A `User`
:param dt: Only include events taking place on/after that date
"""
query = (user.event_persons
.with_entities(EventPerson.event_id, Event._type)
.join(Event, Event.id == EventPerson.event_id)
.filter(EventPerson.event_links.any())
.filter(~Event.is_deleted, Event.ends_after(dt)))
return {event_id: ('lecture_speaker' if event_type == EventType.lecture else 'conference_chair')
for event_id, event_type in query}
def get_random_color(event):
breaks = Break.query.filter(Break.timetable_entry.has(event=event))
used_colors = {s.colors for s in event.sessions} | {b.colors for b in breaks}
unused_colors = set(get_colors()) - used_colors
return random.choice(tuple(unused_colors) or get_colors())
def serialize_event_person(person):
"""Serialize EventPerson to JSON-like object."""
return {'_type': 'EventPerson',
'id': person.id,
'email': person.email,
'name': person.display_full_name,
'firstName': person.first_name,
'familyName': person.last_name,
'title': person.title,
'affiliation': person.affiliation,
'phone': person.phone,
'address': person.address,
'user_id': person.user_id}
def serialize_person_link(person_link):
"""Serialize PersonLink to JSON-like object."""
data = {'email': person_link.person.email,
'name': person_link.display_full_name,
'fullName': person_link.display_full_name,
'firstName': person_link.first_name,
'familyName': person_link.last_name,
'title': person_link.title,
'affiliation': person_link.affiliation,
'phone': person_link.phone,
'address': person_link.address,
'displayOrder': person_link.display_order,
'userId': person_link.person.user_id}
if person_link.person.id is not None:
# In case of a newly added person we only serialize the data again
# if the form's validation failed and the field needs to be displayed
# with the same data. However, now we don't have a person ID since
# the person was never created, so if we send an ID/type in the
# serialized data it'll be sent back and cause an error as it'll appear
# as an existing person instead of a new one when submitting the form
# again.
data.update({'_type': 'PersonLink',
'id': person_link.person.id,
'personId': person_link.person.id})
return data
def update_object_principals(obj, new_principals, read_access=False, full_access=False, permission=None):
"""Update an object's ACL with a new list of principals.
Exactly one argument out of `read_access`, `full_access` and `role` must be specified.
:param obj: The object to update. Must have ``acl_entries``
:param new_principals: The set containing the new principals
:param read_access: Whether the read access ACL should be updated
:param full_access: Whether the full access ACL should be updated
:param permission: The role ACL that should be updated
"""
if read_access + full_access + bool(permission) != 1:
raise ValueError('Only one ACL property can be specified')
if full_access:
existing = {acl.principal for acl in obj.acl_entries if acl.full_access}
grant = {'full_access': True}
revoke = {'full_access': False}
elif read_access:
existing = {acl.principal for acl in obj.acl_entries if acl.read_access}
grant = {'read_access': True}
revoke = {'read_access': False}
elif permission:
existing = {acl.principal
for acl in obj.acl_entries
if acl.has_management_permission(permission, explicit=True)}
grant = {'add_permissions': {permission}}
revoke = {'del_permissions': {permission}}
new_principals = set(new_principals)
added = new_principals - existing
removed = existing - new_principals
for principal in added:
obj.update_principal(principal, **grant)
for principal in removed:
obj.update_principal(principal, **revoke)
return {'added': added, 'removed': removed}
class ListGeneratorBase:
"""Base class for classes performing actions on Indico object lists.
:param event: The associated `Event`
:param entry_parent: The parent of the entries of the list. If it's None,
the parent is assumed to be the event itself.
"""
#: The endpoint of the list management page
endpoint = None
#: Unique list identifier
list_link_type = None
#: The default list configuration dictionary
default_list_config = None
def __init__(self, event, entry_parent=None):
#: The event the list is associated with
self.event = event
#: The parent object of the list items
self.entry_parent = entry_parent or event
#: Columns that originate from the list item's properties,
#: relationships etc, but not from user defined fields (e.g.
#: registration/contribution fields)
self.static_items = {}
self.extra_filters = {}
self.static_link_used = 'config' in request.args
def _get_config_session_key(self):
"""Compose the unique configuration ID.
This ID will be used as a key to set the list's configuration to the
session.
"""
return f'{self.list_link_type}_config_{self.entry_parent.id}'
def _get_config(self):
"""Load the list's configuration from the DB and return it."""
session_key = self._get_config_session_key()
if self.static_link_used:
uuid = request.args['config']
configuration = StaticListLink.load(self.event, self.list_link_type, uuid)
if configuration and configuration['entry_parent_id'] == self.entry_parent.id:
session[session_key] = configuration['data']
return session.get(session_key, self.default_list_config)
def _split_item_ids(self, item_ids, separator_type=None):
"""Separate the dynamic item ids from the static.
:param item_ids: The list of ids to be splitted.
:param separator_type: The type of the item to base the partitioning on.
If the value is 'dynamic', the function will
return a tuple where the first element is a list
of the dynamic item IDs and the second element a
list of the rest item IDs. If the value is
'static', the first element of the returned
tuple will be a list of the static item IDs.
:return: A tuple of 2 lists as a result of the item_ids list
partitioning.
"""
if separator_type == 'dynamic':
dynamic_item_ids = [item_id for item_id in item_ids if not isinstance(item_id, str)]
return dynamic_item_ids, [item_id for item_id in item_ids if item_id not in dynamic_item_ids]
elif separator_type == 'static':
static_item_ids = [item_id for item_id in item_ids if item_id in self.static_items]
return static_item_ids, [item_id for item_id in item_ids if item_id not in static_item_ids]
return item_ids,
def _build_query(self):
"""Return the query of the list's entries.
The query should not take into account the user's filtering
configuration, for example::
return Contribution.query.with_parent(self.event)
"""
raise NotImplementedError
def _filter_list_entries(self, query, filters):
"""Apply user's filters to query and return it."""
raise NotImplementedError
def _get_filters_from_request(self):
"""Get the new filters after the filter form is submitted."""
def get_selected_options(item_id, item):
if item.get('filter_choices') or item.get('type') == 'bool':
return [x if x != 'None' else None for x in request.form.getlist(f'field_{item_id}')]
filters = deepcopy(self.default_list_config['filters'])
for item_id, item in self.static_items.items():
options = get_selected_options(item_id, item)
if options:
filters['items'][item_id] = options
for item_id, item in self.extra_filters.items():
options = get_selected_options(item_id, item)
if options:
filters['extra'][item_id] = options
return filters
def get_list_url(self, uuid=None, external=False):
"""Return the URL of the list management page."""
return url_for(self.endpoint, self.entry_parent, config=uuid, _external=external)
def generate_static_url(self):
"""Return a URL with a uuid referring to the list's configuration."""
session_key = self._get_config_session_key()
configuration = {
'entry_parent_id': self.entry_parent.id,
'data': session.get(session_key)
}
if configuration['data']:
link = StaticListLink.create(self.event, self.list_link_type, configuration)
return self.get_list_url(uuid=link.uuid, external=True)
else:
return self.get_list_url(external=True)
def store_configuration(self):
"""Load the filters from the request and store them in the session."""
filters = self._get_filters_from_request()
session_key = self._get_config_session_key()
self.list_config = session.setdefault(session_key, {})
self.list_config['filters'] = filters
if request.values.get('visible_items'):
visible_items = json.loads(request.values['visible_items'])
self.list_config['items'] = sorted(visible_items, key=str)
session.modified = True
def flash_info_message(self, obj):
raise NotImplementedError
def create_event_logo_tmp_file(event, tmpdir=None):
"""Create a temporary file with the event's logo.
If `tmpdir` is specified, the logo file is created in there and
a path relative to that directory is returned.
"""
logo_meta = event.logo_metadata
logo_extension = guess_extension(logo_meta['content_type']) or os.path.splitext(logo_meta['filename'])[1]
temp_file = NamedTemporaryFile(delete=False, dir=(tmpdir or config.TEMP_DIR), suffix=logo_extension)
temp_file.write(event.logo)
temp_file.flush()
return os.path.relpath(temp_file.name, tmpdir) if tmpdir else temp_file.name
@contextmanager
def track_time_changes(auto_extend=False, user=None):
"""Track time changes of event objects.
This provides a list of changes while the context manager was
active and also triggers `times_changed` signals.
If the code running inside the ``with`` block of this context
manager raises an exception, no signals will be triggered.
:param auto_extend: Whether entry parents will get their boundaries
automatically extended or not. Passing ``'start'`` will
extend only start datetime, ``'end'`` to extend only
end datetime.
:param user: The `User` that will trigger time changes.
"""
if auto_extend:
assert user is not None
if 'old_times' in g:
raise RuntimeError('time change tracking may not be nested')
g.old_times = defaultdict(dict)
changes = defaultdict(dict)
try:
yield changes
except Exception:
del g.old_times
raise
else:
if auto_extend:
by_start = auto_extend in (True, 'start')
by_end = auto_extend in (True, 'end')
initial_changes = set(g.old_times)
# g.old_times changes during iteration
for obj in list(g.old_times):
if not isinstance(obj, Event):
obj.extend_parent(by_start=by_start, by_end=by_end)
cascade_changes = set(g.old_times) - initial_changes
for obj in cascade_changes:
if isinstance(obj, Event):
if not obj.can_manage(user):
# TODO: raise Forbidden exceptions after adding protection check in the UI
raise UserValueError(_('Your action requires modification of event boundaries, but you are '
'not authorized to manage the event.'))
elif not obj.object.can_manage(user):
# TODO: raise Forbidden exceptions after adding protection check in the UI
raise UserValueError(_('Your action requires modification of session block boundaries, but you are '
'not authorized to manage the session block.'))
old_times = g.pop('old_times')
for obj, info in old_times.items():
if isinstance(obj, TimetableEntry):
obj = obj.object
if obj.start_dt != info['start_dt']:
changes[obj]['start_dt'] = (info['start_dt'], obj.start_dt)
if obj.duration != info['duration']:
changes[obj]['duration'] = (info['duration'], obj.duration)
if obj.end_dt != info['end_dt']:
changes[obj]['end_dt'] = (info['end_dt'], obj.end_dt)
for obj, obj_changes in changes.items():
entry = None if isinstance(obj, Event) else obj.timetable_entry
signals.event.times_changed.send(type(obj), entry=entry, obj=obj, changes=obj_changes)
def register_time_change(entry):
"""Register a time-related change for a timetable entry.
This is an internal helper function used in the models to record
changes of the start time or duration. The changes are exposed
through the `track_time_changes` contextmanager function.
"""
# if it's a new object it's not a change so we just ignore it
if not inspect(entry).persistent:
return
try:
old_times = g.old_times
except AttributeError:
msg = f'Time change of {entry} was not tracked'
if current_app.config.get('REPL'):
warnings.warn(msg + ' (exception converted to a warning since you are using the REPL)', stacklevel=2)
return
elif current_app.config['TESTING']:
warnings.warn(msg + ' (exception converted to a warning during tests)', stacklevel=2)
return
else:
raise RuntimeError(msg)
for field in ('start_dt', 'duration', 'end_dt'):
if old_times[entry].get(field) is None:
old_times[entry][field] = getattr(entry, field)
def register_event_time_change(event):
"""Register a time-related change for an event.
This is an internal helper function used in the model to record
changes of the start time or end time. The changes are exposed
through the `track_time_changes` contextmanager function.
"""
# if it's a new object it's not a change so we just ignore it
if not inspect(event).persistent:
return
try:
old_times = g.old_times
except AttributeError:
msg = f'Time change of {event} was not tracked'
if current_app.config.get('REPL'):
warnings.warn(msg + ' (exception converted to a warning since you are using the REPL)', stacklevel=2)
return
elif current_app.config['TESTING']:
warnings.warn(msg + ' (exception converted to a warning during tests)', stacklevel=2)
return
else:
raise RuntimeError(msg)
for field in ('start_dt', 'duration', 'end_dt'):
if old_times[event].get(field) is None:
old_times[event][field] = getattr(event, field)
@contextmanager
def track_location_changes():
"""Track location changes of event objects.
This provides a list of changes while the context manager was
active and also triggers `location_changed` signals.
If the code running inside the ``with`` block of this context
manager raises an exception, no signals will be triggered.
"""
if 'old_locations' in g:
raise RuntimeError('location change tracking may not be nested')
g.old_locations = defaultdict(dict)
changes = defaultdict(dict)
try:
yield changes
except Exception:
del g.old_locations
raise
else:
old_locations = g.pop('old_locations')
cols = ('own_room_id', 'own_room_name', 'own_venue_id', 'own_venue_name', 'own_address', 'inherit_location')
for obj, info in old_locations.items():
for col in cols:
new_value = getattr(obj, col)
if new_value != info[col]:
changes[obj][col] = (info[col], new_value)
for obj, obj_changes in changes.items():
signals.event.location_changed.send(type(obj), obj=obj, changes=obj_changes)
def register_location_change(entry):
"""Register a location-related change for an event object.
This is an internal helper function used in the models to record
changes of the location information. The changes are exposed
through the `track_location_changes` contextmanager function.
"""
# if it's a new object it's not a change so we just ignore it
if not inspect(entry).persistent:
return
try:
old_locations = g.old_locations
except AttributeError:
msg = f'Location change of {entry} was not tracked'
if current_app.config.get('REPL'):
warnings.warn(msg + ' (exception converted to a warning since you are using the REPL)', stacklevel=2)
return
elif current_app.config['TESTING']:
warnings.warn(msg + ' (exception converted to a warning during tests)', stacklevel=2)
return
else:
raise RuntimeError(msg)
for field in ('own_room_id', 'own_room_name', 'own_venue_id', 'own_venue_name', 'own_address', 'inherit_location'):
if old_locations[entry].get(field) is None:
old_locations[entry][field] = getattr(entry, field)
def serialize_event_for_ical(event):
return {
'_fossil': 'conferenceMetadata',
'id': event.id,
'title': event.title,
'description': event.description,
'startDate': event.start_dt,
'endDate': event.end_dt,
'url': event.external_url,
'location': event.venue_name,
'roomFullname': event.room_name,
'speakers': [],
'contributions': []
}
def serialize_event_for_json_ld(event, full=False):
data = {
'@context': 'http://schema.org',
'@type': 'Event',
'url': event.external_url,
'name': event.title,
'startDate': event.start_dt_local.isoformat(),
'endDate': event.end_dt_local.isoformat(),
'location': {
'@type': 'Place',
'name': event.venue_name or 'No location set',
'address': event.address or 'No address set'
}
}
if full and event.description:
data['description'] = strip_tags(event.description)
if full and event.person_links:
data['performer'] = list(map(serialize_person_for_json_ld, event.person_links))
if full and event.has_logo:
data['image'] = event.external_logo_url
return data
def serialize_person_for_json_ld(person):
return {
'@type': 'Person',
'name': person.full_name,
'affiliation': {
'@type': 'Organization',
'name': person.affiliation
}
}
def get_field_values(form_data):
"""Split the form fields between custom and static."""
fields = {x: form_data[x] for x in form_data.keys() if not x.startswith('custom_')}
custom_fields = {x: form_data[x] for x in form_data.keys() if x.startswith('custom_')}
return fields, custom_fields
def set_custom_fields(obj, custom_fields_data):
changes = {}
for custom_field_name, custom_field_value in custom_fields_data.items():
custom_field_id = int(custom_field_name[7:]) # Remove the 'custom_' part
old_value = obj.set_custom_field(custom_field_id, custom_field_value)
if old_value != custom_field_value:
changes[custom_field_name] = (old_value, custom_field_value)
return changes
def check_permissions(event, field, allow_networks=False):
for principal_fossil, permissions in field.data:
principal = principal_from_identifier(principal_fossil['identifier'],
allow_external_users=True,
allow_groups=True,
allow_category_roles=True,
allow_event_roles=True,
allow_emails=True,
allow_registration_forms=True,
allow_networks=allow_networks,
event_id=event.id)
if isinstance(principal, IPNetworkGroup) and set(permissions) - {READ_ACCESS_PERMISSION}:
msg = _('IP networks cannot have management permissions: {}').format(principal.name)
return msg
if isinstance(principal, RegistrationForm) and set(permissions) - {READ_ACCESS_PERMISSION}:
msg = _('Registrants cannot have management permissions: {}').format(principal.name)
return msg
if FULL_ACCESS_PERMISSION in permissions and len(permissions) != 1:
# when full access permission is set, discard rest of permissions
permissions[:] = [FULL_ACCESS_PERMISSION]
def get_event_from_url(url):
data = url_parse(url)
if not all([data.scheme, data.netloc, data.path]):
raise ValueError(_('Invalid event URL'))
event_path = re.match(r'/event/(\d+)(/|$)', data.path)
if not event_path:
raise ValueError(_('Invalid event URL'))
event_id = event_path.group(1)
event = Event.get(event_id, is_deleted=False)
if not event:
raise ValueError(_('This event does not exist'))
return event
class ZipGeneratorMixin:
"""Mixin for RHs that generate zip with files."""
def _adjust_path_length(self, segments):
"""Shorten the path length to < 260 chars.
Windows' built-in ZIP tool doesn't like files whose
total path exceeds ~260 chars. Here we progressively
shorten the total until it matches that constraint.
"""
result = []
total_len = sum(len(seg) for seg in segments) + len(segments) - 1
excess = (total_len - 255) if total_len > 255 else 0
for seg in reversed(segments):
fname, ext = os.path.splitext(seg)
cut = min(excess, (len(fname) - 10) if len(fname) > 14 else 0)
if cut:
excess -= cut
fname = fname[:-cut]
result.append(fname + ext)
return reversed(result)
def _iter_items(self, files_holder):
yield from files_holder
def _generate_zip_file(self, files_holder, name_prefix='material', name_suffix=None, return_file=False):
"""Generate a zip file containing the files passed.
:param files_holder: An iterable (or an iterable containing) object that
contains the files to be added in the zip file.
:param name_prefix: The prefix to the zip file name
:param name_suffix: The suffix to the zip file name
:param return_file: Return the temp file instead of a response
"""
temp_file = NamedTemporaryFile(suffix='.zip', dir=config.TEMP_DIR, delete=False)
with ZipFile(temp_file.name, 'w', allowZip64=True) as zip_handler:
self.used_filenames = set()
for item in self._iter_items(files_holder):
name = self._prepare_folder_structure(item)
self.used_filenames.add(name)
with item.get_local_path() as filepath:
zip_handler.write(filepath, name)
zip_file_name = f'{name_prefix}-{name_suffix}.zip' if name_suffix else f'{name_prefix}.zip'
chmod_umask(temp_file.name)
if return_file:
return temp_file
return send_file(zip_file_name, temp_file.name, 'application/zip', inline=False)
def _prepare_folder_structure(self, item):
file_name = secure_filename(f'{item.id}_{item.filename}', str(item.id))
return os.path.join(*self._adjust_path_length([file_name]))
@memoize_request
def get_all_user_roles(event, user):
event_roles = set(
EventRole.query.with_parent(event)
.filter(EventRole.members.any(User.id == user.id))
)
category_roles = set(
CategoryRole.query
.join(event.category.chain_query.subquery())
.filter(CategoryRole.members.any(User.id == user.id))
)
return event_roles, category_roles
def should_show_draft_warning(event):
return (event.type_ == EventType.conference and
not contribution_settings.get(event, 'published') and
(TimetableEntry.query.with_parent(event).has_rows() or
Contribution.query.with_parent(event).has_rows()))
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5727,
22130,
13,
2
] |
newsXtract.py | selection-bias-www2018/NewsXtract | 1 | 30133 | <reponame>selection-bias-www2018/NewsXtract
import os,json
import requests
BASE_URL = 'http://epfl.elasticsearch.spinn3r.com/content*/_search'
BULK_SIZE = 100
SPINN3R_SECRET = os.environ['SPINN3R_SECRET']
HEADERS = {
'X-vendor': 'epfl',
'X-vendor-auth': SPINN3R_SECRET
}
query = {
"size": BULK_SIZE,
"query":{
"bool":{
"must":{
"match":{
"domain":"afp.com"
}
},
"filter":{
"range":{
"published":{
"gte":"18/02/2017",
"lte":"20/02/2017",
"format":"dd/MM/yyyy"
}
}
}
}
}
}
resp = requests.post(BASE_URL, headers=HEADERS, json=query)
resp_json = json.loads(resp.text)
titles = set()
for r in resp_json['hits']['hits']:
t = r['_source']['title']
if t not in titles:
print t
titles.add(t)
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] |
ally/utils/option.py | rjfranssen/PyAlly | 53 | 18650 | <gh_stars>10-100
# MIT License
#
# Copyright (c) 2020 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import datetime
import math
from .utils import *
############################################################################
def option_format(symbol="", exp_date="1970-01-01", strike=0, direction=""):
"""Returns the OCC standardized option name.
Args:
symbol: the underlying symbol, case insensitive
exp_date: date of expiration, in string-form.
strike: strike price of the option
direction: 'C' or 'call' or the like, for call, otherwise 'p' or 'Put' for put
Returns:
OCC string, like 'IBM201231C00301000'
.. code-block:: python
# Construct the option's OCC symbol
>>> ibm_call = ally.utils.option_format(
exp_date = '2020-12-31',
symbol = 'IBM', # case insensitive
direction = 'call',
strike = 301
)
>>> ibm_call
'IBM201231C00301000'
"""
if not (
check(symbol) and check(exp_date) and check(str(strike)) and check(direction)
):
return ""
# direction into C or P
direction = "C" if "C" in direction.upper() else "P"
# Pad strike with zeros
def format_strike(strike):
x = str(math.floor(float(strike) * 1000))
return "0" * (8 - len(x)) + x
# Assemble
return (
str(symbol).upper()
+ datetime.datetime.strptime(exp_date, "%Y-%m-%d").strftime("%y%m%d")
+ direction
+ format_strike(strike)
)
def option_strike(name):
"""Pull apart an OCC standardized option name and
retreive the strike price, in integer form"""
return int(name[-8:]) / 1000.0
def option_maturity(name):
"""Given OCC standardized option name,
return the date of maturity"""
return datetime.datetime.strptime(name[-15:-9], "%y%m%d").strftime("%Y-%m-%d")
def option_callput(name):
"""Given OCC standardized option name,
return whether its a call or a put"""
return "call" if name.upper()[-9] == "C" else "put"
def option_symbol(name):
"""Given OCC standardized option name, return option ticker"""
return name[:-15]
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] |
falconapi_view.py | splunk-soar-connectors/falconapi | 1 | 66587 | # File: falconapi_view.py
#
# Copyright (c) 2016-2018 Splunk 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.
#
#
# of Phantom Cyber Corporation.
# import json
import phantom.app as phantom
import phantom.utils as util
def _get_hash_type(hash_value):
if util.is_md5(hash_value):
return (phantom.APP_SUCCESS, "md5")
if util.is_sha1(hash_value):
return (phantom.APP_SUCCESS, "sha1")
if util.is_sha256(hash_value):
return (phantom.APP_SUCCESS, "sha256")
return (phantom.APP_ERROR, None)
def _get_ioc_type(ioc):
if util.is_ip(ioc):
return (phantom.APP_SUCCESS, "ip")
if util.is_hash(ioc):
return _get_hash_type(ioc)
if util.is_domain(ioc):
return (phantom.APP_SUCCESS, "domain")
return (phantom.APP_ERROR, "Failed to detect the IOC type")
def _trim_results(data, key):
if (key not in data):
return
if (len(data[key]) <= 100):
return
data[key] = data[key][:101]
data[key][-1] = '...'
def get_ctx_result_hunt(result):
ctx_result = {}
param = result.get_param()
hunt_object = param.get('hash')
if (not hunt_object):
hunt_object = param.get('domain')
param['ioc'] = hunt_object
ret_val, param['ioc_type'] = _get_ioc_type(hunt_object)
ctx_result['param'] = param
message = result.get_message()
ctx_result['message'] = message
summary = result.get_summary()
ctx_result['summary'] = summary
data = result.get_data()
if (not data):
return ctx_result
ctx_result['data'] = data
return ctx_result
def get_ctx_result_ps(result):
ctx_result = {}
param = result.get_param()
if ('ioc' in param):
ioc = param.get('ioc')
ret_val, param['ioc_type'] = _get_ioc_type(ioc)
ctx_result['param'] = param
message = result.get_message()
ctx_result['message'] = message
summary = result.get_summary()
ctx_result['summary'] = summary
data = result.get_data()
if (not data):
return ctx_result
ctx_result['data'] = data
return ctx_result
def get_ctx_result(result):
ctx_result = {}
param = result.get_param()
if ('ioc' in param):
ioc = param.get('ioc')
ret_val, param['ioc_type'] = _get_ioc_type(ioc)
ctx_result['param'] = param
message = result.get_message()
ctx_result['message'] = message
summary = result.get_summary()
ctx_result['summary'] = summary
data = result.get_data()
if (not data):
return ctx_result
data = data[0]
if (not data):
return ctx_result
_trim_results(data, 'ip')
_trim_results(data, 'domain')
_trim_results(data, 'sha256')
_trim_results(data, 'sha1')
_trim_results(data, 'md5')
ctx_result['data'] = data
return ctx_result
def create_alert_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'create_alert_view.html'
def get_alert_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'get_alert_view.html'
def update_alert_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'update_alert_view.html'
def set_status_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'set_status_view.html'
def list_alerts_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'list_alerts_view.html'
def process_list_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result_ps(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'process_list_view.html'
def hunt_view(provides, all_app_runs, context):
context['results'] = results = []
for summary, action_results in all_app_runs:
for result in action_results:
ctx_result = get_ctx_result_hunt(result)
if (not ctx_result):
continue
results.append(ctx_result)
# print context
return 'hunt_view.html'
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] |
ginga/util/mosaic.py | astrofrog/ginga | 0 | 193211 | #! /usr/bin/env python
#
# mosaic.py -- Example of quick and dirty mosaicing of FITS images
#
# <NAME> (<EMAIL>)
#
# Copyright (c) <NAME>. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
import sys, os
import logging
from ginga import AstroImage
STD_FORMAT = '%(asctime)s | %(levelname)1.1s | %(filename)s:%(lineno)d (%(funcName)s) | %(message)s'
def mosaic(paths, logger, outfile=None):
logger.info("Mosaicing images...")
image = AstroImage.AstroImage(logger=logger)
mosaic = image.mosaic(paths)
if outfile:
try:
os.remove(outfile)
except:
pass
logger.info("Writing mosaic file to %s ..." % (outfile))
mosaic.write_fits(outfile)
def main(options, args):
logger = logging.getLogger("mosaic")
logger.setLevel(options.loglevel)
fmt = logging.Formatter(STD_FORMAT)
if options.logfile:
fileHdlr = logging.handlers.RotatingFileHandler(options.logfile)
fileHdlr.setLevel(options.loglevel)
fileHdlr.setFormatter(fmt)
logger.addHandler(fileHdlr)
if options.logstderr:
stderrHdlr = logging.StreamHandler()
stderrHdlr.setLevel(options.loglevel)
stderrHdlr.setFormatter(fmt)
logger.addHandler(stderrHdlr)
if len(args) > 0:
mosaic(args, logger, outfile=options.outfile)
if __name__ == "__main__":
# Parse command line options with nifty optparse module
from optparse import OptionParser
usage = "usage: %prog [options] cmd [args]"
optprs = OptionParser(usage=usage, version=('%%prog'))
optprs.add_option("--debug", dest="debug", default=False, action="store_true",
help="Enter the pdb debugger on main()")
optprs.add_option("--log", dest="logfile", metavar="FILE",
help="Write logging output to FILE")
optprs.add_option("--loglevel", dest="loglevel", metavar="LEVEL",
type='int', default=logging.INFO,
help="Set logging level to LEVEL")
optprs.add_option("-o", "--outfile", dest="outfile", metavar="FILE",
help="Write mosaic output to FILE")
optprs.add_option("--stderr", dest="logstderr", default=False,
action="store_true",
help="Copy logging also to stderr")
optprs.add_option("--profile", dest="profile", action="store_true",
default=False,
help="Run the profiler on main()")
(options, args) = optprs.parse_args(sys.argv[1:])
# Are we debugging this?
if options.debug:
import pdb
pdb.run('main(options, args)')
# Are we profiling this?
elif options.profile:
import profile
print "%s profile:" % sys.argv[0]
profile.run('main(options, args)')
else:
main(options, args)
# END
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] |
atest/testdata/standard_libraries/builtin/FailUntilSucceeds.py | rdagum/robotframework | 7,073 | 193706 | import time
class FailUntilSucceeds:
ROBOT_LIBRARY_SCOPE = 'TESTCASE'
def __init__(self, times_to_fail=0):
self.times_to_fail = int(times_to_fail)
def set_times_to_fail(self, times_to_fail):
self.__init__(times_to_fail)
def fail_until_retried_often_enough(self, message="Hello", sleep=0):
self.times_to_fail -= 1
time.sleep(sleep)
if self.times_to_fail >= 0:
raise Exception('Still %d times to fail!' % self.times_to_fail)
return message
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alfred_client/tests/pushdata_test.py | coyote240/alfred-client | 0 | 118914 | <gh_stars>0
import unittest
from alfred_client.message import PushData
from alfred_client.packet.struct import alfred_packet
expected_mac = '5e:5c:ce:ca:93:58'
expected_message = 'raspberrypi\n'
expected_length = len(expected_message)
expected_bytes = b'\x00\x00\x00\x1a\x00\x00\x00\x00^\\\xce\xca\x93Xc\x00\x00\x0craspberrypi\n' # noqa
class TestCreatePushData(unittest.TestCase):
def setUp(self):
self.message = PushData()
self.message.source_mac_address = expected_mac
def test_message_type(self):
self.assertEqual(self.message.type, 0)
def test_source_mac_address(self):
self.assertEqual(self.message.source_mac_address, expected_mac)
def test_add_data_block(self):
self.message.add_data_block(99, 0, expected_message)
self.assertIn('length', self.message._data[0])
def test_calculates_correct_data_value_length(self):
self.message.add_data_block(99, 0, expected_message)
self.assertEqual(self.message._data[0]['length'], expected_length)
def test_calculates_correct_message_length(self):
self.message.add_data_block(99, 0, expected_message)
self.assertEqual(self.message.length, 26)
def test_bytes(self):
self.message.add_data_block(99, 0, expected_message)
self.assertEqual(bytes(self.message), expected_bytes)
class TestParsePushData(unittest.TestCase):
def setUp(self):
self.container = alfred_packet.parse(expected_bytes)
self.message = PushData(self.container)
def test_message_type(self):
self.assertEqual(self.message.type, 0)
def test_message_version(self):
self.assertEqual(self.message.version, 0)
def test_message_length(self):
self.assertEqual(self.message.length, 26)
def test_transaction_id(self):
self.assertEqual(self.message.transaction_id, 0)
def test_sequence_number(self):
self.assertEqual(self.message.sequence_number, 0)
@unittest.skip('TODO: move _data to packet_body.alfred_data')
def test_alfred_data_length(self):
self.assertEqual(len(self.message.alfred_data), 1)
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tools/mkblocks.py | Commodore-Bench/u5remastered | 14 | 9918 | <filename>tools/mkblocks.py
#!/usr/bin/env python3
# ----------------------------------------------------------------------------
# Copyright 2019 Drunella
#
# 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 os
import sys
import glob
import subprocess
import argparse
import hashlib
import traceback
import pprint
def readblockmap_info(filename):
directory = dict()
with open(filename) as f:
result = [line.split() for line in f]
for l in result:
directory[l[0]] = l[1:]
return directory
def readdisks_info(filename):
disks = []
with open(filename) as f:
result = [line.split() for line in f]
#pprint.pprint(result)
return result
def readdisks_getdiskinfo(disks, diskname):
for d in disks:
if d[0] == diskname:
return d
return []
def map_initialize():
global bank_data, map_data
map_data = bytearray([0xff] * 0x800)
def crtmap_appendentry(filename, block, name, address):
with open(filename, "at") as f:
content = "{0} f {1} addr 0x{2:04x}\n".format(block, name, address)
return f.write(content)
def load_file(filename):
with open(filename, "rb") as f:
return f.read()
def write_prg(dirname, lowhigh, data):
if lowhigh == 0:
# low
a = bytearray(2)
a[0] = 0
a[1] = 0x80
elif lowhigh == 1:
# high
a = bytearray(2)
a[0] = 0
a[1] = 0xA0
else:
raise Exception("lowhigh can only be 0 or 1")
with open(dirname, "wb") as f:
#f.write(a)
f.write(data)
def blockmap_appendentry(diskid, line, bank, highaddress):
global map_data
base = diskid * 256 + line * 2
map_data[base] = bank
map_data[base+1] = highaddress
#print("blockmap_appendentry: " + str(base) + ": " + str(bank) + " " + str(highaddress))
def calculate_address(lowhigh):
if lowhigh == 0:
# low
a = 0x80
elif lowhigh == 1:
# high
a = 0xA0
else:
raise Exception("lowhigh can only be 0 or 1")
return a
def main(argv):
global bank_data, map_data
p = argparse.ArgumentParser()
p.add_argument("-v", dest="verbose", action="store_true", help="Verbose output.")
p.add_argument("-o", dest="disks", action="store", required=True, help="disk configuration file.")
p.add_argument("-f", dest="files", action="store", required=True, help="files directory.")
p.add_argument("-m", dest="crtfile", action="store", required=True, help="crt.map file")
p.add_argument("-d", dest="destination", action="store", required=True, help="destination directory.")
p.add_argument("-b", dest="blockmap", action="store", required=True, help="blockmap file.")
#p.add_argument("-f", dest="fileoutput", action="store", required=True, help="output data content file.")
args = p.parse_args()
#temp_path = os.path.join(args.build, "temp")
#os.makedirs(temp_path, exist_ok=True)
files_path = args.files #os.path.join(args.build, "files")
os.makedirs(files_path, exist_ok=True)
destination_path = args.destination #os.path.join(args.build, "obj")
os.makedirs(destination_path, exist_ok=True)
disks = readdisks_info(args.disks)
blockmap = readblockmap_info(args.blockmap)
map_initialize()
if os.path.exists(args.crtfile):
os.remove(args.crtfile)
# add blocks file
for d in ("britannia", "towne", "dwelling", "castle", "keep", "dungeon", "underworld"):
diskinfo = readdisks_getdiskinfo(disks, d)
starttrack = int(diskinfo[2], 0)
height = int(diskinfo[4], 0) - int(diskinfo[2], 0) + 1
diskid = int(diskinfo[1], 0) - 0x41
startbank = int(blockmap[d][0], 0)
lowhigh = int(blockmap[d][1], 0)
block_data = load_file(os.path.join(files_path, d + ".data"))
# build map and blocks
map_data[diskid*256+255] = starttrack
for b in range(0, height, 2):
# double line or single line
#factor = 2
#if b+1 >= height:
# factor = 1
# make data
bank_data = bytearray([0xff] * 0x2000)
baseaddress = calculate_address(lowhigh)
if b+1 >= height:
# one line
s = b * 256*16
l = 0x1000
bank_data[0:l] = block_data[s:s+l]
blockmap_appendentry(diskid, b, startbank, baseaddress)
else:
# two lines
s = b * 256*16
l = 0x2000
bank_data[0:l] = block_data[s:s+l]
blockmap_appendentry(diskid, b, startbank, baseaddress)
blockmap_appendentry(diskid, b+1, startbank, baseaddress+0x10)
# write data and map
filename = "{0}_{1:02d}.aprg".format(d, b)
write_prg(os.path.join(destination_path, filename), lowhigh, bank_data)
crtmap_appendentry(args.crtfile, startbank, filename, baseaddress * 0x100)
# increase values
startbank += 1
# write block map
blockmap_bank = int(blockmap["blockmap"][0], 0)
blockmap_lowhigh = int(blockmap["blockmap"][1], 0)
blockmap_address = calculate_address(blockmap_lowhigh) * 256
#blockmap_appendentry(0, b, startbank, baseaddress)
blockmapname = os.path.join(destination_path, "blockmap.aprg")
write_prg(blockmapname, blockmap_lowhigh, map_data)
crtmap_appendentry(args.crtfile, blockmap_bank, "blockmap.aprg", blockmap_address)
return 0
if __name__ == '__main__':
try:
retval = main(sys.argv)
sys.exit(retval)
except Exception as e:
print(e)
traceback.print_exc()
sys.exit(1)
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] |
maxwell/publisher.py | maxwell-dev/maxwell-client-python | 1 | 92236 | import pycommons.logger
import maxwell.protocol.maxwell_protocol_pb2 as protocol_types
logger = pycommons.logger.get_instance(__name__)
class Publisher(object):
# ===========================================
# apis
# ===========================================
def __init__(self, master, connection_mgr, options, loop):
self.__master = master
self.__connection_mgr = connection_mgr
self.__options = options
self.__loop = loop
self.__connections = {}
def close(self):
for connection in self.__connections.values():
self.__disconnect_from_backend(connection)
async def publish(self, topic, value):
endpoint = await self.__master.resolve_backend(topic)
connection = self.__connect_to_backend(endpoint)
await self.__publish(connection, topic, value)
# ===========================================
# connector
# ===========================================
def __connect_to_backend(self, endpoint):
connection = self.__connections.get(endpoint)
if connection == None:
connection = self.__connection_mgr.fetch(endpoint)
self.__connections[endpoint] = connection
return connection
def __disconnect_from_backend(self, endpoint):
connection = self.__connections.pop(endpoint, None)
if connection != None:
self.__connection_mgr.release(connection)
# ===========================================
# internal coros
# ===========================================
async def __publish(self, connection, topic, value):
await connection.wait_until_open()
await connection.request(
self.__build_publish_req(topic, value)
)
# ===========================================
# req builders
# ===========================================
def __build_publish_req(self, topic, value):
push_req = protocol_types.push_req_t()
push_req.topic = topic
push_req.value = value
return push_req
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] |
src/zocalo/wrapper.py | rjgildea/python-zocalo | 2 | 180625 | <reponame>rjgildea/python-zocalo
import logging
import threading
import workflows.util
import zocalo
class BaseWrapper:
def set_recipe_wrapper(self, recwrap):
self.recwrap = recwrap
def prepare(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("starting", payload)
def update(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("updates", payload)
def done(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("completed", payload)
def success(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("success", payload)
def failure(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("failure", str(payload))
def run(self):
raise NotImplementedError()
def record_result_individual_file(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("result-individual-file", payload)
def record_result_all_files(self, payload=""):
if getattr(self, "recwrap", None):
self.recwrap.send_to("result-all-files", payload)
class DummyWrapper(BaseWrapper):
def run(self):
logging.getLogger("zocalo.wrapper.DummyWrapper").info(
"This is a dummy wrapper that simply waits for a minute."
)
import time
time.sleep(10)
self.status_thread.taskname += " (still running)"
time.sleep(50)
return True
class StatusNotifications(threading.Thread):
def __init__(self, send_function, taskname):
super().__init__(name="zocalo status notification")
self.daemon = True
self._send_status = send_function
self._lock = threading.Condition(threading.Lock())
self._status_dict = {
"host": workflows.util.generate_unique_host_id(),
"task": taskname,
"workflows": workflows.version(),
"zocalo": zocalo.__version__,
}
self.set_status(workflows.services.common_service.Status.STARTING)
self._keep_running = True
self.start()
def set_static_status_field(self, field, value):
"""
Add an additional static field to status notifications.
"""
with self._lock:
self._status_dict[field] = value
def set_status(self, status):
with self._lock:
self._status_dict["status"], self._status_dict["statustext"] = (
status.intval,
status.description,
)
self._lock.notify()
@property
def taskname(self):
"""Return the name displayed on service monitors for this task."""
return self._status_dict["task"]
@taskname.setter
def taskname(self, value):
"""Set/update the name displayed on service monitors for this task."""
with self._lock:
self._status_dict["task"] = value
self._lock.notify()
def shutdown(self):
"""Stop the status notification thread."""
self._keep_running = False
def send_status(self, dictionary):
try:
self._send_status(dictionary)
except workflows.Disconnected:
pass
def run(self):
"""Status notification thread main loop."""
with self._lock:
self.send_status(self._status_dict)
while self._keep_running:
self._lock.wait(3)
self.send_status(self._status_dict)
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] |
pyreto/continuous/environment.py | rwl/pylon | 11 | 184120 | <reponame>rwl/pylon<filename>pyreto/continuous/environment.py
#------------------------------------------------------------------------------
# Copyright (C) 2007-2010 <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.
#------------------------------------------------------------------------------
""" Defines an environment for market participants.
"""
#------------------------------------------------------------------------------
# Imports:
#------------------------------------------------------------------------------
import logging
from numpy import array, mean, r_, zeros
from pylon import PQ, PV
from pyreto.discrete import MarketEnvironment as DiscreteMarketEnvironment
#------------------------------------------------------------------------------
# Logging:
#------------------------------------------------------------------------------
logger = logging.getLogger(__name__)
#------------------------------------------------------------------------------
# "MarketEnvironment" class:
#------------------------------------------------------------------------------
class MarketEnvironment(DiscreteMarketEnvironment):
""" Defines a continuous representation of an electricity market
participant's environment.
"""
def __init__(self, generators, market, numOffbids=1, maxMarkup=0.0,
maxWithhold=None):
""" A market environment has a portfolios of generation and a market.
"""
super(MarketEnvironment, self).__init__(generators, market,
numStates=0, markups=None, withholds=None, numOffbids=numOffbids)
#: Maximum price markup.
self.maxMarkup = maxMarkup
#: Maximum quantity withhold.
self.maxWithhold = maxWithhold
#--------------------------------------------------------------------------
# "Environment" interface:
#--------------------------------------------------------------------------
def getSensors(self):
""" Returns the currently visible state of the world as a numpy array
of doubles.
"""
sensors = array([])
sensors = r_[sensors, self._getTotalDemandSensor()]
# sensors = r_[sensors, self._getDemandSensor()]
# sensors = r_[sensors, self._getPriceSensor()]
# sensors = r_[sensors, self._getBusVoltageSensor()]
# sensors = r_[sensors, self._getBusVoltageMagnitudeSensor()]
# sensors = r_[sensors, self._getBusVoltageLambdaSensor()]
# sensors = r_[sensors, self._getBranchFlowSensor()]
# logger.info("State: %s" % sensors)
return sensors
def performAction(self, action):
""" Performs an action on the world that changes it's internal state.
@param action: an action that should be executed in the Environment
@type action: array: [ g1_prc, g2_prc, g1_qty, g2_qty, ... ]
"""
self._lastAction = []
n = self.numOffbids * len(self.generators)
if self.maxWithhold is not None:
markups = action[:n]
withholds = action[n:]
else:
markups = action
withholds = [0.0] * n
self._offbid(markups, withholds)
@property
def indim(self):
""" The number of action values that the environment accepts.
"""
indim = self.numOffbids * len(self.generators)
if self.maxWithhold is not None:
return indim * 2
else:
return indim
@property
def outdim(self):
""" The number of sensor values that the environment produces.
"""
return len(self.getSensors())
# def reset(self):
# """ Re-initialises the market environment.
# """
# self._lastAction = []
# self.market.reset()
#--------------------------------------------------------------------------
# "MarketEnvironment" interface:
#--------------------------------------------------------------------------
def _getTotalDemandSensor(self):
Pd = sum([b.p_demand for b in self.market.case.buses])
return array([Pd])
def _getDemandSensor(self):
Pd = [b.p_demand for b in self.market.case.buses if b.type == PQ]
return array(Pd)
def _getPriceSensor(self):
offers = [offer for offer in self._lastAction]
if len(offers) > 0:
avgPrice = mean([ob.clearedPrice for ob in offers])
else:
avgPrice = 0.0
f = self.market._solution["f"]
return array([avgPrice, f])
def _getBusVoltageSensor(self):
sensor = zeros(len(self.market.case.connected_buses))
for i, bus in enumerate(self.market.case.connected_buses):
if bus.type == PV:
sensor[i] = -bus.mu_vmin + bus.mu_vmax
else:
sensor[i] = bus.v_magnitude
return sensor
def _getBusVoltageMagnitudeSensor(self):
Vm = array([b.v_magnitude for b in self.market.case.connected_buses])
# Va = array([b.v_angle for b in self.market.case.connected_buses])
return Vm
def _getBusVoltageAngleSensor(self):
Va = array([b.v_angle for b in self.market.case.connected_buses])
return Va
def _getBusVoltageLambdaSensor(self):
""" Returns an array of length nb where each value is the sum of the
Lagrangian multipliers on the upper and the negative of the Lagrangian
multipliers on the lower voltage limits. """
muVmin = array([b.mu_vmin for b in self.market.case.connected_buses])
muVmax = array([b.mu_vmax for b in self.market.case.connected_buses])
muVmin = -1.0 * muVmin
diff = muVmin + muVmax
return diff
def _getBranchFlowSensor(self):
Pf = array([l.p_from for l in self.market.case.online_branches])
# Qf = array([l.q_from for l in self.market.case.online_branches])
return Pf
# def _offbidMarkup(self, action):
# for i, g in enumerate(self.generators):
# ratedPMin = self._g0[g]["p_min"]
# ratedPMax = self._g0[g]["p_max"]
# margPCost = self._g0[g]["p_cost"]
# margPCostModel = self._g0[g]["pcost_model"]
#
# # Index of the first markup in 'action' for the current gen.
# k = i * (len(action) / len(self.generators))
#
# # Determine the cost at zero output.
# if margPCostModel == POLYNOMIAL:
# costNoLoad = margPCost[-1]
# else:
# costNoLoad = 0.0
#
# # Divide the rated capacity equally among the offers/bids.
# if g.is_load:
# qty = ratedPMin / self.numOffbids
# else:
# qty = ratedPMax / self.numOffbids
#
# # Get the marginal cost of generating at this output.
## c = g.total_cost(totQty, marginalPCost, marginalPCostModel)
#
# totQty = 0.0
# for j in range(self.numOffbids):
# # Track the total quantity offered/bid for by the generator.
# totQty += qty
#
# # The markups are cumulative to ensure cost function convexity.
# mk = sum(action[k:k + j + 1])
#
# # Marginal cost.
# if margPCostModel == POLYNOMIAL:
# cmarg = polyval(polyder(margPCost), totQty)
# elif margPCostModel == PW_LINEAR:
# n_segments = len(margPCost) - 1
# for i in range(n_segments):
# x1, y1 = margPCost[i]
# x2, y2 = margPCost[i + 1]
# if x1 <= totQty <= x2:
# cmarg = (y2 - y1) / (x2 - x1)
# else:
# raise ValueError, "Invalid bid quantity [%f]." % totQty
# else:
# raise ValueError
#
# # Markup the marginal cost of the generator.
# if not g.is_load:
# prc = cmarg * ((100.0 + mk) / 100.0)
# else:
# prc = cmarg * ((100.0 + mk) / 100.0)
#
# if not g.is_load:
# offer = Offer(g, qty, prc, costNoLoad)
# self.market.offers.append(offer)
#
# self._lastAction.append(offer)
#
# logger.info("%.2fMW offered at %.2f$/MWh for %s (%.1f%%)."
# % (qty, prc, g.name, mk))
# else:
# bid = Bid(g, -qty, prc, costNoLoad)
# self.market.bids.append(bid)
#
# self._lastAction.append(bid)
#
# logger.info("%.2f$/MWh bid for %.2fMW for %s (%.1f%%)."
# % (prc, -qty, g.name, mk))
#
# return self._lastAction
# EOF -------------------------------------------------------------------------
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sphinx-sources/Examples/ComputerPrac/FresnelPlane.py | jccmak/lightpipes | 132 | 26893 | #!/usr/bin/env python
"""
Computer practical 6.1. Fresnel diffraction, plane wavefront.
=============================================================
This is part of the 'computer practical' set of assignments.
Demonstrates Fresnel diffraction when a plane wavefront enters
a round hole.
Measure the values of z and d for which minima and/or maxima on-axis occur
and apply the Fresnel-zone theory to find the wavelength of the light.
"""
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import sys
import webbrowser
if sys.version_info[0] < 3:
from Tkinter import *
import Tkinter as Tk
else:
from tkinter import *
import tkinter as Tk
from LightPipes import *
root = Tk.Tk()
root.wm_title("Computer practical: 6.1 Fresnel plane wavefront. LP-version = " + LPversion)
root.wm_protocol("WM_DELETE_WINDOW", root.quit)
wavelength=530*nm;
size=5*mm;
N=200; N2=int(N/2)
z=20*cm
R=0.5*mm
D=DoubleVar()
Z=DoubleVar()
D.set(2*R/mm)
Z.set(z/cm)
fig=plt.figure(figsize=(8,8))
ax1 = fig.add_subplot(111)
canvas = FigureCanvasTkAgg(fig, master=root)
canvas._tkcanvas.pack(side=Tk.LEFT, fill=Tk.BOTH, expand=1)
v=StringVar()
def TheExample(event):
global I
F=Begin(size,wavelength,N)
z=Z.get()*cm
R=D.get()/2*mm
F=CircAperture(R,0,0,F)
FN=R*R/z/wavelength
if (FN >= 15.0):
F=Forvard(z,F)
else:
F=Fresnel(z,F)
I=Intensity(0,F)
ax1.clear()
ax1.contourf(I,50,cmap='hot'); ax1.axis('off'); ax1.axis('equal')
str='Intensity distribution\ncenter-irradiance = %3.3f [a.u.]' %I[N2][N2]
ax1.set_title(str)
canvas.draw()
def motion(event):
x=event.xdata;y=event.ydata
if (x and y is not None and x>0 and x<N and y>0 and y<N):
v.set('x=%3.2f mm, y=%3.2f mm\n I=%3.3f [a.u.]' %((-size/2+x*size/N)/mm,(-size/2+y*size/N)/mm,I[int(x)][int(y)]))
root.configure(cursor='crosshair')
else:
v.set('')
root.configure(cursor='arrow')
def openbrowser(event):
webbrowser.open_new(r"https://opticspy.github.io/lightpipes/FresnelDiffraction.html")
def _quit():
root.quit()
Scale( root,
takefocus = 1,
orient='horizontal',
label = 'diameter aperture [mm]',
length = 200, from_=0.5, to=size/2/mm,
resolution = 0.001,
variable = D,
cursor="hand2",
command = TheExample).pack()
Scale( root,
takefocus = 1,
orient='horizontal',
label = 'z [cm]',
length = 200,
from_=0.01, to=200.0,
resolution = 0.01,
variable = Z,
cursor="hand2",
command = TheExample).pack()
Button( root,
width = 24,
text='Quit',
cursor="hand2",
command=_quit).pack(pady=10)
link = Label(root, text="help", fg="blue", cursor="hand2")
link.pack()
link.bind("<Button-1>", openbrowser)
Label(root, textvariable=v).pack(pady=50)
cid = fig.canvas.mpl_connect('motion_notify_event', motion)
TheExample(0)
root.mainloop()
root.destroy()
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7888,
580,
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4632,
29889,
20524,
580,
13,
13,
2
] |
pyffs/automaton_management/__init__.py | rominf/pyffs | 21 | 82724 | <reponame>rominf/pyffs
from .automaton_manager import manager
from .utils import generate_automaton_to_file
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py2cytoscape/cyrest/apps.py | g-simmons2/py2cytoscape | 97 | 117889 | from .base import *
class apps(object):
"""
cytoscape session interface as shown in CyREST's swagger documentation.
:param url: an url of the type 'http://' + host + ':' + str(port) + '/' + version + '/'.
"""
def __init__(self, url):
self.__url = url + 'commands/apps'
self.___url=url
def getAppList(self, verbose=None):
"""
Returns installed Cytoscape Apps that have CyREST accessible Functions or Commands, as a list of App names.
:param verbose: print more
:returns: 200: successful operation
"""
response=api(url=self.___url+'apps', method="H", verbose=verbose, parse_params=False)
return response | [
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monk/tf_keras_1/finetune/level_11_optimizers_main.py | Sanskar329/monk_v1 | 7 | 31468 | from tf_keras_1.finetune.imports import *
from system.imports import *
from tf_keras_1.finetune.level_10_schedulers_main import prototype_schedulers
class prototype_optimizers(prototype_schedulers):
'''
Main class for all optimizers in expert mode
Args:
verbose (int): Set verbosity levels
0 - Print Nothing
1 - Print desired details
'''
@accepts("self", verbose=int, post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def __init__(self, verbose=1):
super().__init__(verbose=verbose);
###############################################################################################################################################
@warning_checks(None, ["lt", 1], momentum=["lt", 1.5], weight_decay=["lt", 0.01], momentum_dampening_rate=None,
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], momentum=["gte", 0], weight_decay=["gte", 0], momentum_dampening_rate=None,
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], momentum=[int, float], weight_decay=[int, float], momentum_dampening_rate=[int, float],
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
'''
Select stochastic gradient descent optimizer
Args:
learning_rate (float): Initial base learning rate
momentum (float): Momentum value for driving the weights towards minima
weight_decay (float): Value for regularizing weights post every update
momentum_dampening_rate (float): Reduction rate for momentum
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = sgd(self.system_dict, learning_rate,
momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt", 1], momentum=["lt", 1.5], weight_decay=["lt", 0.01], momentum_dampening_rate=None,
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], momentum=["gte", 0], weight_decay=["gte", 0], momentum_dampening_rate=None,
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], momentum=[int, float], weight_decay=[int, float], momentum_dampening_rate=[int, float],
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_nesterov_sgd(self, learning_rate, momentum=0, weight_decay=0, momentum_dampening_rate=0, clipnorm=0.0, clipvalue=0.0):
'''
Select stochastic gradient descent optimizer with nesterov acceleration
Args:
learning_rate (float): Initial base learning rate
momentum (float): Momentum value for driving the weights towards minima
weight_decay (float): Value for regularizing weights post every update
momentum_dampening_rate (float): Reduction rate for momentum
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = nesterov_sgd(self.system_dict, learning_rate,
momentum=momentum, weight_decay=weight_decay, momentum_dampening_rate=momentum_dampening_rate, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
ConstraintWarning("ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt", 1], decay_rate=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01],
clipnorm=None, clipvalue=None, post_trace=None)
@error_checks(None, ["gt", 0], decay_rate=["gt", 0], epsilon=["gte", 0], weight_decay=["gte", 0],
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], decay_rate=[int, float], epsilon=[int, float], weight_decay=[int, float],
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_rmsprop(self, learning_rate, decay_rate=0.99, epsilon=1e-08, weight_decay=0, clipnorm=0.0, clipvalue=0.0):
'''
Select root mean score prop optimizer
Args:
learning_rate (float): Initial base learning rate
decay_rate (float): A decay factor of moving average over past squared gradient.
epsilon (float): A value to avoid division by zero
weight_decay (float): Value for regularizing weights post every update
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = rmsprop(self.system_dict , learning_rate,
decay_rate=decay_rate, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt, 1"], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], amsgrad=None,
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epssilon=["gte", 0], weight_decay=["gte", 0], amsgrad=None,
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float], amsgrad=bool,
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False,
clipnorm=0.0, clipvalue=0.0):
'''
Select ADAM optimizer
Args:
learning_rate (float): Initial base learning rate
beta1 (float): Exponential decay rate for first momentum estimates
beta2 (float): Exponential decay rate for first second estimates
weight_decay (float): Value for regularizing weights post every update
amsgrad (bool): If True, AMSGrad variant of this algorithm is used
epsilon (float): A value to avoid division by zero
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = adam(self.system_dict, learning_rate,
beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
ConstraintWarning("ArgumentWarning: amsgrad is active only for keras and pytorch in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt, 1"], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01], amsgrad=None,
momentum_decay=None, clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epssilon=["gte", 0], weight_decay=["gte", 0], amsgrad=None,
momentum_decay=None, clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float], amsgrad=bool,
momentum_decay=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_nesterov_adam(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0, amsgrad=False,
momentum_decay=0.004, clipnorm=0.0, clipvalue=0.0):
'''
Select ADAM optimizer with nesterov momentum acceleration
Args:
learning_rate (float): Initial base learning rate
beta1 (float): Exponential decay rate for first momentum estimates
beta2 (float): Exponential decay rate for first second estimates
weight_decay (float): Value for regularizing weights post every update
amsgrad (bool): If True, AMSGrad variant of this algorithm is used
epsilon (float): A value to avoid division by zero
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = nesterov_adam(self.system_dict, learning_rate,
beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, amsgrad=amsgrad,
momentum_decay=momentum_decay, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("OptimizerWarning: nesterov adam is active only for keras and gluon in current version of Monk");
self.custom_print("");
ConstraintWarning("ArgumentWarning: amsgrad is inactive in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt", 1], beta1=["lt", 1], beta2=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01],
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], beta1=["gte", 0], beta2=["gte", 0], epsilon=["gte", 0], weight_decay=["gte", 0],
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], beta1=[int, float], beta2=[int, float], epsilon=[int, float], weight_decay=[int, float],
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_adamax(self, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, weight_decay=0,
clipnorm=0.0, clipvalue=0.0):
'''
Select Adamax optimizer
Args:
learning_rate (float): Initial base learning rate
beta1 (float): Exponential decay rate for first momentum estimates
beta2 (float): Exponential decay rate for first second estimates
weight_decay (float): Value for regularizing weights post every update
epsilon (float): A value to avoid division by zero
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = adamax(self.system_dict, learning_rate,
beta1=beta1, beta2=beta2, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt", 1], rho=["lt", 1], epsilon=["lt", 0.001], weight_decay=["lt", 0.01],
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], rho=["gt", 0], epsilon=["gte", 0], weight_decay=["gte", 0],
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], rho=[int, float], epsilon=[int, float], weight_decay=[int, float],
clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_adadelta(self, learning_rate, rho=0.9, epsilon=1e-06, weight_decay=0,
clipnorm=0.0, clipvalue=0.0):
'''
Select Adadelta optimizer
Args:
learning_rate (float): Initial base learning rate
rho (float): Exponential decay rate for momentum estimates
weight_decay (float): Value for regularizing weights post every update
epsilon (float): A value to avoid division by zero
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = adadelta(self.system_dict, learning_rate,
rho=rho, epsilon=epsilon, weight_decay=weight_decay, clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
###############################################################################################################################################
###############################################################################################################################################
@warning_checks(None, ["lt", 1], learning_rate_decay=None, weight_decay=["lt", 0.01], epsilon=None,
clipnorm=None, clipvalue=None, post_trace=False)
@error_checks(None, ["gt", 0], learning_rate_decay=None, weight_decay=["gte", 0], epsilon=None,
clipnorm=None, clipvalue=None, post_trace=False)
@accepts("self", [int, float], learning_rate_decay=[int, float], weight_decay=[int, float],
epsilon=[int, float], clipnorm=[int, float], clipvalue=[int, float], post_trace=False)
#@TraceFunction(trace_args=True, trace_rv=True)
def optimizer_adagrad(self, learning_rate, learning_rate_decay=0, weight_decay=0, epsilon=1e-08,
clipnorm=0.0, clipvalue=0.0):
'''
Select Adagrad optimizer
Args:
learning_rate (float): Initial base learning rate
learning_rate_decay (float): Learning rate decay factor
weight_decay (float): Value for regularizing weights post every update
epsilon (float): A value to avoid division by zero
clipnorm (float): Gradient clipping factor
clipvalue (float): Value for clipping
Returns:
None
'''
self.system_dict = adagrad(self.system_dict, learning_rate,
learning_rate_decay=learning_rate_decay, weight_decay=weight_decay, epsilon=epsilon,
clipnorm=clipnorm, clipvalue=clipvalue);
self.custom_print("Optimizer");
self.custom_print(" Name: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["name"]));
self.custom_print(" Learning rate: {}".format(self.system_dict["hyper-parameters"]["learning_rate"]));
self.custom_print(" Params: {}".format(self.system_dict["hyper-parameters"]["optimizer"]["params"]));
self.custom_print("");
ConstraintWarning("ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk");
self.custom_print("");
ConstraintWarning("ArgumentWarning: learning_rate_decay is active only for pytorch in current version of Monk");
self.custom_print("");
###############################################################################################################################################
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test/test_specs.py | USGS-EROS/lcmap-merlin | 0 | 97071 | from cytoolz import cons
from cytoolz import merge
from merlin import specs
def test_only():
registry = [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'},
{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]
expected = tuple([{'key': 1, 'ubid': 'a'}, {'key': 4, 'ubid': 'd'}])
result = specs.only(ubids=['a', 'd'], specs=registry)
assert expected == result
expected = tuple([{'key': 1, 'ubid': 'a'}, {'key': 4, 'ubid': 'd'}])
result = specs.only(ubids=['d', 'a'], specs=registry)
assert expected == result
expected = tuple()
result = specs.only(ubids=['z'], specs=registry)
assert expected == result
def test_mapped():
registry = [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'},
{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]
ubids = {'red': ['a', 'd'], 'blue': ['b', 'c']}
expected = {'red': ({'key': 1, 'ubid': 'a'}, {'key': 4, 'ubid': 'd'}),
'blue': ({'key': 2, 'ubid': 'b'}, {'key': 3, 'ubid': 'c'})}
result = specs.mapped(ubids=ubids, specs=registry)
assert expected == result
def test_exist():
registry = [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'},
{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]
ubids = ['a', 'd']
assert True==specs.exist(ubids=ubids, specs=registry)
ubids = ['a', 'b', 'x']
assert False==specs.exist(ubids=ubids, specs=registry)
def test_index():
registry = [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'},
{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]
results = specs.index(registry)
# check that dicts were rekeyed into a new dict
assert all(map(lambda r: r in results, ['a', 'b', 'c', 'd']))
# check structure of new dict values
assert all(map(lambda r: 'ubid' in r and 'key' in r, results.values()))
def test_ubids():
registry = [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'},
{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]
data = list(cons({'nope': 'z'}, registry))
good = filter(lambda f: 'ubid' in f, data)
assert set(map(lambda u: u['ubid'], good)) == set(specs.ubids(data))
def test_ubids():
ubids = [{'ubid': 1}, {'ubid': 2}, {'ubid': 3}, {'ubid': 4}]
assert len(specs.ubids(ubids)) == 4
def test_refspec():
specmap = {'red': [{'key': 1, 'ubid': 'a'}, {'key': 2, 'ubid': 'b'}],
'blue': [{'key': 3, 'ubid': 'c'}, {'key': 4, 'ubid': 'd'}]}
assert type(specs.refspec(specmap)) is dict
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] |
deem/pytorch/torch_utils.py | kuisu-GDUT/UltraGCN | 0 | 69548 | # ==============================================================================
# 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 sys
import os
import numpy as np
import torch
from torch import nn
import random
def seed_everything(seed=1029):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
def set_device(gpu=-1):
if gpu >= 0 and torch.cuda.is_available():
device = torch.device("cuda:" + str(gpu))
else:
device = torch.device("cpu")
return device
def set_optimizer(optimizer):
if isinstance(optimizer, str):
if optimizer.lower() == "adam":
optimizer = "Adam"
elif optimizer.lower() == "rmsprop":
optimizer = "RMSprop"
elif optimizer.lower() == "sgd":
optimizer = "SGD"
return getattr(torch.optim, optimizer)
def set_loss(loss):
if isinstance(loss, str):
if loss in ["bce", "binary_crossentropy", "binary_cross_entropy"]:
loss = "binary_cross_entropy"
else:
raise NotImplementedError("loss={} is not supported.".format(loss))
return loss
def set_regularizer(reg):
reg_pair = [] # of tuples (p_norm, weight)
if isinstance(reg, float):
reg_pair.append((2, reg))
elif isinstance(reg, str):
try:
if reg.startswith("l1(") or reg.startswith("l2("):
reg_pair.append((int(reg[1]), float(reg.rstrip(")").split("(")[-1])))
elif reg.startswith("l1_l2"):
l1_reg, l2_reg = reg.rstrip(")").split("(")[-1].split(",")
reg_pair.append((1, float(l1_reg)))
reg_pair.append((2, float(l2_reg)))
else:
raise NotImplementedError
except:
raise NotImplementedError("regularizer={} is not supported.".format(reg))
return reg_pair
def set_activation(activation):
if isinstance(activation, str):
if activation.lower() == "relu":
return nn.ReLU()
elif activation.lower() == "sigmoid":
return nn.Sigmoid()
elif activation.lower() == "tanh":
return nn.Tanh()
else:
return getattr(nn, activation)()
else:
return activation
def pad_sequences(sequences, maxlen=None, dtype='int32',
padding='pre', truncating='pre', value=0.):
""" Pads sequences (list of list) to the ndarray of same length
This is an equivalent implementation of tf.keras.preprocessing.sequence.pad_sequences
for Pytorch
"""
assert padding in ["pre", "post"], "Invalid padding={}.".format(padding)
assert truncating in ["pre", "post"], "Invalid truncating={}.".format(truncating)
if maxlen is None:
maxlen = max(len(x) for x in sequences)
arr = np.full((len(sequences), maxlen), value, dtype=dtype)
for idx, x in enumerate(sequences):
if len(x) == 0:
continue # empty list
if truncating == 'pre':
trunc = x[-maxlen:]
else:
trunc = x[:maxlen]
trunc = np.asarray(trunc, dtype=dtype)
if padding == 'pre':
arr[idx, -len(trunc):] = trunc
else:
arr[idx, :len(trunc)] = trunc
return arr | [
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] |
demo/trace_model.py | furkankirac/maskrcnn-benchmark | 0 | 12035 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from __future__ import division
import os
import numpy
from io import BytesIO
from matplotlib import pyplot
import requests
import torch
from PIL import Image
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo
from maskrcnn_benchmark.structures.image_list import ImageList
if __name__ == "__main__":
# load config from file and command-line arguments
project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
cfg.merge_from_file(
os.path.join(project_dir,
"configs/caffe2/e2e_mask_rcnn_R_50_FPN_1x_caffe2.yaml"))
cfg.merge_from_list(["MODEL.DEVICE", "cpu"])
cfg.freeze()
# prepare object that handles inference plus adds predictions on top of image
coco_demo = COCODemo(
cfg,
confidence_threshold=0.7,
show_mask_heatmaps=False,
masks_per_dim=2,
min_image_size=480,
)
def single_image_to_top_predictions(image):
image = image.float() / 255.0
image = image.permute(2, 0, 1)
# we are loading images with OpenCV, so we don't need to convert them
# to BGR, they are already! So all we need to do is to normalize
# by 255 if we want to convert to BGR255 format, or flip the channels
# if we want it to be in RGB in [0-1] range.
if cfg.INPUT.TO_BGR255:
image = image * 255
else:
image = image[[2, 1, 0]]
# we absolutely want fixed size (int) here (or we run into a tracing error (or bug?)
# or we might later decide to make things work with variable size...
image = image - torch.tensor(cfg.INPUT.PIXEL_MEAN)[:, None, None]
# should also do variance...
image_list = ImageList(image.unsqueeze(0), [(int(image.size(-2)), int(image.size(-1)))])
result, = coco_demo.model(image_list)
scores = result.get_field("scores")
keep = (scores >= coco_demo.confidence_threshold)
result = (result.bbox[keep],
result.get_field("labels")[keep],
result.get_field("mask")[keep],
scores[keep])
return result
@torch.jit.script
def my_paste_mask(mask, bbox, height, width, threshold=0.5, padding=1, contour=True, rectangle=False):
# type: (Tensor, Tensor, int, int, float, int, bool, bool) -> Tensor
padded_mask = torch.constant_pad_nd(mask, (padding, padding, padding, padding))
scale = 1.0 + 2.0 * float(padding) / float(mask.size(-1))
center_x = (bbox[2] + bbox[0]) * 0.5
center_y = (bbox[3] + bbox[1]) * 0.5
w_2 = (bbox[2] - bbox[0]) * 0.5 * scale
h_2 = (bbox[3] - bbox[1]) * 0.5 * scale # should have two scales?
bbox_scaled = torch.stack([center_x - w_2, center_y - h_2,
center_x + w_2, center_y + h_2], 0)
TO_REMOVE = 1
w = (bbox_scaled[2] - bbox_scaled[0] + TO_REMOVE).clamp(min=1).long()
h = (bbox_scaled[3] - bbox_scaled[1] + TO_REMOVE).clamp(min=1).long()
scaled_mask = torch.ops.maskrcnn_benchmark.upsample_bilinear(padded_mask.float(), h, w)
x0 = bbox_scaled[0].long()
y0 = bbox_scaled[1].long()
x = x0.clamp(min=0)
y = y0.clamp(min=0)
leftcrop = x - x0
topcrop = y - y0
w = torch.min(w - leftcrop, width - x)
h = torch.min(h - topcrop, height - y)
# mask = torch.zeros((height, width), dtype=torch.uint8)
# mask[y:y + h, x:x + w] = (scaled_mask[topcrop:topcrop + h, leftcrop:leftcrop + w] > threshold)
mask = torch.constant_pad_nd((scaled_mask[topcrop:topcrop + h, leftcrop:leftcrop + w] > threshold),
(int(x), int(width - x - w), int(y), int(height - y - h))) # int for the script compiler
if contour:
mask = mask.float()
# poor person's contour finding by comparing to smoothed
mask = (mask - torch.nn.functional.conv2d(mask.unsqueeze(0).unsqueeze(0),
torch.full((1, 1, 3, 3), 1.0 / 9.0), padding=1)[0, 0]).abs() > 0.001
if rectangle:
x = torch.arange(width, dtype=torch.long).unsqueeze(0)
y = torch.arange(height, dtype=torch.long).unsqueeze(1)
r = bbox.long()
# work around script not liking bitwise ops
rectangle_mask = ((((x == r[0]) + (x == r[2])) * (y >= r[1]) * (y <= r[3]))
+ (((y == r[1]) + (y == r[3])) * (x >= r[0]) * (x <= r[2])))
mask = (mask + rectangle_mask).clamp(max=1)
return mask
@torch.jit.script
def add_annotations(image, labels, scores, bboxes, class_names=','.join(coco_demo.CATEGORIES), color=torch.tensor([255, 255, 255], dtype=torch.long)):
# type: (Tensor, Tensor, Tensor, Tensor, str, Tensor) -> Tensor
result_image = torch.ops.maskrcnn_benchmark.add_annotations(image, labels, scores, bboxes, class_names, color)
return result_image
@torch.jit.script
def combine_masks(image, labels, masks, scores, bboxes, threshold=0.5, padding=1, contour=True, rectangle=False, palette=torch.tensor([33554431, 32767, 2097151])):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, float, int, bool, bool, Tensor) -> Tensor
height = image.size(0)
width = image.size(1)
image_with_mask = image.clone()
for i in range(masks.size(0)):
color = ((palette * labels[i]) % 255).to(torch.uint8)
one_mask = my_paste_mask(masks[i, 0], bboxes[i], height, width, threshold, padding, contour, rectangle)
image_with_mask = torch.where(one_mask.unsqueeze(-1), color.unsqueeze(0).unsqueeze(0), image_with_mask)
image_with_mask = add_annotations(image_with_mask, labels, scores, bboxes)
return image_with_mask
def process_image_with_traced_model(image):
original_image = image
if coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY:
assert (image.size(0) % coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY == 0
and image.size(1) % coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY == 0)
boxes, labels, masks, scores = traced_model(image)
# todo: make this in one large thing
result_image = combine_masks(original_image, labels, masks, scores, boxes, 0.5, 1, rectangle=True)
return result_image
def fetch_image(url):
response = requests.get(url)
return Image.open(BytesIO(response.content)).convert("RGB")
if __name__ == "__main__":
pil_image = fetch_image(
url="http://farm3.staticflickr.com/2469/3915380994_2e611b1779_z.jpg")
# convert to BGR format
image = torch.from_numpy(numpy.array(pil_image)[:, :, [2, 1, 0]])
original_image = image
if coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY:
assert (image.size(0) % coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY == 0
and image.size(1) % coco_demo.cfg.DATALOADER.SIZE_DIVISIBILITY == 0)
for p in coco_demo.model.parameters():
p.requires_grad_(False)
traced_model = torch.jit.trace(single_image_to_top_predictions, (image,))
@torch.jit.script
def end_to_end_model(image):
boxes, labels, masks, scores = traced_model(image)
result_image = combine_masks(image, labels, masks, scores, boxes, 0.5, 1, rectangle=True)
return result_image
end_to_end_model.save('end_to_end_model.pt')
result_image = process_image_with_traced_model(original_image)
# self.show_mask_heatmaps not done
pyplot.imshow(result_image[:, :, [2, 1, 0]])
pyplot.show()
# second image
image2 = fetch_image(
url='http://farm4.staticflickr.com/3153/2970773875_164f0c0b83_z.jpg')
image2 = image2.resize((640, 480), Image.BILINEAR)
image2 = torch.from_numpy(numpy.array(image2)[:, :, [2, 1, 0]])
result_image2 = process_image_with_traced_model(image2)
# self.show_mask_heatmaps not done
pyplot.imshow(result_image2[:, :, [2, 1, 0]])
pyplot.show()
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] |
pymeasure/virtual/power_test_setup.py | tinix84/pymeasure | 2 | 31752 | class EfficiencyMeasurement():
# input_voltage = None
# input_current = None
# output_voltage = None
# output_current = None
# input_power = None
# output_power = None
# loss_power = None
# efficiency = None
def __init__(self, input_voltage: float, input_current: float,
output_voltage: float, output_current: float):
self.input_voltage = input_voltage
self.input_current = input_current
self.output_voltage = output_voltage
self.output_current = output_current
self.input_power = calc_power(voltage = input_voltage, current = input_current)
self.output_power = calc_power(voltage = output_voltage, current = output_current)
self.loss_power = calc_loss_power(input_power=self.input_power, ouput_power=self.output_power)
class TsetPoint():
setup = None
measurement = None #EfficiencyMeasurement()
class TestSet_Efficiency():
def __init__(self, s: dict):
self.source_bias = s['source_bias']
self.dut = s['powertrain']
self.load = s['load'] | [
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dlm/metrics/accuracy.py | romainloiseau/deep-linear-shapes | 20 | 119546 | <gh_stars>10-100
from collections import OrderedDict
import numpy as np
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from scipy.optimize import linear_sum_assignment
import torch
from ..viz import plot_matrix
from .basemetric import DiscreetMetric
class Accuracy(DiscreetMetric):
"""
Compute the following scores:
- global accuracy
- mean accuracy
- accuracy by class
"""
def __init__(self, classes, *args, **kwargs):
self.names = ['global_acc', 'avg_acc']
if type(classes) is int:
self.perclass_names = [f'acc_cls{i}' for i in range(classes)]
self.n_classes = classes
elif type(classes) is list:
self.perclass_names = [f'acc_{c}' for c in classes]
self.n_classes = len(classes)
super(Accuracy, self).__init__(*args, **kwargs)
def compute(self, best_assign_idx = None):
if self.can_compute():
self.compute_confusion_matrix()
acc = np.diag(self.matrix).sum() / self.matrix.sum()
with np.errstate(divide='ignore', invalid='ignore'):
acc_by_class = np.diag(self.matrix) / self.matrix.sum(axis=1)
avg_acc = np.mean(np.nan_to_num(acc_by_class))
self.results = OrderedDict(zip(self.names, [acc, avg_acc]))
self.detailed_results = OrderedDict(zip(self.perclass_names, acc_by_class.tolist()))
self.reset()
def compute_confusion_matrix(self):
matrix = np.bincount(self.n_classes * self.true + self.pred,
minlength=self.n_classes**2)
self.matrix = matrix.reshape(self.n_classes, self.n_classes)
def plot(self):
plot_matrix(self.matrix)
if __name__=="__main__":
acc = Accuracy(10, 10)
acc.update(np.random.randint(0, 10, 200),
np.random.randint(0, 10, 200))
acc.compute()
acc.plot()
print(acc) | [
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elena/emulators/workpiece_emulator.py | FYQ0919/Your_First_Decentralized_Application_Python | 2 | 60571 | <gh_stars>1-10
class Workpiece:
def __init__(self, id):
self.id = id
# self.status = "awaiting production"
self.status = "awaiting next step"
self.actual_quality = None
self.source = None
self.sink = None
self.location = "wc_0" # Starting point. Location refers to workcell id.
self.step_idx = None
self.pos = None
self.count_down = 0
| [
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innn/META_IPT_BD_TLS.py | mengban/traffic-src-spyder | 0 | 67365 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 31 11:26:25 2018
@author: cadu
"""
print("66")
import pandas as pd
import numpy as np
from sklearn import datasets, svm, metrics
from sklearn import cross_validation
import matplotlib.pyplot as plt
import sys
sys.path.append("../src")
from data_prov2 import get_tt
## load data
data_train,label_train=get_tt()
############################################
label_train = np.resize(label_train,(len(label_train),))
X_train,X_test,y_train,y_test=cross_validation.train_test_split(data_train,label_train,test_size=0.2)
X_val,X_test,y_val,y_test=cross_validation.train_test_split(X_test,y_test,test_size=0.5)
print(X_train.shape,X_val.shape,X_test.shape,y_test.shape)
############################################
def extract_batch_size(_train, step, batch_size):# Function to fetch a "batch_size" amount of data from "(X|y)_train" data.
shape = list(_train.shape) #_X 7352 128 9
shape[0] = batch_size # 1500 128 9
batch_s = np.empty(shape)
for i in range(batch_size):
# Loop index
index = ((step-1)*batch_size + i) % len(_train) # step=1
batch_s[i] = _train[index]
return batch_s
############################################
import tensorflow as tf
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 671])
W = tf.Variable(tf.zeros([671, 8]))
b = tf.Variable(tf.zeros([8]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.int64, [None])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.losses.sparse_softmax_cross_entropy on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=y)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
batch_size=256
print_fre=10
for _ in range(1000):
#batch_xs, batch_ys = mnist.train.next_batch(100)
batch_xs = extract_batch_size(X_train,_ ,batch_size)
batch_xss = extract_batch_size(X_test,_ ,batch_size)
batch_ys = extract_batch_size(y_train,_,batch_size)
batch_yss = extract_batch_size(y_test,_,batch_size)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), y_)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
if _%print_fre==0:
print(sess.run(
accuracy, feed_dict={
x: batch_xss,
y_: batch_yss
}))
print(sess.run(
tf.argmax(y, 1), feed_dict={
x: batch_xss,
y_: batch_yss
}))
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src/gridemissions/scripts/ampd.py | elijahjpassmore/gridemissions | 4 | 175876 | <reponame>elijahjpassmore/gridemissions
import os
from os.path import join
import pandas as pd
import argparse
import logging.config
from gridemissions import config
from gridemissions.ampd import AMPD_download, extract_state
def main():
# Parse command-line arguments
argparser = argparse.ArgumentParser()
argparser.add_argument("--job", default="d", help="Which job to run")
argparser.add_argument("--state", default="all", help="Which state to process")
argparser.add_argument("--year", default="all", help="Which year to process")
args = argparser.parse_args()
logger = logging.getLogger("scraper")
DATA_PATH = config["DATA_PATH"]
# Download data
if args.job == "d":
if args.year == "all":
raise NotImplemented("Not done yet")
count_timed_out = 0
status = "timedout"
y = int(args.year)
while status == "timedout":
ampd = AMPD_download(os.path.join(DATA_PATH, "raw", "AMPD"))
ampd.login()
ampd.getStatus(y)
status = ampd.download(y)
if status == "timed_out":
count_timed_out += 1
if count_timed_out > 5:
raise ValueError("Too many timeouts")
ampd.close()
if args.job == "e":
if args.state == "all":
raise NotImplemented("Not done yet")
if args.year == "all":
year_lst = ["2015", "2016", "2017", "2018", "2019"]
else:
year_lst = [args.year]
for year in year_lst:
path_in = join(config["DATA_PATH"], "raw", "AMPD", year)
path_out = join(config["DATA_PATH"], "analysis", "AMPD")
os.makedirs(path_out, exist_ok=True)
extract_state((path_in, path_out, year, args.state))
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Leetcode/1000-2000/1202. Smallest String With Swaps/1202.py | Next-Gen-UI/Code-Dynamics | 0 | 174222 | <filename>Leetcode/1000-2000/1202. Smallest String With Swaps/1202.py
class UF:
def __init__(self, n: int):
self.id = list(range(n))
def union(self, u: int, v: int) -> None:
self.id[self.find(u)] = self.find(v)
def find(self, u: int) -> int:
if self.id[u] != u:
self.id[u] = self.find(self.id[u])
return self.id[u]
class Solution:
def smallestStringWithSwaps(self, s: str, pairs: List[List[int]]) -> str:
ans = ''
uf = UF(len(s))
map = defaultdict(list)
for p, q in pairs:
uf.union(p, q)
for i in range(len(s)):
map[uf.find(i)].append(s[i])
for key in map.keys():
map[key].sort(reverse=True)
for i in range(len(s)):
ans += map[uf.find(i)].pop()
return ans
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src/waldur_core/core/migrations/0003_enlarge_username.py | geant-multicloud/MCMS-mastermind | 26 | 166176 | # Generated by Django 1.11.13 on 2018-09-11 14:40
import re
import django.core.validators
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0002_remove_organization'),
]
operations = [
migrations.AlterField(
model_name='user',
name='username',
field=models.CharField(
help_text='Required. 128 characters or fewer. Letters, numbers and @/./+/-/_ characters',
max_length=128,
unique=True,
validators=[
django.core.validators.RegexValidator(
re.compile('^[\\w.@+-]+$'), 'Enter a valid username.', 'invalid'
)
],
verbose_name='username',
),
),
]
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] |
documentation/tutorial/service_v5.py | F-Secure/resource-api | 10 | 153954 | import shelve
import os
import re
from resource_api.interfaces import Resource as BaseResource, Link as BaseLink, AbstractUriPolicy
from resource_api.schema import StringField, DateTimeField, IntegerField
from resource_api.service import Service
from resource_api.errors import ValidationError
RE_SHA1 = re.compile("^[a-f0-9]{40}$")
SHELVE_PATH = "/tmp/school.shelve.db"
class User(object):
def __init__(self, email=None):
if email is None:
self.is_anonymous = True
else:
self.is_anonymous = False
if email == "<EMAIL>":
self.is_admin = True
else:
self.is_admin = False
self.email = email
class ShelveService(Service):
def __init__(self):
super(ShelveService, self).__init__()
self._storage = shelve.open(SHELVE_PATH, writeback=True)
def _get_context(self):
return {"storage": self._storage, "service": self}
def _get_user(self, data):
if data is None:
return User(None)
else:
return User(data.get("email"))
def __del__(self):
self._storage.close()
class Resource(BaseResource):
def __init__(self, context):
super(Resource, self).__init__(context)
self._storage = context["storage"]
def exists(self, user, pk):
return pk in self._storage.get(self.get_name(), {})
def get_data(self, user, pk):
return self._storage.get(self.get_name(), {}).get(pk)
def delete(self, user, pk):
self._storage.get(self.get_name(), {}).pop(pk)
self._storage.sync()
def create(self, user, pk, data):
if self.get_name() not in self._storage:
self._storage[self.get_name()] = {}
self._storage[self.get_name()][pk] = data
self._storage.sync()
def update(self, user, pk, data):
self._storage[self.get_name()][pk].update(data)
self._storage.sync()
def get_uris(self, user, params=None):
return self._storage.get(self.get_name(), {}).keys()
def get_count(self, user, params=None):
return len(self.get_uris(params))
def can_get_data(self, user, pk, data):
""" Only authenticated users can access data """
if user.is_anonymous:
return False
else:
return True
def can_get_uris(self, user):
""" Only authenticated users can access data """
if user.is_anonymous:
return False
else:
return True
class Link(BaseLink):
def __init__(self, context):
super(Link, self).__init__(context)
self._storage = context["storage"]
def exists(self, user, pk, rel_pk):
return rel_pk in self._storage.get((pk, self.get_name()), {})
def get_data(self, user, pk, rel_pk):
return self._storage.get((pk, self.get_name()), {}).get(rel_pk)
def create(self, user, pk, rel_pk, data=None):
key = (pk, self.get_name())
if key not in self._storage:
self._storage[key] = {}
self._storage[key][rel_pk] = data
self._storage.sync()
def update(self, user, pk, rel_pk, data):
self._storage[key][rel_pk].update(data)
self._storage.sync()
def delete(self, user, pk, rel_pk):
self._storage.get((pk, self.get_name()), {}).pop(rel_pk)
self._storage.sync()
def get_uris(self, user, pk, params=None):
return self._storage.get((pk, self.get_name()), {}).keys()
def get_count(self, user, pk, params=None):
return len(self.get_uris(pk, params))
def can_get_data(self, user, pk, rel_pk, data):
""" Only authenticated users can access data """
if user.is_anonymous:
return False
else:
return True
def can_get_uris(self, user, pk):
""" Only authenticated users can access data """
if user.is_anonymous:
return False
else:
return True
class Person(Resource):
class Schema:
email = StringField(regex="[^@]+@[^@]+\.[^@]+", pk=True,
description="Addess to which the notifications shall be sent")
first_name = StringField(description="Given name(s)")
last_name = StringField(description="Family name(s)")
def can_update(self, user, pk):
""" Only a person himself can update his own information """
return user.email == pk or user.is_admin
def can_delete(self, user, pk):
""" Only admins can delete people """
return user.is_admin
class Student(Person):
""" A pupil """
class Schema(Person.Schema):
birthday = DateTimeField()
class Links:
class courses(Link):
""" Courses the student has ever attended """
target = "Course"
related_name = "students"
master = True
class Schema:
grade = IntegerField(min_val=1, max_val=5)
def _is_course_teacher(self, user, pk, rel_pk):
""" Only teachers can add/remove students to/from courses and grade them.
And they can do it with their courses ONLY.
"""
if user.is_admin:
return True
teacher_data = self._storage.get(Teacher.get_name(), {}).get(user.email)
if teacher_data is None:
return False
else:
teacher_courses = self._storage.get((user.email, Teacher.Links.courses.get_name()), {})
return rel_pk in teacher_courses
def can_create(self, user, pk, rel_pk, data=None):
return self._is_course_teacher(user, pk, rel_pk)
can_update = can_create
can_delete = _is_course_teacher
class comments(Link):
""" Comments made by the student """
target = "Comment"
related_name = "student"
class ratings(Link):
""" Ratings given by the student """
target = "TeacherRating"
related_name = "student"
class Teacher(Person):
""" A lecturer """
class Schema(Person.Schema):
category = StringField(description="TQS Category",
choices=["four", "five", "five plus", "six"])
class Links:
class ratings(Link):
""" Ratings given to the teacher """
target = "TeacherRating"
related_name = "teacher"
class courses(Link):
""" Courses the teacher is responsible for """
target = "Course"
related_name = "teacher"
class PersonalLink(Link):
""" Users can link things to their accounts only """
def can_update(self, user, pk, rel_pk, data):
return user.email == rel_pk or user.is_admin
def can_create(self, user, pk, rel_pk, data):
return user.email == rel_pk or user.is_admin
def can_delete(self, user, pk, rel_pk):
return user.email == rel_pk or user.is_admin
class Course(Resource):
""" An educational unit represinting the lessons for a specific set of topics """
class Schema:
name = StringField(pk=True, description="Name of the course. E.g. physics, maths.")
duration = IntegerField(description="Length of the course in weeks")
class Links:
class teacher(PersonalLink):
""" The lecturer of the course """
target = "Teacher"
related_name = "courses"
cardinality = Link.cardinalities.ONE
master = True
required = True
class comments(Link):
""" All comments made about the course """
target = "Comment"
related_name = "course"
class ratings(Link):
""" All ratings that were given to the teachers of the specific course """
target = "TeacherRating"
related_name = "course"
class students(Link):
""" All pupils who attend the course """
target = "Student"
related_name = "courses"
class AutoGenSha1UriPolicy(AbstractUriPolicy):
""" Uses a randomly generated sha1 as a primary key """
@property
def type(self):
return "autogen_policy"
def generate_pk(self, data):
return os.urandom(16).encode('hex')
def serialize(self, pk):
return pk
def deserialize(self, pk):
if not isinstance(pk, basestring):
raise ValidationError("Has to be string")
if not RE_SHA1.match(value):
raise ValidationError("PK is not a valid SHA1")
return pk
class Comment(Resource):
""" Student's comment about the course """
UriPolicy = AutoGenSha1UriPolicy
class Schema:
pk = StringField(pk=True, description="Identifier of the resource")
value = StringField(description="Text of the comment")
creation_time = DateTimeField(description="Time when the comment was added (for sorting purpose)")
class Links:
class student(PersonalLink):
""" The pupil who made the comment """
target = "Student"
related_name = "comments"
cardinality = Link.cardinalities.ONE
master = True
required = True
def can_delete(self, user, pk, rel_pk):
""" It does not make any sense to let anyone touch the comment's student once it is created """
return False
class course(Link):
""" The subject the comment was made about """
target = "Course"
related_name = "comments"
cardinality = Link.cardinalities.ONE
master = True
required = True
def can_delete(self, user, pk, rel_pk):
""" It does not make any sense to let anyone touch the comment's course once it is created """
return False
class TeacherRating(Resource):
""" Student's rating about teacher's performance """
UriPolicy = AutoGenSha1UriPolicy
class Schema:
pk = StringField(pk=True, description="Identifier of the resource")
value = IntegerField(min_val=0, max_val=100, description="Lecturer's performance identifier ")
creation_time = DateTimeField(description="Time when the rating was added (for sorting purpose)")
class Links:
class student(PersonalLink):
""" The pupil who gave the rating to the teacher """
target = "Student"
related_name = "ratings"
cardinality = Link.cardinalities.ONE
master = True
required = True
def can_delete(self, user, pk, rel_pk):
""" It does not make any sense to let anyone touch the rating's student once it is created """
return False
class course(Link):
""" The subject with respect to which the rating was given """
target = "Course"
related_name = "ratings"
cardinality = Link.cardinalities.ONE
master = True
required = True
def can_delete(self, user, pk, rel_pk):
""" It does not make any sense to let anyone touch the ratings's course once it is created """
return False
class teacher(Link):
""" The lecturer to whom the rating is related """
target = "Teacher"
related_name = "ratings"
cardinality = Link.cardinalities.ONE
master = True
required = True
def can_delete(self, user, pk, rel_pk):
""" It does not make any sense to let anyone touch the rating's teacher once it is created """
return False
srv = ShelveService()
srv.register(Student)
srv.register(Teacher)
srv.register(Course)
srv.register(Comment)
srv.register(TeacherRating)
srv.setup()
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13,
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319,
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28319,
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13,
13,
1990,
6325,
344,
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6848,
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13,
1678,
9995,
530,
28976,
5190,
10981,
524,
292,
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3109,
787,
363,
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2702,
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310,
23820,
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] |
wechat_config/customize_userManual.py | William-An/wechat_server | 0 | 41757 | import requests
import os
import sys # provide option
import argparse # parse options
import json
PATH = os.path.abspath(os.path.dirname(sys.argv[0]))
token_url = "https://api.weixin.qq.com/cgi-bin/token" # Change to control server
create_interface = "https://api.weixin.qq.com/cgi-bin/menu/create"
get_Allinterface = "https://api.weixin.qq.com/cgi-bin/menu/get"
get_Currentinterface = "https://api.weixin.qq.com/cgi-bin/get_current_selfmenu_info"
del_interface = "https://api.weixin.qq.com/cgi-bin/menu/delete"
# del, getall, getcurrent could be merge into one method by changing params
class userInterface:
credential = dict()
@staticmethod
def init():
# Initialization: acquire access_token from control server
with open(PATH+r"\..\Static\accesskey_token.json","r") as credential_file:
credential = eval("".join([i.strip() for i in credential_file.readlines()]))
#print(credential)
credential_file.close()
try:
key = requests.get(url=token_url,params=credential).json()
token = key["access_token"]
userInterface.credential.update([("access_token",token)])
#print(token)
except Exception as err:
if "errcode" in key:
print("ERROR: errcode:%\t%",(key["errcode"],key["errmsg"]))
else:
print("ERROR: "+str(err))
exit()
@staticmethod
def createManual(file_addr):
with open(file_addr,"rb") as config_file:
try:
# print([ i.decode() for i in config_file.readlines()])
config = eval("".join([i.strip().decode() for i in config_file.readlines()]))
# print(config)
except Exception as err:
print(str(err))
exit()
response = requests.post(create_interface,params=userInterface.credential,data=json.dumps(config)).json() # Must use json
print("Result\nerrcode:",response["errcode"],response["errmsg"])
@staticmethod
def delInterface():
# Write into function? paras are url and credential?
response = requests.get(del_interface,params=userInterface.credential).json()
print("Result\nerrcode:",response["errcode"],response["errmsg"])
@staticmethod
def viewAllInterface():
response = requests.get(get_Allinterface,params=userInterface.credential).json()
if "errcode" in response:
print("Result\nerrcode:",response["errcode"],response["errmsg"])
else:
print(response)
@staticmethod
def viewCurrentInterface():
response = requests.get(get_Currentinterface,params=userInterface.credential).json()
if "errcode" in response:
print("Result\nerrcode:",response["errcode"],response["errmsg"])
else:
print(response)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Provide access to modify/view wechat customized usermanual")
parser.add_argument('-d','--delete',action='store_true',help='Delete ALL interfaces')
parser.add_argument('-l','--list',action="store_true",help="List all userinterfaces")
parser.add_argument('-i','--inspect',action="store_true",help="List current userinterface")
parser.add_argument('-c','--config',help="upload the userinterface configuration")
option=parser.parse_args()
userInterface.init()
if option.delete:
userInterface.delInterface()
elif option.list:
userInterface.viewAllInterface()
elif option.inspect:
userInterface.viewCurrentInterface()
elif option.config:
userInterface.createManual(option.config)
exit() | [
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player_roll.py | jeff012345/clue-part-duo | 0 | 113082 | from __future__ import annotations
from typing import List, Set, Dict, Tuple, Optional
import pygame
import pygame_gui
from player import HumanPlayer
from paths import Board, board_spaces, doors
from game_board_util import scale_position
from definitions import roll_dice
from log_book_ui import LogBookPanel
from definitions import RoomPosition
class PlayerRoll:
### Static
SPACE_COLOR = (0, 0, 0)
def _is_adjacent(p1, p2):
return (abs(p1[0] - p2[0]) == 1 and p1[1] == p2[1]) \
or (abs(p1[1] - p2[1]) == 1 and p1[0] == p2[0])
def _valid_position(pos):
return pos[0] >= 0 and pos[0] < 25 and pos[1] >= 0 and pos[1] < 24 \
and board_spaces[pos[0]][pos[1]] != 0
player: HumanPlayer
surface: pygame.Surface
on_end_turn: Callable
_rolling: bool
_distance: int
_positions: List[Position]
_rect_to_position: Dict[Tuple, Position]
_rects: List[pygame.Rect]
_drawn_rects: List[pygame.Rect]
def __init__(self, surface: pygame.Surface, player: HumanPlayer, on_end_turn: Callable):
self.surface = surface
self._rolling = False
self.player = player
self.on_end_turn = on_end_turn
def roll(self):
self._rolling = True
self._distance = roll_dice()
print("rolled a " + str(self._distance))
self._calculate_positions()
def _calculate_positions(self):
edge = set(self.player.board_position.connections)
self._positions = list(self._find_all_positions_search(self._distance, edge, set()))
self._rects = []
self._rect_to_position = dict()
for p in self._positions:
coords = list(map(scale_position, Board.coords_from_position(p)))
new_rects = list(map(lambda p: pygame.Rect((p[0] - 5, p[1] - 5), (11, 11)), coords))
self._rects.extend(new_rects)
for r in new_rects:
self._rect_to_position[(r.x, r.y)] = p
def _find_all_positions_search(self, distance: int, edge: Set[Position],
all_positions: Set[Position]) -> Set[Position]:
if distance == 1:
return all_positions.union(edge)
new_edge: Set[Position] = set()
for p in edge:
all_positions.add(p)
if isinstance(p, RoomPosition):
# the path stops at the door
continue
for conn in p.connections:
if conn not in all_positions and conn not in edge \
and conn != self.player.board_position:
# haven't seen this node before
new_edge.add(conn)
return self._find_all_positions_search(distance - 1, new_edge, all_positions)
def draw(self):
if not self._rolling:
return
self._drawn_rects = list(map(lambda rect: pygame.draw.rect(self.surface, PlayerRoll.SPACE_COLOR, rect), self._rects))
def process_events(self, event):
if not self._rolling:
return
if event.type == pygame.MOUSEBUTTONUP:
pos = pygame.mouse.get_pos()
i = 0
for rect in self._drawn_rects:
rect = rect.move(LogBookPanel.PANEL_WIDTH, 0)
if rect.collidepoint(pos):
org_rect = self._rects[i]
self._click_move(self._rect_to_position[(org_rect.x, org_rect.y)])
break
i += 1
def _click_move(self, pos: Position):
self.player.move(pos)
self._rolling = False
self.on_end_turn()
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] |
save.py | regismeyssonnier/NeuralNetwork | 0 | 22096 | <gh_stars>0
def write_file(filess, T):
f = open(filess, "w")
for o in T:
f.write("[\n")
for l in o:
f.write(str(l)+"\n")
f.write("]\n")
f.close()
def save_hidden_weight(nb_hidden, hiddenw):
for i in range(nb_hidden):
write_file("save/base_nn_hid_" + str(i+1) + "w.nn", hiddenw[i])
def load_hiddenw(filess, hiddenw):
f = open(filess, "r")
s = f.read().splitlines()
h = 0
for o in s:
#print(o)
if o == "[":
h = []
elif o == "]":
hiddenw.append(h)
else:
h.append(float(o))
def load_hidden_weight(hiddenw, nb_hidden):
for i in range(nb_hidden):
hiddenw.append([])
load_hiddenw("save/base_nn_hid_" + str(i+1) + "w.nn", hiddenw[i])
def load_hidden_weight_v(hiddenw, nb_hidden):
for i in range(nb_hidden):
hiddenw.append([])
load_hiddenw("valid/NN/base_nn_hid_" + str(i+1) + "w.nn", hiddenw[i])
def display_hidden_weight(hiddenw, nb_hidden):
for i in range(nb_hidden):
for j in hiddenw[i]:
print("------------------------------------")
I = 0
for k in j:
print(k)
I+=1
if I > 3:
break
def write_fileb(filess, T):
f = open(filess, "w")
for o in T:
f.write(str(o)+"\n")
f.close()
def save_hidden_bias(nb_hidden, hiddenb):
for i in range(nb_hidden):
write_fileb("save/base_nn_hid_" + str(i+1) + "b.nn", hiddenb[i])
def load_hiddenb(filess, hiddenb):
f = open(filess, "r")
s = f.read().splitlines()
for o in s:
hiddenb.append(float(o))
def load_hidden_bias(hiddenb, nb_hidden):
for i in range(nb_hidden):
hiddenb.append([])
load_hiddenb("save/base_nn_hid_" + str(i+1) + "b.nn", hiddenb[i])
def load_hidden_bias_v(hiddenb, nb_hidden):
for i in range(nb_hidden):
hiddenb.append([])
load_hiddenb("valid/NN/base_nn_hid_" + str(i+1) + "b.nn", hiddenb[i])
def display_hidden_bias(hiddenb, nb_hidden):
for i in range(nb_hidden):
print("------------------------------------")
for j in hiddenb[i]:
print(j)
def save_output_weight(outputw):
write_file("save/base_nn_out_w.nn", outputw[0])
def load_output_weight(outputw):
outputw.append([])
load_hiddenw("save/base_nn_out_w.nn", outputw[0])
def load_output_weight_v(outputw):
outputw.append([])
load_hiddenw("valid/NN/base_nn_out_w.nn", outputw[0])
def save_output_bias(outputb):
write_fileb("save/base_nn_out_b.nn", outputb[0])
def load_output_bias(outputb):
outputb.append([])
load_hiddenb("save/base_nn_out_b.nn", outputb[0])
def load_output_bias_v(outputb):
outputb.append([])
load_hiddenb("valid/NN/base_nn_out_b.nn", outputb[0])
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home/forms.py | Legaeldan/siobhan-mcgowan-photography | 0 | 24667 | from django import forms
class ContactForm(forms.Form):
user_name = forms.CharField(max_length=60, label='', required=True, widget=forms.TextInput(attrs={'placeholder': '<NAME>'}))
user_email = forms.EmailField(label='', required=True)
message = forms.CharField(label='', required=True, widget=forms.Textarea(attrs={'placeholder': 'Message', 'rows': '4'}))
def __init__(self, *args, **kwargs):
super(ContactForm, self).__init__(*args, **kwargs)
for visible in self.visible_fields():
if visible.name == "user_email":
visible.field.widget.attrs['class'] = 'validate-required validate-email field-error'
visible.field.widget.attrs['placeholder'] = 'Email Address'
else:
visible.field.widget.attrs['class'] = 'validate-required field-error'
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bagua/torch_api/contrib/sync_batchnorm.py | mmathys/bagua | 635 | 10646 | <gh_stars>100-1000
# Copyright (c) Uber Technologies, Inc. and its affiliates.
# Copyright (c) 2021 Kuaishou AI Platform & DS3 Lab.
#
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from distutils.version import LooseVersion
import torch
from torch.autograd.function import Function
import torch.nn.functional as F
from torch.nn.modules.batchnorm import _BatchNorm
import bagua.torch_api as bagua
from bagua.torch_api.communication import allgather, allreduce
# Backward compat for old PyTorch
if not hasattr(torch.jit, "unused"):
torch.jit.unused = lambda x: x
_SYNC_BN_V2 = LooseVersion(torch.__version__) >= LooseVersion("1.5.0") and LooseVersion(
torch.__version__
) <= LooseVersion("1.6.0")
_SYNC_BN_V3 = LooseVersion(torch.__version__) >= LooseVersion("1.6.0")
_SYNC_BN_V4 = LooseVersion(torch.__version__) >= LooseVersion("1.9.0")
class SyncBatchNorm(_BatchNorm):
r"""Applies synchronous BatchNorm for distributed module with N-dimensional BatchNorm layer(s).
See `BatchNorm <https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html?highlight=batchnorm#torch.nn.BatchNorm2d>`_ for more details.
Arguments:
num_features: Number of channels :math:`C` from the shape :math:`(N, C, ...)`.
eps: A value added to the denominator for numerical stability. Default: 1e-5.
momentum: The value used for the running_mean and running_var
computation. Can be set to ``None`` for cumulative moving average
(i.e. simple average). Default: 0.1.
affine: A boolean value that when set to ``True``, this module has
learnable affine parameters. Default: ``True``.
track_running_stats: A boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics and always uses batch
statistics in both training and eval modes. Default: ``True``.
.. note:: Only GPU input tensors are supported in the training mode.
"""
def __init__(
self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
):
super().__init__(num_features, eps, momentum, affine, track_running_stats)
def _check_input_dim(self, input):
if input.dim() < 2:
raise ValueError(
"expected at least 2D input (got {}D input)".format(input.dim())
)
def _run_bn(self, input):
return F.batch_norm(
input,
self.running_mean,
self.running_var,
self.weight,
self.bias,
self.training or not self.track_running_stats,
self.momentum,
self.eps,
)
@torch.jit.unused
def _maybe_run_sync_bn(self, input):
if bagua.get_world_size() == 1:
return self._run_bn(input)
return _SyncBatchNorm.apply(
input,
self.weight,
self.bias,
self.running_mean,
self.running_var,
self.eps,
self.momentum,
)
def forward(self, input):
# currently only GPU input is supported by underlying kernel from PyTorch
if not input.is_cuda:
raise ValueError("SyncBatchNorm expected input tensor to be on GPU")
self._check_input_dim(input)
if self.training and self.track_running_stats:
assert self.num_batches_tracked is not None
self.num_batches_tracked = self.num_batches_tracked + 1
if not self.training and self.track_running_stats:
return self._run_bn(input)
else:
return self._maybe_run_sync_bn(input)
@classmethod
def convert_sync_batchnorm(cls, module):
r"""Helper function to convert all :attr:`BatchNorm*D` layers in the model to
`torch.nn.SyncBatchNorm <https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html?highlight=syncbatchnorm#torch.nn.SyncBatchNorm>`_ layers.
Arguments:
module (nn.Module): Module containing one or more :attr:`BatchNorm*D` layers
Returns:
The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm`
layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer,
a new :class:`torch.nn.SyncBatchNorm` layer object will be returned
instead.
.. note:: This function must be called before :meth:`~bagua.torch_api.distributed.BaguaModule.with_bagua` method.
Example::
>>> # Network with nn.BatchNorm layer
>>> model = torch.nn.Sequential(
... torch.nn.Linear(D_in, H),
... torch.nn.ReLU(),
... torch.nn.Linear(H, D_out),
... )
>>> optimizer = torch.optim.SGD(
... model.parameters(),
... lr=0.01,
... momentum=0.9
... )
>>> sync_bn_model = bagua.torch_api.contrib.sync_batchnorm.SyncBatchNorm.convert_sync_batchnorm(model)
>>> bagua_model = sync_bn_model.with_bagua([optimizer], GradientAllReduce())
"""
module_output = module
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
module_output = SyncBatchNorm(
module.num_features,
module.eps,
module.momentum,
module.affine,
module.track_running_stats,
)
if module.affine:
with torch.no_grad():
module_output.weight = module.weight
module_output.bias = module.bias
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
if hasattr(module, "qconfig"):
module_output.qconfig = module.qconfig
for name, child in module.named_children():
module_output.add_module(name, cls.convert_sync_batchnorm(child))
del module
return module_output
class _SyncBatchNorm(Function):
@staticmethod
def forward(self, input, weight, bias, running_mean, running_var, eps, momentum):
input = input.contiguous()
size = input.numel() // input.size(1)
count = torch.tensor([size])
# calculate mean/invstd for input.
mean, invstd = torch.batch_norm_stats(input, eps)
count, mean, invstd = count.cuda(), mean.cuda(), invstd.cuda()
nums_ranks = bagua.get_world_size()
count_all = torch.tensor(
[torch.empty_like(count).cpu().detach().numpy() for _ in range(nums_ranks)]
).cuda()
mean_all = torch.tensor(
[torch.empty_like(mean).cpu().detach().numpy() for _ in range(nums_ranks)]
).cuda()
invstd_all = torch.tensor(
[torch.empty_like(invstd).cpu().detach().numpy() for _ in range(nums_ranks)]
).cuda()
allgather(count.unsqueeze(0), count_all)
allgather(mean.unsqueeze(0), mean_all)
allgather(invstd.unsqueeze(0), invstd_all)
if _SYNC_BN_V3:
counts_for_bngswc = count_all.view(-1).float().to(input.device)
else:
# backwards compatibility
counts_for_bngswc = count_all.view(-1).tolist()
# calculate global mean & invstd
mean, invstd = torch.batch_norm_gather_stats_with_counts(
input,
mean_all,
invstd_all,
running_mean,
running_var,
momentum,
eps,
counts_for_bngswc,
)
self.save_for_backward(input, weight, mean, invstd, count_all)
# apply element-wise normalization
return torch.batch_norm_elemt(input, weight, bias, mean, invstd, eps)
@staticmethod
def backward(self, grad_output):
grad_output = grad_output.contiguous()
saved_input, weight, mean, invstd, count_all = self.saved_tensors
need_input_grad, need_weight_grad, need_bias_grad = self.needs_input_grad[0:3]
# calculate local stats as well as grad_weight / grad_bias
sum_dy, sum_dy_xmu, grad_weight, grad_bias = torch.batch_norm_backward_reduce(
grad_output,
saved_input,
mean,
invstd,
weight,
need_input_grad,
need_weight_grad,
need_bias_grad,
)
if need_input_grad:
# synchronizing stats used to calculate input gradient.
allreduce(sum_dy, sum_dy)
allreduce(sum_dy_xmu, sum_dy_xmu)
if _SYNC_BN_V4:
# from 1.9.0 on we need a count tensor on all devices
# count_all is calculated as total count across all ranks in forward function
count_all = count_all.to(dtype=torch.int, device=grad_output.device)
elif _SYNC_BN_V2 or _SYNC_BN_V3:
# before 1.9.0 we need the count as an integer to compute means values
count = count_all.sum()
else:
# before 1.5.0, sum_dy was sum of means from every worker, so we just
# need to divide it by number of workers
count = bagua.get_world_size()
# backward pass for gradient calculation
# we are calling into a non-public undocumented function which broke moving to 1.9.0
# https://github.com/pytorch/pytorch/issues/57900
if _SYNC_BN_V4:
# from 1.9.0 on, sums and count parameters expected
grad_input = torch.batch_norm_backward_elemt(
grad_output,
saved_input,
mean,
invstd,
weight,
sum_dy,
sum_dy_xmu,
count_all,
)
else:
# before 1.9.0, mean parameters expected, not sums and count
grad_input = torch.batch_norm_backward_elemt(
grad_output,
saved_input,
mean,
invstd,
weight,
sum_dy / count,
sum_dy_xmu / count,
)
else:
grad_input = None
# synchronizing of grad_weight / grad_bias is not needed as distributed
# training would handle all reduce.
if weight is None or not need_weight_grad:
grad_weight = None
if weight is None or not need_bias_grad:
grad_bias = None
return grad_input, grad_weight, grad_bias, None, None, None, None, None, None
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lang/rust/types.py | adsharma/flattools | 17 | 81345 | <filename>lang/rust/types.py
from fbs.fbs import FBSType
class FBSRustType(FBSType):
_VALUES_TO_RUST_TYPES = {
FBSType.BOOL: "bool",
FBSType.BYTE: "i8",
FBSType.UBYTE: "u8",
FBSType.SHORT: "i16",
FBSType.USHORT: "u16",
FBSType.INT: "i32",
FBSType.UINT: "u32",
FBSType.FLOAT: "f32",
FBSType.LONG: "i64",
FBSType.ULONG: "u64",
FBSType.DOUBLE: "f64",
FBSType.STRING: "String",
FBSType.STRUCT: "interface",
FBSType.TABLE: "interface",
FBSType.UNION: "interface",
FBSType.VECTOR: "interface",
FBSType.ENUM: "enum",
}
def optionalize(primitive):
return f"Option<{primitive}>"
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ptah/ptahsettings.py | timgates42/ptah | 13 | 1610830 | <reponame>timgates42/ptah
""" ptah settings """
import pytz
import logging
import sqlalchemy
import translationstring
import ptah.form
from email.utils import formataddr
from pyramid.events import ApplicationCreated
from pyramid_mailer.interfaces import IMailer
import ptah
from ptah import settings
_ = translationstring.TranslationStringFactory('ptah')
log = logging.getLogger('ptah')
ptah.register_settings(
ptah.CFG_ID_PTAH,
ptah.form.BoolField(
'auth',
title = _('Authentication policy'),
description = _('Enable authentication policy.'),
default = False),
ptah.form.TextField(
'secret',
title = _('Authentication policy secret'),
description = _('The secret (a string) used for auth_tkt '
'cookie signing'),
default = '',
tint = True),
ptah.form.TextField(
'hashalg',
title = _('Authentication policy hash algorithm'),
description = _('The hash algorithm used for auth_tkt '
'cookie generation'),
default = 'sha512'),
ptah.form.TextField(
'manage',
title = 'Ptah manage id',
default = 'ptah-manage'),
ptah.form.LinesField(
'managers',
title = 'Manage login',
description = 'List of user logins with access rights to '\
'ptah management ui.',
default = ()),
ptah.form.TextField(
'manager_role',
title = 'Manager role',
description = 'Specific role with access rights to ptah management ui.',
default = ''),
ptah.form.LinesField(
'disable_modules',
title = 'Hide Modules in Management UI',
description = 'List of modules names to hide in manage ui',
default = ()),
ptah.form.LinesField(
'enable_modules',
title = 'Enable Modules in Management UI',
description = 'List of modules names to enable in manage ui',
default = ()),
ptah.form.LinesField(
'disable_models',
title = 'Hide Models in Model Management UI',
description = 'List of models to hide in model manage ui',
default = ()),
ptah.form.TextField(
'email_from_name',
default = 'Site administrator'),
ptah.form.TextField(
'email_from_address',
validator = ptah.form.Email(),
required = False,
default = 'admin@localhost'),
ptah.form.ChoiceField(
'pwd_manager',
title = 'Password manager',
description = 'Available password managers '\
'("plain", "ssha", "bcrypt")',
vocabulary = ptah.form.Vocabulary(
"plain", "ssha",),
default = 'plain'),
ptah.form.IntegerField(
'pwd_min_length',
title = 'Length',
description = 'Password minimium length.',
default = 5),
ptah.form.BoolField(
'pwd_letters_digits',
title = 'Letters and digits',
description = 'Use letters and digits in password.',
default = False),
ptah.form.BoolField(
'pwd_letters_mixed_case',
title = 'Letters mixed case',
description = 'Use letters in mixed case.',
default = False),
ptah.form.LinesField(
'db_skip_tables',
title = 'Skip table creation',
description = 'Do not create listed tables during data population.',
default = ()),
ptah.form.LinesField(
'default_roles',
title = 'Default roles',
description = 'List of default assigned roles for all principals.',
default = ()),
title = _('Ptah settings'),
)
def set_mailer(cfg, mailer=None):
def action(cfg, mailer):
if not mailer:
mailer = cfg.registry.queryUtility(IMailer).direct_delivery
PTAH = ptah.get_settings(ptah.CFG_ID_PTAH, cfg.registry)
PTAH['mailer'] = mailer
cfg.action('ptah.ptah_mailer', action, (cfg, mailer))
class DummyMailer(object):
def send(self, from_, to_, message):
log.warning("Mailer is not configured.")
log.warning(message)
@ptah.subscriber(ptah.events.SettingsInitializing)
def initialized(ev):
PTAH = ptah.get_settings(ptah.CFG_ID_PTAH, ev.registry)
# mail
if PTAH.get('mailer') is None:
PTAH['mailer'] = DummyMailer()
PTAH['full_email_address'] = formataddr(
(PTAH['email_from_name'], PTAH['email_from_address']))
def enable_manage(cfg, name='ptah-manage', access_manager=None,
managers=None, manager_role=None,
disable_modules=None, enable_modules=None):
"""Implementation for pyramid `ptah_init_manage` directive """
def action(cfg, name, access_manager,
managers, manager_role, disable_modules):
PTAH = cfg.ptah_get_settings(ptah.CFG_ID_PTAH)
PTAH['manage'] = name
if managers is not None:
PTAH['managers'] = managers
if manager_role is not None:
PTAH['manager_role'] = manager_role
if disable_modules is not None:
PTAH['disable_modules'] = disable_modules
if enable_modules is not None:
PTAH['enable_modules'] = enable_modules
if access_manager is None:
access_manager = ptah.manage.PtahAccessManager(cfg.registry)
ptah.manage.set_access_manager(access_manager, cfg.registry)
cfg.add_route('ptah-manage', '/{0}/*traverse'.format(name),
factory=ptah.manage.PtahManageRoute, use_global_views=True)
cfg.action(
'ptah.ptah_manage', action,
(cfg, name, access_manager,
managers, manager_role, disable_modules), order=999999+1)
def initialize_sql(cfg, prefix='sqlalchemy.'):
def action(cfg):
PTAH = cfg.ptah_get_settings(ptah.CFG_ID_PTAH)
PTAH['sqlalchemy_initialized'] = True
cfg.include('pyramid_sqlalchemy')
cfg.action('ptah.initalize_sql', action, (cfg,))
# check_version
from ptah import migrate
cfg.add_subscriber(migrate.check_version, ApplicationCreated)
@ptah.subscriber(ApplicationCreated)
def starting(ev):
settings.load_dbsettings()
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495,
29898,
4873,
20399,
29897,
13,
1753,
6257,
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580,
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2
] |
backend/models/events.py | nonamedb/kazan_backend | 0 | 172762 | # coding: utf-8
from datetime import datetime, timedelta
from sqlalchemy import Column, Integer, String, Sequence, Boolean, ForeignKey, DateTime, Text, Date
from sqlalchemy.dialects.postgresql import JSON
from sqlalchemy.orm import relationship, backref
from .organizers import organizer_event_table
from .volunteers import volunteer_event_table
from .base import BaseModel
class Event(BaseModel):
__tablename__ = 'event'
id = Column(Integer, Sequence('event_id_seq'), primary_key=True)
name = Column(String(250), nullable=False)
description = Column(Text(), nullable=True)
community_id = Column(Integer(), nullable=False)
volunteer_count = Column(Integer(), nullable=False)
reward = Column(Integer(), nullable=True)
img = Column(String(250), nullable=True)
event_date = Column(Date(), default=(datetime.today() + timedelta(days=5)), nullable=False)
event_subject_id = Column(Integer(), ForeignKey('event_subject.id'))
event_subject = relationship('EventSubject', lazy='joined')
organizers = relationship(
"Organizer",
secondary=organizer_event_table,
back_populates="organizer_events")
volunteers = relationship(
"Volunteer",
secondary=volunteer_event_table,
back_populates="volunteer_events")
def marshall(self):
return dict(
id=self.id,
name=self.name,
description=self.description,
community_id=self.community_id,
volunteer_count=self.volunteer_count,
reward=self.reward,
img=self.img,
event_date=self.event_date,
organizers=[org.id for org in self.organizers],
volunteers=[vol.id for vol in self.volunteers],
)
class EventSubject(BaseModel):
__tablename__ = 'event_subject'
id = Column(Integer, Sequence('event_subject_id_seq'), primary_key=True)
name = Column(String(250), nullable=False)
abbr = Column(String(250), nullable=False)
def marshall(self):
return dict(
id=self.id,
name=self.name,
abbr=self.abbr
)
__all__ = [Event, EventSubject]
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1649,
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518,
2624,
29892,
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29962,
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] |
ecs/processors/playerattackprocessor.py | joehowells/7drl2020 | 0 | 173751 | from random import randint
from esper import Processor, World
import script
from action import ActionType
from ecs.components.attacktarget import AttackTarget
from ecs.components.map import Map
from ecs.components.message import Message
from ecs.components.monster import Monster
from ecs.components.player import Player
from ecs.components.position import Position
class PlayerAttackProcessor(Processor):
def process(self):
self.world: World
for _, game_map in self.world.get_component(Map):
for _, (position, player) in self.world.get_components(Position, Player):
if player.action.action_type is ActionType.ATTACK:
for entity, (monster, _) in self.world.get_components(Monster, AttackTarget):
self.world.remove_component(entity, AttackTarget)
if randint(0, monster.defend) < player.attack:
self.world.create_entity(Message(
text=script.PLAYER_HIT.format(name=monster.name),
priority=50,
))
monster.health -= 1
if monster.health <= 0:
player.kills[monster.name] += 1
self.world.delete_entity(entity, immediate=True)
self.world.create_entity(Message(
text=script.PLAYER_KILL.format(name=monster.name),
priority=50,
))
else:
self.world.create_entity(Message(
text=script.PLAYER_MISS.format(name=monster.name),
priority=50,
))
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] |
functions/tools.py | tmsmolnar/cth-uva | 2 | 152436 | import pandas as pd
from bokeh.models import ColumnDataSource, HoverTool, LinearInterpolator, CategoricalColorMapper, Paragraph
from bokeh.plotting import figure
from bokeh.palettes import Category20b_20
from bokeh.layouts import column, row
from bokeh.io import show, curdoc
def load_data(path_to_file, index, sheet):
"""
Load the dataset, select which column should be the index, and which sheet of the dataset should be loaded.
Parameters:
path_to_file: string
The path to the file the user wants to load
The datasets can be found under the datasets folder
index: integer
The column the user wants to consider as index
sheet: string or integer
The name or number of the sheet the user wants to load
0 - Elementary school
1 - Special school
2 - Technical school
3 - Vocational school
4 - High school
Returns:
a DataFrame
"""
dataFrame = pd.read_excel(path_to_file, index_col=index, sheet_name=sheet)
if 'Adatok' in dataFrame.columns:
for value in dataFrame['Adatok']:
if value == 'Összeg / Csongrád-Csanád megye':
value_new = 'Összeg / Csongrád megye'
dataFrame = dataFrame.replace({value: value_new})
else:
pass
#dataFrame = dataFrame.fillna(0).astype(int)
return dataFrame
def merge_dataFrames_index(dataFrame_1, dataFrame_2):
"""
Merge two DataFrames based on their indexes
Parameters:
dataFrame_1, dataFrame_2: string
The name of the DataFrames the user wants to merge
Returns:
a DataFrame
"""
dataFrames = [dataFrame_1, dataFrame_2]
merged_dataFrames = pd.concat(dataFrames)
return merged_dataFrames
def merge_dataFrames_column(dataFrame_1, dataFrame_2):
"""
Merge two DataFrames based on their columns
Parameters:
dataFrame_1, dataFrame_2: string
The name of the DataFrames the user wants to merge
Returns:
a DataFrame
"""
dataFrames = [dataFrame_1, dataFrame_2]
merged_dataFrames = pd.concat(dataFrames, axis=1)
return merged_dataFrames
def clean_index(dataFrame):
"""
Clean the index columns from unnecessary words, punctuations
Parameters:
dataFrame: string
The name of the DataFrame in which the user wants to clean the index
Returns:
a DataFrame
"""
for index in dataFrame.index:
splitted_index = index.split(".")
new_index = splitted_index[0]
dataFrame = dataFrame.rename(index={index:new_index})
return dataFrame
def clean_index_year(dataFrame):
"""
Clean those indexes, where the index contains the years and removes the word 'year'
Parameters:
dataFrame: string
The name of the dataFrame in which the user wants to clean the index
Returns:
a DataFrame
"""
for index in dataFrame.index:
splitted_index = index.split(" ")
new_index = splitted_index[2]
dataFrame = dataFrame.rename(index={index:new_index})
return dataFrame
def clean_regions(dataFrame):
"""
Clean those columns that contains the regions, to contain only the name of the region.
Parameters:
dataFrame: string
The name of the DataFrame in which the users wants to clean the region columns
Returns:
a DataFrame
"""
for value in dataFrame['Region']:
splitted_value = value.split(" ")
new_value = splitted_value[2]
dataFrame = dataFrame.replace({value: new_value})
return dataFrame
def clean_column(dataFrame):
"""
Clean the columns from unnecessary words and punctuations.
Parameters:
dataFrame: string
The name of the DataFrame in which the user wants to clean the columms
Returns:
a DataFrame
"""
for column in dataFrame:
splitted_column = column.split(" ")
new_column = splitted_column[0]
dataFrame = dataFrame.rename(columns={column: new_column})
return dataFrame
def clean_age(dataFrame):
"""
Clean those columns that contains the ages of the students, and change the word 'éves' to 'yrs'.
Parameters:
dataFrame: string
The name of the DataFrame in which the user wants to clean the columns containing the ages
Returns:
a DataFrame
"""
for value in dataFrame['Age']:
splitted_value = value.split(" ")
new_value = splitted_value[0] + " yrs"
dataFrame = dataFrame.replace({value: new_value})
return dataFrame
def two_to_one_regions(data_1, data_2, sheet_index):
"""
This function is the main function to be used on the transformation of data that contains the regions! Loads the data, merges the DataFrames,
cleans the columns, indexes, rename columns to make it readable and understandable.
Parameters:
data_1, data_2: strings
The paths of the files the user wants to load and work with
sheet_index: integer
The number of the sheet the user wants to work with
0 - Elementary school
1 - Special school
2 - Technical school
3 - Vocational school
4 - High school
Returns:
a DataFrame
"""
df1 = load_data(data_1, index = 1, sheet = sheet_index)
df2 = load_data(data_2, index = 1, sheet = sheet_index)
result = merge_dataFrames_index(df1, df2)
result = result.rename(columns={'Adatok': 'Region', 'Összeg': 'Number of students'})
result.index.name = None
result = result.rename_axis("Year", axis='columns')
result = clean_index(result)
result = clean_regions(result)
return result
def two_to_one_years(data_1, data_2, sheet_index):
"""
This function is the main function to be used on the transformation of data that contains the ages of students! Loads the data, merges the DataFrames,
cleans the columns, indexes, rename columns to make it readable and understandable.
Parameters:
data_1, data_2: strings
The paths of the files the user wants to load and work with
sheet_index: integer
Has to be zero, as there is only one sheet for these datasets
Returns:
a DataFrame
"""
df1 = load_data(data_1, index=0, sheet = sheet_index)
df2 = load_data(data_2, index=0, sheet = sheet_index)
result = merge_dataFrames_index(df1, df2)
result = result.rename(columns={'Életkor (köznevelés)': 'Age', 'Összeg': 'Number of students'})
result['Number of students'] = result['Number of students'].fillna(0).astype(int)
result.index.name = None
result = result.rename_axis("Year", axis='columns')
result = clean_index_year(result)
result = clean_index(result)
result = clean_age(result)
return result
def two_to_one_regions_gradszakma(data_1, data_2):
"""
This function is for only one case, where the graduated datasets differ, because of the KSH! Loads the data, merges the DataFrames,
cleans the columns, indexes, rename columns to make it readable and understandable.
Parameters:
data_1, data_2: strings
The paths of the files the user wants to load and work with
Returns:
a DataFrame
"""
df1 = load_data(data_1, index=1, sheet=1)
df2 = load_data(data_1, index=1, sheet=2)
df3 = load_data(data_2, index=1, sheet=1)
df2 = df1 + df2
result = merge_dataFrames_index(df2, df3)
result = result.rename(
columns={'Adatok': 'Region', 'Összeg': 'Number of students'})
result.index.name = None
result = result.rename_axis("Year", axis='columns')
result = clean_index(result)
result = clean_regions(result)
if 'Region' in result.columns:
for value in result['Region']:
if value == 'BudapestÖsszeg':
value_new = 'Budapest'
result = result.replace({value: value_new})
return result
def cds_from_regions(df):
"""
Create the ColumnDataSource for the Bokeh visualization, from datasets containing the regions.
Parameters:
df: string
The DataFrame from which the user wants to create the ColumnDataSource
Returns:
ColumnDataSource
"""
source = ColumnDataSource(data={
'x': df.index,
'y': df['Number of students'],
'Region': df['Region']
})
return source
def plot_by_regions(df):
"""
Create the ColumnDataSource, the figure, the tools, other visualistic elements for the visualizations and plot the data.
This function is to be used on DataFrames that contains the regions!
Parameters:
df: string
The name of the DataFrame that the user wants to visualize
Returns:
A Bokeh visualization
"""
regions = df['Region'].unique()
regions = list(regions)
source = cds_from_regions(df)
dot_size = LinearInterpolator(
x=[df['Number of students'].min(), df['Number of students'].max()],
y=[5, 25]
)
dot_color = CategoricalColorMapper(factors=regions, palette=Category20b_20)
hover = HoverTool(tooltips=[
("Region", "@Region"),
("Number of students", "@y"),
("Year", "@x"),
])
tools = "pan, tap, box_select, lasso_select, wheel_zoom, help"
p = figure(plot_height=800, plot_width=800, tools=[hover, tools])
p.yaxis.formatter.use_scientific = False
p.circle(x='x', y='y', source=source, #legend='Region',
size={'field': 'y', 'transform': dot_size},
color=dict(field='Region', transform=dot_color))
return p
def cds_from_age(df):
"""
Create the ColumnDataSource for the Bokeh visualization, from datasets containing the ages of students.
Parameters:
df: string
The DataFrame from which the user wants to create the ColumnDataSource
Returns:
ColumnDataSource
"""
source = ColumnDataSource(data={
'x': df.index,
'y': df['Number of students'],
'Age': df['Age']
})
return source
def plot_by_age(df):
"""
Create the ColumnDataSource, the figure, the tools, other visualistic elements for the visualizations and plot the data.
This function is to be used on DataFrames that contains the ages of students!
Parameters:
df: string
The name of the DataFrame that the user wants to visualize
Returns:
A Bokeh visualization
"""
ages = df['Age'].unique()
ages = list(ages)
source = cds_from_age(df)
dot_size = LinearInterpolator(
x=[df['Number of students'].min(), df['Number of students'].max()],
y=[5, 25]
)
dot_color = CategoricalColorMapper(factors=ages, palette=Category20b_20)
hover = HoverTool(tooltips=[
('Age of students', '@Age'),
('Number of students', '@y'),
('Year', '@x')
])
tools = "pan, tap, box_select, lasso_select, wheel_zoom, help"
p = figure(plot_height=800, plot_width=800, tools=[hover, tools], x_range=(2013,2020), y_range=(0,100000))
p.yaxis.formatter.use_scientific = False
p.circle(x='x', y='y', source=source,
size={'field': 'y', 'transform': dot_size},
color=dict(field='Age', transform=dot_color))
return p
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] |
website/views.py | nulldiego/cadavre-exquis | 0 | 1603104 | from django.shortcuts import render
from website.models import Cadavre
from django.views import generic
from website.forms import SendCadavrePartForm
from django.views.decorators.http import require_http_methods
from django.shortcuts import get_object_or_404
from django.http import HttpResponseRedirect
from django.urls import reverse
# Create your views here.
def index(request):
"""View function for home page of site."""
# Render the HTML template index.html with the data in the context variable
return render(request, 'index.html')
class PlayCadavreView(generic.DetailView):
model = Cadavre
slug_field = 'code'
slug_url_kwarg = 'code'
def get_context_data(self, **kwargs):
context = super(PlayCadavreView, self).get_context_data(**kwargs)
context['form'] = SendCadavrePartForm
return context
@require_http_methods(["POST"])
def send_cadavre(request, code):
cadavre = get_object_or_404(Cadavre, code=code)
form = SendCadavrePartForm(request.POST)
if form.is_valid():
cadavre.cadavrepart_set.create(content=form.cleaned_data['content'], author=form.cleaned_data['author'])
return render(request, 'success.html')
else:
return HttpResponseRedirect(reverse('cadavre_detail', code=cadavre.code)) | [
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utils/xmlrpc.py | NullReferenceError/rtorrent_orphan_cleanup | 27 | 48588 | import socket
import xmlrpc.client
""" referemce: https://stackoverflow.com/a/14397619 """
class ServerProxy:
def __init__(self, url, timeout=10):
self.__url = url
self.__timeout = timeout
self.__prevDefaultTimeout = None
def __enter__(self):
try:
if self.__timeout:
self.__prevDefaultTimeout = socket.getdefaulttimeout()
socket.setdefaulttimeout(self.__timeout)
proxy = xmlrpc.client.ServerProxy(self.__url, allow_none=True)
except Exception as ex:
raise Exception("Unable create XMLRPC-proxy for url '%s': %s" % (self.__url, ex))
return proxy
def __exit__(self, type, value, traceback):
if self.__prevDefaultTimeout is None:
socket.setdefaulttimeout(self.__prevDefaultTimeout)
| [
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] |
Rover/install_isolated/lib/python2.7/dist-packages/cartographer_ros_msgs/msg/_TrajectoryOptions.py | Rose-Hulman-Rover-Team/Rover-2019-2020 | 1 | 158084 | <reponame>Rose-Hulman-Rover-Team/Rover-2019-2020
# This Python file uses the following encoding: utf-8
"""autogenerated by genpy from cartographer_ros_msgs/TrajectoryOptions.msg. Do not edit."""
import sys
python3 = True if sys.hexversion > 0x03000000 else False
import genpy
import struct
class TrajectoryOptions(genpy.Message):
_md5sum = "7eda9b62c16c18fa1563587e73501e47"
_type = "cartographer_ros_msgs/TrajectoryOptions"
_has_header = False #flag to mark the presence of a Header object
_full_text = """# Copyright 2016 The Cartographer Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
string tracking_frame
string published_frame
string odom_frame
bool provide_odom_frame
bool use_odometry
bool use_nav_sat
bool use_landmarks
bool publish_frame_projected_to_2d
int32 num_laser_scans
int32 num_multi_echo_laser_scans
int32 num_subdivisions_per_laser_scan
int32 num_point_clouds
float64 rangefinder_sampling_ratio
float64 odometry_sampling_ratio
float64 fixed_frame_pose_sampling_ratio
float64 imu_sampling_ratio
float64 landmarks_sampling_ratio
# This is a binary-encoded
# 'cartographer.mapping.proto.TrajectoryBuilderOptions' proto.
string trajectory_builder_options_proto
"""
__slots__ = ['tracking_frame','published_frame','odom_frame','provide_odom_frame','use_odometry','use_nav_sat','use_landmarks','publish_frame_projected_to_2d','num_laser_scans','num_multi_echo_laser_scans','num_subdivisions_per_laser_scan','num_point_clouds','rangefinder_sampling_ratio','odometry_sampling_ratio','fixed_frame_pose_sampling_ratio','imu_sampling_ratio','landmarks_sampling_ratio','trajectory_builder_options_proto']
_slot_types = ['string','string','string','bool','bool','bool','bool','bool','int32','int32','int32','int32','float64','float64','float64','float64','float64','string']
def __init__(self, *args, **kwds):
"""
Constructor. Any message fields that are implicitly/explicitly
set to None will be assigned a default value. The recommend
use is keyword arguments as this is more robust to future message
changes. You cannot mix in-order arguments and keyword arguments.
The available fields are:
tracking_frame,published_frame,odom_frame,provide_odom_frame,use_odometry,use_nav_sat,use_landmarks,publish_frame_projected_to_2d,num_laser_scans,num_multi_echo_laser_scans,num_subdivisions_per_laser_scan,num_point_clouds,rangefinder_sampling_ratio,odometry_sampling_ratio,fixed_frame_pose_sampling_ratio,imu_sampling_ratio,landmarks_sampling_ratio,trajectory_builder_options_proto
:param args: complete set of field values, in .msg order
:param kwds: use keyword arguments corresponding to message field names
to set specific fields.
"""
if args or kwds:
super(TrajectoryOptions, self).__init__(*args, **kwds)
#message fields cannot be None, assign default values for those that are
if self.tracking_frame is None:
self.tracking_frame = ''
if self.published_frame is None:
self.published_frame = ''
if self.odom_frame is None:
self.odom_frame = ''
if self.provide_odom_frame is None:
self.provide_odom_frame = False
if self.use_odometry is None:
self.use_odometry = False
if self.use_nav_sat is None:
self.use_nav_sat = False
if self.use_landmarks is None:
self.use_landmarks = False
if self.publish_frame_projected_to_2d is None:
self.publish_frame_projected_to_2d = False
if self.num_laser_scans is None:
self.num_laser_scans = 0
if self.num_multi_echo_laser_scans is None:
self.num_multi_echo_laser_scans = 0
if self.num_subdivisions_per_laser_scan is None:
self.num_subdivisions_per_laser_scan = 0
if self.num_point_clouds is None:
self.num_point_clouds = 0
if self.rangefinder_sampling_ratio is None:
self.rangefinder_sampling_ratio = 0.
if self.odometry_sampling_ratio is None:
self.odometry_sampling_ratio = 0.
if self.fixed_frame_pose_sampling_ratio is None:
self.fixed_frame_pose_sampling_ratio = 0.
if self.imu_sampling_ratio is None:
self.imu_sampling_ratio = 0.
if self.landmarks_sampling_ratio is None:
self.landmarks_sampling_ratio = 0.
if self.trajectory_builder_options_proto is None:
self.trajectory_builder_options_proto = ''
else:
self.tracking_frame = ''
self.published_frame = ''
self.odom_frame = ''
self.provide_odom_frame = False
self.use_odometry = False
self.use_nav_sat = False
self.use_landmarks = False
self.publish_frame_projected_to_2d = False
self.num_laser_scans = 0
self.num_multi_echo_laser_scans = 0
self.num_subdivisions_per_laser_scan = 0
self.num_point_clouds = 0
self.rangefinder_sampling_ratio = 0.
self.odometry_sampling_ratio = 0.
self.fixed_frame_pose_sampling_ratio = 0.
self.imu_sampling_ratio = 0.
self.landmarks_sampling_ratio = 0.
self.trajectory_builder_options_proto = ''
def _get_types(self):
"""
internal API method
"""
return self._slot_types
def serialize(self, buff):
"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
_x = self.tracking_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.published_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.odom_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self
buff.write(_get_struct_5B4i5d().pack(_x.provide_odom_frame, _x.use_odometry, _x.use_nav_sat, _x.use_landmarks, _x.publish_frame_projected_to_2d, _x.num_laser_scans, _x.num_multi_echo_laser_scans, _x.num_subdivisions_per_laser_scan, _x.num_point_clouds, _x.rangefinder_sampling_ratio, _x.odometry_sampling_ratio, _x.fixed_frame_pose_sampling_ratio, _x.imu_sampling_ratio, _x.landmarks_sampling_ratio))
_x = self.trajectory_builder_options_proto
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize(self, str):
"""
unpack serialized message in str into this message instance
:param str: byte array of serialized message, ``str``
"""
try:
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.tracking_frame = str[start:end].decode('utf-8')
else:
self.tracking_frame = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.published_frame = str[start:end].decode('utf-8')
else:
self.published_frame = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.odom_frame = str[start:end].decode('utf-8')
else:
self.odom_frame = str[start:end]
_x = self
start = end
end += 61
(_x.provide_odom_frame, _x.use_odometry, _x.use_nav_sat, _x.use_landmarks, _x.publish_frame_projected_to_2d, _x.num_laser_scans, _x.num_multi_echo_laser_scans, _x.num_subdivisions_per_laser_scan, _x.num_point_clouds, _x.rangefinder_sampling_ratio, _x.odometry_sampling_ratio, _x.fixed_frame_pose_sampling_ratio, _x.imu_sampling_ratio, _x.landmarks_sampling_ratio,) = _get_struct_5B4i5d().unpack(str[start:end])
self.provide_odom_frame = bool(self.provide_odom_frame)
self.use_odometry = bool(self.use_odometry)
self.use_nav_sat = bool(self.use_nav_sat)
self.use_landmarks = bool(self.use_landmarks)
self.publish_frame_projected_to_2d = bool(self.publish_frame_projected_to_2d)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.trajectory_builder_options_proto = str[start:end].decode('utf-8')
else:
self.trajectory_builder_options_proto = str[start:end]
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
def serialize_numpy(self, buff, numpy):
"""
serialize message with numpy array types into buffer
:param buff: buffer, ``StringIO``
:param numpy: numpy python module
"""
try:
_x = self.tracking_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.published_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self.odom_frame
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self
buff.write(_get_struct_5B4i5d().pack(_x.provide_odom_frame, _x.use_odometry, _x.use_nav_sat, _x.use_landmarks, _x.publish_frame_projected_to_2d, _x.num_laser_scans, _x.num_multi_echo_laser_scans, _x.num_subdivisions_per_laser_scan, _x.num_point_clouds, _x.rangefinder_sampling_ratio, _x.odometry_sampling_ratio, _x.fixed_frame_pose_sampling_ratio, _x.imu_sampling_ratio, _x.landmarks_sampling_ratio))
_x = self.trajectory_builder_options_proto
length = len(_x)
if python3 or type(_x) == unicode:
_x = _x.encode('utf-8')
length = len(_x)
buff.write(struct.pack('<I%ss'%length, length, _x))
except struct.error as se: self._check_types(struct.error("%s: '%s' when writing '%s'" % (type(se), str(se), str(locals().get('_x', self)))))
except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(locals().get('_x', self)))))
def deserialize_numpy(self, str, numpy):
"""
unpack serialized message in str into this message instance using numpy for array types
:param str: byte array of serialized message, ``str``
:param numpy: numpy python module
"""
try:
end = 0
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.tracking_frame = str[start:end].decode('utf-8')
else:
self.tracking_frame = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.published_frame = str[start:end].decode('utf-8')
else:
self.published_frame = str[start:end]
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.odom_frame = str[start:end].decode('utf-8')
else:
self.odom_frame = str[start:end]
_x = self
start = end
end += 61
(_x.provide_odom_frame, _x.use_odometry, _x.use_nav_sat, _x.use_landmarks, _x.publish_frame_projected_to_2d, _x.num_laser_scans, _x.num_multi_echo_laser_scans, _x.num_subdivisions_per_laser_scan, _x.num_point_clouds, _x.rangefinder_sampling_ratio, _x.odometry_sampling_ratio, _x.fixed_frame_pose_sampling_ratio, _x.imu_sampling_ratio, _x.landmarks_sampling_ratio,) = _get_struct_5B4i5d().unpack(str[start:end])
self.provide_odom_frame = bool(self.provide_odom_frame)
self.use_odometry = bool(self.use_odometry)
self.use_nav_sat = bool(self.use_nav_sat)
self.use_landmarks = bool(self.use_landmarks)
self.publish_frame_projected_to_2d = bool(self.publish_frame_projected_to_2d)
start = end
end += 4
(length,) = _struct_I.unpack(str[start:end])
start = end
end += length
if python3:
self.trajectory_builder_options_proto = str[start:end].decode('utf-8')
else:
self.trajectory_builder_options_proto = str[start:end]
return self
except struct.error as e:
raise genpy.DeserializationError(e) #most likely buffer underfill
_struct_I = genpy.struct_I
def _get_struct_I():
global _struct_I
return _struct_I
_struct_5B4i5d = None
def _get_struct_5B4i5d():
global _struct_5B4i5d
if _struct_5B4i5d is None:
_struct_5B4i5d = struct.Struct("<5B4i5d")
return _struct_5B4i5d
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] |
cumulusci/tasks/robotframework/debugger/DebugListener.py | davisagli/CumulusCI | 163 | 90682 | """
Robot Debugger
"""
from cumulusci.tasks.robotframework.debugger import (
Breakpoint,
DebuggerCli,
Keyword,
Suite,
Testcase,
)
class DebugListener(object):
"""A robot framework listener for debugging test cases
This acts as the controller for the debugger. It is responsible
for managing breakpoints, and pausing execution of a test when a
breakpoint is hit.
The listener is also responsible for instantiating the debugger UI
(class DebuggerCli).
"""
ROBOT_LISTENER_API_VERSION = 2
def __init__(self, *breakpoints):
self.show_initial_help = True
self.current_file = None
self.stack = []
self.rdb = DebuggerCli(listener=self)
if breakpoints:
self.breakpoints = list(breakpoints)
else:
self.breakpoints = [
Breakpoint(Keyword, "*::cumulusci.robotframework.Salesforce.Breakpoint")
]
def start_suite(self, name, attrs):
self.stack.append(Suite(name, attrs))
def start_test(self, name, attrs):
self.stack.append(Testcase(name, attrs))
def start_keyword(self, name, attrs):
context = Keyword(name, attrs)
self.stack.append(context)
self.break_if_breakpoint()
def end_keyword(self, name, attrs):
self.stack.pop()
def end_test(self, name, attrs):
self.stack.pop()
def end_suite(self, name, attrs):
self.stack.pop()
def do_step(self):
"""Single-step through the code
This will set a temporary breakpoint on the next keyword in
the current context before continuing. Once the breakpoint
is hit, it will be removed from the list of breakpoints.
"""
breakpoint = Breakpoint(
Keyword, "{}::*".format(self.stack[-2].longname), temporary=True
)
self.breakpoints.append(breakpoint)
def break_if_breakpoint(self):
"""Pause test execution and issue a prompt if we are at a breakpoint"""
# filter breakpoints to only those that match the current context
# (eg: Suite, Testcase, Keyword), and iterate over them looking
# for a match.
for breakpoint in [
bp
for bp in self.breakpoints
if isinstance(self.stack[-1], bp.breakpoint_type)
]:
statement = "{}::{}".format(self.stack[-2].longname, self.stack[-1].name)
if breakpoint.match(statement):
if breakpoint.temporary:
self.breakpoints.remove(breakpoint)
intro = "\n"
if self.show_initial_help:
self.show_initial_help = False
intro += self.rdb.intro
intro += "\n> {}\n-> {}".format(
self.stack[-2].longname, str(self.stack[-1])
)
# Note: this call won't return until a debugger command
# has been issued which returns True (eg: 'continue' or 'step')
self.rdb.cmdloop(intro)
return
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h5Nastran/h5Nastran/h5/nastran/input/dynamic.py | ACea15/pyNastran | 293 | 169099 | <filename>h5Nastran/h5Nastran/h5/nastran/input/dynamic.py
from __future__ import print_function, absolute_import
from collections import defaultdict
from six.moves import range
from six import iteritems
from h5Nastran.defaults import Defaults
from h5Nastran.h5nastrannode import H5NastranNode
from .input_table import InputTable, TableDef
class Dynamic(H5NastranNode):
def __init__(self, h5n, input):
self._h5n = h5n
self._input = input
self.eigr = EIGR(self._h5n, self)
self.eigrl = EIGRL(self._h5n, self)
self.freq = FREQ(self._h5n, self)
self.freq1 = FREQ1(self._h5n, self)
self.randps = RANDPS(self._h5n, self)
def path(self):
return self._input.path() + ['DYNAMIC']
def to_bdf(self, bdf):
for key, item in iteritems(self.__dict__):
if key.startswith('_'):
continue
try:
item.to_bdf(bdf)
except NotImplementedError:
pass
########################################################################################################################
class EIGR(InputTable):
table_def = TableDef.create('/NASTRAN/INPUT/DYNAMIC/EIGR')
def to_bdf(self, bdf):
add_card = bdf.add_eigr
identity = self.identity
sid = identity['SID']
method = identity['METHOD']
f1 = identity['F1']
f2 = identity['F2']
ne = identity['NE']
nd = identity['ND']
norm = identity['NORM']
g = identity['G']
c = identity['C']
to_value_int = self._h5n.defaults.to_value_int
for i in range(sid.size):
_g = to_value_int(g[i])
_c = to_value_int(c[i])
add_card(sid[i], method[i].decode(), f1[i], f2[i], ne[i], nd[i], norm[i].decode(), _g, _c)
def from_bdf(self, cards):
card_ids = sorted(cards.keys())
result = {'IDENTITY': {'SID': [], 'METHOD': [], 'F1': [], 'F2': [], 'NE': [], 'ND': [], 'NORM': [],
'G': [], 'C': [], 'DOMAIN_ID': []}}
identity = result['IDENTITY']
sid = identity['SID']
method = identity['METHOD']
f1 = identity['F1']
f2 = identity['F2']
ne = identity['NE']
nd = identity['ND']
norm = identity['NORM']
g = identity['G']
c = identity['C']
get_value_int = self._h5n.defaults.get_value_int
for card_id in card_ids:
card = cards[card_id]
sid.append(card.sid)
method.append(card.method)
f1.append(card.f1)
f2.append(card.f2)
ne.append(get_value_int(card.ne))
nd.append(get_value_int(card.nd))
norm.append(card.norm)
g.append(get_value_int(card.G))
c.append(get_value_int(card.C))
return result
########################################################################################################################
class EIGRL(InputTable):
table_def = TableDef.create('/NASTRAN/INPUT/DYNAMIC/EIGRL/IDENTITY')
def from_bdf(self, cards):
card_ids = sorted(cards.keys())
freqs = {'IDENTITY': {'FI': []}}
result = {'IDENTITY': {'SID': [], 'V1': [], 'V2': [], 'ND': [], 'MSGLVL': [], 'MAXSET': [],
'SHFSCL': [], 'FLAG1': [], 'FLAG2': [], 'NORM': [], 'ALPH': [], 'FREQS_POS': [],
'FREQS_LEN': [], 'DOMAIN_ID': []},
'FREQS': freqs,
'_subtables': ['FREQS']}
fi = freqs['IDENTITY']['FI']
identity = result['IDENTITY']
sid = identity['SID']
v1 = identity['V1']
v2 = identity['V2']
nd = identity['ND']
msglvl = identity['MSGLVL']
maxset = identity['MAXSET']
shfscl = identity['SHFSCL']
flag1 = identity['FLAG1']
flag2 = identity['FLAG2']
norm = identity['NORM']
alph = identity['ALPH']
freqs_pos = identity['FREQS_POS']
freqs_len = identity['FREQS_LEN']
def _get_option(val, option, option_data, default):
if val in ('', None):
val = option_data.get(option, None)
if val is None:
val = default
return val
_pos = 0
for card_id in card_ids:
card = cards[card_id]
option_data = defaultdict(list)
for i in range(len(card.options)):
option_data[card.options[i]].append(card.values[i])
_v1 = _get_option(card.v1, 'V1', option_data, Defaults.default_double)
_v2 = _get_option(card.v2, 'V2', option_data, Defaults.default_double)
_nd = _get_option(card.nd, 'ND', option_data, Defaults.default_int)
_msglvl = _get_option(card.msglvl, 'MSGLVL', option_data, Defaults.default_int)
_maxset = _get_option(card.maxset, 'MAXSET', option_data, Defaults.default_int)
_shfscl = _get_option(card.shfscl, 'SHFSCL', option_data, Defaults.default_double)
_norm = _get_option(card.norm, 'NORM', option_data, Defaults.default_str)
_alph = _get_option(None, 'ALPH', option_data, Defaults.default_double)
# TODO: EIGRL how is nums used?, what is flag1 and flag2?
# _nums = _get_option(None, 'NUMS', option_data, 1)
_fi = _get_option(None, 'Fi', option_data, [])
sid.append(card.sid)
v1.append(_v1)
v2.append(_v2)
nd.append(_nd)
msglvl.append(_msglvl)
maxset.append(_maxset)
shfscl.append(_shfscl)
norm.append(_norm)
alph.append(_alph)
flag1.append(Defaults.unknown_int)
flag2.append(Defaults.unknown_int)
freqs_pos.append(_pos)
_len = len(_fi)
_pos += _len
freqs_len.append(_len)
fi += _fi
return result
########################################################################################################################
class FREQ(InputTable):
table_def = TableDef.create('/NASTRAN/INPUT/DYNAMIC/FREQ/IDENTITY')
def from_bdf(self, cards):
card_ids = sorted(cards.keys())
freqs = {'IDENTITY': {'F': []}}
result = {'IDENTITY': {'SID': [], 'FREQS_POS': [], 'FREQS_LEN': [], 'DOMAIN_ID': []},
'FREQS': freqs,
'_subtables': ['FREQS']}
f = freqs['IDENTITY']['F']
identity = result['IDENTITY']
sid = identity['SID']
freqs_pos = identity['FREQS_POS']
freqs_len = identity['FREQS_LEN']
pos = 0
for card_id in card_ids:
card_list = cards[card_id]
for card in card_list:
sid.append(card.sid)
freqs_pos.append(pos)
_len = len(card.freqs)
freqs_len.append(_len)
pos += _len
f.extend(card.freqs)
return result
########################################################################################################################
class FREQ1(InputTable):
table_def = TableDef.create('/NASTRAN/INPUT/DYNAMIC/FREQ1')
def from_bdf(self, cards):
card_ids = sorted(cards.keys())
result = {'IDENTITY': {'SID': [], 'F1': [], 'DF': [], 'NDF': [], 'DOMAIN_ID': []}}
identity = result['IDENTITY']
sid = identity['SID']
f1 = identity['F1']
df = identity['DF']
ndf = identity['NDF']
for card_id in card_ids:
card_list = cards[card_id]
for card in card_list:
sid.append(card.sid)
f1.append(card.f1)
df.append(card.df)
ndf.append(card.ndf)
return result
########################################################################################################################
class RANDPS(InputTable):
table_def = TableDef.create('/NASTRAN/INPUT/DYNAMIC/RANDPS')
def from_bdf(self, cards):
card_ids = sorted(cards.keys())
result = {
'IDENTITY': {'SID': [], 'J': [], 'K': [], 'X': [], 'Y': [], 'TID': [], 'DOMAIN_ID': []}
}
i = result['IDENTITY']
sid = i['SID']
j = i['J']
k = i['K']
x = i['X']
y = i['Y']
tid = i['TID']
for card_id in card_ids:
card_list = cards[card_id]
for card in card_list:
sid.append(card.sid)
j.append(card.j)
k.append(card.k)
x.append(card.x)
y.append(card.y)
tid.append(card.tid)
return result
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] |
tccli/services/cam/__init__.py | hapsyou/tencentcloud-cli-intl-en | 0 | 182253 | # -*- coding: utf-8 -*-
from tccli.services.cam.cam_client import register_arg
from tccli.services.cam.cam_client import get_actions_info
from tccli.services.cam.cam_client import AVAILABLE_VERSION_LIST
| [
1,
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2
] |
python lab 5 & 6/l5q3.py | gonewithharshwinds/itt-lab | 1 | 34013 | #!/usr/bin/python3
m1 = int(input("Enter no. of rows : \t"))
n1 = int(input("Enter no. of columns : \t"))
a = []
print("Enter Matrix 1:\n")
for i in range(n1):
row = list(map(int, input().split()))
a.append(row)
print(a)
m2 = int(n1)
print("\n Your Matrix 2 must have",n1,"rows and",m1,"columns \n")
n2 = int(m1)
b = []
for i in range(n2):
row = list(map(int, input().split()))
b.append(row)
print(b)
res = []
res = [ [ 0 for i in range(m2) ] for j in range(n1) ]
for i in range(len(a)):
for j in range(len(b[0])):
for k in range(len(b)):
res[i][j] += a[i][k] * b[k][j]
print(res) | [
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2
] |
main.py | OrionDark7/Alakajam12 | 0 | 1566 | import pygame, math
from game import map, ui
window = pygame.display.set_mode([800, 600])
ui.window = window
screen = "game"
s = {"fullscreen": False}
running = True
gamedata = {"level": 0, "coal": 0, "iron": 1, "copper":0}
tiles = pygame.sprite.Group()
rails = pygame.sprite.Group()
carts = pygame.sprite.Group()
interactables = pygame.sprite.Group()
listmap = []
clock = pygame.time.Clock()
selected = pygame.image.load("./resources/images/selected.png")
selected2 = pygame.image.load("./resources/images/selected2.png")
box = pygame.image.load("./resources/images/box.png")
uibox = pygame.image.load("./resources/images/ui box.png")
class Mouse(pygame.sprite.Sprite):
def __init__(self):
pygame.sprite.Sprite.__init__(self)
self.image = pygame.surface.Surface([1, 1])
self.rect = self.image.get_rect()
self.rect.topleft = [0, 0]
self.clickedcart = None
self.hoveritem = None
self.tl = self.rect.topleft
self.mode = "select"
def pos(self, position):
self.rect.topleft = position
self.tl = self.rect.topleft
m = Mouse()
def snaptogrid(pos):
return [int(math.floor(pos[0] / 40)), int(math.floor(pos[1] / 40))]
def loadlevel(number):
global tiles, rails, carts, gamedata, listmap, interactables
tiles, rails, interactables, listmap = map.loadmap(int(number))
carts.empty()
gamedata["level"] = number
gamedata["coal"] = 0
gamedata["iron"] = 1
gamedata["copper"] = 0
loadlevel(0)
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
elif event.type == pygame.MOUSEBUTTONDOWN:
m.pos(pygame.mouse.get_pos())
if screen == "game":
if pygame.sprite.spritecollide(m, carts, False) and m.mode == "select":
carts.update("select", m, listmap)
if m.clickedcart != None:
m.mode = "action"
elif m.mode == "action" and m.clickedcart != None and listmap[snaptogrid(m.tl)[0]][snaptogrid(m.tl)[1]] > 0:
m.clickedcart.pathfind(listmap, snaptogrid(m.tl))
m.clickedcart = None
m.mode = "select"
elif event.type == pygame.MOUSEMOTION:
m.pos(pygame.mouse.get_pos())
if screen == "game":
m.hoveritem = None
if len(pygame.sprite.spritecollide(m, carts, False)) > 0:
m.hoveritem = pygame.sprite.spritecollide(m, carts, False)[0]
elif len(pygame.sprite.spritecollide(m, interactables, False)) > 0:
m.hoveritem = pygame.sprite.spritecollide(m, interactables, False)[0]
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_SPACE:
carts.add(map.Cart(snaptogrid(m.tl), "miner"))
if screen == "game":
window.fill([100, 100, 100])
tiles.draw(window)
carts.draw(window)
carts.update("update", m, listmap)
if not m.hoveritem == None and not m.mode == "action":
window.blit(box, [m.rect.left+10, m.rect.top+10])
ui.Resize(30)
ui.Text(m.hoveritem.type.upper(), [m.rect.left+27, m.rect.top+25])
if m.hoveritem.type.startswith("mine") and m.hoveritem not in carts:
ui.Resize(18)
ui.Text("Carts Inside: " + str(m.hoveritem.data["carts"]), [m.rect.left+27, m.rect.top+47])
ui.Text("Max Carts: " + str(m.hoveritem.data["max"]), [m.rect.left+27, m.rect.top+60])
if not m.clickedcart == None:
window.blit(selected2, [m.clickedcart.rect.left-2, m.clickedcart.rect.top-2])
if m.mode == "action":
window.blit(box, [m.rect.left+10, m.rect.top+10])
ui.Resize(30)
try:
ui.Text(m.hoveritem.type.upper(), [m.rect.left+27, m.rect.top+25])
except:
ui.Text(m.clickedcart.type.upper(), [m.rect.left+27, m.rect.top+25])
if listmap[snaptogrid(m.tl)[0]][snaptogrid(m.tl)[1]] > 0:
ui.Resize(22)
ui.Text("Click to move", [m.rect.left+27, m.rect.top+45])
ui.Text("Cart Here", [m.rect.left+27, m.rect.top+60])
window.blit(selected, [snaptogrid(m.tl)[0]*40-2, snaptogrid(m.tl)[1]*40-2])
window.blit(uibox, [555, 475])
pygame.display.flip()
clock.tick(60)
fps = clock.get_fps()
pygame.quit()
| [
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] |
textrank.py | ptarau/PyDS | 2 | 150654 | <filename>textrank.py
# ranking a document for most important sentences
import nltk
import networkx as nx
import heapq
# nouns,verbs,adjectives
def good_word(wt) :
w,t=wt
return t[0] in "NJV" and w.lower() not in stopwords
# words not worth using for retrivieng content
stopwords= {'am', 'are', 'be', 'been', 'being', 'did', 'do', 'does', 'doing',
'don', 'few', 'had', 'has', 'have', 'having', 'hers', 'i', 'is',
'may', 'might', 'most', 'other', 'others', 'our', 'ourselves',
'own', 's', 'same', 'such',
'their', 'theirs', 'was', 'were', 'would', 'your',
'yours', 'yourselves'}
# from a file to sentences, tokens and words
def digest(doc) :
text=[]
with open(doc,'r') as f :
for line in f.readlines() :
line=line.replace('\n',' ')
text.append(line) #?
text="".join(text)
sents=nltk.sent_tokenize(text)
wss=map(nltk.word_tokenize,sents)
goodWordsSets=[]
for ws in wss :
ts=set(w.lower() for (w,t) in nltk.pos_tag(ws) if good_word((w,t)))
goodWordsSets.append(ts)
return goodWordsSets,sents
# heuristics for adding edges: bi-directional links
def add_weighted_edge(g,i,j,wss) :
ws,us = wss[i],wss[j]
shared = ws.intersection(us)
l = len(shared)
r = len(ws) + len(us) - l
if l > 0:
g.add_edge(i, j, weight=l/r)
g.add_edge(j, i, weight=l/r)
# build graph connecting sentences with shared words
def build_intersection_graph(wss) :
g=nx.DiGraph()
m=len(wss)
for i in range(m) :
for j in range(0,m) : #?
add_weighted_edge(g, i, j, wss)
return g
# highest ranked k sentences
def best_sents(g,k) :
d=nx.pagerank(g)
ranked=[]
for (w,r) in d.items() :
heapq.heappush(ranked,(1-r,w))
for _ in range(k) :
(r,i) = heapq.heappop(ranked)
yield i
# build graph connecting words that occur in the same sentence
def build_co_occurence_graph(wss) :
g=nx.DiGraph()
for ws in wss :
for w1 in ws :
for w2 in ws :
if w1 != w2 :
g.add_edge(w1,w2)
return g
# keywords, highest ranked first
def extract_keywords(wss,k) :
g=build_co_occurence_graph(wss)
d=nx.pagerank(g)
by_rank = [(r,w) for (w,r) in d.items()]
by_rank.sort()
sorted=by_rank.reverse()
for _,w in by_rank:
if k>0 : yield w
k-=1
# summary and keyword extractor
def summary_and_keywords(name='us_constitution.txt',s_count=6,w_count=6) :
wss,sents=digest(name)
g=build_intersection_graph(wss)
summary=[(i,sents[i]) for i in sorted(best_sents(g,s_count))]
keywords=[w for w in extract_keywords(wss,w_count)]
return (summary,keywords)
def go() :
summary,keywords=summary_and_keywords()
print('SUMMARY:')
for s in summary: print(s)
print('')
print('KEYWORDS:',keywords)
go()
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methods/POST.py | WebisD/http-api-without-lib | 2 | 138259 | import datetime
from databaseUser.HandlerDatabase import HandlerDatabase
from databaseUser.ObjectUser import UserObj
from handler.HandlerErrors import HandlerErrors
from message.Response import Response
from message.StatusCode import StatusCode
import json
class POST:
@staticmethod
def response(request) -> str:
""" Performs an insertion when there is a POST request, returning a response with
the headers and the correct body. Adds the received object within the database.
:param request: Request object, containing the body and headers of that request
:returns: The answer to this request
"""
try:
data = json.loads(request.body)
if data["name"] != "" and data["phone"] != "" and data["pokemon"] != "" and data["image"] != "" and data["image"] != "http://localhost:8083/post":
obj = UserObj.fromDict(data)
obj.setId(datetime.datetime.now().strftime("%d%m%Y%H%M%S"))
status = HandlerDatabase.insertPokemon(obj)
header = {
"Connection": "Closed"
}
if(int(status.value[0]) >= 400):
return HandlerErrors.sendErrorCode(request, status)
response = Response(status_code=status, body=status.value[1], header=header)
print("POST::response called " + request.URI + " -> " + status.value[1])
return response.encodeResponse()
else:
raise TypeError("Invalid data")
except Exception as e:
print(e)
print("POST::response called " + request.URI + " -> BAD_REQUEST")
return HandlerErrors.sendErrorCode(request, StatusCode.BAD_REQUEST)
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bot.py | mmonkey/covid-vaccine-slack-bot | 9 | 174991 | <reponame>mmonkey/covid-vaccine-slack-bot
import json
import os
import pytz
import requests
from apscheduler.schedulers.blocking import BlockingScheduler
from datetime import datetime
from dotenv import load_dotenv
from geopy import distance
from slack_sdk import WebClient
from slack_sdk.errors import SlackApiError
from timezonefinder import TimezoneFinder
# Globals
configs = []
message = ''
timezoneFinder = TimezoneFinder()
hyvee_locations = []
hyvee_availability = {}
newly_available_hyvee_appointments = []
spotter_cache = {}
spotter_locations = []
spotter_availability = {}
newly_available_spotter_appointments = []
# Load config
files = [file for file in os.listdir('config/') if file.endswith('.json')]
for file in files:
with open(f'config/{file}') as f:
configs.append(json.load(f))
print(f'config file loaded: {file}')
# Hy-Vee Pharmacies
def get_hyvee_vaccine_availability(latitude, longitude, radius):
global hyvee_locations
url = 'https://www.hy-vee.com/my-pharmacy/api/graphql'
query = '''
query SearchPharmaciesNearPointWithCovidVaccineAvailability($latitude: Float!, $longitude: Float!, $radius: Int! = 10) {
searchPharmaciesNearPoint(latitude: $latitude, longitude: $longitude, radius: $radius) {
distance
location {
locationId
name
nickname
phoneNumber
businessCode
isCovidVaccineAvailable
covidVaccineEligibilityTerms
address {
line1
line2
city
state
zip
latitude
longitude
__typename
}
__typename
}
__typename
}
}
'''
response = requests.post(url, json={
'query': query,
'variables': {
'latitude': latitude,
'longitude': longitude,
'radius': radius,
}
})
if response.status_code == 200:
json_data = json.loads(response.text)
hyvee_locations = list(map(lambda location: location['location'], json_data['data']['searchPharmaciesNearPoint']))
else:
print(f'Error getting vaccine availability: {response.text}')
hyvee_locations = []
def get_newly_available_hyvee_locations(is_test=False):
global hyvee_locations, hyvee_availability, newly_available_hyvee_appointments
newly_available_hyvee_appointments = []
for location in hyvee_locations:
prev_avail = hyvee_availability[location['locationId']] if location['locationId'] in hyvee_availability else False
if location['isCovidVaccineAvailable'] and not prev_avail:
newly_available_hyvee_appointments.append(location)
if not len(newly_available_hyvee_appointments) and is_test and not prev_avail:
newly_available_hyvee_appointments.append(location)
hyvee_availability[location['locationId']] = location['isCovidVaccineAvailable']
# Other Pharmacies - vaccinespotter.org API
def get_spotter_api_vaccine_availability(latitude, longitude, radius, states):
global spotter_cache, spotter_locations
locations = []
for state in states:
if state not in spotter_cache:
response = requests.get(f'https://www.vaccinespotter.org/api/v0/states/{state}.json')
if response.status_code == 200:
json_data = json.loads(response.text)
spotter_cache[state] = list(
filter(lambda loc: loc['properties']['provider'] != 'hyvee', json_data['features'])
)
else:
spotter_cache[state] = []
locations.extend(spotter_cache[state])
spotter_locations = []
search_coordinates = (latitude, longitude)
for location in locations:
location_coordinates = (location['geometry']['coordinates'][1], location['geometry']['coordinates'][0])
if distance.distance(search_coordinates, location_coordinates).miles <= radius:
spotter_locations.append(location)
def get_newly_available_spotter_locations(is_test=False):
global spotter_locations, spotter_availability, newly_available_spotter_appointments
newly_available_spotter_appointments = []
for location in spotter_locations:
prev_avail = spotter_availability[location['properties']['id']] if location['properties']['id'] in spotter_availability else False
if location['properties']['appointments_available'] and not prev_avail:
newly_available_spotter_appointments.append(location)
if not len(newly_available_spotter_appointments) and is_test and not prev_avail:
newly_available_spotter_appointments.append(location)
spotter_availability[location['properties']['id']] = location['properties']['appointments_available']
# Message blocks
def header_block():
global message
message = ':alert: <!here> *Vaccines Available* :alert:'
return {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': ':alert: <!here> *Vaccines Available* :alert:\n\nThe following locations have COVID-19 vaccination appointments :covid-19: :syringe: available now!'
}
}
def location_hyvee_block(location):
global message
name = location['nickname'] if location['nickname'] else location['name']
address = location['address']['line1']
city = location['address']['city']
state = location['address']['state']
zipcode = location['address']['zip']
message += f'\n\n*{name}*\n{address}\n{city}, {state} {zipcode}'
return {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': f'*{name}*\n{address}\n{city}, {state} {zipcode}'
}
}
def location_spotter_block(location):
global message
if location['properties']['provider'] == 'cvs':
city = location['properties']['city']
state = location['properties']['state']
text = f'*CVS*\n{city}, {state}'
else:
name = '{name} {provider}'.format(
name=location['properties']['name'],
provider=location['properties']['provider_brand_name']
)
address = location['properties']['address']
city = location['properties']['city']
state = location['properties']['state']
zipcode = location['properties']['postal_code']
text = f'*{name}*\n{address}\n{city}, {state} {zipcode}'
message += f'\n\n{text}'
return {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': text
}
}
def url_block(url):
global message
message += f'\n{url}'
return {
'type': 'actions',
'elements': [
{
'type': 'button',
'text': {
'type': 'plain_text',
'emoji': True,
'text': 'Register Now'
},
'style': 'primary',
'url': url
}
]
}
def divider_block():
return {
'type': 'divider'
}
def footer_block(latitude, longitude):
global message, timezoneFinder
timezone = timezoneFinder.timezone_at(lng=longitude, lat=latitude)
tz = pytz.timezone(timezone)
dt = datetime.now(tz)
timestamp = dt.strftime('%b %d, %Y at %I:%M:%S %p %Z')
message += f'\n\n_Posted {timestamp}_'
return {
'type': 'section',
'text': {
'type': 'mrkdwn',
'text': f'_Posted {timestamp}_'
}
}
# Main
def check_for_vaccine_availability():
global configs, message, spotter_cache, newly_available_hyvee_appointments, newly_available_spotter_appointments
# clear the spotter cache
spotter_cache = {}
for config in configs:
enabled = config['enabled'] if 'enabled' in config else True
if enabled:
if config['latitude'] and config['longitude'] and config['radius']:
is_test = config['test'] if 'test' in config else False
get_hyvee_vaccine_availability(config['latitude'], config['longitude'], config['radius'])
get_newly_available_hyvee_locations(is_test)
# clear the spotter cache
states = config['states'] if 'states' in config else ['NE']
get_spotter_api_vaccine_availability(
config['latitude'],
config['longitude'],
config['radius'],
states)
get_newly_available_spotter_locations(is_test)
else:
print('invalid config: missing geolocation data')
if len(newly_available_hyvee_appointments) or len(newly_available_spotter_appointments):
print(f'{len(newly_available_hyvee_appointments)} newly available hy-vee location(s) found')
print(f'{len(newly_available_spotter_appointments)} newly available spotter location(s) found')
blocks = [header_block()]
for location in newly_available_hyvee_appointments:
blocks.append(location_hyvee_block(location))
blocks.append(url_block('https://www.hy-vee.com/my-pharmacy/covid-vaccine-consent'))
blocks.append(divider_block())
for location in newly_available_spotter_appointments:
blocks.append(location_spotter_block(location))
blocks.append(url_block(location['properties']['url']))
blocks.append(divider_block())
blocks.append(footer_block(config['latitude'], config['longitude']))
# send the Slack message
if config['channel']:
try:
client = WebClient(token=os.environ['SLACK_BOT_TOKEN'])
client.chat_postMessage(channel=config['channel'], blocks=blocks, text=message)
except SlackApiError as e:
print('Error sending Slack message: {message}'.format(message=e.response['error']))
else:
print('invalid config: missing Slack channel')
# Run the schedule
load_dotenv()
scheduler = BlockingScheduler()
scheduler.add_job(check_for_vaccine_availability, 'interval', seconds=30)
scheduler.start()
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mediawikiapi/config.py | srevinsaju/MediaWikiAPI | 0 | 34544 | <filename>mediawikiapi/config.py
import requests
from datetime import timedelta
from .language import Language
class Config(object):
"""
Contains global configuration
"""
DEFAULT_USER_AGENT = 'mediawikiapi (https://github.com/lehinevych/MediaWikiAPI/)'
DONATE_URL = 'https://donate.wikimedia.org/w/index.php?title=Special:FundraiserLandingPage'
API_URL = 'https://{}.wikipedia.org/w/api.php'
def __init__(self, language=None, user_agent=None, rate_limit=None):
if language is not None:
self.__lang = Language(language)
else:
self.__lang = Language()
self.__rate_limit_last_call = None
self.__rate_limit = rate_limit
self.timeout = None
self.user_agent = user_agent or self.DEFAULT_USER_AGENT
@classmethod
def donate_url(cls):
'''Return media wiki donate url'''
return cls.DONATE_URL
@property
def language(self):
'''Return current global language'''
return self.__lang.language
@language.setter
def language(self, language):
'''Set a new language
Arguments:
* language - (string or Language instance) specifying the language
'''
if isinstance(language, Language):
self.__lang=language
else:
self.__lang.language = language
def get_api_url(self, language=None):
'''Return api for specified language
Arguments:
* language - (string or Language instance) specifying the language
'''
if language is not None:
if isinstance(language, Language):
return self.API_URL.format(language.language)
else:
# does the language verification
lang = Language(language)
return self.API_URL.format(lang.language)
return self.API_URL.format(self.__lang.language)
@property
def rate_limit(self):
return self.__rate_limit
@property
def rate_limit_last_call(self):
return self.__rate_limit_last_call
@rate_limit_last_call.setter
def rate_limit_last_call(self, last_call):
self.__rate_limit_last_call = last_call
@rate_limit.setter
def rate_limit(self, rate_limit):
'''
Enable or disable rate limiting on requests to the Mediawiki servers.
If rate limiting is not enabled, under some circumstances (depending on
load on Wikipedia, the number of requests you and other `wikipedia` users
are making, and other factors), Wikipedia may return an HTTP timeout error.
Enabling rate limiting generally prevents that issue, but please note that
HTTPTimeoutError still might be raised.
Arguments:
* min_wait - (integer or timedelta) describes the minimum time to wait in miliseconds before requests.
Example timedelta(milliseconds=50). If None, rate_limit won't be used.
'''
if rate_limit is None:
self.__rate_limit = None
elif isinstance(rate_limit, timedelta):
self.__rate_limit = rate_limit
else:
self.__rate_limit = timedelta(milliseconds=rate_limit)
self.__rate_limit_last_call = None
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util/update-status.py | SCNU-SoCoding/mirror | 1 | 198272 | <reponame>SCNU-SoCoding/mirror
#!/bin/python3
import json
import codecs
import sys
import getopt
from collections import OrderedDict
if (len(sys.argv) != 3):
sys.exit(1)
file_dir = "/mnt/socoding/files/fancyindex/data/supported_os.json"
with codecs.open(file_dir, 'r', 'utf-8') as f:
oslist = json.load(f, object_pairs_hook=OrderedDict)
for osobj in oslist:
for tag in osobj['tags']:
if (tag['key'] == sys.argv[1]):
tag['text'] = "stat: " + str(sys.argv[2])
tag['color'] = "mdl-chip mdl-color--green" if str(sys.argv[2]) == "0" else "mdl-chip mdl-color--red"
with codecs.open(file_dir, 'w', 'utf-8') as f:
json.dump(oslist, f, ensure_ascii=False,
indent=4, separators=(',', ': '))
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djangobmf/migrations/0002_removed_notification_and_watch.py | dmatthes/django-bmf | 1 | 52645 | <gh_stars>1-10
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
('djangobmf', '0001_initial'),
]
operations = [
migrations.DeleteModel(
name='Notification',
),
migrations.DeleteModel(
name='Watch',
),
]
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pdeopt/pdeopt/TR.py | TiKeil/NCD-corrected-TR-RB-approach-for-pde-opt | 0 | 186714 | # ~~~
# This file is part of the paper:
#
# "A NON-CONFORMING DUAL APPROACH FOR ADAPTIVE TRUST-REGION REDUCED BASIS
# APPROXIMATION OF PDE-CONSTRAINED OPTIMIZATION"
#
# https://github.com/TiKeil/NCD-corrected-TR-RB-approach-for-pde-opt
#
# Copyright 2019-2020 all developers. All rights reserved.
# License: Licensed as BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
# Authors:
# <NAME> (2019 - 2020)
# <NAME> (2019 - 2020)
# ~~~
import numpy as np
import time
from copy import deepcopy
from pdeopt.tools import truncated_conj_grad as TruncCG
def projection_onto_range(parameter_space, mu):
ranges = parameter_space.ranges
for (key, item) in parameter_space.parameter_type.items():
range_ = ranges[key]
if sum(item) < 2: # these are the cases () and (1,)
if mu[key] < range_[0]:
if item == ():
mu[key] = range_[0]
else:
mu[key] = [range_[0]]
if mu[key] > range_[1]:
if item == ():
mu[key] = range_[1]
else:
mu[key] = [range_[1]]
else:
for j in range(item[0]):
if mu[key][j] < range_[0]:
mu[key][j] = range_[0]
if mu[key][j] > range_[1]:
mu[key][j] = range_[1]
return mu
def active_and_inactive_sets(parameter_space, mu, epsilon):
Act = []
ranges = parameter_space.ranges
for (key,item) in parameter_space.parameter_type.items():
range_ = ranges[key]
if sum(item) < 2:
if mu[key] - range_[0] <= epsilon:
Act.append(1.0)
elif range_[1] - mu[key] <= epsilon:
Act.append(1.0)
else:
Act.append(0.0)
else:
for j in range(item[0]):
if mu[key][j] - range_[0] <= epsilon:
Act.append(1.0)
elif range_[1] - mu[key][j] <= epsilon:
Act.append(1.0)
else:
Act.append(0.0)
Act = np.array(Act)
Inact = np.ones(Act.shape) - Act
return Act, Inact
def armijo_rule(opt_model, parameter_space, TR_parameters, mu_i, Ji, direction):
j = 0
condition = True
while condition and j < TR_parameters['max_iterations_armijo']:
mu_ip1 = mu_i + (TR_parameters['initial_step_armijo'] ** j) * direction
mu_ip1_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_ip1))
mu_ip1_dict = projection_onto_range(parameter_space,mu_ip1_dict)
mu_ip1 = opt_model.parse_parameter_inverse(mu_ip1_dict)
Jip1 = opt_model.output_functional_hat(mu_ip1_dict)
if not TR_parameters['full_order_model']:
u_cp = opt_model.solve(mu_ip1_dict)
p_cp = opt_model.solve_dual(mu_ip1_dict)
est = opt_model.estimate_output_functional_hat(u_cp, p_cp, mu_ip1_dict)
else:
est = 0.0
if Jip1 <= Ji - (TR_parameters['armijo_alpha'] / ((TR_parameters['initial_step_armijo'] ** j))) * (np.linalg.norm(mu_ip1-mu_i)**2) and abs(est / Jip1) <= TR_parameters['radius']:
condition = False
j = j + 1
if condition: # This means that we exit the loop because of maximum iteration reached
print("Maximum iteration for Armijo rule reached")
mu_ip1 = mu_i
mu_ip1_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_ip1))
Jip1 = Ji
est = TR_parameters['radius']*Ji # so that the Qian-Grepl method stops as well
return mu_ip1, mu_ip1_dict, Jip1, abs(est / Jip1) #the last is needed for the boundary criterium
def compute_new_hessian_approximation(new_mu,old_mu,new_gradient,old_gradient,old_B):
gk = new_gradient-old_gradient
pk = new_mu-old_mu
den = gk.dot(pk)
if den>0.0:
Hkgk = old_B.dot(gk)
coeff = gk.dot(Hkgk)
Hkgkpkt = np.outer(Hkgk,pk)
pkHkgkt = np.outer(pk,Hkgk)
pkpkt = np.outer(pk,pk)
new_B = old_B + (den+coeff)/(den*den) * pkpkt - (1.0/den) * Hkgkpkt - (1.0/den)*pkHkgkt
else:
print("Curvature condition: {}".format(den))
print("Reset direction to - gradient")
new_B = np.eye(old_gradient.size)
return new_B
def compute_modified_hessian_action_matrix_version(H,Active,Inactive,eta):
etaA = np.multiply(Active, eta)
etaI = np.multiply(Inactive, eta)
Hessian_prod = H.dot(etaI)
Action_of_modified_H = etaA + np.multiply(Inactive, Hessian_prod)
return Action_of_modified_H
def solve_optimization_subproblem_BFGS(opt_model, parameter_space, mu_k_dict, TR_parameters, timing=False):
if not TR_parameters['full_order_model']:
print('___ starting subproblem')
if 'beta' not in TR_parameters:
print('Setting beta to the default 0.95')
TR_parameters['beta'] = 0.95
else:
print("Starting parameter {}".format(mu_k_dict))
tic_ = time.time()
times = []
mus = []
Js = []
FOCs = []
mu_diff = 1e6
J_diff = 1e6
Ji = opt_model.output_functional_hat(mu_k_dict)
gradient = opt_model.output_functional_hat_gradient(mu_k_dict)
normgrad = np.linalg.norm(gradient)
mu_i = opt_model.parse_parameter_inverse(mu_k_dict)
mu_i_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_i))
mu_i_1 = mu_i - gradient
mu_i_1_dict = projection_onto_range(opt_model.parameter_space, opt_model.parse_parameter(opt_model.pre_parse_parameter(mu_i_1)))
mu_i_1 = opt_model.parse_parameter_inverse(mu_i_1_dict)
epsilon_i = TR_parameters['epsilon_i']
if not isinstance(epsilon_i,float):
epsilon_i = np.linalg.norm(mu_i_1 - mu_i)#/(np.linalg.norm(mu_i)+1e-8)
B = np.eye(mu_i.size)
Active_i, Inactive_i = active_and_inactive_sets(opt_model.parameter_space, mu_i_dict, epsilon_i)
i = 0
while i < TR_parameters['max_iterations_subproblem']:
if i>0:
if not TR_parameters['full_order_model']:
if boundary_TR_criterium >= TR_parameters['beta']*TR_parameters['radius']:
print('boundary criterium of the TR satisfied, so stopping the sub-problem solver')
return mu_ip1_dict, Jcp, i, Jip1, FOCs
if normgrad < TR_parameters['sub_tolerance'] or J_diff < TR_parameters['safety_tolerance'] or mu_diff< TR_parameters['safety_tolerance']:
print("Subproblem converged: FOC = {}, mu_diff = {}, J_diff = {} ".format(normgrad,mu_diff,J_diff))
break
else:
if normgrad < TR_parameters['sub_tolerance']:
print("Converged: FOC = {}".format(normgrad))
break
if i == 0 and not TR_parameters['full_order_model']:
print("Computing the approximate Cauchy point and then start the BFGS method")
direction = -gradient
else:
if Inactive_i.sum() == 0.0:
if TR_parameters["full_order_model"]:
print("All indexes are active, I am using -gradient as direction")
direction = -gradient
else:
direction = compute_modified_hessian_action_matrix_version(B, Active_i, Inactive_i, -gradient)
if np.dot(direction,gradient) > 0:
print('Not a descendent direction ... taking -gradient as direction')
direction = -gradient
if TR_parameters["full_order_model"]:
mu_ip1, mu_ip1_dict, Jip1, _ = armijo_rule(opt_model, parameter_space, TR_parameters, mu_i, Ji, direction)
else:
mu_ip1, mu_ip1_dict, Jip1, boundary_TR_criterium = armijo_rule(opt_model, parameter_space, TR_parameters, mu_i, Ji, direction)
if i == 0:
if not TR_parameters['full_order_model']:
Jcp = Jip1
else:
Jcp = None
mu_diff = np.linalg.norm(mu_i - mu_ip1) / np.linalg.norm(mu_i)
J_diff = abs(Ji - Jip1) / abs(Ji)
old_mu = deepcopy(mu_i)
mu_i_dict = mu_ip1_dict
Ji = Jip1
old_gradient = deepcopy(gradient)
gradient = opt_model.output_functional_hat_gradient(mu_i_dict)
mu_box = opt_model.parse_parameter(opt_model.pre_parse_parameter(opt_model.parse_parameter_inverse(mu_i_dict)-gradient))
first_order_criticity = opt_model.parse_parameter_inverse(mu_i_dict)-opt_model.parse_parameter_inverse(projection_onto_range(parameter_space, mu_box))
normgrad = np.linalg.norm(first_order_criticity)
mu_i = opt_model.parse_parameter_inverse(mu_i_dict)
mu_i_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_i))
mu_i_1 = mu_i - gradient
mu_i_1_dict = projection_onto_range(opt_model.parameter_space,opt_model.parse_parameter(opt_model.pre_parse_parameter(mu_i_1)))
mu_i_1 = opt_model.parse_parameter_inverse(mu_i_1_dict)
if not isinstance(epsilon_i,float):
epsilon_i = np.linalg.norm(mu_i_1 - mu_i)
Active_i, Inactive_i = active_and_inactive_sets(opt_model.parameter_space, mu_i_dict, epsilon_i)
B = compute_new_hessian_approximation(mu_i, old_mu, gradient, old_gradient, B)
if TR_parameters["full_order_model"]:
print("Step {}, functional {} , FOC condition {}".format(mu_ip1, Ji, np.linalg.norm(first_order_criticity)))
times.append(time.time() -tic_)
mus.append(mu_ip1)
Js.append(Ji)
FOCs.append(normgrad)
i = i + 1
print("relative differences mu {} and J {}".format(mu_diff, J_diff))
if timing:
return mu_ip1_dict, Jcp, i, Jip1, times, mus, Js, FOCs
else:
return mu_ip1_dict, Jcp, i, Jip1, FOCs
def modified_hessian_action(mu,Active,Inactive,opt_model,eta):
# Used only by the projected Newton Method
etaA = np.multiply(Active,eta)
etaI = np.multiply(Inactive,eta)
Action_on_I = opt_model.output_functional_hessian_operator(mu, etaI, False)
Action_of_modified_operator = etaA + np.multiply(Inactive,Action_on_I)
return Action_of_modified_operator
def solve_optimization_NewtonMethod(opt_model, parameter_space, mu_k_dict, TR_parameters, timing=False):
#This method is used to compute an accurate approximation of the optimal parameter mu_bar with the FOM.
# (Eventually also in the global Greedy). It is not used in the TR algorithm in this paper.
print("Starting parameter {}".format(mu_k_dict))
if 'global_RB' not in TR_parameters:
TR_parameters['global_RB']=False
tic_toc = time.time()
times = []
mus = []
Js = []
FOCs = []
Jcp = None
mu_diff = 1e6
J_diff = 1e6
Ji = opt_model.output_functional_hat(mu_k_dict)
gradient = opt_model.output_functional_hat_gradient(mu_k_dict)
normgrad = np.linalg.norm(gradient)
mu_i = opt_model.parse_parameter_inverse(mu_k_dict)
mu_i_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_i))
mu_i_1 = mu_i - gradient
mu_i_1_dict = projection_onto_range(opt_model.parameter_space, opt_model.parse_parameter(opt_model.pre_parse_parameter(mu_i_1)))
mu_i_1 = opt_model.parse_parameter_inverse(mu_i_1_dict)
epsilon_i = TR_parameters['epsilon_i']
if not isinstance(epsilon_i,float):
epsilon_i = np.linalg.norm(mu_i_1 - mu_i)
i = 0
while i < TR_parameters['max_iterations']:
if i>0:
if TR_parameters['full_order_model'] or TR_parameters['global_RB']:
if normgrad < TR_parameters['sub_tolerance']:
print("Converged: FOC = {}".format(normgrad))
break
Active_i, Inactive_i = active_and_inactive_sets(opt_model.parameter_space, mu_i_dict, epsilon_i)
if Inactive_i.sum() == 0.0:
deltamu = gradient
if TR_parameters["full_order_model"] or TR_parameters['global_RB']:
print("I am using projected gradient instead of Newton")
else:
print("Using truncated CG for the linear system")
deltamu, itcg,rescg, infocg = TruncCG(A_func=lambda v: modified_hessian_action(mu=mu_i_dict, Active= Active_i, Inactive= Inactive_i, opt_model=opt_model, eta=v), b= gradient, tol = 1.e-10)
if infocg > 0:
print("Choosing the gradient as direction")
deltamu = gradient
if np.dot(-deltamu,gradient) >= -1.e-14:
print('Not a descendent direction ... taking gradient as direction')
deltamu = gradient
mu_ip1, mu_ip1_dict, Jip1, _, = armijo_rule(opt_model, parameter_space, TR_parameters, mu_i, Ji, -deltamu)
mu_diff = np.linalg.norm(mu_i - mu_ip1) / np.linalg.norm(mu_i)
J_diff = abs(Ji - Jip1) / abs(Ji)
mu_i_dict = mu_ip1_dict
Ji = Jip1
gradient = opt_model.output_functional_hat_gradient(mu_i_dict)
mu_box = opt_model.parse_parameter(opt_model.pre_parse_parameter(opt_model.parse_parameter_inverse(mu_i_dict)-gradient))
first_order_criticity = opt_model.parse_parameter_inverse(mu_i_dict)-opt_model.parse_parameter_inverse(projection_onto_range(parameter_space, mu_box))
normgrad = np.linalg.norm(first_order_criticity)
mu_i = opt_model.parse_parameter_inverse(mu_i_dict)
mu_i_dict = opt_model.primal_model.parse_parameter(opt_model.pre_parse_parameter(mu_i))
mu_i_1 = mu_i - gradient
mu_i_1_dict = projection_onto_range(opt_model.parameter_space,opt_model.parse_parameter(opt_model.pre_parse_parameter(mu_i_1)))
mu_i_1 = opt_model.parse_parameter_inverse(mu_i_1_dict)
if not isinstance(epsilon_i,float):
epsilon_i = np.linalg.norm(mu_i_1 - mu_i)
print("Step {}, functional {} , FOC condition {}".format(mu_ip1, Ji, np.linalg.norm(first_order_criticity)))
times.append(time.time() -tic_toc)
mus.append(mu_ip1)
Js.append(Ji)
FOCs.append(normgrad)
i = i + 1
print("relative differences mu {} and J {}".format(mu_diff, J_diff))
if timing:
return mu_ip1_dict, Jcp, i, Jip1, times, mus, Js, FOCs
else:
return mu_ip1_dict, Jcp, i, Jip1, FOCs, 0
def enrichment_step(mu, reductor, opt_fom=None):
new_reductor = deepcopy(reductor)
u, p = new_reductor.extend_bases(mu)
opt_rom = new_reductor.reduce()
return opt_rom, new_reductor, u, p
def TR_algorithm(opt_rom, reductor, TR_parameters=None, extension_params=None, opt_fom=None, return_opt_rom=False):
if TR_parameters is None:
mu_k = opt_rom.parameter_space.sample_randomly(1)[0]
TR_parameters = {'radius': 0.1, 'sub_tolerance': 1e-8, 'max_iterations': 30, 'max_iterations_subproblem':400,
'starting_parameter': mu_k, 'max_iterations_armijo': 50, 'initial_step_armijo': 0.5,
'armijo_alpha': 1e-4, 'full_order_model': False,
'epsilon_i': 1e-8, 'Qian-Grepl': False, 'safety_tolerance': 1e-16, 'beta': 0.95}
else:
if 'radius' not in TR_parameters:
TR_parameters['radius'] = 0.1
if 'sub_tolerance' not in TR_parameters:
TR_parameters['sub_tolerance'] = 1e-8
if 'max_iterations' not in TR_parameters:
TR_parameters['max_iterations'] = 30
if 'max_iterations_subproblem' not in TR_parameters:
TR_parameters['max_iterations_subproblem'] = 400
if 'starting_parameter' not in TR_parameters:
TR_parameters['starting_parameter'] = opt_rom.parameter_space.sample_randomly(1)[0]
if 'max_iterations_armijo' not in TR_parameters:
TR_parameters['max_iterations_armijo'] = 50
if 'initial_step_armijo' not in TR_parameters:
TR_parameters['initial_step_armijo'] = 0.5
if 'armijo_alpha' not in TR_parameters:
TR_parameters['armijo_alpha'] = 1.e-4
if 'full_order_model' not in TR_parameters:
TR_parameters['full_order_model'] = False
if 'printing' not in TR_parameters:
TR_parameters['printing'] = False
if 'epsilon_i' not in TR_parameters:
TR_parameters['epsilon_i'] = 1e-8
if 'Qian-Grepl' not in TR_parameters:
TR_parameters['Qian-Grepl'] = False
if 'safety_tolerance' not in TR_parameters:
TR_parameters['safety_tolerance'] = 1e-16
if 'beta' not in TR_parameters:
TR_parameters['beta'] = 0.95
mu_k = TR_parameters['starting_parameter']
if extension_params is None:
extension_params={'Check_suff_and_nec_conditions': True, 'Enlarge_radius': True, 'opt_fom': None }
elif TR_parameters['Qian-Grepl']:
extension_params['Check_suff_and_nec_conditions'] = True
extension_params['Enlarge_radius'] = False
if 'opt_fom' not in extension_params:
extension_params['opt_fom'] = None
else:
if 'Check_suff_and_nec_conditions' not in extension_params:
extension_params['Check_suff_and_nec_conditions'] = True
if 'Enlarge_radius' not in extension_params:
extension_params['Enlarge_radius'] = True
if 'opt_fom' not in extension_params:
extension_params['opt_fom'] = None
if opt_fom is None:
opt_fom = extension_params['opt_fom']
if 'FOC_tolerance' not in TR_parameters:
TR_parameters['FOC_tolerance'] = TR_parameters['sub_tolerance']
if TR_parameters['Qian-Grepl']:
print('QIAN et al. 2017 Method')
print('starting parameter {}'.format(mu_k))
# timings
tic = time.time()
Js = []
FOCs = []
times = []
parameter_space = opt_rom.parameter_space
mu_list = []
mu_list.append(mu_k)
JFE_list = []
normgrad = 1e6
estimate_gradient = 1e6 # Used only for Qian et al. method
model_has_been_enriched = False
point_rejected = False
J_k = opt_rom.output_functional_hat(mu_k)
print("Starting value of the cost: {}".format(J_k))
print("******************************* \n")
k = 0
while k < TR_parameters['max_iterations']:
if point_rejected:
point_rejected = False
if TR_parameters['radius'] < 2.22*1e-16:
print('\nTR-radius is below machine precision... stopping')
break
else:
if not TR_parameters['Qian-Grepl']:
if (normgrad < TR_parameters['FOC_tolerance']):
print('\nStopping criteria fulfilled: FOM FOC condition {} '.format(normgrad))
break
else:
if (normgrad + estimate_gradient < TR_parameters['FOC_tolerance']):
print('\nStopping criteria fulfilled: normgrad {} + estimate_gradient {}'.format(normgrad,estimate_gradient))
break
mu_kp1, Jcp, j, J_kp1, _ = solve_optimization_subproblem_BFGS(opt_rom, parameter_space, mu_k,
TR_parameters)
u_rom = opt_rom.solve(mu_kp1)
p_rom = opt_rom.solve_dual(mu_kp1, U=u_rom)
estimate_J = opt_rom.estimate_output_functional_hat(u_rom, p_rom, mu_kp1)
if TR_parameters['Qian-Grepl']:
estimate_gradient = opt_rom.estimate_output_functional_hat_gradient_norm(mu_kp1, u_rom, p_rom)
if J_kp1 + estimate_J < Jcp:
print('checked sufficient condition, starting the enrichment')
opt_rom, reductor, u, p = enrichment_step(mu_kp1, reductor, opt_fom=extension_params['opt_fom'])
JFE_list.append(reductor.fom.output_functional_hat(mu_kp1,u))
model_has_been_enriched = True
if extension_params['Enlarge_radius']:
if len(JFE_list) > 2:
if (k-1!= 0) and (JFE_list[-2]-JFE_list[-1])/(J_k-J_kp1) > 0.75:
TR_parameters['radius'] *= 2
print('enlarging the TR radius to {}'.format(TR_parameters['radius']))
print("k: {} - j {} - Cost Functional: {} - mu: {}".format(k, j, J_kp1, mu_kp1))
mu_list.append(mu_kp1)
times.append(time.time() -tic)
Js.append(J_kp1)
mu_k = mu_kp1
J_k = opt_rom.output_functional_hat(mu_k)
elif J_kp1 - estimate_J > Jcp:
print('necessary condition failed')
TR_parameters['radius'] = TR_parameters['radius'] * 0.5
print("Shrinking the TR radius to: {} because Jcp {} and J_kp1 {}".format(TR_parameters['radius'], Jcp,
J_kp1))
point_rejected = True
else:
print('enriching to check the sufficient decrease condition')
new_rom, new_reductor, u, p = enrichment_step(mu_kp1, reductor, opt_fom=extension_params['opt_fom'])
JFE_list.append(reductor.fom.output_functional_hat(mu_kp1, u))
model_has_been_enriched = True
J_kp1 = new_rom.output_functional_hat(mu_kp1)
print("k: {} - j {} - Cost Functional: {} - mu: {}".format(k, j, J_kp1, mu_kp1))
if J_kp1 > Jcp + 1e-8: # add a safety tolerance of 1e-8 for avoiding numerical stability effects
TR_parameters['radius'] = TR_parameters['radius'] * 0.5
print("Shrinking the TR radius to: {} because Jcp {} and J_kp1 {}".format(TR_parameters['radius']
,Jcp,J_kp1))
point_rejected = True
JFE_list.pop(-1) #We need to remove the value from the list, because we reject the parameter
else:
opt_rom = new_rom
reductor = new_reductor
mu_list.append(mu_kp1)
times.append(time.time() -tic)
Js.append(J_kp1)
mu_k = mu_kp1
if extension_params['Enlarge_radius']:
if len(JFE_list) > 2:
if (k-1!= 0) and (JFE_list[-2]-JFE_list[-1])/(J_k-J_kp1) > 0.75:
TR_parameters['radius'] *= 2
print('enlarging the TR radius to {}'.format(TR_parameters['radius']))
J_k = J_kp1
if model_has_been_enriched and TR_parameters['Qian-Grepl']:
# Qian et al. method does not use the fom gradient
model_has_been_enriched = False
if not point_rejected:
if model_has_been_enriched:
print('computing the fom gradient since the model was enriched')
gradient = reductor.fom.output_functional_hat_gradient(mu_k, U=u, P=p)
mu_box = opt_rom.parse_parameter(opt_rom.pre_parse_parameter(opt_rom.parse_parameter_inverse(mu_k)-gradient))
first_order_criticity = opt_rom.parse_parameter_inverse(mu_k)-opt_rom.parse_parameter_inverse(projection_onto_range(parameter_space, mu_box))
normgrad = np.linalg.norm(first_order_criticity)
model_has_been_enriched = False
else:
estimate_gradient = opt_rom.estimate_output_functional_hat_gradient_norm(mu_k, non_assembled=True)
gradient = opt_rom.output_functional_hat_gradient(mu_k)
mu_box = opt_rom.parse_parameter(opt_rom.pre_parse_parameter(opt_rom.parse_parameter_inverse(mu_k)-gradient))
first_order_criticity = opt_rom.parse_parameter_inverse(mu_k)-opt_rom.parse_parameter_inverse(projection_onto_range(parameter_space, mu_box))
normgrad = np.linalg.norm(first_order_criticity)
FOCs.append(normgrad)
if TR_parameters['Qian-Grepl']:
print('estimate_gradient {}'.format(estimate_gradient))
print("First order critical condition: {}".format(normgrad))
k= k + 1
print("******************************* \n")
if extension_params['Enlarge_radius']:
Js = JFE_list # This is for speeding-up the post-processing computation of the error for the TR method
# This procedure does not give additional speed-up to our method,
# but improves only the time of the post-processing step,
# to have the plot of the error, which is not counted in the computational time of the method.
if k >= TR_parameters['max_iterations']:
print (" WARNING: Maximum number of iteration for the TR algorithm reached")
if 'timings' in extension_params:
if extension_params['timings']:
if return_opt_rom:
return mu_list, times, Js, FOCs, opt_rom
else:
return mu_list, times, Js, FOCs
else:
return mu_list, times, Js, FOCs
return mu_list
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] |
src/processing/data_processor.py | niksaz/semantic-code-search | 1 | 128741 | import os
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, Any
from dpu_utils.utils import RichPath
from tqdm import tqdm
class DataProcessor(ABC):
def __init__(self):
self.skipped = 0
self.failed = 0
def parse_subfolders(self, input_folder: Path, output_folder: Path) -> None:
self.skipped = 0
self.failed = 0
subfolders = os.listdir(input_folder)
input_subfolders = [input_folder / f for f in subfolders]
output_subfolders = [output_folder / f for f in subfolders]
for input_subfolder, output_subfolder in zip(input_subfolders, output_subfolders):
self.parse_folder(input_subfolder, output_subfolder)
print(f"Skipped {self.skipped} files\nFailed on {self.failed} files")
def parse_folder(self, input_folder: Path, output_folder: Path) -> None:
if not output_folder.exists():
os.makedirs(output_folder)
files = [input_folder / file for file in os.listdir(input_folder)]
for file in files:
self.parse_jsonl_file(file, output_folder)
def parse_jsonl_file(self, file: Path, output_folder: Path) -> None:
print(f"Parsing data in {file}")
input_file = RichPath.create(str(file))
output_file = RichPath.create(str(output_folder / input_file.basename()))
parsed_code = [
self.process_data(self.extract_from_raw_data(raw_json_object))
for raw_json_object in tqdm(input_file.read_by_file_suffix())
]
print(f"Saving processed data in {output_file}")
output_file.save_as_compressed_file(parsed_code)
@abstractmethod
def extract_from_raw_data(self, raw_json_object: Dict[str, Any]) -> Any:
pass
@abstractmethod
def process_data(self, code: str) -> Dict[str, Any]:
pass
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queue-using-stacks/queue_using_stacks.py | blairg23/common-interview-problems | 0 | 1616899 | <filename>queue-using-stacks/queue_using_stacks.py
# Task
# - Implement a Queue using Stacks
# Example:
# >>> a = Stack()
# >>> a.push(1)
# >>> a.push(2)
# >>> a.push(3)
# >>> a.pop()
# 3
# >>> a.pop()
# 2
# >>> a.pop()
# 1
# >>> a.pop()
# IndexError: list index out of range
import time
# Solutions copied from https://www.geeksforgeeks.org/queue-using-stacks/
#
# Method 1 classic solution (making enqueue method costly)
#
# enQueue = O(N), deQueue = O(1)
#
# enQueue(q, x):
# While stack1 is not empty, push everything from stack1 to stack2.
# Push x to stack1 (assuming size of stacks is unlimited).
# Push everything back to stack1.
# Here time complexity will be O(n)
#
# deQueue(q):
# If stack1 is empty then error
# Pop an item from stack1 and return it
# Here time complexity will be O(1)
class Method1Queue:
def __init__(self):
self.s1 = []
self.s2 = []
def enQueue(self, x):
# Move all elements from s1 to s2
while len(self.s1) != 0:
self.s2.append(self.s1[-1])
self.s1.pop()
# Push item into self.s1
self.s1.append(x)
# Push everything back to s1
while len(self.s2) != 0:
self.s1.append(self.s2[-1])
self.s2.pop()
# Dequeue an item from the queue
def deQueue(self):
# if first stack is empty
if len(self.s1) == 0:
print("Q is Empty")
# Return top of self.s1
x = self.s1[-1]
self.s1.pop()
return x
# Method 2, making dequeue method costly
#
# enQueue = O(1), deQueue = O(N)
# enQueue(q, x)
# 1) Push x to stack1 (assuming size of stacks is unlimited).
# Here time complexity will be O(1)
#
# deQueue(q)
# 1) If both stacks are empty then error.
# 2) If stack2 is empty
# While stack1 is not empty, push everything from stack1 to stack2.
# 3) Pop the element from stack2 and return it.
# Here time complexity will be O(n)
class Method2Queue:
def __init__(self):
self.s1 = []
self.s2 = []
# EnQueue item to the queue
def enQueue(self, x):
self.s1.append(x)
# DeQueue item from the queue
def deQueue(self):
# if both the stacks are empty
if len(self.s1) == 0 and len(self.s2) == 0:
print("Q is Empty")
return
# if s2 is empty and s1 has elements
elif len(self.s2) == 0 and len(self.s1) > 0:
while len(self.s1):
temp = self.s1.pop()
self.s2.append(temp)
return self.s2.pop()
else:
return self.s2.pop()
# Method 3, implementation of Method 2 using one user stack and one Function Call stack
#
# enQueue(x)
# 1) Push x to stack1.
#
# deQueue:
# 1) If stack1 is empty then error.
# 2) If stack1 has only one element then return it.
# 3) Recursively pop everything from the stack1, store the popped item
# in a variable res, push the res back to stack1 and return res
class Method3Queue:
def __init__(self):
self.s = []
# Enqueue an item to the queue
def enQueue(self, data):
self.s.append(data)
# Dequeue an item from the queue
def deQueue(self):
# Return if queue is empty
if len(self.s) <= 0:
print('Queue is empty')
return
# pop an item from the stack
x = self.s[len(self.s) - 1]
self.s.pop()
# if stack become empty
# return the popped item
if len(self.s) <= 0:
return x
# recursive call
item = self.deQueue()
# push popped item back to
# the stack
self.s.append(x)
# return the result of
# deQueue() call
return item
# Complexity of these operations?
def meta(q, N):
for i in range(N):
q.enQueue(i)
for i in range(N):
q.deQueue()
if __name__ == '__main__':
N = 500
print("Using Method 1")
start = time.time()
q = Method1Queue()
meta(q=q, N=N)
end = time.time()
print(f"Method 1 takes {end - start}")
print("Using Method 2")
start = time.time()
q = Method2Queue()
meta(q=q, N=N)
end = time.time()
print(f"Method 2 takes {end - start}")
print("Using Method 3")
start = time.time()
q = Method3Queue()
meta(q=q, N=N)
end = time.time()
print(f"Method 3 takes {end - start}")
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entangle/http.py | radiantone/entangle | 102 | 110889 | <filename>entangle/http.py
"""
http.py - Module that provides http oriented decorators
"""
from functools import partial
import requests
def request(function=None,
timeout=None,
url=None,
method='GET',
sleep=None):
"""
:param function:
:param timeout:
:param url:
:param method:
:param sleep:
:return:
"""
def decorator(func):
def wrapper(f_func):
# Build http request function here, get result
# call func with result
def invoke_request(_func, **kwargs):
def make_request(url, method, data):
if method == 'GET':
response = requests.get(url=url, params=data)
return response.content
return None
response = make_request(url, method, kwargs)
return _func(response)
pfunc = partial(invoke_request, f_func)
pfunc.__name__ = func.__name__
return pfunc
return wrapper(func)
if function is not None:
return decorator(function)
return decorator
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sandbox/order/migrations/0009_surcharge.py | thelabnyc/django-oscar-cch | 14 | 77843 | # Generated by Django 2.2.6 on 2020-02-19 09:16
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
("order", "0008_auto_20190301_1035"),
]
replaces = [
("order", "0008_surcharge"),
]
operations = [
migrations.CreateModel(
name="Surcharge",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("name", models.CharField(max_length=128, verbose_name="Surcharge")),
(
"code",
models.CharField(max_length=128, verbose_name="Surcharge code"),
),
(
"incl_tax",
models.DecimalField(
decimal_places=2,
default=0,
max_digits=12,
verbose_name="Surcharge (inc. tax)",
),
),
(
"excl_tax",
models.DecimalField(
decimal_places=2,
default=0,
max_digits=12,
verbose_name="Surcharge (excl. tax)",
),
),
(
"order",
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
related_name="surcharges",
to="order.Order",
verbose_name="Surcharges",
),
),
],
options={"abstract": False, "ordering": ["pk"]},
),
]
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] |
bcc-apps/python/hello_world.py | Huweicai/ebpf-apps | 59 | 30997 | #!/usr/bin/python3
#
# This is a Hello World example of BPF.
from bcc import BPF
# define BPF program
prog = """
int kprobe__sys_clone(void *ctx)
{
bpf_trace_printk("Hello, World!\\n");
return 0;
}
"""
# load BPF program
b = BPF(text=prog)
b.trace_print()
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] |
src/Pybind11Wraps/Neighbor/TreeNeighbor.py | markguozhiming/spheral | 1 | 28496 | #-------------------------------------------------------------------------------
# TreeNeighbor
#-------------------------------------------------------------------------------
from PYB11Generator import *
from Neighbor import *
from NeighborAbstractMethods import *
@PYB11template("Dimension")
class TreeNeighbor(Neighbor):
PYB11typedefs = """
typedef typename TreeNeighbor<%(Dimension)s>::LevelKey LevelKey;
typedef typename TreeNeighbor<%(Dimension)s>::CellKey CellKey;
typedef typename %(Dimension)s::Scalar Scalar;
typedef typename %(Dimension)s::Vector Vector;
typedef typename %(Dimension)s::Tensor Tensor;
typedef typename %(Dimension)s::SymTensor SymTensor;
typedef NodeList<%(Dimension)s> NodeListType;
typedef GridCellIndex<%(Dimension)s> GridCellIndexType;
typedef GeomPlane<%(Dimension)s> Plane;
"""
#...........................................................................
# Constructors
def pyinit(self,
nodeList = "NodeListType&",
searchType = ("const NeighborSearchType", "NeighborSearchType::GatherScatter"),
kernelExtent = ("const double", "2.0"),
xmin = "const Vector&",
xmax = "const Vector&"):
"Construct a TreeNeighbor"
#...........................................................................
# Methods
@PYB11virtual
def reinitialize(self,
xmin = "const Vector&",
xmax = "const Vector&",
htarget = "const Scalar"):
"Reinitialize to possibly more efficient based on the specified box (xmin,xmax) and htarget size"
return "void"
@PYB11const
def gridLevel(self, h="const double&"):
"Find the tree level appropriate for h (units of length)"
return "unsigned"
@PYB11const
def gridLevel(self, H="const SymTensor&"):
"Find the tree level appropriate for H (units of 1/length)"
return "unsigned"
@PYB11const
def dumpTree(self, globalTree="const bool"):
"Return a dump of the tree structure as a string"
return "std::string"
@PYB11const
def dumpTreeStatistics(self, globalTree="const bool"):
"Return a string describing the overall statistics of the tree"
return "std::string"
@PYB11const
def serialize(self, buffer="std::vector<char>&"):
"Serialize the current tree state to a buffer"
return "void"
@PYB11const
def setTreeMasterList(self,
levelID = "const LevelKey",
cellID = "const CellKey",
masterList = "std::vector<int>&",
coarseNeighbors = "std::vector<int>&"):
"For our parallel algorithm it is useful to be able to set the master/coarse information based on the given (level, cell)."
return "void"
@PYB11virtual
@PYB11const
def valid(self):
"Test if the Neighbor is valid, i.e., ready to be queried for connectivity information."
return "bool"
#...........................................................................
# Properties
xmin = PYB11property("const Vector&", "xmin", doc="The minimum coordinate for the simulation bounding box")
xmax = PYB11property("const Vector&", "xmax", doc="The maximum coordinate for the simulation bounding box")
boxLength = PYB11property("double", "boxLength", doc="The maximum current cardinal coordinate distance across the bounding box")
occupiedCells = PYB11property("std::vector<std::vector<CellKey>>", "occupiedCells", doc="The encoded cell key hashes for the cells currently occupied by nodes")
#-------------------------------------------------------------------------------
# Add the virtual interface
#-------------------------------------------------------------------------------
PYB11inject(NeighborAbstractMethods, TreeNeighbor, virtual=True)
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schema/predict/params/google/cloud/aiplatform/v1beta1/schema/predict/params_v1beta1/types/video_classification.py | SinaChavoshi/python-aiplatform | 0 | 138456 | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import proto # type: ignore
__protobuf__ = proto.module(
package='google.cloud.aiplatform.v1beta1.schema.predict.params',
manifest={
'VideoClassificationPredictionParams',
},
)
class VideoClassificationPredictionParams(proto.Message):
r"""Prediction model parameters for Video Classification.
Attributes:
confidence_threshold (float):
The Model only returns predictions with at
least this confidence score. Default value is
0.0
max_predictions (int):
The Model only returns up to that many top,
by confidence score, predictions per instance.
If this number is very high, the Model may
return fewer predictions. Default value is
10,000.
segment_classification (bool):
Set to true to request segment-level
classification. AI Platform returns labels and
their confidence scores for the entire time
segment of the video that user specified in the
input instance. Default value is true
shot_classification (bool):
Set to true to request shot-level
classification. AI Platform determines the
boundaries for each camera shot in the entire
time segment of the video that user specified in
the input instance. AI Platform then returns
labels and their confidence scores for each
detected shot, along with the start and end time
of the shot.
WARNING: Model evaluation is not done for this
classification type, the quality of it depends
on the training data, but there are no metrics
provided to describe that quality.
Default value is false
one_sec_interval_classification (bool):
Set to true to request classification for a
video at one-second intervals. AI Platform
returns labels and their confidence scores for
each second of the entire time segment of the
video that user specified in the input WARNING:
Model evaluation is not done for this
classification type, the quality of it depends
on the training data, but there are no metrics
provided to describe that quality. Default value
is false
"""
confidence_threshold = proto.Field(
proto.FLOAT,
number=1,
)
max_predictions = proto.Field(
proto.INT32,
number=2,
)
segment_classification = proto.Field(
proto.BOOL,
number=3,
)
shot_classification = proto.Field(
proto.BOOL,
number=4,
)
one_sec_interval_classification = proto.Field(
proto.BOOL,
number=5,
)
__all__ = tuple(sorted(__protobuf__.manifest))
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Subsets and Splits