seq_id string | text string | repo_name string | sub_path string | file_name string | file_ext string | file_size_in_byte int64 | program_lang string | lang string | doc_type string | stars int64 | dataset string | pt string | api list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
18672969440 | import pandas as pd
import argparse
import yaml
import os
import io
import json
def retrieve_params(config):
with open(config) as yaml_file:
params= yaml.safe_load(yaml_file)
return params
def generate_metadata_csv(params):
excel_file_path = params["data"]["standard_excel_file"]
df = pd.read_csv(excel_file_path)
df.columns = [i.strip() for i in df.columns]
buffer = io.StringIO()
df.info(buf=buffer, verbose = True, null_counts = False, memory_usage=False)
s = buffer.getvalue()
s = s.split("\n")
del(s[1])
s = "\n".join(s)
standard_csv_meta = params["data"]["standard_csv_meta"]
with open(standard_csv_meta, "a+") as f:
f.write(s)
f.write("\n")
def generate_metadata_json(params):
json_file_path = params["data"]["standard_json_file"]
with open(json_file_path) as json_file:
standard_dict = json.load(json_file)
standard_json_meta = params["data"]["standard_json_meta"]
keys1 = standard_dict.keys()
for i in standard_dict["items"]:
keys2 = i.keys()
break
b = standard_dict["items"][0]["snippet"]
keys3 = b.keys()
with open(standard_json_meta, "w") as meta:
meta.write(str(list(keys1)))
meta.write("\n")
meta.write(str(list(keys2)))
meta.write("\n")
meta.write(str(list(keys3)))
def generate_metadata(config):
params = retrieve_params(config=config)
generate_metadata_csv(params)
generate_metadata_json(params)
if __name__=="__main__":
args = argparse.ArgumentParser()
args.add_argument("--config", default="params.yaml")
parsed_args = args.parse_args()
generate_metadata(parsed_args.config) | sagar-harry/Youtube_Trending_data | generate_meta_data_file_2.py | generate_meta_data_file_2.py | py | 1,708 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "yaml.safe_load",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "io.StringIO",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_numb... |
15381149189 | import requests
from weather_message import WeatherStation
LINE_URL = 'https://notify-api.line.me/api/notify'
def send_message(token, msg):
headers = {'Authorization': 'Bearer ' + token}
payload = {'message': msg}
response = requests.post(LINE_URL, headers=headers, params=payload)
return response.status_code
def main():
line_token = 'your_line_token' # 你的line權杖
msg = ''
status_code = send_message(line_token, msg)
print(status_code)
if __name__ == '__main__':
main()
| shamiOuO/weather_report | Line_notify.py | Line_notify.py | py | 544 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "requests.post",
"line_number": 10,
"usage_type": "call"
}
] |
21123419493 | from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, Text, TIMESTAMP
from sqlalchemy import func
Base = declarative_base()
class TestData(Base):
__tablename__ = 'test'
id = Column(Integer, primary_key = True)
data = Column(Text)
created_at = Column(TIMESTAMP, server_default = func.sysdate())
updated_at = Column(TIMESTAMP, server_default = func.sysdate())
| tosiaki/windyfall_bot | testdata.py | testdata.py | py | 423 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "sqlalchemy.ext.declarative.declarative_base",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "sqlalchemy.Column",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "sqlalchemy.Integer",
"line_number": 10,
"usage_type": "argument"
},
{
... |
72177063783 | import json
import traceback
from flask import Blueprint, jsonify
from bson.json_util import dumps
from models.users import User
get_users_blueprint = Blueprint("get_users_blueprint", __name__)
@get_users_blueprint.route("/get-users")
def get_users():
try:
users = User.find(User.record_status=="ALIVE").all()
users_list = []
for user in users:
user_dict = {}
for x in user:
user_dict[x[0]] = x[1]
users_list.append(user_dict)
return jsonify({
"status_code": "200",
"status": "success",
"message": "users_retrieved_ok",
"data": users_list
})
except:
traceback.print_exc()
return jsonify({
"status_code": "500",
"status": "error",
"message": "failed_to_retrieve_users",
"data": [],
}) | emacliam/REDIS-HACKERTHON---CRM | CRM BACKEND/controllers/users/get_users.py | get_users.py | py | 947 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "flask.Blueprint",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "models.users.User.find",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "models.users.User",
"line_number": 13,
"usage_type": "name"
},
{
"api_name": "models.users.... |
25175834547 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 2 17:19:48 2023
@author: acomeau
"""
import matplotlib.pyplot as plt
import time
import numpy as np
import math
x=np.zeros((1,100))
x1=np.zeros((1,100))
x2=np.zeros((1,100))
for timeSetepNdx in range(1,100):
x[0,timeSetepNdx]=timeSetepNdx
x1[0,timeSetepNdx]=math.cos(timeSetepNdx*2*math.pi/100.0)
x2[0,timeSetepNdx]=math.sin(timeSetepNdx*2*math.pi/100.0)
plt.figure()
plt.subplot(211)
plt.plot(x,x1,'k-.')
plt.plot(x,x2,'k-.')
plt.xlabel('Sample')
plt.ylabel('x,y')
plt.subplot(212)
plt.plot(x1,x2,'k-.')
plt.axes('square')
plt.xlabel('x1')
plt.ylabel('y1')
plt.show()
time.sleep(0.1)
| adriencomeau/Telescopium | untitled1.py | untitled1.py | py | 749 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.zeros",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "math.cos",
"line_number": 22,... |
27435754821 | import os
import pathlib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imsave, imread
def add_noise(noise_type, image):
if noise_type == "gauss":
shp = image.shape
mean = 0
var = 0.01
sigma = var ** 0.5
gauss = np.random.normal(mean, sigma, shp)
noise = image + gauss
return noise
if noise_type == "uniform":
k = 0.5
if image.ndim == 2:
row, col = image.shape
noise = np.random.rand(row, col)
else:
row, col, chan = image.shape
noise = np.random.rand(row, col, chan)
noise = image + k * (noise - 0.5)
return noise
if noise_type == "s&p":
sh = image.shape
s_vs_p = 0.5
amount = 0.05
out = np.copy(image)
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt)) for i in sh]
out[coords] = 1
num_peper = np.ceil(amount * image.size * (1.0 - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_peper)) for i in sh]
out[coords] = 0
return out
if noise_type == "poisson":
PEAK = 20
noisy = image + np.random.poisson(0.5 * PEAK, image.shape) / PEAK
return noisy
if noise_type == "speckle":
if image.ndim == 2:
row, col = image.shape
noise = np.random.randn(row, col)
else:
row, col, chan = image.shape
noise = np.random.randn(row, col, chan)
noisy = image + image * 0.5 * noise
return noisy
def read_images_1(img_path):
row, column = 3, 2
# Original picture
fig = plt.figure(img_path)
plt.axis("off")
img = imread(img_path)
fig.add_subplot(row, column, 1, title="Original")
plt.imshow(img, cmap="gray")
plt.axis("off")
fig.add_subplot(row, column, 2, title="Gauss")
plt.imshow(add_noise("gauss", img), cmap="gray")
plt.axis("off")
fig.add_subplot(row, column, 3, title="Uniform")
plt.imshow(add_noise("uniform", img), cmap="gray")
plt.axis("off")
fig.add_subplot(row, column, 4, title="s&p")
plt.imshow(add_noise("s&p", img), cmap="gray")
plt.axis("off")
fig.add_subplot(row, column, 5, title="Poisson")
plt.imshow(add_noise("poisson", img), cmap="gray")
plt.axis("off")
fig.add_subplot(row, column, 6, title="Speckle")
plt.imshow(add_noise("speckle", img), cmap="gray")
plt.axis("off")
plt.show(block=True)
if __name__ == "__main__":
img_path = os.path.join(pathlib.Path(__file__).parent.parent, "input1", "coins.png")
read_images_1(img_path)
| 206081/psio | Lab2/zad1.py | zad1.py | py | 2,703 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.random.normal",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "numpy.random",
"line_number": 15,
"usage_type": "attribute"
},
{
"api_name": "numpy.random.rand",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "numpy.random"... |
12532422975 | """empty message
Revision ID: 8f71e60633a3
Revises: 2e7679aa003d
Create Date: 2023-01-24 23:57:44.856118
"""
from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import mysql
# revision identifiers, used by Alembic.
revision = '8f71e60633a3'
down_revision = '2e7679aa003d'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('cities', schema=None) as batch_op:
batch_op.add_column(sa.Column('ascii', sa.String(length=20), nullable=True))
batch_op.add_column(sa.Column('feat_code', sa.String(length=20), nullable=True))
batch_op.drop_column('ascci')
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('cities', schema=None) as batch_op:
batch_op.add_column(sa.Column('ascci', mysql.VARCHAR(length=20), nullable=True))
batch_op.drop_column('feat_code')
batch_op.drop_column('ascii')
# ### end Alembic commands ###
| lusferror/SugestionCties | src/migrations/versions/8f71e60633a3_.py | 8f71e60633a3_.py | py | 1,087 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "alembic.op.batch_alter_table",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "alembic.op",
"line_number": 21,
"usage_type": "name"
},
{
"api_name": "sqlalchemy.Column",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "sqlalchemy.... |
69941827626 | import pickle
import os
import numpy as np
import torch
from sklearn.datasets import make_blobs
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
def prepare_blob_dataset(city_num: int = 50,
feature_dim: int = 2,
sample_num: int = 100000
) -> (np.ndarray, np.ndarray):
samples = np.zeros((sample_num, city_num, feature_dim))
labels = np.zeros((sample_num, city_num))
for sample in range(sample_num):
samples[sample, :, :], labels[sample, :] = make_blobs(city_num,
feature_dim,
cluster_std=0.07,
center_box=(0.0, 1.0))
return samples, labels
class BlobDataset(Dataset):
def __init__(self, city_num: int = 50, feature_dim: int = 2, sample_num: int = 100000):
super(BlobDataset, self).__init__()
self.city_num = city_num
self.feature_dim = feature_dim
self.sample_num = sample_num
self.samples, self.labels = self._generate_dataset()
def __getitem__(self, index) -> T_co:
sample = self.samples[index]
label = self.labels[index]
data_pair = {'sample': sample, 'label': label}
return data_pair
def __len__(self):
return len(self.samples)
def _generate_dataset(self):
samples, labels = prepare_blob_dataset(self.city_num,
self.feature_dim,
self.sample_num)
return torch.from_numpy(samples).float(), torch.from_numpy(labels)
# TSP dataset wrapper from https://github.com/wouterkool/attention-learn-to-route
class TSPDataset(Dataset):
def __init__(self, filename=None, size=20, num_samples=1000000, offset=0, distribution=None):
super(TSPDataset, self).__init__()
self.data_set = []
if filename is not None:
if os.path.splitext(filename)[1] == '.npy':
data = np.load(filename)
assert data.ndim == (2 or 3), "data.ndim should either be 2 or 3"
if data.ndim == 2:
data = np.expand_dims(data, axis=0)
self.data = [torch.FloatTensor(row) for row in (data[offset:offset + num_samples])]
else:
assert os.path.splitext(filename)[1] == '.pkl'
with open(filename, 'rb') as f:
data = pickle.load(f)
self.data = [torch.FloatTensor(row) for row in (data[offset:offset + num_samples])]
else:
# Sample points randomly in [0, 1] square
self.data = [torch.FloatTensor(size, 2).uniform_(0, 1) for _ in range(num_samples)]
self.size = len(self.data)
def __len__(self):
return self.size
def __getitem__(self, idx):
return self.data[idx]
def data_normalisation(self):
self.data = [(self.data[row] - self.data[row].min()) / (self.data[row].max() - self.data[row].min())
for row in range(self.size)]
if __name__ == '__main__':
test = TSPDataset(filename='tmp/platforms.npy')
test2 = TSPDataset(size=20, num_samples=50)
print(len(test))
| ma-shangao/rl_waypoint_mrta | dataset_preparation.py | dataset_preparation.py | py | 3,367 | python | en | code | 2 | github-code | 36 | [
{
"api_name": "numpy.zeros",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "sklearn.datasets.make_blobs",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "numpy.ndarray",
... |
17393417065 | import sqlite3
import constants
#from constants import insert_base_user
DB_NAME = "vehicle_management.db"
db = sqlite3.connect(DB_NAME)
db.row_factory = sqlite3.Row
c = db.cursor()
#
# insert_base_user = """
# INSERT INTO BASE_USER (user_name, email, phone_number, address)
# VALUES (:user_name, :email, :phone_number, :address
# )
# """
# c.execute(insert_base_user, {'user_name': 'dux',
# 'email': 'dux@abv.bg',
# 'phone_number': 121,
# 'address': 'Ruse'})
class Mechanic:
def __init__(self, user_name, email, phone_number, address, type):
self.user_name = user_name
self.email = email
self.phone_number = phone_number
self.address = address
self.type = type
@classmethod
def save_to_db(cls, username, email, phone_number, address, type):
c.execute(constants.insert_base_user,
{'user_name': username,
'email': email,
'phone_number': phone_number,
'address': address,
'type': type})
ids = c.execute('SELECT ID FROM BASE_USER WHERE USER_NAME =?',(username,))
for i in ids.fetchone():
# print(i)
c.execute('INSERT INTO MECHANIC VALUES (?)', (i,))
c.execute('INSERT INTO MECHANIC_SERVICE (MECHANIC_ID) VALUES (?)', (i,))
db.commit()
db.commit()
db.commit()
@classmethod
def add_service_to_mechanic_id(cls, service_name, mechanic_id):
service_id = c.execute('SELECT ID FROM SERVICE WHERE NAME=?', (service_name,))
for i in service_id.fetchone():
service_id = i
c.execute('UPDATE MECHANIC_SERVICE SET SERVICE_ID =? WHERE ID=?', (service_id, mechanic_id))
db.commit()
| bonevb/HackBulgaria-Programming101-Python-2018 | week10/01-Vehicle-Repair-Manager/mechanic.py | mechanic.py | py | 1,904 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "sqlite3.connect",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "sqlite3.Row",
"line_number": 8,
"usage_type": "attribute"
},
{
"api_name": "constants.insert_base_user",
"line_number": 34,
"usage_type": "attribute"
}
] |
42985186741 | import psycopg2
def create_table(connection):
# il cursore è utillizato esequire comandi e accedere alla risposta
cursor = connection.cursor()
try:
# prepara il comando
create_table_query = '''CREATE TABLE students
(ID SERIAL PRIMARY KEY ,
NAME TEXT NOT NULL,
SURNAME TEXT NOT NULL,
unique(NAME,SURNAME)); '''
# eseguo il comando
cursor.execute(create_table_query)
create_table_query = '''CREATE TABLE courses
(ID SERIAL PRIMARY KEY ,
NAME TEXT NOT NULL,
DESCR TEXT NOT NULL); '''
cursor.execute(create_table_query)
create_table_query = '''CREATE TABLE booking
(STUDENT_ID BIGINT ,
COURSE_ID BIGINT ,
PRIMARY KEY (STUDENT_ID,COURSE_ID),
FOREIGN KEY (STUDENT_ID) REFERENCES students(ID) MATCH FULL ON DELETE CASCADE,
FOREIGN KEY (COURSE_ID) REFERENCES courses(ID) MATCH FULL ON DELETE CASCADE
); '''
cursor.execute(create_table_query)
# le modifiche diventano definitive
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error creating tables", error)
finally:
# chiudo il cursore in finally
cursor.close()
def insert_names(connection):
cursor = connection.cursor()
try:
nomi = [("Marco", "Torlaschi"), ("Roberta", "Latini"), ("Giorgo", "Franzini")]
# preparo il comando generico con %s e poi inserisco valori in all'esecuzione
insert = "INSERT INTO students (NAME,SURNAME) VALUES (%s,%s)"
for n in nomi:
cursor.execute(insert, n)
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error inserting names", error)
finally:
cursor.close()
def insert_courses(connection):
cursor = connection.cursor()
try:
corsi = [("SQL", "corso sql"), ("SPRING", "corso spring")]
insert = "INSERT INTO courses (NAME,DESCR) VALUES (%s,%s)"
for n in corsi:
cursor.execute(insert, n)
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error inserting names", error)
finally:
cursor.close()
def insert_booking(connection, name, surname, course):
cursor = connection.cursor()
try:
cursor.execute("SELECT ID FROM students WHERE NAME=%s AND SURNAME=%s", (name, surname))
student_id = cursor.fetchone()[0]
cursor.execute("SELECT ID FROM courses WHERE NAME = %s ", (course,))
course_id = cursor.fetchone()[0]
cursor.execute("INSERT INTO booking (STUDENT_ID,COURSE_ID) VALUES (%s,%s)", (str(student_id), str(course_id)))
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error inserting bookings", error)
finally:
cursor.close()
def delate_names(connetion):
cursor = connection.cursor()
try:
cursor.execute("DELETE FROM students where name='Roberta' ")
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error deleting name", error)
finally:
cursor.close()
def drop_tables(connetion):
cursor = connection.cursor()
try:
cursor.execute("DROP TABLE booking ")
cursor.execute("DROP TABLE students ")
cursor.execute("DROP TABLE courses ")
connection.commit()
except (Exception, psycopg2.Error) as error:
print("Error deleting name", error)
finally:
cursor.close()
if __name__ == '__main__':
print("esempi sql")
try:
# apro la connessione
connection = psycopg2.connect(user="postgres",
password="password",
host="127.0.0.1",
port="5432",
database="sample")
#drop_tables(connection)
create_table(connection)
insert_names(connection)
insert_courses(connection)
insert_booking(connection, "Marco", "Torlaschi", "SQL")
insert_booking(connection, "Marco", "Torlaschi", "SPRING")
insert_booking(connection, "Roberta", "Latini", "SPRING")
cursor = connection.cursor()
cursor.execute("SELECT * FROM students,booking,courses WHERE students.ID=booking.STUDENT_ID AND courses.ID=booking.COURSE_ID")
riga = cursor.fetchall()
print(riga)
drop_tables(connection)
except (Exception, psycopg2.Error) as error:
# catcho l'errore in connessione
print("Error while connecting to PostgreSQL", error)
finally:
# chiudo la connessione in finally per essere sicuro che avvenga sempre
if (connection):
connection.close()
print("PostgreSQL connection is closed")
| Torla/postgres_ex | main.py | main.py | py | 5,031 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "psycopg2.Error",
"line_number": 39,
"usage_type": "attribute"
},
{
"api_name": "psycopg2.Error",
"line_number": 60,
"usage_type": "attribute"
},
{
"api_name": "psycopg2.Error",
"line_number": 79,
"usage_type": "attribute"
},
{
"api_name": "psycopg2.... |
70506020904 | #importing libraries
import pandas as pd
from selenium import webdriver # for webdriver
from selenium.common.exceptions import NoSuchElementException # for exception handling
import time # for delay
# setting platform for selenium
path = r'C:\Users\haqab\Desktop\DS\chromedriver.exe'
driver = webdriver.Chrome(path)
# url
driver.get('https://www.olx.in/cars_c84')
# setting arrays for data.
links = []
URL = []
Company_name = []
prices = []
Location = []
Model = []
Variant = []
Year = []
Fuel = []
Transmission = []
Mileage = []
No_owner = []
#main state wise url links
for i in pages:
driver.get(i)
for j in range(1,15):
try:
time.sleep(1)
# for clicking load more botton
load_more = driver.find_element_by_xpath('//*[@class="rui-3sH3b rui-3K5JC rui-1zK8h"]')
load_more.click()
# escaping errors
except NoSuchElementException:
pass
# car links
# main links of cars to scrape.
posts = driver.find_elements_by_xpath('//*[@class = "EIR5N"]/a')
links = [elem.get_attribute('href') for elem in posts]
# checking the output
#print(len(links))
# individual cars url main scraping process..
for i in links:
driver.get(i)
try:
url = driver.find_element_by_xpath('//*[@rel = "alternate"]')
#print(url.get_attribute('href'))
URL.append(url.get_attribute('href'))
name = driver.find_element_by_xpath('//*[@data-aut-id = "value_make"]').text
#print(name)
Company_name.append(name)
price = driver.find_element_by_xpath('//*[@class="_18gRm"]').text
#print(price)
prices.append(price)
local = driver.find_element_by_xpath('//*[@class="_2FRXm"]').text
#print(local)
Location.append(local.split(',')[2])
model = driver.find_element_by_xpath('//*[@data-aut-id = "value_model"]').text
#print(model)
Model.append(model)
variant = driver.find_element_by_xpath('//*[@data-aut-id = "value_variant"]').text
#print(variant)
Variant.append(variant)
year = driver.find_element_by_xpath('//*[@data-aut-id = "value_year"]').text
#print(year)
Year.append(year)
fuel = driver.find_element_by_xpath('//*[@data-aut-id = "value_petrol"]').text
#print(fuel)
Fuel.append(fuel)
transmission = driver.find_element_by_xpath('//*[@data-aut-id = "value_transmission"]').text
#print(transmission)
Transmission.append(transmission)
mileage = driver.find_element_by_xpath('//*[@data-aut-id = "value_mileage"]').text
#print(mileage)
Mileage.append(mileage)
no_owner = driver.find_element_by_xpath('//*[@data-aut-id = "value_first_owner"]').text
#print(no_owner)
No_owner.append(no_owner)
except NoSuchElementException:
pass
#print('One state complete')
#print(len(links))
# The complete output
# conveting into dict..
dictt = {
'URL' : URL,
'Company_name' : Company_name,
'prices' : prices,
'Loctaion' : Location,
'Model' : Model,
'Varient' : Variant,
'Year' : Year,
'Fuel' : Fuel,
'Transmission' : Transmission,
'Mileage' : Mileage,
'No_owner' : No_owner }
dictt
# saving to data frame using pandas
df2 = pd.DataFrame.from_dict(dictt, orient = 'index')
df2 = df.transpose()
# output file
df.to_csv('output.csv')
| Abdulhaq005/Web-Scraping-scrapy-and-selenium- | cars/spiders/selenium_script.py | selenium_script.py | py | 3,645 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "selenium.webdriver.Chrome",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "selenium.webdriver",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "time.sleep",
"line_number": 35,
"usage_type": "call"
},
{
"api_name": "selenium.comm... |
29261172588 | import xml.etree.ElementTree as ET
def xml_parse(file_byte):
def get_recursive(parent):
res = {}
if not parent.getchildren():
res[parent.tag] = ''
return res
res[parent.tag] = []
for child in parent:
if child.getchildren():
res[parent.tag].append(get_recursive(child))
else:
res[parent.tag].append({child.tag: child.text})
return res
root = ET.fromstring(file_byte)
return get_recursive(root)
| oktavianustjoea/ap-app | xml_converter/utils.py | utils.py | py | 529 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "xml.etree.ElementTree.fromstring",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "xml.etree.ElementTree",
"line_number": 19,
"usage_type": "name"
}
] |
70307229865 | import os
import requests
import json
from requests_oauthlib import OAuth1
from dotenv import load_dotenv
load_dotenv()
CONSUMER_KEY = os.getenv("TWITTER_API_KEY")
CONSUMER_SECRET = os.getenv("TWITTER_API_SECRET")
ACCESS_TOKEN = os.getenv("TWITTER_ACCESS_TOKEN")
ACCESS_TOKEN_SECRET = os.getenv("TWITTER_ACCESS_TOKEN_SECRET")
API_KEY = os.getenv("WAKATIME_API_KEY")
auth = OAuth1(CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
def get_wakatime_stats():
waka_url = (
f"https://wakatime.com/api/v1/users/current/status_bar/today?api_key={API_KEY}"
)
response = requests.get(waka_url)
if response.status_code != 200:
raise Exception(
f"Request failed with error {response.status_code}, {response.text}"
)
stats = {}
top_3_languages = {}
stats["minutes_coded_today"] = response.json()["data"]["grand_total"]["text"]
for language in response.json()["data"]["languages"][0:3]:
top_3_languages[language["name"]] = language["text"]
return stats, top_3_languages
def update_coding_streak():
with open("coding_streak.txt", "r") as f:
coding_streak = int(f.read().strip())
stats, top_3_languages = get_wakatime_stats()
if stats["minutes_coded_today"]:
coding_streak += 1
else:
coding_streak = 0
with open("coding_streak.txt", "w") as f:
f.write(str(coding_streak))
# If the coding streak is broken, do not tweet and end the program. The streak file will be updated to 0.
if coding_streak == 0:
print("Coding streak broken, not tweeting")
exit()
return coding_streak
def format_tweet(coding_streak, minutes_coded_today, languages):
day_of_streak = coding_streak
tweet_text = f"Day {day_of_streak} of my coding streak: I coded {minutes_coded_today} today!\n\n#100DaysOfCode "
# make the hashtags each language
for language in languages.keys():
tweet_text += f"#{language} "
return tweet_text
def post_tweet(tweet_text):
url = "https://api.twitter.com/2/tweets"
headers = {"Content-Type": "application/json"}
data = {"text": tweet_text}
response = requests.post(url, auth=auth, data=json.dumps(data), headers=headers)
if response.status_code != 201:
raise Exception(
f"Request failed with error {response.status_code}, {response.text}"
)
print("Tweet posted successfully!", response.json())
if __name__ == "__main__":
coding_streak = update_coding_streak()
stats, languages = get_wakatime_stats()
tweet_text = format_tweet(coding_streak, stats["minutes_coded_today"], languages)
post_tweet(tweet_text)
| homeGrownCheese/tweet_waka_daily | tweet_stats.py | tweet_stats.py | py | 2,688 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "dotenv.load_dotenv",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": ... |
72215373865 | # Download the helper library from https://www.twilio.com/docs/python/install
from twilio.rest import Client
# Your Account Sid and Auth Token from twilio.com/console
# DANGER! This is insecure. See http://twil.io/secure
account_sid = 'AC3f72ddfebffe2adae5b4efe0c6d9c9b6'
auth_token = 'd45d3fcfab5be3e08985b77a3fd13103'
client = Client(account_sid, auth_token)
from twilio.rest import TwilioRestClient
#填入你申请的号码
twilioNumber = '+19195253692'
#填入你验证的手机号
myNumbers = ["+8618601156335", "+8618601156335", "+8618601156335", "+8613120090157", "+8613120090157"]
#填入你想发送的信息
#Message
message = 'Hi this is ravana!'
client = Client(account_sid, auth_token)
for myNumber in myNumbers:
print("Sending", myNumber)
msg = client.messages.create(to=myNumber, from_=twilioNumber, body=message)
print(msg.sid)
#Phone call:
call = client.calls.create(
url='http://demo.twilio.com/docs/voice.xml',
to=myNumber,
from_=twilioNumber)
print(call.sid)
| leemengwei/tasty_shrimp_skype | phone_with_twilio.py | phone_with_twilio.py | py | 1,069 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "twilio.rest.Client",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "twilio.rest.Client",
"line_number": 19,
"usage_type": "call"
}
] |
39687720281 | import networkx as nx, matplotlib.pyplot as plt, numpy as np, copy
import MarkovChain as SMC
from Randomized import *
from time import time
Pps = 0.4 # float(input('Ingrese la probabilidad de que un dispositivo protegido pase a ser susceptible: '))
Psp = 0.3 # float(input('Ingrese la probabilidad de que un dispositivo susceptible pase a ser protegido: '))
Psc = 0.5 # float(input('Ingrese la probabilidad de que un dispositivo susceptible pase a ser comprometido: '))
Pcp = 0.2 # float(input('Ingrese la probabilidad de que un dispositivo comprometido pase a ser protegido: '))
Pci = 0.1 # float(input('Ingrese la probabilidad de que un dispositivo comprometido pase a ser irrecuperable: '))
prec = 5 # int(input('Ingrese el número de decimales a manejar: '))
t_lim = 100 # int(input('Ingrese el número máximo de momentos a estudiar: '))
base_MT = [[1 - Pps, Pps, 0, 0], [Psp, 1 - Psp - Psc, Psc, 0], [Pcp, 0, 1 - Pcp - Pci, Pci], [0, 0, 0, 1]]
NetGraph, NetDevices, states = None, {}, ['Protegido', 'Susceptible', 'Comprometido', 'Irrecuperable']
HeatMap, gen_distr, N = [], [], 0
def setNonDefaults(Probs):
(Pps, Psp, Psc, Pcp, Pci) = Probs
base_MT = [[1 - Pps, Pps, 0, 0], [Psp, 1 - Psp - Psc, Psc, 0], [Pcp, 0, 1 - Pcp - Pci, Pci], [0, 0, 0, 1]]
return Psp, Psc, base_MT
def createNode(i, fs, nset, MT, adjust):
vi = [0, 0, 0, 0]
vi[states.index(fs)] = 1
if adjust:
NetDevices[i]['factual_state'] = fs
NetDevices[i]['init_vector'] = np.array(vi)
NetDevices[i]['trans_matrix'] = copy.deepcopy(np.array(MT))
for n in nset:
if (n not in NetDevices) or (i not in NetDevices[n]['neighbors']) and (len(NetDevices[n]['neighbors']) < 4):
NetDevices[i]['neighbors'].append(n)
NetDevices[n]['neighbors'].append(i)
else:
NetDevices[i] = {
'factual_state': fs,
'init_vector': np.array(vi),
'trans_matrix': copy.deepcopy(np.array(MT)),
'neighbors': nset }
def setNeighborFactor(u, NetDevices, Psp, Psc):
nset, count = NetDevices[u]['neighbors'], 0
if len(nset) == 0:
NetDevices[u]['trans_matrix'][1][1] = 1 - Psp - Psc
NetDevices[u]['trans_matrix'][1][2] = Psc
else:
for n in nset:
if NetDevices[n]['factual_state'] in states[2:]: count += 1
factor = (Psc + (count / len(nset))) / 2
NetDevices[u]['trans_matrix'][1][1] = np.round_(1 - Psp - factor, prec)
NetDevices[u]['trans_matrix'][1][2] = np.round_(factor, prec)
def setFactualState(j, NetDevices):
ref = states.index(NetDevices[j]['factual_state'])
comp = np.argmax(NetDevices[j]['init_vector'])
if NetDevices[j]['trans_matrix'][ref][comp] == 0:
if ref == 3: comp = ref
elif ref == 2 and comp == 1:
if NetDevices[j]['init_vector'][0] == 0: comp = 3
else: comp = 0
elif NetDevices[j]['init_vector'][ref + 1] == 0: comp = 0
else: comp = ref + 1
return states[comp]
def manualBuildNetwork(N, MT):
base_MT = MT; edges = []
nodes = [(x+1) for x in range(N)]
for y in nodes:
fs = ''
while fs not in states:
fs = input('Ingrese el estado del nodo '+str(y)+' (Protegido, Susceptible, Comprometido o Irrecuperable): ')
n_chain = input('Ingrese los vecinos del nodo ' + str(y) + ' separados por comas: ').split(',')
neighbors = [int(n) for n in n_chain]
createNode(y, fs, neighbors, base_MT, False)
for n in neighbors: edges.append([y, n])
return graphNetwork(nodes, edges)
def UIBuildNetwork(Nstates, Nedges, MT):
nodes, edges = [(x+1) for x in range(len(Nedges))], []
for y in nodes:
fs = Nstates[y-1]
n_chain = Nedges[y-1].split(',')
neighbors = [int(n) for n in n_chain]
createNode(y, fs, neighbors, MT, False)
for n in neighbors: edges.append([y, n])
return nodes, edges, NetDevices
def autoBuildNetwork(N, topology, MT):
base_MT = MT; edges, adjust = [], False
nodes = [(x+1) for x in range(N)]
for y in nodes:
fs, neighbors = randomFactualState(states), []
if y == 1:
for x in range(N):
NetDevices[x + 1] = {'factual_state': '', 'init_vector': [], 'trans_matrix': [], 'neighbors': []}
if topology == 'Lineal':
if y < N: neighbors.append(y + 1)
if y > 1: neighbors.append(y - 1)
elif topology == 'Anillo':
if y < N: neighbors.append(y + 1)
if y > 1: neighbors.append(y - 1)
if y in (1, N): neighbors.append(N - y + 1)
elif topology == 'Estrella':
if y == 1: neighbors = range(2, N + 1)
else: neighbors = [1]
elif topology == 'Cuadricula':
if y < N: neighbors.append(y + 1)
if y > 1: neighbors.append(y - 1)
if y % 2 == 0 and y in range(3, 10): neighbors.append(1)
if y in (2, 9) and N >= 9: neighbors.append(11 - y)
if y == 1: neighbors.extend(range(4, np.min([N+1, 8]), 2))
elif topology == 'Malla':
prev = len(NetDevices[y]['neighbors'])
limnodes = np.min([N, 5-prev])
if limnodes > 1:
tries, checks, adjust = np.random.randint(1, limnodes), 0, True
while checks < tries:
randneighbor = np.random.randint(1, N+1)
if y != randneighbor and randneighbor not in neighbors:
neighbors.append(randneighbor)
checks += 1
createNode(y, fs, neighbors, MT, adjust)
for n in neighbors: edges.append([y, n])
#graphNetwork(nodes, edges)
return NetDevices
def graphNetwork(nodes, edges):
NetGraph = nx.Graph()
NetGraph.add_nodes_from(nodes)
nx.set_node_attributes(NetGraph, NetDevices)
NetGraph.add_edges_from(edges)
MC = plt.figure(figsize=(8, 6))
pos = nx.kamada_kawai_layout(NetGraph)
nx.draw_networkx_nodes(NetGraph, pos, cmap=plt.get_cmap('jet'), node_size=1250, node_color='cyan')
nx.draw_networkx_labels(NetGraph, pos)
nx.draw_networkx_edges(NetGraph, pos)
plt.show()
return MC
def scanDistribution(N, NetDevices):
sums = np.array([0, 0, 0, 0])
for z in range(N): sums[states.index(NetDevices[z + 1]['factual_state'])] += 1
sums = sums / N
return sums
def spotNodeStates(N, NetDevices):
nodestates = np.array([0]*N)
for z in range(N): nodestates[z] = states.index(NetDevices[z + 1]['factual_state'])
return nodestates
def performStepAllNodes(N, NetDevices, Psp, Psc):
report, statereg = '', ''
#print('Distribution: ' + str(scanDistribution(N)))
for a in range(1, N + 1):
#report += str(NetDevices[a]['init_vector']) + ' '
#statereg += str(NetDevices[a]['factual_state']) + ' '
setNeighborFactor(a, NetDevices, Psp, Psc)
#print(NetDevices[a]['init_vector'], NetDevices[a]['trans_matrix'])
step = np.matmul(NetDevices[a]['init_vector'], NetDevices[a]['trans_matrix'])
NetDevices[a]['init_vector'] = np.around(step, prec)
NetDevices[a]['factual_state'] = setFactualState(a, NetDevices)
#print(report + '\n', statereg)
return scanDistribution(N, NetDevices)
def paintProgressMC(progress):
BP = plt.figure()
plt.xlabel('Time (t)')
plt.ylabel('Probability (P)')
for p in progress: plt.plot(p)
plt.legend(states)
plt.show()
return BP
def getPlots(progress, temps, comparetemps, context):
plt.plot(progress)
plt.title('Pps = {0}, Psp = {1}, Psc = {2}, Pcp = {3}, Pci = {4} \nInitialization Vector = {5}'
.format(Pps, Psp, Psc, Pcp, Pci, context))
plt.legend(states)
plt.xlabel('Time (t)')
plt.ylabel('Probability (P)')
plt.show()
difftemps = list(map(list, zip(temps, comparetemps)))
plt.plot(difftemps)
plt.title('Comparación de tiempos de ejecución conforme avanza el algoritmo')
plt.legend(['Múltiple', 'Simple'])
plt.xlabel('Moment (t)')
plt.ylabel('Execution Time (ms)')
plt.show()
def retrieveHeatMap(infection, nodes, moments):
HMNS = plt.figure()
plt.xlabel('Time (t)')
plt.ylabel('Nodes')
plt.imshow(infection, cmap='hot_r', aspect='auto', extent=[1, nodes, moments, 0])
plt.show()
infection = []
return HMNS
def start():
N = int(input('Ingrese el número de nodos de la red: '))
NetGraph = manualBuildNetwork(N, base_MT)
input('Press Enter to continue...')
num_nodes = len(NetGraph.nodes)
context = scanDistribution(num_nodes)
progress, temps, c, executime = [context], [0], 0, 0
while c < t_lim:
start_time = time()
progress.append(performStepAllNodes(num_nodes, NetDevices, Psp, Psc))
total_time = time() - start_time
executime += (total_time*1000); c += 1
temps.append(executime)
comparetemps = SMC.start(N)
getPlots(progress, temps, comparetemps, context)
#paintProgressMC()
retrieveHeatMap(HeatMap, len(HeatMap), len(HeatMap[0]))
#means = getMeanTime(matriz_transicion, vector_inverso)
#for m in range(len(means)): print('Tiempo medio desde el estado', Estados[m]+': ', means[m])
if __name__ == '__main__': start() | Zharet-Bautista-Montes/Markov_Inspector | venv/core/MultipleMC.py | MultipleMC.py | py | 9,302 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.array",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "copy.deepcopy",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number"... |
17169329371 | import os
import telebot
from dotenv import load_dotenv
import client
load_dotenv()
bot_token = os.getenv('BOT_TOKEN')
admin = os.getenv('TG_ADMIN_ID')
bot = telebot.TeleBot(bot_token)
states_list = ["ADDRESS", "AMOUNT", "CONFIRM"]
states_of_users = {}
@bot.message_handler(commands=['start'])
def start_message(message):
"""
markup: создаем объект для работы с кнопками (row_width - определяет количество кнопок по ширине)
создаем три кнопки, типа:KeyboardButton
отправляем пользователю три кнопки типа:KeyboardButton
после команды /start.
"""
try:
client.create_user({"tg_ID": message.from_user.id,
"nick": message.from_user.username})
except Exception as e:
return e
markup = telebot.types.ReplyKeyboardMarkup(
row_width=3,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Кошелек')
btn2 = telebot.types.KeyboardButton('Перевести')
btn3 = telebot.types.KeyboardButton('История')
btn4 = telebot.types.KeyboardButton('Админ')
markup.add(btn1, btn2, btn3, btn4)
text: str = f'Привет {message.from_user.full_name}, я твой бот-криптокошелек, \n' \
'у меня ты можешь хранить и отправлять биткоины'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(regexp='Кошелек')
def wallet(message):
wallet = client.get_user_wallet_by_tg_id(message.from_user.id)
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2, resize_keyboard=True)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
text = (f'Ваш баланс: {wallet["balance"]} BTC \n'
f'Ваш адрес: {wallet["address"]}')
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(regexp='История транзакций')
def history(message):
transactions = client.get_user_transactions(
client.get_user_by_tg_id(message.from_user.id)["id"])
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
text = f'Ваши транзакции: \n{transactions}'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(regexp='Перевести')
def transaction(message):
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
text = f'Введите адрес кошелька куда хотите перевести: '
bot.send_message(message.chat.id, text, reply_markup=markup)
# тут мы даём юзеру состояние при котором ему будет возвращаться следующее
# сообщение
states_of_users[message.from_user.id] = {"STATE": "ADDRESS"}
@bot.message_handler(regexp='История')
def history(message):
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
transactions = ['1', '2', '3'] # сюда мы загрузим транзакции
text = f'Ваши транзакции{transactions}'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(regexp='Меню')
def menu(message):
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Кошелек')
btn2 = telebot.types.KeyboardButton('Перевести')
btn3 = telebot.types.KeyboardButton('История')
markup.add(btn1, btn2, btn3)
text = f'Главное меню'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(func=lambda message: message.from_user.id ==
admin and message.text == 'Админ')
def admin_panel(message):
if message.from_user.id != admin:
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
bot.send_message(
message.chat.id,
'У вас нет прав администратора.',
reply_markup=markup)
else:
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2, resize_keyboard=True)
btn1 = telebot.types.KeyboardButton('Общий баланс')
btn2 = telebot.types.KeyboardButton('Все юзеры')
btn3 = telebot.types.KeyboardButton('Данные по юзеру')
btn4 = telebot.types.KeyboardButton('Удалить юзера')
markup.add(btn1, btn2, btn3, btn4)
text = f'Админ-панель'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(func=lambda message: message.from_user.id ==
admin and message.text == "Все юзеры")
def show_all_users(message):
text = f'Юзеры:'
users = client.get_users()
inline_markup = telebot.types.InlineKeyboardMarkup()
for user in users:
inline_markup.add(telebot.types.InlineKeyboardButton(
text=f'Юзер: {user["tg_ID"]}',
callback_data=f'user_{user["tg_ID"]}'
))
bot.send_message(message.chat.id, text, reply_markup=inline_markup)
@bot.callback_query_handler(func=lambda call: True)
def callback_query(call):
query_type = call.data.split('_')[0]
users = client.get_users()
if query_type == 'user':
user_id = call.data.split('_')[1]
inline_markup = telebot.types.InlineKeyboardMarkup()
for user in users:
if str(user['tg_ID']) == user_id:
inline_markup.add(
telebot.types.InlineKeyboardButton(
text="Назад",
callback_data='users'),
telebot.types.InlineKeyboardButton(
text="Удалить юзера",
callback_data=f'delete_user_{user_id}'))
bot.edit_message_text(
text=f'Данные по юзеру:\n'
f'ID: {user["tg_ID"]}\n'
f'Ник: {user.get("nick")}\n'
f'Баланс: {client.get_user_balance_by_id(user["id"])}',
chat_id=call.message.chat.id,
message_id=call.message.message_id,
reply_markup=inline_markup)
print(f"Запрошен {user}")
break
if query_type == 'users':
inline_markup = telebot.types.InlineKeyboardMarkup()
for user in users:
inline_markup.add(
telebot.types.InlineKeyboardButton(
text=f'Юзер: {user["tg_ID"]}',
callback_data=f"user_{user['tg_ID']}"))
bot.edit_message_text(
text="Юзеры:",
chat_id=call.message.chat.id,
message_id=call.message.message_id,
reply_markup=inline_markup) # прикрепляем нашу разметку к ответному сообщению
if query_type == 'delete' and call.data.split('_')[1] == 'user':
user_id = int(call.data.split('_')[2])
for i, user in enumerate(users):
if user['tg_ID'] == user_id:
print(f'Удален Юзер: {users[i]}')
client.delete_user(users.pop(i)['id'])
inline_markup = telebot.types.InlineKeyboardMarkup()
for user in users:
inline_markup.add(
telebot.types.InlineKeyboardButton(
text=f'Юзер: {user["tg_ID"]}',
callback_data=f"user_{user['tg_ID']}"))
bot.edit_message_text(
text="Юзеры:",
chat_id=call.message.chat.id,
message_id=call.message.message_id,
reply_markup=inline_markup
)
@bot.message_handler(func=lambda message: message.from_user.id ==
admin and message.text == "Общий баланс")
def total_balance(message):
total_bal = client.get_total_balance()
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
btn2 = telebot.types.KeyboardButton('Админка')
markup.add(btn1, btn2)
text = f'Общий баланс: {total_bal} BTC'
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(
func=lambda message: states_of_users.get(
message.from_user.id)["STATE"] == 'AMOUNT')
def get_confirmation_of_transaction(message):
if message.text == "Меню":
del states_of_users[message.from_user.id]
menu(message)
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2,
resize_keyboard=True
)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
if message.text.isdigit():
text = f'Вы хотите перевести {message.text} сатоши,\n' \
f'на биткоин-адрес {states_of_users[message.from_user.id]["ADDRESS"]}: '
confirm = telebot.types.KeyboardButton('Подтверждаю')
markup.add(confirm)
bot.send_message(message.chat.id, text, reply_markup=markup)
# тут мы даём юзеру состояние при котором ему будет возвращаться
# следующее сообщение
states_of_users[message.from_user.id]["STATE"] = "CONFIRM"
states_of_users[message.from_user.id]["AMOUNT"] = int(message.text)
else:
text = f'Вы ввели не число, попробуйте заново: '
bot.send_message(message.chat.id, text, reply_markup=markup)
@bot.message_handler(
func=lambda message: states_of_users.get(
message.from_user.id)["STATE"] == 'CONFIRM')
def get_hash_of_transaction(message):
if message.text == "Меню":
del states_of_users[message.from_user.id]
menu(message)
elif message.text == "Подтверждаю":
bot.send_message(
message.chat.id,
f" Ваша транзакция: " + str(client.create_transaction(message.from_user.id,
states_of_users[message.from_user.id]['ADDRESS'],
states_of_users[message.from_user.id]['AMOUNT']))
)
del states_of_users[message.from_user.id]
menu(message)
@bot.message_handler(
func=lambda message: states_of_users.get(
message.from_user.id)["STATE"] == 'ADDRESS')
def get_amount_of_transaction(message):
if message.text == "Меню":
del states_of_users[message.from_user.id]
menu(message)
markup = telebot.types.ReplyKeyboardMarkup(
row_width=2, resize_keyboard=True)
btn1 = telebot.types.KeyboardButton('Меню')
markup.add(btn1)
text = f'Введите сумму в сатоши, которую хотите перевести: '
bot.send_message(message.chat.id, text, reply_markup=markup)
# тут мы даём юзеру состояние при котором ему будет возвращаться следующее
# сообщение
states_of_users[message.from_user.id]["STATE"] = "AMOUNT"
states_of_users[message.from_user.id]["ADDRESS"] = message.text
bot.infinity_polling(timeout=10)
| Lexxar91/bitcoin_api_bot | tg_bot/bot.py | bot.py | py | 12,039 | python | ru | code | 0 | github-code | 36 | [
{
"api_name": "dotenv.load_dotenv",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "telebot.TeleBot",
"line_num... |
31850440252 | import matplotlib.pyplot as plt
import numpy as np
# x axis
u = np.arange(0.0,2.74,0.01,dtype=np.cdouble)
v = np.arange(2.74,5.0,0.01,dtype=np.cdouble)
x = np.arange(0.0,5.0,0.01,dtype=np.cdouble)
y = np.arange(0.0,3.83,0.01, dtype=np.cdouble)
z = np.arange(3.83,5.0,0.01, dtype=np.cdouble)
# y axis
def f(t, option = 0):
c = 1
b = np.sqrt(t ** 4 - 16 * t ** 2 + 20)
a = -t ** 2 +8
if option % 2 == 1:
b *= -1
if option > 1:
c = -1
return c * np.sqrt(a+b)/np.sqrt(complex(22))
def g(t, option = 0):
c = 1
b = np.sqrt(t ** 4 - 8 * t ** 2 + 4)
a = 4 - t ** 2
if option % 2 == 1:
b *= -1
if option > 1:
c = -1
return c * np.sqrt(a+b)/np.sqrt(complex(6))
# use LaTeX fonts in the plot
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
n = plt.figure()
# plot
plt.subplot(2,1,1)
for i in range(4):
plt.plot(x, np.real(f(x,i)), color="black")
plt.plot(x, np.real(g(x,i)), color="black")
plt.axvline(x=0.733, color = "black", alpha = 0.5, dashes = (2,2))
plt.axvline(x=3.83, color = "black", alpha = 0.5, dashes = (2,2))
# set labels (LaTeX can be used)
plt.title(r'\textbf{Symmetry Breaking}', fontsize=11)
plt.ylabel(r'\textbf{Re[$\omega$]}', fontsize=11)
plt.subplot(2,1,2)
for i in range(4):
plt.plot(y, np.imag(f(y,i)), color="black")
plt.plot(z, np.imag(f(z,i)), color="black")
plt.plot(u, np.imag(g(u,i)), color="black")
plt.plot(v, np.imag(g(v,i)), color="black")
# set labels (LaTeX can be used)
plt.axvline(x=0.733, color = "black", alpha = 0.5, dashes = (2,2))
plt.axvline(x=3.83, color = "black", alpha = 0.5, dashes = (2,2))
plt.xlabel(r'\textbf{$\gamma$}', fontsize=11)
plt.ylabel(r'\textbf{Im[$\omega$]}', fontsize=11)
plt.show()
# save as PDF
n.savefig("pt_breaking.pdf",)
| cesaregarza/QMResearch | plotter.py | plotter.py | py | 1,812 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.arange",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "numpy.cdouble",
"line_number": 5,
"usage_type": "attribute"
},
{
"api_name": "numpy.arange",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "numpy.cdouble",
"line_n... |
26626844923 | import astropy.units as u
from astropy.coordinates.sky_coordinate import SkyCoord
from astropy.units import Quantity
from astropy.io.votable import parse
from astropy.table import Table
from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
import numpy as np
extra_data = np.genfromtxt("Data/Mean_extinction_excess_SourceID_500pc_AG-4.4_BminR-1.7.dat", names = True, dtype=None)
source_id = extra_data['source_id']
readresults = Table.read('Data/All-star-500pc-AG-4.4-BminR-1.7.fits',format='fits')
results = np.array(readresults)
#Find the nonmatching source_id
nonmatches = np.array([])
j=0
for i in range(len(source_id)):
not_found = True
while not_found:
if source_id[i]!=results['source_id'][j]:
nonmatches = np.append(nonmatches,j)
j+=1
else:
not_found = False
j+=1
print(j)
#Delete the rows with source_id that have no AG and EBminR
for k in range(len(nonmatches)):
results = np.delete(results,nonmatches[k]-k)
#Check if the deletion was succesful
j=0
nonmatches_check = np.array([])
for i in range(len(source_id)):
not_found = True
while not_found:
if source_id[i]!=results['source_id'][j]:
nonmatches_check = np.append(nonmatches_check,j)
j+=1
else:
not_found = False
j+=1
print(j)
x=results['bp_rp']-extra_data['EBminR']
y_gaia=results['phot_g_mean_mag']+5*np.log10(results['parallax'])-10-extra_data['AG']
k=np.linspace(-4, 1, 1000)
counts_gaia,xbins_gaia,ybins_gaia,image_gaia = plt.hist2d(x,y_gaia,bins=600,normed=True,norm=LogNorm(),cmap = plt.cm.jet)
plt.colorbar()
plt.vlines(x=0, ymin=-5, ymax=2,color=(1.0,0.2,0.3))
plt.vlines(x=0.33, ymin=-5, ymax=4,color=(1.0,0.2,0.3))
#plt.contour(counts_gaia.transpose(), extent=[xbins_gaia.min(),xbins_gaia.max(),ybins_gaia.min(),ybins_gaia.max()], colors='k', linewidth=0.01, levels = [0.1])
plt.text(-0.6, -3.5, 'OB')
plt.text(0.1, -3.5, 'A')
plt.plot(k,3*k+2.1,linestyle='-',color=(1.0,0.2,0.3))
plt.xlim(np.min(x),1.7)
plt.ylim(-4,4.4)
plt.xlabel(r'$(G_{BP}-G_{RP})-<E(B-R)>$')
plt.ylabel(r'$M_G-<A_G>$')
plt.gca().invert_yaxis()
plt.savefig('CM-Diagram_All-star-500pc-AG-4.4-BminR-1.7-Corrected_mean_AG_EBminR.png')
| spacer730/Gaia_research | Queries-CM-Diagrams/CM-Diagram-corrected_mean_AG-EBminR .py | CM-Diagram-corrected_mean_AG-EBminR .py | py | 2,142 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.genfromtxt",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "astropy.table.Table.read",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "astropy.table.Table",
"line_number": 13,
"usage_type": "name"
},
{
"api_name": "numpy.a... |
31987030282 | # !/usr/bin/env python
# -*- coding: utf-8 -*-
import contextlib
import os
import zipfile
import os.path
import yaml
import json
applets_index = []
@contextlib.contextmanager
def change_dir(path):
old_path = os.getcwd()
os.chdir(path)
yield
os.chdir(old_path)
def read_applet_config(applet_path) -> dict:
config_path = os.path.join(applet_path, 'manifest.yml')
if not os.path.exists(config_path):
raise Exception(f'{applet_path} not exist manifest.yml')
with open(config_path, 'r', encoding='utf8') as f:
return yaml.safe_load(f)
def zip_applet(applet_path, dst_dir):
dir_path = os.path.dirname(applet_path)
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
print(f'creat zip build folder: {dst_dir}')
with change_dir(dir_path):
app_config = read_applet_config(applet_path)
if app_config.get("name"):
applets_index.append(app_config)
applet_name = os.path.basename(applet_path)
zip_name = os.path.join(dst_dir, applet_name + '.zip')
filelist = []
if os.path.isfile(applet_path):
filelist.append(applet_path)
else:
for root, dirs, files in os.walk(applet_name):
for name in files:
filelist.append(os.path.join(root, name))
with zipfile.ZipFile(zip_name, "w", zipfile.ZIP_DEFLATED) as zf:
for tar in filelist:
zf.write(tar, tar)
print(f'zip {applet_name} applet to {zip_name} success')
ignore_dirs = ['dist', 'node_modules', 'build', 'venv', '.git', '.idea', '.vscode',
'__pycache__', 'demo', 'pip_packages', ]
def zip_all_applets(project_path):
applets_dir = []
for file in os.listdir(project_path):
applet_path = os.path.join(project_path, file)
if not os.path.isdir(applet_path):
continue
if file.startswith(".") or file in ignore_dirs:
continue
applets_dir.append(applet_path)
dist_dir = os.path.join(project_path, 'build')
for applet in applets_dir:
zip_applet(applet, dist_dir)
def write_index_json(project_path):
dst_dir = os.path.join(project_path, 'build')
with change_dir(dst_dir):
with open('index.json', 'w', encoding='utf8') as f:
json.dump(applets_index, f, ensure_ascii=False, indent=4)
def run():
root_path = os.path.dirname(os.path.abspath(__file__))
zip_all_applets(root_path)
write_index_json(root_path)
if __name__ == '__main__':
run()
| jumpserver/applets | build.py | build.py | py | 2,560 | python | en | code | 9 | github-code | 36 | [
{
"api_name": "os.getcwd",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "os.chdir",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "os.chdir",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "contextlib.contextmanager",
"line_num... |
40861542036 | import numpy as np
from typing import List, Dict
from math import ceil
def _valleys(hist: Dict[int, int]) -> List[int]:
"""
Find the valleys of a histogram of gray levels
Arguments:
hist frequencies in the histogram of gray levels 0,1,...,L-1 (dictionary)
Value:
valleys returns an object with class 'list', list of integer values
"""
L = len(hist)
toRight = list([False]*L)
toLeft = list([False]*L)
# Find when a frequency is less than or equal to the following one
for i in range(0, L-1):
toRight[i] = hist[i] <= hist[i+1]
# Find when a frequency is less than the previous one
for i in range(1, L):
toLeft[i] = hist[i] < hist[i-1]
# Find when both condition hold
both = list(i and j for i, j in zip(toRight, toLeft))
val = list(i for i, x in enumerate(both) if x == True)
return val
def _valley_clustering(L: int, val: List[int]) -> np.ndarray:
"""
Find limits of the clusters of a histogram of gray levels according to given valleys
Arguments:
L number of gray levels 1,...,L
val list of valleys
Value:
valley_clustering returns an object with class 'np.ndarray'
"""
# Find the amount of clusters
n = len(val) + 1
clust = np.zeros((n, 2), dtype=np.uint8)
# Find clusters
clust[0] = [0, val[0] - 1]
for i in range(1, n-1):
clust[i] = [val[i-1], val[i] - 1]
clust[n-1] = [val[n-2], L-1]
return clust
def _searching_window(clust: List[int]) -> np.ndarray:
# Find length of the initial cluster
n = clust[1] - clust[0] + 1
if n == 2:
w = np.zeros((1, 2), dtype=np.uint16)
w[0] = np.transpose(np.array(clust))
else:
# Find length of the searching windows
length = ceil(n / 2)
total = n - length + 1
w = np.zeros((int(total), 2), dtype=np.uint16)
# Find searching windows
for j in range(total):
w[j] = [clust[0] + j, ceil(clust[0] + j + length - 1)]
return w
| image-multithresholding/Image-multithresholding | src/image_multi_thresholding/thresholding_windows.py | thresholding_windows.py | py | 2,140 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "typing.Dict",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "typing.List",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "typing.List",
"line_number": 41,
"usage_type": "name"
},
{
"api_name": "numpy.zeros",
"line_number": 57... |
29028425647 | import json
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
from src.constants import (
DICT_CLASS,
EMB_ARRAY,
EMBEDDINGS,
METADATA,
NUM_CLOSEST_PLOT,
NUMBER_RANDOM_IMAGES,
PREDICTION,
SKETCHY,
TUBERLIN,
QUICKDRAW,
SKTU,
SKTUQD,
)
from src.data.loader_factory import load_data
from src.data.utils import default_image_loader, get_loader, get_dict
from src.models.utils import get_model, normalise_attention, get_parameters
from src.models.metrics import get_similarity
class Inference:
"""
Class to infer closest images of a sketch
Parent class of
- PlotInference called from main to plot closest images of random sketch
- ApiInference called from the api to retrieve the closest images of a hand-drawn sketch
"""
def __init__(self, args):
"""
Initialises the inference with the trained model and precomputed embeddings
Args:
- args: arguments received from the command line (argparse)
- mode: 'train', 'valid' or 'test'
"""
self.args = args
self.transform = transforms.Compose([transforms.ToTensor()])
self.loader = default_image_loader
self.im_net, self.sk_net = get_model(args, args.load)
self.im_net.eval()
self.sk_net.eval()
torch.set_grad_enabled(False)
self.prediction_folder = os.path.join(args.save, PREDICTION)
os.makedirs(self.prediction_folder, exist_ok=True)
self.embedding_path = os.path.join(args.save, EMBEDDINGS)
os.makedirs(self.embedding_path, exist_ok=True)
self.__get_data()
def __get_processed_images(self):
"""
Get the data of images to match with the sketches
Args:
- mode: 'train', 'valid' or 'test'
Return:
- dict_class: dictionnary mapping numbers to classes names
- paths to the images
- classes of the images
- images_embeddings: embeddings of the images
"""
dict_path = os.path.join(self.embedding_path, self.args.dataset + DICT_CLASS)
with open(dict_path, "r") as fp:
dict_class = json.load(fp)
array_path = os.path.join(
self.embedding_path, self.args.dataset + "_images" + EMB_ARRAY
)
with open(array_path, "rb") as f:
images_embeddings = np.load(f)
meta_path = os.path.join(
self.embedding_path, self.args.dataset + "_images" + METADATA
)
df = pd.read_csv(meta_path, sep=" ")
return dict_class, df["fnames"].values, df["classes"].values, images_embeddings
def __get_data(self):
"""
Loads the paths, classes and embeddings of the images of different datasets
"""
dataset = self.args.dataset
if dataset in [SKETCHY, TUBERLIN, QUICKDRAW]:
(
self.dict_class,
self.images_fnames,
self.images_classes,
self.images_embeddings,
) = self.__get_processed_images()
self.sketchy_limit = None
self.tuberlin_limit = None
elif dataset in [SKTU, SKTUQD]:
self.args.dataset = SKETCHY
(
dict_class_sk,
self.images_fnames,
self.images_classes,
self.images_embeddings,
) = self.__get_processed_images()
self.sketchy_limit = len(self.images_fnames)
self.tuberlin_limit = None
self.args.dataset = TUBERLIN
(
dict_class_tu,
images_fnames,
images_classes,
images_embeddings,
) = self.__get_processed_images()
self.dict_class = [dict_class_sk, dict_class_tu]
self.images_fnames = np.concatenate(
(self.images_fnames, images_fnames), axis=0
)
self.images_classes = np.concatenate(
(self.images_classes, images_classes), axis=0
)
self.images_embeddings = np.concatenate(
(self.images_embeddings, images_embeddings), axis=0
)
if dataset == SKTUQD:
self.args.dataset = QUICKDRAW
self.tuberlin_limit = len(self.images_fnames)
(
dict_class_qd,
images_fnames,
images_classes,
images_embeddings,
) = self.__get_processed_images()
self.dict_class.append(dict_class_qd)
self.images_fnames = np.concatenate(
(self.images_fnames, images_fnames), axis=0
)
self.images_classes = np.concatenate(
(self.images_classes, images_classes), axis=0
)
self.images_embeddings = np.concatenate(
(self.images_embeddings, images_embeddings), axis=0
)
else:
raise Exception(self.args.dataset + " not implemented.")
self.args.dataset = dataset
def inference_sketch(self, sk):
"""
Find the closest images of a sketch and plot them
"""
self.sketch = self.transform(sk).unsqueeze(0)
if self.args.cuda:
self.sketch = self.sketch.cuda()
sketch_embedding, self.attn_sk = self.sk_net(self.sketch)
if self.args.cuda:
sketch_embedding = sketch_embedding.cpu()
similarity = get_similarity(
sketch_embedding.detach().numpy(), self.images_embeddings
)
arg_sorted_sim = (-similarity).argsort()
self.sorted_fnames = [
self.images_fnames[i]
for i in arg_sorted_sim[0][0 : NUM_CLOSEST_PLOT + 1] # noqa E203
]
self.sorted_labels = [
self.images_classes[i]
for i in arg_sorted_sim[0][0 : NUM_CLOSEST_PLOT + 1] # noqa E203
]
return sketch_embedding
def get_heatmap(self):
""" Normalise the attention output of the model for heatmap plots """
attn_sk = normalise_attention(self.attn_sk, self.sketch)
self.heat_map = attn_sk.squeeze().cpu().detach().numpy()
def prepare_image(self, index):
""" Gets an an image and its label based on its index """
dataset = self.sorted_fnames[index].split("/")[-4]
loader = get_loader(dataset)
image = loader(self.sorted_fnames[index])
dict_class = get_dict(dataset, self.dict_class)
label = dict_class[str(self.sorted_labels[index])]
return image, label
class PlotInference(Inference):
""" Plot inference of a random sketch with its closest images in the latent space"""
def __init__(self, args, dataset_type):
super().__init__(args, dataset_type)
def random_images_inference(self, number_sketches):
""" Selects number_sketches random sketched and find the closest images """
_, _, [test_sk_loader, _], _ = load_data(self.args, self.transform)
rand_samples_sk = np.random.randint(
0, high=len(test_sk_loader), size=number_sketches
)
for i in range(len(rand_samples_sk)):
_, sketch_fname, _ = test_sk_loader[rand_samples_sk[i]]
self.sk = self.loader(sketch_fname)
self.inference_sketch(self.sk)
self.get_heatmap()
self.plot_closest(sketch_fname)
def plot_closest(self, sketch_fname):
"""
Plots a sketch with its closest images in the embedding space.
The images are stored in the same folder as the best model in a subfolder called 'predictions'
"""
fig, axes = plt.subplots(
1, NUM_CLOSEST_PLOT + 2, figsize=((NUM_CLOSEST_PLOT + 1) * 4, 8)
)
axes[0].imshow(self.sk)
axes[0].set(title="Sketch \n Label: " + sketch_fname.split("/")[-2])
axes[0].axis("off")
axes[1].imshow(self.sk)
axes[1].imshow(255 * self.heat_map, alpha=0.7, cmap="Spectral_r")
axes[1].set(title=sketch_fname.split("/")[-2] + "\n Attention Map")
axes[1].axis("off")
for i in range(2, NUM_CLOSEST_PLOT + 2):
im, label = self.prepare_image(i - 1)
axes[i].imshow(im)
axes[i].set(title="Closest image " + str(i) + "\n Label: " + label)
axes[i].axis("off")
plt.subplots_adjust(wspace=0.25, hspace=-0.35)
img_name = "_".join(sketch_fname.split("/")[-2:])
plt.savefig(os.path.join(self.prediction_folder, img_name))
plt.close(fig)
def main(args):
""" From here, the inference is done on a random sketch and a plot with its closest images is made """
inference = PlotInference(args, "test")
inference.random_images_inference(number_sketches=NUMBER_RANDOM_IMAGES)
if __name__ == "__main__":
args = get_parameters()
args.cuda = args.ngpu > 0 and torch.cuda.is_available()
main(args)
| VisiumCH/AMLD-2021-Sketchy | src/models/inference/inference.py | inference.py | py | 9,160 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "torchvision.transforms.Compose",
"line_number": 47,
"usage_type": "call"
},
{
"api_name": "torchvision.transforms",
"line_number": 47,
"usage_type": "name"
},
{
"api_name": "torchvision.transforms.ToTensor",
"line_number": 47,
"usage_type": "call"
},
{
... |
72547899303 | from tools.build_utils import *
import os, shutil
import argparse
def main():
# Command line parser options
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument(
'--tf_version',
type=str,
help="TensorFlow tag/branch/SHA\n",
action="store",
default="v2.9.3")
parser.add_argument(
'--output_dir',
type=str,
help="Location where TensorFlow build will happen\n",
action="store",
required=True)
parser.add_argument(
'--target_arch',
help=
"Architecture flag to use (e.g., haswell, core-avx2 etc. Default \'native\'\n",
default="native")
parser.add_argument(
'--use_intel_tensorflow',
help="Build using Intel TensorFlow.",
action="store_true")
parser.add_argument(
'--cxx11_abi_version',
help="Desired version of ABI to be used while building Tensorflow",
default='1')
parser.add_argument(
'--resource_usage_ratio',
help="Ratio of CPU / RAM resources to utilize during Tensorflow build",
default=0.5)
arguments = parser.parse_args()
# Check bazel version
bazel_kind, bazel_ver = get_bazel_version()
got_correct_bazel_version = bazel_kind == 'Bazelisk version'
if (not got_correct_bazel_version and int(bazel_ver[0]) < 2):
raise Exception("Need bazel version >= 2.0.0 \n" + "Got: " +
'.'.join(bazel_ver))
if not os.path.isdir(arguments.output_dir):
os.makedirs(arguments.output_dir)
if not os.path.isdir(arguments.output_dir):
raise AssertionError("Did not find output directory: " +
arguments.output_dir)
if not os.path.exists(arguments.output_dir):
raise AssertionError("path doesn't exist {0}".format(
arguments.output_dir))
os.chdir(arguments.output_dir)
if (platform.system() == 'Windows'):
venv_dir = '.\\venv3\\'
else:
venv_dir = './venv3/'
install_virtual_env(venv_dir)
load_venv(venv_dir)
setup_venv(venv_dir, arguments.tf_version)
if not os.path.exists(arguments.output_dir):
raise AssertionError("Directory doesn't exist {0}".format(
arguments.output_dir))
if not os.path.isdir(os.path.join(arguments.output_dir, "tensorflow")):
# Download TensorFlow
download_repo("tensorflow",
"https://github.com/tensorflow/tensorflow.git",
arguments.tf_version)
else:
pwd = os.getcwd()
if not os.path.exists(arguments.output_dir):
raise AssertionError("Path doesn't exist {}".format(
arguments.output_dir))
os.chdir(os.path.join(arguments.output_dir, "tensorflow"))
call(["git", "fetch"])
command_executor(["git", "checkout", arguments.tf_version])
call(["git", "pull"])
if not os.path.exists(pwd):
raise AssertionError("Path doesn't exist {0}".format(pwd))
os.chdir(pwd)
# Build TensorFlow
build_tensorflow(
arguments.tf_version,
"tensorflow",
'artifacts',
arguments.target_arch,
False,
arguments.use_intel_tensorflow,
arguments.cxx11_abi_version,
resource_usage_ratio=float(arguments.resource_usage_ratio))
# Build TensorFlow C++ Library
build_tensorflow_cc(
arguments.tf_version, "tensorflow", 'artifacts', arguments.target_arch,
False, arguments.use_intel_tensorflow, arguments.cxx11_abi_version)
pwd = os.getcwd()
if (platform.system() == 'Windows'):
artifacts_dir = os.path.join(pwd, 'tensorflow')
else:
artifacts_dir = os.path.join(pwd, 'artifacts/tensorflow')
os.chdir("tensorflow")
copy_tf_to_artifacts(arguments.tf_version, artifacts_dir, None,
arguments.use_intel_tensorflow)
print('\033[1;35mTensorFlow Build finished\033[0m')
print(
'When building openvino_tensorflow using this prebuilt tensorflow, use:'
)
print('\033[3;34mpython3 build_ovtf.py --use_tensorflow_from_location ' +
os.path.abspath(arguments.output_dir) + '\033[1;0m')
if __name__ == '__main__':
main()
# Usage
# Build TF once
# ./build_tf.py --tf_version v1.15.2 --output_dir /prebuilt/tf/dir
#
# Reuse TF in different openvino_tensorflow builds
# mkdir ovtf_1; cd ovtf_1
# git clone https://github.com/openvinotoolkit/openvino_tensorflow.git
# ./build_ovtf.py --use_tensorflow_from_location /prebuilt/tf/dir
# cd ..; mkdir ovtf_2; cd ovtf_2
# git clone https://github.com/openvinotoolkit/openvino_tensorflow.git
# ./build_ovtf.py --use_tensorflow_from_location /prebuilt/tf/dir
| openvinotoolkit/openvino_tensorflow | build_tf.py | build_tf.py | py | 4,841 | python | en | code | 176 | github-code | 36 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "argparse.RawTextHelpFormatter",
"line_number": 9,
"usage_type": "attribute"
},
{
"api_name": "os.path.isdir",
"line_number": 48,
"usage_type": "call"
},
{
"api_name":... |
13019170409 | import torch
import math
from torch import nn
import torch.nn.functional as F
# Objective: learn embedding vector for each "relative" position
# Steps: (1) Identify matrix of possible relatives (sent_len, sent_len) "clamped values"
# (2) Identify embedding vector with possible vocabs of relatives (vocab, emb)
# (3) Input 1 into 2 to have position matrix to learn on (PER SENTENCE)
class RelativePosition(nn.Module): # 2 stages: embeddings with vocab of available relativities + tokens of indices/relatives to learn each relative position
def __init__(self, max_relative_position, head_dim, device):
super().__init__()
self.head_dim = head_dim
self.max_relative_position = max_relative_position
# embedding: (vocab x emb_dim): vocab here is of size --> max * 2 (left/right) + 1 (relation with myself). e.g. (max = 3; "3*2+1 = 7")
# embedding table is table of random embeddings that learns by time. we only specify the size of available vocab here
# Embedding we have 512 vector as embedding representing each token in vocab (Initialization)
# When we use the embedding (pass sentence to it) --> we get the corresponding 512 vector of each token from sentence (vocab) then we change this by learning until its suitable for all sentences
self.embedding_table = nn.Embedding(max_relative_position*2 + 1, head_dim)
# nn.embedding (num_possible_emb_dict, emb_dim), Out shape: (*, emb_dim) --> where * is same as input shape
self.device = device
def forward(self, len_q, len_k): # for self attention (q/k/v) have same length as sequence, but this isn't always the case
possible_relatives_1d_q = torch.arange(len_q)
possible_relatives_1d_k = torch.arange(len_k)
# Make row matrix - column matrix [0, 1, 2, 3] - [[0, 1, 2, 3]] --> subtract full row from each token in column
# q is fixed in 2nd position (for its the query in hand?)
possible_relatives_2d = possible_relatives_1d_k[None, :] - possible_relatives_1d_q[:, None] # (Instead of None: put 1)
clamped_relatives_2d = torch.clamp(possible_relatives_2d, -self.max_relative_position, self.max_relative_position) # clamp that no min is less than specified min and same for max (relativity won't differ beyond that)
# shape: len_q x len_k (self attention: seq_len x seq_len)
# To make all positives (no -ve input to network)
clamped_relatives_positive = clamped_relatives_2d + self.max_relative_position # converted from distance matrix (-ve & +ve) to a positive distance matrix represented as tokens for each position
# Make in type long
clamped_relatives_positive = torch.LongTensor(clamped_relatives_positive).to(self.device) # Q: should we make .device from here? Q: why to make it in "Long"?
# this is the matrix that we want to input for embeddings to be learnt (each position will be represented by 512 embedding vector)
# get the relative position embeddings [embedding vector for each relative position token]
relative_position_embeddings = self.embedding_table(clamped_relatives_positive)
# shape: len_q x len_k x head_dim
return relative_position_embeddings
class RelativePositionalEmbedding(nn.Module):
def __init__(self, emb_dim, n_heads, max_relative_position, dropout, device): # here no need of vocab size as we can already infer from max_relative_pos
super().__init__() # Q: do we need to add device information here?
self.dropout, self.max_relative_position = dropout, max_relative_position
# N_heads: instead of having one big embedding vector (512) we make 2 (256) --> both will pass on the same input and same everything
self.emb_dim, self.n_heads = emb_dim, n_heads
self.head_dim = emb_dim // n_heads
# layers
# This takes embedding and output embedding all of same dimension
# --> reduction of head_dim happens during operations of attention but at last concatenated and pass through the linear
self.q_linear, self.k_linear, self.v_linear = nn.Linear(emb_dim, emb_dim), nn.Linear(emb_dim, emb_dim), nn.Linear(emb_dim, emb_dim) #
self.out = nn.Linear(emb_dim, emb_dim) # the output is emb_dim since we still want to o.p emb as this is pos emb (not vocab)
self.relative_position_k = RelativePosition(max_relative_position, self.head_dim, device)
self.relative_position_v = RelativePosition(max_relative_position, self.head_dim, device)
self.scale_factor = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)
self.dropout = nn.Dropout(dropout)
def forward(self, q, k, v, atten_mask=None): # Q: mask here is the attention mask, right?
# --------------------------------- Attention QK ----------------------- Question: what is the output from this? similarity between each word with all other words?
# Beginning of Equation (5) --> e_ij
# 1. normal self attention (qk) --> [ x*WQ * (x*WK)^T ]
# q.shape: [batch_size, seq_len, emb_dim]
batch_size, len_q, len_k, len_v = q.shape[0], q.shape[1], k.shape[1], v.shape[1]
q, k = self.q_linear(q), self.k_linear(k)
# QA: what is the difference between view and permute --> view: split/ chunk/ combine (reads data sequentially in any way you want), permute: only transpose what's already there
q_heads = q.view(batch_size, -1 ,self.n_heads, self.head_dim)
k_heads = k.view(batch_size, -1 ,self.n_heads, self.head_dim)
# for each batch: we have each sequence defined by n heads, each head has head dim (10 sequences, each seq has 2 heads, each head has 256 dim)
# what we want is to have, n heads: each head has n sequences and each sequence is defined by head_dim (2 heads, each head has 10 sequences; each sequence has 256 emb vector to define it)
# We do this to multiply the needed parts together which are the emb dim and seq_len; as matrix multiple only happen to the last 2 indices
q_head_perm, k_head_perm = q_heads.permute(0, 2, 1, 3), k_heads.permute(0, 2, 1, 3) # for we want to calculate emb_dim * seq (this is what matters)
# q: [1, 2, 10, 256] -- k: [1, 2, 10, 256] --> k.T: [1, 2, 256, 10]
qk = torch.matmul(q_head_perm, k_head_perm.permute(0, 1, 3, 2))
# shape: batch_size, n_heads, len_q, len_k
# ----------------------- Relatives ------------------------
# 2. Relative of k --> [a_k]
# [batch_size, len_sequence, emb_dim]
r_k = self.relative_position_k(len_q, len_k)
# shape: len_q x len_k x head_dim --> r_k.T: len_q x head_dim x len_k
q2_r = q_heads.permute(1, 0, 2, 3).contiguous().view(len_q, batch_size*self.n_heads, self.head_dim)
# shape: len_q x bsz*n_head x head_dim
q_rk = torch.matmul(q2_r, r_k.transpose(1, 2)) # transpose only swaps the two indices together
# shape: len_q x bsz*n_head x len_k --> we want to make bsz and n_head at first and leave interesting points to last 2 indices
q_rk = q_rk.transpose(0, 1)
# shape: bsz*n_head x len_q x len_k
q_rk = q_rk.contiguous().view(batch_size, self.n_heads, len_q, len_k)
# shape: batch_size, n_heads, len_q, len_k
attn1, attn2 = qk, q_rk
attn_total = (attn1 + attn2) / self.scale_factor
# shape: batch_size, n_heads, len_q, len_k
#if atten_mask is not None: # Question: ask hazem on how to adjust for mask
# attn_total = attn_total.masked_fill(atten_mask == 0, -1e10)
# End of Equation (5)
# ------------------------ Value --------------------------
# Begining of Equation (3)
# 1. Softmax of total pre attention: alpha_ij = softmax (e_ij)
attn_soft = self.dropout(F.softmax(attn_total, dim=-1))
# shape: batch_size, n_heads, len_q, len_k
# 3. Linear v --> x*W_v
v = self.v_linear(v)
# shape: batch_size, seq_len, emb_dim
v_heads = v.view(batch_size, -1, self.n_heads, self.head_dim) # multiply last from 1st with prelast from 2nd
v_heads = v_heads.permute(0, 2, 1, 3)
# shape: batch_size, n_heads, seq_len, head_dim
# 4. Softmax * linear v --> alpha_ij * [x*W_v]
# For matrix mult: they should be same exact sizes except for last two + last_first == prelast_sec
weight1 = torch.matmul(attn_soft, v_heads)
# shape: batch_size, n_heads, len_q, head_dim
# 2. Relative position of v --> a_v
r_v = self.relative_position_v(len_q, len_v)
# shape: len_q, len_v, head_dim
attn_soft = attn_soft.permute(2, 0, 1, 3).contiguous().view(len_q, batch_size*self.n_heads, len_k)
# shape: len_q, bsz*n_heads, len_k ## Question: how is len_v same as len_k? (for correct multiplication -- how do we know)
# 5. Softmax * relative v --> alpha_ij * [a_v]
weight2 = torch.matmul(attn_soft, r_v)
# shape: len_q, bsz*n_heads, head_dim
weight2 = weight2.transpose(0, 1).contiguous().view(batch_size, self.n_heads, len_q, self.head_dim)
# shape: batch_size, n_heads, len_q, head_dim
# 6. summation of (4) & (5)
weights = weight1 + weight2
# shape: batch_size, n_heads, len_q, head_dim
weights = weights.permute(0, 2, 1, 3).contiguous().view(batch_size, -1, self.emb_dim)
# shape: batch_size, len_q, emb_dim
### linear over summation ###
out = self.out(weights)
# needed out_shape: batch_size, len_q, emb_dim
return out
# class SelfAttention(nn.Module):
# def __init__(self, emb_size, clip, seq_len):
# self.emb_size = emb_size
# self.q_linear = nn.Linear(emb_size, emb_size)
# self.k_linear = nn.Linear(emb_size, emb_size)
# self.v_linear = nn.Linear(emb_size, emb_size)
# self.max_clip = max(-clip, min(seq_len, clip))
# clip = self.clip
# def forward(self, q, k, v, mask=None): # which one is the attention weights? and which is q/k/v weights?
# q, k, v = self.q_linear(q), self.k_linear(k), self.v_linear(v)
# # eq(5.1) == eq(2) --> qk_scale
# qk_scale = (torch.matmul(q, k.transpose(-2, -1)))/math.sqrt(self.emb_size)
# # eq(5.2)
# k_relative = k[] # k[-] -- put clip?
# qk_relative = (torch.matmul(q, k_relative.transpose(-1, -2)))/math.sqrt(self.emb_size)
# qk_total = qk_scale + qk_relative
# qk_total_soft = F.softmax(qk_total, dim=-1)
# # eq(3)
# v_relative = v[]
# v_new = v + v_relative
# qkv = torch.matmul(qk_total_soft, v_new)
| hosnaa/bert-implement | src/relative_position.py | relative_position.py | py | 10,928 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "torch.nn.Module",
"line_number": 10,
"usage_type": "attribute"
},
{
"api_name": "torch.nn",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "torch.nn.Embedding",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "torch.nn",
"line... |
36478211178 | from django import forms
from ..models import Modelo
class ModeloForm(forms.ModelForm):
class Meta:
model = Modelo
fields = ["marca", "nombre"]
def __init__(self, *args, **kwargs):
super(ModeloForm, self).__init__(*args, **kwargs)
for i, (fname, field) in enumerate(self.fields.iteritems()):
field.widget.attrs['class'] = 'form-control' | xcarlx/Venta | apps/producto/formstotal/modelo.py | modelo.py | py | 390 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "django.forms.ModelForm",
"line_number": 4,
"usage_type": "attribute"
},
{
"api_name": "django.forms",
"line_number": 4,
"usage_type": "name"
},
{
"api_name": "models.Modelo",
"line_number": 6,
"usage_type": "name"
}
] |
35291375172 | """
Created on Tue Apr 26 20:17:37 2022
@author: celiaberon
"""
import os
import numpy as np
import pandas as pd
import scipy
from nta.features.select_trials import match_trial_ids
from nta.preprocessing.signal_processing import snr_photo_signal
def QC_included_trials(ts: pd.DataFrame,
trials: pd.DataFrame,
allow_discontinuity: bool = False,
**kwargs) -> dict:
'''
Quality control within sessions to match timeseries and trial dataframes
starting and ending trial ID values. Also remove -- remove high timeout
blocks.
Args:
ts:
Timeseries containing states and neural data for a single
session.
trials:
Trial data for single session.
allow_discontinuity:
Whether or not continuous trial structure can be broken. If False,
only trials from beginning/end can be excluded. If False, also
permits mismatch in trial ids between min and max trial.
Returns:
trials_matched (dict):
{'trials': trial data, 'ts': timeseries data}
Contain only identical trial sets.
'''
assert ts.session.dropna().nunique() == 1, (
'multi-session not implemented')
assert trials.Session.dropna().nunique() == 1, (
'multi-session not implemented')
trials_ = trials.copy()
ts_ = ts.copy()
# flag only blocks that don't disrupt photometry timeseries
min_trial = trials_.query('~flag_block').nTrial.min()
max_trial = trials_.query('~flag_block').nTrial.max()
flagged_blocks = (trials_
.query('~nTrial.between(@min_trial, @max_trial)')
.nTrial.dropna().values)
trials_ = trials_.loc[~trials_.nTrial.isin(flagged_blocks)]
# Match min and max trial IDs only (can differ internally but offset will)
# be consistent.
trials_, ts_ = match_trial_ids(trials_, ts_,
allow_discontinuity=allow_discontinuity)
return {'trials': trials_, 'ts': ts_}
def QC_enl_penalty_rate(trials: pd.DataFrame) -> list:
'''
Generate list of sessions satisfying ENL penalty criteria, defined
as no greater than 2 std above mean penalty rate in final sessions.
Args:
trials (pandas.DataFrame):
Trial data.
Returns:
qc_sessions (list):
Sessions that pass ENLP quality control criteria.
'''
trials_ = trials.copy()
trials_['penalty'] = trials_['n_ENL'] > 1
trials_['Date'] = pd.to_datetime(trials_['Date'], format='%Y_%m_%d')
penalties = (trials_
.sort_values(by='Date')
.groupby(['Mouse', 'Date', 'Session'], as_index=False)
.penalty.mean())
for mouse, mouse_penalties in penalties.groupby('Mouse'):
late_dates = np.sort(mouse_penalties.Date.unique())[-6:]
late_sessions = mouse_penalties.query('Date.isin(@late_dates)')
late_sessions_mean = np.nanmean(late_sessions['penalty'])
late_sessions_std = np.nanstd(late_sessions['penalty'])
qc_criteria = late_sessions_mean + (2 * late_sessions_std)
penalties.loc[penalties.Mouse == mouse, 'QC_criteria'] = qc_criteria
penalties['Pass'] = penalties['penalty'] <= penalties['QC_criteria']
qc_sessions = penalties.query('Pass == True').Session.values
return qc_sessions
def get_sess_val(trials, trial_variable):
val = (trials
.groupby('Session')
.apply(lambda x: x[trial_variable].unique())
.squeeze().item())
return val
def QC_session_performance(trials: pd.DataFrame,
ts: pd.DataFrame,
update_log: bool = False,
**kwargs) -> bool:
'''
Filter out sessions that don't meet certain criteria:
target_avg_threshold:
Proportion of trials to high value port, must exceed
side_bias_threshold:
Proportion of trials that can favor one side (above/below 0.5)
spout_bias_threshold:
Proportion of licks to one spout (slightly more inclusive than
choice)
Args:
trials (pandas.DataFrame):
Trial data.
ts (pandas.DataFrame):
Timeseries data.
update_log (bool):
TRUE if saving .csv overview of session qc stats.
filename_suffix (str):
Suffix for session log filename.
Returns:
criteria_met (bool):
TRUE if passes quality control.
'''
criteria_met = True
# Set thresholds for session-level behavioral performance.
condition_perf_thresh = {9010: 0.7,
'9010': 0.7,
8020: 0.6,
'8020': 0.6}
TARGET_AVG = condition_perf_thresh.get(trials.Condition.unique()[0])
SIDE_BIAS = 0.1
SPOUT_BIAS = 0.15
MIN_TRIALS = 100
# Evaluate spout bias on same trials as trial-level QC (i.e., not
# including flagged blocks).
trial_ids = trials.nTrial.unique()
ts_ = ts.copy().query('nTrial.isin(@trial_ids)')
n_valid_trials = (trials
.query('flag_block==False & timeout==False')['nTrial']
.nunique())
target_avg = trials.selHigh.mean()
if target_avg < TARGET_AVG:
criteria_met = False
right_avg = trials.direction.mean()
if np.abs(right_avg - 0.5) > SIDE_BIAS:
criteria_met = False
spout_avg = (ts_.query('iSpout.ne(0)')
.iSpout.value_counts(normalize=True)[2.])
if np.abs(spout_avg - 0.5) > SPOUT_BIAS:
criteria_met = False
if n_valid_trials < MIN_TRIALS:
criteria_met = False
if update_log:
enlp_rate = np.mean(trials['n_ENL'] > 1)
qc_summary = pd.DataFrame({'Mouse': get_sess_val(trials, 'Mouse'),
'Date': get_sess_val(trials, 'Date'),
'Session': get_sess_val(trials, 'Session'),
'P(right)': round(right_avg, 2),
'P(high)': round(target_avg, 2),
'P(spout)': round(spout_avg, 2),
'N_valid_trials': n_valid_trials,
'enl_penalty_rate': round(enlp_rate, 2),
'Pass': criteria_met},
index=[0])
save_session_log(qc_summary, **kwargs)
return criteria_met
def load_session_log(path_to_file: str):
'''
Load existing session log if it exists, otherwise initialize a new one.
Args:
path_to_file:
Path including filename to try loading in file.
Returns:
session_log:
DataFrame containing session summary quality control stats.
logged_sessions:
List of sessions already included in session_log.
'''
try:
session_log = pd.read_csv(path_to_file, index_col=None)
logged_sessions = ['_'.join(sess)
for sess in session_log[['Mouse', 'Date']].values]
return session_log, logged_sessions
except FileNotFoundError:
return pd.DataFrame(), []
def save_session_log(sess_qc: pd.DataFrame,
fname_suffix: str = 'photometry',
root: str = '',
**kwargs):
'''
Save summary of session quality control metrics.
Args:
sess_qc:
DataFrame containing quality control metrics for single session.
filename_suffix:
Suffix for session_log filename.
Returns:
None
'''
if not root:
root = input('Please provide a path for logging:')
filename = f'session_log_{fname_suffix}.csv'
path_to_file = os.path.join(root, filename)
session_log, logged_sessions = load_session_log(path_to_file)
curr_session = f'{sess_qc.Mouse.item()}_{sess_qc.Date.item()}'
if curr_session not in logged_sessions:
tmp_log = pd.DataFrame({'Mouse': sess_qc['Mouse'].item(),
'Date': sess_qc['Date'].item()},
index=[0])
session_log = pd.concat((session_log, tmp_log)).reset_index(drop=True)
if 'N_valid_trials' not in session_log.columns:
updated_log = pd.merge(session_log, sess_qc,
on=['Mouse', 'Date'], how='left')
else:
updated_log = session_log.copy()
for col in sess_qc.columns.drop(['Mouse', 'Date']):
mouse, date, val = sess_qc[['Mouse', 'Date', col]].iloc[0].values
idx = updated_log.query('Mouse == @mouse & Date == @date').index
updated_log.loc[idx, col] = val
updated_log.to_csv(path_to_file, index=False)
def QC_photometry_signal(timeseries: pd.DataFrame,
mouse: str,
session_date: str,
) -> pd.DataFrame:
'''
Quality control on signal strength in photometry channels using FFT
method. If bilateral signals pass, take delta between right and left.
Args:
timeseries (pandas.DataFrame):
Timeseries data containing photometry signal.
mouse (str):
Mouse ID.
session_date (str):
Session ID.
pp_style (bool):
Deprecated, if changing standardization method for photometry.
Returns:
timeseries (pandas.DataFrame):
Copy of input but replace data with NaNs where QC fails.
ALTERNATE: early return with FALSE if no channels pass QC.
'''
ts_ = timeseries.copy()
# always use z-scored data just for QC consistency
y_cols = ts_.columns.intersection(['z_grnR', 'z_grnL'])
# need different thresholds for dLight vs GRAB-DA
qc_thresh = 2 if mouse in ['C32', 'C33', 'C34', 'C35'] else 5
y_cols = [col for col in y_cols if snr_photo_signal(ts_, col) < qc_thresh]
if len(y_cols) == 2:
print(f'insufficient photometry data...discarding {session_date}')
return None
for y_col in y_cols:
ts_[y_col] = np.nan # NaNs for cols that don't pass QC
if len(y_cols) == 2:
ts_['z_grnDelta'] = ts_['z_grnR'] - ts_['z_grnL']
y_cols_pass = {'z_grnR', 'z_grnL'} - set(y_cols)
return ts_, y_cols_pass
def is_normal(ts, include_score=False, verbose=False, thresh_score=0,
sensor='grabda_vls'):
'''
Test for normality as a measure of signal to noise. Result of normally
distributed data fails to pass QC protocol. Normality is determined via
collective evaluation of skew, kurtosis, and K-S test p-value.
Args:
ts:
Timeseries to be evaluated.
include_score:
Whether or not to include the number of metrics that passed as
normally distributed.
thresh_score:
Threshold for permissable tests that return normality=True.
Default of 1 means any test returning normal dist is sufficient to
accept dist as normal (most conservative inclusion level).
Returns:
result:
True if any metric is consistent with normal distribution. False if
all metrics deviate from normal distribution.
score:
Number of metrics that are consistent with normal distribution (0
to 3).
'''
if ts is None: # needs to be a distribution to have signal
return True
thresholds = {'grabda_vls': (0.5, 0.8),
'grabda_dms': (0.1, 0.2)}
skew_thresh, kurt_thresh = thresholds.get(sensor)
skew = np.abs(ts.skew()) < skew_thresh
kurtosis = np.abs(ts.kurtosis()) < kurt_thresh
rand_normal = np.random.normal(0, np.nanstd(ts), len(ts))
_, p_value = scipy.stats.ks_2samp(ts, rand_normal, alternative="two-sided")
ks_test = p_value > 0.05
score = sum((skew, kurtosis, ks_test))
# print(skew, kurtosis, ks_test)
result = score > thresh_score
if include_score:
if verbose:
print(f'skew = {np.abs(ts.skew())}\n',
f'kurtosis = {np.abs(ts.kurtosis())}\n',
f'p_value = {p_value}')
return result, score
else:
if verbose:
print(f'skew = {np.abs(ts.skew())}\n',
f'kurtosis = {np.abs(ts.kurtosis())}\n',
f'p_value = {p_value}')
return result
| celiaberon/neural-timeseries-analysis | nta/preprocessing/quality_control.py | quality_control.py | py | 12,580 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "pandas.DataFrame",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "pandas.DataFrame",
"line_number": 18,
"usage_type": "attribute"
},
{
"api_name": "nta.features.select_trials.match_trial_ids",
"line_number": 63,
"usage_type": "call"
},
{... |
33938514931 | import pytest
from schema import cities
async def test_database(sqlite_database):
tables_query = "SELECT * FROM sqlite_master where type='table';"
tables = await sqlite_database.fetch_all(query=tables_query)
assert len(tables) != 0
city_query = cities.insert(
values={"name": 'London',
"lattitude": 51.509865,
"longitude": -0.118092})
last_record_id = await sqlite_database.execute(query=city_query)
assert last_record_id == 1
| roman808080/route_planner | tests/test_db_manager.py | test_db_manager.py | py | 497 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "schema.cities.insert",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "schema.cities",
"line_number": 12,
"usage_type": "name"
}
] |
1141324769 | import re
import base64
import math
from glob import glob
from getpass import getpass
from pprint import pprint
from marshals.interface import api
import tns.sedm_auto_tns as tns
fritz_base_url = 'https://fritz.science/api/'
fritz_classification_url = fritz_base_url + 'classification'
fritz_redshift_update_url = fritz_base_url + 'sources/'
def add_spec_attachment(obj_id, comment, fname, spec_id=None, testing=False):
"""
adds a comment (not autoannotation) with attachment to a particular SEDM
spectrum on the view_spec page, which will also appear elsewhere.
obj_id: <str>
comment: <str>
fname: <str>
spec_id: <int>
testing: <bool> are we just testing?
return: True if success, False if not
"""
if obj_id is None or spec_id is None:
print("ERROR - Unable to get info required to post comment")
return False
# read in file
with open(fname, 'rb') as image_file:
encoded = base64.b64encode(image_file.read()).decode('ascii')
# create payload
ddict = {'obj_id': obj_id, 'spectrum_id': spec_id, # these go
'text': comment,
'attachment': {'body': encoded,
'name': fname.split('/')[-1]}}
if testing:
print("TESTING add_spec_attachment(): no data sent to marshal")
print("%s: %s encoded with length %d" % (obj_id, fname.split('/')[-1],
len(encoded)))
return True
else:
fritz_comment_url = fritz_base_url + 'spectra/%d/comments' % spec_id
r = api("POST", fritz_comment_url, data=ddict)
if 'success' in r['status']:
r_data = r['data']
if 'comment_id' in r_data:
print("Comment id = %d" % int(r_data['comment_id']))
print('{} uploaded'.format(fname.split('/')[-1]))
return True
else:
print('error submitting comment with attachment')
print(r['status'])
print(r['message'])
return False
def add_spec_autoannot(obj_id, andic, spec_id=None, origin=None, testing=False):
"""
adds an autoannotation without attachment to a particular SEDM spectrum
on the view_spec page, which will also appear elsewhere.
obj_id: 'ZTF18aaaaaa', for example
andic: <dict> of annotations
spec_id: <int>
origin: <str> giving origin of annotations
testing: <bool> are we testing only?
return: True if success, False if not
"""
ddict = {'origin': origin, 'data': andic}
if testing:
print("TESTING add_spec_autoannot(): no data sent to marshal")
print(ddict)
return True
else:
fritz_annotation_url = fritz_base_url + \
'spectra/%d/annotations' % spec_id
# fritz_annotation_url = fritz_base_url + \
# 'sources/%s/annotations' % obj_id
r = api("POST", fritz_annotation_url, data=ddict)
if 'success' in r['status']:
r_data = r['data']
if 'annotation_id' in r_data:
print("annotation id = %d" % int(r_data['annotation_id']))
print('{}: {} posted'.format(obj_id, origin))
return True
else:
print('error submitting annotation')
print(r['status'])
print(r['message'])
return False
def add_SNIascore_pysedm_autoannot(fname, object_id=None, spec_id=None,
testing=False, upload_tns=True):
"""
adds autoannotations with SNIASCORE and error
if SNIASCORE > 0.9, also adds SNIASCORE redshift and error
fname: '*ZTF18aaaaaaa.txt' that has a bunch of
"# SNIASCORE[something]: [val]" in the header
object_id: (str)
spec_id: (int)
testing: (bool)
upload_tns: (bool)
returns: True if autoannotation works, False
(and it'll exit early) otherwise
"""
tns_upl = False
ann_upl = False
file_ext = fname.split('.')[-1]
assert file_ext == 'txt' or file_ext == 'ascii'
with open(fname) as f:
header = {line.split(':', 1)[0][1:].strip():
line.split(':', 1)[-1].strip()
for line in f if line[0] == '#'}
# SNIascore RESULTS
if 'SNIASCORE' not in header:
print(fname, "never run through SNIascore?")
return ann_upl, tns_upl
if float(header['SNIASCORE']) < 0:
print('no score')
return ann_upl, tns_upl
# construct annotations dictionary
if float(header['SNIASCORE']) >= 0.9:
# Post classification
if add_SNIascore_classification(fname, object_id=object_id,
testing=testing):
print("POSTed Ia classification to fritz")
# Attempt to post to TNS
if upload_tns:
try:
if tns.sedm_tns_classify(fname, ztfname=object_id,
testing=testing):
print("Uploaded SNIa classification to TNS")
tns_upl = True
else:
print("Unable to upload SNIa classification to TNS")
except:
print("Problems connecting")
else:
print("Unable to post Ia classification to fritz")
# Generate annotation dictionary
andic = {
'SNIascore': header['SNIASCORE'],
'SNIascore_err': header['SNIASCORE_ERR'],
'SNIa_z': header['SNIASCORE_Z'],
'SNIa_z_err': header['SNIASCORE_ZERR']
}
else:
andic = {
'SNIascore': header['SNIASCORE'],
'SNIascore_err': header['SNIASCORE_ERR']}
# construct origin
# origin = 'SNIascore:spc%d' % spec_id
origin = 'sedm:SNIascore'
print(andic)
return add_spec_autoannot(object_id, andic, spec_id=spec_id,
origin=origin, testing=testing), tns_upl
def add_SNIascore_classification(fname, object_id=None, testing=False):
"""
adds SNIASCORE "Ia" classification if SNIASCORE > 0.9
fname: '*ZTF18aaaaaaa.txt' that has a bunch of
"# SNIASCORE[something]: [val]" in the header
object_id: (str)
testing: (bool)
returns: True if classification works, False
(and it'll exit early) otherwise
"""
file_ext = fname.split('.')[-1]
assert file_ext == 'txt' or file_ext == 'ascii'
with open(fname) as f:
header = {line.split(':', 1)[0][1:].strip():
line.split(':', 1)[-1].strip()
for line in f if line[0] == '#'}
# SNIascore RESULTS
if 'SNIASCORE' not in header:
print(fname, "never run through SNIascore?")
return False
if float(header['SNIASCORE']) < 0:
print('no score')
return False
# construct annotations dictionary
if float(header['SNIASCORE']) >= 0.9:
cldict = {
"obj_id": object_id,
"classification": "Ia",
"taxonomy_id": 3,
"probability": float(header['SNIASCORE'])
}
if testing:
print("TESTING add_SNIascore_classification():"
" no data sent to marshal")
print(cldict)
return True
else:
r = api("POST", fritz_classification_url, data=cldict)
if 'success' in r['status']:
r_data = r['data']
if 'classification_id' in r_data:
print("classification id = %d" %
int(r_data['classification_id']))
print('{}: Ia classification posted'.format(object_id))
# now add redshift
if 'SNIASCORE_Z' in header and 'SNIASCORE_ZERR' in header:
# What is the current redshift set to?
rc = api("GET", fritz_redshift_update_url + object_id)
if 'success' in rc['status']:
rc_data = rc['data']
current_redshift = None
if 'redshift' in rc_data:
current_redshift = rc_data['redshift']
# Only set redshift if it is not already set
if current_redshift is None:
new_redshift = float(header['SNIASCORE_Z'])
new_redshift_error = float(header['SNIASCORE_ZERR'])
try:
new_z_round = math.ceil(abs(
math.log10(new_redshift_error)))
# Handle negative, NaN, Inf, None and <str> values
except (ValueError, OverflowError, TypeError):
new_z_round = 1
new_z = round(new_redshift,
1 if new_z_round < 1 else new_z_round)
new_error = round(new_redshift_error,
1 if new_z_round < 1 else
new_z_round)
rsdict = {"redshift": new_z,
"redshift_error": new_error}
rr = api("PATCH",
fritz_redshift_update_url + object_id,
data=rsdict)
if 'success' in rr['status']:
print("redshift for %s updated to %.4f +- %.4f"
% (object_id, rsdict['redshift'],
rsdict['redshift_error']))
else:
print('error updating %s redshift' % object_id)
print(rr['status'])
print(rr['message'])
else:
print('Redshift for %s already set to %.4f' %
(object_id, float(rc_data['redshift'])))
else:
print('error getting current redshift for %s' %
object_id)
else:
print('No SNIascore redshift records found for %s ' %
object_id)
return True
else:
print('error submitting classification')
print(r['status'])
print(r['message'])
return False
return False
def add_SNID_pysedm_autoannot(fname, object_id=None, spec_id=None,
testing=False):
"""
if z < 0.3 and rlap > 5.0
adds autoannotations with SNID rlap, z, type, etc
adds a comment with the SNID plot attached
fname: '*ZTF18aaaaaaa.txt' that has a bunch of
"# SNIDMATCH[something]: [val]" in the header
cred: ('username', 'password')
reducedby: (str)
testing: (bool)
returns: True if all four comments/attachments works, False
(and it'll exit early) otherwise
"""
file_ext = fname.split('.')[-1]
assert file_ext == 'txt' or file_ext == 'ascii'
with open(fname) as f:
header = {line.split(':', 1)[0][1:].strip().lower():
line.split(':', 1)[-1].strip()
for line in f if line[0] == '#'}
# Upload pysedm_report
try:
pysedm_report = glob(fname.replace('spec',
'pysedm_report').replace('.txt',
'.png'))[0]
pr_posted = add_spec_attachment(object_id,
'pysedm_report:spc%d' % spec_id,
pysedm_report, spec_id=spec_id,
testing=testing)
except IndexError:
print('no pysedm_report for {}?'.format(header['name']))
pr_posted = False
# SNID RESULTS
if 'snidmatchtype' not in header:
print(fname, "never run through snid?")
return False
if header['snidmatchtype'].lower() == 'none':
print('no match')
return False
elif float(header['snidmatchrlap']) < 5:
print('bad rlap, only {}'.format(header['snidmatchrlap']))
return False
elif (header['snidmatchtype'][0] == 'I') \
and not (0.01 <= float(header['snidmatchredshift']) <= 0.3):
print('bad redshift, {snidmatchredshift} '
'for {snidmatchtype}'.format(**header))
return False
if header['snidmatchsubtype'] == '-':
header['snidmatchmatch'] = header['snidmatchtype']
else:
header['snidmatchmatch'] = '-'.join([header['snidmatchtype'],
header['snidmatchsubtype']])
# construct annotations dictionary
andic = {'match': 'None', 'rlap': 0., 'redshift': 0., 'age': 0.}
for key in andic:
andic[key] = header['snidmatch' + key]
# construct origin
# origin = 'sedm:spc%d' % spec_id
origin = 'sedm:SNID'
if not add_spec_autoannot(object_id, andic, spec_id=spec_id,
origin=origin, testing=testing):
return False
if pr_posted:
return True # we already have an attachment comment so don't overwrite
# SNID PLOT
# NOTE: this makes a major assumption about the naming scheme of snid plots
image_filename = fname.replace('.txt',
'_{}.png'.format(header['snidmatchtype']))
if not glob(image_filename):
return False
ret = add_spec_attachment(object_id, 'AUTO_SNID_plot', image_filename,
spec_id=spec_id, testing=testing)
return ret
if __name__ == "__main__":
auth = (input('GROWTH marshal username:'), getpass())
successes = []
for filename in glob('*ZTF?????????.txt'):
if add_SNID_pysedm_autoannot(filename, auth):
print('success!')
successes.append(re.findall(r'ZTF\d{2}[a-z]{7}', filename)[-1])
break
pprint(successes)
| scizen9/sedmpy | fritz/fritz_commenter.py | fritz_commenter.py | py | 14,311 | python | en | code | 5 | github-code | 36 | [
{
"api_name": "base64.b64encode",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "marshals.interface.api",
"line_number": 49,
"usage_type": "call"
},
{
"api_name": "marshals.interface.api",
"line_number": 88,
"usage_type": "call"
},
{
"api_name": "tns.se... |
32331024727 | #!/usr/bin/python3
"""Contains a class named Base"""
import json
class Base:
"""Class named Base"""
__nb_objects = 0
def __init__(self, id=None):
"""initialize Base class"""
if id is None:
Base.__nb_objects += 1
self.id = Base.__nb_objects
else:
self.id = id
@staticmethod
def to_json_string(list_dictionaries):
"""returns JSON string representation of list_dictionaries"""
if not list_dictionaries or list_dictionaries is None:
return "[]"
return json.dumps(list_dictionaries)
@staticmethod
def from_json_string(json_string):
"""returns list of the JSON string json_string"""
if not json_string or json_string is None:
return []
return json.loads(json_string)
@classmethod
def save_to_file(cls, list_objs):
"""writes JSON string of list_objs to a file"""
file_name = cls.__name__ + ".json"
with open(file_name, "w", encoding="utf-8") as f:
if list_objs is None:
f.write("[]")
else:
f.write(cls.to_json_string([i.to_dictionary()
for i in list_objs]))
@classmethod
def create(cls, **dictionary):
"""returns instance with attributes already set"""
if cls.__name__ == "Rectangle":
dummy = cls(1, 1)
if cls.__name__ == "Square":
dummy = cls(1)
dummy.update(**dictionary)
return dummy
@classmethod
def load_from_file(cls):
"""returns a list of instances"""
file_name = cls.__name__ + ".json"
ls = []
try:
with open(file_name, "r", encoding="utf-8") as f:
ls2 = cls.from_json_string(f.read())
for i in ls2:
ls.append(cls.create(**i))
except:
pass
return ls
| ammartica/holbertonschool-higher_level_programming | 0x0C-python-almost_a_circle/models/base.py | base.py | py | 1,956 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "json.dumps",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 31,
"usage_type": "call"
}
] |
15618824234 | #!/usr/bin/env python3
import argparse
from datetime import datetime
import json
import os
import sys
import time
import uuid
from aiosmtpd.controller import Controller
import mailparser
DATA_PATH = 'messages'
class Handler(object):
# noinspection PyPep8Naming,PyMethodMayBeStatic
async def handle_DATA(self, _, session, envelope):
print('Recieved message...')
# noinspection PyBroadException
try:
file_name = os.path.join('messages', str(uuid.uuid4().hex))
with open(file_name, 'w') as open_file:
content = envelope.original_content.decode('utf-8').replace("\r\r\n", "\r\n")
message = mailparser.parse_from_string(content)
json.dump({'mail_from': envelope.mail_from,
'rcpt_tos': envelope.rcpt_tos,
'subject': message.subject,
'body': message.body,
'data': content,
'timestamp': datetime.utcnow().isoformat()},
open_file)
return '250 OK'
except Exception:
return '500 Could not process your message'
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('host', type=str)
parser.add_argument('port', type=int)
args = parser.parse_args()
handler = Handler()
controller = Controller(handler, hostname=args.host, port=args.port)
# noinspection PyBroadException
try:
controller.start()
pid = str(os.getpid())
pidfile = "/run/postoffice/postoffice.pid"
open(pidfile, 'w').write(pid)
print("Running smtpd server on {}:{}\n".format(args.host, args.port))
while True:
time.sleep(3)
except Exception as exc:
print(exc, file=sys.stderr)
controller.stop()
finally:
os.unlink(pidfile)
| OPSnet/PostOffice | smtpd.py | smtpd.py | py | 1,929 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "os.path.join",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 23,
"usage_type": "attribute"
},
{
"api_name": "uuid.uuid4",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "mailparser.parse_from_string",
... |
9588266467 | import unittest
from tests import PluginTest
from plugins.basketball import basketball
from mock import patch, call
import requests
import datetime
from packages.memory.memory import Memory
class BasketballTest(PluginTest):
"""
Tests For Basketball Plugin
!!! test will be executed only if user has added his own api.basketball.com API_KEY
"""
def setUp(self):
self.test = self.load_plugin(basketball)
m = Memory("basketball.json")
self.unable_to_test_plugin = False
if m.get_data("API_KEY") is None:
self.unable_to_test_plugin = True
else:
self.headers = {"x-rapidapi-host": "api-basketball.p.rapidapi.com", "x-rapidapi-key": m.get_data("API_KEY")}
def test_todays_games(self):
if self.unable_to_test_plugin:
return
with patch.object(requests, 'get', headers=self.headers) as get_mock:
self.test.get_api_key(self.jarvis_api)
self.test.todays_games(self.jarvis_api)
date = datetime.datetime.now().strftime('%Y-%m-%d')
get_mock.assert_called_with(
"https://api-basketball.p.rapidapi.com/games?date={}".format(date), headers=self.headers)
def test_search_team(self):
if self.unable_to_test_plugin:
return
with patch.object(requests, 'get', headers=self.headers) as get_mock:
self.test.get_api_key(self.jarvis_api)
self.queue_input("boston")
self.test.search_team(self.jarvis_api)
get_mock.assert_called_with(
"https://api-basketball.p.rapidapi.com/teams?search=boston", headers=self.headers)
def test_search_league(self):
if self.unable_to_test_plugin:
return
with patch.object(requests, 'get', headers=self.headers) as get_mock:
self.test.get_api_key(self.jarvis_api)
self.queue_input("nba")
self.test.search_league(self.jarvis_api)
get_mock.assert_called_with(
"https://api-basketball.p.rapidapi.com/leagues?search=nba", headers=self.headers)
def test_list_leagues(self):
if self.unable_to_test_plugin:
return
with patch.object(requests, 'get', headers=self.headers) as get_mock:
self.test.get_api_key(self.jarvis_api)
self.test.list_leagues(self.jarvis_api)
get_mock.assert_called_with(
"https://api-basketball.p.rapidapi.com/leagues", headers=self.headers)
if __name__ == '__main__':
unittest.main()
| sukeesh/Jarvis | jarviscli/tests/test_basketball.py | test_basketball.py | py | 2,571 | python | en | code | 2,765 | github-code | 36 | [
{
"api_name": "tests.PluginTest",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "plugins.basketball.basketball",
"line_number": 17,
"usage_type": "argument"
},
{
"api_name": "packages.memory.memory.Memory",
"line_number": 18,
"usage_type": "call"
},
{
"... |
29138207241 |
from random import randint as rand
import pygame
import time
row_max =16
column_max =16
mine_count = 50
square_list = []
square_size = 30
BLACK = (0,0,0)
WHITE = (255,255,255)
BLUE = (0,0,255)
RED = (255,0,0)
pygame.init()
stage = "setup"
check_list = []
screen = pygame.display.set_mode((square_size*column_max, square_size*row_max))
pygame.display.set_caption("Minesweeper")
class square:
def __init__(self, row, column):
self.row = row
self.column = column
self.mine = False
self.hidden = True
self.flag = False
self.mark_mine = False
self.mark_safe = False
def adjacents(self, choice):
adjacent_list = []
empty_list = []
for row_difference in range(-1,2):
new_row = self.row + row_difference
for column_difference in range(-1,2):
new_column = self.column + column_difference
if (0 <= new_column < column_max) and (0 <= new_row < row_max) and not (column_difference == 0 and row_difference == 0):
adjacent_list.append([self.row+row_difference, self.column + column_difference])
output_list = []
for item in adjacent_list:
thing = square_list[item[0]][item[1]]
output_list.append(thing)
if thing.hidden and not thing.flag:
empty_list.append(thing)
if choice == 0:
return output_list
else:
return empty_list
def adjacent_mines(self, type):
num_mines = 0
num_safe = 0
num_hidden = 0
num_flag = 0
num_blank = 0
for item in self.adjacents(0):
if item.mine:
num_mines += 1
else:
num_safe +=1
if item.hidden:
num_hidden +=1
if item.flag:
num_flag +=1
else:
num_blank += 1
if type == 'm':
return num_mines
elif type == 's':
return num_safe
elif type == "h":
return num_hidden
elif type == "f":
return num_flag
elif type == "b":
return num_blank
def draw_me(self):
if self.hidden:
colour = WHITE
text_colour = BLACK
text = "F"
else:
colour = BLACK
text_colour = WHITE
text = str(self.adjacent_mines("m"))
pygame.draw.rect(screen, colour, (self.column*square_size, self.row*square_size, square_size, square_size))
if ((not self.hidden) or self.flag) and text != "0":
font = pygame.font.SysFont(None, square_size)
screen.blit(font.render(text, True, text_colour), (self.column *square_size + square_size/3, self.row*square_size + square_size/3))
def reveal(self):
global reveal_count
global stage
global win
global check_list
if self.hidden:
reveal_count += 1
self.flag = False
if self.mine:
stage = "end"
win = False
print(self.row, self.column)
else:
self.hidden = False
check_win()
if self.adjacent_mines("m") == 0:
for item in self.adjacents(0):
if item.hidden:
item.reveal()
if self.adjacent_mines("b") > 0:
check_list.append(self)
def autocomplete(self):
worked = False
if (not self.hidden) and self.adjacent_mines("b") > 0:
if self.adjacent_mines("b") == self.adjacent_mines("m")-self.adjacent_mines("f"):
worked = True
for item in self.adjacents(1):
item.flag = True
elif self.adjacent_mines("m") == self.adjacent_mines("f"):
worked = True
for item in self.adjacents(1):
item.reveal()
return worked
for row in range(row_max):
square_list.append([])
for column in range(column_max):
square_object = square(row, column)
square_list[row].append(square_object)
def setup(chosen_row, chosen_column):
global reveal_count
square_list[chosen_row][chosen_column].hidden = False
reveal_count = 1
for item in square_list[chosen_row][chosen_column].adjacents(0):
item.hidden = False
reveal_count += 1
for _ in range(mine_count):
valid = False
while not valid:
valid = True
random_column = rand(0, column_max-1)
random_row = rand(0, row_max -1)
if square_list[random_row][random_column].mine or (not square_list[random_row][random_column].hidden):
valid = False
else:
square_list[random_row][random_column].mine = True
for item in square_list[random_row][random_column].adjacents(0):
if item.adjacent_mines('s') == 0:
valid = False
square_list[random_row][random_column].mine = False
break
for row in square_list:
for item in row:
if not item.hidden:
item.reveal()
def draw_grid():
for i in range(row_max + 1):
pygame.draw.line(screen, BLUE, (0, i*square_size), (column_max*square_size, i*square_size))
for i in range(column_max + 1):
pygame.draw.line(screen, BLUE, (i*square_size, 0), (i*square_size, row_max*square_size))
def draw_board():
for row in square_list:
for item in row:
item.draw_me()
draw_grid()
def list_possibilities(num_things, num_spaces):
if num_things > num_spaces:
return([])
elif num_things == 0:
string = ""
for _ in range(num_spaces):
string += "0"
return([string])
else:
output_list = []
position_list = []
for _ in range(num_things):
position_list.append("")
def recursive(ordinal, smaller):
nonlocal output_list
nonlocal position_list
for current in range(smaller+1, num_spaces -ordinal ):
position_list[ordinal] = current
if ordinal == 0:
placeholder = []
for item in position_list:
placeholder.append(item)
output_list.append(placeholder)
else:
recursive(ordinal - 1, current)
recursive(num_things-1, -1)
return output_list
def wait():
draw_board()
pygame.display.update()
def brute(working):
global check_list
for square in check_list:
print(square.row, square.column)
wait()
if working:
print("going")
working = False
count = 0
for i in range(len(check_list)):
if stage == "play":
item = check_list[count]
if item.adjacent_mines("b") > 0:
attempt = item.autocomplete()
pygame.event.get()
if attempt:
working = True
del check_list[count]
wait()
check_win()
else:
count += 1
else:
del check_list[count]
else:
break
else:
print("stuck")
wait()
stuck_v2()
# thread = threading.Thread(stuck())
# thread.start()
# thread.join()
working = True
check_win()
return working
def check_board():
valid = True
for row in square_list:
for item in row:
if (not item.hidden) and item.adjacent_mines("m") > 0:
suspicious_count = 0
for adjacent in item.adjacents(0):
if adjacent.flag or adjacent.mark_mine:
suspicious_count += 1
if suspicious_count != item.adjacent_mines("m"):
valid = False
break
return valid
def check_win():
global win
global stage
if reveal_count == row_max*column_max - mine_count:
stage = "end"
win = True
timer_event = pygame.USEREVENT + 1
pygame.time.set_timer(timer_event, 1)
def game(option):
global screen
global stage
global win
global check_list
win = False
done = False
screen.fill(WHITE)
draw_grid()
autocomplete_working = True
stage_done = True
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
if stage == "setup":
if option == "c":
setup(rand(0, row_max-1), rand(0,column_max-1))
stage = "play"
draw_board()
for row in square_list:
for square in row:
if not square.hidden and square.adjacent_mines("b") > 0:
check_list.append(square)
wait()
time.sleep(1.5)
if event.type == pygame.MOUSEBUTTONDOWN and option == "p":
mouse_row = pygame.mouse.get_pos()[1]//square_size
mouse_column = pygame.mouse.get_pos()[0]//square_size
setup(mouse_row, mouse_column)
stage = "play"
draw_board()
elif stage == "play":
if event.type == pygame.MOUSEBUTTONDOWN and option == "p":
mouse_row = pygame.mouse.get_pos()[1]//square_size
mouse_column = pygame.mouse.get_pos()[0]//square_size
button = event.button
if button == 1:
if square_list[mouse_row][mouse_column].hidden:
square_list[mouse_row][mouse_column].reveal()
else:
_ = square_list[mouse_row][mouse_column].autocomplete()
print(square_possibilities(square_list[mouse_row][mouse_column]))
check_win()
draw_board()
#Left click
elif button == 3:
#Right click
square_list[mouse_row][mouse_column].flag = not square_list[mouse_row][mouse_column].flag
draw_board()
print(square_possibilities(square_list[mouse_row][mouse_column]))
elif option == "c" and event.type == timer_event:
autocomplete_working = brute(autocomplete_working)
elif stage == "end":
if stage_done == True:
if win == False:
print("lose")
# for row in square_list:
# for square in row:
# if square.flag:
# print(square.row, square.column, "flag")
else:
print("win")
if option == "c":
for row in square_list:
for item in row:
if not item.flag and item.hidden:
item.flag = True
wait()
stage_done = False
# # if event.type == pygame.MOUSEBUTTONDOWN:
# # done = True
# # else:
# if win:
# text = "You Win!"
# else:
# text = "You Lose!"
# screen.fill(BLACK)
# font = pygame.font.SysFont(None, square_size)
# screen.blit(font.render(text, True, WHITE), (square_size*column_max/3, square_size*row_max/3))
pygame.display.update()
def square_possibilities(square):
spaces = square.adjacent_mines("b")
temp_list = list_possibilities(square.adjacent_mines("m") - square.adjacent_mines("f"), spaces)
output_list = []
for item in temp_list:
binary_string = ''
for i in range(spaces):
if i in item:
binary_string += "1"
else:
binary_string += "0"
output_list.append(binary_string)
return output_list
def convert_overlap(indexes, string):
new_bin = ""
for index in indexes:
new_bin += string[index]
return new_bin
#reduce string to just overlaps
def make_new_bin(overlap_indexes, possibilities):
output_list = []
for bin in possibilities:
new_bin = convert_overlap(overlap_indexes, bin)
if new_bin not in output_list:
output_list.append(new_bin)
return output_list
#make new list of just overlap possibilities
def check_in_list(possibility_list, indexes, overlap_possibilities):
count = 0
for i in range(len(possibility_list)):
if convert_overlap(indexes, possibility_list[count]) not in overlap_possibilities:
possibility_list.pop(count)
else:
count += 1
return possibility_list
#Cull board_possibilities to possible lists
def stuck_v2():
global check_list
num_flags = 0
for row in square_list:
for item in row:
if item.flag:
num_flags += 1
board_possibilities = square_possibilities(check_list[0])
# for item in check_list:
# print(item.row, item.column, item.hidden)
keys = check_list[0].adjacents(1)
for key in keys:
print(key.row, key.column, "key")
# for key in keys:
# print("keys", key.row, key.column)
big_success = False
for k in range(1,len(check_list)):
for key in keys:
print(key.row, key.column, "key")
item = check_list[k]
# print(item, item.row, item.column)
big_overlap_indexes = []
small_overlap_indexes = []
item_adjacents = item.adjacents(1)
# for adjacent in item_adjacents:
# # print("adjacents",adjacent.row, adjacent.column )
item_possibilities = square_possibilities(item)
print(item_possibilities, item.row, item.column)
for i in range(len(keys)):
for k in range(len(item_adjacents)):
if keys[i] == item_adjacents[k]:
big_overlap_indexes.append(i)
small_overlap_indexes.append(k)
#Finds which squares overlap
big_overlap_possibilities = make_new_bin(big_overlap_indexes, board_possibilities)
print(big_overlap_possibilities, "big overlap possibilities")
small_overlap_possibilities = make_new_bin(small_overlap_indexes, item_possibilities)
print(small_overlap_possibilities, "small """)
overlap_possibilities = small_overlap_possibilities
for bin in overlap_possibilities:
if bin not in big_overlap_possibilities:
overlap_possibilities.remove(bin)
board_possibilities = check_in_list(board_possibilities, big_overlap_indexes, overlap_possibilities)
item_possibilities = check_in_list(item_possibilities, small_overlap_indexes, overlap_possibilities)
pygame.event.get()
stripped_item_possibilities = []
for bin in item_possibilities:
new_bin = ""
for i in range(len(bin)):
if i not in small_overlap_indexes:
new_bin += bin[i]
if new_bin not in stripped_item_possibilities:
stripped_item_possibilities.append(new_bin)
stripped_item_keys = []
pygame.event.get()
for i in range(len(item_adjacents)):
if i not in small_overlap_indexes:
stripped_item_keys.append(item_adjacents[i])
new_board_possibilities = []
for bin in board_possibilities:
for item_bin in stripped_item_possibilities:
potential_mines = 0
new_bin = bin + item_bin
check_bin = item_bin
for i in range(len(big_overlap_indexes)):
check_bin = check_bin[:small_overlap_indexes[i]] + bin[big_overlap_indexes[i]] + check_bin[small_overlap_indexes[i]:]
if check_bin in item_possibilities:
for character in new_bin:
if character == "1":
potential_mines += 1
if num_flags + potential_mines <= mine_count:
new_board_possibilities.append(new_bin)
#Check if there will be too many mines on the board
# print(board_possibilities)
# print(big_overlap_indexes)
# print(item_possibilities)
# print(small_overlap_indexes)
board_possibilities = new_board_possibilities
# print(new_board_possibilities)
keys += stripped_item_keys
# for key in keys:
# print("keys", key.row, key.column)
pygame.event.get()
print(board_possibilities)
for i in range(len(board_possibilities[0])):
small_success = True
number = board_possibilities[0][i]
for q in range(1, len(board_possibilities)):
if number != board_possibilities[q][i]:
small_success = False
break
if small_success:
big_success = True
print("big_success")
if number == "0":
keys[i].reveal()
elif number == "1":
keys[i].flag = True
wait()
if big_success:
for key in keys:
print(key.row, key.column, "key")
break
if not big_success:
random_num = rand(0, len(keys)-1)
keys[random_num].reveal()
print(keys[random_num].row, keys[random_num].column, "random selection")
wait()
print("random")
game("c")
| OOCam1/Minesweeper | Minesweeper.py | Minesweeper.py | py | 19,511 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "pygame.init",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "pygame.display.set_mode",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "pygame.display",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "pygame.displa... |
11249142498 | import torch
import copy
import numpy as np
from torch.nn import Dropout
from torch.nn import Linear
from torch.nn import LayerNorm
from torch.nn import functional as F
from RprMultiheadAttention import MultiheadAttention
def _get_activation_fn(activation):
if activation == "relu":
return F.relu
elif activation == "gelu":
return F.gelu
else:
raise RuntimeError("activation should be relu/gelu, not %s." % activation)
class MemTransformerEncoderLayer(torch.nn.Module):
"""TransformerEncoderLayer is made up of self-attn and feedforward network.
This standard encoder layer is based on the paper "Attention Is All You Need".
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in
Neural Information Processing Systems, pages 6000-6010. Users may modify or implement
in a different way during application.
Args:
d_model: the number of expected features in the input (required).
nhead: the number of heads in the multiheadattention models (required).
dim_feedforward: the dimension of the feedforward network model (default=2048).
dropout: the dropout value (default=0.1).
activation: the activation function of intermediate layer, relu or gelu (default=relu).
Examples::
>>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = torch.rand(10, 32, 512)
>>> out = encoder_layer(src)
"""
def __init__(self, d_model, nhead, frame_len=29, dim_feedforward=2048, dropout=0.1, activation="relu"):
super(MemTransformerEncoderLayer, self).__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = Linear(d_model, dim_feedforward)
self.dropout = Dropout(dropout)
self.linear2 = Linear(dim_feedforward, d_model)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout1 = Dropout(dropout)
self.dropout2 = Dropout(dropout)
self.activation = _get_activation_fn(activation)
# rpr encoding
self.frame_len = frame_len
self.k = 5
self.create_rpr_table(self.k)
self.pk_embed = nn.Embedding(2*self.k + 1, d_model)
self.pv_embed = nn.Embedding(2*self.k + 1, d_model)
def idx_clip(x, k):
return max(-k, min(k, x))
def create_rpr_table(self, k):
self.rpr_table = np.zeros((self.frame_len, self.frame_len))
for i in range(1, self.frame_len + 1):
for j in range(1, self.frame_len + 1):
self.rpr_table[i-1, j-1] = idx_clip(j-i, k) + k
return
def forward(self, src, src_mask=None, src_key_padding_mask=None):
r"""Pass the input through the endocder layer.
Args:
src: the sequnce to the encoder layer (required).
src_mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
Shape:
see the docs in Transformer class.
"""
src2 = self.self_attn(src, src, src, posk, posv, attn_mask=src_mask,
key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
if hasattr(self, "activation"):
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
else: # for backward compatibility
src2 = self.linear2(self.dropout(F.relu(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
| perathambkk/lipreading | short1.27fast_nlls_xl_2mem_lattransconv_p_OSL_500_cosine/RprTransformerEncoderLayer.py | RprTransformerEncoderLayer.py | py | 3,370 | python | en | code | 3 | github-code | 36 | [
{
"api_name": "torch.nn.functional.relu",
"line_number": 13,
"usage_type": "attribute"
},
{
"api_name": "torch.nn.functional",
"line_number": 13,
"usage_type": "name"
},
{
"api_name": "torch.nn.functional.gelu",
"line_number": 15,
"usage_type": "attribute"
},
{
"a... |
6797122561 | import logging
import requests
from django.urls import reverse
from django.conf import settings
from django.core.mail import get_connection
from django.core.mail.backends.base import BaseEmailBackend
from .engine import send_email
from mail.models import Message
logger = logging.getLogger('mails')
def get_admins():
return [mail for name, mail in settings.ADMINS]
class StoreBackend(BaseEmailBackend):
def __get_category_from_header(self, email):
category = None
if settings.MAIL_CATEGORY_HEADER_KEY in email.extra_headers:
category = email.extra_headers.get(settings.MAIL_CATEGORY_HEADER_KEY, None)
email.extra_headers.pop(settings.MAIL_CATEGORY_HEADER_KEY)
return category
def __is_error_or_test_mail(self, subject):
return subject.startswith(settings.MAIL_ERROR_MESAGE_SUBJECT) or subject.startswith(
settings.MAIL_TEST_MESSAGE_SUBJECT)
def send_messages(self, email_messages, notify_webhook=None):
AUTOSEND = getattr(settings, 'MAILER_AUTOSEND', False)
num_sent = 0
for email in email_messages:
if self.__is_error_or_test_mail(email.subject):
email.to = get_admins()
email.connection = get_connection(
backend=settings.MAILER_EMAIL_BACKEND
)
email.send()
else:
msg = Message.objects.create(
subject=email.subject,
to_email=email.to,
from_email=email.from_email,
reply_to=email.reply_to,
cc=email.cc,
bcc=email.bcc,
body=email.body,
category=self.__get_category_from_header(email),
attachment=email.attachments)
if notify_webhook:
mail_url = '{}{}'.format(
settings.DOMAIN_NAME,
reverse('mail:inbox-message', kwargs={'pk': msg.pk}),
)
try:
requests.put(
notify_webhook,
data={
'email_url': mail_url,
'email_status': settings.MAIL_NOTIFY_WEBHOOK_STATUS_OK,
},
)
except Exception as e: # noqa
logger.error('Notify webhook Exception: {}'.format(e))
if AUTOSEND:
send_email(msg)
num_sent += 1
return num_sent
| tomasgarzon/exo-services | service-exo-mail/mail/backend.py | backend.py | py | 2,657 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "logging.getLogger",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "mail.models",
"line_number": 17,
"usage_type": "name"
},
{
"api_name": "django.conf.settings.ADMINS",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "django... |
37412068955 | ##############################################################################
#
# File format versions:
#
# 1: initial version
#
# 2: now contains reciprocal planck opacity and rosseland opacity
# previous rosseland opacity was actually reciprocal planck opacity
#
##############################################################################
from __future__ import print_function, division
import os
import hashlib
import h5py
import numpy as np
from astropy import log as logger
import six
from ..version import __version__
from ..util.constants import c
from ..util.functions import FreezableClass
from ..util.interpolate import interp1d_fast_loglog
from ..util.integrate import integrate_loglog
from ..util.nans import check_for_nans
from .optical_properties import OpticalProperties
from .emissivities import Emissivities
from .mean_opacities import MeanOpacities
def henyey_greenstein(mu, g, p_lin_max):
P1 = (1. - g * g) / (1. + g * g - 2. * g * mu) ** 1.5
P2 = - p_lin_max * P1 * (1. - mu * mu) / (1. + mu * mu)
P3 = P1 * 2. * mu / (1. + mu * mu)
P4 = 0.
return P1, P2, P3, P4
class SphericalDust(FreezableClass):
r"""
This class should be used in cases where fully arbitrary dust properties
need to be specified, within the framework of randomly oriented grains,
which means that the scattering phase function has the general form:
.. math:: R(\theta) = \left[\begin{array}{cccc}P_1 & P_2 & 0 & 0 \\P_2 & P_1 & 0 & 0 \\0 & 0 & P_3 & -P_4 \\0 & 0 & P_4 & P_3\end{array}\right]
This class is initialized with::
d = SphericalDust()
and the properties should then be set manually. See
`here <http://docs.hyperion-rt.org/en/stable/setup/setup_dust.html#fully-customized-4-element-dust>`_
for a description of the available properties and how to set them.
"""
def __init__(self, *args):
self._file = None
self.md5 = None
self.optical_properties = OpticalProperties()
self.mean_opacities = MeanOpacities()
self.emissivities = Emissivities()
self.set_sublimation_specific_energy('no', 0.)
self._freeze()
if len(args) == 0:
pass
elif len(args) == 1:
self.read(args[0])
else:
raise Exception("SphericalDust cannot take more than one argument")
def hash(self):
h = hashlib.md5()
if self.optical_properties is None or not self.optical_properties.all_set():
h.update('none'.encode('utf-8'))
else:
h.update(self.optical_properties.hash().encode('utf-8'))
if self.emissivities is None or not self.emissivities.all_set():
h.update('none'.encode('utf-8'))
else:
h.update(self.emissivities.hash().encode('utf-8'))
if self.mean_opacities is None or not self.mean_opacities.all_set():
h.update('none'.encode('utf-8'))
else:
h.update(self.mean_opacities.hash().encode('utf-8'))
import struct
h.update(self.sublimation_mode.encode('utf-8'))
h.update(struct.pack('>d', self.sublimation_energy))
return h.hexdigest()
def set_lte_emissivities(self, n_temp=1200, temp_min=0.1, temp_max=100000.):
'''
Calculate the emissivities assuming LTE
Parameters
----------
n_temp : int, optional
The number of temperatures to calculate the emissivities for
temp_min : float, optional
The minimum temperature to calculate the emissivities for
temp_max : float, optional
The maximum temperature to calculate the emissivities for
'''
self.mean_opacities.compute(self.optical_properties, n_temp=n_temp,
temp_min=temp_min, temp_max=temp_max)
self.emissivities.set_lte(self.optical_properties, self.mean_opacities)
def plot(self, filename):
# Check that the optical properties have been set
self.optical_properties.ensure_all_set()
import matplotlib.pyplot as plt
# Save original rc parameters
rc_orig = plt.rcParams
# Reset to defaults
plt.rcdefaults()
plt.rc('legend', fontsize=7)
plt.rc('axes', titlesize='x-small')
plt.rc('axes', labelsize='x-small')
plt.rc('xtick', labelsize='xx-small')
plt.rc('ytick', labelsize='xx-small')
plt.rc('axes', linewidth=0.5)
plt.rc('patch', linewidth=0.5)
# Compute mean opacities if not already existent
self._compute_mean_opacities()
# Check that emissivities are set (before computing mean opacities)
if not self.emissivities.all_set():
logger.info("Computing emissivities assuming LTE")
self.emissivities.set_lte(self.optical_properties, self.mean_opacities)
# Initialize figure
fig = plt.figure(figsize=(10, 12))
# Plot optical properties
fig = self.optical_properties.plot(fig, [421, 423, 424, 425, 426])
# Plot mean opacities
fig = self.mean_opacities.plot(fig, 428)
# Plot emissivities
fig = self.emissivities.plot(fig, 427)
# Adjust spacing between subplots
fig.subplots_adjust(left=0.08, right=0.92, wspace=0.22, hspace=0.30)
# Save figure
fig.savefig(filename, bbox_inches='tight')
# Close figure to save RAM
plt.close(fig)
# Restore rc parameters
plt.rc(rc_orig)
def set_sublimation_temperature(self, mode, temperature=0.):
'''
Set the dust sublimation mode and temperature.
Parameters
----------
mode : str
The dust sublimation mode, which can be:
* 'no' - no sublimation
* 'fast' - remove all dust in cells exceeding the
sublimation temperature
* 'slow' - reduce the dust in cells exceeding the
sublimation temperature
* 'cap' - any temperature exceeding the sublimation
temperature is reset to the sublimation
temperature.
temperature : float, optional
The dust sublimation temperature, in K
'''
if mode not in ['no', 'fast', 'slow', 'cap']:
raise Exception("mode should be one of no/fast/slow/cap")
if mode != 'no' and temperature is None:
raise Exception("Need to specify a sublimation temperature")
self.sublimation_mode = mode
self.sublimation_energy = self.temperature2specific_energy(temperature)
def set_sublimation_specific_energy(self, mode, specific_energy=0.):
'''
Set the dust sublimation mode and specific energy.
Parameters
----------
mode : str
The dust sublimation mode, which can be:
* 'no' - no sublimation
* 'fast' - remove all dust in cells exceeding the
sublimation specific energy
* 'slow' - reduce the dust in cells exceeding the
sublimation specific energy
* 'cap' - any specific energy exceeding the sublimation
specific energy is reset to the sublimation
specific energy.
specific_energy : float, optional
The dust sublimation specific energy, in cgs
'''
if mode not in ['no', 'fast', 'slow', 'cap']:
raise Exception("mode should be one of no/fast/slow/cap")
if mode != 'no' and specific_energy is None:
raise Exception("Need to specify a sublimation specific_energy")
self.sublimation_mode = mode
self.sublimation_energy = specific_energy
def _write_dust_sublimation(self, group):
group.attrs['sublimation_mode'] = np.string_(self.sublimation_mode)
if self.sublimation_mode in ['slow', 'fast', 'cap']:
group.attrs['sublimation_specific_energy'] = self.sublimation_energy
def _read_dust_sublimation(self, group):
if 'sublimation_mode' in group.attrs:
self.sublimation_mode = group.attrs['sublimation_mode'].decode('ascii')
if self.sublimation_mode in ['slow', 'fast', 'cap']:
self.sublimation_energy = group.attrs['sublimation_specific_energy']
def _compute_mean_opacities(self):
if not self.mean_opacities.all_set():
self.mean_opacities.compute(self.optical_properties)
def write(self, filename, compression=True):
'''
Write out to a standard dust file, including calculations of the mean
opacities and optionally thermal emissivities.
'''
# Check that the optical properties have been set
self.optical_properties.ensure_all_set()
# Compute mean opacities if not already existent
self._compute_mean_opacities()
# Check that emissivities are set (before computing mean opacities)
if not self.emissivities.all_set():
logger.info("Computing emissivities assuming LTE")
self.emissivities.set_lte(self.optical_properties, self.mean_opacities)
# Create dust table set
if isinstance(filename, six.string_types):
dt = h5py.File(filename, 'w')
else:
dt = filename
# Add standard keywords to header
dt.attrs['version'] = 2
dt.attrs['type'] = 1
dt.attrs['python_version'] = np.string_(__version__)
if self.md5:
dt.attrs['asciimd5'] = np.string_(self.md5)
# Add optical properties and scattering angle tables
self.optical_properties.to_hdf5_group(dt)
# Add mean opacities table
self.mean_opacities.to_hdf5_group(dt)
# Add emissivities and emissivity variable tables
self.emissivities.to_hdf5_group(dt)
# Dust sublimation parameters
self._write_dust_sublimation(dt)
# Check that there are no NaN values in the file - if there are, a
# warning is emitted.
check_for_nans(dt)
# Close dust file
if isinstance(dt, h5py.File):
dt.close()
self._file = (filename, self.hash())
def read(self, filename):
'''
Read in from a standard dust file
'''
from ..util.functions import asstr
if isinstance(filename, six.string_types):
# Check file exists
if not os.path.exists(filename):
raise Exception("File not found: %s" % filename)
# Read in dust table set
dt = h5py.File(filename, 'r')
close = True
else:
# Read in dust table set
dt = filename
close = False
# Check version and type
if dt.attrs['version'] not in [1, 2]:
raise Exception("Version should be 1 or 2")
if dt.attrs['type'] != 1:
raise Exception("Type should be 1")
if 'asciimd5' in dt.attrs:
self.md5 = asstr(dt.attrs['asciimd5'])
else:
self.md5 = None
# Read in the optical properties
self.optical_properties.from_hdf5_group(dt)
# Read in the planck and rosseland mean opacities
if dt.attrs['version'] == 1:
logger.warning("Version 1 dust file detected - discarding mean opacities and recomputing them")
self.mean_opacities.compute(self.optical_properties)
else:
self.mean_opacities.from_hdf5_group(dt)
# Read in emissivities
self.emissivities.from_hdf5_group(dt)
# Dust sublimation parameters
self._read_dust_sublimation(dt)
# Close file object if needed
if close:
dt.close()
self._file = (filename, self.hash())
def chi_nu_temperature(self, temperature):
"""
Compute the mean opacity to extinction for a blackbody at a given temperature.
Parameters
----------
temperature : float
The temperature of the blackbody to use
Returns
-------
chi_nu_mean : float
The mean opacity to extinction
"""
self._compute_mean_opacities()
return interp1d_fast_loglog(self.mean_opacities.temperature,
self.mean_opacities.chi_planck,
temperature,
bounds_error=True)
def kappa_nu_temperature(self, temperature):
"""
Compute the mean opacity to absorption for a blackbody at a given temperature.
Parameters
----------
temperature : float
The temperature of the blackbody to use
Returns
-------
kappa_nu_mean : float
The mean opacity to absorption
"""
self._compute_mean_opacities()
return interp1d_fast_loglog(self.mean_opacities.temperature,
self.mean_opacities.kappa_planck,
temperature,
bounds_error=True)
def chi_nu_spectrum(self, nu, fnu):
"""
Compute the mean opacity to extinction for a given spectrum.
Parameters
----------
nu : array_like
The frequencies, in Hz
fnu : array_like
The monochromatic fluxes per unit frequency. Units are unimportant
since proportionality constants are cancelled out in the
computation.
Returns
-------
chi_nu_mean : float
The mean opacity to extinction
"""
if nu.max() > self.optical_properties.nu.max() or nu.min() < self.optical_properties.nu.min():
raise Exception("Opacity to extinction is not defined at all "
"spectrum frequencies")
chi_nu = self.optical_properties.interp_chi_nu(nu)
return (integrate_loglog(nu, fnu * chi_nu) /
integrate_loglog(nu, fnu))
def kappa_nu_spectrum(self, nu, fnu):
"""
Compute the mean opacity to absorption for a given spectrum.
Parameters
----------
nu : array_like
The frequencies, in Hz
fnu : array_like
The monochromatic fluxes per unit frequency. Units are unimportant
since proportionality constants are cancelled out in the
computation.
Returns
-------
kappa_nu_mean : float
The mean opacity to absorption
"""
if nu.max() > self.optical_properties.nu.max() or nu.min() < self.optical_properties.nu.min():
raise Exception("Opacity to absorption is not defined at all "
"spectrum frequencies")
kappa_nu = self.optical_properties.interp_kappa_nu(nu)
return (integrate_loglog(nu, fnu * kappa_nu) /
integrate_loglog(nu, fnu))
def temperature2specific_energy(self, temperature):
"""
Convert a temperature to its corresponding specific energy value.
Parameters
----------
temperature : float or array_like
The temperature to convert
Returns
-------
specific_energy : float or array_like
The specific energy corresponding to the input temperature
"""
self._compute_mean_opacities()
specific_energy = interp1d_fast_loglog(self.mean_opacities.temperature,
self.mean_opacities.specific_energy,
temperature,
bounds_error=False,
fill_value=np.nan)
if np.isscalar(temperature):
if temperature < self.mean_opacities.temperature[0]:
specific_energy = self.mean_opacities.specific_energy[0]
elif temperature > self.mean_opacities.temperature[-1]:
specific_energy = self.mean_opacities.specific_energy[-1]
else:
specific_energy[temperature < self.mean_opacities.temperature[0]] = self.mean_opacities.specific_energy[0]
specific_energy[temperature > self.mean_opacities.temperature[-1]] = self.mean_opacities.specific_energy[-1]
return specific_energy
def specific_energy2temperature(self, specific_energy):
"""
Convert a specific energy value to its corresponding temperature.
Parameters
----------
specific_energy : float or array_like
The specific energy to convert
Returns
-------
temperature : float or array_like
The temperature corresponding to the input specific energy
"""
self._compute_mean_opacities()
temperature = interp1d_fast_loglog(self.mean_opacities.specific_energy,
self.mean_opacities.temperature,
specific_energy,
bounds_error=False,
fill_value=np.nan)
if np.isscalar(specific_energy):
if specific_energy < self.mean_opacities.specific_energy[0]:
temperature = self.mean_opacities.temperature[0]
elif specific_energy > self.mean_opacities.specific_energy[-1]:
temperature = self.mean_opacities.temperature[-1]
else:
temperature[specific_energy < self.mean_opacities.specific_energy[0]] = self.mean_opacities.temperature[0]
temperature[specific_energy > self.mean_opacities.specific_energy[-1]] = self.mean_opacities.temperature[-1]
return temperature
class IsotropicDust(SphericalDust):
"""
This class should be used for dust properties that include isotropic
scattering. The dust properties should be instatiated as::
d = IsotropicDust(nu, albedo, chi)
where ``nu``, ``albedo``, and ``chi`` are 1-D Numpy arrays containing the
frequencies, albedo, and opacity to extinction respectively.
"""
def __init__(self, nu, albedo, chi):
SphericalDust.__init__(self)
# Set cos(theta) grid for computing the scattering matrix elements
self.optical_properties.mu = np.linspace(-1., 1., 2)
# Set optical properties
self.optical_properties.nu = nu
self.optical_properties.albedo = albedo
self.optical_properties.chi = chi
# Compute scattering matrix elements
self.optical_properties.initialize_scattering_matrix()
# Set scattering matrix to isotropic values
self.optical_properties.P1[:, :] = 1.
self.optical_properties.P2[:, :] = 0.
self.optical_properties.P3[:, :] = 1.
self.optical_properties.P4[:, :] = 0.
# Sort optical properties
self.optical_properties._sort()
class HenyeyGreensteinDust(SphericalDust):
"""
This class should be used for dust properties that include
scattering parameterized by the `Henyey-Greenstein, 1941
<http://dx.doi.org/10.1086/144246>`_ function. The dust properties should
be instatiated as::
d = HenyeyGreensteinDust(nu, albedo, chi, g, p_lin_max)
where ``nu``, ``albedo``, and ``chi`` are 1-D Numpy arrays containing the
frequencies, albedo, and opacity to extinction respectively, and ``g`` and
``p_lin_max`` are also 1-D Numpy arrays containing the asymmetry parameter
and the maximum linear polarization.
"""
def __init__(self, nu, albedo, chi, g, p_lin_max):
SphericalDust.__init__(self)
# Set cos(theta) grid for computing the scattering matrix elements
n_mu = 100
self.optical_properties.mu = np.linspace(-1., 1., n_mu)
# Set optical properties
self.optical_properties.nu = nu
self.optical_properties.albedo = albedo
self.optical_properties.chi = chi
# Compute scattering matrix elements
self.optical_properties.initialize_scattering_matrix()
for i in range(n_mu):
self.optical_properties.P1[:, i], \
self.optical_properties.P2[:, i], \
self.optical_properties.P3[:, i], \
self.optical_properties.P4[:, i] = henyey_greenstein(self.optical_properties.mu[i], g, p_lin_max)
class HOCHUNKDust(HenyeyGreensteinDust):
"""
This class should be used for dust properties that include
scattering parameterized by the `Henyey-Greenstein, 1941
<http://dx.doi.org/10.1086/144246>`_ function, which are formatted for the
`HOCHUNK code <http://gemelli.colorado.edu/~bwhitney/codes/>`_. The dust
properties should be instatiated as::
d = HOCHUNKDust(filename)
where ``filename`` is the name of the file containing the dust properties
in the HOCHUNK format.
"""
def __init__(self, filename):
# Read in dust file
dustfile = np.loadtxt(filename, dtype=[('wav', float), ('c_ext', float),
('c_sca', float), ('chi', float), ('g', float),
('p_lin_max', float)], usecols=[0, 1, 2, 3, 4, 5])
# Ensure file is ordered in increasing frequency
if dustfile['wav'][-1] > dustfile['wav'][0]:
dustfile = dustfile[::-1]
# Compute frequency and albedo
nu = c / dustfile['wav'] * 1.e4
albedo = dustfile['c_sca'] / dustfile['c_ext']
self.md5 = hashlib.md5(open(filename, 'rb').read()).hexdigest()
HenyeyGreensteinDust.__init__(self, nu, albedo, dustfile['chi'], dustfile['g'], dustfile['p_lin_max'])
TTsreDust = HOCHUNKDust
class CoatsphSingle(SphericalDust):
def __init__(self, directory, size, density):
'''
Initialize single-component dust.
Parameters
----------
directory : str
Directory containing all the files describing the dust
size : float
Grain size, in cm
density : float
Dust grain density, in g/cm^3
'''
SphericalDust.__init__(self)
f = open('%s/coatsph_forw.dat' % directory, 'rb')
version = f.readline()
n_components = int(f.readline().strip().split()[5])
# Read in main dust file
dustfile = np.loadtxt(f, skiprows=3,
dtype=[('x', float), ('radius', float), ('wav', float),
('q_ext', float), ('q_sca', float), ('q_back', float),
('g', float)])
n_wav = len(dustfile)
self.optical_properties.nu = c / dustfile['wav'] * 1.e4
self.optical_properties.albedo = dustfile['q_sca'] / dustfile['q_ext']
self.optical_properties.chi = 0.75 * dustfile['q_ext'] / size / density
# Read in scattering matrix elements
for i in range(n_wav):
filename = '%s/coatsph_scat_%04i_0001.dat' % (directory, i + 1)
phasefile = np.loadtxt(filename, skiprows=9,
dtype=[('theta', float), ('s11', float), ('polariz',
float), ('s12', float), ('s33', float), ('s34',
float)])
if i == 0:
self.optical_properties.mu = np.cos(np.radians(phasefile['theta']))
self.optical_properties.initialize_scattering_matrix()
self.optical_properties.P1[i, :] = phasefile['s11']
self.optical_properties.P2[i, :] = phasefile['s12']
self.optical_properties.P3[i, :] = phasefile['s33']
self.optical_properties.P4[i, :] = phasefile['s34']
class CoatsphMultiple(SphericalDust):
def __init__(self, directory):
'''
Initialize multi-component dust.
Parameters
----------
directory : str
Directory containing all the files describing the dust
'''
SphericalDust.__init__(self)
f = open('%s/coatsph_forw.dat' % directory, 'rb')
version = f.readline()
n_components = int(f.readline().strip().split()[5])
# Read in main dust file
dustfile = np.loadtxt(f, skiprows=7,
dtype=[('wav', float), ('c_ext', float), ('c_sca', float),
('chi', float), ('g', float), ('pmax', float),
('thetmax', float)])
n_wav = len(dustfile)
self.optical_properties.nu = c / dustfile['wav'] * 1.e4
self.optical_properties.albedo = dustfile['c_sca'] / dustfile['c_ext']
self.optical_properties.chi = dustfile['chi']
# Read in scattering matrix elements
for i in range(n_wav):
filename = '%s/coatsph_scat.%04i.dat' % (directory, i + 1)
phasefile = np.loadtxt(filename, skiprows=7,
dtype=[('theta', float), ('s11', float), ('polariz',
float), ('s12', float), ('s33', float), ('s34',
float)])
if i == 0:
self.optical_properties.mu = np.cos(np.radians(phasefile['theta']))
self.optical_properties.initialize_scattering_matrix()
self.optical_properties.P1[i, :] = phasefile['s11']
self.optical_properties.P2[i, :] = phasefile['s12']
self.optical_properties.P3[i, :] = phasefile['s33']
self.optical_properties.P4[i, :] = phasefile['s34']
class MieXDust(SphericalDust):
def __init__(self, model):
SphericalDust.__init__(self)
wav = np.loadtxt('%s.alb' % model, usecols=[0])
self.optical_properties.albedo = np.loadtxt('%s.alb' % model, usecols=[1])
kappa = np.loadtxt('%s.k_abs' % model, usecols=[1])
self.optical_properties.chi = kappa / (1 - self.optical_properties.albedo)
# Check for NaN values
for quantity in ['chi', 'albedo']:
values = self.optical_properties.__dict__[quantity]
if np.any(np.isnan(values)):
logger.warning("NaN values found inside MieX %s file - interpolating" % quantity)
invalid = np.isnan(values)
values[invalid] = interp1d_fast_loglog(wav[~invalid], values[~invalid], wav[invalid])
if np.any(np.isnan(values)):
raise Exception("Did not manage to fix NaN values in MieX %s" % quantity)
self.optical_properties.nu = c / wav * 1.e4
n_wav = len(wav)
n_mu = (len(open('%s.f11' % model).readlines()) // n_wav) - 1
mu = np.zeros(n_mu)
# Read mu
f11 = open('%s.f11' % model)
f11.readline()
f11.readline()
for i in range(n_mu):
mu[i] = np.cos(np.radians(float(f11.readline().split()[0])))
f11.close()
self.optical_properties.mu = mu[::-1]
# Read in matrix elements
self.optical_properties.initialize_scattering_matrix()
f11 = open('%s.f11' % model)
f12 = open('%s.f12' % model)
f33 = open('%s.f33' % model)
f34 = open('%s.f34' % model)
f11.readline()
f12.readline()
f33.readline()
f34.readline()
for j in range(n_wav):
if float(f11.readline()) != wav[j]:
raise Exception("Incorrect wavelength in f11")
if float(f12.readline()) != wav[j]:
raise Exception("Incorrect wavelength in f12")
if float(f33.readline()) != wav[j]:
raise Exception("Incorrect wavelength in f33")
if float(f34.readline()) != wav[j]:
raise Exception("Incorrect wavelength in f34")
for i in range(n_mu):
self.optical_properties.P1[j, n_mu - i - 1] = float(f11.readline().split()[1])
self.optical_properties.P2[j, n_mu - i - 1] = float(f12.readline().split()[1])
self.optical_properties.P3[j, n_mu - i - 1] = float(f33.readline().split()[1])
self.optical_properties.P4[j, n_mu - i - 1] = float(f34.readline().split()[1])
for i in range(n_mu):
for quantity in ['P1', 'P2', 'P3', 'P4']:
values = self.optical_properties.__dict__[quantity]
if np.any(np.isnan(values[:, i])):
logger.warning("NaN values found inside MieX %s file - interpolating" % quantity)
invalid = np.isnan(values[:, i])
values[:, i][invalid] = interp1d_fast_loglog(wav[~invalid], values[:, i][~invalid], wav[invalid])
if np.any(np.isnan(values[:, i])):
raise Exception("Did not manage to fix NaN values in MieX %s" % quantity)
class BHDust(SphericalDust):
"""
This class should be used for dust properties that were computed using
`this dust calculation code <https://github.com/hyperion-rt/bhmie>`_ which
is a wrapper to the ``bhmie`` routine originally written by C.F. Bohren and
D. Huffman and improved by B. Draine.
When using the ``bhmie`` code, you should set the output format to ``2``,
which will create a number of files ending in ``.wav``, ``.mu``, ``.alb``,
etc. Then, instantiate this class with the name of the directory containing
these output files along with the prefix used. For example, if you use
``directory/mydust`` as a prefix in ``bhmie``, you can import this dust
with::
>>> from hyperion.dust import BHDust
>>> d = BHDust('directory/mydust')
"""
def __init__(self, model):
SphericalDust.__init__(self)
mu = np.loadtxt('%s.mu' % model)
nu = c / np.loadtxt('%s.wav' % model) * 1.e4
albedo = np.loadtxt('%s.alb' % model)
chi = np.loadtxt('%s.chi' % model)
P1 = np.loadtxt('%s.f11' % model)
P2 = np.loadtxt('%s.f12' % model)
P3 = np.loadtxt('%s.f33' % model)
P4 = np.loadtxt('%s.f34' % model)
if nu[-1] < nu[0]:
nu = nu[::-1]
albedo = albedo[::-1]
chi = chi[::-1]
P1 = P1[::-1, :]
P2 = P2[::-1, :]
P3 = P3[::-1, :]
P4 = P4[::-1, :]
if mu[-1] < mu[0]:
mu = mu[::-1]
P1 = P1[:, ::-1]
P2 = P2[:, ::-1]
P3 = P3[:, ::-1]
P4 = P4[:, ::-1]
self.optical_properties.mu = mu
self.optical_properties.nu = nu
self.optical_properties.albedo = albedo
self.optical_properties.chi = chi
self.optical_properties.P1 = P1
self.optical_properties.P2 = P2
self.optical_properties.P3 = P3
self.optical_properties.P4 = P4
| hyperion-rt/hyperion | hyperion/dust/dust_type.py | dust_type.py | py | 31,342 | python | en | code | 51 | github-code | 36 | [
{
"api_name": "util.functions.FreezableClass",
"line_number": 43,
"usage_type": "name"
},
{
"api_name": "optical_properties.OpticalProperties",
"line_number": 65,
"usage_type": "call"
},
{
"api_name": "mean_opacities.MeanOpacities",
"line_number": 66,
"usage_type": "call"... |
21891037955 | import json
import logging
import traceback
from django.views import View
from django.http import JsonResponse
from backend.models import AnnotationCategory, Video
# from django.core.exceptions import BadRequest
class AnnoatationCategoryCreate(View):
def post(self, request):
try:
if not request.user.is_authenticated:
return JsonResponse({"status": "error"})
# decode data
try:
body = request.body.decode("utf-8")
except (UnicodeDecodeError, AttributeError):
body = request.body
try:
data = json.loads(body)
except Exception as e:
return JsonResponse({"status": "error", "type": "wrong_request_body"})
if "name" not in data:
return JsonResponse({"status": "error", "type": "missing_values"})
try:
query_args = {"name": data.get("name"), "owner": request.user}
if "video_id" in data:
query_args["video__id"] = data.get("video_id")
annotation_category_db = AnnotationCategory.objects.get(**query_args)
except AnnotationCategory.DoesNotExist:
create_args = {"name": data.get("name"), "owner": request.user}
if "color" in data:
create_args["color"] = data.get("color")
if "video_id" in data:
try:
video_db = Video.objects.get(id=data.get("video_id"))
except Video.DoesNotExist:
return JsonResponse({"status": "error", "type": "not_exist"})
create_args["video"] = video_db
annotation_category_db = AnnotationCategory.objects.create(**create_args)
return JsonResponse({"status": "ok", "entry": annotation_category_db.to_dict()})
except Exception as e:
logging.error(traceback.format_exc())
return JsonResponse({"status": "error"})
class AnnoatationCategoryList(View):
def get(self, request):
try:
if not request.user.is_authenticated:
return JsonResponse({"status": "error"})
query_args = {}
query_args["owner"] = request.user
if "video_id" in request.GET:
query_args["video__id"] = request.GET.get("video_id")
query_results = AnnotationCategory.objects.filter(**query_args)
entries = []
for annotation_category in query_results:
entries.append(annotation_category.to_dict())
return JsonResponse({"status": "ok", "entries": entries})
except Exception as e:
logging.error(traceback.format_exc())
return JsonResponse({"status": "error"})
| TIBHannover/tibava-backend | backend/views/annotation_category.py | annotation_category.py | py | 2,854 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "django.views.View",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "django.http.JsonResponse",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "django.http.Jso... |
5102702223 | """fask_dj URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^tasks', views.tasks, name = 'tasks'),
url(r'^task/(?P<task_id>[0-9]+)/$', views.task, name = 'task'),
url(r'^task_delete/(?P<task_id>[0-9]+)/$', views.task_delete, name = 'task_delete'),
url(r'^task_project/(?P<task_id>[0-9]+)/(?P<project_id>[0-9]+)/(?P<redirect_name>[a-z]+)$', views.task_project, name = 'task_project'),
]
| ZompaSenior/fask | fask/src/fask_dj/task/urls.py | urls.py | py | 1,085 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "django.conf.urls.url",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "django.conf.urls.url",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "django.conf.urls.url",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "django... |
6377162291 | from django.db import models
import uuid
class apiKeys(models.Model):
id = models.CharField(max_length=200, null=False, blank=False)
key = models.TextField(primary_key=True, default=uuid.uuid4().hex)
def __unicode__(self):
return self.id
class ButtonTable(models.Model):
SKRANKE = 0
TELEFON = 1
RANDOM = 2
BUTTON_CHOICES= (
(SKRANKE, 'Skranke'),
(TELEFON, 'Telefon'),
(RANDOM, 'Random')
)
sender = models.ForeignKey(apiKeys, editable=False, null=False)
button = models.IntegerField(choices=BUTTON_CHOICES, default=0)
date_registered = models.DateTimeField(auto_now_add=True, auto_now=False)
def __unicode__(self):
return str(self.button)
class Meta:
verbose_name_plural = 'Case registers'
verbose_name = 'Registered case'
| OrakeltjenestenDragvoll/nix | apps/bujumbura/models.py | models.py | py | 837 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "django.db.models.Model",
"line_number": 5,
"usage_type": "attribute"
},
{
"api_name": "django.db.models",
"line_number": 5,
"usage_type": "name"
},
{
"api_name": "django.db.models.CharField",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "... |
74146205863 | from flair.data import Sentence
from flair.models import SequenceTagger
from flair.embeddings import (DocumentPoolEmbeddings,
FlairEmbeddings, StackedEmbeddings)
from torch import dot, norm, squeeze
from pathlib import Path
import pickle
from odm.nlp.tensors import PCA, plot_embeddings, normalize_tensors
from odm.nlp.parse import parse_veclang
class Model:
modelpath = '/data/model/'
def __init__(self):
# Sequence Tagging Model
tagger_file = self.modelpath + 'tagger.pt'
if Path(tagger_file).is_file():
print('loading tagger from file')
self.tagger = SequenceTagger.load_from_file(tagger_file)
else:
print('downloading pretrained tagger')
self.tagger = SequenceTagger.load('ner-ontonotes')
self.tagger.save(tagger_file)
# Text Embedding Model
embeddings_file = self.modelpath + 'embeddings.pickle'
if Path(embeddings_file).is_file():
print('loading embedder from file')
filestream = open(embeddings_file, 'rb')
self.embeddings = pickle.load(filestream)
else:
print('downloading pretrained embedders')
self.embeddings = [
# WordEmbeddings('glove'),
FlairEmbeddings('multi-forward')
# FlairEmbeddings('multi-backward')
]
filestream = open(embeddings_file, 'wb')
pickle.dump(self.embeddings, filestream)
self.token_embedder = StackedEmbeddings(self.embeddings)
self.doc_embedder = DocumentPoolEmbeddings(self.embeddings)
def parse(self, text):
sentence = Sentence(text)
self.tagger.predict(sentence)
self.token_embedder.embed(sentence)
self.doc_embedder.embed(sentence)
return sentence
def mindmap(self, text):
# parsing
lines, arrows = parse_veclang(text)
sentences = [self.parse(line) for line in lines]
tensors = [s.get_embedding() for s in sentences]
# tensor processing
norm_tensors = normalize_tensors(tensors)
flat_tensors = PCA(norm_tensors)
# plot map
filename = plot_embeddings(lines, flat_tensors, arrows)
return f'Plotted mindmap to {filename}'
def similarity(self, text):
lines = text.split('//')
sentences = [self.parse(line) for line in lines]
vecs = [squeeze(s.embedding) for s in sentences]
sim = dot(vecs[0], vecs[1])/(norm(vecs[0])*norm(vecs[1]))
return f'the similarity is {sim}'
| ixxie/odm | src/odm/nlp/model.py | model.py | py | 2,615 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "pathlib.Path",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "flair.models.SequenceTagger.load_from_file",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "flair.models.SequenceTagger",
"line_number": 26,
"usage_type": "name"
},
{
... |
7919024721 | from __future__ import division
import geojson
from helper import load_geom, degrees_to_radians, get_coord, PRECISION
from math import pow, sqrt, pi, tan, cos, sin
from measurement import rhumb_destination
from transformation import transform_rotate
def ellipse(center, x_semi_axis, y_semi_axis, options={}):
steps = 64
if 'steps' in options:
steps = options['steps']
units = 'kilometers'
units_mapping = {
'miles': 'mi',
'kilometers': 'km',
'meters': 'm',
'degrees': 'degrees',
}
if 'units' in options:
units = options['units']
angle = 0
if 'angle' in options:
angle = options['angle']
# validation
if center is None:
raise Exception('center is required')
if x_semi_axis is None:
raise Exception('x_semi_axis is required')
if y_semi_axis is None:
raise Exception('y_semi_axis is required')
if units not in units_mapping:
raise Exception('non valid units')
units = units_mapping[units]
center = load_geom(center)
center_coords = get_coord(center)
angle_rad = 0
if units == 'degrees':
angle_rad = degrees_to_radians(angle)
else:
x_semi_axis = rhumb_destination(center, x_semi_axis, 90, {'units': units})
y_semi_axis = rhumb_destination(center, y_semi_axis, 0, {'units': units})
x_semi_axis = get_coord(x_semi_axis)[0] - center_coords[0]
y_semi_axis = get_coord(y_semi_axis)[1] - center_coords[1]
coordinates = []
for i in range(0, steps):
step_angle = (i * -360) / steps
x = (x_semi_axis * y_semi_axis) / sqrt(
pow(y_semi_axis, 2) + pow(x_semi_axis, 2) * pow(get_tan_deg(step_angle), 2)
)
y = (
0
if pow(get_tan_deg(step_angle), 2) == 0
else (x_semi_axis * y_semi_axis)
/ sqrt(
pow(x_semi_axis, 2)
+ pow(y_semi_axis, 2) / pow(get_tan_deg(step_angle), 2)
)
)
if step_angle < -90 and step_angle >= -270:
x = -x
if step_angle < -180 and step_angle >= -360:
y = -y
if units == 'degrees':
newx = x * cos(angle_rad) + y * sin(angle_rad)
newy = y * cos(angle_rad) - x * sin(angle_rad)
x = newx
y = newy
coordinates.append([x + center_coords[0], y + center_coords[1]])
coordinates.append(coordinates[0])
if units == 'degrees':
return geojson.dumps(
geojson.Feature(
geometry=geojson.Polygon([coordinates], precision=PRECISION)
)['geometry']
)
else:
return geojson.dumps(
transform_rotate(
geojson.Feature(
geometry=geojson.Polygon([coordinates], precision=PRECISION)
),
angle,
mutate=True,
)['geometry']
)
def get_tan_deg(deg):
rad = (deg * pi) / 180
return tan(rad)
| CartoDB/analytics-toolbox-core | clouds/redshift/libraries/python/lib/constructors/ellipse/__init__.py | __init__.py | py | 3,027 | python | en | code | 181 | github-code | 36 | [
{
"api_name": "helper.load_geom",
"line_number": 37,
"usage_type": "call"
},
{
"api_name": "helper.get_coord",
"line_number": 38,
"usage_type": "call"
},
{
"api_name": "helper.degrees_to_radians",
"line_number": 41,
"usage_type": "call"
},
{
"api_name": "measureme... |
29317958483 | from keras.utils.data_utils import get_file
import os
import numpy as np
from os import listdir
from os.path import isfile, join, isdir
import cv2
from random import shuffle
import math
from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, LearningRateScheduler
from keras.models import Model
from keras import optimizers
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, BatchNormalization, Activation, Dropout, Embedding, LSTM, Dense, Conv2D, MaxPooling2D, TimeDistributed, Flatten, concatenate, Reshape, Lambda
from keras.applications.vgg16 import VGG16
import scipy.ndimage as ndimage
import operator
from keras.applications.vgg16 import preprocess_input
# annot_path='/home/lfan/Dropbox/JointAttention/Data/annotation/'
#
# train_path='/home/lfan/Dropbox/JointAttention/faces/train_union/'
# train_sf=[f for f in listdir(train_path) if isdir(join(train_path,f))]
# validate_path='/home/lfan/Dropbox/JointAttention/faces/validate_union/'
# validate_sf=[f for f in listdir(validate_path) if isdir(join(validate_path,f))]
# test_path='/home/lfan/Dropbox/runCoAtt/rawData/images/separate/test/'
# test_sf=[f for f in listdir(test_path) if isdir(join(test_path,f))]
#
# vid_set=test_sf #train_sf
#
# annot_sf=[join(annot_path,f) for f in listdir(annot_path) if isdir(join(annot_path,f))]
#
# with open('/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_test_new.txt','w') as F:
# for i in range(len(vid_set)):
# sf = annot_path+vid_set[i]
# vid = vid_set[i]
#
# with open(join(sf, 'coattention.txt'), 'r') as r1:
# lines = r1.readlines()
# for j in range(0,len(lines),10):
# list_now = lines[j].split()
# frame_now = str(int(list_now[1])+1)
# img_name = '/home/lfan/Dropbox/runCoAtt/rawData/images/all/' + frame_now.zfill(5) + '_' + vid + '.jpg'
#
# ca_xmin=float(list_now[2])
# ca_ymin=float(list_now[3])
# ca_xmax=float(list_now[4])
# ca_ymax=float(list_now[5])
# ca_x=(ca_xmin+ca_xmax)/2
# ca_y=(ca_ymin+ca_ymax)/2
#
#
# num_face = (len(list_now) - 2) / 4 - 1
# for k in range(num_face):
# face = list_now[(6 + k * 4):(10 + k * 4)]
# xmin=float(face[0])
# ymin=float(face[1])
# xmax=float(face[2])
# ymax=float(face[3])
# face_x=(xmin+xmax)/2
# face_y=(ymin+ymax)/2
#
# dir_x=ca_x-face_x
# dir_y=ca_y-face_y
# L=math.sqrt(dir_x ** 2 + dir_y ** 2)
# dir_x=dir_x/L
# dir_y=dir_y/L
#
# # if dir_y >= 0:
# # direction = math.acos(dir_x / math.sqrt(dir_x ** 2 + dir_y ** 2))
# # elif dir_y < 0:
# # direction = 2*math.pi - math.acos(dir_x / math.sqrt(dir_x ** 2 + dir_y ** 2))
#
# F.write(img_name+' '+str(xmin)+' '+str(ymin)+' '+str(xmax)+' '+str(ymax)+' '+str(dir_x)+' '+str(dir_y)+'\n')
# with open('/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_train.txt','r') as f:
# lines=f.readlines()
#
# for i in range(len(lines)):
# list=lines[i].split()
# if not isfile(list[0]):
#
# print(list[0])
# with open('/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_train.txt','r') as f:
# lines=f.readlines()
#
# dir_hist=np.zeros(shape=(10,1))
# for i in range(len(lines)):
# list=lines[i].split()
#
# dir=int(float(list[5])//(2*math.pi/10))
# if dir==10:
# dir=0
# dir_hist[dir,0]+=1
#
# print(dir_hist)
# with open('/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_test_new.txt','r') as f:
# lines=f.readlines()
# with open('/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_test_flipped_new.txt','w') as f2:
# for i in range(len(lines)):
# list = lines[i].split()
# xmin=float(list[1])
# xmax=float(list[3])
#
# xmax_n=480-xmin
# xmin_n=480-xmax
#
# dir_x=float(list[5])
# dir_y=float(list[6])
#
# dir_x=-dir_x
# # if dir<0.5*(2*math.pi):
# # dir_n=0.5-dir
# # else:
# # dir_n=1.5*(2*math.pi)-dir
#
# f2.write(list[0]+' '+str(xmin_n)+' '+list[2]+' '+str(xmax_n)+' '+list[4]+' '+str(dir_x)+' '+str(dir_y)+' '+'f\n')
# gfdatapath='/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/'
#
# with open('/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_test.txt','r') as f:
# lines=f.readlines()
# with open('/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_test_flipped.txt','w') as f2:
# for i in range(len(lines)):
# list = lines[i].split()
# img=cv2.imread(join(gfdatapath,list[0]))
# h, w, ch = img.shape
#
# xmin=float(list[1])
# xmax=float(list[3])+float(list[1])
#
# xmax_n=w-xmin
# xmin_n=w-xmax
# w_n=xmax_n-xmin_n
#
# dir=float(list[9])
# if dir<0.5:
# dir_n=0.5-dir
# else:
# dir_n=1.5-dir
#
# f2.write(list[0]+' '+str(xmin_n)+' '+list[2]+' '+str(w_n)+' '+list[4]+' '+list[5]+' '+list[6]+' '+list[7]+' '+list[8]+' '+str(dir_n)+' '+'f\n')
batch_size=25
epochs=25
nb_train_samples=12000 # 13706
nb_validate_samples=6000 #8533
nb_test_samples=6000
model_weights_path = '/home/lfan/Dropbox/runCoAtt/new_experiment/gazedir/gazedir_finalweights.hdf5'
model_path = '/home/lfan/Dropbox/runCoAtt/new_experiment/gazedir/gazedir_finalmodel.h5'
tensorboard_log_dir = '/home/lfan/Dropbox/runCoAtt/new_experiment/gazedir/tb_log/'
gfdatapath='/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/'
def mygenerator(mode):
if mode==1:
file_name='/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_train_new.txt'
#file_name_flip='/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_train_flipped.txt'
#file_name_gazefollow='/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_train.txt'
#file_name_gazefollow_flipped = '/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_train_flipped.txt'
sample_len=nb_train_samples
elif mode==2:
file_name = '/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_validate_new.txt'
#file_name_flip = '/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_validate_flipped.txt'
#file_name_gazefollow = '/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_test.txt'
#file_name_gazefollow_flipped = '/home/lfan/Dropbox/runCoAtt/vgg16/gazefollow_data/annotation_test_flipped.txt'
sample_len=nb_validate_samples
with open(file_name,'r') as reader:
lines=reader.readlines()
# with open(file_name_flip,'r') as reader:
# lines_flipped=reader.readlines()
# with open(file_name_gazefollow,'r') as reader:
# lines_gazefollow=reader.readlines()
# with open(file_name_gazefollow_flipped,'r') as reader:
# lines_gazefollow_flipped=reader.readlines()
lines=lines[0:sample_len]
#lines_flipped=lines_flipped[0:sample_len]
#lines[0:0]=lines_flipped
# lines[0:0]=lines_gazefollow
# lines[0:0]=lines_gazefollow_flipped
#lines=lines[0:sample_len]
shuffle(lines)
shuffle(lines)
cur_batch_index=0
while True:
x_batch = np.zeros(shape=(batch_size, 224, 224, 3))
y_batch = np.zeros(shape=(batch_size, 2))
start_id = cur_batch_index * batch_size
end_id = start_id + batch_size
files_batch_now = lines[start_id:end_id]
for j in range(batch_size):
list_now=files_batch_now[j].split()
if len(list_now)<8:
img = cv2.imread(list_now[0])
# if len(list_now) == 7:
# img = cv2.flip(img, 1)
# cv2.imshow('flipped',img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
xmin = int(float(list_now[1]))# - (float(list_now[3]) - float(list_now[1])) * 0)
ymin = int(float(list_now[2]))# - (float(list_now[4]) - float(list_now[2])) * 0)
xmax = int(float(list_now[3]))# + (float(list_now[3]) - float(list_now[1])) * 0)
ymax = int(float(list_now[4]) )#+ (float(list_now[4]) - float(list_now[2])) * 0)
# w = float(xmax - xmin)
# h = float(ymax - ymin)
# wh_list = [w, h]
# max_index, max_value = max(enumerate(wh_list), key=operator.itemgetter(1))
# pad = (wh_list[max_index] - wh_list[1 - max_index]) / 2
# if max_index == 0:
# ymin = ymin - pad
# ymax = ymax + pad
# elif max_index == 1:
# xmin -= pad
# xmax += pad
xmin = int(max(0, xmin))
ymin = int(max(0, ymin))
xmax = max(xmin + 1, xmax)
ymax = max(ymin + 1, ymax)
xmax = int(min(479, xmax))
ymax = int(min(319, ymax))
#direction = float(list_now[5]) / (2 * math.pi)
dir_x=float(list_now[5])
dir_y=float(list_now[6])
# print(img.shape)
face = img[ymin:ymax, xmin:xmax, :]
face = cv2.resize(face, (224, 224))
#face=face.astype('float32')
# face[:, :, 0] -= 123.68
# face[:, :, 1] -= 116.779
# face[:, :, 2] -= 103.939
#face=(face/255)*2-1
#print(direction)
#cv2.putText(face, str(direction), (10, 500), cv2.FONT_HERSHEY_SIMPLEX, 4, (255, 255, 255), 2, cv2.LINE_AA)
# cv2.imshow('face',face)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
x_batch[j, :, :, :] = face
y_batch[j, 0] = dir_x
y_batch[j, 1] = dir_y
elif len(list_now)>9:
name = list_now[0]
bbox_x = int(float(list_now[1]))
bbox_y = int(float(list_now[2]))
bbox_w = int(float(list_now[3]))
bbox_h = int(float(list_now[4]))
eye_x = int(np.float32(list_now[5]))
eye_y = int(np.float32(list_now[6]))
direction = np.float32(list_now[9])
img = cv2.imread(gfdatapath + name)
if len(list_now)==11:
img = cv2.flip(img, 1)
h, w, ch = img.shape
totop = np.abs(eye_y - bbox_y)
if int(totop) == 0:
totop = 10
face_h = int(2* totop)
face_w = int(2 * totop)
face_x = int(eye_x - totop)
face_y = int(eye_y - totop)
if face_x < bbox_x:
face_x = bbox_x
if face_y < bbox_y:
face_y = bbox_y
if (face_x + face_w) > (bbox_x + bbox_w):
face_w = bbox_x + bbox_w - face_x
if (face_y + face_h) > (bbox_y + bbox_h):
face_h = bbox_y + bbox_h - face_y
if face_x < 0:
face_x = 0
if face_y < 0:
face_y = 0
face_w=max(1,face_w)
face_h=max(1,face_h)
if (face_x + face_w) > w:
face_w = w - face_x
if (face_y + face_h) > h:
face_h = h - face_y
face_pro = img[face_y:(face_y + face_h), face_x:(face_x + face_w), :]
face_pro = cv2.resize(face_pro, (224, 224))
face_pro=face_pro.astype('float32')
# face_pro[:, :, 0] -= 103.939
# face_pro[:, :, 1] -= 116.779
# face_pro[:, :, 2] -= 123.68
face_pro = np.float32(face_pro) / 255
#direction = direction / (math.pi * 2)
x_batch[j,:,:,:] = face_pro
y_batch[j,0] = direction
# cv2.imshow(str(direction),x_batch[j,:,:,:])
# cv2.waitKey(0)
# cv2.destroyAllWindows()
yield x_batch, y_batch
cur_batch_index = cur_batch_index + 1
if cur_batch_index >= (len(lines) // batch_size):
cur_batch_index = 0
shuffle(lines)
#
def myloss(y_true,y_pred):
lamb=0.9
loss=lamb*tf.reduce_sum(tf.square(y_true-y_pred))
sess=tf.Session()
sess.run(loss)
#+(1-lamb)*tf.abs(1-tf.reduce_sum(tf.square(y_pred)))
#loss= (1 - tf.matmul(y_true,y_pred,transpose_b=True)/tf.sqrt(tf.reduce_sum(tf.square(y_true))*tf.reduce_sum(tf.square(y_pred)))) #+ tf.abs(1-tf.reduce_sum(tf.square(y_pred)))
# offset=np.abs(y_true-y_pred)
# loss=2*((0.5-np.abs(offset-0.5))**2)
return loss
def lr_decay(epoch):
initial_lr = 1e-3
drop = 0.8
epochs_drop = 2
lr = initial_lr * math.pow(drop, math.floor(epoch / epochs_drop))
return lr
def train_model():
img_input = Input(shape=(224, 224, 3), name='img_input')
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
base_output = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
base_model=Model(inputs=img_input,outputs=base_output)
base_model.trainable=True
WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',
WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
base_model.load_weights(weights_path)
output = Flatten(name='flatten')(base_output)
output = Dense(4096, kernel_initializer='normal',activation='relu',name='fc1')(output)
output = Dropout(0.5)(output)
output = Dense(4096, kernel_initializer='normal',name='fc2')(output)
output=BatchNormalization()(output)
output=Activation('relu')(output)
output = Dropout(0.5)(output)
predict= Dense(2, kernel_initializer='normal',activation='tanh', name='predict')(output)
# Create your own model
mymodel = Model(inputs=img_input, outputs=predict)
mymodel.summary()
mycallback = []
tbCallback = TensorBoard(log_dir=tensorboard_log_dir,
histogram_freq=0, batch_size=batch_size,
write_graph=True, write_images=True)
lrscheduler = LearningRateScheduler(lr_decay)
model_checkpoint = ModelCheckpoint(
'/home/lfan/Dropbox/runCoAtt/new_experiment/gazedir/checkpoint.weights.{epoch:02d}-{val_loss:.2f}.hdf5',
save_best_only=False,
save_weights_only=True, monitor='val_loss')
mycallback.append(tbCallback)
mycallback.append(model_checkpoint)
mycallback.append(lrscheduler)
#sgd = optimizers.SGD(lr=1e-4)
mymodel.compile(optimizer='sgd', loss='mse', metrics=['mae','acc'])
history = mymodel.fit_generator(mygenerator(1),
steps_per_epoch=(2*nb_train_samples) // batch_size,
epochs=epochs,
validation_data=mygenerator(2),
validation_steps=(2*nb_validate_samples) // batch_size,
callbacks=mycallback)
mymodel.save_weights(model_weights_path)
mymodel.save(model_path)
score = mymodel.evaluate_generator(mygenerator(2), (2*nb_validate_samples) // batch_size)
print("Validation Accuracy= ", score[1])
def testmodel():
# img_input = Input(shape=(224, 224, 3), name='img_input')
#
# # Block 1
# x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
# x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
#
# # Block 2
# x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
# x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
#
# # Block 3
# x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
# x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
# x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
#
# # Block 4
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
# x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
#
# # Block 5
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
# x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
# base_output = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
#
# output = Flatten(name='flatten')(base_output)
# output = Dense(1024, activation='tanh', name='fc1')(output)
# output = Dropout(0.5)(output)
# output = Dense(512, activation='tanh', name='fc2')(output)
# output = Dropout(0.5)(output)
# output = Dense(128, activation='tanh', name='fc3')(output)
# output = Dropout(0.5)(output)
# predict = Dense(2, activation='tanh', name='predict')(output)
img_input = Input(shape=(224, 224, 3), name='img_input')
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
base_output = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
output = Flatten(name='flatten')(base_output)
output = Dense(4096, kernel_initializer='normal', activation='relu', name='fc1')(output)
output = Dropout(0.5)(output)
output = Dense(4096, kernel_initializer='normal', name='fc2')(output)
output = BatchNormalization()(output)
output = Activation('relu')(output)
output = Dropout(0.5)(output)
predict = Dense(2, kernel_initializer='normal', activation='tanh', name='predict')(output)
# Create your own model
mymodel = Model(inputs=img_input, outputs=predict)
mymodel.summary()
mymodel.load_weights('/home/lfan/Dropbox/runCoAtt/new_experiment/gazedir/checkpoint.weights.19-0.47.hdf5')
file_name='/home/lfan/Dropbox/runCoAtt/rawData/gaze_summary_test_new.txt'
sample_len=nb_test_samples
with open(file_name,'r') as reader:
lines=reader.readlines()
lines=lines[0:sample_len]
shuffle(lines)
LOSS=0
cnt=0
for j in range(sample_len):
list_now = lines[j].split()
img = cv2.imread(list_now[0])
xmin = int(float(list_now[1])) # - (float(list_now[3]) - float(list_now[1])) * 0)
ymin = int(float(list_now[2])) # - (float(list_now[4]) - float(list_now[2])) * 0)
xmax = int(float(list_now[3])) # + (float(list_now[3]) - float(list_now[1])) * 0)
ymax = int(float(list_now[4])) # + (float(list_now[4]) - float(list_now[2])) * 0)
xmin = max(0, xmin)
ymin = max(0, ymin)
xmax = max(xmin + 1, xmax)
ymax = max(ymin + 1, ymax)
xmax = min(479, xmax)
ymax = min(319, ymax)
dir_x=float(list_now[5])
dir_y=float(list_now[6])
#direction = float(list_now[5]) / (2 * math.pi)
# print(img.shape)
face = img[ymin:ymax, xmin:xmax, :]
face = cv2.resize(face, (224, 224))
#face = face.astype('float32')
#face=face[:,:,::-1]
# face[:, :, 0] -= 123.68
# face[:, :, 1] -= 116.779
# face[:, :, 2] -= 103.939
#face=face/255
# print(direction)
# cv2.putText(face, str(direction), (10, 500), cv2.FONT_HERSHEY_SIMPLEX, 4, (255, 255, 255), 2, cv2.LINE_AA)
x_batch=np.zeros(shape=(1,224,224,3))
x_batch[0,:,:,:]=face
res=mymodel.predict(x_batch,batch_size=1)
res=res[0]
print(res)
print('GT: '+str(dir_x)+' '+str(dir_y))
o_x=112
o_y=112
cv2.arrowedLine(face,(o_x,o_y),(o_x+int(res[0]*100),o_y+int(res[1]*100)),(0,0,255),5)
cv2.arrowedLine(face, (o_x, o_y), (o_x + int(dir_x*100), o_y + int(dir_y*100)), (0, 255, 255), 5)
cv2.imshow('face',face)
cv2.waitKey(0)
cv2.destroyAllWindows()
LOSS+=((dir_x-res[0])**2+(dir_y-res[1])**2)/2
cnt+=1
# cv2.imshow(str(res[0])+' '+str(res[1]), face)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
print('Average loss: {}'.format(LOSS/cnt))
#train_model()
testmodel()
| LifengFan/Shared-Attention | src/gazemap.py | gazemap.py | py | 23,892 | python | en | code | 4 | github-code | 36 | [
{
"api_name": "random.shuffle",
"line_number": 199,
"usage_type": "call"
},
{
"api_name": "random.shuffle",
"line_number": 200,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 205,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_... |
18735221514 | import scrapy
from module.items import HotlineItem
class HotlineSpider(scrapy.Spider):
name = "hotline"
allowed_domains = ["hotline.ua"]
start_urls = [f"https://hotline.ua/ua/bt/holodilniki/?p={page}" for page in range(1, 4)]
def parse(self, response):
catalog = response.xpath('//div[contains(@class, "list-body__content")]').xpath('//div[contains(@class, "list-item ")]')
for item in catalog:
url = item.xpath('.//a[contains(@class, "list-item__title")]/@href').get()
yield scrapy.Request(
url=f"https://hotline.ua" + url,
callback=self.parse_hotline
)
def parse_hotline(self, response):
product_name = response.xpath('.//h1[contains(@class, "title__main")]/text()').get()
store_name = response.xpath('.//div[1][@class="list__item row flex"]//a[@data-eventcategory="Pages Product Prices" and @class="shop__title"]/text()').get()
price = response.xpath('.//div[1][@class="list__item row flex"]//span[@data-eventcategory="Pages Product Prices"]/span[@class="price__value"]/text()').get()
yield HotlineItem(
store_name=store_name,
product_name=product_name,
price=price
)
| Ivan7281/Lab-Data-Scraping | Lab7/module/spiders/hotline.py | hotline.py | py | 1,258 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "scrapy.Spider",
"line_number": 5,
"usage_type": "attribute"
},
{
"api_name": "scrapy.Request",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "module.items.HotlineItem",
"line_number": 26,
"usage_type": "call"
}
] |
34530261292 | from __future__ import absolute_import
import hashlib
import json
import os
import re
import socket
from mercurial.i18n import _
from mercurial import (
error,
pathutil,
url as urlmod,
util,
vfs as vfsmod,
worker,
)
from ..largefiles import lfutil
# 64 bytes for SHA256
_lfsre = re.compile(r'\A[a-f0-9]{64}\Z')
class lfsvfs(vfsmod.vfs):
def join(self, path):
"""split the path at first two characters, like: XX/XXXXX..."""
if not _lfsre.match(path):
raise error.ProgrammingError('unexpected lfs path: %s' % path)
return super(lfsvfs, self).join(path[0:2], path[2:])
def walk(self, path=None, onerror=None):
"""Yield (dirpath, [], oids) tuple for blobs under path
Oids only exist in the root of this vfs, so dirpath is always ''.
"""
root = os.path.normpath(self.base)
# when dirpath == root, dirpath[prefixlen:] becomes empty
# because len(dirpath) < prefixlen.
prefixlen = len(pathutil.normasprefix(root))
oids = []
for dirpath, dirs, files in os.walk(self.reljoin(self.base, path or ''),
onerror=onerror):
dirpath = dirpath[prefixlen:]
# Silently skip unexpected files and directories
if len(dirpath) == 2:
oids.extend([dirpath + f for f in files
if _lfsre.match(dirpath + f)])
yield ('', [], oids)
class filewithprogress(object):
"""a file-like object that supports __len__ and read.
Useful to provide progress information for how many bytes are read.
"""
def __init__(self, fp, callback):
self._fp = fp
self._callback = callback # func(readsize)
fp.seek(0, os.SEEK_END)
self._len = fp.tell()
fp.seek(0)
def __len__(self):
return self._len
def read(self, size):
if self._fp is None:
return b''
data = self._fp.read(size)
if data:
if self._callback:
self._callback(len(data))
else:
self._fp.close()
self._fp = None
return data
class local(object):
"""Local blobstore for large file contents.
This blobstore is used both as a cache and as a staging area for large blobs
to be uploaded to the remote blobstore.
"""
def __init__(self, repo):
fullpath = repo.svfs.join('lfs/objects')
self.vfs = lfsvfs(fullpath)
usercache = lfutil._usercachedir(repo.ui, 'lfs')
self.cachevfs = lfsvfs(usercache)
self.ui = repo.ui
def open(self, oid):
"""Open a read-only file descriptor to the named blob, in either the
usercache or the local store."""
# The usercache is the most likely place to hold the file. Commit will
# write to both it and the local store, as will anything that downloads
# the blobs. However, things like clone without an update won't
# populate the local store. For an init + push of a local clone,
# the usercache is the only place it _could_ be. If not present, the
# missing file msg here will indicate the local repo, not the usercache.
if self.cachevfs.exists(oid):
return self.cachevfs(oid, 'rb')
return self.vfs(oid, 'rb')
def download(self, oid, src):
"""Read the blob from the remote source in chunks, verify the content,
and write to this local blobstore."""
sha256 = hashlib.sha256()
with self.vfs(oid, 'wb', atomictemp=True) as fp:
for chunk in util.filechunkiter(src, size=1048576):
fp.write(chunk)
sha256.update(chunk)
realoid = sha256.hexdigest()
if realoid != oid:
raise error.Abort(_('corrupt remote lfs object: %s') % oid)
# XXX: should we verify the content of the cache, and hardlink back to
# the local store on success, but truncate, write and link on failure?
if not self.cachevfs.exists(oid):
self.ui.note(_('lfs: adding %s to the usercache\n') % oid)
lfutil.link(self.vfs.join(oid), self.cachevfs.join(oid))
def write(self, oid, data):
"""Write blob to local blobstore.
This should only be called from the filelog during a commit or similar.
As such, there is no need to verify the data. Imports from a remote
store must use ``download()`` instead."""
with self.vfs(oid, 'wb', atomictemp=True) as fp:
fp.write(data)
# XXX: should we verify the content of the cache, and hardlink back to
# the local store on success, but truncate, write and link on failure?
if not self.cachevfs.exists(oid):
self.ui.note(_('lfs: adding %s to the usercache\n') % oid)
lfutil.link(self.vfs.join(oid), self.cachevfs.join(oid))
def read(self, oid, verify=True):
"""Read blob from local blobstore."""
if not self.vfs.exists(oid):
blob = self._read(self.cachevfs, oid, verify)
# Even if revlog will verify the content, it needs to be verified
# now before making the hardlink to avoid propagating corrupt blobs.
# Don't abort if corruption is detected, because `hg verify` will
# give more useful info about the corruption- simply don't add the
# hardlink.
if verify or hashlib.sha256(blob).hexdigest() == oid:
self.ui.note(_('lfs: found %s in the usercache\n') % oid)
lfutil.link(self.cachevfs.join(oid), self.vfs.join(oid))
else:
self.ui.note(_('lfs: found %s in the local lfs store\n') % oid)
blob = self._read(self.vfs, oid, verify)
return blob
def _read(self, vfs, oid, verify):
"""Read blob (after verifying) from the given store"""
blob = vfs.read(oid)
if verify:
_verify(oid, blob)
return blob
def has(self, oid):
"""Returns True if the local blobstore contains the requested blob,
False otherwise."""
return self.cachevfs.exists(oid) or self.vfs.exists(oid)
class _gitlfsremote(object):
def __init__(self, repo, url):
ui = repo.ui
self.ui = ui
baseurl, authinfo = url.authinfo()
self.baseurl = baseurl.rstrip('/')
useragent = repo.ui.config('experimental', 'lfs.user-agent')
if not useragent:
useragent = 'git-lfs/2.3.4 (Mercurial %s)' % util.version()
self.urlopener = urlmod.opener(ui, authinfo, useragent)
self.retry = ui.configint('lfs', 'retry')
def writebatch(self, pointers, fromstore):
"""Batch upload from local to remote blobstore."""
self._batch(pointers, fromstore, 'upload')
def readbatch(self, pointers, tostore):
"""Batch download from remote to local blostore."""
self._batch(pointers, tostore, 'download')
def _batchrequest(self, pointers, action):
"""Get metadata about objects pointed by pointers for given action
Return decoded JSON object like {'objects': [{'oid': '', 'size': 1}]}
See https://github.com/git-lfs/git-lfs/blob/master/docs/api/batch.md
"""
objects = [{'oid': p.oid(), 'size': p.size()} for p in pointers]
requestdata = json.dumps({
'objects': objects,
'operation': action,
})
batchreq = util.urlreq.request('%s/objects/batch' % self.baseurl,
data=requestdata)
batchreq.add_header('Accept', 'application/vnd.git-lfs+json')
batchreq.add_header('Content-Type', 'application/vnd.git-lfs+json')
try:
rawjson = self.urlopener.open(batchreq).read()
except util.urlerr.httperror as ex:
raise LfsRemoteError(_('LFS HTTP error: %s (action=%s)')
% (ex, action))
try:
response = json.loads(rawjson)
except ValueError:
raise LfsRemoteError(_('LFS server returns invalid JSON: %s')
% rawjson)
return response
def _checkforservererror(self, pointers, responses, action):
"""Scans errors from objects
Raises LfsRemoteError if any objects have an error"""
for response in responses:
# The server should return 404 when objects cannot be found. Some
# server implementation (ex. lfs-test-server) does not set "error"
# but just removes "download" from "actions". Treat that case
# as the same as 404 error.
notfound = (response.get('error', {}).get('code') == 404
or (action == 'download'
and action not in response.get('actions', [])))
if notfound:
ptrmap = {p.oid(): p for p in pointers}
p = ptrmap.get(response['oid'], None)
if p:
filename = getattr(p, 'filename', 'unknown')
raise LfsRemoteError(
_(('LFS server error. Remote object '
'for "%s" not found: %r')) % (filename, response))
else:
raise LfsRemoteError(
_('LFS server error. Unsolicited response for oid %s')
% response['oid'])
if 'error' in response:
raise LfsRemoteError(_('LFS server error: %r') % response)
def _extractobjects(self, response, pointers, action):
"""extract objects from response of the batch API
response: parsed JSON object returned by batch API
return response['objects'] filtered by action
raise if any object has an error
"""
# Scan errors from objects - fail early
objects = response.get('objects', [])
self._checkforservererror(pointers, objects, action)
# Filter objects with given action. Practically, this skips uploading
# objects which exist in the server.
filteredobjects = [o for o in objects if action in o.get('actions', [])]
return filteredobjects
def _basictransfer(self, obj, action, localstore):
"""Download or upload a single object using basic transfer protocol
obj: dict, an object description returned by batch API
action: string, one of ['upload', 'download']
localstore: blobstore.local
See https://github.com/git-lfs/git-lfs/blob/master/docs/api/\
basic-transfers.md
"""
oid = str(obj['oid'])
href = str(obj['actions'][action].get('href'))
headers = obj['actions'][action].get('header', {}).items()
request = util.urlreq.request(href)
if action == 'upload':
# If uploading blobs, read data from local blobstore.
with localstore.open(oid) as fp:
_verifyfile(oid, fp)
request.data = filewithprogress(localstore.open(oid), None)
request.get_method = lambda: 'PUT'
for k, v in headers:
request.add_header(k, v)
response = b''
try:
req = self.urlopener.open(request)
if action == 'download':
# If downloading blobs, store downloaded data to local blobstore
localstore.download(oid, req)
else:
while True:
data = req.read(1048576)
if not data:
break
response += data
if response:
self.ui.debug('lfs %s response: %s' % (action, response))
except util.urlerr.httperror as ex:
if self.ui.debugflag:
self.ui.debug('%s: %s\n' % (oid, ex.read()))
raise LfsRemoteError(_('HTTP error: %s (oid=%s, action=%s)')
% (ex, oid, action))
def _batch(self, pointers, localstore, action):
if action not in ['upload', 'download']:
raise error.ProgrammingError('invalid Git-LFS action: %s' % action)
response = self._batchrequest(pointers, action)
objects = self._extractobjects(response, pointers, action)
total = sum(x.get('size', 0) for x in objects)
sizes = {}
for obj in objects:
sizes[obj.get('oid')] = obj.get('size', 0)
topic = {'upload': _('lfs uploading'),
'download': _('lfs downloading')}[action]
if len(objects) > 1:
self.ui.note(_('lfs: need to transfer %d objects (%s)\n')
% (len(objects), util.bytecount(total)))
self.ui.progress(topic, 0, total=total)
def transfer(chunk):
for obj in chunk:
objsize = obj.get('size', 0)
if self.ui.verbose:
if action == 'download':
msg = _('lfs: downloading %s (%s)\n')
elif action == 'upload':
msg = _('lfs: uploading %s (%s)\n')
self.ui.note(msg % (obj.get('oid'),
util.bytecount(objsize)))
retry = self.retry
while True:
try:
self._basictransfer(obj, action, localstore)
yield 1, obj.get('oid')
break
except socket.error as ex:
if retry > 0:
self.ui.note(
_('lfs: failed: %r (remaining retry %d)\n')
% (ex, retry))
retry -= 1
continue
raise
# Until https multiplexing gets sorted out
if self.ui.configbool('experimental', 'lfs.worker-enable'):
oids = worker.worker(self.ui, 0.1, transfer, (),
sorted(objects, key=lambda o: o.get('oid')))
else:
oids = transfer(sorted(objects, key=lambda o: o.get('oid')))
processed = 0
for _one, oid in oids:
processed += sizes[oid]
self.ui.progress(topic, processed, total=total)
self.ui.note(_('lfs: processed: %s\n') % oid)
self.ui.progress(topic, pos=None, total=total)
def __del__(self):
# copied from mercurial/httppeer.py
urlopener = getattr(self, 'urlopener', None)
if urlopener:
for h in urlopener.handlers:
h.close()
getattr(h, "close_all", lambda : None)()
class _dummyremote(object):
"""Dummy store storing blobs to temp directory."""
def __init__(self, repo, url):
fullpath = repo.vfs.join('lfs', url.path)
self.vfs = lfsvfs(fullpath)
def writebatch(self, pointers, fromstore):
for p in pointers:
content = fromstore.read(p.oid(), verify=True)
with self.vfs(p.oid(), 'wb', atomictemp=True) as fp:
fp.write(content)
def readbatch(self, pointers, tostore):
for p in pointers:
with self.vfs(p.oid(), 'rb') as fp:
tostore.download(p.oid(), fp)
class _nullremote(object):
"""Null store storing blobs to /dev/null."""
def __init__(self, repo, url):
pass
def writebatch(self, pointers, fromstore):
pass
def readbatch(self, pointers, tostore):
pass
class _promptremote(object):
"""Prompt user to set lfs.url when accessed."""
def __init__(self, repo, url):
pass
def writebatch(self, pointers, fromstore, ui=None):
self._prompt()
def readbatch(self, pointers, tostore, ui=None):
self._prompt()
def _prompt(self):
raise error.Abort(_('lfs.url needs to be configured'))
_storemap = {
'https': _gitlfsremote,
'http': _gitlfsremote,
'file': _dummyremote,
'null': _nullremote,
None: _promptremote,
}
def _verify(oid, content):
realoid = hashlib.sha256(content).hexdigest()
if realoid != oid:
raise error.Abort(_('detected corrupt lfs object: %s') % oid,
hint=_('run hg verify'))
def _verifyfile(oid, fp):
sha256 = hashlib.sha256()
while True:
data = fp.read(1024 * 1024)
if not data:
break
sha256.update(data)
realoid = sha256.hexdigest()
if realoid != oid:
raise error.Abort(_('detected corrupt lfs object: %s') % oid,
hint=_('run hg verify'))
def remote(repo):
"""remotestore factory. return a store in _storemap depending on config"""
url = util.url(repo.ui.config('lfs', 'url') or '')
scheme = url.scheme
if scheme not in _storemap:
raise error.Abort(_('lfs: unknown url scheme: %s') % scheme)
return _storemap[scheme](repo, url)
class LfsRemoteError(error.RevlogError):
pass
| bruno-oliveira/twilioHackathon-whats-around-me | env/lib/python2.7/site-packages/hgext/lfs/blobstore.py | blobstore.py | py | 17,091 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "re.compile",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "mercurial.vfs.vfs",
"line_number": 25,
"usage_type": "attribute"
},
{
"api_name": "mercurial.vfs",
"line_number": 25,
"usage_type": "name"
},
{
"api_name": "mercurial.error.Progr... |
12185429190 | # Creating a regression line - aka line of best fit
# Y = mx + b
from statistics import mean
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
# defined as numpy arrays
xs = np.array([1,2,3,4,5,6], dtype=np.float64)
ys = np.array([5,4,6,5,6,7], dtype=np.float64)
def best_fit_slope_and_intercept(xs,ys):
m = (((mean(xs) * mean(ys)) - mean(xs*ys)) / ( (mean(xs) * mean(xs)) - mean(xs**2) ) )
b = mean(ys) - m*mean(xs)
return m,b;
m,b = best_fit_slope_and_intercept(xs,ys)
#Creating line via for loop
regression_line = [(m*x)+b for x in xs]
#print(m,b)
predict_x = 8
predict_y = (m*predict_x + b)
#Create plots
plt.scatter(xs,ys)
plt.scatter(predict_x,predict_y)
plt.plot(xs, regression_line)
plt.show()
| Wcdunn3/pythonProjects | MachineLearning/MlLesson9LineFit.py | MlLesson9LineFit.py | py | 790 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "matplotlib.style.use",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "matplotlib.style",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "numpy.array",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "numpy.float64",
"... |
13509904582 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Resize and add text to videos
Usage example:
$ python transform.py
"""
import random
import os
import shutil
from moviepy.editor import *
from moviepy.video.tools.subtitles import SubtitlesClip
from sqlite import *
class VideoTransform:
def __init__(self, videos=[]):
print(' ----> {} videos to transform'.format(len(videos)))
for video in videos:
video['clip'] = VideoFileClip(video['filepath'])
self.videos = videos
print(' --> {} videos converted to clips'.format(len(self.videos)))
def adjustSize(self, clip,width,height):
# get current dimensions and ratio
clipWidth,clipHeight = clip.size
ratio = clipWidth / clipHeight
# resize the clip
if ratio > (width/height):
clip2 = clip.resize(width=width)
else:
clip2 = clip.resize(height=height)
return clip2
def adjustSizes(self,width=1920,height=1080):
for video in self.videos:
video['clip'] = self.adjustSize(video['clip'],width,height)
def addSubtitle(self, clip, subtitle, howLong, maxLength=15):
# trim the subtitle if it's too long
if len(subtitle) > maxLength:
subtitle = subtitle[:maxLength] + '...'
# set how long the subtitle will show
howLong = min(howLong,clip.duration)
subs = [((0, howLong), subtitle)]
# create clip with subtitle
subtitles = SubtitlesClip(subs, lambda txt: TextClip(txt, font='Arial', fontsize=72, color='white', stroke_color='black'))
# merge clip and subtitles
result = CompositeVideoClip([clip, subtitles.set_position(('center','bottom'))])
# return resulting clip
return result
def addSubtitles(self, howLong=10):
print(' ----> Adding subtitles')
for index, video in enumerate(self.videos):
video['clip'] = self.addSubtitle(video['clip'],video['title'],howLong)
print(' --> {}/{} subtitles added'.format(index+1,len(self.videos)))
def addSource(self,clip,source,howLong):
howLong = min(howLong,clip.duration)
subs = [((0, howLong), source)]
# create clip with source
subtitles = SubtitlesClip(subs, lambda txt: TextClip(txt, font='Arial', fontsize=24, color='white', bg_color='black'))
# merge clip and text
result = CompositeVideoClip([clip, subtitles.set_position((0,0))])
# return resulting clip
return result
def addSources(self, howLong=10):
print(' ----> Adding sources')
for index, video in enumerate(self.videos):
video['clip'] = self.addSource(video['clip'],video['link'],howLong)
print(' --> {}/{} sources added'.format(index+1,len(self.videos)))
def save(self, clip, filename, threads=1):
filepath = os.path.join(os.getcwd(),filename)
clip.write_videofile(filepath, codec='mpeg4', audio=True, threads=threads)
return filepath
if __name__ == '__main__':
# get videos from DB
sql = Sqlite()
result = sql.run('SELECT * FROM videos WHERE duration IS NULL')
videos = [ row for row in result if os.path.isfile(row['filepath']) ]
# convert them to clips
limit = 10
transform = VideoTransform(videos[:limit])
# resize them
transform.adjustSizes()
# add subtitles
transform.addSubtitles()
# add link to original
transform.addSources()
# backup the original videos
# and save the updated versions
for video in transform.videos:
shutil.move(video['filepath'],video['filepath']+'.original')
transform.save(video['clip'],video['filepath'])
# update their durations and sizes on the DB
for video in transform.videos:
filepath = video['filepath']
duration = video['clip'].duration
width,height = video['clip'].size
sql.run('''
UPDATE videos
SET duration = {duration},
width = {width},
height = {height}
WHERE
filepath = '{filepath}'
'''.format(
duration=duration,
filepath=filepath,
width=width,
height=height
))
| miguel-faggioni/zap-bot | transform.py | transform.py | py | 4,335 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "moviepy.video.tools.subtitles.SubtitlesClip",
"line_number": 48,
"usage_type": "call"
},
{
"api_name": "moviepy.video.tools.subtitles.SubtitlesClip",
"line_number": 64,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 77,
"usage_type": "... |
17299275140 | from django.shortcuts import render, get_object_or_404
from .models import Post, Comment
from django.core.paginator import Paginator, EmptyPage, PageNotAnInteger
from .forms import CommentForm
from django.views.decorators.http import require_POST
from taggit.models import Tag
"""
This is the Count aggregation function of the Django ORM. This function will
allow you to perform aggregated counts of tags. django.db.models includes the
following aggregation functions:
• Avg: The mean value
• Max: The maximum value
• Min: The minimum value
• Count: The total number of objects
"""
from django.db.models import Count
from itertools import chain
# Define the number of elements in one page
page_elements_count = 6
# Define number of similar posts
similar_posts_count = 3
# Create your views here.
def blog_list(request, tag_slug=None):
post_list = Post.published.all()
tag = None
if tag_slug:
tag = get_object_or_404(Tag, slug=tag_slug)
post_list = post_list.filter(tags__in=[tag])
posts_count = post_list.count()
# Pagination with page_elements_count per page
paginator = Paginator(post_list, page_elements_count)
# f the page parameter is not in the GET parameters of the request, we use
# the default value 1 to load the first page of results.
page_number = request.GET.get('page', 1)
# Try get the requested page
try:
posts = paginator.page(page_number)
# If the requests page is out of range, retrun the last page
except EmptyPage:
# If page_number is out of range deliver last page of results
posts = paginator.page(paginator.num_pages)
# If the requests page is not an integer, return the first page
except PageNotAnInteger:
posts = paginator.page(1)
return render(
request,
'blog/blog_home.html',
{
'posts': posts,
'posts_count': posts_count,
'tag': tag,
}
)
def blog_post(request, post_slug):
post = get_object_or_404(
Post,
slug=post_slug,
status=Post.Status.PUBLISHED
)
# List of active comments for this post
comments = post.comments.filter(active=True)
if request.method == 'GET':
# Form for users to comment
form = CommentForm()
else:
# Define a comment variable with the initial value None. This variable will
# be used to store the comment object when it gets created.
comment = None
form = CommentForm(data=request.POST)
if form.is_valid():
comment = form.save(commit=False)
# Assign the post attribute of the comment to the current post
comment.post = post
# Save the comment to the database
comment.save()
# List of similar posts
# retrieve a Python list of IDs for the tags of the current post. The
# values_list() QuerySet returns tuples with the values for the given
# fields. We pass flat=True to it to get single values such as
# [1, 2, 3, ...] instead of one-tuples such as [(1,), (2,), (3,) ...]
post_tags_ids = post.tags.values_list('id', flat=True)
# We get all posts that contain any of these tags, excluding the current
# post itself.
similar_posts = Post.published.filter(tags__in=post_tags_ids).exclude(id=post.id)
# use the Count aggregation function to generate a calculated
# field (same_tags) that contains the number of tags shared with all the
# tags queried.
# We order the result by the number of shared tags (descending order) and
# by publish to display recent posts first for the posts with the same
# number of shared tags. We slice the result to retrieve only the first
# three posts.
similar_posts = similar_posts.annotate(same_tags=Count('tags'))\
.order_by('-same_tags', '-created')[:similar_posts_count]
# Fill similar posts with normal posts; if the similar posts are less than
# the required similar_posts_count
if len(similar_posts) < similar_posts_count:
extra_posts_count = similar_posts_count - len(similar_posts)
extra_posts = Post.published.order_by('-created').exclude(id=post.id)\
.exclude(id__in=similar_posts)[:extra_posts_count]
similar_posts = list(chain(similar_posts, extra_posts))
return render(
request,
'blog/blog_post.html',
{
'post': post,
'form': form,
'comments': comments,
'similar_posts': similar_posts,
}
)
| m-abdelgawad/automagic-developer-django-postgres-docker-compose-stack | automagic-website/blog/views.py | views.py | py | 4,620 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "models.Post.published.all",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": "models.Post.published",
"line_number": 30,
"usage_type": "attribute"
},
{
"api_name": "models.Post",
"line_number": 30,
"usage_type": "name"
},
{
"api_name": "djan... |
74540930982 | # -*- coding: utf-8 -*-
from enum import Enum
class SolarTerms(Enum):
the_beginning_of_spring = "the Beginning of Spring", "立春"
rain_water = "Rain Water", "雨水"
the_waking_of_insects = "the Waking of Insects", "惊蛰"
the_spring_equinox = "the Spring Equinox", "春分"
pure_brightness = "Pure Brightness", "清明"
grain_rain = "Grain Rain", "谷雨"
the_beginning_of_summer = "the Beginning of Summer", "立夏"
lesser_fullness_of_grain = "Lesser Fullness of Grain", "小满"
grain_in_beard = "Grain in Beard", "芒种"
the_summer_solstice = "the Summer Solstice", "夏至"
lesser_heat = "Lesser Heat", "小暑"
greater_heat = "Greater Heat", "大暑"
the_beginning_of_autumn = "the Beginning of Autumn", "立秋"
the_end_of_heat = "the End of Heat", "处暑"
white_dew = "White Dew", "白露"
the_autumn_equinox = "the Autumn Equinox", "秋分"
code_dew = "Cold Dew", "寒露"
frost_descent = "Frost's Descent", "霜降"
the_beginning_of_winter = "the Beginning of Winter", "立冬"
lesser_snow = "Lesser Snow", "小雪"
greater_snow = "Greater Snow", "大雪"
the_winter_solstice = "the Winter Solstice", "冬至"
lesser_cold = "Lesser Cold", "小寒"
greater_cold = "Greater Cold", "大寒"
# 计算节气用的 C 值
# 2000年的小寒、大寒、立春、雨水按照20世纪的C值来算
SOLAR_TERMS_C_NUMS = {
# 节气: [20世纪值， 21世纪值]
SolarTerms.the_beginning_of_spring: [4.6295, 3.87],
SolarTerms.rain_water: [19.4599, 18.73],
SolarTerms.the_waking_of_insects: [6.3926, 5.63],
SolarTerms.the_spring_equinox: [21.4155, 20.646],
SolarTerms.pure_brightness: [5.59, 4.81],
SolarTerms.grain_rain: [20.888, 20.1],
SolarTerms.the_beginning_of_summer: [6.318, 5.52],
SolarTerms.lesser_fullness_of_grain: [21.86, 21.04],
SolarTerms.grain_in_beard: [6.5, 5.678],
SolarTerms.the_summer_solstice: [22.2, 21.37],
SolarTerms.lesser_heat: [7.928, 7.108],
SolarTerms.greater_heat: [23.65, 22.83],
SolarTerms.the_beginning_of_autumn: [28.35, 7.5],
SolarTerms.the_end_of_heat: [23.95, 23.13],
SolarTerms.white_dew: [8.44, 7.646],
SolarTerms.the_autumn_equinox: [23.822, 23.042],
SolarTerms.code_dew: [9.098, 8.318],
SolarTerms.frost_descent: [24.218, 23.438],
SolarTerms.the_beginning_of_winter: [8.218, 7.438],
SolarTerms.lesser_snow: [23.08, 22.36],
SolarTerms.greater_snow: [7.9, 7.18],
SolarTerms.the_winter_solstice: [22.6, 21.94],
SolarTerms.lesser_cold: [6.11, 5.4055],
SolarTerms.greater_cold: [20.84, 20.12],
}
# 月份和节气对应关系
SOLAR_TERMS_MONTH = {
1: [SolarTerms.lesser_cold, SolarTerms.greater_cold],
2: [SolarTerms.the_beginning_of_spring, SolarTerms.rain_water],
3: [SolarTerms.the_waking_of_insects, SolarTerms.the_spring_equinox],
4: [SolarTerms.pure_brightness, SolarTerms.grain_rain],
5: [SolarTerms.the_beginning_of_summer, SolarTerms.lesser_fullness_of_grain],
6: [SolarTerms.grain_in_beard, SolarTerms.the_summer_solstice],
7: [SolarTerms.lesser_heat, SolarTerms.greater_heat],
8: [SolarTerms.the_beginning_of_autumn, SolarTerms.the_end_of_heat],
9: [SolarTerms.white_dew, SolarTerms.the_autumn_equinox],
10: [SolarTerms.code_dew, SolarTerms.frost_descent],
11: [SolarTerms.the_beginning_of_winter, SolarTerms.lesser_snow],
12: [SolarTerms.greater_snow, SolarTerms.the_winter_solstice],
}
# 有些节气使用公式计算计算的不够准确，需要进行偏移
SOLAR_TERMS_DELTA = {
(2026, SolarTerms.rain_water): -1,
(2084, SolarTerms.the_spring_equinox): 1,
(1911, SolarTerms.the_beginning_of_summer): 1,
(2008, SolarTerms.lesser_fullness_of_grain): 1,
(1902, SolarTerms.grain_in_beard): 1,
(1928, SolarTerms.the_summer_solstice): 1,
(1925, SolarTerms.lesser_heat): 1,
(2016, SolarTerms.lesser_heat): 1,
(1922, SolarTerms.greater_heat): 1,
(2002, SolarTerms.the_beginning_of_autumn): 1,
(1927, SolarTerms.white_dew): 1,
(1942, SolarTerms.the_autumn_equinox): 1,
(2089, SolarTerms.frost_descent): 1,
(2089, SolarTerms.the_beginning_of_winter): 1,
(1978, SolarTerms.lesser_snow): 1,
(1954, SolarTerms.greater_snow): 1,
(1918, SolarTerms.the_winter_solstice): -1,
(2021, SolarTerms.the_winter_solstice): -1,
(1982, SolarTerms.lesser_cold): 1,
(2019, SolarTerms.lesser_cold): -1,
(2000, SolarTerms.greater_cold): 1,
(2082, SolarTerms.greater_cold): 1,
}
| LKI/chinese-calendar | chinese_calendar/solar_terms.py | solar_terms.py | py | 4,541 | python | en | code | 866 | github-code | 36 | [
{
"api_name": "enum.Enum",
"line_number": 5,
"usage_type": "name"
}
] |
70539338983 | from intrinsic_types import intrinsic_types, is_float
from bitstring import Bits, BitArray
import random
from tempfile import NamedTemporaryFile
import os
import subprocess
from interp import interpret
from compiler import compile
from bit_util import *
import math
import z3
import functools
import operator
from spec_serializer import dump_spec, load_spec
src_path = os.path.dirname(os.path.abspath(__file__))
def get_imm_mask(imm8, outs):
'''
Given imm8 and the semantic of the outputs,
figure out a mask that identifies the bits of imm8
that are actually useful
'''
# TODO: don't assume there is only one output
y = outs[0]
mask = (1 << 33)-1
s = z3.Solver()
for i in range(33, 0, -1):
cur_mask = (1<<(i+1))-1
y_masked = z3.substitute(y, (imm8, imm8 & cur_mask))
ok = z3.is_true(z3.simplify(y_masked == y))
if not ok:
return mask
mask = cur_mask
return mask
def extract_float(bv, i, bitwidth):
'''
extract i'th float from bv
bitwidth is the size of a one floating point (either 32 or 64)
'''
if bitwidth == 32:
ty = z3.Float32()
else:
assert bitwidth == 64
ty = z3.Float64()
sub_bv = z3.Extract(i * bitwidth + bitwidth - 1, i * bitwidth, bv)
return z3.fpBVToFP(sub_bv, ty)
def equal(a, b, ty):
if is_float(ty):
if ty.is_float:
elem_bw = 32
else:
elem_bw = 64
assert ty.bitwidth % elem_bw == 0
num_elems = ty.bitwidth // elem_bw
return z3.Or(a == b, z3.And([
z3.fpAbs(extract_float(a, i, elem_bw) - extract_float(b, i, elem_bw)) < 1e-5
for i in range(num_elems)]))
return a == b
def check_compiled_spec_with_examples(param_vals, outs, out_types, inputs, expected_outs):
s = z3.Solver()
s.set(timeout=20000)
constraints = []
for input, expected in zip(inputs, expected_outs):
subs = [(param, z3.BitVecVal(x, param.size())) for param, x in zip(param_vals, input)]
outs_concrete = [z3.simplify(z3.substitute(out, *subs))
for out in outs]
constraints.append(
z3.And([equal(z3.BitVecVal(y_expected, y.size()), y, out_type)
for y_expected, y, out_type in zip(expected, outs_concrete, out_types)]))
#preconditions = [x_sym == x_conc
#for x_sym, x_conc in zip(param_vals, input)]
#postconditions = [equal(z3.BitVecVal(y_expected, y.size()), y, out_type)
# for y_expected, y, out_type in zip(expected, outs, out_types)]
#constraints.append(
# z3.Implies(z3.And(preconditions), z3.And(postconditions)))
spec_correct = z3.And(constraints)
s.add(z3.Not(spec_correct))
correct = s.check() == z3.unsat
#if not correct:
# print(s.model().evaluate(outs[0]), expected_outs[0][0])
return correct
def line_to_bitvec(line, ty):
qwords = list(map(int, line.split()))
if ty.bitwidth <= 64:
assert len(qwords) == 1
[bits] = qwords
mask = (1 << ty.bitwidth) - 1
return bits & mask
assert ty.bitwidth % 64 == 0
components = [bits << (i * 64) for i, bits in enumerate(qwords)]
return functools.reduce(operator.or_, components, 0)
load_intrinsics = {
'_m512i': '_mm512_loadu_si512',
'__m512i': '_mm512_loadu_si512',
'__m256i': '_mm256_loadu_si256',
'__m128i': '_mm_loadu_si128',
# single precision floats
'__m512': '_mm512_loadu_ps',
'__m256': '_mm256_loadu_ps',
'__m128': '_mm_loadu_ps',
'_m512': '_mm512_loadu_ps',
'_m256': '_mm256_loadu_ps',
'_m128': '_mm_loadu_ps',
# double precision floats
'__m512d': '_mm512_loadu_pd',
'__m256d': '_mm256_loadu_pd',
'__m128d': '_mm_loadu_pd',
'_m512d': '_mm512_loadu_pd',
'_m256d': '_mm256_loadu_pd',
'_m128d': '_mm_loadu_pd',
}
# functions that prints these vector registers
printers = {
'_m512i': 'print__m512i',
'__m512i': 'print__m512i',
'__m256i': 'print__m256i',
'__m128i': 'print__m128i',
# single precision floats
'__m512': 'print__m512i',
'__m256': 'print__m256i',
'__m128': 'print__m128i',
'_m512': 'print__m512i',
'_m256': 'print__m256i',
'_m128': 'print__m128i',
# double precision floats
'__m512d': 'print__m512i',
'__m256d': 'print__m256i',
'__m128d': 'print__m128i',
'_m512d': 'print__m512i',
'_m256d': 'print__m256i',
'_m128d': 'print__m128i',
}
def emit_load(outf, dst, src, typename):
if typename in load_intrinsics:
load_intrinsic = load_intrinsics[typename]
outf.write('%s %s = %s((%s *)%s);\n' % (
typename, dst, load_intrinsic, typename, src
))
else:
outf.write('%s %s = *(%s *)(%s);\n' % (
typename, dst, typename, src
))
def gen_rand_data(outf, name, typename):
'''
declare a variable of `data_type` called `name`
print result to `outf` and return the actual bytes in bits
e.g. for ty=__m512i, name = x1, we declare the following
unsigned char x1[64] = { ... };
'''
if typename.endswith('*'):
is_pointer = True
typename = typename[:-1].strip()
else:
is_pointer = False
ty = intrinsic_types[typename]
# generate floats separates for integer because we don't
# want to deal with underflow and other floating point issues
if not is_float(ty):
num_bytes = ty.bitwidth // 8
bytes = [random.randint(0, 255) for _ in range(num_bytes)]
outf.write('unsigned char %s_aux[%d] = { %s };\n' % (
name, num_bytes, ','.join(map(str, bytes))
))
bits = BitArray(length=ty.bitwidth)
for i, byte in enumerate(bytes):
update_bits(bits, i*8, i*8+8, byte)
else:
float_size = 32 if ty.is_float else 64
c_type = 'float' if ty.is_float else 'double'
num_floats = ty.bitwidth // float_size
floats = [random.random() for _ in range(num_floats)]
outf.write('%s %s_aux[%d] = { %s };\n' % (
c_type, name, num_floats, ','.join(map(str, floats))
))
bits = float_vec_to_bits(floats, float_size=float_size)
if not is_pointer:
# in this case we need to load the bytes
emit_load(outf, src='%s_aux'%name, dst=name, typename=typename)
else:
# in this case we just take the address
outf.write('%s *%s = (%s *)(&%s_aux);\n' % (
typename, name, typename, name
))
return bits
def emit_print(outf, var, typename):
if typename.endswith('*'):
typename = typename[:-1].strip()
ty = intrinsic_types[typename]
is_pointer = True
else:
is_pointer = False
ty = intrinsic_types[typename]
if is_pointer:
# need to load the value first first
tmp = get_temp_name()
emit_load(outf, dst=tmp, src=var, typename=typename)
var = tmp
if typename in printers:
# use the predefined printers
printer = printers[typename]
if ty.is_float:
param_ty = 'unsigned long'
elif ty.is_double:
param_ty = 'unsigned long'
else:
param_ty = 'unsigned long'
outf.write('%s((%s *)&%s);\n' % (printer, param_ty, var))
else:
if ty.is_float:
outf.write('printf("%%u\\n", float2int(%s));\n' % var)
elif ty.is_double:
outf.write('printf("%%lu\\n", double2long(%s));\n' % var)
else:
outf.write('printf("%%lu\\n", (unsigned long)%s);\n' % var)
counter = 0
def get_temp_name():
global counter
counter += 1
return 'tmp%d' % counter
def fuzz_intrinsic_once(outf, spec, sema):
'''
1) generate test (in C) that exercises the intrinsic
2) run the interpreter per the spec and return the expected output
'''
xs, ys = sema
# generate random arguments
c_vars = []
arg_vals = []
out_params = []
out_param_types = []
inst_form = spec.inst_form.split(', ')
no_imm8 = 'imm8' not in (param.name for param in spec.params)
# TODO: refactor out this signature parsing logic
for i, param in enumerate(spec.params):
if ((no_imm8 and i < len(inst_form) and inst_form[i] == 'imm') or
param.name == 'imm8'):
param_id = len(arg_vals)
mask = get_imm_mask(xs[param_id], ys)
byte = random.randint(0, 255) & mask
c_vars.append(str(byte))
arg_vals.append(Bits(uint=byte, length=8))
continue
c_var = get_temp_name()
arg_val = gen_rand_data(outf, c_var, param.type)
c_vars.append(c_var)
arg_vals.append(arg_val)
if param.type.endswith('*'):
out_params.append(c_var)
out_param_types.append(param.type[:-1].strip())
# call the intrinsic
has_return_val = spec.rettype != 'void'
if has_return_val:
ret_var = get_temp_name()
outf.write('%s %s = %s(%s);\n' % (
spec.rettype, ret_var, spec.intrin, ','.join(c_vars)
))
# print the result
emit_print(outf, ret_var, spec.rettype)
else:
outf.write('%s(%s);\n' % (
spec.intrin, ','.join(c_vars)
))
out_types = []
for param, param_type in zip(out_params, out_param_types):
emit_print(outf, param, param_type+'*')
return arg_vals, out_param_types, has_return_val
def get_err(a, b, is_float):
err = a - b
if not is_float:
return err
if math.isnan(a) and math.isnan(b):
return 0
return err
def identical_vecs(a, b, is_float):
errs = [get_err(aa, bb, is_float)
for aa,bb in zip(a, b)]
if is_float:
return all(abs(err) <= 1e-6 for err in errs)
return all(err == 0 for err in errs)
def bits_to_vec(bits, typename):
if typename.endswith('*'):
ty = intrinsic_types[typename[:-1]]
else:
ty = intrinsic_types[typename]
if ty.is_float:
return bits_to_float_vec(bits, float_size=32)
elif ty.is_double:
return bits_to_float_vec(bits, float_size=64)
# integer type
return bits_to_long_vec(bits)
def fuzz_intrinsic(spec, num_tests=100):
'''
spec -> (spec correct, can compile)
'''
param_vals, outs = compile(spec)
sema = param_vals, outs
interpreted = []
exe = NamedTemporaryFile(delete=False)
exe.close()
with NamedTemporaryFile(suffix='.c', mode='w') as outf:
outf.write('''
#include <emmintrin.h>
#include <immintrin.h>
#include <nmmintrin.h>
#include <pmmintrin.h>
#include <smmintrin.h>
#include <tmmintrin.h>
#include <wmmintrin.h>
#include <xmmintrin.h>
#include <stdio.h>
#include "printers.h"
#define __int64_t __int64
#define __int64 long long
union float_and_int {
float f;
unsigned int i;
};
union double_and_long {
double d;
unsigned long l;
};
unsigned int float2int(float f) {
union float_and_int x;
x.f = f;
return x.i;
}
unsigned long double2long(double d) {
union double_and_long x;
x.d = d;
return x.l;
}
int main() {
''')
x = []
y = []
for _ in range(num_tests):
arg_vals, out_param_types, has_return_val = fuzz_intrinsic_once(outf, spec, sema)
out_types = [intrinsic_types[ty] for ty in out_param_types]
if spec.rettype != 'void':
out_types = [intrinsic_types[spec.rettype]] + out_types
x.append([val.uint for val in arg_vals])
outf.write('''
return 0;
}
''')
outf.flush()
os.system('cp %s %s' % (outf.name, 'debug.c'))
# TODO: add CPUIDs
try:
subprocess.check_output(
'clang %s -o %s -I%s %s/printers.o >/dev/null 2>/dev/null -mavx -mavx2 -march=native -mfma' % (
outf.name, exe.name, src_path, src_path),
shell=True)
except subprocess.CalledProcessError:
return False, False
num_outputs_per_intrinsic = len(out_types)
stdout = subprocess.check_output([exe.name])
lines = stdout.decode('utf-8').strip().split('\n')
assert(len(lines) == num_tests * num_outputs_per_intrinsic)
os.system('rm '+exe.name)
for i in range(0, len(lines), num_outputs_per_intrinsic):
outputs = []
for ty, line in zip(out_types, lines[i:i+num_outputs_per_intrinsic]):
outputs.append(line_to_bitvec(line, ty))
y.append(outputs)
correct = check_compiled_spec_with_examples(param_vals, outs, out_types, x, y)
return correct, True
if __name__ == '__main__':
import sys
import xml.etree.ElementTree as ET
from manual_parser import get_spec_from_xml
from intrinsic_types import IntegerType
sema = '''
<intrinsic tech="Other" rettype='unsigned int' name='_bextr_u32'>
<type>Integer</type>
<CPUID>BMI1</CPUID>
<category>Bit Manipulation</category>
<parameter type='unsigned int' varname='a' />
<parameter type='unsigned int' varname='start' />
<parameter type='unsigned int' varname='len' />
<description>Extract contiguous bits from unsigned 32-bit integer "a", and store the result in "dst". Extract the number of bits specified by "len", starting at the bit specified by "start".</description>
<operation>
tmp := ZeroExtend_To_512(a)
dst := ZeroExtend(tmp[start[7:0]+len[7:0]-1:start[7:0]])
</operation>
<instruction name='bextr' form='r32, r32, r32'/>
<header>immintrin.h</header>
</intrinsic>
'''
intrin_node = ET.fromstring(sema)
spec = get_spec_from_xml(intrin_node)
ok = fuzz_intrinsic(spec, num_tests=32)
print(ok)
| ychen306/upgraded-succotash | fuzzer.py | fuzzer.py | py | 12,802 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "os.path.dirname",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 16,
"usage_type": "attribute"
},
{
"api_name": "os.path.abspath",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "z3.Solver",
"line_nu... |
23565614159 | #!/usr/bin/python3
import sys
import pyvips
im = pyvips.Image.new_from_file(sys.argv[1], access="sequential")
text = pyvips.Image.text(f"<span color=\"red\">{sys.argv[3]}</span>",
width=500,
dpi=100,
align="centre",
rgba=True)
# scale the alpha down to make the text semi-transparent
text = (text * [1, 1, 1, 0.3]).cast("uchar")
text = text.rotate(45)
# tile to the size of the image page, then tile again to the full image size
text = text.embed(10, 10, text.width + 20, text.width + 20)
page_height = im.get_page_height()
text = text.replicate(1 + im.width / text.width, 1 + page_height / text.height)
text = text.crop(0, 0, im.width, page_height)
text = text.replicate(1, 1 + im.height / text.height)
text = text.crop(0, 0, im.width, im.height)
# composite the two layers
im = im.composite(text, "over")
im.write_to_file(sys.argv[2])
| libvips/pyvips | examples/watermark.py | watermark.py | py | 945 | python | en | code | 558 | github-code | 36 | [
{
"api_name": "pyvips.Image.new_from_file",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "pyvips.Image",
"line_number": 6,
"usage_type": "attribute"
},
{
"api_name": "sys.argv",
"line_number": 6,
"usage_type": "attribute"
},
{
"api_name": "pyvips.Image.... |
35701573370 | from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(899, 563)
MainWindow.setMinimumSize(QtCore.QSize(700, 480))
MainWindow.setStyleSheet("*{\n"
"background:#18162A;\n"
"border:none;\n"
"color:white;\n"
"}")
self.centralwidget = QtWidgets.QWidget(MainWindow)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.centralwidget.sizePolicy().hasHeightForWidth())
self.centralwidget.setSizePolicy(sizePolicy)
self.centralwidget.setObjectName("centralwidget")
self.hboxlayout = QtWidgets.QHBoxLayout(self.centralwidget)
self.hboxlayout.setContentsMargins(0, 0, 0, 0)
self.hboxlayout.setSpacing(0)
self.hboxlayout.setObjectName("hboxlayout")
self.leftMenu = QtWidgets.QFrame(self.centralwidget)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.leftMenu.sizePolicy().hasHeightForWidth())
self.leftMenu.setSizePolicy(sizePolicy)
self.leftMenu.setMinimumSize(QtCore.QSize(200, 0))
self.leftMenu.setMaximumSize(QtCore.QSize(0, 16777215))
self.leftMenu.setStyleSheet("background:#060117; border:none;")
self.leftMenu.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.leftMenu.setFrameShadow(QtWidgets.QFrame.Raised)
self.leftMenu.setLineWidth(0)
self.leftMenu.setObjectName("leftMenu")
self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.leftMenu)
self.verticalLayout_2.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_2.setSpacing(0)
self.verticalLayout_2.setObjectName("verticalLayout_2")
self.frame_4 = QtWidgets.QFrame(self.leftMenu)
self.frame_4.setMinimumSize(QtCore.QSize(0, 50))
self.frame_4.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_4.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_4.setObjectName("frame_4")
self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.frame_4)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.dvsoft = QtWidgets.QPushButton(self.frame_4)
self.dvsoft.setText("DVsoft")
self.dvsoft.setObjectName("pushButton_9")
self.dvsoft.setStyleSheet("font-size:32px;")
self.verticalLayout_3.addWidget(self.dvsoft, 0, QtCore.Qt.AlignLeft)
self.verticalLayout_2.addWidget(self.frame_4, 0, QtCore.Qt.AlignTop)
self.frame_5 = QtWidgets.QFrame(self.leftMenu)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.frame_5.sizePolicy().hasHeightForWidth())
self.frame_5.setSizePolicy(sizePolicy)
self.frame_5.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_5.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_5.setObjectName("frame_5")
self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.frame_5)
self.verticalLayout_4.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_4.setSpacing(0)
self.verticalLayout_4.setObjectName("verticalLayout_4")
self.Menu2 = QtWidgets.QToolBox(self.frame_5)
self.Menu2.setStyleSheet("QToolBox{\n"
" background-color:rgb(24,24,36);\n"
" text-align:left;\n"
" font-size:20px;\n"
" color:white;\n"
"}\n"
"QToolBox::tab{\n"
" border-radius:5px;\n"
" background-color:rgb(17,16,26);\n"
" text-align:left;\n"
" font-size:20px;\n"
" color:white;\n"
"}")
self.Menu2.setObjectName("Menu2")
self.Menu1 = QtWidgets.QWidget()
self.Menu1.setGeometry(QtCore.QRect(0, 0, 200, 357))
self.Menu1.setStyleSheet("")
self.Menu1.setObjectName("Menu1")
self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.Menu1)
self.verticalLayout_5.setObjectName("verticalLayout_5")
self.frame_7 = QtWidgets.QFrame(self.Menu1)
self.frame_7.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_7.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_7.setObjectName("frame_7")
self.verticalLayout_6 = QtWidgets.QVBoxLayout(self.frame_7)
self.verticalLayout_6.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_6.setSpacing(10)
self.verticalLayout_6.setObjectName("verticalLayout_6")
self.pushButton = QtWidgets.QPushButton(self.frame_7)
self.pushButton.setStyleSheet("font-size:20px")
self.pushButton.setObjectName("pushButton")
self.verticalLayout_6.addWidget(self.pushButton)
self.pushButton_2 = QtWidgets.QPushButton(self.frame_7)
self.pushButton_2.setStyleSheet("font-size:20px")
self.pushButton_2.setObjectName("pushButton_2")
self.verticalLayout_6.addWidget(self.pushButton_2)
self.pushButton_3 = QtWidgets.QPushButton(self.frame_7)
self.pushButton_3.setStyleSheet("font-size:20px")
self.pushButton_3.setObjectName("pushButton_3")
self.verticalLayout_6.addWidget(self.pushButton_3)
self.verticalLayout_5.addWidget(self.frame_7, 0, QtCore.Qt.AlignTop)
icon = QtGui.QIcon()
icon.addPixmap(QtGui.QPixmap("icons/strelka3.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.Menu2.addItem(self.Menu1, icon, "")
self.menu2 = QtWidgets.QWidget()
self.menu2.setGeometry(QtCore.QRect(0, 0, 200, 357))
self.menu2.setObjectName("menu2")
self.verticalLayout_7 = QtWidgets.QVBoxLayout(self.menu2)
self.verticalLayout_7.setObjectName("verticalLayout_7")
self.frame_8 = QtWidgets.QFrame(self.menu2)
self.frame_8.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_8.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_8.setObjectName("frame_8")
self.verticalLayout_8 = QtWidgets.QVBoxLayout(self.frame_8)
self.verticalLayout_8.setContentsMargins(0, 0, 0, 0)
self.verticalLayout_8.setSpacing(15)
self.verticalLayout_8.setObjectName("verticalLayout_8")
self.pushButton_4 = QtWidgets.QPushButton(self.frame_8)
self.pushButton_4.setStyleSheet("font-size:20px;")
self.pushButton_4.setObjectName("pushButton_4")
self.verticalLayout_8.addWidget(self.pushButton_4)
self.pushButton_5 = QtWidgets.QPushButton(self.frame_8)
self.pushButton_5.setStyleSheet("font-size:20px")
self.pushButton_5.setObjectName("pushButton_5")
self.verticalLayout_8.addWidget(self.pushButton_5)
self.label_2 = QtWidgets.QLabel(self.frame_8)
self.label_2.setStyleSheet("font-size:20px;")
self.label_2.setWordWrap(True)
self.label_2.setObjectName("label_2")
self.verticalLayout_8.addWidget(self.label_2)
self.verticalLayout_7.addWidget(self.frame_8)
self.Menu2.addItem(self.menu2, icon, "")
self.verticalLayout_4.addWidget(self.Menu2)
self.verticalLayout_2.addWidget(self.frame_5)
self.frame_6 = QtWidgets.QFrame(self.leftMenu)
self.frame_6.setMinimumSize(QtCore.QSize(0, 50))
self.frame_6.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_6.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_6.setObjectName("frame_6")
self.horizontalLayout_8 = QtWidgets.QHBoxLayout(self.frame_6)
self.horizontalLayout_8.setObjectName("horizontalLayout_8")
self.exit = QtWidgets.QPushButton(self.frame_6)
self.exit.setStyleSheet("font-size:20px;")
icon1 = QtGui.QIcon()
icon1.addPixmap(QtGui.QPixmap("icons/выход.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.exit.setIcon(icon1)
self.exit.setObjectName("pushButton_12")
self.horizontalLayout_8.addWidget(self.exit, 0, QtCore.Qt.AlignLeft)
self.verticalLayout_2.addWidget(self.frame_6)
self.hboxlayout.addWidget(self.leftMenu)
self.MainFrame = QtWidgets.QFrame(self.centralwidget)
self.MainFrame.setStyleSheet("border:none;")
self.MainFrame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.MainFrame.setFrameShadow(QtWidgets.QFrame.Raised)
self.MainFrame.setObjectName("MainFrame")
self.verticalLayout = QtWidgets.QVBoxLayout(self.MainFrame)
self.verticalLayout.setContentsMargins(0, 0, 0, 0)
self.verticalLayout.setSpacing(0)
self.verticalLayout.setObjectName("verticalLayout")
self.header = QtWidgets.QFrame(self.MainFrame)
self.header.setMinimumSize(QtCore.QSize(0, 50))
self.header.setStyleSheet("background:#060117; border:none;")
self.header.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.header.setFrameShadow(QtWidgets.QFrame.Raised)
self.header.setLineWidth(0)
self.header.setObjectName("header")
self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.header)
self.horizontalLayout_2.setObjectName("horizontalLayout_2")
self.frame = QtWidgets.QFrame(self.header)
self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame.setObjectName("frame")
self.horizontalLayout = QtWidgets.QHBoxLayout(self.frame)
self.horizontalLayout.setContentsMargins(0, 0, 0, 0)
self.horizontalLayout.setSpacing(0)
self.horizontalLayout.setObjectName("horizontalLayout")
self.menuTurn = QtWidgets.QPushButton(self.frame)
self.menuTurn.setText("")
icon2 = QtGui.QIcon()
icon2.addPixmap(QtGui.QPixmap("icons/свернуть бар.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.menuTurn.setIcon(icon2)
self.menuTurn.setIconSize(QtCore.QSize(32, 32))
self.menuTurn.setObjectName("pushButton_11")
self.horizontalLayout.addWidget(self.menuTurn)
self.lineEdit = QtWidgets.QLineEdit(self.frame)
self.lineEdit.setStyleSheet("border-bottom:2px solid #19E67E;\n"
"font-size:24px;")
self.lineEdit.setObjectName("lineEdit")
self.horizontalLayout.addWidget(self.lineEdit)
self.pushButton_10 = QtWidgets.QPushButton(self.frame)
self.pushButton_10.setText("")
icon3 = QtGui.QIcon()
icon3.addPixmap(QtGui.QPixmap("icons/search.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.pushButton_10.setIcon(icon3)
self.pushButton_10.setIconSize(QtCore.QSize(32, 32))
self.pushButton_10.setObjectName("pushButton_10")
self.horizontalLayout.addWidget(self.pushButton_10)
self.horizontalLayout_2.addWidget(self.frame, 0, QtCore.Qt.AlignLeft)
self.frame_2 = QtWidgets.QFrame(self.header)
self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_2.setObjectName("frame_2")
self.horizontalLayout_4 = QtWidgets.QHBoxLayout(self.frame_2)
self.horizontalLayout_4.setContentsMargins(0, 0, 0, 0)
self.horizontalLayout_4.setSpacing(0)
self.horizontalLayout_4.setObjectName("horizontalLayout_4")
self.username = QtWidgets.QPushButton(self.frame_2)
self.username.setText("USERNAME")
icon4 = QtGui.QIcon()
icon4.addPixmap(QtGui.QPixmap("icons/пользователь.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.username.setIcon(icon4)
self.username.setIconSize(QtCore.QSize(32, 32))
self.username.setObjectName("pushButton_9")
self.username.setStyleSheet("font-size:24px;")
self.horizontalLayout_4.addWidget(self.username, 0, QtCore.Qt.AlignLeft)
self.horizontalLayout_2.addWidget(self.frame_2, 0, QtCore.Qt.AlignHCenter)
self.frame_3 = QtWidgets.QFrame(self.header)
self.frame_3.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_3.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_3.setObjectName("frame_3")
self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.frame_3)
self.horizontalLayout_3.setContentsMargins(0, 0, 0, 0)
self.horizontalLayout_3.setSpacing(0)
self.horizontalLayout_3.setObjectName("horizontalLayout_3")
self.turn = QtWidgets.QPushButton(self.frame_3)
self.turn.setText("")
icon5 = QtGui.QIcon()
icon5.addPixmap(QtGui.QPixmap("icons/свернуть.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.turn.setIcon(icon5)
self.turn.setIconSize(QtCore.QSize(45, 45))
self.turn.setObjectName("pushButton_6")
self.horizontalLayout_3.addWidget(self.turn)
self.expand = QtWidgets.QPushButton(self.frame_3)
self.expand.setText("")
icon6 = QtGui.QIcon()
icon6.addPixmap(QtGui.QPixmap("icons/двойная диагональная стрелка.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.expand.setIcon(icon6)
self.expand.setIconSize(QtCore.QSize(30, 40))
self.expand.setObjectName("pushButton_7")
self.horizontalLayout_3.addWidget(self.expand)
self.close = QtWidgets.QPushButton(self.frame_3)
self.close.setText("")
icon7 = QtGui.QIcon()
icon7.addPixmap(QtGui.QPixmap("icons/x.svg"), QtGui.QIcon.Normal, QtGui.QIcon.Off)
self.close.setIcon(icon7)
self.close.setIconSize(QtCore.QSize(40, 40))
self.close.setObjectName("pushButton_8")
self.horizontalLayout_3.addWidget(self.close)
self.horizontalLayout_2.addWidget(self.frame_3, 0, QtCore.Qt.AlignRight)
self.verticalLayout.addWidget(self.header)
self.main = QtWidgets.QFrame(self.MainFrame)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Preferred, QtWidgets.QSizePolicy.Expanding)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.main.sizePolicy().hasHeightForWidth())
self.main.setSizePolicy(sizePolicy)
font = QtGui.QFont()
font.setStyleStrategy(QtGui.QFont.PreferDefault)
self.main.setFont(font)
self.main.setAutoFillBackground(False)
self.main.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.main.setFrameShadow(QtWidgets.QFrame.Raised)
self.main.setObjectName("main")
self.verticalLayout_9 = QtWidgets.QVBoxLayout(self.main)
self.verticalLayout_9.setObjectName("verticalLayout_9")
self.label_5 = QtWidgets.QLabel(self.main)
self.label_5.setStyleSheet("border:10px solid #19E67E;\n"
"border-radius:90px;\n"
"padding: 15px")
self.label_5.setText("")
self.label_5.setPixmap(QtGui.QPixmap("icons/логотип.svg"))
self.label_5.setObjectName("label_5")
self.verticalLayout_9.addWidget(self.label_5, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignBottom)
self.label_6 = QtWidgets.QLabel(self.main)
self.label_6.setStyleSheet("font-size:50px;")
self.label_6.setObjectName("label_6")
self.verticalLayout_9.addWidget(self.label_6, 0, QtCore.Qt.AlignHCenter|QtCore.Qt.AlignTop)
self.verticalLayout.addWidget(self.main)
self.footer = QtWidgets.QFrame(self.MainFrame)
self.footer.setMinimumSize(QtCore.QSize(50, 50))
self.footer.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.footer.setFrameShadow(QtWidgets.QFrame.Raised)
self.footer.setObjectName("footer")
self.horizontalLayout_5 = QtWidgets.QHBoxLayout(self.footer)
self.horizontalLayout_5.setContentsMargins(0, 0, 0, 0)
self.horizontalLayout_5.setSpacing(0)
self.horizontalLayout_5.setObjectName("horizontalLayout_5")
self.frame_10 = QtWidgets.QFrame(self.footer)
self.frame_10.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_10.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_10.setObjectName("frame_10")
self.horizontalLayout_6 = QtWidgets.QHBoxLayout(self.frame_10)
self.horizontalLayout_6.setObjectName("horizontalLayout_6")
self.label_4 = QtWidgets.QLabel(self.frame_10)
self.label_4.setStyleSheet("font-size:14px;\n"
"")
self.label_4.setObjectName("label_4")
self.horizontalLayout_6.addWidget(self.label_4, 0, QtCore.Qt.AlignLeft|QtCore.Qt.AlignBottom)
self.horizontalLayout_5.addWidget(self.frame_10, 0, QtCore.Qt.AlignLeft)
self.frame_9 = QtWidgets.QFrame(self.footer)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.frame_9.sizePolicy().hasHeightForWidth())
self.frame_9.setSizePolicy(sizePolicy)
self.frame_9.setFrameShape(QtWidgets.QFrame.StyledPanel)
self.frame_9.setFrameShadow(QtWidgets.QFrame.Raised)
self.frame_9.setObjectName("frame_9")
self.horizontalLayout_7 = QtWidgets.QHBoxLayout(self.frame_9)
self.horizontalLayout_7.setObjectName("horizontalLayout_7")
self.horizontalLayout_5.addWidget(self.frame_9)
self.verticalLayout.addWidget(self.footer)
self.hboxlayout.addWidget(self.MainFrame)
MainWindow.setCentralWidget(self.centralwidget)
self.retranslateUi(MainWindow)
self.Menu2.setCurrentIndex(1)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.dvsoft.setText(_translate("MainWindow", "DVsoft"))
self.pushButton.setText(_translate("MainWindow", "Item1"))
self.pushButton_2.setText(_translate("MainWindow", "Item2"))
self.pushButton_3.setText(_translate("MainWindow", "Item3"))
self.Menu2.setItemText(self.Menu2.indexOf(self.Menu1), _translate("MainWindow", "Menu1"))
self.pushButton_4.setText(_translate("MainWindow", "Weather"))
self.pushButton_5.setText(_translate("MainWindow", "Course Dollar/Euro"))
self.label_2.setText(_translate("MainWindow", "Больше информации вы можете прочеcть на нашем сайте, который ещё не готов :)"))
self.Menu2.setItemText(self.Menu2.indexOf(self.menu2), _translate("MainWindow", "Menu2"))
self.exit.setText(_translate("MainWindow", "Exit"))
self.lineEdit.setText(_translate("MainWindow", "Search"))
self.label_6.setText(_translate("MainWindow", "DVsoft"))
self.label_4.setText(_translate("MainWindow", "DVsoft.project.1"))
import icons_rc
| MyLongCode/project1 | ui_interface.py | ui_interface.py | py | 19,084 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "PyQt5.QtCore.QSize",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "PyQt5.QtCore",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "PyQt5.QtWidgets.QWidget",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "PyQt5.QtWidgets... |
8346930934 | import os
import random
import tempfile
from copy import copy
from functools import partial
from multiprocessing import cpu_count
import pickle
import numpy as np
import tensorflow as tf
from joblib import Parallel, delayed
from .tokenization import printable_text
from .utils import (BOS_TOKEN, EOS_TOKEN, add_special_tokens_with_seqs,
create_instances_from_document, create_mask_and_padding,
create_masked_lm_predictions, punc_augument,
tokenize_text_with_seqs, truncate_seq_pair)
def create_single_problem_single_instance(problem,
ex_index,
example,
label_encoder,
params,
tokenizer,
mode,
problem_type,
is_seq):
raw_inputs, raw_target = example
# punctuation augumentation
if params.punc_replace_prob > 0 and mode == 'train':
raw_inputs = punc_augument(raw_inputs, params)
# tokenize inputs, now the length is fixed, target == raw_target
if isinstance(raw_inputs, dict):
tokens_a, target = tokenize_text_with_seqs(
tokenizer, raw_inputs['a'], raw_target, is_seq)
tokens_b, _ = tokenize_text_with_seqs(
tokenizer, raw_inputs['b'], raw_target)
else:
tokens_a, target = tokenize_text_with_seqs(
tokenizer, raw_inputs, raw_target, is_seq)
tokens_b = None
if tokens_b is not None and is_seq:
raise NotImplementedError(
'Sequence Labeling with tokens b is not implemented')
if not tokens_a:
return
# check whether tokenization changed the length
if is_seq:
if len(target) != len(tokens_a):
tf.logging.warning('Data %d broken' % ex_index)
return
# truncate tokens and target to max_seq_len
tokens_a, tokens_b, target = truncate_seq_pair(
tokens_a, tokens_b, target, params.max_seq_len, is_seq=is_seq)
# add [SEP], [CLS] tokens
tokens, segment_ids, target = add_special_tokens_with_seqs(
tokens_a, tokens_b, target, is_seq)
# train mask lm as augument task while training
if params.augument_mask_lm and mode == 'train':
rng = random.Random()
(mask_lm_tokens, masked_lm_positions,
masked_lm_labels) = create_masked_lm_predictions(
tokens,
params.masked_lm_prob,
params.max_predictions_per_seq,
list(tokenizer.vocab.keys()), rng)
_, mask_lm_tokens, _, _ = create_mask_and_padding(
mask_lm_tokens,
copy(segment_ids),
copy(target),
params.max_seq_len,
is_seq,
dynamic_padding=params.dynamic_padding)
masked_lm_weights, masked_lm_labels, masked_lm_positions, _ = create_mask_and_padding(
masked_lm_labels, masked_lm_positions, None, params.max_predictions_per_seq)
mask_lm_input_ids = tokenizer.convert_tokens_to_ids(
mask_lm_tokens)
masked_lm_ids = tokenizer.convert_tokens_to_ids(masked_lm_labels)
input_mask, tokens, segment_ids, target = create_mask_and_padding(
tokens, segment_ids, target, params.max_seq_len, is_seq, dynamic_padding=params.dynamic_padding)
# create mask and padding for labels of seq2seq problem
if problem_type in ['seq2seq_tag', 'seq2seq_text']:
# tokenize text if target is text
if problem_type == 'seq2seq_text':
# assign num_classes for text generation problem
params.num_classes[problem] = len(label_encoder.vocab)
target, _ = tokenize_text_with_seqs(
label_encoder, target, None, False)
target, _, _ = truncate_seq_pair(
target, None, None, params.decode_max_seq_len, is_seq=is_seq)
# since we initialize the id to 0 in prediction, we need
# to make sure that BOS_TOKEN is [PAD]
target = [BOS_TOKEN] + target + [EOS_TOKEN]
label_mask, target, _, _ = create_mask_and_padding(
target, [0] * len(target), None, params.decode_max_seq_len)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
if isinstance(target, list):
if problem_type == 'seq2seq_text':
label_id = label_encoder.convert_tokens_to_ids(target)
else:
label_id = label_encoder.transform(target).tolist()
label_id = [np.int32(i) for i in label_id]
else:
label_id = label_encoder.transform([target]).tolist()[0]
label_id = np.int32(label_id)
if not params.dynamic_padding:
assert len(input_ids) == params.max_seq_len
assert len(input_mask) == params.max_seq_len
assert len(segment_ids) == params.max_seq_len, segment_ids
if is_seq:
assert len(label_id) == params.max_seq_len
# logging in debug mode
if ex_index < 5:
tf.logging.debug("*** Example ***")
tf.logging.debug("tokens: %s" % " ".join(
[printable_text(x) for x in tokens]))
tf.logging.debug("input_ids: %s" %
" ".join([str(x) for x in input_ids]))
tf.logging.debug("input_mask: %s" %
" ".join([str(x) for x in input_mask]))
tf.logging.debug("segment_ids: %s" %
" ".join([str(x) for x in segment_ids]))
if is_seq or problem_type in ['seq2seq_tag', 'seq2seq_text']:
tf.logging.debug("%s_label_ids: %s" %
(problem, " ".join([str(x) for x in label_id])))
tf.logging.debug("%s_label: %s" %
(problem, " ".join([str(x) for x in target])))
else:
tf.logging.debug("%s_label_ids: %s" %
(problem, str(label_id)))
tf.logging.debug("%s_label: %s" %
(problem, str(target)))
if params.augument_mask_lm and mode == 'train':
tf.logging.debug("mask lm tokens: %s" % " ".join(
[printable_text(x) for x in mask_lm_tokens]))
tf.logging.debug("mask lm input_ids: %s" %
" ".join([str(x) for x in mask_lm_input_ids]))
tf.logging.debug("mask lm label ids: %s" %
" ".join([str(x) for x in masked_lm_ids]))
tf.logging.debug("mask lm position: %s" %
" ".join([str(x) for x in masked_lm_positions]))
# create return dict
if not params.augument_mask_lm:
return_dict = {
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
'%s_label_ids' % problem: label_id
}
else:
if mode == 'train' and random.uniform(0, 1) <= params.augument_rate:
return_dict = {
'input_ids': mask_lm_input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
'%s_label_ids' % problem: label_id,
"masked_lm_positions": masked_lm_positions,
"masked_lm_ids": masked_lm_ids,
"masked_lm_weights": masked_lm_weights,
}
else:
return_dict = {
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
'%s_label_ids' % problem: label_id,
"masked_lm_positions": np.zeros([params.max_predictions_per_seq]),
"masked_lm_ids": np.zeros([params.max_predictions_per_seq]),
"masked_lm_weights": np.zeros([params.max_predictions_per_seq]),
}
if problem_type in ['seq2seq_tag', 'seq2seq_text']:
return_dict['%s_mask' % problem] = label_mask
return return_dict
def _multiprocessing_wrapper(
problem,
example_list,
label_encoder,
params,
tokenizer,
mode,
problem_type,
is_seq):
return_list = []
for ex_index, example in enumerate(example_list):
return_list.append(create_single_problem_single_instance(
problem,
999,
example,
label_encoder,
params,
tokenizer,
mode,
problem_type,
is_seq))
return return_list
def create_single_problem_generator(problem,
inputs_list,
target_list,
label_encoder,
params,
tokenizer,
mode):
"""Function to create iterator for single problem
This function will:
1. Do some text cleaning using original bert tokenizer, if
problem type is sequential tagging, corresponding labels
will be removed.
Example:
Before: inputs: ['a', '&', 'c'] target: [0, 0, 1]
After: inputs: ['a', 'c'] target: [0, 1]
2. Add [CLS], [SEP] tokens
3. Padding
4. yield result dict
Arguments:
problem {str} -- problem name
inputs_list {list } -- inputs list
target_list {list} -- target list, should have the same length as inputs list
label_encoder {LabelEncoder} -- label encoder
params {Params} -- params
tokenizer {tokenizer} -- Bert Tokenizer
epoch {int} -- Deprecate
"""
problem_type = params.problem_type[problem]
# whether this problem is sequential labeling
# for sequential labeling, targets needs to align with any
# change of inputs
is_seq = problem_type in ['seq_tag']
if not params.multiprocess:
for ex_index, example in enumerate(zip(inputs_list, target_list)):
return_dict = create_single_problem_single_instance(problem,
ex_index,
example,
label_encoder,
params,
tokenizer,
mode,
problem_type,
is_seq)
yield return_dict
else:
return_dict_list = []
pickle_file = os.path.join(
params.tmp_file_dir, '{0}_{1}_data.pkl'.format(problem, mode))
if not os.path.exists(pickle_file):
# params.tmp_file_dir = tempfile.mkdtemp(dir='.')
os.makedirs('tmp', exist_ok=True)
params.tmp_file_dir = 'tmp'
pickle_file = os.path.join(
params.tmp_file_dir, '{0}_{1}_data.pkl'.format(problem, mode))
tf.logging.info(
'Saving preprocessing files to {0}'.format(pickle_file))
partial_fn = partial(_multiprocessing_wrapper, problem=problem, label_encoder=label_encoder,
params=params, tokenizer=tokenizer,
mode=mode, problem_type=problem_type, is_seq=is_seq)
example_list = list(zip(inputs_list, target_list))
num_process = cpu_count()
def split(a, n):
k, m = divmod(len(a), n)
return (a[i * k + min(i, m):(i + 1) * k + min(i + 1, m)] for i in range(n))
example_list = list(split(example_list, num_process))
return_dict_list_list = Parallel(num_process)(delayed(partial_fn)(
example_list=example) for example in example_list
)
pickle.dump(return_dict_list_list, open(pickle_file, 'wb'))
else:
return_dict_list_list = pickle.load(open(pickle_file, 'rb'))
for return_dict_list in return_dict_list_list:
for return_dict in return_dict_list:
yield return_dict
def create_pretraining_generator(problem,
inputs_list,
target_list,
label_encoder,
params,
tokenizer
):
"""Slight modification of original code
Raises:
ValueError -- Input format not right
"""
if not isinstance(inputs_list[0][0], list):
raise ValueError('inputs is expected to be list of list of list.')
all_documents = []
for document in inputs_list:
all_documents.append([])
for sentence in document:
all_documents[-1].append(tokenizer.tokenize('\t'.join(sentence)))
all_documents = [d for d in all_documents if d]
rng = random.Random()
rng.shuffle(all_documents)
vocab_words = list(tokenizer.vocab.keys())
instances = []
print_count = 0
for _ in range(params.dupe_factor):
for document_index in range(len(all_documents)):
instances = create_instances_from_document(
all_documents,
document_index,
params.max_seq_len,
params.short_seq_prob,
params.masked_lm_prob,
params.max_predictions_per_seq,
vocab_words, rng)
for instance in instances:
tokens = instance.tokens
segment_ids = list(instance.segment_ids)
input_mask, tokens, segment_ids, _ = create_mask_and_padding(
tokens, segment_ids, None, params.max_seq_len)
masked_lm_positions = list(instance.masked_lm_positions)
masked_lm_weights, masked_lm_labels, masked_lm_positions, _ = create_mask_and_padding(
instance.masked_lm_labels, masked_lm_positions, None, params.max_predictions_per_seq)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
masked_lm_ids = tokenizer.convert_tokens_to_ids(
masked_lm_labels)
next_sentence_label = 1 if instance.is_random_next else 0
yield_dict = {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
"masked_lm_positions": masked_lm_positions,
"masked_lm_ids": masked_lm_ids,
"masked_lm_weights": masked_lm_weights,
"next_sentence_label_ids": next_sentence_label
}
if print_count < 3:
tf.logging.debug('%s : %s' %
('tokens', ' '.join([str(x) for x in tokens])))
for k, v in yield_dict.items():
if not isinstance(v, int):
tf.logging.debug('%s : %s' %
(k, ' '.join([str(x) for x in v])))
print_count += 1
yield yield_dict
def create_generator(params, mode):
"""Function to create iterator for multiple problem
This function dose the following things:
1. Create dummy labels for each problems.
2. Initialize all generators
3. Sample a problem to train at this batch. If eval, take turns
4. Create a loss multiplier
5. Tried to generate samples for target problem, if failed, init gen
6. Add dummy label to other problems
Example:
Problem: cws|NER|weibo_ner&weibo_cws
1. Dummy labels: cws_label_ids: [0,0,0] ...
2. Blablabla
3. Sample, say (weibo_ner&weibo_cws)
4. loss multipliers: {'cws_loss_multiplier': 0, ..., 'weibo_ner_loss_multiplier': 1, ...}
...
Arguments:
params {Params} -- params
mode {mode} -- mode
"""
# example
# problem_list: ['NER', 'cws', 'weibo_ner', 'weibo_cws']
# problem_chunk: [['NER'], ['cws'], ['weibo_ner', 'weibo_cws']]
problem_list = []
problem_chunk = []
for problem_dict in params.run_problem_list:
problem_list += list(problem_dict.keys())
problem_chunk.append(list(problem_dict.keys()))
# get dummy labels
def _create_dummpy_label(problem_type):
if problem_type == 'cls':
return 0
elif problem_type == 'seq_tag':
return [0]*params.max_seq_len
else:
return [0]*params.decode_max_seq_len
dummy_label_dict = {problem+'_label_ids': _create_dummpy_label(
params.problem_type[problem]) for problem in problem_list if params.problem_type[problem] != 'pretrain'}
dummy_label_dict.update({problem+'_mask': _create_dummpy_label(
params.problem_type[problem]) for problem in problem_list if params.problem_type[problem] in ['seq2seq_tag', 'seq2seq_text']})
# init gen
try:
gen_dict = {problem: params.read_data_fn[problem](params, mode)
for problem in problem_list}
except KeyError:
not_exist_problem = [
p for p in problem_list if p not in params.read_data_fn]
raise KeyError(
'The preprocessing function of {0} not found, make sure you called params.add_problem. If you\'re using train_bert_multitask, please make sure you provided problem_type_dict and processing_fn_dict.'.format(not_exist_problem))
while gen_dict:
# sample problem to train
if len(problem_chunk) > 1:
data_num_list = [params.data_num_dict[chunk[0]]
for chunk in problem_chunk]
if params.multitask_balance_type == 'data_balanced':
sample_prob = np.array(data_num_list) / np.sum(data_num_list)
current_problem_chunk_ind = np.random.choice(
list(range(len(problem_chunk))), p=sample_prob)
current_problem_chunk = problem_chunk[current_problem_chunk_ind]
elif params.multitask_balance_type == 'problem_balanced':
sample_prob = np.array(
[1]*len(data_num_list)) / np.sum([1]*len(data_num_list))
current_problem_chunk_ind = np.random.choice(
list(range(len(problem_chunk))), p=sample_prob)
current_problem_chunk = problem_chunk[current_problem_chunk_ind]
else:
current_problem_chunk = problem_chunk[0]
# create loss multiplier
loss_multiplier = {
problem+'_loss_multiplier': 0 for problem in problem_list}
for problem in current_problem_chunk:
loss_multiplier[problem+'_loss_multiplier'] = 1
base_dict = {}
base_input = None
for problem in current_problem_chunk:
try:
instance = next(gen_dict[problem])
except StopIteration:
if mode == 'train':
gen_dict[problem] = params.read_data_fn[problem](
params, mode)
instance = next(gen_dict[problem])
else:
del gen_dict[problem]
continue
except KeyError:
continue
if not instance:
continue
base_dict.update(instance)
if base_input is None:
base_input = instance['input_ids']
elif not params.augument_mask_lm:
assert base_input == instance[
'input_ids'], 'Inputs id of two chained problem not aligned. Please double check!'
if not base_dict:
continue
for problem in problem_list:
problem_type = params.problem_type[problem]
problem_label_name = '{0}_label_ids'.format(problem)
if problem_label_name not in base_dict:
if problem_type == 'seq_tag':
base_dict[problem_label_name] = dummy_label_dict[problem_label_name][:len(
base_dict['input_ids'])]
else:
base_dict[problem_label_name] = dummy_label_dict[problem_label_name]
if problem_type in ['seq2seq_tag', 'seq2seq_text']:
base_dict['{0}_mask'.format(
problem)] = dummy_label_dict['{0}_mask'.format(problem)]
# add loss multipliers
base_dict.update(loss_multiplier)
yield base_dict
| hauwenc/bert-multitask-learning | bert_multitask_learning/create_generators.py | create_generators.py | py | 20,858 | python | en | code | null | github-code | 36 | [
{
"api_name": "utils.punc_augument",
"line_number": 34,
"usage_type": "call"
},
{
"api_name": "utils.tokenize_text_with_seqs",
"line_number": 38,
"usage_type": "call"
},
{
"api_name": "utils.tokenize_text_with_seqs",
"line_number": 40,
"usage_type": "call"
},
{
"a... |
35614129449 | import msgpack
import redis
import click
import socket
import pandas as pd
import numpy as np
import ujson as json
#Extra Functions (for enrichment purpose)
df_port_name = pd.read_csv('enrichments/port_name.txt',delimiter=",", names=['port_num','port_name'])
df_ip_proto_name = pd.read_csv('enrichments/ip_proto_name.txt',delimiter=",", names=['proto_num','proto_name'])
@click.group()
def cli():
pass
@click.command()
def receive():
r = redis.StrictRedis(host='localhost', port=6379, db=0)
p = r.pubsub()
p.subscribe('patterns')
try:
# non-blocking
# while True:
# print(p.get_message())
# blocking
for message in p.listen():
if isinstance(message['data'], bytes):
payload = msgpack.unpackb(message['data'], encoding='utf-8')
print(payload)
except KeyboardInterrupt as e:
print('Keyboard interrupt')
def get_ip_proto_name(ip_proto_number):
try:
return df_ip_proto_name[df_ip_proto_name['proto_num']==ip_proto_number]['proto_name'].values[0]
except:
return str(ip_proto_number)
def get_port_name(port_number):
try:
return df_port_name[df_port_name['port_num']==port_number]['port_name'].values[0]+" service port"
except:
return "port "+str(int(port_number))
def get_tcp_flag_name(tcp_flags_str):
tcp_flags=""
try:
tcp_flags += ("FIN" if (tcp_flags_str.find('F') != -1) else next)
except:
next
try:
tcp_flags += ("SYN" if (tcp_flags_str.find('S')!= -1) else next)
except:
next
try:
tcp_flags += ("RST" if tcp_flags_str.find('R') != -1 else next)
except:
next
try:
tcp_flags += ("PUSH" if tcp_flags_str.find('P') != -1 else next)
except:
next
try:
tcp_flags += ("ACK" if tcp_flags_str.find('A') != -1 else next)
except:
next
try:
tcp_flags += ("URG" if tcp_flags_str.find('U') != -1 else next)
except:
next
try:
tcp_flags += ("ECE" if tcp_flags_str.find('E') != -1 else next)
except:
next
try:
tcp_flags += ("CWR" if tcp_flags_str.find('C') != -1 else next)
except:
next
return tcp_flags
def analyse(df):
"""
Analysis only top traffic stream
:param df:
:return:
"""
debug = True
attack_case = "-1"
ttl_variation_threshold = 4
result = {
"reflected":False,
"spoofed":False,
"fragmented":False,
"pattern_traffic_share":0.0,
"pattern_packet_count":0,
"pattern_total_megabytes":0,
"start_timestamp":0,
"end_timestamp":0,
"dst_ports":[], #(port,share)
"src_ports":[], #(port,share)
"ttl_variation":[],
"src_ips":[],
"dst_ips":[],
"packets":[]
}
if debug: print("\n\n\n")
top_ip_dst = df['ip_dst'].value_counts().index[0]
if debug: print("Top dst IP: "+ top_ip_dst)
result["dst_ips"] = top_ip_dst
top_ip_proto = df[df['ip_dst'] == top_ip_dst]['ip_proto'].value_counts().index[0]
if debug: print("Top IP protocol: "+str(top_ip_proto))
####
# Performing a first filter based on the top_ip_dst (target IP), the source IPs canNOT be from the \16 of the
# target IP, and the top IP protocol that targeted the top_ip_dst
df_filtered = df[
(df['ip_dst'] == top_ip_dst) & ~df['ip_src'].str.contains(".".join(top_ip_dst.split('.')[0:2]), na=False) & (
df['ip_proto'] == top_ip_proto)]
####
# Calculating the number of packets after the first filter
total_packets_filtered = len(df_filtered)
if debug: print("Number of packets: "+str(total_packets_filtered))
result["total_nr_packets"] = total_packets_filtered
####
# For attacks in the IP protocol level
attack_label = get_ip_proto_name(top_ip_proto) + "-based attack"
result["transport_protocol"] = get_ip_proto_name(top_ip_proto)
####
# For attacks based on TCP or UDP, which have source and destination ports
if ((top_ip_proto == 6) or (top_ip_proto == 17)):
if debug: print("\n####################\nREMAINING :\n####################")
####
# Calculating the distribution of source ports based on the first filter
percent_src_ports = df_filtered['sport'].value_counts().divide(float(total_packets_filtered) / 100)
if debug: print("\nSource ports frequency")
if debug: print(percent_src_ports.head())
####
# Calculating the distribution of destination ports after the first filter
percent_dst_ports = df_filtered['dport'].value_counts().divide(float(total_packets_filtered) / 100)
if debug: print("\nDestination ports frequency")
if debug: print(percent_dst_ports.head())
#####
## WARNING packets are filtered here again#####
# Using the top 1 (source or destination) port to analyse a pattern of packets
if (len(percent_src_ports) > 0) and (len(percent_dst_ports) > 0):
if percent_src_ports.values[0] > percent_dst_ports.values[0]:
if debug: print('Using top source port: ', percent_src_ports.keys()[0])
df_pattern = df_filtered[df_filtered['sport'] == percent_src_ports.keys()[0]]
result["selected_port"] = "src_" + str(percent_src_ports.keys()[0])
else:
if debug: print('Using top dest port: ', percent_dst_ports.keys()[0])
df_pattern = df_filtered[df_filtered['dport'] == percent_dst_ports.keys()[0]]
result["selected_port"] = "dst_" + str(percent_dst_ports.keys()[0])
else:
if debug: print('no top source/dest port')
return None
if debug: print("\n####################\nPATTERN "+ "\n####################")
#####
# Calculating the total number of packets involved in the attack
pattern_packets = len(df_pattern)
result["pattern_packet_count"] = pattern_packets
#WARNING Can be wrong
result['raw_attack_size_megabytes'] = (df_pattern['raw_size'].sum() /1000000).item()
result["pattern_total_megabytes"] = (df_pattern[df_pattern['fragments'] == 0]['ip_length'].sum() / 1000000).item()
#####
# Calculating the percentage of the current pattern compared to the raw input file
representativeness = float(pattern_packets) * 100 / float(total_packets_filtered)
result["pattern_traffic_share"] = representativeness
attack_label = 'In %.2f' % representativeness + "\n " + attack_label
#####
# Checking the existence of HTTP data
http_data = df_pattern['http_data'].value_counts().divide(float(pattern_packets) / 100)
#####
# Checking the existence of TCP flags
percent_tcp_flags = df_pattern['tcp_flag'].value_counts().divide(float(pattern_packets) / 100)
#####
# Calculating the number of source IPs involved in the attack
ips_involved = df_pattern['ip_src'].unique()
attack_label = attack_label + "\n"+ str(len(ips_involved)) + " source IPs"
result["src_ips"] = ips_involved.tolist()
#####
# Calculating the number of source IPs involved in the attack
result["start_timestamp"] = df_pattern['timestamp'].min().item()
result["end_timestamp"] = df_pattern['timestamp'].max().item()
####
# Calculating the distribution of TTL variation (variation -> number of IPs)
ttl_variations = df_pattern.groupby(['ip_src'])['ip_ttl'].agg(np.ptp).value_counts().sort_index()
if debug: print('TTL variation : NR of source IPs')
if debug: print(ttl_variations)
#use to_json
result["ttl_variation"] = json.loads(ttl_variations.to_json())
####
# Calculating the distribution of IP fragments (fragmented -> percentage of packets)
percent_fragments = df_pattern['fragments'].value_counts().divide(float(pattern_packets) / 100)
####
# Calculating the distribution of source ports that remains
percent_src_ports = df_pattern['sport'].value_counts().divide(float(pattern_packets) / 100)
if debug: print("\nSource ports frequency")
if debug: print(percent_src_ports.head())
#use to_json()
result["src_ports"] = json.loads(percent_src_ports.to_json())
####
# Calculating the distribution of destination ports after the first filter
percent_dst_ports = df_pattern['dport'].value_counts().divide(float(pattern_packets) / 100)
if debug: print("\nDestination ports frequency")
if debug: print(percent_dst_ports.head())
#use to_json
result["dst_ports"] = json.loads(percent_dst_ports.to_json())
####
# There are 3 possibilities of attacks cases!
if (percent_src_ports.values[0] == 100):
if (len(percent_dst_ports) == 1):
# if debug: print("\nCASE 1: 1 source port to 1 destination port") if debug else next
attack_label = attack_label + "; using " + get_port_name(
percent_src_ports.keys()[0]) + "; to target " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.1f' % percent_dst_ports.values[0] + "%]"
else:
# if debug: print("\nCASE 2: 1 source port to a set of destination ports") if debug else next
if (percent_dst_ports.values[0] >= 50):
attack_label = attack_label + "; using " + get_port_name(
percent_src_ports.keys()[0]) + "; to target a set of (" + str(
len(percent_dst_ports)) + ") ports, such as " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.2f' % percent_dst_ports.values[
0] + "%]" + " and " + get_port_name(percent_dst_ports.keys()[1]) + "[" + '%.2f' % \
percent_dst_ports.values[
1] + "%]"
elif (percent_dst_ports.values[0] >= 33):
attack_label = attack_label + "; using " + get_port_name(
percent_src_ports.keys()[0]) + "; to target a set of (" + str(
len(percent_dst_ports)) + ") ports, such as " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.2f' % percent_dst_ports.values[
0] + "%]" + "; " + get_port_name(percent_dst_ports.keys()[1]) + "[" + '%.2f' % \
percent_dst_ports.values[
1] + "%], and " + get_port_name(
percent_dst_ports.keys()[2]) + "[" + '%.2f' % percent_dst_ports.values[2] + "%]"
else:
attack_label = attack_label + "; using " + get_port_name(
percent_src_ports.keys()[0]) + "; to target a set of (" + str(
len(percent_dst_ports)) + ") ports, such as " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.2f' % percent_dst_ports.values[
0] + "%]" + "; " + get_port_name(percent_dst_ports.keys()[1]) + "[" + '%.2f' % \
percent_dst_ports.values[
1] + "%], and " + get_port_name(
percent_dst_ports.keys()[2]) + "[" + '%.2f' % percent_dst_ports.values[2] + "%]"
else:
if (len(percent_src_ports) == 1):
# if debug: print("\nCASE 1: 1 source port to 1 destination port") if debug else next
attack_label = attack_label + "; using " + get_port_name(percent_src_ports.keys()[0]) + "[" + '%.1f' % \
percent_src_ports.values[
0] + "%]" + "; to target " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.1f' % percent_dst_ports.values[0] + "%]"
else:
# if debug: print("\nCASE 3: 1 source port to a set of destination ports") if debug else next
if (percent_src_ports.values[0] >= 50):
attack_label = attack_label + "; using a set of (" + str(
len(percent_src_ports)) + ") ports, such as " + get_port_name(
percent_src_ports.keys()[0]) + "[" + '%.2f' % percent_src_ports.values[
0] + "%] and " + get_port_name(percent_src_ports.keys()[1]) + "[" + '%.2f' % \
percent_src_ports.values[
1] + "%]" + "; to target " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.1f' % percent_dst_ports.values[0] + "%]"
elif (percent_src_ports.values[0] >= 33):
attack_label = attack_label + "; using a set of (" + str(
len(percent_src_ports)) + ") ports, such as " + get_port_name(
percent_src_ports.keys()[0]) + "[" + '%.2f' % percent_src_ports.values[
0] + "%], " + get_port_name(percent_src_ports.keys()[1]) + "[" + '%.2f' % \
percent_src_ports.values[
1] + "%], and " + get_port_name(
percent_src_ports.keys()[2]) + "[" + '%.2f' % percent_src_ports.values[
2] + "%]" + "; to target " + get_port_name(percent_dst_ports.keys()[0]) + "[" + '%.1f' % \
percent_dst_ports.values[
0] + "%]"
else:
attack_label = attack_label + "; using a set of (" + str(
len(percent_src_ports)) + ") ports, such as " + get_port_name(
percent_src_ports.keys()[0]) + "[" + '%.2f' % percent_src_ports.values[
0] + "%], " + get_port_name(percent_src_ports.keys()[1]) + "[" + '%.2f' % \
percent_src_ports.values[
1] + "%], " + get_port_name(
percent_src_ports.keys()[2]) + "[" + '%.2f' % percent_src_ports.values[
2] + "%]" + "; and " + get_port_name(percent_src_ports.keys()[3]) + "[" + '%.2f' % \
percent_src_ports.values[
3] + "%]" + "; to target " + get_port_name(
percent_dst_ports.keys()[0]) + "[" + '%.1f' % percent_dst_ports.values[0] + "%]"
####
# Testing HTTP request
if len(http_data) > 0 and ((percent_dst_ports.index[0] == 80) or (percent_dst_ports.index[0] == 443)):
attack_label = attack_label + "; " + http_data.index[0]
####
# Testing TCP flags
if (len(percent_tcp_flags) > 0) and (percent_tcp_flags.values[0] > 50):
attack_label = attack_label + "; TCP flags: " + get_tcp_flag_name(
percent_tcp_flags.index[0]) + "[" + '%.1f' % percent_tcp_flags.values[0] + "%]"
####
# IP fragmentation
if (percent_fragments.values[0] > 0) and (percent_fragments.index[0] == 1):
attack_label = attack_label + "; involving IP fragmentation"
result["fragmented"] = True
####
# IP spoofing (if (more than 0) src IPs had the variation of the ttl higher than a treshold)
if (ttl_variations[::-1].values[0] > 0) and (ttl_variations[::-1].index[0] >= ttl_variation_threshold):
result["spoofed"]=True
attack_label = attack_label + "; (likely involving) spoofed IPs"
else:
####
# Reflection and Amplification
if percent_src_ports.values[0] >= 1:
result["reflected"]=True
attack_label = attack_label + "; Reflection & Amplification"
if debug: print("\n####################\nATTACK VECTOR LABEL:"+ "\n####################")
if debug: print(attack_label)
result["label"] = attack_label
print(result)
return result
if __name__ == "__main__":
cli.add_command(receive)
cli()
| jdcc2/ddoshackathon | packet_patterns.py | packet_patterns.py | py | 17,497 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "pandas.read_csv",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "click.group",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "redis.StrictRedis",
"... |
22143984130 | from art import logo
def greet(name, location, time):
print(f"Hi {name} from {location}. Good {time}.")
print(f"Hey {name} from {location}. Good {time}.")
print(f"Hello {name} from {location}. Good {time}.")
#greet("Angela", location="London", time="evening")
def caeser(direction, text, shift):
caeser_text =""
shift = shift % 26
if direction == "encode":
shift = 26 - shift
for letter in text:
if letter in alphabet:
caeser_text += alphabet[alphabet.index(letter)-shift]
else:
caeser_text += letter
print(caeser_text)
def encrypt(text, shift):
encrypt_text =""
for letter in text:
encrypt_text += alphabet[alphabet.index(letter)+shift-26]
print(encrypt_text)
def decrypt(text, shift):
decrypt_text =""
for letter in text:
decrypt_text += alphabet[alphabet.index(letter)-shift]
print(decrypt_text)
print(logo)
alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
run = "y"
while run == "y":
direction = input("Type 'encode' to encrypt, type 'decode' to decrypt:\n")
text = input("Type your message:\n").lower()
shift = int(input("Type the shift number:\n"))
caeser(direction, text, shift)
run = input('Type "y" to continue. Type any other key to end.\n').lower()
print("Goodbye from Caeser") | tarunchandel/python-ceaser-cipher | main.py | main.py | py | 1,367 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "art.logo",
"line_number": 38,
"usage_type": "argument"
}
] |
35478168713 | #!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import argparse
import codecs
import sys
import openapc_toolkit as oat
ARG_HELP_STRINGS = {
"csv_file": "The csv file where rows should be reordered",
"column": "The numerical index of the column to be used as sorting key",
"encoding": "The encoding of the CSV file. Setting this argument will " +
"disable automatic guessing of encoding.",
"quotemask": "A quotemask to apply to the result file after the action " +
"has been performed. A quotemask is a string consisting " +
"only of the letters 't' and 'f' (true/false) and has " +
"the same length as there are columns in the (resulting) " +
"csv file. Only the columns where the index is 't' will be " +
"quoted.",
"openapc_quote_rules": "Determines if the special openapc quote rules " +
"should be applied, meaning that the keywords " +
"NA, TRUE and FALSE will never be quoted. If in " +
"conflict with a quotemask, openapc_quote_rules " +
"will take precedence.",
"other_csv_file": "An optional second csv file. If given, the rows in " +
"the first file will not be sorted alphabetically. " +
"Instead, they will be rearranged so that the values in " +
"the denoted column mirror their order " +
"of occurence in the given column of the second file " +
"(this will obviously only make sense if the columns " +
"have common values and the values represent some sort " +
"of key). Rows with a column value not occuring in the " +
"second file will be appended to the end of the " +
"output file in original order.",
"other_column": "The numerical index of the column to use in the second " +
"file. If omitted, the same index as in the first file " +
"is used.",
"other_encoding": "The optional encoding of the second CSV file.",
"ignore_case": "Ignore case when comparing values for reordering between two files"
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("csv_file", help=ARG_HELP_STRINGS["csv_file"])
parser.add_argument("column", type=int, help=ARG_HELP_STRINGS["column"])
parser.add_argument("other_csv_file", nargs="?", help=ARG_HELP_STRINGS["other_csv_file"])
parser.add_argument("other_column", type=int, nargs="?", help=ARG_HELP_STRINGS["other_column"])
parser.add_argument("-e2", "--other_encoding", help=ARG_HELP_STRINGS["other_encoding"])
parser.add_argument("-e", "--encoding", help=ARG_HELP_STRINGS["encoding"])
parser.add_argument("-i", "--ignore_case", action="store_true", default=False,
help=ARG_HELP_STRINGS["ignore_case"])
parser.add_argument("-q", "--quotemask", help=ARG_HELP_STRINGS["quotemask"])
parser.add_argument("-o", "--openapc_quote_rules",
help=ARG_HELP_STRINGS["openapc_quote_rules"],
action="store_true", default=False)
args = parser.parse_args()
quote_rules = args.openapc_quote_rules
encs = [] #CSV file encodings
for encoding in [args.encoding, args.other_encoding]:
if encoding:
try:
codec = codecs.lookup(encoding)
msg = "Encoding '{}' found in Python's codec collection as '{}'"
print(msg.format(encoding, codec.name))
except LookupError:
print("Error: '" + encoding + "' not found Python's " +
"codec collection. Either look for a valid name here " +
"(https://docs.python.org/2/library/codecs.html#standard-" +
"encodings) or omit this argument to enable automated " +
"guessing.")
sys.exit()
encs.append(encoding)
mask = None
if args.quotemask:
reduced = args.quotemask.replace("f", "").replace("t", "")
if reduced:
print("Error: A quotemask may only contain the letters 't' and 'f'!")
sys.exit()
mask = [True if x == "t" else False for x in args.quotemask]
header, content = oat.get_csv_file_content(args.csv_file, enc=encs[0])
column = args.column
if not args.other_csv_file:
rearranged_content = header + sorted(content, key=lambda x: x[column])
else:
rearranged_content = []
_, second_content = oat.get_csv_file_content(args.other_csv_file, enc=encs[1])
other_column = column # default: use same column index as in first file
if args.other_column:
other_column = args.other_column
for other_row in second_content:
if args.ignore_case:
matching_rows = [row for row in content if row[column].lower() == other_row[other_column].lower()]
else:
matching_rows = [row for row in content if row[column] == other_row[other_column]]
rearranged_content += matching_rows
for matching_row in matching_rows:
content.remove(matching_row)
unmatched_msg = ("{} rows could not be rearranged (unmatched in second csv file) " +
"and were appended to the end of the result file " +
"in original order.")
if content:
oat.print_y(unmatched_msg.format(len(content)))
else:
oat.print_g("All rows matched.")
rearranged_content = header + rearranged_content + content # append any unmatched rows
with open('out.csv', 'w') as out:
writer = oat.OpenAPCUnicodeWriter(out, mask, quote_rules, False)
writer.write_rows(rearranged_content)
if __name__ == '__main__':
main()
| OpenAPC/openapc-de | python/csv_row_reorder.py | csv_row_reorder.py | py | 6,006 | python | en | code | 114 | github-code | 36 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 44,
"usage_type": "call"
},
{
"api_name": "codecs.lookup",
"line_number": 67,
"usage_type": "call"
},
{
"api_name": "sys.exit",
"line_number": 76,
"usage_type": "call"
},
{
"api_name": "sys.exit",
"line_n... |
74297991462 | from collections import defaultdict
from itertools import product, chain
class Rule:
def __init__(self, registry):
self.registry = registry
self.or_rules = []
self._generated = None
def __iter__(self):
for r in self.value:
yield r
def parse_rule(self, rule):
self.raw_rule = rule
for or_def in rule.split(" | "):
or_rule = []
for dependency in or_def.split(" "):
if dependency in '"a""b"':
or_rule.append([dependency[1]])
continue
or_rule.append(self.registry[dependency])
self.or_rules.append(or_rule)
@property
def value(self):
if not self._generated:
for idx, rules in enumerate(self.or_rules):
for i, rule in enumerate(rules):
if isinstance(rule, Rule):
rules[i] = rule.value
self.or_rules[idx] = ["".join(comb) for comb in product(*rules)]
self._generated = list(chain(*self.or_rules))
return self._generated
def parse_input(lines):
messages = []
rules = defaultdict(lambda: Rule(rules))
for line in lines:
if not line:
continue
if ':' in line:
index, rule = line.split(': ')
rules[index].parse_rule(rule)
else:
messages.append(line)
return rules, messages
def solve1(puzzle_input):
rules, messages = parse_input(puzzle_input)
valid_counter = 0
for message in messages:
if message in rules["0"].value:
valid_counter += 1
return valid_counter
def solve2(puzzle_input):
rules, messages = parse_input(puzzle_input)
valid_messages = 0
invalid_message = []
for message in messages:
if message in rules["0"].value:
valid_messages += 1
else:
invalid_message.append(message)
# I'm not smart enough for this (yet ;)) So imma just hack it.
def find_start(message):
for start in rules["8"].value:
if message.startswith(start):
return message[len(start):]
def find_end(message):
for start in rules["42"].value:
for end in rules["31"].value:
if message.startswith(start) and message.endswith(end):
return message[len(start): -len(end)]
msgs = 0
for message in invalid_message:
candidates = []
while message:
message = find_start(message)
if message:
candidates.append(message)
for message in candidates:
while message:
message = find_end(message)
if message == '':
msgs += 1
return valid_messages + msgs
| maarten-dp/adventofcode2020 | solvers/day19.py | day19.py | py | 2,835 | python | en | code | 2 | github-code | 36 | [
{
"api_name": "itertools.product",
"line_number": 33,
"usage_type": "call"
},
{
"api_name": "itertools.chain",
"line_number": 34,
"usage_type": "call"
},
{
"api_name": "collections.defaultdict",
"line_number": 40,
"usage_type": "call"
}
] |
16746507107 | import os
import sys
import gensim
import smart_open
import collections
import random
import json
import os.path
from gensim.models.doc2vec import Doc2Vec, TaggedDocument
from gensim.test.utils import get_tmpfile
import time
from corpus_indexer import CorpusIndexer
class CorpusModeler:
def __init__(self, model_name, corpus_name=""):
self.model_name = model_name
self.corpus_name = corpus_name
self.corpus_file = f"{self.corpus_name}.cor"
self.corpus_index = f"{self.corpus_name}.json"
self.settings_file = f"/models/{self.model_name}.json"
self.model_file = f"/models/{self.model_name}.model"
self.load()
def build(self):
self.__build_model()
def load(self):
self.__load_or_create_settings()
self.__load_model()
def save(self):
self.__save_settings()
self.__save_model()
def __load_or_create_settings(self):
if(os.path.exists(self.settings_file)):
with open(self.settings_file) as f:
content = f.read()
self.settings = json.loads(content)
self.corpus_file = self.settings["corpus_file"]
self.corpus_index = self.settings["corpus_index"]
self.model_file = self.settings["model_file"]
self.corpus_name = self.settings["corpus_name"]
self.model_name = self.settings["model_name"]
else:
with open(self.settings_file, "w+") as f:
empty_content = json.dumps({})
f.write(empty_content)
self.settings = {
"vector_size": 70,
"min_count":2,
"epochs":20,
"dm":0,
"corpus_name": self.corpus_name,
"corpus_file": self.corpus_file,
"corpus_index": self.corpus_index,
"model_file": self.model_file,
"model_name": self.model_name
}
self.__save_settings()
def __save_settings(self):
content = json.dumps(self.settings)
with open(self.settings_file, "w") as f:
f.write(content)
def __save_model(self):
fname = get_tmpfile(self.model_file)
self.model.save(fname)
def __read_corpus(self, fname, tokens_only=False):
with smart_open.open(fname, encoding="iso-8859-1") as f:
for i, line in enumerate(f):
tokens = gensim.utils.simple_preprocess(line)
if tokens_only:
yield tokens
else:
# For training data, add tags
yield gensim.models.doc2vec.TaggedDocument(tokens, [i])
def __build_model(self):
train_corpus = list(self.__read_corpus(self.corpus_file))
model = gensim.models.doc2vec.Doc2Vec(vector_size=self.settings["vector_size"],
min_count=self.settings["min_count"],
epochs=self.settings["epochs"],
dm=self.settings["dm"])
model.build_vocab(train_corpus)
model.train(train_corpus, total_examples=model.corpus_count, epochs=model.epochs)
self.model = model
def __load_model(self):
if (os.path.exists(self.model_file)):
self.model = Doc2Vec.load(self.model_file)
else:
model = None
def gen_data(self, test_file):
if(self.model == None):
print("Must Load of Build a Model to test")
else:
self.__gen_data(test_file)
def __gen_data(self, test_file):
test_corpus = list(self.__read_corpus(test_file, tokens_only=True))
data = []
for doc in test_corpus:
inferred_vector = self.model.infer_vector(doc)
sims = self.model.docvecs.most_similar([inferred_vector], topn=len(self.model.docvecs))
ranks = self.__build_ranks(sims)
data.append({"doc": doc, "ranks": ranks})
self.__save_data(data)
#print('Test Document ({}): «{}»\n'.format(doc_id, ' '.join(test_corpus[doc_id])))
#print(u'SIMILAR/DISSIMILAR DOCS PER MODEL %s:\n' % self.model)
#for label, index in [('MOST', 0), ('MEDIAN', len(sims)//2), ('LEAST', len(sims) - 1)]:
# print(u'%s %s: «%s»\n' % (label, sims[index], ' '.join(train_corpus[sims[index][0]].words)))
def __save_data(self, data):
content = json.dumps(data)
with open(f"/outputs/{self.model_name}-{time.time()}output.json", "w") as f:
f.write(content)
def __build_ranks(self, sims):
corpus_indexer = CorpusIndexer(self.corpus_index)
data = []
for sim in sims:
desc = corpus_indexer.get_description(sim[0])
point = {"id": int(sim[0]), "rank": float(sim[1]), "description": desc}
data.append(point)
return data
| kcculhwch/prayer2vec | build/scripts/corpus_modeler.py | corpus_modeler.py | py | 5,038 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "os.path.exists",
"line_number": 40,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 40,
"usage_type": "attribute"
},
{
"api_name": "json.loads",
"line_number": 43,
"usage_type": "call"
},
{
"api_name": "json.dumps",
"line_number"... |
14144674644 | import numpy as np
from PIL import Image
import torch
class GenericDataset:
def __init__(self):
self.patch_width = None
self.image_height = None
def add(self, dataset):
return SummedDataset(self, dataset)
def resize_image(self, image):
original_width, original_height = image.size
original_height = max(original_height, 1)
original_width = max(original_width, 1)
scale = self.image_height / original_height
resized_width = int(round(scale * original_width, 0))
new_width = resized_width + (self.patch_width - (resized_width % self.patch_width)) # Adjusted this line
return image.resize((new_width, self.image_height))
def __add__(self, dataset):
return self.add(dataset)
class SummedDataset(GenericDataset):
def __init__(self, dataset_left, dataset_right) -> None:
self.left = dataset_left
self.right = dataset_right
def __len__(self):
return len(self.left) + len(self.right)
def __getitem__(self, idx):
if idx > (len(self.left) - 1):
idx_corrected = idx % len(self.left)
return self.right[idx_corrected]
return self.left[idx]
class DummyDataset(GenericDataset):
def __init__(self, number=30, name='dummy_v1', split='test') -> None:
self.samples = list(range(number))
self.name = name
self.split = split
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
return {
'dataset_name': self.name,
'split': self.split,
'image': self.samples[idx]
}
class CollateFNs:
def __init__(self, patch_width, image_height, character_tokenizer, seq2seq_same_size=False, max_size=224,
transforms=lambda x: x) -> None:
self.patch_width = patch_width
self.image_height = image_height
self.character_tokenizer = character_tokenizer
self.visual_padding_token = torch.zeros((3, self.image_height, self.patch_width))
self.total_visual_padding = torch.zeros((3, max_size, max_size))
self.same_size = seq2seq_same_size
self.max_size = max_size
self.transforms = transforms
def collate(self, batch):
max_tokens = max([len(x['tokens']) for x in batch])
max_patches = max([x['input_tensor'].shape[2] // self.patch_width for x in batch])
max_tokens_both = max(max_patches, max_tokens)
visual_tokens = []
text_tokens = []
raw_texts = []
sources = []
resized_images = []
original_images = []
images_tensor_full = []
patched_images = []
masks = []
images_full_resized = []
simply_padded_labels = []
for item in batch:
original_image, image, text_token, raw_text, split, dataset, resized_image = item['original_image'], item[
'input_tensor'], item['tokens'], item['annotation'], item['split'], item['dataset'], item[
'resized_image']
original_images.append(original_image)
resized_image = resize_to_max_size(resized_image, self.max_size)
image_to_be_patched = self.transforms(resized_image)
images_full_resized.append(self.transforms(resized_image.resize((self.max_size, self.max_size))))
_, w, h = image_to_be_patched.shape
final_image = self.total_visual_padding.clone()
# mask = self.total_visual_padding.clone()
final_image[:, :w, :h] = image_to_be_patched
# mask[:, w:, h:] = 1
# mask = 1 - mask
# masks.append(mask)
patched_images.append(final_image)
resized_images.append(resized_image)
sources.append(f"{split}_ {dataset}")
raw_texts.append(raw_text.lower())
patches = list(image.chunk(image.shape[2] // self.patch_width, dim=-1))
patches = patches + [self.visual_padding_token] * (max_tokens_both - len(patches))
text_tokenized = torch.from_numpy(
self.character_tokenizer(
text_token + [self.character_tokenizer.padding_token] * (max_tokens_both - len(text_token))
)
)
text_simply_tokenized = torch.from_numpy(
self.character_tokenizer(
text_token + [self.character_tokenizer.padding_token] * (max_tokens - len(text_token))
)
)
simply_padded_labels.append(text_simply_tokenized)
text_tokens.append(text_tokenized)
visual_tokens.append(
torch.stack(
patches
)
)
images_tensor_full.append(
torch.cat(patches, dim=2)
)
return {
'input_visual_seq': torch.stack(visual_tokens),
'images_tensor': torch.stack(images_tensor_full),
'padded_labels_to_text': torch.stack(simply_padded_labels),
'labels': torch.stack(text_tokens),
'raw_text_gt': raw_texts,
'sources': sources,
# 'original_images': resized_images,
'input_lengths': [x.size[0] // self.patch_width for x in resized_images],
'output_lengths': [len([char for char in x]) for x in raw_texts],
# 'pre_resize_images': original_images,
'totally_padded_image': torch.stack(patched_images),
'input_lengths_clip': [224 // self.patch_width for _ in resized_images],
'masks': masks,
'square_full_images': torch.stack(images_full_resized)
}
def collate_while_debugging(self, batch):
try:
return self.collate(batch)
except:
print(batch)
import pdb
pdb.set_trace()
def resize_to_max_size(image, max_size):
width, height = image.size
return image.resize((min(max_size, width), min(max_size, height)))
if __name__ == '__main__':
dataset_0_3 = DummyDataset(3, name='dataset_1', split='test')
dataset_3_5 = DummyDataset(2, name='dataset_2', split='test')
dataset_5_10 = DummyDataset(5, name='dataset_3', split='val')
dataset_0_5 = dataset_0_3 + dataset_3_5
dataset_0_10 = dataset_0_5 + dataset_5_10
print(dataset_0_10[0]) # Prints dataset_1: 0
print(dataset_0_10[3]) # Prints dataset_2: 0
print(dataset_0_10[5]) # Prints dataset_3: 0
print(dataset_0_10[2]) # Prints dataset_1: 2
print(dataset_0_10[4]) # Prints dataset_2: 1
print(dataset_0_10[9]) # Prints dataset_3: 4
| EauDeData/oda_ocr | src/dataloaders/summed_dataloader.py | summed_dataloader.py | py | 6,678 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "torch.zeros",
"line_number": 72,
"usage_type": "call"
},
{
"api_name": "torch.zeros",
"line_number": 73,
"usage_type": "call"
},
{
"api_name": "torch.from_numpy",
"line_number": 125,
"usage_type": "call"
},
{
"api_name": "torch.from_numpy",
"lin... |
37338865985 | from distutils.core import setup
import os.path
import setuptools
with open("README.md", "r") as fh:
long_description = fh.read()
setuptools.setup(
name="python_obfuscator",
packages=setuptools.find_packages(),
version="0.0.2",
license="MIT",
description="It's a python obfuscator.",
author="David Teather",
author_email="contact.davidteather@gmail.com",
url="https://github.com/davidteather/python-obfuscator",
long_description=long_description,
long_description_content_type="text/markdown",
download_url="https://github.com/davidteather/python-obfuscator/tarball/master",
keywords=["obfuscator"],
install_requires=["regex"],
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"Topic :: Software Development :: Build Tools",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
],
entry_points={"console_scripts": ["pyobfuscate=python_obfuscator.cli:cli"]},
)
| davidteather/python-obfuscator | setup.py | setup.py | py | 1,182 | python | en | code | 133 | github-code | 36 | [
{
"api_name": "setuptools.setup",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "setuptools.find_packages",
"line_number": 10,
"usage_type": "call"
}
] |
69904854506 | from flask import Flask, request, jsonify
from config import Config
from flask_cors import CORS, cross_origin
from database import db
def create_app(config_class=Config):
app = Flask(__name__)
app.config.from_object(config_class)
app.config['CORS_HEADERS'] = 'Content-Type'
db.init_app(app)
# import controllers
from controllers.weo.weo_controller import weo_bp
from controllers.econ.econ_controller import econ_bp
from controllers.money_supply.money_supply_controller import money_supply_bp
from controllers.oil.oil_controller import oil_production_bp
# register controllers
app.register_blueprint(weo_bp)
app.register_blueprint(econ_bp, url_prefix='/econ')
app.register_blueprint(money_supply_bp)
app.register_blueprint(oil_production_bp, url_prefix='/oil')
# test route
@app.route('/')
def hello_world():
return "Izzy is pretty"
# error handler
@app.errorhandler(404)
def not_found(error):
return jsonify({"error": "Not Found"}), 404
return app
app = create_app()
cors = CORS(app, resources={r"/*": {"origins": "*"}})
@app.after_request
def add_cors_headers(response):
response.headers.add('Access-Control-Allow-Origin', '*')
response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
return response
if __name__ == '__main__':
app.run() | danme-l/website-v2-backend | app.py | app.py | py | 1,479 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "config.Config",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "flask.Flask",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "database.db.init_app",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "database.db",
"line_... |
30872218901 | # -*- coding: utf-8 -*-
# from rest_framework.generics import ListAPIView, CreateAPIView, UpdateAPIView, RetrieveAPIView, DestroyAPIView
# from django.shortcuts import render
# from django.views import View
# from rest_framework.views import APIView
from rest_framework.authentication import BasicAuthentication, SessionAuthentication
from rest_framework.permissions import IsAuthenticatedOrReadOnly, IsAuthenticated
from rest_framework.generics import GenericAPIView
from rest_framework.response import Response
from rest_framework.mixins import ListModelMixin, CreateModelMixin
from django.http import JsonResponse
from django.http.request import QueryDict
from rest_framework.viewsets import GenericViewSet, ModelViewSet
from crawlers.models import Book as Book_model, Comment as Comment_model
from book_drf.serializer import BookSerializer, CommentSerializer
# Create your views here.
class Books(GenericAPIView):
queryset = Book_model.objects.all()
serializer_class = BookSerializer
def get_res(self):
temp = {'result': False, 'data': None, 'msg': ''}
return dict(**temp)
def get(self, request):
# print(request.data, request.query_params)
# books = Book_model.objects.all()
books = self.get_queryset()
# c1 = BookContent.objects.get(id=1)
# print(c1.book_name.book_name)
# 关联查询 主表对象.(foreign_model.lower())_set
# book1 = Book_model.objects.get(book_id=1)
# contents = BookContent.objects.filter(book_name_id=book_id)
# contents = book1.bookcontent_set.all()
# print(contents)
ser = self.get_serializer(books, many=True)
# ser = BookSerializer(books, many=True)
res = self.get_res()
res['data'] = ser.data
res['result'] = True
# return JsonResponse(ser.data, safe=False, json_dumps_params={
# 'ensure_ascii': False})
return Response(data=res)
# class Book(GenericAPIView):
# queryset = Book_model.objects.all()
# serializer_class = BookSerializer
#
# def get_res(self):
# temp = {'result': False, 'data': None, 'msg': ''}
# return dict(**temp)
#
# def get(self, request, book_id):
# print(request.data, request.query_params)
# res = self.get_res()
# try:
# book1 = Book_model.objects.get(book_id=book_id)
# print('121:', book_id, book1)
# # books = self.get_queryset()
#
# ser = BookSerializer(book1, many=False)
# # ser.is_valid()
#
# res['result'] = True
# res['data'] = ser.data
#
# except Book_model.DoesNotExist:
# res['msg'] = 'not found'
# return JsonResponse(res, json_dumps_params={
# 'ensure_ascii': False}, status=404)
#
# # return JsonResponse(res, json_dumps_params={
# # 'ensure_ascii': False})
# return Response(data=res)
class Book(ModelViewSet):
authentication_classes = (BasicAuthentication, SessionAuthentication)
permission_classes = (IsAuthenticated, )
queryset = Book_model.objects.all()
serializer_class = BookSerializer
class Comment(GenericAPIView, ListModelMixin, CreateModelMixin):
queryset = Comment_model.objects.all()
serializer_class = CommentSerializer
def get(self, request):
return self.list(request)
# comments = self.get_queryset().filter(book_id=book_id, chapter_count=chapter_count)
# ser = self.get_serializer(comments, many=True)
# return Response(data=ser.data)
# def post(self, request: QueryDict, book_id, chapter_count):
def post(self, request: QueryDict):
try:
return self.create(request)
except Exception as e:
import traceback
traceback.print_exc()
# print(e)
return Response({'msg': str(e)}, 400)
# c = self.get_object()
# print('c=', c)
data = request.data
print(data, type(data))
temp = {'book_id': book_id, 'chapter_count': chapter_count}
temp.update(data)
print('temp:', temp)
# new_comment = Comment_model.objects.create()
ser = self.get_serializer(data=temp)
if ser.is_valid():
ser.save()
return Response(data=ser.data)
else:
# ser.save()
res = {'msg': 'invalid',
'data': ser.errors}
return Response(data=res, status=400)
| ietar/huluobei | book_drf/views2.py | views2.py | py | 4,527 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "rest_framework.generics.GenericAPIView",
"line_number": 20,
"usage_type": "name"
},
{
"api_name": "crawlers.models.Book.objects.all",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "crawlers.models.Book.objects",
"line_number": 22,
"usage_type": "... |
70442787624 | #coding: utf-8
# __author__ = jqy
import logging
class Logger(object):
def __init__(self, log_file):
"""Initialize logging module."""
# disable requests log
logging.getLogger("requests").setLevel(logging.DEBUG)
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s')
# Create a file handler to store error messages
fh = logging.FileHandler(log_file, mode='w')
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
# Create a stream handler to print all messages to console
ch = logging.StreamHandler()
ch.setLevel(logging.WARNING)
ch.setFormatter(formatter)
logger.addHandler(fh)
logger.addHandler(ch)
self.logger = logger
def getLogger(self):
return self.logger
| stkaeljason/TjgbSpider | logdealer.py | logdealer.py | py | 932 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "logging.getLogger",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "logging.DEBUG",
"line_number": 12,
"usage_type": "attribute"
},
{
"api_name": "logging.getLogger",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "logging.DEBUG"... |
36748155431 | from collections import OrderedDict
from fudge.index import Index, read_index, write_index
from fudge.object import Object, load_object, store_object
from fudge.parsing.builder import Builder
from fudge.parsing.parser import Parser
from fudge.utils import FudgeException
class Node(object):
def __init__(self, name, mode, object_id):
self.name = name
self.mode = mode
self.object_id = object_id
self.children = OrderedDict()
def __iter__(self):
for child in self.children.values():
yield child
def add(self, child):
self.children[child.name] = child
def get(self, name):
return self.children.get(name)
@property
def is_branch(self):
return len(self.children) > 0
@property
def is_leaf(self):
return len(self.children) == 0
def get_node(root, path):
path = path.split('/')
current = root
for dirname in path:
current = current.get(dirname)
if not current:
break
return current
def walk_tree2(root, recurse, basepath):
for child in root:
path = basepath + child.name
yield path, child
if child.is_branch and recurse:
yield from walk_tree2(child, recurse, path + '/')
def walk_tree(root, recurse):
return walk_tree2(root, recurse, '')
def print_tree(root, recurse):
types = {
'100644': 'blob',
'100755': 'blob',
'40000': 'tree',
'160000': 'commit',
}
for path, node in walk_tree(root, recurse):
if node.is_branch and recurse:
continue
node_type = types.get(node.mode) or 'unknown'
print('{:0>6} {} {} {}'.format(node.mode, node_type, node.object_id, path))
def build_tree_from_object2(root):
obj = load_object(root.object_id)
if obj.type != 'tree':
raise FudgeException('the specified object is not a tree')
parser = Parser(obj.contents, padding=False)
while not parser.eof:
info = parser.get_utf8()
mode, name = info.split()
object_id = parser.get_sha1()
node = Node(name, mode, object_id)
root.add(node)
if mode == '40000':
build_tree_from_object2(node)
return root
def build_tree_from_object(object_id):
root = Node('root', None, object_id)
return build_tree_from_object2(root)
def read_tree2(index, root, basepath):
for child in root:
path = basepath + child.name
if child.is_branch:
read_tree2(index, child, path + '/')
else:
index.add_object(child.mode, child.object_id, path)
def read_tree(object_id):
index = Index()
root = build_tree_from_object(object_id)
read_tree2(index, root, '')
write_index(index)
def build_tree_from_index():
root = Node('root', None, None)
index = read_index()
for entry in index:
parts = entry.path.split('/')
if len(parts) == 1:
path, filename = [], parts[0]
else:
path, filename = parts[:-1], parts[-1]
current = root
for dirname in path:
if not current.get(dirname):
node = Node(dirname, '40000', None)
current.add(node)
current = current.get(dirname)
node = Node(filename, entry.perms, entry.object_id)
current.add(node)
return root
def write_tree2(root):
builder = Builder(padding=False)
for child in root:
if child.is_branch:
object_id = write_tree2(child)
child.object_id = object_id
info = '{} {}'.format(child.mode, child.name)
builder.set_utf8(info)
builder.set_sha1(child.object_id)
data = builder.data
obj = Object('tree', len(data), data)
store_object(obj)
return obj.id
def write_tree():
root = build_tree_from_index()
return write_tree2(root)
| QuantamKawts/fudge | fudge/tree.py | tree.py | py | 3,926 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "collections.OrderedDict",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "fudge.object.load_object",
"line_number": 80,
"usage_type": "call"
},
{
"api_name": "fudge.utils.FudgeException",
"line_number": 82,
"usage_type": "call"
},
{
"api_n... |
24770123438 | #!/usr/bin/python3
import requests # pip3 install requests
import sys
from lxml import etree # pip3 install lxml
import re
class Item:
category = ''
detName = ''
detDesc = ''
link = ''
def __init__(self, category, detName, detDesc, link):
self.category = category
self.detName = detName
self.detDesc = detDesc
self.link = link
def parseHtmlForItems(html):
items = []
trs = html.xpath('//table[@id="searchResult"]/tr')
for tr in trs:
cInfo = tr.xpath('./td[@class="vertTh"]/center/a/text()')
cStr = _paresCategoryInfo(cInfo)
if cStr != None:
detName = tr.xpath('.//a[@class="detLink"]/text()')[0].strip()
detDescTailNode = tr.xpath('.//font[@class="detDesc"]/a/text()')
detDescTail = ""
if len(detDescTailNode) > 0:
detDescTail = detDescTailNode[0]
detDesc = (tr.xpath('.//font[@class="detDesc"]/text()')[0] + detDescTail).strip()
link = tr.xpath('.//a[@title="Download this torrent using magnet"]/@href')[0].strip()
items.append(Item(cStr, detName, detDesc, link))
return items
def _paresCategoryInfo(categoryInfo):
if len(categoryInfo) != 2:
return None
return "{0}({1})".format(categoryInfo[0].strip(), categoryInfo[1].strip())
def showItems(items):
for i, item in enumerate(items):
print(i, item.category, item.detName, item.detDesc, sep=" | ")
def showItem(items, i):
if (i < 0) | (i >= len(items)):
print("无指定条目!")
return
item = items[i]
print(i, item.category, item.detName, item.detDesc, sep=" | ", end=" :\n")
print(item.link)
class Searcher:
searchName = ''
pageNum = 1
orderBy = 99
category = "0"
categoryStr = "All"
categoryNode = None
begin = 0
end = 0
totle = 0
count = 0
items = []
def search(self):
if (self.searchName == "") | (self.searchName.isspace()):
return
# 搜索
url = "https://thepiratebay10.org/search/{0}/{1}/{2}/{3}".format(self.searchName, self.pageNum, self.orderBy, self.category)
resp = requests.get(url)
if resp.status_code != 200:
print("网络请求出错, 请检查网络或访问的地址!!")
sys.exit(1)
resp.encoding = "utf-8"
html = etree.HTML(resp.text)
# 设置page信息
countInfo = html.xpath('//body/h2[1]/text()')[0].strip()
self._parseCountInfo(countInfo)
# 设置categoryNode
categoryNodes = html.xpath('//select[@id="category"]')
if len(categoryNodes) > 0:
self.categoryNode = categoryNodes[0]
else:
self.categoryNode = None
# 设置items
self.items = parseHtmlForItems(html)
def _parseCountInfo(self, countInfo):
matchObj = re.match(r"Displaying hits from (\d+) to (\d+) \(approx (\d+) found\)", countInfo, re.I)
if matchObj != None:
self.begin = int(matchObj.group(1))
self.end = int(matchObj.group(2))
self.totle = int(matchObj.group(3))
self.count = (self.totle + 29) // 30
else:
self.begin = 0
self.end = 0
self.totle = 0
self.count = 0
def setupCategory(self):
buf = {"0": "All"}
gBuf = {}
print("0 : All")
if self.categoryNode != None:
optgroups = self.categoryNode.xpath('./optgroup')
for optgroup in optgroups:
gCode = optgroup.xpath('./option[1]/@value')[0][:1] + "00"
gDesc = optgroup.xpath('./@label')[0]
print("[ {0} : {1} ]:".format(gCode, gDesc))
gBuf[gCode] = gDesc
opts = optgroup.xpath('./option')
for opt in opts:
code = opt.xpath('./@value')[0]
desc = opt.xpath('./text()')[0].strip()
buf[code] = desc
print("{0} : {1}".format(code, desc))
print("当前类型: {0}({1})".format(self.category, self.categoryStr))
while True:
inputStr = input("请输出类别编码(可以使用','分隔以同时输入多个组编码; 输入'q'将取消设置): ").strip()
if "q" == inputStr:
return
elif inputStr in buf:
self.category = inputStr
self.categoryStr = buf[inputStr]
return
else:
gCodes = inputStr.split(",")
cs = self._getCategoryStrByGCodes(gBuf, gCodes)
if cs != None:
self.category = inputStr
self.categoryStr = cs
return
else:
print("输入有误!")
def _getCategoryStrByGCodes(self, gBuf, gCodes):
gDescs = []
for gc in gCodes:
if gc in gBuf:
gDescs.append(gBuf[gc])
else:
return None
return ",".join(gDescs)
def showMeta(self):
print("类别: {0}, 搜索: {1}, 页码: {2}, 总条数: {3}, 总页数: {4}, 显示条码: [{5},{6})".format(
self.categoryStr, self.searchName, self.pageNum, self.totle, self.count, self.begin, self.end))
def searchAndShow(self):
self.search()
showItems(self.items)
self.showMeta()
def setPageNum(self, pageNum):
if pageNum < 1:
pageNum = 1
elif pageNum > self.count:
if self.count == 0:
pageNum = 1
else:
pageNum = self.count
self.pageNum = pageNum
class TopCategory:
title = ""
url = ""
ended = False
def __init__(self, title, url, ended = False):
self.title = title
self.url = url
self.ended = ended
class Toper:
categories = []
index = 0
items = []
def __init__(self):
url = "https://thepiratebay10.org/top"
resp = requests.get(url)
if resp.status_code != 200:
print("网络请求出错, 请检查网络或访问的地址!!")
sys.exit(1)
resp.encoding = "utf-8"
html = etree.HTML(resp.text)
nodes = html.xpath('//table[@id="categoriesTable"]/tr/td/dl/*')
for node in nodes:
if node.tag == "dt":
title = node.xpath('./a[1]/text()')[0].strip()
url = node.xpath('./a[1]/@href')[0]
self.categories.append(TopCategory("[ {0} ] ".format(title), "https://thepiratebay10.org"+url))
title2 = "[ {0}({1}) ]".format(title, node.xpath('./a[2]/text()')[0].strip())
url2 = node.xpath('./a[2]/@href')[0]
self.categories.append(TopCategory(title2, "https://thepiratebay10.org"+url2, True))
elif node.tag == "dd":
subNodes = node.xpath('.//a')
for subNode in subNodes:
title = subNode.xpath('./text()')[0].strip()
url = subNode.xpath('./@href')[0]
self.categories.append(TopCategory(title, "https://thepiratebay10.org"+url))
self.categories[-1].ended = True
def showCategories(self):
for i, v in enumerate(self.categories):
print(i , v.title, sep=":", end=" ")
if v.ended:
print()
def setCategoryIndex(self, index):
if (index < 0) | (index >= len(self.categories)):
return False
else:
self.index = index
return True
def getTop(self):
url = self.categories[self.index].url
resp = requests.get(url)
resp.encoding = "utf-8"
html = etree.HTML(resp.text)
# 设置items
self.items = parseHtmlForItems(html)
def getAndShow(self):
self.getTop()
showItems(self.items)
def runSearcher(searcher):
inputStr = input("请输入搜索关键词(输出空白字符将退出): ").strip()
if inputStr == "":
return
searcher.searchName = inputStr
searcher.searchAndShow()
while True:
ops = input("i <item> 查看指定条目详情; g <page> 跳转到指定页码; p 上一页; n 下一页; s <keyword> 搜索指定关键词; r 刷新; c 设置查询类型; q 退出;\n")
op = ops[:1]
arg = ops[1:].strip()
if op == "i":
showItem(searcher.items, (int(arg)))
elif op == "g":
searcher.setPageNum(int(arg))
searcher.searchAndShow()
elif op == "p":
searcher.setPageNum(searcher.pageNum - 1)
searcher.searchAndShow()
elif op == "n":
searcher.setPageNum(searcher.pageNum + 1)
searcher.searchAndShow()
elif op == "s":
if arg == "":
print("输入有误(不允许为空)!")
else:
searcher.searchName = arg
searcher.pageNum = 1
searcher.searchAndShow()
elif op == "r":
searcher.searchAndShow()
elif op == "c":
searcher.setupCategory()
elif op == "q":
return
def runToper(toper):
toper.showCategories()
while True:
inputStr = input("请输入类别编号[0, {0})(输入'q'将退出): ".format(len(toper.categories))).strip()
if inputStr == "q":
return
index = int(inputStr)
flag = toper.setCategoryIndex(index)
if flag:
toper.getAndShow()
break
else:
print("输入有误!")
while True:
ops = input("i <item> 查看指定条目详情; g <no> 查询指定类别编号的热门条目; r 刷新; c 查看类别目录; q 退出;\n")
op = ops[:1]
arg = ops[1:].strip()
if op == "i":
showItem(toper.items, int(arg))
elif op == "g":
flag = toper.setCategoryIndex(int(arg))
if flag:
toper.getAndShow()
else:
print("输入有误!")
elif op == "r":
toper.getAndShow()
elif op == "c":
toper.showCategories()
elif op == "q":
return
if __name__ == "__main__":
searcher = None
toper = None
while True:
m = input("请输入模式('s'(搜索), 't'(热门))(输入'q'将退出): ").strip()
if m == "q":
break
elif m == "s":
if searcher == None:
searcher = Searcher()
runSearcher(searcher)
elif m == "t":
if toper == None:
toper = Toper()
runToper(toper)
| echcz/sak | piratebay-cli.py | piratebay-cli.py | py | 10,776 | python | en | code | 2 | github-code | 36 | [
{
"api_name": "requests.get",
"line_number": 71,
"usage_type": "call"
},
{
"api_name": "sys.exit",
"line_number": 74,
"usage_type": "call"
},
{
"api_name": "lxml.etree.HTML",
"line_number": 76,
"usage_type": "call"
},
{
"api_name": "lxml.etree",
"line_number":... |
20336422398 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.6.2)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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qInitResources()
| NIRVANALAN/TU_rcm | resources.py | resources.py | py | 612,926 | python | ja | code | 2 | github-code | 36 | [
{
"api_name": "PyQt5.QtCore.qRegisterResourceData",
"line_number": 9381,
"usage_type": "call"
},
{
"api_name": "PyQt5.QtCore",
"line_number": 9381,
"usage_type": "name"
},
{
"api_name": "PyQt5.QtCore.qUnregisterResourceData",
"line_number": 9384,
"usage_type": "call"
},... |
24428229816 | import numpy as np
import matplotlib.pyplot as plt
import random
class Tuihuo:
path = []
def __init__(self, graph, num, value=0): # 初始化类
self.num = num
self.graph = graph
self.value = 0
def output_graph(self): # 输出初始图
for i in self.graph:
for j in i:
print(j, end=',')
print()
def distance(self, tuihuocurrent):
valuenow = 0
for i in range(self.num - 1):
valuenow += self.graph[tuihuocurrent[i]][tuihuocurrent[i + 1]]
valuenow += self.graph[tuihuocurrent[self.num - 1]][0]
return valuenow
def threetuihuo(self, tuihuocurrent, x, y, w):
if x > y:
x, y = y, x
if w > y:
w, y = y, w
if w > x:
w, x = x, w
temp1 = tuihuocurrent[x:y + 1] + tuihuocurrent[w + 1:x]
tuihuocurrent[w + 1:y + 1] = temp1
return tuihuocurrent
def calculate(self): # 计算近似最短路径并且将路径存储在path中
result = [] # 用于生成可视化图表
tuihuosolution = list(range(self.num))
valuenow = 0
valuenow = self.distance(tuihuosolution)
valuebest = valuenow
tuihuocurrent = tuihuosolution.copy()
tuihuonow = tuihuosolution.copy()
t = 100000 # 初始温度
t1 = 1 # 最后底线温度
r = 0.99999 # 降温参数
while t > t1:
# 使用两路扰乱和三路扰乱两种方式
p1 = np.random.rand()
if p1 > 0.5: # 使用二路扰乱
while 1:
x = random.randrange(1, self.num - 1)
y = random.randrange(1, self.num - 1) # 生成交换坐标点
if x != y:
break
tuihuocurrent[x], tuihuocurrent[y] = tuihuocurrent[y], tuihuocurrent[x]
else: # 使用三路扰乱
while 1:
x = random.randrange(1, self.num - 1)
y = random.randrange(1, self.num - 1)
w = random.randrange(1, self.num - 1) # 生成交换坐标点
if x != y and x != w and y != w:
break
tuihuocurrent = self.threetuihuo(tuihuocurrent, x, y, w)
valuethen = self.distance(tuihuocurrent)
# 判断是否接受该解
if valuethen < valuenow:
tuihuonow = tuihuocurrent.copy()
valuenow = valuethen
if valuethen < valuebest:
tuihuosolution = tuihuocurrent.copy()
valuebest = valuethen
elif np.random.rand() < np.exp(-(valuethen - valuebest) / t): # 一定概率在不是优解的情况下接受
valuenow = valuethen
tuihuonow = tuihuocurrent.copy()
else:
tuihuocurrent = tuihuonow.copy()
t = t * r
result.append(valuenow)
plt.plot(np.array(result))
plt.ylabel("valuenow")
plt.xlabel("t")
plt.show()
self.value = valuebest
return tuihuosolution
def print_outcome(self):
num = self.calculate()
print("模拟退火算法近似最短路径为",end='')
print(0, end='')
for i in range(len(num)):
if i != 0:
print("->%d" % num[i], end='')
print("-> 0")
print("模拟退火算法近似最短路径长度为" + str(self.value))
| blue-vegetable/AI_TSP_HOMEWORK | SA.py | SA.py | py | 3,558 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "numpy.random.rand",
"line_number": 53,
"usage_type": "call"
},
{
"api_name": "numpy.random",
"line_number": 53,
"usage_type": "attribute"
},
{
"api_name": "random.randrange",
"line_number": 56,
"usage_type": "call"
},
{
"api_name": "random.randrange... |
72172630823 | """
Inventarios Componentes v3, CRUD (create, read, update, and delete)
"""
from typing import Any
from sqlalchemy.orm import Session
from lib.exceptions import MyIsDeletedError, MyNotExistsError
from lib.safe_string import safe_string
from ...core.inv_componentes.models import InvComponente
from ..inv_categorias.crud import get_inv_categoria
from ..inv_equipos.crud import get_inv_equipo
def get_inv_componentes(
database: Session,
inv_categoria_id: int = None,
inv_equipo_id: int = None,
generacion: str = None,
) -> Any:
"""Consultar los componentes activos"""
consulta = database.query(InvComponente)
if inv_categoria_id is not None:
inv_categoria = get_inv_categoria(database, inv_categoria_id=inv_categoria_id)
consulta = consulta.filter(InvComponente.inv_categoria == inv_categoria)
if inv_equipo_id is not None:
inv_equipo = get_inv_equipo(database, inv_equipo_id=inv_equipo_id)
consulta = consulta.filter(InvComponente.inv_equipo == inv_equipo)
if generacion is not None:
generacion = safe_string(generacion)
if generacion != "":
consulta = consulta.filter_by(generacion=generacion)
return consulta.filter_by(estatus="A").order_by(InvComponente.id.desc())
def get_inv_componente(database: Session, inv_componente_id: int) -> InvComponente:
"""Consultar un componente por su id"""
inv_componente = database.query(InvComponente).get(inv_componente_id)
if inv_componente is None:
raise MyNotExistsError("No existe ese componente")
if inv_componente.estatus != "A":
raise MyIsDeletedError("No es activo ese componente, está eliminado")
return inv_componente
| PJECZ/pjecz-plataforma-web-api-key | plataforma_web/v4/inv_componentes/crud.py | crud.py | py | 1,709 | python | es | code | 0 | github-code | 36 | [
{
"api_name": "sqlalchemy.orm.Session",
"line_number": 17,
"usage_type": "name"
},
{
"api_name": "core.inv_componentes.models.InvComponente",
"line_number": 23,
"usage_type": "argument"
},
{
"api_name": "inv_categorias.crud.get_inv_categoria",
"line_number": 25,
"usage_ty... |
33815850991 | from django.shortcuts import get_object_or_404
from rest_framework.decorators import api_view, permission_classes
from rest_framework.response import Response
from rest_framework import status
from rest_framework.permissions import IsAdminUser, IsAuthenticated
from .models import Exercise
from .serializers import ExerciseSerializer
import requests
import os
from workouts.models import Workout
# Create your views here.
@api_view(['GET'])
@permission_classes([IsAuthenticated])
def api_exercises_list(request):
if request.method == 'GET':
url = 'https://exercisedb.p.rapidapi.com/exercises'
headers = {
'X-RapidAPI-Key': os.environ.get("EXERCISE_DB_RAPID_API_KEY"),
'X-RapidAPI-Host': 'exercisedb.p.rapidapi.com'
}
response = requests.get(url=url, headers=headers)
return Response(response.json(), status=status.HTTP_200_OK)
return Response(status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'POST'])
@permission_classes([IsAdminUser])
def admin_exercise_list(request):
if request.method == 'GET':
workout_id = request.query_params.get('workout_id')
if workout_id is not None:
exercises = Workout.objects.get(pk=workout_id).exercise_set.all()
serializer = ExerciseSerializer(exercises, many=True)
return Response(serializer.data)
if request.method == 'POST':
workout_id = request.data.pop('workouts_id')
serializer = ExerciseSerializer(data=request.data)
serializer.is_valid(raise_exception=True)
serializer.save()
new_exercise = get_object_or_404(
Exercise, pk=serializer.data.get('id'))
if new_exercise is not None:
workout = get_object_or_404(Workout, id=workout_id)
new_exercise.workouts.add(workout)
new_exercise = get_object_or_404(
Exercise, pk=serializer.data.get('id'))
serializer = ExerciseSerializer(new_exercise)
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(status=status.HTTP_400_BAD_REQUEST)
@api_view(['GET', 'PATCH', 'DELETE'])
@permission_classes([IsAuthenticated])
def exercise_detail(request, pk):
exercise = get_object_or_404(Exercise, pk=pk)
if request.method == 'GET':
serializer = ExerciseSerializer(exercise)
return Response(serializer.data, status=status.HTTP_200_OK)
elif request.method == 'PATCH' and request.user.is_staff:
serializer = ExerciseSerializer(
exercise, data=request.data, partial=True)
serializer.is_valid(raise_exception=True)
serializer.save()
return Response(serializer.data, status=status.HTTP_200_OK)
elif request.method == 'DELETE' and request.user.is_staff:
exercise.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
return Response(status=status.HTTP_400_BAD_REQUEST)
| christianbeckham/capstone-app | backend/exercises/views.py | views.py | py | 2,944 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "os.environ.get",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "os.environ",
"line_number": 22,
"usage_type": "attribute"
},
{
"api_name": "requests.get",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "rest_framework.response.R... |
40660876105 | # -*- coding:utf-8 -*-
import os
import sys
import time
import tensorflow as tf
import seq2seqModel
import getConfig
import io
gConfig = {}
gConfig=getConfig.get_config(config_file='seq2seq.ini')
vocab_inp_size = gConfig['enc_vocab_size']
vocab_tar_size = gConfig['dec_vocab_size']
embedding_dim=gConfig['embedding_dim']
units=gConfig['layer_size']
BATCH_SIZE=gConfig['batch_size']
max_length_inp,max_length_tar=20,20
def preprocess_sentence(w):
w ='start '+ w + ' end'
#print(w)
return w
def create_dataset(path, num_examples):
lines = io.open(path, encoding='UTF-8').read().strip().split('\n')
word_pairs = [[preprocess_sentence(w)for w in l.split('\t')] for l in lines[:num_examples]]
return zip(*word_pairs)
def max_length(tensor):
return max(len(t) for t in tensor)
def read_data(path,num_examples):
input_lang,target_lang=create_dataset(path,num_examples)
input_tensor,input_token=tokenize(input_lang)
target_tensor,target_token=tokenize(target_lang)
return input_tensor,input_token,target_tensor,target_token
def tokenize(lang):
lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=gConfig['enc_vocab_size'], oov_token=3)
lang_tokenizer.fit_on_texts(lang)
tensor = lang_tokenizer.texts_to_sequences(lang)
tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor, maxlen=max_length_inp,padding='post')
return tensor, lang_tokenizer
input_tensor,input_token,target_tensor,target_token= read_data(gConfig['seq_data'], gConfig['max_train_data_size'])
def train():
print("Preparing data in %s" % gConfig['train_data'])
steps_per_epoch = len(input_tensor) // gConfig['batch_size']
print(steps_per_epoch)
enc_hidden = seq2seqModel.encoder.initialize_hidden_state()
checkpoint_dir = gConfig['model_data']
ckpt=tf.io.gfile.listdir(checkpoint_dir)
if ckpt:
print("reload pretrained model")
seq2seqModel.checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
BUFFER_SIZE = len(input_tensor)
dataset = tf.data.Dataset.from_tensor_slices((input_tensor,target_tensor)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
checkpoint_dir = gConfig['model_data']
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
start_time = time.time()
while True:
start_time_epoch = time.time()
total_loss = 0
for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
batch_loss = seq2seqModel.train_step(inp, targ,target_token, enc_hidden)
total_loss += batch_loss
print(batch_loss.numpy())
step_time_epoch = (time.time() - start_time_epoch) / steps_per_epoch
step_loss = total_loss / steps_per_epoch
current_steps = +steps_per_epoch
step_time_total = (time.time() - start_time) / current_steps
print('训练总步数: {} 每步耗时: {} 最新每步耗时: {} 最新每步loss {:.4f}'.format(current_steps, step_time_total, step_time_epoch,
step_loss.numpy()))
seq2seqModel.checkpoint.save(file_prefix=checkpoint_prefix)
sys.stdout.flush()
def predict(sentence):
checkpoint_dir = gConfig['model_data']
seq2seqModel.checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
sentence = preprocess_sentence(sentence)
inputs = [input_token.word_index.get(i,3) for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs],maxlen=max_length_inp,padding='post')
inputs = tf.convert_to_tensor(inputs)
result = ''
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = seq2seqModel.encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([target_token.word_index['start']], 0)
for t in range(max_length_tar):
predictions, dec_hidden, attention_weights = seq2seqModel.decoder(dec_input, dec_hidden, enc_out)
predicted_id = tf.argmax(predictions[0]).numpy()
if target_token.index_word[predicted_id] == 'end':
break
result += target_token.index_word[predicted_id] + ' '
dec_input = tf.expand_dims([predicted_id], 0)
return result
if __name__ == '__main__':
if len(sys.argv) - 1:
gConfig = getConfig.get_config(sys.argv[1])
else:
gConfig = getConfig.get_config()
print('\n>> Mode : %s\n' %(gConfig['mode']))
if gConfig['mode'] == 'train':
train()
elif gConfig['mode'] == 'serve':
print('Serve Usage : >> python3 app.py')
| zhangzhiqiangccm/NLP-project | chineseChatbotWeb/execute.py | execute.py | py | 4,645 | python | en | code | 120 | github-code | 36 | [
{
"api_name": "getConfig.get_config",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "io.open",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "tensorflow.keras.preprocessing.text.Tokenizer",
"line_number": 45,
"usage_type": "call"
},
{
"api_na... |
5603104189 | import discord
cogs_list = [
"help",
"unveil",
"bet",
"account"
]
class Dealer(discord.Bot):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
for cog in cogs_list:
self.load_extension(f"cogs.{cog}")
async def on_ready(self):
print(f"{self.user} has connected to Discord!")
| liang799/rivenDealer | bot.py | bot.py | py | 359 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "discord.Bot",
"line_number": 11,
"usage_type": "attribute"
}
] |
12338769948 | ################011011100110010101101111####
### neo Command Line #######################
############################################
def getcmdlist():
cmds = {
"os" :"Open active Schedule View in Excel.",
"ov" :"Open selected views in Project Browser."
}
return cmds
def runcmd(cmd, msg, recallCL=False):
if cmd == 'os':
from lib.views import neocl_open_schedule_xl as os
os.ExportActiveScheduleViewToExcel()
elif cmd == 'ov':
from lib.views import neocl_views_open as ov
ov.OpenSelectedViews()
else:
from neocl import unknowncmd
unknowncmd(cmd, recallCL, getcmdlist()) | 0neo/pyRevit.neoCL | neoCL.extension/neocl_o.py | neocl_o.py | py | 700 | python | en | code | 7 | github-code | 36 | [
{
"api_name": "lib.views.neocl_open_schedule_xl.ExportActiveScheduleViewToExcel",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "lib.views.neocl_open_schedule_xl",
"line_number": 16,
"usage_type": "name"
},
{
"api_name": "lib.views.neocl_views_open.OpenSelectedViews",
... |
15442973460 | from __future__ import absolute_import, print_function
import json
import os
import tempfile
import mock
import mesos.cli.cfg
import mesos.cli.cmds.config
from .. import utils
config_path = os.path.normpath(os.path.join(
os.path.dirname(__file__), "..", "data", "config.json"))
class TestConfig(utils.MockState):
@mock.patch('os.environ', {"MESOS_CLI_CONFIG": config_path})
@utils.patch_args(["mesos-config"])
def test_output(self):
mesos.cli.cmds.config.CFG = mesos.cli.cfg.Config()
mesos.cli.cmds.config.main()
out = json.loads(self.stdout)
assert "master" in out["test"]
@mock.patch('os.environ', {"MESOS_CLI_CONFIG": config_path})
@utils.patch_args([
"mesos-config",
"master"
])
def test_get_key(self):
mesos.cli.cmds.config.CFG = mesos.cli.cfg.Config()
mesos.cli.cmds.config.main()
assert "zk://localhost:2181/mesos" in self.stdout
@utils.patch_args([
"mesos-config",
"master",
"zk://localhost:2181/mesos"
])
def test_set_key(self):
fd, fname = tempfile.mkstemp()
with open(fname, "w") as fobj:
fobj.write("{}")
try:
with mock.patch('os.environ', {"MESOS_CLI_CONFIG": fname}):
mesos.cli.cmds.config.CFG = mesos.cli.cfg.Config()
mesos.cli.cmds.config.main()
with open(fname, "r") as fobj:
assert "zk://localhost:2181" in json.loads(
fobj.read())["default"]["master"]
finally:
os.remove(fname)
| mesosphere-backup/mesos-cli | tests/integration/test_config.py | test_config.py | py | 1,613 | python | en | code | 116 | github-code | 36 | [
{
"api_name": "os.path.normpath",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 14,
"usage_type": "attribute"
},
{
"api_name": "os.path.join",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "os.path.dirname",
"lin... |
7551769686 | from web3 import Web3, EthereumTesterProvider
from etherscan import Etherscan
import json
class Node:
def __init__(self, name, value, address, children=[]):
self.name = name
self.value = value
self.children = children
self.searched = False
self.address = Web3.toChecksumAddress(address)
class NodeOperations:
def __init__(self, web3, etherscan):
self.web3 = web3
self.etherscan = etherscan
def print_tree(self, node, level=0):
print(' ' * level + node.name + ": \t" + node.address)
for child in node.children:
self.print_tree(child, level+1)
def depth_first_search(self, node, depth, current_depth=0, nodeIterator=0):
if current_depth >= depth: #Escape after depth reached or exceeded
return
if node.searched == True:
return
availibleABI = True
#Handler for non verified smart contracts on etherscan
try:
contractABI = self.etherscan.get_contract_abi(node.address) #Use Etherscan API to get the contracts ABI
except:
print('Problem with the ABI')
availibleABI = False #unavailible ABI (contract code not verified)
if availibleABI: #If contract's ABI is verified
contractInstance = self.web3.eth.contract(address=node.address, abi=contractABI) #Use web3 Library to create an instantiation of the contract
contractABI = json.loads(contractABI) #convert ABI to json format
for i in range(len(contractABI)): #Examine all functions/methods/variables in the ABI
try:
if contractABI[i]['outputs'][0]['type'] == 'address': #Searching exclusively for addresses on the contract
if len(contractABI[i]['inputs']) == 0: #Check function call does not require input
childAddress = eval("contractInstance."+"functions."+contractABI[i]['name']+"()"+".call()") #RPC call to the contract, return the 20byte address
child = Node(contractABI[i]['name'], nodeIterator, childAddress, []) #create node
node.children.append(child) #Append child node
#nodeIterator += 1
#print(f'Searching node...')
elif len(contractABI[i]['inputs']) == 1 and contractABI[i]['inputs'][0]['type'] == 'uint256': #This is an array of addresses:
print('Searching Array Node...')
try:
for j in range(0,10):
#print(j)
childAddress = eval("contractInstance."+"functions."+contractABI[i]['name']+"("+str(j)+ ")"+".call()") #RPC call to the contract, return the 20byte address
child = Node(contractABI[i]['name'], nodeIterator, childAddress, []) #create node
node.children.append(child) #Append child node
nodeIterator += 1
#print(f'Searching node...') #NOTE: Might need to put a check in for 0x0000... etc address.
except:
break
else:
pass
except:
pass
for child in node.children:
child.name
self.depth_first_search(child, depth, current_depth+1, nodeIterator)
node.searched = True
else:
print(node.address)
node.name = 'NO ABI AVAILIBLE'
node.searched = True
pass | DoominEth/Web3DataSandbox | ContractCompossitionNode.py | ContractCompossitionNode.py | py | 3,793 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "web3.Web3.toChecksumAddress",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "web3.Web3",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "json.loads",
"line_number": 42,
"usage_type": "call"
}
] |
34168133396 | from bs4 import BeautifulSoup
import requests
def wsearch(word):
word = word.replace(" ", "-")
url = f"https://dictionary.cambridge.org/dictionary/english/{word}"
user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246'
headers = {'User-Agent': user_agent}
page = requests.get(url, headers=headers).text
doc = BeautifulSoup(page, 'html.parser')
try:
entries = doc.find(
class_="hfl-s lt2b lmt-10 lmb-25 lp-s_r-20 x han tc-bd lmt-20 english").find_all(class_="pr entry-body__el")
if entries == []:
# for a single entry
entries = doc.find(
class_="hfl-s lt2b lmt-10 lmb-25 lp-s_r-20 x han tc-bd lmt-20 english").find_all(class_="pr di superentry")
return entries
except AttributeError:
return None
def compileResult(entries):
id = 0
res = list()
for entry in entries:
# get the word
word = entry.find(class_="di-title").text
# get the word type
try:
wordType = entry.find(
True, class_="pos dpos").text
except AttributeError:
wordType = ''
# get the list of audio tags
audioTags = []
for tag in entry.find_all(class_="region dreg"):
audioTags.append(tag.text)
# get the list of audio links
audioLinks = []
for tag in entry.find_all('source', {"type": "audio/mpeg"}):
audioLinks.append("https://dictionary.cambridge.org" + tag['src'])
# merge the two lists
mergedAudioList = []
for tag, link in zip(audioTags, audioLinks):
mergedAudioList.append({"tag": tag,
"link": link})
groups = entry.find_all(class_="def-block ddef_block")
explanation = list()
for group in groups:
groupList = list()
groupText = group.find_all(
True, {"class": ["def ddef_d db", "eg deg"]})
for text in groupText:
c = ' '.join(text['class'])
if c == "def ddef_d db":
groupList.append(
{
"type": "main",
"content": text.text
}
)
elif c == "eg deg":
groupList.append(
{
"type": "example",
"content": text.text
}
)
explanation.append(groupList)
res.append(
{"id": id,
"word": word,
"wordType": wordType,
"audioLinks": mergedAudioList,
"explanation": explanation
}
)
id += 1
return res
if __name__ == "__main__":
entries = wsearch("hi")
res = compileResult(entries)
| Chris4496/TheDictionaryHubAPI | app/scrapers/cambridge.py | cambridge.py | py | 3,117 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "requests.get",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "bs4.BeautifulSoup",
"line_number": 13,
"usage_type": "call"
}
] |
8919279602 | import subprocess
import json
import argparse
import os
import requests
args = None
def main():
global args
parser = argparse.ArgumentParser(
description='update cloudflare dns records')
parser.add_argument('Domain',
metavar='domain',
type=str,
help='domain to update')
parser.add_argument('--dryrun',
action='store_true',
help='dry run')
parser.add_argument('--kind',
action='store',
type=str,
default='A',
help='type of record, e.g. A')
parser.add_argument('--zone',
action='store',
type=str,
help='dns zone')
parser.add_argument('--id',
action='store',
type=str,
help='ID of record to update')
parser.add_argument('--email',
action='store',
type=str,
help='email')
parser.add_argument('--api-token',
action='store',
type=str,
help='API token')
parser.add_argument('--api-key',
action='store',
type=str,
help='API key')
parser.add_argument('--a-name',
action='store',
type=str,
help='record A name')
parser.add_argument('--a-ip',
action='store',
type=str,
help='record A IP')
args = parser.parse_args()
domain = args.Domain
if (args.api_token == None):
atoken = os.getenv('CF_API_TOKEN')
if (atoken == None):
print("can't find CF_API_TOKEN")
os.exit(1)
args.api_token = atoken
if (args.api_key == None):
akey = os.getenv('CF_API_KEY')
if (akey == None):
print("can't find CF_API_KEY")
os.exit(1)
args.api_key = akey
if (args.zone== None):
azone = os.getenv('CF_ZONE_ID')
if (azone== None):
print("can't find CF_ZONE_ID")
os.exit(1)
args.zone = azone
find_gcp_vms_ip(callback1)
def find_gcp_vms_ip(cb):
instances = subprocess.Popen(['gcloud', 'compute', 'instances', 'list', '--format=json'], stdout=subprocess.PIPE).communicate()[0]
instances2 = json.loads(instances.decode('utf-8').replace('\n', ' '))
for inst in instances2:
name = inst['name']
interface = inst['networkInterfaces'][0]
if (interface == None):
return
accessConfigs = interface['accessConfigs']
if (accessConfigs == None):
return
externalIP = accessConfigs[0]['natIP']
if (externalIP == None):
return
cb(name,externalIP)
def cloudflare_update(name,ip,domain):
url = f"https://api.cloudflare.com/client/v4/zones/{args.zone}/dns_records"
# print(url)
bearer_token = f"Bearer {args.api_token}"
headers = {
'Authorization': bearer_token,
'Content-Type': 'application/json',
'Accept': 'application/json'
}
# print(bearer_token)
# 'X-Auth-Email': f"{args.email}",
# 'X-Auth-Key': f"{args.api_key}",
response = requests.post(
url,
headers=headers,
data = {
'type': args.kind,
'name': name,
'content': ip,
'priority': 10,
'ttl':120,
'proxied':'false'
}
)
print(response.json())
def callback1(name,externalIP):
#print(args)
#print(name,externalIP)
cloudflare_update(name,externalIP,args.Domain)
if __name__ == "__main__":
main()
| bobbae/examples-2021 | Python/cloudflare/update_dns.py | update_dns.py | py | 4,011 | python | en | code | 3 | github-code | 36 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_number": 69,
"usage_type": "call"
},
{
"api_name": "os.exit",
"line_number": 72,
"usage_type": "call"
},
{
"api_name": "os.getenv",
"line_numbe... |
36041433257 | #!/usr/bin/python
import httplib
import random
import argparse
import sys
#Get options
parser = argparse.ArgumentParser(
description='Testing vote app')
parser.add_argument(
'-port',
type=int,
help='port of server',
default=8000)
parser.add_argument(
'-host',
type=str,
help='server name/ip',
default="localhost")
args = parser.parse_args()
#Color table
colorList = ["blue", "orange", "red", "green", "yellow" ]
colorSize = len(colorList) - 1
#Connect with server
conn = httplib.HTTPConnection(args.host, args.port)
#initial request
conn.request("GET", "/")
r1 = conn.getresponse()
#print(r1.status, r1.reason)
print(r1.read())
#vote loop
count = 0
while count < 100 :
count = count + 1
nColor = random.randint(0, colorSize)
conn.request("GET", "/v1/vote?color="+colorList[nColor])
r1 = conn.getresponse()
#print(r1.read())
print
# view current results
conn.request("GET", "/v1/listVotes")
r1 = conn.getresponse()
print(r1.read())
conn.request("GET", "/v1/listWorkers")
r1 = conn.getresponse()
print(r1.read())
conn.close() | JoseIbanez/testing | redis/p02-vote/client/c02.py | c02.py | py | 1,136 | python | en | code | 3 | github-code | 36 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "httplib.HTTPConnection",
"line_number": 34,
"usage_type": "call"
},
{
"api_name": "random.randint",
"line_number": 47,
"usage_type": "call"
}
] |
14557064993 |
import sys
import cv2
import numpy
import copy
import scipy.misc
import itertools
from PIL import Image, ImageOps, ImageDraw
from scipy.ndimage import morphology, label
from copy import deepcopy
from operator import itemgetter
from statistics import median, mean
from math import sqrt
from random import randint
from scipy.misc import toimage
#sketch analysis
computer_vision_size_threshold = 50
threshold_block_width = 5 # minimum block width in pixels
threshold_block_height = 5 # minimum block height in pixels
#Scaling method
scaling_method = int(sys.argv[2]) # 0=Max 1=Min 2=MidRange 3=Mean 4=Median
#rules when picking a block type
check_overlap = 1 # Check that no blocks overlap
height_error_allowed_overlap = 0.03 # prevents rounding errors and gives bit of flexability
check_local_stability = 0 # Check that the structure is locally stable after each added block
check_global_stability = 1 # Check that the structure is globally stable after each added block
check_global_stability_method = 2 # 1 is enforce global stability only for blocks currently added (doesn't take into account future blocks)
# 2 is use both blocks added so far and sketch blocks for those not yet added
check_all_supporters = 1 # Check that all supporters for a block are present (could possibly not be required if global stability checking is enforced)
required_support_amount = 0.01
check_groups = 1 # Check that group height requirements are enforced
average_single_block_groups_heights = 1 # average the height of all single blocks in groups with other single blocks (helps with very poor drawings...)
height_error_allowed_groups = 0.05
use_similarity_grouping = 1
average_same_block_groups_heights = 1
error_percentage_shape = 0.05
check_era_relations = 0 # Check that ERA relations hold
check_composite_block_stability = 1 # check that composite blocks are stable (local)
shift_blocks_sideways = 1 # Makes structures more likely to pass but takes longer, Helps with making structures stable/no overlap
moves_to_try = [-0.8,0.7,-0.6,0.5,-0.4,0.3,-0.2,0.1]
# Alternative horizontal distance options:
#-0.4,0.35,-0.3,0.25,-0.2,0.15,-0.1,0.05
#-2.8,2.6,-2.4,2.2,-2.0,1.8,-1.6,1.4,-1.2,1.0,-0.8,0.6,-0.4,0.2
#-1.4,1.3,-1.2,1.1,-1.0,0.9,-0.8,0.7,-0.6,0.5,-0.4,0.3,-0.2,0.1
order_blocks_smart = 1 # re-order blocks into a more logical order based on support graph (rather than simply bottom to top)
#add extra blocks into sketch
add_extra_blocks_to_make_stable = 1 # add extra blocks to sketch to make structure globally stable
push_back_distance = 5 # distance to push extra blocks inwards (in pixels), helps deal with minor imperfections in the sketches / vision
#generate composite blocks
composite_blocks_allowed = 1 # composite blocks are allowed within the structure
rearrange_special_block_order = 1 # also include rearrangements of composite blocks as alternative options
max_composite_block_width = 3 # number of blocks wide that a composite block can be
composite_block_interweaving = 1
composite_block_penalty_picking = 1.5 # error difference when picking block type multiplied by this value if it is composite
composite_block_penalty_end = 0.0 # final error value score multiplied by this times ratio of composite blocks to non-composite blocks (NOT CURRENTLY INCLUDED)
limit_number_block_type_changes = 1 # limit the number of times a block type can change before rolling back a block
max_number_block_type_changes = 20 # increasing will give better final results but dramatically increases generation time, when using composite blocks
# SHOULD ONLY BE USED ON ACCURATE STRUCTURES WITH ORTHOGONAL/RECTILINEAR POLYGONS
corner_splitting_allowed = int(sys.argv[3]) # split polygons into rectangles based on their corners
seperate_vision_corners = 1 # 0 = associate each corner with the MBR that it is within (problem if within two or more MBRs)
# 1 = associte each corner with the MBR whose original shape it is closest too
max_distance_allowed = 3000 # maximum distance a corner can be from an MBR (removes dots) ERROR WHEN STRUCTURE HAS HOLE IN IT!!!
corner_detection_quality_threshold = 0.2 # quality of corner required for detection
corner_detection_min_distance = 20 # minimum ditance between corners (euclidean)
threshold_corner_amount_x = 10 # make x values for corners same if wihtin this pixel distance
threshold_corner_amount_y = 10 # make y values for corners same if wihtin this pixel distance
add_togethor_similar_x = 1 # combines groups if they share a block
add_togethor_similar_y = 1
GARY_INITIAL = 1
MATTHEW_INITIAL = 1
OTHER_INITIAL = 1
ground = -3.5 # position of the level ground
# blocks number and size
blocks = {'1':[0.84,0.84], '2':[0.85,0.43], '3':[0.43,0.85], '4':[0.43,0.43],
'5':[0.22,0.22], '6':[0.43,0.22], '7':[0.22,0.43], '8':[0.85,0.22],
'9':[0.22,0.85], '10':[1.68,0.22], '11':[0.22,1.68],
'12':[2.06,0.22], '13':[0.22,2.06]}
original_number_blocks = len(blocks)
# blocks number and name
# (blocks 3, 7, 9, 11 and 13) are their respective block names rotated 90 derees clockwise
block_names = {'1':"SquareHole", '2':"RectFat", '3':"RectFat", '4':"SquareSmall",
'5':"SquareTiny", '6':"RectTiny", '7':"RectTiny", '8':"RectSmall",
'9':"RectSmall",'10':"RectMedium",'11':"RectMedium",
'12':"RectBig",'13':"RectBig"}
# Generic list merging functions
def mergeOrNot(list1,list2):
merge=False
for item in list1:
if item in list2:
merge=True
break
return merge
def uniqueList(list1,list2):
result = list1
for j in list2:
if j not in list1:
result.append(j)
return result
def cleverMergeLists(lists):
anotherLoopRequired=False
newList = []
for myList in lists:
addMyList=True
if not anotherLoopRequired:
for myList2 in lists:
if not anotherLoopRequired:
if(myList==myList2):
continue
if(mergeOrNot(myList,myList2)):
anotherLoopRequired=True
addMyList=False
newList.append(uniqueList(myList,myList2))
else:
newList.append(myList2)
if(addMyList):
newList.append(myList)
if anotherLoopRequired:
return cleverMergeLists(newList)
else:
return newList
# COMPUTER VISION ANALYSIS FUNCTIONS
# returns the MBRs for a given image
def boxes(orig):
img = ImageOps.grayscale(orig)
im = numpy.array(img)
# Inner morphological gradient.
im = morphology.grey_dilation(im, (3, 3)) - im
# Binarize.
mean, std = im.mean(), im.std()
t = mean + std
im[im < t] = 0
im[im >= t] = 1
# Connected components.
lbl, numcc = label(im)
# Size threshold.
min_size = computer_vision_size_threshold # pixels
box = []
for i in range(1, numcc + 1):
py, px = numpy.nonzero(lbl == i)
if len(py) < min_size:
im[lbl == i] = 0
continue
xmin, xmax, ymin, ymax = px.min(), px.max(), py.min(), py.max()
# Four corners and centroid.
box.append([
[(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)],
(numpy.mean(px), numpy.mean(py))])
return im.astype(numpy.uint8) * 255, box
# returns both the MBRs for a given image and the segmented sections of the image
def boxes_sep(orig):
img = ImageOps.grayscale(orig)
im = numpy.array(img)
# Inner morphological gradient.
im = morphology.grey_dilation(im, (3, 3)) - im
# Binarize.
mean, std = im.mean(), im.std()
t = mean + std
im[im < t] = 0
im[im >= t] = 1
# Connected components.
lbl, numcc = label(im)
# Size threshold.
min_size = computer_vision_size_threshold # pixels
box = []
segmented_images = []
for i in range(1, numcc + 1):
im2 = deepcopy(lbl)
py, px = numpy.nonzero(lbl == i)
if len(py) < min_size:
im[lbl == i] = 0
continue
segmented_images.append([])
for j in range(len(lbl)):
for k in range(len(lbl[j])):
if lbl[j][k] == i:
segmented_images[-1].append([k,j])
for i in range(1, numcc + 1):
py, px = numpy.nonzero(lbl == i)
if len(py) < min_size:
im[lbl == i] = 0
continue
xmin, xmax, ymin, ymax = px.min(), px.max(), py.min(), py.max()
# Four corners and centroid.
box.append([
[(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)],
(numpy.mean(px), numpy.mean(py))])
return im.astype(numpy.uint8) * 255, box, segmented_images
print("DOING COMPUTER VISION")
# find the corners for the given image
img = cv2.imread(sys.argv[1])
img_orig = copy.copy(img)
grayimg = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(grayimg,100,corner_detection_quality_threshold,corner_detection_min_distance)
corners = numpy.float32(corners)
for item in corners:
x,y = item[0]
cv2.circle(img,(x,y),5,255,-1)
Image.fromarray(img).save("sketch_corners.jpg")
new_corners = []
for item in corners:
x,y = item[0]
new_corners.append([x,y])
corners = deepcopy(new_corners)
print("Number of corners found:")
print(len(corners))
# find the MBRs for the given image
orig = Image.open(sys.argv[1])
if (seperate_vision_corners == 1) and (corner_splitting_allowed == 1):
im, box, seg_points = boxes_sep(orig)
else:
im, box = boxes(orig)
Image.fromarray(im).save("sketch_lines.jpg")
# Draw perfect rectangles and the component centroid.
img = Image.fromarray(im)
visual = img.convert('RGB')
draw = ImageDraw.Draw(visual)
for b, centroid in box:
draw.line(b + [b[0]], fill='yellow')
cx, cy = centroid
draw.ellipse((cx - 2, cy - 2, cx + 2, cy + 2), fill='red')
visual.save("sketch_MBRs.jpg")
# Draw perfect rectangles and the component centroid.
img = Image.fromarray(im)
visual = img.convert('RGB')
draw = ImageDraw.Draw(visual)
for b, centroid in box:
draw.rectangle([b[0],b[2]], fill='white')
visual.save("sketch_MBRs_filled.jpg")
all_boxes = []
# bounding boxes for all rectangles found
# all boxes is a list of all blocks [x,y,w,h], center points (x,y) and width (w) and height (h)
for b, centroid in box:
width = float(b[1][0] - b[0][0])
height = float(b[2][1] - b[0][1])
center_x = float(b[0][0]+(width/2.0))
center_y = float(b[0][1]+(height/2.0))
all_boxes.append([center_x,center_y,width,height])
#all_boxes.append([centroid[0],centroid[1],width,height])
# remove all boxes with a width or height less than size threshold (wrong)
# already done in computer vision section
new_all_boxes = []
for box in all_boxes:
if (box[2] > threshold_block_width) and (box[3] > threshold_block_height):
new_all_boxes.append(box)
all_boxes = deepcopy(new_all_boxes)
# remove all boxes that are fully inside other boxes (holes)
to_remove = []
for i in range(len(all_boxes)):
for j in range(len(all_boxes)):
if i!=j:
if ((all_boxes[i][0]-(all_boxes[i][2]/2.0)) > (all_boxes[j][0]-(all_boxes[j][2]/2.0))):
if ((all_boxes[i][0]+(all_boxes[i][2]/2.0)) < (all_boxes[j][0]+(all_boxes[j][2]/2.0))):
if ((all_boxes[i][1]-(all_boxes[i][3]/2.0)) > (all_boxes[j][1]-(all_boxes[j][3]/2.0))):
if ((all_boxes[i][1]+(all_boxes[i][3]/2.0)) < (all_boxes[j][1]+(all_boxes[j][3]/2.0))):
to_remove.append(i)
new_all_boxes = []
for i in range(len(all_boxes)):
if i not in to_remove:
new_all_boxes.append(all_boxes[i])
all_boxes = deepcopy(new_all_boxes)
if (seperate_vision_corners == 1) and (corner_splitting_allowed == 1):
new_seg_points = []
for i in range(len(seg_points)):
if i not in to_remove:
new_seg_points.append(seg_points[i])
seg_points = deepcopy(new_seg_points)
# split non-rectangular orthogonal polygons into rectangles
if (corner_splitting_allowed==1):
if seperate_vision_corners == 1:
print("SPLITTING CORNERS")
# associte each corner with the MBR whose original shape it is closest too
corner_association = []
for j in range(len(seg_points)):
corner_association.append([])
closest_corners = []
to_remove = []
for c in corners:
min_distance = 99999999
closest_seg = -1
for j in range(len(seg_points)):
for k in seg_points[j]:
x1 = c[0]
x2 = k[0]
y1 = c[1]
y2 = k[1]
distance = (sqrt( (x2 - x1)**2 + (y2 - y1)**2 ))
if distance < min_distance:
min_distance = distance
closest_seg = j
if min_distance > max_distance_allowed:
to_remove.append(c)
else:
closest_corners.append(closest_seg)
for c in to_remove:
corners.remove(c)
for j in range(len(corners)):
corner_association[closest_corners[j]].append(corners[j])
else:
# associate each corner with the MBR that it is within (problem if within two or more MBRs)
corner_association = []
for i in range(len(all_boxes)):
corner_association.append([])
for i in range(len(corners)):
mbr_within = -1
extra_give = 5
found_counter = 0
for j in range(len(all_boxes)):
if corners[i][0] < all_boxes[j][0]+(all_boxes[j][2]/2.0)+extra_give:
if corners[i][0] > all_boxes[j][0]-(all_boxes[j][2]/2.0)-extra_give:
if corners[i][1] < all_boxes[j][1]+(all_boxes[j][3]/2.0)+extra_give:
if corners[i][1] > all_boxes[j][1]-(all_boxes[j][3]/2.0)-extra_give:
mbr_within = j
found_counter = found_counter+1
if mbr_within == -1:
print("error: no MBR found to associate with")
if found_counter > 1:
print("error: too many MBR possibilities")
corner_association[mbr_within].append(corners[i])
# split up every shape with more than 5 corners into multiple rectangles
final_to_remove = []
final_to_add = []
for i in range(len(all_boxes)):
if len(corner_association[i]) > 5:
if (len(corner_association[i]) % 2) == 1:
print("error: odd number of associated corners")
# make the y values similar
split_lines_y = []
split_y = []
for c in corner_association[i]:
found = 0
for y in range(len(split_lines_y)):
max_y = max([sublist[1] for sublist in split_lines_y[y]])
min_y = min([sublist[1] for sublist in split_lines_y[y]])
if min_y < c[1] + threshold_corner_amount_y:
if max_y > c[1] - threshold_corner_amount_y:
split_lines_y[y].append(c)
found = found+1
if found == 0:
split_lines_y.append([c])
split_y.append([])
if found > 1:
print("error: multiple y values found")
if add_togethor_similar_y == 1:
split_lines_y = cleverMergeLists(split_lines_y)
for y in range(len(split_lines_y)):
split_y[y] = 0
for j in split_lines_y[y]:
split_y[y] = split_y[y] + j[1]
split_y[y] = split_y[y] / len(split_lines_y[y])
new_cor = []
for c in range(len(corner_association[i])):
match = 0
for j in range(len(split_lines_y)):
if corner_association[i][c] in split_lines_y[j]:
match = 1
new_cor.append([corner_association[i][c][0],split_y[j]])
if match == 0:
print("error: no matching y value found")
corner_association[i] = deepcopy(new_cor)
# make the x values similar
split_lines_x = []
split_x = []
for c in corner_association[i]:
found = 0
for x in range(len(split_lines_x)):
max_x = max([sublist[0] for sublist in split_lines_x[x]])
min_x = min([sublist[0] for sublist in split_lines_x[x]])
if min_x < c[0] + threshold_corner_amount_x:
if max_x > c[0] - threshold_corner_amount_x:
split_lines_x[x].append(c)
found = found+1
if found == 0:
split_lines_x.append([c])
split_x.append([])
if found > 1:
print("error: multiple x values found")
if add_togethor_similar_x == 1:
split_lines_x = cleverMergeLists(split_lines_x)
for x in range(len(split_lines_x)):
split_x[x] = 0
for j in split_lines_x[x]:
split_x[x] = split_x[x] + j[0]
split_x[x] = split_x[x] / len(split_lines_x[x])
new_cor = []
for c in range(len(corner_association[i])):
match = 0
for j in range(len(split_lines_x)):
if corner_association[i][c] in split_lines_x[j]:
match = 1
new_cor.append([split_x[j],corner_association[i][c][1]])
if match == 0:
print("error: no matching x value found")
corner_association[i] = deepcopy(new_cor)
# find horizontal and vertical edges
y_orderings = []
x_orderings = []
edges_all_x = []
edges_all_y = []
for c in corner_association[i]:
chosen_x = 0
chosen_y = 0
for j in range(len(x_orderings)):
if c[0] == x_orderings[j][0][0]:
x_orderings[j].append(c)
chosen_x = 1
if chosen_x == 0:
x_orderings.append([c])
for j in range(len(y_orderings)):
if c[1] == y_orderings[j][0][1]:
y_orderings[j].append(c)
chosen_y = 1
if chosen_y == 0:
y_orderings.append([c])
for xx in range(len(x_orderings)):
x_orderings[xx] = sorted(x_orderings[xx], key = lambda x: int(x[1]))
for yy in range(len(y_orderings)):
y_orderings[yy] = sorted(y_orderings[yy], key = lambda x: int(x[0]))
connect = True
for o in range(len(x_orderings)):
for c in range(len(x_orderings[o])):
if c < len(x_orderings[o]):
if connect == True:
edges_all_x.append([x_orderings[o][c],x_orderings[o][c+1]])
connect = False
else:
connect = True
for o in range(len(y_orderings)):
for c in range(len(y_orderings[o])):
if c < len(y_orderings[o]):
if connect == True:
edges_all_y.append([y_orderings[o][c],y_orderings[o][c+1]])
connect = False
else:
connect = True
# associate edges with each corner
corner_edges = []
for c in corner_association[i]:
edge_ver = []
edge_hor = []
for e in edges_all_x:
if c in e:
edge_ver = e
for e in edges_all_y:
if c in e:
edge_hor = e
corner_edges.append([edge_hor,edge_ver])
# identify concave and convex corners
convex_corners = [] # point outside
concave_corners = [] # point inside (ones that we want/use)
ori_edges_all_x = deepcopy(edges_all_x)
for j in range(len(corner_edges)):
point_to_test = deepcopy(corner_association[i][j])
shift_amount_corner_test = 0.01
if corner_edges[j][0][0][0] < corner_edges[j][0][1][0]:
if corner_edges[j][0][0][0] == point_to_test[0]:
point_to_test[0] = point_to_test[0]-shift_amount_corner_test
else:
point_to_test[0] = point_to_test[0]+shift_amount_corner_test
else:
if corner_edges[j][0][0][0] == point_to_test[0]:
point_to_test[0] = point_to_test[0]+shift_amount_corner_test
else:
point_to_test[0] = point_to_test[0]-shift_amount_corner_test
if corner_edges[j][1][0][1] < corner_edges[j][1][1][1]:
if corner_edges[j][1][0][1] == point_to_test[1]:
point_to_test[1] = point_to_test[1]-shift_amount_corner_test
else:
point_to_test[1] = point_to_test[1]+shift_amount_corner_test
else:
if corner_edges[j][1][0][1] == point_to_test[1]:
point_to_test[1] = point_to_test[1]+shift_amount_corner_test
else:
point_to_test[1] = point_to_test[1]-shift_amount_corner_test
num_line_intersections = 0
for linex in edges_all_x:
if linex[0][1] < linex[1][1]:
if point_to_test[1] < linex[1][1]:
if point_to_test[1] > linex[0][1]:
if point_to_test[0] > linex[0][0]:
num_line_intersections = num_line_intersections + 1
else:
if point_to_test[1] > linex[1][1]:
if point_to_test[1] < linex[0][1]:
if point_to_test[0] > linex[0][0]:
num_line_intersections = num_line_intersections + 1
if (num_line_intersections%2) == 0:
convex_corners.append(j)
else:
concave_corners.append(j)
# identify extra horzontal edges between concave corners
extra_edges_hor = []
for j in concave_corners:
current_point = corner_association[i][j]
intersecting_lines = []
for linex in edges_all_x:
if linex[0][0]!=current_point[0]:
if linex[0][1] < linex[1][1]:
if current_point[1] < linex[1][1]+shift_amount_corner_test:
if current_point[1] > linex[0][1]-shift_amount_corner_test:
intersecting_lines.append(linex)
else:
if current_point[1] > linex[1][1]-shift_amount_corner_test:
if current_point[1] < linex[0][1]+shift_amount_corner_test:
intersecting_lines.append(linex)
left_intersecting_closest = []
left_distance = 99999999
right_intersecting_closest = []
right_distance = 99999999
for line in intersecting_lines:
if current_point[0] > line[0][0]:
if current_point[0] - line[0][0] < left_distance:
left_distance = current_point[0] - line[0][0]
left_intersecting_closest = line
else:
if line[0][0] - current_point[0] < right_distance:
right_distance = line[0][0] - current_point[0]
right_intersecting_closest = line
extra_edges_hor.append([current_point,[right_intersecting_closest[0][0],current_point[1]]])
extra_edges_hor.append([[left_intersecting_closest[0][0],current_point[1]],current_point])
# identify extra vertical edges between concave corners
extra_edges_ver = []
for j in concave_corners:
current_point = corner_association[i][j]
intersecting_lines = []
for liney in edges_all_y:
if liney[0][1]!=current_point[1]:
if liney[0][0] < liney[1][0]:
if current_point[0] < liney[1][0]+shift_amount_corner_test:
if current_point[0] > liney[0][0]-shift_amount_corner_test:
intersecting_lines.append(liney)
else:
if current_point[0] > liney[1][0]-shift_amount_corner_test:
if current_point[0] < liney[0][0]+shift_amount_corner_test:
intersecting_lines.append(liney)
up_intersecting_closest = []
up_distance = 99999999
down_intersecting_closest = []
down_distance = 99999999
for line in intersecting_lines:
if current_point[1] > line[0][1]:
if current_point[1] - line[0][1] < up_distance:
up_distance = current_point[1] - line[0][1]
up_intersecting_closest = line
else:
if line[0][1] - current_point[1] < down_distance:
down_distance = line[0][1] - current_point[1]
down_intersecting_closest = line
extra_edges_ver.append([current_point,[current_point[0],up_intersecting_closest[0][1]]])
extra_edges_ver.append([[current_point[0],down_intersecting_closest[0][1]],current_point])
# remove duplicates
extra_edges_ver2 = []
extra_edges_hor2 = []
for j in extra_edges_ver:
if j not in extra_edges_ver2:
extra_edges_ver2.append(j)
for j in extra_edges_hor:
if j not in extra_edges_hor2:
extra_edges_hor2.append(j)
extra_edges_ver = deepcopy(extra_edges_ver2)
extra_edges_hor = deepcopy(extra_edges_hor2)
#order edges (left to right, top to bottom)
for edge_test in range(len(extra_edges_ver)):
if extra_edges_ver[edge_test][0][1] > extra_edges_ver[edge_test][1][1]:
extra_edges_ver[edge_test] = [extra_edges_ver[edge_test][1],extra_edges_ver[edge_test][0]]
for edge_test in range(len(extra_edges_hor)):
if extra_edges_hor[edge_test][0][0] > extra_edges_hor[edge_test][1][0]:
extra_edges_hor[edge_test] = [extra_edges_hor[edge_test][1],extra_edges_hor[edge_test][0]]
for edge_test in range(len(edges_all_x)):
if edges_all_x[edge_test][0][1] > edges_all_x[edge_test][1][1]:
edges_all_x[edge_test] = [edges_all_x[edge_test][1],edges_all_x[edge_test][0]]
for edge_test in range(len(edges_all_y)):
if edges_all_y[edge_test][0][0] > edges_all_y[edge_test][1][0]:
edges_all_y[edge_test] = [edges_all_y[edge_test][1],edges_all_y[edge_test][0]]
#split extra edges into two if it intersects another extra edge
no_change = 0
while(no_change==0):
to_add_hor = []
to_add_ver = []
to_remove_hor = []
to_remove_ver = []
no_change = 1
for j in extra_edges_hor:
for k in extra_edges_ver:
if j[0][0] < k[0][0]:
if j[1][0] > k[0][0]:
if k[0][1] < j[0][1]:
if k[1][1] > j[0][1]:
to_add_hor.append([j[0],[k[0][0],j[0][1]]])
to_add_hor.append([[k[0][0],j[0][1]],j[1]])
to_remove_hor.append(j)
to_add_ver.append([k[0],[k[0][0],j[0][1]]])
to_add_ver.append([[k[0][0],j[0][1]],k[1]])
to_remove_ver.append(k)
no_change = 0
if no_change == 0:
extra_edges_hor.append(to_add_hor[0])
extra_edges_hor.append(to_add_hor[1])
extra_edges_hor.remove(to_remove_hor[0])
extra_edges_ver.append(to_add_ver[0])
extra_edges_ver.append(to_add_ver[1])
extra_edges_ver.remove(to_remove_ver[0])
#get all touching line points for creating small blocks
all_touching_line_points = []
for j in corner_association[i]:
if j not in all_touching_line_points:
all_touching_line_points.append(j)
for j in extra_edges_ver:
for k in j:
if k not in all_touching_line_points:
all_touching_line_points.append(k)
for j in extra_edges_hor:
for k in j:
if k not in all_touching_line_points:
all_touching_line_points.append(k)
# mark extra points that were not already corners
extra_added_points = []
for j in all_touching_line_points:
if j not in corner_association[i]:
extra_added_points.append(j)
#order edges (left to right, top to bottom)
for edge_test in range(len(extra_edges_ver)):
if extra_edges_ver[edge_test][0][1] > extra_edges_ver[edge_test][1][1]:
extra_edges_ver[edge_test] = [extra_edges_ver[edge_test][1],extra_edges_ver[edge_test][0]]
for edge_test in range(len(extra_edges_hor)):
if extra_edges_hor[edge_test][0][0] > extra_edges_hor[edge_test][1][0]:
extra_edges_hor[edge_test] = [extra_edges_hor[edge_test][1],extra_edges_hor[edge_test][0]]
for edge_test in range(len(edges_all_x)):
if edges_all_x[edge_test][0][1] > edges_all_x[edge_test][1][1]:
edges_all_x[edge_test] = [edges_all_x[edge_test][1],edges_all_x[edge_test][0]]
for edge_test in range(len(edges_all_y)):
if edges_all_y[edge_test][0][0] > edges_all_y[edge_test][1][0]:
edges_all_y[edge_test] = [edges_all_y[edge_test][1],edges_all_y[edge_test][0]]
#split lines into sub-lines based on extra contact edges added
no_change_split = 0
while(no_change_split == 0):
no_change_split = 1
to_remove = []
to_add = []
for j in edges_all_x:
for k in extra_added_points:
if k[1] < j[1][1]:
if k[1] > j[0][1]:
if k[0] == j[0][0]:
to_remove.append(j)
to_add.append([j[0],k])
to_add.append([k,j[1]])
no_change_split = 0
if no_change_split == 0:
edges_all_x.remove(to_remove[0])
edges_all_x.append(to_add[0])
edges_all_x.append(to_add[1])
else:
for j in edges_all_y:
for k in extra_added_points:
if k[0] < j[1][0]:
if k[0] > j[0][0]:
if k[1] == j[0][1]:
to_remove.append(j)
to_add.append([j[0],k])
to_add.append([k,j[1]])
no_change_split = 0
if no_change_split == 0:
edges_all_y.remove(to_remove[0])
edges_all_y.append(to_add[0])
edges_all_y.append(to_add[1])
# remove duplicates and order
for new_edge_x in extra_edges_ver:
if new_edge_x not in edges_all_x:
edges_all_x.append(new_edge_x)
for new_edge_y in extra_edges_hor:
if new_edge_y not in edges_all_y:
edges_all_y.append(new_edge_y)
small_edges_hor = deepcopy(edges_all_y)
small_edges_ver = deepcopy(edges_all_x)
for edge_test in range(len(small_edges_ver)):
if small_edges_ver[edge_test][0][1] > small_edges_ver[edge_test][1][1]:
small_edges_ver[edge_test] = [small_edges_ver[edge_test][1],small_edges_ver[edge_test][0]]
for edge_test in range(len(small_edges_hor)):
if small_edges_hor[edge_test][0][0] > small_edges_hor[edge_test][1][0]:
small_edges_hor[edge_test] = [small_edges_hor[edge_test][1],small_edges_hor[edge_test][0]]
#get all the small boxes (maximum)
new_boxes = []
for j in small_edges_hor:
for k in small_edges_hor:
above = 0
below = 0
connect_left = []
connect_right = []
if j!=k:
if k[0][0] == j[0][0]:
if k[1][0] == j[1][0]:
if k[0][1] > j[0][1]:
below = 1
else:
above = 1
if below == 1:
for m in small_edges_ver:
if m[0] == j[0]:
if m[1] == k[0]:
connect_left = m
if m[0] == j[1]:
if m[1] == k[1]:
connect_right = m
if above == 1:
for m in small_edges_ver:
if m[0] == k[0]:
if m[1] == j[0]:
conect_left = m
if m[0] == k[1]:
if m[1] == j[1]:
connect_right = m
if (above == 1) and (connect_left != []) and (connect_right != []):
new_boxes.append([k,connect_right,j,connect_left])
if (below == 1) and (connect_left != []) and (connect_right != []):
new_boxes.append([j,connect_right,k,connect_left])
#convert to correct format
new_boxes2 = []
for j in new_boxes:
width = j[0][1][0] - j[0][0][0]
height = j[1][1][1] - j[1][0][1]
center_x = j[0][0][0] + (width/2.0)
center_y = j[1][0][1] + (height/2.0)
new_boxes2.append([center_x,center_y,width,height])
# remove boxes that are actually holes
new_boxes3 = []
for j in new_boxes2:
num_line_intersections = 0
point_to_test = [j[0],j[1]]
for linex in ori_edges_all_x:
if linex[0][1] < linex[1][1]:
if point_to_test[1] < linex[1][1]:
if point_to_test[1] > linex[0][1]:
if point_to_test[0] > linex[0][0]:
num_line_intersections = num_line_intersections + 1
else:
if point_to_test[1] > linex[1][1]:
if point_to_test[1] < linex[0][1]:
if point_to_test[0] > linex[0][0]:
num_line_intersections = num_line_intersections + 1
if (num_line_intersections%2) == 1:
new_boxes3.append(j)
# merge two boxes togethor if they are horizontally next to each other and have the same height
new_boxes4 = deepcopy(new_boxes3)
no_change = 1
to_merge = [0]
while(len(to_merge)>0):
to_merge = []
no_change = 0
for j in new_boxes4:
for k in new_boxes4:
if j != k:
if abs(j[1] - k[1]) < 0.1:
if abs(j[3] - k[3]) < 0.1:
if abs((j[0]+(j[2]/2.0)) - (k[0]-(k[2]/2.0))) < 0.1:
to_merge.append([j,k])
if len(to_merge)>0:
j = to_merge[0][0]
k = to_merge[0][1]
width = j[2]+k[2]
height = j[3]
center_x = (j[0]-(j[2]/2.0)) + (width/2.0)
center_y = j[1]
new_boxes4.append([center_x,center_y,width,height])
new_boxes4.remove(j)
new_boxes4.remove(k)
# add the new boxes to all_boxes and remove the original
final_to_remove.append(all_boxes[i])
for j in new_boxes4:
final_to_add.append(j)
for i in final_to_remove:
all_boxes.remove(i)
for i in final_to_add:
all_boxes.append(i)
stab_all_boxes = deepcopy(all_boxes)
for i in range(len(stab_all_boxes)):
stab_all_boxes[i][1] = (-1*(stab_all_boxes[i][1]))+2000
lowest_y = 99999999
for i in stab_all_boxes:
if i[1]-(i[3]/2.0) < lowest_y:
lowest_y = i[1]-(i[3]/2.0)
down_amount = lowest_y - 100.0
for i in range(len(stab_all_boxes)):
stab_all_boxes[i][1] = stab_all_boxes[i][1] - down_amount
f = open("sketch_blocks_data.txt", "w")
for i in stab_all_boxes:
f.write('%s %s %s %s\n' % (i[0],i[1]-(i[3]/2.0),i[2],i[3]))
f.close()
#find the largest and smallest block dimensions:
largest_value = 0
smallest_value = 99999999
largest_width = 0
smallest_width = 99999999
largest_height = 0
smallest_height = 99999999
widths = []
heights = []
areas = []
center_mass_ori_x = 0
center_mass_ori_y = 0
total_mass_ori = 0
for box in all_boxes:
widths.append(box[2])
heights.append(box[3])
areas.append(box[2]*box[3])
center_mass_ori_x = center_mass_ori_x + (box[0]*box[2]*box[3])
center_mass_ori_y = center_mass_ori_y + (box[1]*box[2]*box[3])
total_mass_ori = total_mass_ori + (box[2]*box[3])
if box[2] > largest_value:
largest_value = box[2]
if box[2] < smallest_value:
smallest_value = box[2]
if box[3] > largest_value:
largest_value = box[3]
if box[3] < smallest_value:
smallest_value = box[3]
if box[2] > largest_width:
largest_width = box[2]
if box[2] < smallest_width:
smallest_width = box[2]
if box[3] > largest_height:
largest_height = box[3]
if box[3] < smallest_height:
smallest_height = box[3]
center_mass_ori_x = center_mass_ori_x / total_mass_ori
center_mass_ori_y = center_mass_ori_y / total_mass_ori
sizes = widths+heights
mean_width = mean(widths)
mean_height = mean(heights)
mean_size = mean(sizes)
mean_area = mean(areas)
median_width = median(widths)
median_height = median(heights)
median_size = median(sizes)
median_area = median(areas)
actual_block_sizes = []
for key,value in blocks.items():
actual_block_sizes.append(value[0])
actual_block_mean = mean(actual_block_sizes)
actual_block_median = median(actual_block_sizes)
maximum_width_gap_touching = 0 # extra number of pixels to add to a blocks width when determining touching blocks
maximum_height_gap_touching = smallest_value*3 - 1 # extra number of pixels to add to a blocks height when determining touching blocks
# finds all supporters (direct and indirect) for a given block
def get_all_supporters(query):
indirect = []
to_check = [query]
while len(to_check) > 0:
doin = to_check.pop()
indirect.append(doin)
if doin != 9999:
for j in graph_supporters[doin]:
if j not in indirect:
if j not in to_check:
to_check.append(j)
new_indirect = []
for i in indirect:
if i not in new_indirect:
new_indirect.append(i)
new_indirect.remove(query)
return new_indirect
# finds all supportees (direct and indirect) for a given block
def get_all_supportees(query):
indirect = []
to_check = [query]
while len(to_check) > 0:
doin = to_check.pop()
indirect.append(doin)
for j in graph_supportees[doin]:
if j not in indirect:
if j not in to_check:
to_check.append(j)
new_indirect = []
for i in indirect:
if i not in new_indirect:
new_indirect.append(i)
new_indirect.remove(query)
return new_indirect
# finds all support paths from start block, upwards to end block
def find_all_paths(graph, start, end, path=[]):
path = path + [start]
if start == end:
return [path]
if start not in graph:
return []
paths = []
for node in graph[start]:
if node not in path:
newpaths = find_all_paths(graph, node, end, path)
for newpath in newpaths:
paths.append(newpath)
return paths
# not used but always good to have in case
def find_shortest_path(graph, start, end, path=[]):
path = path + [start]
if start == end:
return path
if start not in graph:
return None
shortest = None
for node in graph[start]:
if node not in path:
newpath = find_shortest_path(graph, node, end, path)
if newpath:
if not shortest or len(newpath) < len(shortest):
shortest = newpath
return shortest
# check if structure has local stability
def check_local_stability(all_boxes):
for i in range(len(all_boxes)):
left_support = 0
right_support = 0
box = all_boxes[i]
for j in graph_supporters[i]:
if j == 9999:
left_support = 1
right_support = 1
else:
box2 = all_boxes[j]
box2_left = box2[0]-(box2[2]/2.0)
box2_right = box2[0]+(box2[2]/2.0)
if box2_left < box[0]:
left_support = 1
if box2_right > box[0]:
right_support = 1
if left_support == 0:
print("UNSTABLE LOCAL (L) !!!!!")
print(i)
if right_support == 0:
print("UNSTABLE LOCAL (R) !!!!!")
print(i)
def isCentreSupporter(RAx):
if (RAx =="ERA.MOST_START_I" or RAx =="ERA.LESS_START_I" or RAx =="ERA.MOST_FINISH_I" or RAx =="ERA.LESS_FINISH_I" or RAx =="ERA.CENTRE_DURING" or RAx =="ERA.CENTRE_DURING_I" or RAx =="ERA.LEFT_DURING_I" or RAx =="ERA.RIGHT_DURING_I" or RAx =="ERA.MOST_START" or RAx =="ERA.MOST_FINISH" or RAx =="ERA.MOST_OVERLAP_MOST" or RAx =="ERA.LESS_OVERLAP_MOST" or RAx =="ERA.MOST_OVERLAP_MOST_I" or RAx =="ERA.MOST_OVERLAP_LESS_I" or RAx =="ERA.EQUAL"):
return True
return False
def isLeftSupporter(RAx):
if (RAx =="ERA.LESS_OVERLAP_LESS" or RAx =="ERA.MOST_OVERLAP_LESS" or RAx =="ERA.LESS_START" or RAx =="ERA.LEFT_DURING"):
return True
return False
def isRightSupporter(RAx):
if (RAx =="ERA.LESS_OVERLAP_MOST_I" or RAx =="ERA.LESS_OVERLAP_LESS_I" or RAx =="ERA.LESS_FINISH" or RAx =="ERA.RIGHT_DURING"):
return True
return False
# Calculate the ERA relations based on touching blocks
def find_era_relation(touching_line):
ERA_relations = []
ERA_threshold = 0.06
s1 = touching_line[0]
s2 = touching_line[2] # these are in the wrong order (should be 2,0,3,1) but the RAx rules are also wrong (filpped)
e1 = touching_line[1]
e2 = touching_line[3]
RA = "unknown"
if (s2 - e1 >=ERA_threshold):
RA ="ERA.BEFORE"
elif (s1 - e2 >=ERA_threshold):
RA ="ERA.AFTER"
elif (s2 - e1 <ERA_threshold and s2 - e1 >= 0 and s1 < e2):
RA ="ERA.MEET"
elif (s1 - e2 <ERA_threshold and s1 - e2 >= 0 and s2 < e1):
RA ="ERA.MEET_I"
elif (s1 == s2 and e2 - e1 >= 0 and (e2 - s2) / 2 < e1 - s1):
RA ="ERA.MOST_START"
elif (s1 == s2 and e1 - e2 > 0 and e2 - s2 > (e1 - s1) / 2):
RA ="ERA.MOST_START_I"
elif (s1 == s2 and e2 - e1 > 0 and (e2 - s2) / 2 >= e1 - s1):
RA ="ERA.LESS_START"
elif (s1 == s2 and e1 - e2 > 0 and e2 - s2 <= (e1 - s1) / 2):
RA ="ERA.LESS_START_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and e1 <= (s2 + e2) / 2):
RA ="ERA.LEFT_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and e2 <= (s1 + e1) / 2):
RA ="ERA.LEFT_DURING_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and s1 >= (s2 + e2) / 2):
RA ="ERA.RIGHT_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and s2 >= (s1 + e1) / 2):
RA ="ERA.RIGHT_DURING_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and s1 < (s2 + e2) / 2 and e1 > (s2 + e2) / 2):
RA ="ERA.CENTRE_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and s2 < (s1 + e1) / 2 and e2 > (s1 + e1) / 2):
RA ="ERA.CENTRE_DURING_I"
elif (s1 - s2 > 0 and e1 == e2 and (e2 - s2) / 2 < e1 - s1):
RA ="ERA.MOST_FINISH"
elif (s2 - s1 > 0 and e1 == e2 and e2 - s2 > (e1 - s1) / 2):
RA ="ERA.MOST_FINISH_I"
elif (s1 - s2 > 0 and e1 == e2 and (e2 - s2) / 2 >= e1 - s1):
RA ="ERA.LESS_FINISH"
elif (s2 - s1 > 0 and e1 == e2 and e2 - s2 <= (e1 - s1) / 2):
RA ="ERA.LESS_FINISH_I"
elif (abs(s1 - s2) <ERA_threshold and abs(e1 - e2) <ERA_threshold):
RA ="ERA.EQUAL"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 >= s2 - s1 and e1 - s2 >= e2 - e1):
RA ="ERA.MOST_OVERLAP_MOST"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 < s2 - s1 and e1 - s2 >= e2 - e1):
RA ="ERA.LESS_OVERLAP_MOST"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 >= s2 - s1 and e1 - s2 < e2 - e1):
RA ="ERA.MOST_OVERLAP_LESS"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 < s2 - s1 and e1 - s2 < e2 - e1):
RA ="ERA.LESS_OVERLAP_LESS"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e1 - s2 > 0 and e2 - s1 >= s1 - s2 and e2 - s1 >= e1 - e2):
RA ="ERA.MOST_OVERLAP_MOST_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 < s1 - s2 and e2 - s1 >= e1 - e2):
RA ="ERA.LESS_OVERLAP_MOST_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 >= s1 - s2 and e2 - s1 < e1 - e2):
RA ="ERA.MOST_OVERLAP_LESS_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 < s1 - s2 and e2 - s1 < e1 - e2):
RA ="ERA.LESS_OVERLAP_LESS_I"
ERA_relations.append(RA)
return ERA_relations
# Calculate the ERA relations based on touching blocks
def find_era_relations():
ERA_relations = []
ERA_threshold = 5
for i in range(len(touching_lines)):
s1 = touching_lines[i][0]
s2 = touching_lines[i][2] # these are in the wrong order (should be 2,0,3,1) but the RAx rules are also wrong (filpped)
e1 = touching_lines[i][1]
e2 = touching_lines[i][3]
RA = "unknown"
if (s2 - e1 >=ERA_threshold):
RA ="ERA.BEFORE"
elif (s1 - e2 >=ERA_threshold):
RA ="ERA.AFTER"
elif (s2 - e1 <ERA_threshold and s2 - e1 >= 0 and s1 < e2):
RA ="ERA.MEET"
elif (s1 - e2 <ERA_threshold and s1 - e2 >= 0 and s2 < e1):
RA ="ERA.MEET_I"
elif (s1 == s2 and e2 - e1 >= 0 and (e2 - s2) / 2 < e1 - s1):
RA ="ERA.MOST_START"
elif (s1 == s2 and e1 - e2 > 0 and e2 - s2 > (e1 - s1) / 2):
RA ="ERA.MOST_START_I"
elif (s1 == s2 and e2 - e1 > 0 and (e2 - s2) / 2 >= e1 - s1):
RA ="ERA.LESS_START"
elif (s1 == s2 and e1 - e2 > 0 and e2 - s2 <= (e1 - s1) / 2):
RA ="ERA.LESS_START_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and e1 <= (s2 + e2) / 2):
RA ="ERA.LEFT_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and e2 <= (s1 + e1) / 2):
RA ="ERA.LEFT_DURING_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and s1 >= (s2 + e2) / 2):
RA ="ERA.RIGHT_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and s2 >= (s1 + e1) / 2):
RA ="ERA.RIGHT_DURING_I"
elif (s1 - s2 > 0 and e2 - e1 > 0 and s1 < (s2 + e2) / 2 and e1 > (s2 + e2) / 2):
RA ="ERA.CENTRE_DURING"
elif (s2 - s1 > 0 and e1 - e2 > 0 and s2 < (s1 + e1) / 2 and e2 > (s1 + e1) / 2):
RA ="ERA.CENTRE_DURING_I"
elif (s1 - s2 > 0 and e1 == e2 and (e2 - s2) / 2 < e1 - s1):
RA ="ERA.MOST_FINISH"
elif (s2 - s1 > 0 and e1 == e2 and e2 - s2 > (e1 - s1) / 2):
RA ="ERA.MOST_FINISH_I"
elif (s1 - s2 > 0 and e1 == e2 and (e2 - s2) / 2 >= e1 - s1):
RA ="ERA.LESS_FINISH"
elif (s2 - s1 > 0 and e1 == e2 and e2 - s2 <= (e1 - s1) / 2):
RA ="ERA.LESS_FINISH_I"
elif (abs(s1 - s2) <ERA_threshold and abs(e1 - e2) <ERA_threshold):
RA ="ERA.EQUAL"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 >= s2 - s1 and e1 - s2 >= e2 - e1):
RA ="ERA.MOST_OVERLAP_MOST"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 < s2 - s1 and e1 - s2 >= e2 - e1):
RA ="ERA.LESS_OVERLAP_MOST"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 >= s2 - s1 and e1 - s2 < e2 - e1):
RA ="ERA.MOST_OVERLAP_LESS"
elif (s2 - s1 > 0 and e2 - e1 > 0 and e1 - s2 > 0 and e1 - s2 < s2 - s1 and e1 - s2 < e2 - e1):
RA ="ERA.LESS_OVERLAP_LESS"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e1 - s2 > 0 and e2 - s1 >= s1 - s2 and e2 - s1 >= e1 - e2):
RA ="ERA.MOST_OVERLAP_MOST_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 < s1 - s2 and e2 - s1 >= e1 - e2):
RA ="ERA.LESS_OVERLAP_MOST_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 >= s1 - s2 and e2 - s1 < e1 - e2):
RA ="ERA.MOST_OVERLAP_LESS_I"
elif (s1 - s2 > 0 and e1 - e2 > 0 and e2 - s1 > 0 and e2 - s1 < s1 - s2 and e2 - s1 < e1 - e2):
RA ="ERA.LESS_OVERLAP_LESS_I"
ERA_relations.append(RA)
return ERA_relations
# Use the ERA rules to determine is the sketch drawing is stable (qualitative LOCAL)
def calc_era_stability(all_boxes, ERA_relations,touching_blocks):
ERA_stable = []
no_change_ERA = 0
for i in range(len(all_boxes)):
ERA_stable.append(0)
while(no_change_ERA == 0):
old_ERA_stable = deepcopy(ERA_stable)
for i in range(len(all_boxes)):
rightSupporter = False
leftSupporter = False
centreSupporter = False
if graph_supporters[i] == [9999]:
ERA_stable[i] = 1
else:
for j in graph_supporters[i]:
if (ERA_stable[j]==1):
for x in range(len(ERA_relations)):
if touching_blocks[x] == [j,i]:
if isLeftSupporter(ERA_relations[x]):
leftSupporter = True
for k in graph_supporters[i]:
if (ERA_stable[k]==1):
for y in range(len(ERA_relations)):
if touching_blocks[y] == [k,i]:
if isRightSupporter(ERA_relations[y]):
rightSupporter = True
for m in graph_supporters[i]:
if (ERA_stable[m]==1):
for z in range(len(ERA_relations)):
if touching_blocks[z] == [m,i]:
if isCentreSupporter(ERA_relations[z]):
centreSupporter = True
if ((leftSupporter and rightSupporter) or centreSupporter):
ERA_stable[i] = 1
if (sorted(ERA_stable) == sorted(old_ERA_stable)):
no_change_ERA = 1
for i in ERA_stable:
if i==0:
print("UNSTABLE ERA")
# Analyse global stability of the sketch drawing (qualitative GARY)
def calc_gary_stability(all_boxes):
global_stability = []
for q in range(len(all_boxes)):
supporters_list = deepcopy(graph_supporters[q])
supportees_list = get_all_supportees(q)
for i in supportees_list:
for j in range(len(all_boxes)):
if (j in graph_supporters[i]) and (j not in get_all_supportees(q)) and j!=q:
supporters_list.append(j)
supportees_list.append(q)
center_mass_x = 0
total_mass = 0
for k in supportees_list:
center_mass_x = center_mass_x + (all_boxes[k][0]*all_boxes[k][2]*all_boxes[k][3])
total_mass = total_mass + (all_boxes[k][2]*all_boxes[k][3])
center_mass_x = center_mass_x / total_mass
leftmost_support = 99999999
rightmost_support = -99999999
for m in supporters_list:
if m == 9999:
leftmost_support = -99999999
rightmost_support = 99999999
else:
if all_boxes[m][0]-(all_boxes[m][2]/2.0) < leftmost_support:
leftmost_support = all_boxes[m][0]-(all_boxes[m][2]/2.0)
if all_boxes[m][0]+(all_boxes[m][2]/2.0) > rightmost_support:
rightmost_support = all_boxes[m][0]+(all_boxes[m][2]/2.0)
if (center_mass_x > leftmost_support) and (center_mass_x < rightmost_support):
global_stability.append(1)
else:
global_stability.append(0)
for s in global_stability:
if s == 0:
print("UNSTABLE GLOBAL GARY !!!!!")
print(global_stability)
return 0
return 1
# checks if point (vp,current_y) interescts a box in all boxes, and that this block is below the current one (b)
# returns the box that does intersect the point
def get_point_in_block(vp,current_y,all_boxes,b):
current_box = all_boxes[b]
for bb in range(len(all_boxes)):
box = all_boxes[bb]
if vp <= box[0]+(box[2]/2.0):
if vp >= box[0]-(box[2]/2.0):
if current_y <= box[1]+(box[3]/2.0):
if current_y >= box[1]-(box[3]/2.0):
if current_box == box:
return bb
else:
if ((box[1]) > (current_box[1]+(current_box[3]/2.0))): #below block must have center point below top blocks bottom
if ((box[1]-(box[3]/2.0)) > (current_box[1]-(current_box[3]/2.0))): #below block must have top point below top blocks top point
return bb
return None
# checks if point (vp,current_y) is within a box in all boxes, and that this block is below the current one (b)
def check_point_in_block(vp,current_y,all_boxes,b):
current_box = all_boxes[b]
for box in all_boxes:
if vp <= box[0]+(box[2]/2.0):
if vp >= box[0]-(box[2]/2.0):
if current_y <= box[1]+(box[3]/2.0):
if current_y >= box[1]-(box[3]/2.0):
if current_box == box:
return True
else:
if ((box[1]) > (current_box[1]+(current_box[3]/2.0))): #below block must have center point below top blocks bottom
if ((box[1]-(box[3]/2.0)) > (current_box[1]-(current_box[3]/2.0))): #below block must have top point below top blocks top point
return True
return False
#MORE COMPLEX METHOD
def calc_matthew_stability(all_boxes,valid_supportees):
global_stability = []
all_boxes_ori = deepcopy(all_boxes)
# just make safe area formed by direct supporters of the block:
safe_areas2 = []
for b in range(len(all_boxes)):
if b in valid_supportees:
leftmost = 99999999
rightmost = -99999999
for gg in graph_supporters[b]:
if gg == 9999:
leftmost = -99999999
rightmost = 99999999
else:
if (all_boxes[gg][0]-(all_boxes[gg][2]/2.0)) < leftmost:
leftmost = (all_boxes[gg][0]-(all_boxes[gg][2]/2.0))
if (all_boxes[gg][0]+(all_boxes[gg][2]/2.0)) > rightmost:
rightmost = (all_boxes[gg][0]+(all_boxes[gg][2]/2.0))
safe_areas2.append([leftmost,rightmost])
all_boxes = deepcopy(all_boxes_ori)
for b in range(len(all_boxes)):
if b in valid_supportees:
new_stable_check = 1
z = []
z2 = []
eligible_supporters = get_all_supporters(b)
bb = []
for cc in get_all_supportees(b):
if cc in valid_supportees:
eligible_supporters.append(cc)
for x in get_all_supportees(b):
if x in valid_supportees:
invalid = 0
for y in get_all_supporters(x):
if y != b:
if y not in eligible_supporters:
invalid = 1
if invalid == 0:
z.append(x)
z.append(b)
center_mass_x = 0
total_mass = 0
for k in z:
center_mass_x = center_mass_x + (all_boxes[k][0]*all_boxes[k][2]*all_boxes[k][3])
total_mass = total_mass + (all_boxes[k][2]*all_boxes[k][3])
center_mass_x = center_mass_x / total_mass
if (center_mass_x < safe_areas2[b][0]) or (center_mass_x > safe_areas2[b][1]):
if (center_mass_x < safe_areas2[b][0]):
pivot_point = safe_areas2[b][0]
good_side = "right"
else:
pivot_point = safe_areas2[b][1]
good_side = "left"
for k in get_all_supportees(b):
if k in valid_supportees:
if k not in z:
d = []
for n in get_all_supporters(k):
block_on_good_side = 0
if n in graph_supportees[b]:
if n in valid_supportees:
if good_side == "right":
if (all_boxes[n][0]+(all_boxes[n][2]/2.0)) > pivot_point:
block_on_good_side = 1
if good_side == "left":
if (all_boxes[n][0]-(all_boxes[n][2]/2.0)) < pivot_point:
block_on_good_side = 1
if block_on_good_side == 1:
for m in all_boxes:
if m in get_all_supporters(k):
if m in get_all_supportees(n):
if m in valid_supportees:
if good_side == "right":
if all_boxes[m][0] > pivot_point:
d.append(m)
if good_side == "left":
if all_boxes[m][0] < pivot_point:
d.append(m)
if good_side == "right":
if all_boxes[k][0] > pivot_point:
d.append(k)
if all_boxes[n][0] > pivot_point:
d.append(n)
if good_side == "left":
if all_boxes[k][0] < pivot_point:
d.append(k)
if all_boxes[n][0] > pivot_point:
d.append(n)
if d != []:
max_distance = -99999999
best_com = d[0]
for ii in range(len(d)):
if abs(all_boxes[ii][0]-pivot_point) > max_distance:
max_distance = abs(all_boxes[ii][0]-pivot_point)
best_com = d[ii]
new_block = [all_boxes[best_com][0],0,all_boxes[k][2],all_boxes[k][3]]
z2.append(new_block)
for jj in z:
z2.append(all_boxes[jj])
center_mass_x = 0
total_mass = 0
for bob in z2:
center_mass_x = center_mass_x + (bob[0]*bob[2]*bob[3])
total_mass = total_mass + (bob[2]*bob[3])
center_mass_x = center_mass_x / total_mass
if good_side == "right":
if center_mass_x < pivot_point:
new_stable_check = 0
if good_side == "left":
if center_mass_x > pivot_point:
new_stable_check = 0
global_stability.append(new_stable_check)
for s in global_stability:
if s == 0:
return 0
return 1
def calc_matthew_stability_ori(all_boxes):
global_stability = []
pivot_points = []
safe_areas2 = []
for b in range(len(all_boxes)):
leftmost = 99999999
rightmost = -99999999
for gg in graph_supporters[b]:
if gg == 9999:
leftmost = -99999999
rightmost = 99999999
else:
if (all_boxes[gg][0]-(all_boxes[gg][2]/2.0)) < leftmost:
leftmost = (all_boxes[gg][0]-(all_boxes[gg][2]/2.0))
if (all_boxes[gg][0]+(all_boxes[gg][2]/2.0)) > rightmost:
rightmost = (all_boxes[gg][0]+(all_boxes[gg][2]/2.0))
safe_areas2.append([leftmost,rightmost])
all_boxes = deepcopy(all_boxes_ori)
for b in range(len(all_boxes)):
new_stable_check = 1
z = []
z2 = []
good_side = None
eligible_supporters = get_all_supporters(b)+get_all_supportees(b)
for x in get_all_supportees(b):
invalid = 0
for y in get_all_supporters(x):
if y != b:
if y not in eligible_supporters:
invalid = 1
if invalid == 0:
z.append(x)
z.append(b)
center_mass_x = 0
total_mass = 0
for k in z:
center_mass_x = center_mass_x + (all_boxes[k][0]*all_boxes[k][2]*all_boxes[k][3])
total_mass = total_mass + (all_boxes[k][2]*all_boxes[k][3])
center_mass_x = center_mass_x / total_mass
if (center_mass_x < safe_areas2[b][0]) or (center_mass_x > safe_areas2[b][1]):
if (center_mass_x < safe_areas2[b][0]):
pivot_point = safe_areas2[b][0]
good_side = "right"
else:
pivot_point = safe_areas2[b][1]
good_side = "left"
for k in get_all_supportees(b):
if k not in z:
if good_side == "right":
if all_boxes[k][0]+(all_boxes[k][2]/2.0) > pivot_point:
z2.append(k)
if good_side == "left":
if all_boxes[k][0]-(all_boxes[k][2]/2.0) < pivot_point:
z2.append(k)
for jj in z:
z2.append(jj)
supporters_list = deepcopy(graph_supporters[b])
supportees_list = z2
for i in supportees_list:
for j in range(len(all_boxes)):
if (j in graph_supporters[i]) and (j not in get_all_supportees(b)) and j!=b:
supporters_list.append(j)
center_mass_x = 0
total_mass = 0
for k in supportees_list:
center_mass_x = center_mass_x + (all_boxes[k][0]*all_boxes[k][2]*all_boxes[k][3])
total_mass = total_mass + (all_boxes[k][2]*all_boxes[k][3])
center_mass_x = center_mass_x / total_mass
leftmost_support = 99999999
rightmost_support = -99999999
for m in supporters_list:
if m == 9999:
leftmost_support = -99999999
rightmost_support = 99999999
else:
if all_boxes[m][0]-(all_boxes[m][2]/2.0) < leftmost_support:
leftmost_support = all_boxes[m][0]-(all_boxes[m][2]/2.0)
if all_boxes[m][0]+(all_boxes[m][2]/2.0) > rightmost_support:
rightmost_support = all_boxes[m][0]+(all_boxes[m][2]/2.0)
if (center_mass_x > leftmost_support) and (center_mass_x < rightmost_support):
new_stable_check = 1
else:
new_stable_check = 0
pivot_points.append(good_side)
global_stability.append(new_stable_check)
return [global_stability,pivot_points]
def add_extra_supports(all_boxes,pivot_points,chosen_block):
new_all_boxes = deepcopy(all_boxes)
added_block = []
right_side = 0
if pivot_points[chosen_block] == "left":
right_side = 1
x_position = 0
if right_side == 1:
x_position = all_boxes[chosen_block][0]+(all_boxes[chosen_block][2]/2.0) - push_back_distance
else:
x_position = all_boxes[chosen_block][0]-(all_boxes[chosen_block][2]/2.0) + push_back_distance
y_position_top = all_boxes[chosen_block][1]+(all_boxes[chosen_block][3]/2.0)
lowest_point = 0
for i in range(len(all_boxes)):
if (all_boxes[i][1]+(all_boxes[i][3]/2.0)) > lowest_point:
lowest_point = (all_boxes[i][1]+(all_boxes[i][3]/2.0))
to_check_hit = []
for ii in range(len(all_boxes)):
if all_boxes[ii][1] > all_boxes[chosen_block][1]:
if ii != 9999:
to_check_hit.append(all_boxes[ii][1]-(all_boxes[ii][3]/2.0))
to_check_hit = sorted(to_check_hit,reverse=True)
new_to_check_hit = []
for ppp in range(len(to_check_hit)):
if to_check_hit[ppp] > (all_boxes[chosen_block][1]+(all_boxes[chosen_block][3]/2.0)):
new_to_check_hit.append(to_check_hit[ppp])
to_check_hit = new_to_check_hit
y_position_bottom = lowest_point
found = 0
while (len(to_check_hit))>0:
point_to_check = [x_position,to_check_hit[-1]]
if check_point_in_block(x_position,to_check_hit[-1],all_boxes,chosen_block):
if found == 0:
y_position_bottom = to_check_hit[-1]
found = 1
to_check_hit.pop()
added_block_x = x_position
added_block_width = 1
added_block_height = y_position_bottom-y_position_top
added_block_y = y_position_top+(added_block_height/2.0)
added_block = [added_block_x,added_block_y,added_block_width,added_block_height]
all_boxes.append(added_block)
print("ADDED BLOCK:")
print(added_block)
return(all_boxes)
def find_below_blocks(all_boxes, box):
below_blocks = []
for block2 in complete_locations:
if block2[2]<box[2]:
if ( (round(box[0],10) <= round((block2[1]+(blocks[str(block2[0])][0]/2)),10))
and (round(box[1],10) >= round((block2[1]-(blocks[str(block2[0])][0]/2)),10))
and (round(box[2],10) <= round((block2[2]+(blocks[str(block2[0])][1]/2)),10))
and (round(box[3],10) >= round((block2[2]-(blocks[str(block2[0])][1]/2)),10)) ):
below_blocks.append(block2)
return below_blocks
#currently doesn't work if multiple structures in image, need to test each structure separately
def calc_other_stability(all_boxes):
structure_stable = True
# checks the global stability of level by testing the stability of every block (as peak block)
highest_block = -1
highest_com = 99999999
for block in range(len(all_boxes)):
if all_boxes[block][1] < highest_com:
highest_com = all_boxes[block][1]
highest_block = block
current_box = [highest_block]
hit_ground = 0
if graph_supporters[block] == [9999]:
hit_ground = 1
while hit_ground == 0: # while not at bottom of structure
support_area = [99999999,0]
current_com = 0
total_mass = 0
supo = []
for jj in current_box:
for kk in graph_supporters[jj]:
if kk not in current_box:
supo.append(kk)
for jj in current_box:
current_com = current_com + all_boxes[jj][0]*(all_boxes[jj][2]*all_boxes[jj][3])
total_mass = total_mass + (all_boxes[jj][2]*all_boxes[jj][3])
current_com = current_com / total_mass
for jj in supo:
if all_boxes[jj][0] - (all_boxes[jj][2]/2.0) < support_area[0]:
support_area[0] = all_boxes[jj][0] - (all_boxes[jj][2]/2.0)
if all_boxes[jj][0] + (all_boxes[jj][2]/2.0) > support_area[1]:
support_area[1] = all_boxes[jj][0] + (all_boxes[jj][2]/2.0)
if (current_com >= support_area[1]) or (current_com <= support_area[0]):
structure_stable = False
to_add = []
highest_block = -1
highest_com = 99999999
for block in range(len(all_boxes)):
if block not in current_box:
if all_boxes[block][1] < highest_com:
highest_com = all_boxes[block][1]
highest_block = block
to_add.append(highest_block)
current_box = current_box + to_add
if graph_supporters[current_box[-1]] == [9999]:
hit_ground = 1
if structure_stable:
print("STABLE!")
return 1
else:
print("NOT STABLE!")
return 0
all_boxes_ori_very = deepcopy(all_boxes)
all_stable = 0
while all_stable == 0:
all_boxes = sorted(all_boxes, key=itemgetter(1), reverse=True) # sort boxes from bottom to top
all_boxes_ori = deepcopy(all_boxes)
#find blocks that touch each other (above and below)
touching_blocks = []
touching_lines = []
width_extra = maximum_width_gap_touching
height_extra = maximum_height_gap_touching
for i in range(len(all_boxes)):
current_box = all_boxes[i]
for j in range(len(all_boxes)):
box2 = all_boxes[j]
if ( (current_box[0]-((current_box[2]+width_extra)/2.0) < box2[0]+(box2[2]/2.0)) and
(current_box[0]+((current_box[2]+width_extra)/2.0) > box2[0]-(box2[2]/2.0)) and
(current_box[1]+((current_box[3]+height_extra)/2.0) > box2[1]-(box2[3]/2.0)) and
(current_box[1]-((current_box[3]+height_extra)/2.0) < box2[1]+(box2[3]/2.0)) ):
if (i != j):
if ((current_box[1]) > (box2[1]+(box2[3]/2.0))): #below block must have center point below top blocks bottom
if ((current_box[1]-(current_box[3]/2.0)) > (box2[1]-(box2[3]/2.0))): #below block must have top point below top blocks top point
touching_blocks.append([i,j]) #first box supports the second
touching_lines.append([current_box[0]-(current_box[2]/2.0),
current_box[0]+(current_box[2]/2.0),
box2[0]-(box2[2]/2.0),
box2[0]+(box2[2]/2.0)]) #bottom block first then top
new_touching_blocks = []
new_touching_lines = []
for i in range(len(all_boxes)):
for j in range(len(all_boxes)):
for k in range(len(all_boxes)):
if [i,j] in touching_blocks:
if [i,k] in touching_blocks:
if [j,k] in touching_blocks:
posie = touching_blocks.index([i,k])
touching_blocks.pop(posie)
touching_lines.pop(posie)
# finds the supportees and supporters (direct) for each block
all_boxes = deepcopy(all_boxes_ori)
graph_supportees = {}
graph_supporters = {}
for i in range(len(all_boxes)):
graph_supportees[i] = []
for support in touching_blocks:
if support[0] == i:
graph_supportees[i].append(support[1])
for i in range(len(all_boxes)):
graph_supporters[i] = []
for support in touching_blocks:
if support[1] == i:
graph_supporters[i].append(support[0])
if (graph_supporters[i] == []):
graph_supporters[i] = [9999] # the ground is represented as block number 9999
all_boxes = deepcopy(all_boxes_ori)
check_local_stability(all_boxes)
all_boxes = deepcopy(all_boxes_ori)
ERA_relations = find_era_relations()
all_boxes = deepcopy(all_boxes_ori)
calc_era_stability(all_boxes, ERA_relations, touching_blocks)
all_boxes = deepcopy(all_boxes_ori)
testg = calc_gary_stability(all_boxes)
if testg == 0:
GARY_INITIAL = 0
all_boxes = deepcopy(all_boxes_ori)
testg = calc_other_stability(all_boxes)
if testg == 0:
OTHER_INITIAL = 0
# Analyse global stability of the sketch drawing (new qualitative method)
all_boxes = deepcopy(all_boxes_ori)
chosen_block = 99999999
global_stable_sketch = 1
both = calc_matthew_stability_ori(all_boxes)
global_stability = both[0]
pivot_points = both[1]
for s in global_stability:
if s == 0:
global_stable_sketch = 0
print("GLOBALLY UNSTABLE MATTHEW")
MATTHEW_INITIAL = 0
print(both)
if (global_stable_sketch == 0):
for j in range(len(global_stability)):
if global_stability[j] == 0:
chosen_block = j
all_boxes = add_extra_supports(all_boxes,pivot_points,chosen_block)
else:
all_stable = 1
if add_extra_blocks_to_make_stable == 0:
all_stable = 1
else:
all_boxes_ori = deepcopy(all_boxes)
def merge_groups(groupings):
to_merge = []
for g1 in range(len(groupings)):
for g2 in range(len(groupings)):
if (g1 != g2):
for g1_block in groupings[g1]:
for g2_block in groupings[g2]:
if g1_block == g2_block:
to_merge.append([g1,g2])
return to_merge
return to_merge
def remove_groupings(groupings):
to_remove = []
for g1 in range(len(groupings)):
for g2 in range(len(groupings[g1])):
for g3 in range(len(groupings[g1])):
if (g2<g3):
if groupings[g1][g2] == groupings[g1][g3]:
to_remove.append([g1,g3])
return to_remove
return to_remove
# splits block sets into groupings that must have the same height
all_boxes = deepcopy(all_boxes_ori)
groupings = []
no_change1 = 0
if check_groups==1:
no_change1 = 1
old_groupings = deepcopy(groupings)
for i in range(len(all_boxes)):
for j in range(len(all_boxes)):
if (i < j):
# checks if i and j share a direct supporter
direct_supporter = 0
for b1 in graph_supporters[i]:
for b2 in graph_supporters[j]:
if (b1==b2):
direct_supporter = 1
if (direct_supporter == 1): # check if i and j share a supportee
for k in range(len(all_boxes)):
if len(find_all_paths(graph_supportees,i,k)) > 0:
if len(find_all_paths(graph_supportees,j,k)) > 0:
groupings.append([])
for aa in find_all_paths(graph_supportees,i,k):
aa.pop()
groupings[-1].append(aa)
for bb in find_all_paths(graph_supportees,j,k):
bb.pop()
groupings[-1].append(bb)
# merge groups togethor (originally the same indentation level as the above paragraph of code)
cleverMergeLists(groupings)
#remove duplicates
no_change3 = 0
while (no_change3 == 0):
to_remove = remove_groupings(groupings)
if len(to_remove) == 0:
no_change3 = 1
else:
del groupings[to_remove[0][0]][to_remove[0][1]]
if sorted(old_groupings) != sorted(groupings):
no_change1=0
# make all single blocks in groups the average height of all single blocks in same group
all_boxes = deepcopy(all_boxes_ori)
if (average_single_block_groups_heights==1):
for g in groupings:
to_average = []
average_height = 0
total_height = 0
for block_set in g:
if len(block_set)==1:
to_average.append(block_set[0])
if len(to_average)>0:
for b in to_average:
total_height = total_height+all_boxes[b][3]
average_height = total_height/float(len(to_average))
for b in to_average:
all_boxes[b][3] = average_height
if (use_similarity_grouping == 1):
close_distance = largest_value*2
blocks_same = []
for i in range(len(all_boxes)):
for j in range(len(all_boxes)):
same_shape = 0
close = 0
no_inbetween = 1
if i != j:
if all_boxes[i][0] < (all_boxes[j][0] + all_boxes[j][0]*error_percentage_shape):
if all_boxes[i][0] > (all_boxes[j][0] - all_boxes[j][0]*error_percentage_shape):
if all_boxes[i][2] < (all_boxes[j][2] + all_boxes[j][2]*error_percentage_shape):
if all_boxes[i][2] > (all_boxes[j][2] - all_boxes[j][2]*error_percentage_shape):
if all_boxes[i][3] < (all_boxes[j][3] + all_boxes[j][3]*error_percentage_shape):
if all_boxes[i][3] > (all_boxes[j][3] - all_boxes[j][3]*error_percentage_shape):
same_shape = 1
elif all_boxes[i][1] < (all_boxes[j][1] + all_boxes[j][1]*error_percentage_shape):
if all_boxes[i][1] > (all_boxes[j][1] - all_boxes[j][1]*error_percentage_shape):
if all_boxes[i][2] < (all_boxes[j][2] + all_boxes[j][2]*error_percentage_shape):
if all_boxes[i][2] > (all_boxes[j][2] - all_boxes[j][2]*error_percentage_shape):
if all_boxes[i][3] < (all_boxes[j][3] + all_boxes[j][3]*error_percentage_shape):
if all_boxes[i][3] > (all_boxes[j][3] - all_boxes[j][3]*error_percentage_shape):
same_shape = 1
if all_boxes[i][0] < (all_boxes[j][0] + close_distance):
close = 1
if all_boxes[i][0] > (all_boxes[j][0] - close_distance):
close = 1
if all_boxes[i][1] < (all_boxes[j][1] + close_distance):
close = 1
if all_boxes[i][1] > (all_boxes[j][1] - close_distance):
close = 1
for k in range(len(all_boxes)):
k_top = all_boxes[k][1] + (all_boxes[k][3]/2.0)
k_bottom = all_boxes[k][1] - (all_boxes[k][3]/2.0)
k_left = all_boxes[k][0] - (all_boxes[k][2]/2.0)
k_right = all_boxes[k][0] + (all_boxes[k][2]/2.0)
i_top = all_boxes[i][1] + (all_boxes[i][3]/2.0)
i_bottom = all_boxes[i][1] - (all_boxes[i][3]/2.0)
i_left = all_boxes[i][0] - (all_boxes[i][2]/2.0)
i_right = all_boxes[i][0] + (all_boxes[i][2]/2.0)
j_top = all_boxes[j][1] + (all_boxes[j][3]/2.0)
j_bottom = all_boxes[j][1] - (all_boxes[j][3]/2.0)
j_left = all_boxes[j][0] - (all_boxes[j][2]/2.0)
j_right = all_boxes[j][0] + (all_boxes[j][2]/2.0)
if (k_top > i_bottom) and (k_top > j_bottom) and (k_bottom < i_top) and (k_bottom < j_top) and (all_boxes[k][0] > all_boxes[i][0]) and (all_boxes[k][0] < all_boxes[j][0]):
no_inbetween = 0
if (k_right > i_left) and (k_right > j_left) and (k_left < i_right) and (k_left < j_right) and (all_boxes[k][1] > all_boxes[i][1]) and (all_boxes[k][1] < all_boxes[j][1]):
no_inbetween = 0
if (no_inbetween==1 and close==1 and same_shape==1):
blocks_same.append([i,j])
if ((average_same_block_groups_heights==1) and (use_similarity_grouping == 1)):
blocks_same2 = deepcopy(blocks_same)
no_change2 = 0
while(no_change2 == 0):
to_merge = []
for g1 in range(len(blocks_same2)):
for g1_block in blocks_same2[g1]:
for g2 in range(len(blocks_same2)):
for g2_block in blocks_same2[g2]:
if (g1 != g2):
if g1_block == g2_block:
to_merge.append([g1,g2])
if len(to_merge) == 0:
no_change2 = 1
else:
blocks_same2[to_merge[0][0]] = blocks_same2[to_merge[0][0]]+blocks_same2[to_merge[0][1]]
blocks_same2.pop(to_merge[0][1])
#remove duplicates
no_change3 = 0
while (no_change3 == 0):
to_remove = []
no_change3=1
for g1 in range(len(blocks_same2)):
for g2 in range(len(blocks_same2[g1])):
for g3 in range(len(blocks_same2[g1])):
if (g2<g3):
if blocks_same2[g1][g2] == blocks_same2[g1][g3]:
no_change3=0
to_remove.append([g1,g3])
if (no_change3 == 0):
del blocks_same2[to_remove[0][0]][to_remove[0][1]]
# make same average height
for g in blocks_same2:
to_average = []
average_height = 0
total_height = 0
for blockz in g:
to_average.append(blockz)
if len(to_average)>0:
for b in to_average:
total_height = total_height+all_boxes[b][3]
average_height = total_height/float(len(to_average))
for b in to_average:
all_boxes[b][3] = average_height
# adds composite blocks to set of possible block types, made up of multiple smaller blocks
# can also rearrange the ordering of this sub-blocks to create even more possible options
if composite_blocks_allowed == 1:
specials = {}
horizontal = [5,6,8,10,12]
counter = 14
for i in range(max_composite_block_width):
for j in horizontal:
new_block_width = (2.06*i) + blocks[str(j)][0]
height_counter = 0.22
height_num = 1
while height_counter < new_block_width*2.0:
pos_j = deepcopy(i)
if rearrange_special_block_order == 1:
while pos_j >= 0:
blocks[str(counter)] = [round(new_block_width,2),round(height_counter,2)]
block_names[str(counter)] = "special"
specials[str(counter)] = [i,j,height_num,pos_j]
counter = counter + 1
pos_j = pos_j - 1
height_counter = round(height_counter + 0.22,2)
height_num = height_num+1
else:
blocks[str(counter)] = [round(new_block_width,2),round(height_counter,2)]
block_names[str(counter)] = "special"
specials[str(counter)] = [i,j,height_num,pos_j]
counter = counter + 1
height_counter = round(height_counter + 0.22,2)
height_num = height_num+1
# divide the size and position of all blocks by the scale factor
scale_factor = 1
if scaling_method == 0:
scale_factor = largest_value/2.06 # BIG APPROACH
if scaling_method == 1:
scale_factor = smallest_value/0.22 # SMALL APPROACH
if scaling_method == 2:
middle_block_size = (largest_value+smallest_value)/2.0
scale_factor = middle_block_size/1.14 # MIDDLE APPROACH
if scaling_method == 3:
scale_factor = mean_size/actual_block_mean # MEAN APPROACH (0.667)
if scaling_method == 4:
scale_factor = median_size/actual_block_median # MEDIAN APPROACH (0.43)
all_boxes2 = []
for box in all_boxes:
box2 = []
box2.append(box[0]/scale_factor)
box2.append(box[1]/scale_factor)
box2.append(box[2]/scale_factor)
box2.append(box[3]/scale_factor)
all_boxes2.append(box2)
block_order= []
for i in range(len(all_boxes2)):
block_order.append(i)
# re-order list so that blocks are place straight after their direct supporters (or as close as possible to straight after, lower blocks get priority)
# re-orders blocks from being supporters before supportees (closer togethor) rather than top to bottom
# add very bottom block to list
# add supporter of block to list (only if all supporters of this block are present)
# if not all supporters are present, then add all supportees of this block to the list
if order_blocks_smart == 1:
block_order = [0]
block_order2 = [0]
while(len(block_order) < len(all_boxes2)):
added_block = 0
for i in reversed(block_order):
for j in graph_supportees[i]:
if j not in block_order:
if j not in block_order2:
all_supporters = 1
for k in graph_supporters[j]:
if k not in block_order:
all_supporters = 0
check_order = []
to_check = [k]
while(len(to_check)>0):
value_checking = to_check.pop()
check_order.append(value_checking)
for yup in graph_supporters[value_checking]:
if yup != 9999:
to_check.append(yup)
for kk in check_order:
if (kk not in block_order2) and (added_block == 0):
add_me = 1
for gg in graph_supporters[kk]:
if (gg not in block_order2) and gg!=9999:
add_me = 0
if add_me == 1:
block_order2.append(kk)
added_block = 1
if (all_supporters == 1) and (added_block == 0):
block_order2.append(j)
added_block = 1
if (block_order == block_order2):
for rem in range(len(all_boxes2)):
if rem not in block_order2:
block_order2.append(rem)
block_order = deepcopy(block_order2)
# find block type with most similar size to each block
block_keys = numpy.empty((len(all_boxes2), 0)).tolist()
already_tried = [[]]
count_loops = 0
ori_blocks3 = deepcopy(all_boxes2)
all_done = 0
while (all_done == 0) and (count_loops < 10000):
current_index = 0
for qqq in block_keys:
if qqq != []:
current_index = current_index + 1
current_box = block_order[current_index]
box = ori_blocks3[current_box]
count_loops = count_loops+1
if count_loops % 1000 == 0:
print("generating...") # prints every 1000 loops
# choose a block type for the next block to be added
# based on the sum of the squared differences between their widths and heights (width_dif^2 + height_dif^2)
width = box[2]
height = box[3]
best_difference = 99999999
best_name = ""
for key,value in blocks.items():
width_difference = abs(width-value[0])
height_difference = abs(height-value[1])
total_difference = width_difference**2 + height_difference**2
if int(key) > original_number_blocks:
total_difference = total_difference*composite_block_penalty_picking
if (best_difference > total_difference):
if (key not in already_tried[-1]):
best_difference = total_difference
best_name = key
block_keys[current_box] = best_name
already_tried[-1].append(best_name)
# move block to correct height (based on supporting block height)
if graph_supporters[current_box] == [9999]:
new = []
new.append(ori_blocks3[current_box][0])
new.append(ground+(blocks[block_keys[current_box]][1]/2.0))
new.append(blocks[block_keys[current_box]][0])
new.append(blocks[block_keys[current_box]][1])
else:
new = []
new.append(ori_blocks3[current_box][0])
new.append(all_boxes2[graph_supporters[current_box][0]][1]+
(blocks[block_keys[graph_supporters[current_box][0]]][1]/2.0)+ # error might happen here if structure not possible
(blocks[block_keys[current_box]][1]/2.0))
new.append(blocks[block_keys[current_box]][0])
new.append(blocks[block_keys[current_box]][1])
all_boxes2[current_box] = new
# CHECK THAT BLOCK JUST ADDED TO BLOCK KEYS DOESNT VIOLATE ANY RULES
# if it does then pop the key off block_keys
# do iteratively, removing previous block if no block types for the current block are possible
must_pop = 0
if use_similarity_grouping:
for tim in blocks_same:
if tim[0] == current_box:
if block_keys[tim[1]] != []:
if block_keys[tim[0]] != block_keys[tim[1]] :
must_pop = 1
if tim[1] == current_box:
if block_keys[tim[0]] != []:
if block_keys[tim[0]] != block_keys[tim[1]] :
must_pop = 1
# ensures that chosen block type is the right height to fulfil all grouping requirments
# outside of the horizontal movement shift option as that won't help correct this
if (check_groups == 1) and must_pop==0:
for g in groupings:
height_set = 0
for block_set1 in g:
valid = 1
for n in block_set1:
if (block_keys[n]==[]):
valid = 0
if valid == 1:
height_set2 = 0
for nn in block_set1:
height_set2 += blocks[block_keys[nn]][1]
if height_set == 0:
height_set = height_set2
else:
if abs(height_set - height_set2) > height_error_allowed_groups:
must_pop = 1
# Check if comoposite block is locally stable (all blocks that make it up are supported)
if (check_composite_block_stability == 1) and (composite_blocks_allowed == 1) and must_pop==0:
block_num_special = block_keys[current_box]
i = block_keys[current_box]
j = all_boxes2[current_box]
if int(block_num_special) > original_number_blocks:
info = specials[i]
total_width = round((2.06*info[0])+blocks[str(info[1])][0],2)
total_height = info[2]*0.22
positions_long = []
position_extra = []
added_j = 0
current_pos = j[0]-(total_width/2.0)
y_pos = j[1] - (total_height/2.0) + 0.11
for a in range(info[0]):
if a == info[3]:
added_j = 1
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
position_extra = current_pos
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
current_pos = current_pos + 1.03
positions_long.append(current_pos)
current_pos = current_pos + 1.03
if added_j == 0:
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
position_extra = current_pos
all_boxes_special = []
block_keys_special = []
for iii in range(len(positions_long)):
all_boxes_special.append([positions_long[iii],y_pos,2.06,0.22])
block_keys_special.append(12)
all_boxes_special.append([position_extra,y_pos,blocks[str(info[1])][0],0.22])
block_keys_special.append(info[1])
# check local stability
width_error_allowed_local_composite = 0.0
for ii in range(len(all_boxes_special)):
left_support = 0
right_support = 0
box = all_boxes_special[ii]
for jj in graph_supporters[current_box]:
if jj == 9999:
left_support = 1
right_support = 1
else:
box2 = all_boxes2[jj]
box2_left = box2[0]-((blocks[block_keys[jj]][0])/2.0)
box2_right = box2[0]+((blocks[block_keys[jj]][0])/2.0)
if box2_left < box[0] + width_error_allowed_local_composite:
if box2_right > (box[0] - (box[2]/2.0)):
left_support = 1
if box2_right > box[0] - width_error_allowed_local_composite:
if box2_left < (box[0] + (box[2]/2.0)):
right_support = 1
if left_support == 0:
must_pop = 1
if right_support == 0:
must_pop = 1
if must_pop == 0:
tried_all_moving = 0
while (tried_all_moving==0):
must_pop = 0
# ensures the chosen block does not overlap any other already chosen blocks
if (check_overlap == 1) and must_pop==0:
width_error_allowed_overlap = 0.0
for i in range(len(all_boxes2)):
if (block_keys[i]!=[]) and (i!=current_box):
box_width = blocks[best_name][0]-width_error_allowed_overlap
box_height = blocks[best_name][1]-height_error_allowed_overlap
box2 = all_boxes2[i]
box2_width = blocks[block_keys[i]][0]-width_error_allowed_overlap
box2_height = blocks[block_keys[i]][1]-height_error_allowed_overlap
if ( (all_boxes2[current_box][0]-(box_width/2.0) < box2[0]+(box2_width/2.0)) and
(all_boxes2[current_box][0]+(box_width/2.0) > box2[0]-(box2_width/2.0)) and
(all_boxes2[current_box][1]+(box_height/2.0) > box2[1]-(box2_height/2.0)) and
(all_boxes2[current_box][1]-(box_height/2.0) < box2[1]+(box2_height/2.0)) ):
must_pop = 1
# ensures that chosen block type is wide enough to be supported by all direct supporter blocks
if (check_all_supporters == 1) and must_pop==0:
for i in graph_supporters[current_box]:
if (i < 9999):
test_box = all_boxes2[i]
if (all_boxes2[current_box][0]-(blocks[best_name][0]/2.0) + required_support_amount) > (test_box[0]+(blocks[block_keys[i]][0]/2.0)):
must_pop = 1
if (all_boxes2[current_box][0]+(blocks[best_name][0]/2.0) - required_support_amount) < (test_box[0]-(blocks[block_keys[i]][0]/2.0)):
must_pop = 1
# CHECK ERA RELATIONS (OPTIONAL) NOT SURE IF WORKS 100% BUT SHOULDN'T BE USED ANYWAY AS PREVENTS STABILITY CORRECTION AND VERY RESTRICTIVE
if (check_era_relations == 1) and must_pop==0:
width_extra_era = 0.06
height_extra_era = 0.02
touching_blocks2 = []
touching_lines2 = []
era_relations2 = []
for i in range(len(all_boxes2)):
if block_keys[i] != []:
current_box2 = all_boxes2[i]
current_box2[2] = current_box2[2]+width_extra_era
current_box2[3] = current_box2[3]+height_extra_era
for j in range(len(all_boxes2)):
if block_keys[j] != []:
box2 = all_boxes2[j]
if ( (current_box2[0]-(current_box2[2]/2.0) < box2[0]+(box2[2]/2.0)) and
(current_box2[0]+(current_box2[2]/2.0) > box2[0]-(box2[2]/2.0)) and
(current_box2[1]+(current_box2[3]/2.0) > box2[1]-(box2[3]/2.0)) and
(current_box2[1]-(current_box2[3]/2.0) < box2[1]+(box2[3]/2.0)) ):
if (i != j):
if ((current_box2[1]) > (box2[1]+(box2[3]/2.0))):
if ((current_box2[1]-(current_box2[3]/2.0)) > (box2[1]-(box2[3]/2.0))):
touching_blocks2.append([j,i])
touching_lines2.append([box2[0]-(box2[2]/2.0),
box2[0]+(box2[2]/2.0),
current_box2[0]-(current_box2[2]/2.0),
current_box2[0]+(current_box2[2]/2.0)])
for pairin in range(len(touching_blocks)):
if block_keys[touching_blocks[pairin][0]] != []:
if block_keys[touching_blocks[pairin][1]] != []:
if touching_blocks[pairin] not in touching_blocks2:
must_pop=1
for line2 in touching_lines2:
era_relations2.append(find_era_relation(line2))
for ori1 in range(len(touching_blocks)):
if block_keys[touching_blocks[ori1][0]]!=[]:
first_block = touching_blocks[ori1][0]
if block_keys[touching_blocks[ori1][1]]!=[]:
second_block = touching_blocks[ori1][1]
correct_index_new = 99999999
for new1 in range(len(touching_blocks2)):
if touching_blocks2[new1] == [first_block,second_block]:
correct_index_new = new1
if correct_index_new < 99999999:
if ERA_relations[ori1] != era_relations2[correct_index_new][0]:
must_pop = 1
# check if structure has local stability
# BETTER TO CHECK GLOBAL STABILITY UNLESS TRYING TO BE FAST
if (check_local_stability == 1) and must_pop==0:
width_error_allowed_local = 0.0
for i in range(len(all_boxes2)):
if (block_keys[i]!=[]):
left_support = 0
right_support = 0
box = all_boxes2[i]
for j in graph_supporters[i]:
if j == 9999:
left_support = 1
right_support = 1
else:
box2 = all_boxes2[j]
box2_left = box2[0]-((blocks[block_keys[j]][0])/2.0)
box2_right = box2[0]+((blocks[block_keys[j]][0])/2.0)
if box2_left < box[0] + width_error_allowed_local:
left_support = 1
if box2_right > box[0] - width_error_allowed_local:
right_support = 1
if left_support == 0:
must_pop = 1
if right_support == 0:
must_pop = 1
# check if structure has global stability
if (check_global_stability == 1) and must_pop==0:
stable_global = 0
valid_supportees = []
new_joint_all_boxes = []
if check_global_stability_method == 1:
new_joint_all_boxes = deepcopy(all_boxes2)
for k in range(len(block_keys)):
if block_keys[k] != []:
valid_supportees.append(k)
elif check_global_stability_method == 2:
for k in range(len(all_boxes2)):
valid_supportees.append(k)
if block_keys[k] != []:
new_joint_all_boxes.append(all_boxes2[k])
else:
new_joint_all_boxes.append(ori_blocks3[k])
else:
print ("ERROR!! WRONG CHECK GLOBAL STABILITY METHOD")
stable_global = calc_matthew_stability(new_joint_all_boxes,valid_supportees)
if stable_global == 0:
must_pop = 1
# move sideways if selected as viable response option
if must_pop == 1:
if len(moves_to_try)==0:
tried_all_moving=1
elif shift_blocks_sideways==0:
tried_all_moving=1
else:
all_boxes2[current_box][0] = all_boxes2[current_box][0]+moves_to_try[-1]
moves_to_try.pop()
else:
tried_all_moving = 1
# block fails one or more requirments so remove it and try again
if must_pop == 1:
block_keys[current_box]=[]
# if already tried all block types then remove the block AND the previous block
if (limit_number_block_type_changes == 1) and (len(blocks) > max_number_block_type_changes):
while len(already_tried[-1]) == max_number_block_type_changes:
current_index = current_index-1
block_keys[block_order[current_index]]=[]
already_tried.pop()
else:
while len(already_tried[-1]) == len(blocks):
current_index = current_index-1
block_keys[block_order[current_index]]=[]
already_tried.pop()
else:
already_tried.append([])
all_done=1
for qqq in block_keys:
if qqq == []:
all_done=0
if (count_loops >= 10000):
print("generating structure took too long, suggest trying a different scale_calculation_option")
#calculate measure of difference betweent the original sketch and the generated structure (average percentage ratio difference)
avg_ratio_error_score = 0
for i in range(len(all_boxes_ori)):
ratio_ori = all_boxes_ori[i][3]/all_boxes_ori[i][2]
ratio_new = blocks[block_keys[i]][1]/blocks[block_keys[i]][0]
avg_ratio_error_score = avg_ratio_error_score + (abs(ratio_ori-ratio_new)/((ratio_ori+ratio_new)/2.0))
avg_ratio_error_score = avg_ratio_error_score/len(all_boxes_ori)
print("AVG RATIO ERROR:")
print(avg_ratio_error_score)
avg_mean_error_score = 0
old_mean_area = deepcopy(mean_area)
new_mean_area = 0
for i in range(len(all_boxes_ori)):
new_mean_area = new_mean_area + (blocks[block_keys[i]][1]*blocks[block_keys[i]][0])
new_mean_area = new_mean_area / len(all_boxes_ori)
old_scale = old_mean_area/100.0
new_scale = new_mean_area/100.0
for i in range(len(all_boxes_ori)):
area_old = all_boxes_ori[i][3]*all_boxes_ori[i][2]
area_new = ((blocks[block_keys[i]][1]*blocks[block_keys[i]][0]))
area_old = area_old / old_scale
area_new = area_new / new_scale
avg_mean_error_score = avg_mean_error_score + abs((area_old)-(area_new))
avg_mean_error_score = avg_mean_error_score
avg_mean_error_score = avg_mean_error_score/len(all_boxes_ori)
print("AVG MEAN AREA ERROR:")
print(avg_mean_error_score)
avg_location_error_score = 0
old_mean_area = deepcopy(mean_area)
new_mean_area = 0
for i in range(len(all_boxes_ori)):
new_mean_area = new_mean_area + (blocks[block_keys[i]][1]*blocks[block_keys[i]][0])
new_mean_area = new_mean_area / len(all_boxes_ori)
old_scale = sqrt(old_mean_area)/100.0
new_scale = sqrt(new_mean_area)/100.0
center_mass_new_x = 0
center_mass_new_y = 0
total_mass_new = 0
for i in range(len(all_boxes_ori)):
box = all_boxes2[i]
center_mass_new_x = center_mass_new_x + (box[0]*box[2]*box[3])
center_mass_new_y = center_mass_new_y + (box[1]*box[2]*box[3])
total_mass_new = total_mass_new + (box[2]*box[3])
center_mass_new_x = center_mass_new_x / total_mass_new
center_mass_new_y = center_mass_new_y / total_mass_new
for i in range(len(all_boxes_ori)):
position_old_x = abs(all_boxes_ori[i][0]-center_mass_ori_x)
position_old_y = abs(all_boxes_ori[i][1]-center_mass_ori_y)
position_new_x = abs(all_boxes2[i][0]-center_mass_new_x)
position_new_y = abs(all_boxes2[i][1]-center_mass_new_y)
position_old_x = position_old_x / (old_scale)
position_old_y = position_old_y / (old_scale)
position_new_x = position_new_x / (new_scale)
position_new_y = position_new_y / (new_scale)
distance = sqrt( (abs(position_old_x-position_new_x)*abs(position_old_x-position_new_x)) + (abs(position_old_y-position_new_y)*abs(position_old_y-position_new_y)) )
avg_location_error_score = avg_location_error_score + distance
avg_location_error_score = avg_location_error_score
avg_location_error_score = avg_location_error_score/len(all_boxes_ori)
print("AVG LOCATION AREA ERROR:")
print(avg_location_error_score)
penalty_composite = 0.0
number_composite = 0.0
total_number_blocks = len(block_keys)
for i in range(len(block_keys)):
if int(block_keys[i]) > original_number_blocks:
number_composite = number_composite + 1
ratio_composite = number_composite/total_number_blocks
penalty_composite = composite_block_penalty_end*ratio_composite
print("PENALTY COMPOSITE:")
print(penalty_composite)
penalty_extra = 0
penalty_weight = 1.0
for i in range(len(all_boxes2)):
if i >= len(all_boxes_ori_very):
penalty_extra = penalty_extra + ((blocks[block_keys[i]][1]*blocks[block_keys[i]][0]) / (new_scale))
print("PENALTY EXTRA:")
print(penalty_extra)
print("FINAL ERROR SCORE:")
print((avg_ratio_error_score*avg_mean_error_score*avg_location_error_score)+penalty_composite+penalty_extra) # Not normlaised
# flip y_axis direction (upwards is now positive rather than negative)
all_boxes3 = []
need_move_up = 0
for i in all_boxes2:
new = []
new.append(i[0])
new.append(i[1]*-1)
new.append(i[2])
new.append(i[3])
all_boxes3.append(new)
# move blocks to correct height (needs to be done again after flipping y-axis)
for i in range(len(all_boxes3)):
if graph_supporters[i] == [9999]:
new = []
new.append(all_boxes3[i][0])
new.append(ground+(blocks[block_keys[i]][1]/2.0))
new.append(all_boxes3[i][2])
new.append(all_boxes3[i][3])
else:
new = []
new.append(all_boxes3[i][0])
new.append(all_boxes3[graph_supporters[i][0]][1]+
(blocks[block_keys[graph_supporters[i][0]]][1]/2.0)+
(blocks[block_keys[i]][1]/2.0))
new.append(all_boxes3[i][2])
new.append(all_boxes3[i][3])
all_boxes3[i] = new
all_boxes4 = all_boxes3
# write XML
number_birds=3
f = open("level-4.xml", "w")
f.write('<?xml version="1.0" encoding="utf-16"?>\n')
f.write('<Level width ="2">\n')
f.write('<Camera x="0" y="2" minWidth="20" maxWidth="30">\n')
f.write('<Birds>\n')
for i in range(number_birds):
f.write('<Bird type="BirdRed"/>\n')
f.write('</Birds>\n')
f.write('<Slingshot x="-8" y="-2.5">\n')
f.write('<GameObjects>\n')
for index in range(len(all_boxes4)):
i = block_keys[index]
j = all_boxes4[index]
if int(i) > original_number_blocks:
rotation = 0
info = specials[i]
total_width = round((2.06*info[0])+blocks[str(info[1])][0],2)
total_height = info[2]*0.22
y_pos = j[1] - (total_height/2.0) + 0.11
pos_j = info[3]
for jj in range(info[2]):
positions_long = []
position_extra = []
added_j = 0
current_pos = j[0]-(total_width/2.0)
for a in range(info[0]):
if a == pos_j:
added_j = 1
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
position_extra = current_pos
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
current_pos = current_pos + 1.03
positions_long.append(current_pos)
current_pos = current_pos + 1.03
if added_j == 0:
current_pos = current_pos + (blocks[str(info[1])][0]/2.0)
position_extra = current_pos
for aa in range(len(positions_long)):
f.write('<Block type="RectBig" material="stone" x="%s" y="%s" rotation="0" />\n' % (str(positions_long[aa]), str(y_pos)))
f.write('<Block type="%s" material="stone" x="%s" y="%s" rotation="0" />\n' % (str(block_names[str(info[1])]),str(position_extra), str(y_pos)))
y_pos = y_pos + 0.22
if composite_block_interweaving == 1:
pos_j = pos_j + 1
if pos_j > info[0]:
pos_j = 0
else:
rotation = 0
if (int(i) in (3,7,9,11,13)):
rotation = 90
f.write('<Block type="%s" material="stone" x="%s" y="%s" rotation="%s" />\n' % (block_names[str(i)],str(j[0]), str(j[1]), str(rotation)))
f.write('</GameObjects>\n')
f.write('</Level>\n')
f.close()
| stepmat/ScienceBirds_sketch_generation | generate_sketch.py | generate_sketch.py | py | 114,707 | python | en | code | 4 | github-code | 36 | [
{
"api_name": "sys.argv",
"line_number": 24,
"usage_type": "attribute"
},
{
"api_name": "sys.argv",
"line_number": 77,
"usage_type": "attribute"
},
{
"api_name": "PIL.ImageOps.grayscale",
"line_number": 161,
"usage_type": "call"
},
{
"api_name": "PIL.ImageOps",
... |
37853605555 | #!/usr/bin/env python
#
#
import common
import os, sys, re, random, itertools, functools
from atoslib import utils
from atoslib import atos_lib
from atoslib import generators
from atoslib import process
TEST_CASE = "ATOS generators - pruning"
args = common.atos_setup_args(ATOS_DEBUG_FILE="debug.log")
# ####################################################################
#
# creation of fake atos-config directory
#
atos_config = "atos-configurations"
process.commands.mkdir(atos_config)
db = atos_lib.atos_db.db(atos_config)
process.commands.touch("sha1-c")
atos_lib.generate_script(
"profile.sh", ";" "true", {})
process.commands.mkdir("csv")
process.commands.touch("plugin.so")
with open('atos-configurations/flags.inline.cfg', 'w') as flagsf:
print >>flagsf, '-fgood1|-fbad1'
print >>flagsf, '-fgood2|-fgood2'
print >>flagsf, '-fbad2|-fbad2'
print >>flagsf, '-fnone1|-fnone1'
with open('atos-configurations/flags.loop.cfg', 'w') as flagsf:
print >>flagsf, '-fgood3|-fbad3'
print >>flagsf, '-fgood4|-fbad4'
print >>flagsf, '-fnone2|-fnone2'
with open('atos-configurations/flags.optim.cfg', 'w') as flagsf:
print >>flagsf, '-fgood5|-fbad5'
print >>flagsf, '-fnone3|-fnone3'
print >>flagsf, '-fcrash1|-fnone4'
#
# generation of profile files (fctmap.out, oprof.out)
#
def profile_gen():
with open('fctmap.out', 'w') as flagsf:
print >>flagsf, 'sha1.o SHA1Reset'
print >>flagsf, 'sha1.o SHA1Result'
print >>flagsf, 'sha1.o SHA1PadMessage'
print >>flagsf, 'sha1.o SHA1Input'
print >>flagsf, 'sha1.o SHA1ProcessMessageBlock'
print >>flagsf, 'sha.o main'
print >>flagsf, 'sha.o usage'
with open('oprof.out', 'w') as flagsf:
print >>flagsf, 'vma samples % linenr_info image_name symbol_name'
print >>flagsf, '0000000000400b93 353965337 52.2748 (no localization information) sha1-c SHA1ProcessMessageBlock'
print >>flagsf, '0000000000400a86 260243456 38.4336 (no localization information) sha1-c SHA1Input'
print >>flagsf, '00000000004006e4 62914627 9.2914 (no localization information) sha1-c main'
print >>flagsf, '0000000000400f6c 732 0.0001 (no localization information) sha1-c SHA1PadMessage'
print >>flagsf, '0000000000401110 25 0.0000 (no localization information) sha1-c __libc_csu_init'
print >>flagsf, '00000000004009c8 25 0.0000 (no localization information) sha1-c SHA1Reset'
print >>flagsf, '0000000000400a3f 20 0.0000 (no localization information) sha1-c SHA1Result'
#
# estimation of time/size results given a set of flags
#
def get_cfg_result(cfg):
good_flags = [
["-fgood1"], ["-fgood2"], ["-fgood3"], ["-fgood4"], ["-fgood5"],
["-fgood-g1-1", "-fgood-g1-2"],
["-fgood-g2-1", "-fgood-g2-2", "-fgood-g2-3"],
]
bad_flags = [
["-fbad1"], ["-fbad2"], ["-fbad3"], ["-fbad4"], ["-fbad5"],
["-fbad-g1-1", "-fbad-g1-2"],
["-fbad-g2-1", "-fbad-g2-2", "-fbad-g2-3"],
]
crash_flags = [["-fcrash1"]]
#
flag_list = generators.optim_flag_list.parse_line(cfg.flags)
nb_good = len(
filter(lambda f: (len(set(f) - set(flag_list)) == 0), good_flags))
nb_bad = len(
filter(lambda f: (len(set(f) - set(flag_list)) == 0), bad_flags))
nb_crash = len(
filter(lambda f: (len(set(f) - set(flag_list)) == 0), crash_flags))
if nb_crash: return None, None
# file-by-file acf flag
acf_file_flag = filter(
functools.partial(re.search, '--atos-optfile'),
flag_list)
if acf_file_flag:
fbf_file = acf_file_flag[0][14:].strip('= ')
fbf_config = (
generators.gen_file_by_file_cfg.read_config_csv(fbf_file))
for (obj, flags) in fbf_config.items():
flags = generators.optim_flag_list.parse_line(flags)
acf_nb_good = len(
filter(lambda f: (len(set(f) - set(flags)) == 0), good_flags))
acf_nb_bad = len(
filter(lambda f: (len(set(f) - set(flags)) == 0), bad_flags))
nb_good += acf_nb_good * 0.2
nb_bad += acf_nb_bad * 0.2
# function-by-function acf flag
acf_func_flag = filter(
functools.partial(re.search, '-fplugin-arg-acf_plugin-csv_file'),
flag_list)
if acf_func_flag:
fbf_file = re.search(
'-fplugin-arg-acf_plugin-csv_file=([^ ]*)',
acf_func_flag[0]).group(1)
fbf_config = (
generators.gen_function_by_function_cfg.read_config_csv(fbf_file))
for (obj, flags) in fbf_config.items():
flags = generators.optim_flag_list.parse_line(flags)
acf_nb_good = len(
filter(lambda f: (len(set(f) - set(flags)) == 0), good_flags))
acf_nb_bad = len(
filter(lambda f: (len(set(f) - set(flags)) == 0), bad_flags))
nb_good += acf_nb_good * 0.2
nb_bad += acf_nb_bad * 0.2
score = 1000 - ((nb_good - nb_bad) * 100)
return float(score), score
#
# exploration loop and database update
#
def db_add_result(cfg, variant_id=None):
db = atos_lib.atos_db.db(atos_config)
time, size = get_cfg_result(cfg)
if None in [time, size]:
return None
new_entry = {
'variant': variant_id or atos_lib.variant_id(options=cfg.flags),
'target': 'target',
'conf': cfg.flags or '', 'time': time, 'size': size,
'cookies': ','.join(cfg.cookies or [])
}
db.add_results([new_entry])
return new_entry
def generator_loop(generator):
all_results = []
for cfg in generator:
if cfg.profile: profile_gen()
new_res = db_add_result(cfg)
if new_res: all_results.append(new_res)
return all_results
# ####################################################################
#
#
#
ref_entry = db_add_result(
generators.config())
new_entry = db_add_result(
generators.config(
flags="-fgood1 -fbad1 -fgood-g1-1 -fgood2 "
"-fgood-g1-2 -fbad-g1-1 -fbad-g1-2 -fgood3 -fbad-g2-1"),
variant_id="VAR-1")
# good: 4: fgood1 fgood2 fgood3 fgood-g1-1_fgood-g1-2
# bad : 2: -fbad1 fbad-g1-1_fbad-g1-2
# score = 4 - 2 = 2 -> 1000 - 2 * 100 -> 800
assert new_entry['size'] == 800
assert new_entry['time'] == 800.0
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
tradeoff=5.0, threshold=0.0,
configuration_path=atos_config)
generator_loop(generator)
assert generator.final_flags == [
'-fgood1', '-fgood2']
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
update_reference=True,
tradeoff=5.0, threshold=0.0,
configuration_path=atos_config)
generator_loop(generator)
assert generator.final_flags == [
'-fgood1', '-fgood-g1-1', '-fgood2', '-fgood-g1-2', '-fgood3']
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
one_by_one=True,
tradeoff=5.0, threshold=0.0,
configuration_path=atos_config)
generator_loop(generator)
assert generator.final_flags == [
'-fgood1', '-fgood-g1-1', '-fgood2', '-fgood-g1-2', '-fgood3']
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
tradeoff=5.0, threshold=0.0,
selection_size=2,
configuration_path=atos_config)
generator_loop(generator)
assert generator.final_flags == [
'-fgood2', '-fgood3', '-fbad-g2-1']
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
tradeoff=5.0, threshold=0.0,
random_selection=1,
configuration_path=atos_config)
generator_loop(generator)
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
tradeoff=5.0, threshold=-10,
configuration_path=atos_config)
generator_loop(generator)
generator = generators.gen_flags_pruning(
variant_id='VAR-1',
tradeoff=5.0, threshold=-10,
keep_opt_level=1,
configuration_path=atos_config)
generator_loop(generator)
#
# ACF FUNCTION
#
generator = generators.gen_function_by_function(
acf_plugin_path="plugin.so", hwi_size="8",
prof_script="./profile.sh", imgpath=".",
csv_dir="csv", hot_th="70", cold_opts="-Os",
base_flags="-O2", per_func_nbiters=100, optim_levels="-O2",
configuration_path=atos_config)
results = generator_loop(generator)
generator = generators.gen_flags_pruning(
variant_id=results[-1]['variant'],
update_reference=True,
tradeoff=5.0, threshold=0.0,
configuration_path=atos_config)
generator_loop(generator)
#
# ACF FILE
#
generator = generators.gen_file_by_file(
imgpath=".", csv_dir="csv", hot_th="70", cold_opts="-Os",
base_flags="-O2", per_file_nbiters=100, optim_levels="-O2",
configuration_path=atos_config)
results = generator_loop(generator)
generator = generators.gen_flags_pruning(
variant_id=results[-1]['variant'],
update_reference=True,
tradeoff=5.0, threshold=0.0,
configuration_path=atos_config)
generator_loop(generator)
| atos-tools/atos-utils | tests/test116.py | test116.py | py | 9,009 | python | en | code | 5 | github-code | 36 | [
{
"api_name": "common.atos_setup_args",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "atoslib.process.commands.mkdir",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "atoslib.process.commands",
"line_number": 25,
"usage_type": "attribute"
},
{
... |
70606542505 | import json
from wsgiref import simple_server
from wsgiref.simple_server import make_server
def load_html(file_name,**kwargs):
try:
with open(file_name,'r',encoding='utf-8') as file:
content = file.read()
if kwargs: # kwargs = {'username ': 'zhangsan ', 'age':19, 'gender': 'male'}
content = content.format(**kwargs)
# {username}, 迎回来,你今年{age}岁,你的性别是(gender}.format(**kwargs)
return content
except FileExistsError:
print('文件未找到')
# 第1个参数，表示环境（电脑的环境；请求路径相关的环境）
# 第2个参数，是一个函数，用来返回响应头
# 这个函数需要一个返回值，返回值是一个列表
# 列表里有一个元素，是一个二进制，表示返回给浏览器的数据
def demo_app(environ,start_response):
print(environ) # environ是一个字典，保存了很多数据
# 其中重要的一个是 PATH_INFO 能够获取到用户的访问路径
path = environ['PATH_INFO']
# print(environ. get('QUERY-STRING')) # QUERY_STRING ==> 获取到客户端GET请求方式传递的参数
# POST 请求数据的方式
# 状态码: RESTFUL ==> 前后端分离
# 2xx：请求响应成功
# 3Xx:重定向
# 4xx：客户端的错误。404客户端访问了一个不存在的地址 405:请求方式不被允许
# 5xx：服务器的错误。
status_code = '200 OK' # 默认状态码
if path == '/':
response = '欢迎来到我的首页'
elif path == '/test':
response = json.dumps({'name':'zhagnsna','age':18})
elif path == '/demo':
with open('hello1.txt','r',encoding='utf-8') as file:
response = file.read()
elif path == '/info':
# html 文件查询数据库，获取用户名
name = 'jack'
with open('info.html','r',encoding='utf-8') as file:
# '{username}，欢迎回来'.format(username=name)
# flask django 模块，渲染引擎
response = file.read().format(username=name,age=18,gender='男')
else:
status_code = '404 Not Found'
response = '页面走丢了'
print('path={}'.format(path))
start_response(status_code, [('Content-Type', 'text/plain; charset=utf-8'),('Connection', 'keep-alive')])
return [response.encode("utf-8")] # 浏览器显示的内容
if __name__ == '__main__':
# demo_app 是一个函数，用来处理用户的请求
httpd = make_server('', 8000, demo_app)
sa = httpd.socket.getsockname()
print("Serving HTTP on", sa[0], "port", sa[1], "...")
# 代码的作用是打开电脑的浏览器，并在浏览器里输入 http://localhost:8000/xyz?abc
# import webbrowser
# webbrowser.open('http://localhost:8000/xyz?abc')
# httpd.handle_request() # 只处理一次请求
httpd.serve_forever() # 服务器在后台一直运行 | cgyPension/pythonstudy_space | 01_base/$05_wsg服务器.py | $05_wsg服务器.py | py | 2,943 | python | zh | code | 7 | github-code | 36 | [
{
"api_name": "json.dumps",
"line_number": 38,
"usage_type": "call"
},
{
"api_name": "wsgiref.simple_server.make_server",
"line_number": 60,
"usage_type": "call"
}
] |
38841107343 | import re
import random
from django.shortcuts import render
from django.views.decorators.csrf import csrf_exempt
import razorpay
from django.db.models import Q
from operator import itemgetter
from django.core.paginator import Paginator
import datetime
from property_management_system.settings import razorpay_api_key, razorpay_api_secret_key
from django.contrib.auth.decorators import login_required
from django.shortcuts import redirect, render
from django.contrib import messages
from django.contrib.auth.models import User
from django.conf import settings
from django.db.models import Sum, Func, Count
from django.contrib.auth import login, logout, authenticate
from pms.send_mail import send_customise_mail
from django.db import models
from pms.models import Blogs, Contact, Property, PropertyContacts, PropertyImages, PropertyMembership, UserProfile
months_mapping = {
1: "January",
2: "February",
3: "March",
4: "April",
5: "May",
6: "June",
7: "July",
8: "August",
9: "September",
10: "October",
11: "November",
12: "December",
}
def index(request):
try:
property_list = list(Property.objects.filter(
is_listed=True).order_by('-membership_purchased'))
if len(property_list) > 6:
random_properties = random.sample(property_list, 6)
else:
random_properties = (Property.objects.filter(
is_listed=True).order_by('-membership_purchased'))
except Exception as e:
print(e)
try:
agents_list = list(UserProfile.objects.filter(
user_type="Agent").order_by('-created_at'))
if len(agents_list) > 3:
random_agents = random.sample(agents_list, 3)
else:
random_agents = UserProfile.objects.filter(
user_type="Agent").order_by('-created_at')
except Exception as e:
print(e)
all_properties = Property.objects.filter(is_listed=True)
blogs = Blogs.objects.all().order_by('-created_at')
index_page_blogs = None
if len(blogs) > 6:
index_page_blogs = blogs[:6]
else:
index_page_blogs = blogs
context = {
'random_properties': random_properties,
'all_properties': all_properties,
'random_agents': random_agents,
'index_page_blogs': index_page_blogs,
}
return render(request, 'ui_templates/index.html', context)
def error_404_view(request, exception):
return render(request, 'ui_templates/404.html')
def about(request):
registered_users = UserProfile.objects.filter(user_type='').count()
agents = UserProfile.objects.filter(user_type='').count()
total_properties = Property.objects.all().count()
context = {
'registered_users': registered_users,
'agents': agents,
'total_properties': total_properties
}
return render(request, 'ui_templates/about.html', context)
def all_properties(request):
filtered_properties = Property.objects.filter(
is_listed=True).order_by('-membership_purchased')
if request.method == 'GET':
search_filter = request.GET.get('searching_filters')
sorting_properties = request.GET.get('sort')
property_size = request.GET.get('property_size')
nearbuy_location = request.GET.get('nearbuy_location')
if search_filter:
filtered_properties = Property.objects.filter(
Q(city__icontains=search_filter) |
Q(state__icontains=search_filter) |
Q(address__icontains=search_filter) |
Q(type__icontains=search_filter) |
Q(zip_code__icontains=search_filter) |
Q(furnishing__icontains=search_filter) |
Q(availability__icontains=search_filter) |
Q(ownership__icontains=search_filter) |
Q(expected_price__icontains=search_filter) |
Q(details__icontains=search_filter)
).order_by('-membership_purchased')
if sorting_properties and sorting_properties == 'lth':
filtered_properties = filtered_properties.order_by(
'expected_price')
if sorting_properties and sorting_properties == 'htl':
filtered_properties = filtered_properties.order_by(
'-expected_price')
if property_size:
filtered_properties = filtered_properties.filter(
area__gte=property_size)
if nearbuy_location:
filtered_properties = filtered_properties.filter(
Q(city__icontains=nearbuy_location) |
Q(state__icontains=nearbuy_location) |
Q(address__icontains=nearbuy_location))
paginator = Paginator(filtered_properties, 9)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
context = {
'active_properties': filtered_properties,
'page_obj': page_obj,
}
return render(request, 'ui_templates/properties.html', context)
else:
active_properties = Property.objects.filter(
is_listed=True).order_by('-membership_purchased')
paginator = Paginator(active_properties, 9)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
context = {
'active_properties': active_properties,
'page_obj': page_obj,
}
return render(request, 'ui_templates/properties.html', context)
def property_details(request, id):
property = Property.objects.get(id=id)
property_images = PropertyImages.objects.filter(property=property)
amenities = []
if property.amenities:
amenities = [amenity.strip()
for amenity in property.amenities.split(',')]
context = {
'property': property,
'amenities': amenities,
'property_images': property_images,
}
return render(request, 'ui_templates/property_detail.html', context)
def blog(request):
recent_blogs = Blogs.objects.all().order_by('-created_at')
paginator = Paginator(recent_blogs, 8)
page_number = request.GET.get('page')
page_obj = paginator.get_page(page_number)
context = {
'recent_blogs': recent_blogs,
'page_obj': page_obj,
}
return render(request, 'ui_templates/blog.html', context)
def blog_description(request, id):
requested_blog = Blogs.objects.filter(id=id)
context = {'requested_blog': requested_blog}
return render(request, 'ui_templates/blog_description.html', context)
@login_required(login_url='login')
def add_new_blog(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
if request.method == 'POST':
blog_image = None
title = request.POST.get('title')
body = request.POST.get('body')
try:
blog_image = request.FILES.get('blog_image')
except Exception as e:
print(e)
if blog_image:
create_new_blog = Blogs(
blog_title=title, blog_body=body, blog_image=blog_image,
uploaded_by=user)
create_new_blog.save()
messages.add_message(request, messages.WARNING,
"New Blog Created Successfully.")
else:
messages.add_message(request, messages.WARNING,
"Blog Image is Required")
return render(request, 'dashboard_templates/blog/add_new_blog.html')
@login_required(login_url='login')
def view_blogs_list(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
all_blogs = Blogs.objects.all()
context = {'all_blogs': all_blogs}
return render(request, 'dashboard_templates/blog/blogs_view.html', context)
@login_required(login_url='login')
def update_blog(request, id):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
requested_blog = Blogs.objects.filter(id=id)
if request.method == 'POST':
blog_update = Blogs.objects.filter(id=id).first()
updated_title = request.POST.get('title')
updated_body = request.POST.get('body')
blog_update.blog_title = updated_title
blog_update.blog_body = updated_body
try:
updated_blog_image = request.FILES['blog_image']
blog_update.blog_image = updated_blog_image
except Exception as e:
print(e)
blog_update.save()
messages.add_message(request, messages.WARNING,
"Blog Updated Successfully.")
context = {
'requested_blog': requested_blog
}
return render(request, 'dashboard_templates/blog/update_blog.html', context)
@login_required(login_url='login')
def delete_blog(request, id):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
requested_blog = Blogs.objects.filter(id=id)
requested_blog.delete()
return redirect('blog_list')
def auth_login(request):
if request.user.is_authenticated:
messages.add_message(request, messages.WARNING,
"You already logged in.")
return redirect('index')
else:
if request.method == 'POST':
email = request.POST.get('email')
password = request.POST.get('password')
user = authenticate(username=email, password=password)
if user:
login(request, user)
messages.add_message(
request, messages.WARNING, f"Welcome, {request.user.first_name} {request.user.last_name} you logged in successfully.")
return redirect('index')
else:
messages.add_message(
request, messages.WARNING, "Sorry, check again your email or password.")
return render(request, 'ui_templates/login_page.html')
@login_required(login_url='login')
def user_logout(request):
logout(request)
messages.add_message(request, messages.SUCCESS,
"Success, You Logged Out Successfully.")
return redirect('index')
@login_required(login_url='login')
def profile(request):
requested_user = request.user
user_profile = UserProfile.objects.get(user=requested_user)
context = {
'profile_details': user_profile
}
return render(request, 'dashboard_templates/profile/user_profile.html', context)
def contact_us(request):
if request.method == 'POST':
name = request.POST.get('name')
email = request.POST.get('email')
contact_no = request.POST.get('contact_no')
subject = request.POST.get('subject')
message = request.POST.get('message')
if name and email and contact_no and subject and message:
new_contact = Contact(name=name, email=email, contact_no=contact_no,
subject=subject, message=message)
new_contact.save()
messages.add_message(
request, messages.SUCCESS, "Thanks for Contacting us. We will contact you shortly.")
return redirect('index')
else:
messages.add_message(request, messages.WARNING,
"Please fill all the required details.")
return render(request, 'ui_templates/contact.html')
return render(request, 'ui_templates/contact.html')
def auth_register(request):
password_validation = False
if request.method == 'POST':
profile_image = None
fname = request.POST.get('fname')
lname = request.POST.get('lname')
email = request.POST.get('email')
contact = request.POST.get('contact')
password = request.POST.get('password')
user_type = request.POST.get('user_type')
try:
profile_image = request.FILES.get('profile_image')
except Exception as e:
print(e)
user_exists = User.objects.filter(username=email)
# Minimum six characters, Maximum 20 Characters, at least one Uppercase and Lowecase letter and one number:
password_regex = (
"(?=.*[a-z])(?=.*[A-Z])(?=.*\d)(?=.*[@$!%*#?&])[A-Za-z\d@$!#%*?&]{6,20}$")
password_regex_compile = re.compile(password_regex)
if (re.search(password_regex_compile, password)):
password_validation = True
if password_validation:
if profile_image:
if not user_exists:
if user_type == "Agent":
new_user = User.objects.create_user(
first_name=fname, last_name=lname, username=email, email=email, password=password, is_active=True, is_staff=True)
new_user.save()
subject = "Dream Homes: Registration Successfully"
body = (
"Hi "+fname+". Thanks for Registering with Dream Homes as a " + user_type+".")
send_customise_mail(
subject=subject,
body=body,
email_id=email)
else:
new_user = User.objects.create_user(
first_name=fname, last_name=lname, username=email, email=email, password=password, is_active=True)
new_user.save()
subject = "Dream Homes: Registration Successfully"
body = (
"Hi "+fname+". Thanks for Registering with Dream Homes as a " + user_type+".")
send_customise_mail(
subject=subject,
body=body,
email_id=email)
user_profile = UserProfile(
contact=contact, user=new_user, user_type=user_type, user_image=profile_image)
user_profile.save()
messages.add_message(
request, messages.SUCCESS, "Thanks for Registering with us.Please Login")
return redirect('login')
else:
messages.add_message(
request, messages.WARNING, "You Already have an Account. Please Login")
else:
messages.add_message(
request, messages.SUCCESS, "Profile Image is Required")
else:
messages.add_message(
request, messages.WARNING, "Please Create Strong Password as mentioned criteria.")
return render(request, 'ui_templates/register_page.html')
class Month(Func):
function = 'EXTRACT'
template = "%(function)s(MONTH from %(expressions)s)"
output_field = models.IntegerField()
@login_required(login_url='login')
def dashboard(request):
logged_in_user = request.user
if logged_in_user.is_superuser:
total_membership_amount = PropertyMembership.objects.aggregate(
Sum('property_membership_amount'))['property_membership_amount__sum']
total_membership_taken = PropertyMembership.objects.all().count()
approved_properties = Property.objects.filter(
approved=True).count()
pending_properties = Property.objects.filter(
approved=False).count()
listed_properties = Property.objects.filter(
is_listed=True).count()
unlisted_properties = Property.objects.filter(
is_listed=False).count()
# In case of admin all responses
current_time = datetime.datetime.now()
response_summary = (PropertyContacts.objects.filter(created_at__year=current_time.year)
.annotate(response_by_month=Month('created_at'))
.values('response_by_month')
.annotate(total=Count('id')))
response_data = []
for data in response_summary:
response_data.append(
{
"month": months_mapping.get(data.get('response_by_month')),
"total_responses": int(data.get('total'))
}
)
for month in months_mapping.keys():
result = [i for i in response_data if i['month'] == month]
if not result:
response_data.append(
{"month": months_mapping.get(month), "total_responses": 0})
sorted_response_data = sorted(response_data, key=lambda d: datetime.datetime.strptime(d['month'], "%B")
)
context = {
'approved_properties': approved_properties,
'listed_properties': listed_properties,
'unlisted_properties': unlisted_properties,
'pending_properties': pending_properties,
'total_membership_amount': total_membership_amount,
'total_membership_taken': total_membership_taken,
'response_data': sorted_response_data,
'current_year': current_time.year,
}
else:
approved_properties = Property.objects.filter(
uploaded_by=logged_in_user, approved=True).count()
pending_properties = Property.objects.filter(
uploaded_by=logged_in_user, approved=False).count()
listed_properties = Property.objects.filter(
uploaded_by=logged_in_user, is_listed=True).count()
unlisted_properties = Property.objects.filter(
uploaded_by=logged_in_user, is_listed=True).count()
properties_uploaded_by_user = Property.objects.filter(
uploaded_by=logged_in_user)
current_time = datetime.datetime.now()
response_summary = (PropertyContacts.objects.filter(property_id__in=properties_uploaded_by_user, created_at__year=current_time.year)
.annotate(response_by_month=Month('created_at'))
.values('response_by_month')
.annotate(total=Count('id')))
response_data = []
for data in response_summary:
response_data.append(
{
"month": months_mapping.get(data.get('response_by_month')),
"total_responses": int(data.get('total'))
}
)
for month in months_mapping.keys():
result = [i for i in response_data if i['month'] == month]
if not result:
response_data.append(
{"month": months_mapping.get(month), "total_responses": 0})
sorted_response_data = sorted(response_data, key=lambda d: datetime.datetime.strptime(d['month'], "%B")
)
context = {
'approved_properties': approved_properties,
'listed_properties': listed_properties,
'unlisted_properties': unlisted_properties,
'pending_properties': pending_properties,
'response_data': sorted_response_data,
'current_year': current_time.year,
}
return render(request, 'dashboard_templates/dashboard.html', context)
@login_required(login_url='login')
def add_new_property(request):
user = request.user
last_property_id = Property.objects.all().order_by('id').last()
if not last_property_id:
prop_id = 'WHPR' + '000001'
else:
last_prop_id = last_property_id.property_id
prop_no_int = int(last_prop_id[4:10])
new_property_id = prop_no_int + 1
prop_id = 'WHPR' + str(new_property_id).zfill(6)
if request.method == 'POST':
property_sr = request.POST.get('property_sr')
property_type = request.POST.get('property_type')
property_address = request.POST.get('property_address')
property_city = request.POST.get('property_city')
property_state = request.POST.get('property_state')
no_bedrooms = request.POST.get('no_bedrooms')
no_bathrooms = request.POST.get('no_bathrooms')
no_balconies = request.POST.get('no_balconies')
area_details = request.POST.get('area_details')
furnishing_type = request.POST.get('furnishing_type')
open_parking = request.POST.get('open_parking')
covered_parking = request.POST.get('covered_parking')
availability_status = request.POST.get('availability_status')
property_age = request.POST.get('property_age')
property_ownership = request.POST.get('property_ownership')
expected_price = request.POST.get('expected_price')
price_sq_ft = request.POST.get('price_sq_ft')
property_unique_details = request.POST.get('property_unique_details')
zip_code = request.POST.get('zip_code')
amenities = request.POST.get('amenities')
create_new_property = Property(
property_id=prop_id,
sell_rent=property_sr,
type=property_type,
address=property_address,
city=property_city,
state=property_state,
zip_code=zip_code,
bedrooms=no_bedrooms,
bathrooms=no_bathrooms,
balconies=no_balconies,
area=area_details,
furnishing=furnishing_type,
open_parking=open_parking,
covered_parking=covered_parking,
availability=availability_status,
age=property_age,
ownership=property_ownership,
expected_price=expected_price,
area_price=price_sq_ft,
details=property_unique_details,
approved=False,
uploaded_by=request.user,
amenities=amenities
)
create_new_property.save()
try:
property_images = request.FILES.getlist('property_images')
for img in property_images:
new_img = PropertyImages(
property=create_new_property,
images=img
)
new_img.save()
except Exception as e:
print(e)
subject = "Dream Homes: Property Added Successfully"
body = ("Hi "+user.first_name+". New Property Added Successfully. Your Property ID: " +
prop_id+". It will be listed once all details are verified.")
send_customise_mail(
subject=subject,
body=body,
email_id=user.email)
messages.add_message(request, messages.WARNING,
"New Property Created Successfully.")
return render(request, 'dashboard_templates/property/add_new_property.html')
@login_required(login_url='login')
def list_uploaded_properties(request):
properties_list_by_user = Property.objects.filter(uploaded_by=request.user)
membership_amount = 50000 # In Paisa = Rs 500
membership_currency = 'INR'
client = razorpay.Client(auth=(razorpay_api_key, razorpay_api_secret_key))
payment_data = {"amount": membership_amount,
"currency": membership_currency, 'payment_capture': '1'}
payment_order = client.order.create(data=payment_data)
payment_order_id = payment_order['id']
context = {
'amount': membership_amount,
'api_key': razorpay_api_key,
'order_id': payment_order_id,
'property_list': properties_list_by_user
}
return render(request, 'dashboard_templates/property/list_uploaded_properties.html', context)
@login_required(login_url='login')
def list_property_contacts(request, id):
property_contacts = PropertyContacts.objects.filter(property=id,
property__membership_purchased=True)
context = {
'property_contacts': property_contacts
}
return render(request, 'dashboard_templates/property/property_contacts.html', context)
@login_required(login_url='login')
def delete_property(request, id):
requested_property = Property.objects.get(id=id)
requested_property.delete()
return redirect('property_list')
@login_required(login_url='login')
def update_property(request, id, view=None):
user = request.user
requested_property = Property.objects.filter(id=id)
if request.method == 'POST':
property_update = Property.objects.filter(id=id).first()
property_sr = request.POST.get('property_sr')
property_type = request.POST.get('property_type')
property_address = request.POST.get('property_address')
property_city = request.POST.get('property_city')
property_state = request.POST.get('property_state')
no_bedrooms = request.POST.get('no_bedrooms')
no_bathrooms = request.POST.get('no_bathrooms')
no_balconies = request.POST.get('no_balconies')
area_details = request.POST.get('area_details')
furnishing_type = request.POST.get('furnishing_type')
open_parking = request.POST.get('open_parking')
covered_parking = request.POST.get('covered_parking')
availability_status = request.POST.get('availability_status')
property_age = request.POST.get('property_age')
property_ownership = request.POST.get('property_ownership')
expected_price = request.POST.get('expected_price')
price_sq_ft = request.POST.get('price_sq_ft')
property_unique_details = request.POST.get('property_unique_details')
zip_code = request.POST.get('zip_code')
amenities = request.POST.get('amenities')
property_update.sell_rent = property_sr
property_update.type = property_type
property_update.address = property_address
property_update.city = property_city
property_update.state = property_state
property_update.bedrooms = no_bedrooms
property_update.bathrooms = no_bathrooms
property_update.balconies = no_balconies
property_update.area = area_details
property_update.furnishing = furnishing_type
property_update.open_parking = open_parking
property_update.covered_parking = covered_parking
property_update.availability = availability_status
property_update.age = property_age
property_update.ownership = property_ownership
property_update.expected_price = expected_price
property_update.area_price = price_sq_ft
property_update.zip_code = zip_code
property_update.details = property_unique_details
property_update.amenities = amenities
try:
updated_blog_image = request.FILES['blog_image']
property_update.blog_image = updated_blog_image
except Exception as e:
print(e)
requested_property_images = PropertyImages.objects.filter(
property=property_update)
try:
property_images = request.FILES.getlist('property_images')
if property_images:
for old_img in requested_property_images:
old_img.delete()
for img in property_images:
new_images = PropertyImages(
property=property_update,
images=img
)
new_images.save()
except Exception as e:
print(e)
property_update.save()
subject = "Dream Homes: Property Updated Successfully"
body = ("Hi "+user.first_name+". Your Property " +
property_update.property_id+" Updated Successfully.")
send_customise_mail(
subject=subject,
body=body,
email_id=user.email)
messages.add_message(request, messages.WARNING,
"Property Details Updated Successfully.")
if view:
context = {'requested_property': requested_property,
'view_property': True}
else:
context = {'requested_property': requested_property}
return render(request, 'dashboard_templates/property/update_property.html', context)
@login_required(login_url='login')
def view_property_images(request, id):
property_images = PropertyImages.objects.filter(property=id)
context = {
'property_images': property_images
}
return render(request, 'dashboard_templates/property/property_images.html', context)
@csrf_exempt
def payment_success_membership(request, id):
user = request.user
if request.method == 'POST':
payment_id = request.POST.get('razorpay_payment_id', '')
property = Property.objects.get(id=id)
property.membership_purchased = True
amount = 500
create_property_membership = PropertyMembership(
property=property,
user=request.user,
property_membership_plan=True,
payment_order_id=payment_id,
property_membership_amount=amount
)
create_property_membership.save()
property.save()
subject = "Dream Homes: Property Membership"
body = ("Hi "+user.first_name+". Thanks for Purchasing Property " +
property.property_id+" Membership.")
send_customise_mail(
subject=subject,
body=body,
email_id=user.email)
return redirect('property_list')
@login_required(login_url='login')
def pending_properties_view(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
pending_properties_list = Property.objects.filter(
approved=False)
context = {
'pending_properties_data': pending_properties_list
}
return render(request, 'dashboard_templates/property/pending_properties.html', context)
@login_required(login_url='login')
def approve_property(request, id):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
property = Property.objects.get(id=id)
property.approved = True
property.approved_on = datetime.datetime.now()
property.is_listed = True
property.save()
return redirect('pending_properties')
@login_required(login_url='login')
def unlist_property(request, id):
property = Property.objects.get(id=id)
property.is_listed = False
property.unlisted_date = datetime.datetime.now()
property.save()
return redirect('unlisted_properties')
@login_required(login_url='login')
def listed_properties_view(request):
listed_properties_list = Property.objects.filter(
approved=True, is_listed=True, uploaded_by=request.user)
context = {
'listed_properties_data': listed_properties_list
}
return render(request, 'dashboard_templates/property/listed_properties.html', context)
@login_required(login_url='login')
def unlisted_properties_view(request):
unlisted_properties_list = Property.objects.filter(
approved=True, is_listed=False, uploaded_by=request.user)
context = {
'unlisted_properties_data': unlisted_properties_list
}
return render(request, 'dashboard_templates/property/unlisted_properties.html', context)
@login_required(login_url='login')
def calculate_emi(request):
if request.method == 'POST':
amount = request.POST.get('amount')
interest = request.POST.get('interest')
tenure = request.POST.get('tenure')
if not amount or not interest or not tenure:
messages.add_message(request, messages.WARNING,
"Fill All the Details Properly")
return render(request, 'emi_calculate.html')
else:
rate_of_interest_per_month = float(interest)/(12*100)
monthly_emi = float(amount) * float(rate_of_interest_per_month) * ((1+float(rate_of_interest_per_month))
** float(tenure))/((1+float(rate_of_interest_per_month)) ** float(tenure) - 1)
total_interest_amount = (
float(monthly_emi)*float(tenure)) - float(amount)
context = {
'amount': amount,
'rate_of_interest_per_month': rate_of_interest_per_month,
'tenure': tenure,
'monthly_emi': monthly_emi,
'total_interest_amount': total_interest_amount,
'interest': interest,
'total_amount': round((float(amount)+float(total_interest_amount)), 2),
}
return render(request, 'dashboard_templates/emi_calculate.html', context)
else:
return render(request, 'dashboard_templates/emi_calculate.html')
@login_required(login_url='login')
def agents_list_view(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
agents_data = UserProfile.objects.filter(user_type='Agent').all()
context = {
'agents_list': agents_data
}
return render(request, 'dashboard_templates/reports/agents_list.html', context)
@login_required(login_url='login')
def properties_uploaded_by_user(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
properties = Property.objects.all()
context = {
'properties': properties
}
return render(request, 'dashboard_templates/reports/user_uploaded_properties.html', context)
@login_required(login_url='login')
def customers_list_view(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
customers_data = UserProfile.objects.filter(user_type='Buyer').all()
context = {
'customers_data': customers_data
}
return render(request, 'dashboard_templates/reports/customers_list.html', context)
@login_required(login_url='login')
def total_uploaded_properties(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
all_uploaded_properties = Property.objects.all()
context = {
'all_uploaded_properties': all_uploaded_properties
}
return render(request, 'dashboard_templates/reports/total_uploaded_properties.html', context)
@login_required(login_url='login')
def total_listed_properties(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
all_listed_properties = Property.objects.filter(is_listed=True)
context = {
'all_listed_properties': all_listed_properties
}
return render(request, 'dashboard_templates/reports/total_listed_properties.html', context)
@login_required(login_url='login')
def total_unlisted_properties(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
all_unlisted_properties = Property.objects.filter(
is_listed=False, unlisted_date__isnull=False)
context = {
'all_unlisted_properties': all_unlisted_properties
}
return render(request, 'dashboard_templates/reports/total_unlisted_properties.html', context)
@login_required(login_url='login')
def revenue_details(request):
user = request.user
if not user.is_superuser:
return redirect('index')
else:
if request.method == 'POST':
from_date = request.POST.get('from_date')
to_date = request.POST.get('to_date')
if from_date and to_date:
from_date = datetime.datetime.strptime(from_date, "%Y-%m-%d")
to_date = datetime.datetime.strptime(to_date, "%Y-%m-%d")
to_date = datetime.datetime(
to_date.year, to_date.month, to_date.day, 23, 59, 59)
revenue_details = PropertyMembership.objects.filter(
payment_date__gte=from_date, payment_date__lte=to_date)
context = {
'revenue_details': revenue_details
}
if revenue_details:
messages.add_message(request, messages.SUCCESS,
"Revenue Details From: "+str(from_date)+" To: "+str(to_date))
else:
messages.add_message(request, messages.WARNING,
"No Revenue Details Found !!")
return render(request, 'dashboard_templates/reports/revenue_details.html', context)
else:
return render(request, 'dashboard_templates/reports/revenue_details.html')
else:
return render(request, 'dashboard_templates/reports/revenue_details.html')
def property_comments(request, id):
if request.method == 'POST':
property = Property.objects.get(id=id)
name = request.POST.get('name')
email = request.POST.get('email')
contact = request.POST.get('contact')
comment = request.POST.get('comment')
new_property_contact = PropertyContacts(
name=name, email=email, contact=contact, comments=comment, property=property)
new_property_contact.save()
subject = "Dream Homes: Property Response"
body = ("Hi "+property.uploaded_by.first_name +
". Someone Shows Interest in your Property. Property ID: "+property.property_id+".")
send_customise_mail(
subject=subject,
body=body,
email_id=property.uploaded_by.email)
messages.add_message(request, messages.WARNING,
"Query Submitted Successfully...")
return redirect("property_details", id=id)
else:
return redirect("index")
@login_required(login_url='login')
def view_user_image(request, id):
user_profile_image = UserProfile.objects.filter(user=id)
context = {
'user_profile_image': user_profile_image
}
return render(request, 'dashboard_templates/reports/user_images.html', context)
| karan7864/property_management | pms/views.py | views.py | py | 38,362 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "pms.models.Property.objects.filter",
"line_number": 42,
"usage_type": "call"
},
{
"api_name": "pms.models.Property.objects",
"line_number": 42,
"usage_type": "attribute"
},
{
"api_name": "pms.models.Property",
"line_number": 42,
"usage_type": "name"
},
... |
43231808566 | """
<风格>复古</风格>的旗袍款式
1. 先分词，再标签。
"""
import os
import re
from transformers import BertTokenizer
from collections import defaultdict, Counter
PATTEN_BIO = re.compile('<?.*>?')
def parse_tag_words(subwords):
""" HC<领型>圆领</领型><风格>拼接</风格>连衣裙 """
tags = []
new_subwords = []
i = 0
entity = ''
while i < len(subwords):
if subwords[i] == '<':
if entity == '': # <领型>
for j in range(i + 1, len(subwords)):
if subwords[j] == '>':
break
entity += subwords[j]
else: # </领型>
for j in range(i + 1, len(subwords)):
if subwords[j] == '>':
break
entity = ''
i = j + 1
continue
if entity != '': # 圆领
for j in range(i, len(subwords)):
if subwords[j] == '<':
i = j
break
new_subwords.append(subwords[j])
if j == i:
tags.append('B-' + entity)
else:
tags.append('I-' + entity)
continue
tags.append('O')
new_subwords.append(subwords[i])
i = i + 1
return new_subwords, tags
def bpe(part='train', export_dict=False):
all_tags = []
data_dir = '/workspace/fairseq/data/JD/kb_ner'
bpe_dir = data_dir + '/bpe'
bpe = BertTokenizer('/workspace/fairseq/data/vocab/vocab.jd.txt') # never_split参数对tokenizer不起作用
f_src = open(os.path.join(bpe_dir, part + '.src'), 'w', encoding='utf-8')
f_tgt = open(os.path.join(bpe_dir, part + '.tgt'), 'w', encoding='utf-8')
for line in open(data_dir + '/raw/jdai.jave.fashion.' + part, 'r', encoding='utf-8'):
cid, sid, sent, tag_sent = line.strip().split('\t')
subwords = bpe._tokenize(tag_sent)
subwords, tags = parse_tag_words(subwords)
f_src.write(' '.join(subwords) + '\n')
f_tgt.write(' '.join(tags) + '\n')
all_tags += tags
if export_dict:
with open(os.path.join(bpe_dir, 'vocab.tgt.txt'), 'w', encoding='utf-8') as f_out:
f_out.write('\n'.join([k for k, cnt in Counter(all_tags).items()]) + '\n')
def find_all_entity():
all_tag_sent = []
for line in open('raw/jdai.jave.fashion.train', 'r', encoding='utf-8'):
cid, sid, sent, tag_sent = line.strip().split('\t')
all_tag_sent.append(tag_sent)
entity_list = re.findall('<.*?>', ''.join(all_tag_sent))
aa = Counter(entity_list)
f_out = open('raw/vocab_bioattr.txt', 'w', encoding='utf-8')
f_out.write(''.join(['{}\t{}\n'.format(k, cnt) for k, cnt in aa.items()]))
if __name__ == "__main__":
bpe('train', export_dict=True)
bpe('valid')
bpe('test')
| WaveLi123/m-kplug | m_kplug/data_process/bpe_kb_encoder.py | bpe_kb_encoder.py | py | 2,910 | python | en | code | 4 | github-code | 36 | [
{
"api_name": "re.compile",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "transformers.BertTokenizer",
"line_number": 60,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 61,
"usage_type": "call"
},
{
"api_name": "os.path",
"lin... |
18338901118 | import os
import wave
import numpy as np
import calRMSE
import dsp
import pylab as pl
import scipy.signal as signal
import numpy as np
import cv2
import matplotlib.pyplot as plt
import scipy
from scipy.fftpack import fft
from scipy.io import wavfile as wav
from scipy import signal as sig
from scipy.signal import windows
'''
1.录取敲击声音频，音频中包含多个敲击声
2.将数据流中的多个敲击声切割出来，转换为stft矩阵，进行组合为3通道
3.load模型
4.将数据输入进模型进行测试
5.输出识别结果
'''
def read_wav_scipy(wav_path):
"""[使用scipy读取wav]
Args:
wav_path ([str]): [路径]
Returns:
[np.array, numeric]: [信号及采样率]
"""
wf = wave.open(wav_path, "rb") # 打开wav
params = wf.getparams() # 参数获取
nchannels, sampwidth, framerate, nframes = params[:4] #nframes指bufsize
#声道数, 量化位数（byte单位）, 采样频率,
#采样点数, 压缩类型, 压缩类型的描述
str_data = wf.readframes(nframes)
wf.close() # 关闭wave
#####2.将波形数据转换为数组
# N-1 一维数组，右声道接着左声道
wave_data = np.fromstring(str_data, dtype=np.short)
# 2-N N维数组
wave_data.shape = -1, 1
# 将数组转置为 N-2 目标数组
wave_data = wave_data.T
#
#fs, signal = wav.read(wav_path)
#if(len(signal.shape) > 1):
#signal = signal[:,0]
#return_signal = signal
#
return_signal=wave_data[0];
if return_signal.dtype.name == "int16":
return_signal = return_signal / 32767
fs=framerate
return return_signal, fs
def enframe(x, win, inc):
nx = len(x) # 取数据长度
nwin = len(win) # 取窗长
if nwin == 1: # 判断窗长是否为1，若为1，即表示没有设窗函数
len_temp = win # 是，帧长=win
else:
len_temp = nwin # 否，帧长=窗长
if inc is None: # 如果只有两个参数，设帧inc=帧长
inc = len_temp
nf = int(np.fix((nx-len_temp+inc)/inc)) # 计算帧数
frameout = np.zeros((nf, len_temp)) # 初始化
indf = np.reshape(inc * np.arange(0, nf), (nf, 1))
inds = np.reshape(np.arange(0, len_temp), (1, len_temp))
## 此处没有matlab的语法糖，无法做到简单复制
## Caution: 此处数值已经产生了细微的不同
# frameout = x[indf[:, np.ones((1, len_temp))] + inds[np.ones((nf, 1)), :]]
def myfunc(a):
return x[a]
# 方式1: 使用vectorize来映射
# vfunc = np.vectorize(myfunc)
# frameout = vfunc(np.repeat(indf, len_temp, axis=1) + np.repeat(inds, nf, axis=0))
# 方式2: 直接遍历来映射
temp = np.repeat(indf, len_temp, axis=1) + np.repeat(inds, nf, axis=0)
frameout = np.zeros(np.shape(temp))
for idx,i in enumerate(temp):
for idxx,j in enumerate(i):
frameout[idx, idxx] = myfunc(j)
if nwin > 1:
frameout = frameout * win
return frameout
def vad_specEn(x, wnd, inc, NIS, th1, th2, fs):
frames = enframe(x, wnd, inc).T # 分帧
fn = np.size(frames, axis=1) # 求帧数
if len(wnd) == 1:
wlen = wnd # 求出帧长
else:
wlen = len(wnd)
df = fs / wlen # 求出FFT后频率分辨率
fx1 = int(np.fix(250 / df) + 1)
fx2 = int(np.fix(3500 / df) + 1) # 找出250Hz和3500Hz的位置
km = int(np.floor(wlen / 8)) # 计算出子带个数
K = 0.5 # 常数K
Hb = np.zeros((fn))
for i in range(fn):
A = np.abs(fft(frames[:, i])) # 取来一帧数据FFT后取幅值
E = A[fx1 + 1 : fx2 - 1] # 只取250～3500Hz之间的分量
E = E * E # 计算能量
P1 = E / np.sum(E) # 幅值归一化? -> 应该是计算概率
index = np.argwhere(P1 >= 0.9) # 寻找是否有分量的概率大于0.9
if len(index) != 0:
E[index] = 0 # 若有,该分量置0
Eb = np.zeros((km))
for m in range(km): # 计算子带能量
Eb[m] = np.sum(E[4 * m : 4 * (m + 1)])
prob = (Eb + K) / np.sum(Eb + K) # 计算子带概率
Hb[i] = -np.sum(prob * np.log(prob + 10 ** -23))
Enm = sig.medfilt(Hb, 5) # 1次平滑处理
for i in range(9):
Enm = sig.medfilt(Enm, 5) # 9次平滑处理
Me = np.min(Enm) # 设置阈值
eth = np.mean(Enm[1:NIS])
Det = eth - Me
T1 = th1 * Det + Me
T2 = th2 * Det + Me
voiceseg, vsl, SF, NF = vad_revr(Enm, T1, T2)
return voiceseg, vsl, SF, NF, Enm
def vad_revr(dst1, T1, T2):
fn = len(dst1) # 取得帧数
maxsilence = 8 # 初始化
minlen = 5
status = 0
count = [0]
silence = [0]
x1 = [0]
x2 = [0]
# 开始端点检测
xn = 0
for n in range(1, fn):
if status == 0 or status == 1: # 0 = 静音, 1 = 可能开始
if dst1[n] < T2: # 确信进入语音段
x1[xn] = np.max((n - count[xn] - 1, 1))
status = 2
silence[xn] = 0
count[xn] += 1
elif dst1[n] < T1: # 可能处于语音段
status = 1
count[xn] += 1
else: # 静音状态
status = 0
count[xn] = 0
x1[xn] = 0
x2[xn] = 0
elif status == 2: # 2 = 语音段
if dst1[n] < T1: # 保持在语音段
count[xn] += 1
else: # 语音将结束
silence[xn] += 1
if silence[xn] < maxsilence: # 静音还不够长，尚未结束
count[xn] += 1
elif count[xn] < minlen: # 语音长度太短，认为是噪声
status = 0
silence[xn] = 0
count[xn] = 0
else:
status = 3
x2[xn] = x1[xn] + count[xn]
elif status == 3: # 语音结束，为下一个语音准备
status = 0
xn += 1
count.append(0)
silence.append(0)
x1.append(0)
x2.append(0)
el = len(x1) - 1
if x1[el] == 0:
el = el - 1 # 获得x1的实际长度
if el == 0:
return None,None,None,None
if x2[el] == 1: # 如果x2最后一个值为0，对它设置为fn
print("Error: Not find endding point!")
x2[el] = fn
SF = np.zeros((1, fn))
NF = np.ones((1, fn))
for i in range(el+1): # 按x1和x2，对SF和NF赋值
SF[0, x1[i] : x2[i]] = 1
NF[0, x1[i] : x2[i]] = 0
speechIndex = np.argwhere(SF == 1)
voiceseg = findSegment(speechIndex) # 计算voiceseg
vsl = len(voiceseg)
return voiceseg, vsl, SF, NF
def findSegment(express):
express_temp = [e[0] if e[0] != 0 else e[1] for e in express]
express = express_temp
if express[0] == 0:
voiceIndex = np.argwhere(express != 0) # 寻找express中为1的位置
else:
voiceIndex = express
class seg_object(object):
"""
保存begin/end/duration属性
"""
def __init__(self) -> None:
pass
soundSegment = [seg_object()]
k = 0
soundSegment[k].begin = voiceIndex[0] # 设置第一组有话段的起始位置
for i in range(len(voiceIndex)-1):
if voiceIndex[i + 1] - voiceIndex[i] > 1: # 本组有话段结束
soundSegment[k].end = voiceIndex[i] # 设置本组有话段的结束位置
soundSegment.append(seg_object())
soundSegment[k + 1].begin = voiceIndex[i + 1] # 设置下一组有话段的起始位置
k += 1
soundSegment[k].end = voiceIndex[-1] # 最后一组有话段的结束位置
# 计算每组有话段的长度
for i in range(k+1):
soundSegment[i].duration = soundSegment[i].end - soundSegment[i].begin + 1
return soundSegment
def FrameTimeC(frameNum, framelen, inc, fs):
frameTime = ((np.arange(1, frameNum + 1) - 1) * inc + framelen / 2) / fs
return frameTime
def read_data(i):
global list1;
x=list1[i];
return np.array(x,float);
def seg_data(wavfile,th1,th2,tap_num = 3,threshold = 30):
global list1;
list1=[];
# 遍历文件开始处理
signal, fs = read_wav_scipy(wavfile)
# step2.0: 变量使用及预处理
IS = 0.7 # 设置前导无话段长度
wlen = 200 # 设置帧长为25ms
inc = 80 # 求帧移
signal = signal[int(np.round(0.25 * fs))-1:] # 去除开头的硬件噪声?
signal_normalized = signal / np.max(np.abs(signal)) # 幅值非严格归一 -> [-1, 1]
N = len(signal_normalized) # 信号长度
time = np.arange(0, N) / fs
wnd = sig.windows.hamming(wlen, sym=False) # 此处matlab的显示精度需要设置，数值应该是一样的
overlap = wlen - inc
NIS = int(np.fix((IS * fs - wlen) / inc + 1))
#th1=0.7;
#th2=0.8;
'''
# step2.1: 阈值选取
material_list = ["6061", "yCu", "bxg304"]
# assert material_type in material_list, "材料类型不在列表中，请更换材料类型参数；Material_type not in the list... Please change the \"material_type\" parameter."
if material_type in material_list:
th1 = 0.35
th2 = 0.3
# th1 = 0.25
# th2 = 0.2
else:
th1 = 0.7
th2 = 0.8
'''
# step3.0: 谱熵法计算
voiceseg, vsl, SF, NF, Enm = vad_specEn(
signal_normalized, wnd, inc, NIS, th1, th2, fs
) # 谱熵法
if voiceseg==None and vsl==None:
return np.array([[[1]]], float)
fn = np.max(np.size(SF))
frameTime = FrameTimeC(fn, wlen, inc, fs) # 计算各帧对应的时间
# step3.1: 切分开始
up_cut_time_point = []
down_cut_time_point = []
for k in range(vsl):
nx1 = voiceseg[k].begin
nx2 = voiceseg[k].end
up_cut_time_point.append(frameTime[nx1])
down_cut_time_point.append(frameTime[nx2])
multi_data=[];
for i in range(vsl):
'''
if loc.find("10cm") + 1:
t1 = up_cut_time_point[i] - 0.0025
else:
t1 = up_cut_time_point[i] - 0.001
'''
t1 = up_cut_time_point[i] - 0.001
t2 = t1 + 0.009
touchsound = signal[int(np.round(t1 * fs)) : int(np.round(t2 * fs))]
# 观察结果
N_final = len(touchsound)
time_final = N_final / fs
Xtime_final = np.linspace(0, time_final, N_final, endpoint=True)
"""
plt.close()
plt.figure()
touchsound_final=touchsound
plt.plot(Xtime_final, touchsound_final)
touchsound_final2=touchsound_final.reshape(1,-1);
list1.append(Xtime_final);
list1.append(touchsound_final2[0]);
plt.title("touchsound_final")
plt.show()
"""
# 求切割完成的信号的STFT得到时频分析的矩阵，进行多通道的组合
# window_size = 32
#window_size = 32
#stft_stride = 10
window_size = 64
stft_stride = 10
#window_size = 128
#stft_stride = 5
stft_n_downsample = 1
stft_no_log = False
log_alpha = 0.1
#print(touchsound)
# [f, t, Zxx] = signal.spectrogram(touchsound_final, 44100, window="hamm", nperseg=window_size, noverlap=stft_stride, detrend=False)
spectrum = dsp.signal2spectrum(touchsound, window_size,stft_stride, stft_n_downsample, not stft_no_log)
#plt.close()#############
#plt.figure()############
# y_max = np.max(spectrum)
#plt.xlim([2, spectrum.shape[1]])########
#plt.ylim([0, spectrum.shape[0]])#######
# plt.axis("off")
#plt.imshow(spectrum)#########
# plt.savefig();
#print(spectrum)
multi_data.append(spectrum)
multi_data1 = np.array(multi_data, dtype = np.float32)
ch = 3
print("vsl : "+str(vsl))
if vsl<ch :
return np.array([[[0]]], float)
#for i in range(ch):
#print (multi_data1[i]);
#multi_data1[i] = normliaze(multi_data[i], mode = 'z-score', truncated=5)
#print (multi_data1[i])
return np.array(multi_data1,float)
def normliaze(data, mode = 'z-score', sigma = 0, dtype=np.float32, truncated = 2):
'''
mode: norm | std | maxmin | 5_95
dtype : np.float64,np.float16...
'''
data = data.astype(dtype)
data_calculate = data.copy()
if mode == 'norm':
result = (data-np.mean(data_calculate))/sigma
elif mode == 'z-score':
mu = np.mean(data_calculate)
sigma = np.std(data_calculate)
print(np.mean(data_calculate))
print(np.std(data_calculate))
result = (data - mu) / sigma
return result.astype(dtype)
| wzt1512978386/Surtify | app/src/main/python/DataPreprocessing2.py | DataPreprocessing2.py | py | 12,746 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "wave.open",
"line_number": 34,
"usage_type": "call"
},
{
"api_name": "numpy.fromstring",
"line_number": 43,
"usage_type": "call"
},
{
"api_name": "numpy.short",
"line_number": 43,
"usage_type": "attribute"
},
{
"api_name": "numpy.fix",
"line_num... |
6508918488 | import numpy as np
import csv
import chainer
import chainer.functions as F
import chainer.links as L
import sys
import matplotlib.pyplot as plt
class LaughNeuralNet(chainer.Chain):
def __init__(self):
super(LaughNeuralNet, self).__init__(
l1=L.Linear(None, 200),
l2=L.Linear(None, 100),
l3=L.Linear(None, 2)
)
def __call__(self, x):
h = F.dropout(F.relu(self.l1(x)))
h = F.dropout(F.relu(self.l2(h)))
h = self.l3(h)
return h
class LaughNeuralNet2(chainer.Chain):
def __init__(self):
super(LaughNeuralNet2, self).__init__(
l1=L.Linear(None, 1000),
b1=L.BatchNormalization(1000),
l2=L.Linear(None, 1000),
b2=L.BatchNormalization(1000),
l3=L.Linear(None, 1000),
l4=L.Linear(None, 500),
b3=L.BatchNormalization(500),
l5=L.Linear(None, 500),
l6=L.Linear(None, 2)
)
def __call__(self, x):
h = F.dropout(self.b1(F.relu(self.l1(x))))
h = F.dropout(self.b2(F.relu(self.l2(h))))
h = F.dropout(F.relu(self.l3(h)))
h = F.dropout(self.b3(F.relu(self.l4(h))))
h = F.dropout(F.relu(self.l5(h)))
h = self.l6(h)
return h
class LaughNet(chainer.Chain):
def __init__(self):
super(LaughNet, self).__init__(
l1=L.LSTM(None, 200),
l2=L.Linear(None, 100),
l3=L.Linear(None, 2)
)
def __call__(self, x):
self.l1.reset_state()
h = self.l1(x)
h = F.dropout(self.l2(h))
h = self.l3(h)
return h
class LaughNet2(chainer.Chain):
def __init__(self):
super(LaughNet2, self).__init__(
l1=L.Linear(None, 200),
l2=L.Linear(None, 100),
l3=L.Linear(None, 2)
)
def __call__(self, x):
h = F.dropout(F.relu(self.l1(x)))
h = F.dropout(F.relu(self.l2(h)))
h = self.l3(h)
return h
# test predicting
def test_predict(path='movie_model'):
model = LaughNet()
chainer.serializers.load_npz(path, model)
with open('confused_data/Raw/v_0.csv', 'r') as f:
reader = csv.reader(f)
csv0 = [(np.array(row, dtype=np.float32), 0) for row in reader]
with open('confused_data/Raw/v_1.csv', 'r') as f:
reader = csv.reader(f)
csv1 = [(np.array(row, dtype=np.float32), 1) for row in reader]
all_ary = csv0 + csv1
ans_counts = 0
incorrect = 0
question_num = len(all_ary)
plt_ary = []
for data in all_ary:
correct = data[1]
np_data = data[0].reshape(1, 50)
y = np.argmax(F.softmax(model(np_data)).data)
if y == correct:
ans_counts += 1
plt_ary.append((model(np_data).data, y, 1))
else:
incorrect += 1
plt_ary.append((model(np_data).data, y, 0))
print('accuracy:', str(ans_counts / question_num))
print('incorrect rate:', str(incorrect / question_num))
# ｐｌｔ
x_ary0 = []
y_ary0 = []
x_ary1 = []
y_ary1 = []
incorrect_x0 = []
incorrect_y0 = []
incorrect_x1 = []
incorrect_y1 = []
for dt in plt_ary:
if dt[2] == 0:
if dt[1] == 0:
incorrect_x0.append(dt[0][0][0])
incorrect_y0.append(dt[0][0][1])
continue
else:
incorrect_x1.append(dt[0][0][0])
incorrect_y1.append(dt[0][0][1])
continue
if dt[1] == 0:
x_ary0.append(dt[0][0][0])
y_ary0.append(dt[0][0][1])
else:
x_ary1.append(dt[0][0][0])
y_ary1.append(dt[0][0][1])
np_plt_x0 = np.array(x_ary0, dtype=np.float32)
np_plt_y0 = np.array(y_ary0, dtype=np.float32)
np_plt_x1 = np.array(x_ary1, dtype=np.float32)
np_plt_y1 = np.array(y_ary1, dtype=np.float32)
np_incorrectx0 = np.array(incorrect_x0, dtype=np.float32)
np_incorrecty0 = np.array(incorrect_y0, dtype=np.float32)
np_incorrectx1= np.array(incorrect_x1, dtype=np.float32)
np_incorrecty1 = np.array(incorrect_y1, dtype=np.float32)
plt.plot(np_plt_x1, np_plt_y1, 'o', color='r', alpha=0.5)
plt.plot(np_plt_x0, np_plt_y0, 'o', color='b', alpha=0.5)
plt.plot(np_incorrectx0, np_incorrecty0, 'o', color='g', alpha=0.5)
plt.plot(np_incorrectx1, np_incorrecty1, 'o', color='m', alpha=0.5)
plt.show()
# predict
test_predict()
| awkrail/laugh_maker | validation_src/predict.py | predict.py | py | 4,519 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "chainer.Chain",
"line_number": 9,
"usage_type": "attribute"
},
{
"api_name": "chainer.links.Linear",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "chainer.links",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "chainer.links.Li... |
22530028718 | """Implementation of a space that represents textual strings."""
from typing import Any, Dict, FrozenSet, Optional, Set, Tuple, Union
import numpy as np
from gym.spaces.space import Space
alphanumeric: FrozenSet[str] = frozenset(
"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
)
class Text(Space[str]):
r"""A space representing a string comprised of characters from a given charset.
Example::
>>> # {"", "B5", "hello", ...}
>>> Text(5)
>>> # {"0", "42", "0123456789", ...}
>>> import string
>>> Text(min_length = 1,
... max_length = 10,
... charset = string.digits)
"""
def __init__(
self,
max_length: int,
*,
min_length: int = 1,
charset: Union[Set[str], str] = alphanumeric,
seed: Optional[Union[int, np.random.Generator]] = None,
):
r"""Constructor of :class:`Text` space.
Both bounds for text length are inclusive.
Args:
min_length (int): Minimum text length (in characters). Defaults to 1 to prevent empty strings.
max_length (int): Maximum text length (in characters).
charset (Union[set], str): Character set, defaults to the lower and upper english alphabet plus latin digits.
seed: The seed for sampling from the space.
"""
assert np.issubdtype(
type(min_length), np.integer
), f"Expects the min_length to be an integer, actual type: {type(min_length)}"
assert np.issubdtype(
type(max_length), np.integer
), f"Expects the max_length to be an integer, actual type: {type(max_length)}"
assert (
0 <= min_length
), f"Minimum text length must be non-negative, actual value: {min_length}"
assert (
min_length <= max_length
), f"The min_length must be less than or equal to the max_length, min_length: {min_length}, max_length: {max_length}"
self.min_length: int = int(min_length)
self.max_length: int = int(max_length)
self._char_set: FrozenSet[str] = frozenset(charset)
self._char_list: Tuple[str, ...] = tuple(charset)
self._char_index: Dict[str, np.int32] = {
val: np.int32(i) for i, val in enumerate(tuple(charset))
}
self._char_str: str = "".join(sorted(tuple(charset)))
# As the shape is dynamic (between min_length and max_length) then None
super().__init__(dtype=str, seed=seed)
def sample(
self, mask: Optional[Tuple[Optional[int], Optional[np.ndarray]]] = None
) -> str:
"""Generates a single random sample from this space with by default a random length between `min_length` and `max_length` and sampled from the `charset`.
Args:
mask: An optional tuples of length and mask for the text.
The length is expected to be between the `min_length` and `max_length` otherwise a random integer between `min_length` and `max_length` is selected.
For the mask, we expect a numpy array of length of the charset passed with `dtype == np.int8`.
If the charlist mask is all zero then an empty string is returned no matter the `min_length`
Returns:
A sampled string from the space
"""
if mask is not None:
assert isinstance(
mask, tuple
), f"Expects the mask type to be a tuple, actual type: {type(mask)}"
assert (
len(mask) == 2
), f"Expects the mask length to be two, actual length: {len(mask)}"
length, charlist_mask = mask
if length is not None:
assert np.issubdtype(
type(length), np.integer
), f"Expects the Text sample length to be an integer, actual type: {type(length)}"
assert (
self.min_length <= length <= self.max_length
), f"Expects the Text sample length be between {self.min_length} and {self.max_length}, actual length: {length}"
if charlist_mask is not None:
assert isinstance(
charlist_mask, np.ndarray
), f"Expects the Text sample mask to be an np.ndarray, actual type: {type(charlist_mask)}"
assert (
charlist_mask.dtype == np.int8
), f"Expects the Text sample mask to be an np.ndarray, actual dtype: {charlist_mask.dtype}"
assert charlist_mask.shape == (
len(self.character_set),
), f"expects the Text sample mask to be {(len(self.character_set),)}, actual shape: {charlist_mask.shape}"
assert np.all(
np.logical_or(charlist_mask == 0, charlist_mask == 1)
), f"Expects all masks values to 0 or 1, actual values: {charlist_mask}"
else:
length, charlist_mask = None, None
if length is None:
length = self.np_random.integers(self.min_length, self.max_length + 1)
if charlist_mask is None:
string = self.np_random.choice(self.character_list, size=length)
else:
valid_mask = charlist_mask == 1
valid_indexes = np.where(valid_mask)[0]
if len(valid_indexes) == 0:
if self.min_length == 0:
string = ""
else:
# Otherwise the string will not be contained in the space
raise ValueError(
f"Trying to sample with a minimum length > 0 ({self.min_length}) but the character mask is all zero meaning that no character could be sampled."
)
else:
string = "".join(
self.character_list[index]
for index in self.np_random.choice(valid_indexes, size=length)
)
return "".join(string)
def contains(self, x: Any) -> bool:
"""Return boolean specifying if x is a valid member of this space."""
if isinstance(x, str):
if self.min_length <= len(x) <= self.max_length:
return all(c in self.character_set for c in x)
return False
def __repr__(self) -> str:
"""Gives a string representation of this space."""
return (
f"Text({self.min_length}, {self.max_length}, characters={self.characters})"
)
def __eq__(self, other) -> bool:
"""Check whether ``other`` is equivalent to this instance."""
return (
isinstance(other, Text)
and self.min_length == other.min_length
and self.max_length == other.max_length
and self.character_set == other.character_set
)
@property
def character_set(self) -> FrozenSet[str]:
"""Returns the character set for the space."""
return self._char_set
@property
def character_list(self) -> Tuple[str, ...]:
"""Returns a tuple of characters in the space."""
return self._char_list
def character_index(self, char: str) -> np.int32:
"""Returns a unique index for each character in the space's character set."""
return self._char_index[char]
@property
def characters(self) -> str:
"""Returns a string with all Text characters."""
return self._char_str
@property
def is_np_flattenable(self) -> bool:
"""The flattened version is an integer array for each character, padded to the max character length."""
return True
| openai/gym | gym/spaces/text.py | text.py | py | 7,660 | python | en | code | 33,110 | github-code | 36 | [
{
"api_name": "typing.FrozenSet",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "gym.spaces.space.Space",
"line_number": 13,
"usage_type": "name"
},
{
"api_name": "typing.Union",
"line_number": 31,
"usage_type": "name"
},
{
"api_name": "typing.Set",
... |
37697513017 |
import streamlit as st
from streamlit_option_menu import option_menu
from pages import home, dashboard, login
class MultiApp:
def __init__(self):
self.apps = []
def add_app(self, title, func):
self.apps.append({
"title": title,
"function": func
})
def run():
# app = st.sidebar(
with st.sidebar:
app = option_menu(
menu_title='Navigation',
options=['Home','Dashboard','Login'],
icons=['house','book','envelope'],
menu_icon='cast',
default_index=0,
)
if app == "Home":
home.app()
if app == "Dashboard":
dashboard.app()
if app == "Login":
login.app()
run()
| Ashwani132003/Attendace-Tracker-using-Face-recognition | main.py | main.py | py | 976 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "streamlit.sidebar",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "streamlit_option_menu.option_menu",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "pages.home.app",
"line_number": 35,
"usage_type": "call"
},
{
"api_name"... |
24192841460 | import requests
def check_security_headers(url):
headers_to_check = [
'Strict-Transport-Security',
'Content-Security-Policy',
'X-Content-Type-Options',
'X-Frame-Options',
'X-XSS-Protection',
'Referrer-Policy',
'Permissions-Policy'
]
missing_headers = []
try:
response = requests.get(url)
response_headers = response.headers
for header in headers_to_check:
if header not in response_headers:
missing_headers.append(header)
except requests.exceptions.RequestException as e:
print(f"Error: {e}")
return missing_headers
if __name__ == "__main__":
url = input("Enter the URL of the web app: ").strip()
missing_headers = check_security_headers(url)
if missing_headers:
print("Missing security headers:")
for header in missing_headers:
print(f"- {header}")
else:
print("All security headers are present.")
| hexodotsh/WebSite_Header_Scanner | headers-security-check.py | headers-security-check.py | py | 999 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "requests.get",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "requests.exceptions",
"line_number": 24,
"usage_type": "attribute"
}
] |
23331780439 | from utility_functions import *
import matplotlib.pyplot as plt
def lemke_optimizer_sparse(eco, payoff_matrix = None, dirac_mode = True):
A = np.zeros((eco.populations.size, eco.populations.size * eco.layers))
for k in range(eco.populations.size):
A[k, k * eco.layers:(k + 1) * eco.layers] = -1
q = np.zeros(eco.populations.size + eco.populations.size * eco.layers)
q[eco.populations.size * eco.layers:] = -1
q = q.reshape(-1, 1)
if payoff_matrix is None:
payoff_matrix = total_payoff_matrix_builder(eco)
H = np.block([[-payoff_matrix, A.T], [-A, np.zeros((A.shape[0], eco.populations.size))]])
lcp = sn.LCP(H, q)
ztol = 1e-8
#solvers = [sn.SICONOS_LCP_PGS, sn.SICONOS_LCP_QP,
# sn.SICONOS_LCP_LEMKE, sn.SICONOS_LCP_ENUM]
z = np.zeros((eco.layers*eco.populations.size+eco.populations.size,), np.float64)
w = np.zeros_like(z)
options = sn.SolverOptions(sn.SICONOS_LCP_PIVOT)
#sn.SICONOS_IPARAM_MAX_ITER = 10000000<
options.iparam[sn.SICONOS_IPARAM_MAX_ITER] = 1000000
options.dparam[sn.SICONOS_DPARAM_TOL] = 10**(-5)
info = sn.linearComplementarity_driver(lcp, z, w, options)
if sn.lcp_compute_error(lcp,z,w, ztol) > 10**(-5):
print(sn.lcp_compute_error(lcp,z,w, ztol), "Error")
return z
#@njit(parallel = True)
def total_payoff_matrix_builder_sparse(eco, current_layered_attack = None, dirac_mode = False):
total_payoff_matrix = np.zeros((eco.populations.size*eco.layers, eco.populations.size*eco.layers))
if current_layered_attack is None:
current_layered_attack = eco.parameters.layered_attack
for i in range(eco.populations.size):
for j in range(eco.populations.size):
if i != j:
i_vs_j = payoff_matrix_builder(eco, i, j, current_layered_attack = current_layered_attack, dirac_mode = dirac_mode)
elif i == j:
i_vs_j = np.zeros((eco.layers, eco.layers))
#if i == 1:
# total_payoff_matrix[i*eco.layers:(i+1)*eco.layers, j*eco.layers: (j+1)*eco.layers] = i_vs_j.T
#else:
total_payoff_matrix[i * eco.layers:(i + 1) * eco.layers, j * eco.layers: (j + 1) * eco.layers] = i_vs_j
# print("MAXIMM PAYDAY ORIGINAL", np.max(total_payoff_matrix))
total_payoff_matrix[total_payoff_matrix != 0] = total_payoff_matrix[total_payoff_matrix != 0] - np.max(total_payoff_matrix) #- 1 #Making sure everything is negative #- 0.00001
#total_payoff_matrix = total_payoff_matrix/np.max(-total_payoff_matrix)
print(np.where(total_payoff_matrix == 0))
return total_payoff_matrix
depth = 45
layers = 100
segments = 1
size_classes = 2
lam = 100
simulate = False
verbose = True
l2 = False
min_attack_rate = 10**(-3)
mass_vector = np.array([0.05, 0.05*408]) # np.array([1, 30, 300, 400, 800, 16000])
obj = spectral_method(depth, layers, segments=segments)
logn = stats.lognorm.pdf(obj.x, 1, 0)
norm_dist = stats.norm.pdf(obj.x, loc=0, scale=3)
res_start = 4*norm_dist # 0.1*(1-obj.x/depth)
res_max = 8*norm_dist
water_start = water_column(obj, res_start, layers=layers * segments, resource_max=res_max, replacement=lam, advection=0,
diffusion=0, logistic = True)
params = ecosystem_parameters(mass_vector, obj, lam=0.3, min_attack_rate = min_attack_rate, forage_mass = 0.05/408)
params.handling_times = np.zeros(2)
eco = ecosystem_optimization(mass_vector, layers * segments, params, obj, water_start, l2=l2, movement_cost=0)
eco.population_setter(np.array([1, 0.05]))
eco.heat_kernel_creator(10**(-1))
eco.heat_kernels[1] = eco.heat_kernels[0]
S = lemke_optimizer_sparse(eco, total_payoff_matrix_builder_sparse(eco))
S1 = lemke_optimizer(eco)
plt.plot(eco.spectral.x, S[0:layers]@eco.heat_kernels[0])
plt.plot(eco.spectral.x, S[layers:2*layers]@eco.heat_kernels[0])
S2 = quadratic_optimizer(eco, payoff_matrix=total_payoff_matrix_builder_sparse(eco))
S3 = quadratic_optimizer(eco)
plt.show()
plt.plot(eco.spectral.x, S1[0:layers]@eco.heat_kernels[0])
plt.plot(eco.spectral.x, S1[layers:2*layers]@eco.heat_kernels[0])
plt.show()
plt.plot(eco.spectral.x, S2[0:layers]@eco.heat_kernels[0])
plt.plot(eco.spectral.x, S2[layers:2*layers]@eco.heat_kernels[0])
plt.show()
plt.plot(eco.spectral.x, S3[0:layers]@eco.heat_kernels[0])
plt.plot(eco.spectral.x, S3[layers:2*layers]@eco.heat_kernels[0])
plt.show() | jemff/food_web | old_sims/sparse_testing.py | sparse_testing.py | py | 4,391 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "matplotlib.pyplot.plot",
"line_number": 88,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 88,
"usage_type": "name"
},
{
"api_name": "matplotlib.pyplot.plot",
"line_number": 89,
"usage_type": "call"
},
{
"api_name": "matpl... |
74477215464 | from rdflib import Graph, RDF, Literal, RDFS, plugin, OWL, XSD, SKOS, PROV
plugin.register('json-ld', 'Serializer', 'rdfextras.serializers.jsonld', 'JsonLDSerializer')
import csv
import pandas as pd
from collections import Counter
from nltk.tag import StanfordNERTagger
import spacy
import jellyfish as jf
import json
import validators
import re
csv_links = "../results/yago-links.csv"
csv_node_labels = "../results/yago-node-labels.csv"
csv_nodes = "../results/yago-nodes.csv"
entity_file = "../results/entities_nodes.csv"
data_type_json = "../results/data-type-validation.json"
csv_with_features = "../results/features_selected_yago_nodes.csv"
############################Triple Features############################################################################
def pred_occur():
#counts the no. of times a predicate occurs within the entire dataset
print("inside pred_occur")
df_node_labels = pd.read_csv(csv_node_labels)
df_node_labels['CountPredOccur'] = df_node_labels['predicate'].map(Counter(list(df_node_labels['predicate'])))
df_node_labels.to_csv(csv_node_labels, index=False)
subj_pred_occur()
def subj_pred_occur():
# counts the no. of times a predicate occurs within the entity's group
print("inside subj_pred_occur")
df_node_labels = pd.read_csv(csv_node_labels)
total_groups = df_node_labels.groupby(['subject', 'predicate'])
label_count = {}
for group in total_groups.groups:
label_count[group] = len(total_groups.get_group(group))
df_node_labels['CountPredOccurofSubject'] = df_node_labels.set_index(['subject', 'predicate']).index.map(label_count.get)
df_node_labels.to_csv(csv_node_labels, index=False)
dup_triples()
def dup_triples():
# counts the no. of times a predicate occurs within the entity's group
print("inside dup_triples")
df_node_labels = pd.read_csv(csv_node_labels)
total_groups = df_node_labels.groupby(['subject', 'predicate', 'object'])
label_count = {}
for group in total_groups.groups:
try:
label_count[group] = len(total_groups.get_group(group))
except:
continue
df_node_labels['CountDupTriples'] = df_node_labels.set_index(['subject', 'predicate', 'object']).index.map(label_count.get)
df_node_labels.to_csv(csv_node_labels, index=False)
cal_haslabel_similarity()
def cal_haslabel_similarity():
# Calculate the similarity between two labels given a has_label predicate
print("inside cal_haslabel_similarity")
df_node_labels = pd.read_csv(csv_node_labels)
label_count = {}
for index, row in df_node_labels.iterrows():
if row['predicate'] == "has_label":
entity_split = row['subject'].split("/")[-1]
underscore_removed = entity_split.replace("_", " ")
wordnet_removed = underscore_removed.replace('wordnet', "")
wikicat_removed = wordnet_removed.replace('wikicategory', "")
subject_final = "".join(filter(lambda x: not x.isdigit(), wikicat_removed))
subject_final = subject_final.strip()
print(subject_final)
similarity = jf.jaro_distance(subject_final, row['object'])
label_count[(row['subject'], row['object'])] = round(similarity,2)
else:
label_count[(row['subject'], row['object'])] = "na"
df_node_labels['SimSubjectObject'] = df_node_labels.set_index(['subject', 'object']).index.map(label_count.get)
df_node_labels.to_csv(csv_node_labels, index=False)
tot_literals()
def entity_recognition():
print("inside entity_recognition")
web_sm = spacy.load(r'/Users/asara/Documents/data_enrichment/assets/en_core_web_sm-3.0.0/en_core_web_sm/en_core_web_sm-3.0.0')
st = StanfordNERTagger('assets/stanford-ner-4.2.0/classifiers/english.all.3class.distsim.crf.ser.gz',
'assets/stanford-ner-4.2.0/stanford-ner-4.2.0.jar', encoding='utf-8')
df_node_labels = pd.read_csv(csv_node_labels)
dict_entity_type = {}
entities = set(list(df_node_labels['subject']) + list(df_node_labels['object']))
total_entities = len(entities)
print("total_entities: " ,total_entities)
count=0
with open(entity_file, "a") as outfile:
writer = csv.writer(outfile)
writer.writerow(["entity", "entity_type"])
for entity in entities:
try:
count = count + 1
print("entity number: ", count)
print("more to go number: ", total_entities-count)
entity_types = []
entity_split = entity.split("/")[-1]
underscore_removed = entity_split.replace("_", " ")
wordnet_removed = underscore_removed.replace('wordnet', "")
wikicat_removed = wordnet_removed.replace('wikicategory', "")
entity_final = "".join(filter(lambda x: not x.isdigit(), wikicat_removed))
entity_final = entity_final.strip()
print(entity_final)
spacy_ner = [(X.text, X.label_) for X in web_sm(entity_final).ents]
if spacy_ner:
for item in spacy_ner:
entity_types.append(item[1])
else:
stanford_ner = st.tag([entity_final])
for item in stanford_ner + spacy_ner:
entity_types.append(item[1])
replacements = {"ORG": "ORGANIZATION", "GPE": "LOCATION", "LOC": "LOCATION"}
replacer = replacements.get # For faster gets.
entity_types = [replacer(n, n) for n in entity_types]
dict_entity_type[entity] = set(entity_types)
writer.writerow([entity, set(entity_types)])
except:
dict_entity_type[entity] = "{O}"
writer.writerow([entity, set(entity_types)])
df_node_labels['SubjectEntityType'] = df_node_labels.set_index(['subject']).index.map(dict_entity_type.get)
df_node_labels['ObjectEntityType'] = df_node_labels.set_index(['object']).index.map(dict_entity_type.get)
df_node_labels.to_csv(csv_node_labels, index=False)
pred_entity_type_occur()
def pred_entity_type_occur():
#this method counts the no. of times a particular predicate occurs with the two given entity types of subject and object
print("inside pred_entity_type_occur")
df_node_labels = pd.read_csv(csv_node_labels)
total_groups = df_node_labels.groupby(['SubjectEntityType','ObjectEntityType','predicate'])
label_count = {}
for group in total_groups.groups:
try:
label_count[group] = len(total_groups.get_group(group))
except:
continue
df_node_labels['CountPredOccurEntityType'] = df_node_labels.set_index(['SubjectEntityType','ObjectEntityType','predicate']).index.map(label_count.get)
df_node_labels.to_csv(csv_node_labels, index=False)
find_com_rare_entity_type()
def validate_literal_data_type():
print("inside validate_literal_data_type")
df_node_labels = pd.read_csv(csv_node_labels)
json_file = open(data_type_json,)
data_type_dict = json.load(json_file)
print(data_type_dict)
validity_score = []
for index, row in df_node_labels.iterrows():
pred_extracted = row['predicate']
if validators.url(pred_extracted):
pred_extracted = row['predicate'].split("/")[-1]
validity = "na"
for key in data_type_dict.keys():
if key in pred_extracted.lower():
data_type = data_type_dict[key]
try:
if data_type == "url":
validity = is_url(row['object'])
break
if data_type == "date":
validity = is_date(row['object'])
break
if data_type == 'integer':
validity = row['object'].isnumeric()
break
if data_type == 'time':
validity = re.match('\d{2}:\d{2}:\d{2}', row['object'])
break
if data_type == 'string':
validity = ((not row['object'].isnumeric()) and (not row['object'] == "") and (not validators.url(row['object'])\
and (not is_date(row['object']))))
break
except:
validity = 0
validity_score.append(validity)
df_node_labels['ValidityOfLiteral'] = validity_score
df_node_labels.to_csv(csv_node_labels, index=False)
count_occur_dup_triples()
############################Node Features############################################################################
def tot_literals():
#count total number of literal based triples an entity has
print("inside tot_predicates")
df_node_labels = pd.read_csv(csv_node_labels)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
try:
label_count[group] = len(total_groups.get_group(group))
except:
continue
data = {'node':list(label_count.keys()), 'CountLiterals': list(label_count.values()) }
df_nodes = pd.DataFrame.from_dict(data)
df_nodes.to_csv(csv_nodes, index=False)
count_dif_literal_types()
def count_dif_literal_types():
#count different types of predicates an entity has got where the object is a literal
print("inside count_dif_literal_types")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
print(fetched_group)
count_dif_literals = len(fetched_group['predicate'].unique())
label_count[group] = count_dif_literals
df_nodes['CountDifLiteralTypes'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_isa_triples()
def count_isa_triples():
#count different types of predicates an entity has got where the object is a literal
print("inside count_isa_triples")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
print(fetched_group)
count_dif_literals = len(fetched_group[fetched_group['predicate']=='isa'])
label_count[group] = count_dif_literals
df_nodes['CountIsaPredicate'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_haslabel_triples()
def count_haslabel_triples():
#count different types of predicates an entity has got where the object is a literal
print("inside count_haslabel_triples")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
print(fetched_group)
count_dif_literals = len(fetched_group[fetched_group['predicate']=='has_label'])
label_count[group] = count_dif_literals
df_nodes['CountHaslabelPredicate'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_subclassof_triples()
def count_subclassof_triples():
#count different types of predicates an entity has got where the object is a literal
print("inside count_subclassof_triples")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
print(fetched_group)
count_dif_literals = len(fetched_group[fetched_group['predicate']=='subClassOf'])
label_count[group] = count_dif_literals
df_nodes['CountSubclassofPredicate'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_subpropertyof_triples()
def count_subpropertyof_triples():
#count different types of predicates an entity has got where the object is a literal
print("inside count_subpropertyof_triples")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
print(fetched_group)
count_dif_literals = len(fetched_group[fetched_group['predicate']=='subPropertyOf'])
label_count[group] = count_dif_literals
df_nodes['CountSubpropofPredicate'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_high_sim_labels()
def count_high_sim_labels():
#count different types of predicates an entity has got where the object is a literal
print("inside count_high_sim_labels")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
count = 0
for index, row in fetched_group.iterrows():
if row['SimSubjectObject'] != 'na' and float(row['SimSubjectObject']) >=0.5:
count+=1
label_count[group] = count
df_nodes['CountHighSimLabels'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_low_sim_labels()
def count_low_sim_labels():
#count different types of predicates an entity has got where the object is a literal
print("inside count_low_sim_labels")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
fetched_group = total_groups.get_group(group)
count = 0
for index, row in fetched_group.iterrows():
if row['SimSubjectObject'] != 'na' and float(row['SimSubjectObject']) <0.5:
count+=1
label_count[group] = count
df_nodes['CountLowSimLabels'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
tot_outgoing_links()
def tot_outgoing_links():
print("inside tot_incoming_links")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
df_links = pd.read_csv(csv_links)
groups_in_node_labels = df_node_labels.groupby(['subject'])
groups_in_links = df_links.groupby(['subject'])
label_count = {}
for group in df_nodes['node']:
try:
fetched_node_label_groups = len(groups_in_node_labels.get_group(group))
except:
fetched_node_label_groups = 0
try:
fetched_link_groups = len(groups_in_links.get_group(group))
except:
fetched_link_groups = 0
label_count[group] = fetched_node_label_groups + fetched_link_groups
df_nodes['OutDegree'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
tot_incoming_links()
def tot_incoming_links():
print("inside tot_outgoing_links")
df_nodes = pd.read_csv(csv_nodes)
df_links = pd.read_csv(csv_links)
groups_in_links = df_links.groupby(['object'])
label_count = {}
for group in df_nodes['node']:
try:
fetched_link_groups = len(groups_in_links.get_group(group))
except:
fetched_link_groups = 0
label_count[group] = fetched_link_groups
df_nodes['InDegree'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
validate_literal_data_type()
def find_com_rare_entity_type():
# counts the no. of times a predicate occurs within the entity's group
print("inside find_com_rare_entity_type")
df_node_links = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_links.groupby(['subject'])
entity_count_node_max, entity_count_node_min = {}, {}
for group in total_groups.groups:
entity_count = {}
sub_group = total_groups.get_group(group).groupby(['SubjectEntityType','ObjectEntityType'])
for entity_group in sub_group.groups:
entity_count[entity_group] = len(sub_group.get_group(entity_group))
key_max = max(entity_count.keys(), key=(lambda k: entity_count[k]))
key_min = min(entity_count.keys(), key=(lambda k: entity_count[k]))
entity_count_node_max[group] = [key_max]
entity_count_node_min[group] = [key_min]
df_nodes['CommonPredType'] = df_nodes.set_index(['node']).index.map(entity_count_node_max.get)
df_nodes['RarePredType'] = df_nodes.set_index(['node']).index.map(entity_count_node_min.get)
df_nodes.to_csv(csv_nodes, index=False)
def count_occur_dup_triples():
# counts the no. of duplicate triples a node has got
print("inside count_occur_dup_triples")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
count=0
for index, row in total_groups.get_group(group).iterrows():
if row['CountDupTriples'] > 1:
count+=row['CountDupTriples']
label_count[group] = count
df_nodes['CountDupTriples'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
count_invalid_literals()
def count_invalid_literals():
print("inside count_invalid_literals")
df_node_labels = pd.read_csv(csv_node_labels)
df_nodes = pd.read_csv(csv_nodes)
total_groups = df_node_labels.groupby(['subject'])
label_count = {}
for group in total_groups.groups:
count=0
for index, row in total_groups.get_group(group).iterrows():
if row['ValidityOfLiteral'] == False:
count+=1
label_count[group] = count
df_nodes['CountInvalidTriples'] = df_nodes.set_index(['node']).index.map(label_count.get)
df_nodes.to_csv(csv_nodes, index=False)
entity_recognition()
########################################Special Functions###################################
def is_date(string, fuzzy=False):
"""
Return whether the string can be interpreted as a date.
:param string: str, string to check for date
:param fuzzy: bool, ignore unknown tokens in string if True
"""
from dateutil.parser import parse
try:
parse(string, fuzzy=fuzzy)
return True
except ValueError:
return False
def is_url(url):
#check for valid URL
if not validators.url(url):
return False
else:
return True
def feature_reduction():
print("Inside feature reduction")
df = pd.read_csv(csv_node_labels)
df_fileterd = df.iloc[:,3:]
for feature in df_fileterd.columns:
if df_fileterd.dtypes[feature] != np.int64 or df_fileterd.dtypes[feature] != np.float64:
df_fileterd[feature].replace(['1','0','True','False',True,False],[1,0,1,0,1,0], inplace=True)
df_fileterd[feature] = df_fileterd[feature].astype(np.float)
for col in df_fileterd.columns:
count_unique = len(df[col].unique())
if count_unique == 1:
print(col)
df_fileterd.drop(col, inplace=True, axis=1)
columns = list(df_fileterd.columns)
corr_feature_list = []
for i in range(0, len(columns)-1):
for j in range(i+1, len(columns)):
print(columns[i],columns[j])
correlation = df_fileterd[columns[i]].corr(df_fileterd[columns[j]])
if correlation == 1:
print(columns[i], columns[j])
corr_feature_list.append(columns[i])
corr_feature_list.append(columns[j])
remove_corr_features(corr_feature_list, df_fileterd, df)
def remove_corr_features(corr_feature_list, df_fileterd, df):
print("Correlated Features: ", corr_feature_list)
features_to_remove = [input("Enter the features to remove seperated by a comma without spaces: ")]
if features_to_remove[0] == '':
gen_binary_feature(df_fileterd, df)
else:
for feature in features_to_remove:
df_fileterd.drop(feature, inplace=True, axis=1)
gen_binary_feature(df_fileterd, df)
def gen_binary_feature(df_fileterd, df):
print("inside binary_features")
columns = df_fileterd.columns
for column in columns:
new_col = []
new_col_name = "Freq" + column
for index, row in df_fileterd.iterrows():
if row[column] > df_fileterd[column].median():
new_col.append(1)
else:
new_col.append(0)
df[new_col_name] = new_col
df.to_csv(csv_with_features, index=False)
| AsaraSenaratne/anomaly-detection-kg | source-files/yago_nodes.py | yago_nodes.py | py | 21,397 | python | en | code | 2 | github-code | 36 | [
{
"api_name": "rdflib.plugin.register",
"line_number": 2,
"usage_type": "call"
},
{
"api_name": "rdflib.plugin",
"line_number": 2,
"usage_type": "name"
},
{
"api_name": "pandas.read_csv",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "collections.Counte... |
43103024978 | from celery import shared_task
from celery.utils.log import get_task_logger
from decouple import config
logger = get_task_logger("tasks")
expiration_time = config("EXPIRATION_TIME", default=1800, cast=int)
@shared_task(
bind=True,
default_retry_delay=3,
eta=expiration_time,
retry_kwargs={"max_retries": 5},
)
def delete_qrcode_task(self, qrcode_id):
from qrcode_api.apps.api.models import QrCode
try:
qrcode = QrCode.objects.get(id=qrcode_id)
logger.info(f"Deactivating qrcode {qrcode_id}")
qrcode.active = False
qrcode.image.delete()
qrcode.save()
except Exception as e:
logger.error(e)
self.retry(exc=e)
| guilhermehgbrito/qrcode-api | qrcode_api/apps/api/tasks.py | tasks.py | py | 721 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "celery.utils.log.get_task_logger",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "decouple.config",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "qrcode_api.apps.api.models.QrCode.objects.get",
"line_number": 19,
"usage_type": "call"
... |
37208016314 | from datetime import datetime, timedelta
birthdate = input("Tell us your bidrthay in DD.MM.YYYY format ")
print(birthdate)
date_obj = datetime.strptime(birthdate, '%d.%m.%Y').date()
print(date_obj)
time_difference = datetime.now().date() - date_obj
time_now = datetime.now()
if (time_now.year < date_obj.year):
print("You aren't even born yet.")
exit()
age = time_now.year - date_obj.year
if (time_now.month < date_obj.month):
age -= 1
elif (time_now.month == date_obj.month):
if (time_now.day < date_obj.day):
age -= 1
needed_age = age % 10
print(needed_age)
top = " ______"
top2 = " |:H:a:p:p:y:|"
top3 = " __|___________|__"
bottom1 = " |^^^^^^^^^^^^^^^^^|"
bottom2 = " |:B:i:r:t:h:d:a:y:|"
bottom3 = " | |"
bottom4 = " ~~~~~~~~~~~~~~~~~~~"
if (needed_age < 5):
top = top[:(10+((5-needed_age)//2))] + "i"*needed_age + "_"*(5-needed_age) + top[(10+((5-needed_age)//2)):]
elif(needed_age == 5):
top = top[:10] + "i"*needed_age + "_"*(5-needed_age) + top[10:]
else :
top = top[:(10-((needed_age-5)// 2))] + "i"*needed_age + top[(10-((needed_age-5)// 2)):-(needed_age-5)]
print(top)
print(top2)
print(top3)
print(bottom1)
print(bottom2)
print(bottom3)
print(bottom4)
| KyleKiske/DI-Bootcamp | Week2/Day2/ChallengeGold.py | ChallengeGold.py | py | 1,248 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "datetime.datetime.strptime",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "datetime.datetime",
"line_number": 5,
"usage_type": "name"
},
{
"api_name": "datetime.datetime.now",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "datet... |
17300324237 | from django.conf.urls import url
from django.views.generic import TemplateView
from .views import (
klasses_list_view,
klasses_detail_view,
klasses_create_view,
klasses_delete_view,
klasses_update_view,
)
urlpatterns =[
# This is Klasses pages
url(r'^list/$', klasses_list_view, name='klasses_list_view'),
url(r'^create/$', klasses_create_view, name='klasses_create_view'),
url(r'^(?P<id>[\w-]+)/$', klasses_detail_view, name='klasses_detail_view'),
url(r'^(?P<id>[\w-]+)/delete$', klasses_delete_view, name='klasses_delete_view'),
url(r'^(?P<id>[\w-]+)/edit$', klasses_update_view, name='klasses_update_view'),
#End klasses pages
]
| SaramCodes/School-Management-System | klass/urls.py | urls.py | py | 683 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "django.conf.urls.url",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "views.klasses_list_view",
"line_number": 13,
"usage_type": "argument"
},
{
"api_name": "django.conf.urls.url",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": ... |
42576581221 | """ Exercício para mostras as faces encontradas com variação de parâmetro """
import cv2
classificador = cv2.CascadeClassifier('cascades\\haarcascade_frontalface_default.xml')
imagem = cv2.imread('pessoas\\pessoas3.jpg')
imagemcinza = cv2.cvtColor(imagem, cv2.COLOR_BGR2GRAY)
facesdetectadas = classificador.detectMultiScale(imagemcinza, scaleFactor = 1.2, minNeighbors=3, minSize= (35,35))
print(len(facesdetectadas))
for (x, y, l, a) in facesdetectadas:
cv2.rectangle(imagem, (x, y), (x + l, y + a), (0, 0, 255), 2)
cv2.imshow('Faces Detectadas', imagem)
cv2.waitKey() | alans96/PythonProject | Computer Vision/1 Detecção de Faces com Python e OpenCV/3 exe.py | 3 exe.py | py | 583 | python | pt | code | 0 | github-code | 36 | [
{
"api_name": "cv2.CascadeClassifier",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "cv2.imread",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "cv2.cvtColor",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "cv2.COLOR_BGR2GRAY",
"... |
36829740540 | import pytest
from launch_jenkins import launch_jenkins
from launch_jenkins import log
from launch_jenkins import errlog
from launch_jenkins import CaseInsensitiveDict
def test_log(monkeypatch, capsys):
monkeypatch.setitem(launch_jenkins.CONFIG, 'quiet', False)
log('hello', 'world')
out, err = capsys.readouterr()
assert not out
assert err == 'hello world\n'
monkeypatch.setitem(launch_jenkins.CONFIG, 'quiet', True)
log('hello', 'world')
out, err = capsys.readouterr()
assert not out
assert not err
def test_errlog(monkeypatch, capsys):
monkeypatch.setitem(launch_jenkins.CONFIG, 'quiet', False)
errlog('hello', 'world')
out, err = capsys.readouterr()
assert not out
assert err == 'hello world\n'
monkeypatch.setitem(launch_jenkins.CONFIG, 'quiet', True)
errlog('hello', 'world')
out, err = capsys.readouterr()
assert not out
assert err == 'hello world\n'
def test_caseinsensitivedict():
cid = CaseInsensitiveDict()
cid['key'] = 'value'
cid['other'] = 'othervalue'
del cid['other']
assert cid['key'] == cid['KEY']
assert list(cid) == ['key']
assert len(cid) == 1
assert cid == {'key': 'value'}
assert cid.copy() == cid
assert cid != 'somethingelse'
assert repr(cid)
@pytest.mark.parametrize('millis,expect', [
(0, '00:00'),
(1000, '00:01'),
(60000, '01:00'),
(61000, '01:01'),
(120000, '02:00'),
(630000, '10:30'),
(3599000, '59:59'),
(3600000, '1:00:00'),
(3661000, '1:01:01'),
(36061000, '10:01:01'),
])
def test_format_millis(millis, expect):
assert launch_jenkins.format_millis(millis) == expect
| ocaballeror/jenkins-launch | tests/test_misc.py | test_misc.py | py | 1,681 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "launch_jenkins.launch_jenkins.CONFIG",
"line_number": 10,
"usage_type": "attribute"
},
{
"api_name": "launch_jenkins.launch_jenkins",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "launch_jenkins.log",
"line_number": 11,
"usage_type": "call"
},... |
38916812011 | import discord
from discord.ext.commands import Bot
from discord.ext import commands
import asyncio
import time
import random
from discord import Game
Client = discord.client
client = commands.Bot(command_prefix = '-')
Clientdiscord = discord.Client()
TOKEN = ("NTczNDkxODgzOTc2ODE4Njk4.XMroNg.Pzwl-RFnKN_qTQFcqeFmllynIj0")
@client.event
async def on_member_join(member):
print('Recognised that a member called ' + member.name + ' joined')
await client.send_message(member, 'Welcome in Official MajklCraft discord for help write "-pomoc"')
print('Sent message to ' + member.name)
@client.event
async def on_ready():
await client.change_presence(game=Game(name='-pomoc'))
print('Bot Is Running Sucesfully')
async def on_message(message):
author = message.author
content = message.content
print('{}: {}'.format(author, content))
@client.event
async def on_message(message):
if message.content == '-web':
await client.send_message(message.channel,'www.futurik.majklcraft.eu')
if message.content == '-cheers':
em = discord.Embed(description='Cheers')
em.set_image(url='https://cdn.discordapp.com/attachments/528194410924605440/529441936323510273/download_1.jpg')
await client.send_message(message.channel, embed=em)
if message.content.startswith('-coinflip'):
randomlist = ["head", "tail", ]
await client.send_message(message.channel, (random.choice(randomlist)))
if message.content == '-prikazy':
await client.send_message(message.channel,'-web,-cheers,-coinflip,-vyhody,-pomoc,-ts')
if message.content == '-vyhody':
await client.send_message(message.channel,'http://futurik.buycraft.net/')
if message.content == '-pomoc':
await client.send_message(message.channel,'Pro pomoc kontaktujte kohokoli z AT.')
if message.content == '-ts':
await client.send_message(message.channel,'81.0.217.180:7399')
async def on_message(message):
author = message.author
content = message.content
print('{}: {}'.format(author, content))
client.run(TOKEN)
| ANATLANTIDA/BOT | Bot.py | Bot.py | py | 2,165 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "discord.client",
"line_number": 10,
"usage_type": "attribute"
},
{
"api_name": "discord.ext.commands.Bot",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "discord.ext.commands",
"line_number": 11,
"usage_type": "name"
},
{
"api_name": "dis... |
7870435087 | from bs4 import BeautifulSoup
from uk_bin_collection.uk_bin_collection.common import *
from uk_bin_collection.uk_bin_collection.get_bin_data import \
AbstractGetBinDataClass
# import the wonderful Beautiful Soup and the URL grabber
class CouncilClass(AbstractGetBinDataClass):
"""
Concrete classes have to implement all abstract operations of the
base class. They can also override some operations with a default
implementation.
"""
def parse_data(self, page: str, **kwargs) -> dict:
requests.packages.urllib3.disable_warnings()
user_uprn = kwargs.get("uprn")
check_uprn(user_uprn)
headers = {
"Accept": "*/*",
"Accept-Language": "en-GB,en;q=0.6",
"Connection": "keep-alive",
"Referer": "https://www.valeofglamorgan.gov.uk/",
"Sec-Fetch-Dest": "script",
"Sec-Fetch-Mode": "no-cors",
"Sec-Fetch-Site": "same-site",
"Sec-GPC": "1",
"sec-ch-ua": '"Not?A_Brand";v="8", "Chromium";v="108", "Brave";v="108"',
"sec-ch-ua-mobile": "?0",
"sec-ch-ua-platform": '"Windows"',
}
params = {
"RequestType": "LocalInfo",
"ms": "ValeOfGlamorgan/AllMaps",
"group": "Community and Living|Refuse HIDE2",
"type": "json",
"callback": "AddressInfoCallback",
"uid": user_uprn,
"import": "jQuery35108514154283927682_1673022974838",
"_": "1673022974840",
}
# Get a response from the council
response = requests.get(
"https://myvale.valeofglamorgan.gov.uk/getdata.aspx",
params=params,
headers=headers,
).text
# Load the JSON and seek out the bin week text, then add it to the calendar URL. Also take the weekly
# collection type and generate dates for it. Then make a GET request for the calendar
bin_week = str(
json.loads(response)["Results"]["Refuse_HIDE2"]["Your_Refuse_round_is"]
).replace(" ", "-")
weekly_collection = str(
json.loads(response)["Results"]["Refuse_HIDE2"]["Recycling__type"]
).capitalize()
weekly_dates = get_weekday_dates_in_period(
datetime.now(), days_of_week.get(bin_week.split("-")[0].strip()), amount=48
)
schedule_url = f"https://www.valeofglamorgan.gov.uk/en/living/Recycling-and-Waste/collections/Black-Bag-Collections/{bin_week}.aspx"
response = requests.get(schedule_url, verify=False)
# BS4 parses the calendar
soup = BeautifulSoup(response.text, features="html.parser")
soup.prettify()
# Some scraper variables
collections = []
# Get the calendar table and find the headers
table = soup.find("table", {"class": "TableStyle_Activities"}).find("tbody")
table_headers = table.find("tr").find_all("th")
# For all rows below the header, find all details in th next row
for tr in soup.find_all("tr")[1:]:
row = tr.find_all("td")
# Parse month and year - month needs converting from text to number
month_and_year = row[0].text.split()
if month_and_year[0] in list(calendar.month_abbr):
collection_month = datetime.strptime(month_and_year[0], "%b").month
elif month_and_year[0] == "Sept":
collection_month = int(9)
else:
collection_month = datetime.strptime(month_and_year[0], "%B").month
collection_year = datetime.strptime(month_and_year[1], "%Y").year
# Get the collection dates column, remove anything that's not a number or space and then convert to dates
for day in remove_alpha_characters(row[1].text.strip()).split():
try:
bin_date = datetime(collection_year, collection_month, int(day))
collections.append((table_headers[1].text.strip().replace(" collection date", ""), bin_date))
except Exception as ex:
continue
# Add in weekly dates to the tuple
for date in weekly_dates:
collections.append(
(weekly_collection, datetime.strptime(date, date_format))
)
# Order all the data, only including future dates
ordered_data = sorted(collections, key=lambda x: x[1])
data = {"bins": []}
for item in ordered_data:
collection_date = item[1]
if collection_date.date() >= datetime.now().date():
dict_data = {
"type": item[0],
"collectionDate": collection_date.strftime(date_format),
}
data["bins"].append(dict_data)
return data
| robbrad/UKBinCollectionData | uk_bin_collection/uk_bin_collection/councils/ValeofGlamorganCouncil.py | ValeofGlamorganCouncil.py | py | 4,863 | python | en | code | 51 | github-code | 36 | [
{
"api_name": "uk_bin_collection.uk_bin_collection.get_bin_data.AbstractGetBinDataClass",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "bs4.BeautifulSoup",
"line_number": 65,
"usage_type": "call"
}
] |
44145316591 | import json
import logging
import typing
class JSONDumpReader(typing.Iterator[dict]):
def __init__(self, dump_path: str):
self.__dump_path = dump_path
def __iter__(self):
with open(self.__dump_path) as f:
for l in f:
l = JSONDumpReader.__clean_line(l)
try:
yield json.loads(l)
except ValueError:
logging.log(level=logging.DEBUG, msg="encountered illegal string while parsing JSON dump")
@staticmethod
def __clean_line(l: str)->str:
return l.strip().rstrip(',')
class JSONDumpWriter(object):
def __init__(self, output_path: str, batch_size: int=500):
self.__output_path = output_path
self.__batch_size = batch_size
def write(self, objects: typing.Iterable[dict]):
with open(self.__output_path, mode='w') as f:
f.write('[\n')
batch = list()
for idx, o in enumerate(objects):
batch.append(json.dumps(o))
if idx and idx % self.__batch_size == 0:
logging.log(level=logging.INFO, msg='wrote {} objects'.format(idx + 1))
f.write('\n'.join(batch) + '\n')
batch = list()
f.write('\n'.join(batch) + '\n')
f.write(']\n')
| AlexandraBaier/bachelorthesis | data_analysis/dumpio.py | dumpio.py | py | 1,344 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "typing.Iterator",
"line_number": 7,
"usage_type": "attribute"
},
{
"api_name": "json.loads",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "logging.log",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "logging.DEBUG",
"line_... |
37955494687 | from flask import Flask, request, jsonify
import util
app = Flask(__name__)
# @app.route decorator exposes the http enedpoint
@app.route("/hello")
def test():
return "hello world"
@app.route("/get-locations")
def get_locations():
response = jsonify(
{
"locations": util.get_locations()
}
)
response.headers.add("Access-Control-Allow-Origin", "*")
return response
@app.route("/predict-price", methods=['POST'])
def predict_price():
loc = request.form["loc"]
sqft = float(request.form["sft"])
bedrooms = int(request.form["bedrooms"])
bath = int(request.form["bath"])
response = jsonify(
{
"approximate_price": util.get_approx_price(loc, sqft, bedrooms, bath)
}
)
response.headers.add("Access-Control-Allow-Origin", "*")
return response
if __name__ == "__main__":
print("python flask server started...")
util.load_artifacts()
app.run() | Chiemerie1/house_prices_ML_model_deployment | server/server.py | server.py | py | 963 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "flask.Flask",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "flask.jsonify",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "util.get_locations",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "flask.request.form",
... |
4640657000 | from django.http import Http404
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status, permissions
from .models import CustomUser
from .serializers import CustomUserSerializer
# Create your views here.
class CustomUserList(APIView):
permission_classes = [permissions.IsAuthenticated]
def get(self, request):
user = CustomUser.objects.all()
serializer = CustomUserSerializer(user, many=True)
return Response(serializer.data)
def post(self, request):
serializer = CustomUserSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_201_CREATED)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class CustomUserDetail(APIView):
permission_classes = [permissions.IsAuthenticated]
def get_object(self, pk):
try:
user = CustomUser.objects.get(pk=pk)
self.check_object_permissions(self.request, user)
return user
except CustomUser.DoesNotExist:
raise Http404
def get(self, request, pk):
user = self.get_object(pk)
serializer = CustomUserSerializer(user)
return Response(serializer.data)
def put(self, request, pk):
user = self.get_object(pk)
serializer = CustomUserSerializer(
instance=user, data=request.data, partial=True
)
if serializer.is_valid():
serializer.save()
return Response(serializer.data, status=status.HTTP_200_OK)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
def delete(self, request, pk):
user = self.get_object(pk)
user.delete()
return Response(
status=status.HTTP_204_NO_CONTENT
)
| SheCodesAus/heading_for_success_backend_bris_2023 | SheFunds_backend/users/views.py | views.py | py | 1,915 | python | en | code | 1 | github-code | 36 | [
{
"api_name": "rest_framework.views.APIView",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "rest_framework.permissions.IsAuthenticated",
"line_number": 13,
"usage_type": "attribute"
},
{
"api_name": "rest_framework.permissions",
"line_number": 13,
"usage_type"... |
42603056105 | import os
import json
import shutil
import time
import traceback
__author__ = 'Michael Ryan Harlich'
def update_paths(paths):
paths['partial_prediction'] = paths['output'] + 'partial_predication.ent'
paths['partial_ground_truth'] = paths['output'] + 'partial_ground_truth.ent'
paths['aligned_prediction'] = paths['output'] + 'aligned_prediction.pdb'
def execute(paths):
align(paths)
start_residue, end_residue = get_start_and_end_residue(paths)
save_partial_protein(start_residue, end_residue, paths)
pass
def save_partial_protein(start_residue, end_residue, paths):
if start_residue is None and end_residue is None:
shutil.copyfile(paths['ground_truth'], paths['partial_ground_truth'])
return
else:
gt_file = open(paths['ground_truth'], 'r')
gt_partial_file = open(paths['partial_ground_truth'], 'w')
save_partial_file(start_residue, end_residue, gt_file, gt_partial_file)
gt_partial_file.close()
gt_file.close()
return
def save_partial_file(start_residue, end_residue, src_file, des_file):
for line in src_file:
tokens = line.split()
if len(tokens) > 0:
if tokens[0] == 'ATOM':
if int(tokens[5]) >= int(start_residue) and int(tokens[5]) <= int(end_residue):
des_file.write(line)
def get_start_and_end_residue(paths):
if 'selections_file' in paths:
emdb_id = paths['prediction'].split('/')[-2]
with open(paths['selections_file']) as f:
selections = json.load(f)
if emdb_id in selections:
return selections[emdb_id]
return (None, None)
def align(paths):
try:
os.system(paths['tmalign_path'] + ' ' + paths['prediction'] + ' ' + paths['ground_truth'] + ' -o ' + paths['output'] + 'TM.sup')
time.sleep(1)
ap_file = open(paths['aligned_prediction'], 'w')
tm_sup = open(paths['output'] + 'TM.sup_all', 'r')
on = False
for line in tm_sup:
tokens = line.split()
if len(tokens) > 0:
if tokens[0] == 'TER':
on = False
if on == True:
ap_file.write(line)
if len(tokens) > 1:
if tokens[1] == 'Aligned':
on = True
ap_file.close()
tm_sup.close()
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
pass | RyanHarlich/Ca-Prediction-Automated-Testing-Quick-Tools | segments_rmsd/partial_protein/partial_protein.py | partial_protein.py | py | 2,534 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "shutil.copyfile",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_number": 51,
"usage_type": "call"
},
{
"api_name": "os.system",
"line_number": 60,
"usage_type": "call"
},
{
"api_name": "time.sleep",
"line_number": 6... |
28930913211 | #!/usr/bin/python3
# -*- coding: utf8 -*-
# Code from here:
# https://stackoverflow.com/a/26289475
import psutil
import subprocess
import time
import os
class SSHTunnel(object):
"""
A context manager implementation of an ssh tunnel opened from python
"""
def __init__(self, tunnel_command):
assert "-fN" in tunnel_command, "need to open the tunnel with -fN"
self._tunnel_command = tunnel_command
self._delay = 0.1
self.ssh_tunnel = None
def create_tunnel(self):
tunnel_cmd = self._tunnel_command
ssh_process = subprocess.Popen(tunnel_cmd, universal_newlines=True,
shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
stdin=subprocess.PIPE)
# Assuming that the tunnel command has "-f" and "ExitOnForwardFailure=yes", then the
# command will return immediately so we can check the return status with a poll().
while True:
p = ssh_process.poll()
if p is not None: break
time.sleep(self._delay)
if p == 0:
# Unfortunately there is no direct way to get the pid of the spawned ssh process, so we'll find it
# by finding a matching process using psutil.
current_username = psutil.Process(os.getpid()).username()
ssh_processes = [proc for proc in psutil.process_iter() if proc.cmdline() == tunnel_cmd.split() and proc.username() == current_username]
if len(ssh_processes) == 1:
self.ssh_tunnel = ssh_processes[0]
return ssh_processes[0]
else:
raise RuntimeError('multiple (or zero?) tunnel ssh processes found: ' + str(ssh_processes))
else:
raise RuntimeError('Error creating tunnel: ' + str(p) + ' :: ' + str(ssh_process.stdout.readlines()))
def release(self):
""" Get rid of the tunnel by killin the pid
"""
if self.ssh_tunnel:
self.ssh_tunnel.terminate()
def __enter__(self):
self.create_tunnel()
return self
def __exit__(self, type, value, traceback):
self.release()
def __del__(self):
self.release()
| Vasilesk/quotes-posting | sshtunnel.py | sshtunnel.py | py | 2,205 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "subprocess.Popen",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "subprocess.PIPE",
"line_number": 26,
"usage_type": "attribute"
},
{
"api_name": "subprocess.STDOUT",
"line_number": 26,
"usage_type": "attribute"
},
{
"api_name": "subproce... |
21594616095 | import spacy
import plac
import numpy as np
import time
import re
import os
import sys
import argparse
from sklearn.metrics import accuracy_score
from conllToSpacy import main
# Parsing argument for command-line.
parser = argparse.ArgumentParser(description="Testing an NER model with SpaCy.")
parser.add_argument("-tp", "--test_path", help="Path to CoNLL test dataset.")
parser.add_argument("-model", help="Path to the model.")
args = parser.parse_args()
if args.test_path:
testing, ent_true = main(args.test_path, test='test')
print("Got test data at", args.test_path)
else:
print("No test data path given ! Interrupting the script...")
sys.exit()
if args.model:
model = args.model
print("Model loaded at", model)
else:
print("No model path given !")
sys.exit()
testing_tokens = [x.split() for x in testing]
print('Length testing sentences: ', len(testing))
print('Length testing tokens: ', len(testing_tokens))
def compute_score(L1, L2):
correct_answers = 0
nb_answers = 0
for i in range(len(L1)):
if L1[i] != 'O':
nb_answers += 1
if L1[i] == L2[i]:
correct_answers += 1
print('%d correct out of %d answers.' % (correct_answers, nb_answers))
return correct_answers/nb_answers
def main():
# test the saved model
print("Loading from", model)
nlp2 = spacy.load(model)
ent_pred = []
testing_pred = []
print("Start predicttion...")
count = 1
k = 1
for text in testing:
start = time.time()
doc = nlp2(text)
entities = ['O'] * len(text.split())
for ent in doc.ents:
try:
entities[text.split().index(ent.text)] = ent.label_
except IndexError:
print('Index Error! Ent:', list(ent.text), '. Text:', text)
except ValueError:
print('Value Error! Ent:', list(ent.text), '. Text:', text)
ent_pred.append(entities)
testing_pred.append([t.text for t in doc])
print(str(count)+'/'+str(len(testing))+' done in %fs' % (time.time()-start))
count += 1
# Check whether there are the same number of sentences, and the same number of words in each sentence.
print('Length pred sentences: ', len(testing_pred))
for i in range(len(testing_tokens)):
if len(ent_true[i]) != len(ent_pred[i]):
print("NOT THE SAME LENGTH !")
print("True Text: ", testing_tokens[i])
print("Pred Text: ", testing_pred[i])
print("Entities true: ", ent_true[i])
print("Entities pred: ", ent_pred[i])
print('Pred Entity: ', set([x for y in ent_pred for x in y]))
print('True Entity: ', set([x for y in ent_true for x in y]))
y_pred = [x for y in ent_pred for x in y]
y_true = [x for y in ent_true for x in y]
Precision = compute_score(y_pred, y_true)
Recall = compute_score(y_true, y_pred)
F1_score = 2*(Recall * Precision) / (Recall + Precision)
print("Random accuracy: %0.2f" % (accuracy_score(y_true, ['O']*len(y_true))))
print("Accuracy score: %0.2f" % (accuracy_score(y_true, y_pred)))
print('Precision: %0.2f' % (Precision))
print('Recall: %0.2f' % (Recall))
print('F1 score: %0.2f' % (F1_score))
# with open('score.csv', 'a', encoding='utf-8') as f:
# f.write(os.path.basename(model) + ',' + str(Precision) + ',' + str(Recall) + ',' + str(F1_score) + '\n')
if __name__ == '__main__':
main()
| Djia09/Named-Entity-Recognition-spaCy | test_ner_spacy.py | test_ner_spacy.py | py | 3,496 | python | en | code | 3 | github-code | 36 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "conllToSpacy.main",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "sys.exit",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "sys.exit",
"li... |
74582104744 | from rest_framework.decorators import api_view, permission_classes
from rest_framework.permissions import IsAuthenticated
from rest_framework.permissions import AllowAny
from rest_framework.response import Response
from .models import File
from .models import Folder
from .serializers import FileSerializer
from .serializers import FolderSerializer
import os
from django.conf import settings
#####################################################################
###################### Get All Files of a User ######################
#####################################################################
@api_view(['GET'])
@permission_classes([IsAuthenticated])
def get_all_files(request):
user = request.user
files = File.objects.filter(user=user)
serializer = FileSerializer(files, many=True, context={'request': request})
return Response(serializer.data)
#######################################################################
###################### Get All Folders of a User ######################
#######################################################################
@api_view(['GET'])
@permission_classes([IsAuthenticated])
def get_all_folders(request):
user = request.user
folders = Folder.objects.filter(user=user)
serializer = FolderSerializer(folders, many=True)
return Response(serializer.data)
#####################################################################
#####################################################################
@api_view(['GET'])
@permission_classes([IsAuthenticated])
def get_files(request,pk):
user = request.user
if pk=='null':
files = File.objects.filter(user=user,folder_id=None)
else:
files = File.objects.filter(user=user,folder_id=pk)
serializer = FileSerializer(files, many=True, context={'request': request})
return Response(serializer.data)
@api_view(['POST'])
@permission_classes([IsAuthenticated])
def upload_file(request):
user = request.user
serializer = FileSerializer(data=request.data, context={'request': request})
if serializer.is_valid():
original_filename = request.data['file'].name
folder_id = request.data.get('folder_id', None)
serializer.save(user=user, filename=original_filename, folder_id=folder_id)
return Response(serializer.data, status=201)
return Response(serializer.errors, status=400)
@api_view(['DELETE'])
@permission_classes([IsAuthenticated])
def delete_file(request, pk):
user = request.user
try:
file = File.objects.get(id=pk, user=user)
file_path = os.path.join(settings.MEDIA_ROOT, str(file.file))
# Delete the actual file from the server
if os.path.exists(file_path):
os.remove(file_path)
file.delete()
return Response(status=204)
except File.DoesNotExist:
return Response({"detail": "File not found."}, status=404)
@api_view(['POST'])
@permission_classes([IsAuthenticated])
def create_folder(request):
user = request.user
try:
parent_folder_id = request.data['parent_folder_id']
if parent_folder_id=='null':
parent_folder = Folder.objects.get(user=user, id=None)
else:
parent_folder = Folder.objects.get(user=user, id=parent_folder_id)
except (KeyError, Folder.DoesNotExist):
parent_folder=None
serializer = FolderSerializer(data=request.data)
if serializer.is_valid():
serializer.save(user=user, parentFolder=parent_folder)
return Response(serializer.data, status=201)
return Response(serializer.errors, status=400)
@api_view(['GET'])
@permission_classes([IsAuthenticated])
def get_folders(request, pk):
user = request.user
if pk=='null':
folders = Folder.objects.filter(user=user, parentFolder=None)
else:
folders = Folder.objects.filter(user=user, parentFolder=pk)
serializer = FolderSerializer(folders, many=True)
return Response(serializer.data)
@api_view(['PUT'])
@permission_classes([IsAuthenticated])
def updated_folder_title(request,pk):
user=request.user
folder = Folder.objects.get(user=user,id=pk)
newTitle=request.data.get('title',None)
if newTitle:
folder.title=newTitle
folder.save()
serializer = FolderSerializer(folder)
return Response(serializer.data)
return Response({"error": "Title is required"}, status=400)
@api_view(['DELETE'])
@permission_classes([IsAuthenticated])
def delete_folder(request, pk):
user = request.user
try:
folder = Folder.objects.get(id=pk, user=user)
folder.delete()
return Response(status=204)
except Folder.DoesNotExist:
return Response({"detail": "Folder not found."}, status=404) | balibabu/backend | fileapi/views.py | views.py | py | 4,763 | python | en | code | 0 | github-code | 36 | [
{
"api_name": "models.File.objects.filter",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "models.File.objects",
"line_number": 19,
"usage_type": "attribute"
},
{
"api_name": "models.File",
"line_number": 19,
"usage_type": "name"
},
{
"api_name": "seria... |
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