text string | size int64 | token_count int64 |
|---|---|---|
import os, sys
srcFolder = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'src')
sys.path.append(srcFolder)
from metrics import nss
from metrics import auc
from metrics import cc
from utils import *
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Evaluate predicted saliency map')
parser.add_argument('--output', type=str, default='')
parser.add_argument('--fixation_folder', type=str, default='')
parser.add_argument('--salmap_folder', type=str, default='')
parser.add_argument('--split_file', type=str, default='')
parser.add_argument('--fxt_loc_name', type=str, default='fixationPts')
parser.add_argument('--fxt_size', type=str, default='',
help='fixation resolution: (600, 800) | (480, 640) | (320, 640)')
parser.add_argument('--appendix', type=str, default='')
parser.add_argument('--file_extension', type=str, default='jpg')
args = parser.parse_args()
if args.fxt_size != '':
spl_tokens = args.fxt_size.split()
args.fxt_size = (int(spl_tokens[0]), int(spl_tokens[1]))
else:
args.fxt_size = (480, 640)
fixation_folder = args.fixation_folder
salmap_folder = args.salmap_folder
fxtimg_type = detect_images_type(fixation_folder)
split_file = args.split_file
if split_file != '' and os.path.isfile(split_file):
npzfile = np.load(split_file)
salmap_names = [os.path.join(salmap_folder, x) for x in npzfile['val_imgs']]
gtsal_names = [os.path.join(fixation_folder, x[:x.find('.')+1]+fxtimg_type) for x in npzfile['val_imgs']]
fxtpts_names = [os.path.join(fixation_folder, '{}mat'.format(x[:x.find('.')+1])) for x in npzfile['val_imgs']]
else:
salmap_names = load_allimages_list(salmap_folder)
gtsal_names = []
fxtpts_names = []
for sn in salmap_names:
file_name = sn.split('/')[-1]
gtsal_names.append(os.path.join(fixation_folder,'{}{}'.format(file_name[:file_name.find('.')+1], fxtimg_type)))
fxtpts_names.append(os.path.join(fixation_folder,'{}mat'.format(file_name[:file_name.find('.')+1])))
nss_score, _ = nss.compute_score(salmap_names, fxtpts_names, image_size=args.fxt_size, fxt_field_in_mat=args.fxt_loc_name)
cc_score, _ = cc.compute_score(salmap_names, gtsal_names, image_size=args.fxt_size)
auc_score, _ = auc.compute_score(salmap_names, fxtpts_names, image_size=args.fxt_size, fxt_field_in_mat=args.fxt_loc_name)
with open(args.output, 'a') as f:
f.write('{:0.4f}, {:0.4f}, {:0.4f}{}\n'.format(
nss_score, auc_score, cc_score, args.appendix)) | 2,507 | 965 |
from pandas import Series
class Evaluator:
series: Series
def __init__(self, series: Series):
self.series = series
self.unique_series = [value for value in self.series.dropna().unique()]
def series_match(self, pattern: str):
return Series(self.unique_series).astype(str).str.match(pattern).eq(True).all()
def series_contains(self, pattern: str):
return Series(self.unique_series).astype(str).str.contains(pattern).eq(True).any()
| 482 | 146 |
# -*- coding: utf-8 -*-
# Importação da biblioteca
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 3, 7, 1, 0]
titulo = "Gráfico de barras"
eixoX = "EixoX"
eixoY = "EixoY"
# Legendas
plt.title(titulo)
plt.xlabel(eixoX)
plt.ylabel(eixoY)
plt.bar(x, y)
plt.show() | 294 | 168 |
# Org_v01 by Orgdot (www.orgdot.com/aliasfonts). A tiny,
# stylized font with all characters within a 6 pixel height.
Org_01Bitmaps = [
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0xC0, 0x82, 0x08, 0x20, 0x80, 0xD5, 0xC0, 0x54, 0xF8, 0x80, 0xF1, 0xFF,
0x8F, 0x99, 0xF0, 0xF8, 0x8F, 0x1F, 0x99, 0xF0, 0xFF, 0x8F, 0x6B, 0xA4,
0xF9, 0x9F, 0x10, 0x8F, 0x99, 0x90, 0xF0, 0x55, 0xC0, 0x8A, 0xF9, 0x90,
0xF8, 0xFD, 0x63, 0x10, 0xF9, 0x99, 0xF9, 0x9F, 0xF9, 0x9F, 0x80, 0xF9,
0x9F, 0x20, 0xF8, 0x88, 0x47, 0x1F, 0x27, 0xC8, 0x42, 0x00, 0x99, 0x9F,
0x99, 0x97, 0x8C, 0x6B, 0xF0, 0x96, 0x69, 0x99, 0x9F, 0x10, 0x2E, 0x8F,
0x2B, 0x22, 0xF8, 0x89, 0xA8, 0x0F, 0xE0 ]
Org_01Glyphs = [
[ 0, 0, 0, 6, 0, 1 ], # 0x20 ' '
[ 0, 1, 5, 2, 0, -4 ], # 0x21 '!'
[ 1, 3, 1, 4, 0, -4 ], # 0x22 '"'
[ 2, 5, 5, 6, 0, -4 ], # 0x23 '#'
[ 6, 5, 5, 6, 0, -4 ], # 0x24 '$'
[ 10, 5, 5, 6, 0, -4 ], # 0x25 '%'
[ 14, 5, 5, 6, 0, -4 ], # 0x26 '&'
[ 18, 1, 1, 2, 0, -4 ], # 0x27 '''
[ 19, 2, 5, 3, 0, -4 ], # 0x28 '('
[ 21, 2, 5, 3, 0, -4 ], # 0x29 ')'
[ 23, 3, 3, 4, 0, -3 ], # 0x2A '#'
[ 25, 3, 3, 4, 0, -3 ], # 0x2B '+'
[ 27, 1, 2, 2, 0, 0 ], # 0x2C ','
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[ 29, 1, 1, 2, 0, 0 ], # 0x2E '.'
[ 30, 5, 5, 6, 0, -4 ], # 0x2F '/'
[ 34, 5, 5, 6, 0, -4 ], # 0x30 '0'
[ 38, 1, 5, 2, 0, -4 ], # 0x31 '1'
[ 39, 5, 5, 6, 0, -4 ], # 0x32 '2'
[ 43, 5, 5, 6, 0, -4 ], # 0x33 '3'
[ 47, 5, 5, 6, 0, -4 ], # 0x34 '4'
[ 51, 5, 5, 6, 0, -4 ], # 0x35 '5'
[ 55, 5, 5, 6, 0, -4 ], # 0x36 '6'
[ 59, 5, 5, 6, 0, -4 ], # 0x37 '7'
[ 63, 5, 5, 6, 0, -4 ], # 0x38 '8'
[ 67, 5, 5, 6, 0, -4 ], # 0x39 '9'
[ 71, 1, 4, 2, 0, -3 ], # 0x3A ':'
[ 72, 1, 4, 2, 0, -3 ], # 0x3B ''
[ 73, 3, 5, 4, 0, -4 ], # 0x3C '<'
[ 75, 4, 3, 5, 0, -3 ], # 0x3D '='
[ 77, 3, 5, 4, 0, -4 ], # 0x3E '>'
[ 79, 5, 5, 6, 0, -4 ], # 0x3F '?'
[ 83, 5, 5, 6, 0, -4 ], # 0x40 '@'
[ 87, 5, 5, 6, 0, -4 ], # 0x41 'A'
[ 91, 5, 5, 6, 0, -4 ], # 0x42 'B'
[ 95, 5, 5, 6, 0, -4 ], # 0x43 'C'
[ 99, 5, 5, 6, 0, -4 ], # 0x44 'D'
[ 103, 5, 5, 6, 0, -4 ], # 0x45 'E'
[ 107, 5, 5, 6, 0, -4 ], # 0x46 'F'
[ 111, 5, 5, 6, 0, -4 ], # 0x47 'G'
[ 115, 5, 5, 6, 0, -4 ], # 0x48 'H'
[ 119, 5, 5, 6, 0, -4 ], # 0x49 'I'
[ 123, 5, 5, 6, 0, -4 ], # 0x4A 'J'
[ 127, 5, 5, 6, 0, -4 ], # 0x4B 'K'
[ 131, 5, 5, 6, 0, -4 ], # 0x4C 'L'
[ 135, 5, 5, 6, 0, -4 ], # 0x4D 'M'
[ 139, 5, 5, 6, 0, -4 ], # 0x4E 'N'
[ 143, 5, 5, 6, 0, -4 ], # 0x4F 'O'
[ 147, 5, 5, 6, 0, -4 ], # 0x50 'P'
[ 151, 5, 5, 6, 0, -4 ], # 0x51 'Q'
[ 155, 5, 5, 6, 0, -4 ], # 0x52 'R'
[ 159, 5, 5, 6, 0, -4 ], # 0x53 'S'
[ 163, 5, 5, 6, 0, -4 ], # 0x54 'T'
[ 167, 5, 5, 6, 0, -4 ], # 0x55 'U'
[ 171, 5, 5, 6, 0, -4 ], # 0x56 'V'
[ 175, 5, 5, 6, 0, -4 ], # 0x57 'W'
[ 179, 5, 5, 6, 0, -4 ], # 0x58 'X'
[ 183, 5, 5, 6, 0, -4 ], # 0x59 'Y'
[ 187, 5, 5, 6, 0, -4 ], # 0x5A 'Z'
[ 191, 2, 5, 3, 0, -4 ], # 0x5B '['
[ 193, 5, 5, 6, 0, -4 ], # 0x5C '\'
[ 197, 2, 5, 3, 0, -4 ], # 0x5D ']'
[ 199, 3, 2, 4, 0, -4 ], # 0x5E '^'
[ 200, 5, 1, 6, 0, 1 ], # 0x5F '_'
[ 201, 1, 1, 2, 0, -4 ], # 0x60 '`'
[ 202, 4, 4, 5, 0, -3 ], # 0x61 'a'
[ 204, 4, 5, 5, 0, -4 ], # 0x62 'b'
[ 207, 4, 4, 5, 0, -3 ], # 0x63 'c'
[ 209, 4, 5, 5, 0, -4 ], # 0x64 'd'
[ 212, 4, 4, 5, 0, -3 ], # 0x65 'e'
[ 214, 3, 5, 4, 0, -4 ], # 0x66 'f'
[ 216, 4, 5, 5, 0, -3 ], # 0x67 'g'
[ 219, 4, 5, 5, 0, -4 ], # 0x68 'h'
[ 222, 1, 4, 2, 0, -3 ], # 0x69 'i'
[ 223, 2, 5, 3, 0, -3 ], # 0x6A 'j'
[ 225, 4, 5, 5, 0, -4 ], # 0x6B 'k'
[ 228, 1, 5, 2, 0, -4 ], # 0x6C 'l'
[ 229, 5, 4, 6, 0, -3 ], # 0x6D 'm'
[ 232, 4, 4, 5, 0, -3 ], # 0x6E 'n'
[ 234, 4, 4, 5, 0, -3 ], # 0x6F 'o'
[ 236, 4, 5, 5, 0, -3 ], # 0x70 'p'
[ 239, 4, 5, 5, 0, -3 ], # 0x71 'q'
[ 242, 4, 4, 5, 0, -3 ], # 0x72 'r'
[ 244, 4, 4, 5, 0, -3 ], # 0x73 's'
[ 246, 5, 5, 6, 0, -4 ], # 0x74 't'
[ 250, 4, 4, 5, 0, -3 ], # 0x75 'u'
[ 252, 4, 4, 5, 0, -3 ], # 0x76 'v'
[ 254, 5, 4, 6, 0, -3 ], # 0x77 'w'
[ 257, 4, 4, 5, 0, -3 ], # 0x78 'x'
[ 259, 4, 5, 5, 0, -3 ], # 0x79 'y'
[ 262, 4, 4, 5, 0, -3 ], # 0x7A 'z'
[ 264, 3, 5, 4, 0, -4 ], # 0x7B '['
[ 266, 1, 5, 2, 0, -4 ], # 0x7C '|'
[ 267, 3, 5, 4, 0, -4 ], # 0x7D ']'
[ 269, 5, 3, 6, 0, -3 ] ] # 0x7E '~'
Org_01 = [
Org_01Bitmaps,
Org_01Glyphs,
0x20, 0x7E, 7 ]
# Approx. 943 bytes
| 7,189 | 4,904 |
# Generated by Django 3.1.8 on 2021-05-26 11:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('manager', '0004_auto_20210519_1334'),
]
operations = [
migrations.AddField(
model_name='director',
name='service_mesh_label',
field=models.CharField(default='', max_length=128),
),
]
| 411 | 150 |
from django.db import models
from resources_portal.models.material import Material
from resources_portal.models.user import User
class MaterialShareEvent(models.Model):
class Meta:
db_table = "material_share_events"
get_latest_by = "created_at"
objects = models.Manager()
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
material = models.ForeignKey(
Material, blank=False, null=False, on_delete=models.CASCADE, related_name="share_events"
)
# TODO: add possible choices
event_type = models.CharField(max_length=255, blank=True, null=True)
time = models.DateTimeField()
created_by = models.ForeignKey(
User,
blank=False,
null=False,
on_delete=models.CASCADE,
related_name="material_share_events",
)
assigned_to = models.ForeignKey(
User,
blank=False,
null=False,
on_delete=models.CASCADE,
related_name="material_share_assignments",
)
| 1,053 | 323 |
""" Archived flu data
http://webarchive.nationalarchives.gov.uk/20130107105354/http://www.dh.gov.uk/en/Publicationsandstatistics/Statistics/Performancedataandstatistics/DailySituationReports/index.htm
"""
import collections
import calendar
import datetime
import re
import urllib
from lxml.html import fromstring, tostring
import requests
import slugify
from publish.lib.helpers import to_markdown, anchor_to_resource, get_dom, hd
ROOT = "http://webarchive.nationalarchives.gov.uk/20130107105354/http://www.dh.gov.uk/en/Publicationsandstatistics/Statistics/Performancedataandstatistics/DailySituationReports/index.htm"
DESCRIPTION = None
def scrape_block(block, title):
global DESCRIPTION
dataset = {
"title": title,
"notes": DESCRIPTION,
"tags": ["sitrep", "winter"],
"origin": ROOT,
"resources": [anchor_to_resource(a) for a in block.cssselect('.itemLinks li a')],
"groups": ['winter']
}
dataset["name"] = slugify.slugify(dataset["title"]).lower()
for r in dataset["resources"]:
r['description'] = r['description'].replace('Download ', '')
return dataset
def scrape(workspace):
print "Scraping Archived Flu Data with workspace {}".format(workspace)
global DESCRIPTION
datasets = []
page = get_dom(ROOT)
DESCRIPTION = to_markdown(unicode(page.cssselect('.introText')[0].text_content().strip()))
containers = page.cssselect('.itemContainer')[1:]
datasets.append(scrape_block(containers[0], "Daily Hospital Situation Report 2011-12"))
datasets.append(scrape_block(containers[1], "Daily Hospital Situation Report 2010-11"))
datasets.append(scrape_block(containers[2], "Daily Flu Situation Report 2010-11"))
datasets.append(scrape_block(containers[3], "Daily SitRep Guidance 2011-12"))
datasets = filter(lambda x: x is not None, datasets)
print "Found {} datasets".format(len(datasets))
return datasets | 1,941 | 652 |
from rest_framework.serializers import ModelSerializer
from .models import MonthlyBudget
class MonthlyBudgetSerializer(ModelSerializer):
class Meta:
model = MonthlyBudget
fields = '__all__'
| 213 | 60 |
from behave import *
from google.cloud import datastore
import os
import uuid
import pandas as pd
@given('site "{site}" exists')
def step_impl(context, site):
print(f'STEP: Given provider {site} exists')
context.domain = os.getenv('DOMAIN')
# Instantiates a client
datastore_client = datastore.Client()
context.site_one = site
provider_key = datastore_client.key('Site', context.site_one)
datastore_client.delete(provider_key)
entity = datastore.Entity(key=provider_key)
entity['site'] = context.site_one
code = str(uuid.uuid4())
entity['code'] = code
context.site_one_link = 'https://' + context.domain + '/register?site=' + site + '&code=' + code
entity['link'] = context.site_one_link
datastore_client.put(entity)
@step('site "{site}" does not exist')
def step_impl(context, site):
print(f'STEP: And site {site} does not exists')
context.provider_two = site
# Instantiates a client
datastore_client = datastore.Client()
datastore_client.delete(datastore_client.key('Site', site))
@step("both sites are included in the input file")
def step_impl(context):
context.file = 'features/resources/input-file.xlsx'
print(f'STEP: And both sites are included in the input file at {context.file}')
@when("the input file is processed")
def step_impl(context):
print(u'STEP: When the input file is processed')
context.output_file = 'features/resources/output-file.xlsx'
command = f'python3 scripts/new-providers/new-providers.py {context.domain} {context.file} {context.output_file}'
print(command)
os.system(command)
@then('site "{site}" is updated with the original link')
def step_impl(context, site):
print(f'STEP: And site {site} is updated with the original link')
# Instantiates a client
datastore_client = datastore.Client()
key = datastore_client.key('Site', site)
assert key is not None
entity = datastore_client.get(key)
assert entity['link'] == context.site_one_link
print(entity)
@then('site "{site}" is created with a new link')
def step_impl(context, site):
print(f'STEP: Then {site} is created')
# Instantiates a client
datastore_client = datastore.Client()
key = datastore_client.key('Site', site)
assert key is not None
entity = datastore_client.get(key)
assert entity['link'] == 'https://' + context.domain + '/register?site=' + site + '&code=' + entity['code']
print(entity)
@step('site "{site}" appears in the output file as "{status}"')
def step_impl(context, site, status):
print(f'STEP: And site {site} appears in the output file as {status}')
df = pd.read_excel(context.output_file)
row = df.loc[df['site'] == site]
print(row)
assert row['comment'].values[0] == status
| 2,786 | 898 |
import os
gpu_flag = False
gpu_name = 'cpu'
x_dim = 2048
num_class = 87
num_query = 5
batch_size = 84
eval_batch_size = 128
glove_dim = 200
pretrain_lr = 1e-4
num_epochs_pretrain = 20
eval_step_pre = 1
fusion_iter_len = 100000
# num_epochs_pretrain = 30
num_epochs_style = 30
num_epochs_fusion = 50
log_step_pre = 60
folder_path = os.path.dirname(os.path.realpath(__file__)) + '/'
path_class_list = folder_path + 'extra/common_class_list.txt'
dir_saved_model = folder_path + 'saved_model_qd/'
dir_saved_feature = folder_path + 'saved_features_qd/'
dir_dataset = folder_path + 'dataset/'
dir_extra = folder_path + 'extra/'
os.makedirs( dir_saved_model, exist_ok = True)
os.makedirs( dir_saved_feature, exist_ok = True )
os.makedirs( dir_extra, exist_ok = True )
path_model_image = dir_saved_model + 'resnet_50_image.pt'
path_model_sketchy = dir_saved_model + 'resnet_50_sketchy.pt'
path_z_encoder_sketchy = dir_saved_model + 'z_encoder_sketch.pt'
path_s_encoder_sketchy = dir_saved_model + 's_encoder_sketch.pt'
path_adv_model_sketchy = dir_saved_model + 'adv_sketch.pt'
path_recon_model_sketchy = dir_saved_model + 'reconstruck_sketch.pt'
path_z_encoder_image = dir_saved_model + 'z_encoder_image.pt'
path_s_encoder_image = dir_saved_model + 's_encoder_image.pt'
path_adv_model_image = dir_saved_model + 'adv_image.pt'
path_recon_model_image = dir_saved_model + 'reconstruck_image.pt'
path_fusion_model = dir_saved_model + 'fusion_model.pt'
path_image_dataset = dir_dataset + 'images/'
path_sketchy_dataset = dir_dataset + 'sketchy/'
path_quickdraw_dataset = dir_dataset + 'quick_draw/'
path_image_features = dir_saved_feature + 'image_features.p'
path_sketchy_features = dir_saved_feature + 'sketchy_features.p'
path_quickdraw_features = dir_saved_feature + 'quick_draw_features.p'
path_image_file_list = dir_extra + 'images_file_list.p'
path_sketchy_file_list = dir_extra + 'sketchy_file_list.p'
path_quickdraw_file_list = dir_extra + 'quick_draw_file_list.p'
path_model = folder_path + 'resnet_50_da.pt'
path_sketch_z_encoder = folder_path + 'sketch_encoder.pt'
path_glove_vector = folder_path + 'glove_vector'
| 2,137 | 895 |
# -*- coding: utf-8 -*-
from __future__ import print_function
import caffe
from caffe import params as P
from google.protobuf import text_format
#import inputParam
import os
import sys
import math
sys.path.append('../')
from username import USERNAME
sys.dont_write_bytecode = True
# #################################################################################
caffe_root = "/home/{}/work/repository".format(USERNAME)
# Projects name
Project = "RtPose"
ProjectName = "Rtpose_COCO"
Results_dir = "/home/{}/Models/Results".format(USERNAME)
# Pretrained_Model = "/home/{}/Models/PoseModels/pose_iter_440000.caffemodel".format(USERNAME)
# Pretrained_Model = "/home/{}/Models/PoseModels/VGG19_3S_0_iter_20000.caffemodel".format(USERNAME)
Pretrained_Model = "/home/{}/Models/PoseModels/DarkNet_3S_0_iter_450000_merge.caffemodel".format(USERNAME)
gpus = "0"
solver_mode = P.Solver.GPU
| 882 | 315 |
# -*- coding: utf-8 -*-
from typing import Any
from typing import Dict
from typing import Optional
from typing import Set
from unittest import TestCase
from flask import abort
from app import create_app
from app import db
from app.configuration import TestConfiguration
from app.userprofile import Permission
from app.userprofile import Role
from app.userprofile import User
class ViewTestCase(TestCase):
"""
This class is a base test case for all view tests, providing helpful methods that are needed in many situations
when testing views.
"""
# region Test Setup
def setUp(self) -> None:
"""
Prepare the test cases.
"""
self.app = create_app(TestConfiguration)
self.client = self.app.test_client()
self.app_context = self.app.app_context()
self.app_context.push()
self.request_context = self.app.test_request_context()
self.request_context.push()
db.create_all()
def tearDown(self) -> None:
"""
Clean up after each test case.
"""
db.session.remove()
db.drop_all()
self.request_context.pop()
self.app_context.pop()
# endregion
# region Route Accessing
def get(self, url: str, expected_status: int = 200, follow_redirects: bool = True) -> str:
"""
Access the given URL via HTTP GET. Assert that the returned status code is the given one.
The status code is checked using `self.assertEqual`.
:param url: The URL to access.
:param expected_status: The status code that should be returned. Defaults to `200`.
:param follow_redirects: Set to `False` if redirects by the route should not be followed. Defaults to
`True`.
:return: The response of accessing the URL as a string.
"""
response = self.client.get(url, follow_redirects=follow_redirects)
data = response.get_data(as_text=True)
self.assertEqual(expected_status, response.status_code, msg='Expected Status Code')
return data
def post(self, url: str, data: Dict[str, Any] = None, expected_status: int = 200, follow_redirects: bool = True) \
-> str:
"""
Access the given URL via HTTP POST, sending the given data. Assert that the returned status code is the
given one.
The status code is checked using `self.assertEqual`.
:param url: The URL to access.
:param data: The data to send in the POST request. Defaults to `dict()`.
:param expected_status: The status code that should be returned. Defaults to `200`.
:param follow_redirects: Set to `False` if redirects by the route should not be followed. Defaults to
`True`.
:return: The response of accessing the URL as a string.
"""
if data is None:
data = dict()
response = self.client.post(url, follow_redirects=follow_redirects, data=data)
data = response.get_data(as_text=True)
self.assertEqual(expected_status, response.status_code, msg='Expected Status Code')
return data
# TODO: Rename assert_allowed_methods().
def check_allowed_methods(self, url: str, allowed_methods: Optional[Set[str]] = None, allow_options: bool = True) \
-> None:
"""
Check if the given URL can be accessed only by the specified methods.
This method will assert that the URL can be accessed by all HTTP methods listed in `allowed_methods` by
checking that a request via each of these methods to the URL does not return a response code of 405.
Likewise, it will test that all methods not listed in `allowed_methods` return a response code of 405.
Flask by default allows 'OPTIONS'. This method follows this behaviour and automatically adds 'OPTIONS' to
the set of allowed methods unless configured otherwise.
Flask also automatically allows 'HEAD' if 'GET' is allowed. This method follows this behaviour and always
adds 'HEAD' to the set of allowed methods if 'GET' is included in the set.
:param url: The URL to check.
:param allowed_methods: A set of all HTTP methods via which the URL can be accessed. If the set is not given
or an empty set of allowed methods is passed, 'GET' will automatically be allowed
to mimic Flask's behaviour. Defaults to `None`.
:param allow_options: If this parameter is set to `True`, 'OPTIONS' will automatically be added to the set
of allowed methods to follow Flask's behaviour. Defaults to `True`.
"""
all_methods = ['DELETE', 'GET', 'HEAD', 'OPTIONS', 'PATCH', 'POST', 'PUT']
# Be default, 'GET' is the only allowed method.
if allowed_methods is None or not allowed_methods:
allowed_methods = {'GET'}
# If 'GET' is allowed, Flask also allows 'HEAD' automatically.
if 'GET' in allowed_methods:
allowed_methods.add('HEAD')
# Follow Flask's behaviour and add 'OPTIONS' by default.
if allow_options:
allowed_methods.add('OPTIONS')
prohibited_methods = [method for method in all_methods if method not in allowed_methods]
for allowed_method in allowed_methods:
status_code = self._get_status_code_for_method(url, allowed_method)
self.assertNotEqual(405, status_code, f'{allowed_method} {url} is not allowed, but should be.')
for prohibited_method in prohibited_methods:
status_code = self._get_status_code_for_method(url, prohibited_method)
self.assertEqual(405, status_code, f'{prohibited_method} {url} is allowed, but should not be.')
def _get_status_code_for_method(self, url: str, method: str) -> int:
"""
Access the given URL via the given HTTP method and return the response status code.
:param url: The URL to access.
:param method: The HTTP method used to access the URL.
:return: The HTTP status code that accessing the URL via the method returned.
:raise ValueError: if the HTTP method is invalid.
"""
if method == 'DELETE':
response = self.client.delete(url)
elif method == 'GET':
response = self.client.get(url)
elif method == 'HEAD':
response = self.client.head(url)
elif method == 'OPTIONS':
response = self.client.options(url)
elif method == 'PATCH':
response = self.client.patch(url)
elif method == 'POST':
response = self.client.post(url)
elif method == 'PUT':
response = self.client.put(url)
else:
raise ValueError(f'Invalid HTTP method {method}')
return response.status_code
# endregion
# region Permissions
def assert_no_permission_required(self, url: str, method: str = 'GET') -> None:
"""
Assert that accessing the URL via the given method requires no permission at all and that accessing the URL
with any permission is actually possible.
The test checks for a response code of 403. This can lead to a false positive if a route aborts with an
error 403.
:param url: The URL to access.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
allowed_permissions = Permission.get_permissions(include_empty_permission=True, all_combinations=True)
self._assert_permissions(url, allowed_permissions, method)
def assert_permission_required(self, url: str, permission: Permission, method: str = 'GET') -> None:
"""
Assert that accessing the URL via the given method requires the specified permission and that accessing the
URL with the permission is actually possible.
The test checks for a response code of 403. This can lead to a false positive if a route does not require
the specified permission but aborts with an error 403 in some other case.
:param url: The URL to access.
:param permission: The permission that must be required to access the URL.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
all_permissions = Permission.get_permissions(include_empty_permission=True, all_combinations=True)
allowed_permissions = {p for p in all_permissions if p.includes_permission(permission)}
self._assert_permissions(url, allowed_permissions, method)
def assert_permission_required_one_of(self, url: str, *permissions: Permission, method: str = 'GET') -> None:
"""
Assert that accessing the URL via the given method requires one of the specified permissions and that
accessing the URL with the permission is actually possible, while accessing the URL with any other
permission fails.
:param url: The url to access.
:param permissions: The permissions that must be required to access the URL.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
all_permissions = Permission.get_permissions(include_empty_permission=True, all_combinations=True)
allowed_permissions = set({})
for permission in permissions:
allowed_permissions.update({p for p in all_permissions if p.includes_permission(permission)})
self._assert_permissions(url, allowed_permissions, method)
def assert_permission_required_all(self, url: str, *permissions: Permission, method: str = 'GET') -> None:
"""
Assert that accessing the URL via the given method requires all of the specified permissions and that
accessing the URL with the permissions is actually possible.
:param url: The URL to access.
:param permissions: The permissions that must be required to access the URL.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
all_permissions = Permission.get_permissions(include_empty_permission=True, all_combinations=True)
#
allowed_permission = Permission(0)
for permission in permissions:
allowed_permission |= permission
allowed_permissions = {p for p in all_permissions if p.includes_permission(allowed_permission)}
self._assert_permissions(url, allowed_permissions, method)
def _assert_permission_grants_access(self, url: str, permission: Permission, method: str = 'GET') -> None:
"""
Assert that the given permission is sufficient to access the given URL.
:param url: The URL to access.
:param permission: The permission that should be able to able to access the URL.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
# Create and log in a user with the given permission.
role = self.create_role(permission)
user = self.create_and_login_user(role=role)
# Ensure that accessing the URL with the given permission is possible.
status_code = self._get_status_code_for_method(url, method)
self.assertNotEqual(403, status_code,
f'{method} {url} must be accessible with permission {permission}, but it is not.')
# Delete the user and role so that this method can be called multiple times in the same test.
# Since the role might be the only role with permissions to edit roles, we cannot use role.delete() which will
# fail in such a case.
user._delete()
db.session.delete(role)
db.session.commit()
def _assert_permission_does_not_grant_access(self, url: str, permission: Permission, method: str = 'GET') -> None:
"""
Assert that the given permission is not sufficient to access the given URL.
:param url: The URL to access.
:param permission: The permission that should not be able to able to access the URL.
:param method: The HTTP method to access the URL by. Defaults to `'GET'`.
"""
# Create and log in a user with the given permission.
role = self.create_role(permission)
user = self.create_and_login_user(role=role)
# Ensure that accessing the URL with the given permission is impossible.
status_code = self._get_status_code_for_method(url, method)
self.assertEqual(403, status_code,
f'{method} {url} must not be accessible with permission {permission}, but it is.')
# Delete the user and role so that this method can be called multiple times in the same test.
# Since the role might be the only role with permissions to edit roles, we cannot use role.delete() which will
# fail in such a case.
user._delete()
db.session.delete(role)
db.session.commit()
def _assert_permissions(self, url: str, allowed_permissions: Set[Permission], method: str) -> None:
"""
Assert that the given URL can be accessed with the allowed permissions, but not with any other permission.
:param url: The URL to access.
:param allowed_permissions: List of permissions that must be able to access the URL.
:param method: The HTTP method to access the URL by.
"""
for allowed_permission in allowed_permissions:
self._assert_permission_grants_access(url, allowed_permission, method)
all_permissions = Permission.get_permissions(include_empty_permission=True, all_combinations=True)
prohibited_permissions = {permission for permission in all_permissions if permission not in allowed_permissions}
for prohibited_permission in prohibited_permissions:
self._assert_permission_does_not_grant_access(url, prohibited_permission, method)
# endregion
# region Application Entities
@staticmethod
def create_user(email: str, name: str, password: str, role: Optional[Role] = None) -> User:
"""
Create a user with the given parameters. If a role is given, assign the role to the user. Commit this user
to the DB.
:param email: The email address of the user.
:param name: The name of the user.
:param password: The password of the user.
:param role: The role for the user. Defaults to `None`.
:return: The created user.
"""
user = User(email, name)
user.set_password(password)
if role:
user.role = role
db.session.add(user)
db.session.commit()
return user
def create_and_login_user(self,
email: str = 'doe@example.com',
name: str = 'Jane Doe',
password: str = 'ABC123!',
role: Optional[Role] = None
) -> User:
"""
Create a user with the given parameters and log them in. If a role is given, assign the role to the user.
The user is committed to the DB.
:param email: The email address of the user. Defaults to `'doe@example.com'`.
:param name: The name of the user. Defaults to `'Jane Doe'`.
:param password: The password of the user. Defaults to `'ABC123!'`.
:param role: The role for the user. Defaults to `None`.
:return: The created user.
"""
user = self.create_user(email, name, password, role)
self.client.post('/user/login', follow_redirects=True, data=dict(
email=email,
password=password,
))
return user
@staticmethod
def create_role(*permissions: Permission, name: str = 'Test Role') -> Role:
"""
Create a role with the given permissions.
:param permissions: The permissions of the role.
:param name: The name of the new role. Defaults to `'Test Role'`.
:return: The created role.
"""
role = Role(name)
for permission in permissions:
role.permissions |= permission
db.session.add(role)
db.session.commit()
return role
# endregion
# region Routes
@staticmethod
def aborting_route(code: int) -> None:
"""
A route handler that aborts with the given status code.
:param code: The status code of the HTTP response.
"""
abort(code)
@staticmethod
def example_route() -> str:
"""
A route handler that returns an example string as its response.
:return: 'Hello, world!'
"""
return 'Hello, world!'
# endregion
# region Other Helper Methods
@classmethod
def get_false(cls) -> bool:
"""
Get `False`. Useful for mocking.
This method must be a class method so that it can be used in the patch decorator when mocking another
method.
:return: `False`
"""
return False
# endregion
| 17,567 | 4,488 |
# Copyright 2021, Peter Birch, mailto:peter@lightlogic.co.uk
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .container import Container
from .scalar import Scalar
from .struct import Struct
class Union(Container):
""" Packed data structure formed of other structures or unions """
def __init__(self, name, fields, desc=None):
""" Initialise union with name and fields
Args:
name : Name of the container
fields: Dictionary of fields
desc : Optional description
"""
# Perform container construction
super().__init__(name, fields, desc=desc, legal=[Scalar, Struct, Union])
# Check all fields are the same width
widths = [x._pt_width for x in self._pt_values()]
assert len(set(widths)) == 1, \
f"Unmatched widths of fields in union {self._pt_name}: {widths}"
# Calculate the width
self._pt_width = next((x._pt_width for x in self._pt_values()))
| 1,483 | 415 |
# Zipfile Module
import zipfile
# Open and List
zip = zipfile.ZipFile('Archive.zip', 'r')
print(zip.namelist()) # Lists everything within zip file
# Metadata in the zip folder
for meta in zip.infolist(): # List of the metadata within zip file
print(meta)
info = zip.getinfo("purchased.txt")
# Access to files in zip folder
print(zip.read("wishlist.txt"))
with zip.open('wishlist.txt') as f: # establishing f = zip.open('wishlist.txt')
print(f.read())
# Extracting files
zip.extract('purchased.txt') # Extract just the one file
input("Continue? Press any key.")
zip.extractall() # Extracts everything
# Closing the zip
zip.close()
| 644 | 216 |
"""Recommendation Algorithm Base Class
This module is a base class for algorithms using sparse matrices
The required packages can be found in requirements.txt
"""
import pandas as pd
import numpy as np
from lenskit import batch, topn, util
from lenskit import crossfold as xf
from lenskit.algorithms import Recommender, Predictor, als, basic, user_knn
from lenskit.data import sparse_ratings
from scipy import sparse
from sklearn.metrics.pairwise import cosine_similarity
from abc import ABCMeta, abstractmethod
# Visualizations and debugging
import plotly.graph_objs as go
import logging
class SparseBasedAlgo(Recommender, Predictor, metaclass=ABCMeta):
# def __init__(self):
# pass
@abstractmethod
def __str__(self):
"""Name of the class"""
@abstractmethod
def get_num_users(self):
"""Get the number of users in the recommender system"""
@abstractmethod
def get_num_items(self):
"""Get the number of items in the recommender system"""
@abstractmethod
def fit(self, ratings, **kwargs):
"""Fit the algorithm over the initial dataset
:param ratings: user item ratings in a dataframe
:type ratings: pandas DataFrame
"""
@abstractmethod
def update(self):
"""Refit the algorithm over the internal sparse matrix"""
# Add a user to the ratings matrix
def add_user(self, user_id):
# Check if user_id to be added already exists
try:
assert (
user_id in self.user_index_
) == False, "User ID already exists! Not adding anything..."
except AssertionError as e:
print(e)
exit(1)
# Build a sparse matrix of length of number of items
tmp_sparse_row = sparse.csr_matrix(np.zeros((1, len(self.item_index_))))
# Vertically stack temporary matrix to original matrix
self.rating_matrix_ = sparse.vstack([self.rating_matrix_, tmp_sparse_row])
# Update user index
self.user_index_ = self.user_index_.append(pd.Index([user_id]))
# Add an item to the ratings matrix
def add_item(self, item_id):
# Check if item_id to be added already exists
try:
assert (item_id in self.item_index_) == False, "Item ID already exists!"
except AssertionError as e:
print(e)
exit(1)
# Build a sparse matrix of length of number of users
tmp_sparse_col = sparse.csr_matrix(np.zeros((len(self.user_index_), 1)))
# Horizotnally stack temporary matrix to original matrix
self.rating_matrix_ = sparse.hstack(
[self.rating_matrix_, tmp_sparse_col]
).tocsr()
# Update item index
self.item_index_ = self.item_index_.append(pd.Index([item_id]))
# Add a user-item interaction for existing users and items
def add_interactions(self, user_id, item_id, rating):
# Check if inputs are lists and all input list lengths are equal
assert type(user_id) == list, "Input user_id is not a list"
assert type(item_id) == list, "Input item_id is not a list"
assert type(rating) == list, "Input rating is not a list"
assert (
len(user_id) == len(item_id) == len(rating)
), "Input lists are not of the same length"
# Build a temporary sparse LIL matrix
tmp_ratings = sparse.lil_matrix(self.rating_matrix_.shape)
for i in range(len(user_id)):
# Obtain locations from ID
(user_pos,) = np.where(self.user_index_ == user_id[i])[0]
(item_pos,) = np.where(self.item_index_ == item_id[i])[0]
# If rating does not exist
if self.rating_matrix_[user_pos, item_pos] == 0:
# Fill into temporary sparse matrix
tmp_ratings[user_pos, item_pos] = rating[i]
# Convert temporary LIL to CSR
tmp_ratings = tmp_ratings.tocsr()
# Add temporary CSR to main ratings matrix
self.rating_matrix_ += tmp_ratings | 4,059 | 1,175 |
# -*- coding: utf-8 -*-
"""
This module implements the User functionality of TheTVDb API.
Allows to retrieve, add and delete user favorites and ratings.
See [Users API section](https://api.thetvdb.com/swagger#!/Users)
"""
from .base import TVDB
class User(TVDB):
"""
User class to retrieve, add and delete user favorites and ratings.
Requires username and user-key.
"""
_BASE_PATH = 'user'
_URLS = {
'info': '',
'favorites': '/favorites',
'alter_favorite': '/favorites/{id}'
}
def __init__(self, user, key):
"""
Initialize the User class.
`user` is the username for login. `key` is the userkey needed to
authenticate with the user, you can find it in the
[account info](http://thetvdb.com/?tab=userinfo) under account identifier.
"""
super(User, self).__init__(user=user, key=key)
self.Ratings = User_Ratings(user, key)
"""
Allows to retrieve, add and delete user ratings.
"""
def info(self):
"""
Get the basic user info and set its values to local attributes.
Returns a dict with all the information of the user.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> user = tvdb.User('username', 'userkey')
>>> response = user.info()
>>> user.userName
'username'
"""
path = self._get_path('info')
response = self._GET(path)
self._set_attrs_to_values(response)
return response
def favorites(self):
"""
Get the a list of the favorite series of the user and
sets it to `favorites` attribute.
Returns a list of the favorite series ids.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> user = tvdb.User('username', 'userkey')
>>> response = user.favorites()
>>> user.favorites[0]
'73545'
"""
path = self._get_path('favorites')
response = self._GET(path)
self._set_attrs_to_values(response)
return self._clean_return(response)
def _clean_return(self,jsn):
if 'favorites' in jsn:
return jsn['favorites']
return jsn
def add_favorite(self, id):
"""
Add a series to user favorite series from its series id.
`id` is the series id you want to add to favorites.
Returns the updated list of the favorite series ids.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> user = tvdb.User('username', 'userkey')
>>> response = user.add_favorite(78804)
>>> response[-1]
'78804'
"""
path = self._get_path('alter_favorite').format(id=id)
return self._clean_return(self._PUT(path))
def delete_favorite(self, id):
"""
Delete a series from user favorite series from its series id.
`id` is the series id you want to delete from favorites.
Returns the updated list of the favorite series ids.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> user = tvdb.User('username', 'userkey')
>>> response = user.delete_favorite(78804)
>>> response[-1]
'73545'
"""
path = self._get_path('alter_favorite').format(id=id)
return self._clean_return(self._DELETE(path))
class User_Ratings(TVDB):
"""
Class needed to organize user ratings. Allows to retrieve, add and delete user ratings.
Requires username and user-key.
"""
_BASE_PATH = 'user/ratings'
_URLS = {
'all': '',
'query': '/query',
'query_params': '/query/params',
'add': '/{itemType}/{itemId}/{itemRating}',
'delete': '/{itemType}/{itemId}'
}
_PAGES = -1
_PAGES_LIST = {}
_FILTERS = {}
def __init__(self, user, key, **kwargs):
"""
Initialize the class.
`user` is the username for login. `key` is the userkey needed to
authenticate with the user, you can find it in the
[account info](http://thetvdb.com/?tab=userinfo) under account identifier.
It's possible to provide `itemType` that filters ratings by type.
Can be either 'series', 'episode', or 'banner'.
"""
super(User_Ratings, self).__init__(user=user, key=key)
self._FILTERS = {}
self.update_filters(**kwargs)
def update_filters(self, **kwargs):
"""
Set the filters for the user rating.
It's possible to provide `itemType` that filters ratings by type.
Can be either 'series', 'episode', or 'banner'.
"""
self._FILTERS = kwargs
def query_params(self):
"""
Get the query parameters allowed for filtering and set it to `query_params` attribute.
Returns a list of parameters you can set to filters.
"""
path = self._get_id_path('query_params')
response = self._GET(path)
self._set_attrs_to_values({'query_params': response})
return response
def pages(self):
"""
Get the number of rating pages available for filtered ratings of the specific user.
Returns the number of rating pages available with current filters.
"""
if self._PAGES < 0:
self.page(1)
return self._PAGES
def add(self, type, id, rating):
"""
Add a new rating to the user's ratings..
`type` is the item type of the item you want to rate. Can be either
'series', 'episode', or 'image'.
`id` is the ID of the item that you want to rate.
`rating` is the `integer` rating you want to set.
Returns a list with the new updated rating.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> rtn = tvdb.User_Ratings('username', 'userkey')
>>> response = rtn.add('series', 78804, 8)
"""
path = self._get_path('add').format(itemType=type, itemId=id, itemRating=rating)
return self._PUT(path)
def delete(self, type, id):
"""
Delete an existing user's rating..
`type` is the item type of the item rating you want to delete. Can be either
'series', 'episode', or 'image'.
`id` is the ID of the item rating that you want to delete.
Returns an empty dictionary.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> rtn = tvdb.User_Ratings('username', 'userkey')
>>> response = rtn.delete('series', 78804)
"""
path = self._get_path('delete').format(itemType=type, itemId=id)
return self._DELETE(path)
def all(self):
"""
Get the full rating list filtered for the user and adds it
to the `ratings` attribute.
Returns a list of ratings info.
For example
#!python
>>> import tvdbsimple as tvdb
>>> tvdb.KEYS.API_KEY = 'YOUR_API_KEY'
>>> rtn = tvdb.User_Rating('phate89', '3EF7CF9BBC8BB430')
>>> response = rtn.all()
>>> rtn.ratings[0]['ratingType']
'episode'
"""
ratings = []
for i in range (1, self.pages()+1):
ratings.extend(self.page(i))
self._set_attrs_to_values({'ratings': ratings})
return ratings
def page(self, page):
"""
Get the rating list for a specific page for the user.
`page` is the rating page number.
Returns a list ratings available in the page.
"""
if page in self._PAGES_LIST:
return self._PAGES_LIST[page]
if self._FILTERS:
path = self._get_path('query')
else:
path = self._get_path('all')
filters = self._FILTERS.copy()
filters['page'] = page
response = self._GET(path, params=filters, cleanJson=False)
if 'links' in response and 'last' in response['links']:
self._PAGES = response['links']['last']
self._PAGES_LIST[page] = response['data']
return response['data']
def __iter__(self):
for i in range (1, self.pages()+1):
yield self.page(i) | 8,871 | 2,626 |
#!/usr/bin/env python
import re
from binaryninja.log import log_info
from binaryninja.architecture import Architecture
from binaryninja.function import RegisterInfo, InstructionInfo, InstructionTextToken
from binaryninja.enums import InstructionTextTokenType, BranchType, FlagRole, LowLevelILFlagCondition
from . import LR35902IL
from .hardware_documentation import IO_REGISTERS as IO_REGS
from lr35902dis.lr35902 import *
CC_TO_STR = {
CC.ALWAYS:'1', CC.NOT_Z:'nz', CC.Z:'z',
CC.NOT_C:'nc', CC.C:'c'
}
class LR35902(Architecture):
name = 'LR35902'
address_size = 2
default_int_size = 1
instr_alignment = 1
max_instr_length = 4
# register related stuff
regs = {
# main registers
'AF': RegisterInfo('AF', 2),
'BC': RegisterInfo('BC', 2),
'DE': RegisterInfo('DE', 2),
'HL': RegisterInfo('HL', 2),
# main registers (sub)
"A": RegisterInfo("AF", 1, 1),
"F": RegisterInfo("AF", 1, 0),
"B": RegisterInfo("BC", 1, 1),
"C": RegisterInfo("BC", 1, 0),
"D": RegisterInfo("DE", 1, 1),
"E": RegisterInfo("DE", 1, 0),
"H": RegisterInfo("HL", 1, 1),
"L": RegisterInfo("HL", 1, 0),
"Flags": RegisterInfo("AF", 0),
# index registers
'SP': RegisterInfo('SP', 2),
# program counter
'PC': RegisterInfo('PC', 2),
# status
# 'status': RegisterInfo('status', 1)
}
stack_pointer = "SP"
IO_REGISTERS = IO_REGS
#------------------------------------------------------------------------------
# FLAG fun
#------------------------------------------------------------------------------
flags = ['z', 'h', 'n', 'c']
# remember, class None is default/integer
semantic_flag_classes = ['class_bitstuff']
# flag write types and their mappings
flag_write_types = ['dummy', '*', 'c', 'z', 'not_c']
flags_written_by_flag_write_type = {
'dummy': [],
'*': ['z', 'h', 'n', 'c'],
'c': ['c'],
'z': ['z'],
'not_c': ['z', 'h', 'n'] # eg: LR35902's DEC
}
semantic_class_for_flag_write_type = {
# by default, everything is type None (integer)
# '*': 'class_integer',
# 'c': 'class_integer',
# 'z': 'class_integer',
# 'cszpv': 'class_integer',
# 'not_c': 'class_integer'
}
# groups and their mappings
semantic_flag_groups = ['group_e', 'group_ne', 'group_lt']
flags_required_for_semantic_flag_group = {
'group_lt': ['c'],
'group_e': ['z'],
'group_ne': ['z']
}
flag_conditions_for_semantic_flag_group = {
#'group_e': {None: LowLevelILFlagCondition.LLFC_E},
#'group_ne': {None: LowLevelILFlagCondition.LLFC_NE}
}
# roles
flag_roles = {
'z': FlagRole.ZeroFlagRole,
'h': FlagRole.HalfCarryFlagRole,
'n': FlagRole.SpecialFlagRole, # set if last instruction was a subtraction (incl. CP)
'c': FlagRole.CarryFlagRole
}
# MAP (condition x class) -> flags
def get_flags_required_for_flag_condition(self, cond, sem_class):
#LogDebug('incoming cond: %s, incoming sem_class: %s' % (str(cond), str(sem_class)))
if sem_class == None:
lookup = {
# Z, zero flag for == and !=
LowLevelILFlagCondition.LLFC_E: ['z'],
LowLevelILFlagCondition.LLFC_NE: ['z'],
# Z, zero flag for == and !=
LowLevelILFlagCondition.LLFC_E: ['z'],
LowLevelILFlagCondition.LLFC_NE: ['z'],
# H, half carry for ???
# P, parity for ???
# s> s>= s< s<= done by sub and overflow test
#if cond == LowLevelILFlagCondition.LLFC_SGT:
#if cond == LowLevelILFlagCondition.LLFC_SGE:
#if cond == LowLevelILFlagCondition.LLFC_SLT:
#if cond == LowLevelILFlagCondition.LLFC_SLE:
# C, for these
LowLevelILFlagCondition.LLFC_UGE: ['c'],
LowLevelILFlagCondition.LLFC_ULT: ['c']
}
if cond in lookup:
return lookup[cond]
return []
#------------------------------------------------------------------------------
# CFG building
#------------------------------------------------------------------------------
def get_instruction_info(self, data, addr):
decoded = decode(data, addr)
# on error, return nothing
if decoded.status == DECODE_STATUS.ERROR or decoded.len == 0:
return None
# on non-branching, return length
result = InstructionInfo()
result.length = decoded.len
if decoded.typ != INSTRTYPE.JUMP_CALL_RETURN:
return result
# jp has several variations
if decoded.op == OP.JP:
(oper_type, oper_val) = decoded.operands[0]
# jp pe,0xDEAD
if oper_type == OPER_TYPE.COND:
assert decoded.operands[1][0] == OPER_TYPE.ADDR
result.add_branch(BranchType.TrueBranch, decoded.operands[1][1])
result.add_branch(BranchType.FalseBranch, addr + decoded.len)
# jp hl
elif oper_type in [OPER_TYPE.REG]:
result.add_branch(BranchType.IndirectBranch)
# jp 0xDEAD
elif oper_type == OPER_TYPE.ADDR:
result.add_branch(BranchType.UnconditionalBranch, oper_val)
else:
print(f"Missed JP handling type: {decoded}")
raise Exception('handling JP')
# jr can be conditional
elif decoded.op == OP.JR:
(oper_type, oper_val) = decoded.operands[0]
# jr c,0xdf07
if oper_type == OPER_TYPE.COND:
assert decoded.operands[1][0] == OPER_TYPE.ADDR
result.add_branch(BranchType.TrueBranch, decoded.operands[1][1])
result.add_branch(BranchType.FalseBranch, addr + decoded.len)
# jr 0xdf07
elif oper_type == OPER_TYPE.ADDR:
result.add_branch(BranchType.UnconditionalBranch, oper_val)
else:
raise Exception('handling JR')
# call can be conditional
elif decoded.op == OP.CALL:
(oper_type, oper_val) = decoded.operands[0]
# call c,0xdf07
if oper_type == OPER_TYPE.COND:
assert decoded.operands[1][0] == OPER_TYPE.ADDR
result.add_branch(BranchType.CallDestination, decoded.operands[1][1])
# call 0xdf07
elif oper_type == OPER_TYPE.ADDR:
result.add_branch(BranchType.CallDestination, oper_val)
else:
raise Exception('handling CALL')
# ret can be conditional
elif decoded.op == OP.RET:
if decoded.operands and decoded.operands[0][0] == OPER_TYPE.COND:
# conditional returns dont' end block
pass
else:
result.add_branch(BranchType.FunctionReturn)
# ret from interrupts
elif decoded.op == OP.RETI:
result.add_branch(BranchType.FunctionReturn)
return result
#------------------------------------------------------------------------------
# STRING building, disassembly
#------------------------------------------------------------------------------
def reg2str(self, reg):
reg_name = reg.name if isinstance(reg, REG) else reg
# enum AF_ should be returned as AF'
return reg_name if reg_name[-1] != '_' else reg_name[:-1]+"'"
# from api/python/function.py:
#
# TextToken Text that doesn't fit into the other tokens
# InstructionToken The instruction mnemonic
# OperandSeparatorToken The comma or whatever else separates tokens
# RegisterToken Registers
# IntegerToken Integers
# PossibleAddressToken Integers that are likely addresses
# BeginMemoryOperandToken The start of memory operand
# EndMemoryOperandToken The end of a memory operand
# FloatingPointToken Floating point number
def get_instruction_text(self, data, addr):
decoded = decode(data, addr)
if decoded.status != DECODE_STATUS.OK or decoded.len == 0:
return None
result = []
# opcode
result.append(InstructionTextToken( \
InstructionTextTokenType.InstructionToken, decoded.op.name))
# space for operand
if decoded.operands:
result.append(InstructionTextToken(InstructionTextTokenType.TextToken, ' '))
# operands
for i, operand in enumerate(decoded.operands):
(oper_type, oper_val) = operand
if oper_type == OPER_TYPE.REG:
result.append(InstructionTextToken( \
InstructionTextTokenType.RegisterToken, self.reg2str(oper_val)))
elif oper_type == OPER_TYPE.REG_DEREF:
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.RegisterToken, self.reg2str(oper_val)),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
elif oper_type in {OPER_TYPE.REG_DEREF_DEC, OPER_TYPE.REG_DEREF_INC}:
update = '-' if oper_type == OPER_TYPE.REG_DEREF_DEC else '+'
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.RegisterToken, self.reg2str(oper_val)),
(InstructionTextTokenType.TextToken, update),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
elif oper_type in {OPER_TYPE.REG_DEREF_FF00}:
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.PossibleAddressToken, '0xFF00', 0xFF00),
(InstructionTextTokenType.TextToken, '+'),
(InstructionTextTokenType.RegisterToken, self.reg2str(oper_val)),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
elif oper_type == OPER_TYPE.ADDR:
oper_val = oper_val & 0xFFFF
txt = '0x%04x' % oper_val
result.append(InstructionTextToken( \
InstructionTextTokenType.PossibleAddressToken, txt, oper_val))
elif oper_type == OPER_TYPE.ADDR_DEREF:
txt = '0x%04x' % oper_val
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.PossibleAddressToken, txt, oper_val),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
elif oper_type == OPER_TYPE.ADDR_DEREF_FF00:
val = 0xFF00 + (oper_val & 0xff)
txt = '0x{:04x}'.format(val)
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.PossibleAddressToken, txt, val),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
elif oper_type == OPER_TYPE.IMM:
if oper_val == 0:
txt = '0'
elif oper_val >= 16:
txt = '0x%x' % oper_val
else:
txt = '%d' % oper_val
result.append(InstructionTextToken( \
InstructionTextTokenType.IntegerToken, txt, oper_val))
elif oper_type == OPER_TYPE.COND:
txt = CC_TO_STR[oper_val]
result.append(InstructionTextToken( \
InstructionTextTokenType.TextToken, txt))
elif oper_type == OPER_TYPE.SP_OFFSET:
offset = '{:+02x}'.format(oper_val)
sign, offset = offset[0], offset[1:]
toks = [
(InstructionTextTokenType.BeginMemoryOperandToken, '('),
(InstructionTextTokenType.RegisterToken, 'SP'),
(InstructionTextTokenType.TextToken, sign),
(InstructionTextTokenType.IntegerToken, offset, abs(oper_val)),
(InstructionTextTokenType.EndMemoryOperandToken, ')'),
]
result.extend([InstructionTextToken(*ts) for ts in toks])
else:
raise Exception('unknown operand type: ' + str(oper_type))
# if this isn't the last operand, add comma
if i < len(decoded.operands)-1:
result.append(InstructionTextToken( \
InstructionTextTokenType.OperandSeparatorToken, ','))
return result, decoded.len
#------------------------------------------------------------------------------
# LIFTING
#------------------------------------------------------------------------------
def get_flag_write_low_level_il(self, op, size, write_type, flag, operands, il):
flag_il = LR35902IL.gen_flag_il(op, size, write_type, flag, operands, il)
if flag_il:
return flag_il
return Architecture.get_flag_write_low_level_il(self, op, size, write_type, flag, operands, il)
def get_instruction_low_level_il(self, data, addr, il):
decoded = decode(data, addr)
if decoded.status != DECODE_STATUS.OK or decoded.len == 0:
return None
LR35902IL.gen_instr_il(addr, decoded, il)
return decoded.len
| 14,178 | 4,188 |
import os
import paramiko
class SftpHandle(paramiko.SFTPHandle):
def stat(self):
try:
return paramiko.SFTPAttributes.from_stat(os.fstat(self.readfile.fileno()))
except OSError as e:
return paramiko.SFTPServer.convert_errno(e.errno)
def chattr(self, attr):
pass
| 278 | 110 |
from brownie import ETH_ADDRESS, accounts
def test_brownie():
"""Test if the brownie module is working.
"""
acc = accounts[0]
assert type(acc.address) == str
| 176 | 56 |
"""
Copyright (c) 2022 Julien Posso
"""
import torch
import optuna
from config import Config
from pose_net import POSENet
from submission import SubmissionWriter
from print_results import print_training_loss, print_training_score, print_beta_tuning, print_error_distance
import os
import numpy as np
import random
def main():
# SELECT DEVICE AUTOMATICALLY: if available, select the GPU with the most available memory, else select the CPU
if torch.cuda.is_available():
if torch.cuda.device_count() > 1:
# The following branch works with nvidia 470 drivers + cuda 11.4
# Do not work with nvidia 510 + cuda 11.6: Free memory is replaced with reserved memory
# get the GPU id with max memory available
os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >../tmp')
memory_available = [int(x.split()[2]) for x in open('../tmp', 'r').readlines()]
os.remove('../tmp') # remove the temporary file
gpu_id = np.argmax(memory_available)
device = torch.device(f"cuda:{gpu_id}")
print(f"Device used: {device} with {memory_available[gpu_id]} MB memory available")
else:
device = torch.device("cuda")
else:
device = torch.device("cpu")
print("Device used:", device)
# Create config and Pose estimation class
config = Config(device)
pose_estimation = POSENet(config)
# Set manual seeds for reproducibility. See https://pytorch.org/docs/stable/notes/randomness.html#reproducibility
torch.manual_seed(config.SEED)
random.seed(config.SEED) # Python random module.
np.random.seed(config.SEED) # Numpy module.
torch.use_deterministic_algorithms(True)
if torch.cuda.is_available():
torch.cuda.manual_seed(config.SEED)
torch.cuda.manual_seed_all(config.SEED) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ":4096:8"
# Count number of model parameters
pytorch_total_params = pose_estimation.get_n_params()
print(f"Number of trainable parameters in the model :{pytorch_total_params:,}")
# Submissions on ESA website
sub = SubmissionWriter()
if config.HPARAM_TUNING:
print("hyperparameter tuning")
sampler = optuna.samplers.TPESampler(seed=config.SEED) # Make the sampler behave in a deterministic way.
study = optuna.create_study(sampler=sampler, direction="minimize")
print(f"Sampler is {study.sampler.__class__.__name__}")
study.optimize(pose_estimation.objective, n_trials=config.N_TRIALS)
data = study.trials_dataframe(("number", "value", "intermediate_values", "datetime_start", "datetime_complete",
"duration", "params", "user_attrs", "system_attrs", "state"))
data.to_csv('../optuna_tuning/hyperparameter_tuning_result.csv', encoding='utf-8')
pruned_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.PRUNED]
complete_trials = [t for t in study.trials if t.state == optuna.structs.TrialState.COMPLETE]
print("Study statistics: ")
print(" Number of finished trials: ", len(study.trials))
print(" Number of pruned trials: ", len(pruned_trials))
print(" Number of complete trials: ", len(complete_trials))
print("Best trial:")
trial = study.best_trial
print(" Value: ", trial.value)
print(" Params: ")
for key, value in trial.params.items():
print(" {}: {}".format(key, value))
if config.TRAINING:
print("Training...")
model, loss, score = pose_estimation.train()
# Save model
model.cpu()
torch.save(model.state_dict(), config.MODEL_PATH)
# Print training
print_training_loss(loss, show=False, save=True)
print_training_score(score, show=False, save=True)
else:
# If not training, try to load the model from config.MODEL_PATH instead of saving the model to config.MODEL_PATH
model = pose_estimation.get_model()
model.load_state_dict(torch.load(config.MODEL_PATH))
# Move model to GPU if needed and prepare it for evaluation
model.to(config.DEVICE)
model.eval()
if config.EVALUATION:
print("Evaluation on valid and real set")
pose_estimation.evaluate('valid')
pose_estimation.evaluate('real')
if config.EVAL_SUBMIT:
print("Evaluation for submission...")
pose_estimation.evaluate_submit(sub)
sub.export(out_dir='../submissions/', suffix="pytorch")
sub.reset()
if config.EVAL_DISTANCE:
print("Evaluate by distance...")
ori_err, pos_err, distance = pose_estimation.eval_error_distance()
print_error_distance(ori_err, pos_err, distance, show=False, save=True)
print("The end!")
if __name__ == '__main__':
main()
| 5,023 | 1,598 |
from enum import IntEnum, unique
"""Stores the unique values for interpretting message signatures and command types.
Extra key-value pairs have been added to simply to demonstrate how to modify this file. Not all key-value pairs have not implemented.
"""
@unique
class MessageSource(IntEnum):
UNKNOWN = 0
TEST_PROCESS = 1 # Used in testing modules
SOURCE2 = 2 # Example of an additional source. Change name appropriately
@unique
class MessageCommand(IntEnum):
CLOSE_COM = 0
XMIT_DATA = 1
ERROR = 2
| 526 | 156 |
"""fontTools.misc.fixedTools.py -- tools for working with fixed numbers.
"""
from __future__ import print_function, division, absolute_import
from fontTools.misc.py23 import *
__all__ = [
"fixedToFloat",
"floatToFixed",
]
def fixedToFloat(value, precisionBits):
"""Converts a fixed-point number to a float, choosing the float
that has the shortest decimal reprentation. Eg. to convert a
fixed number in a 2.14 format, use precisionBits=14. This is
pretty slow compared to a simple division. Use sporadically.
>>> fixedToFloat(13107, 14)
0.8
>>> fixedToFloat(0, 14)
0.0
>>> fixedToFloat(0x4000, 14)
1.0
"""
if not value: return 0.0
scale = 1 << precisionBits
value /= scale
eps = .5 / scale
digits = (precisionBits + 2) // 3
fmt = "%%.%df" % digits
lo = fmt % (value - eps)
hi = fmt % (value + eps)
out = []
length = min(len(lo), len(hi))
for i in range(length):
if lo[i] != hi[i]:
break;
out.append(lo[i])
outlen = len(out)
if outlen < length:
out.append(max(lo[outlen], hi[outlen]))
return float(strjoin(out))
def floatToFixed(value, precisionBits):
"""Converts a float to a fixed-point number given the number of
precisionBits. Ie. int(round(value * (1<<precisionBits))).
>>> floatToFixed(0.8, 14)
13107
>>> floatToFixed(1.0, 14)
16384
>>> floatToFixed(1, 14)
16384
>>> floatToFixed(0, 14)
0
"""
return int(round(value * (1<<precisionBits)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 1,482 | 627 |
from action_hero.utils import PipelineAction, DebugAction
from action_hero.net import (
EmailIsValidAction,
IPIsValidIPAddressAction,
IPIsValidIPv4AddressAction,
IPIsValidIPv6AddressAction,
URLIsNotReachableAction,
URLIsReachableAction,
URLWithHTTPResponseStatusCodeAction,
)
from action_hero.path import (
DirectoryDoesNotExistAction,
DirectoryExistsAction,
DirectoryIsExecutableAction,
DirectoryIsNotExecutableAction,
DirectoryIsNotReadableAction,
DirectoryIsNotWritableAction,
DirectoryIsReadableAction,
DirectoryIsValidAction,
DirectoryIsWritableAction,
EnsureDirectoryAction,
EnsureFileAction,
FileDoesNotExistAction,
FileExistsAction,
FileHasExtensionAction,
FileIsEmptyAction,
FileIsExecutableAction,
FileIsNotEmptyAction,
FileIsNotExecutableAction,
FileIsNotReadableAction,
FileIsNotWritableAction,
FileIsReadableAction,
FileIsWritableAction,
PathDoesNotExistsAction,
PathExistsAction,
PathIsExecutableAction,
PathIsNotExecutableAction,
PathIsNotReadableAction,
PathIsNotWritableAction,
PathIsReadableAction,
PathIsValidAction,
PathIsWritableAction,
ResolvePathAction,
)
from action_hero.types import (
IsConvertibleToFloatAction,
IsConvertibleToIntAction,
IsConvertibleToUUIDAction,
IsFalsyAction,
IsTruthyAction,
)
from action_hero.misc import (
ChoicesAction,
CollectIntoDictAction,
CollectIntoListAction,
CollectIntoTupleAction,
ConfirmAction,
GetInputAction,
GetSecretInputAction,
LoadJSONFromFileAction,
LoadPickleFromFileAction,
LoadYAMLFromFileAction,
NotifyAndContinueAction,
NotifyAndExitAction,
)
__all__ = [
# utils
"PipelineAction",
"DebugAction",
# net & email
"EmailIsValidAction",
"IPIsValidIPAddressAction",
"IPIsValidIPv4AddressAction",
"IPIsValidIPv6AddressAction",
"URLIsNotReachableAction",
"URLIsReachableAction",
"URLWithHTTPResponseStatusCodeAction",
# path
"DirectoryDoesNotExistAction",
"DirectoryExistsAction",
"DirectoryIsExecutableAction",
"DirectoryIsNotExecutableAction",
"DirectoryIsNotReadableAction",
"DirectoryIsNotWritableAction",
"DirectoryIsReadableAction",
"DirectoryIsValidAction",
"DirectoryIsWritableAction",
"EnsureDirectoryAction",
"EnsureFileAction",
"FileDoesNotExistAction",
"FileExistsAction",
"FileHasExtensionAction",
"FileIsEmptyAction",
"FileIsExecutableAction",
"FileIsNotEmptyAction",
"FileIsNotExecutableAction",
"FileIsNotReadableAction",
"FileIsNotWritableAction",
"FileIsReadableAction",
"FileIsValidAction",
"FileIsWritableAction",
"PathDoesNotExistsAction",
"PathExistsAction",
"PathIsExecutableAction",
"PathIsNotExecutableAction",
"PathIsNotReadableAction",
"PathIsNotWritableAction",
"PathIsReadableAction",
"PathIsValidAction",
"PathIsWritableAction",
"ResolvePathAction",
# types
"IsConvertibleToFloatAction",
"IsConvertibleToIntAction",
"IsConvertibleToUUIDAction",
"IsFalsyAction",
"IsTruthyAction",
# misc
"ChoicesAction",
"CollectIntoDictAction",
"CollectIntoListAction",
"CollectIntoTupleAction",
"ConfirmAction",
"GetInputAction",
"GetSecretInputAction",
"LoadJSONFromFileAction",
"LoadPickleFromFileAction",
"LoadYAMLFromFileAction",
"NotifyAndContinueAction",
"NotifyAndExitAction",
]
| 3,548 | 1,114 |
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
# import selenium exceptions module
from selenium.common.exceptions import *
browser = webdriver.Chrome()
browser.get("http://www.seleniumframework.com/python-course/")
browser.maximize_window()
browser.implicitly_wait(10)
try:
element = WebDriverWait(browser, 10).until(
EC.presence_of_all_elements_located((By.TAG_NAME, "randomtag"))
)
except TimeoutException:
print("Encounted timeout while waiting for the element")
else:
print("Successfully located all the elements by tag name")
browser.quit() | 725 | 203 |
"""Stacker custom lookup to get a Cognito User Pool App Client Secret."""
import logging
from stacker.session_cache import get_session
TYPE_NAME = 'CognitoUserPoolAppClientSecret'
LOGGER = logging.getLogger(__name__)
def handler(value, provider, **kwargs): # pylint: disable=W0613
""" Lookup a Cognito User Pool App Client secret by UserPoolId::AppClientId.
Need to specify the Cognito User Pool ID and App Client ID
Region is obtained from the environment file
[in the environment file]:
region: us-west-2
For example:
[in the stacker yaml (configuration) file]:
lookups:
CognitoUserPoolAppClientSecret: lookups.instance-attribute-by-name-tag-lookup.handler
stacks:
variables:
AppClientSecret: ${CognitoUserPoolAppClientSecret ${user-pool-id}::${app-client-id}}
"""
user_pool_id = value.split('::')[0]
app_client_id = value.split('::')[1]
session = get_session(provider.region)
cognito_client = session.client('cognito-idp')
try:
desc_user_pool_client_output = cognito_client.describe_user_pool_client(
ClientId=app_client_id,
UserPoolId=user_pool_id)
except Exception as e:
LOGGER.error('could not describe user pool client: %s', e)
return 'error: could not describe user pool client'
secret = desc_user_pool_client_output['UserPoolClient'].get('ClientSecret')
if secret:
LOGGER.debug('found user pool app client secret')
return secret
else:
LOGGER.debug('did not find user pool app client secret')
return 'not found'
| 1,670 | 488 |
# Generated by Django 3.0.7 on 2020-11-18 11:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('core', '0088_matierepremiere_is_huile_vegetale'),
]
operations = [
migrations.AddField(
model_name='depot',
name='address',
field=models.CharField(default='', max_length=128),
preserve_default=False,
),
migrations.AddField(
model_name='depot',
name='ownership_type',
field=models.CharField(choices=[('OWN', 'Propre'), ('THIRD_PARTY', 'Tiers')], default='THIRD_PARTY', max_length=32),
),
migrations.AddField(
model_name='depot',
name='postal_code',
field=models.CharField(default='', max_length=32),
preserve_default=False,
),
]
| 884 | 285 |
r"""2018 - Day 7 Part 1: The Sum of Its Parts.
You find yourself standing on a snow-covered coastline; apparently, you landed
a little off course. The region is too hilly to see the North Pole from here,
but you do spot some Elves that seem to be trying to unpack something that
washed ashore. It's quite cold out, so you decide to risk creating a paradox by
asking them for directions.
"Oh, are you the search party?" Somehow, you can understand whatever Elves from
the year 1018 speak; you assume it's Ancient Nordic Elvish. Could the device on
your wrist also be a translator? "Those clothes don't look very warm; take
this." They hand you a heavy coat.
"We do need to find our way back to the North Pole, but we have higher
priorities at the moment. You see, believe it or not, this box contains
something that will solve all of Santa's transportation problems - at least,
that's what it looks like from the pictures in the instructions." It doesn't
seem like they can read whatever language it's in, but you can: "Sleigh kit.
Some assembly required."
"'Sleigh'? What a wonderful name! You must help us assemble this 'sleigh' at
once!" They start excitedly pulling more parts out of the box.
The instructions specify a series of steps and requirements about which steps
must be finished before others can begin (your puzzle input). Each step is
designated by a single letter. For example, suppose you have the following
instructions:
Step C must be finished before step A can begin.
Step C must be finished before step F can begin.
Step A must be finished before step B can begin.
Step A must be finished before step D can begin.
Step B must be finished before step E can begin.
Step D must be finished before step E can begin.
Step F must be finished before step E can begin.
Visually, these requirements look like this:
-->A--->B--
/ \ \
C -->D----->E
\ /
---->F-----
Your first goal is to determine the order in which the steps should be
completed. If more than one step is ready, choose the step which is first
alphabetically. In this example, the steps would be completed as follows:
Only C is available, and so it is done first.
Next, both A and F are available. A is first alphabetically, so it is done
next.
Then, even though F was available earlier, steps B and D are now also
available, and B is the first alphabetically of the three.
After that, only D and F are available. E is not available because only
some of its prerequisites are complete. Therefore, D is completed next.
F is the only choice, so it is done next.
Finally, E is completed.
So, in this example, the correct order is CABDFE.
In what order should the steps in your instructions be completed?
"""
from collections import defaultdict
from string import ascii_uppercase
from typing import DefaultDict
from typing import Generator
from typing import Optional
from typing import Set
Step = str
Parents = DefaultDict[Step, Set[Step]]
StepGenerator = Generator[Step, None, None]
def process_date(data: str) -> Parents:
"""Generate a dict of step: parents from raw data."""
parents: Parents = defaultdict(set)
for line in data.strip().split("\n"):
parent, child = line[5], line[36]
parents[child].add(parent)
return parents
def next_step(
parents: Parents, done: Set[Step], todo: Set[Step]
) -> Optional[Step]:
"""Get next available step to take."""
ready = set()
for step in todo:
if parents[step].issubset(done):
ready.add(step)
return min(ready) if ready else None
def ordered_steps(parents: Parents, steps: str) -> StepGenerator:
"""Yield next available step in the correct order."""
done: Set[Step] = set()
todo: Set[Step] = set(steps)
while todo:
new_step = next_step(parents, done, todo)
if new_step:
yield new_step
done.add(new_step)
todo.remove(new_step)
def solve(task: str, steps=ascii_uppercase) -> str:
"""Find the sequence of steps."""
parents = process_date(task)
return "".join(ordered_steps(parents, steps))
| 4,202 | 1,169 |
import pytest
from riotwatcher import TftWatcher
@pytest.mark.tft
@pytest.mark.usefixtures("reset_globals")
class TestTftWatcher:
def test_require_api_key(self):
with pytest.raises(ValueError):
TftWatcher()
def test_allows_positional_api_key(self):
TftWatcher("RGAPI-this-is-a-fake")
def test_allows_keyword_api_key(self):
TftWatcher(api_key="RGAPI-this-is-a-fake")
| 419 | 164 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy
import copy
def get_loss_from_z(model, z, t, reduction):
if model.name_loss == 'multi-class classification':
criterion = torch.nn.CrossEntropyLoss()
loss = criterion(z, t.type(torch.LongTensor).to(z.device))
elif model.name_loss == 'binary classification':
loss = torch.nn.BCEWithLogitsLoss(reduction = reduction)(z, t.float().unsqueeze_(1))
if reduction == 'none':
loss = loss.squeeze(1)
elif model.name_loss == 'logistic-regression':
if reduction == 'none':
loss = torch.nn.BCEWithLogitsLoss(reduction = reduction)(z, t.float())
loss = torch.sum(loss, dim=1)
elif reduction == 'mean':
loss = torch.nn.BCEWithLogitsLoss(reduction = 'sum')(z, t.float())
loss = loss / z.size(0) / z.size(1)
elif reduction == 'sum':
loss = torch.nn.BCEWithLogitsLoss(reduction = reduction)(z, t.float())
elif model.name_loss == 'logistic-regression-sum-loss':
if reduction == 'none':
loss = torch.nn.BCEWithLogitsLoss(reduction = reduction)(z, t.float())
loss = torch.sum(loss, dim=1)
elif reduction == 'mean':
loss = torch.nn.BCEWithLogitsLoss(reduction = 'sum')(z, t.float())
loss = loss / z.size(0)
elif reduction == 'sum':
loss = torch.nn.BCEWithLogitsLoss(reduction = reduction)(z, t.float())
elif model.name_loss == 'linear-regression-half-MSE':
if reduction == 'mean':
loss = torch.nn.MSELoss(reduction = 'sum')(z, t) / 2
loss = loss / z.size(0)
elif reduction == 'none':
loss = torch.nn.MSELoss(reduction = 'none')(z, t) / 2
loss = torch.sum(loss, dim=1)
elif model.name_loss == 'linear-regression':
if reduction == 'mean':
loss = torch.nn.MSELoss(reduction = 'sum')(z, t)
loss = loss / z.size(0)
elif reduction == 'none':
loss = torch.nn.MSELoss(reduction = 'none')(z, t)
loss = torch.sum(loss, dim=1)
return loss
def get_zero_torch(params):
layers_params = params['layers_params']
device = params['device']
delta = []
for l in range(len(layers_params)):
delta_l = {}
delta_l['W'] = torch.zeros(layers_params[l]['output_size'], layers_params[l]['input_size'], device=device)
delta_l['b'] = torch.zeros(layers_params[l]['output_size'], device=device)
delta.append(delta_l)
return delta
def get_subtract(model_grad, delta, params):
diff_p = get_zero(params)
for l in range(params['numlayers']):
for key in diff_p[l]:
diff_p[l][key] = np.subtract(model_grad[l][key], delta[l][key])
return diff_p
def get_subtract_torch(model_grad, delta):
diff_p = []
for l in range(len(model_grad)):
diff_p_l = {}
for key in model_grad[l]:
diff_p_l[key] = torch.sub(model_grad[l][key], delta[l][key])
diff_p.append(diff_p_l)
return diff_p
def get_plus(model_grad, delta):
sum_p = []
for l in range(len(model_grad)):
sum_p_l = {}
for key in model_grad[l]:
sum_p_l[key] = np.add(model_grad[l][key], delta[l][key])
sum_p.append(sum_p_l)
return sum_p
def get_plus_torch(model_grad, delta):
sum_p = []
for l in range(len(model_grad)):
sum_p_l = {}
for key in model_grad[l]:
sum_p_l[key] = model_grad[l][key] + delta[l][key]
sum_p.append(sum_p_l)
return sum_p
def get_if_nan(p):
for l in range(len(p)):
for key in p[l]:
if torch.sum(p[l][key] != p[l][key]):
return True
return False
def get_torch_tensor(p, params):
p_torch = []
for l in range(len(p)):
p_torch_l = {}
for key in p[l]:
p_torch_l[key] = torch.from_numpy(p[l][key]).to(params['device'])
p_torch.append(p_torch_l)
return p_torch
def get_plus_scalar(alpha, model_grad):
sum_p = []
for l in range(len(model_grad)):
sum_p_l = {}
for key in model_grad[l]:
sum_p_l[key] = model_grad[l][key] + alpha
sum_p.append(sum_p_l)
return sum_p
def get_multiply_scalar(alpha, delta):
alpha_p = []
for l in range(len(delta)):
alpha_p_l = {}
for key in delta[l]:
alpha_p_l[key] = alpha * delta[l][key]
alpha_p.append(alpha_p_l)
return alpha_p
def get_multiply_scalar_no_grad(alpha, delta):
alpha_p = []
for l in range(len(delta)):
alpha_p_l = {}
for key in delta[l]:
alpha_p_l[key] = alpha * delta[l][key].data
alpha_p.append(alpha_p_l)
return alpha_p
def get_multiply_scalar_blockwise(alpha, delta, params):
alpha_p = []
for l in range(params['numlayers']):
alpha_p_l = {}
for key in delta[l]:
alpha_p_l[key] = alpha[l] * delta[l][key]
alpha_p.append(alpha_p_l)
return alpha_p
def get_multiply_torch(alpha, delta):
alpha_p = []
for l in range(len(delta)):
alpha_p_l = {}
for key in delta[l]:
alpha_p_l[key] = torch.mul(alpha[l][key], delta[l][key])
alpha_p.append(alpha_p_l)
return alpha_p
def get_multiply(alpha, delta):
alpha_p = []
for l in range(len(delta)):
alpha_p_l = {}
for key in delta[l]:
alpha_p_l[key] = np.multiply(alpha[l][key], delta[l][key])
alpha_p.append(alpha_p_l)
return alpha_p
def get_weighted_sum_batch(hat_v, batch_grads_test, params):
alpha_p = get_zero(params)
for l in range(params['numlayers']):
alpha_p['W'][l] = np.sum(hat_v[:, None, None] * batch_grads_test['W'][l], axis=0)
alpha_p['b'][l] = np.sum(hat_v[:, None] * batch_grads_test['b'][l], axis=0)
return alpha_p
def get_opposite(delta):
numlayers = len(delta)
p = []
for l in range(numlayers):
p_l = {}
for key in delta[l]:
p_l[key] = -delta[l][key]
p.append(p_l)
return p
def get_model_grad(model, params):
model_grad_torch = []
for l in range(model.numlayers):
model_grad_torch_l = {}
for key in model.layers_weight[l]:
model_grad_torch_l[key] = copy.deepcopy(model.layers_weight[l][key].grad)
model_grad_torch.append(model_grad_torch_l)
return model_grad_torch
def get_regularized_loss_and_acc_from_x_whole_dataset(model, x, t, reduction, params):
N1 = params['N1']
N1 = np.minimum(N1, len(x))
i = 0
device = params['device']
list_loss = []
list_acc = []
model.eval()
while i + N1 <= len(x):
# with torch.no_grad():
z, test_a, test_h = model.forward(torch.from_numpy(x[i: i+N1]).to(device))
torch_t_mb = torch.from_numpy(t[i: i+N1]).to(params['device'])
list_loss.append(
get_regularized_loss_from_z(model, z, torch_t_mb,
reduction, params['tau']).item())
list_acc.append(
get_acc_from_z(model, params, z, torch_t_mb))
i += N1
model.train()
return sum(list_loss) / len(list_loss), sum(list_acc) / len(list_acc)
def get_regularized_loss_from_z(model, z, t, reduction, tau):
loss = get_loss_from_z(model, z, t, reduction)
loss += 0.5 * tau *\
get_dot_product_torch(model.layers_weight, model.layers_weight)
return loss
def get_if_stop(args, i, iter_per_epoch, timesCPU):
if args['if_max_epoch']:
if i < int(args['max_epoch/time'] * iter_per_epoch):
return False
else:
return True
else:
if timesCPU[-1] < args['max_epoch/time']:
return False
else:
return True
def get_square(delta_1):
numlayers = len(delta_1)
sqaure_p = []
for l in range(numlayers):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = np.square(delta_1[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_square_torch(delta_1):
numlayers = len(delta_1)
sqaure_p = []
for l in range(numlayers):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = torch.mul(delta_1[l][key], delta_1[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_sqrt(delta_1):
sqaure_p = []
for l in range(len(delta_1)):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = np.sqrt(delta_1[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_sqrt_torch(delta_1):
sqaure_p = []
for l in range(len(delta_1)):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = torch.sqrt(delta_1[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_max_with_0(delta_1):
sqaure_p = []
for l in range(len(delta_1)):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = F.relu(delta_1[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_divide(delta_1, delta_2):
numlayers = len(delta_1)
sqaure_p = []
for l in range(numlayers):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = np.divide(delta_1[l][key], delta_2[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_divide_torch(delta_1, delta_2):
numlayers = len(delta_1)
sqaure_p = []
for l in range(numlayers):
sqaure_p_l = {}
for key in delta_1[l]:
sqaure_p_l[key] = torch.div(delta_1[l][key], delta_2[l][key])
sqaure_p.append(sqaure_p_l)
return sqaure_p
def get_dot_product_torch(delta_1, delta_2):
dot_product = 0
for l in range(len(delta_1)):
for key in delta_1[l]:
dot_product += torch.sum(torch.mul(delta_1[l][key], delta_2[l][key]))
return dot_product
def get_dot_product_blockwise_torch(delta_1, delta_2):
dot_product = []
for l in range(len(delta_1)):
dot_product_l = 0
for key in delta_1[l]:
dot_product_l += torch.sum(torch.mul(delta_1[l][key], delta_2[l][key]))
dot_product.append(dot_product_l)
return dot_product
def get_dot_product_batch(model_grad, batch_grads_test, params):
# numlayers = params['numlayers']
dot_product = np.zeros(len(batch_grads_test['W'][0]))
for l in range(params['numlayers']):
dot_product += np.sum(
np.sum(np.multiply(model_grad['W'][l][None, :], batch_grads_test['W'][l]), axis=-1), axis=-1)
dot_product += np.sum(np.multiply(model_grad['b'][l][None, :], batch_grads_test['b'][l]), axis=-1)
return dot_product
def get_acc_from_z(model, params, z, torch_t):
if model.name_loss == 'multi-class classification':
y = z.argmax(dim=1)
acc = torch.mean((y == torch_t).float())
elif model.name_loss == 'binary classification':
z_1 = torch.sigmoid(z)
y = (z_1 > 0.5)
y = y[:, 0]
acc = np.mean(y.cpu().data.numpy() == np_t)
elif model.name_loss in ['logistic-regression',
'logistic-regression-sum-loss']:
z_sigmoid = torch.sigmoid(z)
criterion = nn.MSELoss(reduction = 'mean')
acc = criterion(z_sigmoid, torch_t)
elif model.name_loss in ['linear-regression',
'linear-regression-half-MSE']:
acc = nn.MSELoss(reduction = 'mean')(z, torch_t)
acc = acc.item()
return acc
def get_homo_grad(model_grad_N1, params):
device = params['device']
homo_model_grad_N1 = []
for l in range(params['numlayers']):
homo_model_grad_N1_l = torch.cat((model_grad_N1[l]['W'], model_grad_N1[l]['b'].unsqueeze(1)), dim=1)
homo_model_grad_N1.append(homo_model_grad_N1_l)
return homo_model_grad_N1
| 12,140 | 4,570 |
print("Hello")
1 / 0
print("world!")
| 38 | 18 |
from db_manager import db
class MilkCollection(db.Model):
id = db.Column(db.Integer, primary_key=True)
shift = db.Column(db.String(80))
member_id = db.Column(db.Integer, db.ForeignKey('member.id'))
member = db.relationship('Member',
backref=db.backref('milk_collections', lazy='dynamic'))
fat = db.Column(db.Float(precision=2))
snf = db.Column(db.Float(precision=2))
qty = db.Column(db.Float(precision=2))
clr = db.Column(db.Float(precision=2))
aw = db.Column(db.Float(precision=2))
rate = db.Column(db.Float(precision=2))
total = db.Column(db.Float(precision=2))
can_no = db.Column(db.Integer)
created_at = db.Column(db.DateTime, nullable=False)
created_by = db.Column(db.Integer, nullable=False)
updated_at = db.Column(db.DateTime, nullable=True)
updated_by = db.Column(db.Integer, nullable=True)
status = db.Column(db.Boolean, default=True) | 924 | 337 |
'''
- Leetcode problem: 352
- Difficulty: Hard
- Brief problem description:
Given a data stream input of non-negative integers a1, a2, ..., an, ..., summarize the numbers seen so far as a list of
disjoint intervals.
For example, suppose the integers from the data stream are 1, 3, 7, 2, 6, ..., then the summary will be:
[1, 1]
[1, 1], [3, 3]
[1, 1], [3, 3], [7, 7]
[1, 3], [7, 7]
[1, 3], [6, 7]
Follow up:
What if there are lots of merges and the number of disjoint intervals are small compared to the data stream's size?
- Solution Summary:
- Used Resources:
--- Bo Zhou
'''
class SummaryRanges:
def __init__(self):
"""
Initialize your data structure here.
"""
self.ih = [] # interval heap
def addNum(self, val: int) -> None:
heapq.heappush(self.ih, [val, val])
def getIntervals(self) -> List[List[int]]:
newh = []
while self.ih:
newInter = heapq.heappop(self.ih)
if newh and newh[-1][1] + 1 >= newInter[0]:
newh[-1][1] = max(newh[-1][1], newInter[1])
else:
heapq.heappush(newh, newInter)
self.ih = newh
return self.ih
# Your SummaryRanges object will be instantiated and called as such:
# obj = SummaryRanges()
# obj.addNum(val)
# param_2 = obj.getIntervals() | 1,332 | 481 |
from mongo_test.utils.dal.identity_dal import *
from mongo_test.utils.dal.object_dal import *
from mongo_test.utils.dal.detection_dal import *
from mongo_test.utils.dal.user_dal import *
from mongo_test.utils.dal.process_dal import *
from mongo_test.utils.dal.camera_dal import *
from mongo_test.utils.dal.logger_dal import *
from mongo_test.utils.dal.cluster_dal import *
from mongo_test.utils.dal.cluster_element_dal import *
from mongo_test.utils.dal.parameter_dal import * | 476 | 154 |
# flake8: noqa
from .stdout import StdOut
| 42 | 17 |
# vim:fileencoding=utf-8
import os
import vim
import socket
import struct
import contextlib
fcitxsocketfile = vim.eval('s:fcitxsocketfile')
class FcitxComm(object):
STATUS = struct.pack('i', 0)
ACTIVATE = struct.pack('i', 1 | (1 << 16))
DEACTIVATE = struct.pack('i', 1)
INT_SIZE = struct.calcsize('i')
def __init__(self, socketfile):
if socketfile[0] == '@': # abstract socket
socketfile = '\x00' + socketfile[1:]
self.socketfile = socketfile
self.sock = None
def status(self):
return self._with_socket(self._status) == 2
def activate(self):
self._with_socket(self._command, self.ACTIVATE)
def deactivate(self):
self._with_socket(self._command, self.DEACTIVATE)
def _error(self, e):
estr = str(e).replace('"', r'\"')
file = self.socketfile.replace('"', r'\"')
vim.command('echohl WarningMsg | echo "fcitx.vim: socket %s error: %s" | echohl NONE' % (file, estr))
def _connect(self):
self.sock = sock = socket.socket(socket.AF_UNIX)
sock.settimeout(0.5)
try:
sock.connect(self.socketfile)
return True
except (socket.error, socket.timeout) as e:
self._error(e)
return False
def _with_socket(self, func, *args, **kwargs):
# fcitx doesn't support connection reuse
if not self._connect():
return
with contextlib.closing(self.sock):
try:
return func(*args, **kwargs)
except (socket.error, socket.timeout, struct.error) as e:
self._error(e)
def _status(self):
self.sock.send(self.STATUS)
return struct.unpack('i', self.sock.recv(self.INT_SIZE))[0]
def _command(self, cmd):
self.sock.send(cmd)
Fcitx = FcitxComm(fcitxsocketfile)
def fcitx2en():
if Fcitx.status():
vim.command('let b:inputtoggle = 1')
Fcitx.deactivate()
def fcitx2zh():
if vim.eval('exists("b:inputtoggle")') == '1':
if vim.eval('b:inputtoggle') == '1':
Fcitx.activate()
vim.command('let b:inputtoggle = 0')
else:
vim.command('let b:inputtoggle = 0')
| 2,032 | 761 |
# -*- coding: utf-8 -*-
__author__ = "R. Bauer"
__copyright__ = "MedPhyDO - Machbarkeitsstudien des Instituts für Medizinische Strahlenphysik und Strahlenschutz am Klinikum Dortmund im Rahmen von Bachelor und Masterarbeiten an der TU-Dortmund / FH-Dortmund"
__credits__ = ["R.Bauer", "K.Loot"]
__license__ = "MIT"
__version__ = "0.1.0"
__status__ = "Prototype"
import matplotlib.pyplot as plt
from isp.plot import plotClass
import logging
logger = logging.getLogger( "MQTT" )
class ispCheckClass( plotClass ):
""" Hilfsfunktionen für alle check Module
Attributes
----------
image : instance of BaseImage
baseImage : instance of BaseImage
das zum normalisieren zu verwendende Bild
infos : dict
die infos aus self.image.infos
checkField : dict
die für die Tests zu verwendende Bildinformatioen
baseField : dict
die für das normalisieren zu verwendende Bildinformatioen
"""
def __init__( self, image=None, baseImage=None, normalize="none" ):
"""Check Klasse initialisieren
"""
#self.checkField = None
self.image = image
self.baseImage = baseImage
#self.baseField = None
self.infos = None
# ist auch das baseImage da dann ggf normalisieren
if not self.baseImage == None:
self.normalize( normalize )
# infos auch über die eigene Klasse erreichbar machen
if not self.image == None:
self.infos = self.image.infos
#print("ispCheckClass.__init__",self.baseImage, normalize)
def show(self):
'''
Ruft plt.show auf um die erzeugten Grafiken auszugeben
Returns
-------
None.
'''
plt.show()
def normalize( self, normalize: str="diff" ):
'''Normalisiert checkField mit baseField
in self.image.array liegen anschließend die normalisierten Daten
Parameters
----------
normalize : str, optional
Art der Normalisierung. The default is "diff".
- none: keine Normalisierung durchführen
- diff: test / open
- prozent: (test - open) / open
Returns
-------
None.
'''
# image.array als image.arrayOriginal merken
self.image.arrayOriginal = self.image.array.copy()
#print("### ispCheckClass.normalize", self.image, self.baseImage, normalize)
"""
if basefilename:
if self.debug:
print("---------------------------")
print("OpenImage: %s, min: %1.3f, max: %1.3f, DPMM: %1.3f, DPI: %1.3f, CAX-x: %1.3f CAX-y:%1.3f"
% (self.openfilename, np.amin(openImg.array), np.amax(openImg.array),
openImg.dpmm, openImg.dpi, openImg.cax.x, openImg.cax.y ) )
self.printMetaInfo( openImg.metadata )
if self.debug:
print("---------------------------")
print("CheckImage: %s, min: %1.3f, max: %1.3f, DPMM: %1.3f, DPI: %1.3f, CAX-x: %1.3f CAX-y:%1.3f"
% (testfilename, np.amin(checkImage.array), np.amax(checkImage.array),
checkImage.dpmm, checkImage.dpi, checkImage.cax.x, checkImage.cax.y ) )
self.printMetaInfo( checkImage.metadata )
"""
base = self.baseImage.array.copy()
check = self.image.array.copy()
if normalize == "diff":
# Beide Arrays um 0.000001 erhöhen und geschlossenes durch offene teilen
self.image.array = (check + 0.000001) / (base + 0.000001)
elif normalize == "prozent":
self.image.array = ( (check + 0.000001) - (base + 0.000001) ) / (base + 0.000001)
def getMeanDose( self, field=None ):
"""Die mittlere Dosis eines Angegebenen Bereichs ermitteln
"""
if not field: # pragma: no cover
field = { "X1":-2, "X2": 2, "Y1": -2, "Y2":2 }
# holt den angegebenen Bereich um dort die Dosis zu bestimmen
roi = self.image.getRoi( field ).copy()
#print( roi.mean() )
return roi.mean()
#print( self.metadata )
| 4,373 | 1,399 |
# Generated by Django 3.1.2 on 2020-10-28 05:15
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Contribution',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('description', models.CharField(max_length=200)),
],
options={
'ordering': ['description'],
},
),
migrations.CreateModel(
name='Skill',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=30)),
('experience', models.CharField(choices=[('Training', 'Training'), ('Novice', 'Novice'), ('Intermediate', 'Intermediate'), ('Expert', 'Expert')], default='Training', max_length=30)),
],
options={
'ordering': ['name'],
},
),
migrations.CreateModel(
name='Project',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=40)),
('skill', models.ManyToManyField(to='resume.Skill')),
],
options={
'ordering': ['name'],
},
),
migrations.CreateModel(
name='Job',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('name', models.CharField(max_length=40)),
('title', models.CharField(max_length=40)),
('type', models.CharField(max_length=40)),
('company', models.CharField(max_length=30)),
('client', models.CharField(max_length=30)),
('start_date', models.DateField()),
('end_date', models.DateField(null=True)),
('contribution', models.ManyToManyField(to='resume.Contribution')),
('project', models.ManyToManyField(to='resume.Project')),
('skill', models.ManyToManyField(to='resume.Skill')),
],
options={
'ordering': ['start_date'],
},
),
]
| 2,499 | 677 |
# -*- coding: utf-8 -*-
import hashlib
import base64
from Crypto.Cipher import AES
import json
from src.services.utils import Request
class WXAPPError(Exception):
def __init__(self, code, description):
self.code = code
self.description = description
def __str__(self):
return '%s: %s' % (self.code, self.description)
class WXAPPAPI(object):
# 默认https
host = "api.weixin.qq.com"
def __init__(self, appid=None, app_secret=None):
self.appid = appid
self.app_secret = app_secret
def pre_params(self):
return dict(secret=self.app_secret,
appid=self.appid)
def jscode2session(self, js_code):
path = '/sns/jscode2session'
params = self.pre_params()
params.update(js_code=js_code,
grant_type='authorization_code')
response = Request.get(self.host, path, params)
content = json.loads(response.content.decode())
if content.get('errcode', 0):
raise WXAPPError(content.get('errcode', 0),
content.get("errmsg", ""))
return content
def client_credential_for_access_token(self):
path = '/cgi-bin/token'
params = self.pre_params()
params.update(grant_type='client_credential')
response = Request.get(self.host, path, params)
content = json.loads(response.content.decode())
if content.get('errcode', 0):
raise WXAPPError(content.get('errcode', 0),
content.get("errmsg", ""))
return content
def getwxacode(self, access_token, page_path):
# 接口A 数量有限 A＋C 100000个
path = '/wxa/getwxacode?access_token=%s' % access_token
params = {
'path': page_path,
}
response = Request.post(self.host, path, params)
try:
content = json.loads(response.content.decode())
if content.get('errcode', 0):
raise WXAPPError(content.get('errcode', 0),
content.get("errmsg", ""))
return content, None
except:
return base64.standard_b64encode(response.content), len(response.content)
def getwxacodeunlimit(self, access_token, scene):
# 接口B 数量无限 scene strint(32)
path = '/wxa/getwxacodeunlimit?access_token=%s' % access_token
params = {
'scene': scene,
}
response = Request.post(self.host, path, params)
try:
content = json.loads(response.content.decode())
if content.get('errcode', 0):
raise WXAPPError(content.get('errcode', 0),
content.get("errmsg", ""))
return content, None
except:
return base64.standard_b64encode(response.content), len(response.content)
def createwxaqrcode(self, access_token, page_path):
# 接口C 数量有限 A＋C 100000个
path = '/cgi-bin/wxaapp/createwxaqrcode?access_token=%s' % access_token
params = {
'path': page_path,
}
response = Request.post(self.host, path, params)
try:
content = json.loads(response.content.decode())
if content.get('errcode', 0):
raise WXAPPError(content.get('errcode', 0),
content.get("errmsg", ""))
return content, None
except:
return base64.standard_b64encode(response.content), len(response.content)
class WXBizDataCrypt:
def __init__(self, appid, session_key):
self.app_id = appid
self.session_key = session_key
def decrypt(self, encryptedData, iv):
# base64 decode
sessionKey = base64.b64decode(self.session_key)
encryptedData = base64.b64decode(encryptedData)
iv = base64.b64decode(iv)
cipher = AES.new(sessionKey, AES.MODE_CBC, iv)
decrypted = json.loads(self._unpad(cipher.decrypt(encryptedData)))
if decrypted['watermark']['appid'] != self.app_id:
raise Exception('Invalid Buffer')
return decrypted
def check_raw_data(self, raw_data, session_key, signature):
return hashlib.sha1(raw_data + session_key).hexdigest() == signature
def _unpad(self, s):
return s[:-ord(s[len(s) - 1:])]
| 4,354 | 1,364 |
from tkinter import *
from placeholder import EntPlaceHold
from gradiente import GradientFrame
from tkinter import ttk, messagebox
from funcoes_sqlite import FuncsSqlite
from funcoes_txt import ArquivosTexto
import os
class Front(FuncsSqlite, ArquivosTexto):
def __init__(self):
self.window = Tk()
self.CreateTable()
self.path = os.getcwd() # Caminho do diretório para referenciar as imagens em qualquer computador
def WindowConfigure(self):
"""
Faz as configurações inicais da tela e põe a imagem de fundo na Interface
"""
self.back_interface = PhotoImage(
file=os.path.join(self.path, 'img\\tecnology7.png'))
label_interface = Label(self.window, image=self.back_interface, bg='white')
self.window.geometry("1919x1056")
self.window.title('Contas')
label_interface.pack()
def CreateFrames(self):
"""
Esse método cria todos os frames da aplicação de uma vez sem definir a posição deles. Tornado mais fácil sua
manipulação.
"""
self.frame_cad = GradientFrame(self.window, bd=4, bg='#dfe3ee', highlightbackground='#759fe6',
highlightthickness=2,
color1='#00FF00', color2='#ADFF2F')
self.frame_del = GradientFrame(self.window, bd=4, bg='#dfe3ee', highlightbackground='#759fe6',
highlightthickness=2,
color1='#FFD700', color2='#FF0000')
self.frame_lista = GradientFrame(self.window, bd=4, highlightbackground='#759fe6',
highlightthickness=2,
color1='#00BFFF', color2='#1E90FF')
self.frame_creditos = GradientFrame(self.window, bd=4, highlightbackground='#759fe6',
highlightthickness=2,
color1='#00BFFF', color2='#1E90FF')
def WidgetsIniciais(self):
"""
Cria os botões da Interface Inicial
"""
# Ícones dos botões da Interface Inicial
self.img_cadastro = PhotoImage(
file=os.path.join(self.path, 'img\\cliente_cadastro.png'))
self.img_excluir = PhotoImage(
file=os.path.join(self.path, 'img\\cliente_delete.png'))
self.img_conta = PhotoImage(
file=os.path.join(self.path, 'img\\atualizando.png'))
# Label e Botão de cadastro
self.button_cadastro = Button(self.window, image=self.img_cadastro, cursor='hand2',
command=self.IntCadastro)
self.button_cadastro.place(relx=0.02, rely=0.07)
self.label_cadastro = Label(self.window, text='Cadastrar Cliente', font=("Verdana Bold", 12), bg='white')
self.label_cadastro.place(relx=0.02, rely=0.05)
# Label e Botão de Exclusão do cliente
self.button_excluir = Button(self.window, image=self.img_excluir, cursor='hand2', command=self.IntDelete)
self.button_excluir.place(relx=0.02, rely=0.3)
self.label_excluir = Label(self.window, text='Deletar Cliente', font=("Verdana Bold", 12), bg='white')
self.label_excluir.place(relx=0.02, rely=0.28)
# Label e Botão da conta
self.button_conta = Button(self.window, image=self.img_conta, cursor='hand2', command=self.IntVerConta)
self.button_conta.place(relx=0.02, rely=0.53)
self.label_conta = Label(self.window, text='Ver Conta', font=("Verdana Bold", 12), bg='white')
self.label_conta.place(relx=0.02, rely=0.51)
# Essa função limpa os widgets iniciais da interface para prosseguir para outra sessão.
def LimparWidgetsIniciais(self):
self.widgtes_principais = [self.button_cadastro, self.button_excluir,
self.button_conta,
self.label_cadastro, self.label_excluir,
self.label_conta]
for widget in self.widgtes_principais:
widget.place_forget()
# Essa função cria os Widgets que serão utilizados na função 'IntCadastro()' que criará a interface de cadastro.
def WidgetsCadastro(self):
self.frame_cad.place(relx=0.03, rely=0.1, relwidth=0.3, relheight=0.15)
# Label e Entry do nome do cliente
self.label_nome = Label(self.frame_cad, text='Nome', fg='black', bg='#7CFC00')
self.label_nome.place(relx=0.22, rely=0.4)
self.entry_nome = EntPlaceHold(self.frame_cad, 'Digite o nome do cliente para cadastrá-lo')
self.entry_nome.place(relx=0.32, rely=0.4, relwidth=0.5)
self.btn_cadastro = Button(self.frame_cad, text='Cadastrar', bd=4, command=self.ComandoCadastro)
self.btn_cadastro.place(relx=0.46, rely=0.6)
# Essa função cria os Widgets que serão utilizados na função 'IntDelete()' que criará o frame de deletar cliente
def WidgetsDelete(self):
self.frame_del.place(relx=0.03, rely=0.1, relwidth=0.3, relheight=0.15)
self.label_nome_delete = Label(self.frame_del, text='Nome', fg='black', bg='#FFA500')
self.label_nome_delete.place(relx=0.22, rely=0.4)
self.entry_nome_del = EntPlaceHold(self.frame_del, 'Digite o nome do cliente para Deletá-lo')
self.entry_nome_del.place(relx=0.32, rely=0.4, relwidth=0.5)
self.btn_delete = Button(self.frame_del, text='Deletar', bd=4, command=self.ComandoDelete)
self.btn_delete.place(relx=0.46, rely=0.6)
# Essa função cria os widgets relacionados ao botão Ver Conta
def WidgetsListaVerConta(self):
"""
Elementos relacionados a sessão Ver Conta
"""
# imagens
self.icone_pesquisa = PhotoImage(
file=os.path.join(self.path, 'img\\icone_pesquisa.png'))
self.icone_atualizar_conta = PhotoImage(
file=os.path.join(self.path, 'img\\icone_att.png'))
self.icone_abrir = PhotoImage(
file=os.path.join(self.path, 'img\\icone_abrir.png'))
# Lista onde aparecem os clientes e suas contas.
self.lista_ver_conta = ttk.Treeview(self.frame_lista, height=3, column='nome')
self.frame_lista.place(relx=0.28, rely=0.05, relwidth=0.4, relheight=0.9)
self.entry_buscar_lista = EntPlaceHold(self.frame_lista, 'Digite o nome do cliente')
self.entry_buscar_lista.place(relx=0.25, rely=0.03, relwidth=0.5)
self.botao_buscar_lista = Button(self.frame_lista, image=self.icone_pesquisa, command=self.ComandoBuscarCliente)
self.botao_buscar_lista.place(relx=0.76, rely=0.027)
self.area_txt = Text(self.frame_lista)
self.area_txt.place(relx=0.2, rely=0.6, relwidth=0.6, relheight=0.37)
self.area_txt.configure(font=('Courier', 13), bg='#D3D3D3')
self.botao_abrir_conta = Button(self.frame_lista, image=self.icone_abrir, command=self.InsertArchEntry)
self.botao_abrir_conta.place(relx=0.812, rely=0.62)
self.botao_atualizar = Button(self.frame_lista, image=self.icone_atualizar_conta, command=self.CommandAttAcount)
self.botao_atualizar.place(relx=0.812, rely=0.68)
self.lista_ver_conta.heading('#0', text='')
self.lista_ver_conta.heading('#1', text='Clientes')
self.lista_ver_conta.column('#0', width=1)
self.lista_ver_conta.column('#1', width=100)
self.lista_ver_conta.place(relx=0.3, rely=0.07, relwidth=0.4, relheight=0.5)
self.Scroollista = Scrollbar(self.frame_lista, orient='vertical')
self.lista_ver_conta.configure(yscroll=self.Scroollista.set)
self.Scroollista.place(relx=0.67, rely=0.071, relwidth=0.03, relheight=0.499)
self.SelectLista(self.lista_ver_conta)
self.lista_ver_conta.bind('<Double-1>', self.SelectDoubleClick)
def WidgetsCreditos(self):
self.frame_creditos.place(relx=0.28, rely=0.05, relwidth=0.4, relheight=0.9)
self.imagem_back = PhotoImage(file=r'/AppDesktopMercadinho/img/back_interface_credito.png')
self.imagem_icone_cadastro = PhotoImage(file=r'/AppDesktopMercadinho/img/cliente_cadastro.png')
self.imagem_icone_atualizar = PhotoImage(file=r'/AppDesktopMercadinho/img/atualizando.png')
self.label_back = Label(self.frame_creditos, image=self.imagem_back)
self.label_cadastro_credito = Label(self.frame_creditos, image=self.imagem_icone_cadastro)
self.label_delete_credito = Label(self.frame_creditos, image=self.imagem_icone_atualizar)
self.label_back.place(relx=0.05, rely=0.1)
self.label_cadastro_credito.place(relx=0.05, rely=0.4)
self.label_delete_credito.place(relx=0.28, rely=0.4)
self.label_back_texto = Label(self.frame_creditos, text='Link da Imagem original: https://pt.vecteezy.com/vetor-gratis/computador')
self.label_back_texto.place(relx=0.46, rely=0.2)
self.label_icones_texto = Label(self.frame_creditos, text=f'Links do Site original dos ícones:{os.linesep} https://www.freepik.com{os.linesep}'
'https://www.flaticon.com/br/')
self.label_icones_texto.place(relx=0.61, rely=0.44)
# As funções abaixo são os comandos que serão executados ao acionar os botões da interface inicial
def IntCadastro(self):
self.LimparWidgetsIniciais()
self.WidgetsCadastro()
def IntDelete(self):
self.LimparWidgetsIniciais()
self.WidgetsDelete()
def IntVerConta(self):
self.LimparWidgetsIniciais()
self.WidgetsListaVerConta()
def ComandoCadastro(self):
"""
Essa função gera o comando que irá cadastrar o cliente no banco de dados.
"""
nome_cadastro = self.entry_nome.get()
if nome_cadastro == '' or nome_cadastro == 'Digite o nome do cliente para cadastrá-lo':
messagebox.showerror('Erro', 'É preciso digitar o nome do cliente para cadastrá-lo')
else:
try:
self.CreateClient(nome_cadastro)
self.CreateArch(nome_cadastro)
messagebox.showinfo('Sucesso', 'Cliente Cadastrado com sucesso!')
except:
messagebox.showerror('Erro', 'Houve um erro ao cadastrar o cliente')
def ComandoDelete(self):
"""
Essa função gera o comando que irá deletar o cliente do banco de dados. Ao apertar o botão.
"""
nome_delete = self.entry_nome_del.get()
if nome_delete == '' or nome_delete == 'Digite o nome do cliente para Deletá-lo':
messagebox.showerror('Erro', 'Digite um nome válido')
else:
confirm_delete = messagebox.askyesno('Confirme', 'Você tem certeza que deseja deletar esse cliente?')
if confirm_delete:
try:
self.DeleteClient(nome_delete)
self.DeleteArch(nome_delete)
messagebox.showinfo('Sucesso', 'Cliente deletado com sucesso')
except FileNotFoundError as erro:
print(erro)
messagebox.showerror('Erro', 'Houve um erro ao deletar o cliente do banco. Verifique se o cliente '
'já foi excluído do banco de dados')
def InsertArchEntry(self):
""" Esta Função insere o conteúdo do arquivo txt na area de texto do tkinter"""
self.area_txt.delete('1.0', END)
try:
nome = self.entry_buscar_lista.get()
arquivo_read = self.ReadAccount(nome)
self.area_txt.insert(END, arquivo_read)
except FileNotFoundError:
messagebox.showerror('Erro', 'O cliente digitado não está cadastrado na lista')
def CommandAttAcount(self):
""" Função que atualiza o conteúdo do arquivo txt"""
nome_entry = self.entry_buscar_lista.get()
if nome_entry == '' or nome_entry == 'Digite o nome do cliente':
messagebox.showerror('Erro', 'Digite um nome válido')
else:
confirm_att = messagebox.askyesno('Confirme', 'Você deseja realmente atualizar a conta desta maneira?')
if confirm_att:
try:
txt_anotacoes = self.area_txt.get('1.0', END)
self.WriteAccount(nome_entry, txt_anotacoes)
messagebox.showinfo('Sucesso', 'Conta atualizada com sucesso!')
except FileNotFoundError as erro:
print(erro)
messagebox.showerror('Erro', 'O cliente selecionado não está cadastrado. Verifique se o nome está '
'esxrito exatamente igual como está na lista!')
def ComandoBuscarCliente(self):
""" Comando que seleciona o cliente na lista ao buscar ele"""
self.BuscarCliente(self.lista_ver_conta, self.entry_buscar_lista)
def Menu(self):
"""
Menu do topo da tela
"""
self.barra_menu = Menu(self.window)
self.window.config(menu=self.barra_menu)
self.menu_volta = Menu(self.barra_menu)
self.barra_menu.add_cascade(label='Menu', menu=self.menu_volta)
self.menu_volta.add_command(label='Voltar', command=self.BackMenu)
self.menu_volta.add_command(label='Créditos', command=self.MostrarCreditos)
def SelectDoubleClick(self, event):
"""Função que insere o nome do cliente dentro da entry de busca ao dar um duplo clique no nome do cliente"""
self.entry_buscar_lista.delete(0, END)
self.lista_ver_conta.selection()
for nome in self.lista_ver_conta.selection():
nome_ins = self.lista_ver_conta.item(nome, 'values')
for n in nome_ins:
self.entry_buscar_lista.insert(END, n)
def BackMenu(self):
"""
Comando que retorna para a interface Inicial a partir de qualquer sessão
"""
if self.frame_cad:
self.frame_cad.place_forget()
if self.frame_del:
self.frame_del.place_forget()
if self.frame_lista:
self.frame_lista.place_forget()
if self.frame_creditos:
self.frame_creditos.place_forget()
self.WidgetsIniciais()
def LimparTodosWidgets(self):
"""
Comando que apaga todos os Widgets da tela. Que será utilizado para mostrar os créditos das imagens na opção créditos do menu.
"""
if self.frame_cad:
self.frame_cad.place_forget()
if self.frame_del:
self.frame_del.place_forget()
if self.frame_lista:
self.frame_lista.place_forget()
if self.button_cadastro:
self.LimparWidgetsIniciais()
def MostrarCreditos(self):
"""
Comando que 'chama' a interface que mostra os créditos das imagen na tela.
"""
self.LimparTodosWidgets()
self.WidgetsCreditos()
def Iniciar(self):
"""
Método que inicia a aplicação
"""
self.WindowConfigure()
self.CreateFrames()
self.WidgetsIniciais()
self.Menu()
self.window.mainloop()
app = Front()
app.Iniciar()
| 15,265 | 5,209 |
"""
Code for domain classes.
"""
| 33 | 12 |
import sys
import os
__all__ = []
__version__ = '0.0.1'
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '.' + '/CryptoVinaigrette'))) | 164 | 73 |
import json
import os.path
import falcon
from app.__root__ import __path__
class MockServerResource:
def __init__(self):
self.__root_path__ = __path__()
async def on_get(self, req, resp, route: str):
if not route.endswith(".json"):
route += ".json"
filepath = os.path.join(self.__root_path__, "assets", route)
if not os.path.exists(filepath):
raise falcon.HTTPNotImplemented("File Requested Not Available")
with open(filepath, 'r') as fp:
data = json.load(fp)
resp.text = json.dumps(data)
| 586 | 185 |
import asyncio
import aiohttp
async def duck_search(search_term):
search_term = "+".join(search_term.split())
url = f"https://api.duckduckgo.com/?q={search_term}&format=json"
async with aiohttp.ClientSession() as session:
async with session.get(url) as r:
if r.status == 200:
content = await r.json(content_type="application/x-javascript")
result = content["AbstractText"]
if content["AbstractText"] == "":
result = content["RelatedTopics"][0]["Text"]
return result
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(duck_search(input("> ")))
| 703 | 216 |
from __future__ import annotations
from prettyqt import widgets
from prettyqt.qt import QtWidgets
QtWidgets.QStatusBar.__bases__ = (widgets.Widget,)
class StatusBar(QtWidgets.QStatusBar):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.progress_bar = widgets.ProgressBar()
def __add__(self, other: QtWidgets.QAction | QtWidgets.QWidget) -> StatusBar:
if isinstance(other, QtWidgets.QAction):
self.addAction(other)
return self
elif isinstance(other, QtWidgets.QWidget):
self.addWidget(other)
return self
else:
raise TypeError(other)
def setup_default_bar(self) -> None:
# This is simply to show the bar
self.progress_bar.hide()
self.progress_bar.setRange(0, 0)
self.progress_bar.setFixedSize(200, 20)
self.progress_bar.setTextVisible(False)
self.addPermanentWidget(self.progress_bar)
def add_action(self, action: QtWidgets.QAction) -> None:
self.addAction(action)
def add_widget(self, widget: QtWidgets.QWidget, permanent: bool = False) -> None:
if permanent:
self.addPermanentWidget(widget)
else:
self.addWidget(widget)
def show_message(self, message: str, timeout: int = 0) -> None:
self.showMessage(message, timeout)
if __name__ == "__main__":
app = widgets.app()
dlg = widgets.MainWindow()
status_bar = StatusBar()
status_bar.set_color("black")
label = widgets.Label("test")
status_bar.addWidget(label)
status_bar.setup_default_bar()
dlg.setStatusBar(status_bar)
dlg.show()
app.main_loop()
| 1,709 | 544 |
# Function to clear data in instances folder before new run
import os
import shutil
def clear_instances(directory):
instances_folder = directory
folders = os.listdir(instances_folder)
for folder in folders:
shutil.rmtree(instances_folder + folder)
print('\nInstances folder cleared\n')
def clear_all():
img_dir = './data/images/'
anno_dir = './data/annotations/'
save_dir = './data/instances/'
imgs = os.listdir(img_dir)
annos = os.listdir(anno_dir)
object_folders = os.listdir(save_dir)
for img in imgs:
os.remove(img_dir + img)
for anno in annos:
os.remove(anno_dir + anno)
for folder in object_folders:
shutil.rmtree(save_dir + folder) | 670 | 249 |
""" Activity commands """
from discord.ext import commands
from bot.components.logging import log_all
class Activities(commands.Cog):
""" Activity Commands """
def __init__(self, api):
self.api = api
@commands.command()
@log_all
async def work(self, ctx):
""" (NOT YET IMPLEMENTED) Work while idle to earn gold """
log = self.api.logger.entry()
log.color("warn")
log.title("Command not yet implemented")
log.buffer(ctx.author)
await self.api.logger.send_buffer()
await ctx.message.add_reaction(u'❌')
@commands.command()
@log_all
async def explore(self, ctx):
""" (NOT YET IMPLEMENTED) Explore while idle to find loot """
log = self.api.logger.entry()
log.color("warn")
log.title("Command not yet implemented")
log.buffer(ctx.author)
await self.api.logger.send_buffer()
await ctx.message.add_reaction(u'❌')
@commands.command()
@log_all
async def train(self, ctx):
""" (NOT YET IMPLEMENTED) Train while idle to gain experience """
log = self.api.logger.entry()
log.color("warn")
log.title("Command not yet implemented")
log.buffer(ctx.author)
await self.api.logger.send_buffer()
await ctx.message.add_reaction(u'❌')
| 1,338 | 412 |
"""Python functions to handle the ISO-14443-A protocol basics (parity and CRC)"""
# Pynfc is a python wrapper for the libnfc library
# Copyright (C) 2009 Mike Auty
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
def crc(inbytes):
"""Calculates the ISO-14443A CRC checksum"""
wCrc = 0x6363
for i in range(len(inbytes)):
byte = ord(inbytes[i])
byte = (byte ^ (wCrc & 0x00FF))
byte = ((byte ^ (byte << 4)) & 0xFF)
wCrc = ((wCrc >> 8) ^ (byte << 8) ^ (byte << 3) ^ (byte >> 4)) & 0xFFFF
res = chr(wCrc & 0xFF) + chr((wCrc >> 8) & 0xff)
return res
def parity(inbytes):
"""Calculates the odd parity bits for a byte string"""
res = ""
for i in inbytes:
tempres = 1
for j in range(8):
tempres = tempres ^ ((ord(i) >> j) & 0x1)
res += chr(tempres)
return res
| 1,535 | 570 |
import pytest
import sqlalchemy as sa
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.ext.hybrid import hybrid_property
from attrs_sqlalchemy import attrs_sqlalchemy
class TestAttrsSqlalchemy:
def test_deprecation(self):
with pytest.warns(UserWarning, match='attrs_sqlalchemy is deprecated'):
@attrs_sqlalchemy
class MyModel(declarative_base()):
__tablename__ = 'mymodel'
id = sa.Column(sa.Integer, primary_key=True)
@pytest.mark.parametrize('decorator', [attrs_sqlalchemy, attrs_sqlalchemy()])
def test_attrs_sqlalchemy(self, decorator):
"""
Decorating a class with ``@attrs_sqlalchemy`` or
``@attrs_sqlalchemy()`` should add ``__repr__``, ``__eq__``, and
``__hash__`` methods based on the fields on the model.
"""
TestBase = declarative_base()
@attrs_sqlalchemy
class MyModel(TestBase):
__tablename__ = 'mymodel'
id = sa.Column(sa.Integer, primary_key=True)
text = sa.Column(sa.String)
instance = MyModel(id=1, text='hello')
same_data = MyModel(id=1, text='hello')
same_pk = MyModel(id=1, text='world')
# All fields are the same
assert instance == same_data
# Primary key is not enough for equality
assert instance != same_pk
# Instances should have a repr containing their keys and type
assert repr(instance) == "MyModel(id=1, text='hello')"
# Instances should be hashable by their fields and used in a dict
d = {instance: True}
assert d.get(same_data) == d[instance]
assert d.get(same_pk) is None
def test_field_name_not_column_name(self):
"""
``@attrs_sqlalchemy`` should use attribute/field names, not column names.
"""
@attrs_sqlalchemy
class MyModel(declarative_base()):
__tablename__ = 'mymodel'
_id = sa.Column('id', sa.Integer, primary_key=True)
text = sa.Column(sa.String)
assert {attr.name for attr in MyModel.__attrs_attrs__} == {'_id', 'text'}
def test_subclass(self):
"""
When used on a subclass, ``@attrs_sqalchemy`` should also include the
attributes from the parent class(es), even if a parent class is also
decorated with ``@attrs_sqlalchemy``.
"""
@attrs_sqlalchemy
class ParentModel(declarative_base()):
__tablename__ = 'parent'
id = sa.Column(sa.Integer, primary_key=True)
type = sa.Column(sa.String)
__mapper_args__ = {
'polymorphic_identity': 'parent',
'polymorphic_on': type,
}
@attrs_sqlalchemy
class ChildModel(ParentModel):
__tablename__ = 'child'
id = sa.Column(sa.Integer, sa.ForeignKey('parent.id'), primary_key=True)
child_field = sa.Column(sa.Integer)
__mapper_args__ = {
'polymorphic_identity': 'child',
}
@attrs_sqlalchemy
class ChildChildModel(ChildModel):
__tablename__ = 'very_child'
id = sa.Column(sa.Integer, sa.ForeignKey('child.id'), primary_key=True)
very_child_field = sa.Column(sa.String)
__mapper_args__ = {
'polymorphic_identity': 'childchild',
}
assert {attr.name for attr in ChildModel.__attrs_attrs__} == {
'id', 'type', 'child_field',
}
assert {attr.name for attr in ChildChildModel.__attrs_attrs__} == {
'id', 'type', 'child_field', 'very_child_field',
}
def test_hybrid_property(self):
"""
Hybrid properties should not be included in the attributes used by
``@attrs_sqlalchemy``.
"""
@attrs_sqlalchemy
class MyModel(declarative_base()):
__tablename__ = 'mymodel'
id = sa.Column(sa.Integer, primary_key=True)
text = sa.Column(sa.String)
@hybrid_property
def tiny_text(self): # pragma: no cover
return self.text[:10]
assert {attr.name for attr in MyModel.__attrs_attrs__} == {'id', 'text'}
| 4,294 | 1,331 |
"""
Generate predictions for AIcrowd.
Usage:
python3 run.py
The csv file produced will be "out/predictions.csv".
"""
# flake8: noqa: E402
import os
import numpy as np
# Import functions from scripts/
from csv_utils import create_csv_submission, load_csv_data
from path import (add_src_to_path, load_json_parameters, DATA_TEST_PATH,
DATA_TRAIN_PATH, OUT_DIR)
# Add src/ to path to import functions
add_src_to_path()
# Import functions from src/
from clean_data import clean_train_test_data_by_jet
from cross_validation import build_poly
from implementations import ridge_regression
from split_data import split_by_jet
from helpers import predict_labels
from print_utils import (print_shapes, print_shapes_by_jet, print_subset_label,
NB_SUBSETS)
# Classifier to use
CLASSIFIER = 'ridge_regression'
# Output file path
OUTPUT_PATH = os.path.join(OUT_DIR, 'predictions.csv')
def main():
"""
Main function to generate predictions for AIcrowd.
"""
# Load the train data
print('[1/8] Load train data')
y_tr, x_tr, ids_tr = load_csv_data(DATA_TRAIN_PATH)
print_shapes(y_tr, x_tr)
# Load the test data
print('[2/8] Load test data')
y_te, x_te, ids_te = load_csv_data(DATA_TEST_PATH)
print_shapes(y_te, x_te)
# Split and clean train data
print('[3/8] Split train and test data by jet')
y_tr_by_jet, x_tr_by_jet, _ = split_by_jet(y_tr, x_tr, ids_tr)
y_te_by_jet, x_te_by_jet, ids_te_by_jet = split_by_jet(y_te, x_te, ids_te)
print('[4/8] Clean train and test data')
y_tr_by_jet, x_tr_by_jet, y_te_by_jet, x_te_by_jet = \
clean_train_test_data_by_jet(y_tr_by_jet, x_tr_by_jet,
y_te_by_jet, x_te_by_jet)
print('Train:')
print_shapes_by_jet(y_tr_by_jet, x_tr_by_jet)
print('Test:')
print_shapes_by_jet(y_te_by_jet, x_te_by_jet)
# Load parameters
print('[5/7] Load parameters')
params = load_json_parameters()
print(params[CLASSIFIER])
lambda_ = params[CLASSIFIER]['lambda_']
degrees = params[CLASSIFIER]['degree']
# Run ridge regression
print('[6/8] Run classification algorithm')
w_by_jet = list()
for i in range(NB_SUBSETS):
print_subset_label(i)
# Get train subset
x_tr_jet, y_tr_jet = x_tr_by_jet[i], y_tr_by_jet[i]
# Build polynomial basis
phi_tr_jet = build_poly(x_tr_jet, degrees[i])
# Run ridge regression
w, loss = ridge_regression(y_tr_jet, phi_tr_jet, lambda_[i])
print(f'Loss = {loss:.3f}')
# Add weights to list
w_by_jet.append(w)
# Generate predictions
print('[7/8] Generate predictions')
y_pred_by_jet = list()
for i in range(NB_SUBSETS):
# Get subset
x_te_jet, w = x_te_by_jet[i], w_by_jet[i]
# Build polynomial basis
phi_te_jet = build_poly(x_te_jet, degrees[i])
# Predict labels
y_pred = predict_labels(w, phi_te_jet)
y_pred_by_jet.append(y_pred)
# Create submission
print('[8/8] Create submission')
y_pred = np.concatenate(y_pred_by_jet)
ids_pred = np.concatenate(ids_te_by_jet)
create_csv_submission(ids_pred, y_pred, OUTPUT_PATH)
print(f'File {OUTPUT_PATH} created')
if __name__ == '__main__':
main()
| 3,331 | 1,272 |
DESC = f"""{__file__} - A code sample to help get you started with proceedurally generated terrain / chunkloading in Ursina.
More complex terrain can be achieved by using a more complicated function in GameWithChunkloading.get_heightmap().
Supports both Perlin Noise and Open Simplex Noise.
Written by Lyfe.
"""
TERRAIN_X_Z_SCALE = 0.5 #Higher numbers yield rougher terrain
TERRAIN_Y_SCALE = 5 #Terrain Y amplification, results in y values in the range of +- MAP_MODEL_SCALE
CHUNK_DIVISIONS = 5 #Number of subdivisions per tile, more divisions is more detail
DEFAULT_NUM_GENERATORS = 1
RENDER_DISTANCE = 4
MAP_SCALE = 6 #how big the tiles are
USE_PERLIN = True
import os, json, random
import numpy as np
from ursina import *
from ursina.prefabs.first_person_controller import FirstPersonController
#This class is a trimmed version of terrain.py that takes a numpy array directly rather than an image
class HeightMesh(Mesh):
def __init__(self, heightmap):
heightmap = np.swapaxes(heightmap,0,1)
self.vertices, self.triangles, self.uvs, self.normals = list(), list(), list(), list()
lhm = len(heightmap)
l=lhm-1
i = 0
for z in range(lhm):
for x in range(lhm):
self.vertices.append(Vec3(x/l, heightmap[x][z], z/l))
self.uvs.append((x/l, z/l))
if x > 0 and z > 0:
self.triangles.append((i, i-1, i-l-2, i-l-1))
if x < l-1 and z < l-1:
rl = heightmap[x+1][z] - heightmap[x-1][z]
fb = heightmap[x][z+1] - heightmap[x][z-1]
self.normals.append(Vec3(rl, 1, fb).normalized())
i += 1
super().__init__(vertices=self.vertices, triangles=self.triangles, uvs=self.uvs, normals=self.normals)
#This entity is a base, when writing a game you would want to do stuff like spawning foliage etc when the chunk is initialized.
class Chunk(Entity):
def __init__(self, game, chunk_id, heightmap, **kwargs):
Entity.__init__(self, model=HeightMesh(heightmap),**kwargs)
self.game, self.chunk_id, self.heightmap, self.collider, = game, chunk_id, heightmap, self.model
def save(self):
#You could implement a more complicated save system here,
#adding more to the chunk save than just the heightmap
#You would also need to add something during the chunk init that
#checked if a save file existed for it and if so loaded that
#save data
x,z = self.chunk_id
filename = self.game.saves_dir+f"{x}x{z}#{self.game.seed}.json"
with open(filename, "w+") as f:
json.dump({"heightmap":self.heightmap}, f)
#A basic example of dynamic terrain generation and chunkloading
#More generators will result in smoother terrain
#The seed determines the terrain that spawns, leave empty for random
class GameWithChunkloading(Ursina):
def __init__(self,*args,**kwargs):
#Set defaults
self.seed = random.randint(1,9999999999) #Random overwritten by kwargs
self.radius = RENDER_DISTANCE
self.use_perlin = USE_PERLIN
self.chunk_divisions = CHUNK_DIVISIONS
self.terrain_scale = TERRAIN_X_Z_SCALE
self.terrain_y_scale = TERRAIN_Y_SCALE
self.map_scale = MAP_SCALE
self.num_generators = DEFAULT_NUM_GENERATORS
self.enable_save_system = False
self.saves_dir="saves"
#Override passed non-defaults
for key in ('seed', 'use_perlin', 'radius', 'chunk_divisions',\
'terrain_scale', 'terrain_y_scale', 'map_scale',\
'num_generators', 'enable_save_system', 'saves_dir'):
if key in kwargs:
setattr(self, key, kwargs[key])
del kwargs[key]
super().__init__(*args, **kwargs)
##Chunkloading
self.loaded = [] # loaded chunks
self.last_chunk = None #Last place chunkload occured
##Terrain Generation
print(f"Using seed {self.seed}")
if self.use_perlin:
print("Using Perlin Noise")
from perlin_noise import PerlinNoise
self.generators = [PerlinNoise(seed=self.seed,octaves=self.num_generators)]
else:
print("Using Open Simplex")
from opensimplex import OpenSimplex
self.generators = [OpenSimplex(seed=(self.seed + i)*(i+1)).noise2 for i in range(self.num_generators)]
##Save system
if self.enable_save_system:
self.saves_dir = "saves/"
os.makedirs(self.saves_dir, exist_ok=True) #Make saves dir
self.player = FirstPersonController(model='cube', z=-10, color=color.clear, origin_y=-.5, speed=8)
self.player.collider = BoxCollider(self.player, Vec3(0,1,0), Vec3(1,2,1))
self.player.y = self.get_heightmap(0,0)*self.map_scale*self.terrain_y_scale+1 #Ensure player is above map when spawned
self.update() #Build terrain for first time
def get_chunk_id_from_position(self,x,z):#Takes an x/z position and returns a chunk id
return int(x/MAP_SCALE),int(z/MAP_SCALE)
def get_heightmap(self,x,z):#Get terrain y at a given x/z position
pos = (x*self.terrain_scale,z*self.terrain_scale) #Get adjusted position in heightmap
#Get the sum of the heights for all generators at a given position
#If using perlin noise this is handled internally using 'octaves' option
height = sum(g(pos) if self.use_perlin else g(*pos) for g in self.generators)
if len(self.generators)>1: height /= len(self.generators) #scale output back to range of [-1...1]
return height
def update(self): #update which chunks are loaded
current = self.get_chunk_id_from_position(self.player.position.x, self.player.position.z)
if current == self.last_chunk: return #Don't update if in same chunk as last update
needed_chunk_ids = [] #List of chunks that should currently be loaded
for z in range(int(current[1]-self.radius),int(current[1]+self.radius)):
for x in range(int(current[0]-self.radius),int(current[0]+self.radius)):
needed_chunk_ids.append((x,z))
for c in self.loaded.copy(): #Remove unneeded chunks
if c.chunk_id not in needed_chunk_ids:
cid = c.chunk_id
self.loaded.remove(c)
destroy(c)
print(f"Unloaded chunk {cid}")
current_chunk_ids = [c.chunk_id for c in self.loaded]
for chunk_id in needed_chunk_ids: #Show the needed chunks
if not chunk_id in current_chunk_ids:
x,z=chunk_id
heightmap = []
chunk_needs_save = False #Flag for if a chunk is new and needs to be saved
if self.enable_save_system:
filename = self.saves_dir+f"{x}x{z}#{self.seed}.json"
#Check if saved chunk already exists:
if os.path.exists(filename):
print(f"Found saved chunk {chunk_id}")
with open(filename, 'r') as f:
heightmap = json.load(f)["heightmap"]
else:
heightmap = self.generate_chunk_heightmap(x,z)
chunk_needs_save = True
else:
heightmap = self.generate_chunk_heightmap(x,z)
c = Chunk(
self,
chunk_id = chunk_id,
heightmap=list(heightmap),
scale=self.map_scale,
scale_y=self.terrain_y_scale,
texture="white_cube",
position=(x*self.map_scale,0,z*self.map_scale)
)
self.loaded.append(c)
if chunk_needs_save: c.save() #Save chunk if newly loaded
print(f"Loaded chunk {chunk_id}")
self.last_chunk = current #Update last rendered chunk to prevent unneeded re-draws
def generate_chunk_heightmap(self,x,z):
heightmap = []
for _z in range(self.chunk_divisions+1):
heightmap_row = []
for _x in range(self.chunk_divisions+1):
heightmap_row.append(self.get_heightmap(x+_x/self.chunk_divisions,z+_z/self.chunk_divisions))
heightmap.append(heightmap_row)
return heightmap
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description=DESC)
parser.add_argument("-s", "--seed", help = "Select terrain seed. No seed / a seed of zero results in a random seed. (Int)")
parser.add_argument("-d", "--divisions", help = "Number of divisions per chunk edge. (Int)")
parser.add_argument("-r", "--renderdistance", help = "Render distance radius in chunks. (Int)")
parser.add_argument("-t", "--terrainscale", help = "Higher values yield rougher terrain. (Float)")
parser.add_argument("-y", "--yscale", help = "Terrain Y amplification. (Float)")
parser.add_argument("-m", "--mapscale", help = "Length of chunk edge. (Float)")
parser.add_argument("-n", "--numgenerators", help = "Length of chunk edge. (Float)")
parser.add_argument("-l", "--loadchunks", action='store_true', help="Enable example chunk save/load system.")
parser.add_argument("-o", "--opensimplex", action='store_true', help = "Set this flag to use Open Simplex Noise rather than Perlin Noise")
args = parser.parse_args()
kwargs = {}
if args.seed: kwargs["seed"]= int(args.seed)
if args.opensimplex: kwargs["use_perlin"]=not bool(args.opensimplex)
if args.renderdistance: kwargs["radius"]= int(args.renderdistance)
if args.divisions: kwargs["chunk_divisions"]= int(args.divisions)
if args.terrainscale: kwargs["terrain_scale"]= float(args.terrainscale)
if args.yscale: kwargs["terrain_y_scale"]= float(args.yscale)
if args.mapscale: kwargs["map_scale"]= float(args.mapscale)
if args.loadchunks: kwargs["enable_save_system"]=bool(args.loadchunks)
num_terrain_generators = args.numgenerators or DEFAULT_NUM_GENERATORS
app = GameWithChunkloading(**kwargs)
update = app.update
app.run() | 8,958 | 3,414 |
"""CLI console script entry point."""
import clc
def main():
clc.v1.Args()
clc.v1.ExecCommand()
| 105 | 46 |
from namedivider import NameDivider
from controller.model import DivisionRequest
from controller import division_controller
def test_division_controller():
divider = NameDivider()
undivided_names = ["菅義偉"]
division_request = DivisionRequest(names=undivided_names)
res = division_controller.divide(divider=divider, division_request=division_request)
assert res.divided_names[0].family == "菅"
assert res.divided_names[0].given == "義偉"
assert res.divided_names[0].separator == " "
assert res.divided_names[0].score == 0.6328842762252201
assert res.divided_names[0].algorithm == "kanji_feature"
if __name__ == '__main__':
test_division_controller() | 688 | 236 |
from urllib.parse import urlparse
from toolkit.lib.http_tools import request_page
from bs4 import BeautifulSoup
class AuditPage():
def __init__(self, url):
parsed_url = urlparse(url)
self.domain = parsed_url.netloc
self.scheme = parsed_url.scheme
self.path = parsed_url.path
self.request = request_page(self.generate_url())
self.status_code = self.request.status_code
self.headers = self.request.headers
self.soup = BeautifulSoup(self.request.content, 'html.parser')
def __str__(self):
a = "--------------------\n"
a += "Domain: " + self.domain + "\n"
a += "Scheme: " + self.scheme + "\n"
a += "Path: " + self.path + "\n"
a += "Status Code: " + str(self.status_code) + "\n"
a += "Headers: " + str([x for x in self.headers]) + "\n"
return a
def generate_url(self):
return self.scheme + "://" + self.domain + "/" + self.path
| 1,001 | 324 |
import os
import shutil
import zipfile
from http.server import HTTPServer, CGIHTTPRequestHandler
import threading
import subprocess
import sys
import traceback
PORT = 3000
os.chdir('autoupdate')
def create_folder_and_zip():
def zipdir(path, ziph):
for root, dirs, files in os.walk(path):
for file in files:
ziph.write(os.path.join(root, file))
os.mkdir('cmu_graphics_installer')
shutil.copytree('../../cmu_graphics', 'cmu_graphics_installer/cmu_graphics')
with zipfile.ZipFile('cmu_graphics_installer.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:
zipdir('cmu_graphics_installer', zipf)
shutil.move('cmu_graphics_installer/cmu_graphics', 'cmu_graphics')
os.rmdir('cmu_graphics_installer')
os.mkdir('srv')
shutil.move('cmu_graphics_installer.zip', 'srv/cmu_graphics_installer.zip')
# server version
with open('srv/version.txt', 'w+') as f:
f.write('0.0.1')
# local version
with open('cmu_graphics/meta/version.txt', 'w') as f:
f.write('0.0.0')
def set_mock_urls():
def replace_in_file(filename, old_string, new_string):
content = None
with open(filename, 'r') as f:
content = f.read()
with open(filename, 'w') as f:
f.write(content.replace(old_string, new_string))
replace_in_file(
'cmu_graphics/updater.py',
'https://s3.amazonaws.com/cmu-cs-academy.lib.prod/desktop-cmu-graphics/cmu_graphics_installer.zip',
'http://localhost:%d/srv/cmu_graphics_installer.zip' % PORT
)
replace_in_file(
'cmu_graphics/cmu_graphics.py',
'https://s3.amazonaws.com/cmu-cs-academy.lib.prod/desktop-cmu-graphics/version.txt',
'http://localhost:%d/srv/version.txt' % PORT
)
def run_server():
httpd = HTTPServer(('', PORT), CGIHTTPRequestHandler)
httpd.serve_forever()
def spawn_server():
daemon = threading.Thread(target=run_server)
daemon.setDaemon(True)
daemon.start()
def run_student_code():
p = subprocess.Popen(
[sys.executable, 'update_trigger.py'],
env={**os.environ, 'CMU_GRAPHICS_AUTO_UPDATE': 'YES'}
)
assert(p.wait() == 0)
def assert_update_succeeded():
with open('cmu_graphics/meta/version.txt', 'r') as f:
assert f.read() != '0.0.0'
run_student_code()
def cleanup():
for dir in ('cmu_graphics', 'cmu_graphics_installer', 'srv'):
if os.path.exists(dir):
shutil.rmtree(dir)
for file in ('cmu_graphics_installer.zip', 'version.txt'):
if os.path.exists(file):
os.remove(file)
def main():
exit_code = 0
try:
create_folder_and_zip()
set_mock_urls()
spawn_server()
run_student_code() # causes an update
assert_update_succeeded()
except:
traceback.print_exc()
exit_code = 1
finally:
cleanup()
os._exit(exit_code)
if __name__ == "__main__":
main()
| 2,968 | 1,069 |
import RPi.GPIO as GPIO
import time
import os
class Axis:
"""
Base Class for one of the XY gantry axes
All move commands are written to be called in threads
self.position and/or self.step position can safely be queried
while the axis is moving.
"""
def __init__(self, name, pin_list, steps_per_cm, logfile=None):
self.name = name
self.ena = pin_list['ena']
self.pul = pin_list['pul']
self.dir = pin_list['dir']
self.eot_ccw = pin_list['eot_ccw']
self.eot_cw = pin_list['eot_cw']
self.setup_pins()
self.hold_enable = False
self.keep_moving = False
self.step_position = 0
self.logfile = logfile
if self.logfile is not None and os.path.exists(self.logfile):
with open(self.logfile, "r") as pos_file:
self.step_position = int(pos_file.read())
self.steps_per_cm = steps_per_cm
self.max_vel = 1.27 ## cm / s
self.homed = False
@property
def position(self):
return self.step_position / self.steps_per_cm
@position.setter
def position(self, value):
if self.keep_moving:
raise ValueError("Cannot update position while moving")
self.step_position = value*self.steps_per_cm
@property
def limits(self):
'''
Returns: (home limit, far side limit)
'''
self.set_limits()
return self.lim_cw, self.lim_ccw
def setup_pins(self):
GPIO.setmode(GPIO.BCM)
for pin in [self.ena, self.pul, self.dir]:
GPIO.setup(pin, GPIO.OUT)
GPIO.output(pin, GPIO.HIGH)
for pin in [self.eot_ccw, self.eot_cw]:
GPIO.setup(pin, GPIO.IN)
def set_limits(self):
### pins go low when they are engaged
self.lim_ccw = GPIO.input(self.eot_ccw) == GPIO.LOW
self.lim_cw = GPIO.input(self.eot_cw) == GPIO.LOW
return self.lim_ccw or self.lim_cw
def home(self, max_dist=150, reset_pos=True):
"""Move axis at 1 cm/s toward the home limit.
Arguments
----------
max_dist : float
the maximum number of cm to move for homing
reset_pos : bool
if true, axis position is reset to zero
"""
while not self.lim_cw:
self.move_cm(True, max_dist, velocity=1)
if reset_pos:
self.step_position = 0
self.homed = True
def move_cm(self, dir, distance, velocity=None):
'''
Axis Moves the commanded number of cm. Converts to steps
and calls the move_step function
Args:
dir -- True goes toward home (the motors)
distance -- number of cm to move
velocity -- how quickly to move
'''
steps = distance*self.steps_per_cm
if velocity is None:
velocity = self.max_vel
if velocity > self.max_vel:
print('Requested Velocity too high, setting to {} cm/s'.format(self.max_vel))
velocity = self.max_vel
wait = 1.0/(2*velocity*self.steps_per_cm)
success, steps = self.move_step(dir, steps, wait)
return success, steps/self.steps_per_cm
def move_to_cm(self, new_position, velocity=None, require_home=True):
'''
Move Axis to the requested position.
Args:
new_position -- the position you want to move to
velocity -- how fast to move (cm/s)
require_home -- if true, requires the axis position to be calibrated
defaults to true to prevent mistakes
'''
if not self.homed:
if require_home:
print('ERROR -- Axis Position Not Calibrated')
return False
print('WARNING -- Axis Position Not calibrated')
distance = new_position - self.position
if distance < 0:
return self.move_cm( True, abs(distance), velocity)
return self.move_cm(False, abs(distance), velocity)
def enable(self):
self.hold_enable = True
GPIO.output(self.ena, GPIO.LOW)
def disable(self):
self.hold_enable = False
GPIO.output(self.ena, GPIO.HIGH)
def move_step(self, dir, steps=100, wait=0.005):
## direction = False is toward the CCW limit
## direction = True is toward the CW limit
steps = int(round(steps))
self.keep_moving = True
if dir:
increment = -1
else:
increment = 1
if not self.hold_enable:
GPIO.output( self.ena, GPIO.LOW)
GPIO.output(self.dir, dir)
time.sleep(0.25)
while steps > 0 and self.keep_moving:
if self.set_limits():
if (not dir) and self.lim_ccw:
#print('Hit CCW limti with {} steps left'.format(steps))
self.keep_moving = False
break
elif dir and self.lim_cw:
### true goes to home
self.keep_moving = False
break
#print('LIMIT!')
#print('CCW: ', self.lim_ccw, 'CW:', self.lim_cw)
GPIO.output(self.pul, GPIO.HIGH)
time.sleep(wait)
GPIO.output(self.pul, GPIO.LOW)
time.sleep(wait)
self.step_position += increment
steps -= 1
if self.logfile is not None:
with open(self.logfile, "w") as pos_file:
pos_file.write(self.step_position)
if not self.hold_enable:
GPIO.output(self.ena, GPIO.HIGH)
if not self.keep_moving:
#print('I think I hit a limit with {} steps left'.format(steps))
return False, steps
self.keep_moving = False
return True, steps
def stop(self):
self.keep_moving = False
def cleanup(self):
GPIO.cleanup()
class CombinedAxis(Axis):
"""
Two axes where the control outputs are electrically connected
This assumes there's two limit switches per axes so there's two
limits on each side.
"""
def setup_pins(self):
GPIO.setmode(GPIO.BCM)
for pin in [self.ena, self.pul, self.dir]:
GPIO.setup(pin, GPIO.OUT)
GPIO.output(pin, GPIO.HIGH)
for pins in [self.eot_ccw, self.eot_cw]:
for pin in pins:
GPIO.setup(pin, GPIO.IN)
def set_limits(self):
### pins go low when they are engaged
self.lim_ccw = (GPIO.input(self.eot_ccw[0]) == GPIO.LOW) or (GPIO.input(self.eot_ccw[1]) == GPIO.LOW)
self.lim_cw = (GPIO.input(self.eot_cw[0]) == GPIO.LOW) or (GPIO.input(self.eot_cw[1]) == GPIO.LOW)
return self.lim_ccw or self.lim_cw
if __name__ == '__main__':
from threading import Thread
STEP_PER_CM = 1574.80316
### used when only one axis is plugged in to Xa
x_axis = Axis('X',
pin_list={
'ena':2, 'pul':4, 'dir':3,
'eot_ccw':17, 'eot_cw':27},
steps_per_cm = STEP_PER_CM)
'''
#### BCM PIN NUMBERS
x_axis = CombinedAxis('X',
pin_list={
'ena':2, 'pul':4, 'dir':3,
'eot_ccw':[17,23], 'eot_cw':[27,24]},
steps_per_cm = STEP_PER_CM)
y_axis = Axis('Y',
pin_list={
'ena':16, 'pul':21, 'dir':20,
'eot_ccw':19, 'eot_cw':26},
steps_per_cm = STEP_PER_CM)
'''
#x = Thread(target=x_axis.move_to_cm, args=(10, 1, False))
x = Thread(target=x_axis.home, args=() )
print('starting')
x.start()
time.sleep(0.01)
while x_axis.keep_moving:
time.sleep(1)
print(x_axis.position)
if x_axis.position > 4:
x_axis.position=0
x.join()
print('all done')
x_axis.stop()
#x_axis.move_step(False, 5000, 0.0001)
#time.sleep(0.1)
#test.move(True,2000, 0.0001)
#test.print_limits(nread=40, wait=0.25)
#time.sleep(30)
| 8,202 | 2,778 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import os
import json
from pymongo import MongoClient
class LogImporter(object):
def __init__(self, data, constring):
self.data_dir = data
self.db = MongoClient(constring).get_default_database()
self.messages = self.db.messages
self.channels = self.__load_channels()
self.channel_ids = {chan["name"]: chan["id"] for chan in self.channels}
self.users = self.__load_users()
self.user_names = {user["id"]: user["name"] for user in self.users}
def __load_channels(self):
chan_file = os.path.join(self.data_dir, "channels.json")
f = open(chan_file)
channels = json.load(f)
f.close()
return channels
def __load_users(self):
user_file = os.path.join(self.data_dir, "users.json")
f = open(user_file)
users = json.load(f)
f.close()
return users
def channel(self, chan):
def insert_messages(msgs):
if len(msgs) == 0:
return
self.messages.insert(msgs)
def insert_chunk(filename):
f = open(filename)
msgs = json.load(f)
f.close()
for msg in msgs:
msg["channel"] = self.channel_ids[chan]
msg["channel_name"] = chan
if "user" not in msg:
continue
if msg["user"] == "USLACKBOT":
msg["user_name"] = "slackbot"
elif msg["user"] in self.user_names:
msg["user_name"] = self.user_names[msg["user"]]
else:
msg["user_name"] = "unknown"
insert_messages(msgs)
chan_dir = os.path.join(self.data_dir, chan)
if not (os.path.exists(chan_dir) and os.path.isdir(chan_dir)):
raise ValueError("Channel could not be found in the log directory")
for filename in os.listdir(chan_dir):
insert_chunk(os.path.join(chan_dir, filename))
def all_channels(self):
chans = [f for f in os.listdir(self.data_dir)
if os.path.isdir(os.path.join(self.data_dir, f))]
for chan in chans:
self.channel(chan)
def main():
parser = argparse.ArgumentParser(description="Import Slack logs into MongoDB")
parser.add_argument("--logs", required=True, type=str, metavar="LOGDIR", help="Directory containing Slack log files")
parser.add_argument("--mongo", required=True, type=str, metavar="MONGOCON", help="MongoDB connection string")
args = parser.parse_args()
importer = LogImporter(data=os.path.realpath(args.logs), constring=args.mongo)
importer.all_channels()
if __name__ == "__main__":
main()
| 2,788 | 873 |
from moai.parameters.optimization.optimizers.swa import SWA as Outer
import torch
import omegaconf.omegaconf
import hydra.utils as hyu
import typing
__all__ = ['SWA']
#NOTE: modified from https://github.com/alphadl/lookahead.pytorch
class SWA(object):
"""Implements Stochastic Weight Averaging (SWA).
- **Paper**: [Averaging Weights Leads to Wider Optima and Better Generalization](https://arxiv.org/pdf/1803.05407.pdf)
- **Implementation**: [GitHub @ pytorch](https://github.com/pytorch/contrib/tree/master/torchcontrib)
"""
def __init__(self,
parameters: typing.Iterator[torch.nn.Parameter],
optimizers: omegaconf.DictConfig,
swa_start=None,
swa_freq=None,
swa_lr=None
):
self.optimizers = [
Outer(
optimizer=hyu.instantiate(optimizers[0], parameters),
swa_start=swa_start, swa_lr=swa_lr, swa_freq=swa_freq,
)
]
| 973 | 335 |
#/usr/bin/env python
'''
@2018-01 by lanhin
Generate cluster json file.
Usage: python2 clustergen.py
The output file name is cluster.json
'''
import getopt
import sys
import os
def jsonout(fileout, devices, edges, nodelinks):
with open(fileout, "wb") as result:
outItem = "{\n \"devices\":\n [\n"
result.write(outItem)
# Write devices
for i in range(len(devices)-1):
outItem = "\t{\"id\":\""+(str)(devices[i][0])+"\",\n\t\"compute\":\""+(str)(devices[i][1])+"\",\n\t\"RAM\":\""+(str)(devices[i][2])+"\",\n\t\"bw\":\""+(str)(devices[i][3])+"\",\n\t\"loc\":\""+str(devices[i][4])+"\"\n\t},\n"
result.write(outItem)
outItem = "\t{\"id\":\""+(str)(devices[-1][0])+"\",\n\t\"compute\":\""+(str)(devices[-1][1])+"\",\n\t\"RAM\":\""+(str)(devices[-1][2])+"\",\n\t\"bw\":\""+(str)(devices[-1][3])+"\",\n\t\"loc\":\""+str(devices[-1][4])+"\"\n\t}\n"
result.write(outItem)
outItem = " ],\n \"links\":\n [\n"
result.write(outItem)
# Write links
for i in range(len(edges)-1):
outItem = "\t{\"src\":\""+(str)(edges[i][0])+"\",\n\t\"dst\":\""+(str)(edges[i][1])+"\",\n\t\"bw\":\""+(str)(edges[i][2])+"\"\n\t},\n"
result.write(outItem)
outItem = "\t{\"src\":\""+(str)(edges[-1][0])+"\",\n\t\"dst\":\""+(str)(edges[-1][1])+"\",\n\t\"bw\":\""+(str)(edges[-1][2])+"\"\n\t}"
result.write(outItem)
if len(nodelinks) > 0:
outItem = ",\n"
else:
outItem = "\n"
result.write(outItem)
for i in range(len(nodelinks)-1):
outItem = "\t{\"src\":\""+(str)(nodelinks[i][0])+"\",\n\t\"dst\":\""+(str)(nodelinks[i][1])+"\",\n\t\"bw\":\""+(str)(nodelinks[i][2])+"\",\n\t\"BetweenNode\":\"true\"\n\t},\n"
result.write(outItem)
outItem = "\t{\"src\":\""+(str)(nodelinks[-1][0])+"\",\n\t\"dst\":\""+(str)(nodelinks[-1][1])+"\",\n\t\"bw\":\""+(str)(nodelinks[-1][2])+"\",\n\t\"BetweenNode\":\"true\"\n\t}\n"
result.write(outItem)
outItem = " ]\n}\n"
result.write(outItem)
# compute, RAM, bw, network
devs = [("1000000", "1048576", "1111490", "1600000"),
("10000000", "1048576", "1342177", "3200000"),
("100000000", "2097152", "1342177", "3200000")]
# id, output network bandwidth in KB/s
comnodes = [(0,1000000), (1,20000000), (2,1000000), (3,1000000),
(4,1000000), (5,1000000), (6,1000000), (7,1000000)]
# id, compute, RAM, bw, location, network bandwidth
devices = list()
edges = list()
nodelinks = list()
if (len(sys.argv)) != 2:
print "Usage: python clustergen.py <cluster input file>"
exit(1)
filein = sys.argv[1]
fileout = filein + '.json'
print "Input:",filein
print "Output:",fileout
if os.path.isfile(fileout):
os.remove(fileout)
with open(filein, "rb") as source:
for line in source: # id, index, location
splited = line.strip().split(' ')
devices.append((int(splited[0]), devs[int(splited[1])][0], devs[int(splited[1])][1], devs[int(splited[1])][2], splited[2], devs[int(splited[1])][3]))
for i in range(len(devices)):
if int(devices[i][4]) == int(splited[-1]) and devices[i][0] != int(splited[0]): # in the same node
bw = min(float(devices[i][-1]), float(devs[int(splited[1])][-1]))
edges.append((splited[0], devices[i][0], str(bw)))
for i in range(len(comnodes)):
for j in range(i+1, len(comnodes)):
if j >= len(comnodes):
continue
nodelinks.append((comnodes[i][0], comnodes[j][0], min(float(comnodes[i][1]), float(comnodes[j][1]))))
#print devices
#print edges
#print nodelinks
jsonout(fileout, devices, edges, nodelinks)
| 3,763 | 1,600 |
import unittest
from xie.graphics.drawing import DrawingSystem
from xie.graphics.canvas import EncodedTextCanvasController
from xie.graphics.factory import StrokeSpec, StrokeFactory
class DrawingSystemTestCase(unittest.TestCase):
def setUp(self):
self.controller = EncodedTextCanvasController()
self.ds = DrawingSystem(self.controller)
self.strokeFactory = StrokeFactory()
def tearDown(self):
pass
def generateStroke(self, name, params, startPoint):
strokeSpec = StrokeSpec(name, params)
return self.strokeFactory.generateStrokeBySpec(strokeSpec, startPoint=startPoint)
def test_draw_stroke_1(self):
stroke = self.generateStroke("橫", [222], startPoint=(20, 123))
self.ds.draw(stroke)
self.assertEqual("0.20.123,1.242.123", self.controller.getStrokeExpression())
def test_draw_stroke_2(self):
stroke = self.generateStroke("豎", [211], startPoint=(124, 27))
self.ds.draw(stroke)
self.assertEqual("0.124.27,1.124.238", self.controller.getStrokeExpression())
def test_draw_stroke_3(self):
stroke = self.generateStroke("豎彎", [146, 126, 32], startPoint=(43, 54))
self.ds.draw(stroke)
self.assertEqual("0.43.54,1.43.180,2.43.212,1.75.212,1.221.212", self.controller.getStrokeExpression())
def test_translate(self):
stroke = self.generateStroke("橫", [222], startPoint=(20, 123))
self.ds.translate(29, 105)
self.ds.draw(stroke)
self.assertEqual("0.49.228,1.271.228", self.controller.getStrokeExpression())
def test_scale(self):
stroke = self.generateStroke("橫", [222], startPoint=(20, 123))
self.ds.scale(0.5, 1.2)
self.ds.draw(stroke)
self.assertEqual("0.10.148,1.121.148", self.controller.getStrokeExpression())
def test_complex_transform(self):
stroke = self.generateStroke("橫", [222], startPoint=(20, 123))
self.ds.translate(-10, -110)
self.ds.scale(0.5, 1.2)
self.ds.translate(26, 80)
self.ds.draw(stroke)
self.assertEqual("0.31.96,1.142.96", self.controller.getStrokeExpression())
| 1,956 | 846 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from PyQt5 import QtGui, QtWidgets, QtCore
from sharedcomponets import GUIToolKit
import logging
from simpleFOCConnector import SimpleFOCDevice
class DROGroupBox(QtWidgets.QGroupBox):
def __init__(self, parent=None, simpleFocConn=None):
"""Constructor for ToolsWidget"""
super().__init__(parent)
self.device = simpleFocConn
self.setTitle("Simple FOC Digital Read Out")
self.setObjectName("digitalReadOutGroupBox")
self.droHorizontalLayout = QtWidgets.QHBoxLayout(self)
self.droHorizontalLayout.setObjectName("droHorizontalLayout")
self.signal0Label = QtWidgets.QLabel(self)
self.signal0Label.setObjectName("signal0Label")
self.signal0Label.setText("Signal 0")
self.droHorizontalLayout.addWidget(self.signal0Label)
self.signal0LCDNumber = QtWidgets.QLCDNumber(self)
self.putStyleToLCDNumber(self.signal0LCDNumber)
self.signal0LCDNumber.setObjectName("signal0LCDNumber")
self.droHorizontalLayout.addWidget(self.signal0LCDNumber)
self.signal1Label = QtWidgets.QLabel(self)
self.signal1Label.setObjectName("signal1Label")
self.signal1Label.setText("Signal 1")
self.droHorizontalLayout.addWidget(self.signal1Label)
self.signal1LCDNumber = QtWidgets.QLCDNumber(self)
self.putStyleToLCDNumber(self.signal1LCDNumber)
self.signal1LCDNumber.setObjectName("signal1LCDNumber")
self.droHorizontalLayout.addWidget(self.signal1LCDNumber)
self.signal2Label = QtWidgets.QLabel(self)
self.signal2Label.setObjectName("voltageLable")
self.signal2Label.setText("Signal 2")
self.droHorizontalLayout.addWidget(self.signal2Label)
self.signal2LCDNumber = QtWidgets.QLCDNumber(self)
self.putStyleToLCDNumber(self.signal2LCDNumber)
self.signal2LCDNumber.setObjectName("signal2LCDNumber")
self.droHorizontalLayout.addWidget(self.signal2LCDNumber)
self.device.commProvider.telemetryDataReceived.connect(self.updateDRO)
self.device.addControlLoopModeListener(self)
self.controlLoopModeChanged(self.device.controlType)
self.initDiplay()
self.disableUI()
self.device.addConnectionStateListener(self)
def updateLabels(self, label0, label1, label2):
self.signal0Label.setText(label0)
self.signal1Label.setText(label1)
self.signal2Label.setText(label2)
def deviceConnected(self, deviceConnected):
if deviceConnected is True:
self.enabeUI()
self.initDiplay()
else:
self.initDiplay()
self.disableUI()
def enabeUI(self):
self.setEnabled(True)
def disableUI(self):
self.setEnabled(False)
def initDiplay(self):
self.signal0LCDNumber.display(0.0)
self.signal1LCDNumber.display(0.0)
self.signal2LCDNumber.display(0.0)
def putStyleToLCDNumber(self, lcdNumber):
lcdNumber.setStyleSheet("""QLCDNumber {background-color: white;}""")
palette = self.setColor(lcdNumber.palette(),GUIToolKit.RED_COLOR)
lcdNumber.setPalette(palette)
def setColor(self, palette, colorTouple):
R = colorTouple[0]
G = colorTouple[1]
B = colorTouple[2]
# foreground color
palette.setColor(palette.WindowText, QtGui.QColor(R,G,B))
# background color
palette.setColor(palette.Background, QtGui.QColor(R,G,B))
# "light" border
palette.setColor(palette.Light, QtGui.QColor(R,G,B))
# "dark" border
palette.setColor(palette.Dark, QtGui.QColor(R,G,B))
return palette
def setValues(self, values):
self.signal0LCDNumber.display(values[0])
self.signal1LCDNumber.display(values[1])
self.signal2LCDNumber.display(values[2])
def updateDRO(self, signal0, signal1, signal2):
try:
if type(signal0) is float and type(signal1) is float and type(signal2) is float:
self.signal0LCDNumber.display(signal0)
self.signal2LCDNumber.display(signal1)
self.signal1LCDNumber.display(signal2)
except IndexError as error:
logging.error(error, exc_info=True)
def controlLoopModeChanged(self, controlMode):
label0, label1, label2 = SimpleFOCDevice.getSignalLabels(controlMode)
self.updateLabels(label0, label1, label2) | 4,500 | 1,424 |
#!/usr/bin/env python
# coding=utf-8
'''
###
这个文件用来接管 控制 coredump 的存储
### 需手动
echo "|/usr/bin/python /docker_host/1.py" > /proc/sys/kernel/core_pattern
### sys.stdin 如果没有数据 返回 ''，不是 None
###
容器里对 core 对操作会持久化到 image 里
预期在 container 中对 /proc/sys/kernel/core_pattern 做的修改 如果不 commit ，
下次从相同到 image 进入 container core 应该还是未修改的，
在 macOS 测试却是修改过的
###
core dump 的小说明
https://favoorr.github.io/2017/02/10/learn-gdb-from-brendan/
'''
import os
import sys
import datetime
import io
import contextlib
import gzip
import argparse
import time
curpath = os.path.realpath(__file__)
curpath = os.path.dirname(curpath)
CORE_FILE_PREFIX = "core_time"
def redirect_to_file(fullpath, inputs):
'''
Write input to file, not compress
'''
if os.path.exists(fullpath):
os.remove(fullpath)
with open(fullpath, "wb") as fw:
while 1:
c = inputs.read(1024)
if not c:
break
fw.write(c)
return fullpath
def redirect_to_gzfile(fullpath, inputs):
'''
Write input to file, with compress
'''
if os.path.exists(fullpath):
os.remove(fullpath)
if not fullpath.endswith(".gz"):
fullpath = fullpath + ".gz"
if os.path.exists(fullpath):
os.remove(fullpath)
with gzip.open(fullpath, "wb", compresslevel=9) as fw:
with contextlib.closing(io.BufferedWriter(fw)) as fww:
while 1:
c = inputs.read(1024)
if not c:
break
fww.write(c)
return fullpath
def tick_called(msg):
'''
用来记录被调用 测试脚本时使用
'''
called = os.path.join(curpath, "called")
with open(called, "ab+") as fw:
fw.write("{t} called msg={m}\n".format(t=datetime.datetime.now(), m=msg))
def core_read(**kwargs):
'''
path_corepattern
'''
with open(kwargs.get("path_corepattern", ""), "rb") as fr:
c = fr.read()
return c
def core_set(**kwargs):
'''
path_corepattern
'''
path_corepattern = kwargs.get("path_corepattern", "")
print("[+] Before register the content of {f} ={c}"
.format(f=path_corepattern, c=core_read(**kwargs)))
towrite = "|/usr/bin/python {f} %p %u %h %e".format(f=os.path.realpath(__file__))
with open(path_corepattern, "wb") as fw:
fw.write(towrite)
print("[+] After register the content of {f} ={c}"
.format(f=path_corepattern, c=core_read(**kwargs)))
def check_core_file_limits(**kwargs):
'''
kwargs contains:
total_core_count_limit
core_saved_path
根据配置文件的配置和已经生成的core 决定要不要继续保存core
默认保存 Return True
'''
count_limit = kwargs.get('total_core_count_limit', None)
path_saved = kwargs.get('core_saved_path', None)
if not (count_limit and path_saved):
return True
counts = 0
sizes = 0
for child in os.listdir(path_saved):
p = os.path.join(path_saved, child)
if os.path.isfile(p) and child.startswith(CORE_FILE_PREFIX):
counts += 1
sizes += os.path.getsize(p)
if count_limit is not None and count_limit > 0 and counts >= count_limit:
return False
return True
def core_restore(**kwargs):
'''
path_corepattern
'''
path_corepattern = kwargs.get('path_corepattern', "")
print('[+] Before restore the content of {f} ={c}'
.format(f=path_corepattern, c=core_read(**kwargs)))
towrite = 'core'
with open(path_corepattern, 'wb') as fw:
fw.write(towrite)
print('[+] After restore the content of {f} ={c}'
.format(f=path_corepattern, c=core_read(**kwargs)))
def core_generate(**kwargs):
'''
core_pid
core_uid
core_hostname
core_execname
core_saved_path
core_input
:return fullpath_core
'''
now = time.time() * 1000
now = int(now)
filename = "{pf}-{t}_pid-{pid}_uid-{uid}_host-{hostname}_name-{execname}".format(
pf = CORE_FILE_PREFIX,
t=now, pid=kwargs.get("core_pid", ""), uid=kwargs.get("core_uid", ""),
hostname=kwargs.get("core_hostname", ""), execname=kwargs.get("core_execname", "")
)
saved = kwargs.get("core_saved_path", None) or curpath
fullpath = os.path.join(saved, filename)
return redirect_to_gzfile(fullpath, kwargs.get("core_input", sys.stdin))
def logging(**kwargs):
''' 生成 core 时， 写业务日志 说我们生成 core 了
'''
# format 2018-04-28 02:20:47:[INFO]===>load_black_white_list_conf==>enter hc->hid=142
logf = kwargs.get('path_log', '')
fp = kwargs.get('path_core_file', '')
nowutc = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if logf and os.path.exists(os.path.dirname(logf)):
with open(logf, 'a+') as fw:
fw.write("{t}:[ERROR]===>pycore==>cored".format(t=nowutc))
if fp and os.path.exists(fp):
fw.write(" write core file {f}".format(f=fp))
fw.write("\n")
def entry():
fullpath_core_pattern = "/proc/sys/kernel/core_pattern"
fullpath_log = "/home/logs"
if not os.path.exists(fullpath_log):
os.makedirs(fullpath_log)
fullpath_log = os.path.join(fullpath_log, "cores.log")
function_args = {
"path_corepattern": fullpath_core_pattern,
"path_log": fullpath_log,
"core_saved_path":"/home",
"total_core_count_limit": 3,
}
parser = argparse.ArgumentParser(description='Use for take over the core by pipe',
version='1.0')
parser.add_argument('--set', action='store_true',
help='Register this python file in {f} for core dump pipe'.format(f=fullpath_core_pattern))
parser.add_argument('--restore', action='store_true',
help='Restore the file {f}'.format(f=fullpath_core_pattern))
parser.add_argument('--testcore', action='store_true',
help='Test core generate')
args, unknownargs = parser.parse_known_args()
if args.restore:
core_restore(**function_args)
elif args.set:
core_set(**function_args)
elif args.testcore:
fp = open(os.path.join(curpath, 'core_test'), 'rb')
function_args.update({
'core_pid': 1, 'core_uid': 2,
'core_hostname': 3,
'core_execname': 4,
'core_saved_path': curpath,
'core_input': fp,
})
v = core_generate(**function_args)
fp.close()
print('[+] Generate core {f}'.format(f=v))
elif len(unknownargs) > 3:
function_args.update({
'core_pid': unknownargs[0], 'core_uid': unknownargs[1],
'core_hostname': unknownargs[2],
'core_execname': unknownargs[3],
'core_input': sys.stdin,
})
if check_core_file_limits(**function_args):
core_file = core_generate(**function_args)
logging(**function_args, path_core_file=core_file)
else:
parser.print_help()
if __name__ == '__main__':
sy = sys.version_info
if not (sy.major >= 2 and sy.minor >= 7):
raise ValueError('only support Python version up 2.7.x')
entry()
| 7,157 | 2,603 |
plot = Plot()
# Uneven error bars
line = Line()
line.xValues = range(6)
line.yValues = [25, 21, 30, 23, 10, 30]
line.yMins = [10, 18, 10, 10, 5, 20]
line.yMaxes = [30, 50, 40, 30, 20, 45]
line.label = "Asymmetric Errors"
line.color = "red"
line.xValues = range(len(line.yValues))
# Even error bars
line2 = Line()
line2.xValues = range(6)
line2.yValues = [35, 40, 45, 40, 55, 50]
line2.color = "blue"
line2.label = "Symmetric Errors"
line2.yErrors = [3, 6, 5, 3, 5, 4]
plot.add(line)
plot.add(line2)
plot.xLabel = "X Label"
plot.yLabel = "Y Label"
plot.hasLegend()
plot.save("errorbars.png")
| 595 | 308 |
#!/usr/bin/env python
from wurst import *
from wurst.searching import *
from wurst.transformations.activity import change_exchanges_by_constant_factor
from wurst.transformations.uncertainty import rescale_exchange
from wurst.IMAGE.io import *
from wurst.IMAGE import *
from wurst.ecoinvent.electricity_markets import *
from wurst.ecoinvent.filters import *
from wurst.transformations.geo import *
import pandas as pd
from pprint import pprint
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
image_filename = os.path.join(dir_path, 'assets/Image variable names.xlsx')
image_variable_names = pd.read_excel(image_filename)
REGIONS = image_variable_names['Regions'].dropna().values
scenarios = {'BAU': {}, '450': {}}
scenarios['BAU']['filepath'] = r'SSPs_data/SSPs/SSP2'
scenarios['BAU']['description'] = "Middle of the Road (Medium challenges to mitigation and adaptation)"
scenarios['450']['filepath'] = r'SSPs_data/SSPs/SSP2_450'
scenarios['450']['description'] = "Middle of the Road (Medium challenges to mitigation and adaptation), 4.5W.m-2 target"
#
# <p>We import some datasets from simapro. These functions clean up the datasets:</p>
#
def fix_unset_technosphere_and_production_exchange_locations(db, matching_fields=('name', 'unit')):
for ds in db:
for exc in ds['exchanges']:
if exc['type'] == 'production' and exc.get('location') is None:
exc['location'] = ds['location']
elif exc['type'] == 'technosphere' and exc.get('location') is None:
locs = find_location_given_lookup_dict(db,
{k: exc.get(k) for k in matching_fields})
if len(locs) == 1:
exc['location'] = locs[0]
else:
print("No unique location found for exchange:\n{}\nFound: {}".format(
pprint.pformat(exc), locs
))
def find_location_given_lookup_dict(db, lookup_dict):
return [x['location'] for x in get_many(db, *[equals(k, v) for k, v in lookup_dict.items()])]
exists = lambda x: {k: v for k, v in x.items() if v is not None}
def remove_nones(db):
for ds in db:
ds['exchanges'] = [exists(exc) for exc in ds['exchanges']]
def set_global_location_for_additional_datasets(db):
""" This function is needed because the wurst function relink_technosphere exchanges needs global datasets if if can't find a regional one."""
non_ecoinvent_datasets = [x['name'] for x in input_db if x['database'] != 'ecoinvent']
ecoinvent_datasets = [x['name'] for x in input_db if x['database'] == 'ecoinvent']
for ds in [x for x in db if x['database'] in ['Carma CCS', 'CSP']]:
ds['location'] = 'GLO'
for exc in [x for x in ds['exchanges'] if x['type'] != 'biosphere']:
if exc['name'] in non_ecoinvent_datasets:
if exc['name'] in ecoinvent_datasets and exc['location'] != 'GLO':
print(exc['name'], exc['location'])
else:
exc['location'] = 'GLO'
def add_new_locations_to_added_datasets(db):
# We create a new version of all added electricity generation datasets for each IMAGE region.
# We allow the upstream production to remain global, as we are mostly interested in regionalizing
# to take advantage of the regionalized IMAGE data.
# step 1: make copies of all datasets for new locations
# best would be to regionalize datasets for every location with an electricity market like this:
# locations = {x['location'] for x in get_many(db, *electricity_market_filter_high_voltage)}
# but this takes quite a long time. For now, we just use 1 location that is uniquely in each IMAGE region.
possibles = {}
for reg in REGIONS[:-1]:
temp = [x for x in geomatcher.intersects(('IMAGE', reg)) if type(x) != tuple]
possibles[reg] = [x for x in temp if len(ecoinvent_to_image_locations(x)) == 1]
if not len(possibles[reg]): print(reg, ' has no good candidate')
locations = [v[0] for v in possibles.values()]
# This code would modify every new dataset, but this would be quite large:
# for ds in pyprind.prog_bar([ds for ds in db if ds['database'] in ['CSP','Carma CCS']]):
# so we consider only the final electricity production dataset and not the upstream impacts:
for ds in pyprind.prog_bar([ds for ds in db if ds['name'] in carma_electricity_ds_name_dict.keys()]):
for location in locations:
new_ds = copy_to_new_location(ds, location)
db.append(new_ds)
def regionalize_added_datasets(db):
# step 2: relink all processes in each dataset
# This code would modify every new dataset, but this would be quite large:
# for ds in pyprind.prog_bar([ds for ds in db if ds['database'] in ['CSP','Carma CCS']]):
# so we consider only the final electricity production dataset and not the upstream impacts:
for ds in [ds for ds in db if ds['name'] in carma_electricity_ds_name_dict.keys()]:
ds = relink_technosphere_exchanges(ds, db, exclusive=True, drop_invalid=False, biggest_first=False,
contained=False)
#
# <h1 id="Modify-electricity-datasets">Modify electricity datasets<a class="anchor-link" href="#Modify-electricity-datasets">¶</a></h1>
#
# In[12]:
def update_ecoinvent_efficiency_parameter(ds, scaling_factor):
parameters = ds['parameters']
possibles = ['efficiency', 'efficiency_oil_country', 'efficiency_electrical']
for key in possibles:
try:
parameters[key] /= scaling_factor
return
except KeyError:
pass
# In[13]:
def find_coal_efficiency_scaling_factor(ds, year, image_efficiency, agg_func=np.average):
# Input a coal electricity dataset and year. We look up the efficiency for this region and year
# from the Image model and return the scaling factor by which to multiply all efficiency dependent exchanges.
# If the ecoinvent region corresponds to multiple Image regions we simply average them.
ecoinvent_eff = find_ecoinvent_coal_efficiency(ds)
image_locations = ecoinvent_to_image_locations(ds['location'])
image_eff = agg_func(
image_efficiency.loc[year][image_locations].values) # we take an average of all applicable image locations
return ecoinvent_eff / image_eff
# In[14]:
def find_gas_efficiency_scaling_factor(ds, year, image_efficiency, agg_func=np.average):
# Input a gas electricity dataset and year. We look up the efficiency for this region and year
# from the Image model and return the scaling factor by which to multiply all efficiency dependent exchanges.
# If the ecoinvent region corresponds to multiple Image regions we simply average them.
ecoinvent_eff = find_ecoinvent_gas_efficiency(ds)
image_locations = ecoinvent_to_image_locations(ds['location'])
image_eff = agg_func(
image_efficiency.loc[year][image_locations].values) # we take an average of all applicable image locations
return ecoinvent_eff / image_eff
# In[15]:
def find_oil_efficiency_scaling_factor(ds, year, image_efficiency, agg_func=np.average):
# Input a oil electricity dataset and year. We look up the efficiency for this region and year
# from the Image model and return the scaling factor by which to multiply all efficiency dependent exchanges.
# If the ecoinvent region corresponds to multiple Image regions we simply average them.
ecoinvent_eff = find_ecoinvent_oil_efficiency(ds)
image_locations = ecoinvent_to_image_locations(ds['location'])
image_eff = agg_func(
image_efficiency.loc[year][image_locations].values) # we take an average of all applicable image locations
return ecoinvent_eff / image_eff
# In[16]:
def find_biomass_efficiency_scaling_factor(ds, year, image_efficiency, agg_func=np.average):
# Input an electricity dataset and year. We look up the efficiency for this region and year
# from the Image model and return the scaling factor by which to multiply all efficiency dependent exchanges.
# If the ecoinvent region corresponds to multiple Image regions we simply average them.
ecoinvent_eff = find_ecoinvent_biomass_efficiency(ds)
image_locations = ecoinvent_to_image_locations(ds['location'])
image_eff = agg_func(
image_efficiency.loc[year][image_locations].values) # we take an average of all applicable image locations
return ecoinvent_eff / image_eff
# In[17]:
def find_nuclear_efficiency_scaling_factor(ds, year, image_efficiency, agg_func=np.average):
# Input an electricity dataset and year. We look up the efficiency for this region and year
# from the Image model and return the scaling factor compared to the improvement since 2012.
# We do not consider the ecoinvent efficiency in 2012 as it is rather difficult to calculate and
# the burnup is not available.
# This is a simplification and certainly has it's weaknesses,
# however we argue that it's better than not chaning the datasets at all.
# If the ecoinvent region corresponds to multiple Image regions we simply average them.
image_locations = ecoinvent_to_image_locations(ds['location'])
image_uranium_efficiency = agg_func(
image_efficiency.loc[year][image_locations].values) # we take an average of all applicable image locations
image_uranium_efficiency_2012 = agg_func(image_efficiency.loc[2012][image_locations].values)
return image_uranium_efficiency_2012 / image_uranium_efficiency
# In[18]:
def find_ecoinvent_coal_efficiency(ds):
# Nearly all coal power plant datasets have the efficiency as a parameter.
# If this isn't available, we back calculate it using the amount of coal used and
# an average energy content of coal.
try:
return ds['parameters']['efficiency']
except KeyError:
pass
# print('Efficiency parameter not found - calculating generic coal efficiency factor', ds['name'], ds['location'])
fuel_sources = technosphere(ds,
either(contains('name', 'hard coal'), contains('name', 'lignite')),
doesnt_contain_any('name', ('ash', 'SOx')),
equals('unit', 'kilogram'))
energy_in = 0
for exc in fuel_sources:
if 'hard coal' in exc['name']:
energy_density = 20.1 / 3.6 # kWh/kg
elif 'lignite' in exc['name']:
energy_density = 9.9 / 3.6 # kWh/kg
else:
raise ValueError("Shouldn't happen because of filters!!!")
energy_in += (exc['amount'] * energy_density)
ds['parameters']['efficiency'] = reference_product(ds)['amount'] / energy_in
# print(ds['parameters']['efficiency'])
return reference_product(ds)['amount'] / energy_in
# In[19]:
def find_ecoinvent_gas_efficiency(ds):
# Nearly all gas power plant datasets have the efficiency as a parameter.
# If this isn't available, we back calculate it using the amount of gas used and an average energy content of gas.
try:
return ds['parameters']['efficiency']
except KeyError:
pass
# print('Efficiency parameter not found - calculating generic gas efficiency factor', ds['name'], ds['location'])
fuel_sources = technosphere(ds,
either(contains('name', 'natural gas, low pressure'),
contains('name', 'natural gas, high pressure')),
equals('unit', 'cubic meter'))
energy_in = 0
for exc in fuel_sources:
# (based on energy density of natural gas input for global dataset
# 'electricity production, natural gas, conventional power plant')
if 'natural gas, high pressure' in exc['name']:
energy_density = 39 / 3.6 # kWh/m3
# (based on average energy density of high pressure gas, scaled by the mass difference listed between
# high pressure and low pressure gas in the dataset:
# natural gas pressure reduction from high to low pressure, RoW)
elif 'natural gas, low pressure' in exc['name']:
energy_density = 39 * 0.84 / 3.6 # kWh/m3
else:
raise ValueError("Shouldn't happen because of filters!!!")
energy_in += (exc['amount'] * energy_density)
ds['parameters']['efficiency'] = reference_product(ds)['amount'] / energy_in
# print(ds['parameters']['efficiency'])
return reference_product(ds)['amount'] / energy_in
# In[20]:
def find_ecoinvent_oil_efficiency(ds):
# Nearly all oil power plant datasets have the efficiency as a parameter.
# If this isn't available, we use global average values to calculate it.
try:
return ds['parameters']['efficiency_oil_country']
except KeyError:
pass
# print('Efficiency parameter not found - calculating generic oil efficiency factor', ds['name'], ds['location'])
fuel_sources = [x for x in technosphere(ds, *[contains('name', 'heavy fuel oil'),
equals('unit', 'kilogram')]
)]
energy_in = 0
for exc in fuel_sources:
# (based on energy density of heavy oil input and efficiency parameter for dataset
# 'electricity production, oil, RoW')
energy_density = 38.5 / 3.6 # kWh/m3
energy_in += (exc['amount'] * energy_density)
ds['parameters']['efficiency'] = reference_product(ds)['amount'] / energy_in
# print(ds['parameters']['efficiency'])
return reference_product(ds)['amount'] / energy_in
# In[21]:
def find_ecoinvent_biomass_efficiency(ds):
# Nearly all power plant datasets have the efficiency as a parameter. If this isn't available, we excl.
try:
return ds['parameters']['efficiency_electrical']
except:
pass
if ds['name'] == 'heat and power co-generation, biogas, gas engine, label-certified':
ds['parameters'] = {'efficiency_electrical': 0.32}
return ds['parameters']['efficiency_electrical'] # in general comments for dataset
elif ds['name'] == 'wood pellets, burned in stirling heat and power co-generation unit, 3kW electrical, future':
ds['parameters'] = {'efficiency_electrical': 0.23}
return ds['parameters']['efficiency_electrical'] # in comments for dataset
print(ds['name'], ds['location'], ' Efficiency not found!')
return 0
# In[22]:
def get_exchange_amounts(ds, technosphere_filters=None, biosphere_filters=None):
result = {}
for exc in technosphere(ds, *(technosphere_filters or [])):
result[(exc['name'], exc['location'])] = exc['amount']
for exc in biosphere(ds, *(biosphere_filters or [])):
result[(exc['name'], exc.get('categories'))] = exc['amount']
return result
# In[23]:
retained_filter = doesnt_contain_any('name', (
'market for NOx retained',
'market for SOx retained'
))
image_air_pollutants = {
'Methane, fossil': 'CH4',
'Sulfur dioxide': 'SO2',
'Carbon monoxide, fossil': 'CO',
'Nitrogen oxides': 'NOx',
'Dinitrogen monoxide': 'N2O'
}
no_al = [exclude(contains('name', 'aluminium industry'))]
no_ccs = [exclude(contains('name', 'carbon capture and storage'))]
no_markets = [exclude(contains('name', 'market'))]
no_imports = [exclude(contains('name', 'import'))]
generic_excludes = no_al + no_ccs + no_markets
image_mapping = {
'Coal ST': {
'fuel2': 'Coal',
'eff_func': find_coal_efficiency_scaling_factor,
'technology filters': coal_electricity + generic_excludes,
'technosphere excludes': [retained_filter],
},
'Coal CHP': {
'fuel2': 'Coal',
'eff_func': find_coal_efficiency_scaling_factor,
'technology filters': coal_chp_electricity + generic_excludes,
'technosphere excludes': [retained_filter],
},
'Natural gas OC': {
'fuel2': 'Natural gas',
'eff_func': find_gas_efficiency_scaling_factor,
'technology filters': gas_open_cycle_electricity + generic_excludes + no_imports,
'technosphere excludes': [],
},
'Natural gas CC': {
'fuel2': 'Natural gas',
'eff_func': find_gas_efficiency_scaling_factor,
'technology filters': gas_combined_cycle_electricity + generic_excludes + no_imports,
'technosphere excludes': [],
},
'Natural gas CHP': {
'fuel2': 'Natural gas',
'eff_func': find_gas_efficiency_scaling_factor,
'technology filters': gas_chp_electricity + generic_excludes + no_imports,
'technosphere excludes': [],
},
'Oil ST': {
'fuel2': 'Heavy liquid fuel',
'eff_func': find_oil_efficiency_scaling_factor,
'technology filters': oil_open_cycle_electricity + generic_excludes + [exclude(contains('name', 'nuclear'))],
'technosphere excludes': [],
},
'Oil CC': {
'fuel2': 'Heavy liquid fuel',
'eff_func': find_oil_efficiency_scaling_factor,
'technology filters': oil_combined_cycle_electricity + generic_excludes + [
exclude(contains('name', 'nuclear'))],
'technosphere excludes': [],
},
'Oil CHP': {
'fuel2': 'Heavy liquid fuel',
'eff_func': find_oil_efficiency_scaling_factor,
'technology filters': oil_chp_electricity + generic_excludes + [exclude(contains('name', 'nuclear'))],
'technosphere excludes': [],
},
'Biomass ST': {
'fuel2': 'Biomass',
'eff_func': find_biomass_efficiency_scaling_factor,
'technology filters': biomass_electricity + generic_excludes,
'technosphere excludes': [],
},
'Biomass CHP': {
'fuel2': 'Biomass',
'eff_func': find_biomass_efficiency_scaling_factor,
'technology filters': biomass_chp_electricity + generic_excludes,
'technosphere excludes': [],
},
'Biomass CC': {
'fuel2': 'Biomass',
'eff_func': find_biomass_efficiency_scaling_factor,
'technology filters': biomass_combined_cycle_electricity + generic_excludes,
'technosphere excludes': [],
},
'Nuclear': {
'fuel2': None, # image parameter doesn't exist for nuclear
'eff_func': find_nuclear_efficiency_scaling_factor,
'technology filters': nuclear_electricity + generic_excludes,
'technosphere excludes': [],
},
}
def update_electricity_datasets_with_image_data(db, year, scenario, agg_func=np.average, update_efficiency=True,
update_emissions=True):
"""
#for the moment we assume that particulates reduce according to the efficiency as we don't have any better data.
"""
changes = {}
for image_technology in image_mapping:
print('Changing ', image_technology)
md = image_mapping[image_technology]
image_efficiency = get_image_efficiencies(scenario, image_technology)
if image_technology != 'Nuclear':
image_emissions = get_image_electricity_emission_factors(scenario, image_efficiency, fuel2=md.get('fuel2'))
for ds in get_many(db, *md['technology filters']):
changes[ds['code']] = {}
changes[ds['code']].update({('meta data', x): ds[x] for x in ['name', 'location']})
changes[ds['code']].update({('meta data', 'Image technology'): image_technology})
changes[ds['code']].update({('original exchanges', k): v for k, v in get_exchange_amounts(ds).items()})
if update_efficiency == True:
# Modify using IMAGE efficiency values:
scaling_factor = md['eff_func'](ds, year, image_efficiency, agg_func)
update_ecoinvent_efficiency_parameter(ds, scaling_factor)
change_exchanges_by_constant_factor(ds, scaling_factor, md['technosphere excludes'],
[doesnt_contain_any('name', image_air_pollutants)])
if image_technology != 'Nuclear': # We don't update emissions for nuclear
if update_emissions == True:
# Modify using IMAGE specific emissions data
for exc in biosphere(ds, either(*[contains('name', x) for x in image_air_pollutants])):
image_locations = (ds['location'])
flow = image_air_pollutants[exc['name']]
amount = agg_func(image_emissions[flow].loc[year][image_locations].values)
# if new amount isn't a number:
if np.isnan(amount):
print('Not a number! Setting exchange to zero' + ds['name'], exc['name'], ds['location'])
rescale_exchange(exc, 0)
# if old amound was zero:
elif exc['amount'] == 0:
exc['amount'] = 1
rescale_exchange(exc, amount / exc['amount'], remove_uncertainty=True)
else:
rescale_exchange(exc, amount / exc['amount'])
changes[ds['code']].update({('updated exchanges', k): v for k, v in get_exchange_amounts(ds).items()})
return changes
#
# <h2 id="Modify-Carma-Datasets">Modify Carma Datasets<a class="anchor-link" href="#Modify-Carma-Datasets">¶</a></h2>
#
#
# <p>Carma datasets are CCS datasets taken from project Carma - see Volkart 2013.</p>
#
# In[24]:
carma_electricity_ds_name_dict = {
'Electricity, at wood burning power plant 20 MW, truck 25km, post, pipeline 200km, storage 1000m/2025': 'Biomass CCS',
'Electricity, at power plant/natural gas, NGCC, no CCS/2025/kWh': 'Natural gas CC',
'Electricity, at power plant/natural gas, pre, pipeline 400km, storage 3000m/2025': 'Natural gas CCS',
'Electricity, at BIGCC power plant 450MW, pre, pipeline 200km, storage 1000m/2025': 'Biomass CCS',
'Electricity, at power plant/hard coal, PC, no CCS/2025': 'Coal ST',
'Electricity, at power plant/hard coal, IGCC, no CCS/2025': 'IGCC',
'Electricity, at wood burning power plant 20 MW, truck 25km, no CCS/2025': 'Biomass ST',
'Electricity, at power plant/natural gas, pre, pipeline 200km, storage 1000m/2025': 'Natural gas CCS',
'Electricity, at power plant/lignite, PC, no CCS/2025': 'Coal ST',
'Electricity, at power plant/hard coal, pre, pipeline 200km, storage 1000m/2025': 'Coal CCS',
'Electricity, from CC plant, 100% SNG, truck 25km, post, pipeline 200km, storage 1000m/2025': 'Biomass CCS',
'Electricity, at wood burning power plant 20 MW, truck 25km, post, pipeline 400km, storage 3000m/2025': 'Biomass CCS',
'Electricity, at power plant/hard coal, oxy, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, oxy, pipeline 200km, storage 1000m/2025': 'Coal CCS',
'Electricity, at power plant/hard coal, post, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, pre, pipeline 200km, storage 1000m/2025': 'Coal CCS',
'Electricity, at BIGCC power plant 450MW, pre, pipeline 400km, storage 3000m/2025': 'Biomass CCS',
'Electricity, at power plant/natural gas, post, pipeline 400km, storage 1000m/2025': 'Natural gas CCS',
'Electricity, at power plant/lignite, post, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/hard coal, post, pipeline 400km, storage 1000m/2025': 'Coal CCS',
'Electricity, from CC plant, 100% SNG, truck 25km, post, pipeline 400km, storage 3000m/2025': 'Biomass CCS',
'Electricity, at power plant/natural gas, ATR H2-CC, no CCS/2025': 'Natural gas CCS',
'Electricity, at power plant/hard coal, pre, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, IGCC, no CCS/2025': 'IGCC',
'Electricity, at power plant/hard coal, post, pipeline 200km, storage 1000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, oxy, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, post, pipeline 200km, storage 1000m/2025': 'Coal CCS',
'Electricity, at power plant/lignite, pre, pipeline 400km, storage 3000m/2025': 'Coal CCS',
'Electricity, at power plant/natural gas, post, pipeline 200km, storage 1000m/2025': 'Natural gas CCS',
'Electricity, at power plant/natural gas, post, pipeline 400km, storage 3000m/2025': 'Natural gas CCS',
'Electricity, at BIGCC power plant 450MW, no CCS/2025': 'Biomass ST',
'Electricity, from CC plant, 100% SNG, truck 25km, no CCS/2025': 'Biomass ST',
'Electricity, at power plant/hard coal, oxy, pipeline 200km, storage 1000m/2025': 'Coal CCS'
}
# In[25]:
def modify_all_carma_electricity_datasets(db, year, scenario, update_efficiency=True, update_emissions=True):
# First determine which image efficiency dataset needs to be used:
image_emissions = {}
for fuel2 in ['Coal', 'Natural gas', 'Biomass']:
image_emissions[fuel2] = get_image_electricity_emissions_per_input_energy(scenario, fuel2,
sector='Power generation')
fuel_dict = {'Biomass CCS': 'Biomass',
'Biomass ST': 'Biomass',
'Coal CCS': 'Coal',
'Coal ST': 'Coal',
'IGCC': 'Coal',
'Natural gas CC': 'Natural gas',
'Natural gas CCS': 'Natural gas'}
for name, tech in carma_electricity_ds_name_dict.items():
image_efficiency = get_image_efficiencies(scenario, tech)
for ds in get_many(db, equals('name', name)):
if update_efficiency:
if 'Electricity, at BIGCC power plant 450MW' in ds['name']:
modify_carma_BIGCC_efficiency(ds, year, scenario, image_efficiency)
else:
modify_standard_carma_dataset_efficiency(ds, year, scenario, image_efficiency)
if update_emissions:
modify_carma_dataset_emissions(db, ds, year, scenario, image_emissions[fuel_dict[tech]])
# The efficiency defined by image also includes the electricity consumed in the carbon capture process,
# so we have to set this exchange amount to zero:
if update_efficiency:
for ds in get_many(db, contains('name', 'CO2 capture')):
for exc in technosphere(ds, *[contains('name', 'Electricity'), equals('unit', 'kilowatt hour')]):
exc['amount'] = 0
# In[26]:
def modify_carma_dataset_emissions(db, ds, year, scenario, emission_df):
# The dataset passed to this function doesn't have the biosphere flows directly.
# Rather, it has an exchange (with unit MJ) that contains the biosphere flows per unit fuel input.
biosphere_mapping = {'CH4': 'Methane, fossil',
'SO2': 'Sulfur dioxide',
'CO': 'Carbon monoxide, fossil',
'NOx': 'Nitrogen oxides',
'N2O': 'Dinitrogen monoxide'}
image_locations = ecoinvent_to_image_locations(ds['location'])
exc_dataset_names = [x['name'] for x in technosphere(ds, equals('unit', 'megajoule'))]
for exc_dataset in get_many(db, *[
either(*[equals('name', exc_dataset_name) for exc_dataset_name in exc_dataset_names])]):
if len(list(biosphere(exc_dataset))) == 0:
modify_carma_dataset_emissions(db, exc_dataset, year, scenario, emission_df)
continue
# Modify using IMAGE emissions data
for key, value in biosphere_mapping.items():
for exc in biosphere(exc_dataset, contains('name', value)):
exc['amount'] = np.average(emission_df[key].loc[year][image_locations].values)
if np.isnan(exc['amount']):
print('Not a number! Setting exchange to zero' + ds['name'], exc['name'], ds['location'])
exc['amount'] = 0
return
# In[27]:
def modify_standard_carma_dataset_efficiency(ds, year, scenario, image_efficiency):
if 'Electricity, at BIGCC power plant 450MW' in ds['name']:
print("This function can't modify dataset: ", ds['name'], "It's got a different format.")
return
image_locations = ecoinvent_to_image_locations(ds['location'])
image_efficiency = np.average(image_efficiency.loc[year][image_locations].values)
# All other carma electricity datasets have a single exchange that is the combustion of a fuel in MJ.
# We can just scale this exchange and efficiency related changes will be done
for exc in technosphere(ds):
exc['amount'] = 3.6 / image_efficiency
return
# In[28]:
def modify_carma_BIGCC_efficiency(ds, year, scenario, image_efficiency):
image_locations = ecoinvent_to_image_locations(ds['location'])
image_efficiency = np.average(image_efficiency.loc[year][image_locations].values)
old_efficiency = 3.6 / get_one(technosphere(ds), *[contains('name', 'Hydrogen, from steam reforming')])['amount']
for exc in technosphere(ds):
exc['amount'] = exc['amount'] * old_efficiency / image_efficiency
return
#
# <h1 id="Get-image-model-results">Get image model results<a class="anchor-link" href="#Get-image-model-results">¶</a></h1>
#
# In[29]:
def read_image_data_Reg_x_Tech(scenario, technology, filename_extension, world_calc_type):
# This function imports a set of results from image for a certain scenario and returns
# a dataframe with the values for all years and regions for a specific technology.
# Possible choices are listed in image_variable_names['Technology']
fp = os.path.join(scenarios[scenario]['filepath'], filename_extension)
image_output = load_image_data_file(fp)
result = {}
lookup_number = np.where(image_variable_names['Technology'] == technology)[0][0]
for year in image_output.years:
result[year] = {}
for region, vector in zip(REGIONS[:],
image_output.data[:, lookup_number, list(image_output.years).index(year)]):
result[year][region] = vector
result = pd.DataFrame.from_dict(result, orient='index')
if world_calc_type == 'mean':
result['World'] = result.mean(axis=1)
elif world_calc_type == 'sum':
result['World'] = result.sum(axis=1)
else:
print("can't calculate world")
return result
#
# <h2 id="Nuclear-Electricity-Efficiency">Nuclear Electricity Efficiency<a class="anchor-link" href="#Nuclear-Electricity-Efficiency">¶</a></h2>
#
# In[30]:
def read_electricity_uranium_consumption(scenario):
# This function imports the efficiency of nuclear power plants in GJ electricity /kg uranium
fp = os.path.join(scenarios[scenario]['filepath'], "tuss", "nuclfueleff.out")
image_output = load_image_data_file(fp)
result = {}
for year in image_output.years:
result[year] = {}
for region, vector in zip(REGIONS[:], image_output.data[:, list(image_output.years).index(year)]):
result[year][region] = vector
df = pd.DataFrame.from_dict(result, orient='index')
df.replace({0: np.nan},
inplace=True) # we set all zero values to NaN so that the global average is calcuated only from values that exist.
df['World'] = df.mean(axis=1)
return df
#
# <h2 id="Fossil-Electricity-Efficiency">Fossil Electricity Efficiency<a class="anchor-link" href="#Fossil-Electricity-Efficiency">¶</a></h2>
#
# In[31]:
def get_image_efficiencies(scenario, technology):
# This function imports a set of results from image for a certain scenario and returns
# a dataframe with the efficiency values for all years and regions for a specific technology.
# possible choices are listed in image_variable_names['Technology']
fp = os.path.join(scenarios[scenario]['filepath'], "tuss", "EPG", "ElecEffAvg.out")
elec_eff_avg = load_image_data_file(fp)
image_efficiency = {}
lookup_number = np.where(image_variable_names['Technology'] == technology)[0][0]
for year in elec_eff_avg.years:
image_efficiency[year] = {}
for region, vector in zip(REGIONS[:],
elec_eff_avg.data[:, lookup_number, list(elec_eff_avg.years).index(year)]):
image_efficiency[year][region] = vector
image_efficiency = pd.DataFrame.from_dict(image_efficiency, orient='index')
image_efficiency['World'] = image_efficiency.mean(axis=1)
return image_efficiency
#
# <h2 id="Fossil-Electricity-Emissions">Fossil Electricity Emissions<a class="anchor-link" href="#Fossil-Electricity-Emissions">¶</a></h2>
#
# In[32]:
def get_image_electricity_emission_factors(scenario, image_efficiency, fuel2, sector='Power generation'):
# This function imports a set of results from image for a certain scenario and returns
# a dictionary of dataframes each with the emission values for all years and regions for one pollutant.
# possible fuel2 choices are listed in image_variable_names['Fuel2']
emission_dict = {'CH4': "ENEFCH4.out",
'CO': "ENEFCO.out",
'N2O': "ENEFN2O.out",
'NOx': "ENEFNOx.out",
'SO2': "ENEFSO2.out",
'BC': "ENEFBC.out",
}
elec_emission_factors = {}
for k, v in emission_dict.items():
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", v)
elec_emission_factors[k] = load_image_data_file(fp)
# We currently don't have a good way to deal with the fact that ecoinvent has many different VOCs listed.
# For the moment we just allow them to scale with the efficiency.
# Note that we don't import CO2 results as these are calculated by scaling using efficiency.
# This is more accurate as it considers that ecoinvent is more accurate regarding the energy content
# of the specific fuel used.
image_emissions = {}
fuel2_number = np.where(image_variable_names['Fuel2'] == fuel2)[0][0]
sector_number = np.where(image_variable_names['Sector'] == sector)[0][0]
for key, value in elec_emission_factors.items():
image_emissions[key] = {}
for year in elec_emission_factors[key].years:
image_emissions[key][year] = {}
for region, vector in zip(REGIONS[:-1], value.data[:, sector_number, fuel2_number,
list(elec_emission_factors[key].years).index(year)]):
image_emissions[key][year][region] = vector
image_emissions[key] = pd.DataFrame.from_dict(image_emissions[key], orient='index')
# Note that Image reports emissions pre unit of fuel in, so we have to make a couple of calculations
if key == 'BC':
image_emissions[key] = (image_emissions[key].divide(image_efficiency,
axis=0)) * 3.6e-3 # convert to kg/kWh of electricity
else:
image_emissions[key] = (image_emissions[key].divide(image_efficiency,
axis=0)) * 3.6e-6 # convert to kg/kWh of electricity
image_emissions[key].replace({0: np.nan},
inplace=True) # we set all zero values to NaN so that the global average is calcuated only from values that exist.
image_emissions[key]['World'] = image_emissions[key].mean(axis=1)
image_emissions[key].fillna(0, inplace=True) # set nan values back to zero.
return image_emissions
# In[33]:
def get_image_electricity_emissions_per_input_energy(scenario, fuel2, sector='Power generation'):
# This function imports a set of results from image for a certain scenario and returns
# a dictionary of dataframes each with the emission values for all years and regions for one pollutant.
# possible fuel2 choices are listed in image_variable_names['Fuel2']
elec_emission_factors = {}
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFCH4.out")
elec_emission_factors['CH4'] = load_image_data_file(fp)
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFCO.out")
elec_emission_factors['CO'] = load_image_data_file(fp)
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFN2O.out")
elec_emission_factors['N2O'] = load_image_data_file(fp)
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFNOx.out")
elec_emission_factors['NOx'] = load_image_data_file(fp)
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFSO2.out")
elec_emission_factors['SO2'] = load_image_data_file(fp)
fp = os.path.join(scenarios[scenario]['filepath'], "indicatoren", "ENEFBC.out")
elec_emission_factors['BC'] = load_image_data_file(fp)
# We currently don't have a good way to deal with the fact that ecoinvent has many different VOCs listed.
# For the moment we just allow them to scale with the efficiency.
# Note that we don't import CO2 results as these are calculated by scaling using efficiency.
# This is more accurate as it considers that ecoinvent is more accurate regarding the energy content of coal.
image_emissions = {}
fuel2_number = np.where(image_variable_names['Fuel2'] == fuel2)[0][0]
sector_number = np.where(image_variable_names['Sector'] == sector)[0][0]
for key, value in elec_emission_factors.items():
image_emissions[key] = {}
for year in elec_emission_factors[key].years:
image_emissions[key][year] = {}
for region, vector in zip(REGIONS[:-1], value.data[:, sector_number, fuel2_number,
list(elec_emission_factors[key].years).index(year)]):
image_emissions[key][year][region] = vector
image_emissions[key] = pd.DataFrame.from_dict(image_emissions[key], orient='index')
# Note that Image reports emissions pre unit of fuel in, so we have to make a couple of calculations
if key == 'BC':
image_emissions[key] = image_emissions[key] * 1e-3 # convert to kg/MJ of input energy
else:
image_emissions[key] = image_emissions[key] * 1e-6 # convert to kg/MJ of input energy
image_emissions[key].replace({0: np.nan},
inplace=True) # we set all zero values to NaN so that the global average is calcuated only from values that exist.
image_emissions[key]['World'] = image_emissions[key].mean(axis=1)
image_emissions[key].fillna(0, inplace=True) # set nan values back to zero.
return image_emissions
#
# <h1 id="Electricity-markets:">Electricity markets:<a class="anchor-link" href="#Electricity-markets:">¶</a></h1>
#
#
# <h2 id="Define-available-technologies:">Define available technologies:<a class="anchor-link" href="#Define-available-technologies:">¶</a></h2>
#
# In[34]:
available_electricity_generating_technologies = {
'Solar PV': ['electricity production, photovoltaic, 3kWp slanted-roof installation, multi-Si, panel, mounted',
'electricity production, photovoltaic, 3kWp slanted-roof installation, single-Si, panel, mounted',
'electricity production, photovoltaic, 570kWp open ground installation, multi-Si'],
'CSP': ['Electricity production for a 50MW parabolic trough power plant', # Will be available in ecoinvent 3.4
'Electricity production at a 20MW solar tower power plant'], # Will be available in ecoinvent 3.4
'Wind onshore': ['electricity production, wind, <1MW turbine, onshore',
'electricity production, wind, 1-3MW turbine, onshore',
'electricity production, wind, >3MW turbine, onshore'],
'Wind offshore': ['electricity production, wind, 1-3MW turbine, offshore'],
'Hydro': ['electricity production, hydro, reservoir, alpine region',
'electricity production, hydro, reservoir, non-alpine region',
'electricity production, hydro, reservoir, tropical region',
'electricity production, hydro, run-of-river'],
'Other renewables': ['electricity production, deep geothermal'],
'Nuclear': ['electricity production, nuclear, boiling water reactor',
'electricity production, nuclear, pressure water reactor, heavy water moderated',
'electricity production, nuclear, pressure water reactor'],
'Coal ST': ['electricity production, hard coal',
'electricity production, lignite'],
'Coal CHP': ['heat and power co-generation, hard coal',
'heat and power co-generation, lignite'],
'IGCC': ['Electricity, at power plant/hard coal, IGCC, no CCS/2025', # From Carma project
'Electricity, at power plant/lignite, IGCC, no CCS/2025'], # From Carma project
'Oil ST': ['electricity production, oil'],
'Oil CHP': ['heat and power co-generation, oil'],
'Oil CC': ['electricity production, oil'], # Use copy of Oil ST here as this doesn't exist in ecoinvent
'Natural gas OC': ['electricity production, natural gas, conventional power plant'],
'Natural gas CC': ['electricity production, natural gas, combined cycle power plant'],
'Natural gas CHP': ['heat and power co-generation, natural gas, combined cycle power plant, 400MW electrical',
'heat and power co-generation, natural gas, conventional power plant, 100MW electrical'],
'Biomass CHP': ['heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014',
'heat and power co-generation, wood chips, 6667 kW',
'heat and power co-generation, biogas, gas engine'],
'Biomass CC': ['heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014',
# Use copy of Biomass CHP here as this not available in ecoinvent
'heat and power co-generation, wood chips, 6667 kW',
'heat and power co-generation, biogas, gas engine'],
'Biomass ST': ['heat and power co-generation, wood chips, 6667 kW, state-of-the-art 2014',
# Use copy of Biomass CHP here as this not available in ecoinvent
'heat and power co-generation, wood chips, 6667 kW',
'heat and power co-generation, biogas, gas engine'],
'Coal CCS': ['Electricity, at power plant/hard coal, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, oxy, pipeline 200km, storage 1000m/2025'],
'Coal CHP CCS': ['Electricity, at power plant/hard coal, pre, pipeline 200km, storage 1000m/2025',
# Carma project didn't include Coal CHP CCS
'Electricity, at power plant/lignite, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, oxy, pipeline 200km, storage 1000m/2025'],
'Oil CCS': ['Electricity, at power plant/hard coal, pre, pipeline 200km, storage 1000m/2025',
# Carma project didn't include oil - we just use all coal and gas datasets as a proxy
'Electricity, at power plant/lignite, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/natural gas, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/natural gas, post, pipeline 200km, storage 1000m/2025'],
'Oil CHP CCS': ['Electricity, at power plant/hard coal, pre, pipeline 200km, storage 1000m/2025',
# Carma project didn't include oil - we just use all coal and gas datasets as a proxy
'Electricity, at power plant/lignite, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, post, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/lignite, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/hard coal, oxy, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/natural gas, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/natural gas, post, pipeline 200km, storage 1000m/2025'],
'Natural gas CCS': ['Electricity, at power plant/natural gas, pre, pipeline 200km, storage 1000m/2025',
'Electricity, at power plant/natural gas, post, pipeline 200km, storage 1000m/2025'],
'Natural gas CHP CCS': ['Electricity, at power plant/natural gas, pre, pipeline 200km, storage 1000m/2025',
# Copy normal natural gas CCS datasets here
'Electricity, at power plant/natural gas, post, pipeline 200km, storage 1000m/2025'],
'Biomass CCS': ['Electricity, from CC plant, 100% SNG, truck 25km, post, pipeline 200km, storage 1000m/2025',
'Electricity, at wood burning power plant 20 MW, truck 25km, post, pipeline 200km, storage 1000m/2025',
'Electricity, at BIGCC power plant 450MW, pre, pipeline 200km, storage 1000m/2025'],
'Biomass CHP CCS': ['Electricity, from CC plant, 100% SNG, truck 25km, post, pipeline 200km, storage 1000m/2025',
# Copy normal wood CCS datasets here as CHP not available
'Electricity, at wood burning power plant 20 MW, truck 25km, post, pipeline 200km, storage 1000m/2025',
'Electricity, at BIGCC power plant 450MW, pre, pipeline 200km, storage 1000m/2025'],
}
#
# <h2 id="Overall-function-to-change-markets">Overall function to change markets<a class="anchor-link" href="#Overall-function-to-change-markets">¶</a></h2>
#
#
# <p>Function that returns all IMAGE locations that are interested by an ecoinvent location:</p>
#
# In[35]:
# these locations aren't found correctly by the constructiive geometries library - we correct them here:
fix_names = {'CSG': 'CN-CSG',
'SGCC': 'CN-SGCC',
'RFC': 'US-RFC',
'SERC': 'US-SERC',
'TRE': 'US-TRE',
'ASCC': 'US-ASCC',
'HICC': 'US-HICC',
'FRCC': 'US-FRCC',
'SPP': 'US-SPP',
'MRO, US only': 'US-MRO',
'NPCC, US only': 'US-NPCC',
'WECC, US only': 'US-WECC',
'IAI Area, Africa': 'IAI Area 1, Africa',
'IAI Area, South America': 'IAI Area 3, South America',
'IAI Area, Asia, without China and GCC': 'IAI Area 4&5, without China',
'IAI Area, North America, without Quebec': 'IAI Area 2, without Quebec',
'IAI Area, Gulf Cooperation Council': 'IAI Area 8, Gulf'
}
# In[36]:
def ecoinvent_to_image_locations(loc):
if loc == 'RoW':
loc = 'GLO'
if loc in fix_names.keys():
new_loc_name = fix_names[loc]
return [r[1] for r in geomatcher.intersects(new_loc_name) if r[0] == 'IMAGE']
else:
return [r[1] for r in geomatcher.intersects(loc) if r[0] == 'IMAGE']
# In[37]:
def update_electricity_markets(db, year, scenario):
# import the image market mix from the image result files:
image_electricity_market_df = get_image_markets(year, scenario)
# Remove all electricity producers from markets:
db = empty_low_voltage_markets(db)
db = empty_medium_voltage_markets(db)
db = empty_high_voltage_markets(db) # This function isn't working as expected - it needs to delete imports as well.
changes = {}
# update high voltage markets:
for ds in get_many(db, *electricity_market_filter_high_voltage):
changes[ds['code']] = {}
changes[ds['code']].update({('meta data', x): ds[x] for x in ['name', 'location']})
changes[ds['code']].update({('original exchanges', k): v for k, v in get_exchange_amounts(ds).items()})
delete_electricity_inputs_from_market(
ds) # This function will delete the markets. Once Wurst is updated this can be deleted.
add_new_datasets_to_electricity_market(ds, db, image_electricity_market_df)
changes[ds['code']].update({('updated exchanges', k): v for k, v in get_exchange_amounts(ds).items()})
return changes
#
# <h2 id="Define-electricity-market-filters">Define electricity market filters<a class="anchor-link" href="#Define-electricity-market-filters">¶</a></h2>
#
# In[38]:
electricity_market_filter_high_voltage = [contains('name', 'market for electricity, high voltage'),
doesnt_contain_any('name',
['aluminium industry', 'internal use in coal mining'])]
electricity_market_filter_medium_voltage = [contains('name', 'market for electricity, medium voltage'),
doesnt_contain_any('name', ['aluminium industry',
'electricity, from municipal waste incineration'])]
electricity_market_filter_low_voltage = [contains('name', 'market for electricity, low voltage')]
#
# <h2 id="Modify-high-voltage-markets">Modify high voltage markets<a class="anchor-link" href="#Modify-high-voltage-markets">¶</a></h2>
#
# In[39]:
def delete_electricity_inputs_from_market(ds):
# This function reads through an electricity market dataset and deletes all electricity inputs that are not own consumption.
ds['exchanges'] = [exc for exc in get_many(ds['exchanges'], *[either(*[exclude(contains('unit', 'kilowatt hour')),
contains('name',
'market for electricity, high voltage'),
contains('name',
'market for electricity, medium voltage'),
contains('name',
'market for electricity, low voltage'),
contains('name',
'electricity voltage transformation')])])]
# In[40]:
def get_image_markets(year, scenario):
# This returns a pandas dataframe containing the electricity mix for a certain year for all image locations.
# This function is totally inefficient and should be rewritten to consider the year in question. Currently it calculates for all years and then filters out the year in question!
fp = os.path.join(scenarios[scenario]['filepath'], "T2RT", "ElecProdSpec.out")
elec_production = load_image_data_file(fp)
elec_prod = {}
elec_prod_dfs = {}
for i, region in enumerate(REGIONS):
elec_prod[region] = elec_production.data[i, :, :]
elec_prod_dfs[region] = pd.DataFrame(elec_production.data[i, :, :], columns=elec_production.years,
index=image_variable_names['Technology'].dropna().values).T.drop(
'EMPTY CATEGORY!!', axis=1)
for region in REGIONS[:-1]:
elec_prod_dfs['World'] += elec_prod_dfs[region]
df = pd.concat([pd.Series(elec_prod_dfs[region].loc[year], name=region) for region in REGIONS[:-1]], axis=1)
df['World'] = df.sum(axis=1)
empty_columns = find_empty_columns(df)
df = df.divide(df.sum(axis=0)).sort_values(by='World', ascending=False).T.drop(empty_columns, axis=1)
return df
def find_average_mix(df):
# This function considers that there might be several image regions that match the ecoinvent region. This function returns the average mix across all regions.
return df.divide(df.sum().sum()).sum()
# In[41]:
def find_ecoinvent_electricity_datasets_in_same_ecoinvent_location(tech, location, db):
# first try ecoinvent location code:
try:
return [x for x in get_many(db, *[
either(*[equals('name', name) for name in available_electricity_generating_technologies[tech]]),
equals('location', location), equals('unit', 'kilowatt hour')])]
# otherwise try image location code (for new datasets)
except:
return [x for x in get_many(db, *[
either(*[equals('name', name) for name in available_electricity_generating_technologies[tech]]),
equals('location', ecoinvent_to_image_locations(location)), equals('unit', 'kilowatt hour')])]
# In[42]:
def find_other_ecoinvent_regions_in_image_region(loc):
if loc == 'RoW':
loc = 'GLO'
if loc in fix_names:
new_loc_name = fix_names[loc]
image_regions = [r for r in geomatcher.intersects(new_loc_name) if r[0] == 'IMAGE']
else:
image_regions = [r for r in geomatcher.intersects(loc) if r[0] == 'IMAGE']
temp = []
for image_region in image_regions:
temp.extend([r for r in geomatcher.contained(image_region)])
result = []
for temp in temp:
if type(temp) == tuple:
result.append(temp[1])
else:
result.append(temp)
return set(result)
# In[43]:
def find_ecoinvent_electricity_datasets_in_image_location(tech, location, db):
return [x for x in get_many(db, *[
either(*[equals('name', name) for name in available_electricity_generating_technologies[tech]]),
either(*[equals('location', loc) for loc in find_other_ecoinvent_regions_in_image_region(location)]),
equals('unit', 'kilowatt hour')
])]
# In[44]:
def find_ecoinvent_electricity_datasets_in_all_locations(tech, db):
return [x for x in get_many(db, *[
either(*[equals('name', name) for name in available_electricity_generating_technologies[tech]]),
equals('unit', 'kilowatt hour')])]
# In[45]:
def add_new_datasets_to_electricity_market(ds, db, df):
# This function adds new electricity datasets to a market based on image results. We pass not only a dataset to modify, but also a pandas dataframe containing the new electricity mix information, and the db from which we should find the datasets
# find out which image regions correspond to our dataset:
image_locations = ecoinvent_to_image_locations(ds['location'])
# here we find the mix of technologies in the new market and how much they contribute:
mix = find_average_mix(df.loc[image_locations]) # could be several image locations - we just take the average
# here we find the datasets that will make up the mix for each technology
datasets = {}
for i in mix.index:
if mix[i] != 0:
print('Next Technology: ',i)
# First try to find a dataset that is from that location (or image region for new datasets):
datasets[i] = find_ecoinvent_electricity_datasets_in_same_ecoinvent_location(i, ds['location'], db)
print('First round: ',i, [(ds['name'], ds['location']) for ds in datasets[i]])
# If this doesn't work, we try to take a dataset from another ecoinvent region within the same image region
if len(datasets[i]) == 0:
datasets[i] = find_ecoinvent_electricity_datasets_in_image_location(i, ds['location'], db)
print('Second round: ',i, [(ds['name'], ds['location']) for ds in datasets[i]])
# If even this doesn't work, try taking a global datasets
if len(datasets[i]) == 0:
datasets[i] = find_ecoinvent_electricity_datasets_in_same_ecoinvent_location(i, 'GLO', db)
print('Third round: ',i, [(ds['name'], ds['location']) for ds in datasets[i]])
# if no global dataset available, we just take the average of all datasets we have:
if len(datasets[i]) == 0:
datasets[i] = find_ecoinvent_electricity_datasets_in_all_locations(i, db)
print('Fourth round: ',i, [(ds['name'], ds['location']) for ds in datasets[i]])
# If we still can't find a dataset, we just take the global market group
if len(datasets[i]) == 0:
print('No match found for location: ', ds['location'], ' Technology: ', i,
'. Taking global market group for electricity')
datasets[i] = [x for x in get_many(db, *[equals('name', 'market group for electricity, high voltage'),
equals('location', 'GLO')])]
# Now we add the new exchanges:
for i in mix.index:
if mix[i] != 0:
total_amount = mix[i]
amount = total_amount / len(datasets[i])
for dataset in datasets[i]:
ds['exchanges'].append({
'amount': amount,
'unit': dataset['unit'],
'input': (dataset['database'], dataset['code']),
'type': 'technosphere',
'name': dataset['name'],
'location': dataset['location']
})
# confirm that exchanges sum to 1!
sum = np.sum([exc['amount'] for exc in technosphere(ds, *[equals('unit', 'kilowatt hour'),
doesnt_contain_any('name', [
'market for electricity, high voltage'])])])
if round(sum, 4) != 1.00: print(ds['location'], " New exchanges don't add to one! something is wrong!", sum)
return
# In[ ]:
| 59,134 | 18,926 |
"""
FJTYFilt_estimator.py
Creates a vectorizer and estimated an SVM model from the files in FILE_NAMES; run a TRAIN_PROP train/test on these
then saves these
TO RUN PROGRAM:
python3 SVM_filter_estimate.py
PROGRAMMING NOTES:
1. There are no summary statistics across the experiments, as these are currently just eyeballed to make sure nothing is
badly out of line.
SYSTEM REQUIREMENTS
This program has been successfully run under Mac OS 10.10.5; it is standard Python 3.5
so it should also run in Unix or Windows.
PROVENANCE:
Programmer: Philip A. Schrodt
Parus Analytics
Charlottesville, VA, 22901 U.S.A.
http://eventdata.parusanalytics.com
Copyright (c) 2017 Philip A. Schrodt. All rights reserved.
This code is covered under the MIT license: http://opensource.org/licenses/MIT
Report bugs to: schrodt735@gmail.com
REVISION HISTORY:
16-Jan-17: Initial version
31-Jan-17: modified to save vectorizer and model, clean up output
02-Jan-17: cmd-line for file list; save estimates
05-Mar-19: modified from SVM_filter_estimate.py for FJ project
=========================================================================================================
"""
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from time import time
import datetime
import utilFJML
import pickle
import random
import os
N_EXPERIMENTS = 5
TRAIN_PROP = 0.33 # proportion of cases in the training file.
INPUT_FILELIST = "filt-estimator-filelist.txt"
FILE_PATH = "../FJML-Filter/FJTY_training_wordlists"
FILE_NAMES = [line[:-1] for line in open(INPUT_FILELIST, "r")]
LABELS = ["codeable", "sports", "culture/entertainment", "business/finance", "opinion", "crime", "accidents",
"natural disaster", "open", "no codeable content"]
TEST_RESULT_FILE_NAME = "SVM_test_results-"
VECTORZ_PFILE_NAME = "save-vectorizer-Mk2.p"
MODEL_PFILE_NAME = "save-lin_clf-Mk2.p"
N_MODE = 10 # maximum number of unique modes
random.seed()
# Evaluate the model
suffix = utilFJML.get_timed_suffix()
fout = open(TEST_RESULT_FILE_NAME + suffix + ".txt", 'w')
fout.write("SVM_FILTER_ESTIMATE.PY TRAIN/TEST RESULTS\nRun datetime: {:s}\n".format(datetime.datetime.now().strftime('%y-%m-%d %H:%M:%S')))
fout.write("Training cases proportion: {:0.3f}\nTraining files\n".format(TRAIN_PROP))
fout.write("FILE_PATH: " + FILE_PATH + "\n")
for stnm in FILE_NAMES:
fout.write(" " + stnm + '\n')
for kex in range(N_EXPERIMENTS):
fout.write("\n ============ Experiment {:d} ============\n".format(kex + 1))
Y = []
corpus = []
Ytest = []
testcase = []
for filename in FILE_NAMES:
reader = utilFJML.read_file(os.path.join(FILE_PATH, filename))
print("Reading", FILE_PATH + filename)
for krec, rec in enumerate(reader):
if random.random() < TRAIN_PROP:
Y.append(int(rec['mode'][0]))
corpus.append(rec['textInfo']['wordlist'])
else:
Ytest.append(int(rec['mode'][0]))
testcase.append(rec['textInfo']['wordlist'])
vectorizer = TfidfVectorizer(min_df=1)
tfidf2 = vectorizer.fit_transform(corpus)
X = tfidf2.toarray()
t0 = time()
lin_clf = svm.LinearSVC()
lin_clf.fit(X, Y)
print("Time to estimate: {:0.3f} sec".format(time() - t0))
fout.write("Time to estimate: {:0.3f} sec\n".format(time() - t0))
"""LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
verbose=0)"""
#dec = lin_clf.decision_function([[1,1]])
kcorr = 0
classmat = []
for ka in range(N_MODE):
classmat.append(N_MODE*[0])
for ka, xv in enumerate(X):
pred = lin_clf.predict([xv])
classmat[Y[ka]][pred[0]] +=1
if Y[ka] == pred[0]:
kcorr +=1
print('Training set')
fout.write('Training set\n')
for ka, kv in enumerate(classmat):
print(ka,end=' | ')
fout.write(str(ka) + ' | ')
tot = 0
for kb, num in enumerate(kv):
print("{:4d} ".format(num),end='')
fout.write("{:4d} ".format(num))
tot += num
if ka == kb:
main = num
if tot > 0:
print(' {:.2f}'.format(float(main*100)/tot))
fout.write(' {:.2f}\n'.format(float(main*100)/tot))
else:
print(' {:.2f}'.format(0.0))
fout.write(' {:.2f}\n'.format(0.0))
X_test = vectorizer.transform(testcase).toarray()
kt = 0
kcorr = 0
classmat = []
for ka in range(N_MODE):
classmat.append(N_MODE*[0])
t0 = time()
for ka, xv in enumerate(X_test):
kt += 1
pred = lin_clf.predict([xv])
classmat[Ytest[ka]][pred[0]] +=1
if Ytest[ka] == pred[0]:
kcorr +=1
print("Time to fit {:d} cases {:0.3f} sec".format(kt, time() - t0))
fout.write("\nTime to fit {:d} cases {:0.3f} sec\n".format(kt, time() - t0))
print('Test set')
fout.write('Test set\n')
for ka, kv in enumerate(classmat):
tot = 0
print("{:>22s} | ".format(LABELS[ka]),end='')
fout.write("{:>22s} | ".format(LABELS[ka]))
nnc = 0
for kb, num in enumerate(kv):
print("{:4d} ".format(num),end='')
fout.write("{:4d} ".format(num))
tot += num
if ka == kb:
main = num
if kb != 0:
nnc += num
if tot > 0:
print(' {:4d} ({:6.2f}%) {:6.2f}%'.format(tot,float(tot*100)/kt, float(main*100)/tot), end="")
fout.write(' {:4d} ({:6.2f}%) {:6.2f}%'.format(tot,float(tot*100)/kt, float(main*100)/tot))
if ka == 0:
print(' {:6.2f}%'.format(float(main*100)/tot))
fout.write(' {:6.2f}%\n'.format(float(main*100)/tot))
else:
print(' {:6.2f}%'.format(float(nnc*100)/tot))
fout.write(' {:6.2f}%\n'.format(float(nnc*100)/tot))
else:
print(' ---')
fout.write(' ---\n')
fout.close()
print('Saving model using all cases')
Y = []
corpus = []
for filename in FILE_NAMES:
reader = utilFJML.read_file(os.path.join(FILE_PATH, filename))
print("Reading", FILE_PATH + filename)
for krec, rec in enumerate(reader):
Y.append(int(rec['mode'][0]))
corpus.append(rec['textInfo']['wordlist'])
vectorizer = TfidfVectorizer(min_df=1)
tfidf2 = vectorizer.fit_transform(corpus)
pickle.dump(vectorizer, open(VECTORZ_PFILE_NAME, "wb"))
X = tfidf2.toarray()
lin_clf = svm.LinearSVC()
lin_clf.fit(X, Y)
pickle.dump(lin_clf, open(MODEL_PFILE_NAME, "wb"))
print("Finished")
| 6,996 | 2,594 |
import sys
print("Hello, "+sys.argv[1]+"!") | 43 | 18 |
import cv2
import pickle
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
def load_cal_dist():
# load the pickle file
file = open("wide_dist_pickle.p", "rb")
dist_pickle = pickle.load(file)
objpoints = dist_pickle["objpoints"]
imgpoints = dist_pickle["imgpoints"]
return objpoints, imgpoints
# Function to undistorted the image
def cal_undistort(img, objpoints, imgpoints):
# Use cv2.calibrateCamera() and cv2.undistort()
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
undist = cv2.undistort(img, mtx, dist,None,mtx)
return undist
| 694 | 252 |
# for loop
numbers = [1, 2, 3, 4, 5]
new_list = []
for n in numbers:
add_1 = n + 1
new_list.append(add_1)
print(new_list)
# List Comprehension
new_list_comprehension = [n + 1 for n in numbers]
print(new_list_comprehension)
# Rang List Comprehension
range_list = [n * 2 for n in range(1, 5)]
print(range_list)
# Conditional List Comprehension
only_pairs = [n for n in range(1, 11) if n % 2 == 0]
print(only_pairs)
names = ["Alex", "Beth", "Caroline", "Dave", "Eleanor", "Freddie"]
short_names = [name for name in names if len(name) < 5]
print(short_names)
uppercase_names = [name.upper() for name in names if len(name) > 5]
print(uppercase_names) | 658 | 274 |
from .iid_equal import distribute_batches_equally
| 50 | 17 |
from io import StringIO
import os
from pathlib import Path
import pytest
from herethere.everywhere import ConnectionConfig, runcode
from herethere.everywhere import config
code_with_definition = """
def foo(a, b):
return a + b
print(foo(1, 2))
"""
@pytest.mark.parametrize(
"code, expected",
[
('print("1")', "1\n"),
('print("1")\nprint("2")', "1\n2\n"),
(code_with_definition, "3\n"),
],
)
def test_runcode_expected_result(code, expected):
assert runcode(code) == expected
def test_runcode_syntax_error():
assert "SyntaxError: invalid syntax" in runcode("syntax error here")
@pytest.mark.parametrize(
"code, expected",
[
('print("1")\nprint("2")', "1\n2\n"),
],
)
def test_runcode_expected_io(code, expected):
stdout = StringIO()
assert not runcode(code, stdout=stdout)
assert stdout.getvalue() == expected
def test_runcode_namespace_used():
assert "NameError:" in runcode("print(runcode_global_var)")
namespace = globals()
global runcode_global_var
runcode_global_var = 111
assert "NameError:" in runcode("print(runcode_global_var)")
assert runcode("print(runcode_global_var)", namespace=namespace) == "111\n"
assert (
runcode(
"runcode_global_var *= 3 ; print(runcode_global_var)", namespace=namespace
)
== "333\n"
)
assert runcode_global_var == 333
@pytest.mark.parametrize(
"path,env,expected",
(
(
"",
{
"THERE_HOST": "1",
"THERE_PORT": "2",
"THERE_USERNAME": "3",
"THERE_PASSWORD": "4",
},
ConnectionConfig("1", "2", "3", "4"),
),
(
"tests/connection.env",
{},
ConnectionConfig("localhost", "9022", "here", "there"),
),
),
)
def test_connection_config_loaded(path, env, expected, tmp_environ):
tmp_environ.update(env)
assert ConnectionConfig.load(path=path, prefix="there") == expected
def test_connection_not_found(tmp_environ):
with pytest.raises(config.ConnectionConfigError):
ConnectionConfig.load(path="no-such-config-here", prefix="there")
@pytest.mark.parametrize("prefix", ("", "test"))
def test_connection_config_saved(tmpdir, prefix):
path = Path(tmpdir) / "test-config-saved.env"
assert not os.path.exists(path)
with pytest.raises(config.ConnectionConfigError):
ConnectionConfig.load(path=path, prefix=prefix)
ConnectionConfig("localhost", "9022", "here", "there").save(path, prefix=prefix)
ConnectionConfig.load(path=path, prefix=prefix)
| 2,673 | 877 |
import os
import re
import joblib
import numpy as np
import pandas as pd
import traceback
from .logger import create_logger
from .ml_utils import fetch_ohlcv, normalize_position
from .agent_api import submit_prediction
agent_base_url = os.getenv('ALPHASEA_AGENT_BASE_URL')
model_id = os.getenv('ALPHASEA_MODEL_ID')
model_path = os.getenv('ALPHASEA_MODEL_PATH')
log_level = os.getenv('ALPHASEA_LOG_LEVEL')
position_noise = float(os.getenv('ALPHASEA_POSITION_NOISE'))
if not re.match(r'^[a-z_][a-z0-9_]{3,30}$', model_id):
raise Exception('model_id must be ^[a-z_][a-z0-9_]{3,30}$')
def predict_job(dry_run=False):
logger = create_logger(log_level)
model = joblib.load(model_path)
# fetch data
interval_sec = 60 * 60
max_retry_count = 5
for _ in range(max_retry_count):
try:
df = fetch_ohlcv(symbols=model.symbols, logger=logger, interval_sec=interval_sec)
max_timestamp = df.index.get_level_values('timestamp').max()
df = df.loc[max_timestamp - pd.to_timedelta(model.max_data_sec, unit='S') <= df.index.get_level_values('timestamp')]
break
except Exception as e:
logger.error(e)
logger.error(traceback.format_exc())
logger.info('fetch_ohlcv error. retrying')
# predict
df['position'] = model.predict(df)
# add noise (for debug)
df['position'] += np.random.normal(0, position_noise, size=df.shape[0])
normalize_position(df)
# filter last timestamp
df = df.loc[df.index.get_level_values('timestamp') == max_timestamp]
# submit
if dry_run:
logger.info('dry run submit {}'.format(df))
else:
result = submit_prediction(
agent_base_url=agent_base_url,
model_id=model_id,
df=df,
prediction_license='CC0-1.0'
)
logger.info(result)
| 1,879 | 661 |
import cv2
import numpy as np
img1 = cv2.imread('image.tif')
img2 = cv2.imread('imglogo.png')
rows, cols, channels = img2.shape
roi = img1[0:rows, 0:cols]
img2gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 230, 255, cv2.THRESH_BINARY_INV)
mask_inv = cv2.bitwise_not(mask)
img1_bg = cv2.bitwise_and(roi, roi, mask = mask_inv)
img2_fg = cv2.bitwise_and(img2, img2, mask = mask)
dst = cv2.add(img1_bg, img2_fg)
img1[0:rows, 0:cols] = dst
cv2.imshow('final', img1)
cv2.waitKey(0)
cv2.destroyAllWindows()
| 545 | 269 |
from django.contrib.auth.models import User
from django import forms
from constance import config
#from constance.admin import ConstanceForm
from django.conf import settings
from .models import DeviceFamily, Firmware, Board
###################################################################################################################
# Firmware Flash Forms
###################################################################################################################
class FirmwareFamilyForm(forms.Form):
DEVICE_FAMILY_CHOICES = (
)
device_family = forms.ChoiceField(label="Device Family",
widget=forms.Select(attrs={'class': 'form-control',
'data-toggle': 'select'}),
choices=DEVICE_FAMILY_CHOICES, required=True)
def __init__(self, *args, **kwargs):
super(FirmwareFamilyForm, self).__init__(*args, **kwargs)
for this_field in self.fields:
self.fields[this_field].widget.attrs['class'] = "form-control"
family_choices = [(fam.id, fam.name) for fam in DeviceFamily.objects.all()]
self.fields['device_family'].choices = family_choices
class BoardForm(forms.Form):
DEVICE_BOARD_CHOICES = (
)
board_type = forms.ChoiceField(label="Board Type",
widget=forms.Select(attrs={'class': 'form-control', 'data-toggle': 'select'}),
choices=DEVICE_BOARD_CHOICES, required=True)
def set_choices(self, family):
# There's probably a better way of doing this
board_choices = [(brd.id, brd.name) for brd in Board.objects.filter(family=family)]
self.fields['board_type'].choices = board_choices
# class GuidedDeviceFlashForm(forms.Form):
# DEVICE_FAMILY_CHOICES = GuidedDeviceSelectForm.DEVICE_FAMILY_CHOICES
#
# device_family = forms.ChoiceField(label="Device Family",
# widget=forms.Select(attrs={'class': 'form-control',
# 'data-toggle': 'select'}),
# choices=DEVICE_FAMILY_CHOICES, required=True)
# should_flash_device = forms.BooleanField(widget=forms.HiddenInput, required=False, initial=False)
#
#
| 2,378 | 646 |
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nsitivity":"all"},"children":{"Name":"children","Phase":"link","Transitivity":"all"},"metadata":{"Name":"metadata","Phase":"link","Transitivity":"never"}},"Item3":[{"reference":{"sourceType":"uid","value":"SortingVisualizer"},"file":"~/obj/api/SortingVisualizer.yml","reportedBy":"~/obj/api/SortingVisualizer.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.#ctor"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.initWindowSettings"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.initParameters"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.genRandomNumber(System.Random)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.enableSortButtons"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.disableSortButtons"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.FrmMain_Load(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.exitToolStripMenuItem_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.githubToolStripMenuItem_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnGenerateArray_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnMergeSort_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnQuickSort_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnInsertionSort_Click(System.Object,System.EventArgs)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.printArray"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.Dispose(System.Boolean)"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.#ctor*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.initWindowSettings*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.initParameters*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.genRandomNumber*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.enableSortButtons*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.disableSortButtons*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.FrmMain_Load*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.exitToolStripMenuItem_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.githubToolStripMenuItem_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnGenerateArray_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnMergeSort_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnQuickSort_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.btnInsertionSort_Click*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.printArray*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"},{"reference":{"sourceType":"uid","value":"SortingVisualizer.FrmMain.Dispose*"},"file":"~/obj/api/SortingVisualizer.FrmMain.yml","reportedBy":"~/obj/api/SortingVisualizer.FrmMain.yml"}]} | 296,545 | 92,491 |
from . import auth
from flask_login import login_user,login_required,logout_user
from flask import render_template,url_for,flash,redirect,request
from .forms import SignupForm,SigninForm
from ..models import User
from .. import db
from ..email import mail_message
@auth.route('/signup', methods = ["GET","POST"])
def signup():
form = SignupForm()
if form.validate_on_submit():
user = User(email = form.email.data, username = form.name.data, password = form.password.data)
db.session.add(user)
db.session.commit()
mail_message("Welcome to 1 Min Of You","email/welcome_user",user.email,user=user)
return redirect(url_for('auth.login'))
return render_template('auth/signup.html', signup_form = form)
@auth.route('/login', methods = ['GET','POST'])
def login():
signin_form = SigninForm()
if signin_form.validate_on_submit():
user = User.query.filter_by(email = signin_form.email.data).first()
if user is not None and user.verify_password(signin_form.password.data):
login_user(user,signin_form.remember_me.data)
return redirect(request.args.get('next') or url_for('main.index'))
flash('Invalid username or Password')
title = "1 Min Of You Login"
return render_template('auth/login.html', signin_form=signin_form,title=title)
@auth.route('/logout')
@login_required
def logout():
logout_user()
return redirect(url_for("main.index")) | 1,395 | 449 |
#
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from pyspark.sql.types import *
label = 'delinquency_12'
schema = StructType([
StructField('orig_channel', FloatType()),
StructField('first_home_buyer', FloatType()),
StructField('loan_purpose', FloatType()),
StructField('property_type', FloatType()),
StructField('occupancy_status', FloatType()),
StructField('property_state', FloatType()),
StructField('product_type', FloatType()),
StructField('relocation_mortgage_indicator', FloatType()),
StructField('seller_name', FloatType()),
StructField('mod_flag', FloatType()),
StructField('orig_interest_rate', FloatType()),
StructField('orig_upb', IntegerType()),
StructField('orig_loan_term', IntegerType()),
StructField('orig_ltv', FloatType()),
StructField('orig_cltv', FloatType()),
StructField('num_borrowers', FloatType()),
StructField('dti', FloatType()),
StructField('borrower_credit_score', FloatType()),
StructField('num_units', IntegerType()),
StructField('zip', IntegerType()),
StructField('mortgage_insurance_percent', FloatType()),
StructField('current_loan_delinquency_status', IntegerType()),
StructField('current_actual_upb', FloatType()),
StructField('interest_rate', FloatType()),
StructField('loan_age', FloatType()),
StructField('msa', FloatType()),
StructField('non_interest_bearing_upb', FloatType()),
StructField(label, IntegerType()),
])
features = [ x.name for x in schema if x.name != label ]
default_params = {
'eta': 0.1,
'gamma': 0.1,
'missing': 0.0,
'maxDepth': 10,
'maxLeaves': 256,
'growPolicy': 'depthwise',
'objective': 'binary:logistic',
'minChildWeight': 30.0,
'lambda_': 1.0,
'scalePosWeight': 2.0,
'subsample': 1.0,
'nthread': 1,
'numRound': 100,
'numWorkers': 1,
}
| 2,433 | 785 |
# _*_coding:utf-8_*_
# author：qq823947488
# date：2019/4/24 20:12
host='localhost'
port=3306
user='root'
passwd='123'
db='flask_learn'
charset='utf8'
| 150 | 88 |
"""
These function implement functions or other functionalities provided by the linux userspace dynamic loader
"""
| 115 | 24 |
from wrappers import inner_f
def compile_f(contents, attributes, doc, name):
new_contents = []
for f in contents:
sub_contents = getattr(f, "contents", None)
if sub_contents:
new_contents.extend(sub_contents)
else:
new_contents.append(f)
new_func = inner_f(new_contents)
if attributes:
new_func.__dict__ = attributes.copy()
new_func.__doc__ = doc
new_func.name = name
return new_func
def compile_word_f(f, name=None):
contents = getattr(f, 'contents', None)
if contents and len(contents) > 1:
return compile_f(contents, f.__dict__, f.__doc__, name)
return f
| 664 | 211 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Filip Stefanak <f.stefanak@rare-technologies.com>
# Copyright (C) 2017 Rare Technologies
#
# This code is distributed under the terms and conditions
# from the MIT License (MIT).
import sys
import unittest
from bounter import HashTable
long_long_max = 9223372036854775807
class MyClass(object):
pass
class HashTableGetSetTest(unittest.TestCase):
"""
Functional tests for setting and retrieving values of the counter
"""
def setUp(self):
self.ht = HashTable(buckets=64)
def test_unknown_is_zero(self):
self.assertEqual(self.ht['foo'], 0)
def test_set_and_get(self):
foo_value = 42
bar_value = 53
self.ht['foo'] = foo_value
self.ht['bar'] = bar_value
self.ht['max'] = long_long_max
self.assertEqual(self.ht['foo'], foo_value)
self.assertEqual(self.ht['max'], long_long_max)
self.assertEqual(self.ht['bar'], bar_value)
def test_set_and_get_bytes(self):
first_value = 42
second_value = 53
self.ht[b'foo'] = first_value
self.assertEqual(self.ht['foo'], first_value)
self.assertEqual(self.ht[b'foo'], first_value)
self.ht['foo'] = second_value
self.assertEqual(self.ht['foo'], second_value)
self.assertEqual(self.ht[b'foo'], second_value)
def test_replace(self):
"""
Test that a set successfully replaces existing value of the counter
"""
foo_value = 42
new_value = 53
self.ht['foo'] = foo_value
self.ht['foo'] = new_value
self.assertEqual(self.ht['foo'], new_value)
def test_add_from_zero(self):
"""
Test simple addition from zero value.
Note that this is NOT recommended (use HashTable.increment instead) because python interprets it inefficiently,
but we do want to support this case.
"""
# SLOW, always prefer to use ht.increment('foo', 1) or ht.increment('foo') instead!
self.ht['foo'] += 1
self.assertEqual(self.ht['foo'], 1)
def test_add_from_existing(self):
"""
Test simple addition from existing value.
Note that this is NOT recommended (use HashTable.increment instead) because python interprets it inefficiently,
but we do want to support this case.
"""
self.ht['foo'] = 1
# SLOW, always prefer to use ht.increment('foo', 2) or ht.increment('foo') instead!
self.ht['foo'] += 2
self.assertEqual(self.ht['foo'], 3)
def test_get_set_int_key(self):
"""
Negative test: integer keys are not supported and yield TypeError
"""
with self.assertRaises(TypeError):
self.ht[1] = 42
with self.assertRaises(TypeError):
foo = self.ht[1]
def test_delete_key(self):
foo_value = 42
bar_value = 53
self.ht['foo'] = foo_value
self.ht['bar'] = bar_value
self.assertEqual(self.ht['foo'], foo_value)
self.assertEqual(self.ht['bar'], bar_value)
del self.ht['foo']
self.assertEqual(self.ht['foo'], 0)
self.assertEqual(self.ht['bar'], bar_value)
del self.ht['bar']
self.assertEqual(self.ht['foo'], 0)
self.assertEqual(self.ht['bar'], 0)
def test_get_set_object_key(self):
"""
Negative test: object keys are not supported and yield TypeError
"""
o = MyClass()
with self.assertRaises(TypeError):
self.ht[o] = 42
with self.assertRaises(TypeError):
foo = self.ht[o]
def test_get_set_empty_string(self):
self.ht['foo'] = 42
self.ht['bar'] = 53
self.assertEqual(self.ht[''], 0)
self.ht[''] = 3
self.assertEqual(self.ht[''], 3)
del self.ht['']
self.assertEqual(self.ht[''], 0)
def test_get_set_long_string(self):
long_string = 'l' + ('o' * 100) + 'ng'
longer_string = 'l' + ('o' * 120) + 'ng'
self.ht[long_string] = 2
self.ht[longer_string] = 3
self.assertEqual(self.ht[long_string], 2)
self.assertEqual(self.ht[longer_string], 3)
del self.ht[longer_string]
self.assertEqual(self.ht[longer_string], 0)
def test_get_set_nonascii_string(self):
non_ascii_string = "Non-ascii dôverivá Čučoriedka 9#8\\%7 平仮名\n☃\t+☀\t=\t☹ "
# the second line contains a different symbol
similar_string = "Non-ascii dôverivá Čučoriedka 9#8\\%7 平仮名\n☃\t+☀\t=\t☺ "
self.ht[non_ascii_string] = 2
self.ht[similar_string] = 3
self.assertEqual(self.ht[non_ascii_string], 2)
self.assertEqual(self.ht[similar_string], 3)
del self.ht[non_ascii_string]
self.assertEqual(self.ht[non_ascii_string], 0)
def test_get_set_nonascii_unicode(self):
non_ascii_unicode = u"Non-ascii dôverivá Čučoriedka 9#8\\%7 平仮名\n☃\t+☀\t=\t☹ "
# the second line contains a different symbol
similar_unicode = u"Non-ascii dôverivá Čučoriedka 9#8\\%7 平仮名\n☃\t+☀\t=\t☺ "
self.ht[non_ascii_unicode] = 2
self.ht[similar_unicode] = 3
self.assertEqual(self.ht[non_ascii_unicode], 2)
self.assertEqual(self.ht[similar_unicode], 3)
del self.ht[non_ascii_unicode]
self.assertEqual(self.ht[non_ascii_unicode], 0)
def test_set_float_value(self):
"""
Negative test: float values are not supported and yield TypeError
In Python 2, float values are converted to integers automatically without error, this is a "feature" of argument
parsing.
"""
if sys.version_info < (3, 0):
return
with self.assertRaises(TypeError):
self.ht['foo'] = float(42.0)
def test_set_string_value(self):
"""
Negative test: string values are not supported and yield TypeError
"""
with self.assertRaises(TypeError):
self.ht['foo'] = 'bar'
def test_set_object_value(self):
"""
Negative test: object values are not supported and yield TypeError
"""
class MyClass(object):
pass
with self.assertRaises(TypeError):
self.ht['foo'] = MyClass()
def test_get_set_big_number(self):
big_number = 206184125847
self.ht['big number'] = big_number
self.assertEqual(self.ht['big number'], big_number)
def test_set_negative(self):
"""
Negative test, raises ValueError on negative values
"""
# new value
with self.assertRaises(ValueError):
self.ht['foo'] = -4
self.assertEqual(self.ht['foo'], 0, "value should remain unaffected")
self.ht['foo'] = 3
# existing value
with self.assertRaises(ValueError):
self.ht['foo'] = -2
self.assertEqual(self.ht['foo'], 3, "value should remain unaffected")
def test_set_number_greater_than_long_long_max(self):
"""
Negative test: set fails on a number which is larger than long long's max
"""
self.ht['toomuch'] = 42
with self.assertRaises(OverflowError):
self.ht['toomuch'] = long_long_max + 1
self.assertEqual(self.ht['toomuch'], 42, 'value should remain unaffected')
def test_set_zero(self):
"""
Setting the zero value
"""
self.ht['foo'] = 0
self.assertEqual(self.ht['foo'], 0)
self.ht['bar'] = 4
self.assertEqual(self.ht['bar'], 4)
self.ht['bar'] = 0
self.assertEqual(self.ht['bar'], 0)
if __name__ == '__main__':
unittest.main()
| 7,732 | 2,646 |
import sys
newick = ''
with open(sys.argv[1]) as f:
orig = f.read().rstrip()
orig = orig.replace('(', '(\n').replace(',', '\n,\n').replace(')', ')\n')
for line in orig.split('\n'):
fields = line.rstrip().split(':')
if len(fields) == 0:
continue
elif len(fields) == 1:
newick += fields[0]
else:
prefix, suffix = fields
newick += prefix[:30]
if suffix != '':
newick += ':'
newick += suffix
with open(sys.argv[2], 'w') as of:
of.write(newick + '\n')
| 540 | 192 |
# -*- coding: utf-8 -*-
#
# Based on bitcraze example project:
# https://github.com/bitcraze/crazyflie-lib-python/blob/master/examples/step-by-step/sbs_motion_commander.py
import logging
import sys
import time
from threading import Event
import cflib.crtp
from cflib.crazyflie import Crazyflie
from cflib.crazyflie.log import LogConfig
from cflib.crazyflie.syncCrazyflie import SyncCrazyflie
from cflib.positioning.motion_commander import MotionCommander
from cflib.utils import uri_helper
URI_interface = 'radio://0/80/2M/E7E7E7E7E7' # 'debug://0/1/E7E7E7E7E7' #
URI = uri_helper.uri_from_env(default=URI_interface)
DEFAULT_HEIGHT = 0.5
logging.basicConfig(level=logging.ERROR)
position_estimate = [0, 0]
def move_linear_simple(scf):
with MotionCommander(scf, default_height=DEFAULT_HEIGHT) as mc:
time.sleep(1)
mc.forward(0.5)
time.sleep(1)
mc.turn_left(180)
time.sleep(1)
mc.forward(0.5)
time.sleep(1)
def take_off_simple(scf):
with MotionCommander(scf, default_height=DEFAULT_HEIGHT) as mc:
time.sleep(3)
mc.stop()
def log_pos_callback(timestamp, data, logconf):
print(data)
global position_estimate
position_estimate[0] = data['stateEstimate.x']
position_estimate[1] = data['stateEstimate.y']
if __name__ == '__main__':
cflib.crtp.init_drivers()
with SyncCrazyflie(URI, cf=Crazyflie(rw_cache='./cache')) as scf:
logconf = LogConfig(name='Position', period_in_ms=10)
logconf.add_variable('stateEstimate.x', 'float')
logconf.add_variable('stateEstimate.y', 'float')
scf.cf.log.add_config(logconf)
logconf.data_received_cb.add_callback(log_pos_callback)
logconf.start()
take_off_simple(scf)
move_linear_simple(scf)
logconf.stop()
| 1,830 | 703 |
from datetime import datetime
from discord.errors import NotFound
from discord import Embed
from discord_slash.utils.manage_commands import create_option, SlashCommandOptionType
from data import get_invites_by_guild_and_code
from .infrastructure import record_usage, CommandDefinition, registered_guild_and_admin_or_mod_only
async def _track(ctx, code):
await registered_guild_and_admin_or_mod_only(ctx)
record_usage(ctx)
if "discord" not in code:
full_code = f"https://discord.gg/{code}"
else:
full_code = code
invites = await get_invites_by_guild_and_code(ctx.guild.id, full_code)
if not invites:
return await ctx.send("Invite not found in database.")
try:
creator = await ctx.bot.fetch_user(invites[0]["InviteIssuerId"])
except NotFound:
creator = None
invitees = {}
count = 0 # only do this for the first 20 users
for invite in invites:
if count > 20:
break
if invite["JoinedUserId"] not in invitees:
count += 1
invitees[invite["JoinedUserId"]] = await ctx.bot.fetch_user(invite["JoinedUserId"])
embed = Embed()
if creator:
embed.set_author(name=f"{creator.name}#{creator.discriminator}", icon_url=creator.avatar_url, url=creator.avatar_url)
embed.description = f"`{full_code}` was created by {creator.mention} at `{invites[0]['InviteCreatedAt'].strftime('%d %b %Y %H:%M:%S')}`."
else:
embed.description = f"`{full_code}` was created by `{creator.id}` at `{invites[0]['InviteCreatedAt'].strftime('%d %b %Y %H:%M:%S')}`."
used_by = ""
for invite in invites:
if len(used_by) > 900:
used_by += "[...]"
break
if invitees.get(invite['JoinedUserId']):
used_by += f"- `{invitees[invite['JoinedUserId']].name}#{invitees[invite['JoinedUserId']].discriminator}` `{invite['JoinedUserId']}` - `{invite['JoinedAt'].strftime('%d %b %Y %H:%M:%S')}`\n"
else:
used_by += f"- `{invite['JoinedUserId']}` - `{invite['JoinedAt'].strftime('%d %b %Y %H:%M:%S')}`\n"
embed.add_field(name=f"Used by [{len(invites)}]", value=used_by, inline=False)
embed.set_footer(text=f"Invite: {full_code}")
embed.timestamp = datetime.now()
return await ctx.send(embed=embed)
track = CommandDefinition(
func=_track,
short_help="Track an invite, its creator and its users.",
long_help="Track an invite in your guild, its creator and its users.\nEither enter the invite code or the url in the format `https://discord.gg/<code>`.",
usage="track <code|url>",
options=[
create_option("code", "the invite code or link.", SlashCommandOptionType.STRING, False)
]
)
| 2,745 | 949 |
from .coco import COCO
from .dac import DAC
datasets = {
"COCO": COCO,
"DAC": DAC
}
| 94 | 43 |
from typing import List
class Solution:
def createTargetArray(self, nums: List[int], index: List[int]) -> List[int]:
target = []
for n, i in zip(nums, index):
target.insert(i, n)
return target
| 235 | 72 |
class Tile:
"""
represents one space on the board
responsible for storing and giving information about itself in a safe manner,
usually generated by a board object
Attributes:
_x (integer): the x coordinate, should line up with our other methods
_y (integer): the y coordinate etc
visited (boolean): whether or not a tile has been visited, useful for some algorithms
cost: the cost for a-star to travers a tile, can be used to determine if a tile is pathable
Object Methods:
get_coord(): int list, returns a list of the x position, then the y position
get_x(): int, returns the x coordinate of the tile
get_y(): int, returns the y coordinate of the tile
visit(): void, sets visited to true
get_visited(): boolean, returns current state of visited
get_cost(): int, returns cost attribute of the tile
set_cost(): void, allows you to hard set a tiles cost, use with caution
modify_cost(): void, changes the cost by passed in val with some safety conditions
"""
def __init__(self, x_coord, y_coord, path_cost, is_food, debug_char):
self._x = x_coord
self._y = y_coord
self.cost = path_cost
self._is_food = is_food
self._debug_char = debug_char
self.visited = False
def __str__(self):
'''
the to string method
'''
return_string = ""
return_string += "Location: (" + str(self.get_x()) + ", " + str(self.get_y()) + ")\n"
return_string += "Cost: " + str(self.get_cost()) + "\n"
return_string += "Char: " + str(self.get_char())
return return_string
# Getters and setters
def get_x(self):
return self._x
def get_y(self):
return self._y
def set_visited(self, visit):
self.visited = visit
def get_visited(self):
return self.visited
def get_cost(self):
return self.cost
# be careful with this one, make sure to set it back after you're done
def set_cost(self, new_cost):
self.cost = new_cost
# doesn't change "walls" and won't make a pathable tile un-pathable
def modify_cost(self, change_by):
if self.get_cost() < 1:
return
if self.get_cost() + change_by < 1:
self.cost = 1
return
self.cost += change_by
def get_food(self):
return self._is_food
def get_char(self):
return self._debug_char
| 2,513 | 737 |
list = ['Tiago','Ivan','Henrique', 'Paulo']
nome = 'teste'
nome = input('Digite um nome: ') # Python input prompt
print(nome)
for i in range(len(list)):
if list[i] == nome:
print(nome,'is number',i + 1,'on the list')
break
else:
print(nome,'is not on the list') | 285 | 101 |
import numpy as np
import math
import random
class Ai():
def __init__(self, resolution, all_pos: list, all_ids, options: list):
self.board_resolution = resolution
self.all_pos = all_pos
self.player_option, self.ai_option = options
self.available_tiles = self.all_pos[:]
self.ai_move = []
self.id_array = np.array(all_ids)
self.id_array = np.reshape(self.id_array, (3, 3))
self.player_moves = []
self.ai_moves = []
# All the possible matches
# Board:
# np.array([1 2 3]
# [4 5 6]
# [7 8 9])
# Sideway matches
self.a_match_1 = self.id_array[0, 0], self.id_array[1, 1], self.id_array[2, 2] # (1, 5, 9)
self.a_match_2 = self.id_array[0, 2], self.id_array[1, 1], self.id_array[2, 0] # (3, 5, 7)
# Straight matches
self.a_match_3 = self.id_array[0, 0], self.id_array[1, 0], self.id_array[2, 0] # (1, 4, 7)
self.a_match_4 = self.id_array[0, 1], self.id_array[1, 1], self.id_array[2, 1] # (2, 5, 8)
self.a_match_5 = self.id_array[0, 2], self.id_array[1, 2], self.id_array[2, 2] # (3, 6, 9)
# Side matches
self.a_match_6 = self.id_array[0, 0], self.id_array[0, 1], self.id_array[0, 2] # (1, 2, 3)
self.a_match_7 = self.id_array[1, 0], self.id_array[1, 1], self.id_array[1, 2] # (4, 5, 6)
self.a_match_8 = self.id_array[2, 0], self.id_array[2, 1], self.id_array[2, 2] # (7, 8, 9)
self.all_matches = [self.a_match_1, self.a_match_2, self.a_match_3,
self.a_match_4,
self.a_match_5,
self.a_match_6,
self.a_match_7,
self.a_match_8,
]
def calculate_move(self, user_pos: list):
self.available_tiles.remove(user_pos)
if len(self.available_tiles) > 1:
self.ai_move = random.choice(self.available_tiles)
self.available_tiles.remove(self.ai_move)
def decide_winner(self, butt_dict, player_butt_id, ai_butt_id):
self.player_moves.append(player_butt_id)
self.ai_moves.append(ai_butt_id)
# won_buttons keep of the buttons to illuminate (or change color)
won_buttons = tuple()
won = ""
# Looping through all the matches and checking if any of those matches are in Player/Ai moves
for matches in self.all_matches:
if set(matches).issubset(self.player_moves) or set(matches).issubset(self.ai_moves):
won_buttons = matches
won = "Player" if set(matches).issubset(self.player_moves) else "Ai"
# This changes the color of the buttons that won the match
for button in butt_dict.values():
if button[1] in won_buttons:
button[0].background_normal = ""
# Green when the player wins and red when the Ai wins
button[0].background_color = (0, 1, 0, 1) if won == "Player" else (1, 0, 0, 1)
return won | 3,221 | 1,165 |
class CoreException(Exception):
def __init__(self, message):
self.message = message
class ValidationError(CoreException):
def __init__(self, message, errors=[]):
super().__init__(message)
self.errors = errors
| 244 | 68 |
"""
test to generate noise but failed for 2d
"""
import math
import random
maxfreq = 1/500
myfrequencies = [random.random() * maxfreq * 2 * math.pi for _ in range(5)]
myphases = [random.random() * math.pi for _ in range(5)]
def myfunction(x, y):
global myfrequencies
global myphases
start = 1
for f, p in zip(myfrequencies, myphases):
start = start * math.sin(f * x + p)
return int((start + 1) * 127)
x = [n for n in range(display_width)]
y = [myfunction(n, 0) for n in x]
| 507 | 199 |
from django.contrib import admin
from django_summernote.admin import SummernoteModelAdmin
from .models import (
Event,
EventDateAndTime,
EventDescription,
EventLocation)
# Apply summernote to all TextField in model.
class SomeModelAdmin(SummernoteModelAdmin): # instead of ModelAdmin
summer_note_fields = '__all__'
class EventTime(admin.TabularInline):
model = EventDateAndTime
extra = 0
class EventDescAdmin(admin.TabularInline):
model = EventDescription
extra = 0
class EventLocationAdmin(admin.TabularInline):
model = EventLocation
extra = 0
class EventAdmin(admin.ModelAdmin):
list_display = (
'event_title',
'event_slug',
'event_user',
'event_date_created'
)
inlines = [
EventTime,
EventDescAdmin,
EventLocationAdmin
]
admin.site.register(Event, EventAdmin)
admin.site.register(EventDescription, SomeModelAdmin)
| 947 | 288 |
# Generated by Django 2.2.10 on 2020-02-13 05:16
from django.db import migrations, models
import djangocms_bootstrap4.contrib.bootstrap4_link.validators
class Migration(migrations.Migration):
dependencies = [
('bootstrap4_link', '0005_auto_20190709_1626'),
]
operations = [
migrations.AddField(
model_name='bootstrap4link',
name='modal_id',
field=models.CharField(blank=True, default='', help_text='Do not include a preceding "#" symbol.', max_length=60, verbose_name='Modal Id'),
),
migrations.AlterField(
model_name='bootstrap4link',
name='external_link',
field=models.CharField(blank=True, help_text='Provide a link to an external source.', max_length=2040, validators=[djangocms_bootstrap4.contrib.bootstrap4_link.validators.LocalORIntranetURLValidator()], verbose_name='External link'),
),
]
| 930 | 300 |
"""Support files for podman API implementation."""
import datetime
import re
import threading
__all__ = [
'cached_property',
'datetime_parse',
'datetime_format',
]
class cached_property(object):
"""cached_property() - computed once per instance, cached as attribute.
Maybe this will make a future version of python.
"""
def __init__(self, func):
"""Construct context manager."""
self.func = func
self.__doc__ = func.__doc__
self.lock = threading.RLock()
def __get__(self, instance, cls=None):
"""Retrieve previous value, or call func()."""
if instance is None:
return self
attrname = self.func.__name__
try:
cache = instance.__dict__
except AttributeError: # objects with __slots__ have no __dict__
msg = ("No '__dict__' attribute on {}"
" instance to cache {} property.").format(
repr(type(instance).__name__), repr(attrname))
raise TypeError(msg) from None
with self.lock:
# check if another thread filled cache while we awaited lock
if attrname not in cache:
cache[attrname] = self.func(instance)
return cache[attrname]
def datetime_parse(string):
"""Convert timestamp to datetime.
Because date/time parsing in python is still pedantically stupid,
we rip the input string apart throwing out the stop characters etc;
then rebuild a string strptime() can parse. Igit!
- Python >3.7 will address colons in the UTC offset.
- There is no ETA on microseconds > 6 digits.
- And giving an offset and timezone name...
# match: 2018-05-08T14:12:53.797795191-07:00
# match: 2018-05-08T18:24:52.753227-07:00
# match: 2018-05-08 14:12:53.797795191 -0700 MST
# match: 2018-05-09T10:45:57.576002 (python isoformat())
Some people, when confronted with a problem, think “I know,
I'll use regular expressions.” Now they have two problems.
-- Jamie Zawinski
"""
ts = re.compile(r'^(\d+)-(\d+)-(\d+)'
r'[ T]?(\d+):(\d+):(\d+).(\d+)'
r' *([-+][\d:]{4,5})? *')
x = ts.match(string)
if x is None:
raise ValueError('Unable to parse {}'.format(string))
# converting everything to int() not worth the readablity hit
igit_proof = '{}T{}.{}{}'.format(
'-'.join(x.group(1, 2, 3)),
':'.join(x.group(4, 5, 6)),
x.group(7)[0:6],
x.group(8).replace(':', '') if x.group(8) else '',
)
format = '%Y-%m-%dT%H:%M:%S.%f'
if x.group(8):
format += '%z'
return datetime.datetime.strptime(igit_proof, format)
def datetime_format(dt):
"""Format datetime in consistent style."""
if isinstance(dt, str):
return datetime_parse(dt).isoformat()
elif isinstance(dt, datetime.datetime):
return dt.isoformat()
else:
raise ValueError('Unable to format {}. Type {} not supported.'.format(
dt, type(dt)))
| 3,063 | 1,019 |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: ResourceSecretService.proto
"""Generated protocol buffer code."""
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='ResourceSecretService.proto',
package='org.apache.custos.resource.secret.service',
syntax='proto3',
serialized_options=b'P\001',
create_key=_descriptor._internal_create_key,
serialized_pb=b'\n\x1bResourceSecretService.proto\x12)org.apache.custos.resource.secret.service\"\xda\x03\n\x0eSecretMetadata\x12P\n\nowner_type\x18\x01 \x01(\x0e\x32<.org.apache.custos.resource.secret.service.ResourceOwnerType\x12N\n\rresource_type\x18\x02 \x01(\x0e\x32\x37.org.apache.custos.resource.secret.service.ResourceType\x12I\n\x06source\x18\x03 \x01(\x0e\x32\x39.org.apache.custos.resource.secret.service.ResourceSource\x12\x0c\n\x04name\x18\x04 \x01(\t\x12\r\n\x05value\x18\x05 \x01(\t\x12K\n\x04type\x18\x06 \x01(\x0e\x32=.org.apache.custos.resource.secret.service.ResourceSecretType\x12\x10\n\x08tenantId\x18\x07 \x01(\x03\x12\x10\n\x08owner_id\x18\x08 \x01(\t\x12\x16\n\x0epersisted_time\x18\t \x01(\x03\x12\r\n\x05token\x18\n \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x0b \x01(\t\x12\x11\n\tclient_id\x18\x0c \x01(\t\"\xab\x01\n\x10GetSecretRequest\x12K\n\x08metadata\x18\x01 \x01(\x0b\x32\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\x10\n\x08tenantId\x18\x02 \x01(\x03\x12\x10\n\x08\x63lientId\x18\x03 \x01(\t\x12\x11\n\tclientSec\x18\x04 \x01(\t\x12\x13\n\x0b\x61\x63\x63\x65ssToken\x18\x05 \x01(\t\"\xc6\x01\n\x15\x43\x65rtificateCredential\x12K\n\x08metadata\x18\x01 \x01(\x0b\x32\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\x11\n\tx509_cert\x18\x03 \x01(\t\x12\x11\n\tnot_after\x18\x04 \x01(\t\x12\x13\n\x0bprivate_key\x18\x05 \x01(\t\x12\x11\n\tlife_time\x18\x06 \x01(\x03\x12\x12\n\nnot_before\x18\x07 \x01(\t\"s\n\x12PasswordCredential\x12K\n\x08metadata\x18\x01 \x01(\x0b\x32\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\x10\n\x08password\x18\x03 \x01(\t\"\x99\x01\n\rSSHCredential\x12K\n\x08metadata\x18\x01 \x01(\x0b\x32\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\x12\n\npassphrase\x18\x03 \x01(\t\x12\x12\n\npublic_key\x18\x04 \x01(\t\x12\x13\n\x0bprivate_key\x18\x05 \x01(\t\"o\n#GetResourceCredentialByTokenRequest\x12\x10\n\x08tenantId\x18\x01 \x01(\x03\x12\r\n\x05token\x18\x02 \x01(\t\x12\x14\n\x0cperformed_by\x18\x03 \x01(\t\x12\x11\n\tclient_id\x18\x04 \x01(\t\"\xd3\x01\n%GetResourceCredentialSummariesRequest\x12\x45\n\x04type\x18\x01 \x01(\x0e\x32\x37.org.apache.custos.resource.secret.service.ResourceType\x12\x19\n\x11\x61\x63\x63\x65ssible_tokens\x18\x02 \x03(\t\x12\x10\n\x08tenantId\x18\x03 \x01(\x03\x12\x10\n\x08owner_id\x18\x04 \x01(\t\x12\x11\n\tall_types\x18\x05 \x01(\x08\x12\x11\n\tclient_id\x18\x06 \x01(\t\"j\n\x1bResourceCredentialSummaries\x12K\n\x08metadata\x18\x01 \x03(\x0b\x32\x39.org.apache.custos.resource.secret.service.SecretMetadata\".\n\x1d\x41\x64\x64ResourceCredentialResponse\x12\r\n\x05token\x18\x01 \x01(\t\"3\n!ResourceCredentialOperationStatus\x12\x0e\n\x06status\x18\x01 \x01(\x08*<\n\x11ResourceOwnerType\x12\x0f\n\x0bTENANT_USER\x10\x00\x12\n\n\x06\x43USTOS\x10\x01\x12\n\n\x06TENANT\x10\x02*Y\n\x0cResourceType\x12\x16\n\x12SERVER_CERTIFICATE\x10\x00\x12\x1b\n\x17JWT_SIGNING_CERTIFICATE\x10\x01\x12\x14\n\x10VAULT_CREDENTIAL\x10\x02*D\n\x0eResourceSource\x12\x08\n\x04KUBE\x10\x00\x12\t\n\x05LOCAL\x10\x01\x12\x0c\n\x08\x45XTERNAL\x10\x02\x12\x0f\n\x0bLETSENCRYPT\x10\x03*A\n\x12ResourceSecretType\x12\x07\n\x03SSH\x10\x00\x12\x0c\n\x08PASSWORD\x10\x01\x12\x14\n\x10X509_CERTIFICATE\x10\x02\x32\x8f\x10\n\x15ResourceSecretService\x12\x83\x01\n\tgetSecret\x12;.org.apache.custos.resource.secret.service.GetSecretRequest\x1a\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\xa9\x01\n\x1cgetResourceCredentialSummary\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1a\x39.org.apache.custos.resource.secret.service.SecretMetadata\x12\xbd\x01\n!getAllResourceCredentialSummaries\x12P.org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest\x1a\x46.org.apache.custos.resource.secret.service.ResourceCredentialSummaries\x12\x96\x01\n\x10\x61\x64\x64SSHCredential\x12\x38.org.apache.custos.resource.secret.service.SSHCredential\x1aH.org.apache.custos.resource.secret.service.AddResourceCredentialResponse\x12\xa0\x01\n\x15\x61\x64\x64PasswordCredential\x12=.org.apache.custos.resource.secret.service.PasswordCredential\x1aH.org.apache.custos.resource.secret.service.AddResourceCredentialResponse\x12\xa6\x01\n\x18\x61\x64\x64\x43\x65rtificateCredential\x12@.org.apache.custos.resource.secret.service.CertificateCredential\x1aH.org.apache.custos.resource.secret.service.AddResourceCredentialResponse\x12\x9c\x01\n\x10getSSHCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1a\x38.org.apache.custos.resource.secret.service.SSHCredential\x12\xa6\x01\n\x15getPasswordCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1a=.org.apache.custos.resource.secret.service.PasswordCredential\x12\xac\x01\n\x18getCertificateCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1a@.org.apache.custos.resource.secret.service.CertificateCredential\x12\xb3\x01\n\x13\x64\x65leteSSHCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1aL.org.apache.custos.resource.secret.service.ResourceCredentialOperationStatus\x12\xb3\x01\n\x13\x64\x65letePWDCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1aL.org.apache.custos.resource.secret.service.ResourceCredentialOperationStatus\x12\xbb\x01\n\x1b\x64\x65leteCertificateCredential\x12N.org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest\x1aL.org.apache.custos.resource.secret.service.ResourceCredentialOperationStatusB\x02P\x01\x62\x06proto3'
)
_RESOURCEOWNERTYPE = _descriptor.EnumDescriptor(
name='ResourceOwnerType',
full_name='org.apache.custos.resource.secret.service.ResourceOwnerType',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='TENANT_USER', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='CUSTOS', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='TENANT', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=1735,
serialized_end=1795,
)
_sym_db.RegisterEnumDescriptor(_RESOURCEOWNERTYPE)
ResourceOwnerType = enum_type_wrapper.EnumTypeWrapper(_RESOURCEOWNERTYPE)
_RESOURCETYPE = _descriptor.EnumDescriptor(
name='ResourceType',
full_name='org.apache.custos.resource.secret.service.ResourceType',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='SERVER_CERTIFICATE', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='JWT_SIGNING_CERTIFICATE', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='VAULT_CREDENTIAL', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=1797,
serialized_end=1886,
)
_sym_db.RegisterEnumDescriptor(_RESOURCETYPE)
ResourceType = enum_type_wrapper.EnumTypeWrapper(_RESOURCETYPE)
_RESOURCESOURCE = _descriptor.EnumDescriptor(
name='ResourceSource',
full_name='org.apache.custos.resource.secret.service.ResourceSource',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='KUBE', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='LOCAL', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='EXTERNAL', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='LETSENCRYPT', index=3, number=3,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=1888,
serialized_end=1956,
)
_sym_db.RegisterEnumDescriptor(_RESOURCESOURCE)
ResourceSource = enum_type_wrapper.EnumTypeWrapper(_RESOURCESOURCE)
_RESOURCESECRETTYPE = _descriptor.EnumDescriptor(
name='ResourceSecretType',
full_name='org.apache.custos.resource.secret.service.ResourceSecretType',
filename=None,
file=DESCRIPTOR,
create_key=_descriptor._internal_create_key,
values=[
_descriptor.EnumValueDescriptor(
name='SSH', index=0, number=0,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='PASSWORD', index=1, number=1,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
_descriptor.EnumValueDescriptor(
name='X509_CERTIFICATE', index=2, number=2,
serialized_options=None,
type=None,
create_key=_descriptor._internal_create_key),
],
containing_type=None,
serialized_options=None,
serialized_start=1958,
serialized_end=2023,
)
_sym_db.RegisterEnumDescriptor(_RESOURCESECRETTYPE)
ResourceSecretType = enum_type_wrapper.EnumTypeWrapper(_RESOURCESECRETTYPE)
TENANT_USER = 0
CUSTOS = 1
TENANT = 2
SERVER_CERTIFICATE = 0
JWT_SIGNING_CERTIFICATE = 1
VAULT_CREDENTIAL = 2
KUBE = 0
LOCAL = 1
EXTERNAL = 2
LETSENCRYPT = 3
SSH = 0
PASSWORD = 1
X509_CERTIFICATE = 2
_SECRETMETADATA = _descriptor.Descriptor(
name='SecretMetadata',
full_name='org.apache.custos.resource.secret.service.SecretMetadata',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='owner_type', full_name='org.apache.custos.resource.secret.service.SecretMetadata.owner_type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='resource_type', full_name='org.apache.custos.resource.secret.service.SecretMetadata.resource_type', index=1,
number=2, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='source', full_name='org.apache.custos.resource.secret.service.SecretMetadata.source', index=2,
number=3, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='name', full_name='org.apache.custos.resource.secret.service.SecretMetadata.name', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='value', full_name='org.apache.custos.resource.secret.service.SecretMetadata.value', index=4,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='type', full_name='org.apache.custos.resource.secret.service.SecretMetadata.type', index=5,
number=6, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tenantId', full_name='org.apache.custos.resource.secret.service.SecretMetadata.tenantId', index=6,
number=7, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='owner_id', full_name='org.apache.custos.resource.secret.service.SecretMetadata.owner_id', index=7,
number=8, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='persisted_time', full_name='org.apache.custos.resource.secret.service.SecretMetadata.persisted_time', index=8,
number=9, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='org.apache.custos.resource.secret.service.SecretMetadata.token', index=9,
number=10, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='description', full_name='org.apache.custos.resource.secret.service.SecretMetadata.description', index=10,
number=11, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_id', full_name='org.apache.custos.resource.secret.service.SecretMetadata.client_id', index=11,
number=12, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=75,
serialized_end=549,
)
_GETSECRETREQUEST = _descriptor.Descriptor(
name='GetSecretRequest',
full_name='org.apache.custos.resource.secret.service.GetSecretRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='metadata', full_name='org.apache.custos.resource.secret.service.GetSecretRequest.metadata', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tenantId', full_name='org.apache.custos.resource.secret.service.GetSecretRequest.tenantId', index=1,
number=2, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='clientId', full_name='org.apache.custos.resource.secret.service.GetSecretRequest.clientId', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='clientSec', full_name='org.apache.custos.resource.secret.service.GetSecretRequest.clientSec', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='accessToken', full_name='org.apache.custos.resource.secret.service.GetSecretRequest.accessToken', index=4,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=552,
serialized_end=723,
)
_CERTIFICATECREDENTIAL = _descriptor.Descriptor(
name='CertificateCredential',
full_name='org.apache.custos.resource.secret.service.CertificateCredential',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='metadata', full_name='org.apache.custos.resource.secret.service.CertificateCredential.metadata', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='x509_cert', full_name='org.apache.custos.resource.secret.service.CertificateCredential.x509_cert', index=1,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='not_after', full_name='org.apache.custos.resource.secret.service.CertificateCredential.not_after', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='private_key', full_name='org.apache.custos.resource.secret.service.CertificateCredential.private_key', index=3,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='life_time', full_name='org.apache.custos.resource.secret.service.CertificateCredential.life_time', index=4,
number=6, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='not_before', full_name='org.apache.custos.resource.secret.service.CertificateCredential.not_before', index=5,
number=7, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=726,
serialized_end=924,
)
_PASSWORDCREDENTIAL = _descriptor.Descriptor(
name='PasswordCredential',
full_name='org.apache.custos.resource.secret.service.PasswordCredential',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='metadata', full_name='org.apache.custos.resource.secret.service.PasswordCredential.metadata', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='password', full_name='org.apache.custos.resource.secret.service.PasswordCredential.password', index=1,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=926,
serialized_end=1041,
)
_SSHCREDENTIAL = _descriptor.Descriptor(
name='SSHCredential',
full_name='org.apache.custos.resource.secret.service.SSHCredential',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='metadata', full_name='org.apache.custos.resource.secret.service.SSHCredential.metadata', index=0,
number=1, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='passphrase', full_name='org.apache.custos.resource.secret.service.SSHCredential.passphrase', index=1,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='public_key', full_name='org.apache.custos.resource.secret.service.SSHCredential.public_key', index=2,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='private_key', full_name='org.apache.custos.resource.secret.service.SSHCredential.private_key', index=3,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1044,
serialized_end=1197,
)
_GETRESOURCECREDENTIALBYTOKENREQUEST = _descriptor.Descriptor(
name='GetResourceCredentialByTokenRequest',
full_name='org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='tenantId', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest.tenantId', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='token', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest.token', index=1,
number=2, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='performed_by', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest.performed_by', index=2,
number=3, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_id', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest.client_id', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1199,
serialized_end=1310,
)
_GETRESOURCECREDENTIALSUMMARIESREQUEST = _descriptor.Descriptor(
name='GetResourceCredentialSummariesRequest',
full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.type', index=0,
number=1, type=14, cpp_type=8, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='accessible_tokens', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.accessible_tokens', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='tenantId', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.tenantId', index=2,
number=3, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='owner_id', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.owner_id', index=3,
number=4, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='all_types', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.all_types', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
_descriptor.FieldDescriptor(
name='client_id', full_name='org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest.client_id', index=5,
number=6, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1313,
serialized_end=1524,
)
_RESOURCECREDENTIALSUMMARIES = _descriptor.Descriptor(
name='ResourceCredentialSummaries',
full_name='org.apache.custos.resource.secret.service.ResourceCredentialSummaries',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='metadata', full_name='org.apache.custos.resource.secret.service.ResourceCredentialSummaries.metadata', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1526,
serialized_end=1632,
)
_ADDRESOURCECREDENTIALRESPONSE = _descriptor.Descriptor(
name='AddResourceCredentialResponse',
full_name='org.apache.custos.resource.secret.service.AddResourceCredentialResponse',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='token', full_name='org.apache.custos.resource.secret.service.AddResourceCredentialResponse.token', index=0,
number=1, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=b"".decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1634,
serialized_end=1680,
)
_RESOURCECREDENTIALOPERATIONSTATUS = _descriptor.Descriptor(
name='ResourceCredentialOperationStatus',
full_name='org.apache.custos.resource.secret.service.ResourceCredentialOperationStatus',
filename=None,
file=DESCRIPTOR,
containing_type=None,
create_key=_descriptor._internal_create_key,
fields=[
_descriptor.FieldDescriptor(
name='status', full_name='org.apache.custos.resource.secret.service.ResourceCredentialOperationStatus.status', index=0,
number=1, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),
],
extensions=[
],
nested_types=[],
enum_types=[
],
serialized_options=None,
is_extendable=False,
syntax='proto3',
extension_ranges=[],
oneofs=[
],
serialized_start=1682,
serialized_end=1733,
)
_SECRETMETADATA.fields_by_name['owner_type'].enum_type = _RESOURCEOWNERTYPE
_SECRETMETADATA.fields_by_name['resource_type'].enum_type = _RESOURCETYPE
_SECRETMETADATA.fields_by_name['source'].enum_type = _RESOURCESOURCE
_SECRETMETADATA.fields_by_name['type'].enum_type = _RESOURCESECRETTYPE
_GETSECRETREQUEST.fields_by_name['metadata'].message_type = _SECRETMETADATA
_CERTIFICATECREDENTIAL.fields_by_name['metadata'].message_type = _SECRETMETADATA
_PASSWORDCREDENTIAL.fields_by_name['metadata'].message_type = _SECRETMETADATA
_SSHCREDENTIAL.fields_by_name['metadata'].message_type = _SECRETMETADATA
_GETRESOURCECREDENTIALSUMMARIESREQUEST.fields_by_name['type'].enum_type = _RESOURCETYPE
_RESOURCECREDENTIALSUMMARIES.fields_by_name['metadata'].message_type = _SECRETMETADATA
DESCRIPTOR.message_types_by_name['SecretMetadata'] = _SECRETMETADATA
DESCRIPTOR.message_types_by_name['GetSecretRequest'] = _GETSECRETREQUEST
DESCRIPTOR.message_types_by_name['CertificateCredential'] = _CERTIFICATECREDENTIAL
DESCRIPTOR.message_types_by_name['PasswordCredential'] = _PASSWORDCREDENTIAL
DESCRIPTOR.message_types_by_name['SSHCredential'] = _SSHCREDENTIAL
DESCRIPTOR.message_types_by_name['GetResourceCredentialByTokenRequest'] = _GETRESOURCECREDENTIALBYTOKENREQUEST
DESCRIPTOR.message_types_by_name['GetResourceCredentialSummariesRequest'] = _GETRESOURCECREDENTIALSUMMARIESREQUEST
DESCRIPTOR.message_types_by_name['ResourceCredentialSummaries'] = _RESOURCECREDENTIALSUMMARIES
DESCRIPTOR.message_types_by_name['AddResourceCredentialResponse'] = _ADDRESOURCECREDENTIALRESPONSE
DESCRIPTOR.message_types_by_name['ResourceCredentialOperationStatus'] = _RESOURCECREDENTIALOPERATIONSTATUS
DESCRIPTOR.enum_types_by_name['ResourceOwnerType'] = _RESOURCEOWNERTYPE
DESCRIPTOR.enum_types_by_name['ResourceType'] = _RESOURCETYPE
DESCRIPTOR.enum_types_by_name['ResourceSource'] = _RESOURCESOURCE
DESCRIPTOR.enum_types_by_name['ResourceSecretType'] = _RESOURCESECRETTYPE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
SecretMetadata = _reflection.GeneratedProtocolMessageType('SecretMetadata', (_message.Message,), {
'DESCRIPTOR' : _SECRETMETADATA,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.SecretMetadata)
})
_sym_db.RegisterMessage(SecretMetadata)
GetSecretRequest = _reflection.GeneratedProtocolMessageType('GetSecretRequest', (_message.Message,), {
'DESCRIPTOR' : _GETSECRETREQUEST,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.GetSecretRequest)
})
_sym_db.RegisterMessage(GetSecretRequest)
CertificateCredential = _reflection.GeneratedProtocolMessageType('CertificateCredential', (_message.Message,), {
'DESCRIPTOR' : _CERTIFICATECREDENTIAL,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.CertificateCredential)
})
_sym_db.RegisterMessage(CertificateCredential)
PasswordCredential = _reflection.GeneratedProtocolMessageType('PasswordCredential', (_message.Message,), {
'DESCRIPTOR' : _PASSWORDCREDENTIAL,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.PasswordCredential)
})
_sym_db.RegisterMessage(PasswordCredential)
SSHCredential = _reflection.GeneratedProtocolMessageType('SSHCredential', (_message.Message,), {
'DESCRIPTOR' : _SSHCREDENTIAL,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.SSHCredential)
})
_sym_db.RegisterMessage(SSHCredential)
GetResourceCredentialByTokenRequest = _reflection.GeneratedProtocolMessageType('GetResourceCredentialByTokenRequest', (_message.Message,), {
'DESCRIPTOR' : _GETRESOURCECREDENTIALBYTOKENREQUEST,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.GetResourceCredentialByTokenRequest)
})
_sym_db.RegisterMessage(GetResourceCredentialByTokenRequest)
GetResourceCredentialSummariesRequest = _reflection.GeneratedProtocolMessageType('GetResourceCredentialSummariesRequest', (_message.Message,), {
'DESCRIPTOR' : _GETRESOURCECREDENTIALSUMMARIESREQUEST,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.GetResourceCredentialSummariesRequest)
})
_sym_db.RegisterMessage(GetResourceCredentialSummariesRequest)
ResourceCredentialSummaries = _reflection.GeneratedProtocolMessageType('ResourceCredentialSummaries', (_message.Message,), {
'DESCRIPTOR' : _RESOURCECREDENTIALSUMMARIES,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.ResourceCredentialSummaries)
})
_sym_db.RegisterMessage(ResourceCredentialSummaries)
AddResourceCredentialResponse = _reflection.GeneratedProtocolMessageType('AddResourceCredentialResponse', (_message.Message,), {
'DESCRIPTOR' : _ADDRESOURCECREDENTIALRESPONSE,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.AddResourceCredentialResponse)
})
_sym_db.RegisterMessage(AddResourceCredentialResponse)
ResourceCredentialOperationStatus = _reflection.GeneratedProtocolMessageType('ResourceCredentialOperationStatus', (_message.Message,), {
'DESCRIPTOR' : _RESOURCECREDENTIALOPERATIONSTATUS,
'__module__' : 'ResourceSecretService_pb2'
# @@protoc_insertion_point(class_scope:org.apache.custos.resource.secret.service.ResourceCredentialOperationStatus)
})
_sym_db.RegisterMessage(ResourceCredentialOperationStatus)
DESCRIPTOR._options = None
_RESOURCESECRETSERVICE = _descriptor.ServiceDescriptor(
name='ResourceSecretService',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService',
file=DESCRIPTOR,
index=0,
serialized_options=None,
create_key=_descriptor._internal_create_key,
serialized_start=2026,
serialized_end=4089,
methods=[
_descriptor.MethodDescriptor(
name='getSecret',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getSecret',
index=0,
containing_service=None,
input_type=_GETSECRETREQUEST,
output_type=_SECRETMETADATA,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='getResourceCredentialSummary',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getResourceCredentialSummary',
index=1,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_SECRETMETADATA,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='getAllResourceCredentialSummaries',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getAllResourceCredentialSummaries',
index=2,
containing_service=None,
input_type=_GETRESOURCECREDENTIALSUMMARIESREQUEST,
output_type=_RESOURCECREDENTIALSUMMARIES,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='addSSHCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.addSSHCredential',
index=3,
containing_service=None,
input_type=_SSHCREDENTIAL,
output_type=_ADDRESOURCECREDENTIALRESPONSE,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='addPasswordCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.addPasswordCredential',
index=4,
containing_service=None,
input_type=_PASSWORDCREDENTIAL,
output_type=_ADDRESOURCECREDENTIALRESPONSE,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='addCertificateCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.addCertificateCredential',
index=5,
containing_service=None,
input_type=_CERTIFICATECREDENTIAL,
output_type=_ADDRESOURCECREDENTIALRESPONSE,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='getSSHCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getSSHCredential',
index=6,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_SSHCREDENTIAL,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='getPasswordCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getPasswordCredential',
index=7,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_PASSWORDCREDENTIAL,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='getCertificateCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.getCertificateCredential',
index=8,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_CERTIFICATECREDENTIAL,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='deleteSSHCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.deleteSSHCredential',
index=9,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_RESOURCECREDENTIALOPERATIONSTATUS,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='deletePWDCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.deletePWDCredential',
index=10,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_RESOURCECREDENTIALOPERATIONSTATUS,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
_descriptor.MethodDescriptor(
name='deleteCertificateCredential',
full_name='org.apache.custos.resource.secret.service.ResourceSecretService.deleteCertificateCredential',
index=11,
containing_service=None,
input_type=_GETRESOURCECREDENTIALBYTOKENREQUEST,
output_type=_RESOURCECREDENTIALOPERATIONSTATUS,
serialized_options=None,
create_key=_descriptor._internal_create_key,
),
])
_sym_db.RegisterServiceDescriptor(_RESOURCESECRETSERVICE)
DESCRIPTOR.services_by_name['ResourceSecretService'] = _RESOURCESECRETSERVICE
# @@protoc_insertion_point(module_scope)
| 46,271 | 16,899 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
###############################################################################
# Girder plugin framework and tests adapted from Kitware Inc. source and
# documentation by the Imaging and Visualization Group, Advanced Biomedical
# Computational Science, Frederick National Laboratory for Cancer Research.
#
# Copyright Kitware Inc.
#
# Licensed under the Apache License, Version 2.0 ( the "License" );
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
###############################################################################
import datetime
import json
from bson.objectid import ObjectId
from girder import plugin
from girder import events, logger
from girder.settings import SettingDefault
from girder.exceptions import ValidationException
from girder.models.item import Item
from girder.models.notification import Notification
from girder.utility import setting_utilities
from girder_jobs.constants import JobStatus
from girder_jobs.models.job import Job
from .constants import PluginSettings
from .rest import HistogramResource
from .models.histogram import Histogram
from girder.utility.model_importer import ModelImporter
def _onRemoveItem(event):
"""
When a resource containing histograms is about to be deleted, we delete
all of the histograms that are attached to it.
"""
histogramModel = Histogram()
for histogram in Histogram().find({'itemId': ObjectId(event.info['_id'])}):
histogramModel.remove(histogram)
def _onRemoveFile(event):
"""
When a histogram file is deleted, we remove the parent histogram.
"""
histogramModel = Histogram()
for histogram in Histogram().find({'fileId': ObjectId(event.info['_id'])}):
histogramModel.remove(histogram, keepFile=True)
def _onUpload(event):
"""
Histogram creation can be requested on file upload by passing a reference
'histogram' that is a JSON object of the following form:
{
"histogram": {
"bins": 255,
"label": True,
"bitmask": False
}
}
bins, label, and bitmask arguments are optional
"""
file_ = event.info['file']
user = event.info['currentUser']
token = event.info['currentToken']
if 'itemId' not in file_:
return
try:
ref = json.loads(event.info.get('reference', ''))
except (TypeError, ValueError):
return
if not isinstance(ref, dict):
return
if ref.get('isHistogram'):
# jobId = ref.get('jobId')
fakeId = ref.get('fakeId')
if not fakeId:
msg = 'Histogram file %s uploaded without fakeId reference.'
logger.warning(msg % file_['_id'])
return
histograms = list(Histogram().find({'fakeId': fakeId}, limit=2))
if len(histograms) == 1:
histogram = histograms[0]
del histogram['expected']
histogram['fileId'] = file_['_id']
Histogram().save(histogram)
else:
msg = 'Failed to retrieve histogram for file %s using fakeId %s.'
logger.warning(msg % (file_['_id'], fakeId))
return
elif isinstance(ref.get('histogram'), dict):
item = Item().load(file_['itemId'], force=True)
Histogram().createHistogram(item, file_, user, token,
**ref['histogram'])
def _updateJob(event):
"""
Called when a job is saved, updated, or removed. If this is a histogram
job and it is ended, clean up after it.
"""
if event.name == 'jobs.job.update.after':
job = event.info['job']
else:
job = event.info
meta = job.get('meta', {})
if (meta.get('creator') != 'histogram' or
meta.get('task') != 'createHistogram'):
return
status = job['status']
if event.name == 'model.job.remove' and status not in (
JobStatus.ERROR, JobStatus.CANCELED, JobStatus.SUCCESS):
status = JobStatus.CANCELED
if status not in (JobStatus.ERROR, JobStatus.CANCELED, JobStatus.SUCCESS):
return
histograms = list(Histogram().find({'fakeId': meta.get('fakeId')}, limit=2))
if len(histograms) != 1:
msg = 'Failed to retrieve histogram using fakeId %s.'
logger.warning(msg % meta.get('fakeId'))
return
histogram = histograms[0]
if histogram.get('expected'):
# We can get a SUCCESS message before we get the upload message, so
# don't clear the expected status on success.
if status != JobStatus.SUCCESS:
del histogram['expected']
notify = histogram.get('notify')
msg = None
if notify:
del histogram['notify']
if status == JobStatus.SUCCESS:
msg = 'Histogram created'
elif status == JobStatus.CANCELED:
msg = 'Histogram creation canceled'
else: # ERROR
msg = 'FAILED: Histogram creation failed'
msg += ' for item %s' % histogram['itemId']
msg += ', file %s' % histogram['fileId']
if status == JobStatus.SUCCESS:
Histogram().save(histogram)
else:
Histogram().remove(histogram)
if msg and event.name != 'model.job.remove':
Job().updateJob(job, progressMessage=msg)
if notify:
Notification().createNotification(
type='histogram.finished_histogram',
data={
'histogram_id': histogram['_id'],
'item_id': histogram['itemId'],
'file_id': histogram['fileId'],
'fakeId': histogram['fakeId'],
'success': status == JobStatus.SUCCESS,
'status': status
},
user={'_id': job.get('userId')},
expires=datetime.datetime.utcnow() + datetime.timedelta(seconds=30)
)
@setting_utilities.validator({
PluginSettings.DEFAULT_BINS,
})
def validateNonnegativeInteger(doc):
val = doc['value']
try:
val = int(val)
if val < 0:
raise ValueError
except ValueError:
msg = '%s must be a non-negative integer.' % doc['key']
raise ValidationException(msg, 'value')
doc['value'] = val
# Default settings values
SettingDefault.defaults.update({
PluginSettings.DEFAULT_BINS: 256,
})
class HistogramPlugin(plugin.GirderPlugin):
DISPLAY_NAME = 'Histogram'
CLIENT_SOURCE_PATH = 'web_client'
def load(self, info):
ModelImporter.registerModel('histogram', Histogram, 'histogram')
info['apiRoot'].histogram = HistogramResource()
events.bind('model.item.remove', 'Histogram', _onRemoveItem)
events.bind('model.file.remove', 'Histogram', _onRemoveFile)
events.bind('data.process', 'Histogram', _onUpload)
events.bind('jobs.job.update.after', 'Histogram', _updateJob)
events.bind('model.job.save', 'Histogram', _updateJob)
events.bind('model.job.remove', 'Histogram', _updateJob)
| 7,421 | 2,170 |
from keras.models import Sequential, load_model
from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten
from keras.optimizers import Adam
from keras.callbacks import TensorBoard
import tensorflow as tf
from tqdm import tqdm
from collections import deque
from gym.bug_env.environment import Environment
import numpy as np
import random
import time
import os
LOAD_MODEL = "benchmarked_models/2x256____34.40max___26.53avg___18.00min__1621204339.model"
DISCOUNT = 0.99
REPLAY_MEMORY_SIZE = 50_000 # How many last steps to keep for model training
MIN_REPLAY_MEMORY_SIZE = 1_000 # Minimum number of steps in a memory to start training
MINIBATCH_SIZE = 64 # How many steps (samples) to use for training
UPDATE_TARGET_EVERY = 5 # Terminal states (end of episodes)
MODEL_NAME = '2x256'
MIN_REWARD = 0 # For model save
MEMORY_FRACTION = 0.20
# Environment settings
EPISODES = 20_000
# Stats settings
AGGREGATE_STATS_EVERY = 50 # episodes
SHOW_PREVIEW = True
class DQNAgent:
def __init__(self, env):
# Main model (gets trained every step)
self.model = self.create_model(env)
# Target model (this is what we .predict against every step)
self.target_model = self.create_model(env)
self.target_model.set_weights(self.model.get_weights())
self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE)
self.target_update_counter = 0
self.env = env
def create_model(self, env):
if LOAD_MODEL is not None:
print(f"Loading {LOAD_MODEL}")
model = load_model(LOAD_MODEL)
print(f"Model {LOAD_MODEL} loaded!")
return model
else:
print("ERROR! No model to load!")
def update_replay_memory(self, transition):
self.replay_memory.append(transition)
def get_qs(self, state, step):
return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0]
def train(self, terminal_state, step):
if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE:
return
minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE)
current_states = np.array([transition[0] for transition in minibatch])/255
current_qs_list = self.model.predict(current_states)
new_current_states = np.array([transition[3] for transition in minibatch])/255
future_qs_list = self.target_model.predict(new_current_states)
# Feature sets (images from game)
X = []
# Labels (action we decide to take)
y = []
for index, (current_state, action, reward, new_current_states, done) in enumerate(minibatch):
if not done:
max_future_q = np.max(future_qs_list[index])
new_q = reward + DISCOUNT * max_future_q
else:
new_q = reward
current_qs = current_qs_list[index]
current_qs[action] = new_q
X.append(current_state)
y.append(current_qs)
self.model.fit(np.array(X) / 255, np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False)
# Updating to determine if we want to update target_model yet
if terminal_state:
self.target_update_counter += 1
if self.target_update_counter > UPDATE_TARGET_EVERY:
self.target_model.set_weights(self.model.get_weights())
self.target_update_counter = 0
def main():
# Exploration settings
epsilon = 0 # not a constant, going to be decayed
EPSILON_DECAY = 0.99975
MIN_EPSILON = 0.001
print("Running Inference App...")
# For stats
ep_rewards = [-200]
# For more repetitive results
#random.seed(1)
#np.random.seed(1)
#tf.set_random_seed(1)
# Create models folder
if not os.path.isdir("models"):
os.mkdir("models")
# Create Environment
env = Environment(map_img_path="gym/bug_env/res/map_small_edge.jpg",
fov=15,
food_spawn_threshold=255,
percent_for_game_over=100,
steps_for_game_over=100,
wait_key=300,
render=True)
agent = DQNAgent(env)
for episode in tqdm(range(1, EPISODES+1), ascii=True, unit="episode"):
episode_reward = 0
step = 1
current_state = env.reset()
done = False
while not done:
if np.random.random() > epsilon:
action = np.argmax(agent.get_qs(current_state, step))
else:
action = np.random.randint(0, env.get_action_space_size())
new_state, reward, done = env.step(action)
episode_reward += reward
if SHOW_PREVIEW and not episode % 1:
env.render_map()
env.render_sub_map()
#agent.update_replay_memory((current_state, action, reward, new_state, done))
#agent.train(done, step)
current_state = new_state
step += 1
print("reward: {}".format(reward),
"total reward: {}".format(env.agent_reward),
"game_over: {}".format(done),
"total_gen_reward: {}".format(env.total_generated_rewards)
)
if __name__ == '__main__':
main()
| 5,325 | 1,789 |
# -*- coding: utf-8 -*-
'''
Быстрая сортировка
'''
from typing import MutableSequence, Callable
def _qsort(data: MutableSequence,
start: int,
end: int,
process_func: Callable[[MutableSequence, int, int], int]) -> None:
if start < end:
pivot_index = process_func(data, start, end)
_qsort(data, start, pivot_index - 1, process_func)
_qsort(data, pivot_index + 1, end, process_func)
def _process_end(data: MutableSequence, start: int, end: int) -> int:
i = start - 1
j = end
pivot = data[end]
while i < j:
i += 1
j -= 1
while data[i] < pivot:
i += 1
while data[j] > pivot and j > start:
j -= 1
if i < j:
data[i], data[j] = data[j], data[i]
data[i], data[end] = data[end], data[i]
return i
def _process_middle(data: MutableSequence, start: int, end: int) -> int:
i = start - 1
j = end + 1
pivot_index = (start + end) // 2
pivot = data[pivot_index]
while i < j:
if i < pivot_index:
i += 1
while data[i] < pivot and i < pivot_index:
i += 1
if j > pivot_index:
j -= 1
while data[j] > pivot and j > pivot_index:
j -= 1
if i < j:
data[i], data[j] = data[j], data[i]
if i == pivot_index:
pivot_index = j
elif j == pivot_index:
pivot_index = i
return i
def qsort_end(data: MutableSequence):
_qsort(data, 0, len(data) - 1, _process_end)
def qsort_middle(data: MutableSequence):
_qsort(data, 0, len(data) - 1, _process_middle)
| 1,701 | 601 |
from flask import Flask,render_template,request
from flask_wtf.csrf import CSRFProtect
from check_domain_result import compute
from check_traceroute import get_traceroute_result
from check_mtr import get_mtr_result
from check_ping import get_ping_result
from geo_latency_info import *
import folium
import os
import logging
from waitress import serve
logging.basicConfig(filename="/var/log/latency_app.log",level = logging.INFO,format = '%(levelname)s %(asctime)s %(message)s',datefmt = '%Y-%m-%d %H:%M:%S',filemode = 'a')
logger = logging.getLogger()
secret_key=os.urandom(12)
app = Flask(__name__)
app.config['SECRET_KEY']=secret_key
csrf = CSRFProtect(app)
@app.route('/',methods=['GET'])
def home():
return render_template('home.html')
@app.route('/latency',methods=['GET','POST'])
def latency():
if request.method=='POST':
name=request.form['name']
if name == '':
return render_template('latency.html')
else:
result=compute(name)
result=[ int(res*1000) for res in result ]
return render_template('show_latency_result.html',url=name,result=result)
return render_template('latency.html')
@app.route('/traceroute',methods=['GET','POST'])
def traceroute():
if request.method=='POST':
name=request.form['name']
if name == '':
return render_template('traceroute.html')
else:
traceroute_result=get_traceroute_result(name)
mtr_result=get_mtr_result(name)
ping_result=get_ping_result(name)
return render_template('show_traceroute_result.html',url=name,traceroute_result=traceroute_result,mtr_result=mtr_result,ping_result=ping_result)
return render_template('traceroute.html')
@app.route('/geo_trace',methods=['GET','POST'])
def geo_trace():
if request.method=='POST':
name=request.form['name']
if name == '':
return render_template('geo_trace.html')
else:
#draw trace on map
#remove old map
#os.remove('templates/show_geo_trace.html')
#logging.info("removed templates/show_geo_trace.html")
m=folium.Map()
Cordinate_list=[]
geo_obj=GeoTrace(name)
SOURCE_LOCATION,TRACES=geo_obj.get_geo_traceroute_data()
Cordinate_list.append(list(SOURCE_LOCATION))
if TRACES:
for hop,cordinate in sorted(TRACES.items()):
Cordinate_list.append(cordinate)
coordinates =[Cordinate_list]
logger.info(coordinates)
m = folium.Map(location=SOURCE_LOCATION, zoom_start=4)
folium.TileLayer('stamentoner').add_to(m)
folium.TileLayer('stamenterrain').add_to(m)
folium.TileLayer('openstreetmap').add_to(m)
folium.map.LayerControl().add_to(m)
COORDINATE_LIST=coordinates[0]
#add marker
count=0
for i in range(0,len(COORDINATE_LIST)):
lat=COORDINATE_LIST[i][0]
lon=COORDINATE_LIST[i][1]
count+=1
folium.Marker(location=[lat,lon],popup=('Route {}'.format(count)),icon = folium.Icon(color='green',icon='plus')).add_to(m)
my_PolyLine=folium.PolyLine(locations=coordinates,weight=5,color='red')
m.add_child(my_PolyLine)
f_name=name.split('.')[0]
file_name='show_geo_trace_'+str(f_name)+'.html'
saved_file_name=os.path.join('templates/',file_name)
m.save(saved_file_name)
logging.info("saved new result to geo_trace.html")
return render_template(file_name)
return render_template('geo_trace.html')
if __name__=='__main__':
serve(app,port=5000,threads=100)
| 3,834 | 1,267 |
import time
import queue
import threading
import RPi.GPIO as GPIO
_GPIO_PIN = 25
class Pixels:
def __init__(self):
self.is_light_on = 1
self.count_down = 0
self.light_on_count = 0
self.light_off_count = 0
GPIO.setwarnings(False)
GPIO.setmode(GPIO.BCM)
GPIO.setup(_GPIO_PIN, GPIO.OUT)
self.queue = queue.Queue()
self.off()
self.thread = threading.Thread(target=self._run)
self.thread.daemon = True
self.thread.start()
def wakeup(self, direction=0):
self.queue.put((1, 1))
def listen(self):
self.queue.put((5, 5))
def think(self):
self.queue.put((3, 3))
def speak(self):
self.queue.put((10, 0))
def off(self):
self.queue.put((0, 10))
def _set_light_on(self, on):
pos = GPIO.HIGH if on else GPIO.LOW
GPIO.output(_GPIO_PIN, pos)
def _run(self):
while True:
while not self.queue.empty():
(self.light_on_count, self.light_off_count) = self.queue.get()
self.is_light_on = 0
self.count_down = 0
while self.queue.empty():
if self.count_down == 0:
self.is_light_on = not self.is_light_on
if self.is_light_on:
self.count_down = self.light_on_count
else:
self.count_down = self.light_off_count
if self.count_down == 0:
continue
self._set_light_on(self.is_light_on)
time.sleep(0.1)
self.count_down -= 1
pixels = Pixels()
if __name__ == '__main__':
while True:
try:
pixels.wakeup()
time.sleep(3)
pixels.listen()
time.sleep(3)
pixels.think()
time.sleep(3)
pixels.speak()
time.sleep(3)
pixels.off()
time.sleep(3)
except KeyboardInterrupt:
break
pixels.off()
time.sleep(1)
| 2,111 | 716 |
##
# This software was developed and / or modified by Raytheon Company,
# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
#
# U.S. EXPORT CONTROLLED TECHNICAL DATA
# This software product contains export-restricted data whose
# export/transfer/disclosure is restricted by U.S. law. Dissemination
# to non-U.S. persons whether in the United States or abroad requires
# an export license or other authorization.
#
# Contractor Name: Raytheon Company
# Contractor Address: 6825 Pine Street, Suite 340
# Mail Stop B8
# Omaha, NE 68106
# 402.291.0100
#
# See the AWIPS II Master Rights File ("Master Rights File.pdf") for
# further licensing information.
##
#
# Name:
# TAMPGenerator.py
# GFS1-NHD:A10032.0000-SCRIPT;10
#
# Status:
# DELIVERED
#
# History:
# Revision 10 (DELIVERED)
# Created: 07-AUG-2009 10:22:17 OBERFIEL
# Use of CB is now constrained LTE a configurable height used
# by TAFGen
# when creating guidance TAFs.
#
# Revision 9 (DELIVERED)
# Created: 17-JUL-2009 16:40:07 OBERFIEL
# Viewers now use a resource to determine the Routine button
# setting.
#
# Revision 8 (REVIEW)
# Created: 05-MAY-2009 14:54:52 OBERFIEL
# Additional filter for cloud layers
#
# Revision 7 (DELIVERED)
# Created: 01-MAY-2009 13:57:47 OBERFIEL
# Added exception handling when VRB is encountered during
# wind averaging.
#
# Revision 6 (DELIVERED)
# Created: 01-AUG-2008 15:44:45 OBERFIEL
# Synch'd up with changes in OB8.3.1
#
# Revision 5 (DELIVERED)
# Created: 19-JUN-2008 14:20:42 OBERFIEL
# Allowed variable length of TAF -- not just 24h.
#
# Revision 4 (DELIVERED)
# Created: 18-APR-2008 14:19:09 OBERFIEL
# Numerous enhancements more robust error checking added.
#
# Revision 3 (DELIVERED)
# Created: 18-MAR-2008 14:40:28 OBERFIEL
# Fixed numerous formatting errors when TAFs are combined.
#
# Revision 2 (DELIVERED)
# Created: 14-MAR-2008 14:56:22 OBERFIEL
# Fixed some wind and cloud bugs
#
# Revision 1 (DELIVERED)
# Created: 20-NOV-2007 16:36:36 OBERFIEL
# New module to create TAF combined with GFSLAMP guidance.
#
# Change Document History:
# 1:
# Change Document: GFS1-NHD_SPR_7417
# Action Date: 06-OCT-2009 09:42:01
# Relationship Type: In Response to
# Status: CLOSED
# Title: AvnFPS: TUG code does not handle transition from warm to cold seasons
#
#
##
# This is a base file that is not intended to be overridden.
##
import copy, math, re, time
from itertools import groupby
import Avn, AvnLib, TafGen
#
# LAMP categories for ceiling and visibility
#
_LAMPCeilings=[0,200,500,1000,2000,3100,6600,12100]
_LAMPVisibilities=[0.,.5,1.0,2.0,3.0,5.1,6.1]
#
# Upper and lower limits within LAMP categories, allowed for insertion into TAF
#
_LAMPNewCeilings=[(),(0,1),(2,4),(5,9),(10,19),(20,30),(35,60),(70,120),(250,250)]
_LAMPNewVisibilities=[(),(0.1,0.25),(0.25,0.75),(1.0,1.5),(2.0,2.0),(3.0,5.0),(6.0,6.0),(12.0,12.0)]
re_TS = re.compile('TS\s?')
#
# Custom exception class
class NoEntries(Exception):
"Used when empty list is found"
class OcnlTimeLimit(Exception):
"Used when conditional group reaches its maximum hours in length"
def _findBestCategory(probabilities,thresholds):
cat=len(probabilities)
for cat,probability,threshold in enumerate(probabilities,thresholds):
if probability >= threshold:
break
return cat
def _findCategory(probability,thresholds):
cat=len(thresholds)
for cat,threshold in enumerate(thresholds):
if probability >= threshold:
break
return cat
def _inCategory(cat,thresholds,probabilities,delta):
try:
t=thresholds[cat]
except IndexError:
t=thresholds[-1]
return abs(t-probabilities[cat]) < t*delta
def _getCldHt(cldlyr):
try:
if cldlyr.endswith('CB'):
return int(cldlyr[-5:-2])*100
else:
return int(cldlyr[-3:])*100
except ValueError:
return 25000
def _nearesthr(t=0):
"""Top of the hour nearest within 30 minutes"""
hr=3600
d=t%hr
if d<1800:
hr=0
return t+hr-d
def _checkOcnlGrp(TAFLines):
"""Check the TEMPO/PROB30 group for elimination"""
#
# Check for last two strings in list
try:
if (TAFLines[-1].startswith('TEMPO') or TAFLines[-1].startswith('PROB30')):
pass
else:
return
except IndexError:
return
#
# Parse the last two lines into seperate tokens
ocnltokens = TAFLines[-1].split(' ')[2:]
prvlngtokens = TAFLines[-2].split(' ')[1:]
#
# If differences are found return, otherwise remove occasional group.
for token in ocnltokens:
if token not in prvlngtokens:
break
else:
TAFLines.pop(-1)
return
def _prjAverage(prjs,tafDuration=24):
"""Average elements together"""
#
# Initialization
TAFStrings = []
ocnlgrp = []
spos = n = 0
#
for n,prj in enumerate(prjs):
if prj.tocnl and prj.tocnl['type']:
#
# Make sure its of the same type and continuous in time
try:
if ocnlgrp[2] != prj.tocnl['type'] or \
ocnlgrp[0] + ocnlgrp[1] != n:
raise IndexError
if ocnlgrp[1:] == [4,'TEMPO'] or ocnlgrp[1:] == [6,'PROB']:
raise OcnlTimeLimit
ocnlgrp[1] += 1
#
# If occasional group is discontinuous in time or exceeds four
# hours in length
#
except (IndexError, OcnlTimeLimit):
if ocnlgrp:
# start position of occasional group and its duration
ospos,duration = ocnlgrp[:2]
TAFStrings.append(' '.join(_buildString([proj.tprev for proj in prjs[spos:n]],
prjs[spos].lampdata,tafDuration)))
TAFStrings.append(' '.join(_buildString([prjs[ospos+x].tocnl
for x in xrange(duration)])))
_checkOcnlGrp(TAFStrings)
spos = n
ocnlgrp = [n,1,prj.tocnl['type']]
#
prjs[spos].tprev['type']='FM'
if spos == n:
_tafstring = ' '.join(_buildString([prjs[spos].tprev],prjs[spos].lampdata,tafDuration))
else:
_tafstring = ' '.join(_buildString([prj.tprev for prj in prjs[spos:n]],
prjs[spos].lampdata,tafDuration))
TAFStrings.append(_tafstring)
try:
ospos,duration = ocnlgrp[:2]
_tafstring = ' '.join(_buildString([prjs[ospos+n].tocnl for n in xrange(duration)]))
TAFStrings.append(_tafstring)
_checkOcnlGrp(TAFStrings)
except (ValueError,TypeError):
pass
return TAFStrings
def _avgNSW(alist):
"""Just return NSW if present"""
if alist:
return 'NSW'
raise NoEntries
def _avgLLWS(alist):
"""Just return the first one, if present"""
if alist:
return alist[0]['str']
raise NoEntries
def _mostFrequent(alist):
"""Returns most frequent string found in list"""
strings = [element.get('str','') for element in alist]
return _freqList(strings)[-1]
def _freqList(alist):
"""Count unique items in list"""
if alist:
freqs = [(len(list(g)),k) for k, g in groupby(sorted(alist))]
return [b for a, b in sorted(freqs)]
raise NoEntries
def _avgWind(winds):
"""Average winds"""
n = len(winds)
if n == 0:
raise NoEntries
elif n == 1:
return winds[0]['str']
wspd = [x['ff'] for x in winds]
wdir = [x['dd'] for x in winds]
gsts = []
for x in winds:
try:
gsts.append(x['gg'])
except KeyError:
continue
ff = int(float(sum(wspd))/len(wspd)+0.5)
try:
gg = sum(gsts)/len(gsts)
except ZeroDivisionError:
gg = 0
pass
dd = 'VRB'
if ff > 3:
try:
u = sum([math.cos(math.radians(270 - dir))*spd \
for dir,spd in zip(wdir,wspd)])
v = sum([math.sin(math.radians(270 - dir))*spd \
for dir,spd in zip(wdir,wspd)])
dd = math.degrees(math.atan2(v,u))
if dd >= -90:
dd = 270-dd
else:
dd = -90-dd
dd = '%03d' % (10*((dd+5)//10))
if dd == '000':
dd = '360'
except TypeError:
pass
elif ff < 3:
dd = '000'
ff = 0
if gg - ff > 5:
return '%s%02dG%02dKT' % (dd,ff,gg)
else:
return '%s%02dKT' % (dd,ff)
def _avgSky(clouds):
"""Returns most frequently occurring layers"""
#
cldlayers=[]
newstring=[]
#
allrpts=Avn.flatten([y.split() for y in [x['str'] for x in clouds]])
toofew = len(allrpts)/4
cldlayers=[rpt for num, rpt in [(len(list(g)),k) for k, g in groupby(sorted(allrpts))] if num > toofew]
if len(cldlayers) == 0:
mostfreqlyrs=_freqList(allrpts)
else:
mostfreqlyrs=_freqList(cldlayers)
if mostfreqlyrs[-1] == 'SKC':
return 'SKC'
#
# Remove all occurrences of SKC
while True:
try:
ignored = mostfreqlyrs.pop(mostfreqlyrs.index('SKC'))
except:
break
#
# Remove layers at the same height
uniqclddict=dict([(_getCldHt(x),x) for x in mostfreqlyrs])
#
# Prepare to order the most frequent layers
lyrs = dict([(x,0) for x in ['OVC','BKN','SCT','FEW','VV']])
sortedlayers=sorted([_getCldHt(x) for x in mostfreqlyrs[-4:]])
lastheight = 0
for height in sortedlayers:
try:
strng = uniqclddict[height]
except KeyError:
continue
if height <= lastheight:
continue
lastheight = height
coverage=strng[:3]
if strng.startswith('V'):
coverage='VV'
#
# Don't allow VV, SCT or FEW if a BKN is present
if lyrs['BKN']:
if coverage in ['FEW','SCT','VV']:
continue
#
# Don't allow FEW or VV above a SCT layer
if lyrs['SCT']:
if coverage in ['FEW','VV']:
continue
try:
lyrs[coverage] += 1
newstring.append(strng)
except KeyError:
continue
#
# First overcast or VV stops the loop.
if lyrs['OVC'] or lyrs['VV']:
break
#
# Or two BKN layers
if lyrs['BKN'] == 2:
break
#
# Three cloud layers results in breakout
if lyrs['FEW'] + lyrs['SCT'] + lyrs['BKN'] > 2:
break
return ' '.join(newstring)
def _buildString(elements,lampdata=None,tafDuration=24):
"""Examine each element in TAF and come up with a string"""
#
# The presence of lampdata indicates a FM group, bleh.
rec = {}
rec['time'] = { 'from':elements[0]['time']['from'] }
rec['type'] = elements[0]['type']
if not lampdata:
rec['time']['to']=elements[-1]['time']['to']
for _function,_element in [(_avgWind,'wind'),(_mostFrequent,'vsby'),
(_mostFrequent,'pcp'),(_mostFrequent,'obv'),
(_avgSky,'sky'),(_avgNSW,'nsw'),
(_mostFrequent,'vcnty'),(_avgLLWS,'llws')]:
try:
rec[_element]={'str':_function([v[_element] for v in elements if v.has_key(_element)])}
except NoEntries:
pass
#
# Sanity checks
try:
if rec['vsby']['str'] == 'P6SM':
del rec['obv']
except KeyError:
pass
#
#
try:
if rec['nsw']:
if rec['type'] in ['FM','PROB']:
del rec['nsw']
elif rec['pcp']:
del rec['nsw']
except KeyError:
pass
try:
if re_TS.match(rec['pcp']['str']):
for lyr in rec['sky']['str'].split():
if lyr.endswith('CB'):
break
else:
rec['sky']['str'] += 'CB'
except KeyError:
pass
if lampdata and lampdata.has_key('ident'):
return AvnLib.formatRecForMAIN(rec,lampdata['ident'],lampdata['amd'],
tafDuration=tafDuration)
else:
if rec['type'] == 'FM':
return AvnLib.formatRecForFM(rec)
elif len(rec.keys()) > 2:
return AvnLib.formatRecForOCNL(rec)
def _addPrj(grpnum,pos,currentFltCat,nextFltCat,prevFltCat):
"""Almost always insures that that two or more of the same flight category exists"""
#
# Beginning hour of TAF is always added, regardless of any impending flight category
# change
#
if grpnum == 0 and pos == 0:
return True
elif pos:
return currentFltCat in [nextFltCat,prevFltCat]
else:
return currentFltCat == nextFltCat
def _summarizeTAFPrjs(TAFPrjs,TAFData,tafDuration=24):
"""Attempt to group based on previous TAF"""
#
# Initialization
TAFText = []
#
# Save Station Identifier and whether its an amendment
try:
ident=TAFPrjs[0].lampdata['ident']
amd=TAFPrjs[0].lampdata['amd']
except (KeyError, IndexError):
return
#
# Start with breakpoints in the official TAF
for grpnum,grp in enumerate(TAFData):
try:
shr = grp['prev']['time']['from']
ehr = grp['prev']['time']['to']
except KeyError:
continue
#
# Identify those projections and the flight category
# they correspond to.
#
prjs = [(x.flightCategory(),x) for x in TAFPrjs if shr <= x.vtime < ehr]
#
for n,cat in enumerate(prjs):
if n == 0:
numPrjs = len(prjs)-1
prjs2avg = []
crntPrj = cat[0]
nextPrj = prjs[min(n+1,numPrjs)][0]
prevPrj = prjs[max(0,n-1)][0]
if _addPrj(grpnum,n,crntPrj,nextPrj,prevPrj):
#
prjs2avg.append(cat[1])
#
# If there's a change in flight category ahead,
# average the projections gathered so far
#
if crntPrj != nextPrj:
if TAFText == []:
prjs2avg[0].lampdata['ident']=ident
prjs2avg[0].lampdata['amd']=amd
TAFText.extend(_prjAverage(prjs2avg,tafDuration))
prjs2avg = []
#
if prjs and prjs2avg:
if TAFText == []:
prjs2avg[0].lampdata['ident']=ident
prjs2avg[0].lampdata['amd']=amd
TAFText.extend(_prjAverage(prjs2avg,tafDuration))
return TAFText
class TUGPrj:
def __init__(self,**kwds):
self.__dict__.update(kwds)
try:
self.tprev['time']['from']=self.vtime
self.tprev['time']['to']=self.vtime+3600.0
except KeyError:
pass
try:
self.tocnl['time']['from']=self.vtime
self.tocnl['time']['to']=self.vtime+3600.0
except KeyError:
pass
self.wet = self._isWet()
self.pcpn_changed = self.changed = False
def checkSky(self,tafGrpInstructions,dthresholds={'up':.1,'down':.1},
wthresholds={'up':.1,'down':.1}):
"""Make changes to ceiling when guidance strongly differs"""
maxcbhgt=tafGrpInstructions.get('cbhight',50)
#
# For prevailing and occasional groups, adjust if necessary
try:
for group in [self.tprev,self.tocnl]:
if self._isGroupWet(group):
self._checkCeiling(group['sky'],wthresholds,maxcbhgt,True)
else:
self._checkCeiling(group['sky'],dthresholds,maxcbhgt,False)
#
# Determine if the sky condition is duplicated.
new_OcnlSky = []
for layer in self.tocnl['sky']['str'].split():
if not (layer.endswith('CB') or layer.startswith('VV')) and \
layer in self.tprev['sky']['str']:
continue
new_OcnlSky.append(layer)
if len(new_OcnlSky) == 0:
del self.tocnl['sky']
except KeyError:
pass
def _checkCeiling(self,taf,deltas,maxcbhgt,wet=False):
"""Adjust ceilings if necessary"""
#
if wet:
lamp=self.lampdata['csky']
lampBestCat=self.lampdata['ccig_bestCat']
probabilities=self.lampdata['ccprob']
thresholds=self.ccigthr
else:
lamp=self.lampdata['sky']
lampBestCat=self.lampdata['cig_bestCat']
probabilities=self.lampdata['cprob']
thresholds=self.cigthr
tcat = Avn.category(taf['cig'],_LAMPCeilings)
if tcat == lampBestCat:
return
#
# If LAMP and TAF both do not have a ceiling, return early
if lamp['cig'] == taf['cig'] == 99999:
return
#
# Adjust thresholds, determine if we can hit taf's category.
if tcat > lampBestCat and _inCategory(lampBestCat,thresholds,probabilities,deltas['up']):
return
if tcat < lampBestCat and _inCategory(tcat,thresholds,probabilities,deltas['down']):
return
#
# Otherwise, the guidance 'strongly' disagrees with TAF
self.cig_changed = self.changed = True
newsky = []
newceiling = []
#
# Preserve CB in sky condition, cb_skyamt serves as a flag as well
cb_skyamt = None
for lyr in taf['str'].split():
if lyr.endswith('CB'):
cb_skyamt = lyr[:3]
#
# Find layers at or below LAMP ceiling category
if lampBestCat < tcat:
# They have to be FEW or SCT layers
for layer in [x for x in taf['str'].split() if Avn.category(_getCldHt(x),_LAMPCeilings) <= lampBestCat]:
# SCT layers that match LAMP category, change to BKN
if layer[:3] == 'SCT' and Avn.category(_getCldHt(layer),_LAMPCeilings) == lampBestCat:
newceiling.append('BKN%03d' % int(_getCldHt(layer)*0.01))
else:
newsky.append(layer)
#
# If no ceiling found in LAMP category add one
if not newceiling:
maxCeiling = _LAMPNewCeilings[lampBestCat][1]
if lamp['str'] != 'SKC':
newceiling.append(lamp['str'][:3]+'%03d'%maxCeiling)
else:
newceiling.append(lamp['str'])
cb_skyamt = None
newsky = []
#
newsky.extend(newceiling)
else:
# Remove ceilings below lamp category, leave FEW and SCT alone
newsky = [x for x in taf['str'].split()
if x[:3] in ['FEW','SCT'] and \
Avn.category(_getCldHt(x),_LAMPCeilings) < lampBestCat]
newceiling = [x for x in taf['str'].split()
if Avn.category(_getCldHt(x),_LAMPCeilings) == lampBestCat]
#
if not newceiling:
if lamp['str']=='SKC':
newsky=['SKC']
else:
newsky.extend([lamp['str'][:3]+'%03d'%(_LAMPNewCeilings[lampBestCat][0])])
else:
newsky.extend(newceiling)
if cb_skyamt:
#
# If there's already a CB present, break
for i, lyr in enumerate(newsky):
if lyr.endswith('CB'):
break
else:
#
# If there's a cloud amount that matches the original TAF CB amount and its
# below a configurable max CB height
#
for i, lyr in enumerate(newsky):
try:
if cb_skyamt == lyr[:3] and int(lyr[3:6]) <= maxcbhgt:
newsky[i]+='CB'
break
except (ValueError,IndexError):
pass
else:
#
# Otherwise, use the first acceptable layer found below a configurable
# max CB height
#
for i, lyr in enumerate(newsky):
try:
if lyr[:3] in ['SCT','BKN','OVC'] and int(lyr[3:6]) <= maxcbhgt:
newsky[i]+='CB'
break
except (ValueError,IndexError):
pass
taf['str'],taf['cig'] = ' '.join(newsky), _getCldHt(newsky[-1])
def checkVsby(self,dthresholds={'up':.1,'down':.1},wthresholds={'up':.1,'down':.1}):
"""Make changes to visibility when guidance disagrees"""
# For prevailing and occasional groups, adjust if necessary
try:
for group in [self.tprev,self.tocnl]:
if self._isGroupWet(group):
self._checkVisibility(group,wthresholds,True)
else:
self._checkVisibility(group,dthresholds,False)
except KeyError:
pass
def _adjustSNDZIntensity(self,pcpn_str,intensity=None):
"""Based on visibility, the intensity of snow or drizzle may need to be adjusted"""
newPcpnStr = []
for pcp in pcpn_str.split():
result = re.compile('(?P<Pint>[+-])?[A-Z]{,6}(DZ|SN)').match(pcp)
#
# If SN and/or drizzle present
if result:
oldintensity = None
try:
oldintensity = result.group('Pint')
except AttributeError:
pass
if intensity == oldintensity:
return pcpn_str
elif intensity and not oldintensity:
newPcpnStr.append('%c%s' % (intensity,pcp))
elif oldintensity and not intensity:
newPcpnStr.append(pcp[1:])
else:
newPcpnStr.append('%c%s' % (intensity,pcp[1:]))
else:
newPcpnStr.append(pcp)
return ' '.join(newPcpnStr)
def _checkVisibility(self,taf,deltas,wet=False):
"""Adjust ceilings if necessary"""
#
if wet:
lamp=self.lampdata['cvsby']
lampBestCat=self.lampdata['cvis_bestCat']
probabilities=self.lampdata['cvprob']
thresholds=self.cvisthr
else:
lamp=self.lampdata['vsby']
lampBestCat=self.lampdata['vis_bestCat']
probabilities=self.lampdata['vprob']
thresholds=self.visthr
tcat = Avn.category(taf['vsby']['value'],_LAMPVisibilities)
if tcat == lampBestCat:
try:
if taf['obv']['str'] in ['BR','FG']:
if taf['vsby']['value'] <= 0.5:
taf['obv']['str'] = 'FG'
else:
taf['obv']['str'] = 'BR'
except KeyError:
pass
return
#
# Determine if we can hit taf's category by seeing how much its off
if tcat > lampBestCat and _inCategory(lampBestCat,thresholds,probabilities,deltas['up']):
return
if tcat < lampBestCat and _inCategory(tcat,thresholds,probabilities,deltas['down']):
return
#
# Check precip/obvis in the VFR/VLIFR cases, all other cases, TAF obvis will be accepted.
if lampBestCat < tcat:
taf['vsby'] = AvnLib.fixTafVsby(_LAMPNewVisibilities[lampBestCat][1])
#
# If LAMP forecasting VLIFR and TAF obvis is BR, change that
if lampBestCat == 1:
try:
if taf['obv'] and taf['obv']['str'] == 'BR':
taf['obv']['str'] = 'FG'
except KeyError:
pass
#
# Tedious for precipitation
try:
if lampBestCat == 1:
taf['pcp']['str'] = self._adjustSNDZIntensity(taf['pcp']['str'],'+')
else:
taf['pcp']['str'] = self._adjustSNDZIntensity(taf['pcp']['str'],'-')
except KeyError:
pass
if not taf.has_key('pcp') and not taf.has_key('obv'):
taf['obv'] = copy.copy(self.lampdata['obv'])
if taf['obv']['str'] == 'FG' and lampBestCat > 1:
taf['obv']['str'] = 'BR'
else:
#
# If there's obstruction to vision or precipitation, and LAMP indicates VFR
# better to accept forecaster's value in this case.
#
if lampBestCat > 5 and ('obv' in taf.keys() or self._isGroupWet(taf)):
return
#
# Otherwise, adjust according.
taf['vsby'] = AvnLib.fixTafVsby(_LAMPNewVisibilities[lampBestCat][0])
#
# Change occurrence of FG to BR
try:
if lampBestCat > 2 and taf['obv'] and taf['obv']['str'] == 'FG':
taf['obv']['str'] = 'BR'
except KeyError:
pass
#
# Tedious for precipitation
try:
if lampBestCat == 2:
taf['pcp']['str'] = self._adjustSNDZIntensity(taf['pcp']['str'],'+')
else:
taf['pcp']['str'] = self._adjustSNDZIntensity(taf['pcp']['str'],'-')
except KeyError:
pass
if lampBestCat < 7 and not taf.has_key('pcp') and not taf.has_key('obv'):
taf['obv'] = copy.copy(self.lampdata['obv'])
if taf['obv']['str'] == 'FG' and lampBestCat > 1:
taf['obv']['str'] = 'BR'
def checkWind(self):
"""Simply copies LAMP winds into TAF"""
#
# Provide LAMP winds aren't missing!
if not self.lampdata['wind']['str'].startswith('?'):
self.tprev['wind']=copy.copy(self.lampdata['wind'])
def _genOcnlPcp(self,otype):
"""Add precipitation to occasional group"""
if hasattr(self,'tocnl') and self.tocnl.has_key('pcp'):
return
if not hasattr(self,'tocnl'):
self.tocnl = { 'time': { 'from':self.vtime,'to':self.vtime+3600.0 }}
else:
self.tocnl['time'] = { 'from':self.vtime,'to':self.vtime+3600.0 }
self.tocnl['type']=otype
self.tocnl['pcp'] = self.lampdata['pcp']
self.tocnl['vsby'] = self.lampdata['cvsby']
self.tocnl['sky'] = self.lampdata['csky']
try:
self.tocnl['obv'] = self.lampdata['obv']
except KeyError:
pass
def _genPrevailingPcp(self):
"""Add precipitation to prevailing group"""
self.tprev['pcp'] = self.lampdata['pcp']
self.tprev['vsby'] = self.lampdata['cvsby']
try:
if not self.tprev.has_key('obv'):
self.tprev['obv'] = self.lampdata['obv']
except KeyError:
pass
def checkPrecip(self,bbound=-.1,tbound=.1):
"""Compare guidance and official TAF to see if they agree w.r.t precipitation"""
#
# Probability 'score' combines 6-h POP and relative probability over
# climatology 0.17 ~= 1/6, bleh.
#
score = self.lampdata.get('pop6hr',0)*.17+self.lampdata['pcp']['pop']
#
# A dry TAF
if not self.wet:
# Look at the 'score' value to determine if precip is warranted
if score <= 30.0:
return
elif 30 < score <= 50.0:
if self.lampprj > 9:
self._genOcnlPcp('PROB')
elif 50 < score <= 70.0:
self._genOcnlPcp('TEMPO')
else:
self._genPrevailingPcp()
return
#
# TAF is wet, but LAMP indicates dry
elif self.lampdata['pcp']['pcat'] == 0:
#
# if prevailing group of TAF is wet...
if self._isGroupWet(self.tprev):
#
# Use the freezing precipitation that LAMP suggests
if 'FZ' in self.lampdata['pcp']['str'] and \
not 'FZ' in self.tprev['pcp']['str']:
self.tprev['pcp']=self.lampdata['pcp']
#
# but if probablity is low, demote or remove
if score < 40.0:
if self._tsCheck(self.tprev):
self.tprev['pcp']['str'] = 'TS'
else:
del self.tprev['pcp']
#
# Add the appropriate group
if 30 > score >= 40.0:
self._genOcnlPcp('TEMPO')
elif score <= 30.0 and self.lampprj > 9:
self._genOcnlPcp('PROB')
#
# If in TEMPO or PROB30 don't remove unless really low.
else:
if score <= 20.0:
#
# PROB30 is used only for precipiation and/or thunderstorms,
# so if its too low for PROB30, remove it entirely.
#
if self.tocnl['type'] == 'PROB':
self.tocnl = {}
else:
del self.tocnl['pcp']
#
# Both TAF and LAMP indicate precipitation
elif not self._isGroupWet(self.tprev):
#
# Promote PROB30 precip group as appropriate
if self.lampprj <= 9 and self.tocnl['type'] == 'PROB':
if score <= 70:
self.tocnl['type'] = 'TEMPO'
else:
self.tprev['pcp'] = copy.copy(self.tocnl['pcp'])
self.tocnl = {}
def _tsCheck(self,g):
"""See if TS is present"""
return 'pcp' in g and re_TS.match(g['pcp']['str'])
def _rmvTS(self,g):
"""Remove TS from weather string"""
new_wx=re_TS.sub('',g['str'])
if len(new_wx) < 2:
return ''
return new_wx
def checkTstms(self,bbound=-.1,tbound=.1):
"""Check for thunderstorms"""
#
if not 'tcat' in self.lampdata['pcp']:
return
#
# If LAMP suggests no thunderstorms, remove them if pot is low enough
if self.lampdata['pcp']['tcat'] == 0:
for group,threshold in [(self.tprev,0.9),(self.tocnl,0.5)]:
try:
score = self.lampdata['pcp']['pot']/self.poptthr
if self._tsCheck(group) and score <= threshold:
new_wx = self._rmvTS(group)
if new_wx:
group['pcp']['str']=new_wx
else:
del group
self.pcpn_changed=True
except KeyError:
pass
#
# Otherwise add them if threshold high enough
else:
for group,threshold in [(self.tprev,2.0),(self.tocnl,1.25)]:
try:
score = self.lampdata['pcp']['pot']/self.poptthr
if not self._tsCheck(group) and score >= threshold:
try:
plist = group['pcp'].get('str').split()
plist.insert(0,'TS')
except KeyError:
plist =['TS']
self.pcpn_changed=True
group['pcp']['str'] = TafGen.fixPcp(plist)
break
except KeyError:
pass
def _isGroupWet(self,g):
try:
return len(self._rmvTS(g['pcp'])) > 1
except KeyError:
return False
def _isWet(self):
return self._isGroupWet(self.tprev) or self._isGroupWet(self.tocnl)
def checkSyntax(self):
"""Checks for inconsistency in forecast"""
#
# Check the occasional group for duplication in the prevailing
items =self.tocnl.keys()
for item in items:
if item in ['time','type']: continue
try:
if self.tprev[item]['str'] != self.tocnl[item]['str']:
break
except KeyError:
break
else:
for item in items:
if item in ['time','type']: continue
del self.tocnl[item]
def printOfficalTAFPrj(self,tafDuration):
"""Print hourly TAF groups"""
taf=[]
try:
try:
taf=AvnLib.formatRecForMAIN(self.tprev,self.lampdata['ident'],
self.lampdata['amd'],
tafDuration=tafDuration)
except KeyError:
taf=AvnLib.formatRecForFM(self.tprev)
if len(self.tocnl.keys()) > 2:
taf.extend(AvnLib.formatRecForOCNL(self.tocnl))
except KeyError:
pass
return ' '.join(taf)
def flightCategory(self):
return AvnLib.flightCategory(self.tprev)
def _rmvBestCategoryBounces(key,startIdx,LAMPData):
index = 1
for i in xrange(startIdx,len(LAMPData)):
try:
p1,p2,p3 = LAMPData[startIdx+index-1][key],LAMPData[startIdx+index][key], \
LAMPData[startIdx+index+1][key]
if p1 == p3 and p1 != p2:
LAMPData[startIdx+index][key] = p1
index=index+1
except IndexError:
pass
def TAMPGenerator(LAMP,TAFData,thresholdsDict,amdmt=' ',cvOnly=True,longFmt=True,
tafDuration=24):
"""Combine latest TAF with LAMP guidance"""
LAF = None
#
# Find first LAMP projection for forecast
LAMPData = LAMP.data['group']
now = AvnLib.getValidTime('taf',amdmt)
#
startIdx=0
for startIdx,prj in enumerate(LAMPData):
if now <= prj['time']['from']:
break
else:
raise Avn.AvnError('No guidance available')
#
if len(thresholdsDict) == 0:
raise Avn.AvnError('No thresholds available')
#
# Create lists of proper thresholds and valid times for the LAMP data we're going to
# use
#
thresholds = [thresholdsDict[x] for x in xrange(startIdx+1,len(thresholdsDict)+1)]
ValidTimes = [prj['time']['from'] for prj in LAMPData[startIdx:]]
#
# Remove 1hr bounces
index = 1
for i in xrange(startIdx,len(LAMPData)):
try:
p1,p2,p3 = LAMPData[startIdx+index-1]['pcp']['pcat'],LAMPData[startIdx+index]['pcp']['pcat'], \
LAMPData[startIdx+index+1]['pcp']['pcat']
if p1 == p3 and p1 != p2:
LAMPData[startIdx+index]['pcp']['pcat'] = p1
index=index+1
except IndexError:
pass
_rmvBestCategoryBounces('ccig_bestCat',startIdx,LAMPData)
_rmvBestCategoryBounces('cvis_bestCat',startIdx,LAMPData)
_rmvBestCategoryBounces('cig_bestCat',startIdx,LAMPData)
_rmvBestCategoryBounces('vis_bestCat',startIdx,LAMPData)
#
# Generate LAMP TAF based on 'smoothed' data and append to
# original TAF when creating the next regular issued TAF
#
if amdmt[0] == ' ':
tafGen = TafGen.TafGen('gfslamp',LAMP.data,amdmt,now)
tafCfgDict = tafGen.grpTaf.copy()
LAF = tafGen.formNewDic(False)
pos = len(TAFData)
#
# LAMP may not be able to add anything here.
try:
TAFData.extend([copy.deepcopy(group) for group in LAF \
if group['prev']['time']['to'] >= TAFData[-1]['prev']['time']['to']])
if TAFData[pos]['prev']['time']['from'] < TAFData[pos-1]['prev']['time']['to']:
TAFData[pos]['prev']['time']['from'] = TAFData[pos-1]['prev']['time']['to']
except (KeyError, IndexError):
pass
else:
tafCfgDict = TafGen.Config(LAMP.data['ident']['str'],'gfslamp').grpTaf().copy()
#
# Map the TAF to each LAMP forecast hour
TAFIndex=[]
o = len(TAFData)
for i,grp in enumerate(TAFData):
for vtime in ValidTimes:
o = len(TAFData)
if grp.has_key('ocnl'):
shr = _nearesthr(grp['ocnl']['time']['from'])
if shr <= vtime < grp['ocnl']['time']['to']:
o = i
shr = _nearesthr(grp['prev']['time']['from'])
if shr <= vtime < grp['prev']['time']['to']:
TAFIndex.append((i,o))
if vtime >= grp['prev']['time']['to']:
break
#
# Fill out the rest of sequence
for x in xrange(len(ValidTimes)-len(TAFIndex)):
TAFIndex.append((o,o))
TAFData.append({'prev':{},'ocnl':{}})
#
# First projection object needs additional information to be formatted correctly.
LAMPData[startIdx]['amd']=amdmt
LAMPData[startIdx]['ident']=LAMP.data['ident']['str']
#
# Construct the objects
TAFPrjs = [TUGPrj(lampprj=p,vtime=v,lampdata=l,tprev=tp,tocnl=to,visthr=th['vis'],cvisthr=th['cvis'],
cigthr=th['cig'],ccigthr=th['ccig'],popothr=th.get('popt',[0])[0],
poptthr=th.get('pott',[0])[0])
for p,v,l,tp,to,th in zip(xrange(1,len(LAMPData[startIdx:])+1),ValidTimes,LAMPData[startIdx:],
[copy.deepcopy(TAFData[n[0]]['prev']) for n in TAFIndex],
[copy.deepcopy(TAFData[n[1]]['ocnl']) for n in TAFIndex],
thresholds)]
newTAF=[]
for prj in TAFPrjs:
if not cvOnly:
prj.checkPrecip()
prj.checkTstms()
prj.checkSky(tafCfgDict)
prj.checkVsby()
prj.checkWind()
prj.checkSyntax()
if longFmt:
newTAF.append(prj.printOfficalTAFPrj(tafDuration).rstrip())
if not longFmt:
newTAF = _summarizeTAFPrjs(TAFPrjs,TAFData,tafDuration)
newTAF = AvnLib.indentTaf(newTAF)
if amdmt.startswith('A'):
newTAF.insert(0,'TAF AMD')
else:
newTAF.insert(0,'TAF')
return newTAF
| 40,231 | 13,006 |
from __future__ import division, print_function
import time
import os
import configargparse
import bz2
import csv
import json
import sys
from datetime import datetime
from os import listdir
from os.path import isfile, join
import multiprocessing as mp
def process_file(records_reader, file):
core_fields = {"file": file, "lines": 0, "calls": 0, "texts": 0, "NotWellFormedTooLong": 0,
"NotWellFormedTooShort": 0, "NotWellFormedWrongCallType": 0, "NotWellFormedWrongNumberFormat": 0,
"NotWellFormedDate": 0, "NotWellFormedCallDuration": 0}
stats = dict(core_fields)
stats["lines"] = 0
for row in records_reader:
# We compute the statistics for the row
stats["lines"] = stats["lines"] + 1
fields_vals = row[0].split(';')
if len(fields_vals) < 9:
stats["NotWellFormedTooShort"] = stats["NotWellFormedTooShort"] + 1
continue
if len(fields_vals) > 9:
stats["NotWellFormedTooLong"] = stats["NotWellFormedTooLong"] + 1
continue
try:
call_type = int(fields_vals[0])
if not call_type in [1, 2]:
stats["NotWellFormedWrongCallType"] = stats["NotWellFormedWrongCallType"] + 1
continue
except ValueError:
stats["NotWellFormedWrongCallType"] = stats["NotWellFormedWrongCallType"] + 1
continue
try:
int(fields_vals[1])
except ValueError:
stats["NotWellFormedWrongNumberFormat"] = stats["NotWellFormedWrongNumberFormat"] + 1
continue
try:
int(fields_vals[6])
except ValueError:
stats["NotWellFormedWrongNumberFormat"] = stats["NotWellFormedWrongNumberFormat"] + 1
continue
try:
datetime.strptime(fields_vals[2], '%Y-%m-%d %H:%M:%S')
except ValueError:
stats["NotWellFormedDate"] = stats["NotWellFormedDate"] + 1
continue
try:
call_duration = int(fields_vals[8])
except ValueError:
stats["NotWellFormedCallDuration"] = stats["NotWellFormedCallDuration"] + 1
continue
if call_duration == 1 :
stats["texts"] = stats["texts"] + 1
else:
stats["calls"] = stats["calls"] + 1
return stats
# STATS to be extracted :
# 1) Total number of call and text per hour
# 2) Total number of unique numbers per hour
# 3) Check the pseudonymization (that the same number appear in all files)
# We process a file by first extracting the compressed data into a csv
def process_day(writing_queue, zip_files, path):
for i in range(len(zip_files)):
zip_file = zip_files[i]
file_name = zip_file[:-4] # assuming the filepath ends with .bz2
zipfile = bz2.BZ2File(path + '/' + zip_file) # open the file
data = zipfile.read() # get the decompressed data
open(path + '/' + file_name, 'wb').write(data) # write a uncompressed file
csv_path = os.path.join(path, file_name)
# csv_path = path + '/' + zip_files[i]
with open(csv_path, 'r') as csvfile:
records_reader = csv.reader(csvfile, delimiter=',')
next(records_reader, None)
stats_final = process_file(records_reader, zip_files[i])
csvfile.close()
writing_queue.put(stats_final)
os.remove(csv_path)
return True
def write_stats_to_csv(writing_queue, save_path):
"""Write user in writing_queue to csv."""
while True:
# wait for result to appear in the queue
stats = writing_queue.get()
# if got signal 'kill' exit the loop
if stats == 'kill':
break
csv_path = os.path.join(save_path, 'stats.csv')
with open(csv_path, 'a') as csv:
json.dump(stats, csv)
csv.write('\n')
csv.close()
def chunks(l, n):
for i in range(0, len(l), n):
yield l[i:i + n]
#####################################
# main program #
#####################################
parser = configargparse.ArgumentParser(
description='Generate statistics for OPAL raw dataset.')
parser.add_argument('--num_threads', type=int, required=True,
help='Number of threads to be used to create data.')
parser.add_argument('--data_path', required=True,
help='Data path where generated csv have to be saved.')
args = parser.parse_args()
fileDirectory = args.data_path
if __name__ == "__main__":
# Prevent attempt to start a new process before the current process has finished its bootstrapping phase in Windows.
if os.name == 'nt':
mp.freeze_support()
print("Starting...")
start_time = time.time()
# We check if a stats file already exists, if it exists we cancel the operation
csv_path = os.path.join(args.data_path, 'stats.csv')
if os.path.exists(csv_path):
print("The stats file already exists. I am not overwriting it. Cancelling operations.")
sys.exit()
# set up parallel processing
manager = mp.Manager()
writing_queue = manager.Queue()
jobs = []
# additional 1 process is for which shouldn't take up much CPU power
pool = mp.Pool(processes=args.num_threads + 1)
pool.apply_async(write_stats_to_csv, (writing_queue, args.data_path))
if os.path.exists(fileDirectory):
filesName = [f for f in listdir(fileDirectory) if isfile(join(fileDirectory, f))]
chunks = chunks(filesName, args.num_threads - 1)
chunksList = list(chunks)
for n in range(len(chunksList)):
print(chunksList[n])
jobs.append(pool.apply_async(
process_day, (writing_queue, chunksList[n], fileDirectory)))
# clean up parallel processing (close pool, wait for processes to
# finish, kill writing_queue, wait for queue to be killed)
pool.close()
for job in jobs:
job.get()
writing_queue.put('kill')
pool.join()
elapsed_time = time.time() - start_time
print("The threads are done and it took: %f" % (elapsed_time))
| 6,154 | 1,857 |