content stringlengths 5 1.05M |
|---|
from __future__ import absolute_import, print_function
from django.conf import settings
from sentry.plugins import Plugin2
from .processor import SourceProcessor
def preprocess_event(data):
if data.get('platform') != 'javascript':
return
processor = SourceProcessor()
return processor.process(data)
class JavascriptPlugin(Plugin2):
def get_event_preprocessors(self, **kwargs):
if not settings.SENTRY_SCRAPE_JAVASCRIPT_CONTEXT:
return []
return [preprocess_event]
|
import json
import os
import random
import warnings
from argparse import Namespace
import numpy as np
import torch
warnings.simplefilter(action="ignore", category=DeprecationWarning)
def set_seed(seed=42):
random.seed(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
class AttrDict(Namespace):
def __init__(self, dictionary: dict):
for key, value in dictionary.items():
value = AttrDict(value) if isinstance(value, dict) else value
setattr(self, key, value)
def __setattr__(self, key, value):
value = AttrDict(value) if isinstance(value, dict) else value
super().__setattr__(key, value)
def to_dict(self):
return vars(self)
def load_checkpoint_config(checkpoint_path: str) -> AttrDict:
root, _ = os.path.split(checkpoint_path)
config_path = os.path.join(root, "config.json")
return load_config(config_path)
def load_config(config_path: str) -> AttrDict:
with open(config_path) as f:
return AttrDict(json.load(f))
def load_trainer(checkpoint_path):
# avoid circular import
from trainer import Trainer
config = load_checkpoint_config(checkpoint_path)
trainer = Trainer(config)
trainer.load_checkpoint(checkpoint_path)
return trainer
def load_model(checkpoint_path, eval=False):
config = load_checkpoint_config(checkpoint_path)
model = init_model(config.model)
try:
checkpoint = torch.load(checkpoint_path)
except RuntimeError:
checkpoint = torch.load(checkpoint_path, map_location="cuda")
state_dict = checkpoint["model"]
model.load_state_dict(state_dict)
if eval:
model.eval()
return model
def init_model(model_config):
from model import MLPSinger
model = MLPSinger(model_config)
return model
def to_device(xs, device):
moved_xs = []
for x in xs:
if isinstance(x, torch.Tensor):
moved_xs.append(x.to(device))
else:
moved_xs.append(x)
return moved_xs
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
class EarlyStopMonitor:
def __init__(self, patience, mode="min"):
mode = mode.lower()
assert mode.lower() in {
"min",
"max",
}, f"Expected `mode` to be one of 'min' or 'max', but got {mode} instead"
self.log = []
self.mode = mode
self.patience = patience
def check(self, metric):
if not self.log:
self.log.append(metric)
return False
flag = metric > self.log[-1]
if flag == (self.mode == "min"):
self.log.append(metric)
else:
self.log = []
return len(self.log) > self.patience
def make_directory(path):
if not os.path.exists(path):
os.makedirs(path)
|
import torch
from torch import nn
from torch.utils.data import DataLoader
import wandb
from config import get_params
from dataset import DatasetNorm
from utils.read_data import parse_data
from utils import make_dataset
from utils.utils import set_random_seed, save_model, load_model, accuracy
from model.model import TempoCNN
from train import train
def main():
params = get_params()
set_random_seed(params.RANDOM_SEED)
parse_data()
data = DatasetNorm('cutted_data')
train_set, test_set = torch.utils.data.random_split(data, [data.__len__() - 100, 100])
trainloader = DataLoader(dataset=train_set, batch_size=params.BATCH_SIZE, shuffle=True, num_workers=8)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
tcnn = TempoCNN().to(device)
wandb.init(project="tcnn")
config = wandb.config
config.learning_rate = 0.001
wandb.watch(tcnn)
if not params.LOAD_MODEL:
model = train(tcnn, trainloader)
save_model(model)
else:
model = load_model().to(device)
testloader = DataLoader(dataset=test_set, batch_size=params.BATCH_SIZE, shuffle=True)
iters = 0
loss = 0.0
cr_loss = nn.BCELoss()
for i, data in enumerate(testloader, 0):
tcnn.eval()
mels, labels = data[0].to(device), data[1].to(device)
pred = model(mels.unsqueeze(-1).permute(0, 3, 1, 2)).to('cpu').detach()
res = accuracy(pred, labels)
print(res)
loss += cr_loss(pred.float(), labels.float().to('cpu').detach()).item()
iters += 1
print(loss / iters)
if __name__ == '__main__':
make_dataset(params.OSU_TRACKS_DIR, params.DATA_PATH + '/audio_normal', + params.DATA_PATH + '/text_normal', params.ENUMERATE_FROM)
parse_data()
|
import numpy as np
import os
import sys
import math
import random
import glob
import cv2
import torch
from scipy import io
from opts import market1501_train_map, duke_train_map, get_opts
market_dict = {'age':[1,2,3,4], # young(1), teenager(2), adult(3), old(4)
'backpack':[1,2], # no(1), yes(2)
'bag':[1,2], # no(1), yes(2)
'handbag':[1,2], # no(1), yes(2)
'downblack':[1,2], # no(1), yes(2)
'downblue':[1,2], # no(1), yes(2)
'downbrown':[1,2], # no(1), yes(2)
'downgray':[1,2], # no(1), yes(2)
'downgreen':[1,2], # no(1), yes(2)
'downpink':[1,2], # no(1), yes(2)
'downpurple':[1,2], # no(1), yes(2)
'downwhite':[1,2], # no(1), yes(2)
'downyellow':[1,2], # no(1), yes(2)
'upblack':[1,2], # no(1), yes(2)
'upblue':[1,2], # no(1), yes(2)
'upgreen':[1,2], # no(1), yes(2)
'upgray':[1,2], # no(1), yes(2)
'uppurple':[1,2], # no(1), yes(2)
'upred':[1,2], # no(1), yes(2)
'upwhite':[1,2], # no(1), yes(2)
'upyellow':[1,2], # no(1), yes(2)
'clothes':[1,2], # dress(1), pants(2)
'down':[1,2], # long lower body clothing(1), short(2)
'up':[1,2], # long sleeve(1), short sleeve(2)
'hair':[1,2], # short hair(1), long hair(2)
'hat':[1,2], # no(1), yes(2)
'gender':[1,2]}# male(1), female(2)
duke_dict = {'gender':[1,2], # male(1), female(2)
'top':[1,2], # short upper body clothing(1), long(2)
'boots':[1,2], # no(1), yes(2)
'hat':[1,2], # no(1), yes(2)
'backpack':[1,2], # no(1), yes(2)
'bag':[1,2], # no(1), yes(2)
'handbag':[1,2], # no(1), yes(2)
'shoes':[1,2], # dark(1), light(2)
'downblack':[1,2], # no(1), yes(2)
'downwhite':[1,2], # no(1), yes(2)
'downred':[1,2], # no(1), yes(2)
'downgray':[1,2], # no(1), yes(2)
'downblue':[1,2], # no(1), yes(2)
'downgreen':[1,2], # no(1), yes(2)
'downbrown':[1,2], # no(1), yes(2)
'upblack':[1,2], # no(1), yes(2)
'upwhite':[1,2], # no(1), yes(2)
'upred':[1,2], # no(1), yes(2)
'uppurple':[1,2], # no(1), yes(2)
'upgray':[1,2], # no(1), yes(2)
'upblue':[1,2], # no(1), yes(2)
'upgreen':[1,2], # no(1), yes(2)
'upbrown':[1,2]} # no(1), yes(2)
__dict_factory={
'market_attribute': market_dict,
'dukemtmcreid_attribute': duke_dict
}
def get_keys(dict_name):
for key, value in __dict_factory.items():
if key == dict_name:
return value.keys()
def get_target_withattr(attr_matrix, dataset_name, attr_list, pids, pids_raw):
attr_key, attr_value = attr_list
attr_name = 'duke_attribute' if dataset_name == 'dukemtmcreid' else 'market_attribute'
mapping = duke_train_map if dataset_name == 'dukemtmcreid' else market1501_train_map
column = attr_matrix[attr_name][0]['train'][0][0][attr_key][0][0]
n = pids_raw.size(0)
targets = np.zeros_like(column)
for i in range(n):
if column[mapping[pids_raw[i].item()]] == attr_value:
targets[pids[i].item()] = 1
return torch.from_numpy(targets).view(1,-1).repeat(n, 1) |
from tkinter import *
import requests
from bs4 import BeautifulSoup
from tkinter.font import Font
from tkinter import ttk
import time
import threading
root = Tk()
root.title("COVID-19")
root.geometry("300x360")
root.resizable(0, 0)
root.iconbitmap("icon.ico")
label_frame_1 = LabelFrame(root, text="Details")
label_frame_1.pack(expand="yes", fill="both", padx=5)
def my_threading():
progress["value"] = 0
total_cases.set("")
new_cases.set("")
total_deaths.set("")
new_deaths.set("")
total_recovered.set("")
active_cases.set("")
t = threading.Thread(target=main_procedure)
t.start()
def main_procedure():
options_menu.config(state="disabled")
progress["value"] += 20
label_frame_1.update_idletasks()
find_button.config(state="disabled")
html = requests.get("https://www.worldometers.info/coronavirus/").text
progress["value"] += 20
label_frame_1.update_idletasks()
html_soup = BeautifulSoup(html, "html.parser")
rows = html_soup.find_all("tr")
progress["value"] += 20
label_frame_1.update_idletasks()
def extract_text(row, tag):
element = BeautifulSoup(row, 'html.parser').find_all(tag)
text = [col.get_text() for col in element]
return text
progress["value"] += 20
label_frame_1.update_idletasks()
data = []
for row in rows:
data.append(extract_text(str(row), 'td')[1:9])
progress["value"] += 10
label_frame_1.update_idletasks()
for sublists in data:
try:
if sublists[0] == str(selected_country.get()):
progress["value"] += 10
label_frame_1.update_idletasks()
total_cases.set(sublists[1])
new_cases.set(str(sublists[2]).replace("+", ""))
total_deaths.set(sublists[3])
new_deaths.set(str(sublists[4]).replace("+", ""))
total_recovered.set(sublists[5])
active_cases.set(sublists[7])
find_button.config(state="normal")
options_menu.config(state="normal")
exit_button.config(state="normal")
break
except:
pass
total_cases = StringVar()
new_cases = StringVar()
total_deaths = StringVar()
new_deaths = StringVar()
total_recovered = StringVar()
active_cases = StringVar()
label_font = Font(size=11, family="Myraid Pro", weight="bold")
options = ["World", "USA", "UK", "Oman", "Australia", "Sri Lanka"]
selected_country = StringVar()
selected_country.set("World")
options_menu = OptionMenu(root, selected_country, *options)
options_menu.pack()
find_button = Button(root, text="Find", bg="#f53d3d", fg="white", font=label_font, width=40, height=2, command=my_threading)
find_button.pack()
exit_button = Button(root, text="Exit", bg="#7703fc", fg="white", font=label_font, width=40, height=2, command=root.destroy)
exit_button.pack()
exit_button.config(state="disabled")
title_label = Label(root, text="CREATED BY: YATHURSHAN", font="Roboto 10 bold")
title_label.pack()
######################################
total_cases_label = Label(label_frame_1, text="Total Cases", fg="red", font=label_font)
total_cases_label.place(x=32, y=5)
total_cases_box = Entry(label_frame_1, textvariable=total_cases, justify="center", bd=5)
total_cases_box.place(x=10, y=25)
total_cases_box.config(state="disabled")
new_cases_label = Label(label_frame_1, text="New Cases", fg="red", font=label_font)
new_cases_label.place(x=32, y=55)
new_cases_box = Entry(label_frame_1, textvariable=new_cases, justify="center", bd=5)
new_cases_box.place(x=10, y=75)
new_cases_box.config(state="disabled")
active_cases_label = Label(label_frame_1, text="Active Cases", fg="red", font=label_font)
active_cases_label.place(x=32, y=105)
active_cases_box = Entry(label_frame_1, textvariable=active_cases, justify="center", bd=5)
active_cases_box.place(x=10, y=125)
active_cases_box.config(state="disabled")
##############################################
total_deaths_label = Label(label_frame_1, text="Total Deaths", fg="red", font=label_font)
total_deaths_label.place(x=172, y=5)
total_deaths_box = Entry(label_frame_1, textvariable=total_deaths, justify="center", bd=5)
total_deaths_box.place(x=150, y=25)
total_deaths_box.config(state="disabled")
new_deaths_label = Label(label_frame_1, text="New Deaths", fg="red", font=label_font)
new_deaths_label.place(x=172, y=55)
new_deaths_box = Entry(label_frame_1, textvariable=new_deaths, justify="center", bd=5)
new_deaths_box.place(x=150, y=75)
new_deaths_box.config(state="disabled")
total_recovered_label = Label(label_frame_1, text="Total Recovered", fg="red", font=label_font)
total_recovered_label.place(x=155, y=105)
total_recovered_box = Entry(label_frame_1, textvariable=total_recovered, justify="center", bd=5)
total_recovered_box.place(x=150, y=125)
total_recovered_box.config(state="disabled")
progress = ttk.Progressbar(label_frame_1, orient=HORIZONTAL, length=200, mode="determinate")
progress.place(x=50, y=160)
root.mainloop()
|
"""
백준 1967번 : 트리의 지름
"""
import sys
sys.setrecursionlimit(10**9)
input = sys.stdin.readline
# dfs로 한 점 기준으로 거리를 잴 수 있다.
def dfs(start, weight):
for i in tree[start]:
node, w = i
if dist[node] == -1:
dist[node] = weight + w
dfs(node, weight + w)
node = int(input())
tree = {x: [] for x in range(node+1)}
for _ in range(node-1):
parent, child, weight = map(int, input().split())
tree[parent].append([child, weight])
tree[child].append([parent, weight])
# 한 점 기준으로 가장 먼 곳 찾기 -> n1
dist = [-1] * (node + 1)
dist[1] = 0
dfs(1, 0)
# n1 노드 찾고, n1 기준으로 가장 먼 곳 찾기 -> n2
start = dist.index(max(dist))
dist = [-1] * (node + 1)
dist[start] = 0
dfs(start, 0)
# n1 -> n2가 트리의 지름이 됨.
print(max(dist)) |
# Copyright 2016: Mirantis Inc.
# 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 unittest import mock
import ddt
from rally.plugins.task.hook_triggers import periodic
from rally.task import hook
from tests.unit import test
@ddt.ddt
class PeriodicTriggerTestCase(test.TestCase):
def setUp(self):
super(PeriodicTriggerTestCase, self).setUp()
self.hook_cls = mock.MagicMock(__name__="name")
self.trigger = periodic.PeriodicTrigger(
{"trigger": ("periodic", {"unit": "iteration", "step": 2}),
"action": ("foo", {})},
mock.MagicMock(), self.hook_cls)
@ddt.data((dict(unit="time", step=1), True),
(dict(unit="time", step=0), False),
(dict(unit="time", step=1, start=0), True),
(dict(unit="time", step=1, start=-1), False),
(dict(unit="time", step=1, start=0, end=1), True),
(dict(unit="time", step=1, start=0, end=0), False),
(dict(unit="time", wrong_prop=None), False),
(dict(unit="time"), False),
(dict(unit="iteration", step=1), True),
(dict(unit="iteration", step=0), False),
(dict(unit="iteration", step=1, start=1), True),
(dict(unit="iteration", step=1, start=0), False),
(dict(unit="iteration", step=1, start=1, end=1), True),
(dict(unit="iteration", step=1, start=1, end=0), False),
(dict(unit="iteration", wrong_prop=None), False),
(dict(unit="iteration"), False),
(dict(unit="wrong-unit", step=1), False),
(dict(step=1), False))
@ddt.unpack
def test_validate(self, config, valid):
results = hook.HookTrigger.validate("periodic", None, None, config)
if valid:
self.assertEqual([], results)
else:
self.assertEqual(1, len(results))
def test_get_listening_event(self):
event_type = self.trigger.get_listening_event()
self.assertEqual("iteration", event_type)
@ddt.data((1, True), (2, False), (3, True), (4, False), (5, True),
(6, False), (7, True), (8, False), (9, True), (10, False))
@ddt.unpack
def test_on_event(self, value, should_call):
self.trigger.on_event("iteration", value)
self.assertEqual(should_call, self.hook_cls.called)
@ddt.data((0, False), (1, False), (2, True), (3, False), (4, False),
(5, True), (6, False), (7, False), (8, True), (9, False))
@ddt.unpack
def test_on_event_start_end(self, value, should_call):
trigger = periodic.PeriodicTrigger(
{"trigger": ("periodic", {"unit": "time",
"step": 3, "start": 2, "end": 9}),
"action": ("foo", {})},
mock.MagicMock(), self.hook_cls)
trigger.on_event("time", value)
self.assertEqual(should_call, self.hook_cls.called)
|
import time
from compute.process import dispatch_to_do
from compute.gpu import gpus_all_available
from comm.utils import CacheToResultFile
from algs.regularized_evolution.genetic.dag import DAG, DAGValidationError
from algs.regularized_evolution.utils import Utils
from algs.regularized_evolution.genetic.statusupdatetool import StatusUpdateTool
import numpy as np
def generate_dag(matrix):
# create nodes for the graph
l = matrix.shape[0]
nodes = np.empty((0), dtype=np.str)
for n in range(0, l):
nodes = np.append(nodes, ''.join(["node", "_", str(n)]))
# initialize directed asyclic graph (DAG) and add nodes to it
dag = DAG()
for n in nodes:
dag.add_node(n)
for i in range(2, l):
for j in range(0, i):
if matrix[i][j] != 0:
dag.add_edge(''.join(["node", "_", str(j)]), ''.join(["node", "_", str(i)]))
dag.add_edge_type(''.join(["node", "_", str(j)]), ''.join(["node", "_", str(i)]), matrix[i][j])
# delete nodes not connected to anyother node from DAG
for n in nodes:
if len(dag.predecessors(n)) == 0 and len(dag.downstream(n)) == 0:
dag.delete_node(n)
nodes = np.delete(nodes, np.where(nodes == n)[0][0])
return dag, nodes
class layer(object):
def __init__(self, in_name, out_name, type):
self.type = type
self.in_name = in_name
self.out_name = out_name
if type == 2:
conv_sizes = StatusUpdateTool.get_conv_size()
conv_size = conv_sizes[np.random.randint(0, len(conv_sizes))]
self.kernel_size = conv_size
else:
self.kernel_size = 3
class Network(object):
def __init__(self, id):
self.units = []
self.skipconnections = []
self.without_towards = []
self.without_predecessors = []
self.id = id
def add_edge(self, in_name, out_name, type):
conv = layer(in_name, out_name, type)
self.units.append(conv)
def add_skip(self, ind_node_name, dep_node_name, ops_type):
conv = layer(ind_node_name, dep_node_name, ops_type)
self.skipconnections.append(conv)
def add_without_predecessors(self, node_name):
self.without_predecessors.append(node_name)
def add_without_towards(self, node_name):
self.without_towards.append(node_name)
def get_net(cell, id):
normal_cell_dag, normal_cell_nodes = generate_dag(cell)
without_predecessors = normal_cell_dag.ind_nodes()
without_successors = normal_cell_dag.all_leaves()
net = Network(id)
for wop in without_predecessors:
net.add_without_predecessors(wop)
for n in normal_cell_nodes:
predecessors = normal_cell_dag.predecessors(n)
if len(predecessors) == 0:
continue
elif len(predecessors) > 1:
for prd in range(1, len(predecessors)):
net.add_skip(predecessors[prd], n, normal_cell_dag.type[predecessors[prd] + "_" + n])
net.add_edge(predecessors[0], n, normal_cell_dag.type[predecessors[0] + "_" + n])
elif len(predecessors) == 1:
net.add_edge(predecessors[0], n, normal_cell_dag.type[predecessors[0] + "_" + n])
if len(without_successors) > 0:
for suc in range(0, len(without_successors)):
net.add_without_towards(without_successors[suc])
return net
def decode_generate_file(individual, F, test=False):
normal_cell_net = get_net(individual.normal_cell, individual.id)
reduction_cell_net = get_net(individual.reduction_cell, individual.id)
Utils.generate_pytorch_file(normal_cell_net, reduction_cell_net, F, test)
class FitnessEvaluate(object):
def __init__(self, individuals, params, log):
self.individuals = list(individuals)
self.params = params
self.log = log
def generate_to_python_file(self, test=False):
self.log.info("Begin to generate python files")
for indi in list(self.individuals):
decode_generate_file(indi, self.params['F'], test)
self.log.info("Finished the generation of python files")
def evaluate(self):
"""
load fitness from cache file
"""
self.log.info('Query fitness from cache')
_map = Utils.load_cache_data()
_count = 0
for indi in self.individuals:
_key, _str = indi.uuid()
if _key in _map:
_count += 1
_acc = _map[_key]
self.log.info('Hit the cache for %s, key:%s, acc:%.5f' % (_key, _key, float(_acc)))
CacheToResultFile.do(indi.id, float(_acc))
indi.acc = float(_acc)
for indi in self.individuals:
if indi.acc < 0:
_id = indi.id
_uuid, _ = indi.uuid()
dispatch_to_do(_id, _uuid)
all_have_been_evaluated = False
while all_have_been_evaluated is not True:
time.sleep(120)
all_have_been_evaluated = gpus_all_available()
|
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# ----------------------------------------------------------------------------
from __future__ import division
from unittest import TestCase, main
from collections import Counter, defaultdict
import numpy as np
from skbio.core.sequence import NucleotideSequence, DNASequence, RNASequence
from skbio.core.alignment import SequenceCollection, Alignment
from skbio.core.exception import SequenceCollectionError
from skbio.core.distance import DistanceMatrix
class SequenceCollectionTests(TestCase):
"""Tests of the SequenceCollection class """
def setUp(self):
"""Initialize values to be used in tests
"""
self.d1 = DNASequence('GATTACA', identifier="d1")
self.d2 = DNASequence('TTG', identifier="d2")
self.d1_lower = DNASequence('gattaca', identifier="d1")
self.d2_lower = DNASequence('ttg', identifier="d2")
self.r1 = RNASequence('GAUUACA', identifier="r1")
self.r2 = RNASequence('UUG', identifier="r2")
self.r3 = RNASequence('U-----UGCC--', identifier="r3")
self.i1 = DNASequence('GATXACA', identifier="i1")
self.seqs1 = [self.d1, self.d2]
self.seqs1_lower = [self.d1_lower, self.d2_lower]
self.seqs2 = [self.r1, self.r2, self.r3]
self.seqs3 = self.seqs1 + self.seqs2
self.seqs1_t = [('d1', 'GATTACA'), ('d2', 'TTG')]
self.seqs2_t = [('r1', 'GAUUACA'), ('r2', 'UUG'),
('r3', 'U-----UGCC--')]
self.seqs3_t = self.seqs1_t + self.seqs2_t
self.s1 = SequenceCollection(self.seqs1)
self.s1_lower = SequenceCollection(self.seqs1_lower)
self.s2 = SequenceCollection(self.seqs2)
self.s3 = SequenceCollection(self.seqs3)
self.empty = SequenceCollection([])
self.invalid_s1 = SequenceCollection([self.i1])
def test_init(self):
"""Initialization functions as expected with varied input types
"""
SequenceCollection(self.seqs1)
SequenceCollection(self.seqs2)
SequenceCollection(self.seqs3)
SequenceCollection([])
def test_init_fail(self):
"""initialization with sequences with overlapping identifiers fails
"""
s1 = [self.d1, self.d1]
self.assertRaises(SequenceCollectionError, SequenceCollection, s1)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
SequenceCollection(self.seqs1, validate=True)
SequenceCollection(self.seqs1, validate=True)
# can't validate self.seqs2 as a DNASequence
self.assertRaises(SequenceCollectionError, SequenceCollection,
self.invalid_s1, validate=True)
def test_from_fasta_records(self):
"""Initialization from list of tuples functions as expected
"""
SequenceCollection.from_fasta_records(self.seqs1_t, DNASequence)
SequenceCollection.from_fasta_records(self.seqs2_t, RNASequence)
SequenceCollection.from_fasta_records(self.seqs3_t, NucleotideSequence)
def test_contains(self):
"""in operator functions as expected
"""
self.assertTrue('d1' in self.s1)
self.assertTrue('r2' in self.s2)
self.assertFalse('r2' in self.s1)
def test_eq(self):
"""equality operator functions as expected
"""
self.assertTrue(self.s1 == self.s1)
self.assertFalse(self.s1 == self.s2)
# different objects can be equal
self.assertTrue(self.s1 == SequenceCollection([self.d1, self.d2]))
self.assertTrue(SequenceCollection([self.d1, self.d2]) == self.s1)
# SequenceCollections with different number of sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertFalse(self.s1 == FakeSequenceCollection([self.d1, self.d2]))
self.assertFalse(self.s1 == Alignment([self.d1, self.d2]))
# SequenceCollections with different sequences are not equal
self.assertFalse(self.s1 == SequenceCollection([self.d1, self.r1]))
def test_getitem(self):
"""getitem functions as expected
"""
self.assertEqual(self.s1[0], self.d1)
self.assertEqual(self.s1[1], self.d2)
self.assertEqual(self.s2[0], self.r1)
self.assertEqual(self.s2[1], self.r2)
self.assertRaises(IndexError, self.empty.__getitem__, 0)
self.assertRaises(KeyError, self.empty.__getitem__, '0')
def test_iter(self):
"""iter functions as expected
"""
s1_iter = iter(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, s1_iter.next)
def test_len(self):
"""len functions as expected
"""
self.assertEqual(len(self.s1), 2)
self.assertEqual(len(self.s2), 3)
self.assertEqual(len(self.s3), 5)
self.assertEqual(len(self.empty), 0)
def test_ne(self):
"""inequality operator functions as expected
"""
self.assertFalse(self.s1 != self.s1)
self.assertTrue(self.s1 != self.s2)
# SequenceCollections with different number of sequences are not equal
self.assertTrue(self.s1 != SequenceCollection([self.d1]))
class FakeSequenceCollection(SequenceCollection):
pass
# SequenceCollections of different types are not equal
self.assertTrue(self.s1 != FakeSequenceCollection([self.d1, self.d2]))
self.assertTrue(self.s1 != Alignment([self.d1, self.d2]))
# SequenceCollections with different sequences are not equal
self.assertTrue(self.s1 !=
SequenceCollection([self.d1, self.r1]))
def test_repr(self):
"""repr functions as expected
"""
self.assertEqual(repr(self.s1),
"<SequenceCollection: n=2; "
"mean +/- std length=5.00 +/- 2.00>")
self.assertEqual(repr(self.s2),
"<SequenceCollection: n=3; "
"mean +/- std length=7.33 +/- 3.68>")
self.assertEqual(repr(self.s3),
"<SequenceCollection: n=5; "
"mean +/- std length=6.40 +/- 3.32>")
self.assertEqual(repr(self.empty),
"<SequenceCollection: n=0; "
"mean +/- std length=0.00 +/- 0.00>")
def test_reversed(self):
"""reversed functions as expected
"""
s1_iter = reversed(self.s1)
count = 0
for actual, expected in zip(s1_iter, self.seqs1[::-1]):
count += 1
self.assertEqual(actual, expected)
self.assertEqual(count, len(self.seqs1))
self.assertRaises(StopIteration, s1_iter.next)
def test_k_word_frequencies(self):
"""k_word_frequencies functions as expected
"""
expected1 = defaultdict(int)
expected1['A'] = 3/7.
expected1['C'] = 1/7.
expected1['G'] = 1/7.
expected1['T'] = 2/7.
expected2 = defaultdict(int)
expected2['G'] = 1/3.
expected2['T'] = 2/3.
self.assertEqual(self.s1.k_word_frequencies(k=1),
[expected1, expected2])
expected1 = defaultdict(int)
expected1['GAT'] = 1/2.
expected1['TAC'] = 1/2.
expected2 = defaultdict(int)
expected2['TTG'] = 1/1.
self.assertEqual(self.s1.k_word_frequencies(k=3, overlapping=False),
[expected1, expected2])
self.assertEqual(self.empty.k_word_frequencies(k=1), [])
def test_str(self):
"""str functions as expected
"""
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(str(self.s1), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(str(self.s2), exp2)
exp4 = ""
self.assertEqual(str(self.empty), exp4)
def test_distribution_stats(self):
"""distribution_stats functions as expected
"""
actual1 = self.s1.distribution_stats()
self.assertEqual(actual1[0], 2)
self.assertAlmostEqual(actual1[1], 5.0, 3)
self.assertAlmostEqual(actual1[2], 2.0, 3)
actual2 = self.s2.distribution_stats()
self.assertEqual(actual2[0], 3)
self.assertAlmostEqual(actual2[1], 7.333, 3)
self.assertAlmostEqual(actual2[2], 3.682, 3)
actual3 = self.s3.distribution_stats()
self.assertEqual(actual3[0], 5)
self.assertAlmostEqual(actual3[1], 6.400, 3)
self.assertAlmostEqual(actual3[2], 3.323, 3)
actual4 = self.empty.distribution_stats()
self.assertEqual(actual4[0], 0)
self.assertEqual(actual4[1], 0.0)
self.assertEqual(actual4[2], 0.0)
def test_degap(self):
"""degap functions as expected
"""
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs2_t]
expected = SequenceCollection.from_fasta_records(expected, RNASequence)
actual = self.s2.degap()
self.assertEqual(actual, expected)
def test_get_seq(self):
"""getseq functions asexpected
"""
self.assertEqual(self.s1.get_seq('d1'), self.d1)
self.assertEqual(self.s1.get_seq('d2'), self.d2)
def test_identifiers(self):
"""identifiers functions as expected
"""
self.assertEqual(self.s1.identifiers(), ['d1', 'd2'])
self.assertEqual(self.s2.identifiers(), ['r1', 'r2', 'r3'])
self.assertEqual(self.s3.identifiers(),
['d1', 'd2', 'r1', 'r2', 'r3'])
self.assertEqual(self.empty.identifiers(), [])
def test_int_map(self):
"""int_map functions as expected
"""
expected1 = {"1": self.d1, "2": self.d2}
expected2 = {"1": "d1", "2": "d2"}
self.assertEqual(self.s1.int_map(), (expected1, expected2))
expected1 = {"h-1": self.d1, "h-2": self.d2}
expected2 = {"h-1": "d1", "h-2": "d2"}
self.assertEqual(self.s1.int_map(prefix='h-'), (expected1, expected2))
def test_is_empty(self):
"""is_empty functions as expected
"""
self.assertFalse(self.s1.is_empty())
self.assertFalse(self.s2.is_empty())
self.assertFalse(self.s3.is_empty())
self.assertTrue(self.empty.is_empty())
def test_is_valid(self):
"""is_valid functions as expected
"""
self.assertTrue(self.s1.is_valid())
self.assertTrue(self.s2.is_valid())
self.assertTrue(self.s3.is_valid())
self.assertTrue(self.empty.is_valid())
self.assertFalse(self.invalid_s1.is_valid())
def test_iteritems(self):
"""iteritems functions as expected
"""
self.assertEqual(list(self.s1.iteritems()),
[(s.identifier, s) for s in self.s1])
def test_lower(self):
"""lower functions as expected
"""
self.assertEqual(self.s1.lower(), self.s1_lower)
def test_sequence_count(self):
"""num_seqs functions as expected
"""
self.assertEqual(self.s1.sequence_count(), 2)
self.assertEqual(self.s2.sequence_count(), 3)
self.assertEqual(self.s3.sequence_count(), 5)
self.assertEqual(self.empty.sequence_count(), 0)
def test_sequence_lengths(self):
"""sequence_lengths functions as expected
"""
self.assertEqual(self.s1.sequence_lengths(), [7, 3])
self.assertEqual(self.s2.sequence_lengths(), [7, 3, 12])
self.assertEqual(self.s3.sequence_lengths(), [7, 3, 7, 3, 12])
self.assertEqual(self.empty.sequence_lengths(), [])
def test_to_fasta(self):
"""to_fasta functions as expected
"""
exp1 = ">d1\nGATTACA\n>d2\nTTG\n"
self.assertEqual(self.s1.to_fasta(), exp1)
exp2 = ">r1\nGAUUACA\n>r2\nUUG\n>r3\nU-----UGCC--\n"
self.assertEqual(self.s2.to_fasta(), exp2)
def test_upper(self):
"""upper functions as expected
"""
self.assertEqual(self.s1_lower.upper(), self.s1)
class AlignmentTests(TestCase):
def setUp(self):
self.d1 = DNASequence('..ACC-GTTGG..', identifier="d1")
self.d2 = DNASequence('TTACCGGT-GGCC', identifier="d2")
self.d3 = DNASequence('.-ACC-GTTGC--', identifier="d3")
self.r1 = RNASequence('UUAU-', identifier="r1")
self.r2 = RNASequence('ACGUU', identifier="r2")
self.seqs1 = [self.d1, self.d2, self.d3]
self.seqs2 = [self.r1, self.r2]
self.seqs1_t = [('d1', '..ACC-GTTGG..'), ('d2', 'TTACCGGT-GGCC'),
('d3', '.-ACC-GTTGC--')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.a1 = Alignment(self.seqs1)
self.a2 = Alignment(self.seqs2)
self.empty = Alignment([])
def test_degap(self):
"""degap functions as expected
"""
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs1_t]
expected = SequenceCollection.from_fasta_records(expected, DNASequence)
actual = self.a1.degap()
self.assertEqual(actual, expected)
expected = [(id_, seq.replace('.', '').replace('-', ''))
for id_, seq in self.seqs2_t]
expected = SequenceCollection.from_fasta_records(expected, RNASequence)
actual = self.a2.degap()
self.assertEqual(actual, expected)
def test_distances(self):
"""distances functions as expected
"""
expected = [[0, 6./13, 4./13],
[6./13, 0, 7./13],
[4./13, 7./13, 0]]
expected = DistanceMatrix(expected, ['d1', 'd2', 'd3'])
actual = self.a1.distances()
self.assertEqual(actual, expected)
def test_subalignment(self):
"""subalignment functions as expected
"""
# keep seqs by identifiers
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by indices
actual = self.a1.subalignment(seqs_to_keep=[0, 2])
expected = Alignment([self.d1, self.d3])
self.assertEqual(actual, expected)
# keep seqs by identifiers (invert)
actual = self.a1.subalignment(seqs_to_keep=['d1', 'd3'],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep seqs by indices (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
invert_seqs_to_keep=True)
expected = Alignment([self.d2])
self.assertEqual(actual, expected)
# keep positions
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', identifier="d1")
d2 = DNASequence('TAC', identifier="d2")
d3 = DNASequence('.AC', identifier="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep positions (invert)
actual = self.a1.subalignment(positions_to_keep=[0, 2, 3],
invert_positions_to_keep=True)
d1 = DNASequence('.C-GTTGG..', identifier="d1")
d2 = DNASequence('TCGGT-GGCC', identifier="d2")
d3 = DNASequence('-C-GTTGC--', identifier="d3")
expected = Alignment([d1, d2, d3])
self.assertEqual(actual, expected)
# keep seqs and positions
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3])
d1 = DNASequence('.AC', identifier="d1")
d3 = DNASequence('.AC', identifier="d3")
expected = Alignment([d1, d3])
self.assertEqual(actual, expected)
# keep seqs and positions (invert)
actual = self.a1.subalignment(seqs_to_keep=[0, 2],
positions_to_keep=[0, 2, 3],
invert_seqs_to_keep=True,
invert_positions_to_keep=True)
d2 = DNASequence('TCGGT-GGCC', identifier="d2")
expected = Alignment([d2])
self.assertEqual(actual, expected)
def test_init_validate(self):
"""initialization with validation functions as expected
"""
Alignment(self.seqs1, validate=True)
# invalid DNA character
invalid_seqs1 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTXGC--', identifier="i1")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs1, validate=True)
# invalid lengths (they're not all equal)
invalid_seqs2 = [self.d1, self.d2, self.d3,
DNASequence('.-ACC-GTGC--', identifier="i2")]
self.assertRaises(SequenceCollectionError, Alignment,
invalid_seqs2, validate=True)
def test_is_valid(self):
"""is_valid functions as expected
"""
self.assertTrue(self.a1.is_valid())
self.assertTrue(self.a2.is_valid())
self.assertTrue(self.empty.is_valid())
# invalid because of length mismatch
d1 = DNASequence('..ACC-GTTGG..', identifier="d1")
d2 = DNASequence('TTACCGGT-GGC', identifier="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
# invalid because of invalid charaters
d1 = DNASequence('..ACC-GTXGG..', identifier="d1")
d2 = DNASequence('TTACCGGT-GGCC', identifier="d2")
self.assertFalse(Alignment([d1, d2]).is_valid())
def test_iter_positions(self):
"""iter_positions functions as expected
"""
actual = list(self.a2.iter_positions())
expected = [map(RNASequence, list('UA')),
map(RNASequence, list('UC')),
map(RNASequence, list('AG')),
map(RNASequence, list('UU')),
map(RNASequence, list('-U'))]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
actual = list(self.a2.iter_positions(constructor=str))
expected = [list('UA'),
list('UC'),
list('AG'),
list('UU'),
list('-U')]
self.seqs2_t = [('r1', 'UUAU-'), ('r2', 'ACGUU')]
self.assertEqual(actual, expected)
def test_majority_consensus(self):
"""majority_consensus functions as expected
"""
d1 = DNASequence('TTT', identifier="d1")
d2 = DNASequence('TT-', identifier="d2")
d3 = DNASequence('TC-', identifier="d3")
a1 = Alignment([d1, d2, d3])
self.assertEqual(a1.majority_consensus(), DNASequence('TT-'))
d1 = DNASequence('T', identifier="d1")
d2 = DNASequence('A', identifier="d2")
a1 = Alignment([d1, d2])
self.assertTrue(a1.majority_consensus() in
[DNASequence('T'), DNASequence('A')])
self.assertEqual(self.empty.majority_consensus(), '')
def test_omit_gap_positions(self):
"""omitting gap positions functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_positions(1.0), expected)
self.assertEqual(self.a2.omit_gap_positions(0.51), expected)
r1 = RNASequence('UUAU', identifier="r1")
r2 = RNASequence('ACGU', identifier="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.49), expected)
r1 = RNASequence('UUAU', identifier="r1")
r2 = RNASequence('ACGU', identifier="r2")
expected = Alignment([r1, r2])
self.assertEqual(self.a2.omit_gap_positions(0.0), expected)
self.assertEqual(self.empty.omit_gap_positions(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_positions(0.49), self.empty)
self.assertEqual(self.empty.omit_gap_positions(1.0), self.empty)
def test_omit_gap_sequences(self):
"""omitting gap sequences functions as expected
"""
expected = self.a2
self.assertEqual(self.a2.omit_gap_sequences(1.0), expected)
self.assertEqual(self.a2.omit_gap_sequences(0.20), expected)
expected = Alignment([self.r2])
self.assertEqual(self.a2.omit_gap_sequences(0.19), expected)
self.assertEqual(self.empty.omit_gap_sequences(0.0), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(0.2), self.empty)
self.assertEqual(self.empty.omit_gap_sequences(1.0), self.empty)
def test_position_counters(self):
"""position_counters functions as expected
"""
expected = [Counter({'U': 1, 'A': 1}),
Counter({'U': 1, 'C': 1}),
Counter({'A': 1, 'G': 1}),
Counter({'U': 2}),
Counter({'-': 1, 'U': 1})]
self.assertEqual(self.a2.position_counters(), expected)
self.assertEqual(self.empty.position_counters(), [])
def test_position_frequencies(self):
"""computing position frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 0.5, 'A': 0.5}),
defaultdict(int, {'U': 0.5, 'C': 0.5}),
defaultdict(int, {'A': 0.5, 'G': 0.5}),
defaultdict(int, {'U': 1.0}),
defaultdict(int, {'-': 0.5, 'U': 0.5})]
self.assertEqual(self.a2.position_frequencies(), expected)
self.assertEqual(self.empty.position_frequencies(), [])
def test_position_entropies(self):
"""computing positional uncertainties functions as expected
tested by calculating values as described in this post:
http://stackoverflow.com/a/15476958/3424666
"""
expected = [0.69314, 0.69314, 0.69314, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(),
expected, 5)
expected = [1.0, 1.0, 1.0, 0.0, np.nan]
np.testing.assert_almost_equal(self.a2.position_entropies(base=2),
expected, 5)
np.testing.assert_almost_equal(self.empty.position_entropies(base=2),
[])
def test_k_word_frequencies(self):
"""k_word_frequencies functions as expected
"""
expected = [defaultdict(int, {'U': 3/5, 'A': 1/5, '-': 1/5}),
defaultdict(int, {'A': 1/5, 'C': 1/5, 'G': 1/5, 'U': 2/5})]
actual = self.a2.k_word_frequencies(k=1)
for a, e in zip(actual, expected):
a_keys = a.keys()
a_keys.sort()
a_values = a.values()
a_values.sort()
e_keys = e.keys()
e_keys.sort()
e_values = e.values()
e_values.sort()
self.assertEqual(a_keys, e_keys, 5)
np.testing.assert_almost_equal(a_values, e_values, 5)
def test_sequence_length(self):
"""sequence_length functions as expected
"""
self.assertEqual(self.a1.sequence_length(), 13)
self.assertEqual(self.a2.sequence_length(), 5)
self.assertEqual(self.empty.sequence_length(), 0)
def test_to_phylip(self):
"""to_phylip functions as expected
"""
d1 = DNASequence('..ACC-GTTGG..', identifier="d1")
d2 = DNASequence('TTACCGGT-GGCC', identifier="d2")
d3 = DNASequence('.-ACC-GTTGC--', identifier="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = a.to_phylip(map_labels=False)
self.assertEqual(id_map, {'d1': 'd1',
'd3': 'd3',
'd2': 'd2'})
expected = "\n".join(["3 13",
"d1 ..ACC-GTTGG..",
"d2 TTACCGGT-GGCC",
"d3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_to_phylip_map_labels(self):
"""to_phylip functions as expected with label mapping
"""
d1 = DNASequence('..ACC-GTTGG..', identifier="d1")
d2 = DNASequence('TTACCGGT-GGCC', identifier="d2")
d3 = DNASequence('.-ACC-GTTGC--', identifier="d3")
a = Alignment([d1, d2, d3])
phylip_str, id_map = a.to_phylip(map_labels=True, label_prefix="s")
self.assertEqual(id_map, {'s1': 'd1',
's3': 'd3',
's2': 'd2'})
expected = "\n".join(["3 13",
"s1 ..ACC-GTTGG..",
"s2 TTACCGGT-GGCC",
"s3 .-ACC-GTTGC--"])
self.assertEqual(phylip_str, expected)
def test_validate_lengths(self):
"""
"""
self.assertTrue(self.a1._validate_lengths())
self.assertTrue(self.a2._validate_lengths())
self.assertTrue(self.empty._validate_lengths())
self.assertTrue(Alignment([
DNASequence('TTT', identifier="d1")])._validate_lengths())
self.assertFalse(Alignment([
DNASequence('TTT', identifier="d1"),
DNASequence('TT', identifier="d2")])._validate_lengths())
if __name__ == "__main__":
main()
|
"""usgs_topo_tiler.usgs_topo: USGS Historical topo processing."""
import json
from typing import Any, List, Tuple
import numpy as np
import rasterio
from rasterio.crs import CRS
from rasterio.warp import transform_bounds
from rio_tiler import reader
from usgs_topo_tiler.cutline import get_cutline
from usgs_topo_tiler.extent import estimate_extent
def tile(
address: str,
tile_x: int,
tile_y: int,
tile_z: int,
tilesize: int = 256,
map_bounds: List[float] = None,
parse_asset_as_json: bool = True,
**kwargs: Any,
) -> Tuple[np.ndarray, np.array]:
"""
Create mercator tile from any images.
Attributes
----------
address : str
file url.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
tilesize : int, optional (default: 256)
Output image size.
map_bounds : List[float], optional (default: inferred)
Bounds of map excluding border in WGS84
Normal order: (minx, miny, maxx, maxy)
parse_asset_as_json : bool, optional (default: True)
Whether to attempt to parse address as a JSON object with "url" and
"map_bounds keys"
kwargs: dict, optional
These will be passed to the 'rio_tiler.reader.tile' function.
Returns
-------
data : np ndarray
mask : np array
"""
# Custom hack that encodes url and map_bounds into address string
if parse_asset_as_json:
try:
asset_dict = json.loads(address)
address = asset_dict['url']
map_bounds = asset_dict['map_bounds']
except json.JSONDecodeError:
pass
with rasterio.open(address) as src_dst:
# Convert image bounds to wgs84
image_wgs_bounds = transform_bounds(
src_dst.crs, CRS.from_epsg(4326), *src_dst.bounds)
# Get extent and cutline
if not map_bounds:
map_bounds = estimate_extent(image_wgs_bounds, address)
cutline = get_cutline(src_dst, map_bounds)
return reader.tile(
src_dst,
tile_x,
tile_y,
tile_z,
tilesize,
warp_vrt_option={'cutline': cutline},
**kwargs)
|
import numpy as np
class Solution(object):
def XXX(self, s):
maxLen = 0
maxStr = ""
s_len = len(s)
p = np.arange(0.5, s_len, 0.5)
for i in p:
if i % 1 == 0.5:
start = (int)(i - 0.5)
end = (int)(i + 0.5)
else:
start = (int)(i - 1)
end = (int)(i + 1)
while (start >= 0 and end < s_len):
if s[start] == s[end]:
if maxLen < end - start + 1:
maxLen = end - start + 1
maxStr = s[start:end+1]
start = start - 1
end = end + 1
else:
break
if maxStr == "":
maxStr = s[0]
return maxStr
|
import pydicom
from PIL import Image, ImageQt
class Dicom:
def __init__(self, filename):
self.filename = filename
# Information related to image
self.data = pydicom.read_file(filename)
self.size = (self.data.Rows, self.data.Columns)
self.image = self.get_8bit_image()
def get_8bit_image(self):
# Get pixel data as float numbers
array = self.data.pixel_array.astype('float64')
# Normalize pixel data to 0-255
array = (array / array.max()) * 255
# Create and return image in right mode
image = Image.fromarray(array)
converted = image.convert('L')
return ImageQt.ImageQt(converted)
|
import numpy as np
import cv2
class Detection(object):
"""
This class represents a bounding box detection for a single image.
Parameters
----------
tlbr : array_like
Bounding box in format `(min x, min y, max x, max y)`.
class_id : int
Class id
confidence : float
Detector confidence score.
"""
def __init__(self, tlbr, class_id, confidence):
for coord in tlbr:
if coord < 0:
raise ValueError('Coordinate value should be greater then 0.')
if tlbr[0] >= tlbr[2] or tlbr[1] >= tlbr[3]:
raise ValueError('xmax(ymax) should be greater then xmin(ymin).')
self.tlbr = np.asarray(tlbr, dtype=np.float16)
self.class_id = int(class_id)
self.confidence = float(confidence)
def __repr__(self):
return "<Detection(class_id: {}, conf: {})>".format(self.class_id, self.confidence)
@classmethod
def from_tlwh(cls, tlwh, label, confidence):
tlbr = np.asarray((tlwh[0], tlwh[1], tlwh[0]+tlwh[2], tlwh[1]+tlwh[3]), dtype=np.float)
return cls(tlbr, label, float(confidence))
def to_tlwh(self):
"""Convert bounding box to format `(x, y, w, h)`, i.e.,
`(top left, bottom right)`.
"""
ret = self.tlbr.copy()
ret[2] = ret[2] - ret[0]
ret[3] = ret[3] - ret[1]
return ret
def to_xyah(self):
"""Convert bounding box to format `(center x, center y, aspect ratio,
height)`, where the aspect ratio is `width / height`.
"""
ret = self.tlbr.copy()
center_x = (ret[0] + ret[2]) / 2
center_y = (ret[1] + ret[3]) / 2
ratio = (ret[2] - ret[0])/(ret[3] - ret[1])
height = ret[3] - ret[1]
return center_x, center_y, ratio, height
def get_center(self):
ret = self.tlbr.copy()
return (ret[0] + ret[2]) / 2, (ret[1] + ret[3]) / 2
def __key(self):
return self.class_id, self.confidence
def __hash__(self):
return hash(self.__key())
def __eq__(self, other):
if not isinstance(other, Detection):
return self.__key() == other.__key()
return sum(self.tlbr == other.tlbr) == 4 and self.class_id == other.class_id \
and self.confidence == other.confidence
def _intersect(self, box):
def _interval_overlap(interval_a, interval_b):
x1, x2 = interval_a
x3, x4 = interval_b
if x3 < x1:
if x4 < x1:
return 0
else:
return min(x2, x4) - x1
else:
if x2 < x3:
return 0
else:
return min(x2, x4) - x3
intersect_w = _interval_overlap([self.tlbr[0], self.tlbr[2]], [box[0], box[2]])
intersect_h = _interval_overlap([self.tlbr[1], self.tlbr[3]], [box[1], box[3]])
intersect = intersect_w * intersect_h
return intersect
def get_iou(self, tlbr):
"""
Get IOU metric value with tlbr object
:param tlbr: Bounding box in format `(min x, min y, max x, max y)`
:return: float
"""
intersect = self._intersect(tlbr)
w1, h1 = self.tlbr[2] - self.tlbr[0], self.tlbr[3] - self.tlbr[1]
w2, h2 = tlbr[2] - tlbr[0], tlbr[3] - tlbr[1]
union = w1 * h1 + w2 * h2 - intersect
return float(intersect) / union
def draw(self, image, labels, colors, print_labels=True):
image_h, image_w, _ = image.shape
color = colors[int(self.class_id) % len(colors)]
xmin, ymin, xmax, ymax = self.tlbr
f = lambda x: x < 1
if sum(list(map(f, self.tlbr))) == 4:
xmin = int(image_w * xmin)
xmax = int(image_w * xmax)
ymin = int(image_h * ymin)
ymax = int(image_h * ymax)
else:
xmin = int(xmin)
xmax = int(xmax)
ymin = int(ymin)
ymax = int(ymax)
cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
if print_labels:
title = "{} : {}%".format(labels[self.class_id], int(100*self.confidence))
cv2.putText(image, title, (xmin, ymin - 13), cv2.FONT_HERSHEY_SIMPLEX, 0.0007 * image_h, color, 2)
return image
def to_json(self):
"""
Serialize Detection to JSON object
"""
data = {'tlbr': self.tlbr.tolist(),
'class_id': self.class_id,
'confidence': self.confidence}
return data
@classmethod
def from_json(cls, json_data=None):
"""
Load Detection object from JSON file or object
:param json_data: JSON object
:return: Detection object
"""
instance = cls(tlbr=np.array(json_data['tlbr']),
class_id=json_data['class_id'],
confidence=json_data['confidence'])
return instance
|
""" Not fully examined in the latest version of code.. """
import os
import sys
from pathlib import Path
from typing import List
import torch
from torch import nn
from diffabs import DeeppolyDom, IntervalDom
sys.path.append(str(Path(__file__).resolve().parent.parent))
from art.acas import AcasNet, AcasProp
from art.external_verifier import _CEX, Reluplex, ReluVal
def _errors(arr1: List, arr2: List) -> float:
from math import sqrt
assert len(arr1) == len(arr2)
err = 0.0
for (n1, n2) in zip(arr1, arr2):
err += (n1 - n2) ** 2
err /= len(arr1)
return sqrt(err)
def test_reluplex(logs_dir: str = './reluplex_logs/'):
""" Use property 2 logs from Reluplex for thoroughly examination.
Need to run Reluplex's script 2 first and prepare the logs in proper location.
:param logs_dir: directory of logs
"""
if not Path(logs_dir).is_dir():
print(f'{logs_dir} is not valid path for all logs.')
return
dom = DeeppolyDom()
def validate_normalize(dnn: AcasNet, cex: _CEX, mins, maxs):
""" Validate that the normalize() function works the same as NNET's normalizeInput/Output(). """
ni = torch.tensor([cex.inputs])
ni = dnn.normalize_inputs(ni, mins, maxs)
ni = ni[0].detach().numpy()
target = cex.inputs_normed
err = _errors(ni, target)
print('My PyTorch normalizing:', ni)
print('NNET normalizing:', target)
print('Error:', err)
return err
def validate_dnn(dnn, cex):
""" Validate that the DNN outputs the same result as NNET does. """
oi = torch.tensor([cex.inputs])
oo = dnn(oi)
oo = oo[0].detach().numpy()
target = cex.nnet_outputs_normed
err = _errors(oo, target)
print('PyTorch :', oo)
print('NNET C++:', target)
print('Error:', err)
return err
def validate_cex(c, log_path):
id = log_path[-7:-4] # e.g. 2_1 for property2_stats_2_1.txt
net_path = './acas_nets/ACASXU_run2a_%s_batch_2000.nnet' % id
print(net_path)
dnn, mins, maxs = AcasNet.load_nnet(net_path, dom)
err1 = validate_normalize(dnn, c, mins, maxs)
print('---')
err2 = validate_dnn(dnn, c)
print()
return err1, err2
reluplex = Reluplex()
log_files = [fn for fn in os.listdir(logs_dir) if not fn.endswith('_summary.txt')]
all_cexs = []
err1s = []
err2s = []
for log_name in log_files:
with open(Path(logs_dir, log_name), 'r') as f:
log_data = f.read()
cexs = reluplex.extract(log_data)
all_cexs.extend(cexs)
for c in cexs:
err1, err2 = validate_cex(c, log_name)
err1s.append(err1)
err2s.append(err2)
pass
print('Errors for normalization:')
for err in err1s:
print(err)
print('Avg:', sum(err1s) / len(err1s))
print('Errors for forward propagation:')
for err in err2s:
print(err)
print('Avg:', sum(err2s) / len(err2s))
print('Example:')
print(all_cexs[0])
return
def test_reluval(logs_dir: str = './reluval_logs/'):
""" Use property 2 logs from ReluVal for thoroughly examination.
Need to run ReluVal's script 2 first and prepare the logs in proper location.
:param logs_dir: directory of logs
"""
if not Path(logs_dir).is_dir():
print(f'{logs_dir} is not valid path for all logs.')
return
dom = IntervalDom()
def validate_dnn(dnn, cex):
""" Validate that the DNN outputs the same result as NNET does. """
oi = torch.tensor([cex.inputs])
oo = dnn(oi)
oo = oo[0].detach().numpy()
target = cex.outputs
err = _errors(oo, target)
print('My PyTorch:', oo)
print('ReluVal C++:', target)
print('Error:', err)
return err
def validate_by_prop(dnn, cex, prop_id: int = 2):
""" It seems the computed outputs are quite different (10^-2 error). So confirm it's true CEX instead? """
oi = torch.tensor([cex.inputs])
oo = dnn(oi)
if prop_id != 2:
raise NotImplementedError()
prop = AcasProp.property2(dom)
e = dom.Ele.by_intvl(oo, oo)
dist = prop.safe_dist(e)
mse = nn.MSELoss()
loss = mse(dist, torch.zeros_like(dist))
print(f'My PyTorch loss for property{prop_id}: {loss}')
return loss
def validate_cex(c, log_path):
log_name = Path(log_path).name
prefix = 'ACASXU_run2a_'
assert prefix in log_name
id = log_name[len(prefix):len(prefix) + 3] # e.g. 2_1 for ACASXU_run2a_2_1_batch_2000.nnet.log
net_path = f'./acas_nets/ACASXU_run2a_{id}_batch_2000.nnet'
print(net_path)
dnn, mins, maxs = AcasNet.load_nnet(net_path, dom)
# err = validate_dnn(dnn, c)
err = validate_by_prop(dnn, c)
print()
return err
reluval = ReluVal()
log_files = [fn for fn in os.listdir(logs_dir) if fn.endswith('.nnet.log')]
errs = []
for log_name in log_files:
with open(Path(logs_dir, log_name), 'r') as f:
log_data = f.read()
cexs = reluval.extract(log_data)
for c in cexs:
print('Validing', c)
err = validate_cex(c, log_name)
errs.append(err)
pass
print('Losses for forward propagation (should be > 0, so that CEX is genuine):')
for err in errs:
print(err)
print('Avg:', sum(errs) / len(errs))
return
def test_reluval_cex(nitems: int = 5):
""" Try to call ReluVal and collect its CEX. Validate that things are working. """
dom = DeeppolyDom()
reluval = ReluVal()
prop = AcasProp.property2(dom)
lb, ub = prop.lbub()
for npath in prop.applicable_net_paths()[:nitems]:
print('Using network from path', npath)
net, bound_mins, bound_maxs = AcasNet.load_nnet(npath, dom)
cexs = reluval.verify(lb, ub, net, task_name='2')
print(cexs)
# validation
for i in range(len(cexs)):
print('------ Validating cex', i)
cex = cexs[i:i + 1]
cex = net.normalize_inputs(cex, bound_mins, bound_maxs)
print('CEX:', cex)
with torch.no_grad():
out = net(cex)
print('Concrete Outs:', out)
assert out.argmax(dim=-1).item() == 0
absin = dom.Ele.by_intvl(cex, cex)
with torch.no_grad():
absout = net(absin)
print('Distance:', prop.safe_dist(absout))
print('------')
return
|
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
dataset_ml_query = """
CREATE OR REPLACE TABLE `@ML_TABLE_ID`
AS (
WITH
-- Calculate features before CUTOFF_DATE date.
features AS (
SELECT
customer_id,
customer_country,
COUNT(n_purchases) AS n_purchases,
AVG(order_qty) AS avg_purchase_size,
AVG(revenue) AS avg_purchase_revenue,
DATE_DIFF(MAX(order_date), MIN(order_date), DAY) AS customer_age,
DATE_DIFF(DATE('2011-09-01'), MAX(order_date), DAY) AS days_since_last_purchase
FROM
`@CLEAN_TABLE_ID`
WHERE
order_date <= DATE('2011-09-01')
GROUP BY
customer_id,
customer_country),
-- Calculate customer target monetary value over historical period + 3M future period.
label AS (
SELECT
customer_id,
SUM(revenue) AS target_monetary_value_3M
FROM
`@CLEAN_TABLE_ID`
WHERE
order_date < DATE('2011-12-01')
GROUP BY
customer_id
)
SELECT
features.customer_id,
features.customer_country,
features.n_purchases, -- frequency
features.avg_purchase_size,
features.avg_purchase_revenue,
features.customer_age,
features.days_since_last_purchase, --recency
label.target_monetary_value_3M, --monetary
CASE
WHEN MOD(ABS(FARM_FINGERPRINT(CAST(features.customer_id AS STRING))), 10) < 8
THEN 'TRAIN'
WHEN MOD(ABS(FARM_FINGERPRINT(CAST(features.customer_id AS STRING))), 10) = 9
THEN 'VALIDATE'
ELSE
'TEST' END AS data_split
FROM
features
INNER JOIN label
ON features.customer_id = label.customer_id
);
""" |
from django import forms
class LoginForm(forms.Form):
username=forms.CharField(max_length=50, required=True)
password=forms.CharField(max_length=150, required=True)
back_url=forms.CharField(max_length=150, required=False)
class ResetPasswordForm(forms.Form):
username=forms.CharField(required=True,max_length=200,widget=forms.TextInput(attrs={'class':'form-control leo-farsi mt-3','placeholder':'موبایل','type':'tel'}))
old_password=forms.CharField(max_length=150, required=False)
new_password=forms.CharField(max_length=150, required=True)
class RegisterForm(forms.Form):
username=forms.CharField(max_length=50, required=True)
password=forms.CharField(max_length=150, required=True)
first_name=forms.CharField(max_length=50, required=True)
last_name=forms.CharField(max_length=50, required=True)
class UploadProfileImageForm(forms.Form):
profile_id=forms.IntegerField(required=True)
image=forms.ImageField(required=True)
class UploadProfileHeaderForm(forms.Form):
profile_id=forms.IntegerField(required=True)
header_image=forms.ImageField(required=True)
class EditProfileForm(forms.Form):
profile_id=forms.IntegerField(required=True)
first_name=forms.CharField(max_length=50, required=True)
last_name=forms.CharField(max_length=50, required=True)
mobile=forms.CharField(max_length=50, required=False)
address=forms.CharField(max_length=50, required=False)
slogan=forms.CharField(max_length=50, required=False)
bio=forms.CharField(max_length=500, required=False)
address=forms.CharField(max_length=100, required=False)
postal_code=forms.CharField(max_length=50, required=False) |
#!/bin/python3
from mavlink_arbiter.singleton import singleton
import queue
class BCOLORS:
"""
Static class for OS
Constant use on headers.
"""
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
SUCCESS = '\033[1;42m'
UNDERLINE = '\033[4m'
ERROR = '\033[1;41m'
@singleton
class Utils:
def __init__(self):
"""
Utilities class for loging messages in the console
class takes on a singleton pattern.
"""
self.logs = []
self.currLog = 'Initialized'
def log(self, string):
"""
Logs a message in the entire program.
:string: String that you want to log.
"""
print(BCOLORS.BOLD + string + BCOLORS.ENDC)
self.currLog = string
self.logs.append(string)
# def prompt(self, question, affirm_opt):
# '''
# TODO: Not functional yet. Will be used for CLI.
# '''
# opt = input(BCOLORS.BOLD + question + BCOLORS.ENDC)
# if opt in affirm_opt:
# return True
# else:
# return False
def errLog(self, string):
"""
Logs an error in the entire program.
Will highlight red.
:string: String that you want to log.
"""
print(BCOLORS.ERROR + string + BCOLORS.ENDC)
self.currLog = string
self.logs.append(string)
def succLog(self, string):
"""
Logs a success message in the entire program.
Will highlight green.
:string: String that you want to log.
"""
print(BCOLORS.SUCCESS + string + BCOLORS.ENDC)
self.currLog = string
self.logs.append(string)
def getPreviousLogs(self):
"""
Get the last log that was made.
"""
return self.logs
@staticmethod
def meters_to_feet(meters):
"""
Meters to Feet conversion function
for use by Interoperbility publishing
"""
feet = meters * 3.280839895
return feet
if __name__ == "main":
print("Testing Queue buffer utility:")
queue = queue.Queue()
obj1 = dict()
obj1['1'] = 'String1'
obj2 = dict()
obj2['2'] = 'String2'
print(queue.empty())
queue.put(obj1)
print(queue.empty())
# https://mavlink.io/en/messages/common.html#MAV_MODE
class ModeMavlink:
MAV_MODE_PREFLIGHT = 0
MAV_MODE_STABILIZE_DISARMED = 80
MAV_MODE_STABILIZE_ARMED = 208
MAV_MODE_MANUAL_DISARMED = 64
MAV_MODE_MANUAL_ARMED = 192
MAV_MODE_GUIDED_ARMED = 216
MAV_MODE_GUIDED_DISARMED = 88
MAV_MODE_AUTO_DISARMED = 92
MAV_MODE_AUTO_ARMED = 220
MAV_MODE_TEST_DISARMED = 66
MAV_MODE_TEST_ARMED = 194
# https://mavlink.io/en/messages/common.html
class CommandMavlink:
CMD_NAV_WAYPOINT = 16
CMD_NAV_LOITER_UNLIM = 17
CMD_NAV_LOITER_TURNS = 18
CMD_NAV_LOITER_TIME = 19
CMD_NAV_RETURN_TO_LAUNCH = 20
CMD_NAV_LAND = 21
CMD_NAV_TAKEOFF = 22
CMD_NAV_ROI = 80
CMD_NAV_PATHPLANNING = 81
CMD_NAV_LAST = 95
CMD_CONDITION_DELAY = 112
CMD_CONDITION_CHANGE_ALT = 113
CMD_CONDITION_DISTANCE = 114
CMD_CONDITION_YAW = 115
CMD_CONDITION_LAST = 159
CMD_DO_SET_MODE = 176
CMD_DO_JUMP = 177
CMD_DO_CHANGE_SPEED = 178
CMD_DO_SET_HOME = 179
CMD_DO_SET_PARAMETER = 180
CMD_DO_SET_RELAY = 181
CMD_DO_REPEAT_RELAY = 182
CMD_DO_SET_SERVO = 183
CMD_DO_REPEAT_SERVO = 184
CMD_DO_CONTROL_VIDEO = 200
CMD_DO_DIGICAM_CONFIGURE = 202
CMD_DO_DIGICAM_CONTROL = 203
CMD_DO_MOUNT_CONFIGURE = 204
CMD_DO_MOUNT_CONTROL = 205
CMD_DO_LAST = 240
CMD_PREFLIGHT_CALIBRATION = 241
CMD_PREFLIGHT_SET_SENSOR_OFFSETS = 242
CMD_PREFLIGHT_STORAGE = 245
CMD_PREFLIGHT_REBOOT_SHUTDOWN = 246
CMD_OVERRIDE_GOTO = 252
CMD_MISSION_START = 300
D_COMPONENT_ARM_DISARM = 400
|
from __future__ import unicode_literals
from django.conf.urls import url
from . import views
from mezzanine.conf import settings
# Trailing slash for urlpatterns based on setup.
_slash = "/" if settings.APPEND_SLASH else ""
# patterns
urlpatterns = [
url("^/events/feeds/(?P<format>.*)%s$" % _slash,
views.event_feed, name="event_feed"),
]
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright (c) 2016, Silvio Peroni <essepuntato@gmail.com>
#
# Permission to use, copy, modify, and/or distribute this software for any purpose
# with or without fee is hereby granted, provided that the above copyright notice
# and this permission notice appear in all copies.
#
# THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
# REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT,
# OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE,
# DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
# ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS
# SOFTWARE.
from __future__ import annotations
from typing import TYPE_CHECKING
from oc_ocdm.decorators import accepts_only
from oc_ocdm.graph.graph_entity import GraphEntity
from oc_ocdm.prov.prov_entity import ProvEntity
from rdflib import XSD
if TYPE_CHECKING:
from typing import Optional, List
from rdflib import URIRef
class SnapshotEntity(ProvEntity):
# HAS CREATION DATE
def get_generation_time(self) -> Optional[str]:
return self._get_literal(ProvEntity.iri_generated_at_time)
@accepts_only('literal')
def has_generation_time(self, string: str) -> None:
"""The date on which a particular snapshot of a bibliographic entity's metadata was
created.
"""
self.remove_generation_time()
self._create_literal(ProvEntity.iri_generated_at_time, string, XSD.dateTime)
def remove_generation_time(self) -> None:
self.g.remove((self.res, ProvEntity.iri_generated_at_time, None))
# HAS INVALIDATION DATE
def get_invalidation_time(self) -> Optional[str]:
return self._get_literal(ProvEntity.iri_invalidated_at_time)
@accepts_only('literal')
def has_invalidation_time(self, string: str) -> None:
"""The date on which a snapshot of a bibliographic entity's metadata was invalidated due
to an update (e.g. a correction, or the addition of some metadata that was not specified
in the previous snapshot), or due to a merger of the entity with another one.
"""
self.remove_invalidation_time()
self._create_literal(ProvEntity.iri_invalidated_at_time, string, XSD.dateTime)
def remove_invalidation_time(self) -> None:
self.g.remove((self.res, ProvEntity.iri_invalidated_at_time, None))
# IS SNAPSHOT OF
def get_is_snapshot_of(self) -> Optional[URIRef]:
uri: Optional[URIRef] = self._get_uri_reference(ProvEntity.iri_specialization_of)
return uri
def is_snapshot_of(self, en_res: GraphEntity) -> None:
"""This property is used to link a snapshot of entity metadata to the bibliographic entity
to which the snapshot refers.
"""
self.remove_is_snapshot_of()
self.g.add((self.res, ProvEntity.iri_specialization_of, en_res.res))
def remove_is_snapshot_of(self) -> None:
self.g.remove((self.res, ProvEntity.iri_specialization_of, None))
# IS DERIVED FROM
def get_derives_from(self) -> List[ProvEntity]:
uri_list: List[URIRef] = self._get_multiple_uri_references(ProvEntity.iri_was_derived_from, 'se')
result: List[ProvEntity] = []
for uri in uri_list:
# TODO: what is the prov_subject of these snapshots?
result.append(self.p_set.add_se(None, uri))
return result
@accepts_only('se')
def derives_from(self, se_res: ProvEntity) -> None:
"""This property is used to identify the immediately previous snapshot of entity metadata
associated with the same bibliographic entity.
"""
self.g.add((self.res, ProvEntity.iri_was_derived_from, se_res.res))
@accepts_only('se')
def remove_derives_from(self, se_res: ProvEntity = None) -> None:
if se_res is not None:
self.g.remove((self.res, ProvEntity.iri_was_derived_from, se_res.res))
else:
self.g.remove((self.res, ProvEntity.iri_was_derived_from, None))
# HAS PRIMARY SOURCE
def get_primary_source(self) -> Optional[URIRef]:
uri: Optional[URIRef] = self._get_uri_reference(ProvEntity.iri_had_primary_source)
return uri
@accepts_only('thing')
def has_primary_source(self, any_res: URIRef) -> None:
"""This property is used to identify the primary source from which the metadata
described in the snapshot are derived (e.g. Crossref, as the result of querying the
CrossRef API).
"""
self.remove_primary_source()
self.g.add((self.res, ProvEntity.iri_had_primary_source, any_res))
def remove_primary_source(self) -> None:
self.g.remove((self.res, ProvEntity.iri_had_primary_source, None))
# HAS UPDATE ACTION
def get_update_action(self) -> Optional[str]:
return self._get_literal(ProvEntity.iri_has_update_query)
@accepts_only('literal')
def has_update_action(self, string: str) -> None:
"""The UPDATE SPARQL query that specifies which data, associated to the bibliographic
entity in consideration, have been modified (e.g. for correcting a mistake) in the
current snapshot starting from those associated to the previous snapshot of the entity.
"""
self.remove_update_action()
self._create_literal(ProvEntity.iri_has_update_query, string)
def remove_update_action(self) -> None:
self.g.remove((self.res, ProvEntity.iri_has_update_query, None))
# HAS DESCRIPTION
def get_description(self) -> Optional[str]:
return self._get_literal(ProvEntity.iri_description)
@accepts_only('literal')
def has_description(self, string: str) -> None:
"""A textual description of the events that have resulted in the current snapshot (e.g. the
creation of the initial snapshot, the creation of a new snapshot following the
modification of the entity to which the metadata relate, or the creation of a new
snapshot following the merger with another entity of the entity to which the previous
snapshot related).
"""
self.remove_description()
self._create_literal(ProvEntity.iri_description, string)
def remove_description(self) -> None:
self.g.remove((self.res, ProvEntity.iri_description, None))
# IS ATTRIBUTED TO
def get_resp_agent(self) -> Optional[URIRef]:
uri: Optional[URIRef] = self._get_uri_reference(ProvEntity.iri_was_attributed_to)
return uri
@accepts_only('thing')
def has_resp_agent(self, se_agent: URIRef) -> None:
"""The agent responsible for the creation of the current entity snapshot.
"""
self.remove_resp_agent()
self.g.add((self.res, ProvEntity.iri_was_attributed_to, se_agent))
def remove_resp_agent(self) -> None:
self.g.remove((self.res, ProvEntity.iri_was_attributed_to, None))
|
import re
# import sys
import ast
import unicodedata
from decimal import Decimal
from fractions import Fraction
from collections import defaultdict, namedtuple
from apps.products.models import Unit, Product
from apps.recipes.models import Dish, DishLabel, Recipe, RecipeInstructions, Ingredient
# def print(s):
# # Overwrite standard print for command use
# sys.stdout.write(s)
UNICODE_VULGAR_FRACTIONS = '¼½¾⅐⅑⅒⅓⅔⅕⅖⅗⅘⅙⅚⅛⅜⅝⅞'
possible_units = [
'unit: unit',
'gram: g gram grams gramo gramos gr grs g. gr. grs.',
'kilogram: kg kilogram kilograms kilo kilos kilogramo kilogramos kg.',
'liter: L l liter liters litro litros l.',
'milliliter: mL ml milliliter milliliters mililitro mililitros ml. cc cc.',
'cup: cup cups taza tazas',
('teaspoon: tsp teaspoon teaspoons cucharita cucharitas cucharadita cucharaditas '
'cdta cdita cditas cdta. cdtas. cdita. cditas.'),
'tablespoon: Tbsp tbsp tablespoon tablespoons cuchara cucharas cucharada cucharadas cda cdas cds. cda. cdas.',
'pound: lb pound pounds libra libras lb.',
'ounce: oz ounce ounces onza onzas oz.',
]
# Map from possible unit names to their corresponding Unit object
units_mapping = {u: Unit.objects.get(name=uu[0]) # {..., 'gram': <Unit: gram>, 'grams': <Unit: gram>, ...}
for uu in map(lambda x: x.split(': '), possible_units) # [..., ['g', 'gram grams...']. ...]
for u in uu[1].split()} # ['gram', 'grams', ...]
default_unit = units_mapping['unit']
unrecognized_unit_names = defaultdict(int)
""" INGREDIENT PARSING """
IngredientTuple = namedtuple('Ingredient', 'quantity unit product remarks section')
def pretty_ingr(ingr):
return f'[{ingr.quantity} {ingr.unit}] de [{ingr.product}]' + (f' ({ingr.remarks})' if ingr.remarks else '')
def parse_ingredient(ingr_line, section_name):
"""Parses typical ingredient line format: '<quantity> [<unit> of] <ingredient>[, <remarks>]/[ (<remarks>)]'.
Returns IngredientTuple, with None for any N/A."""
ingr_line = ingr_line.strip()
quantity, unit, remarks = None, None, None
# look for number at beginning of string
if (quantity_match := re.match(rf'^\s*(?:([0-9{UNICODE_VULGAR_FRACTIONS}.,/]+)\s*)+', ingr_line)):
quantity = Decimal()
# convert each part to Decimal and get full quantity (e.g. '2 ½' => Decimal('2.5'))
# TODO: parse decimal comma
# TODO: parse integer next to vulgar fraction (e.g. 2½)
for p in quantity_match.group().split():
try:
f = float(Fraction(p))
except ValueError:
f = unicodedata.numeric(p)
quantity += Decimal(str(f))
ingr_line = ingr_line[quantity_match.end():]
unit = default_unit
# look for possible unit name
if unit_match := re.match(r'^\s*(?:de\s*)?([^\d\W]+\b\.?)\s*(de\b\s*)?', ingr_line, re.IGNORECASE):
try:
unit = units_mapping[unit_match.group(1).lower()]
ingr_line = ingr_line[unit_match.end():]
except KeyError:
unrecognized_unit_names[unit_match.group(1).lower()] += 1
# look for comment at string end, after comma or between parentheses, and grab first match
if (remarks_match := re.search(r'\s*(?:,\s*(.*)|\((.*)\))\s*$', ingr_line)):
remarks = next((x.strip(' .') for x in remarks_match.groups() if x), None)
ingr_line = ingr_line[:remarks_match.start()]
# whatever's left should be the product name
product = ingr_line.lower().strip(' ⠀.') # includes U+2800 ("braille pattern blank")
section_name = section_name.strip(': ') if section_name else None
return IngredientTuple(quantity=quantity, unit=unit, product=product, remarks=remarks, section=section_name)
def seems_like_ingredient(line):
"""Check whether `line` starts with number."""
return bool(
re.search(rf'^[0-9{UNICODE_VULGAR_FRACTIONS}]', line)
)
def seems_like_section_name(line):
"""Check whether `line` starts with 'Para' or ends with ':', ignoring case and whitespace."""
return bool(
re.search(r'(^[^a-záéíóúü0-9]*para\b|:\s*$)', line, re.IGNORECASE)
)
def parse_ingredient_list(raw_ingrs_list):
print('\n--- Original ---\n')
_ = [print(line) for line in raw_ingrs_list]
ingrs = []
d = defaultdict(list) # TODO: this structure is redundant against ingrs but easier to print
first_lines = True
next_is_section_title = False
section = None
# TODO: try to simplify logic
for line in raw_ingrs_list:
if line and first_lines and not seems_like_ingredient(line):
# something at the beginning which doesn't look like an ingredient is probably a section name
section = line
next_is_section_title = False
elif not line:
# empty lines usually precede section names
if first_lines:
continue
next_is_section_title = True
elif (next_is_section_title and not seems_like_ingredient(line)) \
or seems_like_section_name(line):
section = line
next_is_section_title = False
elif not (parsed_ingr := parse_ingredient(line, section)):
# if ingredient parsing fails, just ignore it
continue
else:
# else we have a new ingredient
d[section].append(parsed_ingr)
ingrs.append(parsed_ingr)
first_lines = False
print('\n--- Parsed ---')
for section_ingrs in d.items():
print(f'\nSección: {section_ingrs[0]}')
_ = [print(f' {pretty_ingr(ingr)}') for ingr in section_ingrs[1]]
# TODO: should use sys.stdin?
if not input('\nInput any character IF the parsing seems CORRECT >'):
return None
return ingrs
""" RECIPE PARSING """
def parse_and_create_recipe(raw_recipe, dish, assets):
print(f"\n\n\n[{dish.name}] *{raw_recipe['name'].strip()}*")
# First verify we get a nice parsing, otherwise skip the recipe
if not (parsed_ingrs := parse_ingredient_list(raw_recipe['ingredients'])):
return False
# Now we can create the recipe and all of its ingredients
recipe, newly_created = Recipe.objects.get_or_create(
dish=dish,
title=raw_recipe['name'].strip(),
defaults={
'description': f"Source: {raw_recipe['source'].strip()}\nVideo: {assets}",
'instructions': RecipeInstructions.objects.create(steps=raw_recipe['steps']),
},
)
if not newly_created:
print("Recipe with this name for this dish already exists; won't create ingredients")
return False
# Create each ingredient
for parsed_ingr in parsed_ingrs:
product = Product.objects.get_or_create(name=parsed_ingr.product)[0]
Ingredient.objects.create(
recipe=recipe,
product=product,
quantity=parsed_ingr.quantity,
unit=(parsed_ingr.unit if parsed_ingr.unit else default_unit),
notes=(parsed_ingr.remarks if parsed_ingr.remarks else ''),
section=(parsed_ingr.section if parsed_ingr.section else '')
)
return True
""" DISH PARSING """
class DishParser:
def __init__(self):
self.rows_added_count = 0
def unrecognized_unit_names(self):
return unrecognized_unit_names
def parse_and_create_dish(self, row):
dish, is_new = Dish.objects.get_or_create(
name=row['title'].strip(),
# If a dish already exists with that title, description won't be overwritten
defaults={
'description': row['description'].strip(),
},
)
# row['recipes'] is a string which holds a Python list of dicts in a string
recipes = ast.literal_eval(row['recipes'])
if (not any([parse_and_create_recipe(recipe_dict, dish, row['assets'])
for recipe_dict in recipes])
and is_new):
# If no recipes were created for a newly created dish, delete it
dish.delete()
return False
# row['tags'] is a string which holds a Python list of string tags
for tag in ast.literal_eval(row['tags']):
print(f'Tag: {tag.strip().lower()}')
dish.labels.add(
DishLabel.objects.get_or_create(name=tag.strip().lower())[0]
)
self.rows_added_count += 1
return True
|
import os
import numpy as np
import cv2
import json
from util.process_box import corner_to_yolo_box
def read_json_label(filename):
with open(filename) as fp:
str_json_data = fp.read()
json_data = json.loads(str_json_data)
label=[]
for shape in json_data['shapes']:
cls = np.asarray([shape['label']],dtype=np.float32)
box = np.asarray(shape['points'],dtype=np.float32).reshape(4)
label.append(np.concatenate([cls,box]))
return np.asarray(label)
def load_data(path,mode='test'):
if len(path) > 1 and os.path.isfile(path[0]):
image_dir = ''
file_list = path
label_dir = os.path.join(os.path.dirname(os.path.dirname(path[0])), 'label')
elif os.path.isdir(path):
image_dir = os.path.join(path, 'image')
file_list = os.listdir(image_dir)
label_dir = os.path.join(path,'label')
elif os.path.isfile(path):
image_dir = ''
file_list = [path]
label_dir = os.path.join(os.path.dirname(os.path.dirname(path)),'label')
image_data = []
labels = []
for file_name in file_list:
if file_name.endswith('.jpg'):
image = cv2.imread(os.path.join(image_dir, file_name))
image_data.append(image)
if mode=='train':
label_filename = os.path.splitext(file_name)[0] + '.json'
label = read_json_label(os.path.join(label_dir,label_filename))
labels.append(label)
if mode == 'train':
return image_data,labels
else:
return image_data
def to_one_hot(label,num_class):
one_hot_label = np.zeros(num_class)
one_hot_label[int(label)] = 1
return one_hot_label
def preprocess_label(label,num_class,image_size,output_size):
labels = np.zeros((49,5+num_class))
for obj in label:
cls = obj[0]
box = obj[1:]
one_hot_label = to_one_hot(cls,num_class)
yolo_box,frame_x,frame_y = corner_to_yolo_box(box, image_size,output_size)
ind = frame_y*7+frame_x
labels[ind, 0] = 1
labels[ind, 1:5] = yolo_box
labels[ind, 5:] = one_hot_label
return labels
def preprocess_data(images,labels=None,target_size=(448,448),output_size=(7,7),num_class=20,mode='test'):
image_num = len(images)
w,h = target_size
image_data = np.zeros((image_num,w,h,3))
if mode == 'train':
y_true = np.zeros((image_num,49,5+num_class))
for i in range(image_num):
ih,iw,_=images[i].shape
image_data[i] = cv2.resize(images[i], target_size, interpolation=cv2.INTER_CUBIC)/255.
if mode == 'train':
y_true[i] = preprocess_label(labels[i], num_class, (iw,ih),output_size)
if mode == 'train':
return image_data,y_true
else:
return image_data
if __name__=='__main__':
images,labels = load_data('../dataset/yolo_test_data',mode='train')
print(labels)
|
#print(ad) || total
#print(ad[0]) || Name
#print(ad[1]) || Passhashed sha3_512
#print(ad[2]) || Wins
#print(ad[3]) || Losses
#print(ad[4]) || Draws
#print(ad[5]) || Account type || 0/Standard | 1/SuperUser | 2/Admin | 3/Debug
#print(ad[6]) || Profile Picture Location
#Imports
import tkinter as tk
from tkinter import filedialog
import PIL
from PIL import ImageTk, Image
import hashlib
import pickle
import os
import time
#Start menu
class startMenu(tk.Frame):
def __init__(self, parent):
tk.Frame.__init__(self, parent)
startwindow.title("PyTTT")
self.config(bg="white")
self.logo = ImageTk.PhotoImage(Image.open("assests/Logo.pgm"))
logoLabel = tk.Label(self, image=self.logo, bg="white")
self.promptUpper = tk.Label(self, text="PyTTT", anchor="c", font="TkFixedFont 18 bold", bg="white")
self.buttonSingle = tk.Button(self, text="Single Player", font="TkFixedFont 12", bg="white", command = self.playSingle)
self.buttonMulti = tk.Button(self, text="Two Players", font="TkFixedFont 12", bg="white", command = self.playMulti)
self.buttonProfile = tk.Button(self, text="Profile", font="TkFixedFont 12", bg="white", command = self.profile)
self.buttonExit = tk.Button(self, text="Exit", font="TkFixedFont 12", bg="white", command = self.sysExit)
self.promptLower = tk.Label(self, text="Made with Python 3.7 by Sasith De Abrew", bg="white", anchor="s")
self.promptUpper.pack(side="top", fill="x", expand=1)
logoLabel.pack(side="right", anchor="e", padx=10)
self.buttonSingle.pack(side="top", anchor="w", expand=1, padx=10)
self.buttonMulti.pack(side="top", anchor="w", expand=1, padx=10)
self.buttonProfile.pack(side="top", anchor="w", expand=1, padx=10)
self.buttonExit.pack(side="top", anchor="w", expand=1, padx=10)
self.promptLower.pack(side="bottom", fill="x", anchor="s")
def playSingle(self):
startwindow.destroy()
try:
profilewindow.destroy()
except:
pass
#game.singlePlayer()
def playMulti(self):
startwindow.destroy()
try:
profilewindow.destroy()
except:
pass
#game.multiPlayer()
def profile(self):
if currentUser != "":
global profilewindow
profilewindow = tk.Toplevel()
profileMenu(profilewindow).pack(fill="both", expand=True)
profilewindow.mainloop()
else:
global loginwindow
loginwindow = tk.Tk()
loginMenu(loginwindow).pack(fill="both", expand=True)
loginwindow.mainloop()
def sysExit(self):
startwindow.destroy()
raise SystemExit
#Login Class
class loginMenu(tk.Frame):
def __init__(self, parent):
tk.Frame.__init__(self, parent)
loginwindow.title("PyTTT Login")
self.config(bg="white")
self.title = tk.Label(self, text="PyTTT Login", font="TkFixedFont 14", bg="white", anchor="n")
self.subtitle = tk.Label(self, text="Please register or log in", font="TkFixedFont 12", bg="white", anchor="n")
self.loginButton = tk.Button(self, text="Login", font="TkFixedFont 12", bg="white", command=self.login)
self.registerButton = tk.Button(self, text="register", font="TkFixedFont 12", bg="white", command=self.register)
self.title.pack(side="top", anchor="n")
self.subtitle.pack(side="top", anchor="n")
self.loginButton.pack(side="left", padx=20, pady=10)
self.registerButton.pack(side="right", padx=20, pady=10)
def register(self):
loginwindow.title("PyTTT Register")
for widget in self.winfo_children():
widget.destroy()
self.config(bg="white")
self.title = tk.Label(self, text="PyTTT Register", font="TkFixedFont 14", bg="white", anchor="n")
self.subtitle = tk.Label(self, text="Enter a username and password", font="TkFixedFont 12", bg="white", anchor="n")
self.userSubtitle = tk.Label(self, text="Username:", font="TkFixedFont 10", bg="white")
self.userInputForm = tk.Entry(self)
self.userInputForm.config(highlightbackground="white", highlightthickness=0)
self.passwordSubtitle = tk.Label(self, text="Password:", font="TkFixedFont 10", bg="white")
self.passwordInputForm = tk.Entry(self, show="*")
self.passwordInputForm.config(highlightbackground="white", highlightthickness=0)
self.credentialsSubmitButton = tk.Button(self, text="Register", bg="white", font="TkFixedFont 12", command=self.registeraccount)
self.title.grid(column=1, row=1, sticky="n")
self.subtitle.grid(column=1, row=2, sticky="n")
self.userSubtitle.grid(column=1, row=3, sticky="w")
self.userInputForm.grid(column=1, row=3, padx=20, sticky="e")
self.passwordSubtitle.grid(column=1, row=4, sticky="w")
self.passwordInputForm.grid(column=1, row=4, padx=20, sticky="e")
self.credentialsSubmitButton.grid(column=1, row=5, sticky="s")
def registeraccount(self):
if len(self.userInputForm.get()) == 0 or self.userInputForm.get() == " ":
self.usernameWarning = tk.Label(self, text="Username cannot be empty", bg="red")
self.usernameWarning.grid(column=1, row=6, sticky="s")
if len(self.passwordInputForm.get()) == 0 or self.passwordInputForm.get() == " ":
self.passwordWarning = tk.Label(self, text="Password cannot be empty", bg="red")
self.passwordWarning.grid(column=1, row=7, sticky="s")
if len(self.userInputForm.get()) > 0 and self.userInputForm.get() != " ":
if len(self.passwordInputForm.get()) > 0 and self.passwordInputForm.get() != " ":
self.initializeaccount()
def initializeaccount(self):
usrvar = self.userInputForm.get()
passvar = self.passwordInputForm.get()
usrDirectory = (f"./usr/{usrvar}")
try:
os.mkdir(usrDirectory)
passvarHashed = hashlib.sha3_512(passvar.encode("utf-8")).hexdigest()
global currentUser
currentUser = str(usrvar)
del usrvar
del passvar
initWins = 0
initLoses = 0
initDraws = 0
initAccountType = 0
initProfilePicture = "./assets/Blank-Profile-Picture.pgm"
usrVariables = [currentUser, passvarHashed, initWins, initLoses, initDraws, initAccountType, initProfilePicture]
DataLocation = (f"./usr/{currentUser}/AccountDetails.dat")
with open(DataLocation, "wb+") as data:
pickle.dump(usrVariables, data, 0)
data.close()
for widget in self.winfo_children():
widget.destroy()
self.maintext = tk.Label(self, text="Account successfully registered", bg="white", font="TkFixedFont 14")
self.subtext = tk.Label(self, text="You may now close this window", bg="white", font="TkFixedFont 12")
global Wins
global Loses
global Draws
global profilePicture
global accountType
Wins = initWins
Loses = initLoses
Draws = initDraws
accountType = initAccountType
profilePicture = initProfilePicture
self.closeWindowButton = tk.Button(self, text="Close", bg="white", font="TkFixedFont 12", command=loginwindow.destroy)
self.maintext.grid(column=0, row=0)
self.subtext.grid(column=0, row=1)
self.closeWindowButton.grid(column=0, row=2)
except FileExistsError:
self.UsrExists = tk.Label(self, text="User already exists", bg="red")
self.UsrExists.grid(column=1, row=8, sticky="s")
def login(self):
loginwindow.title("PyTTT Login")
for widget in self.winfo_children():
widget.destroy()
self.config(bg="white")
self.title = tk.Label(self, text="PyTTT Login", font="TkFixedFont 14", bg="white", anchor="n")
self.subtitle = tk.Label(self, text="Enter your username:", font="TkFixedFont 12", bg="white", anchor="n")
self.userInputForm = tk.Entry(self)
self.userInputForm.config(highlightbackground="white", highlightthickness=0)
self.credentialsSubmitButton = tk.Button(self, text="Login", bg="white", font="TkFixedFont 12", command=self.loginusersubmit)
self.title.grid(column=1, row=1, sticky="n")
self.subtitle.grid(column=1, row=2, sticky="n")
self.userInputForm.grid(column=1, row=3, padx=20, sticky="e")
self.credentialsSubmitButton.grid(column=1, row=4, sticky="s")
def loginusersubmit(self):
global usrvar
usrvar = self.userInputForm.get()
DataLocation = (f"./usr/{usrvar}")
if len(usrvar) > 0:
try:
os.mkdir(DataLocation)
os.rmdir(DataLocation)
self.usrNotExists = tk.Label(self, text="User does not exists", bg="red")
self.usrNotExists.grid(column=1, row=6, sticky="s")
except FileExistsError:
for widget in self.winfo_children():
widget.destroy()
self.config(bg="white")
self.title = tk.Label(self, text=f"Welcome {usrvar}", font="TkFixedFont 14", bg="white", anchor="n")
self.subtitle = tk.Label(self, text="Enter your password:", font="TkFixedFont 12", bg="white", anchor="n")
self.passwordInputForm = tk.Entry(self, show="*")
self.passwordInputForm.config(highlightbackground="white", highlightthickness=0)
self.credentialsSubmitButton = tk.Button(self, text="Login", bg="white", font="TkFixedFont 12", command=self.loginpasswordsubmit)
self.title.grid(column=1, row=1, sticky="n")
self.subtitle.grid(column=1, row=2, sticky="n")
self.passwordInputForm.grid(column=1, row=3, padx=20, sticky="e")
self.credentialsSubmitButton.grid(column=1, row=4, sticky="s")
else:
self.usrBlank = tk.Label(self, text="Username cannot be Blank", bg="red")
self.usrBlank.grid(column=1, row=5, sticky="s")
def loginpasswordsubmit(self):
passvar = self.passwordInputForm.get()
DataLocation = (f"./usr/{usrvar}/AccountDetails.dat")
with open(DataLocation, "rb+") as UsrData:
logonDetails = pickle.load(UsrData)
UsrData.close()
if hashlib.sha3_512(passvar.encode("utf-8")).hexdigest() == logonDetails[1]:
global currentUser
currentUser = usrvar
for widget in self.winfo_children():
widget.destroy()
global Wins
global Loses
global Draws
global accountType
global profilePicture
Wins = logonDetails[2]
Loses = logonDetails[3]
Draws = logonDetails[4]
accountType = logonDetails[5]
profilePicture = logonDetails[6]
self.config(bg="white")
self.title = tk.Label(self, text=f"Welcome {currentUser}", font="TkFixedFont 14", bg="white", anchor="n")
self.subtitle = tk.Label(self, text="Your Infomation has been accepted", font="TkFixedFont 12", bg="white", anchor="n")
self.subsubtitle = tk.Label(self, text="You may close this window", font="TkFixedFont 12", bg="white", anchor="n")
self.closeWindowButton = tk.Button(self, text="Close", bg="white", font="TkFixedFont 12", command=loginwindow.destroy)
self.title.grid(column=1, row=1, sticky="n")
self.subtitle.grid(column=1, row=2, sticky="n")
self.subsubtitle.grid(column=1, row=3, padx=20, sticky="e")
self.closeWindowButton.grid(column=1, row=4, sticky="s")
else:
self.passNotValid = tk.Label(self, text="Password is not valid", bg="red")
self.passNotValid.grid(column=1, row=5, sticky="s")
#Profile class
class profileMenu(tk.Frame):
def __init__(self, parent):
tk.Frame.__init__(self, parent)
self.config(bg="white")
profilewindow.title(f"{currentUser}'s Profile")
self.title = tk.Label(self, text=f"Welcome {currentUser}", font="TkFixedFont 18 bold", bg="white")
self.logo = ImageTk.PhotoImage(Image.open(profilePicture))
self.logoLabel = tk.Label(self, image=self.logo, bg="white")
self.statsDisplay = tk.Label(self, text=f"Wins: {Wins}\nLoses: {Loses}\nDraws: {Draws}", font="TkFixedFont 15 bold", bg="white")
self.editProfileButton = tk.Button(self, text="Edit Profile", bg="white", font="TkFixedFont 12", command=self.editprofile)
self.exitProfileButton = tk.Button(self, text="Exit", bg="white", font="TkFixedFont 12", command=profilewindow.destroy)
self.title.grid(column=0, row=0, columnspan=2)
self.logoLabel.grid(column=0, row=1, sticky="w")
self.statsDisplay.grid(column=1, row=1, sticky="w")
self.editProfileButton.grid(column=0, row=2, sticky="news")
self.exitProfileButton.grid(column=1, row=2, sticky="news")
def editprofile(self):
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change account details", bg="white", font="TkFixedFont 15 bold")
self.changePictureButton = tk.Button(self, text="Change profile picture", bg="white", command=self.changeprofilepicture)
self.changeUsernameButton = tk.Button(self, text="Change account username", bg="white", command=self.changeaccountusername)
self.changePasswordButton = tk.Button(self, text="Change account password", bg="white", command=self.changeaccountpassword)
self.title.grid(column=0, row=0)
self.changePictureButton.grid(column=0, row=1, sticky="news")
self.changeUsernameButton.grid(column=0, row=2, sticky="news")
self.changePasswordButton.grid(column=0, row=3, sticky="news")
def changeprofilepicture(self):
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change profile picture", bg="white", font="TkFixedFont 15 bold")
self.pathEntry = tk.Entry(self, width=60)
self.selectPathButton = tk.Button(self, text="Select image", bg="white", command=self.loadprofilepicturepath)
self.next = tk.Button(self, text="Next", bg="white", command=self.confirmprofilepicture)
self.back = tk.Button(self, text="Back", bg="white", command=self.__init__)
self.title.grid(column=0, row=0, columnspan=2)
self.pathEntry.grid(column=0, row=1, columnspan=2)
self.absDefaultPath = os.path.abspath(profilePicture)
self.pathEntry.insert(0, self.absDefaultPath)
self.selectPathButton.grid(column=1, row=2, sticky="e")
self.next.grid(column=1, row=3, sticky="news")
self.back.grid(column=0, row=3, sticky="news")
def loadprofilepicturepath(self):
filename = filedialog.askopenfilename(filetypes = (("Jpeg files", "*.jpg"), ("Png files", "*.png")))
self.pathEntry.delete(0, tk.END)
self.pathEntry.insert(0, filename)
def confirmprofilepicture(self):
try:
self.profilePicturePreDownsample = Image.open(self.pathEntry.get())
basewidth = 180
wpercent = (basewidth/float(self.profilePicturePreDownsample.size[0]))
hsize = int((float(self.profilePicturePreDownsample.size[1])*float(wpercent)))
self.profilePictureDownsample = self.profilePicturePreDownsample.resize((basewidth, hsize), PIL.Image.ANTIALIAS)
self.profilePictureDownsample.save(f"./usr/{currentUser}/profilePicture.gif")
global profilePicture
profilePicture = f"./usr/{currentUser}/profilePicture.gif"
except IOError:
self.notImageFormat = tk.Label(self, text="That is not a valid image", bg="red")
self.notImageFormat.grid(column=0, row=4, columnspan=2)
#########################################################################################
def changeaccountusername(self):
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change your username", bg="white", font="TkFixedFont 16 bold")
self.passwordSubtitle = tk.Label(self, text="Current password:", bg="white")
self.passwordInputForm = tk.Entry(self, show="*")
self.passwordConfirmSubtitle = tk.Label(self, text="Confirm password:", bg="white")
self.passwordConfirmForm = tk.Entry(self, show="*")
self.passwordSubmitButton = tk.Button(self, text="Submit", bg="white", command=self.changeuser)
self.title.grid(column=0, row=0, sticky="news", columnspan=2)
self.passwordSubtitle.grid(column=0, row=1)
self.passwordInputForm.grid(column=1, row=1)
self.passwordConfirmSubtitle.grid(column=0, row=2)
self.passwordConfirmForm.grid(column=1, row=2)
self.passwordSubmitButton.grid(column=0, row=3, sticky="news", columnspan=2)
def changeuser(self):
if self.passwordInputForm.get() == self.passwordConfirmForm.get():
DataLocation = (f"./usr/{currentUser}/AccountDetails.dat")
with open(DataLocation, "rb+") as UsrData:
Details = pickle.load(UsrData)
UsrData.close()
if Details[1] == hashlib.sha3_512(self.passwordInputForm.get().encode("utf-8")).hexdigest() and Details[1] == hashlib.sha3_512(self.passwordConfirmForm.get().encode("utf-8")).hexdigest():
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change your username", bg="white", font="TkFixedFont 16 bold")
self.currentUserSubtitle = tk.Label(self, text="Current username:", bg="white")
self.currentUserForm = tk.Entry(self)
self.newUserSubtitle = tk.Label(self, text="New username:", bg="white")
self.newUserForm = tk.Entry(self)
self.usernameChangeConfirm = tk.Button(self, text="Confirm Changes", bg="white", command=self.confirmusernamechange)
self.title.grid(column=0, row=0, columnspan=2)
self.currentUserSubtitle.grid(column=0, row=1)
self.currentUserForm.grid(column=1, row=1)
self.newUserSubtitle.grid(column=0, row=2)
self.newUserForm.grid(column=1, row=2)
self.usernameChangeConfirm.grid(column=0, row=3, columnspan=2)
else:
self.passIncorrect = tk.Label(self, text="Password is incorrect", bg="red")
self.passIncorrect.grid(column=0, row=4, sticky="news", columnspan=2)
else:
self.passNotMatch = tk.Label(self, text="Passwords do not match", bg="red")
self.passNotMatch.grid(column=0, row=5, sticky="news", columnspan=2)
def confirmusernamechange(self):
if currentUser == self.currentUserForm.get():
if self.newUserForm.get() != " " and len(self.newUserForm.get()) > 0:
DataLocation = (f"./usr/{currentUser}/AccountDetails.dat")
with open(DataLocation, "rb+") as UsrData:
Details = pickle.load(UsrData)
UsrData.close()
newUser = self.newUserForm.get()
passvarHashed = Details[1]
if Details[6] != "./assests/Blank-Profile-Picture.pgm":
profileUser = self.newUserForm.get()
profilePicture = f"./usr/{profileUser}/Profile Picture.gif"
usrVariables = [newUser, passvarHashed, Wins, Loses, Draws, accountType, profilePicture]
with open(DataLocation, "wb+") as data:
pickle.dump(usrVariables, data, 0)
data.close()
os.rename(f"./usr/{currentUser}", f"./usr/{self.newUserForm.get()}")
global OldUsername
OldUsername = currentUser
self.inituserchange()
else:
self.newUserInvalid = tk.Label(self, text="New username cannot be empty", bg="red")
self.newUserInvalid.grid(column=0, row=4, sticky="news", columnspan=2)
else:
self.currentUserIncorrect = tk.Label(self, text="Current username is incorrect", bg="red")
self.currentUserIncorrect.grid(column=0, row=5, sticky="news", columnspan=2)
def inituserchange(self):
global currentUser
currentUser = self.newUserForm.get()
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Username Successfully changed", bg="white", font="TkFixedFont 15 bold")
self.subtitle = tk.Label(self, text=f"From {OldUsername} --> {currentUser}", bg="white", font="TkFixedFont 12 bold")
#self.profileWarning = tk.Label(self, text="Due to limitations, your profile picture has been changed to the default picture", bg="white")
#self.profileWarningFix = tk.Label(self, text="Please change it again to access your profile profile picture", bg="white")
self.quitButton = tk.Button(self, text="Exit", bg="white", command=profilewindow.destroy)
self.title.grid(column=0, row=0)
self.subtitle.grid(column=0, row=1)
#self.profileWarning.grid(column=0, row=2)
#self.profileWarningFix.grid(column=0, row=3)
self.quitButton.grid(column=0, row=4)
def changeaccountpassword(self):
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change your password", bg="white", font="TkFixedFont 16 bold")
self.passwordSubtitle = tk.Label(self, text="Current password:", bg="white")
self.passwordConfirmSubtitle = tk.Label(self, text="Confirm password:", bg="white")
self.passwordInputForm = tk.Entry(self, show="*")
self.passwordConfirmForm = tk.Entry(self, show="*")
self.passwordSubmitButton = tk.Button(self, text="Submit", bg="white", command=self.changepassword)
self.title.grid(column=0, row=0, sticky="news", columnspan=2)
self.passwordSubtitle.grid(column=0, row=1)
self.passwordInputForm.grid(column=1, row=1)
self.passwordConfirmSubtitle.grid(column=0, row=2)
self.passwordConfirmForm.grid(column=1, row=2)
self.passwordSubmitButton.grid(column=0, row=3, sticky="news", columnspan=2)
def changepassword(self):
if self.passwordInputForm.get() == self.passwordConfirmForm.get():
DataLocation = (f"./usr/{currentUser}/AccountDetails.dat")
with open(DataLocation, "rb+") as UsrData:
Details = pickle.load(UsrData)
UsrData.close()
if Details[1] == hashlib.sha3_512(self.passwordInputForm.get().encode("utf-8")).hexdigest() and Details[1] == hashlib.sha3_512(self.passwordConfirmForm.get().encode("utf-8")).hexdigest():
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Change your password", bg="white", font="TkFixedFont 16 bold")
self.newPasswordSubtitle = tk.Label(self, text="New password:", bg="white")
self.newPasswordForm = tk.Entry(self, show="*")
self.passwordConfirmSubtitle = tk.Label(self, text="Confirm password:", bg="white")
self.passwordConfirmForm = tk.Entry(self, show="*")
self.passwordChangeConfirm = tk.Button(self, text="Confirm Changes", bg="white", command=self.confirmpasswordchange)
self.title.grid(column=0, row=0, columnspan=2)
self.newPasswordSubtitle.grid(column=0, row=1)
self.newPasswordForm.grid(column=1, row=1)
self.passwordConfirmSubtitle.grid(column=0, row=2)
self.passwordConfirmForm.grid(column=1, row=2)
self.passwordChangeConfirm.grid(column=0, row=3, columnspan=2)
else:
self.passIncorrect = tk.Label(self, text="Password is incorrect", bg="red")
self.passIncorrect.grid(column=0, row=4, sticky="news", columnspan=2)
else:
self.passNotMatch = tk.Label(self, text="Passwords do not match", bg="red")
self.passNotMatch.grid(column=0, row=5, sticky="news", columnspan=2)
def confirmpasswordchange(self):
if self.newPasswordForm.get() == self.passwordConfirmForm.get():
if self.newPasswordForm.get() != " " and len(self.newPasswordForm.get()) > 0:
DataLocation = (f"./usr/{currentUser}/AccountDetails.dat")
with open(DataLocation, "rb+") as UsrData:
Details = pickle.load(UsrData)
UsrData.close()
passvarHashed = hashlib.sha3_512(self.newPasswordForm.get().encode("utf-8")).hexdigest()
usrVariables = [currentUser, passvarHashed, Wins, Loses, Draws, accountType, profilePicture]
with open(DataLocation, "wb+") as data:
pickle.dump(usrVariables, data, 0)
data.close()
for widget in self.winfo_children():
widget.destroy()
self.title = tk.Label(self, text="Password successfuly changed", bg="white", font="TkFixedFont 16 bold")
self.closeButton = tk.Button(self, text="Close", bg="white", command=profilewindow.destroy)
self.title.grid(column=0, row=0)
self.closeButton.grid(column=0, row=1)
else:
newUserInvalid = tk.Label(self, text="New password cannot be empty", bg="red")
newUserInvalid.grid(column=0, row=4, sticky="news", columnspan=2)
else:
self.newPassNotMatch = tk.Label(self, text="New passwords do not match", bg="red")
self.newPassNotMatch.grid(column=0, row=5, sticky="news", columnspan=2)
currentUser = ""
startwindow = tk.Tk()
startMenu(startwindow).pack(fill="both", expand=True)
startwindow.mainloop()
|
import unittest
from formation import AppBuilder
from formation.tests.support import get_resource
class CanvasTestCase(unittest.TestCase):
builder = None
@classmethod
def setUpClass(cls) -> None:
cls.builder = AppBuilder(path=get_resource("canvas.xml"))
cls.canvas1 = cls.builder.canvas1
cls.canvas2 = cls.builder.canvas2
def test_loading(self):
self.assertEqual(len(self.canvas1.find_all()), 19)
self.assertEqual(len(self.canvas2.find_all()), 6)
def test_line(self):
line = self.builder.cv1_line
coords = self.canvas1.coords(line)
self.assertListEqual(
list(coords),
[25, 33, 292, 33, 382, 128, 542, 128, 542, 226]
)
def test_polygon(self):
poly = self.builder.cv1_polygon
coords = self.canvas1.coords(poly)
self.assertListEqual(
list(coords),
[68, 216, 67, 284, 151, 339, 366, 340, 448, 272, 448, 216]
)
self.assertEqual(self.canvas1.itemcget(poly, "fill"), "#1d731d")
def test_rectangle(self):
rec = self.builder.cv2_rectangle
coords = self.canvas2.coords(rec)
self.assertListEqual(list(coords), [372, 88, 423, 136])
self.assertEqual(self.canvas2.itemcget(rec, "stipple"), "gray12")
self.assertEqual(self.canvas2.itemcget(rec, "fill"), "#1d731d")
def test_oval(self):
circle = self.builder.cv1_circle2
coords = self.canvas1.coords(circle)
self.assertListEqual(list(coords), [177, 59, 288, 169])
self.assertEqual(self.canvas1.itemcget(circle, "stipple"), "gray12")
self.assertEqual(self.canvas1.itemcget(circle, "fill"), "#ff0000")
self.assertEqual(self.canvas1.itemcget(circle, "outline"), "#1d731d")
def test_arc(self):
arc = self.builder.cv2_arc1
coords = self.canvas2.coords(arc)
self.assertListEqual(list(coords), [78, 37, 190, 133])
self.assertEqual(float(self.canvas2.itemcget(arc, "extent")), 90.0)
self.assertEqual(float(self.canvas2.itemcget(arc, "start")), 0.0)
self.assertEqual(self.canvas2.itemcget(arc, "style"), "pieslice")
def test_image(self):
image = self.builder.cv1_image
self.assertListEqual(list(self.canvas1.coords(image)), [472, 67])
self.assertTrue(bool(self.canvas1.itemcget(image, "image")))
def test_bitmap(self):
bit = self.builder.cv1_bitmap
self.assertListEqual(list(self.canvas1.coords(bit)), [84, 115])
self.assertEqual(self.canvas1.itemcget(bit, "bitmap"), "gray12")
self.assertEqual(self.canvas1.itemcget(bit, "anchor"), "nw")
self.assertEqual(self.canvas1.itemcget(bit, "background"), "#1d731d")
def test_text(self):
text = self.builder.cv2_text
self.assertListEqual(list(self.canvas2.coords(text)), [280, 114])
self.assertEqual(self.canvas2.itemcget(text, "text"), "yet another layout")
self.assertEqual(self.canvas2.itemcget(text, "fill"), "#1d731d")
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages
import labyrinth
with open('requirements.txt') as f:
requires = f.read().split('\n')
setup(
name='nz-labyrinth',
version=3.6,
packages=find_packages(),
install_requires=requires,
author='Nico Zhan',
author_email='nicozhan@hyperloop.fr',
description='Help Mc Gyver to leave the maze',
long_description=open('README.md').read(),
# include file from manifest.in
include_package_data=True,
url='https://github.com/Hyperyon/p3-labyrinthe',
classifiers=[
'Programming Language :: Python',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3.5',
],
) |
"""
file contains various functions used as subroutines by the spiders and crawler
"""
import os
import urlparse
# creates the project directory
def create_main_directory(path):
if not os.path.isdir(path):
os.makedirs(path)
# used as subroutine in create_directory
# splits the path into directory_name and filename and returns them as tuple
def path_split(path):
u = urlparse.urlparse(path)
path = get_domain_name(path) + u.path
return os.path.split(path)
# creates the directory with the specified path, and also creates an empty file for the corresponding URL
def create_directory(path):
org_dir = os.getcwd()
path_tuple = path_split(path)
dir_path = path_tuple[0]
resource = path_tuple[1]
if not os.path.isdir(dir_path):
os.makedirs(dir_path)
change_directory(dir_path)
if resource == '':
resource = dir_path.split('/')[-1]
resource.replace('.', '_')
create_file(resource)
change_directory(org_dir)
def change_directory(path):
os.chdir(path)
# utility function to create file and write data
def create_file(path, data=''):
f = open(path, 'w')
f.write(data.encode('utf-8'))
f.close()
# overwrites already existing file
def add_content(path, data):
create_file(path, data)
# takes a set() as parameter, produces a string containing all the links in that set, and writes it to a file
def set_to_file(path, link_data):
s = ''
for link in link_data:
s += link + '\n'
add_content(path, s)
# takes a file_path and converts the contents of that file into a set
def file_to_set(path):
f = open(path, 'r')
links = set()
for line in f:
links.add(line.strip('\n'))
f.close()
return links
# given an url which may be of form A.B.C.D.tld(in general), the function parses it and returns the main domain
# consisting of the hostname and tld
def get_domain_name(url):
domain_name = ''
try:
parse_result = urlparse.urlparse(url)
domain_name = parse_result.netloc.split('.')
domain_name = '.'.join(domain_name[-2:])
return domain_name
except:
return ''
if __name__ == "__main__":
create_directory('https://docs.python.org/2/library/os.path.html')
create_file('hi.c', '')
|
from django.http import Http404
from awards.models import FilmAward, FilmAwardReceived
from awards.serializers import ExtendedFilmAwardSerializer
from movies.models import Film
from rest_framework.viewsets import ModelViewSet
from movie_geeks_django.mixins import SerializerDifferentiationMixin
from .models import Performer
from .nested_serializers import PerformerSerializerForDisplayInLists
from .serializers import BasicPerformerSerializer, ExtendedPerformerSerializer
class PerformerView(SerializerDifferentiationMixin, ModelViewSet):
serializer_class = BasicPerformerSerializer
queryset = Performer.objects.all()
lookup_field = "url_name"
GET_serializer = ExtendedPerformerSerializer
POST_serializer = BasicPerformerSerializer
class PerformerViewForCastLists(ModelViewSet):
serializer_class = PerformerSerializerForDisplayInLists
def get_queryset(self):
film = Film.objects.all().filter(url_name=self.kwargs["film_url_name"]).first()
if not film:
raise Http404
return Performer.objects.all().filter(starred_in=film)
class PerformerViewForRecipientLists(ModelViewSet):
serializer_class = PerformerSerializerForDisplayInLists
def get_queryset(self):
"""
Works slightly differently than other methods for the nested router viewsets.
1) queries database for the relevant FilmAward model;
2) queries database for FilmAwardReceived objects of the same type;
3) iterates over the retrieved FilmAwardsReceived and creates a list of their recipients, effectively creating
a list of all recipients of a given type of award.
"""
award = FilmAward.objects.all().filter(url_name=self.kwargs['filmaward_url_name']).first()
if not award:
raise Http404
all_performers = Performer.objects.all().filter(awards__isnull=False, awards__name=award).distinct()
return all_performers
|
from . import account_payment
from . import payment_acquirer
from . import payment_transaction
|
import re
from fabric.api import put, sudo, task, env, hide, settings, run
from fabric.contrib import files
def _read_lines_from_file(file_name):
with open(file_name) as f:
packages = f.readlines()
return map(lambda x: x.strip('\n\r'), packages)
def user_exists(username):
exists = False
with settings(hide('everything'), warn_only=True):
exists = run(u"grep ^%s /etc/passwd" % username)
return exists
def group_exists(name):
exists = False
with settings(hide('everything'), warn_only=True):
exists = run(u"grep ^%s /etc/group" % name)
return exists
@task
def install_packages(*packages):
"""Install apt packages from a list."""
sudo(u"apt-get install -y %s" % u" ".join(packages))
@task
def install_packages_from_file(file_name):
"""Install apt packages from a file list."""
install_packages(*_read_lines_from_file(file_name))
@task
def update_apt_sources():
"""Update apt source."""
sudo(u"apt-get update")
@task
def upgrade_apt_packages():
"""Safe upgrade of all packages."""
update_apt_sources()
sudo(u"apt-get upgrade -y")
@task
def add_ppa(name, update=True):
"""Add personal package archive."""
sudo(u"add-apt-repository %s" % name)
if update:
update_apt_sources()
@task
def add_ppas_from_file(file_name, update=True):
"""Add personal package archive from a file list."""
for ppa in _read_lines_from_file(file_name):
add_ppa(ppa, update=False)
if update:
update_apt_sources()
@task
def add_apt_source(source, key=None, update=True):
"""Adds source url to apt sources.list. Optional to pass the key url."""
# Make a backup of list
source_list = u'/etc/apt/sources.list'
sudo("cp %s{,.bak}" % source_list)
files.append(source_list, source, use_sudo=True)
if key:
# Fecth key from url and add
sudo(u"wget -q %s -O - | sudo apt-key add -" % key)
if update:
update_apt_sources()
@task
def add_sources_from_file(file_name, update=True):
"""
Add source urls from a file list.
The file should contain the source line to add followed by the
key url, if any, enclosed in parentheses.
Ex:
deb http://example.com/deb lucid main (http://example.com/key)
"""
key_regex = re.compile(r'(?P<source>[^()]*)(\s+\((?P<key>.*)\))?$')
for line in _read_lines_from_file(file_name):
kwargs = key_regex.match(line).groupdict()
kwargs['update'] = False
add_apt_source(**kwargs)
if update:
update_apt_sources()
@task
def create_user(name, groups=None, key_file=None):
"""Create a user. Adds a key file to authorized_keys if given."""
groups = groups or []
if not user_exists(name):
for group in groups:
if not group_exists(group):
sudo(u"addgroup %s" % group)
groups = groups and u'-G %s' % u','.join(groups) or ''
sudo(u"useradd -m %s -s /bin/bash %s" % (groups, name))
sudo(u"passwd -d %s" % name)
if key_file:
sudo(u"mkdir -p /home/%s/.ssh" % name)
put(key_file, u"/home/%s/.ssh/authorized_keys" % name, use_sudo=True)
sudo(u"chown -R %(name)s:%(name)s /home/%(name)s/.ssh" % {'name': name})
@task
def service_command(name, command):
"""Run an init.d/upstart command."""
service_command_template = getattr(env, 'ARGYLE_SERVICE_COMMAND_TEMPLATE',
u'/etc/init.d/%(name)s %(command)s')
sudo(service_command_template % {'name': name,
'command': command}, pty=False)
@task
def start_service(name):
"""Start an init.d service."""
service_command(name, u"start")
@task
def stop_service(name):
"""Stop an init.d service."""
service_command(name, u"stop")
@task
def restart_service(name):
"""Restart an init.d service."""
service_command(name, u"restart")
|
class DeviceType:
GENERIC = 'generic'
BUTTON = 'button'
DISPLAY = 'display'
SPEAKER = 'speaker'
CHOICES = (
(BUTTON, 'Button'),
(DISPLAY, 'Display'),
(SPEAKER, 'Speaker'),
)
|
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from .raymarching import AbsorptionOnlyRaymarcher, EmissionAbsorptionRaymarcher
from .raysampling import GridRaysampler, MonteCarloRaysampler, NDCGridRaysampler
from .renderer import ImplicitRenderer, VolumeRenderer, VolumeSampler
from .utils import (
RayBundle,
ray_bundle_to_ray_points,
ray_bundle_variables_to_ray_points,
)
__all__ = [k for k in globals().keys() if not k.startswith("_")]
|
import sys
from PyQt5 import QtWidgets
def window():
app = QtWidgets.QApplication(sys.argv)
w = QtWidgets.QWidget()
w.setWindowTitle('PYQt5 lesson 1')
w.show()
sys.exit(app.exec_())
window() |
from ggmodel_dev.graphmodel import GraphModel
from ggmodel_dev.utils import get_model_properties
PM25_nodes = {
"AEUi": {
"name": "Total agricultural energy use per type",
"type": "input",
"unit": "TWH"
},
"BMB": {
"name": "Total biomass burned",
"type": "input",
"unit": "kg dm"
},
"ECR_PM25eq": {
"name": "PM25 emissions from burning crop residues",
"type": "variable",
"unit": "tonnes",
"computation": lambda BMB, EFCRBI_pm25, **kwargs: BMB * EFCRBI_pm25
},
"EFCRBI_pm25": {
"name": "Emission factors burning crop residues",
"type": "input",
"unit": "kg/mg waste"
},
"EFPM25Ai": {
"name": "Emission factor PM2.5 from live animals",
"type": "input",
"unit": "kg/heads"
},
"EFPM25Ci": {
"name": "Emission factors PM2.5 from crops",
"type": "input",
"unit": "kg/ha"
},
"EFPM25Ei": {
"name": "Emission factors PM2.5 agricultural fuel consumption",
"type": "input",
"unit": "g/tonne fuel"
},
"PM25": {
"name": "Total agricultural PM25 emissions",
"type": "variable",
"unit": "tonnes",
"computation": lambda PM25A, PM25C, PM25E, ECR_PM25eq, **kwargs: PM25A + PM25C + PM25E + ECR_PM25eq
},
"PM25A": {
"name": "PM25 emissions from live animals",
"type": "variable",
"unit": "tonnes",
"computation": lambda TAi, EFPM25Ai, **kwargs: (TAi * EFPM25Ai).sum()
},
"PM25C": {
"name": "PM25 emissions from crops",
"type": "variable",
"unit": "tonnes",
"computation": lambda TCLDi, EFPM25Ci, **kwargs: (TCLDi * EFPM25Ci).sum()
},
"PM25E": {
"name": "PM25 emissions from agricultural energy use",
"type": "variable",
"unit": "tonnes",
"computation": lambda AEUi, EFPM25Ei, **kwargs: (AEUi * EFPM25Ei).sum()
},
"TAi": {
"name": "Total animal population",
"type": "input",
"unit": "head"
},
"TCLDi": {
"name": "Cropland demand",
"type": "input",
"unit": "ha"
}
}
PM25_model = GraphModel(PM25_nodes)
model_dictionnary = {"PM25_model": PM25_model}
model_properties = get_model_properties('models/landuse/PM25_properties.json') |
import pygame
from pygame.locals import *
from pygame.event import wait
from deck import *
from game import *
from init import *
deck = Deck()
King = Game("Pit","Dotti","Lella","Rob")
giocata=0
position=[0,0,0,0]
carteGiocate=[[],[],[],[],[],[],[],[],[],[],[],[],[]]
timerScomparsa=0
timerGiocata=0
primaCarta = None
Turno = init(deck, King)
#Inizializzare pygame
pygame.init()
clock = pygame.time.Clock()
#Mostra lo schermo
screen = pygame.display.set_mode((800,600))
#Impostazioni del gioco
pygame.display.set_caption("King")
icon = pygame.image.load("img\icon.png")
pygame.display.set_icon(icon)
font = pygame.font.SysFont("monospace", 16)
#funzione per mostrare la mano a video
def mostraMano(self,ypos,sel):
xpos=400-len(self.Mano)*50/2
for carta in range(len(self.Mano)):
thisy=ypos
if carta == sel :
thisy-=35
screen.blit(self.Mano[carta].img, (xpos,thisy))
xpos+=50
def primaGiocata(Turno,giocata):
if King.Primo == 0 :
primaCarta=King.g1.Mano[position[0]]
Turno=(Turno+1)%4
return primaCarta, Turno
position[King.Primo]=random.randint(0,len(King.allg[King.Primo].Mano)-1)
primaCarta=King.allg[King.Primo].Mano[position[King.Primo]]
carteGiocate[giocata].append(primaCarta)
King.allg[Turno].Mano.pop(position[Turno])
Turno=(Turno+1)%4
return primaCarta, Turno, giocata
def altraGiocata(Turno, primaCarta, position):
position[Turno]=random.randint(0,len(King.allg[Turno].Mano)-1)
cartaGiocata=King.allg[Turno].Mano[position[Turno]]
while King.checkSuit(position, primaCarta, Turno):
position[Turno]=random.randint(0,len(King.allg[Turno].Mano)-1)
cartaGiocata=King.allg[Turno].Mano[position[Turno]]
carteGiocate[giocata].append(cartaGiocata)
King.allg[Turno].Mano.pop(position[Turno])
Turno=(Turno+1)%4
return Turno
def checkVincitore(primaCarta, giocata, carteGiocate, Primo):
cartaVincente = primaCarta
newPrimo = Primo
for i in [1,2,3]:
if (cartaVincente.suit == carteGiocate[giocata][i].suit) & (cartaVincente.value < carteGiocate[giocata][i].value):
cartaVincente = carteGiocate[giocata][i]
newPrimo = (i + Primo) % 4
print("La mano è stata vinta da {} con la carta ".format(King.allg[newPrimo].Nome), end="")
cartaVincente.show()
return newPrimo
def mostraGiocata(giocata):
cordcarte=[(375,310),(425,230),(375,150),(325,230)]
for i in range(len(carteGiocate[giocata])):
screen.blit(carteGiocate[giocata][i].img, cordcarte[(King.Primo+i)%4])
def stampaUHD():
pos=[(350,540),(600,300),(350,40),(100,300)]
pos2=[(350,556),(600,316),(350,56),(100,316)]
for i in range(4):
label = font.render('{}'.format(King.allg[i].Nome), 1, (0,0,0), (160,160,160))
label2 = font.render('Punti: {}'.format(King.allg[i].Punti), 1, (0,0,0), (160,160,160))
screen.blit(label, pos[i])
screen.blit(label2, pos2[i])
#Loop del gioco
running = True
while running:
screen.fill((0,255,0))
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
#Muoversi tra le carte
if (event.type == pygame.KEYDOWN) & (len(carteGiocate[giocata]) < 4):
if (event.key == pygame.K_LEFT) :
if position[0] > 0:
position[0]-=1
else:
position[0]=len(King.g1.Mano)-1
elif (event.key == pygame.K_RIGHT) :
if (position[0]<len(King.g1.Mano)-1) :
position[0]+=1
else :
position[0]= 0
elif (event.key == pygame.K_RETURN):
if (Turno == 0):
if (King.Primo == 0):
primaCarta, Turno = primaGiocata(Turno, giocata)
carteGiocate[giocata].append(King.g1.Mano[position[0]])
King.g1.Mano.pop(position[0])
position[0]=0
else:
if King.checkSuit(position, primaCarta, 0):
print("Devi rispondere a seme")
continue
carteGiocate[giocata].append(King.g1.Mano[position[0]])
King.g1.Mano.pop(position[0])
position[0]=0
Turno += 1
else :
print('Non è il tuo turno')
elif (event.key == pygame.K_ESCAPE):
pygame.quit()
quit()
#Premo un pulsante per far giocare quello dopo
#Primo
elif (event.key == pygame.K_p and len(carteGiocate[giocata]) == 0):
if (Turno == 0 ):
print('è il tuo turno')
continue
primaCarta, Turno, giocata = primaGiocata(Turno, giocata)
#Altri
elif (event.key == pygame.K_n):
if (Turno == 0):
print('è il tuo turno')
continue
if (primaCarta == None):
print('deve giocare il primo di mano')
continue
Turno = altraGiocata(Turno, primaCarta, position)
#Premere S per stampare cose
elif (event.key == pygame.K_s):
print(carteGiocate)
print(giocata)
#Premere T per stampare carta selezionata
elif (event.key == pygame.K_t):
King.allg[0].Mano[position[0]].show()
#Andamento del gioco
if (Turno != 0):
if timerGiocata>10:
if (len(carteGiocate[giocata]) == 0):
primaCarta, Turno, giocata = primaGiocata(Turno, giocata)
elif (len(carteGiocate[giocata]) < 4):
Turno = altraGiocata(Turno, primaCarta, position)
timerGiocata = 0
timerGiocata += 1
#Check di fine turno
if (len(carteGiocate[giocata])>3):
if timerScomparsa>16 :
King.Primo=checkVincitore(primaCarta,giocata, carteGiocate,King.Primo)
King.allg[King.Primo].Punti+=1
giocata+=1
Turno=King.Primo
timerScomparsa=0
primaCarta=None
King.contaSemi()
King.punteggio()
timerScomparsa+=1
if (giocata == 13):
Turno = init(deck, King)
giocata=0
position=[0,0,0,0]
carteGiocate=[[],[],[],[],[],[],[],[],[],[],[],[],[]]
timerScomparsa=0
primaCarta = None
mostraMano(King.g1,450,position[0])
stampaUHD()
# mostraMano(King.g2,50,position[0])
# mostraMano(King.g3,100,position[0])
# mostraMano(King.g4,150,position[0])
mostraGiocata(giocata)
pygame.display.update()
clock.tick(10) |
import aws_cdk_lib as cdk
from constructs import Construct, IConstruct
|
import matplotlib.pyplot as plt
import numpy as np
plt.rc('text', usetex=True)
plt.rc('font', family='serif')
n12 = [ 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 0.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 0.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 0.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 0.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 0.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 1.58496250072115618147, 0.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618100, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 1.00000000000000000000, 0.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 0.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 0.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 1.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618100, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 0.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, ]
n13 = [ 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410700, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.00000000000000000000, 0.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 0.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 1.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.32192809488736234781, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 0.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 0.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 0.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 1.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618100, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 0.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 0.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234700, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 0.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.32192809488736234781, 0.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, ]
n14 = [ 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 0.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 1.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 0.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 1.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 0.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 0.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 3.32192809488736234700, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.80735492205760410749, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 0.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234700, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, ]
n15 = [ 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410700, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 0.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 1.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 0.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 0.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618100, 3.45943161863729725615, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 0.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 0.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 0.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 3.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, ]
n16 = [ 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 0.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 0.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 0.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 3.45943161863729725615, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 0.00000000000000000000, 1.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 1.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 1.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 0.00000000000000000000, 0.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234700, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 1.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618100, 3.16992500144231236295, 1.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 1.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, ]
n17 = [ 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410700, 1.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410700, 2.58496250072115618147, 1.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.90689059560851852928, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.32192809488736234781, 1.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 1.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618100, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 1.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 1.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 3.90689059560851852928, 1.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 3.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 1.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410700, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 3.70043971814109216032, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 1.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.00000000000000000000, 3.32192809488736234781, 3.70043971814109216032, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, ]
n18 = [ 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.70043971814109216032, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.90689059560851852928, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.58496250072115618147, 1.58496250072115618147, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.70043971814109216032, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.90689059560851852928, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 1.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 1.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.70043971814109216032, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 1.00000000000000000000, 2.32192809488736234781, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 3.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.70043971814109216032, 3.00000000000000000000, 1.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 4.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 1.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.70043971814109216032, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 1.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.70043971814109216032, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.70043971814109216032, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 1.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.90689059560851852928, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234700, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 3.32192809488736234781, 1.58496250072115618147, 1.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 3.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 4.00000000000000000000, ]
n19 = [ 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.90689059560851852928, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 1.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.90689059560851852928, 3.90689059560851852928, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 1.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.70043971814109216032, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 3.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 2.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 4.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 3.90689059560851852928, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.70043971814109216032, 2.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.70043971814109216032, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618100, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234700, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.45943161863729725615, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 1.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.70043971814109216032, 3.16992500144231236295, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 1.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234700, 2.32192809488736234781, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, ]
n20 = [ 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 1.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 2.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 3.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.90689059560851852928, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 4.08746284125033940843, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.70043971814109216032, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 2.58496250072115618147, 1.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.45943161863729725615, 3.45943161863729725615, 2.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.80735492205760410749, 3.70043971814109216032, 1.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.70043971814109216032, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 1.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 1.00000000000000000000, 3.70043971814109216032, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.70043971814109216032, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.70043971814109216032, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618100, 2.80735492205760410749, 3.70043971814109216032, 3.70043971814109216032, 3.45943161863729725615, 3.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 1.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.80735492205760410749, 3.80735492205760410749, 3.00000000000000000000, 3.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 1.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 2.00000000000000000000, 1.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.70043971814109216032, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 1.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 1.00000000000000000000, 3.16992500144231236295, 2.58496250072115618100, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 2.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, ]
n21 = [ 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.70043971814109216032, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 3.45943161863729725615, 3.16992500144231236295, 1.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 4.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 3.90689059560851852928, 3.70043971814109216032, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 3.70043971814109216032, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 3.70043971814109216032, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 3.16992500144231236295, 3.90689059560851852928, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.70043971814109216032, 2.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.58496250072115618147, 4.08746284125033940843, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.90689059560851852928, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.70043971814109216032, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 3.70043971814109216032, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 3.16992500144231236295, 1.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 3.32192809488736234781, 3.90689059560851852928, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 3.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 0.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 3.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 3.70043971814109216032, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 1.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 1.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.70043971814109216032, 3.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.80735492205760410749, 3.16992500144231236295, 3.90689059560851852928, 2.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.70043971814109216032, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.90689059560851852928, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.70043971814109216032, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 3.70043971814109216032, 1.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.45943161863729725615, 2.32192809488736234781, 3.58496250072115618147, 3.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 1.58496250072115618147, 3.32192809488736234781, 3.70043971814109216032, 2.80735492205760410749, 3.45943161863729725615, 2.00000000000000000000, 3.16992500144231236295, 2.00000000000000000000, 1.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 1.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.90689059560851852928, ]
n22 = [ 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.70043971814109216032, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 4.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.70043971814109216032, 1.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.80735492205760410749, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.70043971814109216032, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 4.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.80735492205760410749, 2.32192809488736234781, 3.70043971814109216032, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.90689059560851852928, 3.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 3.70043971814109216032, 3.70043971814109216032, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 3.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.70043971814109216032, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.70043971814109216032, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.80735492205760410749, 2.80735492205760410749, 3.70043971814109216032, 3.45943161863729725615, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.80735492205760410749, 2.80735492205760410749, 4.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 4.24792751344358549383, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 1.00000000000000000000, 3.16992500144231236295, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 4.24792751344358549383, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.70043971814109216032, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 2.00000000000000000000, 3.90689059560851852928, 3.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 3.58496250072115618147, 4.08746284125033940843, 3.00000000000000000000, 2.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.32192809488736234781, 3.16992500144231236295, 3.70043971814109216032, 4.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 3.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 3.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.90689059560851852928, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.90689059560851852928, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 3.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.80735492205760410749, 0.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.90689059560851852928, 1.00000000000000000000, 1.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.90689059560851852928, 3.45943161863729725615, 3.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.90689059560851852928, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.45943161863729725615, 3.70043971814109216032, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.70043971814109216032, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.90689059560851852928, 2.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 2.80735492205760410700, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.58496250072115618147, 3.90689059560851852928, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.70043971814109216032, 2.00000000000000000000, 2.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 3.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 2.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 1.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 3.70043971814109216032, 2.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 4.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.80735492205760410749, 3.32192809488736234781, 4.00000000000000000000, 3.16992500144231236295, 4.08746284125033940843, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 1.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 1.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.80735492205760410749, ]
n23 = [ 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 1.00000000000000000000, 3.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.00000000000000000000, 3.70043971814109216032, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.90689059560851852928, 2.80735492205760410749, 2.58496250072115618147, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 1.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.58496250072115618147, 3.70043971814109216032, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 2.32192809488736234781, 3.58496250072115618147, 3.70043971814109216032, 3.45943161863729725615, 2.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.58496250072115618147, 3.45943161863729725615, 3.70043971814109216032, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.70043971814109216032, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 3.70043971814109216032, 3.32192809488736234781, 3.70043971814109216032, 2.80735492205760410749, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 3.90689059560851852928, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.70043971814109216032, 3.58496250072115618147, 3.58496250072115618147, 3.70043971814109216032, 2.32192809488736234781, 2.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 3.00000000000000000000, 3.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 3.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 3.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410700, 3.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 3.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 3.58496250072115618147, 2.58496250072115618147, 3.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 3.70043971814109216032, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 4.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.90689059560851852928, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.70043971814109216032, 1.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.70043971814109216032, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.70043971814109216032, 3.90689059560851852928, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.70043971814109216032, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 1.58496250072115618147, 3.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 3.45943161863729725615, 4.16992500144231236295, 3.45943161863729725615, 3.70043971814109216032, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 1.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.70043971814109216032, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.16992500144231236295, 1.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.90689059560851852928, 2.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.00000000000000000000, 2.00000000000000000000, 3.70043971814109216032, 2.00000000000000000000, 3.32192809488736234700, 3.70043971814109216032, 3.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 3.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 4.08746284125033940843, 3.90689059560851852928, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.58496250072115618147, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 4.08746284125033940843, 3.00000000000000000000, 2.58496250072115618147, 3.80735492205760410749, 2.00000000000000000000, 3.58496250072115618147, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 3.80735492205760410749, 3.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.80735492205760410749, 3.58496250072115618147, 3.45943161863729725615, 2.32192809488736234781, 1.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, ]
n24 = [ 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 3.90689059560851852928, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.70043971814109216032, 3.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 1.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.70043971814109216032, 3.16992500144231236295, 3.70043971814109216032, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 3.90689059560851852928, 3.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.80735492205760410749, 3.70043971814109216032, 2.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.80735492205760410749, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 1.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 2.00000000000000000000, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 2.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 3.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 4.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 3.70043971814109216032, 4.00000000000000000000, 3.32192809488736234781, 3.58496250072115618147, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.00000000000000000000, 3.90689059560851852928, 3.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 3.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.70043971814109216032, 3.70043971814109216032, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.70043971814109216032, 3.70043971814109216032, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.70043971814109216032, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 3.70043971814109216032, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 4.08746284125033940843, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 3.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.45943161863729725615, 3.70043971814109216032, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.70043971814109216032, 3.16992500144231236295, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.70043971814109216032, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.90689059560851852928, 2.80735492205760410749, 3.32192809488736234781, 3.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.80735492205760410749, 2.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.00000000000000000000, 2.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 3.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 3.70043971814109216032, 3.80735492205760410749, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 3.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.70043971814109216032, 3.00000000000000000000, 2.58496250072115618147, 4.00000000000000000000, 3.70043971814109216032, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 3.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, 3.45943161863729725615, 2.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 2.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.58496250072115618147, 3.70043971814109216032, 3.45943161863729725615, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 3.70043971814109216032, 3.16992500144231236295, 3.00000000000000000000, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 3.70043971814109216032, 3.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 3.58496250072115618147, 2.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.45943161863729725615, 3.58496250072115618147, 2.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 3.70043971814109216032, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.90689059560851852928, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 2.00000000000000000000, 3.32192809488736234781, 3.70043971814109216032, 3.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.16992500144231236295, 3.70043971814109216032, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.90689059560851852928, 2.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 4.00000000000000000000, 3.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 3.32192809488736234700, 3.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.90689059560851852928, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.70043971814109216032, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.58496250072115618147, 1.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.80735492205760410749, 3.45943161863729725615, 4.32192809488736234781, 3.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 3.70043971814109216032, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 2.32192809488736234781, 3.32192809488736234781, 3.58496250072115618147, 2.80735492205760410749, 3.80735492205760410749, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 3.58496250072115618147, 3.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 3.90689059560851852928, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 3.32192809488736234781, 3.70043971814109216032, 3.16992500144231236295, 3.32192809488736234781, 3.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.45943161863729725615, 2.58496250072115618147, 3.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.70043971814109216032, 2.58496250072115618147, 3.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 4.08746284125033940843, 3.45943161863729725615, 2.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 4.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 3.32192809488736234781, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 3.16992500144231236295, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.70043971814109216032, 3.90689059560851852928, 2.58496250072115618147, 1.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.80735492205760410749, 3.90689059560851852928, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 4.00000000000000000000, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.90689059560851852928, 3.16992500144231236295, 3.45943161863729725615, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 4.08746284125033940843, 3.90689059560851852928, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.90689059560851852928, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 3.90689059560851852928, 3.00000000000000000000, ]
n25 = [ 3.70043971814109216032, 3.16992500144231236295, 3.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.45943161863729725615, 3.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.58496250072115618147, 3.70043971814109216032, 3.00000000000000000000, 3.70043971814109216032, 3.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 2.80735492205760410749, 4.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 3.45943161863729725615, 3.32192809488736234781, 3.90689059560851852928, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.45943161863729725615, 3.70043971814109216032, 3.16992500144231236295, 2.80735492205760410749, 3.45943161863729725615, 3.16992500144231236295, 3.58496250072115618147, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 1.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 2.80735492205760410749, 4.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 4.08746284125033940843, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.70043971814109216032, 3.45943161863729725615, 3.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 2.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 3.70043971814109216032, 2.32192809488736234781, 3.80735492205760410749, 3.32192809488736234781, 3.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.58496250072115618147, 3.70043971814109216032, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 4.08746284125033940843, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 2.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 2.32192809488736234781, 2.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.32192809488736234781, 3.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 1.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 3.80735492205760410749, 2.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.58496250072115618147, 3.32192809488736234781, 3.80735492205760410749, 3.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.00000000000000000000, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 1.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 3.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.70043971814109216032, 3.32192809488736234781, 2.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 2.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 4.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.16992500144231236295, 3.16992500144231236295, 3.90689059560851852928, 3.45943161863729725615, 3.45943161863729725615, 3.00000000000000000000, 3.16992500144231236295, 3.90689059560851852928, 2.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 2.80735492205760410749, 3.70043971814109216032, 3.45943161863729725615, 2.80735492205760410749, 3.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.32192809488736234781, 3.70043971814109216032, 3.16992500144231236295, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 3.80735492205760410749, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 2.80735492205760410749, 2.80735492205760410749, 3.00000000000000000000, 3.45943161863729725615, 3.70043971814109216032, 3.45943161863729725615, 3.70043971814109216032, 1.00000000000000000000, 3.32192809488736234781, 3.70043971814109216032, 3.80735492205760410749, 3.32192809488736234781, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.58496250072115618147, 3.45943161863729725615, 1.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.58496250072115618147, 3.70043971814109216032, 3.70043971814109216032, 3.58496250072115618147, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.90689059560851852928, 4.00000000000000000000, 3.32192809488736234781, 3.90689059560851852928, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 3.32192809488736234781, 2.80735492205760410749, 4.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 3.32192809488736234781, 3.45943161863729725615, 2.80735492205760410749, 2.80735492205760410749, 2.58496250072115618147, 3.70043971814109216032, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 3.90689059560851852928, 3.16992500144231236295, 3.70043971814109216032, 2.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.45943161863729725615, 3.45943161863729725615, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 3.58496250072115618147, 2.58496250072115618147, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.70043971814109216032, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 2.32192809488736234781, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.90689059560851852928, 3.16992500144231236295, 3.00000000000000000000, 1.00000000000000000000, 3.00000000000000000000, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 3.70043971814109216032, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.70043971814109216032, 3.45943161863729725615, 2.58496250072115618147, 1.58496250072115618147, 3.00000000000000000000, 3.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.16992500144231236295, 3.00000000000000000000, 3.16992500144231236295, 2.58496250072115618147, 3.90689059560851852928, 3.45943161863729725615, 3.16992500144231236295, 2.80735492205760410749, 3.70043971814109216032, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.80735492205760410749, 3.70043971814109216032, 3.00000000000000000000, 3.00000000000000000000, 3.80735492205760410749, 3.58496250072115618147, 3.58496250072115618147, 2.58496250072115618147, 2.80735492205760410749, 3.90689059560851852928, 3.00000000000000000000, 3.16992500144231236295, 2.80735492205760410749, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 3.70043971814109216032, 3.80735492205760410749, 3.80735492205760410749, 3.16992500144231236295, 3.32192809488736234781, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 2.80735492205760410749, 3.90689059560851852900, 3.45943161863729725615, 3.80735492205760410749, 3.16992500144231236295, 3.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 3.00000000000000000000, 2.32192809488736234781, 3.32192809488736234781, 3.00000000000000000000, 3.32192809488736234781, 3.90689059560851852928, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 2.32192809488736234781, 1.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 3.90689059560851852928, 3.32192809488736234781, 4.08746284125033940843, 3.80735492205760410749, 3.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 3.58496250072115618147, 2.80735492205760410749, 4.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.00000000000000000000, 3.16992500144231236295, 2.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 2.80735492205760410749, 3.70043971814109216032, 3.16992500144231236295, 2.58496250072115618147, 3.00000000000000000000, 3.45943161863729725615, 2.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 4.00000000000000000000, 3.32192809488736234781, 3.80735492205760410749, 3.70043971814109216032, 3.45943161863729725615, 3.58496250072115618147, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.00000000000000000000, 2.32192809488736234781, 3.70043971814109216032, 3.00000000000000000000, 2.58496250072115618147, 3.16992500144231236295, 2.32192809488736234781, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 3.70043971814109216032, 2.80735492205760410749, 3.00000000000000000000, 3.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.45943161863729725615, 3.45943161863729725615, 3.00000000000000000000, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236200, 1.58496250072115618147, 3.58496250072115618147, 3.16992500144231236295, 3.00000000000000000000, 2.80735492205760410749, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.16992500144231236295, 3.16992500144231236295, 2.00000000000000000000, 3.16992500144231236295, 3.45943161863729725615, 3.80735492205760410749, 1.58496250072115618147, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 3.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.70043971814109216032, 3.58496250072115618147, 3.45943161863729725615, 3.45943161863729725615, 2.80735492205760410749, 3.32192809488736234781, 2.32192809488736234781, 2.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, 2.32192809488736234781, 3.00000000000000000000, 4.08746284125033940843, 3.32192809488736234781, 3.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 2.80735492205760410749, 1.58496250072115618147, 2.80735492205760410749, 2.80735492205760410749, 3.32192809488736234781, 4.32192809488736234781, 3.00000000000000000000, 3.80735492205760410749, 3.58496250072115618147, 2.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.16992500144231236295, 3.45943161863729725615, 3.58496250072115618147, 2.80735492205760410749, 2.32192809488736234781, 3.00000000000000000000, 2.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.90689059560851852928, 3.45943161863729725615, 2.32192809488736234781, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 3.58496250072115618147, 3.16992500144231236295, 3.80735492205760410749, 3.00000000000000000000, 3.90689059560851852928, 3.70043971814109216032, 3.00000000000000000000, 3.45943161863729725615, 3.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.32192809488736234781, 3.80735492205760410749, 2.00000000000000000000, 3.58496250072115618147, 3.32192809488736234781, 3.00000000000000000000, 2.58496250072115618147, 3.70043971814109216032, 3.70043971814109216032, 3.45943161863729725615, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.45943161863729725615, 2.32192809488736234781, 3.00000000000000000000, 3.00000000000000000000, 3.16992500144231236295, 3.00000000000000000000, 2.58496250072115618147, 2.58496250072115618147, 3.58496250072115618147, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.90689059560851852928, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 2.00000000000000000000, 4.08746284125033940843, 1.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.58496250072115618147, 2.32192809488736234781, 2.80735492205760410749, 3.58496250072115618147, 2.80735492205760410749, 3.32192809488736234781, 3.45943161863729725615, 3.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.32192809488736234781, 2.58496250072115618147, 3.70043971814109216032, 2.58496250072115618147, 3.00000000000000000000, 3.32192809488736234781, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 3.32192809488736234781, 3.16992500144231236295, 3.45943161863729725615, 2.80735492205760410749, 3.16992500144231236295, 3.00000000000000000000, 3.00000000000000000000, 3.70043971814109216032, 3.16992500144231236295, 3.70043971814109216032, 3.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 2.32192809488736234781, 3.00000000000000000000, 3.45943161863729725615, 3.00000000000000000000, 2.32192809488736234781, 2.58496250072115618147, 2.80735492205760410749, 3.45943161863729725615, 4.08746284125033940843, 3.90689059560851852928, 3.80735492205760410749, 3.16992500144231236295, 2.58496250072115618147, 3.70043971814109216032, 2.80735492205760410749, 3.70043971814109216032, 3.80735492205760410749, 3.32192809488736234781, 2.80735492205760410749, 2.58496250072115618147, 3.16992500144231236295, 3.80735492205760410749, 3.32192809488736234781, 3.80735492205760410749, 2.58496250072115618147, 2.80735492205760410749, 3.00000000000000000000, 3.58496250072115618147, 3.00000000000000000000, 2.80735492205760410749, 3.58496250072115618147, 3.16992500144231236295, 3.58496250072115618147, 2.00000000000000000000, 3.32192809488736234781, 2.32192809488736234781, 2.80735492205760410749, 2.80735492205760410749, 2.80735492205760410749, 3.45943161863729725615, 3.32192809488736234781, 3.58496250072115618147, 3.70043971814109216032, 3.16992500144231236295, 3.16992500144231236295, 2.58496250072115618147, 3.45943161863729725615, 3.32192809488736234781, 3.16992500144231236295, 2.00000000000000000000, 2.58496250072115618147, 3.00000000000000000000, 3.00000000000000000000, 3.00000000000000000000, ]
yt = [ 2.58496250072115618147, 2.70043971814109216032, 2.80735492205760410749, 2.90689059560851852928, 3.00000000000000000000, 3.08746284125033940821, 3.16992500144231236295, 3.24792751344358549383, 3.32192809488736234781, 3.39231742277876028896, 3.45943161863729725615, 3.52356195605701287229, 3.58496250072115618147, 3.64385618977472469583, ]
xt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, ]
nlist = [ n12, n13, n14, n15, n16, n17, n18, n19, n20, n21, n22, n23, n24, n25]
# multiple box plots on one figure
fig, ax1 = plt.subplots()
bp = plt.boxplot(nlist, notch=0, sym='+', vert=1)
plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12,13,14],[12,13,14,15,16,17,18,19,20,21,22,23,24,25])
for i in range(0,14):
med = bp['medians'][i]
plt.plot([np.average(med.get_xdata())], [np.average(nlist[i])], color='r', marker='*', markeredgecolor='k')
# Add a horizontal grid to the plot, but make it very light in color
# so we can use it for reading data values but not be distracting
ax1.yaxis.grid(True, linestyle='--', which='major', color='lightgrey', alpha=0.5)
# Hide these grid behind plot objects
ax1.set_axisbelow(True)
ax1.set_title('Number of component in FG of chopped AES-128 ($768$ random samplings)', fontsize=10, fontweight='bold')
ax1.set_xlabel('$\log_2(N)$')
ax1.set_ylabel('$\log_2(\#\mathrm{component})$')
the, = plt.plot(xt,yt,color='blue',label='Theoretical average value')
average_line, = plt.plot([], color='w', marker='*', markerfacecolor='red', markeredgecolor='k', markersize=8, label='Experimental average value')
plt.legend([the, average_line], ['Theoretical average value', 'Experimental average value'])
plt.savefig('n12_n25_componentN.pdf')
plt.savefig('n12_n25_componentN.png')
plt.show()
|
'''
Created on Nov 2, 2014
@author: ehenneken
'''
import simplejson as json
from sqlalchemy import Column, Integer, String, DateTime, Boolean
from sqlalchemy.ext.declarative import DeclarativeMeta
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm.exc import NoResultFound
from sqlalchemy.dialects import postgresql
from flask_sqlalchemy import SQLAlchemy
from flask import current_app
Base = declarative_base()
class AlchemyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj.__class__, DeclarativeMeta):
# an SQLAlchemy class
fields = {}
for field in [x for x in dir(obj)
if not x.startswith('_') and x != 'metadata']:
data = obj.__getattribute__(field)
try:
# this will fail on non-encodable values, like other
# classes
json.dumps(data)
fields[field] = data
except TypeError:
fields[field] = None
# a json-encodable dict
return fields
return json.JSONEncoder.default(self, obj)
class GraphicsModel(Base):
__tablename__ = 'graphics'
id = Column(Integer, primary_key=True)
bibcode = Column(String, nullable=False, index=True)
doi = Column(String)
source = Column(String)
eprint = Column(Boolean)
figures = Column(postgresql.JSON)
thumbnails = Column(postgresql.ARRAY(String), default=[])
baseurl = Column(String)
modtime = Column(DateTime)
def execute_SQL_query(bibc):
with current_app.session_scope() as session:
resp = session.query(GraphicsModel).filter(
GraphicsModel.bibcode == bibc).one()
results = json.loads(json.dumps(resp, cls=AlchemyEncoder))
return results
def get_graphics_record(bibcode):
try:
res = execute_SQL_query(bibcode)
except NoResultFound:
res = {'Error': 'Unable to get results!', 'Error Info': 'No database entry found for %s' % bibcode}
except Exception as err:
res = {'Error': 'Unable to get results!', 'Error Info': 'Graphics query failed for %s: %s'%(bibcode, err)}
return res
|
from django.conf.urls.defaults import *
from web_frontend.views import *
from web_frontend.condor_copasi_db import views as db
from web_frontend.condor_copasi_db.views import tasks as db_tasks
# Uncomment the next two lines to enable the admin:
from django.contrib import admin
from django.conf import settings
admin.autodiscover()
urlpatterns = patterns('',
# Example:
# (r'^web_frontend/', include('web_frontend.foo.urls')),
# Uncomment the admin/doc line below to enable admin documentation:
#(r'^admin/doc/', include('django.contrib.admindocs.urls')),
# Uncomment the next line to enable the admin:
url(r'^admin/', include(admin.site.urls), name="adminsite"),
url(r'^my_admin/jsi18n', 'django.views.i18n.javascript_catalog'),
url(r'^$', mainPage, name="index"),
url(r'^login/$', loginPage, name="login"),
url(r'^logout/$',logoutPage, name="logout"),
url(r'^tasks/$', db_tasks, name="tasks_home"),
url(r'^tasks/new/sensitivity_optimization/$', db.newTask, {'type': 'SO'}, name="new_SO"),
url(r'^tasks/new/stochastic_simulation/$', db.newTask, {'type': 'SS'}, name="new_SS"),
url(r'^tasks/new/parallel_scan/$', db.newTask, {'type': 'PS'}, name="new_PS"),
url(r'^tasks/new/raw/$', db.newTask, {'type': 'RW'}, name="new_RW"),
url(r'^tasks/new/optimization_repeat/$', db.newTask, {'type': 'OR'}, name="new_OR"),
url(r'^tasks/new/parameter_estimation_repeat/$', db.newTask, {'type': 'PR'}, name="new_PR"),
url(r'^tasks/new/optimization_repeat_different_algorithms/$', db.newTask, {'type': 'OD'}, name="new_OD"),
url(r'^tasks/new/sigma_point_method/$', db.newTask, {'type': 'SP'}, name="new_SP"),
url(r'^tasks/new/confirm/(?P<job_id>\w+)/$', db.taskConfirm, name="confirm_task"),
url(r'^my_account/$', db.myAccount, name="my_account"),
url(r'^my_account/change_password$', db.change_password, name="change_password"),
url(r'^my_account/jobs/running/$', db.myAccountRunningJobs, name="running_jobs"),
url(r'^my_account/jobs/completed/$', db.myAccountCompletedJobs, name="completed_jobs"),
url(r'^my_account/jobs/errors/$', db.myAccountJobErrors, name="job_errors"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/download/best_results/$', db.prModelDownload, name="pr_best_results_download"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/download/$', db.jobDownload, name="job_download"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/save/plot.png$', db.ss_plot, name="plot"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/progress/plot.png$', db.so_progress_plot, name="so_progress_plot"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/progress/$', db.so_progress_page, name="so_progress_page"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/save/$', db.jobResultDownload, name="job_save"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/output/$', db.jobOutput, name="job_output"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/remove/$', db.jobRemove, name="job_remove"),
url(r'^my_account/jobs/details/(?P<job_name>.+)/$', db.jobDetails, name="job_details"),
url(r'^my_account/jobs/compare/so/$', db.compareSOJobs, name="so_compare"),
url(r'^help/$', helpPage, name="help"),
url(r'^usage/$', db.usageHome, name="usage_home"),
url(r'^usage/all/$', db.usageByPeriod, {'start':'all', 'end':'all'} ,name="usage_by_period_all"),
url(r'^usage/(?P<start>.+)/to/(?P<end>.+)/$', db.usageByPeriod, name="usage_by_period"),
)
if settings.DEBUG:
urlpatterns += patterns('',
(r'^static/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),
)
|
# coding: utf-8
def test_default_state(printer):
assert printer.is_online is True
def test_online(printer):
printer.online()
assert printer.is_online is True
def test_offline(printer):
printer.offline()
assert printer.is_online is False
def test_online_after_offline(printer):
printer.online()
assert printer.is_online is True
def test_reset_value(printer):
printer.reset()
assert printer.is_online is True
|
from core.portfolio.abstract_portfolio_handler import AbstractPortfolioHandler
from core.portfolio.portfolio import Portfolio
class PortfolioHandler(AbstractPortfolioHandler):
def __init__(self, exchange, capital, base_currency, events_queue,
price_handler, risk_manager):
super().__init__(capital, base_currency, events_queue, price_handler,
risk_manager)
self.portfolio = Portfolio(exchange, self.capital, self.price_handler)
async def on_signal(self, signal_event):
"""
This is called by the backtester or live trading architecture
to form the initial orders from the SignalEvent.
These orders are sent to the RiskManager to verify,modify or
eliminate.
Once received from the RiskManager they are converted into
full OrderEvent objects and sent back to the events queue.
Args:
signal_event (SignalEvent)
"""
# print('Got signal: {}'.format(signal_event))
order_event = self.risk_manager.create_order(
self.portfolio, signal_event)
print('Putting order event', order_event)
await self.events_queue.put(order_event)
async def on_fill(self, fill_event):
"""
This is called by the backtester or live trading architecture
to take a FillEvent and update the Portfolio object with new
or modified Positions.
Upon receipt of a FillEvent, the PortfolioHandler converts
the event into a transaction that gets stored in the Portfolio
object. This ensures that the broker and the local portfolio
are "in sync".
"""
timestamp = fill_event.timestamp
action = fill_event.action
ticker = fill_event.ticker
quantity = fill_event.quantity
price = fill_event.price
commission = fill_event.commission
# Create or sell the position from the fill info
self.portfolio.process_position(
timestamp, action, ticker, quantity, price, commission
)
def get_portfolio_report(self):
return {
'closed_positions': self.portfolio.closed_positions,
'initial_capital': self.portfolio.initial_capital,
'capital': self.portfolio.capital,
'equity': self.portfolio.equity
}
|
import json
import os
from requests.auth import HTTPBasicAuth
import requests
_readme_api_url = "https://dash.readme.io/api/v1/"
class ReadMeSession:
# Passed for every API call
__headers = {"Accept": "application/json", "content-type": "application/json"}
# Map: <version string -> version info>
__versions = None
def __init__(self, auth_token, api_version=None):
self.auth_token = auth_token
self.__refresh_versions()
if api_version:
self.set_api_version(api_version)
# Set the version used for GET requests
def set_api_version(self, version):
self.__verify_version_exists(version)
self.api_version = version
self.__headers["x-readme-version"] = "v" + version
if "categories" not in self.get_version():
self.__refresh_categories()
# Call the readme API. api_func should be a requests-based function.
def __api_call(self, api_func, endpoint, print_info, data=None):
print(print_info + "... ", end="")
response = api_func(
_readme_api_url + endpoint,
headers=self.__headers,
auth=HTTPBasicAuth(self.auth_token, ""),
data=data,
)
if response.status_code not in [200, 201, 204]:
print("Error (code " + str(response.status_code) + "): " + response.text)
return None
else:
print()
return None if api_func == requests.delete else json.loads(response.text)
# API GET call.
# If paginated, gather and concat the output for each page in the endpoint.
def __api_GET(self, endpoint, print_info=None, paginated=False):
if not print_info:
print_info = "API::GET(" + endpoint + ")"
if paginated:
i = 1
out = []
while True:
response = self.__api_call(
requests.get,
endpoint + "?page=" + str(i),
print_info + " (page " + str(i) + ")",
)
if response is None:
return None
if len(response) is 0:
return out
out += response
i += 1
else:
return self.__api_call(requests.get, endpoint, print_info)
# API POST call.
# Data should be passed in as a map. The map will be converted to string.
def __api_POST(self, endpoint, data, print_info=None):
if not print_info:
print_info = "API::POST(" + endpoint + ")"
# Convert data to str
data_str = ""
for x, y in data.items():
data_str += '"' + x + '":"' + y + '",'
data_str = ("{" + data_str[:-1] + "}").encode("utf-8")
data = data_str
return self.__api_call(requests.post, endpoint, print_info, data)
# API DELETE call.
def __api_DELETE(self, endpoint, print_info):
if not print_info:
print_info = "API::DELETE(" + endpoint + ")"
return self.__api_call(requests.delete, endpoint, print_info)
# Populates version_to_info as a map: "version" -> "version info"
def __refresh_versions(self):
response = self.__api_GET("version", print_info="Fetching versions")
if response:
self.__versions = {}
for version in response:
self.get_versions()[version["version"]] = version
# Verify a version exists
def __verify_version_exists(self, version):
if version not in self.get_versions():
raise ValueError("Version " + version + " does not exist.")
# Get all version info
def get_versions(self):
return self.__versions
# Get a version info
def get_version(self):
versions = self.get_versions()
return versions[self.api_version] if self.api_version in versions else None
# Populates categories as a map: "category title" -> "category ID"
def __refresh_categories(self):
version_info = self.get_version()
version_info["categories"] = {}
categories = version_info["categories"]
response = self.__api_GET(
"categories",
paginated=True,
print_info="Fetching categories for version " + self.api_version,
)
if response is not None:
for category in response:
if category[
"reference"
]: # Only get cateories that are in the API reference
if category["title"] in categories:
print(
"Warning: There are two categories with the name "
+ category["title"]
+ " for version "
+ self.api_version
+ ". Which category this title refers"
+ " to will be unpredictable."
)
categories[category["title"]] = category
self.__refresh_category_files(category["title"])
# Populate as a map: map<category, map<title, info object>>
def __refresh_category_files(self, category):
self.__verify_category_exists(category)
category_files = self.__api_GET(
"categories/" + self.get_category(category)["slug"] + "/docs",
print_info="Fetching docs in " + category,
)
# Populate as a map: map<title, info object>>
category = self.get_category(category)
category["files"] = {}
for file in category_files:
category["files"][file["title"]] = file
# Get all category info
def get_categories(self):
return self.get_version()["categories"]
# Get a category info
def get_category(self, category):
categories = self.get_categories()
return categories[category] if category in categories else None
# Get a categories' file list
def get_category_files(self, category):
self.__verify_category_exists(category)
return self.get_category(category)["files"]
# Verify a category exists
def __verify_category_exists(self, category):
if not self.get_category(category):
raise ValueError(
"Category "
+ category
+ " does not exist for version "
+ self.api_version
+ "."
)
# Create a version with default settings.
def create_version(
self, version, from_version=None, is_stable=False, is_beta=False, is_hidden=True
):
if version in self.get_versions():
raise ValueError(
"Version " + version + " already exists! Cannot create it."
)
# If no source version, pick the latest one
if not from_version:
max_version = 0
for ver in self.get_versions():
ver = float(ver)
if ver > max_version:
max_version = ver
from_version = str(max_version)
data = {
"version": "v" + version,
"is_stable": is_stable,
"is_beta": is_beta,
"is_hidden": is_hidden,
"from": from_version,
}
self.get_versions()[version] = self.__api_POST(
"version", data, "Creating version " + version
)
# Update a version
def update_version(self, version, is_stable=None, is_beta=None, is_hidden=None):
self.__verify_version_exists(version)
data = {
"version": "v" + version,
"is_stable": is_stable
if is_stable is not None
else self.get_versions()[version]["is_stable"],
"is_beta": is_beta
if is_beta is not None
else self.get_versions()[version]["is_beta"],
"is_hidden": is_hidden
if is_hidden is not None
else self.get_versions()[version]["is_hidden"],
}
version = self.__api_POST("version", data, "Creating version " + version)
for k, v in version.items():
self.get_versions()[version][k] = v
# Empty a category
def empty_category(self, category):
self.__verify_category_exists(category)
print("Emptying category " + category)
for title, data in self.get_category_files(category).items():
self.__api_DELETE(
"docs/" + data["slug"],
print_info=" Removing file " + category + "/" + title,
)
self.get_category(category)["files"] = {}
# Delete files in the given category with the given title
def delete_file_with_title(self, title, category):
self.__verify_category_exists(category)
# Search for a file with the same title.
files = self.get_category_files(category)
if title in files:
self.__api_DELETE(
"docs/" + files[title]["slug"],
print_info="Removing duplicate file " + category + "/" + title,
)
files.pop(title)
# Uploads all files in the folder at path to ReadMe.
# Can also upload individual files at path.
def upload(self, path, category, recursive=False):
self.__verify_category_exists(category)
if os.path.isdir(path):
if recursive:
# get all subdirs in path and recursively transfer all files in that subdir
subdirpath = path
onlydirs = [
f
for f in os.listdir(subdirpath)
if os.path.isdir(os.path.join(subdirpath, f))
]
for dir in onlydirs:
self.upload(os.path.join(path, dir), category, recursive)
# get all filenames in current dir
files = sorted(
[
os.path.join(path, f)
for f in os.listdir(path)
if os.path.isfile(os.path.join(path, f))
]
)
# iterate through all filenames and import the html files
for currfilename in files:
self.upload(currfilename, category, recursive)
elif not os.path.isfile(path):
raise ValueError("Unable to find file at path: " + path)
currfilename = path
if currfilename.find(".html") != -1:
# open and read file
file = open(currfilename, "r")
filecontents = file.read()
file.close()
filecontents = filecontents.replace("\\", "\")
filecontents = filecontents.replace("\n", "\\\\n")
filecontents = filecontents.replace("¶", "")
filecontents = filecontents.replace('"', "'")
filecontents = (
'[block:html]\\n{\\n \\"html\\": \\"'
+ filecontents
+ '\\"\\n}\\n[/block]'
)
firstheadline = os.path.basename(currfilename)[:-5]
# extract first heading and use as page title
# soup = BeautifulSoup(filecontents, 'html.parser')
# for headlines in soup.find_all("h1"):
# firstheadline = headlines.text.strip()
# break
# Delete files with identical title
self.delete_file_with_title(firstheadline, category)
# Set up HTML _reamde_api_url for ReadMe API
data = {
"hidden": "false",
"title": firstheadline,
"type": "basic",
"body": filecontents,
"category": self.get_category(category)["_id"],
}
# Create the new page
out = self.__api_POST(
"docs", data, "Uploading " + currfilename + " to category " + category
)
self.get_category_files(category)[firstheadline] = out
|
from simple_array.m_kth_largest import KLargestElement
class TestKLargestElement:
def test_empty_array(self):
kl = KLargestElement()
ans = kl.find_bubble_sort(None, 2)
assert ans is None
def test_bubble_sort(self):
kl = KLargestElement()
nums = [3, 2, 1, 5, 6, 4]
ans = kl.find_bubble_sort(nums, 2)
assert ans == 5
nums = [3, 2, 3, 1, 2, 4, 5, 5, 6]
ans = kl.find_bubble_sort(nums, 4)
assert ans == 4
def test_min_heap(self):
kl = KLargestElement()
nums = [3, 2, 1, 5, 6, 4]
ans = kl.find_heap(nums, 2)
assert ans == 5
nums = [3, 2, 3, 1, 2, 4, 5, 5, 6]
ans = kl.find_heap(nums, 4)
assert ans == 4
|
"""
A component that represents the cooling system in a refuelling station is
created through this class.
*****
Scope
*****
As part of the hydrogen refuelling station, a cooling system is required
to precool high pressure hydrogen before it is dispensed into the vehicle's
tank. This is in order to prevent the tank from overheating.
*******
Concept
*******
An oemof Sink component is used which has one electrical bus input that
represents the electricity required to power the cooling system.
.. figure:: /images/h2_refuel_cooling_system.png
:width: 40 %
:alt: h2_refuel_cooling_system.png
:align: center
Fig.1: Simple diagram of a hydrogen refuel cooling system.
The required electricity supply for the cooling system per timestep is
calculated by the following equation:
.. math::
E_{elec,i} = \\frac{D_{H_{2},i} \\cdot E_{spec} + E_{standby}}{3.6}
* :math:`E_{elec,i}` = electrical energy required for the ith timestep [Wh]
* :math:`D_{H_{2},i}` = hydrogen demand for the ith timestep [kg]
* :math:`E_{spec}` = specific energy required relative to the demand [kJ/kg]
* :math:`E_{standby}` = standby energy required per timestep [kJ/h]
The default specific energy is chosen to be 730 kJ/kg, and the standby
energy is chosen to be 8100 kJ/h [find source]. Furthermore, this
cooling system component is only necessary if the hydrogen is compressed
over 350 bar e.g. to 700 bar for passenger cars.
"""
import os
import oemof.solph as solph
from smooth.components.component import Component
import smooth.framework.functions.functions as func
class H2RefuelCoolingSystem(Component):
"""
:param name: unique name given to the H2 refuel cooling system component
:type name: str
:param bus_el: electricity bus that is the input of the cooling system
:type bus_el: str
:param nominal_value: value that the timeseries should be multiplied by, default is 1
:type nominal_value: numerical
:param csv_filename: csv filename containing the desired timeseries, e.g. 'my_filename.csv'
:type csv_filename: str
:param csv_separator: separator of the csv file, e.g. ',' or ';' (default is ',')
:type csv_separator: str
:param column_title: column title (or index) of the timeseries, default is 0
:type column_title: str or int
:param path: path where the timeseries csv file can be located
:type path: str
:param cool_spec_energy: energy required to cool the refuelling station [kJ/kg]
:type cool_spec_energy: numerical
:param standby_energy: required standby energy [kJ/h]
:type standby_energy: numerical
:param life_time: life time of the component [a]
:type life_time: numerical
:param number_of_units: number of units installed
:type number of units: numerical
:param set_parameters(params): updates parameter default values
(see generic Component class)
:type set_parameters(params): function
:param data: dataframe containing data from timeseries
:type data: pandas dataframe
:param electrical_energy: electrical energy required for each hour [Wh]
:type electrical_energy: numerical
"""
def __init__(self, params):
"""Constructor method
"""
# Call the init function of the mother class.
Component.__init__(self)
# ------------------- PARAMETERS -------------------
self.name = 'H2_refuel_default_name'
self.bus_el = None
self.nominal_value = 1
self.csv_filename = None
self.csv_separator = ','
self.column_title = 0
self.path = os.path.dirname(__file__)
self.cool_spec_energy = 730
self.standby_energy = 8100
self.life_time = 20
self.number_of_units = 1
# ------------------- UPDATE PARAMETER DEFAULT VALUES -------------------
self.set_parameters(params)
# ------------------- READ CSV FILES -------------------
self.data = func.read_data_file(self.path, self.csv_filename,
self.csv_separator, self.column_title)
self.electrical_energy = \
(self.data * self.cool_spec_energy + self.standby_energy) / 3.6
def add_to_oemof_model(self, busses, model):
"""Creates an oemof Sink component from information given in
the H2RefuelCoolingSystem class, to be used in the oemof model
:param busses: virtual buses used in the energy system
:type busses: dict
:param model: current oemof model
:type model: oemof model
:return: oemof component
"""
h2_refuel_cooling_system = solph.Sink(
label=self.name,
inputs={busses[self.bus_el]: solph.Flow(
fix=self.electrical_energy.iloc[self.sim_params.i_interval],
nominal_value=self.nominal_value
)})
model.add(h2_refuel_cooling_system)
return h2_refuel_cooling_system
|
from pycamara.base import BaseClient
class PropositionClient(BaseClient):
def all(self):
return self.safe(self._get('/proposicoes'))
def filter(self, **kwargs):
return self.safe(self._get('/proposicoes', **kwargs))
def get(self, proposition_id):
path = '/proposicoes/{}'.format(proposition_id)
return self.safe(self._get(path))
def proceedings(self, proposition_id):
path = '/proposicoes/{}/tramitacoes'.format(proposition_id)
return self.safe(self._get(path))
def votings(self, proposal_id):
path = '/proposicoes/{}/votacoes'.format(proposal_id)
return self.safe(self._get(path))
|
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸 (Blueking) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at https://opensource.org/licenses/MIT
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 django.utils.translation import ugettext as _
from drf_yasg.utils import swagger_auto_schema
from rest_framework import serializers, status
from rest_framework.decorators import action
from rest_framework.response import Response
from apps.generic import APIViewSet
from apps.gsekit.cmdb.handlers.cmdb import CMDBHandler
from apps.gsekit.configfile import exceptions
from apps.gsekit.configfile.handlers.config_version import ConfigVersionHandler
from apps.gsekit.configfile.serializers import config_version as config_version_serializer
from apps.gsekit.process.handlers.process import ProcessHandler
from apps.iam import ActionEnum, ResourceEnum
from apps.iam.handlers.drf import InstanceActionPermission, ViewBusinessPermission
from apps.utils.mako_utils.checker import check_mako_template_safety
from apps.utils.mako_utils.exceptions import ForbiddenMakoTemplateException
from apps.utils.mako_utils.visitor import MakoNodeVisitor
from apps.utils.models import model_to_dict
ConfigVersionViewTags = ["config_version"]
class ConfigVersionViews(APIViewSet):
lookup_field = "config_version_id"
def config_template_ids_getter(self, request):
lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field
config_version_id = self.kwargs[lookup_url_kwarg]
config_template_id = ConfigVersionHandler(config_version_id=config_version_id).data.config_template_id
return [config_template_id]
def get_instance_ids_getter(self, request):
if self.action in ["clone", "update", "retrieve"]:
return self.config_template_ids_getter(request)
return None
def get_permissions(self):
if self.detail:
return [
InstanceActionPermission(
[ActionEnum.EDIT_CONFIG_TEMPLATE], ResourceEnum.CONFIG_TEMPLATE, self.get_instance_ids_getter
),
]
return [ViewBusinessPermission()]
def get_serializer_class(self, *args, **kwargs):
action_serializer_map = {
"update": config_version_serializer.UpdateConfigVersionRequestSerializer,
}
return action_serializer_map.get(self.action, serializers.Serializer)
@swagger_auto_schema(
operation_summary="创建配置模板版本（克隆）",
tags=ConfigVersionViewTags,
request_body=config_version_serializer.CreateConfigVersionRequestSerializer(),
responses={status.HTTP_200_OK: config_version_serializer.CreateConfigVersionResponseSerializer()},
)
@action(
methods=["POST"], detail=True, serializer_class=config_version_serializer.CreateConfigVersionRequestSerializer
)
def clone(self, request, config_version_id, *args, **kwargs):
description = self.validated_data["description"]
return Response(ConfigVersionHandler(config_version_id=config_version_id).clone(description))
@swagger_auto_schema(
operation_summary="编辑配置模板版本（草稿/保存）",
tags=ConfigVersionViewTags,
request_body=config_version_serializer.UpdateConfigVersionRequestSerializer(),
)
def update(self, request, config_version_id, *args, **kwargs):
description = self.validated_data["description"]
content = self.validated_data["content"]
try:
check_mako_template_safety(content, MakoNodeVisitor())
except ForbiddenMakoTemplateException as mako_error:
raise exceptions.ForbiddenMakoTemplateException(str(mako_error))
is_draft = self.validated_data["is_draft"]
is_active = self.validated_data["is_active"]
file_format = self.validated_data.get("file_format")
if "${HELP}" in content:
raise exceptions.ForbiddenConfigContentException(err_msg=_("${HELP}变量仅在预览时提供帮助，保存配置时请不要包含"))
return Response(
ConfigVersionHandler(config_version_id=config_version_id).update(
description, content, is_draft, is_active, file_format
)
)
@swagger_auto_schema(
operation_summary="获取配置模板详情", tags=ConfigVersionViewTags,
)
def retrieve(self, request, config_version_id, *args, **kwargs):
return Response(model_to_dict(ConfigVersionHandler(config_version_id=config_version_id).data))
@swagger_auto_schema(
operation_summary="指定进程实例预览配置模板",
tags=ConfigVersionViewTags,
request_body=config_version_serializer.PreviewConfigRequestSerializer(),
responses={status.HTTP_200_OK: config_version_serializer.PreviewConfigResponseSerializer()},
)
@action(detail=False, methods=["POST"], serializer_class=config_version_serializer.PreviewConfigRequestSerializer)
def preview(self, request, bk_biz_id, *args, **kwargs):
bk_process_id = self.validated_data["bk_process_id"]
content = self.validated_data["content"]
try:
check_mako_template_safety(content, MakoNodeVisitor())
except ForbiddenMakoTemplateException as mako_error:
raise exceptions.ForbiddenMakoTemplateException(str(mako_error))
process_info = ProcessHandler(bk_biz_id=bk_biz_id).process_info(bk_process_id=bk_process_id)
CMDBHandler(bk_biz_id=bk_biz_id).cache_topo_tree_attr(bk_set_env=process_info["set"]["bk_set_env"])
return Response(ConfigVersionHandler.render(bk_biz_id, process_info, content))
|
from flask_restful import Resource
class status(Resource):
def get(self):
return {'status':'OK'}, 200 |
# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from absl.testing import absltest
from absl.testing import parameterized
import itertools
import jax
import jax.numpy as jnp
from jaxopt import objective
from jaxopt import ArmijoSGD
from jaxopt._src import test_util
import numpy as onp
from sklearn import datasets
class ArmijoSgdTest(test_util.JaxoptTestCase):
@parameterized.product(aggressiveness=[0.1, 0.5, 0.9], decrease_factor=[0.8, 0.9])
def test_interpolating_regime(self, aggressiveness, decrease_factor):
"""Test Armijo on a problem on which interpolation holds.
No regularization, one cluster per class, two classes, with enough distance,
to ensure classes are easily separable.
"""
X, y = datasets.make_classification(n_samples=100, n_features=20, n_classes=2,
n_informative=10, n_redundant=10,
n_clusters_per_class=1, class_sep=6.,
random_state=42)
data = (X, y)
l2reg = 0.0
# fun(params, data)
fun = objective.l2_multiclass_logreg_with_intercept
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
pytree_init = (W_init, b_init)
tol = 1e-5
opt = ArmijoSGD(fun=fun, aggressiveness=aggressiveness, decrease_factor=decrease_factor,
maxiter=20*1000, tol=tol)
params, state = opt.run(pytree_init, l2reg=l2reg, data=data)
loss = opt.fun(params, l2reg, data)
self.assertLessEqual(state.error, tol)
self.assertLessEqual(loss, tol) # check interpolation
error = opt.l2_optimality_error(params, l2reg=l2reg, data=data)
self.assertLessEqual(error, tol)
@parameterized.product(l2reg=[1e-1, 1])
def test_non_interpolating_regime(self, l2reg):
"""Test Armijo on a problem on which interpolation does not holds.
No (theoretical) convergence guarantees.
"""
X, y = datasets.make_classification(n_samples=200, n_features=10, n_classes=3,
n_informative=5, n_redundant=3,
n_clusters_per_class=2, class_sep=1.,
random_state=42)
data = (X, y)
# fun(params, data)
fun = objective.l2_multiclass_logreg_with_intercept
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
pytree_init = (W_init, b_init)
tol = 1e-7
opt = ArmijoSGD(fun=fun, aggressiveness=0.7, maxiter=10*1000, tol=tol)
params, state = opt.run(pytree_init, l2reg=l2reg, data=data)
error = opt.l2_optimality_error(params, l2reg=l2reg, data=data)
self.assertLessEqual(error, 0.0015)
def test_logreg_autodiff(self):
X, y = datasets.load_digits(return_X_y=True)
data = (X, y)
l2reg = float(X.shape[0])
fun = objective.l2_multiclass_logreg
jac_num = test_util.logreg_skl_jac(X, y, l2reg)
W_skl = test_util.logreg_skl(X, y, l2reg)
# Make sure the decorator works.
opt = ArmijoSGD(fun=fun, maxiter=5)
def wrapper(l2reg):
return opt.run(W_skl, l2reg=l2reg, data=data).params
jac_custom = jax.jacrev(wrapper)(l2reg)
self.assertArraysAllClose(jac_num, jac_custom, atol=1e-2)
def test_logreg_implicit_diff(self):
X, y = datasets.load_digits(return_X_y=True)
data = (X, y)
l2reg = float(X.shape[0])
fun = objective.l2_multiclass_logreg
jac_num = test_util.logreg_skl_jac(X, y, l2reg)
W_skl = test_util.logreg_skl(X, y, l2reg)
# Make sure the decorator works.
opt = ArmijoSGD(fun=fun, maxiter=5)
def wrapper(l2reg):
# Unfortunately positional arguments are required when implicit_diff=True.
return opt.run(W_skl, l2reg, data).params
jac_custom = jax.jacrev(wrapper)(l2reg)
self.assertArraysAllClose(jac_num, jac_custom, atol=1e-2)
def test_goldstein(self):
X, y = datasets.make_classification(n_samples=10, n_features=5, n_classes=3,
n_informative=3, random_state=0)
def dataset_loader(X, y, n_iter):
rng = onp.random.RandomState(0)
for _ in range(n_iter):
perm = rng.permutation(len(X))
yield X[perm], y[perm]
l2reg = 10.0
fun = objective.l2_multiclass_logreg_with_intercept
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
params = (W_init, b_init)
tol = 1e-3
opt = ArmijoSGD(fun=fun, reset_option='goldstein', maxiter=1000, tol=tol)
iterable = dataset_loader(X, y, n_iter=200)
state = opt.init_state(params, l2reg=l2reg)
@jax.jit
def jitted_update(params, state, data):
return opt.update(params, state, l2reg=l2reg, data=data)
for data in itertools.islice(iterable, 0, opt.maxiter):
params, state = jitted_update(params, state, data)
# Check optimality conditions.
error = opt.l2_optimality_error(params, l2reg=l2reg, data=(X, y))
self.assertLessEqual(error, tol)
def test_run_iterable(self):
X, y = datasets.make_classification(n_samples=10, n_features=5, n_classes=3,
n_informative=3, random_state=0)
def dataset_loader(X, y, n_iter):
rng = onp.random.RandomState(0)
for _ in range(n_iter):
perm = rng.permutation(len(X))
yield X[perm], y[perm]
l2reg = 100.0
fun = objective.l2_multiclass_logreg_with_intercept
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
pytree_init = (W_init, b_init)
tol = 3e-1
opt = ArmijoSGD(fun=fun, maxiter=10, tol=tol) # few iterations due to speed issues
iterable = dataset_loader(X, y, n_iter=200)
params, _ = opt.run_iterator(pytree_init, iterable, l2reg=l2reg)
# Check optimality conditions.
error = opt.l2_optimality_error(params, l2reg=l2reg, data=(X, y))
self.assertLessEqual(error, tol)
@parameterized.product(momentum=[0.5, 0.9])
def test_momentum(self, momentum):
X, y = datasets.make_classification(n_samples=10, n_features=5, n_classes=3,
n_informative=3, random_state=0)
data = (X, y)
l2reg = 100.0
# fun(params, data)
fun = objective.l2_multiclass_logreg_with_intercept
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
pytree_init = (W_init, b_init)
opt = ArmijoSGD(fun=fun, momentum=momentum)
params, state = opt.run(pytree_init, l2reg=l2reg, data=data)
# Check optimality conditions.
error = opt.l2_optimality_error(params, l2reg=l2reg, data=data)
self.assertLessEqual(error, 0.05)
def test_logreg_with_intercept_run(self):
X, y = datasets.make_classification(n_samples=10, n_features=5, n_classes=3,
n_informative=3, random_state=0)
data = (X, y)
l2reg = 100.0
_fun = objective.l2_multiclass_logreg_with_intercept
def fun(params, l2reg, data):
return _fun(params, l2reg, data), None
n_classes = len(jnp.unique(y))
W_init = jnp.zeros((X.shape[1], n_classes))
b_init = jnp.zeros(n_classes)
pytree_init = (W_init, b_init)
opt = ArmijoSGD(fun=fun, maxiter=300, has_aux=True)
# Test positional, keyword and mixed arguments.
for params, _ in (opt.run(pytree_init, l2reg, data),
opt.run(pytree_init, l2reg=l2reg, data=data),
opt.run(pytree_init, l2reg, data=data)):
# Check optimality conditions.
error = opt.l2_optimality_error(params, l2reg=l2reg, data=data)
self.assertLessEqual(error, 0.05)
if __name__ == '__main__':
# Uncomment the line below in order to run in float64.
# jax.config.update("jax_enable_x64", True)
absltest.main()
|
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
try:
import sys
import subprocess
import re
import platform
from os import popen as pipe
except ImportError as e:
print("[!] Required module missing. %s" % e.args[0])
sys.exit(-1)
class ADB(object):
__adb_path = None
__output = None
__error = None
__devices = None
__target = None
# reboot modes
REBOOT_NORMAL = 0
REBOOT_RECOVERY = 1
REBOOT_BOOTLOADER = 2
# default TCP/IP port
DEFAULT_TCP_PORT = 5555
# default TCP/IP host
DEFAULT_TCP_HOST = "localhost"
def __init__(self, adb_path="adb"):
# By default we assume adb is in $PATH
self.__adb_path = adb_path
if not self.check_path():
self.__error = "[!] adb path not valid"
def __clean__(self):
self.__output = None
self.__error = None
def __read_output__(self, fd):
ret = ''
while 1:
line = fd.readline()
if not line:
break
ret += line
if len(ret) == 0:
ret = None
return ret
def __build_command__(self, cmd):
"""
Build command parameters
"""
if self.__devices is not None and len(self.__devices) > 1 and self.__target is None:
self.__error = "[!] Must set target device first"
return None
if type(cmd) is tuple:
a = list(cmd)
elif type(cmd) is list:
a = cmd
else:
# All arguments must be single list items
a = cmd.split(" ")
a.insert(0, self.__adb_path)
if self.__target is not None:
# add target device arguments to the command
a.insert(1, '-s')
a.insert(2, self.__target)
return a
def run_cmd(self, cmd):
"""
Run a command against the adb tool ($ adb <cmd>)
"""
self.__clean__()
if self.__adb_path is None:
self.__error = "[!] ADB path not set"
return False
try:
args = self.__build_command__(cmd)
if args is None:
return
cmdp = subprocess.Popen(args, shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
self.__output, self.__error = cmdp.communicate()
retcode = cmdp.wait()
if "device unauthorized" in self.__output:
self.__error = "[-] Device unauthorized"
return False
return self.__output.rstrip('\n')
except OSError as e:
self.__error = str(e)
return
def get_version(self):
"""
Returns ADB tool version
adb version
"""
ret = self.run_cmd("version")
try:
pattern = re.compile(r"version\s(.+)")
version = pattern.findall(ret)[0]
except:
version = None
return version
def check_path(self):
"""
Verify if adb path is valid
"""
if self.get_version() is None:
print("[-] adb executable not found")
return False
return True
def set_adb_path(self, adb_path):
"""
Set the ADB tool path
"""
self.__adb_path = adb_path
self.check_path()
def get_adb_path(self):
"""
Returns the ADB tool path
"""
return self.__adb_path
def start_server(self):
"""
Starts the ADB server
adb start-server
"""
self.run_cmd('start-server')
return self.__output
def kill_server(self):
"""
Kills the ADB server
adb kill-server
"""
self.run_cmd('kill-server')
def restart_server(self):
"""
Restarts the ADB server
"""
self.kill_server()
return self.start_server()
def restore_file(self, file_name):
"""
Restore device contents from the <file> backup archive
adb restore <file>
"""
self.run_cmd('restore %s' % file_name)
return self.__output
def wait_for_device(self):
"""
Block operations until device is online
adb wait-for-device
"""
self.run_cmd('wait-for-device')
return self.__output
def get_help(self):
"""
Returns ADB help
adb help
"""
self.run_cmd('help')
return self.__output
def get_devices(self):
"""
Return a dictionary of connected devices along with an incremented Id.
adb devices
"""
error = 0
# Clear existing list of devices
self.__devices = None
self.run_cmd("devices")
device_dict = {}
if self.__error is not None:
return None
try:
n = 0
output_list = self.__output.split("\n")
# Split on \r if we are on Windows
if platform.system().lower == "windows":
output_list = self.__output.split("\r")
for line in output_list:
pattern = re.compile(r"([^\s]+)\t+.+$")
device = pattern.findall(line)
if device:
device_dict[n] = device[0]
n += 1
except:
self.__devices = None
error = 1
self.__devices = device_dict
return self.__devices
def set_target_by_name(self, device):
"""
Specify the device name to target
example: set_target_device('emulator-5554')
"""
if device is None or self.__devices is None or device not in self.__devices.values():
self.__error = 'Must get device list first'
print("[!] Device not found in device list")
return False
self.__target = device
return "[+] Target device set: %s" % self.get_target_device()
def set_target_by_id(self, device):
"""
Specify the device ID to target.
The ID should be one from the device list.
"""
if device is None or self.__devices is None or device not in self.__devices:
self.__error = 'Must get device list first'
print("[!] Device not found in device list")
return False
self.__target = self.__devices[device]
return "[+] Target device set: %s" % self.get_target_device()
def get_target_device(self):
"""
Returns the selected device to work with
"""
if self.__target is None:
print("[*] No device target set")
return self.__target
def get_state(self):
"""
Get ADB state. Returns either offline | offline | device
adb get-state
"""
return self.run_cmd('get-state')
def get_model(self):
"""
Get Model name from target device
"""
self.run_cmd("devices -l")
device_model = ""
if self.__error is not None:
return self.__error
try:
for line in self.__output.split("\n"):
if line.startswith(self.__target):
pattern = r"model:(.+)\sdevice"
pat = re.compile(pattern)
device_model = pat.findall(line)
device_model = re.sub("[\[\]\'{\}<>]", '', str(device_model))
except Exception as e:
return "[-] Error: %s" % e.args[0]
return device_model
def get_serialno(self):
"""
Get serialno from target device
adb get-serialno
"""
return self.run_cmd('get-serialno')
def reboot_device(self, mode=0):
"""
Reboot the target device
Specify mode to reboot normally, recovery or bootloader
adb reboot <normally (0)/recovery (1) /bootloader (2)>
"""
if mode not in (self.REBOOT_NORMAL, self.REBOOT_RECOVERY, self.REBOOT_BOOTLOADER):
self.__error = "mode must be REBOOT_NORMAL/REBOOT_RECOVERY/REBOOT_BOOTLOADER"
return self.__output
cmd_str = "reboot"
if mode == self.REBOOT_RECOVERY:
cmd_str += " recovery"
elif mode == self.REBOOT_BOOTLOADER:
cmd_str += " bootloader"
return self.run_cmd(cmd_str)
def set_adb_root(self, mode):
"""
restarts the adbd daemon with root permissions
adb root
"""
return self.run_cmd('root')
def set_system_rw(self):
"""
Mounts /system as rw
adb remount
"""
self.run_cmd("remount")
return self.__output
def get_remote_file(self, remote, local):
"""
Pulls a remote file
adb pull remote local
"""
self.run_cmd('pull \"%s\" \"%s\"' % (remote, local))
if "bytes in" in self.__error:
self.__output = self.__error
self.__error = None
return self.__output
def push_local_file(self, local, remote):
"""
Push a local file
adb push local remote
"""
self.run_cmd('push \"%s\" \"%s\"' % (local, remote))
return self.__output
def shell_command(self, cmd):
"""
Executes a shell command
adb shell <cmd>
"""
self.run_cmd('shell %s' % cmd)
return self.__output
def listen_usb(self):
"""
Restarts the adbd daemon listening on USB
adb usb
"""
self.run_cmd("usb")
return self.__output
def listen_tcp(self, port=DEFAULT_TCP_PORT):
"""
Restarts the adbd daemon listening on the specified port
adb tcpip <port>
"""
self.run_cmd("tcpip %s" % port)
return self.__output
def get_bugreport(self):
"""
Return all information from the device that should be included in a bug report
adb bugreport
"""
self.run_cmd("bugreport")
return self.__output
def get_jdwp(self):
"""
List PIDs of processes hosting a JDWP transport
adb jdwp
"""
return self.run_cmd("jdwp")
def get_logcat(self, lcfilter=""):
"""
View device log
adb logcat <filter>
"""
self.run_cmd("logcat %s" % lcfilter)
return self.__output
def run_emulator(self, cmd=""):
"""
Run emulator console command
"""
self.run_cmd("emu %s" % cmd)
return self.__output
def connect_remote(self, host=DEFAULT_TCP_HOST, port=DEFAULT_TCP_PORT):
"""
Connect to a device via TCP/IP
adb connect host:port
"""
self.run_cmd("connect %s:%s" % (host, port))
return self.__output
def disconnect_remote(self, host=DEFAULT_TCP_HOST, port=DEFAULT_TCP_PORT):
"""
Disconnect from a TCP/IP device
adb disconnect host:port
"""
self.run_cmd("disconnect %s:%s" % (host, port))
return self.__output
def ppp_over_usb(self, tty=None, params=""):
"""
Run PPP over USB
adb ppp <tty> <params>
"""
if tty is None:
return self.__output
cmd = "ppp %s" % tty
if params != "":
cmd += " %s" % params
self.run_cmd(cmd)
return self.__output
def sync_directory(self, directory=""):
"""
Copy host->device only if changed (-l means list but don't copy)
adb sync <dir>
"""
self.run_cmd("sync %s" % directory)
return self.__output
def forward_socket(self, local=None, remote=None):
"""
Forward socket connections
adb forward <local> <remote>
"""
if local is None or remote is None:
return self.__output
self.run_cmd("forward %s %s" % (local, remote))
return self.__output
def uninstall(self, package=None, keepdata=False):
"""
Remove this app package from the device
adb uninstall [-k] package
"""
if package is None:
return self.__output
cmd = "uninstall %s" % (package if keepdata is True else "-k %s" % package)
self.run_cmd(cmd)
return self.__output
def install(self, pkgapp=None, fwdlock=False, reinstall=False, sdcard=False):
"""
Push this package file to the device and install it
adb install [-l] [-r] [-s] <file>
-l -> forward-lock the app
-r -> reinstall the app, keeping its data
-s -> install on sdcard instead of internal storage
"""
if pkgapp is None:
return self.__output
cmd = "install"
if fwdlock is True:
cmd += " -l "
if reinstall is True:
cmd += " -r "
if sdcard is True:
cmd += " -s "
self.run_cmd("%s %s" % (cmd, pkgapp))
return self.__output
def find_binary(self, name=None):
"""
Look for a binary file on the device
"""
self.shell_command("which %s" % name)
if self.__output is None: # not found
self.__error = "'%s' was not found" % name
elif self.__output.strip() == "which: not found": # which binary not available
self.__output = None
self.__error = "which binary not found"
else:
self.__output = self.__output.strip()
return self.__output
def wake_device(self):
return self.run_cmd('shell input keyevent 26')
def sideload(self, otapackage=None):
if otapackage is None:
return self.__output
self.run_cmd("sideload %s" % otapackage)
return self.__output
def get_devpath(self):
return self.run_cmd('get-devpath')
class Fastboot(object):
__fastboot_path = None
__output = None
__error = None
__devices = None
__target = None
def __init__(self, fastboot_path="fastboot"):
"""
By default we assume fastboot is in $PATH.
Alternatively, the path to fasboot can be supplied.
"""
self.__fastboot_path = fastboot_path
if not self.check_path():
self.__error = "[!] fastboot path not valid."
def __clean__(self):
self.__output = None
self.__error = None
def __read_output__(self, fd):
ret = ""
while 1:
line = fd.readline()
if not line:
break
ret += line
if len(ret) == 0:
ret = None
return ret
def __build_command__(self, cmd):
"""
Build command parameters for Fastboot command
"""
if self.__devices is not None and len(self.__devices) > 1 and self.__target is None:
self.__error = "[!] Must set target device first"
return None
if type(cmd) is tuple:
a = list(cmd)
elif type(cmd) is list:
a = cmd
else:
a = cmd.split(" ")
a.insert(0, self.__fastboot_path)
if self.__target is not None:
# add target device arguments to the command
a.insert(1, '-s')
a.insert(2, self.__target)
return a
def run_cmd(self, cmd):
"""
Run a command against the fastboot tool ($ fastboot <cmd>)
"""
self.__clean__()
if self.__fastboot_path is None:
self.__error = "[!] Fastboot path not set"
return False
try:
args = self.__build_command__(cmd)
if args is None:
return
cmdp = subprocess.Popen(args, shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
self.__output, self.__error = cmdp.communicate()
retcode = cmdp.wait()
return self.__output
except OSError as e:
self.__error = str(e)
return
def check_path(self):
"""
Check if the Fastboot path is valid
"""
if self.run_cmd("help") is None:
print("[-] fastboot executable not found")
return False
return True
def set_fastboot_path(self, fastboot_path):
"""
Set the Fastboot tool path
"""
self.__fastboot_path = fastboot_path
self.check_path()
def get_fastboot_path(self):
"""
Returns the Fastboot tool path
"""
return self.__fastboot_path_path
def get_devices(self):
"""
Return a dictionary of fastboot connected devices along with an incremented Id.
fastboot devices
"""
error = 0
# Clear existing list of devices
self.__devices = None
self.run_cmd("devices")
if self.__error is not None:
return ''
try:
device_list = self.__output.replace('fastboot', '').split()
if device_list[1:] == ['no', 'permissions']:
error = 2
self.__devices = None
except:
self.__devices = None
error = 1
i = 0
device_dict = {}
for device in device_list:
# Add list to dictionary with incrementing ID
device_dict[i] = device
i += 1
self.__devices = device_dict
return self.__devices
def set_target_by_name(self, device):
"""
Specify the device name to target
example: set_target_device('emulator-5554')
"""
if device is None or not device in self.__devices.values():
self.__error = 'Must get device list first'
print("[!] Device not found in device list")
return False
self.__target = device
return "[+] Target device set: %s" % self.get_target_device()
def set_target_by_id(self, device):
"""
Specify the device ID to target.
The ID should be one from the device list.
"""
if device is None or not device in self.__devices:
self.__error = 'Must get device list first'
print("[!] Device not found in device list")
return False
self.__target = self.__devices[device]
return "[+] Target device set: %s" % self.get_target_device()
def get_target_device(self):
"""
Returns the selected device to work with
"""
if self.__target == None:
print("[*] No device target set")
return self.__target
def flash_all(self, wipe=False):
"""
flash boot + recovery + system. Optionally wipe everything
"""
if wipe:
self.run_cmd('-w flashall')
else:
self.run_cmd('flashall')
def format(self, partition):
"""
Format the specified partition
"""
self.run_cmd('format %s' % partition)
return self.__output
def reboot_device(self):
"""
Reboot the device normally
"""
self.run_cmd('reboot')
return self.__output
def reboot_device_bootloader(self):
"""
Reboot the device into bootloader
"""
self.run_cmd('reboot-bootloader')
return self.__output
def oem_unlock(self):
"""
unlock bootloader
"""
self.run_cmd('oem unlock')
return self.__output
def oem_lock(self):
"""
lock bootloader
"""
self.run_cmd('oem lock')
return self.__output
|
from unittest import TestCase
from registration.forms import UserRegistrationForm
class TestUserRegistrationForm(TestCase):
def test_register_user(self):
data = {
'email': 'test@email.com',
'password1': 'password',
'password2': 'password'
}
form = UserRegistrationForm(data=data)
self.assertTrue(form.is_valid())
def test_register_user_passwords_mismatch(self):
data = {
'email': 'test@email.com',
'password1': 'password',
'password2': 'passworddddd'
}
form = UserRegistrationForm(data=data)
self.assertFalse(form.is_valid())
|
# AUTOGENERATED! DO NOT EDIT! File to edit: dev/02_data.datasets.ipynb (unless otherwise specified).
__all__ = ['get_cifar10']
# Cell
from ..imports import *
from .load import *
from ..vision.augmentations import *
# Cell
def get_cifar10(ds_dir: str, batch_size: int = None,
normalize: bool = False, padding: int = None, augmentation: str = None):
if not os.path.exists(ds_dir):
os.makedirs(ds_dir)
img_sz = (32, 32)
fname_base = "cifar10"
if normalize:
fname_base += "-normalized"
if padding is not None:
fname_base += f"-{padding}-pad"
if augmentation is not None:
fname_base += f"-{augmentation}-aug"
train_path = os.path.join(ds_dir, f"{fname_base}.train.tfrecords")
test_path = os.path.join(ds_dir, f"{fname_base}.test.tfrecords")
def parser_train(tf_example):
if padding is not None:
train_img_sz = (32+padding*2, 32+padding*2)
else:
train_img_sz = img_sz
x, y = parse_tf_example_img(tf_example, train_img_sz[0], train_img_sz[1])
if padding is not None:
x = tf.image.random_flip_left_right(tf.image.random_crop(x, [img_sz[0], img_sz[1], 3]))
if augmentation and augmentation == 'cutout':
x = cutout(x,h=8,w=8)
return x, y
def parser_test(tf_example):
x, y = parse_tf_example_img(tf_example, img_sz[0], img_sz[1])
return x, y
if not os.path.exists(train_path) or not os.path.exists(test_path):
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
if normalize:
X_train_mean = np.mean(X_train, axis=(0,1,2))
X_train_std = np.std(X_train, axis=(0,1,2))
X_train = (X_train - X_train_mean) / X_train_std
X_test = (X_test - X_train_mean) / X_train_std
if padding is not None:
pad_fn = lambda x: np.pad(x, [(0, 0), (padding, padding), (padding, padding), (0, 0)], mode='reflect')
X_train = pad_fn(X_train)
store_np_imgs_as_tfrecord(train_path, X_train, y_train)
store_np_imgs_as_tfrecord(test_path, X_test, y_test)
num_train = 50000
train = read_tfrecord_as_dataset(ds_path=train_path, parser=parser_train, batch_size=batch_size, shuffle=True, shuffle_buffer_size=num_train)
test = read_tfrecord_as_dataset(ds_path=test_path, parser=parser_test, batch_size=batch_size, shuffle=False)
return train, test |
# coding=utf-8
# Distributed under the MIT software license, see the accompanying
# file LICENSE or http://www.opensource.org/licenses/mit-license.php.
import grpc
from qrl.generated import qrldebug_pb2_grpc, qrldebug_pb2
class StateValidator:
def __init__(self, debug_addresses):
self.debug_addresses = debug_addresses
def get_full_state(self):
debug_api_addresses = self.debug_addresses
full_state_responses = []
for debug_api_address in debug_api_addresses:
channel_public = grpc.insecure_channel(debug_api_address)
stub = qrldebug_pb2_grpc.DebugAPIStub(channel_public)
full_state_responses.append(stub.GetFullState(request=qrldebug_pb2.GetFullStateReq()))
return full_state_responses
@staticmethod
def check_address_state(state1, state2):
if state1.address != state2.address:
raise Exception('Address mismatch %s %s', state1.address, state2.address)
if state1.balance != state2.balance:
raise Exception('Balance mismatch %s %s', state1.balance, state2.balance)
if state1.nonce != state2.nonce:
raise Exception('Nonce mismatch %s %s', state1.nonce, state2.nonce)
if state1.ots_bitfield != state2.ots_bitfield:
raise Exception('OTS Bitfield mismatch %s %s', state1.ots_bitfield, state2.ots_bitfield)
if state1.transaction_hashes != state2.transaction_hashes:
raise Exception('Transaction hashes mismatch %s %s',
state1.transaction_hashes,
state2.transaction_hashes)
if state1.tokens != state2.tokens:
raise Exception('Tokens mismatch %s %s', state1.tokens, state2.tokens)
if state1.latticePK_list != state2.latticePK_list:
raise Exception('LatticePK mismatch %s %s', state1.latticePK_list, state2.latticePK_list)
if state1.slave_pks_access_type != state2.slave_pks_access_type:
raise Exception('Slave PKS mismatch %s %s', state1.slave_pks_access_type, state2.slave_pks_access_type)
if state1.ots_counter != state2.ots_counter:
raise Exception('Slave PKS mismatch %s %s', state1.ots_counter, state2.ots_counter)
def validate_addresses_state(self, state1, state2):
try:
self.check_address_state(state1, state2)
except Exception as e:
raise Exception('Exception for state check between addresses %s %s\nError:\n',
state1.address,
state2.address,
e)
def validate_state(self) -> bool:
full_state_responses = self.get_full_state()
state_response1 = full_state_responses[0]
for state_response in full_state_responses[1:]:
self.validate_addresses_state(state_response1.coinbase_state, state_response.coinbase_state)
if len(state_response1.addresses_state) != len(state_response.addresses_state):
raise Exception('Number of Addresses State mismatch')
for address_state in state_response1.addresses_state:
self.validate_addresses_state(address_state, address_state)
return True
|
from enum import Enum
DEFAULT_CRF = 15
DEFAULT_PRESET = 'fast'
class Resolution(Enum):
# Width, height, auto bitrate
LOW_DEF = (360, 240, 500, 'auto_bitrate_240')
STANDARD_DEF = (720, 480, 1600, 'auto_bitrate_480')
MEDIUM_DEF = (1280, 720, 4500, 'auto_bitrate_720')
HIGH_DEF = (1920, 1080, 8000, 'auto_bitrate_1080')
ULTRA_HIGH_DEF = (3840, 2160, None)
@property
def width(self):
return self.value[0]
@property
def height(self):
return self.value[1]
@property
def auto_bitrate(self):
return self.value[2]
@property
def auto_bitrate_name(self):
return self.value[3]
class BitDepth(Enum):
BIT_8 = (8, 'yuv420p')
BIT_10 = (10, 'yuv420p10le')
BIT_12 = (12, 'yuv420p12le')
@property
def bits(self):
return self.value[0]
@property
def pix_fmt(self):
return self.value[1]
@staticmethod
def get_from_pix_fmt(pix_fmt: str):
for i in BitDepth:
if i.pix_fmt == pix_fmt:
return i
return None
def resolution_name(height):
if height <= 240:
return Resolution.LOW_DEF
elif height <= 576:
return Resolution.STANDARD_DEF
elif height <= 800:
return Resolution.MEDIUM_DEF
elif height <= 1080:
return Resolution.HIGH_DEF
else:
return Resolution.ULTRA_HIGH_DEF
class VideoCodec(Enum):
H264 = ('libx264', ['h264'])
H265 = ('libx265', ['hevc', 'h265'])
MPEG2 = ('mpeg2video', ['mpeg2video','mpeg2'])
COPY = ('copy', ['copy'])
@property
def ffmpeg_encoder_name(self):
return self.value[0]
@property
def ffmpeg_codec_name(self):
return self.value[1][0]
@property
def codec_names(self):
return self.value[1]
@staticmethod
def from_code_name(name):
for vc in VideoCodec:
for n in vc.codec_names:
if name == n:
return vc
return None
def equals(self, to_comp: str) -> bool:
"""
Compares all of the possible names to the value
:param to_comp:
:return:
"""
return to_comp == self.ffmpeg_encoder_name or to_comp in self.codec_names
class AudioCodec(Enum):
AAC = 'aac'
AC3 = 'ac3'
DTS = 'dts'
COPY = 'copy'
@property
def extension(self):
return self.value
@property
def ffmpeg_codec_name(self):
return self.value
@property
def ffmpeg_encoder_name(self):
return self.ffmpeg_codec_name
def equals(self, to_comp: str) -> bool:
"""
Compares all of the possible names to the value
:param to_comp:
:return:
"""
return to_comp == self.ffmpeg_encoder_name
class AudioChannelName(Enum):
MONO = (1, ['mono'])
STEREO = (2, ['stereo'])
SURROUND_5_1 = (6, ['surround', '5.1'])
SURROUND_6_1 = (7, ['6.1'])
SURROUND_7_1 = (8, ['7.1'])
@property
def num_channels(self):
return self.value[0]
@property
def names(self):
return self.value[1]
@property
def name(self):
return self.names()[0]
@staticmethod
def from_name(name):
for ac in AudioChannelName:
for n in ac.names:
if n == name:
return ac
return None
class VideoFileContainer(Enum):
MP4 = 'mp4'
MKV = 'mkv'
MPEG = 'mpeg'
WTV = 'wtv'
@property
def extension(self):
return self.value
class H264Preset(Enum):
ULTRAFAST = 'ultrafast'
SUPERFAST = 'superfast'
VERYFAST = 'veryfast'
FASTER = 'faster'
FAST = 'fast'
MEDIUM = 'medium'
SLOW = 'slow'
VERYSLOW = 'veryslow'
PLACEBO = 'placebo'
@staticmethod
def from_value(value):
for preset in H264Preset:
if preset.value == value:
return preset
return None
|
import torch
import torch.nn as nn
"""
Generalized Matrix Factorization
NCF interpretation of MF. This part of framework
is used to endow model of linearity to learn interactions
between users and items
"""
class GMF(nn.Module):
def __init__(self, config):
super(GMF, self).__init__()
self.user_embeddings = nn.Embedding(config.user_count, config.gmf_dim)
self.item_embeddings = nn.Embedding(config.item_count, config.gmf_dim)
self.out = nn.Sequential(nn.Linear(config.gmf_dim, 1), nn.Sigmoid())
def forward(self, users, items):
user_vector = self.user_embeddings(users)
item_vector = self.item_embeddings(items)
pointwise_product = user_vector * item_vector
prob = self.out(pointwise_product)
return prob
"""
Multy-Layer Perceptron
Simply a vector concatenation does not account for any interactions
between user and item latent features, which is insufficient
for modelling the collaborative filtering effect. To address
this issue, we propose to add hidden layers on the concatenated vector, using a standard MLP to learn the interaction
between user and item latent features. In this sense, we can
endow the model a large level of flexibility and non-linearity
to learn the interactions between user vector and item vector, rather than the
way of GMF that uses only a fixed element-wise product
on them.
"""
class MLP(nn.Module):
def __init__(self, config):
super(MLP, self).__init__()
self.config = config
self.user_embeddings = nn.Embedding(config.user_count, config.mlp_dim * 2**(config.layers_count - 1))
self.item_embeddings = nn.Embedding(config.item_count, config.mlp_dim * 2**(config.layers_count - 1))
self.hidden = MLP.create_hidden_layers(config)
self.out = nn.Sequential(nn.Linear(config.mlp_dim, 1), nn.Sigmoid())
@staticmethod
def create_hidden_layers(config):
hidden_layers = []
for i in range(config.layers_count):
input_size = config.mlp_dim * 2**(config.layers_count - i)
output_size = input_size // 2
if i == 0:
input_size += config.user_features
input_size += config.item_features
hidden_layers.extend([nn.Linear(input_size, output_size), nn.LeakyReLU(config.slope), nn.Dropout(config.dropout)])
hidden = nn.Sequential(*hidden_layers)
return hidden
def forward(self, *net_input):
input_vector = MLP.parse_input(*net_input, user_embeddings_layer=self.user_embeddings, item_embeddings_layer=self.item_embeddings)
hidden_output = self.hidden(input_vector)
prob = self.out(hidden_output)
return prob
@staticmethod
def parse_input(*net_input, user_embeddings_layer, item_embeddings_layer):
assert len(net_input) >= 2, "Input must contain at least user and item"
users = net_input[0]
items = net_input[1]
user_vector = user_embeddings_layer(users)
item_vector = item_embeddings_layer(items)
input_vector = torch.cat((user_vector, item_vector), dim=1)
if len(net_input) > 2:
user_features = net_input[2]
input_vector = torch.cat((input_vector, user_features), dim=1)
if len(net_input) > 3:
item_features = net_input[3]
input_vector = torch.cat((input_vector, item_features), dim=1)
return input_vector
"""
Enseble of GMF and MLP
"""
class NeuMF(nn.Module):
def __init__(self, config):
super(NeuMF, self).__init__()
self.user_embeddings_gmf = nn.Embedding(config.user_count, config.gmf_dim)
self.item_embeddings_gmf = nn.Embedding(config.item_count, config.gmf_dim)
self.user_embeddings_mlp = nn.Embedding(config.user_count, config.mlp_dim * 2**(config.layers_count - 1))
self.item_embeddings_mlp = nn.Embedding(config.item_count, config.mlp_dim * 2**(config.layers_count - 1))
self.hidden = MLP.create_hidden_layers(config)
self.out = nn.Sequential(nn.Linear(config.gmf_dim + config.mlp_dim, 1), nn.Sigmoid())
def forward(self, *net_input):
assert len(net_input) >= 2, "Input must contain at least user and item"
gmf_output = self.forward_gmf(*net_input)
mlp_output = self.forward_mlp(*net_input)
last_layer_input = torch.cat((gmf_output, mlp_output), dim=1)
prob = self.out(last_layer_input)
return prob
def forward_gmf(self, *net_input):
users, items = net_input[0], net_input[1]
user_vector = self.user_embeddings_gmf(users)
item_vector = self.item_embeddings_gmf(items)
pointwise_product = user_vector * item_vector
return pointwise_product
def forward_mlp(self, *net_input):
input_tensor = MLP.parse_input(*net_input, user_embeddings_layer=self.user_embeddings_mlp, item_embeddings_layer=self.item_embeddings_mlp)
output_tensor = self.hidden(input_tensor)
return output_tensor
@staticmethod
def load_pretrained(gmf_model, mlp_model, config):
neu_mf_model = NeuMF(config)
# Load GMF embeddings
neu_mf_model.user_embeddings_gmf.weight = gmf_model.user_embeddings.weight
neu_mf_model.item_embeddings_gmf.weight = gmf_model.item_embeddings.weight
# Load MLP embeddings
neu_mf_model.user_embeddings_mlp.weight = mlp_model.user_embeddings.weight
neu_mf_model.item_embeddings_mlp.weight = mlp_model.item_embeddings.weight
# Load hidden layers from MLP
neu_mf_state_dict = neu_mf_model.state_dict()
hidden_mlp_state_dict = mlp_model.hidden.state_dict()
hidden_state_dict = { "hidden."+k:v for k,v in hidden_mlp_state_dict.items() }
neu_mf_state_dict.update(hidden_state_dict)
neu_mf_model.load_state_dict(neu_mf_state_dict)
# Last output layer is a concatenation of weights of GMF and MLP output layers respectively
# There is a trade-off between weights of small models, which is tuned by alpha (hyperparameter)
# alpha - coefficient for GMF weights, (1 - alpha) - coefficient for MLP Weights
alpha = config.alpha
gmf_out_layer_weights, gmf_out_layer_bias = alpha*gmf_model.out[0].weight, alpha*gmf_model.out[0].bias
mlp_out_layer_weights, mlp_out_layer_bias = (1.0 - alpha)*mlp_model.out[0].weight, (1.0 - alpha)*mlp_model.out[0].bias
out_weights = torch.cat((gmf_out_layer_weights, mlp_out_layer_weights), dim=1)
out_bias = gmf_out_layer_bias + mlp_out_layer_bias
neu_mf_model.out[0].weight = nn.Parameter(out_weights)
neu_mf_model.out[0].bias = nn.Parameter(out_bias)
return neu_mf_model
if __name__ == "__main__":
from torchsummaryX import summary
from hparams.utils import Hparam
import sys
config = Hparam(sys.argv[1])
gmf_model = GMF(config.gmf)
mlp_model = MLP(config.mlp)
BATCH_SIZE = 1024
user, item = [torch.zeros(BATCH_SIZE).long() for _ in range(2)]
print('===============================================================')
print(' GMF ')
_ = summary(gmf_model, user, item)
print('===================================================================')
print(' MLP ')
_ = summary(mlp_model, user, item)
print('====================================================================')
print(' NeuMF ')
neu_mf_model = NeuMF.load_pretrained(gmf_model, mlp_model, config.neu_mf)
_ = summary(neu_mf_model, user, item) |
import scipy.io
import numpy as np
import pickle
import os
import pandas as pd
BIRD_DIR = 'Data/birds'
def load_filenames(data_dir):
filepath = data_dir + '/filenames_flying_251.pickle'
with open(filepath, 'rb') as f:
filenames = pickle.load(f)
print('Load filenames from: %s (%d)' % (filepath, len(filenames)))
# filenames = filenames[0:10]
return filenames
def load_bbox(data_dir):
bbox_path = os.path.join(data_dir, 'CUB_200_2011/bounding_boxes.txt')
df_bounding_boxes = pd.read_csv(bbox_path,
delim_whitespace=True,
header=None).astype(int)
filepath = os.path.join(data_dir, 'CUB_200_2011/images.txt')
df_filenames = pd.read_csv(filepath, delim_whitespace=True, header=None)
filenames = df_filenames[1].tolist()
print('Total filenames: ', len(filenames), filenames[0])
filename_bbox = {img_file[:-4]: [] for img_file in filenames}
numImgs = len(filenames)
for i in xrange(0, numImgs):
# bbox = [x-left, y-top, width, height]
bbox = df_bounding_boxes.iloc[i][1:].tolist()
key = filenames[i][:-4]
filename_bbox[key] = bbox
return filename_bbox
def custom_crop(img, bbox):
# bbox consists of (x-left, y-top, width, height)
imsiz = img.shape # [height, width, channel]
center_x = int((2 * bbox[0] + bbox[2]) / 2)
center_y = int((2 * bbox[1] + bbox[3]) / 2)
R = int(np.maximum(bbox[2], bbox[3]) * 0.75)
y1 = np.maximum(0, center_y - R)
y2 = np.minimum(imsiz[0], center_y + R)
x1 = np.maximum(0, center_x - R)
x2 = np.minimum(imsiz[1], center_x + R)
img_cropped = img[y1:y2, x1:x2]
return img_cropped
def convert_birds_dataset_pickle(inpath, set):
filename_bbox = load_bbox(inpath)
sketches = list()
ids = list()
train_dir = os.path.join(inpath, set)
train_filenames = load_filenames(train_dir)
for i in range(len(train_filenames)):
fn = train_filenames[i]
bbox = filename_bbox[fn]
mat = scipy.io.loadmat('sketches_flying_animated/{}.mat'.format(fn))
sketch = mat['sketch']
sketch = custom_crop(sketch, bbox)
sketch = scipy.misc.imresize(sketch, [76, 76], 'bicubic').astype('float32')
sketch -= 0.5
sketches.append(sketch)
ids.append(np.int64(fn[:3]))
print('Saving to {}'.format('Data/birds/{}/sketches_flying_251_animated.pickle'.format(set)))
pickle.dump(sketches, open('Data/birds/{}/sketches_flying_251_animated.pickle'.format(set), 'wb'))
pickle.dump(ids, open('Data/birds/{}/class_info_flying_251_animated.pickle'.format(set), 'wb'))
# def generate_pickle_filenames(text_file):
# with open(text_file, "r") as f:
# filenames = f.read().splitlines()
# for i in range(len(filenames)):
# filenames[i] = filenames[i][:-4]
# pickle.dump(filenames, open('Data/birds/train/filenames_flying_886.pickle', 'w'))
if __name__ == '__main__':
# generate_pickle_filenames('Data/birds/train/filenames_flying_886.txt')
convert_birds_dataset_pickle(BIRD_DIR, 'test')
# convert_birds_dataset_pickle(BIRD_DIR, 'test') |
#! /usr/bin/env python
# Hi There!
# You may be wondering what this giant blob of binary data here is, you might
# even be worried that we're up to something nefarious (good for you for being
# paranoid!). This is a base64 encoding of a zip file, this zip file contains
# a fully functional basic pytest script.
#
# Pytest is a thing that tests packages, pytest itself is a package that some-
# one might want to install, especially if they're looking to run tests inside
# some package they want to install. Pytest has a lot of code to collect and
# execute tests, and other such sort of "tribal knowledge" that has been en-
# coded in its code base. Because of this we basically include a basic copy
# of pytest inside this blob. We do this because it let's you as a maintainer
# or application developer who wants people who don't deal with python much to
# easily run tests without installing the complete pytest package.
#
# If you're wondering how this is created: you can create it yourself if you
# have a complete pytest installation by using this command on the command-
# line: ``py.test --genscript=runtests.py``.
sources = """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"""
import sys
import base64
import zlib
class DictImporter(object):
def __init__(self, sources):
self.sources = sources
def find_module(self, fullname, path=None):
if fullname == "argparse" and sys.version_info >= (2,7):
# we were generated with <python2.7 (which pulls in argparse)
# but we are running now on a stdlib which has it, so use that.
return None
if fullname in self.sources:
return self
if fullname + '.__init__' in self.sources:
return self
return None
def load_module(self, fullname):
# print "load_module:", fullname
from types import ModuleType
try:
s = self.sources[fullname]
is_pkg = False
except KeyError:
s = self.sources[fullname + '.__init__']
is_pkg = True
co = compile(s, fullname, 'exec')
module = sys.modules.setdefault(fullname, ModuleType(fullname))
module.__file__ = "%s/%s" % (__file__, fullname)
module.__loader__ = self
if is_pkg:
module.__path__ = [fullname]
do_exec(co, module.__dict__) # noqa
return sys.modules[fullname]
def get_source(self, name):
res = self.sources.get(name)
if res is None:
res = self.sources.get(name + '.__init__')
return res
if __name__ == "__main__":
try:
import pkg_resources # noqa
except ImportError:
sys.stderr.write("ERROR: setuptools not installed\n")
sys.exit(2)
if sys.version_info >= (3, 0):
exec("def do_exec(co, loc): exec(co, loc)\n")
import pickle
sources = sources.encode("ascii") # ensure bytes
sources = pickle.loads(zlib.decompress(base64.decodebytes(sources)))
else:
import cPickle as pickle
exec("def do_exec(co, loc): exec co in loc\n")
sources = pickle.loads(zlib.decompress(base64.decodestring(sources)))
importer = DictImporter(sources)
sys.meta_path.insert(0, importer)
entry = "import pytest; raise SystemExit(pytest.cmdline.main())"
do_exec(entry, locals()) # noqa
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import os
import time
import json
import hashlib
import string
import random
import requests
from .base import Map, WeixinError
__all__ = ("WeixinMPError", "WeixinMP")
DEFAULT_DIR = os.getenv("HOME_EP", os.getcwd())
class WeixinMPError(WeixinError):
def __init__(self, msg):
super(WeixinMPError, self).__init__(msg)
class WeixinMP(object):
"""
微信公众号相关接口
当需要全局使用access token可以选择继承WeixinMP实现access_token
class WeixinMPSub(object):
def __init__(self, app_id, app_secret):
WeixinMP.__init__(app_id, app_secret)
@property
def access_token(self):
return requests.get("http://example.com").content
mp = WeixinMPSub("app_id", "app_secret")
也可以选择传入jt_callback
def get_access_token(mp):
return requests.get("http://example.com").content
WeixinMP("app_id", "app_secret", ac_callback=get_access_token)
"""
api_uri = "https://api.weixin.qq.com"
contenttype_config_res = {
r'jpg': r'image/jpeg',
r'jpeg': r'image/jpeg',
r'gif': r'image/gif',
r'png': r'image/png',
}
def __init__(self, app_id, app_secret, ac_path=None, jt_path=None, ac_callback=None, jt_callback=None):
"""
:param :app_id 微信app id
:param :app_secret 微信app secret
:param :ac_path access token 保存路径
:param :jt_path js ticket 保存路径
:param :ac_callback ac_callback
:param :jt_callback jt_callback
"""
self.app_id = app_id
self.app_secret = app_secret
self.session = requests.Session()
if ac_path is None:
ac_path = os.path.join(DEFAULT_DIR, ".access_token")
if jt_path is None:
jt_path = os.path.join(DEFAULT_DIR, ".jsapi_ticket")
self.ac_path = ac_path
self.jt_path = jt_path
self.ac_callback = ac_callback
self.jt_callback = jt_callback
def fetch(self, method, url, params=None, data=None, headers=None, buffer=False, files=None):
if files:
resp = requests.post(url=url, params=params, data=data, files=files)
else:
req = requests.Request(method, url, params=params,
data=data, headers=headers)
prepped = req.prepare()
resp = self.session.send(prepped, timeout=20)
if resp.status_code == 200 and buffer:
return resp.content
data = Map(resp.json())
if data.errcode:
msg = "%(errcode)d %(errmsg)s" % data
raise WeixinMPError(msg)
return data
def get(self, path, params=None, token=True, prefix="/cgi-bin"):
url = "{0}{1}{2}".format(self.api_uri, prefix, path)
params = {} if not params else params
token and params.setdefault("access_token", self.access_token)
return self.fetch("GET", url, params)
def post(self, path, data, prefix="/cgi-bin", json_encode=True, token=True, buffer=False, headers=None, files=None):
url = "{0}{1}{2}".format(self.api_uri, prefix, path)
params = {}
token and params.setdefault("access_token", self.access_token)
if not headers:
headers = {}
if json_encode:
data = json.dumps(data, ensure_ascii=False).encode('utf-8')
# data = json.dumps(data)
headers["Content-Type"] = "application/json;charset=UTF-8"
# print url, params, headers, data
return self.fetch("POST", url, params=params, data=data, headers=headers, buffer=buffer, files=files)
@property
def access_token(self):
"""
获取服务端凭证
当多台服务器需要共用access_token的时候
如果不想自己继承实现access_token，可以传入ac_callback()
接收一个WeixinMP对象作为参数
"""
if self.ac_callback and callable(self.ac_callback):
return self.ac_callback(self)
timestamp = time.time()
if not os.path.exists(self.ac_path) or \
int(os.path.getmtime(self.ac_path)) < timestamp:
params = dict()
params.setdefault("grant_type", "client_credential")
params.setdefault("appid", self.app_id)
params.setdefault("secret", self.app_secret)
data = self.get("/token", params, False)
with open(self.ac_path, 'wb') as fp:
fp.write(data.access_token.encode("utf-8"))
os.utime(self.ac_path, (timestamp, timestamp + data.expires_in - 600))
return open(self.ac_path).read().strip()
@property
def jsapi_ticket(self):
"""
获取jsapi ticket
当多台服务器需要共用js_ticket的时候
如果不想自己继承实现js_ticket，可以传入jt_callback()
接收一个WeixinMP对象作为参数
"""
if self.jt_callback and callable(self.jt_callback):
return self.jt_callback(self)
timestamp = time.time()
if not os.path.exists(self.jt_path) or \
int(os.path.getmtime(self.jt_path)) < timestamp:
params = dict()
params.setdefault("type", "jsapi")
data = self.get("/ticket/getticket", params, True)
with open(self.jt_path, 'wb') as fp:
fp.write(data.ticket.encode("utf-8"))
os.utime(self.jt_path, (timestamp, timestamp + data.expires_in - 600))
return open(self.jt_path).read()
@property
def nonce_str(self):
char = string.ascii_letters + string.digits
return "".join(random.choice(char) for _ in range(32))
def jsapi_sign(self, **kwargs):
"""
生成签名给js使用
"""
timestamp = str(int(time.time()))
nonce_str = self.nonce_str
kwargs.setdefault("jsapi_ticket", self.jsapi_ticket)
kwargs.setdefault("timestamp", timestamp)
kwargs.setdefault("noncestr", nonce_str)
raw = [(k, kwargs[k]) for k in sorted(kwargs.keys())]
s = "&".join("=".join(kv) for kv in raw if kv[1])
sign = hashlib.sha1(s.encode("utf-8")).hexdigest().lower()
return Map(sign=sign, timestamp=timestamp, noncestr=nonce_str, appId=self.app_id)
def groups_create(self, name):
"""
创建分组
:param name: 分组名
"""
data = dict(group=dict(name=name))
return self.post("/groups/create", data)
def groups_get(self):
"""
获取所有分组
"""
return self.get("/groups/get")
def groups_getid(self, openid):
"""
查询用户所在分组
:param openid: 用户id
"""
data = dict(openid=openid)
return self.post("/groups/getid", data)
def groups_update(self, id, name):
"""
修改分组名
:param id: 分组id
:param name: 分组名
"""
data = dict(group=dict(id=id, name=name))
return self.post("/groups/update", data)
def groups_members_update(self, to_groupid, openid):
"""
移动用户分组
:param to_groupid: 分组id
:param openid: 用户唯一标识符
"""
data = dict(openid=openid, to_groupid=to_groupid)
return self.post("/groups/members/update", data)
def groups_members_batchupdate(self, to_groupid, *openid):
"""
批量移动用户分组
:param to_groupid: 分组id
:param openid: 用户唯一标示列表
"""
data = dict(openid_list=openid, to_groupid=to_groupid)
return self.post("/groups/members/batchupdate", data)
def groups_delete(self, id):
"""
删除组
:param id: 分组的id
"""
data = dict(group=dict(id=id))
return self.post("/groups/delete", data)
def user_info_updateremark(self, openid, remark):
"""
设置备注名
:param openid: 用户唯一标识符
:param remark: 备注
"""
data = dict(openid=openid, remark=remark)
return self.post("/user/info/updateremark", data)
def user_info(self, openid):
"""
获取用户信息
包含subscribe字段，可以用来判断用户是否关注公众号
:param openid: 用户id
"""
args = dict(openid=openid, lang="zh_CN")
return self.get("/user/info", args)
def user_info_batchget(self, *openid):
"""
批量获取用户信息
"""
user_list = []
for id in openid:
user_list.append(dict(openid=openid, lang="zh_CN"))
data = dict(user_list=user_list)
return self.post("/user/info/batchget", data)
def user_get(self, next_openid=None):
"""
获取公众号关注列表
一次最多返回1000个
:param next_openid: 第一个拉取的openid,不填默认从头开始
"""
args = dict()
if next_openid:
args.setdefault("next_openid", next_openid)
return self.get("/user/get", args)
def menu_create(self, data):
data = dict(button=data)
return self.post("/menu/create", data)
def menu_get(self):
return self.get("/menu/get")
def menu_delete(self):
return self.get("/menu/delete")
def get_current_selfmenu_info(self):
return self.get("/get_current_selfmenu_info")
def shorturl(self, long_url):
"""
长链接转为短链接
:param long_url: 长链接
"""
data = dict(action="long2short", long_url=long_url)
return self.post("/shorturl", data)
def qrcode_create(self, scene_id, expires=30):
"""
创建qrcode
"""
data = dict(
action_name="QR_SCENE", expire_seconds=expires,
action_info=dict(scene=dict(scene_id=scene_id)),
)
return self.post("/qrcode/create", data)
def qrcode_create_limit(self, input):
"""
创建qrcode限制方式
"""
data = dict()
if isinstance(input, int):
data["action_name"] = "QR_LIMIT_SCENE"
data["action_info"] = dict(scene=dict(
scene_id=input,
))
elif isinstance(input, str):
data["action_name"] = "QR_LIMIT_STR_SCENE"
data["action_info"] = dict(scene=dict(
scene_str=input,
))
else:
raise ValueError("invalid type")
return self.post("/qrcode/create", data)
def qrcode_show(self, ticket):
"""
显示qrcode
"""
url = "https://mp.weixin.qq.com/cgi-bin/showqrcode"
return self.add_query(url, dict(ticket=ticket))
def shop_list(self, pageindex=1, pagesize=10):
"""
门店列表
"""
data = dict(pageindex=pageindex, pagesize=pagesize)
return self.post("/shop/list", data, prefix="/bizwifi")
def shop_get(self, shop_id):
"""
查询门店Wi-Fi信息
"""
return self.post("/shop/get", dict(shop_id=shop_id), prefix="/bizwifi")
def shop_update(self, shop_id, old_ssid, ssid, password=None):
"""
修改门店网络信息
"""
data = dict(shop_id=shop_id, old_ssid=old_ssid, ssid=ssid)
if password:
data.update(dict(password=password))
return self.post("/shop/update", data, prefix="/bizwifi")
def shop_clean(self, shop_id):
"""
通过此接口清空门店的网络配置及所有设备，恢复空门店状态
"""
return self.post("/shop/clean", dict(shop_id=shop_id), prefix="/bizwifi")
def apportal_register(self, shop_id, ssid, reset):
"""
添加portal型设备
"""
data = dict(shop_id=shop_id, ssid=ssid, reset=reset)
return self.post("/apportal/register", data)
def device_list(self, shop_id=None, pageindex=1, pagesize=10, prefix="/bizwifi"):
"""
查询设备
"""
data = dict(pageindex=pageindex, pagesize=pagesize)
if shop_id:
data.update(dict(shop_id=shop_id))
return self.post("/device/list", data, prefix="/bizwifi")
def device_delete(self, bssid):
"""
删除设备
"""
return self.post("/device/delete", dict(bssid=bssid), prefix="/bizwifi")
def qrcode_get(self, shop_id, ssid, img_id):
"""
获取物料二维码
"""
data = dict(shop_id=shop_id, ssid=ssid, img_id=img_id)
return self.post("/qrcode/get", data, prefix="/bizwifi")
def get_all_private_template(self):
"""
获取所有私有模板列表
"""
return self.get("/template/get_all_private_template")
def del_private_template(self, template_id):
"""
删除私有模板
"""
return self.post("/template/del_private_template", dict(template_id=template_id))
def template_send(self, template_id, touser, data, url=None, miniprogram=None, **kwargs):
"""
发送模板消息
:paramas template_id: 模板id
:params touser: openid
:params data: 模板消息对应的内容跟颜色
:params url: 跳转地址
:parms miniprogram: 小程序跳转相关
"""
kwargs.setdefault("template_id", template_id)
kwargs.setdefault("touser", touser)
kwargs.setdefault("data", data)
url and kwargs.setdefault("url", url)
miniprogram and kwargs.setdefault("miniprogram", miniprogram)
# print kwargs
return self.post("/message/wxopen/template/send", kwargs)
def msg_sec_check(self, content):
"""
检查一段文本是否含有违法违规内容。
:param content: 文本内容
"""
return self.post("/msg_sec_check", {'content': content}, prefix="/wxa")
def img_sec_check(self, filename):
"""
校验一张图片是否含有违法违规内容。
:param 要检测的图片文件，格式支持PNG、JPEG、JPG、GIF，图片尺寸不超过 750px x 1334px
"""
contenttype = self.contenttype_config_res.get(str(filename).split('.')[-1])
media = open(filename, 'rb')
files = [(contenttype, media), ]
return self.post('/img_sec_check', data={'media': 'media'}, json_encode=False, files=files, prefix='/wxa')
def get_wxacode_unlimit(self, scene, **kwargs):
"""
:param scene: 参数 a=1&b=2
:param kwargs: {
page: 跳转的小程序页面，默认主页 pages/index/index,
width: 二维码宽度默认430,
auto_color: 自动配置线条颜色,
line_color: auto_color 为 false 时生效，使用 rgb 设置颜色 例如 {"r":"xxx","g":"xxx","b":"xxx"} 十进制表示,
is_hyaline: 是否需要透明底色，为 true 时，生成透明底色的小程序
}
"""
kwargs['scene'] = scene
return self.post('/getwxacodeunlimit', kwargs, prefix='/wxa', buffer=True)
def msg_send(self, user, msg):
"""
该接口每个用户每月最多收到4条，每天调用100次
:param body:
{
"touser":[
"OPENID1",
"OPENID2"
],
"msgtype": "text",
"text": { "content": "hello from boxer."}
}
:return:
"""
body = {
"touser": user,
"msgtype": "text",
"text": {
"content": msg
}
}
return self.post('/message/mass/send', body, buffer=True)
def temp_send(self, user, data, template_id, **kwargs):
"""
服务号模板消息推送
每天调用100000次。
{
"touser":"OPENID",
"template_id":"ngqIpbwh8bUfcSsECmogfXcV14J0tQlEpBO27izEYtY",
"url":"http://weixin.qq.com/download",
"miniprogram":{
"appid":"xiaochengxuappid12345",
"pagepath":"index?foo=bar"
},
"data":{
"first": {
"value":"恭喜你购买成功！",
"color":"#173177"
},
"keyword1":{
"value":"巧克力",
"color":"#173177"
},
"keyword2": {
"value":"39.8元",
"color":"#173177"
},
"keyword3": {
"value":"2014年9月22日",
"color":"#173177"
},
"remark":{
"value":"欢迎再次购买！",
"color":"#173177"
}
}
}
:return:
"""
body = {
"touser": user,
"template_id": template_id,
"data": data
}
if kwargs.get('url'):
body.setdefault('url', kwargs.get('url'))
if kwargs.get('miniprogram'):
body.setdefault('miniprogram', kwargs.get('miniprogram'))
return self.post('/message/template/send', body, buffer=True)
# a = '/message/template/send'
# def ep_access_token(self):
# """
# 获取epaccesstoken
# :return:
# """
# ep_ac_path = os.path.join(DEFAULT_DIR, ".ep_access_token")
# timestamp = time.time()
# if not os.path.exists(ep_ac_path) or \
# int(os.path.getmtime(ep_ac_path)) < timestamp:
# params = {
# "client_id": "ehr-106",
# "client_secret": "5b828d4939b24b11ad6d9c7529105fb6",
# "protocalMustParams": {
# "baseParam": {
# "userCode": "nbzlb"
# },
# "charset": "utf-8",
# "appid": "ehr-106"}
# }
# # headers = {}
# # headers["Content-Type"] = "application/json;charset=UTF-8"
# data = json.dumps(params, ensure_ascii=False).encode('utf-8')
# data = self.fetch("POST","https://epp.epsoft.com.cn/eps-api/oauth/token", data=data, headers={
# "Content-Type":"application/json;charset=UTF-8"})
# from flask import current_app
# current_app.logger.info('get data = {}'.format(data))
# with open(ep_ac_path, 'wb') as fp:
# fp.write(data.accessToken.encode("utf-8"))
# lasttime = time.mktime(time.strptime(str(data.expiration).split('+')[0], '%Y-%m-%dT%H:%M:%S.%f'))
# current_app.logger.info('get ep_access_token lasttime = {}'.format(lasttime))
# os.utime(ep_ac_path, (timestamp, lasttime)) # 过期时间设置
# return open(ep_ac_path).read().strip()
# def ep_test_access_token(self):
# ep_ac_path = os.path.join(DEFAULT_DIR, ".ep_test_access_token")
# timestamp = time.time()
# if not os.path.exists(ep_ac_path) or \
# int(os.path.getmtime(ep_ac_path)) < timestamp:
# params = {
# "client_id": "ehradmin",
# "client_secret": "5f728cee95b048f2a94db9d33d1f6737",
# "protocalMustParams": {
# "baseParam": {
# "userCode": "nbzlb"
# },
# "charset": "utf-8",
# "appid": "app_id"
# }
# }
# data = json.dumps(params, ensure_ascii=False).encode('utf-8')
# data = self.fetch("POST", "https://epp.epsoft.com.cn/eps-api/oauth/token", data=data, headers={
# "Content-Type": "application/json;charset=UTF-8"})
# from flask import current_app
# current_app.logger.info('get data = {}'.format(data))
# with open(ep_ac_path, 'wb') as fp:
# fp.write(data.accessToken.encode("utf-8"))
# lasttime = time.mktime(time.strptime(str(data.expiration).split('+')[0], '%Y-%m-%dT%H:%M:%S.%f'))
# current_app.logger.info('get ep_test_access_token lasttime = {}'.format(lasttime))
# os.utime(ep_ac_path, (timestamp, lasttime)) # 过期时间设置
# return open(ep_ac_path).read().strip()
|
"""Investigates how the optimal sweep distribution is dependent on lift coefficient."""
import optix
import json
import matplotlib
import numpy as np
import machupX as mx
import matplotlib.pyplot as plt
import scipy.optimize as opt
import multiprocessing as mp
from optimization import DragCase, grad, optimize
if __name__=="__main__":
font = {
"family" : "serif",
"size" : 10
}
matplotlib.rc('font', **font)
# Params
N_CLs = 11
TR = 1.0
AR = 12.0
N = 80
N_sweeps = 20
CLs = np.linspace(0.1, 0.5, N_CLs)
sweep_dists = np.zeros((N_CLs, N_sweeps))
color_range = np.linspace(0, 155, N_CLs)
colors = ["#"+"".join([hex(int(x)).replace('0x', '')]*3) for x in color_range]
for i, CL in enumerate(CLs):
_,_,sweep_dists[i] = optimize(np.zeros(N_sweeps), TR, AR, CL, N, 'L-BFGS-B')
# Plot sweep distribution
spans = np.linspace(0.0, 1.0, N_sweeps)
plt.figure(figsize=(5, 5))
for i, CL in enumerate(CLs):
plt.plot(spans, sweep_dists[i], label=str(round(CL, 3)), color=colors[i])
plt.xlabel("Span Fraction")
plt.ylabel("Local Sweep Angle [deg]")
plt.legend(title="$C_L$")
plt.show() |
"""
File for generating MAG data plots
"""
import heliopy.data.cassini as heliopydata
def base_mag_1min_plot(starttime, endtime, coords, ax=None):
"""
Plot spectrogram of MAG data
"""
magdata = heliopydata.mag_1min(starttime, endtime, coords)
for Baxis in ['Bx', 'By', 'Bz']:
ax.plot(magdata.index, magdata.to_dataframe()[Baxis], label=Baxis)
ax.set_ylabel("MAG \n{0} coords \n[\\nT] ".format(coords))
|
# Copyright (c) 2015-2021 Agalmic Ventures LLC (www.agalmicventures.com)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import unittest
from JTL import Interpreter
class InterpreterTest(unittest.TestCase):
def setUp(self):
self._testData = {
'a': {
'X': 3,
'Y': 2,
},
'b': {'p': {'d': {'q': 'test'}}},
'c': 'asdf',
}
def test_transformChain(self):
self.assertEqual(Interpreter.transform(self._testData, 'a.X'), 3)
self.assertEqual(Interpreter.transform(self._testData, 'a $ .X'), 3)
self.assertEqual(Interpreter.transform(self._testData, 'a $ .X $ toString'), "3")
def test_transformArithmetic(self):
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ + a.Y'), 5)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ - a.Y'), 1)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ * a.Y'), 6)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ / a.Y'), 1.5)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ % a.Y'), 1)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ ** a.Y'), 9)
def test_transformComparison(self):
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ == 3'), True)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ != 3'), False)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ >= 3'), True)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ > 3'), False)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ <= 3'), True)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ < 3'), False)
self.assertEqual(Interpreter.transform(self._testData, 'a.X $ == 3 $ not'), False)
def test_transformDictionary(self):
self.assertEqual(Interpreter.transform(self._testData, 'a $ keys $ sorted'), ['X', 'Y'])
self.assertEqual(Interpreter.transform(self._testData, 'a $ values $ sorted'), [2, 3])
|
## 1. Introducing Data Cleaning ##
# Read the text on the left, and then scroll to the bottom
# to find the instructions for the coding exercise
# Write your answer to the instructions below -- the list of
# lists is stored using the variable name `moma`
num_rows = len(moma)
## 2. Reading our MoMA Data Set ##
# import the reader function from the csv module
from csv import reader
# use the python built-in function open()
# to open the children.csv file
opened_file = open('children.csv')
# use csv.reader() to parse the data from
# the opened file
read_file = reader(opened_file)
# use list() to convert the read file
# into a list of lists format
children = list(read_file)
# remove the first row of the data, which
# contains the column names
children = children[1:]
# Write your code here
opened_file = open('artworks.csv')
read_file = reader(opened_file)
moma = list(read_file)
moma = moma[1:]
## 3. Replacing Substrings with the replace Method ##
age1 = "I am thirty-one years old"
age2 = age1.replace("one","two")
## 4. Cleaning the Nationality and Gender Columns ##
# Variables you create in previous screens
# are available to you, so you don't need
# to read the CSV again
for row in moma:
# remove parentheses from the nationality column
nationality = row[2]
nationality = nationality.replace("(","")
nationality = nationality.replace(")","")
row[2] = nationality
# remove parentheses from the gender column
gender = row[5]
gender = gender.replace("(","")
gender = gender.replace(")","")
row[5] = gender
## 5. String Capitalization ##
for row in moma:
# fix the capitalization and missing
# values for the gender column
gender = row[5]
gender = gender.title()
if not gender:
gender = "Gender Unknown/Other"
row[5] = gender
# fix the capitalization and missing
# values for the nationality column
nationality = row[2]
nationality = nationality.title()
if not nationality:
nationality = "Nationality Unknown"
row[2] = nationality
## 6. Errors During Data Cleaning ##
def clean_and_convert(date):
# check that we don't have an empty string
if date != "":
# move the rest of the function inside
# the if statement
date = date.replace("(", "")
date = date.replace(")", "")
date = int(date)
return date
for row in moma:
birth_date = row[3]
death_date = row[4]
birth_date = clean_and_convert(birth_date)
death_date = clean_and_convert(death_date)
row[3] = birth_date
row[4] = death_date
## 7. Parsing Numbers from Complex Strings, Part One ##
test_data = ["1912", "1929", "1913-1923",
"(1951)", "1994", "1934",
"c. 1915", "1995", "c. 1912",
"(1988)", "2002", "1957-1959",
"c. 1955.", "c. 1970's",
"C. 1990-1999"]
bad_chars = ["(",")","c","C",".","s","'", " "]
def strip_characters(string):
for char in bad_chars:
string = string.replace(char,"")
return string
stripped_test_data = []
for d in test_data:
date = strip_characters(d)
stripped_test_data.append(date)
## 8. Parsing Numbers from Complex Strings, Part Two ##
test_data = ["1912", "1929", "1913-1923",
"(1951)", "1994", "1934",
"c. 1915", "1995", "c. 1912",
"(1988)", "2002", "1957-1959",
"c. 1955.", "c. 1970's",
"C. 1990-1999"]
bad_chars = ["(",")","c","C",".","s","'", " "]
def strip_characters(string):
for char in bad_chars:
string = string.replace(char,"")
return string
stripped_test_data = ['1912', '1929', '1913-1923',
'1951', '1994', '1934',
'1915', '1995', '1912',
'1988', '2002', '1957-1959',
'1955', '1970', '1990-1999']
def process_date(date):
if "-" in date:
split_date = date.split("-")
date_one = split_date[0]
date_two = split_date[1]
date = (int(date_one) + int(date_two)) / 2
date = round(date)
else:
date = int(date)
return date
processed_test_data = []
for d in stripped_test_data:
date = process_date(d)
processed_test_data.append(date)
for row in moma:
date = row[6]
date = strip_characters(date)
date = process_date(date)
row[6] = date |
import pandas
import numpy as np
class SortinoRatioIndicator(object):
""" Sortino Ratio of a trading strategy KPI """
KPIdf: np.float64
def __init__(self, a_df: pandas.DataFrame, risk_free_rate: float = 0.022):
self.__setIndicator(a_df, risk_free_rate)
def __setIndicator(self, a_df: pandas.DataFrame, risk_free_rate):
"""function to calculate sortino ratio ; rf is the risk free rate"""
df: pandas.DataFrame = a_df.copy()
df['DailyReturn'] = a_df['Adj Close'].pct_change()
neg_vol = df[df['DailyReturn'] < 0]['DailyReturn'].std() * np.sqrt(252)
self.KPIdf = (self.__getCagr(df) - risk_free_rate)/neg_vol
@staticmethod
def __getCagr(a_df: pandas.DataFrame):
"""function to calculate the Cumulative Annual Growth Rate of a trading strategy"""
df: pandas.DataFrame = a_df.copy()
df['DailyReturn'] = a_df['Adj Close'].pct_change()
df['CumulativeReturn'] = (1 + df["DailyReturn"]).cumprod()
annual_length = len(df) / 252
return (df['CumulativeReturn'][-1]) ** (1 / annual_length) - 1
|
###
# Copyright (c) 2002-2005, Jeremiah Fincher
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions, and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the author of this software nor the name of
# contributors to this software may be used to endorse or promote products
# derived from this software without specific prior written consent.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
###
import re
from cStringIO import StringIO
import supybot.gpg as gpg
from supybot.test import PluginTestCase, network
import supybot.conf as conf
import supybot.world as world
import supybot.ircdb as ircdb
import supybot.utils as utils
PRIVATE_KEY = """
-----BEGIN PGP PRIVATE KEY BLOCK-----
Version: GnuPG v1.4.12 (GNU/Linux)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=UBcd
-----END PGP PRIVATE KEY BLOCK-----
"""
WRONG_TOKEN_SIGNATURE = """
-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1
{a95dc112-780e-47f7-a83a-c6f3820d7dc3}
-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.4.12 (GNU/Linux)
iJwEAQECAAYFAlD7Jb0ACgkQydMyPCCvASv9HgQAhQf/oFMWcKwGncH0hjXC3QYz
7ck3chgL3S1pPAvS69viz6i2bwYZYD8fhzHNJ/qtw/rx6thO6PwT4SpdhKerap+I
kdem3LjM4fAGHRunHZYP39obNKMn1xv+f26mEAAWxdv/W/BLAFqxi3RijJywRkXm
zo5GUl844kpnV+uk0Xk=
=z2Cz
-----END PGP SIGNATURE-----
"""
FINGERPRINT = '2CF3E41500218D30F0B654F5C9D3323C20AF012B'
class UserTestCase(PluginTestCase):
plugins = ('User', 'Admin', 'Config')
prefix1 = 'somethingElse!user@host.tld'
prefix2 = 'EvensomethingElse!user@host.tld'
def setUp(self):
super(UserTestCase, self).setUp()
gpg.loadKeyring()
def testHostmaskList(self):
self.assertError('hostmask list')
original = self.prefix
self.prefix = self.prefix1
self.assertNotError('register foo bar')
self.prefix = original
self.assertError('hostmask list foo')
self.assertNotError('hostmask add foo [hostmask] bar')
self.assertNotError('hostmask add foo')
self.assertNotRegexp('hostmask add foo', 'IrcSet')
def testHostmaskListHandlesEmptyListGracefully(self):
self.assertError('hostmask list')
self.prefix = self.prefix1
self.assertNotError('register foo bar')
self.assertNotError('hostmask remove foo %s' % self.prefix1)
self.assertNotError('identify foo bar')
self.assertRegexp('hostmask list', 'no registered hostmasks')
def testHostmask(self):
self.assertResponse('hostmask', self.prefix)
self.assertError('@hostmask asdf')
m = self.irc.takeMsg()
self.failIf(m is not None, m)
def testRegisterUnregister(self):
self.prefix = self.prefix1
self.assertNotError('register foo bar')
self.assertError('register foo baz')
self.failUnless(ircdb.users.getUserId('foo'))
self.assertError('unregister foo')
self.assertNotError('unregister foo bar')
self.assertRaises(KeyError, ircdb.users.getUserId, 'foo')
def testDisallowedUnregistration(self):
self.prefix = self.prefix1
self.assertNotError('register foo bar')
orig = conf.supybot.databases.users.allowUnregistration()
conf.supybot.databases.users.allowUnregistration.setValue(False)
try:
self.assertError('unregister foo')
m = self.irc.takeMsg()
self.failIf(m is not None, m)
self.failUnless(ircdb.users.getUserId('foo'))
finally:
conf.supybot.databases.users.allowUnregistration.setValue(orig)
def testList(self):
self.prefix = self.prefix1
self.assertNotError('register foo bar')
self.assertResponse('user list', 'foo')
self.prefix = self.prefix2
self.assertNotError('register biff quux')
self.assertResponse('user list', 'biff and foo')
self.assertRegexp('user list --capability testcap', 'no matching')
self.assertNotError('admin capability add biff testcap')
self.assertResponse('user list --capability testcap', 'biff')
self.assertNotError('config capabilities.private testcap')
self.assertRegexp('user list --capability testcap', 'Error:.*private')
self.assertNotError('admin capability add biff admin')
self.assertResponse('user list --capability testcap', 'biff')
self.assertNotError('admin capability remove biff admin')
self.assertRegexp('user list --capability testcap', 'Error:.*private')
self.assertNotError('config capabilities.private ""')
self.assertResponse('user list --capability testcap', 'biff')
self.assertNotError('admin capability remove biff testcap')
self.assertRegexp('user list --capability testcap', 'no matching')
self.assertResponse('user list f', 'biff and foo')
self.assertResponse('user list f*', 'foo')
self.assertResponse('user list *f', 'biff')
self.assertNotError('unregister biff quux')
self.assertResponse('user list', 'foo')
self.assertNotError('unregister foo bar')
self.assertRegexp('user list', 'no registered users')
self.assertRegexp('user list asdlfkjasldkj', 'no matching registered')
def testListHandlesCaps(self):
self.prefix = self.prefix1
self.assertNotError('register Foo bar')
self.assertResponse('user list', 'Foo')
self.assertResponse('user list f*', 'Foo')
def testChangeUsername(self):
self.prefix = self.prefix1
self.assertNotError('register foo bar')
self.prefix = self.prefix2
self.assertNotError('register bar baz')
self.prefix = self.prefix1
self.assertError('changename foo bar')
self.assertNotError('changename foo baz')
def testSetpassword(self):
self.prefix = self.prefix1
self.assertNotError('register foo bar')
password = ircdb.users.getUser(self.prefix).password
self.assertNotEqual(password, 'bar')
self.assertNotError('set password foo bar baz')
self.assertNotEqual(ircdb.users.getUser(self.prefix).password,password)
self.assertNotEqual(ircdb.users.getUser(self.prefix).password, 'baz')
def testStats(self):
self.assertNotError('user stats')
self.assertNotError('load Lart')
self.assertNotError('user stats')
def testUserPluginAndUserList(self):
self.prefix = self.prefix1
self.assertNotError('register Foo bar')
self.assertResponse('user list', 'Foo')
self.assertNotError('load Seen')
self.assertResponse('user list', 'Foo')
if gpg.available and network:
def testGpgAddRemove(self):
self.assertNotError('register foo bar')
self.assertError('user gpg add 51E516F0B0C5CE6A pgp.mit.edu')
self.assertResponse('user gpg add EB17F1E0CEB63930 pgp.mit.edu',
'1 key imported, 0 unchanged, 0 not imported.')
self.assertNotError(
'user gpg remove F88ECDE235846FA8652DAF5FEB17F1E0CEB63930')
self.assertResponse('user gpg add EB17F1E0CEB63930 pgp.mit.edu',
'1 key imported, 0 unchanged, 0 not imported.')
self.assertResponse('user gpg add EB17F1E0CEB63930 pgp.mit.edu',
'Error: This key is already associated with your account.')
if gpg.available:
def testGpgAuth(self):
self.assertNotError('register spam egg')
gpg.keyring.import_keys(PRIVATE_KEY).__dict__
(id, user) = ircdb.users.items()[0]
user.gpgkeys.append(FINGERPRINT)
msg = self.getMsg('gpg gettoken').args[-1]
match = re.search('is: ({.*}).', msg)
assert match, repr(msg)
token = match.group(1)
def fakeGetUrlFd(*args, **kwargs):
return fd
(utils.web.getUrlFd, realGetUrlFd) = (fakeGetUrlFd, utils.web.getUrlFd)
fd = StringIO()
fd.write('foo')
fd.seek(0)
self.assertResponse('gpg auth http://foo.bar/baz.gpg',
'Error: Signature or token not found.')
fd = StringIO()
fd.write(token)
fd.seek(0)
self.assertResponse('gpg auth http://foo.bar/baz.gpg',
'Error: Signature or token not found.')
fd = StringIO()
fd.write(WRONG_TOKEN_SIGNATURE)
fd.seek(0)
self.assertRegexp('gpg auth http://foo.bar/baz.gpg',
'Error: Unknown token.*')
fd = StringIO()
fd.write(str(gpg.keyring.sign(token)))
fd.seek(0)
self.assertResponse('gpg auth http://foo.bar/baz.gpg',
'You are now authenticated as spam.')
utils.web.getUrlFd = realGetUrlFd
# vim:set shiftwidth=4 softtabstop=4 expandtab textwidth=79:
|
# SPDX-FileCopyrightText: 2019 Scott Shawcroft for Adafruit Industries
#
# SPDX-License-Identifier: MIT
"""
`adafruit_ssd1608`
================================================================================
CircuitPython `displayio` driver for SSD1608-based ePaper displays
* Author(s): Scott Shawcroft
Implementation Notes
--------------------
**Hardware:**
* `Adafruit 1.54" Monochrome ePaper Display Breakout <https://www.adafruit.com/product/4196>`_
**Software and Dependencies:**
* Adafruit CircuitPython firmware (version 5+) for the supported boards:
https://github.com/adafruit/circuitpython/releases
"""
import displayio
__version__ = "1.2.4"
__repo__ = "https://github.com/adafruit/Adafruit_CircuitPython_SSD1608.git"
_START_SEQUENCE = (
b"\x12\x00" # Software reset
b"\x01\x03\x00\x00\x00" # driver output control
b"\x3a\x01\x1b" # Set dummy line period
b"\x3b\x01\x0b" # Set gate line width
b"\x11\x01\x03" # Data entry sequence
b"\x2c\x01\x70" # Vcom Voltage
b"\x32\x1e\x02\x02\x01\x11\x12\x12\x22\x22\x66\x69\x69\x59\x58\x99\x99\x88\x00\x00\x00\x00\xf8"
b"\xb4\x13\x51\x35\x51\x51\x19\x01\x00" # LUT
b"\x22\x01\xc7" # Set DISP ctrl2
)
_STOP_SEQUENCE = b"\x10\x01\x01" # Enter deep sleep
# pylint: disable=too-few-public-methods
class SSD1608(displayio.EPaperDisplay):
"""SSD1608 driver"""
def __init__(self, bus, **kwargs):
start_sequence = bytearray(_START_SEQUENCE)
width = kwargs["width"]
start_sequence[4] = (width - 1) & 0xFF
start_sequence[5] = (width - 1) >> 8
super().__init__(
bus,
start_sequence,
_STOP_SEQUENCE,
**kwargs,
ram_width=240,
ram_height=320,
set_column_window_command=0x44,
set_row_window_command=0x45,
set_current_column_command=0x4E,
set_current_row_command=0x4F,
write_black_ram_command=0x24,
refresh_display_command=0x20,
)
|
from nyr.interpreter.interpreter import Interpreter
from nyr.parser.parser import Parser
def testEmptyStatement():
ast = Parser().parse(";;")
env = Interpreter().interpret(ast)
assert env == dict()
def testInterpretMultiple():
parser = Parser()
interpreter = Interpreter()
for i in range(20):
ast = parser.parse(f"let x = {i};")
env = interpreter.interpret(ast)
assert env == {"x": i}
|
#!/usr/bin/env python
#
# Copying a pin is not representative of typical user behavior on Pinterest.
#
# This script is intended to demonstrate how to use the API to developers,
# and to provide functionality that might be convenient for developers.
# For example, it might be used as part of a program to generate an
# account to be used to test an API-based application.
#
import argparse
import sys
from os.path import abspath, dirname, join
sys.path.append(abspath(join(dirname(__file__), "..", "src")))
from api_config import ApiConfig
from arguments import common_arguments
def main(argv=[]):
"""
This script copies a pin to a board, both of which are specified by identifiers
that can be found using the get_user_pins.py and get_user_boards.py script.
If a section identifier is specified in addition to a board identifier,
this script will copy the pin to the board section. Section identifiers can be
found using the get_board.py script. A section identifier may not be specified
without a board identifier.
"""
parser = argparse.ArgumentParser(description="Copy a Pin")
parser.add_argument("-p", "--pin-id", required=True, help="source pin identifier")
parser.add_argument("-m", "--media", help="media path or id")
parser.add_argument(
"-b", "--board-id", required=True, help="destination board identifier"
)
parser.add_argument("-s", "--section", help="destination board section")
common_arguments(parser)
args = parser.parse_args(argv)
# get configuration from defaults and/or the environment
api_config = ApiConfig(verbosity=args.log_level, version=args.api_version)
# imports that depend on the version of the API
from access_token import AccessToken
from oauth_scope import Scope
from pin import Pin
access_token = AccessToken(api_config, name=args.access_token)
access_token.fetch(scopes=[Scope.READ_PINS, Scope.WRITE_BOARDS, Scope.WRITE_PINS])
pin = Pin(args.pin_id, api_config, access_token)
pin_data = pin.get()
print("source pin:")
Pin.print_summary(pin_data)
new_pin_data = pin.create(pin_data, args.board_id, args.section, args.media)
print("new pin:")
Pin.print_summary(new_pin_data)
if __name__ == "__main__":
main(sys.argv[1:])
|
import uuid
from django.db import migrations, models
def get_token():
return str(uuid.uuid4())
def create_uuid(apps, schema_editor):
accounts = apps.get_model("account", "User").objects.all()
for account in accounts:
account.token = get_token()
account.save()
class Migration(migrations.Migration):
dependencies = [("account", "0020_user_token")]
operations = [
migrations.RunPython(create_uuid),
migrations.AlterField(
model_name="user",
name="token",
field=models.UUIDField(default=get_token, editable=False, unique=True),
),
]
|
import sqlite3
import pathlib
import io
from PIL import Image
from get_caged.cage_image import CageImage
from get_caged.target_image import TargetImageSpec
base_path = pathlib.Path(__file__).resolve().parent
def connect():
return sqlite3.connect(base_path / "cage.db")
def dict_factory(cursor, row):
d = {}
for idx, col in enumerate(cursor.description):
d[col[0]] = row[idx]
return d
def initialize_db():
conn = connect()
c = conn.cursor()
c.execute(
"""
CREATE TABLE IF NOT EXISTS nicholas_cage_images
(id integer PRIMARY KEY,
width integer NOT NULL,
height integer NOT NULL,
aspect_ratio float NOT NULL,
image_data blob NOT NULL,
face_height_coord integer NOT NULL,
face_width_coord integer NOT NULL)
"""
)
conn.commit()
def insert_image(img: CageImage):
conn = connect()
c = conn.cursor()
# Discard the id which is None for new images
img_dict = img.dict()
stream = io.BytesIO()
img_dict["image_data"].save(stream, format="JPEG")
img_dict["image_data"] = stream.getvalue()
# If we need we can update the PIL object to bytes here
_, *insert_data = img_dict.values()
insert_data = tuple(insert_data)
print(f"inserting data: {img}")
c.execute(
"""
INSERT INTO nicholas_cage_images
(width,
height,
aspect_ratio,
image_data,
face_height_coord,
face_width_coord
)
VALUES (?, ?, ?, ?, ?, ?)
""",
insert_data,
)
conn.commit()
conn.close()
def find_caged_image(target: TargetImageSpec) -> CageImage:
"Queries the best match based on target"
conn = connect()
conn.row_factory = dict_factory
c = conn.cursor()
c.execute(
"""
select
*
from nicholas_cage_images
where
nicholas_cage_images.width > :width
and nicholas_cage_images.height > :height
order by
ABS(nicholas_cage_images.aspect_ratio - :aspect_ratio) ASC
limit 1
""",
{**target.dict(), "aspect_ratio": target.aspect_ratio},
)
item = c.fetchone()
if item is None:
# If no image with higher resolution is found
c.execute(
"""
select
*
from nicholas_cage_images
order by
ABS(nicholas_cage_images.aspect_ratio - :aspect_ratio) ASC
limit 1
""",
{**target.dict(), "aspect_ratio": target.aspect_ratio},
)
item = c.fetchone()
conn.close()
item["image_data"] = Image.open(io.BytesIO(item["image_data"]))
return CageImage(**item)
|
import time
from datetime import datetime, timedelta
import pandas as pd
import json
import datetime as dt
from dateutil.parser import parse
pd.set_option('mode.chained_assignment', None)
class Collector():
def __init__(self, src, des, table, src_type, date_format, selected_time, selected_columns="all", dcpm=None, encoding=None):
self.src = src
self.des = des
self.table = table
self.date_format = date_format
self.selected_time = selected_time
self.selected_columns = selected_columns
self.dcpm = dcpm
self.encoding = encoding
possible = ['api','xls','csv','influxDB']
if(src_type in possible):
self.src_type = src_type
else:
raise Exception('Source type is not available')
## getter & setter
def set_table(self,table):
self.table = table
def get_table(self):
return self.table
def set_src(self,src):
self.src = src
def get_src(self):
return self.src
def get_src_type(self):
return self.src_type
def get_table_from_csv(self):
if type(self.selected_time) == dict:
result = pd.read_csv(self.get_src(), header=0, index_col=False, encoding=self.encoding, dtype={self.selected_time["Year"]:str})
else:
result = pd.read_csv(self.get_src(), header=0, index_col=False, encoding=self.encoding, dtype={self.selected_time:str})
return result
## time
def timestamp(self,time_string):
timestamp = time.mktime(datetime.strptime(
time_string, '%Y%m%d').timetuple())
timestamp = str(int(timestamp)*(10**9))
return timestamp
def timestampNow(self):
end = (datetime.now() + timedelta(days=1)
).replace(tzinfo=None).strftime('%Y%m%d')
print(end)
end = self.timestamp(end)
return end
def timeNow(self):
return (datetime.now() - timedelta(days=1)
).replace(tzinfo=None).strftime('%Y%m%d')
## clean
def clean_table_from_api(self,data):
return data
def clean_table_from_csv(self,data):
idx = data.fillna(method="ffill").dropna(axis=0, thresh=round(len(self.selected_columns)*0.8)).index
res_idx = data.loc[idx].fillna(method="bfill").dropna(axis=0, thresh=round(len(self.selected_columns)*0.8)).index
data = data.loc[res_idx]
data = data.drop_duplicates()
if type(self.selected_time) == dict:
time_list = list(set(x for x in (self.selected_time.values()) if x != "-"))
for num in range(len(time_list)):
data = data.dropna(subset=[time_list[num]], axis=0)
try:
data = self.time_combine_conversion(data)
except ValueError:
data = self.time_combine_conversion_24(data)
else:
data = data.dropna(subset=[self.selected_time], axis=0)
try:
data.set_index(self.selected_time, inplace=True)
data.index = pd.to_datetime(data.index, format="%s"%self.date_format)
except ValueError:
data = self.time_conversion_24(data)
if type(self.selected_columns) == dict:
data = data.rename(columns=self.selected_columns)
data = data[self.selected_columns.values()]
else:
data = data[self.selected_columns]
data.index.names = ["time"]
if self.dcpm != None:
data = self.duplicate_column(data)
return data
def duplicate_column(self, data):
if self.dcpm == "remove":
data = data.loc[~data.index.duplicated(keep="first")]
elif self.dcpm == "sum":
data = data.groupby("time").sum()
elif self.dcpm == "average":
data = data.groupby("time").mean()
elif self.dcpm == "min":
data = data.groupby("time").min()
elif self.dcpm == "max":
data = data.groupby("time").max()
return data
def clean_table_from_xls(self, data):
return data
def clean_table_from_influxDB(self,data):
return data
def getData(self):
print("Getting Data ...")
## get data process
return ""
def cleanData(self,data):
print("Preprocessing Data ...")
## preprocessing data for collection
if(self.get_src_type() == 'api'):
return self.clean_table_from_api(data)
elif(self.get_src_type() == 'csv'):
return self.clean_table_from_csv(data)
elif(self.get_src_type() == 'xls'):
return self.clean_table_from_xls(data)
elif(self.get_src_type() == 'influxDB'):
return self.clean_table_from_influxDB(data)
return data
def writeData(self, data):
print("Writing Data ...")
## write process
print('\n==========='+self.get_table()+'===========')
print(data.tail())
print('========================')
self.des.write_db(data, self.get_table())
return
def collect(self):
print("Start to Collect Data")
data = self.getData()
data = self.cleanData(data)
#print(data)
self.writeData(data)
print("Finish to Collect Data")
return data
if __name__ == "__main__":
import pandas as pd
#test = Collector()
data = pd.DataFrame()
#test.collect()
|
from unittest import TestCase
from src.leilao.dominio import Usuario, Lance, Leilao
from src.leilao.exception import LanceInvalido
class TestLeilao(TestCase):
def setUp(self): # método do framework para criar o cenário de teste
self.user = Usuario("Matheus", 500)
self.lance_matheus = Lance(self.user, 500)
self.leilao = Leilao("Leilão de carros")
def test_deve_retornar_o_maior_e_menor_valor_quando_adicionados_em_ordem_crescente(self):
user2 = Usuario("João", 500)
lance_joao = Lance(user2, 9000)
self.leilao.propoe(lance_joao)
self.leilao.propoe(self.lance_matheus)
menor_valor_esperado = 9000
maior_valor_esperado = 10000
self.assertEqual(menor_valor_esperado, self.leilao.menor_lance)
self.assertEqual(maior_valor_esperado, self.leilao.maior_lance)
def test_nao_deve_permitir_propor_um_lance_em_ordem_decrescente(self):
with self.assertRaises(LanceInvalido):
user2 = Usuario("João", 500)
lance_joao = Lance(user2, 9000)
self.leilao.propoe(self.lance_matheus)
self.leilao.propoe(lance_joao)
def test_deve_retornar_o_mesmo_valor_para_o_maior_e_para_o_menor_lance_quando_leilao_tiver_um_lance(self):
self.leilao.propoe(self.lance_matheus)
self.assertEqual(10000, self.leilao.menor_lance)
self.assertEqual(10000, self.leilao.maior_lance)
def test_deve_retornar_o_maior_e_o_menor_valor_quando_leilao_tiver_tres_lances(self):
user3 = Usuario("Roberto", 500)
user2 = Usuario("João", 500)
lance_joao = Lance(user2, 9000)
lance_roberto = Lance(user3, 12000)
self.leilao.propoe(lance_joao)
self.leilao.propoe(self.lance_matheus)
self.leilao.propoe(lance_roberto)
self.assertEqual(9000, self.leilao.menor_lance)
self.assertEqual(12000, self.leilao.maior_lance)
def test_deve_permitir_propor_um_lance_caso_o_leilao_nao_tenha_lances(self):
self.leilao.propoe(self.lance_matheus)
self.assertEqual(1, len(self.leilao.lances))
def test_deve_permitir_propor_um_lance_caso_o_ultimo_usuario_seja_diferente(self):
carlos = Usuario("Carlos", 500)
lance_do_carlos = Lance(carlos, 12000)
self.leilao.propoe(self.lance_matheus)
self.leilao.propoe(lance_do_carlos)
self.assertEqual(2, len(self.leilao.lances))
def test_nao_deve_permitir_o_lance_caso_o_usuario_seja_o_mesmo(self):
lance_matheus_12000 = Lance(self.user, 12000)
with self.assertRaises(LanceInvalido):
self.leilao.propoe(self.lance_matheus)
self.leilao.propoe(lance_matheus_12000)
|
#!/usr/bin/python3
"""Script to invoke emacs on currently branched files in a git repo.
Invokes Emacs on the current set of files in a private branch.
Sets GOROOT if this looks like a GO root repository.
"""
import getopt
import os
import re
import sys
import script_utils as u
# Echo command before executing
flag_echo = True
# Dry run mode
flag_dryrun = False
# Branches of interest
flag_branchname = None
def docmd(cmd):
"""Execute a command."""
if flag_echo:
sys.stderr.write("executing: " + cmd + "\n")
if flag_dryrun:
return
u.docmd(cmd)
def doscmd(cmd, nf=None):
"""Execute a command."""
if flag_echo:
sys.stderr.write("executing: " + cmd + "\n")
if flag_dryrun:
return
u.doscmd(cmd, nf)
def perform():
"""Main driver routine."""
if not ingitrepo():
usage("not within git repo")
branch, modifications, untracked, renames, rev_renames = u.get_git_status()
if flag_branchname != branch:
if modifications or renames:
u.error("working copy has modifications, can't proceed")
docmd("git checkout %s" % flag_branchname)
allfiles = {}
for f in rev_renames:
allfiles[f] = 1
for f in modifications:
allfiles[f] = 1
print("not yet implemented")
def usage(msgarg):
"""Print usage and exit."""
me = os.path.basename(sys.argv[0])
if msgarg:
sys.stderr.write("error: %s\n" % msgarg)
print("""\
usage: %s [options]
options:
-b B switch to branch B from master before starting emacs
-e echo commands before executing
-d increase debug msg verbosity level
-D dryrun mode (echo commands but do not execute)
""" % me)
sys.exit(1)
def parse_args():
"""Command line argument parsing."""
global flag_echo, flag_dryrun, flag_branchname
try:
optlist, args = getopt.getopt(sys.argv[1:], "dDeb:")
except getopt.GetoptError as err:
# unrecognized option
usage(str(err))
for b in args:
flag_branches[b] = 1
for opt, arg in optlist:
if opt == "-d":
u.increment_verbosity()
elif opt == "-D":
flag_dryrun = True
flag_echo = True
elif opt == "-e":
flag_echo = True
elif opt == "-b":
flag_branchname = arg
#
#......................................................................
#
# Main portion of script
#
parse_args()
u.setdeflanglocale()
perform()
|
import matplotlib
from PyQt5.QtWidgets import QHBoxLayout, QPushButton, QWidget, QVBoxLayout, QLabel, QCheckBox
from PyQt5.QtCore import Qt
from models.data_key import DataKey
import numpy as np
matplotlib.use('QT5Agg')
import matplotlib.pyplot as plt
from utils import ui_utils
class StandardLineWidget(QWidget):
def __init__(self, results_dialog):
QWidget.__init__(self)
self.results_dialog = results_dialog
self.samples = [sample for sample in self.results_dialog.samples if sample.is_standard]
self.model_data = results_dialog.model_data
layout = QHBoxLayout()
layout.addLayout(self._create_widget())
self.setLayout(layout)
self.results_dialog.configuration_changed.connect(self.replot_graph)
def _create_widget(self):
layout = QVBoxLayout()
layout.addWidget(QLabel("Standard line"))
layout.addWidget(self._create_standard_line_graph())
return layout
def _create_standard_line_graph(self):
graph_and_points = QWidget()
layout = QVBoxLayout()
fig = plt.figure()
self.axes = plt.axes()
graph_widget, self.canvas = ui_utils.create_figure_widget(fig, self)
layout.addWidget(graph_widget)
graph_and_points.setLayout(layout)
return graph_and_points
#############
## Actions ##
#############
def replot_graph(self):
current_item = self.results_dialog.sample_tree.tree.currentItem()
config = self.results_dialog.configuration_widget.current_config
if config and current_item:
self.plot_standard_line_graph(config)
def plot_standard_line_graph(self, config):
axis = self.axes
axis.clear()
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
xs = []
x_errors = []
ys = []
y_errors = []
for sample in self.samples:
for spot in sample.spots:
ratios = spot.data[config][DataKey.ACTIVITY_RATIOS]
if len(ratios) == 0:
continue
for ratio in ratios:
if isinstance(ratio, str):
continue
(x, dx), (y, dy) = ratio
xs.append(x)
x_errors.append(dx)
ys.append(y)
y_errors.append(dy)
axis.errorbar(xs, ys, xerr=x_errors, yerr=y_errors, linestyle='none', marker='o')
standard_line, standard_line_uncertainty = self.model_data[config][DataKey.STANDARD_LINE_GRADIENT]
standard_line_MSWD = self.model_data[config][DataKey.STANDARD_LINE_MSWD]
standard_x = np.arange(0.0, (max(xs)+max(x_errors)), max(xs)/4)
standard_y = standard_line*standard_x
axis.plot(standard_x, standard_y)
string = f"Standard line gradient: {standard_line:.3f} Uncertainty: {standard_line_uncertainty:.3f} MSWD: {standard_line_MSWD:.3f} "
axis.text(0.5, 1, string, transform=axis.transAxes, horizontalalignment="center")
axis.set_xlabel("(238U)/(232Th)")
axis.set_ylabel("(230Th)/(232Th)")
self.canvas.draw()
|
from FUNReader import read_fun_file_as_list_of_lists
from VARReader import read_var_file_as_dictionary
from BestsTool import create_best_fun_seq_files
from Medians import create_seq_with_values_on_median_files
def main():
fun = read_fun_file_as_list_of_lists("Resources/FUN.BB12044.tsv")
var = read_var_file_as_dictionary("Resources/VAR.BB12044.tsv")
main_dictionary = {}
count = 0
for seq in var:
main_dictionary.update({seq: fun[count]})
count += 1
create_best_fun_seq_files(main_dictionary)
create_seq_with_values_on_median_files(main_dictionary) |
"""Cache management on Firebase Hosting.
Cloud Run response caching on Firebase Hosting.
https://firebase.google.com/docs/hosting/manage-cache
"""
from dataclasses import dataclass
@dataclass
class CacheControl:
"""Cache-Control header object.
"""
max_age: int = 0
s_maxage: int = 0
@property
def header_name(self) -> str:
return "Cache-Control"
@property
def header_value(self) -> str:
"""Header value from object attribute.
If you use this class, on default value set "public".
"""
val = "public"
if self.max_age and self.max_age > 0:
val += f", max-age={self.max_age}"
if self.s_maxage and self.s_maxage > 0:
val += f", s-maxage={self.s_maxage}"
return val
|
import tkinter
import sqlite3
from tkinter import *
from Welcome_ui import Welcome
from tkinter import messagebox
class CustomWelcome(Welcome):
print("Welcome Window Opened.")
pass
def about_command(self):
print("About Button Pressed.")
import aboutus
self.newwindow = tkinter.Toplevel()
self.demo=aboutus.CustomAboutus(self.newwindow)
#newwindow.destroy()
pass
def exit_command(self):
print("Exit Button Pressed.")
msg = messagebox.askyesno("Exit","Are you sure you want to exit ?", icon='warning')
if msg:
print("Welcome Window Closed.")
quit(0)
pass
def login_command(self):
print("Login Button Pressed.")
import Login
self.newwindow = tkinter.Toplevel()
self.demo=Login.CustomLogin(self.newwindow)
pass
def newuser_command(self):
print("New User Button Pressed.")
import newuser
self.newwindow = tkinter.Toplevel()
self.demo = newuser.CustomNewuser(self.newwindow)
pass
def main():
root = Tk()
demo = CustomWelcome(root)
root.title('Welcome')
root.mainloop()
if __name__ == '__main__': main() |
# Resource object code (Python 3)
# Created by: object code
# Created by: The Resource Compiler for Qt version 5.15.1
# WARNING! All changes made in this file will be lost!
from PySide2 import QtCore
qt_resource_data = b"\
\x00\x00\x07\xc5\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x0a\x00\x00\x00\x0a\x08\x06\x00\x00\x00\x8d2\xcf\xbd\
\x00\x00\x00\x09pHYs\x00\x00\x0b\x13\x00\x00\x0b\x13\
\x01\x00\x9a\x9c\x18\x00\x00\x06\xbeiTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:xmpMM=\x22htt\
p://ns.adobe.com\
/xap/1.0/mm/\x22 xm\
lns:stEvt=\x22http:\
//ns.adobe.com/x\
ap/1.0/sType/Res\
ourceEvent#\x22 xml\
ns:dc=\x22http://pu\
rl.org/dc/elemen\
ts/1.1/\x22 xmlns:p\
hotoshop=\x22http:/\
/ns.adobe.com/ph\
otoshop/1.0/\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-04-26T14:37:\
13-03:00\x22 xmp:Me\
tadataDate=\x222020\
-05-03T08:46:13-\
03:00\x22 xmp:Modif\
yDate=\x222020-05-0\
3T08:46:13-03:00\
\x22 xmpMM:Instance\
ID=\x22xmp.iid:2ce5\
4e64-1b68-5140-b\
404-101ae4f2390d\
\x22 xmpMM:Document\
ID=\x22adobe:docid:\
photoshop:06387a\
18-9b10-4e44-b0d\
3-6a4c6e30ef94\x22 \
xmpMM:OriginalDo\
cumentID=\x22xmp.di\
d:5349ee02-98da-\
9648-893b-acc1e6\
33f5f6\x22 dc:forma\
t=\x22image/png\x22 ph\
otoshop:ColorMod\
e=\x223\x22 photoshop:\
ICCProfile=\x22sRGB\
IEC61966-2.1\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:5349ee02-98da\
-9648-893b-acc1e\
633f5f6\x22 stEvt:w\
hen=\x222020-04-26T\
14:37:13-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:68\
2e5c79-1a3b-8647\
-b55c-bcf3054d2d\
2f\x22 stEvt:when=\x22\
2020-04-26T14:37\
:13-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> <rdf:li stEv\
t:action=\x22saved\x22\
stEvt:instanceI\
D=\x22xmp.iid:2ce54\
e64-1b68-5140-b4\
04-101ae4f2390d\x22\
stEvt:when=\x22202\
0-05-03T08:46:13\
-03:00\x22 stEvt:so\
ftwareAgent=\x22Ado\
be Photoshop 21.\
0 (Windows)\x22 stE\
vt:changed=\x22/\x22/>\
</rdf:Seq> </xm\
pMM:History> </r\
df:Description> \
</rdf:RDF> </x:x\
mpmeta> <?xpacke\
t end=\x22r\x22?>\xcfr\x9f\xf7\x00\
\x00\x00\xadIDAT\x18\x95\x85\xce\xa1jBq\x14\
\x07\xe0\xcf\xeb\x15e\x93\x81 \x88\xcf`X\x9ba\xc5\
\x17XY_\x11\xc50L\x82\xb0w\xd8\x1e\xc0 \xa2\
\xd9\x22\xa8a\xc2\x9a`\xb2\xdb\x8c\xa2\x82\x0f`\x98\xe5\
\x9f.\xa2'\x1d8\xdf9\xe7\x97\xfa\xe9\x0d\xdc\xa8\x17\
\xbcb\x19\xdfRh\xa2\x81\xfe=8F\x84\xf15X\
\x0a\xc3\x1d\x16X\xe3\x14%P\x01+lPF\x0b[\
|%a\x0aY\xe4B\x9f\xc1\x032\xc9\xd7'T\x90\
\xc6\x11=L\xb0\x8fP\xc5\x08\xef\xf8G\x1d\x9fx\xc4\
3\xda\xa8\xc6\xa8\xe1\x03y\xcc\xf0\x1d\xae\x0f\xf1\x86\x0e\
\x9eb\xfc\xa2\x88?\x9c\xd1\x0dK\x07LC\xe6\xf9\x05\
\x90}\x1d\xd4\x12\xb7,_\x00\x00\x00\x00IEND\
\xaeB`\x82\
\x00\x00\x09\x14\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:28-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:28-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:3a4aa39d-7e80\
-f746-9727-85ee5\
e3b0f1e\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:31ed018a-fd1f-4\
148-9cc1-8f8e475\
43ec2\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:eec531d\
e-d5ee-e043-8fcb\
-5de525e22004\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:eec531de-d5ee\
-e043-8fcb-5de52\
5e22004\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:3a\
4aa39d-7e80-f746\
-9727-85ee5e3b0f\
1e\x22 stEvt:when=\x22\
2020-05-02T17:52\
:28-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xc5\xb2\
\xd3\xa3\x00\x00\x02\xc9IDATH\xc7\x8d\x96{h\
\xceQ\x18\xc7\xdf\xf7\xdd\xc5-\xc9-\x92D\xc9h\xb4\
\xf6\x07\x99)\xf7\x10)\xa5\x95\xcb\x22!\xd1\xfe\x98(\
\xb9\x93kB$in\x85R$\x97\x0dI\x1b\x09!\
\xb9\xcf\x5c#\x7fl\xa6\x89\xc5\xcc\xab\xcd\xeb\xf3\xd4\xf7\
\xd4\xd9\xaf\xbd{\x7f\xa7>\x9d_\xbf\xdf9\xe79\xcf\
s\xbe\xcfs~\xd1\xea\xea\xeaH\x886\x01r \x06\
\x7f\xe0&T\x86\x99\x98\x1eb\xcc\x00\xb8\x06\x19\xde\xbb\
{0\x06\x9aRM\x8e%y\x1f\xf5\x9es\xb5x1\
d\xc1Yy\xd39\xc9\xf8P\x06\x12\xea\xc7\xc1V=\
\x9f\x83\xb7P\x06\x9d`/\x0c\x0e\x8co\xd3\xc0h\xd8\
\x05\xd9\xd0O;\xad\x80n\xb0\x1dj5\xae\x5c\xc6\x16\
@\x15\xac\x957\x93`'\xf4n\x11\x0a\x1drWx\
\x09}\xa0\x01:\xc8\xf8\x1a8\x01_Z\xd9\xdc X\
\x0f\xf3\xe0'\xb4W(\xcf\xc0\x9c\xa0\x07\xab\xb5\xb8\xed\
j\x1f|\x82\x99\xb0#\xc9\xe2\x11\x85\xab\x106@\x1d\
,\x83\xfd0\x1b\xa69\x07\xcc\x83l\xed\xfe$\xcco\
C\x10\xf9\xd0\x03\xde\xa7\x90\xe87E\xc1\x04\xd1h\x1e\
\xfc\x83zi}r+\x8a0\x15=\x84\xdbp\x11\x9e\
\xc0\xa9\x80l\xad\xa5\xc1\x22\xe8\x22\xaf\x9b]\x88\xec\xa0\
&J\x19Wa\x987\xa9'\x5c\x86\xe1\xb0\x0ef\xc0\
Q\xc5\xfdt`3s\xe1\x08\x5c\x81\xe9\xf0\xd7\xbe\xbb\
D{\xa4]-\x95{\xae\xad\x82\xbe\x90\x07\xf7\xf5\xae\
\x14\xbe\xc2F\xcd)\xd3\xfb\xb8\xfa=\xfan\xc6\x13\xbe\
L\xb3\xe4\xda\x87@\xdc_y\x8b\xbb\xf1\x87\xe0\x17\x8c\
\xf7\xc6>S?\xd0\xcf\x8d\x98\xe7\xe6o\xc5\xd1w;\
.\xc9\x06\x13\xb0\xa3\xce\xa0\xd1\xfb\x96\xa9\xbe!\x98h\
nR\xad\xa4\x9a\xe3}/U-*\x0a\x18\xb0\x84l\
\x07\xe7\xbd\xb1y\xea\xbf\x07\x0d\xd8\xae\x17K\xbf\xaf5\
\xc0yqP1>\xa0\x0c>\x0c/\xa0\x006\xc1c\
\xaf\x16}T\xd8\xec\xa0\xc7\xba5,\x0fF\xd0?\x80\
[\xca\xc0\x9a\x80\xfc2U*\x0aU}-\x04\x9b\xe1\
X+9`\xde?Uf\x9b\x02\xe3\xe6\xc1g\xf8!\
\xf7k\x02\x07\x15\x91\xdcV\xaa>\x0dU\xef/\x9e\xa1\
0\xba\xec\xaeW\x226\xb9\x10\x99rV\xa8rZ\xdd\
\xb9\x03oTg\x22\x81\x03\x0fzg\xb9s\x09\xdeI\
\xb2\x15J\xb4\x85J\xb4\xa8\x93\x9d[\xd8jQwe\
\xee\x16\xb8\x01\xa3\xa4\x1a\xbf\xf5R\xb2=\x87\xa9Z\xd8\
\xaa\xe9H\xcd\xabr\xa2Hw\x09\xa1\xf8\xe7*s#\
*^\xdb\xe0\xaen\xb4Y\x92\xb2\x85\xe9\x82\xc2h\x07\
>\x05\xae+\xe6\xf9^\xe2\xb5(\xd7\xc9\x9a\xc9v\xb7\
\x8c\x0f\x91\xca\x8a\xa4\xaa\xe5p\x5cwt4\xd9\xa5\x13\
Kq\xa5\x9a\xf5\x12=\x17\xab\xc6\x14H%%\xa9\x16\
\x0f{\xe9W\xaa\x5c,\x11\x11\x95\xf6D\xaa\xeb2\xac\
\x81:\x95\xf2\xfe\x92^\x9a\x0c6\x87\xf9m\xf9\x0f\xa8\
\x07\xb9\xfe-\x0e]\xa4\x00\x00\x00\x00IEND\xae\
B`\x82\
\x00\x00\x08;\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:10-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:10-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:51122435-2d64\
-4147-9d3f-18244\
2d60738\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:4ac2dc3a-9f2c-e\
a42-a687-2c6d761\
4b980\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:ab442cd\
6-81f6-0345-811f\
-94f91571114c\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:ab442cd6-81f6\
-0345-811f-94f91\
571114c\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:51\
122435-2d64-4147\
-9d3f-182442d607\
38\x22 stEvt:when=\x22\
2020-05-02T17:52\
:10-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>r\x09\
\xf4\x18\x00\x00\x01\xf0IDATH\xc7\xa5\xd6O\x88\
\x8da\x14\xc7\xf1\xcf{\xe7\x0eCc!Iw#\x1b\
J)\xa2\x88\x05\x1b\x16d\xc9B\x16\x12\xc2\xb0\x13\xa9\
\xb1e!f#\xa3\xb0\xb0\xb0\x11\x0be-)l\xec\
\xc8\x9f\x92\x8dt3\x8b\xd90\x19\x8d\x99\xb96g\xf4\
\xf4\xf6\xbc\xf7\xbd3sV\xef\xf3\xef\xfc\xce\xf3=\xe7\
\xdc\xe7\x16\xedv\xdb<l\x0d\xc6z\xdd\xdcj\xb54\
j\xf6\x14\xc9\xf7u\xbc\xc3\xdd\x8a\xf5\xac5j\x9cw\
\xe2{\x14\x17\xf1\x15\xa7\xf0\x0c\xfd\xb1^,T`\xce\
\xf9m\x9c\xc5U\xec\xc29\x1c\xc4S,\xaf\x13i\xd4\
`\xb9\x87!\x8c\xe0Jr\x9b\xe38\x80\xd7\x91\x97N\
\xaf\x02)\x96[8\x89k\xb8\x90\xac\x17x\x80\xc3\xd8\
\x82\xf7\xd8X\x95\x93F\x17,\xe7\xc3\xf9pi}n\
\xcf\x13\xec\xc5j\xbc\xc0\xd6\x1c\xaeFF\xf9~\x82e\
\xb8\xa6Z\x9ecs8~\x83\xdde\x5c\x8dL\xe4'\
JXta\x5cD\xe9\xee\xc0\x17\xbc\xc4\xa1\xb2@\x81\
f8\x1f\xca`QSi\x05\xbea\x1f>\xe01\xce\
\xa4\x02\x1d\x5c\x0a\xe77z\xc0RU\xce?\xb0\x13\xaf\
p\x07\xfbSD\xdb1\x11\xcdT\x87\xa5[c\xfe\x0a\
\xc4\x227\xff\x05&0\x8d>\x8b\xb7\xd4\xa7f)\x82\
~\xcc\x94\x0e\xf4c\x0fV`6\xe6\x9a\xc1\xfdmF\
`i:h\xf6\x10\xd1\xcd\xe8\x89rN~\xe34\x1e\
v;\x5c'\xb0\x0cG\xf1\x09\x97#\xba\xe9\xb8\xd5#\
\x1cY\xac@\x11\xc8>\xc6/hj#\xf3IH7\
\x9b\x8d\x9b\xa4\xa5;\x88%\x99|-H@\x92\xdcN\
2\xee,\xe4\x06\xb3\x15M\xd4\xcb\x8b\xd7\xc9\x9d)\x0b\
L\x95\xc6\x93\x81\xa1\xea\xdd\xf8\x93\x99\x1fO\xc5\xd3$\
\x0f\xc6\xcb5\x13\xf3S\x18\xc0*\xfc\xadH\xfe&\x1c\
\x8b\x1c\xf5Es\xad\xcf\x09\xfc\x8c\x0d\xa3\x15(\xc62\
\xe8\xda\xd8\x16\x8fO\xce&\xa1\x88\xbf-\xeb\xb06\x22\
(s\x1d\xc0\xf7\xe8\xdc\x14\xed\x06\xac\xcc`-\xa2_\
>\xb7Z\xad\xf1\x7f\x19\xe0v\xff\xf8\xf4M@\x00\x00\
\x00\x00IEND\xaeB`\x82\
\x00\x00\x07E\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:11-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:11-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:9f389513-5665\
-2a44-ada7-74153\
67a7420\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:5a793212-f3ad-3\
e43-bc16-023c39f\
160bc\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:e3ac0a5\
a-9612-d14c-9265\
-ee5e3a0f2ff8\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:e3ac0a5a-9612\
-d14c-9265-ee5e3\
a0f2ff8\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:9f\
389513-5665-2a44\
-ada7-7415367a74\
20\x22 stEvt:when=\x22\
2020-05-02T17:52\
:11-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xdf3\
\x9c\xc7\x00\x00\x00\xfaIDATH\xc7\xbd\xd5!N\
\x83A\x10\x86\xe1g[\x0a\x84\xe00\x08N\xc0\x01 \
\xc1\x12L\x15XdC\x15A`\xaa\xb8\x04\x84 \x08\
\x0a\xc5\x158\x0b~\xd0\x98\x06\x0at1[\x83i\x9a\
\xec\xfe+W\xcc\x9b\x9d\xf7\x9b\xd9\x94s\xd6\xf2\xa4\xe6\
\x80\x88\xd8\xc1^\xa3\xfao)\x22\xaep\xd7\x08p\x90\
\x22b\x1fC|\xa1V\xbf\xd6\xf1\x89\x97\x14\x11\xcd\x1d\
la\x17?\x95jf\xf4\xf1\x8b\xf7\x14\x11c<b\
V\xb1Ek\xf8\xc6Q\x8a\x88C\x9c\x96\xe2\xb5\x00=\
\xcc\xf1\xd0\x89\x83^\xb1^s\xe2R\xa97K\x11q\
\x8e\xdb\x22\xa5\xa6\xe4)\x86)\x22N0)\xb9\xad\x05\
\xd8(\x80\xebN\x1c4\x07\x9c\xe1\xa6\xe46Wj\xd1\
\xa0\xcc\xd5h\x01\x98\x94\xdc\xe6\x8a\x92\xe7\xb8X\xb4(\
U\x8ei\xb7\x0e\xfe\xdf\x8dp\xbc\xe2\x8brY\x0f\xcf\
x]\x06\xb8\xc7%>V\x00\xf4\xb1\x8d1\x9e\x96\x01\
\x06\xd8,\xa9Zu\x83N\x8b\xdc\x0e?\xfd\xd6\x80?\
\xd7\x17u030U\xf2\x00\x00\x00\x00IEND\
\xaeB`\x82\
\x00\x00\x07\x19\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:37:\
55-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:18-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:18-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:80fec745-a70d\
-8449-a60e-a7c5e\
379bc07\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:c18f4afb-caeb-5\
c46-9c44-4a51e1d\
4bddb\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:36def88\
3-a73c-1047-b02e\
-2ef8a062d027\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:36def883-a73c\
-1047-b02e-2ef8a\
062d027\x22 stEvt:w\
hen=\x222020-05-02T\
17:37:55-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:80\
fec745-a70d-8449\
-a60e-a7c5e379bc\
07\x22 stEvt:when=\x22\
2020-05-02T17:52\
:18-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>L\xa9\
\x8a\xc7\x00\x00\x00\xceIDATH\xc7c\xfc\xff\xff\
?\x03-\x01\xe3\xa8\x05$Y\xf0\xfc\xf9s\x17 \x95\
\x0b\xc4\x9f@r$\x9a\xc5\x02\xc4\xbf\x81\xb8NRR\
\xf2>.\x0b\x12\x81\xd4< \xfe\x0c\xc4\xffH0\x1c\
d\x08\x17\x10\x7f\x05b\x07\xa0\x05\x97pY\xc0\x04\xa4\
\xd8(\x08\x11\x90a?\x81\x16\xe0\x0c\x22\xaa\x84\xfb\xf0\
\xb6\x80\x19H\xb1\x93\x9b\x22\xa1q\xf0\x03h\xc1?\x5c\
\x16D\x01\xa99\xd0T\xf4\x9f\xc4\xc8e\x87&o/\
\xa0\x05\xd7pY\xe0\x0a\xa4*\xa1\xc9\x8dT\x0b8\xa0\
\xfa\x0a\x81\x16<\x1c\xde\xa9\x88\x91\xc2\x8c6\xb0>\x10\
\x01R\xf2@\xfc\x87\x82dz\x0bh\xc1w\x5c\x16\xe4\
\x00\xa9\xc9\x14z\xc0\x02h\xc1I\x5c\x16h\x01)O\
\xfeEF2e\x83\xea[\x01\xb4\xe0\xedh\x959\
x,\x00\x00\xa0\xd9\x9f\xd1\xbe\xc1\x7f\xfe\x00\x00\x00\x00\
IEND\xaeB`\x82\
\x00\x00\x08\x91\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:39:\
05-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:51:57-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:51:57-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:dc1b8acc-f199\
-5a45-bff7-996c6\
d11f12d\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:0549dbbb-8900-8\
b46-94f5-07c1263\
7fc95\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:688f62d\
f-bffe-4e43-b808\
-153501238893\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:688f62df-bffe\
-4e43-b808-15350\
1238893\x22 stEvt:w\
hen=\x222020-05-02T\
17:39:05-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:dc\
1b8acc-f199-5a45\
-bff7-996c6d11f1\
2d\x22 stEvt:when=\x22\
2020-05-02T17:51\
:57-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xb98\
\xd6u\x00\x00\x02FIDATH\xc7\xad\xd6_h\
\xcdq\x18\xc7\xf1\xd7\xdc #\x91t\xda\xa4V\x14\xf9\
s#+.\x94\xb4\x1a%E)k\xe5_\xac%\xff\
\x1a\xf9\xcf\xcc\x9f\xc9\xfc-\xa4\xb8q\xa1](\x5c\xfa\
\x9b\xd5\xa4\x84\x16\xee\x18v\xb5(\xa2%\x7f\xa2\xe3\xe6\
9u\xfa\xed\xfcv\xce\x96\xa7N\xe7|\x7f\xe7\xf9}\
\xdf\xdf\xe7\xf9}\x9e\xe7\xf9\x95e\xb3Y\xd0\xdb\xdb+\
\xc5\xca\x90\x8d\xdf\xc3\xf1\x07\x7f\x0b\xfc\xd7\xcf2\x99\x8c\
\xb2\x12\x009\xabC\x13\xbe\xa1\x05\x0f\x8a\xdd\x90\x06H\
\x9ej.\xce\xa3:q\xff-l\xc7\xfb\x94h\x0b\x02\
\xf2\x1d&\xe0\x086\xe0;\xf6\xa2\x11\x1f\xf0\x18\xcd\xe1\
\xb7\x17\xa7\xf1\xb3\xd4\x08F`3\xf6\xa3\x1c\x17q\x00\
\x9f\xe3\xb4\xaf\xb0\x14\xd3p\x0a\xb5\xe8\xc1.\xb4\x17\x03\
,\xc1eT\xe0\x11\xb6\xe0e\xf8\x8f\xc4\xdbX\xd7\xe6\
\xed\xb3\x10\x970\x15]X\x87\x17\x85\x00\x0d\xe1\xf8\x09\
\x0d\xb8\x99\xc8\xebpt\x07`q\x22\x1b\xc3\xb0\x0d'\
\xf0\x1b5\xe8L\x02:0\x1b\x93\xd0W@\x14\x03\x01\
r6\x0f\x9d\xa1\xb2\x83I\xc0}T\xc5\xa7\x90\x9a\x06\
\x02\xe4|\xa7\xe0)\xce\xa29\x09\xb8\x8b\xe9\xa8L9\
\xdd\x88<@m\x0a`&\x9eD\xaaZ\x92\x80{\xa1\
\x8c\xca\x01j\xa7;T\xb4,\x050#\x00'q\xb8\
\x18 \x99\xa2\x1a\xdc\x09\xb9\xce\x89z\xf8/\x80J\xb4\
ae\xd4Ay\x14\xe1\x09\xb4\xe2\xebP\x01#\xb1\x07\
\xfbb\xdd\x86\xdd\x18\x873X\x85\x1f\xd8\x89\x0b\xa5\x00\
\xeecr(a}\x94\xffh\xdc\xc6\xd6DJDo\
:\x85\xf9x\x875x\x83\xd7\xd1\xbb\xfa\xa9\xe8&\x16\
\xe19\x16D\xd5n\xc4\xc3\x22M\xb3>\x22\x18\x1d\xbe\
\xd5\xd1\xc3Z\x93\x80\xf6\xc8s\x1f\xd6\xe2\x86\xc1YS\
\xa4Ft\xd93I\xc0\x22\x5c\x8f\x07\xd8\x12\xce})\
\xad8\xa9\xb0\x0a\x1c\x8fh\x9ea\x05z\x0a5\xbb\x89\
\x91\xd7\xba\xe8)\x8d\xb8\x9a\xb7i\xcer\x9b\x8f\xc2\x0e\
\x1c\x8cu+\x0e\xe1\x17\xca2\x99L6m\xa2U\xe3\
\x5c|wE\xf8\x0f\x12\xd1\xd4\xe3X\x9c\xfeV\xa8\xe9\
\xcd`'\xdar\x5c\xc1\xd8\x00\xac\xc6x\x5c\xc3\xacP\
\xcc&t\x94:\xd1\x0a\xd9\x98\x18>My\xd7\xfab\
\xc0\x5cL\x1b\x97C\x19\xfaU8\x8c/8\x8a\x8fC\
\x1d\xfa\xc5^]\x94\xf2\xca\x92\x03\xfc\x03\x06\x8b*\xe0\
\x82B%\xd8\x00\x00\x00\x00IEND\xaeB`\x82\
\
\x00\x00\x06\xa8\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:39-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:39-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:69201227-c2e9\
-0f49-9487-a4cb3\
8178b11\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:c4eedba7-d52f-6\
f4e-bdee-7717e2d\
72b83\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:c85abf9\
a-b449-1145-a89f\
-e95d10179c43\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:c85abf9a-b449\
-1145-a89f-e95d1\
0179c43\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:69\
201227-c2e9-0f49\
-9487-a4cb38178b\
11\x22 stEvt:when=\x22\
2020-05-02T17:52\
:39-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xc72\
\xe9P\x00\x00\x00]IDATH\xc7c\xfc\xff\xff\
?\x03-\x01\xe3\xa8\x05\xa3\x16\x8cZ0j\xc1\xa8\x05\
\xa3\x16\x8cZ@\xb4\x05\xcf\x9f?g\x06\xb2U\x80\x98\
\x03\x88A62Rh\xee{II\xc9G\xc8\x16\xf0\
\x00\xd9W\x80X\x9eJ\x0e_\x0d\xb4 \x0c\xd9\x02\x16\
;\x04j\xc1W\x0a\x0d\x079\xf6\x14\xd0\x82}t\
\x89\x03\x00\xbb\xf0X\xd1\xb2\xc7\xb1\x86\x00\x00\x00\x00I\
END\xaeB`\x82\
\x00\x00\x07\xb8\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:40-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:40-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:d8fea973-0684\
-4d4d-a57f-c19fe\
a411517\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:90e12245-cfea-5\
743-98a2-dcf93af\
ff919\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:9d3d376\
3-8f21-244a-af8d\
-46a43be9c711\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:9d3d3763-8f21\
-244a-af8d-46a43\
be9c711\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:d8\
fea973-0684-4d4d\
-a57f-c19fea4115\
17\x22 stEvt:when=\x22\
2020-05-02T17:52\
:40-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xd7\xf0\
G\x8a\x00\x00\x01mIDATH\xc7\xed\xd5!O\
\xdeP\x14\xc6\xf1_\x05\x0cC^\x12T?\xc2\x0c\x10\
\x0cX<\x02\xb1lf\x09\x09\xc9\xc4\xc4\xc2\xc6\x18\x0a\
K\xc8`\x1b\x8cL\x80%!\x88e\x8a,\x01\x04f\
(\x82G\x0d\xfb~\x85\x19\xe8\xcc\xed\xd2\xf4\xed\xbd\xbc\
%\xc1\x90\x1d\xd5\xde{{\xfe}\x9esN\x9b\x15E\
\xe1!#{<\x80n\xb7[\xae\x0d\xe1\xcf=r\x0d\
\xe0\x06\xb7\x90\xe7y#`\x18G\xb8\xc6B\xf9\x12\x88\
\xc9,\xf7F\xf0\x13\x97XL\x01\x86\xb0\x83W\xf8\x86\
7\x09H\xb96\x8cs\x8c\xe15vc\x80j\x92/\
x\x87-,%l\x19\xc41f0\x8f\xfd\x12\x1cS\
P\x85l\xe0\x03\xb6\x03\xac\xdc\x17\xcetB\xf2i<\
\xc7\xf7*9\x06\xa8\xc7&\x96kv\x95\xb5\xfa\x85q\
\xbc\xc4A\xdd\xc6\xbb\x00\xd5\xc3[x[\xb3\xeb\xacf\
KO\xf4\xa3\xa0\x0a\xf9\x1c\x92o\xe2)f\x9bl\xb9\
\x8fE\xd5\xf8\x88\x95p\xfd\x0c?R-\xdc\x16\x90\xe1\
\x10/\xc2\xfd*\xd6R\x0f\xb4\xb1\xa8\x83\x13La.\
X\xb4^\xabI\x8f\x92~\x8b\xdc\xd4-\xb1\xeej\xd5\
E\xc9!Jt\xd7\x9d\x0a\xb2>\x86\xa8\x0a\xf9\x84\xf7\
\x0d\xc3X\xa4\x14\x8c\xe2\x14\x93\xb1!\x8a\x0c\xe3\xd7\xa0\
\xe4\x16Y\x9e\xe7E\x13`\x00\x17\x98H\x0dQ\x83\x92\
\xed\xf0\x15\xfdW\x93\x98\x82'\xa1\xdf\xaf\xb0\xd7\xcf\xff\
\xa4\x02\xd9\xc1\xef\xa0\xa4\xf5$\xb7\x8e\x1e\xc0\xff\x9f~\
,\xfe\x02\x87\x80\xd4\xd1\x08\xa9\x5c\xc1\x00\x00\x00\x00I\
END\xaeB`\x82\
\x00\x00\x07\xea\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xf1iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:48:\
24-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52:39-03\
:00\x22 xmp:Metadat\
aDate=\x222020-05-0\
2T17:52:39-03:00\
\x22 dc:format=\x22ima\
ge/png\x22 photosho\
p:ColorMode=\x223\x22 \
photoshop:ICCPro\
file=\x22sRGB IEC61\
966-2.1\x22 xmpMM:I\
nstanceID=\x22xmp.i\
id:d2f40a67-74fa\
-e442-af0e-d3a48\
546ea8b\x22 xmpMM:D\
ocumentID=\x22adobe\
:docid:photoshop\
:10e27ed9-a195-6\
b4c-836a-8073dab\
0989d\x22 xmpMM:Ori\
ginalDocumentID=\
\x22xmp.did:479143d\
0-43d3-3645-b607\
-356308a7a2ad\x22> \
<xmpMM:History> \
<rdf:Seq> <rdf:l\
i stEvt:action=\x22\
created\x22 stEvt:i\
nstanceID=\x22xmp.i\
id:479143d0-43d3\
-3645-b607-35630\
8a7a2ad\x22 stEvt:w\
hen=\x222020-05-02T\
17:48:24-03:00\x22 \
stEvt:softwareAg\
ent=\x22Adobe Photo\
shop 21.0 (Windo\
ws)\x22/> <rdf:li s\
tEvt:action=\x22sav\
ed\x22 stEvt:instan\
ceID=\x22xmp.iid:d2\
f40a67-74fa-e442\
-af0e-d3a48546ea\
8b\x22 stEvt:when=\x22\
2020-05-02T17:52\
:39-03:00\x22 stEvt\
:softwareAgent=\x22\
Adobe Photoshop \
21.0 (Windows)\x22 \
stEvt:changed=\x22/\
\x22/> </rdf:Seq> <\
/xmpMM:History> \
</rdf:Descriptio\
n> </rdf:RDF> </\
x:xmpmeta> <?xpa\
cket end=\x22r\x22?>\xa6\x0e\
\xb2\xc4\x00\x00\x01\x9fIDATH\xc7\xb5\xd5\xc1J\
\x16a\x14\x06\xe0gl\xfa\x8d0K\x08\x846Aa\
\xd8Z\x88\xba\x00WAW\xe0V\xb2 \xe8\x02Z\xb6\
o\x13\xb8n\x11]\x81\xee\xda\xd5\xa2m\x8b\xa2B\x94\
\xd0\x9f\xdcX\x8ah\x9a5m\xce\xc00\xfd\xf39\xff\
h\x07\x869\xf3\xcd7\xef;\xe7\xbc\xe7\x9c/\xeb\xf7\
\xfb\x1a\xec:\xa6t\xb7/X\xc9\x1b^\x8e`\x16w\
\xc2\xcf\x86\x04\xff\x8dwX\xccj\x11d(p\x03\x9f\
bm\x0bgZ\x02g\x01>\x11\xcf\xd3M\x11\x5c\x88\
\xfbc,F\x14m\xac\x88\xeb!\x9ea,Ol\x84\
\x1c\x87\x1d\xf2_~s4\xd2\x00\xfc\x11O\xb0\xdcQ\
\xe0\xbc\xee\x8c\xe1\x12~U\x88\x9eG\xee'\x8f\x01\xda\
\xc3\xf7\xd4\x86i\xbc\xc0\xcd\x10\xa8\x8c\xe2O\xa5\x82\x8a\
\x06As\xac\xe2\x01\xde6\x11\xcc\xe2\x16^\xe1+\xce\
7\x00\xd6\xed \xa2\x9e\xc7\xbd\x14A\xa9\xc3\x02v:\
\xe4{\xee81\xf6\xc3\x7f\x84\xcf\xe8\xb5\x8c\xe0\x08\x97\
c\xff\xcf\x14A)\xec\xd3\x13\x8c\x85\x83\x14\xc1h\xf8\
\xf7\xf1!4hc\xfb\xb8\x82\x978\x97\x22(\xc7\xc0\
k\xac\x0c\xf9\xe7\x13\xd1Ty\x8a\xa0\xa8l6\xc4`\
+b\xa4\x0c\x1a\x86\xdb\xfft\x5c\xd4\xbd\x96\x02\xd7;\
\xff02q;pfb\xfdl\xeet\xecGD\xf3\
\xa6\xb2\xb6\x81\xf5\xd3\x22\xb8\x88]\xdc\x8d\xb1\x03\xef\xf1\
-\xaf\xb5~W\x1b\x8d\xbeX\x1etr\x95\xc0]\xba\
x\xafr\x064VQ/\xfc\xa9\xc8\xe5xK\xf0]\
\x5c\x8d\xef{)\x82\xb5\xf0\x97N\x90\xa2\xd5\x14\xc1R\
t\xf1\xb5(\xb7a\xca\xb4\x87\xcd\xe8\xe6\xc1\x87tQ\
\x14\xfe\xa7\xfd\x05\x0b[[\x86\xbbO\x83\xb0\x00\x00\x00\
\x00IEND\xaeB`\x82\
\x00\x00\x08g\
\x89\
PNG\x0d\x0a\x1a\x0a\x00\x00\x00\x0dIHDR\x00\
\x00\x00\x18\x00\x00\x00\x18\x08\x06\x00\x00\x00\xe0w=\xf8\
\x00\x00\x00\x09pHYs\x00\x00\x16%\x00\x00\x16%\
\x01IR$\xf0\x00\x00\x05\xe8iTXtXML\
:com.adobe.xmp\x00\x00\
\x00\x00\x00<?xpacket beg\
in=\x22\xef\xbb\xbf\x22 id=\x22W5M\
0MpCehiHzreSzNTc\
zkc9d\x22?> <x:xmpm\
eta xmlns:x=\x22ado\
be:ns:meta/\x22 x:x\
mptk=\x22Adobe XMP \
Core 5.6-c148 79\
.164036, 2019/08\
/13-01:06:57 \
\x22> <rdf:RDF \
xmlns:rdf=\x22http:\
//www.w3.org/199\
9/02/22-rdf-synt\
ax-ns#\x22> <rdf:De\
scription rdf:ab\
out=\x22\x22 xmlns:xmp\
=\x22http://ns.adob\
e.com/xap/1.0/\x22 \
xmlns:dc=\x22http:/\
/purl.org/dc/ele\
ments/1.1/\x22 xmln\
s:photoshop=\x22htt\
p://ns.adobe.com\
/photoshop/1.0/\x22\
xmlns:xmpMM=\x22ht\
tp://ns.adobe.co\
m/xap/1.0/mm/\x22 x\
mlns:stEvt=\x22http\
://ns.adobe.com/\
xap/1.0/sType/Re\
sourceEvent#\x22 xm\
p:CreatorTool=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22 x\
mp:CreateDate=\x222\
020-05-02T17:39:\
50-03:00\x22 xmp:Mo\
difyDate=\x222020-0\
5-02T17:52-03:00\
\x22 xmp:MetadataDa\
te=\x222020-05-02T1\
7:52-03:00\x22 dc:f\
ormat=\x22image/png\
\x22 photoshop:Colo\
rMode=\x223\x22 photos\
hop:ICCProfile=\x22\
sRGB IEC61966-2.\
1\x22 xmpMM:Instanc\
eID=\x22xmp.iid:ce9\
713d8-2f74-3e4c-\
b357-075ba163a8f\
8\x22 xmpMM:Documen\
tID=\x22adobe:docid\
:photoshop:c0a9c\
365-279d-a541-a0\
c4-10029b2b734b\x22\
xmpMM:OriginalD\
ocumentID=\x22xmp.d\
id:58a663a3-da35\
-6644-aba1-aabce\
1c6b16e\x22> <xmpMM\
:History> <rdf:S\
eq> <rdf:li stEv\
t:action=\x22create\
d\x22 stEvt:instanc\
eID=\x22xmp.iid:58a\
663a3-da35-6644-\
aba1-aabce1c6b16\
e\x22 stEvt:when=\x222\
020-05-02T17:39:\
50-03:00\x22 stEvt:\
softwareAgent=\x22A\
dobe Photoshop 2\
1.0 (Windows)\x22/>\
<rdf:li stEvt:a\
ction=\x22saved\x22 st\
Evt:instanceID=\x22\
xmp.iid:ce9713d8\
-2f74-3e4c-b357-\
075ba163a8f8\x22 st\
Evt:when=\x222020-0\
5-02T17:52-03:00\
\x22 stEvt:software\
Agent=\x22Adobe Pho\
toshop 21.0 (Win\
dows)\x22 stEvt:cha\
nged=\x22/\x22/> </rdf\
:Seq> </xmpMM:Hi\
story> </rdf:Des\
cription> </rdf:\
RDF> </x:xmpmeta\
> <?xpacket end=\
\x22r\x22?>J\xcc\xd92\x00\x00\x02%IDA\
TH\xc7\xc5\xd6K\x88Ha\x14\x07\xf0\xdf5\xc3x\
E$\x0b$\xf2\x98\x84\x10\xb3\xa0\xc4\x82\x11+\xa2\xbc\
6\x8a\xc8c\x90\xd8\x10\x0b\x91W\xf2\x0e\x0b\xa3\xe4\x9d\
\x15\x99\xb1Q\x1e+\x22!\x91\xf2X\xf0-Xh<\
\xa2\x18csFCf\xeeEr6\xf7\xde\xaf\xef\x9c\
\xffw\xbe\xff9\xffs\xb3\x94\x92&\xd6\x1b\x93P\x86\
z?Z)\xeap\x09\xaf\x14\xb4,\xa5\x94\xa1\x01\x9d\
p\x1b\xfds|.cB\xa3\x7f\xf8\xb6\x08\xd0\xf8>\
\x12\xb7\xb0\x15\xc7\xd0\xbe\x89s+|\xc0fLF?\
\xa4\xa2\x19\x94a\x1d\xe6\x84\xe3\x02\x1cif\xff\x0e\xac\
\xc6=\x1c\xc5~|\xc9\x03\x98\x85S\xb8\x83\x178\x88\
\xda\x9f\xf7E6\xf30\x0d\x030\x08\xe3p-\x0f`\
\x0d\xb6\x87\xc3\xa3\x82\xdcM\x0a\xb2\xe7\xe2\xe4\xaf\xe26\
^o\x96RZ\x81\xdd\xbf\x090\x05\x17\xf1\x12\xcf\xd1\
\xe6'\xbe>\xe15Vg)\xa5\xe5\xd8\x83\xe1\xb8[\
\x10`\x04\xb6\xa0$\xca\xb7$\xb8h\xc0g\x0cAO\
T\xfc\x09@i\x13b\x9b+\xd3E8\x84ay\x00\
mQ\x8d\xce\x98\x8a\xd9X\x8c\x0d\xd1\x0f5\xb8\x19\xdf\
\xeb1:\xf6M\xc5\x19\x0c\xcf\x03\xe8\x88\xa7\xe8\x16}\
\xb1\x1ek\xb10\x02\xd4\xe1j4^\x0d&\xa2;\xc6\
\xe3l\x11\x80\x0c\x03\x83\xc4\xfb\xe1\x5c\x1e}\xf0\x0e\xa3\
\x82\xccg\xe8\x8b\xae\xa1\x06Kp\xa0\x08\xc0\x9f\xdaJ\
\xec\xfa\x97\x00\xdfc\xfe\xf7\x0cJ0#\xc8\xae\x8e\xfa\
\x1f\x8b\x0b!+\x0b\xf1$\xa4\xa52\x94\xf80\xe6\xc7\
\xb3P\x15\xbd\x8b\xf7v\xd8\x16\xe9W\xe18\xde\xe0!\
\x06G\xb9\x8eB\x0f\x8c\xc1\xb9\x22\x00\xad\xb1,\xfaa\
G8V\xe2D\x04\xae\x8a\x0cjb\xbd\x1c\xfb0\x13\
\xa7\xff\x86\x83,\xa6\xdf\xfb\xe8\xe4v\xa1?\xf5Q\xd2\
\x8b\xb0\xb1H'7g\xe3\xe3\x84\x1d\x82'\x01\xd4\x10\
\x9db\xad\x22K)\xad\xc2N\xf4\x0au,b\xe3\
p%\x14\xf5Fp\xf5\xb5Iv\xf5\x91]u\x96R\
\xaa\xc2^l\xc2\xe3\xd8\xdc\x92\xbd\x0d\xcdY\x1a\xb2]\
\x9b7p\xfa\xe0<\x86\xfef\xad_\xc7\xf4\x90\x8a\xdc\
\xa1\xdf%\xc6\xe0\xd7\xbc\xbf\x84\xb8\x02x\x80\x8fy\xa7\
\xf8\x06\xb3p\xd6m\xe0\xd0h\xec\x00\x00\x00\x00IE\
ND\xaeB`\x82\
"
qt_resource_name = b"\
\x00\x05\
\x005\xbbT\
\x002\
\x004\x00x\x002\x004\
\x00\x05\
\x004\xdbF\
\x001\
\x006\x00x\x001\x006\
\x00\x05\
\x00o\xa6S\
\x00i\
\x00c\x00o\x00n\x00s\
\x00\x11\
\x0c\x84-\xa7\
\x00c\
\x00i\x00l\x00-\x00s\x00i\x00z\x00e\x00-\x00g\x00r\x00i\x00p\x00.\x00p\x00n\x00g\
\
\x00\x10\
\x0d\xc9]\x07\
\x00c\
\x00i\x00l\x00-\x00s\x00e\x00t\x00t\x00i\x00n\x00g\x00s\x00.\x00p\x00n\x00g\
\x00\x0c\
\x0b\x0b\xb0\xa7\
\x00c\
\x00i\x00l\x00-\x00h\x00o\x00m\x00e\x00.\x00p\x00n\x00g\
\x00\x16\
\x01\x843\xa7\
\x00c\
\x00i\x00l\x00-\x00j\x00u\x00s\x00t\x00i\x00f\x00y\x00-\x00c\x00e\x00n\x00t\x00e\
\x00r\x00.\x00p\x00n\x00g\
\x00\x0c\
\x09k\xbf'\
\x00c\
\x00i\x00l\x00-\x00m\x00e\x00n\x00u\x00.\x00p\x00n\x00g\
\x00\x0a\
\x00\x9ah\xa7\
\x00c\
\x00i\x00l\x00-\x003\x00d\x00.\x00p\x00n\x00g\
\x00\x17\
\x04%\x97\xa7\
\x00c\
\x00i\x00l\x00-\x00w\x00i\x00n\x00d\x00o\x00w\x00-\x00m\x00i\x00n\x00i\x00m\x00i\
\x00z\x00e\x00.\x00p\x00n\x00g\
\x00\x09\
\x0fK\x84\xa7\
\x00c\
\x00i\x00l\x00-\x00x\x00.\x00p\x00n\x00g\
\x00\x16\
\x01D\x10'\
\x00c\
\x00i\x00l\x00-\x00w\x00i\x00n\x00d\x00o\x00w\x00-\x00r\x00e\x00s\x00t\x00o\x00r\
\x00e\x00.\x00p\x00n\x00g\
\x00\x13\
\x06C\xb9'\
\x00c\
\x00i\x00l\x00-\x00c\x00a\x00m\x00e\x00r\x00a\x00-\x00r\x00o\x00l\x00l\x00.\x00p\
\x00n\x00g\
"
qt_resource_struct = b"\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x02\x00\x00\x00\x01\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x10\x00\x02\x00\x00\x00\x01\x00\x00\x00\x0e\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x01\x00\x00\x00\x03\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00 \x00\x02\x00\x00\x00\x01\x00\x00\x00\x04\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x00\x00\x02\x00\x00\x00\x09\x00\x00\x00\x05\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\xec\x00\x00\x00\x00\x00\x01\x00\x00'\x86\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x01R\x00\x00\x00\x00\x00\x01\x00\x00>\x83\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x00\x9c\x00\x00\x00\x00\x00\x01\x00\x00\x19 \
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x01\x06\x00\x00\x00\x00\x00\x01\x00\x000\x1b\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x01\x84\x00\x00\x00\x00\x00\x01\x00\x00Fq\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x00\xce\x00\x00\x00\x00\x00\x01\x00\x00 i\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x00~\x00\x00\x00\x00\x00\x01\x00\x00\x10\xe1\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x00X\x00\x00\x00\x00\x00\x01\x00\x00\x07\xc9\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x01:\x00\x00\x00\x00\x00\x01\x00\x006\xc7\
\x00\x00\x01tr\xe9\xcc\x98\
\x00\x00\x00 \x00\x02\x00\x00\x00\x01\x00\x00\x00\x0f\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x00\x10\x00\x02\x00\x00\x00\x01\x00\x00\x00\x10\
\x00\x00\x00\x00\x00\x00\x00\x00\
\x00\x00\x000\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\
\x00\x00\x01tr\xe9\xcc\x98\
"
def qInitResources():
QtCore.qRegisterResourceData(0x03, qt_resource_struct, qt_resource_name, qt_resource_data)
def qCleanupResources():
QtCore.qUnregisterResourceData(0x03, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
from ..utils import TranspileTestCase
import unittest
def assertTokenizaton(self, source, expected):
self.assertJavaScriptExecution("""
import _compile
s = %s
tok = _compile.Tokenizer(s)
for i in range(10000):
t = tok.get_token()
if t is None:
break
token, a, b = str(t).split(",")
print(i, token, s[int(a):int(b)])
""" % repr(source), expected)
class CompileTests(TranspileTestCase):
def test_basic_tokenize(self):
assertTokenizaton(self, "x = 1; fun.w3 -= 14.0e4j",
"""
0 NAME x
1 EQUAL =
2 NUMBER 1
3 SEMI ;
4 NAME fun
5 DOT .
6 NAME w3
7 MINEQUAL -=
8 NUMBER 14.0e4j
""")
def test_multiline_tokenize(self):
assertTokenizaton(self, '''
LOOPS = 50000
from time import clock
__version__ = "1.2"
[Ident1, Ident2, Ident3, Ident4, Ident5] = range(1, 6)
''',
"""
0 NEWLINE
1 NAME LOOPS
2 EQUAL =
3 NUMBER 50000
4 NEWLINE
5 NEWLINE
6 NAME from
7 NAME time
8 NAME import
9 NAME clock
10 NEWLINE
11 NEWLINE
12 NAME __version__
13 EQUAL =
14 STRING "1.2"
15 NEWLINE
16 NEWLINE
17 LSQB [
18 NAME Ident1
19 COMMA ,
20 NAME Ident2
21 COMMA ,
22 NAME Ident3
23 COMMA ,
24 NAME Ident4
25 COMMA ,
26 NAME Ident5
27 RSQB ]
28 EQUAL =
29 NAME range
30 LPAR (
31 NUMBER 1
32 COMMA ,
33 NUMBER 6
34 RPAR )
""")
def test_pystone_tokenize(self):
assertTokenizaton(self, '''
LOOPS = 50000
from time import clock
__version__ = "1.2"
[Ident1, Ident2, Ident3, Ident4, Ident5] = range(1, 6)
class Record:
def __init__(self, PtrComp = None, Discr = 0, EnumComp = 0,
IntComp = 0, StringComp = 0):
self.PtrComp = PtrComp
self.Discr = Discr
self.EnumComp = EnumComp
self.IntComp = IntComp
self.StringComp = StringComp
def copy(self):
return Record(self.PtrComp, self.Discr, self.EnumComp,
self.IntComp, self.StringComp)
TRUE = 1
FALSE = 0
def main(loops=LOOPS):
benchtime, stones = pystones(loops)
print("Pystone(%s) time for %d passes = %g" % \\
(__version__, loops, benchtime))
print("This machine benchmarks at %g pystones/second" % stones)
def pystones(loops=LOOPS):
return Proc0(loops)
IntGlob = 0
BoolGlob = FALSE
Char1Glob = '\\0'
Char2Glob = '\\0'
Array1Glob = [0]*51
Array2Glob = [x[:] for x in [Array1Glob]*51]
PtrGlb = None
PtrGlbNext = None
def Proc0(loops=LOOPS):
global IntGlob
global BoolGlob
global Char1Glob
global Char2Glob
global Array1Glob
global Array2Glob
global PtrGlb
global PtrGlbNext
starttime = clock()
for i in range(loops):
pass
nulltime = clock() - starttime
PtrGlbNext = Record()
PtrGlb = Record()
PtrGlb.PtrComp = PtrGlbNext
PtrGlb.Discr = Ident1
PtrGlb.EnumComp = Ident3
PtrGlb.IntComp = 40
PtrGlb.StringComp = "DHRYSTONE PROGRAM, SOME STRING"
String1Loc = "DHRYSTONE PROGRAM, 1'ST STRING"
Array2Glob[8][7] = 10
starttime = clock()
for i in range(loops):
Proc5()
Proc4()
IntLoc1 = 2
IntLoc2 = 3
String2Loc = "DHRYSTONE PROGRAM, 2'ND STRING"
EnumLoc = Ident2
BoolGlob = not Func2(String1Loc, String2Loc)
while IntLoc1 < IntLoc2:
IntLoc3 = 5 * IntLoc1 - IntLoc2
IntLoc3 = Proc7(IntLoc1, IntLoc2)
IntLoc1 = IntLoc1 + 1
Proc8(Array1Glob, Array2Glob, IntLoc1, IntLoc3)
PtrGlb = Proc1(PtrGlb)
CharIndex = 'A'
while CharIndex <= Char2Glob:
if EnumLoc == Func1(CharIndex, 'C'):
EnumLoc = Proc6(Ident1)
CharIndex = chr(ord(CharIndex)+1)
IntLoc3 = IntLoc2 * IntLoc1
IntLoc2 = IntLoc3 // IntLoc1
IntLoc2 = 7 * (IntLoc3 - IntLoc2) - IntLoc1
IntLoc1 = Proc2(IntLoc1)
benchtime = clock() - starttime - nulltime
if benchtime == 0.0:
loopsPerBenchtime = 0.0
else:
loopsPerBenchtime = (loops / benchtime)
return benchtime, loopsPerBenchtime
def Proc1(PtrParIn):
PtrParIn.PtrComp = NextRecord = PtrGlb.copy()
PtrParIn.IntComp = 5
NextRecord.IntComp = PtrParIn.IntComp
NextRecord.PtrComp = PtrParIn.PtrComp
NextRecord.PtrComp = Proc3(NextRecord.PtrComp)
if NextRecord.Discr == Ident1:
NextRecord.IntComp = 6
NextRecord.EnumComp = Proc6(PtrParIn.EnumComp)
NextRecord.PtrComp = PtrGlb.PtrComp
NextRecord.IntComp = Proc7(NextRecord.IntComp, 10)
else:
PtrParIn = NextRecord.copy()
NextRecord.PtrComp = None
return PtrParIn
def Proc2(IntParIO):
IntLoc = IntParIO + 10
while 1:
if Char1Glob == 'A':
IntLoc = IntLoc - 1
IntParIO = IntLoc - IntGlob
EnumLoc = Ident1
if EnumLoc == Ident1:
break
return IntParIO
def Proc3(PtrParOut):
global IntGlob
if PtrGlb is not None:
PtrParOut = PtrGlb.PtrComp
else:
IntGlob = 100
PtrGlb.IntComp = Proc7(10, IntGlob)
return PtrParOut
def Proc4():
global Char2Glob
BoolLoc = Char1Glob == 'A'
BoolLoc = BoolLoc or BoolGlob
Char2Glob = 'B'
def Proc5():
global Char1Glob
global BoolGlob
Char1Glob = 'A'
BoolGlob = FALSE
def Proc6(EnumParIn):
EnumParOut = EnumParIn
if not Func3(EnumParIn):
EnumParOut = Ident4
if EnumParIn == Ident1:
EnumParOut = Ident1
elif EnumParIn == Ident2:
if IntGlob > 100:
EnumParOut = Ident1
else:
EnumParOut = Ident4
elif EnumParIn == Ident3:
EnumParOut = Ident2
elif EnumParIn == Ident4:
pass
elif EnumParIn == Ident5:
EnumParOut = Ident3
return EnumParOut
def Proc7(IntParI1, IntParI2):
IntLoc = IntParI1 + 2
IntParOut = IntParI2 + IntLoc
return IntParOut
def Proc8(Array1Par, Array2Par, IntParI1, IntParI2):
global IntGlob
IntLoc = IntParI1 + 5
Array1Par[IntLoc] = IntParI2
Array1Par[IntLoc+1] = Array1Par[IntLoc]
Array1Par[IntLoc+30] = IntLoc
for IntIndex in range(IntLoc, IntLoc+2):
Array2Par[IntLoc][IntIndex] = IntLoc
Array2Par[IntLoc][IntLoc-1] = Array2Par[IntLoc][IntLoc-1] + 1
Array2Par[IntLoc+20][IntLoc] = Array1Par[IntLoc]
IntGlob = 5
def Func1(CharPar1, CharPar2):
CharLoc1 = CharPar1
CharLoc2 = CharLoc1
if CharLoc2 != CharPar2:
return Ident1
else:
return Ident2
def Func2(StrParI1, StrParI2):
IntLoc = 1
while IntLoc <= 1:
if Func1(StrParI1[IntLoc], StrParI2[IntLoc+1]) == Ident1:
CharLoc = 'A'
IntLoc = IntLoc + 1
if CharLoc >= 'W' and CharLoc <= 'Z':
IntLoc = 7
if CharLoc == 'X':
return TRUE
else:
if StrParI1 > StrParI2:
IntLoc = IntLoc + 7
return TRUE
else:
return FALSE
def Func3(EnumParIn):
EnumLoc = EnumParIn
if EnumLoc == Ident3: return TRUE
return FALSE
if __name__ == '__main__':
import sys
def error(msg):
print(msg, end=' ', file=sys.stderr)
print("usage: %s [number_of_loops]" % sys.argv[0], file=sys.stderr)
sys.exit(100)
nargs = len(sys.argv) - 1
if nargs > 1:
error("%d arguments are too many;" % nargs)
elif nargs == 1:
try: loops = int(sys.argv[1])
except ValueError:
error("Invalid argument %r;" % sys.argv[1])
else:
loops = LOOPS
main(loops)
''',
"""
0 NEWLINE
1 NAME LOOPS
2 EQUAL =
3 NUMBER 50000
4 NEWLINE
5 NEWLINE
6 NAME from
7 NAME time
8 NAME import
9 NAME clock
10 NEWLINE
11 NEWLINE
12 NAME __version__
13 EQUAL =
14 STRING "1.2"
15 NEWLINE
16 NEWLINE
17 LSQB [
18 NAME Ident1
19 COMMA ,
20 NAME Ident2
21 COMMA ,
22 NAME Ident3
23 COMMA ,
24 NAME Ident4
25 COMMA ,
26 NAME Ident5
27 RSQB ]
28 EQUAL =
29 NAME range
30 LPAR (
31 NUMBER 1
32 COMMA ,
33 NUMBER 6
34 RPAR )
35 NEWLINE
36 NEWLINE
37 NAME class
38 NAME Record
39 COLON :
40 NEWLINE
41 NEWLINE
42 INDENT
43 NAME def
44 NAME __init__
45 LPAR (
46 NAME self
47 COMMA ,
48 NAME PtrComp
49 EQUAL =
50 NAME None
51 COMMA ,
52 NAME Discr
53 EQUAL =
54 NUMBER 0
55 COMMA ,
56 NAME EnumComp
57 EQUAL =
58 NUMBER 0
59 COMMA ,
60 NAME IntComp
61 EQUAL =
62 NUMBER 0
63 COMMA ,
64 NAME StringComp
65 EQUAL =
66 NUMBER 0
67 RPAR )
68 COLON :
69 NEWLINE
70 INDENT
71 NAME self
72 DOT .
73 NAME PtrComp
74 EQUAL =
75 NAME PtrComp
76 NEWLINE
77 NAME self
78 DOT .
79 NAME Discr
80 EQUAL =
81 NAME Discr
82 NEWLINE
83 NAME self
84 DOT .
85 NAME EnumComp
86 EQUAL =
87 NAME EnumComp
88 NEWLINE
89 NAME self
90 DOT .
91 NAME IntComp
92 EQUAL =
93 NAME IntComp
94 NEWLINE
95 NAME self
96 DOT .
97 NAME StringComp
98 EQUAL =
99 NAME StringComp
100 NEWLINE
101 NEWLINE
102 DEDENT
103 NAME def
104 NAME copy
105 LPAR (
106 NAME self
107 RPAR )
108 COLON :
109 NEWLINE
110 INDENT
111 NAME return
112 NAME Record
113 LPAR (
114 NAME self
115 DOT .
116 NAME PtrComp
117 COMMA ,
118 NAME self
119 DOT .
120 NAME Discr
121 COMMA ,
122 NAME self
123 DOT .
124 NAME EnumComp
125 COMMA ,
126 NAME self
127 DOT .
128 NAME IntComp
129 COMMA ,
130 NAME self
131 DOT .
132 NAME StringComp
133 RPAR )
134 NEWLINE
135 NEWLINE
136 DEDENT
137 DEDENT
138 NAME TRUE
139 EQUAL =
140 NUMBER 1
141 NEWLINE
142 NAME FALSE
143 EQUAL =
144 NUMBER 0
145 NEWLINE
146 NEWLINE
147 NAME def
148 NAME main
149 LPAR (
150 NAME loops
151 EQUAL =
152 NAME LOOPS
153 RPAR )
154 COLON :
155 NEWLINE
156 INDENT
157 NAME benchtime
158 COMMA ,
159 NAME stones
160 EQUAL =
161 NAME pystones
162 LPAR (
163 NAME loops
164 RPAR )
165 NEWLINE
166 NAME print
167 LPAR (
168 STRING "Pystone(%s) time for %d passes = %g"
169 PERCENT %
170 LPAR (
171 NAME __version__
172 COMMA ,
173 NAME loops
174 COMMA ,
175 NAME benchtime
176 RPAR )
177 RPAR )
178 NEWLINE
179 NAME print
180 LPAR (
181 STRING "This machine benchmarks at %g pystones/second"
182 PERCENT %
183 NAME stones
184 RPAR )
185 NEWLINE
186 NEWLINE
187 NEWLINE
188 DEDENT
189 NAME def
190 NAME pystones
191 LPAR (
192 NAME loops
193 EQUAL =
194 NAME LOOPS
195 RPAR )
196 COLON :
197 NEWLINE
198 INDENT
199 NAME return
200 NAME Proc0
201 LPAR (
202 NAME loops
203 RPAR )
204 NEWLINE
205 NEWLINE
206 DEDENT
207 NAME IntGlob
208 EQUAL =
209 NUMBER 0
210 NEWLINE
211 NAME BoolGlob
212 EQUAL =
213 NAME FALSE
214 NEWLINE
215 NAME Char1Glob
216 EQUAL =
217 STRING '\\0'
218 NEWLINE
219 NAME Char2Glob
220 EQUAL =
221 STRING '\\0'
222 NEWLINE
223 NAME Array1Glob
224 EQUAL =
225 LSQB [
226 NUMBER 0
227 RSQB ]
228 STAR *
229 NUMBER 51
230 NEWLINE
231 NAME Array2Glob
232 EQUAL =
233 LSQB [
234 NAME x
235 LSQB [
236 COLON :
237 RSQB ]
238 NAME for
239 NAME x
240 NAME in
241 LSQB [
242 NAME Array1Glob
243 RSQB ]
244 STAR *
245 NUMBER 51
246 RSQB ]
247 NEWLINE
248 NAME PtrGlb
249 EQUAL =
250 NAME None
251 NEWLINE
252 NAME PtrGlbNext
253 EQUAL =
254 NAME None
255 NEWLINE
256 NEWLINE
257 NAME def
258 NAME Proc0
259 LPAR (
260 NAME loops
261 EQUAL =
262 NAME LOOPS
263 RPAR )
264 COLON :
265 NEWLINE
266 INDENT
267 NAME global
268 NAME IntGlob
269 NEWLINE
270 NAME global
271 NAME BoolGlob
272 NEWLINE
273 NAME global
274 NAME Char1Glob
275 NEWLINE
276 NAME global
277 NAME Char2Glob
278 NEWLINE
279 NAME global
280 NAME Array1Glob
281 NEWLINE
282 NAME global
283 NAME Array2Glob
284 NEWLINE
285 NAME global
286 NAME PtrGlb
287 NEWLINE
288 NAME global
289 NAME PtrGlbNext
290 NEWLINE
291 NEWLINE
292 NAME starttime
293 EQUAL =
294 NAME clock
295 LPAR (
296 RPAR )
297 NEWLINE
298 NAME for
299 NAME i
300 NAME in
301 NAME range
302 LPAR (
303 NAME loops
304 RPAR )
305 COLON :
306 NEWLINE
307 INDENT
308 NAME pass
309 NEWLINE
310 DEDENT
311 NAME nulltime
312 EQUAL =
313 NAME clock
314 LPAR (
315 RPAR )
316 MINUS -
317 NAME starttime
318 NEWLINE
319 NEWLINE
320 NAME PtrGlbNext
321 EQUAL =
322 NAME Record
323 LPAR (
324 RPAR )
325 NEWLINE
326 NAME PtrGlb
327 EQUAL =
328 NAME Record
329 LPAR (
330 RPAR )
331 NEWLINE
332 NAME PtrGlb
333 DOT .
334 NAME PtrComp
335 EQUAL =
336 NAME PtrGlbNext
337 NEWLINE
338 NAME PtrGlb
339 DOT .
340 NAME Discr
341 EQUAL =
342 NAME Ident1
343 NEWLINE
344 NAME PtrGlb
345 DOT .
346 NAME EnumComp
347 EQUAL =
348 NAME Ident3
349 NEWLINE
350 NAME PtrGlb
351 DOT .
352 NAME IntComp
353 EQUAL =
354 NUMBER 40
355 NEWLINE
356 NAME PtrGlb
357 DOT .
358 NAME StringComp
359 EQUAL =
360 STRING "DHRYSTONE PROGRAM, SOME STRING"
361 NEWLINE
362 NAME String1Loc
363 EQUAL =
364 STRING "DHRYSTONE PROGRAM, 1'ST STRING"
365 NEWLINE
366 NAME Array2Glob
367 LSQB [
368 NUMBER 8
369 RSQB ]
370 LSQB [
371 NUMBER 7
372 RSQB ]
373 EQUAL =
374 NUMBER 10
375 NEWLINE
376 NEWLINE
377 NAME starttime
378 EQUAL =
379 NAME clock
380 LPAR (
381 RPAR )
382 NEWLINE
383 NEWLINE
384 NAME for
385 NAME i
386 NAME in
387 NAME range
388 LPAR (
389 NAME loops
390 RPAR )
391 COLON :
392 NEWLINE
393 INDENT
394 NAME Proc5
395 LPAR (
396 RPAR )
397 NEWLINE
398 NAME Proc4
399 LPAR (
400 RPAR )
401 NEWLINE
402 NAME IntLoc1
403 EQUAL =
404 NUMBER 2
405 NEWLINE
406 NAME IntLoc2
407 EQUAL =
408 NUMBER 3
409 NEWLINE
410 NAME String2Loc
411 EQUAL =
412 STRING "DHRYSTONE PROGRAM, 2'ND STRING"
413 NEWLINE
414 NAME EnumLoc
415 EQUAL =
416 NAME Ident2
417 NEWLINE
418 NAME BoolGlob
419 EQUAL =
420 NAME not
421 NAME Func2
422 LPAR (
423 NAME String1Loc
424 COMMA ,
425 NAME String2Loc
426 RPAR )
427 NEWLINE
428 NAME while
429 NAME IntLoc1
430 LESS <
431 NAME IntLoc2
432 COLON :
433 NEWLINE
434 INDENT
435 NAME IntLoc3
436 EQUAL =
437 NUMBER 5
438 STAR *
439 NAME IntLoc1
440 MINUS -
441 NAME IntLoc2
442 NEWLINE
443 NAME IntLoc3
444 EQUAL =
445 NAME Proc7
446 LPAR (
447 NAME IntLoc1
448 COMMA ,
449 NAME IntLoc2
450 RPAR )
451 NEWLINE
452 NAME IntLoc1
453 EQUAL =
454 NAME IntLoc1
455 PLUS +
456 NUMBER 1
457 NEWLINE
458 DEDENT
459 NAME Proc8
460 LPAR (
461 NAME Array1Glob
462 COMMA ,
463 NAME Array2Glob
464 COMMA ,
465 NAME IntLoc1
466 COMMA ,
467 NAME IntLoc3
468 RPAR )
469 NEWLINE
470 NAME PtrGlb
471 EQUAL =
472 NAME Proc1
473 LPAR (
474 NAME PtrGlb
475 RPAR )
476 NEWLINE
477 NAME CharIndex
478 EQUAL =
479 STRING 'A'
480 NEWLINE
481 NAME while
482 NAME CharIndex
483 LESSEQUAL <=
484 NAME Char2Glob
485 COLON :
486 NEWLINE
487 INDENT
488 NAME if
489 NAME EnumLoc
490 EQEQUAL ==
491 NAME Func1
492 LPAR (
493 NAME CharIndex
494 COMMA ,
495 STRING 'C'
496 RPAR )
497 COLON :
498 NEWLINE
499 INDENT
500 NAME EnumLoc
501 EQUAL =
502 NAME Proc6
503 LPAR (
504 NAME Ident1
505 RPAR )
506 NEWLINE
507 DEDENT
508 NAME CharIndex
509 EQUAL =
510 NAME chr
511 LPAR (
512 NAME ord
513 LPAR (
514 NAME CharIndex
515 RPAR )
516 PLUS +
517 NUMBER 1
518 RPAR )
519 NEWLINE
520 DEDENT
521 NAME IntLoc3
522 EQUAL =
523 NAME IntLoc2
524 STAR *
525 NAME IntLoc1
526 NEWLINE
527 NAME IntLoc2
528 EQUAL =
529 NAME IntLoc3
530 DOUBLESLASH //
531 NAME IntLoc1
532 NEWLINE
533 NAME IntLoc2
534 EQUAL =
535 NUMBER 7
536 STAR *
537 LPAR (
538 NAME IntLoc3
539 MINUS -
540 NAME IntLoc2
541 RPAR )
542 MINUS -
543 NAME IntLoc1
544 NEWLINE
545 NAME IntLoc1
546 EQUAL =
547 NAME Proc2
548 LPAR (
549 NAME IntLoc1
550 RPAR )
551 NEWLINE
552 NEWLINE
553 DEDENT
554 NAME benchtime
555 EQUAL =
556 NAME clock
557 LPAR (
558 RPAR )
559 MINUS -
560 NAME starttime
561 MINUS -
562 NAME nulltime
563 NEWLINE
564 NAME if
565 NAME benchtime
566 EQEQUAL ==
567 NUMBER 0.0
568 COLON :
569 NEWLINE
570 INDENT
571 NAME loopsPerBenchtime
572 EQUAL =
573 NUMBER 0.0
574 NEWLINE
575 DEDENT
576 NAME else
577 COLON :
578 NEWLINE
579 INDENT
580 NAME loopsPerBenchtime
581 EQUAL =
582 LPAR (
583 NAME loops
584 SLASH /
585 NAME benchtime
586 RPAR )
587 NEWLINE
588 DEDENT
589 NAME return
590 NAME benchtime
591 COMMA ,
592 NAME loopsPerBenchtime
593 NEWLINE
594 NEWLINE
595 DEDENT
596 NAME def
597 NAME Proc1
598 LPAR (
599 NAME PtrParIn
600 RPAR )
601 COLON :
602 NEWLINE
603 INDENT
604 NAME PtrParIn
605 DOT .
606 NAME PtrComp
607 EQUAL =
608 NAME NextRecord
609 EQUAL =
610 NAME PtrGlb
611 DOT .
612 NAME copy
613 LPAR (
614 RPAR )
615 NEWLINE
616 NAME PtrParIn
617 DOT .
618 NAME IntComp
619 EQUAL =
620 NUMBER 5
621 NEWLINE
622 NAME NextRecord
623 DOT .
624 NAME IntComp
625 EQUAL =
626 NAME PtrParIn
627 DOT .
628 NAME IntComp
629 NEWLINE
630 NAME NextRecord
631 DOT .
632 NAME PtrComp
633 EQUAL =
634 NAME PtrParIn
635 DOT .
636 NAME PtrComp
637 NEWLINE
638 NAME NextRecord
639 DOT .
640 NAME PtrComp
641 EQUAL =
642 NAME Proc3
643 LPAR (
644 NAME NextRecord
645 DOT .
646 NAME PtrComp
647 RPAR )
648 NEWLINE
649 NAME if
650 NAME NextRecord
651 DOT .
652 NAME Discr
653 EQEQUAL ==
654 NAME Ident1
655 COLON :
656 NEWLINE
657 INDENT
658 NAME NextRecord
659 DOT .
660 NAME IntComp
661 EQUAL =
662 NUMBER 6
663 NEWLINE
664 NAME NextRecord
665 DOT .
666 NAME EnumComp
667 EQUAL =
668 NAME Proc6
669 LPAR (
670 NAME PtrParIn
671 DOT .
672 NAME EnumComp
673 RPAR )
674 NEWLINE
675 NAME NextRecord
676 DOT .
677 NAME PtrComp
678 EQUAL =
679 NAME PtrGlb
680 DOT .
681 NAME PtrComp
682 NEWLINE
683 NAME NextRecord
684 DOT .
685 NAME IntComp
686 EQUAL =
687 NAME Proc7
688 LPAR (
689 NAME NextRecord
690 DOT .
691 NAME IntComp
692 COMMA ,
693 NUMBER 10
694 RPAR )
695 NEWLINE
696 DEDENT
697 NAME else
698 COLON :
699 NEWLINE
700 INDENT
701 NAME PtrParIn
702 EQUAL =
703 NAME NextRecord
704 DOT .
705 NAME copy
706 LPAR (
707 RPAR )
708 NEWLINE
709 DEDENT
710 NAME NextRecord
711 DOT .
712 NAME PtrComp
713 EQUAL =
714 NAME None
715 NEWLINE
716 NAME return
717 NAME PtrParIn
718 NEWLINE
719 NEWLINE
720 DEDENT
721 NAME def
722 NAME Proc2
723 LPAR (
724 NAME IntParIO
725 RPAR )
726 COLON :
727 NEWLINE
728 INDENT
729 NAME IntLoc
730 EQUAL =
731 NAME IntParIO
732 PLUS +
733 NUMBER 10
734 NEWLINE
735 NAME while
736 NUMBER 1
737 COLON :
738 NEWLINE
739 INDENT
740 NAME if
741 NAME Char1Glob
742 EQEQUAL ==
743 STRING 'A'
744 COLON :
745 NEWLINE
746 INDENT
747 NAME IntLoc
748 EQUAL =
749 NAME IntLoc
750 MINUS -
751 NUMBER 1
752 NEWLINE
753 NAME IntParIO
754 EQUAL =
755 NAME IntLoc
756 MINUS -
757 NAME IntGlob
758 NEWLINE
759 NAME EnumLoc
760 EQUAL =
761 NAME Ident1
762 NEWLINE
763 DEDENT
764 NAME if
765 NAME EnumLoc
766 EQEQUAL ==
767 NAME Ident1
768 COLON :
769 NEWLINE
770 INDENT
771 NAME break
772 NEWLINE
773 DEDENT
774 DEDENT
775 NAME return
776 NAME IntParIO
777 NEWLINE
778 NEWLINE
779 DEDENT
780 NAME def
781 NAME Proc3
782 LPAR (
783 NAME PtrParOut
784 RPAR )
785 COLON :
786 NEWLINE
787 INDENT
788 NAME global
789 NAME IntGlob
790 NEWLINE
791 NEWLINE
792 NAME if
793 NAME PtrGlb
794 NAME is
795 NAME not
796 NAME None
797 COLON :
798 NEWLINE
799 INDENT
800 NAME PtrParOut
801 EQUAL =
802 NAME PtrGlb
803 DOT .
804 NAME PtrComp
805 NEWLINE
806 DEDENT
807 NAME else
808 COLON :
809 NEWLINE
810 INDENT
811 NAME IntGlob
812 EQUAL =
813 NUMBER 100
814 NEWLINE
815 DEDENT
816 NAME PtrGlb
817 DOT .
818 NAME IntComp
819 EQUAL =
820 NAME Proc7
821 LPAR (
822 NUMBER 10
823 COMMA ,
824 NAME IntGlob
825 RPAR )
826 NEWLINE
827 NAME return
828 NAME PtrParOut
829 NEWLINE
830 NEWLINE
831 DEDENT
832 NAME def
833 NAME Proc4
834 LPAR (
835 RPAR )
836 COLON :
837 NEWLINE
838 INDENT
839 NAME global
840 NAME Char2Glob
841 NEWLINE
842 NEWLINE
843 NAME BoolLoc
844 EQUAL =
845 NAME Char1Glob
846 EQEQUAL ==
847 STRING 'A'
848 NEWLINE
849 NAME BoolLoc
850 EQUAL =
851 NAME BoolLoc
852 NAME or
853 NAME BoolGlob
854 NEWLINE
855 NAME Char2Glob
856 EQUAL =
857 STRING 'B'
858 NEWLINE
859 NEWLINE
860 DEDENT
861 NAME def
862 NAME Proc5
863 LPAR (
864 RPAR )
865 COLON :
866 NEWLINE
867 INDENT
868 NAME global
869 NAME Char1Glob
870 NEWLINE
871 NAME global
872 NAME BoolGlob
873 NEWLINE
874 NEWLINE
875 NAME Char1Glob
876 EQUAL =
877 STRING 'A'
878 NEWLINE
879 NAME BoolGlob
880 EQUAL =
881 NAME FALSE
882 NEWLINE
883 NEWLINE
884 DEDENT
885 NAME def
886 NAME Proc6
887 LPAR (
888 NAME EnumParIn
889 RPAR )
890 COLON :
891 NEWLINE
892 INDENT
893 NAME EnumParOut
894 EQUAL =
895 NAME EnumParIn
896 NEWLINE
897 NAME if
898 NAME not
899 NAME Func3
900 LPAR (
901 NAME EnumParIn
902 RPAR )
903 COLON :
904 NEWLINE
905 INDENT
906 NAME EnumParOut
907 EQUAL =
908 NAME Ident4
909 NEWLINE
910 DEDENT
911 NAME if
912 NAME EnumParIn
913 EQEQUAL ==
914 NAME Ident1
915 COLON :
916 NEWLINE
917 INDENT
918 NAME EnumParOut
919 EQUAL =
920 NAME Ident1
921 NEWLINE
922 DEDENT
923 NAME elif
924 NAME EnumParIn
925 EQEQUAL ==
926 NAME Ident2
927 COLON :
928 NEWLINE
929 INDENT
930 NAME if
931 NAME IntGlob
932 GREATER >
933 NUMBER 100
934 COLON :
935 NEWLINE
936 INDENT
937 NAME EnumParOut
938 EQUAL =
939 NAME Ident1
940 NEWLINE
941 DEDENT
942 NAME else
943 COLON :
944 NEWLINE
945 INDENT
946 NAME EnumParOut
947 EQUAL =
948 NAME Ident4
949 NEWLINE
950 DEDENT
951 DEDENT
952 NAME elif
953 NAME EnumParIn
954 EQEQUAL ==
955 NAME Ident3
956 COLON :
957 NEWLINE
958 INDENT
959 NAME EnumParOut
960 EQUAL =
961 NAME Ident2
962 NEWLINE
963 DEDENT
964 NAME elif
965 NAME EnumParIn
966 EQEQUAL ==
967 NAME Ident4
968 COLON :
969 NEWLINE
970 INDENT
971 NAME pass
972 NEWLINE
973 DEDENT
974 NAME elif
975 NAME EnumParIn
976 EQEQUAL ==
977 NAME Ident5
978 COLON :
979 NEWLINE
980 INDENT
981 NAME EnumParOut
982 EQUAL =
983 NAME Ident3
984 NEWLINE
985 DEDENT
986 NAME return
987 NAME EnumParOut
988 NEWLINE
989 NEWLINE
990 DEDENT
991 NAME def
992 NAME Proc7
993 LPAR (
994 NAME IntParI1
995 COMMA ,
996 NAME IntParI2
997 RPAR )
998 COLON :
999 NEWLINE
1000 INDENT
1001 NAME IntLoc
1002 EQUAL =
1003 NAME IntParI1
1004 PLUS +
1005 NUMBER 2
1006 NEWLINE
1007 NAME IntParOut
1008 EQUAL =
1009 NAME IntParI2
1010 PLUS +
1011 NAME IntLoc
1012 NEWLINE
1013 NAME return
1014 NAME IntParOut
1015 NEWLINE
1016 NEWLINE
1017 DEDENT
1018 NAME def
1019 NAME Proc8
1020 LPAR (
1021 NAME Array1Par
1022 COMMA ,
1023 NAME Array2Par
1024 COMMA ,
1025 NAME IntParI1
1026 COMMA ,
1027 NAME IntParI2
1028 RPAR )
1029 COLON :
1030 NEWLINE
1031 INDENT
1032 NAME global
1033 NAME IntGlob
1034 NEWLINE
1035 NEWLINE
1036 NAME IntLoc
1037 EQUAL =
1038 NAME IntParI1
1039 PLUS +
1040 NUMBER 5
1041 NEWLINE
1042 NAME Array1Par
1043 LSQB [
1044 NAME IntLoc
1045 RSQB ]
1046 EQUAL =
1047 NAME IntParI2
1048 NEWLINE
1049 NAME Array1Par
1050 LSQB [
1051 NAME IntLoc
1052 PLUS +
1053 NUMBER 1
1054 RSQB ]
1055 EQUAL =
1056 NAME Array1Par
1057 LSQB [
1058 NAME IntLoc
1059 RSQB ]
1060 NEWLINE
1061 NAME Array1Par
1062 LSQB [
1063 NAME IntLoc
1064 PLUS +
1065 NUMBER 30
1066 RSQB ]
1067 EQUAL =
1068 NAME IntLoc
1069 NEWLINE
1070 NAME for
1071 NAME IntIndex
1072 NAME in
1073 NAME range
1074 LPAR (
1075 NAME IntLoc
1076 COMMA ,
1077 NAME IntLoc
1078 PLUS +
1079 NUMBER 2
1080 RPAR )
1081 COLON :
1082 NEWLINE
1083 INDENT
1084 NAME Array2Par
1085 LSQB [
1086 NAME IntLoc
1087 RSQB ]
1088 LSQB [
1089 NAME IntIndex
1090 RSQB ]
1091 EQUAL =
1092 NAME IntLoc
1093 NEWLINE
1094 DEDENT
1095 NAME Array2Par
1096 LSQB [
1097 NAME IntLoc
1098 RSQB ]
1099 LSQB [
1100 NAME IntLoc
1101 MINUS -
1102 NUMBER 1
1103 RSQB ]
1104 EQUAL =
1105 NAME Array2Par
1106 LSQB [
1107 NAME IntLoc
1108 RSQB ]
1109 LSQB [
1110 NAME IntLoc
1111 MINUS -
1112 NUMBER 1
1113 RSQB ]
1114 PLUS +
1115 NUMBER 1
1116 NEWLINE
1117 NAME Array2Par
1118 LSQB [
1119 NAME IntLoc
1120 PLUS +
1121 NUMBER 20
1122 RSQB ]
1123 LSQB [
1124 NAME IntLoc
1125 RSQB ]
1126 EQUAL =
1127 NAME Array1Par
1128 LSQB [
1129 NAME IntLoc
1130 RSQB ]
1131 NEWLINE
1132 NAME IntGlob
1133 EQUAL =
1134 NUMBER 5
1135 NEWLINE
1136 NEWLINE
1137 DEDENT
1138 NAME def
1139 NAME Func1
1140 LPAR (
1141 NAME CharPar1
1142 COMMA ,
1143 NAME CharPar2
1144 RPAR )
1145 COLON :
1146 NEWLINE
1147 INDENT
1148 NAME CharLoc1
1149 EQUAL =
1150 NAME CharPar1
1151 NEWLINE
1152 NAME CharLoc2
1153 EQUAL =
1154 NAME CharLoc1
1155 NEWLINE
1156 NAME if
1157 NAME CharLoc2
1158 NOTEQUAL !=
1159 NAME CharPar2
1160 COLON :
1161 NEWLINE
1162 INDENT
1163 NAME return
1164 NAME Ident1
1165 NEWLINE
1166 DEDENT
1167 NAME else
1168 COLON :
1169 NEWLINE
1170 INDENT
1171 NAME return
1172 NAME Ident2
1173 NEWLINE
1174 NEWLINE
1175 DEDENT
1176 DEDENT
1177 NAME def
1178 NAME Func2
1179 LPAR (
1180 NAME StrParI1
1181 COMMA ,
1182 NAME StrParI2
1183 RPAR )
1184 COLON :
1185 NEWLINE
1186 INDENT
1187 NAME IntLoc
1188 EQUAL =
1189 NUMBER 1
1190 NEWLINE
1191 NAME while
1192 NAME IntLoc
1193 LESSEQUAL <=
1194 NUMBER 1
1195 COLON :
1196 NEWLINE
1197 INDENT
1198 NAME if
1199 NAME Func1
1200 LPAR (
1201 NAME StrParI1
1202 LSQB [
1203 NAME IntLoc
1204 RSQB ]
1205 COMMA ,
1206 NAME StrParI2
1207 LSQB [
1208 NAME IntLoc
1209 PLUS +
1210 NUMBER 1
1211 RSQB ]
1212 RPAR )
1213 EQEQUAL ==
1214 NAME Ident1
1215 COLON :
1216 NEWLINE
1217 INDENT
1218 NAME CharLoc
1219 EQUAL =
1220 STRING 'A'
1221 NEWLINE
1222 NAME IntLoc
1223 EQUAL =
1224 NAME IntLoc
1225 PLUS +
1226 NUMBER 1
1227 NEWLINE
1228 DEDENT
1229 DEDENT
1230 NAME if
1231 NAME CharLoc
1232 GREATEREQUAL >=
1233 STRING 'W'
1234 NAME and
1235 NAME CharLoc
1236 LESSEQUAL <=
1237 STRING 'Z'
1238 COLON :
1239 NEWLINE
1240 INDENT
1241 NAME IntLoc
1242 EQUAL =
1243 NUMBER 7
1244 NEWLINE
1245 DEDENT
1246 NAME if
1247 NAME CharLoc
1248 EQEQUAL ==
1249 STRING 'X'
1250 COLON :
1251 NEWLINE
1252 INDENT
1253 NAME return
1254 NAME TRUE
1255 NEWLINE
1256 DEDENT
1257 NAME else
1258 COLON :
1259 NEWLINE
1260 INDENT
1261 NAME if
1262 NAME StrParI1
1263 GREATER >
1264 NAME StrParI2
1265 COLON :
1266 NEWLINE
1267 INDENT
1268 NAME IntLoc
1269 EQUAL =
1270 NAME IntLoc
1271 PLUS +
1272 NUMBER 7
1273 NEWLINE
1274 NAME return
1275 NAME TRUE
1276 NEWLINE
1277 DEDENT
1278 NAME else
1279 COLON :
1280 NEWLINE
1281 INDENT
1282 NAME return
1283 NAME FALSE
1284 NEWLINE
1285 NEWLINE
1286 DEDENT
1287 DEDENT
1288 DEDENT
1289 NAME def
1290 NAME Func3
1291 LPAR (
1292 NAME EnumParIn
1293 RPAR )
1294 COLON :
1295 NEWLINE
1296 INDENT
1297 NAME EnumLoc
1298 EQUAL =
1299 NAME EnumParIn
1300 NEWLINE
1301 NAME if
1302 NAME EnumLoc
1303 EQEQUAL ==
1304 NAME Ident3
1305 COLON :
1306 NAME return
1307 NAME TRUE
1308 NEWLINE
1309 NAME return
1310 NAME FALSE
1311 NEWLINE
1312 NEWLINE
1313 DEDENT
1314 NAME if
1315 NAME __name__
1316 EQEQUAL ==
1317 STRING '__main__'
1318 COLON :
1319 NEWLINE
1320 INDENT
1321 NAME import
1322 NAME sys
1323 NEWLINE
1324 NAME def
1325 NAME error
1326 LPAR (
1327 NAME msg
1328 RPAR )
1329 COLON :
1330 NEWLINE
1331 INDENT
1332 NAME print
1333 LPAR (
1334 NAME msg
1335 COMMA ,
1336 NAME end
1337 EQUAL =
1338 STRING ' '
1339 COMMA ,
1340 NAME file
1341 EQUAL =
1342 NAME sys
1343 DOT .
1344 NAME stderr
1345 RPAR )
1346 NEWLINE
1347 NAME print
1348 LPAR (
1349 STRING "usage: %s [number_of_loops]"
1350 PERCENT %
1351 NAME sys
1352 DOT .
1353 NAME argv
1354 LSQB [
1355 NUMBER 0
1356 RSQB ]
1357 COMMA ,
1358 NAME file
1359 EQUAL =
1360 NAME sys
1361 DOT .
1362 NAME stderr
1363 RPAR )
1364 NEWLINE
1365 NAME sys
1366 DOT .
1367 NAME exit
1368 LPAR (
1369 NUMBER 100
1370 RPAR )
1371 NEWLINE
1372 DEDENT
1373 NAME nargs
1374 EQUAL =
1375 NAME len
1376 LPAR (
1377 NAME sys
1378 DOT .
1379 NAME argv
1380 RPAR )
1381 MINUS -
1382 NUMBER 1
1383 NEWLINE
1384 NAME if
1385 NAME nargs
1386 GREATER >
1387 NUMBER 1
1388 COLON :
1389 NEWLINE
1390 INDENT
1391 NAME error
1392 LPAR (
1393 STRING "%d arguments are too many;"
1394 PERCENT %
1395 NAME nargs
1396 RPAR )
1397 NEWLINE
1398 DEDENT
1399 NAME elif
1400 NAME nargs
1401 EQEQUAL ==
1402 NUMBER 1
1403 COLON :
1404 NEWLINE
1405 INDENT
1406 NAME try
1407 COLON :
1408 NAME loops
1409 EQUAL =
1410 NAME int
1411 LPAR (
1412 NAME sys
1413 DOT .
1414 NAME argv
1415 LSQB [
1416 NUMBER 1
1417 RSQB ]
1418 RPAR )
1419 NEWLINE
1420 NAME except
1421 NAME ValueError
1422 COLON :
1423 NEWLINE
1424 INDENT
1425 NAME error
1426 LPAR (
1427 STRING "Invalid argument %r;"
1428 PERCENT %
1429 NAME sys
1430 DOT .
1431 NAME argv
1432 LSQB [
1433 NUMBER 1
1434 RSQB ]
1435 RPAR )
1436 NEWLINE
1437 DEDENT
1438 DEDENT
1439 NAME else
1440 COLON :
1441 NEWLINE
1442 INDENT
1443 NAME loops
1444 EQUAL =
1445 NAME LOOPS
1446 NEWLINE
1447 DEDENT
1448 NAME main
1449 LPAR (
1450 NAME loops
1451 RPAR )
""")
|
import numpy as np
import nibabel as nib
import struct
from scipy.ndimage.interpolation import zoom as zoom
from scipy.ndimage.interpolation import map_coordinates as map_coordinates
#import torch
#import torch.nn as nn
#import torch.nn.functional as F
import argparse
def main():
parser = argparse.ArgumentParser()
#inputdatagroup = parser.add_mutually_exclusive_group(required=True)
parser.add_argument("--input_field", dest="input_field", help="input pdd displacement field (.npz) half resolution", default=None, required=True)
parser.add_argument("--input_moving", dest="input_moving", help="input moving scan (.nii.gz)", default=None, required=True)
parser.add_argument("--output_warped", dest="output_warped", help="output waroed scan (.nii.gz)", default=None, required=True)
options = parser.parse_args()
d_options = vars(options)
input_field = np.load(d_options['input_field'])['arr_0']
_, H1, W1, D1 = input_field.shape
H = int(H1*2); W = int(W1*2); D = int(D1*2);
identity = np.meshgrid(np.arange(H), np.arange(W), np.arange(D), indexing='ij')
disp_field = np.zeros((3,H,W,D)).astype('float32')
disp_field[0] = zoom(input_field[0].astype('float32'),2,order=2)
disp_field[1] = zoom(input_field[1].astype('float32'),2,order=2)
disp_field[2] = zoom(input_field[2].astype('float32'),2,order=2)
moving = nib.load(d_options['input_moving']).get_fdata()
moving_warped = map_coordinates(moving, identity + disp_field, order=0) #assuming a segmentation -> nearest neighbour interpolation
nib.save(nib.Nifti1Image(moving_warped,np.eye(4)),d_options['output_warped'])
if __name__ == '__main__':
main()
|
import asyncio
from bot.bot import bot
from bot.constants import Client
if not Client.in_ci:
async def main() -> None:
"""Entry Async method for starting the bot."""
async with bot:
bot._guild_available = asyncio.Event()
await bot.start(Client.token)
asyncio.run(main())
|
import json
import sys
# Usage
# function run_bril -a opt_name test_name run_opt; bril2json < $test_name | if eval $run_opt; python3 ../$opt_name;
# else; cat; end | bril2txt | tee /tmp/bril_test.bril | bril2json | brili -p; cat /tmp/bril_test.bril; end
#
# function run_opt -a opt_name test_name; printf "OPT OFF:\n"; run_bril $opt_name $test_name false; printf "OPT ON:\n";
# run_bril $opt_name $test_name true; end
#
# // run from `(git_root)/my_examples/test`
# run_opt dce.py dce_1.bril
#
# Added test in `test folder` also.
def dce(instrs):
while True:
last_def = {}
to_remove = set()
for idx, instr in enumerate(instrs):
if "args" in instr:
for arg in instr["args"]:
if arg in last_def:
del last_def[arg]
if "dest" in instr:
if instr["dest"] in last_def:
to_remove.add(last_def[instr["dest"]])
last_def[instr["dest"]] = idx
to_remove = to_remove.union(set(last_def.values()))
if len(to_remove) == 0:
break
instrs = [instr for idx, instr in enumerate(instrs) if idx not in to_remove]
return instrs
def main(verbose=False):
prog = json.load(sys.stdin)
transformed = {"functions": []}
for func in prog["functions"]:
transformed_instrs = dce(func["instrs"])
transformed_func = {"name": func["name"], "instrs": transformed_instrs}
transformed["functions"].append(transformed_func)
print(json.dumps(transformed))
if __name__ == "__main__":
main(False)
|
import speech_recognition as sr
import os
from .mute_shell import mute_on, mute_off
def recognize_speech_from_mic(recognizer, microphone):
with microphone as source:
recognizer.adjust_for_ambient_noise(source)
audio = recognizer.listen(source)
response = {
"success": True,
"error": None,
"transcription": None
}
try:
response["transcription"] = recognizer.recognize_google(audio)
except sr.RequestError:
response["success"] = False
response["error"] = "API unavailable"
except sr.UnknownValueError:
response["error"] = "Unable to recognize speech"
return response
def voice():
recognizer = sr.Recognizer()
if os.name!='nt': mute_on()
microphone = sr.Microphone()
if os.name!='nt': mute_off()
print('Listening... (Press "Ctrl+C" to cancel)')
voice_text = recognize_speech_from_mic(recognizer, microphone)
if voice_text["transcription"]:
print(voice_text['transcription'])
return voice_text['transcription']
if not voice_text["success"]:
print("I didn't catch that. What did you say?\n")
if voice_text["error"]:
print("ERROR: {}".format(voice_text["error"]))
|
from django.contrib import admin
from django.utils.translation import ugettext_lazy as _
from .models import IssueCommentLink, IssueLink
from .utils import get_backend_client
# Register your models here.
def sync_from_source(modeladmin, request, queryset):
gl = get_backend_client()
for x in queryset:
x.sync_with_source(backend_object=gl)
sync_from_source.short_description = _("Sync issue data from remote repository")
class IssueLinkAdmin(admin.ModelAdmin):
list_display = [
"external_id",
"creator",
"cached_title",
"cached_status",
"created",
"sync_status",
"last_sync",
]
ordering = ["-created"]
actions = [sync_from_source]
class IssueCommentLinkAdmin(admin.ModelAdmin):
list_display = ["master_issue", "creator", "created", "sync_status", "last_sync"]
actions = [sync_from_source]
ordering = ["-created"]
admin.site.register(IssueLink, IssueLinkAdmin)
admin.site.register(IssueCommentLink, IssueCommentLinkAdmin)
|
# -*- coding: utf-8 -*-
import os
from django.db import models
from autenticar.models import Gauser
from entidades.models import Entidad
from gauss.funciones import pass_generator
class Categoria_objeto(models.Model):
categoria = models.CharField('Categoría', max_length=100, blank=True, null=True)
subcategoria = models.CharField('Subcategoría', max_length=100, blank=True, null=True)
class Meta:
ordering = ['pk']
def __str__(self):
return u'%s -> %s' % (self.categoria, self.subcategoria)
# Manejo de los ficheros subidos para que se almacenen con el nombre que deseo y no con el que originalmente tenían
def update_fichero(instance, filename):
nombre = filename.rpartition('.')
instance.fich_name = filename
fichero = pass_generator(size=30) + '.' + nombre[2]
return '/'.join(['compraventa', str(instance.entidad.code), fichero])
class Foto_objeto(models.Model):
entidad = models.ForeignKey(Entidad, on_delete=models.CASCADE)
fichero = models.FileField("Fichero con información", upload_to=update_fichero, blank=True)
content_type = models.CharField("Tipo de archivo", max_length=200, blank=True, null=True)
fich_name = models.CharField("Nombre del archivo", max_length=200, blank=True, null=True)
def filename(self):
f = os.path.basename(self.fichero.name)
return os.path.split(f)[1]
def __str__(self):
return u'%s (%s)' % (self.fichero, self.entidad.name)
class Articulo(models.Model):
FORMATOS = (
('FIJ', 'Producto con precio fijo'),
('SUB', 'Producto para ser subastado'),
('SER', 'Oferta de servicio o trabajo'),
)
ESTADOS = (
('DISPONIBLE', 'Está disponible'),
('RESERVADO', 'Está reservado para un posible comprador'),
('VENDIDO', 'Está vendido'),
)
vendedor = models.ForeignKey(Gauser, blank=True, null=True, on_delete=models.CASCADE)
entidad = models.ForeignKey(Entidad, blank=True, null=True, on_delete=models.CASCADE)
nombre = models.CharField("Nombre del objeto a vender", max_length=100, blank=True, null=True)
# Varios objetos iguales vendidos por el mismo vendedor tendrán el mismo codigo:
codigo = models.CharField("Código identificador del objeto", max_length=50, blank=True, null=True)
precio = models.FloatField("Precio de la unidad", blank=True, null=True)
precio_envio = models.FloatField("Precio por enviar", blank=True, null=True)
formato = models.CharField("Formato", max_length=10, choices=FORMATOS)
descripcion = models.TextField("Descripción del producto", blank=True, null=True)
pago = models.TextField("Describe cómo se debe realizar el pago", blank=True, null=True)
entrega = models.TextField("Describe cómo se realiza la entrega del objeto al comprador", blank=True, null=True)
fotos = models.ManyToManyField(Foto_objeto, blank=True)
categorias = models.ManyToManyField(Categoria_objeto, blank=True)
estado = models.CharField("Estado del artículo", default='DISPONIBLE', max_length=15, choices=ESTADOS)
reservado = models.NullBooleanField('¿Ya ha sido reservado?', blank=True, null=True)
comprado = models.NullBooleanField('¿Ya ha sido comprado?', blank=True, null=True)
class Meta:
ordering = ['pk']
def __str__(self):
return u'%s' % (self.nombre)
class Comprador(models.Model):
articulo = models.ForeignKey(Articulo, blank=True, null=True, on_delete=models.CASCADE)
comprador = models.ForeignKey(Gauser, blank=True, null=True, on_delete=models.CASCADE)
entidad = models.ForeignKey(Entidad, blank=True, null=True, on_delete=models.CASCADE)
oferta = models.FloatField('Cantidad ofrecida', blank=True, null=True)
observaciones = models.TextField('Observaciones realizadas del producto', blank=True, null=True)
fecha_hora = models.DateTimeField('Fecha y hora de la oferta', auto_now_add=True)
class Meta:
ordering = ['pk']
def __str__(self):
return u'Compra: %s' % (self.articulo.nombre)
|
import numpy as np
from array_creation import py_arrays
def number_of_axes(array):
"""
Parameters
----------
array Python array
Returns
-------
Number of axes (dimensions) of the array.
"""
return np.array(array).ndim
def shape(array):
"""
The dimension of the array is a tuple of integers indicating the size of the array in each dimension.
For a matrix with n rows and m columns, shape will be (n,m).
The length of the shape tuple is therefore the number of axes, ndim.
Parameters
----------
array Python array
Returns
-------
Dimensions of the array.
"""
return np.array(array).shape
def size(array):
"""
Parameters
----------
array Python array Python array
Returns
-------
Total number of elements of the array. It is equal to the product of the elements of shape.
"""
return np.array(array).size
def dtype(array):
"""
One can create or specify dtype's using standard Python types.
NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples.
Parameters
----------
array Python array
Returns
-------
Object describing the type of the elements in the array.
"""
return np.array(array).dtype
def item_size(array):
"""
Parameters
----------
array Python array
Returns
-------
Size in bytes of each element of the array.
"""
return np.array(array).itemsize
def data(array):
"""
Parameters
----------
array Python array
Returns
-------
The buffer containing the actual elements of the array.
You do not usually need to use this attribute: you will access the elements in an array using indexing facilities.
"""
return np.array(array).data
if __name__ == '__main__':
print('Numpy Array - Info\n')
print('number of axes: ' + str(number_of_axes(py_arrays)))
print('shape: ' + str(shape(py_arrays)))
print('n° of elements: ' + str(size(py_arrays)))
print('dtype: ' + str(dtype(py_arrays)))
print('item size: ' + str(item_size(py_arrays)))
print('data: ' + str(data(py_arrays)))
# switch-case: https://www.simplifiedpython.net/python-switch-case-statement/
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
"""
Alexa Config Flow.
For more details about this platform, please refer to the documentation at
https://community.home-assistant.io/t/echo-devices-alexa-as-media-player-testers-needed/58639
"""
from collections import OrderedDict
import logging
from typing import Text
from alexapy import AlexapyConnectionError
from homeassistant import config_entries
from homeassistant.const import (
CONF_EMAIL,
CONF_NAME,
CONF_PASSWORD,
CONF_SCAN_INTERVAL,
CONF_URL,
EVENT_HOMEASSISTANT_STOP,
)
from homeassistant.core import callback
from homeassistant.helpers import config_validation as cv
import voluptuous as vol
from .const import (
CONF_DEBUG,
CONF_EXCLUDE_DEVICES,
CONF_INCLUDE_DEVICES,
CONF_QUEUE_DELAY,
DEFAULT_QUEUE_DELAY,
DATA_ALEXAMEDIA,
DOMAIN,
)
_LOGGER = logging.getLogger(__name__)
@callback
def configured_instances(hass):
"""Return a set of configured Alexa Media instances."""
return set(entry.title for entry in hass.config_entries.async_entries(DOMAIN))
@config_entries.HANDLERS.register(DOMAIN)
class AlexaMediaFlowHandler(config_entries.ConfigFlow):
"""Handle a Alexa Media config flow."""
VERSION = 1
CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL
def _update_ord_dict(self, old_dict: OrderedDict, new_dict: dict) -> OrderedDict:
result: OrderedDict = OrderedDict()
for k, v in old_dict.items():
for key, value in new_dict.items():
if k == key:
# _LOGGER.debug(
# "Replacing (%s:default(%s), %s) with (%s:default(%s), %s)",
# k,
# k.default() if hasattr(k.default, '__call__') else "",
# v,
# key,
# key.default() if hasattr(key.default, '__call__') else "",
# value,
# )
result.update([(key, value)])
break
if k not in result:
# _LOGGER.debug("Keeping (%s, %s)", k, v)
result.update([(k, v)])
return result
def __init__(self):
"""Initialize the config flow."""
self.login = None
self.config = OrderedDict()
self.data_schema = OrderedDict(
[
(vol.Required(CONF_EMAIL), str),
(vol.Required(CONF_PASSWORD), str),
(vol.Required(CONF_URL, default="amazon.com"), str),
(vol.Optional(CONF_DEBUG, default=False), bool),
(vol.Optional(CONF_INCLUDE_DEVICES, default=""), str),
(vol.Optional(CONF_EXCLUDE_DEVICES, default=""), str),
(vol.Optional(CONF_SCAN_INTERVAL, default=60), int),
]
)
self.captcha_schema = OrderedDict(
[(vol.Required(CONF_PASSWORD), str), (vol.Required("captcha"), str)]
)
self.twofactor_schema = OrderedDict([(vol.Required("securitycode"), str)])
self.claimspicker_schema = OrderedDict(
[
(
vol.Required("claimsoption", default=0),
vol.All(cv.positive_int, vol.Clamp(min=0)),
)
]
)
self.authselect_schema = OrderedDict(
[
(
vol.Required("authselectoption", default=0),
vol.All(cv.positive_int, vol.Clamp(min=0)),
)
]
)
self.verificationcode_schema = OrderedDict(
[(vol.Required("verificationcode"), str)]
)
async def _show_form(
self, step="user", placeholders=None, errors=None, data_schema=None
) -> None:
"""Show the form to the user."""
_LOGGER.debug("show_form %s %s %s %s", step, placeholders, errors, data_schema)
data_schema = data_schema or vol.Schema(self.data_schema)
return self.async_show_form(
step_id=step,
data_schema=data_schema,
errors=errors if errors else {},
description_placeholders=placeholders if placeholders else {},
)
async def async_step_import(self, import_config):
"""Import a config entry from configuration.yaml."""
return await self.async_step_user(import_config)
async def async_step_user(self, user_input=None):
"""Handle the start of the config flow."""
from alexapy import AlexaLogin
if not user_input:
return await self._show_form(data_schema=vol.Schema(self.data_schema))
if "{} - {}".format(
user_input[CONF_EMAIL], user_input[CONF_URL]
) in configured_instances(self.hass):
return await self._show_form(errors={CONF_EMAIL: "identifier_exists"})
self.config[CONF_EMAIL] = user_input[CONF_EMAIL]
self.config[CONF_PASSWORD] = user_input[CONF_PASSWORD]
self.config[CONF_URL] = user_input[CONF_URL]
self.config[CONF_DEBUG] = user_input[CONF_DEBUG]
from datetime import timedelta
self.config[CONF_SCAN_INTERVAL] = (
user_input[CONF_SCAN_INTERVAL]
if not isinstance(user_input[CONF_SCAN_INTERVAL], timedelta)
else user_input[CONF_SCAN_INTERVAL].total_seconds()
)
if isinstance(user_input[CONF_INCLUDE_DEVICES], str):
self.config[CONF_INCLUDE_DEVICES] = (
user_input[CONF_INCLUDE_DEVICES].split(",")
if CONF_INCLUDE_DEVICES in user_input
and user_input[CONF_INCLUDE_DEVICES] != ""
else []
)
else:
self.config[CONF_INCLUDE_DEVICES] = user_input[CONF_INCLUDE_DEVICES]
if isinstance(user_input[CONF_EXCLUDE_DEVICES], str):
self.config[CONF_EXCLUDE_DEVICES] = (
user_input[CONF_EXCLUDE_DEVICES].split(",")
if CONF_EXCLUDE_DEVICES in user_input
and user_input[CONF_EXCLUDE_DEVICES] != ""
else []
)
else:
self.config[CONF_EXCLUDE_DEVICES] = user_input[CONF_EXCLUDE_DEVICES]
try:
if not self.login:
_LOGGER.debug("Creating new login")
self.login = AlexaLogin(
self.config[CONF_URL],
self.config[CONF_EMAIL],
self.config[CONF_PASSWORD],
self.hass.config.path,
self.config[CONF_DEBUG],
)
await self.login.login_with_cookie()
return await self._test_login()
else:
_LOGGER.debug("Using existing login")
await self.login.login(data=user_input)
return await self._test_login()
except AlexapyConnectionError:
return await self._show_form(errors={"base": "connection_error"})
except BaseException as ex:
_LOGGER.warning("Unknown error: %s", ex)
return await self._show_form(errors={"base": "unknown_error"})
async def async_step_captcha(self, user_input=None):
"""Handle the input processing of the config flow."""
return await self.async_step_process(user_input)
async def async_step_twofactor(self, user_input=None):
"""Handle the input processing of the config flow."""
return await self.async_step_process(user_input)
async def async_step_claimspicker(self, user_input=None):
"""Handle the input processing of the config flow."""
return await self.async_step_process(user_input)
async def async_step_authselect(self, user_input=None):
"""Handle the input processing of the config flow."""
return await self.async_step_process(user_input)
async def async_step_verificationcode(self, user_input=None):
"""Handle the input processing of the config flow."""
return await self.async_step_process(user_input)
async def async_step_process(self, user_input=None):
"""Handle the input processing of the config flow."""
if user_input:
if CONF_PASSWORD in user_input:
password = user_input[CONF_PASSWORD]
self.config[CONF_PASSWORD] = password
try:
await self.login.login(data=user_input)
except AlexapyConnectionError:
return await self._show_form(errors={"base": "connection_error"})
except BaseException as ex:
_LOGGER.warning("Unknown error: %s", ex)
return await self._show_form(errors={"base": "unknown_error"})
return await self._test_login()
async def _test_login(self):
login = self.login
config = self.config
_LOGGER.debug("Testing login status: %s", login.status)
if "login_successful" in login.status and login.status["login_successful"]:
_LOGGER.debug("Setting up Alexa devices with %s", dict(config))
await login.close()
return self.async_create_entry(
title="{} - {}".format(login.email, login.url), data=config
)
if "captcha_required" in login.status and login.status["captcha_required"]:
new_schema = self._update_ord_dict(
self.captcha_schema,
{vol.Required(CONF_PASSWORD, default=config[CONF_PASSWORD]): str},
)
_LOGGER.debug("Creating config_flow to request captcha")
return await self._show_form(
"captcha",
data_schema=vol.Schema(new_schema),
errors={},
placeholders={
"email": login.email,
"url": login.url,
"captcha_image": "[]({0})".format(
login.status["captcha_image_url"]
),
"message": "\n> {0}".format(
login.status["error_message"]
if "error_message" in login.status
else ""
),
},
)
elif (
"securitycode_required" in login.status
and login.status["securitycode_required"]
):
_LOGGER.debug("Creating config_flow to request 2FA")
message = "> {0}".format(
login.status["error_message"] if "error_message" in login.status else ""
)
return await self._show_form(
"twofactor",
data_schema=vol.Schema(self.twofactor_schema),
errors={},
placeholders={
"email": login.email,
"url": login.url,
"message": message,
},
)
elif (
"claimspicker_required" in login.status
and login.status["claimspicker_required"]
):
error_message = "> {0}".format(
login.status["error_message"] if "error_message" in login.status else ""
)
_LOGGER.debug("Creating config_flow to select verification method")
claimspicker_message = login.status["claimspicker_message"]
return await self._show_form(
"claimspicker",
data_schema=vol.Schema(self.claimspicker_schema),
errors={},
placeholders={
"email": login.email,
"url": login.url,
"message": "> {0}\n> {1}".format(
claimspicker_message, error_message
),
},
)
elif (
"authselect_required" in login.status
and login.status["authselect_required"]
):
_LOGGER.debug("Creating config_flow to select OTA method")
error_message = (
login.status["error_message"] if "error_message" in login.status else ""
)
authselect_message = login.status["authselect_message"]
return await self._show_form(
"authselect",
data_schema=vol.Schema(self.authselect_schema),
placeholders={
"email": login.email,
"url": login.url,
"message": "> {0}\n> {1}".format(authselect_message, error_message),
},
)
elif (
"verificationcode_required" in login.status
and login.status["verificationcode_required"]
):
_LOGGER.debug("Creating config_flow to enter verification code")
return await self._show_form(
"verificationcode", data_schema=vol.Schema(self.verificationcode_schema)
)
elif "login_failed" in login.status and login.status["login_failed"]:
_LOGGER.debug("Login failed")
return self.async_abort(reason="Login failed")
new_schema = self._update_ord_dict(
self.data_schema,
{
vol.Required(CONF_EMAIL, default=config[CONF_EMAIL]): str,
vol.Required(CONF_PASSWORD, default=config[CONF_PASSWORD]): str,
vol.Required(CONF_URL, default=config[CONF_URL]): str,
vol.Optional(CONF_DEBUG, default=config[CONF_DEBUG]): bool,
vol.Optional(
CONF_INCLUDE_DEVICES,
default=(
config[CONF_INCLUDE_DEVICES]
if isinstance(config[CONF_INCLUDE_DEVICES], str)
else ",".join(map(str, config[CONF_INCLUDE_DEVICES]))
),
): str,
vol.Optional(
CONF_EXCLUDE_DEVICES,
default=(
config[CONF_EXCLUDE_DEVICES]
if isinstance(config[CONF_EXCLUDE_DEVICES], str)
else ",".join(map(str, config[CONF_EXCLUDE_DEVICES]))
),
): str,
vol.Optional(
CONF_SCAN_INTERVAL, default=config[CONF_SCAN_INTERVAL]
): int,
},
)
return await self._show_form(data_schema=vol.Schema(new_schema))
@staticmethod
@callback
def async_get_options_flow(config_entry):
"""Get the options flow for this handler."""
return OptionsFlowHandler(config_entry)
class OptionsFlowHandler(config_entries.OptionsFlow):
"""Handle a option flow for Alexa Media."""
def __init__(self, config_entry: config_entries.ConfigEntry):
"""Initialize options flow."""
self.config_entry = config_entry
async def async_step_init(self, user_input=None):
"""Handle options flow."""
if user_input is not None:
return self.async_create_entry(title="", data=user_input)
data_schema = vol.Schema(
{
vol.Optional(
CONF_QUEUE_DELAY,
default=self.config_entry.options.get(
CONF_QUEUE_DELAY, DEFAULT_QUEUE_DELAY
),
): vol.All(vol.Coerce(float), vol.Clamp(min=0))
}
)
return self.async_show_form(step_id="init", data_schema=data_schema)
|
# pylint: disable=global-statement,redefined-outer-name
""" Module to wrap AWS Cognito APIs """
import json
import sys
from dataclasses import dataclass
import boto3
@dataclass(frozen=True)
class CognitoGroup:
""" Class for AWS Cognito group """
name: str
description: str
@dataclass(frozen=True)
class CognitoUser:
""" Class for AWS Cognito user """
username: str
email: str
custom_name: str = ""
user_status: str = ""
email_verified: str = ""
enabled: bool = True
def name(self) -> str:
""" Generate the name field """
return self.custom_name or self.email
def __convert_aws_user__(aws_user):
email: str = ""
custom_name: str = ""
email_verified: str = ""
enabled = aws_user["Enabled"]
username = aws_user["Username"]
user_status = aws_user["UserStatus"]
for attr in aws_user["Attributes"]:
if attr["Name"] == "email":
email = attr["Value"]
elif attr["Name"] == "custom:name":
custom_name = attr["Value"]
elif attr["Name"] == "email_verified":
email_verified = attr["Value"]
user = CognitoUser(
username=username,
email=email,
custom_name=custom_name,
enabled=enabled,
email_verified=email_verified,
user_status=user_status,
)
return user
def add_to_group(client, profile, user, group_name):
""" Adds the specified user to the specified group """
try:
response = client.admin_add_user_to_group(
UserPoolId=profile["user_pool_id"],
Username=user.email,
GroupName=group_name,
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} added to group {group_name}")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ResourceNotFoundException as error:
print(f"Group {group_name} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to add user {user.email} to group {group_name}")
return error.response
def create_user(client, profile, user, resend=False):
""" Creates a new user in the specified user pool """
try:
if resend:
# Resend confirmation email for get back password
response = client.admin_create_user(
UserPoolId=profile["user_pool_id"],
Username=user.email,
MessageAction="RESEND",
)
else:
response = client.admin_create_user(
UserPoolId=profile["user_pool_id"],
Username=user.email,
UserAttributes=[
{"Name": "email", "Value": user.email},
{"Name": "email_verified", "Value": "true"},
],
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
if resend:
print(f"Resend confirmation to user {user.email} successfully")
else:
print(f"User {user.email} was created successfully")
return response
except client.exceptions.UsernameExistsException as error:
print(f"User {user.email} exists")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to create user {user.email}: {error.response}")
return error.response
def delete_user(client, profile, user):
""" Deletes a user from the pool """
try:
response = client.admin_delete_user(
UserPoolId=profile["user_pool_id"], Username=user.email
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} was deleted successfully")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to delete user {user.email}")
return error.response
def disable_user(client, profile, user):
""" Disables the specified user """
try:
response = client.admin_disable_user(
UserPoolId=profile["user_pool_id"], Username=user.email
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} was disabled successfully")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to disable user {user.email}")
return error.response
def enable_user(client, profile, user):
""" Enables the specified user """
try:
response = client.admin_enable_user(
UserPoolId=profile["user_pool_id"], Username=user.email
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} was enabled successfully")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to disable user {user.email}")
return error.response
def init_client(profile):
client = boto3.client(
"cognito-idp",
aws_access_key_id=profile["access_key_id"],
aws_secret_access_key=profile["secret_access_key"],
region_name=profile["region_name"],
)
return client
def list_group_users(client, profile, group_name, token=""):
""" Lists all user from the specified group """
result = []
try:
if token:
response = client.list_users_in_group(
UserPoolId=profile["user_pool_id"],
GroupName=group_name,
NextToken=token,
)
else:
response = client.list_users_in_group(
UserPoolId=profile["user_pool_id"], GroupName=group_name,
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
aws_users = response["Users"]
for aws_user in aws_users:
result.append(__convert_aws_user__(aws_user))
next_token = response.get("NextToken")
if next_token:
more = list_group_users(client, profile, group_name, next_token)
result.extend(more)
except client.exceptions.ResourceNotFoundException:
print(f"Group {group_name} does not exist")
sys.exit(2)
except client.exceptions.ClientError as error:
print(error.response)
print("Fail to list groups")
sys.exit(2)
return result
def list_groups(client, profile):
""" List existing groups from the pool """
result = []
try:
response = client.list_groups(UserPoolId=profile["user_pool_id"])
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
groups = response["Groups"]
for group in groups:
result.append(
CognitoGroup(
name=group["GroupName"], description=group["Description"]
)
)
except client.exceptions.ClientError as error:
print("Fail to list groups")
print(error.response)
sys.exit(2)
return result
def list_users(client, profile, token=""):
""" Lists all users from the pool """
result = []
try:
if token:
response = client.list_users(
UserPoolId=profile["user_pool_id"], PaginationToken=token,
)
else:
response = client.list_users(UserPoolId=profile["user_pool_id"],)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
aws_users = response["Users"]
for aws_user in aws_users:
result.append(__convert_aws_user__(aws_user))
next_token = response.get("PaginationToken")
if next_token:
more = list_users(client, profile, next_token)
result.extend(more)
except client.exceptions.ClientError as error:
print("Fail to list users")
print(error)
sys.exit(2)
return result
def remove_from_group(client, profile, user, group_name):
""" Removes the specified user from the specified group """
try:
response = client.admin_remove_user_from_group(
UserPoolId=profile["user_pool_id"],
Username=user.email,
GroupName=group_name,
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} removed from the group {group_name}")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ResourceNotFoundException as error:
print(f"Group {group_name} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to remove user {user.email} from group {group_name}")
return error.response
def reset_user_password(client, profile, user):
""" Resets the specified user's password """
try:
response = set_user_password(client, profile, user)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
# Resend confirmation
response = create_user(client, profile, user, True)
print(f"Password of user {user.email} was reset successfully")
return response
except client.exceptions.ClientError as error:
print(f"Fail to reset password of user {user.email}")
return error.response
def set_user_password(client, profile, user, password="N0t-permanent!"):
""" Sets the specified user's password """
try:
response = client.admin_set_user_password(
UserPoolId=profile["user_pool_id"],
Username=user.email,
Password=password,
Permanent=False,
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"Password of user {user.email} was set successfully")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to reset password of user {user.email}")
return error.response
def show_error_response(response, is_show=False):
""" Show error message if any """
if is_show:
if response["ResponseMetadata"]["HTTPStatusCode"] != 200:
# json_string = json.dumps(response, indent=4)
json_string = json.dumps(response.get("Error"), indent=4)
print(json_string)
def update_user_attributes(client, profile, user, attr_name, attr_value):
""" Updates the specified user's attribute """
try:
response = client.admin_update_user_attributes(
UserPoolId=profile["user_pool_id"],
Username=user.email,
UserAttributes=[{"Name": attr_name, "Value": attr_value}],
)
if response["ResponseMetadata"]["HTTPStatusCode"] == 200:
print(f"User {user.email} was updated successfully")
return response
except client.exceptions.UserNotFoundException as error:
print(f"User {user.email} does not exist")
return error.response
except client.exceptions.ClientError as error:
print(f"Fail to disable user {user.email}")
return error.response |
import aspose.email
from aspose.email.clients.imap import ImapClient
from aspose.email.clients import SecurityOptions
def run():
#ExStart: SSLEnabledIMAPServer
client = ImapClient("imap.domain.com", 993, "user@domain.com", "pwd")
client.security_options = SecurityOptions.SSLIMPLICIT
#ExEnd:SSLEnabledIMAPServer
if __name__ == '__main__':
run()
|
import click
from do_cli.contexts import CTX
@click.command('flush-cache')
@CTX
def cli(ctx):
"""
clear the cache
"""
if ctx.verbose:
click.echo("clear the cache")
ctx.cache.flushdb()
if ctx.verbose:
click.echo('---- cmd_flush-cache done ----')
|
import sys, os
import params
#This sets the path in our computer to where the eyetracker stuff is located
#sys.path.append('/Users/Preetpal/desktop/ubc_4/experimenter_platform/modules')
#sys.path.append('E\\Users\\admin\\Desktop\\experimenter_platform\\modules')
sys.path.append(os.path.join(sys.path[0],'Modules'))
sys.path.append(os.path.join(sys.path[0],'tobii_binder'))
import os
import datetime
import time
import tobii_research as tr
import csv
import numpy as np
from tornado import gen
import emdat_utils
import ast
import subprocess
from application.backend.eye_tracker_class import *
class TobiiControllerNewSdk:
""" The singleton class used to communicate with Tobii eye tracker API: it initializes the eye tracker,
stores the raw gaze data, so the detection components can compute the features
they are responsible for, and it stores the EMDAT features computed over the whole execution of the platform.
"""
def __init__(self):
"""Initializes TobiiController instances
keyword arguments
None
"""
print("constructing eyetracker object")
self.gazeData = []
self.eventData = []
self.datafile = None
#Preetpal's code for fixation
self.x = []
self.y = []
self.time = []
self.validity = []
self.pupilsize = []
self.pupilvelocity = []
self.head_distance = []
self.EndFixations = []
self.mouse_clicks = []
self.keyboard_clicks = []
#This contains the websocket to send data to be displayed on front end
self.runOnlineFix = True
# initialize communications
self.aoi_ids = {}
self.dpt_id = 0
# for computing pupil velocity
self.last_pupil_left = -1
self.last_pupil_right = -1
self.LastTimestamp = -1
self.init_emdat_global_features()
# Instantiate an eye tracker class corresponding to the EYETRACKER_TYPE determined inside the params.py file
self.eye_tracker = globals()[EyeTrackerNames[params.EYETRACKER_TYPE].value]()
print("constructed eyetracker object")
############################################################################
# activation methods
############################################################################
def activate(self):
self.eye_tracker.activate(self)
def startTracking(self):
"""Starts the collection of gaze data
arguments
None
keyword arguments
None
returns
None -- resets both self.gazeData and self.eventData, then
sets TobiiTracker.on_gazedata as an event callback
for self.eyetracker.events.OnGazeDataReceived and
calls self.eyetracker.StartTracking()
"""
print("starting tracker")
self.gazeData = []
self.eventData = []
#Preetpal's Code to initialize/empty arrays to be used in fixation algorithm
self.x = []
self.y = []
self.time = []
self.validity = []
self.pupilsize = []
self.pupilvelocity = []
self.head_distance = []
self.mouse_clicks = []
self.keyboard_clicks = []
print("=================== SLEEPING =========================")
time.sleep(1)
print("=================== WOKE UP =========================")
self.eye_tracker.start_tracking(self)
def stopTracking(self):
"""Stops the collection of gaze data
arguments
None
keyword arguments
None
returns
None -- calls self.eyetracker.StopTracking(), then unsets
TobiiTracker.on_gazedata as an event callback for
self.eyetracker.events.OnGazeDataReceived, and
calls TobiiTracker.flushData before resetting both
self.gazeData and self.eventData
"""
self.eye_tracker.stop_tracking(self)
#self.flushData()
self.gazeData = []
self.EndFixations = []
#Preetpals code
#Empty the arrays needed for fixation algorithm
#May need to also empty the websocket set
self.x = []
self.y = []
self.time = []
self.validity = []
self.pupilsize = []
self.pupilvelocity = []
self.head_distance = []
self.mouse_clicks = []
self.keyboard_clicks = []
self.aoi_ids = {}
self.dpt_id = 0
def logFixations(self, user_id, task_id):
"""Log the recorded fixations for the current user and task
arguments
user_id ID of current user_id
task_id ID of current task
keyword arguments
None
returns
None
"""
with open(str(params.LOG_PREFIX) + "_user_" + str(user_id) + "_task_"+str(task_id) + "_raw_fixations.csv", "wb") as f:
f.write( "x,y,duration,start_time\n" ) # header
for fix in self.EndFixations:
f.write( ",".join([str(x) for x in fix])+"\n" )
def on_gazedata(self, gaze):
"""Adds new data point to the raw data arrays. If x, y coordinate data is not available,
stores the coordinates for this datapoint as (-1280, -1024). Any other feature,
if not available, is stored as -1.
arguments
error -- some Tobii error message, isn't used in function
gaze -- Tobii gaze data struct
keyword arguments
None
returns
None -- appends gaze to self.gazeData list
"""
#Don't need raw gaze so this code is commented out
#self.gazeData.append(gaze)
#Below code is just print statements that are commented out
'''
print gaze.keys()
print 'Timestamp: ', gaze["device_time_stamp"]
print 'LeftGazePoint2D: ', gaze["left_gaze_point_on_display_area"]
print 'RightGazePoint2D: ', gaze["right_gaze_point_on_display_area"]
print 'LeftEyePosition3D: ', gaze["left_gaze_point_in_user_coordinate_system"]
print 'RightEyePosition3D', gaze["right_gaze_point_in_user_coordinate_system"]
print 'LeftPupil: ', gaze["left_pupil_diameter"]
print 'RightPupil: ', gaze["right_pupil_diameter"]
print gaze["left_gaze_point_validity"]
print gaze["right_gaze_point_validity"]
'''
#Below code checks to see if the gaze data is valid. If it is valid then
#we average the left and right. Else we use the valid eye. We are multiplying
#by SCREEN_SIZE_X and SCREEN_SIZE_Y because those are the dimensions of the monitor and since
#the gaze values returned are between 0 and 1
if ((gaze["left_gaze_point_on_display_area"][0] >= 0) & (gaze["right_gaze_point_on_display_area"][0] >= 0)):
self.x.append(((gaze["left_gaze_point_on_display_area"][0] + gaze["right_gaze_point_on_display_area"][0])/2) * params.SCREEN_SIZE_X)
self.y.append(((gaze["left_gaze_point_on_display_area"][1] + gaze["right_gaze_point_on_display_area"][1])/2) * params.SCREEN_SIZE_Y)
elif (gaze["left_gaze_point_on_display_area"][0] >= 0):
self.x.append(gaze["left_gaze_point_on_display_area"][0] * params.SCREEN_SIZE_X)
self.y.append(gaze["left_gaze_point_on_display_area"][1] * params.SCREEN_SIZE_Y)
elif (gaze["right_gaze_point_on_display_area"][0] >= 0):
self.x.append(gaze["right_gaze_point_on_display_area"][0] * params.SCREEN_SIZE_X)
self.y.append(gaze["right_gaze_point_on_display_area"][1] * params.SCREEN_SIZE_Y)
else:
self.x.append(-1 * params.SCREEN_SIZE_X)
self.y.append(-1 * params.SCREEN_SIZE_Y)
#print(gaze.RightGazePoint2D.x * params.SCREEN_SIZE_X, gaze.RightGazePoint2D.y * params.SCREEN_SIZE_Y)
# print("%f" % (time.time() * 1000.0))
if (params.USE_EMDAT):
for aoi, polygon in self.AOIs.iteritems():
if utils.point_inside_polygon((self.x[-1], self.y[-1]), polygon):
self.aoi_ids[aoi].append(self.dpt_id)
# Pupil size features
self.pupilsize.append(self.get_pupil_size(gaze["left_pupil_diameter"], gaze["right_pupil_diameter"]))
if (self.last_pupil_right != -1):
self.pupilvelocity.append(self.get_pupil_velocity(self.last_pupil_left, self.last_pupil_right, gaze["left_pupil_diameter"], gaze["right_pupil_diameter"], gaze["device_time_stamp"] - self.LastTimestamp))
else:
self.pupilvelocity.append(-1)
self.time.append(gaze["device_time_stamp"])
self.head_distance.append(self.get_distance(gaze["left_gaze_point_in_user_coordinate_system"][2], gaze["right_gaze_point_in_user_coordinate_system"][2]))
self.validity.append(gaze["left_gaze_point_validity"] == 1 or gaze["right_gaze_point_validity"] == 1)
# for pupil velocity
self.last_pupil_left = gaze["left_pupil_diameter"]
self.last_pupil_right = gaze["right_pupil_diameter"]
self.LastTimestamp = gaze["device_time_stamp"]
self.dpt_id += 1
def on_gazedata_4c(self, x, y, time_stamp):
"""Adds new data point to the raw data arrays. If x, y coordinate data is not available,
stores the coordinates for this datapoint as (-1280, -1024). Any other feature,
if not available, is stored as -1.
arguments
error -- some Tobii error message, isn't used in function
gaze -- Tobii gaze data struct
keyword arguments
None
returns
None -- appends gaze to self.gazeData list
"""
#Don't need raw gaze so this code is commented out
#self.gazeData.append(gaze)
# print(gaze.RightGazePoint2D.x * 1280, gaze.RightGazePoint2D.y * 1024)
# print("%f" % (time.time() * 1000.0))
self.x.append(x)
self.y.append(y)
if (params.USE_EMDAT):
for aoi, polygon in self.AOIs.iteritems():
if utils.point_inside_polygon((self.x[-1], self.y[-1]), polygon):
print("point inside ", aoi)
self.aoi_ids[aoi].append(self.dpt_id)
self.time.append(time_stamp)
self.validity.append(True)
self.LastTimestamp = time_stamp
self.dpt_id += 1
def on_gazedata_simulation(self, x, y, time_stamp):
"""Adds new data point to the raw data arrays. If x, y coordinate data is not available,
stores the coordinates for this datapoint as (-1280, -1024). Any other feature,
if not available, is stored as -1.
arguments
error -- some Tobii error message, isn't used in function
gaze -- Tobii gaze data struct
keyword arguments
None
returns
None -- appends gaze to self.gazeData list
"""
#Don't need raw gaze so this code is commented out
#self.gazeData.append(gaze)
# print(gaze.RightGazePoint2D.x * 1280, gaze.RightGazePoint2D.y * 1024)
# print("%f" % (time.time() * 1000.0))
self.x.append(x)
self.y.append(y)
if (params.USE_EMDAT):
for aoi, polygon in self.AOIs.iteritems():
if utils.point_inside_polygon((self.x[-1], self.y[-1]), polygon):
print("point inside ", aoi)
self.aoi_ids[aoi].append(self.dpt_id)
self.time.append(time_stamp)
self.validity.append(True)
self.LastTimestamp = time_stamp
self.dpt_id += 1
def add_fixation(self, x, y, duration, start_time):
'''
Called by FixationDetector when a new fixation is detected.
Adds a new fixation to data array to be used for EMDAT features calculation.
Args:
x - coordinate of fixation on the screen
y - coordinate of fixation on the screen
duration - duration of fixation in microseconds
'''
self.EndFixations.append((x, y, duration, start_time))
def add_mouse_key_click(self, mouse_click):
'''
Called by MouseKeyboardEventDetector when a new mouse click is detected.
Adds a new mouse click to data array to be used for EMDAT features calculation.
Args:
mouse_click - BasicMouseEvent object
'''
self.mouse_clicks.append(mouse_click)
def add_keyboard_click(self, keyboard_click):
'''
Called by MouseKeyboardEventDetector when a new keyboard click is detected.
Adds a new keyboard click to data array to be used for EMDAT features calculation.
Args:
keyboard_click - KeyboardEvent object
'''
self.keyboard_clicks.append(keyboard_click)
def get_pupil_size(self, pupilleft, pupilright):
'''
Used for extracting pupilsize in on_gazedata(). If recordings for both eyes are available, return their average,
else return value for a recorded eye (if any)
Args:
pupilleft - recording of pupil size on left eye
pupilright - recording of pupil size on right eye
Returns:
pupil size to generate pupil features with.
'''
if pupilleft == 0 and pupilright == 0:
return -1
if pupilleft == 0:
return pupilright
if pupilright == 0:
return pupilleft
return (pupilleft + pupilright) / 2.0
def get_pupil_velocity(self, last_pupilleft, last_pupilright, pupilleft, pupilright, time):
'''
Used for extracting pupilvelocity in on_gazedata().
If pupilsizes for both eyes are available, return the average of their difference,
else return value for a recorded eye (if any)
Args:
last_pupilleft - pupilsize for left eye from previous gaze object
last_pupilright - pupilsize for right eye from previous gaze object
pupilleft - pupilsize for left eye from current gaze object
pupilright - pupilsize for left eye from current gaze object
time - timestamp difference between current and last gaze object
'''
if (last_pupilleft == 0 or pupilleft == 0) and (last_pupilright == 0 or pupilright == 0):
return -1
if (last_pupilleft == 0 or pupilleft == 0):
return abs(pupilright - last_pupilright) / time
if (last_pupilright == 0 or pupilright == 0):
return abs(pupilleft - last_pupilleft) / time
return abs( (pupilleft + pupilright) / 2 - (last_pupilleft + last_pupilright) / 2 ) / time
def get_distance(self, distanceleft, distanceright):
'''
Used for extracting head distance in on_gazedata(). If recordings for both eyes are available, return their average,
else return value for a recorded eye (if any)
Args:
distanceleft - recording of distance on left eye
distanceright - recording of distance size on right eye
'''
if distanceleft == 0 and distanceright == 0:
return -1
if distanceleft == 0:
return distanceright
if distanceright == 0:
return distanceleft
return (distanceleft + distanceright) / 2.0
def update_aoi_storage(self, AOIS):
"""
Add new aois to global EMDAT feature storage dictionary.
Called during a task switch by EMDATComponent.
"""
self.AOIs = AOIS
for event_name in AOIS.keys():
self.aoi_ids[event_name] = []
if event_name not in self.emdat_global_features:
self.emdat_global_features[event_name] = {}
self.emdat_global_features[event_name]['numfixations'] = 0
self.emdat_global_features[event_name]['longestfixation'] = -1
self.emdat_global_features[event_name]['meanfixationduration'] = -1
self.emdat_global_features[event_name]['stddevfixationduration'] = -1
self.emdat_global_features[event_name]['timetofirstfixation'] = -1
self.emdat_global_features[event_name]['timetolastfixation'] = -1
self.emdat_global_features[event_name]['proportionnum'] = 0
self.emdat_global_features[event_name]['proportiontime'] = 0
self.emdat_global_features[event_name]['fixationrate'] = 0
self.emdat_global_features[event_name]['totaltimespent'] = 0
self.emdat_global_features[event_name]['meanpupilsize'] = -1
self.emdat_global_features[event_name]['stddevpupilsize'] = -1
self.emdat_global_features[event_name]['maxpupilsize'] = -1
self.emdat_global_features[event_name]['minpupilsize'] = -1
self.emdat_global_features[event_name]['startpupilsize'] = -1
self.emdat_global_features[event_name]['endpupilsize'] = -1
self.emdat_global_features[event_name]['meanpupilvelocity'] = -1
self.emdat_global_features[event_name]['stddevpupilvelocity'] = -1
self.emdat_global_features[event_name]['maxpupilvelocity'] = -1
self.emdat_global_features[event_name]['minpupilvelocity'] = -1
self.emdat_global_features[event_name]['numpupilsizes'] = 0
self.emdat_global_features[event_name]['numpupilvelocity'] = 0
self.emdat_global_features[event_name]['numdistancedata'] = 0
self.emdat_global_features[event_name]['numdistancedata'] = 0
self.emdat_global_features[event_name]['meandistance'] = -1
self.emdat_global_features[event_name]['stddevdistance'] = -1
self.emdat_global_features[event_name]['maxdistance'] = -1
self.emdat_global_features[event_name]['mindistance'] = -1
self.emdat_global_features[event_name]['startdistance'] = -1
self.emdat_global_features[event_name]['enddistance'] = -1
self.emdat_global_features[event_name]['total_trans_from'] = 0
self.emdat_global_features[event_name]['startpupilvelocity'] = -1
self.emdat_global_features[event_name]['endpupilvelocity'] = 0
self.emdat_global_features[event_name]['numevents'] = 0
self.emdat_global_features[event_name]['numleftclic'] = 0
self.emdat_global_features[event_name]['numrightclic'] = 0
# self.emdat_global_features[event_name]['numdoubleclic'] = 0
self.emdat_global_features[event_name]['numkeypressed'] = 0
# self.emdat_global_features[event_name]['numdragdrop'] = 0
self.emdat_global_features[event_name]['leftclicrate'] = -1
self.emdat_global_features[event_name]['rightclicrate'] = -1
# self.emdat_global_features[event_name]['doubleclicrate'] = -1
self.emdat_global_features[event_name]['keypressedrate'] = -1
# self.emdat_global_features[event_name]['dragdroprate'] = -1
# self.emdat_global_features[event_name]['timetofirstleftclic'] = -1
# self.emdat_global_features[event_name]['timetofirstrightclic'] = -1
# self.emdat_global_features[event_name]['timetofirstdoubleclic'] = -1
# self.emdat_global_features[event_name]['timetofirstkeypressed'] = -1
for cur_aoi in AOIS.keys():
self.emdat_global_features[event_name]['numtransfrom_%s'%(cur_aoi)] = 0
self.emdat_global_features[event_name]['proptransfrom_%s'%(cur_aoi)] = -1
def init_emdat_global_features(self):
'''
Initialize global EMDAT feature storage dictionary. Called by EMDATComponent.
'''
self.emdat_global_features = {}
self.emdat_global_features['length'] = 0
self.emdat_global_features['length_invalid'] = 0
# Pupil features
self.emdat_global_features['numpupilsizes'] = 0
self.emdat_global_features['numpupilvelocity'] = 0
self.emdat_global_features['meanpupilsize'] = -1
self.emdat_global_features['stddevpupilsize'] = -1
self.emdat_global_features['maxpupilsize'] = -1
self.emdat_global_features['minpupilsize'] = -1
self.emdat_global_features['startpupilsize'] = -1
self.emdat_global_features['endpupilsize'] = -1
self.emdat_global_features['meanpupilvelocity'] = -1
self.emdat_global_features['stddevpupilvelocity'] = -1
self.emdat_global_features['maxpupilvelocity'] = -1
self.emdat_global_features['minpupilvelocity'] = -1
self.emdat_global_features['startpupilvelocity'] = -1
self.emdat_global_features['endpupilvelocity'] = -1
# Distance features
self.emdat_global_features['numdistancedata'] = 0
self.emdat_global_features['meandistance'] = -1
self.emdat_global_features['stddevdistance'] = -1
self.emdat_global_features['maxdistance'] = -1
self.emdat_global_features['mindistance'] = -1
self.emdat_global_features['startdistance'] = -1
self.emdat_global_features['enddistance'] = -1
# Path features
self.emdat_global_features['numfixdistances'] = 0
self.emdat_global_features['numabsangles'] = 0
self.emdat_global_features['numrelangles'] = 0
self.emdat_global_features['meanpathdistance'] = -1
self.emdat_global_features['sumpathdistance'] = -1
self.emdat_global_features['stddevpathdistance'] = -1
self.emdat_global_features['eyemovementvelocity'] = -1
self.emdat_global_features['sumabspathangles'] = -1
self.emdat_global_features['abspathanglesrate'] = -1
self.emdat_global_features['meanabspathangles'] = -1
self.emdat_global_features['stddevabspathangles'] = -1
self.emdat_global_features['sumrelpathangles'] = -1
self.emdat_global_features['relpathanglesrate'] = -1
self.emdat_global_features['meanrelpathangles'] = -1
self.emdat_global_features['stddevrelpathangles'] = -1
# Fixation features
self.emdat_global_features['numfixations'] = 0
self.emdat_global_features['fixationrate'] = -1
self.emdat_global_features['meanfixationduration'] = -1
self.emdat_global_features['stddevfixationduration'] = -1
self.emdat_global_features['sumfixationduration'] = -1
self.emdat_global_features['fixationrate'] = -1
# Event features
self.emdat_global_features['numevents'] = 0
self.emdat_global_features['numleftclic'] = 0
self.emdat_global_features['numrightclic'] = 0
# self.emdat_global_features['numdoubleclic'] = 0
self.emdat_global_features['numkeypressed'] = 0
# self.emdat_global_features['numdragdrop'] = 0
self.emdat_global_features['leftclicrate'] = -1
self.emdat_global_features['rightclicrate'] = -1
# self.emdat_global_features['doubleclicrate'] = -1
self.emdat_global_features['keypressedrate'] = -1
# self.emdat_global_features['dragdroprate'] = -1
# self.emdat_global_features['timetofirstleftclic'] = -1
# self.emdat_global_features['timetofirstrightclic'] = -1
# self.emdat_global_features['timetofirstdoubleclic'] = -1
# self.emdat_global_features['timetofirstkeypressed'] = -1
#Original code provided by Roberto showing how to start the the eyetracker
"""
#this will be called from a tornado handler
if __name__ == "__main__":
eb = TobiiController()
eb.waitForFindEyeTracker()
print eb.eyetrackers
eb.activate(eb.eyetrackers.keys()[0])
eb.startTracking()
time.sleep(10)
eb.stopTracking()
eb.destroy()
"""
|
from django.conf.urls import patterns, include, url
from django.contrib import admin
from .views import hello, inbound_route
urlpatterns = patterns('',
url(r'^hello$', hello),
url(r'^inbound$', inbound_route)
)
|
from model import exercise, exercise_step
from music_theory import chord, position
from practice import abstract_practice
class ScaleOnChord(abstract_practice.AbstractPractice):
_TITLE = "Scale on chord"
_SUBTITLE = "Play a scale on top of chord"
def get_exercise(self, quantity: int) -> exercise.Exercise:
random_steps = []
random_chords = chord.Chord().get_random_chords(quantity)
for random_chord in random_chords:
random_step = exercise_step.ExerciseStep(random_chord, super(ScaleOnChord, self).get_random_position_suggestion_text())
random_steps.append(random_step)
output = exercise.Exercise(self._TITLE, self._SUBTITLE, random_steps)
return output
|
import torch
from torch import nn
def wrapmodel(model, loss_fn, sample_spec):
if isinstance(model, nn.DataParallel):
return nn.DataParallel(WrappedModel(
model.module, loss_fn, sample_spec)).cuda()
else:
return WrappedModel(model, loss_fn, sample_spec)
class WrappedModel(nn.Module):
def __init__(self, model, loss_fn, sample_spec):
"""
Wrapping the loss function evaluation with the forward pass to
minimize cross-gpu talk
"""
super(WrappedModel, self).__init__()
self.model = model
self.loss_fn = loss_fn
self.sample_spec = sample_spec
def forward(self, inputs, labels, masks, return_preds=False):
preds = self.model(*inputs)
losses, nmsks = self.eval_error(preds, labels, masks)
if not return_preds:
return losses, nmsks
else:
return losses, nmsks, preds
def eval_error(self, preds, labels, masks):
"""
Evaluates the error of the predictions according to the available
labels and masks
Assumes labels are ordered according to the sample_spec
"""
label_names = self.sample_spec.get_labels()
assert len(label_names) == len(labels), \
"Mismatched labels and label names"
assert len(preds) == len(labels), \
"Mismatched preds and labels"
losses, nmsks = dict(), dict()
for (pred, label, label_name) in zip(preds, labels, label_names):
if self.sample_spec.has_mask(label_name):
mask = masks[self.sample_spec.get_mask_index(label_name)]
losses[label_name] = self.loss_fn(pred, label, mask)
nmsks[label_name] = mask.sum()
else:
losses[label_name] = self.loss_fn(pred, label)
# Wrapping the value in a torch Tensor to give a
# uniform interface
# (particularly for log_errors and DataParallel's gather)
nmsks[label_name] = torch.tensor((label.nelement(),),
device=label.device)
# DataParallel doesn't like concatenating scalars
losses, nmsks = self.format_losses(losses, nmsks)
return losses, nmsks
def format_losses(self, *args):
"""
DataParallel doesn't like concatenating scalars (gives a warning),
so I'll add an extra dimension to those values.
"""
new_args = list()
for arg in args:
new_arg = dict()
for (k, v) in arg.items():
new_arg[k] = v.unsqueeze(0) if v.ndim == 0 else v
new_args.append(new_arg)
return new_args
|
#!/usr/bin/env python
"""Machine Learning Server Unit Testing."""
# System
import tempfile
import warnings
# Third Party
import unittest
from pqueue import Queue
class TestServerFailover(unittest.TestCase):
fake_requests = ["Not a real request", "Also not a real request"]
def queue_persistence(self, queue, tdir, qdir):
self.assertEqual(queue.qsize(), 0)
for request in self.fake_requests:
queue.put(request)
self.assertEqual(queue.qsize(), len(self.fake_requests))
del queue
queue = Queue(qdir, tempdir=tdir)
self.assertEqual(queue.qsize(), len(self.fake_requests))
for _ in range(len(self.fake_requests)):
queue.get()
self.assertEqual(queue.qsize(), 0)
def test_concordance_queue(self):
with warnings.catch_warnings():
warnings.simplefilter('ignore')
self._test_concordance_queue()
def _test_concordance_queue(self):
with tempfile.TemporaryDirectory() as temp_dir:
with tempfile.TemporaryDirectory() as concordance_dir:
concordance_queue = Queue(concordance_dir, tempdir=temp_dir)
self.queue_persistence(concordance_queue, temp_dir,
concordance_dir)
if __name__ == '__main__':
unittest.main()
|
N = input()
print('SAME' if all(N[0] == n for n in N) else 'DIFFERENT')
|
#!/usr/bin/python
import sys, getopt
import argparse;
import time
import stl_path
from trex_stl_lib.api import *
H_VER = "trex-x v0.1 "
class t_global(object):
args=None;
import json
import string
ip_range = {'src': {'start': "16.0.0.1", 'end': "16.0.0.254"},
'dst': {'start': "48.0.0.1", 'end': "48.0.0.254"}}
def read_profile(profilePath):
x = None
with open(profilePath, "r") as f:
x = json.load(f)
f.close()
return x
def get_vm(direction):
if direction == 0:
src = ip_range["src"]
dst = ip_range["dst"]
else:
src = ip_range["dst"]
dst = ip_range["src"]
vm = STLVM()
# define two vars (src and dst)
vm.var(name="src",min_value=src['start'],max_value=src['end'],size=4,op="inc")
vm.var(name="dst",min_value=dst['start'],max_value=dst['end'],size=4,op="inc")
# write them
vm.write(fv_name="src",pkt_offset= "IP.src")
vm.write(fv_name="dst",pkt_offset= "IP.dst")
# fix checksum
vm.fix_chksum()
return vm
def generate_payload(length):
word = ''
alphabet_size = len(string.letters)
for i in range(length):
word += string.letters[(i % alphabet_size)]
return word
def create_pkt (size, vm, vlan):
# Create base packet and pad it to size
if vlan:
base_pkt = Ether()/Dot1Q(vlan = vlan)/IP()
else:
base_pkt = Ether()/IP()
pad = max(0, size - len(base_pkt)) * 'x'
pkt = STLPktBuilder(pkt = base_pkt/pad, vm = vm)
return pkt
def create_steps(outfile, bidir, low_tput, high_tput, pps, levels, profile, aggr_metrics, no_scale_down, sleep_step_secs, duration = 10, vlan=None):
client = STLClient(server=t_global.args.ip)
client.connect()
if bidir:
directions = [0,1]
else:
directions = [0]
client.reset(ports=directions)
client.set_port_attr(ports = directions, promiscuous = False)
passed = True
latency_pgids = []
for direction in directions:
vm = get_vm(direction)
streams = []
total_pps = 1000
for i in range(len(profile)):
x = profile[i]
pps = total_pps * x['ratio']
pkt = create_pkt(x["size"], vm, vlan)
streams.append(STLStream(packet=pkt, mode=STLTXCont(pps = pps), isg=x['isg']))
latency_pgid = 12+direction
lat_stream = STLStream(packet = create_pkt(256, vm, vlan), mode = STLTXCont(pps=1000), flow_stats = STLFlowLatencyStats(pg_id = latency_pgid))
streams.append(lat_stream)
latency_pgids.append(latency_pgid)
client.add_streams(streams, ports=[direction])
client.clear_stats()
ramp_up_time = 15 #seconds
traffic_bws = []
per_level_diff = (high_tput - low_tput)/float(levels)
for l in range(levels):
tput = l*per_level_diff + low_tput
traffic_bws.append(int(tput))
traffic_bws.append(int(high_tput))
if pps:
unit = "kpps"
else:
unit = "mbps"
print (traffic_bws)
print (unit)
if no_scale_down:
total_flow_time = ramp_up_time + duration*levels
else:
total_flow_time = ramp_up_time + duration*(levels*2 + 1)
f = open(outfile, "w")
# choose rate and start traffic for 10 seconds on 5 mpps
mult = "%d%s"%(low_tput, unit)
print ("Enforcing mult %s on directions %s"%(mult, str(directions)))
client.start(ports = directions, mult = mult, duration = total_flow_time)
time.sleep(ramp_up_time)
client.clear_stats()
sleep_time = 0
for bw in traffic_bws:
sleep_time = 0
mult = "%d%s"%(bw, unit)
print ("Enforcing mult %s on directions %s"%(mult, str(directions)))
for d in directions:
client.update_line("--port %d -m %s"%(d, mult))
while sleep_time < duration:
if not aggr_metrics:
client.clear_stats()
data = {}
data["init_stats"] = client.get_stats()
time.sleep(sleep_step_secs)
sleep_time += sleep_step_secs
if not aggr_metrics:
time_ms = int(round(time.time() * 1000))
data["ts"] = time_ms
data["final_stats"] = client.get_stats()
data["bw"] = bw
f.write(json.dumps(data))
f.write("\n")
if not no_scale_down:
sleep_time = 0
for i in range(len(traffic_bws)):
idx = len(traffic_bws)-1-i
bw = traffic_bws[idx]
sleep_time = 0
mult = "%d%s"%(bw, unit)
print ("Enforcing mult %s on directions %s"%(mult, str(directions)))
for d in directions:
client.update_line("--port %d -m %s"%(d, mult))
while sleep_time < duration:
if not aggr_metrics:
client.clear_stats()
data = {}
data["init_stats"] = client.get_stats()
time.sleep(sleep_step_secs)
sleep_time += sleep_step_secs
if not aggr_metrics:
time_ms = int(round(time.time() * 1000))
data["ts"] = time_ms
data["final_stats"] = client.get_stats()
data["bw"] = bw
f.write(json.dumps(data))
f.write("\n")
# block until done
client.wait_on_traffic(ports = directions)
if aggr_metrics:
time_ms = int(round(time.time() * 1000))
data = {}
data["ts"] = time_ms
data["stats"] = client.get_stats()
data["bw"] = bw
f.write(json.dumps(data))
f.write("\n")
f.close()
client.disconnect()
if passed:
print("\nPASSED\n")
else:
print("\nFAILED\n")
def process_options ():
parser = argparse.ArgumentParser();
parser.add_argument("--ip",dest="ip",help='remote trex ip default local',default="127.0.0.1",type = str)
parser.add_argument('-d','--duration-per-level',dest='duration',help='duration in second ',default=10,type = int,)
parser.add_argument('-H','--high-tput',dest='high_tput', help='high throughput',default="1024",type=int)
parser.add_argument('-l','--low-tput',dest='low_tput',help='low throughput ',default="1",type=int)
parser.add_argument('-L','--levels',dest='levels',help='Number of levels between low and high throughput',default="16",type=int)
parser.add_argument('-O','--out-file',dest='outfile',help='Output file to write the stats to',default="/tmp/test.out")
parser.add_argument("--aggr-metrics",dest="aggr_metrics",help='Aggregate metrics over the entire run',action='store_true',default=False)
parser.add_argument("--no-scale-down",dest="no_scale_down",help='Add this flag to prevent traffic rate from going down after peak. Cuts off traffic at peak.',action='store_true',default=False)
parser.add_argument("--bidirectional",dest="bidir",help='Generate traffic in both directions',action='store_true')
parser.add_argument("--vlan",dest="vlan",help='VLAN ID',default=None,type = int)
parser.add_argument("--imix-profile",dest="imix_profile",help='IMIX profile path',type = str,required = True)
parser.add_argument("--sleep-step-secs",dest="sleep_step_secs",help='Seconds to sleep between collecting metrics',default=5, type=int)
t_global.args = parser.parse_args();
print(t_global.args)
def main():
process_options ()
profile = read_profile(t_global.args.imix_profile)
create_steps(duration = t_global.args.duration,
low_tput = t_global.args.low_tput,
high_tput = t_global.args.high_tput,
levels = t_global.args.levels,
outfile = t_global.args.outfile,
bidir=t_global.args.bidir,
pps=True,
vlan = t_global.args.vlan,
profile = profile,
aggr_metrics=t_global.args.aggr_metrics,
no_scale_down=t_global.args.no_scale_down
sleep_step_secs=t_global.args.sleep_step_secs
)
if __name__ == "__main__":
main()
|
#!/usr/bin/env python
import numpy as np
from numpy import *
import os
# pyyaml - https://pyyaml.org/wiki/PyYAMLDocumentation
import yaml
# ROS
import rospy
import rospkg
import std_msgs.msg
from std_msgs.msg import Bool
from std_msgs.msg import Header
import geometry_msgs.msg
from geometry_msgs.msg import PoseStamped
import visualization_msgs.msg
from visualization_msgs.msg import Marker
from visualization_msgs.msg import MarkerArray
import tf_conversions
import tf2_ros
#
import ars_lib_helpers
class ArsSimEnvironmentRos:
#######
# World frame
world_frame = None
#
environment_descript = None
environment_descript_yaml_file_name = None
#
flag_dynamic_obstacles = None
flag_dyn_obst_sub = None
# Dynam obst loop freq
# time step
dynamic_obst_loop_freq = None
# Timer
dynamic_obst_loop_timer = None
# Static obst loop freq
# time step
static_obst_loop_freq = None
# Timer
static_obst_loop_timer = None
# Obstacles static pub
obstacles_static_pub = None
# Obstacles dynamic pub
obstacles_dynamic_pub = None
#
obstacles_static_msg = None
#
obstacles_dynamic_msg = None
#
id_first_available = None
#########
def __init__(self):
# World frame
self.world_frame = 'world'
#
self.environment_descript = None
self.environment_descript_yaml_file_name = ''
#
self.flag_dynamic_obstacles = True
self.flag_dyn_obst_sub = None
# Dynam obst loop freq
# time step
self.dynamic_obst_loop_freq = 10.0
# Timer
self.dynamic_obst_loop_timer = None
# Static obst loop freq
# time step
self.static_obst_loop_freq = 10.0
# Timer
self.static_obst_loop_timer = None
#
self.obstacles_static_msg = MarkerArray()
#
self.obstacles_dynamic_msg = MarkerArray()
#
self.id_first_available = 0
# end
return
def init(self, node_name='ars_sim_environment_node'):
#
# Init ROS
rospy.init_node(node_name, anonymous=True)
# Package path
pkg_path = rospkg.RosPack().get_path('ars_sim_environment')
#### READING PARAMETERS ###
# Environment description
default_environment_descript_yaml_file_name = os.path.join(pkg_path,'config','obstacles_env_01.yaml')
environment_descript_yaml_file_name_str = rospy.get_param('~environment_description_yaml_file', default_environment_descript_yaml_file_name)
print(environment_descript_yaml_file_name_str)
self.environment_descript_yaml_file_name = os.path.abspath(environment_descript_yaml_file_name_str)
###
# Load environment description
with open(self.environment_descript_yaml_file_name,'r') as file:
# The FullLoader parameter handles the conversion from YAML
# scalar values to Python the dictionary format
self.environment_descript = yaml.load(file)
# Obstacles static
self.initObstaclesStatic()
# Obstacles dynamic
self.initObstaclesDynamic()
# End
return
def open(self):
# Publishers
#
self.obstacles_static_pub = rospy.Publisher('obstacles_static', MarkerArray, queue_size=1, latch=True)
#
self.obstacles_dynamic_pub = rospy.Publisher('obstacles_dynamic', MarkerArray, queue_size=1, latch=True)
# Subscribers
self.flag_dyn_obst_sub = rospy.Subscriber('flag_dynamic_obstacles', Bool, self.flagDynamObstCallback)
# Timers
#
self.dynamic_obst_loop_timer = rospy.Timer(rospy.Duration(1.0/self.dynamic_obst_loop_freq), self.dynamicObstaclesLoopTimerCallback)
#
self.static_obst_loop_timer = rospy.Timer(rospy.Duration(1.0), self.staticObstaclesLoopTimerCallback, oneshot=True)
# End
return
def run(self):
rospy.spin()
return
def flagDynamObstCallback(self, flag_dynamic_obstacles_msg):
self.flag_dynamic_obstacles = flag_dynamic_obstacles_msg.data
if(self.flag_dynamic_obstacles == True):
self.initObstaclesDynamic()
else:
self.emptyObstaclesDynamic()
# Publish
self.publishObstacleDynamic()
return
def publishObstacleDynamic(self):
self.obstacles_dynamic_pub.publish(self.obstacles_dynamic_msg)
return
def updateDynObstaclesAndPublish(self, time_stamp_current):
#
if(self.flag_dynamic_obstacles):
# Update
# TODO
# Publish dynamic obstacles
self.publishObstacleDynamic()
# End
return
def staticObstaclesLoopTimerCallback(self, timer_msg):
# Get time
time_stamp_current = rospy.Time.now()
# Publish static obstacles
self.obstacles_static_pub.publish(self.obstacles_static_msg)
# End
return
def dynamicObstaclesLoopTimerCallback(self, timer_msg):
# Get time
time_stamp_current = rospy.Time.now()
#
self.updateDynObstaclesAndPublish(time_stamp_current)
# End
return
def initObstaclesStatic(self):
# Set static obstacles
self.obstacles_static_msg.markers = []
#
print('Obstacles static:')
for object_env in self.environment_descript['static']:
print('Circles:')
for circle in object_env['circles']:
#
print(circle)
# obstacle i
obstacle_i = Marker()
obstacle_i.header = Header()
obstacle_i.header.stamp = rospy.Time()
obstacle_i.header.frame_id = self.world_frame
obstacle_i.ns = 'static'
obstacle_i.id = self.id_first_available
obstacle_i.action = 0
obstacle_i.type = 3
obstacle_i.pose.position.x = circle['position'][0]
obstacle_i.pose.position.y = circle['position'][1]
obstacle_i.pose.position.z = circle['position'][2]
obstacle_i.pose.orientation.w = 1.0
obstacle_i.pose.orientation.x = 0.0
obstacle_i.pose.orientation.y = 0.0
obstacle_i.pose.orientation.z = 0.0
obstacle_i.scale.x = circle['sizes'][0]
obstacle_i.scale.y = circle['sizes'][1]
obstacle_i.scale.z = circle['sizes'][2]
obstacle_i.color.r = 1.0
obstacle_i.color.g = 0.0
obstacle_i.color.b = 0.0
obstacle_i.color.a = 0.3
obstacle_i.lifetime = rospy.Duration(0.0)
#
self.obstacles_static_msg.markers.append(obstacle_i)
#
self.id_first_available += 1
#
return
def removeObstaclesStatic(self):
self.obstacles_static_msg.markers = []
return
def addObstaclesStatic(self):
for obs_i_msg in self.obstacles_static_msg.markers:
obs_i_msg.action = 0
return
def removeObstaclesStatic(self):
for obs_i_msg in self.obstacles_static_msg.markers:
obs_i_msg.action = 2
return
def initObstaclesDynamic(self):
# Set dynamic obstacles
self.obstacles_dynamic_msg.markers = []
#
print('Obstacles dynamic:')
for object_env in self.environment_descript['dynamic']:
print('Circles:')
for circle in object_env['circles']:
#
print(circle)
# obstacle i
obstacle_i = Marker()
obstacle_i.header = Header()
obstacle_i.header.stamp = rospy.Time()
obstacle_i.header.frame_id = self.world_frame
obstacle_i.ns = 'dynamic'
obstacle_i.id = self.id_first_available
obstacle_i.action = 0
obstacle_i.type = 3
obstacle_i.pose.position.x = circle['position'][0]
obstacle_i.pose.position.y = circle['position'][1]
obstacle_i.pose.position.z = circle['position'][2]
obstacle_i.pose.orientation.w = 1.0
obstacle_i.pose.orientation.x = 0.0
obstacle_i.pose.orientation.y = 0.0
obstacle_i.pose.orientation.z = 0.0
obstacle_i.scale.x = circle['sizes'][0]
obstacle_i.scale.y = circle['sizes'][1]
obstacle_i.scale.z = circle['sizes'][2]
obstacle_i.color.r = 0.0
obstacle_i.color.g = 1.0
obstacle_i.color.b = 0.0
obstacle_i.color.a = 0.3
obstacle_i.lifetime = rospy.Duration(1.0/self.dynamic_obst_loop_freq)
#
self.obstacles_dynamic_msg.markers.append(obstacle_i)
#
self.id_first_available += 1
return
def emptyObstaclesDynamic(self):
self.obstacles_dynamic_msg.markers = []
return
|
"""Module holds tests for the orderCategory class"""
import unittest
from app.models import Database, User, orderCategory, order
from app import utilities
class orderCategoryTest(unittest.TestCase):
"""All tests for the orderCategory class"""
def setUp(self):
"""Initiates variables to be used in most tests"""
self.db = Database()
self.user_data = {
'key': 1,
'first_name': 'John',
'last_name': 'Doe',
'email': 'preston@example.com',
'password': 'password',
}
self.user = User(**self.user_data)
self.db = Database()
self.user.save(self.db)
self.category_data = {
'key': 1,
'name': 'cakes',
'description': 'all orders cake!',
'user': self.user.key,
}
self.category = orderCategory(**self.category_data)
self.order_data = {
'name': 'Banana cake',
'description': 'yummy!',
}
def test_name_is_mandatory(self):
"""
In the constructor, the name parameter should be
a string which is not empty
"""
self.assertRaises(TypeError, orderCategory, key=1,
description='', user=self.user.key)
invalid_data = utilities.replace_value_in_dict(self.category_data, 'name', 7)
self.assertRaises(TypeError, orderCategory, **invalid_data)
invalid_data = utilities.replace_value_in_dict(self.category_data, 'name', '')
self.assertRaises(ValueError, orderCategory, **invalid_data)
invalid_data = utilities.replace_value_in_dict(self.category_data, 'name', ' ')
self.assertRaises(ValueError, orderCategory, **invalid_data)
def test_save_method(self):
"""
The save() method should be able to update the parent user's
list of order categories as well as that of the database
"""
self.assertIsInstance(self.category, orderCategory)
self.category.save(self.db)
length_of_db_category_keys = len(self.db.order_category_keys)
length_of_user_categories = len(self.user.order_categories)
self.assertIn(self.category.key, self.db.order_category_keys)
self.assertEqual(self.category, self.db.order_categories[self.category.key])
self.assertIn(self.category.key, self.user.order_categories)
self.assertIn(self.category.name, self.db.order_category_name_key_map.keys())
self.assertEqual(self.category.key,
self.db.order_category_name_key_map[self.category.name])
# the user should exist in database
invalid_data = utilities.replace_value_in_dict(self.category_data, 'user', 78)
new_category = orderCategory(**invalid_data)
self.assertRaises(KeyError, new_category.save, self.db)
# database parameter should be of type Database
self.assertRaises(TypeError, self.category.save,
'string instead of Database object')
# calling save more than once does not increase size of self.db.order_category_keys
self.category.save(self.db)
self.assertEqual(len(self.db.order_category_keys), length_of_db_category_keys)
# calling save more than once does not increase size of self.user.order_categories
self.assertEqual(len(self.user.order_categories), length_of_user_categories)
def test_delete(self):
"""orderCategory can be deleted"""
self.assertIsInstance(self.category, orderCategory)
self.category.save(self.db)
self.assertEqual(self.category, self.db.order_categories[self.category.key])
self.assertEqual(self.category, self.db.order_categories.get(self.category.key))
self.category.delete(self.db)
self.assertRaises(KeyError, utilities.return_value_from_dict,
self.db.order_categories, self.category.key)
self.assertNotIn(self.category.key, self.db.order_category_keys)
self.assertNotIn(self.category.key, self.user.order_categories)
self.assertNotIn(self.category.name, self.db.order_category_name_key_map.keys())
# database parameter should be of type Database
self.assertRaises(TypeError, self.category.delete,
'string instead of Database object')
# calling delete more than once on same Database objec raises KeyError
self.assertRaises(KeyError, self.category.delete, self.db)
def test_set_name(self):
""" The name can be set with a new non-empty string value"""
# try to set a new name
new_name = 'foo'
# save to db
self.category.save(self.db)
self.category.set_name(new_name, self.db)
# the records in db should be updated also
self.assertEqual(self.category, self.db.order_categories[self.category.key])
self.assertIn(self.category.key, self.db.order_category_keys)
self.assertIn(self.category.name, self.db.order_category_name_key_map.keys())
self.assertEqual(self.category.key, self.db.order_category_name_key_map[self.category.name])
# assert that the new name is set
self.assertEqual(new_name, self.category.name)
# try setting with a non string name
self.assertRaises(TypeError, self.category.set_name, 2, self.db)
# try setting with an empty string
self.assertRaises(ValueError, self.category.set_name, '', self.db)
# try setting with a space string
self.assertRaises(ValueError, self.category.set_name, ' ', self.db)
# try setting with a database that is not a Databas
self.assertRaises(TypeError, self.category.set_name, 'new name',
'a string instead of database')
def test_set_description(self):
""" The description can be set with a new non-empty string value"""
# try to set a new description
new_description = 'bar'
# Save to self.db
self.category.save(self.db)
self.category.set_description(new_description, self.db)
self.assertEqual(self.category, self.db.order_categories[self.category.key])
self.assertIn(self.category.key, self.db.order_category_keys)
self.assertIn(self.category.name, self.db.order_category_name_key_map.keys())
self.assertEqual(self.category.key, self.db.order_category_name_key_map[self.category.name])
# assert that the new description is set
self.assertEqual(new_description, self.category.description)
# try setting with a non string description
self.assertRaises(TypeError, self.category.set_description, 2, self.db)
# the records in db should be updated also
# try setting with a database that is not a Databas
self.assertRaises(TypeError, self.category.set_description, 'new description',
'a string instead of database')
def test_category_can_create_orders(self):
"""Category can create orders under it"""
order = self.category.create_order(self.db, self.order_data)
self.assertIsInstance(order, order)
self.assertIn(order.key, self.category.orders)
self.assertIn(order.key, self.db.order_keys)
self.assertEqual(order, self.db.orders[order.key])
self.assertIn(order.name, self.db.order_name_key_map.keys())
self.assertEqual(order.key,
self.db.order_name_key_map[order.name])
self.assertRaises(TypeError, self.category.create_order,
'database should be a Database object', self.order_data)
del(self.order_data['name'])
order = self.category.create_order(self.db, self.order_data)
self.assertIsNone(order)
def test_get_all_orders(self):
"""The get_all_orders function should be able to retrieve all orders"""
names = ('Banana cake', 'fruit cake', 'icy cake')
# create three orders
created_orders = []
# incase a order is ever created in the Setup
key = 2
# save category in db
self.category.save(self.db)
for name in names:
new_data = utilities.replace_value_in_dict(self.order_data, 'name', name)
new_order = order(**new_data, key=key, category=self.category.key)
new_order.save(self.db)
created_orders.append(new_order)
key += 1
orders = self.category.get_all_orders(self.db)
self.assertIsInstance(orders, list)
self.assertEqual(len(self.category.orders), len(orders))
self.assertListEqual(created_orders, orders)
self.assertRaises(TypeError, self.category.get_all_orders,
'expected Database object not string')
if __name__ == '__main__':
unittest.main()
|
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.by import By
import time
PATH = "/mnt/c/Users/pspan/OneDrive - Umich/Fall 2020/EECS 201/eecs201-web/test/chromedriver.exe"
DOMAIN = "https://philspan.github.io/eecs201-web/blog"
class InvalidPageError(Exception):
def __init__(self, *args):
if args:
self.message = args[0]
else:
self.message = None
def __str__(self):
if self.message:
return "InvalidPageError: {0}".format(self.message)
else:
return "InvalidPageError"
driver = webdriver.Chrome(PATH)
driver.get(DOMAIN)
print(driver.title)
# Get link elements
links = driver.find_elements(By.TAG_NAME,'a')
blogPosts = []
for linkElement in links:
blogPosts.append(linkElement.get_attribute("href"))
# Go to each link and print
for link in blogPosts:
try:
driver.get(link)
if ("404" in driver.title):
raise InvalidPageError
print(driver.title)
driver.back()
except InvalidPageError as e:
# Catch 404 Error
print("{e}: This link does not lead to any page yet.")
# Close webdriver
driver.close() |
"""Base module for validators."""
import abc
class ValidatorBase:
"""Base class for validators."""
@abc.abstractmethod
def validate_exists(self, config, header, directive=None, cookie=None):
"""Validates that a header, directive or cookie exists in a set of headers.
Args:
config (CaseInsensitiveDict): The configuration of the exists rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
cookie (str): (optional) The cookie to validate.
"""
@abc.abstractmethod
def validate_not_exists(self, config, header, directive=None, cookie=None):
"""Validates that a header, directive or cookie does not exist in a set of headers.
Args:
config (CaseInsensitiveDict): The configuration of the not-exists rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
cookie (str): (optional) The cookie to validate.
"""
@abc.abstractmethod
def validate_value(self, config, header, directive=None):
"""Validates that a header or directive matches a single expected value.
Args:
config (CaseInsensitiveDict): The configuration of the value rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
"""
@abc.abstractmethod
def validate_value_any_of(self, config, header, directive=None):
"""Validates that a header or directive matches one or more of a list of expected values.
Args:
config (CaseInsensitiveDict): The configuration of the value-any-of rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
"""
@abc.abstractmethod
def validate_value_one_of(self, config, header, directive=None):
"""Validates that a header or directive matches one of a list of expected values.
Args:
config (CaseInsensitiveDict): The configuration of the value-one-of rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
"""
@abc.abstractmethod
def validate_must_avoid(self, config, header, directive=None, cookie=None):
"""Validates that a header, directive or cookie does not contain any of a list of disallowed values.
Args:
config (CaseInsensitiveDict): The configuration of the must-avoid rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
cookie (str): (optional) The cookie to validate.
"""
@abc.abstractmethod
def validate_must_contain(self, config, header, directive=None, cookie=None):
"""Validates that a header, directive or cookie contains all of a list of expected values.
Args:
config (CaseInsensitiveDict): The configuration of the must-contain rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
cookie (str): (optional) The cookie to validate.
"""
@abc.abstractmethod
def validate_must_contain_one(self, config, header, directive=None, cookie=None):
"""Validates that a header, directive or cookie contains one or more of a list of expected values.
Args:
config (CaseInsensitiveDict): The configuration of the must-contain-one rule.
header (str): The header to validate.
directive (str): (optional) The directive to validate.
cookie (str): (optional) The cookie to validate.
"""
class UnsupportedValidationError(Exception):
"""Exception to be raised when an unsupported validation is called.
Attributes:
message (string): A message describing the error.
"""
def __init__(self, message):
"""Initialises an UnsupportedValidationError instance with a message."""
self.message = message
def get_delimiter(config, delimiter_type):
if 'delimiters' in config:
return config['delimiters'].get(delimiter_type)
def get_expected_values(config, key, delimiter):
if isinstance(config[key], list):
return [str(item).strip() for item in config[key]]
else:
return [item.strip() for item in str(config[key]).split(delimiter)]
|
#!/usr/bin/env python
#Title: Module to facilitate Sub-Chandra processing and analysis (subchandra.py)
#Author: Adam Jacobs
#Creation Date: June 26, 2015
#Usage: load as a module
#Description: This module contains code utilized by the Sub-Chandra IPython notebooks and
# Sub-Chandra processing/analysis scripts.
# For now it's just a huge module that I hope to break up as subsets become clear.
#
#TODO:
# 1)
# 2)
#Notes on conventions, programming style:
# 1) All classes are in CamelCase with the first letter capitalized. Class methods
# are also in CamelCase with the first letter lower-case, e.g. myMethod().
# 2) Non-class functions and all variables are lowercase with underscores (_)
# acting as delimiters when needed/wanted.
# 3) An underscore prefix, e.g. _variable, means the named item is intended to be
# private (Python doesn't provide for easily enforcing data hiding, it's all by
# convention).
# 4) Names that are in ALL_CAPS are intended as constants.
###########################################
### Global Imports, Data, and Constants ###
###########################################
from __future__ import print_function
import sys
###############
### Classes ###
###############
class SCSimulationSuite(object):
"""A class representing a suite of sub-Chandra simulations and the different
operations one might want to perform on such such a suite."""
##Global Data##
##Constructor##
def __init__(self, stage_dir, scratch_dir, label):
"""self --> implicitly passed reference to this instance of SCSimulation
stage_dir --> staging directory containing all simulations
scratch_dir --> scratch directory where the work is done but data's purged
label --> this suite's label (e.g. SubChandraII)"""
self._stage_dir = stage_dir.rstrip('/') #Get rid of any trailing '/'
self._scratch_dir = scratch_dir.rstrip('/')
self._label = label
##Public methods##
def listRuns(self):
"""List all active runs in this suite."""
from supercomputer import TermColors, Supercomputer
from os.path import isfile, isdir, join
from glob import glob
active_runs = self._getActiveRuns()
curcomp = Supercomputer.getCurrentSC()
heading = '{0:29s}|{1:14s}|{2:14s}'.format('Label', 'In scratch?', 'In queue?')
list_format = '{0:29s}|{1:14s}|{2:14s}'
yep = TermColors.START_GREEN + '{0:14s}'.format("Yes!") + TermColors.RESET
nope = TermColors.START_RED + '{0:14s}'.format("No!") + TermColors.RESET
purged = TermColors.START_BLUE + '{0:14s}'.format("Purged!") + TermColors.RESET
print(heading)
for r in active_runs:
#Check scratch: is it there, not, or there but purged?
rundir = join(self._scratch_dir, r)
if isdir(rundir):
found_all_expected_files = (
len(glob( join(rundir, 'main.*') )) > 0 and
len(glob( join(rundir, 'inputs*') )) > 0 and
len(glob( join(rundir, 'helm_table.dat') )) > 0
)
if found_all_expected_files:
sc_str = yep
else:
sc_str = purged
else:
sc_str = nope
#Check queue
# ASSUMPTION: run directory is same as queue label
if curcomp.isQueued(r):
q_str = yep
else:
q_str = nope
outstr = list_format.format(r, sc_str, q_str)
print(outstr)
def printStatus(self, temp_tol=5.e8, mach_tol=0.3):
"""Go through all runs in the staging directory, printing out:
run status (queued, running, or inactive)
T_peak
M_peak
Simulation time"""
print('run label:')
print('run status | T_peak | M_peak | time | note \n')
#Loop over all runs in staging directory assuming <run label>/[output, run, plots]
#structure
runs = self._getActiveRuns()
for rr in runs:
#Get properties
rstat = self._getRStat(rr)
tpeak = self._getTPeak(rr, temp_tol)
mpeak = self._getMPeak(rr, mach_tol)
time = self._getTime(rr)
note = self._getNote(rr)
#Print
print(rr + ":\n" + rstat + "|" + tpeak + "|" + mpeak + "|" + time + "|" + note + '\n')
return
##Private methods##
def _getRStat(self, run_label):
"""Determine run status of run_label"""
from supercomputer import Supercomputer
curcomp = Supercomputer.getCurrentSC()
(job_status, qcount) = curcomp.getQStatus(run_label)
return job_status + '*{0:02d}'.format(qcount)
def _getTPeak(self, run_label, temp_tol):
"""Return the largest temperature found in diagnostic .out files with a preference for
files found in the work directory"""
import os
import numpy as np
from supercomputer import Supercomputer, TermColors
stg_dir, wrk_dir = self._stage_dir, self._scratch_dir
#Check work directory
if(os.path.isfile(wrk_dir + '/' + run_label + '/subchandra_temp_diag.out')):
diag_file = wrk_dir + '/' + run_label + '/subchandra_temp_diag.out'
temps = np.loadtxt(diag_file, usecols=(1,), unpack=True)
maxt = max(temps)
if(maxt > temp_tol):
return TermColors.START_RED + '{:6.2f}'.format(maxt/1.e6) + TermColors.RESET
else:
return '{:6.2f}'.format(maxt/1.e6)
#If no luck, check staging directory
elif(os.path.isfile(stg_dir + '/' + run_label + '/output/subchandra_temp_diag.out')):
diag_file = stg_dir + '/' + run_label + '/output/subchandra_temp_diag.out'
temps = np.loadtxt(diag_file, usecols=(1,), unpack=True)
maxt = max(temps)
if(maxt > temp_tol):
return TermColors.START_RED + '{:6.2f}'.format(maxt/1.e6) + TermColors.RESET
else:
return '{:6.2f}'.format(maxt/1.e6)
else:
return 'no files'
def _getMPeak(self, run_label, mach_tol):
"""Return the largest Mach number found in diagnostic .out files with a preference for
files found in the work directory"""
import os
import numpy as np
from supercomputer import TermColors
stg_dir, wrk_dir = self._stage_dir, self._scratch_dir
#Check work directory
if(os.path.isfile(wrk_dir + '/' + run_label + '/subchandra_vel_diag.out')):
diag_file = wrk_dir + '/' + run_label + '/subchandra_vel_diag.out'
machnums = np.loadtxt(diag_file, usecols=(3,), unpack=True)
maxm = max(machnums)
if(maxm > mach_tol):
return TermColors.START_RED + '{:5.3f}'.format(maxm) + TermColors.RESET
else:
return '{:5.3f}'.format(maxm)
#If no luck, check staging directory
elif(os.path.isfile(stg_dir + '/' + run_label + '/output/subchandra_vel_diag.out')):
diag_file = stg_dir + '/' + run_label + '/output/subchandra_vel_diag.out'
machnums = np.loadtxt(diag_file, usecols=(3,), unpack=True)
maxm = max(machnums)
if(maxm > mach_tol):
return TermColors.START_RED + '{:5.3f}'.format(maxm) + TermColors.RESET
else:
return '{:5.3f}'.format(maxm)
else:
return 'no files'
def _getTime(self, run_label):
"""Return the latest time found in diagnostic .out files with a preference for
files found in the work directory"""
import os
stg_dir, wrk_dir = self._stage_dir, self._scratch_dir
#Check work directory
diag_file = None
if(os.path.isfile(wrk_dir + '/' + run_label + '/subchandra_temp_diag.out')):
diag_file = wrk_dir + '/' + run_label + '/subchandra_temp_diag.out'
#If no luck, check staging directory
elif(os.path.isfile(stg_dir + '/' + run_label + '/output/subchandra_temp_diag.out')):
diag_file = stg_dir + '/' + run_label + '/output/subchandra_temp_diag.out'
else:
return 'no files'
with open(diag_file, 'r') as f:
f.seek(-2,2) #Jump to near the end of the file (second-to-last byte)
while f.read(1) != '\n': #Read to EOL
f.seek(-2, 1) #Jump back a bit
last_line = f.readline() #Read current line
tokens = last_line.split()
return '{:06.2f}'.format(float(tokens[0]))
def _getNote(self, run_label):
"""Return the any short note found in the staging directory's out directory
as note.txt."""
import os
stg_dir, wrk_dir = self._stage_dir, self._scratch_dir
#Check for a note, return it if it exists
if(os.path.isfile(stg_dir + '/' + run_label + '/output/note.txt')):
note_file = stg_dir + '/' + run_label + '/output/note.txt'
with open(note_file, 'r') as nf:
for i, l in enumerate(nf):
assert (i == 0), 'Error! note.txt should have only one line!'
txt = l
return txt.strip('\n')
#Otherwise, return a blank
return ' '
def _getActiveRuns(self):
from os import listdir
from os.path import join
from supercomputer import Supercomputer
curcomp = Supercomputer.getCurrentSC()
ret = []
for d in listdir(self._stage_dir):
if d == 'inactive':
continue
ret.append(d)
return ret
class SCSimConfig(object):
"""struct-like class for containing a sub-Chandra simulation's configuration."""
#Constants
OUTLET = 12
SYMMETRY = 13
#Sim metadata
project_label = None
run_label = None
Maestro_home = None
label_extras = []
exe = 'main.Linux.Cray.mpi.omp.exe'
inputs = ''
#Initial model core parameters
Mcore = None
Mhe = None
tcore = None
tbase = None
#Initial model supplementary parameters
delta = 5.e6
rmin = 0.e0
rmax = 1.1e9
#Inputs parameters, files
init_hse = "<full path>/sub_chandra.M_WD*hse*"
init_extras = "<full path>/sub_chandra.M_WD*extras*"
an_cut = None
base_cutoff = 1.e4
sponge_cen_den = None
sponge_st_fac = 2.0
#Grid properties
coarse_res = None
levels = None
drdx = 5 #This is the standard value for the resolution jump
#from fine dx to base state's dr
octant = None
#Required computing resources
node_count = None
mpn = None #MPI tasks / node
omp_thds = None
class SCSimulation(object):
"""A class representing a particular sub-Chandra simulation and the different
operations such a simulation might perform."""
##Global Data##
IGTEMP = 3.5e8
##Constructor##
def __init__(self, label, proj_label):
"""self --> implicitly passed reference to this instance of SCSimulation
label --> this simulation's label (e.g. 12050-107-175-3levs)
proj_label --> the project/suite this simulation belongs to (e.g. SubChandraII)"""
from supercomputer import Supercomputer
from os.path import join, split, dirname
curcomp = Supercomputer.getCurrentSC()
pbase, sbase = curcomp.getProjsAndScratch()
self._stage_dir = join(pbase, proj_label, 'Runs', label)
self._scratch_dir = join(sbase, 'sub_chandra', label)
self._label = label
self._initmod = SCInitialModel(self._stage_dir, self._label)
self._initInputs() #Inits self._inputs
self._out = SCOutput(self._stage_dir, self._scratch_dir, self._label, self)
##Public methods##
def getLabel(self):
"""Get this simulation's label"""
return self._label
def getIMDict(self):
"""Get a dictionary of this simulation's initial model data,
including keys 'radius', 'density', 'temperature', 'pressure', 'species'"""
return self._initmod.getDataDict()
def getIMCuts(self):
"""Get a struct-like class of this simulation's initial model cutoffs"""
return self._initmod.getCuts()
def getIMLims(self):
"""Get a struct-like class of this simulation's initial model limits"""
return self._initmod.getLims()
def getIMConvBounds(self):
"""Get the radius boundaries of convection in the initial model"""
return self._initmod.getConvBounds()
def getStageDir(self):
"""Get the staging directory for this run"""
return self._stage_dir
def getDiagTemp(self):
"""Get the temperature diagnostic data"""
return self._out._diags.getTempData()
def getDiagMach(self):
"""Get the Mach # diagnostic data"""
return self._out._diags.getMachData()
def getDiagEnuc(self):
"""Get the e_nuc diagnostic data"""
return self._out._diags.getEnucData()
def getSliceArray(self):
"""Get an array of SCSlices for this simulation"""
return self._out._slices
@staticmethod
def listRuns(project):
"""List all active runs in the given project."""
from supercomputer import TermColors, Supercomputer
from os.path import isfile, isdir, join
from glob import glob
active_runs = SCSimulation._getActiveRuns(project)
curcomp = Supercomputer.getCurrentSC()
heading = '{0:29s}|{1:14s}|{2:14s}'.format('Label', 'In scratch?', 'In queue?')
list_format = '{0:29s}|{1:14s}|{2:14s}'
yep = TermColors.START_GREEN + '{0:14s}'.format("Yes!") + TermColors.RESET
nope = TermColors.START_RED + '{0:14s}'.format("No!") + TermColors.RESET
purged = TermColors.START_BLUE + '{0:14s}'.format("Purged!") + TermColors.RESET
print(heading)
for r in active_runs:
#Check scratch: is it there, not, or there but purged?
rundir = join(curcomp.myconfig.scratch_base, 'sub_chandra', r)
if isdir(rundir):
found_all_expected_files = found_required_scratch_files(rundir)
if found_all_expected_files:
sc_str = yep
else:
sc_str = purged
else:
sc_str = nope
#Check queue
# ASSUMPTION: run directory is same as queue label
if curcomp.isQueued(r):
q_str = yep
else:
q_str = nope
outstr = list_format.format(r, sc_str, q_str)
print(outstr)
@staticmethod
def generateSimulation(simconfig):
"""A static factory method for building a new SCSimulation"""
from supercomputer import Supercomputer
from shutil import copy
from os.path import join, basename, isfile
#Create all the files and structures that a simulation consists of
curcomp = Supercomputer.getCurrentSC()
SCSimulation._generateDirStructure(simconfig, curcomp)
SCSimulation._generateInitModel(simconfig, curcomp)
SCSimulation._generateInputs(simconfig, curcomp)
SCSimulation._generateScripts(simconfig, curcomp)
#Copy needed files to scratch
exe_src = join(simconfig.Maestro_home, 'Exec', 'SCIENCE', 'sub_chandra', 'build', simconfig.exe)
exe_dst = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label, simconfig.exe)
copy(exe_src, exe_dst)
hse_src = simconfig.init_hse
hse_dst = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label, basename(hse_src))
copy(hse_src, hse_dst)
inputs_src = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',
simconfig.run_label, 'run', simconfig.inputs)
inputs_dst = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label, simconfig.inputs)
copy(inputs_src, inputs_dst)
proc_src = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',
simconfig.run_label, 'run', curcomp.myconfig.proc_script)
proc_dst = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label,
curcomp.myconfig.proc_script)
copy(proc_src, proc_dst)
run_src = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',
simconfig.run_label, 'run', curcomp.myconfig.run_script)
run_dst = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label,
curcomp.myconfig.run_script)
copy(run_src, run_dst)
helmtab = join(curcomp.myconfig.scratch_base, 'sub_chandra', simconfig.run_label, 'helm_table.dat')
if not isfile(helmtab):
copy(join(simconfig.Maestro_home, 'Microphysics', 'EOS', 'helmeos', 'helm_table.dat'),
helmtab)
def printTimestampOverview(self):
"""Print a text summary of timestamps with output data."""
self._out.printTimestampOverview()
def getFineResolution(self):
"""Get the physical length in cm corresponding to the
resolution at the finest level of refinement."""
xmax = float(self._inputs['prob_hi_x'].replace('d','e'))
mlevs = int(self._inputs['max_levs'])
nx = int(self._inputs['n_cellx'])
dxf = xmax/float(nx*2**(mlevs-1))
return dxf
#TODO: Move to supercomputer.py
def getpltdata(self, machine, max=None):
"""Retrieve this run's pltfile data from HPSS, store in this run's scratch directory.
'machine' specifies which system we're on (e.g. titan), max is used to set maximum
number of files to extract."""
from subprocess import PIPE, STDOUT, Popen
from os.path import join, basename, isdir, isfile
from os import listdir
from re import match
if machine.lower().strip() == 'titan':
HPSS_BASE = '/proj/ast106/sub_chandra'
PLT_RE = r'.*_plt[0-9]{5,6}[.]tar$' #<any characters>_plt<5-6 numbers>.tar<end of string>
#Get list of files in this simulation's HPSS directory
avail = []
hsi_ls = Popen(['hsi', 'ls -1 {0}'.format(join(HPSS_BASE, self._label))], stdout=PIPE, stderr=STDOUT)
out = hsi_ls.communicate()[0]
tokens = out.split('\n')
#Find index where directory list starts (i.e. skip the header)
dir_idx = len(tokens) + 1 #force error if not initialized
for i, s in enumerate(tokens):
if s.startswith('Username'):
dir_idx=i+1
break
#Build list of pltfiles
pltfiles = []
for s in tokens[dir_idx:]:
if match(PLT_RE, s.strip()):
pltfiles.append(s)
#Build list of pltfiles already on scratch
scdir = join(self._scratch_dir, self._label, 'plotfiles')
exists = []
for d in listdir(scdir):
hfile = join(scdir, d, 'Header')
pltdir = join(scdir, d)
if isdir(pltdir) and isfile(hfile):
exists.append(d)
#Remove any pltfiles already residing on scratch
#from list of files to extract form HPSS
pltfiles = [pf for pf in pltfiles if exists.count(basename(pf)[:-4]) == 0] #The [:-4] removes .tar
#Retrieve data in background
# htar -xf <full path>.tar >> file.out 2>&1 &
# will write "HTAR SUCCESS" to file.out
self._titanHPSSRetrieve(pltfiles, max)
else:
#TODO: implement other systems
pass
def getTHistData(self, step):
"""Return the temperature histogram object for the given step."""
return self._out.getTHistData(step)
def printOutcome(self, ig_temp=IGTEMP):
"""Analyze output and print outcome details."""
import numpy as np
#First get the diagnostic data
Tpeak, timepeak, rpeak, peakslice = self._out.getPeakState()
tconv = self._out.getTurnover()
#Determine the outcome
outcome = 'quasi-equilibrium'
if Tpeak > ig_temp:
outcome = 'ignition'
#TODO: Add criteria for nova
#Check peakslice
dt = peakslice.timestamp[1] - timepeak
if peakslice is None:
print("Couldn't find slice data!")
return
if abs(dt) > 3.0:
print("WARNING: Couldn't find a slice near (< 3 s) ignition time! dt: ", dt)
#Determine peak radially averaged temperature,
#use its location as radius of "base" of convective envelope
avgTpeak = max(peakslice.datalist[SCSlice.ITEMP])
brho_i = np.where(peakslice.datalist[SCSlice.ITEMP] == avgTpeak)[0][0]
avgBaseRho = peakslice.datalist[SCSlice.IRHO][brho_i]
rBase = peakslice.datalist[SCSlice.IRADIUS][brho_i]
print('Outcome |\n timepeak | <tconv> | (t_peak-50)/tconv | Tpeak | r_peak |\n dt | dr | <Tpeak> | <rho_peak> | <r_base> ')
print(outcome, '|\n', timepeak, tconv, (timepeak-50.)/tconv, Tpeak, rpeak, '\n', dt, rpeak - rBase, avgTpeak, avgBaseRho, rBase)
def validateRun(self, verbose=False):
"""Do various checks to make sure this run is behaving well.
Set verbose to True to get all possible warnings"""
from glob import glob
from os.path import join, isfile
#Output check
self._checkOutfiles(verbose)
#Necessary files check
if found_required_scratch_files(self._scratch_dir):
print('SUCCESS! Found needed scratch files')
else:
print('WARNING! Did not find all needed scratch files')
#Diagnostics check
self._checkDiags(verbose)
##Private methods##
def _titanHPSSRetrieve(self, pfl, max=None):
"""Assuming Titan's system configuration, retrieves files in the plotfiles list pfl
from HPSS archival storage."""
from subprocess import Popen, PIPE, STDOUT
from os.path import join, isfile
HTAR_EXE = '/opt/public/bin/htar'
HTAR_ARGS = '-xf'
PF_DIR = 'plotfiles'
#For each plotfile spawn a subprocess to extract it using htar
#The OLCF/Titan helpdesk recommends no more than two simultaneous,
#active htar instances to make sure you aren't depriving other users
#of fair access to shared resources. So only have 2 going at a time.
proc_list = []
if not max:
max = len(proc_list) - 1 #process all files
else:
max = max - 1 #Convert into 0-based
for i, f in enumerate(pfl):
curp = Popen([HTAR_EXE, HTAR_ARGS + f.strip()], stdout=PIPE, stderr=STDOUT,
cwd = join(self._scratch_dir, self._label, PF_DIR))
print('Spawned process for ' + f + ': ' + str(curp.pid))
proc_list.append(curp)
if i == max:
break
if i % 2 == 0:
#Wait for each to complete, printing their output
print('\nWaiting for some processes to complete, may take a while...')
for p in proc_list:
out = p.communicate()[0]
print(str(p.pid) + ' completed with')
print(' return code: ', p.returncode)
print(' output: ')
print(out)
proc_list = []
#Wait for any remaining processes
if proc_list:
print('\nWaiting for some processes to complete, may take a while...')
for p in proc_list:
out = p.communicate()[0]
print(str(p.pid) + ' completed with')
print(' return code: ', p.returncode)
print(' output: ')
print(out)
def _initInputs(self):
"""Initializes a map of parameters to their values for this run's inputs
file. It's assumed needed class member variables have been initialized."""
from glob import glob
#Find the file
# ASSUMPTION: There's only one inputs file with the prefix 'inputs' in
# <stage dir>/<run label>/run/
inputs_fname = glob(self._stage_dir + '/run/inputs*')[0]
inputs_file = open(inputs_fname)
#Create inputs map
self._inputs = {}
#Go through inputs file, fill in the inputs map
for line in inputs_file:
tokens = line.partition('=')
if tokens[1]: #Only do anything if a '=' was found
key = tokens[0].strip()
strval = tokens[2].strip()
self._inputs[key] = strval
def _checkOutfiles(self, verbose):
"""Check the outfiles (<run label>.o<jobid>)"""
from glob import glob
from os.path import join, basename
MIN_LINES = 250 #All output files should have more line than this,
#typical count for sub-Chandra is about 27000 lines for a 4 hour run
#Check output files
#Does the base-state physical resolution match initial model physical resolution?
outfiles = glob(join(self._stage_dir, 'output', '{}.o*'.format(self._label)))
outfiles += glob(join(self._scratch_dir, '{}.o*'.format(self._label)))
outfiles.sort()
if len(outfiles) == 0:
if verbose:
print('NOTE: No output files yet')
lowcount = 0
total = 0
for fname in outfiles:
if fname.endswith('~'):
#Skip over vim junk
continue
with open(fname, 'r') as f:
total += 1
#Check linecount
num_lines = sum(1 for line in f)
if num_lines < MIN_LINES:
lowcount += 1
if verbose:
print('WARNING! Low line count for {}: {}'.format(basename(fname), num_lines))
#Check outfile data
f.seek(0, 0) #Reset to beginning of file
base_res = None
im_res = None
res_warned = False
for line in f:
#Look for base-state, initial model resolution, make sure they match
if line.count('dr of MAESTRO base state') == 1:
base_res = float(line.partition('=')[2].strip())
if line.count('dr of input file data') == 1:
im_res = float(line.partition('=')[2].strip())
if base_res is not None and im_res is not None:
if not res_warned:
if base_res != im_res:
print('WARNING! Base state and initial model resolution do not match')
print(' Base res: {}'.format(base_res))
print(' IM res: {}'.format(im_res))
print(' file: {}'.format(basename(fname)))
res_warned = True
else:
print('SUCCESS! Base state/initial model resolutions match')
print(' file: {}'.format(basename(fname)))
res_warned = True
if lowcount > 0:
if not verbose:
print('WARNING! {}/{} outfiles had low line count'.format(lowcount, total))
def _checkDiags(self, verbose):
"""Check the status of the diagnostics files"""
from os.path import join, isfile, basename
#Get lists of the diagnostics files
sc_diag = []
st_diag = []
sc_diag_lc = [0, 0, 0]
st_diag_lc = [0, 0, 0]
sc_diag.append(join(self._scratch_dir, 'subchandra_temp_diag.out'))
sc_diag.append(join(self._scratch_dir, 'subchandra_vel_diag.out'))
sc_diag.append(join(self._scratch_dir, 'subchandra_enuc_diag.out'))
st_diag.append(join(self._stage_dir, 'output', 'subchandra_temp_diag.out'))
st_diag.append(join(self._stage_dir, 'output', 'subchandra_vel_diag.out'))
st_diag.append(join(self._stage_dir, 'output', 'subchandra_enuc_diag.out'))
#Count stage files
st_count = 0
for i, f in enumerate(st_diag):
if isfile(f):
st_count += 1
st_diag_lc[i] = sum(1 for line in open(f, 'r'))
else:
if verbose:
print('WARNING! Missing diag file in stage: {}'.format(f))
#Count scratch files
sc_count = 0
for i, f in enumerate(sc_diag):
if isfile(f):
sc_count += 1
sc_diag_lc[i] = sum(1 for line in open(f, 'r'))
else:
if verbose:
print('WARNING! Missing diag file in scratch: {}'.format(f))
#If both are 0, we probably haven't started running yet
if st_count == 0 and sc_count == 0:
if verbose:
print('NOTE: Looks like run has not started, no diag files')
#They don't match, not good
if st_count != sc_count:
print('WARNING! Stage and scratch do not have the same diag file count')
#Do the line counts match?
for i in range(1,3):
if sc_diag_lc[0] != sc_diag_lc[i]:
print("WARNING! line counts don't match")
print(" <scratch>/{}:{}".format(basename(sc_diag[0]), sc_diag_lc[0]))
print(" <scratch>/{}:{}".format(basename(sc_diag[i]), sc_diag_lc[i]))
return
if st_diag_lc[0] != st_diag_lc[i]:
print("WARNING! line counts don't match")
print(" <stage>/{}:{}".format(basename(st_diag[0]), st_diag_lc[0]))
print(" <stage>/{}:{}".format(basename(st_diag[i]), st_diag_lc[i]))
return
#Time to archive?
if sc_diag_lc[0] != st_diag_lc[0]:
if verbose:
print('NOTE: You need to archive scratch diagnostics to stage')
#All's good if all diag files found in only scratch or in both scratch and stage
if st_count == 3 and (sc_count==3 or sc_count==0):
print('SUCCESS! All expected diagnostic files found')
@staticmethod
def _generateDirStructure(simconfig, curcomp):
"""Create a directory convention, including a run directory in backed-up home and
a scratch directory in the purged space of the filesystem.
Run directory will be <home>/Projects/simconfig.project_label/Runs/<run_label>/ containing run, output, and plots.
Scratch directory will be <scratch>/sub_chandra/<run_label>"""
from os import makedirs, error
from os.path import join
import os
from supercomputer import Supercomputer
import math
#Construct <run_label> string
run_label = "{0:02d}{1:03d}-{2:03d}-{3:03d}-{4:1d}lev".format(int(simconfig.Mcore*10),
int(simconfig.Mhe*1000), int(simconfig.tcore/1.e6), int(simconfig.tbase/1.e6),
simconfig.levels)
if simconfig.octant == False:
run_label += "-full"
for note in simconfig.label_extras:
run_label += "-" + note
simconfig.run_label = run_label
#Create Run and scratch directory
(projs_base, scratch_base) = curcomp.getProjsAndScratch()
proj_base = join(projs_base, simconfig.project_label)
run_base = join(proj_base, 'Runs', run_label)
scratch_base = join(scratch_base, 'sub_chandra')
scratch = join(scratch_base, run_label)
try:
makedirs( join(run_base, 'run') )
makedirs( join(run_base, 'output') )
makedirs( join(run_base, 'plots') )
except os.error:
print('WARNING, already exists: {}'.format(run_base))
try:
makedirs(scratch)
except os.error:
print('WARNING, already exists: {}'.format(scratch))
@staticmethod
def _generateInitModel(simconfig, curcomp):
"""Generate 1D initial model."""
from shutil import move
from os.path import join
from glob import glob
#Generate an initial parameters file,
#this one will have an overly large radius, which we'll adjust
proj_base = join(curcomp.myconfig.projs_base, simconfig.project_label)
pfilename = SCSimulation._generateParams(simconfig, proj_base)
#Build the initial model
SCSimulation._buildIM(simconfig, pfilename)
#Repeat the above, adjusting the radius down
(simconfig.rmax, simconfig.an_cut, simconfig.sponge_cen_den) = SCSimulation._computeRmaxAndCutoffs(simconfig, 0.5)
SCSimulation._generateParams(simconfig, proj_base)
SCSimulation._buildIM(simconfig, pfilename)
#Move the parameters and model file to run directory
move(pfilename, join(proj_base, 'Runs', simconfig.run_label, 'run', pfilename))
(hse, extras) = glob('sub_chandra.M_WD*')
simconfig.init_hse = join(proj_base, 'Runs', simconfig.run_label, 'run', hse)
simconfig.init_extras = join(proj_base, 'Runs', simconfig.run_label, 'run', extras)
move(hse, simconfig.init_hse)
move(extras, simconfig.init_extras)
@staticmethod
def _generateParams(simconfig, proj_base):
"""Generate parameters input file for initial model builder."""
from os.path import join
from shutil import move
#Build param dict
param_dict = {}
if simconfig.octant:
param_dict['nx'] = str(simconfig.coarse_res * 2**(simconfig.levels-1) * simconfig.drdx)
else:
#For full star, only half of the coarse res is used for the radial base state
param_dict['nx'] = str(simconfig.coarse_res/2 * 2**(simconfig.levels-1) * simconfig.drdx)
param_dict['M_tot'] = str(simconfig.Mcore)
param_dict['M_He'] = str(simconfig.Mhe)
param_dict['delta'] = str(simconfig.delta)
param_dict['xmin'] = str(simconfig.rmin)
param_dict['xmax'] = str(simconfig.rmax)
param_dict['temp_core'] = str(simconfig.tcore)
param_dict['temp_base'] = str(simconfig.tbase)
#Build from template file
tfilename = join(proj_base, 'Templates', '_params.template')
pfilename = '_params.{}'.format(simconfig.run_label)
SCSimulation._writeKeyValFile(tfilename, pfilename, param_dict)
return pfilename
@staticmethod
def _writeKeyValFile(template_file, outfile, kvdict):
"""Read from a template file, use the given dictionary to write key, value pairs
in a parameter file containing pairs of the form "key = value"."""
with open(outfile, 'w') as ofile, open(template_file, 'r') as tfile:
for line in tfile:
if line.find('=') != -1:
tokens = line.partition('=')
key = tokens[0]
val = tokens[2].strip()
if key.strip() in kvdict:
val = kvdict[key.strip()]
ofile.write(key.rstrip() + ' = ' + val.strip() + '\n')
else:
ofile.write(line)
return outfile
@staticmethod
def _buildIM(simconfig, pfilename):
"""Build the initial model data."""
from subprocess import call, Popen, PIPE, STDOUT
from os.path import join, isfile
from os import remove
from glob import glob
from shlex import split
#Make sure helmtable is linked
if not isfile('helm_table.dat'):
call(['ln', '-s', join(simconfig.Maestro_home, 'Microphysics', 'EOS', 'helmeos', 'helm_table.dat')])
#Build the executable command
init1d_exe = join(simconfig.Maestro_home, 'Util', 'initial_models', 'sub_chandra',
'init_1d.Linux.gfortran.debug.exe') + ' ' + pfilename
#Execute, removing any old IM data files
old_files = glob('sub_chandra.M_WD*')
for f in old_files:
remove(f)
i1d_proc = Popen(split(init1d_exe), stdout=PIPE, stderr=PIPE)
(i1d_out, i1d_err) = i1d_proc.communicate()
if i1d_err:
print('init1d error: ', i1d_err)
print('init1d out, last 5 lines:')
for line in i1d_out.split('\n')[-5:]:
print(line)
@staticmethod
def _computeRmaxAndCutoffs(simconfig, rfac):
"""Read in the initial model data, return adjusted radius to be r_peak + r_peak*rfac,
where r_peak is the radius of peak temperature. Also return the anelastic and
sponge central density cutoffs based on top of convective zone."""
from numpy import loadtxt
from glob import glob
#Read in data
#ASSUMPTION: initial model data for exactly one model
# exists in the current directory
imfile = glob('sub_chandra.M_WD*.hse*')[0]
rad, rho, temp = loadtxt(imfile, usecols=(0,1,2), unpack=True)
rmax = 0
ancut = 0.0
te_old = 0.0
for r, d, t in zip(rad, rho, temp):
dt = t - te_old
if dt < 0.0:
rmax = r
if rmax and dt == 0.0:
ancut = d_old/simconfig.sponge_st_fac
scd = ancut
break
te_old = t
d_old = d
rmax = rmax + rmax*rfac
return (round(rmax, -7), round(ancut, -3), round(scd, -3))
@staticmethod
def _generateInputs(simconfig, curcomp):
"""Generate inputs file for the Maestro executable."""
from os.path import join, basename
from shutil import move
from numpy import log10
#Build inputs dict
inputs_dict = {}
inputs_dict['model_file'] = '"' + basename(simconfig.init_hse) + '"'
simconfig.job_name = '"{0:d}^3 base grid, T_core = 10^{1:d}, '.format(simconfig.coarse_res, int(log10(simconfig.tcore)))
simconfig.job_name += 'T_base = {0:3d} MK -- M_WD={1:3.1f}, M_He={2:4.2f}"'.format(int(simconfig.tbase/1.e6),
simconfig.Mcore, simconfig.Mhe)
inputs_dict['job_name'] = simconfig.job_name
inputs_dict['max_levs'] = str(simconfig.levels)
inputs_dict['n_cellx'] = str(simconfig.coarse_res)
inputs_dict['n_celly'] = str(simconfig.coarse_res)
inputs_dict['n_cellz'] = str(simconfig.coarse_res)
inputs_dict['anelastic_cutoff'] = str(simconfig.an_cut)
inputs_dict['base_cutoff_density'] = str(simconfig.base_cutoff)
inputs_dict['sponge_center_density'] = str(simconfig.sponge_cen_den)
inputs_dict['sponge_start_factor'] = str(simconfig.sponge_st_fac)
if simconfig.octant == True:
inputs_dict['octant'] = '.true.'
inputs_dict['prob_hi_x'] = str(simconfig.rmax)
inputs_dict['prob_hi_y'] = str(simconfig.rmax)
inputs_dict['prob_hi_z'] = str(simconfig.rmax)
inputs_dict['bcx_lo'] = str(simconfig.SYMMETRY)
inputs_dict['bcx_hi'] = str(simconfig.OUTLET)
inputs_dict['bcy_lo'] = str(simconfig.SYMMETRY)
inputs_dict['bcy_hi'] = str(simconfig.OUTLET)
inputs_dict['bcz_lo'] = str(simconfig.SYMMETRY)
inputs_dict['bcz_hi'] = str(simconfig.OUTLET)
elif simconfig.octant == False:
inputs_dict['octant'] = '.false.'
inputs_dict['prob_hi_x'] = str(simconfig.rmax*2)
inputs_dict['prob_hi_y'] = str(simconfig.rmax*2)
inputs_dict['prob_hi_z'] = str(simconfig.rmax*2)
inputs_dict['bcx_lo'] = str(simconfig.OUTLET)
inputs_dict['bcx_hi'] = str(simconfig.OUTLET)
inputs_dict['bcy_lo'] = str(simconfig.OUTLET)
inputs_dict['bcy_hi'] = str(simconfig.OUTLET)
inputs_dict['bcz_lo'] = str(simconfig.OUTLET)
inputs_dict['bcz_hi'] = str(simconfig.OUTLET)
else:
raise ValueError('Invalid value for octant in SCSimConfig')
inputs_dict['plot_base_name'] = '"' + simconfig.run_label + '_plt"'
inputs_dict['check_base_name'] = '"' + simconfig.run_label + '_chk"'
#Build from template file
proj_base = join(curcomp.myconfig.projs_base, simconfig.project_label)
tfilename = join(proj_base, 'Templates', 'inputs3d.template')
ifilename = 'inputs3d.{}'.format(simconfig.run_label)
SCSimulation._writeKeyValFile(tfilename, ifilename, inputs_dict)
#Move to run directory
ipathname = join(proj_base, 'Runs', simconfig.run_label, 'run', ifilename)
move(ifilename, ipathname)
simconfig.inputs = ifilename
@staticmethod
def _generateScripts(simconfig, curcomp):
"""Generate the scripts for running the job in scratch."""
from supercomputer import JobConfig
from datetime import time
from os.path import join
from shutil import copy
#Configure job
jconfig = JobConfig()
jconfig.nodes = simconfig.node_count
jconfig.mpn = simconfig.mpn
jconfig.omp = simconfig.omp_thds
jconfig.label = simconfig.run_label
jconfig.time = time(4,0,0)
jconfig.exe = simconfig.exe
jconfig.inputs = simconfig.inputs
#Make strings
outpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',
simconfig.run_label, 'run')
tpath = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',
'titan.run.template')
#Generate job script
curcomp.generateJobScript(outpath, jconfig, tpath)
#Copy process script over
proc_temp = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Templates',
'process.titan.template')
proc_dst = join(curcomp.myconfig.projs_base, simconfig.project_label, 'Runs',
simconfig.run_label, 'run', 'process.titan')
copy(proc_temp, proc_dst)
@staticmethod
def _getActiveRuns(project):
from os import listdir
from os.path import join
from supercomputer import Supercomputer
curcomp = Supercomputer.getCurrentSC()
proj_root = join(curcomp.myconfig.projs_base, project, 'Runs')
ret = []
for d in listdir(proj_root):
if d == 'inactive':
continue
ret.append(d)
return ret
class SCInitialModel(object):
"""A class representing a Maestro initial model for the Sub-Chandra
problem."""
#TODO: Write Maestro initial model super class
##Data##
#Static variables
_STAT_VAR = 1
#Global constants
#IRADIUS = 0
#Public variables
nspec = 0
#Consructor
def __init__(self, stage_dir, label):
"""SCInitialModel constructor.
self --> reference to this instance of SCInitialModel (implicitly passed)
stage_dir --> directory this run will be staged in
label --> run label (e.g. 12050-107-175-3lev)"""
from glob import glob
#Initialize simple private variables
self._label = label
self._stage_dir = stage_dir.rstrip('/')
#Initialize parameters and model data
self._parameters = {'nx': None,
'M_tot': None,
'M_He': None,
'delta': None,
'xmin': None,
'xmax': None,
'temp_core': None,
'temp_base': None,
'mixed_co_wd': None,
'low_density_cutoff': None,
'temp_fluff': None,
'smallt': None}
self._model_data = {'radius': None,
'density': None,
'temperature': None,
'pressure': None,
'species': None}
#Build filenames
#ASSUMPTIONS:
# 1) All run information can be found in stage_dir/label/run/
# 2) params files are prefixed with '_params' and there's only one in
# stage_dir/label/run/
# 2) data files are prefixed with 'sub_chandra.M_WD' and there's only one set
# of 'hse' and possibly 'extras' in stage_dir/label/run/
params_file = glob(self._stage_dir + '/run/_params*')[0]
data_file = glob(self._stage_dir + '/run/sub_chandra.M_WD*hse*')[0]
#Read file data
self._read_params(params_file)
self._read_data(data_file, extras=True)
#Initialize plotting limits and convection zone boundaries
self.initLimits()
self.initConv()
##Public methods##
def __str__(self):
"""String representation of an instance of SCInitialModel. Used when passed to print()."""
ret = "Label: " + self._label + "\n"
ret += "nspec: " + str(self.nspec) + "\n"
ret += "Parameters: \n"
for key in self._parameters:
ret += " " + key + " --> " + self._parameters[key] + "\n"
ret += "Data: \n"
for key in self._model_data:
ret += " " + key + " --> " + str(self._model_data[key]) + "\n"
ret += "\n\n"
return ret
def initLimits(self):
"""Initializes plotting limits based on parameters in
stage_dir/label/run/inputs*."""
from glob import glob
#Initialize Limits and Cutoffs struct-like objects
self._mylim = _Limits()
self._mycut = _Cutoffs()
#Build inputs filename
#ASSUMPTIONS:
# 1) Inputs file is in self._stage_dir/self._label/run/
# 2) Inputs file is prefixed with 'inputs3d' and there's only one
inputs_file = glob(self._stage_dir + '/run/inputs3d*')[0]
#Determine axis limits
# Temperature
# For now fix it at 1e7 K to 5e8 K
self._mylim.Tlims=(1.e7, 5.e8)
# Density
# For now, fix it at 1 to 1e9
self._mylim.dlims = (1.0, 1.e9)
# Radius
# Go from 0 to xmax in parameters file
rmax = float(self._parameters['xmax'].replace('d', 'e'))
self._mylim.rlims = (0., rmax)
# Soundspeed
self._mylim.clims = (1.e7, 1.e10)
# Entropy
self._mylim.slims = (1.e7, 1.e10)
#self._mylim.slims = (0, 1)
#Determine zoom bounds
rtop, dtop, Ttop = self._get_top()
self._mylim.rzoom = (rtop - rtop*0.05, rtop + rtop*0.15)
self._mylim.dzoom = (dtop - dtop*0.25, dtop + dtop*1.5)
self._mylim.Tzoom = (Ttop - Ttop*0.25, Ttop + Ttop*1.5)
self._mylim.zbounds = (self._mylim.rzoom, self._mylim.dzoom, self._mylim.Tzoom)
#Read in cutoffs
self._mycut.an_cut = float(get_param('anelastic_cutoff', inputs_file).replace('d', 'e'))
self._mycut.sp_cen_den = float(get_param('sponge_center_density', inputs_file).replace('d', 'e'))
self._mycut.sp_st_fac = float(get_param('sponge_start_factor', inputs_file).replace('d', 'e'))
self._mycut.base_cut = float(get_param('base_cutoff_density', inputs_file).replace('d', 'e'))
#Find cutoff radii
# find the r coordinate of the anelastic cutoff
for r, rho in zip(self._model_data['radius'], self._model_data['density']):
#TODO add error checking
if rho <= self._mycut.an_cut:
self._mycut.r_an = r
break
# find the r coordinate of the start of the sponge
sp_st_rho = self._mycut.sp_cen_den * self._mycut.sp_st_fac
for r, rho in zip(self._model_data['radius'], self._model_data['density']):
#TODO add error checking
if rho <= sp_st_rho:
self._mycut.r_sp = r
break
# find the r coordinate of the cutoff density
for r, rho in zip(self._model_data['radius'], self._model_data['density']):
#TODO add error checking
if rho <= self._mycut.base_cut:
self._mycut.r_bc = r
break
return
def initConv(self):
"""Initializes the lower and upper radial bounds of the convection zone
as determined by the entropy."""
from glob import glob
#The DS critical values are based on analyzing models at different extremes and
#are found to accurately demark the convective zone where ds/dr <= 0.
#TODO: I'm noticing that these critical values depend on resolution of the initial model,
# which makes sense as higher resolution will have smaller ds between cells. I should
# normalize by dr.
DSDR_TRIGGER = 5.0 #This triggers the search for the convective zone,
DS_TRIGGER = 1.0e6 #This triggers the search for the convective zone,
#it must be after this point
DSDR_THRESH = 0.4 #When DS falls below this, we're in the convectively unstable zone,
DS_THRESH = 7.5e4 #When DS falls below this, we're in the convectively unstable zone,
#when it rises back above it we're out of the convective zone
###OLD
#S_TOL = 1.e-16
#normalize to avoid differences between huge numbers
#s_norm = self._model_data['entropy']/max(self._model_data['entropy'])
###END OLD
ent = self._model_data['entropy']
#Find radial bounds in which entropy is flat (i.e. isentropic)
# ASSUMPTION: dr is fixed
sold = ent[1]
dr = self._model_data['radius'][2]-self._model_data['radius'][1]
left = None
right = None
trigger = False
#print('trigger: ', DS_TRIGGER)
#print('dr trigger: ', DSDR_TRIGGER)
#print('thresh: ', DS_THRESH)
#print('dr thresh: ', DSDR_THRESH)
for r, s in zip(self._model_data['radius'][2:], ent[2:]):
ds = (s-sold)
dsdr = ds/dr
#print('ds: ', ds)
#print('ds/dr: ', dsdr)
if dsdr > DSDR_TRIGGER and r > 1.e8:
trigger = True
#print('trigger!')
#TODO add error checking
if trigger:
if not left:
if dsdr < DSDR_THRESH:
left = r
#print('found left!')
else:
if not right:
if dsdr >= DSDR_THRESH:
right = r
#print('found right!')
break
sold = s
#Make sure we got them both
assert left != None, "Found no lower convective bound! Check initConv()"
assert right != None, "Found no upper convective bound! Check initConv()"
self._conv_bounds = (left, right)
def getDataDict(self):
"""Get a dictionary of this initial model's data,
including keys 'radius', 'density', 'temperature', 'pressure', 'species'"""
return self._model_data
def getCuts(self):
"""Get a struct-like class of this initial model's cutoffs"""
return self._mycut
def getLims(self):
"""Get a struct-like class of this initial model's limits"""
return self._mylim
def getConvBounds(self):
"""Get a tuple of the lower and upper radius bounds of convection."""
return self._conv_bounds
##Private methods##
def _read_params(self, params_file):
"""Reads in parameters from params_file and stores them in _parameters dictionary."""
pfile = open(params_file)
for line in pfile:
if(line.find('=') > -1):
tokens = line.partition('=')
cur_param = tokens[0].strip()
cur_val = tokens[2]
if(cur_param in self._parameters):
self._parameters[cur_param] = cur_val
pfile.close()
def _read_data(self, data_file, extras=False):
"""Reads in data from data_file and stores them in _model_data. If
extras=True then soundspeend data is loaded from data_file with 'hse'
replaced with 'extras'."""
import numpy as np
#ASSUMPTION!: Data file has a header of this form:
# npts = <some integer>
# num of variables = <some integer>
# <variable label 1>
# ...
# <variable label n>
#The number of non-species variables (radius, density, temperature, pressure)
NONSPEC = 4
#Parse header
dfile = open(data_file)
if extras:
efile = open(data_file.replace('hse', 'extras'))
npts = -1
varc = -1
varcol = {'radius': 0}
evarcol = {}
i = 1
for line in dfile:
#If no # prefix, we've finished reading the header
if(not line.strip().startswith('#')):
break
if(line.find('=') > -1):
#Strip comment characters
sline = line.lstrip('#')
#Extract npts and variable count
tokens = sline.partition('=')
if(tokens[0].strip() == 'npts'):
npts = int(tokens[2])
if(tokens[0].strip() == 'num of variables'):
varc = int(tokens[2])
else:
#Store column number of variable
tokens = line.partition(' ')
varcol[tokens[2].strip()] = i
i = i +1
self.nspec = len(varcol) - NONSPEC
#If needed, parse extras header
#ASSUMPTION: extras file has same npts as hse data file
i=1 #i=0 is still radius
if extras:
for line in efile:
#If no # prefix, we've finished reading the header
if(not line.strip().startswith('#')):
break
if(line.find('=') > -1):
continue
else:
#Store column number of variable
tokens = line.partition(' ')
evarcol[tokens[2].strip()] = i
i = i +1
#Use header info to build non-species _model_data dict
for key in self._model_data:
if key != 'species':
data_arr = np.loadtxt(data_file, usecols=(varcol[key],))
self._model_data[key] = data_arr
del varcol[key]
#Build species part of _model_data dict
species_dict = {}
for key in varcol:
data_arr = np.loadtxt(data_file, usecols=(varcol[key],))
species_dict[key] = data_arr
self._model_data['species'] = species_dict
#Build extras part of _model_data dict
if extras:
for key in evarcol:
data_arr = np.loadtxt(data_file.replace('hse','extras'), usecols=(evarcol[key],))
self._model_data[key] = data_arr
def _get_top(self):
"""Return tuple of (radius, density, temperature) values at the top of the convective zone
(where temperature levels out)."""
#Get data arrays
r = self._model_data['radius']
rho = self._model_data['density']
temp = self._model_data['temperature']
#Search from end of temp array until the temp changes, this is the top of
#the convective zone. return values at this point.
te_prev = temp[len(temp)-1]
r_top = -1.0
rho_top = -1.0
T_top = -1.0
for re, rhoe, te in zip(r[::-1], rho[::-1], temp[::-1]):
if(te_prev != te):
r_top = re
rho_top = rhoe
T_top = te
break
return (r_top, rho_top, T_top)
class SCDiagnostics(object):
"""A class representing the diagnostics printed out every timestep: peak temperature
details, peak velocity/Mach # details, and peak nuclear burning energy details."""
##Shared class data##
##Constructor##
def __init__(self, stage_dir, scratch_dir, label):
"""self --> implicitly passed reference to this instance of SCSimulation
stage_dir --> staging directory containing all simulations
scratch_dir --> scratch directory where the work is done but data's purged
label --> this simulation's label (e.g. 12050-107-175-3levs)"""
from glob import glob
from os.path import join, getmtime, isfile
from warnings import warn
import numpy as np
#Constants
TIME_COL = 0
MAXT_COL = 1
MAXT_X_COL = 2
MAXT_Y_COL = 3
MAXT_Z_COL = 4
MAXT_R_COL = 8
MAXT_VX_COL = 5
MAXT_VY_COL = 6
MAXT_VZ_COL = 7
MAXT_VR_COL = 9
MAXVMAG_COL = 1
MAXMACH_SUB_COL = 2
MAXMACH_FULL_COL = 3
DT_COL = 4
MAXENUC_COL = 1
MAXENUC_X_COL = 2
MAXENUC_Y_COL = 3
MAXENUC_Z_COL = 4
MAXENUC_R_COL = 8
MAXENUC_VX_COL = 5
MAXENUC_VY_COL = 6
MAXENUC_VZ_COL = 7
MAXENUC_VR_COL = 9
#Store args
self._stage_dir = stage_dir.rstrip('/') #Get rid of any trailing '/'
self._scratch_dir = scratch_dir.rstrip('/')
self._label = label
#Find the most up-to-date diagnostics data files
temp_stfname = join(self._stage_dir, 'output', 'subchandra_temp_diag.out')
temp_scfname = join(self._scratch_dir, 'subchandra_temp_diag.out')
if not isfile(temp_scfname):
if not isfile(temp_stfname):
warn('No temperature diagnostic file found for {0}!'.format(self._label))
temp_fname = None
else:
temp_fname = temp_stfname
elif getmtime(temp_scfname) >= getmtime(temp_stfname):
temp_fname = temp_scfname
else:
temp_fname = temp_stfname
vel_stfname = join(self._stage_dir, 'output', 'subchandra_vel_diag.out')
vel_scfname = join(self._scratch_dir, 'subchandra_vel_diag.out')
if not isfile(vel_scfname):
if not isfile(vel_stfname):
warn('No velocity diagnostic file found for {0}!'.format(self._label))
vel_fname = None
else:
vel_fname = vel_stfname
elif getmtime(vel_scfname) >= getmtime(vel_stfname):
vel_fname = vel_scfname
else:
vel_fname = vel_stfname
enuc_stfname = join(self._stage_dir, 'output', 'subchandra_enuc_diag.out')
enuc_scfname = join(self._scratch_dir, 'subchandra_enuc_diag.out')
if not isfile(enuc_scfname):
if not isfile(enuc_stfname):
warn('No enuc diagnostic file found for {0}!'.format(self._label))
enuc_fname = None
else:
enuc_fname = enuc_stfname
elif getmtime(enuc_scfname) >= getmtime(enuc_stfname):
enuc_fname = enuc_scfname
else:
enuc_fname = enuc_stfname
#Load most recent diagnostics data
# max temperature and time (ASSUMPTION: All diag files have same time data)
if temp_fname != None:
(self._time, self._maxT, self._maxT_x, self._maxT_y, self._maxT_z, self._maxT_r,
self._maxT_vx, self._maxT_vy, self._maxT_vz, self._maxT_vr) = np.loadtxt(temp_fname,
usecols=(TIME_COL, MAXT_COL, MAXT_X_COL, MAXT_Y_COL, MAXT_Z_COL, MAXT_R_COL,
MAXT_VX_COL, MAXT_VY_COL, MAXT_VZ_COL, MAXT_VR_COL), unpack=True)
# max velocity
if vel_fname != None:
(self._time, self._maxvmag, self._maxmachsub, self._maxmachfull, self._dt) = np.loadtxt(vel_fname,
usecols=(TIME_COL, MAXVMAG_COL, MAXMACH_SUB_COL, MAXMACH_FULL_COL, DT_COL), unpack=True)
# max enuc
if enuc_fname != None:
(self._time, self._maxenuc, self._maxenuc_x, self._maxenuc_y, self._maxenuc_z, self._maxenuc_r,
self._maxenuc_vx, self._maxenuc_vy, self._maxenuc_vz, self._maxenuc_vr) = np.loadtxt(enuc_fname,
usecols=(TIME_COL, MAXENUC_COL, MAXENUC_X_COL, MAXENUC_Y_COL, MAXENUC_Z_COL, MAXENUC_R_COL,
MAXENUC_VX_COL, MAXENUC_VY_COL, MAXENUC_VZ_COL, MAXENUC_VR_COL), unpack=True)
##Public methods##
def stageOutput(self):
"""Check the scratch directory for any updated output. If found, copy to
the stage directory."""
#TODO: Implement this
pass
def getPeakState(self):
"""Get the temp and time at the time of peak global temperature."""
import numpy as np
Tpeak = self._maxT.max()
peakdex = np.where(self._maxT == Tpeak)[0][0]
#Tpeak = self._maxT[:peakdex-100].max()
#peakdex = np.where(self._maxT == Tpeak)[0][0]
timepeak = self._time[peakdex]
rpeak = self._maxT_r[peakdex]
return Tpeak, timepeak, rpeak
def getTempData(self):
"""Get tuple of data:
(time, peak temp, peak temp's radius, radial velocity at peak temp)"""
return self._time, self._maxT, self._maxT_r, self._maxT_vr
def getMachData(self):
"""Get tuple of data:
(time, peak Mach #)"""
return self._time, self._maxmachfull
def getEnucData(self):
"""Get tuple of data:
(time, peak e_nuc)"""
return self._time, self._maxenuc
##Private methods##
#TODO: Migrate self-plotting objects to plotting via SCPlotter
class SCPlotter(object):
"""A class for executing plots relevant to the Sub-Chandra problem."""
##Data##
#Static variables
#Private variables
#Global constants
#Public variables
#Constructor
def __init__(self):
"""SCPlotter constructor.
self --> reference to this instance of SCPlotter (implicitly passed)"""
pass
##Private methods##
##Public methods##
def __str__(self):
pass
def printHotRhoTemp(self, sims):
"""Print 'step | time | <rho>_base | <temp>_base' for all sims"""
import numpy as np
import matplotlib.pyplot as plt
print('step | time | <rho>_base | <temp>_base')
for sim in sims:
max_temp = 0.0
max_i = -1
max_rho = 0.0
max_ts = None
for s in sim.getSliceArray():
avgTpeak = max(s.datalist[SCSlice.ITEMP])
if avgTpeak > max_temp:
max_temp = avgTpeak
max_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak)
max_rho = s.datalist[SCSlice.IRHO][max_i][0]
max_ts = s.timestamp
print(max_ts[0], max_ts[1], max_rho, max_temp)
def plotHotRhoTemp(self, sims):
"""Print 'step | time | <rho>_base | <temp>_base' for all sims"""
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(nrows=1, ncols=1)
y = []; x = []
for sim in sims:
max_temp = 0.0
max_i = -1
max_rho = 0.0
max_ts = None
for s in sim.getSliceArray():
avgTpeak = max(s.datalist[SCSlice.ITEMP])
if avgTpeak > max_temp:
max_temp = avgTpeak
max_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak)
max_rho = s.datalist[SCSlice.IRHO][max_i][0]
max_ts = s.timestamp
y.append(max_rho)
x.append(max_temp)
ax.scatter(x,y)
def plotTempTimeSeries(self, sim):
"""Plot the core mass vs shell mass."""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import scipy.integrate as spint
#Convenience aliases for initial model data
an_cut = sim._initmod._mycut.an_cut
sp_cen_den = sim._initmod._mycut.sp_cen_den
sp_st_fac = sim._initmod._mycut.sp_st_fac
#Convenience aliases for diagnostic data
t_T, Tpeak, Tpeak_r, Tpeak_vr = sim.getDiagTemp()
#Prepare variables for use in slice loop
H = []
iface = []
rbot = []
rtop = []
t_slice = []
tconv = []
tconvb = []
tnuc_x = []
tnuc_xb = []
tnuc_wk = []
ratio = []
avgTpeak = []
avgBaseRho = []
rhoCrit = []
#Loop over slices in chronological order
for s in sorted(sim.getSliceArray(), key=lambda sl: sl.timestamp[0]):
#Build radius data from slices
rbot.append(s.derived_datalist[SCSlice.IRCONV])
rtop.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.ILCONV])
H.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.IPSCALE])
iface.append(s.derived_datalist[SCSlice.IINTFACE])
t_slice.append(s.timestamp[1])
#Estimate of convective turnover timescale and minimum nuclear timescale
tconv.append(s.derived_datalist[SCSlice.ILCONV] / s.derived_datalist[SCSlice.IUCONV])
tnuc_x.append(min(s.derived_datalist[SCSlice.ITNUC][0]))
tnuc_wk.append(min(s.derived_datalist[SCSlice.ITNUC][1]))
tnuc_xb.append(min(s.derived_datalist[SCSlice.ITNUC][2]))
#Get the peak radially averaged temperature as an estimate of the background
#conditions the hottest spot is being generated in.
avgTpeak.append(max(s.datalist[SCSlice.ITEMP]))
brho_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak[len(avgTpeak)-1])
avgBaseRho.append(s.datalist[SCSlice.IRHO][brho_i])
t8 = avgTpeak[-1:][0] / 1.e8
rctemp = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
rhoCrit.append(rctemp*1.e6)
###TEST: Calculate global convective timescale
#Calculate cutoff radii
sp_st_den = sp_cen_den*sp_st_fac
r_anelastic = None
r_sp_start = None
for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
#TODO add error checking
if not r_anelastic and rho <= an_cut:
r_anelastic = r
if r_sp_start:
break
if not r_sp_start and rho <= sp_st_den:
r_sp_start = r
if r_anelastic:
break
cbot = s.derived_datalist[SCSlice.IRCONV]
ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
magvel = s.datalist[SCSlice.IMV]
mv_rad = s.datalist[SCSlice.IRADIUS]
#Change to dimensionless variables, only care about the convective zone
li = np.where(mv_rad == cbot)[0]
ri = np.where(mv_rad == ctop)[0]
r_norm = mv_rad[li:ri]/ctop
magvel_norm = magvel[li:ri] / magvel.max()
mvn_inv = 1.0 / magvel_norm
#Calculate global convective timescale as integral of 1/v over the convective zone
#Convert back to physical units
tcg = (ctop/magvel.max())*spint.trapz(mvn_inv, r_norm)
tconvb.append(tcg)
###END TEST
ratio.append(tnuc_x[len(tnuc_x)-1]/tconvb[len(tconvb)-1])
#Build plots
fig, ax_list = plt.subplots(nrows=1, ncols=1)
fig.set_size_inches(12.0, 10.0)
#Temp
ax_list.plot(t_T, Tpeak, color='red')
ax_list.plot(t_slice, avgTpeak, color='red', marker='x', linestyle='None', label='avg peak temp')
ax_list.set_ylabel(r'T$_{\mathrm{peak}}$ (K)', color='red')
ax_list.set_title(sim.getLabel() + ' | Peak Temperature')
#ax_list[0].set_ylim(1.74e8, 4.0e8)
#ax_list[0].set_xlim(150, 155)
tw = ax_list.twinx()
#tw.set_xlim(150, 155)
tw.plot(t_T, Tpeak_r, color='green')
tw.plot(t_slice, iface, color='cyan', marker='o', linestyle='None', label='CO/He')
tw.plot(t_slice, H, color='cyan', marker='^', linestyle='None', label='H')
tw.plot(t_slice, rbot, color='cyan', marker='v', linestyle='None', label='rbot')
tw.plot(t_slice, rtop, color='cyan', marker='v', linestyle='None', label='rtop')
tw.set_ylabel(r'T$_{\mathrm{peak}}$ radius (cm)', color='green')
#handles, labels = ax_list[0].get_legend_handles_labels()
#fig.legend(handles, labels)
tw.legend(loc=2)
def plotMinMass(self, core, shell, fontsize='medium'):
"""Plot the core mass vs shell mass."""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.patches import Polygon
# Make Bildsten et al. 2007 data
bswn_core = np.array([0.8, 1.0, 1.2])
bswn_shell = np.array([0.095, 0.0425, 0.012])
bswn_shell_err = np.array([[0.07, 0.0275, 0.0075],
[0.12, 0.055, 0.018]])
vertices = [(0.8, 0.07), (1.0, 0.0275), (1.2, 0.0075),
(1.2, 0.018), (1.0, 0.055), (0.8, 0.12)]
bswn_patch = Polygon(vertices, facecolor='0.9', edgecolor='1.0', label='BSWN2007')
# Make Woosley & Kasen 2011 data
wk_core = np.array([0.8, 1.0, 1.1])
wk_shell = np.array([0.085, 0.05, 0.0375])
wk_shell_err = np.array([[0.075, 0.0375, 0.025],
[0.095, 0.062, 0.05]])
vertices = [(0.8, 0.075), (1.0, 0.0375), (1.1, 0.025),
(1.1, 0.05), (1.0, 0.062), (0.8, 0.095)]
wk_patch = Polygon(vertices, hatch='x', facecolor='1.0', edgecolor='0.0', label=r'W\&K2011')
#make the plots
fig, ax = plt.subplots(nrows=1, ncols=1)
#plot the data
ax.plot(core, shell, 'r', label='this work')
ax.add_patch(bswn_patch)
ax.add_patch(wk_patch)
#ax.errorbar(bswn_core, bswn_shell, yerr=bswn_shell_err, fmt='--k', label='BSWN2007')
#ax.errorbar(wk_core, wk_shell, yerr=wk_shell_err, fmt='-k', label='W&K2011')
#tune plot settings
ax.set_yscale('log')
ax.set_ylabel(r'Helium shell mass (M$_\odot$)', fontsize=fontsize)
ax.set_ylim(0.001, 0.2)
ax.set_xlabel(r'Core mass (M$_\odot$)', fontsize=fontsize)
ax.set_xlim(0.75, 1.35)
ax.tick_params(labelsize=fontsize)
ax.legend(loc=1)
#save fig for pub
fig.savefig("minmass.eps", bbox_inches='tight')
def plotHotspots(self, scsim, step, rlim=None, templog=False, reset=False, paper=True, plot_top=False):
"""Plot radius and temperature histograms for this timestep's top hotspots
as well as temperature contours."""
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm, colors
from mpl_toolkits_ext.basemap import Basemap#, cm
TCRIT = 2.25e7
TMAX = 2.4e9
PFONT_SIZE = 'large'
SFONT_SIZE = 'x-large'
RSCALE = 1.0e8
TSCALE = 1.0e8
RAD2DEG = 180./np.pi
CM2M = 1./100.
CM2KM = 1.0/1.e5
OCTANT_OMEGA = 90.*90. #Square degrees of an octant surface
matplotlib.rc('text', usetex=False)
print(matplotlib.__version__)
#First I need the interesting radii details.
#TODO: This is a stupid inefficient way to get them. Need to rewrite/restructure.
radii = (None, None, None)
for s in scsim._out._slices:
if s.timestamp[0] == step:
radii = (s.derived_datalist[SCSlice.IRCONV],
s.derived_datalist[SCSlice.IPSCALE],
s.derived_datalist[SCSlice.IINTFACE])
dx = scsim.getFineResolution()
print('rad, fine_dx: ')
print(radii)
print(dx)
#Find the right set of hotspots
hspots = None
for hs in scsim._out._hotspots:
if hs.timestamp[0] == step:
hspots = hs
break
if (hspots is None):
print("Couldn't find step {0:6d}".format(step))
return
#When tweaking plots it's nice if I can store the data, but then when I load new
#data I need to reset. Here I delete all data arrays so they'll be rebuilt.
if(reset and hasattr(hspots, 'r')):
del hspots.r
del hspots.theta
del hspots.phi
del hspots.temp
del hspots.logtemp
del hspots.rho6
del hspots.temp_lons
del hspots.temp_lats
#Get data, and save data so we only go through all the arrays once
if not hspots._hsarray: #If no array yet, build it
hspots._buildHSArr()
if not hasattr(hspots, 'r'):
hspots.r = np.array([hs.loc[1][0] for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(hspots, 'theta'):
hspots.theta = np.array([hs.loc[1][1] for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(hspots, 'phi'):
hspots.phi = np.array([hs.loc[1][2] for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(hspots, 'temp'):
hspots.temp = np.array([hs.temp/TSCALE for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
#hspots.temp = np.array([hs.temp for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(hspots, 'logtemp'):
hspots.logtemp = np.log10(hspots.temp)
if not hasattr(hspots, 'rho6'):
hspots.rho6 = np.array([hs.rho/1.e6 for hs in hspots._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(hspots, 'temp_lons'):
hspots.temp_lons = np.array([deg for deg in np.degrees(hspots.phi)])
if not hasattr(hspots, 'temp_lats'):
hspots.temp_lats = np.array([-(deg-90.) for deg in np.degrees(hspots.theta)])
#Local aliases for data
r = hspots.r
avg_r = np.average(r) #Average radius in cm
#theta = hspots.theta
#phi = hspots.phi
temp = hspots.temp
logtemp = hspots.logtemp
rho6 = hspots.rho6
temp_lons = hspots.temp_lons
temp_lats = hspots.temp_lats
#Critical hotspots
#ctemp = np.array([hs.temp for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#ctheta = np.array([hs.loc[1][1] for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#cphi = np.array([hs.loc[1][2] for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#ctemp_lons = np.array([deg for deg in np.degrees(cphi)])
#ctemp_lats = np.array([-(deg-90.) for deg in np.degrees(ctheta)])
crit_temp = 2.3e8
#ctemp = np.array([hs.temp for hs in self._hsarray if hs.temp > crit_temp])
#ctheta = np.array([hs.loc[1][1] for hs in self._hsarray if hs.temp > crit_temp])
#cphi = np.array([hs.loc[1][2] for hs in self._hsarray if hs.temp > crit_temp])
#ctemp_lons = np.array([deg for deg in np.degrees(cphi)])
#ctemp_lats = np.array([-(deg-90.) for deg in np.degrees(ctheta)])
#Get important radii of interest
rbot = radii[0]
H = radii[1]
iface = radii[2]
#Get min, max temp
min_temp = temp.min()
max_temp = temp.max()
hs_count = len(temp)
#Calculate temperature bins for color map
#shsarr = sorted(hspots._hsarray, key=lambda hs: hs.temp)
stemp = sorted(temp)
tlevs = [temp.min()]
tllevs = [logtemp.min()]
count = 0
for t in stemp:
count += 1
if count > (1./9.)*hs_count:
tlevs.append(t)
tllevs.append(np.log10(t))
count = 0
#For hottest temps break into top 9% and top 1%
#if len(tlevs) == 9:
# if count > 0.09*hs_count:
# tlevs.append(hs.temp)
# tllevs.append(np.log10(hs.temp))
# count = 0
#else:
# if count > 0.1*hs_count:
# tlevs.append(hs.temp)
# tllevs.append(np.log10(hs.temp))
# count = 0
else:
tlevs.append(t)
tllevs.append(np.log10(t))
#Build colormap
#TODO: Get nice paper-worthy plot for 'ignition' section
#TODO: Figure out discrepancy between pltfile cells and valid cells
# Update: currently working with OLCF on this, seems to only happen with optimized Cray compiler
#rr = np.linspace(1.0, 0.7, 11)
#gb = np.linspace(1.0, 0.0, 11)
#temp_cmap = colors.ListedColormap([
# (rr[0], gb[0], gb[0]), #Coldest
# (rr[1], gb[1], gb[1]),
# (rr[2], gb[2], gb[2]),
# (rr[3], gb[3], gb[3]),
# (rr[4], gb[4], gb[4]),
# (rr[5], gb[5], gb[5]),
# (rr[6], gb[6], gb[6]),
# (rr[7], gb[7], gb[7]),
# (rr[8], gb[8], gb[8]),
# (rr[9], gb[9], gb[9]),
# (rr[10], gb[10], gb[10])]) #Hottest
# #(1, 1, 0)]) #Hottest
# RGB colors from 9-class OrRd at colorbrewer2.org
temp_cmap = colors.ListedColormap([
(255./255., 247./255., 236./255.), #Coldest
(254./255., 232./255., 200./255.),
(253./255., 212./255., 158./255.),
(253./255., 187./255., 132./255.),
(252./255., 141./255., 89./255.),
(239./255., 101./255., 72./255.),
(215./255., 48./255., 31./255.),
(179./255., 0./255., 0./255.),
(127./255., 0./255., 0./255.)]) #Hottest
tc_bounds = tlevs
tc_norm = colors.BoundaryNorm(tc_bounds, temp_cmap.N)
tmap = cm.ScalarMappable(norm=tc_norm, cmap=temp_cmap)
tmap._A = [] # mpl >= 1.2 want ScalarMAppables to have an _A array.
#Calculate critical density range based on eqn (8) of Woosley & Kasen 2011
t8 = max_temp #/1.e8
rho_critl = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
t8 = min_temp #/1.e8
rho_critr = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
print('Min, Max temp of {0} hottest cells: {1}, {2}'.format(hs_count, min_temp, max_temp))
print('Critical density range: [{0}, {1}]'.format(rho_critl, rho_critr))
print('Scale height: {0}'.format(H))
sys.stdout.flush()
if paper:
#Build plots
print('mark A')
sys.stdout.flush()
plt.clf()
fig = plt.figure()
#subplot2grid call signature: (grid_row, grid_cols), (subplot_row, subplot_col), colspan=1, rowspan=1
ax_rad = plt.subplot2grid((3,3), (0,0))
ax_temp = plt.subplot2grid((3,3), (0,1))
ax_rho = plt.subplot2grid((3,3), (0,2))
ax_proj = plt.subplot2grid((3,3), (1,0), colspan=2, rowspan=2)
#Plot temperature histogram
ax_temp.hist(temp, bins=1000)
ax_temp.set_xlabel("temperature (K)")
#Build projection map for temperature theta, phi locations
map = Basemap(projection='nsper', lon_0=45, lat_0=45,
#llcrnrlon=-180, llcrnrlat=-90, urcrnrlon=180, urcrnrlat=90,
resolution=None, ax=ax_proj, rsphere=avg_r*CM2M)
#map.drawmeridians(np.arange(0, 90, 15), color="0.65", latmax=90)
map.drawparallels([0, 80], color="0.65", latmax=90) #, labels=[1,0,0,1])
map.drawmapscale(-15,45,45,45,H*CM2KM,labelstyle=False,
format='%6.2f', fontsize=11) #Draw scale height
#It's stupid that I have to do this, but below I "erase" extraneous latitude lines
#by writing over them with thick white lines.
#for lat in range(0,90,15):
# for long in range(15,180,15):
# #Erase extraneous latitude lines on the left
# left_long=-long
# map.drawgreatcircle(left_long+15, lat, left_long, lat, linewidth=5, color="w")
# #Erase extraneous latitude lines on the right
# right_long=long+90
# map.drawgreatcircle(right_long-15, lat, right_long, lat, linewidth=5, color="w")
##Same with extraneous longitude lines at the bottom
#map.drawgreatcircle(0, 0, 0, -25, linewidth=5, color="w")
#map.drawgreatcircle(15, 0, 15, -30, linewidth=5, color="w")
#map.drawgreatcircle(30, 0, 30, -35, linewidth=5, color="w")
#map.drawgreatcircle(45, 0, 45, -30, linewidth=5, color="w")
#map.drawgreatcircle(60, 0, 60, -35, linewidth=5, color="w")
#map.drawgreatcircle(75, 0, 75, -30, linewidth=5, color="w")
print('mark B')
sys.stdout.flush()
# draw the boundary of our domain -- we want great circles here
# note that we draw in 15 degree increments. Otherwise the lat/long grid
# doesn't line up with the boundary
#Left boundary
for lat in range(0,90,15):
map.drawgreatcircle(0, lat, 0, lat+15, linewidth=1, color="k", zorder=max_temp+10)
#Right boundary
for lat in range(0,90,15):
map.drawgreatcircle(90, lat, 90, lat+15, linewidth=1, color="k", zorder=max_temp+10)
#Bottom boundary
for lon in range(0,90,15):
map.drawgreatcircle(lon, 0, lon+15, 0, linewidth=1, color="k", zorder=max_temp+10)
print('mark C')
sys.stdout.flush()
if templog:
clevs = np.linspace(logtemp.min(), logtemp.max(), 11)
#cs = map.contourf(temp_lons, temp_lats, logtemp, clevs, latlon=True, tri=True, cmap=cm.Reds)
cs = map.contourf(temp_lons, temp_lats, logtemp, tllevs, latlon=True, tri=True, cmap=cm.jet)
else:
#clevs = np.linspace(temp.min(), temp.max(), 11)
clevs = np.linspace(2.25e8, crit_temp, 11)
#cs = map.contourf(temp_lons, temp_lats, temp, tlevs, latlon=True, tri=True, cmap=temp_cmap, norm=tc_norm)
#map.contourf(ctemp_lons, ctemp_lats, ctemp, clevs, latlon=True, tri=True, cmap=cm.Greens)
#cbar = map.colorbar(cs, location='right', pad='5%', ticks=tlevs)
#cbar.set_label('Kelvin')
temp_cols = tmap.to_rgba(temp)
deg_fine = dx/avg_r * RAD2DEG
#deg_fine = deg_fine*1.e2
print('mark Cx')
sys.stdout.flush()
tx, ty = map(temp_lons, temp_lats)
num_bins = int(np.round(90./deg_fine))
#map.hexbin(tx,ty,C=temp,gridsize=num_bins,cmap=temp_cmap,norm=tc_norm,
# reduce_C_function=np.max)
# reduce_C_function=np.max,rasterized=True)
i = 0
for tln, tlt, t, c in zip(temp_lons, temp_lats, temp, temp_cols):
i = i + 1
if i % 10 == 0:
map.tissot(tln, tlt, deg_fine, 4, zorder=t, color=c)
#map.tissot(tln, tlt, deg_fine, 4, zorder=t, color=c, rasterized=True)
#map.tissot(45,45,10,4)
cbar = map.colorbar(tmap, location='right', pad='5%', ticks=tlevs, ax=ax_proj,
format='%.3f')
cbar.set_label(r'temperature ($\times 10^8$ Kelvin)', fontsize='x-large')
#Plot radius histogram with temperature color-coding
# color-coding is achieved by plotting several bars instead of using
# ax.hist(), and we use the projection map's colorbar
# WD/He iface = radius where <X_He> = 0.9
#ax_rad.hist(r, bins=1000)
dr = dx #use a dr of dx, which is roughly the radial resolution in these simulations
#dr = 1.e5 #use a dr of 1 km, which is roughly the radial resolution in these simulations
radii, counts, cols = hspots._binData(r, temp, dr, cbar)
print('mark D')
sys.stdout.flush()
for r, c, col in zip(radii, counts, cols):
ax_rad.bar(r/RSCALE, c, width=dr/RSCALE, color=col, edgecolor=col, align='center')
#ax_rad.bar(radii, counts, width=dr, color=(1.0, 1.0, 1.0), align='center')
ax_rad.set_xlabel(r"radius ($\times 10^8$ cm)", fontsize='x-large')
if rlim:
ax_rad.set_xlim(rlim[0]/RSCALE, rlim[1]/RSCALE)
# plot the radii (CO/He interface, start of convective region, top of convective region)
ax_rad.plot([iface/RSCALE, iface/RSCALE], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
ax_rad.plot([rbot/RSCALE, rbot/RSCALE], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
if plot_top:
ax_rad.plot([(rbot + H)/RSCALE, (rbot + H)/RSCALE], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
#Annotate the interface line
ifrac = (iface/RSCALE - ax_rad.get_xlim()[0]) / (ax_rad.get_xlim()[1] - ax_rad.get_xlim()[0])
ax_rad.annotate(r'$\langle X_\mathrm{He}\rangle_r = 0.9$',
xy=(ifrac,0.9),
xytext=(-80,-30),
xycoords='axes fraction', textcoords='offset points',
fontsize = PFONT_SIZE, fontweight='bold',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
#Annotate the convective base line
cbfrac = (rbot/RSCALE - ax_rad.get_xlim()[0]) / (ax_rad.get_xlim()[1] - ax_rad.get_xlim()[0])
ax_rad.annotate('Convective\nbase',
xy=(cbfrac,0.8),
xytext=(30,-30),
xycoords='axes fraction', textcoords='offset points',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
#Annotate the pressure scale height
ax_proj.annotate(r'Scale Height [km]'.format(H*CM2KM),
xy=(0.06,0.675),
xytext=(0,0),
xycoords='axes fraction', textcoords='offset points')
print('mark E')
sys.stdout.flush()
#Plot density histogram with color-coding
drho6 = 0.001
rho6_bins, rho_counts, rho_colors = hspots._binData(rho6, temp, drho6, cbar)
for r6, c, col in zip(rho6_bins, rho_counts, rho_colors):
ax_rho.bar(r6, c, width=drho6, color=col, edgecolor=col, align='center')
#ax_rho.hist(rho6, bins=1000)
ax_rho.set_xlabel(r"density ($\times 10^6$ g cm$^{-3}$)", fontsize='x-large')
#ax_rho.fill_between([rho_critl, rho_critr], 0, 500, facecolor='0.9', edgecolor='1.0')
ax_rho.plot([rho_critr, rho_critr], [0, ax_rho.get_ylim()[1]], color="k", linestyle='--')
#Annotate the critical density line
rhofrac = (rho_critr - ax_rho.get_xlim()[0]) / (ax_rho.get_xlim()[1] - ax_rho.get_xlim()[0])
ax_rho.annotate(r'$\rho_{\mathrm{cr},\mathrm{WK}}$',
xy=(rhofrac,0.8), size=12.5,
xytext=(30,-30),
xycoords='axes fraction', textcoords='offset points',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
print('mark F')
sys.stdout.flush()
#Set plot properties
fig.set_size_inches(15.0, 12.5)
#This fixes a problem with mpl's pstoeps converter when using ghostscript as distiller
#matplotlib.rc('ps', usedistiller='xpdf')
#fig.savefig("test_ig.png", bbox_inches='tight')
#fig.savefig("test_ig.pdf")
print('mark G')
sys.stdout.flush()
else:
#TODO: Need to implement non-inline plotting
pass
def plotTHist(self, scsim, step):
"""Plot temperature histograms from the given timestep and simulation."""
import matplotlib
#matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
#Get the data
thd = scsim.getTHistData(step)
ells, temps, counts = thd.ells, thd.temps, thd.counts
TMax = thd.TMax
#make the plots
fig, ax = plt.subplots(nrows=len(ells), ncols=2)
#Plot first lengthscale
for i in range(len(ells)):
#Data for cell's stored temperature
ax[i][0].bar(temps[i], counts[0][i], width=1.0e5)
ax[i][0].set_title('l={0}'.format(ells[i]))
text_bbox = dict(boxstyle="round", fc="white")
ax[i][0].text(0.3, 0.8, r'$T_\mathrm{{max}} = {0}$'.format(TMax[i]), ha='center', va='center', transform=ax[i][0].transAxes,
bbox=text_bbox, size=15)
#Data for cell's temperature derived from rho, h, X
ax[i][1].bar(temps[i], counts[1][i], width=1.0e5)
ax[i][1].set_title('l={0}'.format(ells[i]))
text_bbox = dict(boxstyle="round", fc="white")
ax[i][1].text(0.3, 0.8, r'$T_\mathrm{{max}} = {0}$'.format(TMax[i]), ha='center', va='center', transform=ax[i][1].transAxes,
bbox=text_bbox, size=15)
#ax[1].bar(temps[1], counts[1], width=1.0e5)
#ax[1].set_title('l={0}'.format(ells[1]))
#ax[2].bar(temps[2], counts[2], width=1.0e5)
#ax[2].set_title('l={0}'.format(ells[2]))
#ax[3].bar(temps[3], counts[3], width=1.0e5)
#ax[3].set_title('l={0}'.format(ells[3]))
fig.set_size_inches(10.0, 15.0)
def plotInitModel(self, sim, writefile=False, fontsize='x-large'):
"""Plots this initial model using matplotlib."""
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
#NOTE: fontsize can be [size in points | 'xx-small' | 'x-small' |
# 'small' | 'medium' | 'large' | 'x-large' | 'xx-large'
#This function is just an alias for the following call
self.plotSliceOverview(sim, None, writefile, fontsize)
def plotSliceOverview(self, sim, time, writefile=False, fontsize='x-large'):
"""Plot overview of the available slice data nearest to time. Set time to None
to plot the initial model data."""
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
#NOTE: fontsize can be [size in points | 'xx-small' | 'x-small' |
# 'small' | 'medium' | 'large' | 'x-large' | 'xx-large'
# make the plots
fig, ax_list = plt.subplots(nrows=3, ncols=1)
#Find the right slice, if requested
slicedata = None
if time is not None:
for s in sorted(sim._out._slices, key=lambda sl: sl.timestamp[0]):
if s.timestamp[1] > time:
slicedata = s
break
#Plot the three subplots
self._plotRhoT(ax_list[0], sim, slicedata, fontsize)
self._plotX( ax_list[1], sim, slicedata, fontsize)
self._plotS( ax_list[2], sim, slicedata, fontsize)
#Adjust figure settings
fig.set_size_inches(6.0,12.0)
fig.tight_layout()
#If requested, save a figure
if writefile:
if time is None:
fig.savefig(sim._label + "_initial_model.eps", bbox_inches='tight')
else:
fig.savefig(sim._label + "_slice_overview.eps", bbox_inches='tight')
#Write header for interactive viewing (not for the file)
if time is None:
fig.suptitle(sim._label + ' Initial Model', fontsize=18, y=1.00)
else:
fig.suptitle(sim._label + ' Slice', fontsize=18, y=1.00)
def displayParameters(self, sim):
"""Use IPython's display framework to write out a table of key parameters"""
from glob import glob
from IPython.display import HTML, display
from os.path import dirname
#Assume <run label>/[output | run | plots] naming scheme
#Write opening
htmlstr = r'<table border="1">' + "\n"
#I'm repurposing functions that originally needed a list, so put the
#label in a list
#TODO: Need to rework this so I'm not using single item lists
labellist = [sim.getLabel(),]
stage_dir = dirname(sim.getStageDir())
#Add each row
htmlstr += self._labelRow(labellist)
htmlstr += self._massRow(labellist, stage_dir)
htmlstr += self._paramRow(labellist, stage_dir, r'T$_{\mathrm{CO}}$ [K]', 'temp_core')
htmlstr += self._paramRow(labellist, stage_dir, r'T$_{\mathrm{base}}$ [K]', 'temp_base')
htmlstr += self._rhobaseRow(labellist, stage_dir, r'$\rho_{\mathrm{base}}$ [$\times 10^5$ g cm$^{-3}$]')
htmlstr += self._paramRow(labellist, stage_dir, r'x$_{\mathrm{max}}$ [cm]', 'xmax')
htmlstr += self._inputsRow(labellist, stage_dir, r'anelastic_cutoff [g cm$^{-3}$]', 'anelastic_cutoff')
htmlstr += self._inputsRow(labellist, stage_dir, r'base_cutoff_density [g cm$^{-3}$]', 'base_cutoff_density')
htmlstr += self._inputsRow(labellist, stage_dir, r'species_pred_type', 'species_pred_type')
htmlstr += self._inputsRow(labellist, stage_dir, r'octant', 'octant')
htmlstr += self._inputsRow(labellist, stage_dir, r'Levs', 'max_levs')
htmlstr += self._dxRow(labellist, stage_dir, r'$\Delta x_\mathrm{fine}$ [km]')
#Write closing
htmlstr += r'</table>'
display(HTML(htmlstr))
def plotTimeSeries(self, sim): #an_cut, sp_cen_den, sp_st_fac):
"""Plot data of interest vs time for this simulation's output."""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import scipy.integrate as spint
matplotlib.rc('text', usetex=False)
#Convenience aliases for diagnostic data
t_T, Tpeak, Tpeak_r, Tpeak_vr = sim.getDiagTemp()
t_M, Mpeak = sim.getDiagMach()
t_e, epeak = sim.getDiagEnuc()
#Prepare variables for use in slice loop
H = []
iface = []
rbot = []
rtop = []
t_slice = []
tconv = []
tconvb = []
tnuc_x = []
tnuc_xb = []
tnuc_wk = []
ratio = []
avgTpeak = []
avgBaseRho = []
rhoCrit = []
#Get some data
slices = sim.getSliceArray()
cuts = sim.getIMCuts()
an_cut = cuts.an_cut
sp_cen_den = cuts.sp_cen_den
sp_st_fac = cuts.sp_st_fac
#Loop over slices in chronological order
for s in sorted(slices, key=lambda sl: sl.timestamp[0]):
#Build radius data from slices
rbot.append(s.derived_datalist[SCSlice.IRCONV])
rtop.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.ILCONV])
H.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.IPSCALE])
iface.append(s.derived_datalist[SCSlice.IINTFACE])
t_slice.append(s.timestamp[1])
#Estimate of convective turnover timescale and minimum nuclear timescale
#tconv.append(s.derived_datalist[SCSlice.IPSCALE] / s.derived_datalist[SCSlice.IUCONV])
tconv.append(s.derived_datalist[SCSlice.ILCONV] / s.derived_datalist[SCSlice.IUCONV])
#tconv.append(s.derived_datalist[SCSlice.IUCONV])
tnuc_x.append(min(s.derived_datalist[SCSlice.ITNUC][0]))
tnuc_wk.append(min(s.derived_datalist[SCSlice.ITNUC][1]))
tnuc_xb.append(min(s.derived_datalist[SCSlice.ITNUC][2]))
#ratio.append(tnuc_x[len(tconv)-1]/tconv[len(tconv)-1])
#ratio.append(tnuc_wk[len(tconv)-1]/tconv[len(tconv)-1])
#Get the peak radially averaged temperature as an estimate of the background
#conditions the hottest spot is being generated in.
avgTpeak.append(max(s.datalist[SCSlice.ITEMP]))
brho_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak[len(avgTpeak)-1])
avgBaseRho.append(s.datalist[SCSlice.IRHO][brho_i])
t8 = avgTpeak[-1:][0] / 1.e8
rctemp = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
rhoCrit.append(rctemp*1.e6)
###TEST: Calculate global convective timescale
#Calculate cutoff radii
sp_st_den = sp_cen_den*sp_st_fac
r_anelastic = None
r_sp_start = None
for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
#TODO add error checking
if not r_anelastic and rho <= an_cut:
r_anelastic = r
if r_sp_start:
break
if not r_sp_start and rho <= sp_st_den:
r_sp_start = r
if r_anelastic:
break
cbot = s.derived_datalist[SCSlice.IRCONV]
ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
magvel = s.datalist[SCSlice.IMV]
mv_rad = s.datalist[SCSlice.IRADIUS]
#Change to dimensionless variables, only care about the convective zone
li = np.where(mv_rad == cbot)[0]
ri = np.where(mv_rad == ctop)[0]
r_norm = mv_rad[li:ri]/ctop
magvel_norm = magvel[li:ri] / magvel.max()
mvn_inv = 1.0 / magvel_norm
#Calculate global convective timescale as integral of 1/v over the convective zone
#Convert back to physical units
tcg = (ctop/magvel.max())*spint.trapz(mvn_inv, r_norm)
tconvb.append(tcg)
###END TEST
ratio.append(tnuc_x[len(tnuc_x)-1]/tconvb[len(tconvb)-1])
if 'inline' in matplotlib.get_backend():
#Build plots
fig, ax_list = plt.subplots(nrows=5, ncols=1)
#Temp
ax_list[0].plot(t_T, Tpeak, color='red')
ax_list[0].plot(t_slice, avgTpeak, color='red', marker='x', linestyle='None', label='avg peak temp')
ax_list[0].set_ylabel(r'T$_{\mathrm{peak}}$ (K)', color='red')
ax_list[0].set_title(sim.getLabel() + ' | Peak Temperature')
#ax_list[0].set_ylim(1.74e8, 4.0e8)
#ax_list[0].set_xlim(150, 155)
tw = ax_list[0].twinx()
#tw.set_xlim(150, 155)
#tw.set_ylim(4.26e8, 4.38e8)
tw.plot(t_T, Tpeak_r, color='green')
tw.plot(t_slice, iface, color='cyan', marker='o', linestyle='None', label='CO/He')
tw.plot(t_slice, H, color='cyan', marker='^', linestyle='None', label='H')
tw.plot(t_slice, rbot, color='cyan', marker='v', linestyle='None', label='rbot')
tw.plot(t_slice, rtop, color='cyan', marker='v', linestyle='None', label='rtop')
tw.set_ylabel(r'T$_{\mathrm{peak}}$ radius (cm)', color='green')
#handles, labels = ax_list[0].get_legend_handles_labels()
#fig.legend(handles, labels)
tw.legend(loc=2)
#Mach
ax_list[1].plot(t_M, Mpeak, color='blue')
ax_list[1].set_title(sim.getLabel() + ' | Peak Mach #')
#ax_list[1].set_xlim(0, 70)
#ax_list[1].set_ylim(0, .045)
#enuc
ax_list[2].plot(t_e, epeak, color='blue')
ax_list[2].set_title(sim.getLabel() + ' | Peak H_nuc')
#ax_list[2].set_xlim(0, 70)
#ax_list[2].set_ylim(0.5e13, 4.e13)
#Timescales
ax_list[3].set_yscale('log')
#11050-107-175-3lev
#ax_list[3].set_ylim(1.e3, 1.e4)
#ax_list[3].set_xlim(145.0, 155.0)
#12025-107-175-3lev
#ax_list[3].set_ylim(1.e3, 1.e4)
#ax_list[3].set_xlim(200.0, 225.0)
#12050-107-165-3lev
#ax_list[3].set_ylim(1.e3, 1.e4)
#ax_list[3].set_xlim(60.0, 70.0)
ax_list[3].plot(t_slice, tconv, color='blue', label=r'$\tau_{conv}$')
ax_list[3].plot(t_slice, tconvb, color='blue', linestyle='--', label=r'$\tau_{conv,b}$')
ax_list[3].set_ylabel(r'$\tau_{conv}$ (s)', color='blue')
#ax_list[3].plot(t_slice, tnuc, color='green', label=r'$\tau_{nuc}$')
ax_list[3].set_title(sim.getLabel() + ' | Timescales')
ax_list[3].set_xlabel(r'time [s]')
ax_list[3].grid(which='both')
tw2 = ax_list[3].twinx()
tw2.plot(t_slice, ratio, color='cyan', label=r'$\tau_{nuc,x}/\tau_{conv}$')
tw2.set_ylabel(r'$\tau_{nuc,x}/\tau_{conv,b}$ ratio', color='cyan')
#tw2.set_ylim(200.0, 350.0)
#tw2.set_xlim(200.0, 225.0)
#tw2.set_xlim(60.0, 70.0)
#tw2.set_xlim(145.0, 155.0)
ax_list[3].plot(t_slice, tnuc_x, color='green', label=r'$\tau_{nuc,x}$')
ax_list[3].plot(t_slice, tnuc_xb, color='green', label=r'$\tau_{nuc,xb}$')
ax_list[3].plot(t_slice, tnuc_wk, color='green', linestyle='--', label=r'$\tau_{nuc,wk}$')
ax_list[3].set_ylabel(r'$\tau_{nuc}$ (s)', color='green')
ax_list[3].legend()
#Plot base density (density at radius of peak temp)
ax_list[4].plot(t_slice, avgBaseRho)
ax_list[4].plot(t_slice, rhoCrit, 'b--')
#ax_list[4].set_yscale('log')
#ax_list[4].set_ylim(9.e5, 1.5e6)
ax_list[4].set_ylabel(r'$\rho$ [g cm$^{-3}$]')
ax_list[4].set_title(sim.getLabel() + ' | Base Density')
#Set plot properties
fig.set_size_inches(10.0, 40.0)
#fig.tight_layout()
#fig.savefig("convt.png", bbox_inches='tight')
else:
#TODO: Need to implement non-inline plotting
pass
def plotEntropyDetail(self, sim, time, r_lims=(3.65e8, 4.25e8), ent_lims=(1.e8, 1.e9), ds_lims=(-50000.0, 50000.0)):
"""Plot entropy with ability to focus in on details"""
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
#NOTE: fontsize can be [size in points | 'xx-small' | 'x-small' |
# 'small' | 'medium' | 'large' | 'x-large' | 'xx-large'
fontsize = 'x-large'
# make the plots
fig, ax = plt.subplots(nrows=1, ncols=1)
#Find the right slice, if requested
slicedata = None
if time is not None:
for s in sorted(sim._out._slices, key=lambda sl: sl.timestamp[0]):
if s.timestamp[1] > time:
slicedata = s
break
#Plot the entropy subplot
self._plotSDetail(ax, sim, slicedata, fontsize, r_lims, ent_lims, ds_lims)
#Adjust figure settings
fig.set_size_inches(8.0,6.0)
fig.tight_layout()
#Write header for interactive viewing
fig.suptitle(sim._label + ' Slice', fontsize=18, y=1.00)
##Private methods##
def _plotRhoT(self, sp, sim, slicedata, fontsize):
"""Plot a density,temperature vs. radius subplot sp. If slicedata is given, use it,
if None use initial model data."""
import pylab
import numpy as np
# grab data
if slicedata is None:
model_data = sim.getIMDict()
r = model_data['radius']
rho = model_data['density']
temp = model_data['temperature']
else:
r = slicedata.datalist[SCSlice.IRADIUS]
rho = slicedata.datalist[SCSlice.IRHO]
temp = slicedata.datalist[SCSlice.ITEMP]
# plot density
sp.plot(r, rho, color="blue")
# plot the sponge start, anelastic cutoff, and base cutoff density
cuts = sim.getIMCuts()
lims = sim.getIMLims()
sp.plot([cuts.r_an, cuts.r_an ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
sp.plot([cuts.r_sp, cuts.r_sp ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
sp.plot([cuts.r_bc, cuts.r_bc ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
# shade convection zone
conv_bounds = sim.getIMConvBounds()
lidx = np.where(r >= conv_bounds[0])[0][0]
ridx = np.where(r <= conv_bounds[1])[0][-1]
r_conv = r[lidx:ridx]
sp.fill_between(r_conv, lims.dlims[0], 10.0*lims.dlims[1], facecolor='0.9', edgecolor='1.0')
# set plot properties
# NOTE: the offset text is the 1.e8 that appears under the axis labels when
# doing scientific notation
sp.set_ylabel(r"density (g cm$^{-3}$)", color="blue", fontsize=fontsize)
sp.set_yscale('log')
sp.set_xlim(lims.rlims[0], lims.rlims[1])
sp.set_ylim(lims.dlims[0], lims.dlims[1])
sp.tick_params(labelbottom='off', labelsize=fontsize)
sp.xaxis.offsetText.set_visible(False)
# plot temperature on a twin axis
sp2 = sp.twinx()
sp2.plot(r, temp, color="red")
# set plot properties
sp2.yaxis.tick_right()
sp2.set_yscale('log')
sp2.axis(labelcolor="red")
sp2.tick_params(labelsize=fontsize)
sp2.set_ylabel(r"temperature (K)", color="red", fontsize=fontsize)
sp2.set_xlim(lims.rlims[0], lims.rlims[1])
sp2.set_ylim(lims.Tlims[0], lims.Tlims[1])
# plot zoomed inset
fig = sp.get_figure()
axins = fig.add_axes([0.2, 0.75, 0.30, 0.10])
axins.plot(r, rho, color="blue")
axins_twin = axins.twinx()
axins_twin.plot(r, temp, color="red")
axins.plot([cuts.r_an, cuts.r_an ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
axins.plot([cuts.r_sp, cuts.r_sp ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
axins.plot([cuts.r_bc, cuts.r_bc ], [lims.dlims[0], 10.0*lims.dlims[1]], color="k", linestyle='--')
# set inset properties
axins.set_yscale('log')
axins_twin.set_yscale('log')
axins.set_xlim(lims.zbounds[0][0], lims.zbounds[0][1])
axins.set_ylim(lims.zbounds[1][0], lims.zbounds[1][1])
axins.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
axins.set_yticklabels([' '], visible=False)
axins_twin.set_ylim(lims.zbounds[2][0], lims.zbounds[2][1])
axins_twin.set_yticklabels([' '], visible=False)
return
def _plotX(self, sp, sim, slicedata, fontsize):
"""Plot a mass fraction vs. radius subplot sp. If slicedata is given, use it,
if None use initial model data."""
import pylab
import numpy as np
# grab data
if slicedata is None:
model_data = sim.getIMDict()
r = model_data['radius']
xhe = model_data['species']['helium-4']
xc = model_data['species']['carbon-12']
else:
r = slicedata.datalist[SCSlice.IRADIUS]
xhe = slicedata.datalist[SCSlice.ISPEC]['X(He4)'][0]
xc = slicedata.datalist[SCSlice.ISPEC]['X(C12)'][0]
# Plot mass fractions and a legend
sp.plot(r, xhe, color="red", label=r"$^{4}\mathrm{He}$")
sp.plot(r, xc, color="blue", label=r"$^{12}\mathrm{C}$")
sp.legend(loc=2, fontsize=fontsize)
# shade convection zone
conv_bounds = sim.getIMConvBounds()
lidx = np.where(r >= conv_bounds[0])[0][0]
ridx = np.where(r <= conv_bounds[1])[0][-1]
r_conv = r[lidx:ridx]
sp.fill_between(r_conv, 0, 1.05, facecolor='0.9', edgecolor='1.0')
# draw in the sponge star, anelastic cutoff, and base cutoff density
cuts = sim.getIMCuts()
lims = sim.getIMLims()
sp.plot([cuts.r_an, cuts.r_an], [0, 1.05], color="k", linestyle='--')
sp.plot([cuts.r_sp, cuts.r_sp], [0, 1.05], color="k", linestyle='--')
sp.plot([cuts.r_bc, cuts.r_bc], [0, 1.05], color="k", linestyle='--')
# set plot properties
sp.set_ylabel("mass fraction", fontsize=fontsize)
sp.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
sp.tick_params(labelbottom='off', labelsize=fontsize)
sp.xaxis.offsetText.set_visible(False)
sp.set_xlim(lims.rlims[0], lims.rlims[1])
sp.set_ylim(0.0,1.05)
def _plotCs(self, sp, sim, slicedata, fontsize):
"""Plot a soundspeed vs. radius subplot sp. If slicedata is given, use it,
if None use initial model data."""
# grab data
if slicedata is None:
# Make sure we have soundspeed data
model_data = sim.getIMDict()
if not 'cs' in model_data.keys():
raise ValueError("No soundspeed data loaded! Can't plot.")
r = model_data['radius']
cs = model_data['cs']
else:
raise NotImplementedError("Plotting soundspeed from slicedata not implemented.")
sp.set_yscale('log')
sp.plot(r, cs, color="red")
# draw in the sponge start, anelastic cutoff, and base cutoff density
cuts = sim.getIMCuts()
lims = sim.getIMLims()
sp.plot([cuts.r_an, cuts.r_an], [lims.clims[0], lims.clims[1]], color="0.65", linestyle='--')
sp.plot([cuts.r_sp, cuts.r_sp], [lims.clims[0], lims.clims[1]], color="0.65", linestyle='--')
sp.plot([cuts.r_bc, cuts.r_bc], [lims.clims[0], lims.clims[1]], color="0.65", linestyle='--')
sp.set_xlabel("radius (cm)", fontsize=fontsize)
sp.set_ylabel("sound speed (cm/s)", fontsize=fontsize)
sp.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
sp.tick_params(labelsize=fontsize)
sp.xaxis.offsetText.set_size(fontsize)
#sp.yaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
sp.set_xlim(lims.rlims[0], lims.rlims[1])
sp.set_ylim(lims.clims[0], lims.clims[1])
def _plotS(self, sp, sim, slicedata, fontsize):
"""Plot entropy vs. radius subplot sp. If slicedata is given, use it,
if None use initial model data."""
import pylab
import numpy as np
# grab data
if slicedata is None:
# Make sure we have entropy data
model_data = sim.getIMDict()
if not 'entropy' in model_data.keys():
raise ValueError("No entropy data loaded! Can't plot.")
r = model_data['radius']
S = model_data['entropy']
else:
r = slicedata.datalist[SCSlice.IRADIUS]
S = slicedata.datalist[SCSlice.IS]
#Plot entropy
sp.plot(r, S, color="red")
# draw in the sponge start, anelastic cutoff, and base cutoff density
cuts = sim.getIMCuts()
lims = sim.getIMLims()
sp.plot([cuts.r_sp, cuts.r_sp], [lims.slims[0], lims.slims[1]], color="k", linestyle='--')
sp.plot([cuts.r_an, cuts.r_an], [lims.slims[0], lims.slims[1]], color="k", linestyle='--')
sp.plot([cuts.r_bc, cuts.r_bc], [lims.slims[0], lims.slims[1]], color="k", linestyle='--')
# shade convection zone
conv_bounds = sim.getIMConvBounds()
lidx = np.where(r >= conv_bounds[0])[0][0]
ridx = np.where(r <= conv_bounds[1])[0][-1]
r_conv = r[lidx:ridx]
sp.fill_between(r_conv, lims.slims[0], lims.slims[1], facecolor='0.9', edgecolor='1.0')
# set plot properties
sp.set_yscale('log')
sp.set_xlabel("radius (cm)", fontsize=fontsize)
sp.set_ylabel(r"specific entropy (erg g$^{-1}$ K$^{-1}$)", fontsize=fontsize)
sp.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
sp.tick_params(labelsize=fontsize)
sp.xaxis.offsetText.set_size(fontsize)
sp.set_xlim(lims.rlims[0], lims.rlims[1])
sp.set_ylim(lims.slims[0], lims.slims[1])
def _plotSDetail(self, sp, sim, slicedata, fontsize, r_lims, ent_lims, ds_lims):
"""Plot entropy vs. radius"""
import pylab
import numpy as np
# grab data
r = slicedata.datalist[SCSlice.IRADIUS]
S = slicedata.datalist[SCSlice.IS]
P = slicedata.datalist[SCSlice.IP]
S_SMOOTH = [(S[i+1] + S[i] + S[i-1])/3. for i in range(1,len(S)-1)]
S_SMOOTH.insert(0,S_SMOOTH[0])
S_SMOOTH.append(S_SMOOTH[-1])
DS = [S_SMOOTH[i] - S_SMOOTH[i-1] for i in range(1,len(S_SMOOTH))]
DS.insert(0,DS[0])
#Plot entropy
#sp.plot(r, S, 'rx') #color="red")
sp.plot(r, P, 'rx') #color="red")
#s_lo = ent_lims[0] #2.930e8
#s_hi = ent_lims[1] #2.940e8
#sp.set_xlim(r_lims[0], r_lims[1])
#sp.set_ylim(s_lo, s_hi)
#ent_level = slicedata.derived_datalist[SCSlice.IRCONV]
#sp.plot([ent_level, ent_level], [s_lo, s_hi], color="k", linestyle='--')
#On same axis, plot ds
#tw = sp.twinx()
#tw.plot(r, DS, 'bx')#color="blue")
#tw.set_ylim(ds_lims[0], ds_lims[1])
#tw.set_xlim(r_lims[0], r_lims[1])
# set plot properties
#sp.set_yscale('log')
sp.set_xlabel("radius (cm)", fontsize=fontsize)
sp.set_ylabel(r"specific entropy (erg g$^{-1}$ K$^{-1}$)", fontsize=fontsize)
sp.xaxis.set_major_formatter(pylab.ScalarFormatter(useMathText=True))
sp.tick_params(labelsize=fontsize)
sp.xaxis.offsetText.set_size(fontsize)
def _labelRow(self, dirlist):
"""Return string corresponding to html table row describing labels of dirs in dirlist."""
ret = r' <tr>' + "\n"
ret += r' <th>Model Label</th>' + "\n"
for curdir in dirlist:
ret += r' <th>' + curdir + r'</th>' + "\n"
ret += r' </tr>' + "\n"
return ret
def _massRow(self, dirlist, stg_dir):
"""Return string corresponding to html table row describing mass configuration of runs in dirlist."""
from glob import glob
ret = r' <tr>' + "\n"
ret += r' <th>(Core, Shell) Mass [M$_\odot$]</th>' + "\n"
for curdir in dirlist:
pfile = glob(stg_dir + '/' + curdir + '/run/_params.*') #Grab first parameter file found, should only be one
m_core = get_param('M_tot', pfile[0])
m_shell = get_param('M_He', pfile[0])
mstr = '(' + str(m_core) + ', ' + str(m_shell) + ')'
ret += r' <td>' + mstr + r'</td>' + "\n"
ret += r' </tr>' + "\n"
return ret
def _paramRow(self, dirlist, stg_dir, label, param):
"""Return string corresponding to html table row with the given label and parameter for all runs in dirlist."""
from glob import glob
ret = r' <tr>' + "\n"
ret += r' <th>' + label + r'</th>' + "\n"
for curdir in dirlist:
pfile = glob(stg_dir + '/' + curdir + '/run/_params.*') #Grab first parameter file found, should only be one
pstr = get_param(param, pfile[0])
ret += r' <td>' + pstr + r'</td>' + "\n"
ret += r' </tr>' + "\n"
return ret
def _inputsRow(self, dirlist, stg_dir, label, param):
"""Return string corresponding to html table row with the given label and input parameter for all runs in dirlist."""
from glob import glob
ret = r' <tr>' + "\n"
ret += r' <th>' + label + r'</th>' + "\n"
for curdir in dirlist:
pfile = glob(stg_dir + '/' + curdir + '/run/inputs*') #Grab first inputs file found, should only be one
pstr = get_param(param, pfile[0])
ret += r' <td>' + pstr + r'</td>' + "\n"
ret += r' </tr>' + "\n"
return ret
def _dxRow(self, dirlist, stg_dir, label):
"""Return string corresponding to html table row with the given label and the fine dx resolution for all runs in dirlist."""
from glob import glob
ret = r' <tr>' + "\n"
ret += r' <th>' + label + r'</th>' + "\n"
for curdir in dirlist:
pfile = glob(stg_dir + '/' + curdir + '/run/inputs*') #Grab first inputs file found, should only be one
# get max_levs, n_cellx, and xmax
lev = int(get_param('max_levs', pfile[0]))
nx = int(get_param('n_cellx', pfile[0]))
xmax = float(get_param('prob_hi_x', pfile[0]).replace('d','e'))
dxf = xmax/float(nx*2**(lev-1))
dxf = dxf / 1.e5 #Convert to km
ret += r' <td>' + str(dxf) + r'</td>' + "\n"
ret += r' </tr>' + "\n"
return ret
def _rhobaseRow(self, dirlist, stg_dir, label):
"""Return string corresponding to html table row with the given label and
the density at the base of the convective layer (where T peaks)."""
from glob import glob
import numpy as np
ret = r' <tr>' + "\n"
ret += r' <th>' + label + r'</th>' + "\n"
for curdir in dirlist:
imfile = glob(stg_dir + '/' + curdir + '/run/sub_chandra*hse*') #Grab first hse initial model file, should only be one
# find peak T, store corresponding density
rho, T = np.loadtxt(imfile[0], usecols=(1, 2), unpack=True)
idx = T.argmax()
rbase = rho[idx]
rbase = rbase / 1.e5
ret += r' <td>' + str(rbase) + r'</td>' + "\n"
ret += r' </tr>' + "\n"
return ret
class SCSlice(object):
"""A class representing a 1D radial slice of angle-averaged data from a
sub-Chandra run's plotfile."""
##Class/pseudo-static data, constructor##
#Indices for the datalist
#TODO:I don't like explicitly manipulating so many quantities, but not sure what a better alternative is
IRADIUS = 0
IRHO = 1 #Density
IRHO_RMS = 2
ITEMP = 3 #Temperature
ITEMP_RMS = 4
IP = 5 #Pressure
IP_RMS = 6
IMV = 7 #Magnitude of Velocity vector U
IHNUC = 8 #\sum_k{q_k * omegadot_k}
IS = 9 #Entropy
IS_RMS = 10
ISPEC = 11 #Species, mass fractions and their time derivatives
#Indices for derived_datalist
IINTFACE = 0 #CO/He Interface radius (radius where X_He = 0.9)
IRCONV = 1 #Radius of the bottom of the convective region
ILCONV = 2 #Lengthscale of the convective region. I define the lengthscale
#as the region in which ds/dr is numerically <= 0 (s is specific entropy)
IUCONV = 3 #Average velocity magnitude in the convective region
IPSCALE = 4 #Length of a pressure scale height. From the radius of
#T_peak to the radius where the pressure at that point has
#fallen by 1/e
#TODO: Compare this to common equations used to determine H
ITNUC = 5 #Tuple of radial arrays of different nuclear timescales
# (X/dXdt, WK 2011 eqn 3)
#Size of the non-species part of datalist
_NS_SIZE = 11
#Constructor
def __init__(self, slicefile, parent):
"""self --> implicitly passed reference to this instance of SCSlice
slicefile --> a .slice file generated by fsubchandra.f90
parent --> reference to this slice's parent SCOutput"""
from os.path import basename
(self.timestamp, self.nspec, self.glbs,
self.datalist) = self._parseSlicefile(slicefile)
self.derived_datalist = self._deriveData()
self.parent = parent
#ASSUMPTION: Slicefiles are of form '<pltfile name>.slice'
self.my_pltfile = basename(slicefile[:-6])
##Public methods##
##Private methods##
def _deriveData(self):
"""Derive interesting data from slice data:
pressure scale height, interface radius, convection radii, enuc timescales."""
import numpy as np
#The DS critical values are based on analyzing models at different extremes and
#are found to accurately demark the convective zone where ds/dr <= 0.
DS_TRIGGER = 1.e6 #This triggers the search for the convective zone,
#it must be after this point
DS_THRESH = 2.0e4 #When DS falls below this, we're in the convectively unstable zone,
#when it rises back above it we're out of the convective zone
ENT_TOL = 2.0e5 #Tolerance for distance from entropy level,
#beyond which we consider to not be convective
ENT_TOL = 0.001
TNUC_MAX = 5.e15
retlist = [None, None, None, None, None, None]
tpeak = self.datalist[SCSlice.ITEMP].max()
ppeak = None
l1 = None
l2 = None
vavg = 0.0
vsum = 0.0
vcnt = 0
#tnuc = (tnuc based on X/dXdt, tnuc based on W&K 2011 eqn 3)
tnuc = (np.zeros(len(self.datalist[SCSlice.IRADIUS])), np.zeros(len(self.datalist[SCSlice.IRADIUS])), np.zeros(len(self.datalist[SCSlice.IRADIUS])))
#These are the specific binding energies of [He4, C12, O16] from
#AstroDev/networks/triple_alpha_plus_cago/network.f90
#in units of erg/g
q = [6.8253797e18, 7.4103097e18, 7.6959581e18]
q_tot = sum(q)
#E_CRIT = 1.7e-7 # erg
#TEST_VOL = 4./3.*np.pi*(1e7)**3 # cm^3
trigger = False
trigger_count = 0
min_R = 2.0e8
ent_level = 0.0
ent_count = 0
calc_ent_level = True
for i in range(len(self.datalist[SCSlice.IRADIUS])):
#Calculate estimate of interface radius based on when the helium mass fraction hits 0.9
if not retlist[SCSlice.IINTFACE] and self.datalist[SCSlice.ISPEC]['X(He4)'][0][i] > 0.9:
retlist[SCSlice.IINTFACE] = self.datalist[SCSlice.IRADIUS][i]
#Calculate the entropy level. This is the average value of the specific entropy
#in the region where its radial profile flattens out, which is one criteria for
#determining the convective region
if calc_ent_level and i > 2 and i < len(self.datalist[SCSlice.IRADIUS]):
#Calculate smoothed entropy difference, ds
s_smooth1 = (self.datalist[SCSlice.IS][i-1] + self.datalist[SCSlice.IS][i] + self.datalist[SCSlice.IS][i+1])/3.0
s_smooth2 = (self.datalist[SCSlice.IS][i] + self.datalist[SCSlice.IS][i+1] + self.datalist[SCSlice.IS][i+2])/3.0
ds = s_smooth2 - s_smooth1
#Trigger the search for the entropy level
if ds > DS_TRIGGER and self.datalist[SCSlice.IRADIUS][i] > min_R:
trigger_count += 1
if trigger_count > 3:
#To avoid a false trigger due to noise,
#make sure we've been above the trigger consistently for the
#last few elements in the array. If yes, then trigger away.
trigger = True
else:
trigger_count = 0
#If the entropy trigger was set off and we're below the threshold
#we've found the bottom of the convective envelope
if trigger and ds < DS_THRESH:
ent_level += self.datalist[SCSlice.IS][i]
ent_count += 1
#We're outside of the convecting region, stop calculation of ent_level
if trigger and ent_level > 0.0 and ds >= DS_THRESH:
calc_ent_level = False
#Calculate nuclear timescale with limits on how large it can get
for species in self.datalist[SCSlice.ISPEC]:
x = self.datalist[SCSlice.ISPEC][species][0][i]
dxdt = self.datalist[SCSlice.ISPEC][species][1][i]
if dxdt < 0.0: #If negative, then fuel
dxdt = -dxdt
#Normalize with x
if tnuc[0][i] == 0.0:
tnuc[0][i] = x/dxdt
elif x/dxdt < tnuc[0][i]:
tnuc[0][i] = x/dxdt
if tnuc[0][i] > TNUC_MAX:
tnuc[0][i] = TNUC_MAX
#Raw dxdt
if tnuc[2][i] == 0.0:
tnuc[2][i] = 1.0/dxdt
elif 1.0/dxdt < tnuc[2][i]:
tnuc[2][i] = 1.0/dxdt
if tnuc[2][i] > TNUC_MAX:
tnuc[2][i] = TNUC_MAX
if tnuc[0][i] == 0.0:
tnuc[0][i] = TNUC_MAX
if tnuc[2][i] == 0.0:
tnuc[2][i] = TNUC_MAX
rho6 = self.datalist[SCSlice.IRHO][i]/1.e6
T8 = self.datalist[SCSlice.ITEMP][i]/1.e8
tnuc[1][i] = 3.4e-4 * np.exp(20./T8) * pow(rho6, -2.3)
ent_level = ent_level / ent_count
#print('ent_level: {}'.format(ent_level))
top_i = None
for i in range(len(self.datalist[SCSlice.IRADIUS])):
rr = self.datalist[SCSlice.IRADIUS][i]
ent_norm = self.datalist[SCSlice.IS][i]/ent_level
if np.abs(ent_norm - 1.0) < ENT_TOL:
#We're in the convecting region, calculate various quantities of interest
#First, get the base of this region
if not l1:
l1 = self.datalist[SCSlice.IRADIUS][i]
ppeak = self.datalist[SCSlice.IP][i]
retlist[SCSlice.IRCONV] = l1
#Calculate average velocity magnitude in convective region
vavg += self.datalist[SCSlice.IMV][i]
vcnt += 1
vsum += 1.0 / self.datalist[SCSlice.IMV][i]
#Get the index of the top of the convecting region
#The last value assigned to top_i will be this index
top_i = i
#Calculate pressure scale height
if ppeak and not retlist[SCSlice.IPSCALE]: #ppeak has been found and we haven't found the scale height yet
if self.datalist[SCSlice.IP][i] <= ppeak/np.e: #Found the scale height
retlist[SCSlice.IPSCALE] = rr - l1
l2 = self.datalist[SCSlice.IRADIUS][top_i]
retlist[SCSlice.ILCONV] = l2 - l1
retlist[SCSlice.IUCONV] = vavg / vcnt
#dr = self.datalist[SCSlice.IRADIUS][2] - self.datalist[SCSlice.IRADIUS][1]
#retlist[SCSlice.IUCONV] = dr*vsum
retlist[SCSlice.ITNUC] = tnuc
return retlist
def _parseSlicefile(self, slicefile):
"""Return (timestamp tuple, nspec, globals, data list) based on slicefile."""
import numpy as np
import re
SPEC_COL = 7
sf = open(slicefile)
tret = (None, None) #(time step, time in seconds)
nspec = None
specmap = {}
gret = _Globals()
dlret = [None for i in range(SCSlice._NS_SIZE+1)] #Use list comprehension
#Parse header / global info
#TODO: I make strict assumptions about the structure of the header,
#might be nice to develop a more portable/generic algorithm
for line in sf:
if not line.strip():
continue #If blank line, just loop to next line
if not line.strip().startswith('#') :
break #Reached end of header
if line.count('time') == 1:
tokens = line.split()
time = float( tokens[len(tokens)-1] )
elif line.count('peak temperature') == 1:
tokens = line.split()
gret.Tpeak = float( tokens[len(tokens)-1] )
elif line.count('peak temp loc') == 1:
tokens = line.split()
tcart = tuple( float(val) for val in tokens[-3:] )
elif line.count('peak temp radius') == 1:
tokens = line.split()
trad = float( tokens[len(tokens)-1] )
elif line.count('velocity @ peak T loc (vx') == 1:
tokens = line.split()
tvcart = tuple( float(val) for val in tokens[-3:] )
elif line.count('radial velocity @ peak T') == 1:
tokens = line.split()
tvrad = float( tokens[len(tokens)-1] )
elif line.count('peak enucdot =') == 1:
tokens = line.split()
gret.epeak = float( tokens[len(tokens)-1] )
elif line.count('peak enucdot loc') == 1:
tokens = line.split()
ecart = tuple( float(val) for val in tokens[-3:] )
elif line.count('peak enucdot radius') == 1:
tokens = line.split()
erad = float( tokens[len(tokens)-1] )
elif line.count('pressure') == 2:
#This line labels the data, use it to calculate nspec and get species names
#Split with regex representing two or more spaces -- some labels have one space in them
tokens = re.split(r'\s{2,}', line)
nspec = len(tokens) - SCSlice._NS_SIZE - 1 #-1 for the comment character
nspec = nspec/2 #Divide by 2 because we have both X and dX/dt for each species
speclist = [spec for spec in tokens[SPEC_COL+1:SPEC_COL+nspec+1]] #+1 for comment char
sf.close()
gret.tploc = tcart + (trad,)
gret.tpvel = tvcart + (tvrad,)
gret.eploc = ecart + (erad,)
#Record time info
li = slicefile.index('_plt') + 4
ri = slicefile.index('.slice')
step = int(slicefile[li:ri])
tret = (step, time)
#Read in non-species data. I know, this is hideous.
rs = 7+2*nspec #RMS start
(dlret[SCSlice.IRADIUS], dlret[SCSlice.IRHO], dlret[SCSlice.IRHO_RMS],
dlret[SCSlice.ITEMP], dlret[SCSlice.ITEMP_RMS], dlret[SCSlice.IP],
dlret[SCSlice.IP_RMS], dlret[SCSlice.IMV], dlret[SCSlice.IHNUC],
dlret[SCSlice.IS], dlret[SCSlice.IS_RMS]) = np.loadtxt(slicefile,
usecols=(0,1,rs,2,rs+1,3,rs+2,4,5,6,rs+3), unpack=True)
#Read species data
#Species maps 'X(<species>)' --> (<array of X(r)>, <array of dX/dt(r)>)
for (i, key) in enumerate(speclist):
specmap[key] = (np.loadtxt(slicefile, usecols=(SPEC_COL+i,)), np.loadtxt(slicefile, usecols=(SPEC_COL+nspec+i,)))
dlret[SCSlice.ISPEC] = specmap
return (tret, nspec, gret, dlret)
def _write3DRVIn(self, pltfile, min_rv, conv_bot, conv_top, H, rmax, r_anelastic, r_sp_start, savefile):
"""Write the input needed for the VisIt plotting script."""
from numpy import sqrt
#TODO: If the unused args prove not helpful, clean up
PLOTRV_IN = '/ccs/home/ajacobs/Projects/SubChandra/lensSubmit/plotRV3D.in'
with open(PLOTRV_IN, 'w') as prv_in:
#Write header
prv_in.write('#Inputs for the plotRV3D VisIt script\n\n')
#Write pltfile's location
prv_in.write('#Database/file to be read\n')
prv_in.write('pltfile = {0}\n\n'.format(pltfile))
#Convective region
#prv_in.write('#The radius of the bottom and top of the convective layer\n')
#prv_in.write('#(where entropy is flat, ds/dr <=0) in cm\n')
#prv_in.write('#Radius of conv_bot + 1 pressure scale height\n')
#prv_in.write('conv_bot = {0}\n'.format(conv_bot))
#prv_in.write('conv_top = {0}\n'.format(conv_top))
#prv_in.write('H = {0}\n\n'.format(H))
#Maximum x
prv_in.write('#Maximum x (same as max y, z)\n')
xmax = rmax / sqrt(3.0)
prv_in.write('xmax = {0}\n\n'.format(xmax))
#Minimum radial velocity to plot
prv_in.write('#Minimum radial velocity to plot\n')
prv_in.write('min_rv = {0}\n\n'.format(min_rv))
#Radii of Maestro params
#prv_in.write('#Radii of the anelastic cutoff density and start of the sponge\n')
#prv_in.write('r_anelastic = {0}\n'.format(r_anelastic))
#prv_in.write('r_sp_start = {0}\n\n'.format(r_sp_start))
#File to save to
prv_in.write('#File to save the plot to\n')
prv_in.write('savefile = {0}\n'.format(savefile))
def _writeRVIn(self, infilename, pltfile, conv_bot, conv_top, H, rmax, r_anelastic, r_sp_start, savefile):
"""Write the input needed for the VisIt plotting script."""
from numpy import sqrt
with open(infilename, 'w') as prv_in:
#Write header
prv_in.write('#Inputs for the plotRV VisIt script\n\n')
#Write pltfile's location
prv_in.write('#Database/file to be read\n')
prv_in.write('pltfile = {0}\n\n'.format(pltfile))
#Convective region
prv_in.write('#The radius of the bottom and top of the convective layer\n')
prv_in.write('#(where entropy is flat, ds/dr <=0) in cm\n')
prv_in.write('#Radius of conv_bot + 1 pressure scale height\n')
prv_in.write('conv_bot = {0}\n'.format(conv_bot))
prv_in.write('conv_top = {0}\n'.format(conv_top))
prv_in.write('H = {0}\n\n'.format(H))
#Maximum radius
prv_in.write('#Maximum x (same as max y, z)\n')
xmax = rmax / sqrt(3.0)
prv_in.write('xmax = {0}\n\n'.format(xmax))
#Radii of Maestro params
prv_in.write('#Radii of the anelastic cutoff density and start of the sponge\n')
prv_in.write('r_anelastic = {0}\n'.format(r_anelastic))
prv_in.write('r_sp_start = {0}\n\n'.format(r_sp_start))
#File to save to
prv_in.write('#File to save the plot to\n')
prv_in.write('savefile = {0}\n'.format(savefile))
#################
### Functions ###
#################
#TODO: Would be better to have objects with data dictionaries for this sort of thing.
# Should rework code this way
def get_param(param, param_file):
"""Return the parameter value (as a string) found in a simple parameter
file with <param> = <val> assignments. None is returned if not found."""
pfile = open(param_file)
ret = None
for line in pfile:
if(line.find('=') > -1):
tokens = line.partition('=')
cur_param = tokens[0].strip()
cur_val = tokens[2]
if(cur_param == param):
ret = cur_val
pfile.close()
return ret
def found_required_scratch_files(rundir):
"""Check that all files required for a sub-Chandra simulation are present in rundir"""
from glob import glob
from os.path import join, isdir, isfile
#Make sure either valid chk file or initial model data is available
if not isfile(join(rundir, 'sub_chandra.M_WD*hse*')):
#No initial model, better have a chkfile
chkfiles = []
for f in glob(join(rundir, '*_chk*')):
if isdir(f):
chkfiles.append(f)
chkfiles.sort()
last_chk_file = chkfiles[-1]
if not isfile(join(last_chk_file, 'Header')):
return False
#Make sure we have the executable, inputs file, and EoS data
return (len(glob( join(rundir, 'main.*.exe') )) > 0 and
len(glob( join(rundir, 'inputs*') )) > 0 and
len(glob( join(rundir, 'helm_table.dat') )) > 0
)
#################
### Execution ###
#################
#This is only for testing. subchandra.py is intended to be used as a module.
if __name__== "__main__":
if(len(sys.argv) <= 1):
#TODO: Add any desired testing
pass
######################
### OLD!!!! DELETE ###
######################
class _Limits(object):
"""struct-like class for containing plot limits."""
#Axis limits
Tlims = (None, None)
rlims = (None, None)
dlims = (None, None)
clims = (None, None)
slims = (None, None)
#Zoomed inset limits
rzoom = (None, None)
dzoom = (None, None)
Tzoom = (None, None)
zbounds = (rzoom, dzoom, Tzoom)
class _Cutoffs(object):
"""struct-like class for containing cutoffs."""
an_cut = None
sp_cen_den = None
sp_st_fac = None
base_cut = None
class SCOutput(object):
"""A class representing the output of a particular sub-Chandra simulation."""
##Shared class data##
##Constructor##
def __init__(self, stage_dir, scratch_dir, label, parent):
"""self --> implicitly passed reference to this instance of SCSimulation
stage_dir --> staging directory for this sub-Chandra simulation
scratch_dir --> scratch directory where the work is done but data's purged
label --> this simulation's label (e.g. 12050-107-175-3levs)
parent --> the SCSimulation parent of this SCOutput"""
from glob import glob
from os.path import join
self._stage_dir = stage_dir.rstrip('/') #Get rid of any trailing '/'
self._scratch_dir = scratch_dir.rstrip('/')
self._label = label
self.parent = parent
#Load all available slicefiles
filelist = glob(join(self._stage_dir, 'output', '*.slice'))
self._slices = [SCSlice(sfile, self) for sfile in filelist]
#Load all available hotspots files
filelist = glob(join(self._stage_dir, 'output', '*.hotspots'))
if parent is None:
self._hotspots = [SCHotspots(hsfile) for hsfile in filelist]
else:
self._hotspots = [SCHotspots(hsfile, parent._inputs) for hsfile in filelist]
#Load available temperature histogram files
filelist = glob(join(self._stage_dir, 'output', '*.temphist'))
self._thists = [SCTempHist(thfile, self) for thfile in filelist]
#Load diagnostics data
self._diags = SCDiagnostics(stage_dir, scratch_dir, label)
##Public methods##
def getTHistData(self, step):
"""Return the temperature histogram object for the given step."""
for th in self._thists:
if th.timestamp[0] == step:
return th
def getPeakState(self, t_cut=None):
"""Get the temperature and time at the time of peak global temperature,
as well as the slice with a timestamp nearest the peak temp time."""
Tpeak, timepeak, rpeak = self._diags.getPeakState()
#Find the slice with a timestamp nearest timepeak
dt = 1.e9
peakslice = None
for s in self._slices:
t = s.timestamp[1]
if t_cut and t > t_cut: #Optional time cutoff
break
if (abs(t - timepeak) < dt):
dt = abs(t - timepeak)
peakslice = s
return Tpeak, timepeak, rpeak, peakslice
def getTurnover(self):
"""Get the average convective turnover time, skipping the first 50 seconds during which
convection is being established."""
import numpy as np
tconv = []
for s in self._slices:
t = s.timestamp[1]
if t < 50.:
continue
tconv.append(s.derived_datalist[SCSlice.ILCONV] / s.derived_datalist[SCSlice.IUCONV])
tcavg = sum(tconv)/len(tconv)
return tcavg
def stageOutput(self):
"""Check the scratch directory for any updated output. If found, copy to
the stage directory."""
#TODO: Implement this
pass
def analyzePlts(self, hpcnt=-1.0, full_star=False):
"""Run the Fortran analysis routine 'fsubchandra.f90' on all of this
simulation's plotfiles in the scratch directory. Copy the generated
output to the staging directory's 'output' directory.
hpcnt is optional. It's the percentage of cells to track
when calculating the hottest cells for doing hotspot statistics. If <= 0.0
then no hotspot statistics are calculated."""
from os import listdir
from os.path import join, basename, dirname, isfile
from re import match
from subprocess import call
from glob import glob
from shutil import copy2
FSUB_CMD = '/u/sciteam/ajacobs/Codebase/AmrPostprocessing/F_Src/MAESTRO_sub_chandra/fsubchandra.Linux.gfortran.exe'
#Get a list of valid plotfiles from this simulation's
#base directory and plot directory
pltfiles = self._getPltFiles()
#Loop over plotfiles, analyzing each
for plt in pltfiles:
print('checking ', plt)
outdir = join(self._stage_dir, 'output')
pltfname = basename(plt)
#Only do the analysis if it hasn't been done yet,
#and only calculate hotspot data if asked
#TODO: For now I'm just assuming if the output exists it's current.
# Would be better to check file timestamps
if(hpcnt > 0.0):
found_slice = isfile(join(outdir, pltfname + '.slice'))
found_hs = isfile(join(outdir, pltfname + '.hotspots'))
if not (found_slice and found_hs):
args = ['-h', str(hpcnt), plt + '/']
if(full_star):
args.insert(0, '-f')
print('Running fsubchandra, may take several minutes...')
#Do a flush to make sure the print makes it to stdout before
#call() starts blocking
sys.stdout.flush()
call_list = []
call_list.append(FSUB_CMD)
for a in args:
call_list.append(a)
call(call_list)
#Copy the output files to the staging directory
ofiles = [join(dirname(plt), pltfname + '.slice'), join(dirname(plt), pltfname + '.hotspots')]
for out in ofiles:
dst = join(outdir, basename(out))
copy2(out, dst)
else:
found_slice = isfile(join(outdir, pltfname + '.slice'))
if not found_slice:
args = [plt + '/',]
if(full_star):
args.insert(0, '-f')
print('Running fsubchandra, may take several minutes...')
#Do a flush to make sure the print makes it to stdout before
#call() starts blocking
sys.stdout.flush()
call_list = []
call_list.append(FSUB_CMD)
for a in args:
call_list.append(a)
call(call_list)
#Copy the output files to the staging directory
slice_out = join(dirname(plt), pltfname + '.slice')
dst = join(outdir, basename(slice_out))
copy2(slice_out, dst)
def printTimestampOverview(self):
"""Print a summary of available timestamps with data."""
print('{0:>4s}{1:>16s}{2:>16s}{3:>16s}{4:>16s}{5:>16s}'.format('Step', 'Time (s)', 'CO/He r (cm)', 'l_conv (cm)', 'U_conv (cm/s)', 'H (cm)'))
print('{0:-<4s}{1:-<16s}{2:-<16s}{3:-<16s}{4:-<16s}{5:-<16s}'.format('-', '-', '-', '-', '-', '-'))
for s in sorted(self._slices, key=lambda sl: sl.timestamp[0]):
print('{0:>4d}{1:>16.4f}{2:>16.4g}{3:>16.4g}{4:>16.4g}{5:>16.4g}'.format(
s.timestamp[0], s.timestamp[1], s.derived_datalist[SCSlice.IINTFACE],
s.derived_datalist[SCSlice.ILCONV], s.derived_datalist[SCSlice.IUCONV],
s.derived_datalist[SCSlice.IPSCALE]))
def plotDiags(self):
"""Plot this simulation's diagnostics output: peak temperature, peak Mach #,
etc... vs time."""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
#Get data
t_T, Tpeak, Tpeak_r, Tpeak_vr = self._diags._time, self._diags._maxT, self._diags._maxT_r, self._diags._maxT_vr
t_M, Mpeak = self._diags._time, self._diags._maxmachfull
if 'inline' in matplotlib.get_backend():
#Build plots
fig, ax_list = plt.subplots(nrows=2, ncols=1)
#Temp
ax_list[0].plot(t_T, Tpeak, color='red')
ax_list[0].set_ylabel(r'T$_{\mathrm{peak}}$ (K)', color='red')
ax_list[0].set_title(self._label + ' | Peak Temperature')
tw = ax_list[0].twinx()
tw.plot(t_T, Tpeak_r, color='green')
tw.set_ylabel(r'T$_{\mathrm{peak}}$ radius (cm)', color='green')
#Mach
ax_list[1].plot(t_M, Mpeak, color='blue')
ax_list[1].set_title(self._label + ' | Peak Mach #')
ax_list[1].set_xlabel(r'time [s]')
#Set plot properties
fig.set_size_inches(5.0, 8.0)
fig.tight_layout()
else:
#TODO: Need to implement non-inline plotting
pass
def plotIgTimeFig(self, an_cut, sp_cen_den, sp_st_fac):
"""Plot a figure demonstrating ignition over time for publication, posters, presentation, etc..."""
#TODO: I'm hacking this for a presentation, probably going to be ugly and need reworking
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.ticker import MaxNLocator
import scipy.integrate as spint
#Convenience aliases for diagnostic data
t_T, Tpeak, Tpeak_r, Tpeak_vr = self._diags._time, self._diags._maxT, self._diags._maxT_r, self._diags._maxT_vr
t_M, Mpeak = self._diags._time, self._diags._maxmachfull
t_e, epeak = self._diags._time, self._diags._maxenuc
#Prepare variables for use in slice loop
H = []
iface = []
rbot = []
rtop = []
t_slice = []
tconv = []
tconvb = []
tnuc_x = []
tnuc_xb = []
tnuc_wk = []
ratio = []
avgTpeak = []
avgBaseRho = []
rhoCrit = []
#Loop over slices in chronological order
for s in sorted(self._slices, key=lambda sl: sl.timestamp[0]):
#Build radius data from slices
rbot.append(s.derived_datalist[SCSlice.IRCONV])
rtop.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.ILCONV])
H.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.IPSCALE])
iface.append(s.derived_datalist[SCSlice.IINTFACE])
t_slice.append(s.timestamp[1])
#Estimate of convective turnover timescale and minimum nuclear timescale
#tconv.append(s.derived_datalist[SCSlice.IPSCALE] / s.derived_datalist[SCSlice.IUCONV])
tconv.append(s.derived_datalist[SCSlice.ILCONV] / s.derived_datalist[SCSlice.IUCONV])
#tconv.append(s.derived_datalist[SCSlice.IUCONV])
tnuc_x.append(min(s.derived_datalist[SCSlice.ITNUC][0]))
tnuc_wk.append(min(s.derived_datalist[SCSlice.ITNUC][1]))
tnuc_xb.append(min(s.derived_datalist[SCSlice.ITNUC][2]))
#ratio.append(tnuc_x[len(tconv)-1]/tconv[len(tconv)-1])
#ratio.append(tnuc_wk[len(tconv)-1]/tconv[len(tconv)-1])
#Get the peak radially averaged temperature as an estimate of the background
#conditions the hottest spot is being generated in.
avgTpeak.append(max(s.datalist[SCSlice.ITEMP]))
brho_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak[len(avgTpeak)-1])
avgBaseRho.append(s.datalist[SCSlice.IRHO][brho_i]/1.e5)
t8 = avgTpeak[-1:][0] / 1.e8
rctemp = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
rhoCrit.append(rctemp*1.e6/1.e5)
###TEST: Calculate global convective timescale
#Calculate cutoff radii
sp_st_den = sp_cen_den*sp_st_fac
r_anelastic = None
r_sp_start = None
for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
#TODO add error checking
if not r_anelastic and rho <= an_cut:
r_anelastic = r
if r_sp_start:
break
if not r_sp_start and rho <= sp_st_den:
r_sp_start = r
if r_anelastic:
break
cbot = s.derived_datalist[SCSlice.IRCONV]
ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
magvel = s.datalist[SCSlice.IMV]
mv_rad = s.datalist[SCSlice.IRADIUS]
#Change to dimensionless variables, only care about the convective zone
li = np.where(mv_rad == cbot)[0]
ri = np.where(mv_rad == ctop)[0]
r_norm = mv_rad[li:ri]/ctop
magvel_norm = magvel[li:ri] / magvel.max()
mvn_inv = 1.0 / magvel_norm
#Calculate global convective timescale as integral of 1/v over the convective zone
#Convert back to physical units
tcg = (ctop/magvel.max())*spint.trapz(mvn_inv, r_norm)
tconvb.append(tcg)
###END TEST
ratio.append(tnuc_x[len(tnuc_x)-1]/tconvb[len(tconvb)-1])
#Fix any bad sorting of time array
t_Ts, Tpeak = zip(*sorted(zip(t_T, Tpeak)))
t_T, Tpeak_r = zip(*sorted(zip(t_T, Tpeak_r)))
#NOTE: fontsize can be [size in points | 'xx-small' | 'x-small' |
# 'small' | 'medium' | 'large' | 'x-large' | 'xx-large'
#Build plots
fig, ax_list = plt.subplots(nrows=2, ncols=1)
#Temp
ax_list[0].plot(t_T, Tpeak, color='red')
ax_list[0].plot(t_slice, avgTpeak, color='red', marker='x', linestyle='None', label='avg peak temp')
ax_list[0].set_ylabel(r'T$_{\mathrm{peak}}$ [$\times 10^8$ K]', color='red', fontsize='xx-large')
ax_list[0].set_title('11030 Temperature and Radii', fontsize='xx-large')
ax_list[0].set_ylim(1.85e8, 3.5e8)
ax_list[0].tick_params(labelsize='xx-large')
ax_list[0].yaxis.offsetText.set_visible(False)
#ax_list[0].set_xlim(0, 40)
tw = ax_list[0].twinx()
tw.tick_params(labelsize='xx-large')
tw.yaxis.offsetText.set_visible(False)
tw.yaxis.set_major_locator(MaxNLocator(prune='lower'))
#tw.set_xlim(0, 40)
#tw.set_ylim(4.26e8, 4.38e8)
tw.plot(t_T, Tpeak_r, color='green')
tw.plot(t_slice, iface, color='black', label='CO/He Int.')
tw.plot(t_slice, H, color='cyan', label='H')
tw.plot(t_slice, rbot, color='cyan', label=r'$r_\mathrm{conv}$')
#tw.plot(t_slice, rtop, color='cyan', marker='v', linestyle='None', label='rtop')
tw.set_ylabel(r'T$_{\mathrm{peak}}$ radius [$\times 10^8$ cm]', color='green', fontsize='xx-large')
#handles, labels = ax_list[0].get_legend_handles_labels()
#fig.legend(handles, labels)
tw.legend(loc=2, fontsize='x-large')
#Plot base density (density at radius of peak temp)
ax_list[1].plot(t_slice, avgBaseRho, label=r'$\rho_\mathrm{base}$')
ax_list[1].plot(t_slice, rhoCrit, 'b--',
#label=r'$\rho_{WK} = \left( \frac{0.0607}{4 T_8^2} exp(20/T_8) \right)^{\frac{1}{2.3}} $')
label=r'$\rho_{WK}$')
#Customize axis labels (TODO: figure out how this works at some point)
#label_text = [r'%i' % int(n/10**4) for n in plt.yticks()[0]]
#ax_list[1].set_yticklabels(label_text)
ax_list[1].tick_params(labelsize='xx-large')
#ax_list[4].set_yscale('log')
#ax_list[4].set_ylim(9.e5, 1.5e6)
ax_list[1].set_ylabel(r'$\rho$ [$\times 10^5$ g cm$^{-3}$]', fontsize='xx-large')
ax_list[1].set_xlabel(r'time [s]', fontsize='xx-large')
ax_list[1].set_title('Density', fontsize='xx-large')
ax_list[1].legend(loc=2, fontsize='x-large')
#Set plot properties
fig.set_size_inches(10.0, 10.0)
fig.tight_layout()
fig.savefig("TRD.png", bbox_inches='tight')
def plotConvTimescales(self, an_cut, sp_cen_den, sp_st_fac):
"""Plot a figure demonstrating ignition over time for publication, posters, presentation, etc..."""
#TODO: I'm hacking this for a presentation, probably going to be ugly and need reworking
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.ticker import MaxNLocator
import scipy.integrate as spint
#Convenience aliases for diagnostic data
t_T, Tpeak, Tpeak_r, Tpeak_vr = self._diags._time, self._diags._maxT, self._diags._maxT_r, self._diags._maxT_vr
t_M, Mpeak = self._diags._time, self._diags._maxmachfull
t_e, epeak = self._diags._time, self._diags._maxenuc
#Prepare variables for use in slice loop
H = []
iface = []
rbot = []
rtop = []
t_slice = []
tconv = []
tconvb = []
tnuc_x = []
tnuc_xb = []
tnuc_wk = []
ratio = []
avgTpeak = []
avgBaseRho = []
rhoCrit = []
#Loop over slices in chronological order
for s in sorted(self._slices, key=lambda sl: sl.timestamp[0]):
#Build radius data from slices
rbot.append(s.derived_datalist[SCSlice.IRCONV])
rtop.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.ILCONV])
H.append(s.derived_datalist[SCSlice.IRCONV] + s.derived_datalist[SCSlice.IPSCALE])
iface.append(s.derived_datalist[SCSlice.IINTFACE])
t_slice.append(s.timestamp[1])
#Estimate of convective turnover timescale and minimum nuclear timescale
#tconv.append(s.derived_datalist[SCSlice.IPSCALE] / s.derived_datalist[SCSlice.IUCONV])
tconv.append(s.derived_datalist[SCSlice.ILCONV] / s.derived_datalist[SCSlice.IUCONV])
#tconv.append(s.derived_datalist[SCSlice.IUCONV])
tnuc_x.append(min(s.derived_datalist[SCSlice.ITNUC][0]))
tnuc_wk.append(min(s.derived_datalist[SCSlice.ITNUC][1]))
tnuc_xb.append(min(s.derived_datalist[SCSlice.ITNUC][2]))
#ratio.append(tnuc_x[len(tconv)-1]/tconv[len(tconv)-1])
#ratio.append(tnuc_wk[len(tconv)-1]/tconv[len(tconv)-1])
#Get the peak radially averaged temperature as an estimate of the background
#conditions the hottest spot is being generated in.
avgTpeak.append(max(s.datalist[SCSlice.ITEMP]))
brho_i = np.where(s.datalist[SCSlice.ITEMP] == avgTpeak[len(avgTpeak)-1])
avgBaseRho.append(s.datalist[SCSlice.IRHO][brho_i])
t8 = avgTpeak[-1:][0] / 1.e8
rctemp = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
rhoCrit.append(rctemp*1.e6)
###TEST: Calculate global convective timescale
#Calculate cutoff radii
sp_st_den = sp_cen_den*sp_st_fac
r_anelastic = None
r_sp_start = None
for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
#TODO add error checking
if not r_anelastic and rho <= an_cut:
r_anelastic = r
if r_sp_start:
break
if not r_sp_start and rho <= sp_st_den:
r_sp_start = r
if r_anelastic:
break
cbot = s.derived_datalist[SCSlice.IRCONV]
ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
magvel = s.datalist[SCSlice.IMV]
mv_rad = s.datalist[SCSlice.IRADIUS]
#Change to dimensionless variables, only care about the convective zone
li = np.where(mv_rad == cbot)[0]
ri = np.where(mv_rad == ctop)[0]
r_norm = mv_rad[li:ri]/ctop
magvel_norm = magvel[li:ri] / magvel.max()
mvn_inv = 1.0 / magvel_norm
#Calculate global convective timescale as integral of 1/v over the convective zone
#Convert back to physical units
tcg = (ctop/magvel.max())*spint.trapz(mvn_inv, r_norm)
tconvb.append(tcg)
###END TEST
ratio.append(tnuc_x[len(tnuc_x)-1]/tconvb[len(tconvb)-1])
#Fix any bad sorting of time array
t_Ts, Tpeak = zip(*sorted(zip(t_T, Tpeak)))
t_T, Tpeak_r = zip(*sorted(zip(t_T, Tpeak_r)))
#NOTE: fontsize can be [size in points | 'xx-small' | 'x-small' |
# 'small' | 'medium' | 'large' | 'x-large' | 'xx-large'
#Build plots
fig, ax_list = plt.subplots(nrows=2, ncols=1)
#Temp
ax_list[0].plot(t_T, Tpeak, color='red')
ax_list[0].plot(t_slice, avgTpeak, color='red', marker='x', linestyle='None', label='avg peak temp')
ax_list[0].set_ylabel(r'T$_{\mathrm{peak}}$ [$\times 10^8$ K]', color='red', fontsize='xx-large')
ax_list[0].set_title('Temperature and Radii', fontsize='xx-large')
ax_list[0].set_ylim(1.85e8, 1.95e8)
ax_list[0].tick_params(labelsize='xx-large')
ax_list[0].yaxis.offsetText.set_visible(False)
#ax_list[0].set_xlim(0, 40)
tw = ax_list[0].twinx()
tw.tick_params(labelsize='xx-large')
tw.yaxis.offsetText.set_visible(False)
tw.yaxis.set_major_locator(MaxNLocator(prune='lower'))
#tw.set_xlim(0, 40)
#tw.set_ylim(4.26e8, 4.38e8)
tw.plot(t_T, Tpeak_r, color='green')
tw.plot(t_slice, iface, color='black', label='CO/He Int.')
tw.plot(t_slice, H, color='cyan', label='H')
tw.plot(t_slice, rbot, color='cyan', label=r'$r_\mathrm{conv}$')
#tw.plot(t_slice, rtop, color='cyan', marker='v', linestyle='None', label='rtop')
tw.set_ylabel(r'T$_{\mathrm{peak}}$ radius [$\times 10^8$ cm]', color='green', fontsize='xx-large')
#handles, labels = ax_list[0].get_legend_handles_labels()
#fig.legend(handles, labels)
tw.legend(loc=2, fontsize='x-large')
#Plot base density (density at radius of peak temp)
ax_list[1].plot(t_slice, avgBaseRho, label=r'$\rho_\mathrm{base}$')
ax_list[1].plot(t_slice, rhoCrit, 'b--', label=r'$\rho_{WK}$')
#Customize axis labels (TODO: figure out how this works at some point)
#label_text = [r'%i' % int(n/10**4) for n in plt.yticks()[0]]
#ax_list[1].set_yticklabels(label_text)
ax_list[1].tick_params(labelsize='xx-large')
#ax_list[4].set_yscale('log')
#ax_list[4].set_ylim(9.e5, 1.5e6)
ax_list[1].set_ylabel(r'$\rho$ [g cm$^{-3}$]', fontsize='xx-large')
ax_list[1].set_xlabel(r'time [s]', fontsize='xx-large')
ax_list[1].set_title('Base Density', fontsize='xx-large')
#Set plot properties
fig.set_size_inches(10.0, 10.0)
fig.tight_layout()
fig.savefig("TRD.png", bbox_inches='tight')
def plotTimescales(self, step):
"""Plot averaged timescales as a function of radius for the given timestep."""
for s in self._slices:
if s.timestamp[0] == step:
s.plotTimescales()
def plotHotspots(self, step, rlim=None, templog=False, reset=False):
"""Plot radius and temperature histograms for the given timestep's top hotspots
as well as temperature contours."""
#First I need the interesting radii details.
#TODO: This is a stupid inefficient way to get them. Need to rewrite/restructure.
radii = (None, None, None)
for s in self._slices:
if s.timestamp[0] == step:
radii = (s.derived_datalist[SCSlice.IRCONV],
s.derived_datalist[SCSlice.IPSCALE],
s.derived_datalist[SCSlice.IINTFACE])
#Find the right hotspot, call its plotting function
for hs in self._hotspots:
if hs.timestamp[0] == step:
hs.plotHotspots(radii, rlim=rlim, templog=templog, reset=reset)
def plotHotspotsProj(self, step):
"""Plot a projection of hotspots onto a sphere for all of this timestep's hotspots."""
for hs in self._hotspots:
if hs.timestamp[0] == step:
hs.plotHotspotsProj()
def plotEP(self, step):
"""Plot entropy and pressure vs radius for the given timestep."""
for s in self._slices:
if s.timestamp[0] == step:
s.plotEP()
def plotRVSlice(self, step, poll, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Display a slice of a 3D pseudocolor plot of radial velocity generated by VisIt
for this timestep. If the plot image isn't available, then generate it with VisIt on lens."""
for s in self._slices:
if s.timestamp[0] == step:
s.plotRVSlice(poll, anelastic_cutoff, sp_cen_den, sp_st_fac)
def genRVSlices(self, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Generate RV Slices for all available pltfiles. Each slice can be displayed with
plotRVSlice()."""
from subprocess import PIPE, STDOUT, Popen
#Make the input files needed by the VisIt script
self._genRVSliceInfiles(anelastic_cutoff, sp_cen_den, sp_st_fac)
#Spawn subprocess to execute the VisIt script
lenssub = Popen(['lensSubmit/lenssub.sh', '/ccs/home/ajacobs/Projects/SubChandra/lensSubmit/genRVPlots.py'], stdout=PIPE, stdin=PIPE, stderr=STDOUT)
#The old version of the IPython notebook available on Titan has no ability to
#accept stdin, so remind the user they must enter their password for lens in the
#terminal running the notebook.
print('Enter your lens password into the terminal running this notebook.')
def gen3DRVPlots(self, min_rv, xmax, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Generate 3D RV plots for all available pltfiles. Each plots can be displayed with
plotRV3D()."""
from subprocess import PIPE, STDOUT, Popen
#Make the input files needed by the VisIt script
self._gen3DRVInfiles(min_rv, xmax, anelastic_cutoff, sp_cen_den, sp_st_fac)
#Spawn subprocess to execute the VisIt script
lenssub = Popen(['lensSubmit/lenssub.sh', '/ccs/home/ajacobs/Projects/SubChandra/lensSubmit/gen3DRVPlots.py'], stdout=PIPE, stdin=PIPE, stderr=STDOUT)
#The old version of the IPython notebook available on Titan has no ability to
#accept stdin, so remind the user they must enter their password for lens in the
#terminal running the notebook.
print('Enter your lens password into the terminal running this notebook.')
def plotRV3D(self, step, min_rv, poll, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Display a 3D volume rendering plot of positive radial velocity generated by VisIt
for this timestep. If the plot image isn't available, then generate it with VisIt on lens."""
for s in self._slices:
if s.timestamp[0] == step:
s.plotRV3D(min_rv, poll, anelastic_cutoff, sp_cen_den, sp_st_fac)
##Private methods##
def _getPltFiles(self):
"""Get a list of the full path to all valid pltfile directories for this simulation."""
from os import listdir
from os.path import join, basename, isfile
from re import match
#All plotfiles assumed to be form of <some label>_plt#####
#where the name can end in 5 or 6 numbers
PLTFILERE = '.+_plt[0-9]{5,6}$'
PLOTDIR = 'plotfiles'
pltdir = join(self._scratch_dir, PLOTDIR)
candidates = [join(base, c) for c in listdir(base)] #Use a list comprehension to add the full path to each entry
candidates += [join(pltdir, c) for c in listdir(pltdir)]
pltfiles = []
for c in candidates:
cc = basename(c)
if match(PLTFILERE, cc): #Match the regex
if (cc not in [basename(f) for f in pltfiles]): #Check for duplicate
#We make sure this isn't an empty or incomplete pltfile
#by making sure the last file written, Header, is present
if isfile(join(c, 'Header')):
pltfiles.append(c)
return pltfiles
def _genRVSliceInfiles(self, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Generate the infiles needed to run the script that plots multiple RV slices."""
from os.path import join, basename
from numpy import sqrt
LENSSUB_DIR = '/ccs/home/ajacobs/Projects/SubChandra/lensSubmit'
PLOTRVS_IN = join(LENSSUB_DIR, 'genRVPlots.in')
PLTS_DIR = join(self._stage_dir, 'plots')
#Create the top-level infile
with open(PLOTRVS_IN, 'w') as prvs_in:
#Get a reverse-sorted list of all valid pltfiles
pltfiles = self._getPltFiles()
pltfiles = sorted(pltfiles, key=basename)
pltfiles.reverse()
#All plts have same xmax, so grab one and use it to calculate xmax
rmax = self._slices[0].datalist[SCSlice.IRADIUS].max()
xmax = rmax / sqrt(3.0)
prvs_in.write('#Maximum x (same as max y, z)\n')
prvs_in.write('xmax = {0}\n\n'.format(xmax))
#These 2D RV slices make use of information from the radial slices, so
#we only attempt to plot 2D slices for pltfiles with a corresponding
#1D slice. For each such pair, generate an input file used by the VisIt
#plotting script
i = 0
sub_infiles = []
curplt = pltfiles.pop()
for s in sorted(self._slices, key=lambda sl: sl.timestamp[0]):
if s.my_pltfile == basename(curplt):
#Build and store sub-infile name
infilename = join(LENSSUB_DIR, 'genRVPlots-{0}.in'.format(i))
sub_infiles.append(infilename)
i += 1
#Store important radii
cbot = s.derived_datalist[SCSlice.IRCONV]
ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
H = s.derived_datalist[SCSlice.IPSCALE]
#Calculate cutoff radii
sp_st_den = sp_cen_den*sp_st_fac
r_anelastic = None
r_sp_start = None
for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
#TODO add error checking
if not r_anelastic and rho <= anelastic_cutoff:
r_anelastic = r
if r_sp_start:
break
if not r_sp_start and rho <= sp_st_den:
r_sp_start = r
if r_anelastic:
break
#Build savefilename
base = self._label + '_RV' + str(s.timestamp[0]) + '.png'
savefile = join(PLTS_DIR, base)
#TODO: Only write infile if png doesn't exist?
#Write the sub-infile
pltfull = join(curplt, 'Header')
s._writeRVIn(infilename, pltfull, cbot, ctop, H, rmax, r_anelastic, r_sp_start, savefile)
#Advance to the next pltfile
curplt = pltfiles.pop()
prvs_in.write('#Input files for each individual frame\n')
prvs_in.write('plt_count = {0}\n'.format(i))
for si in sub_infiles:
prvs_in.write('{0}\n'.format(si))
def _gen3DRVInfiles(self, min_rv, xmax, anelastic_cutoff, sp_cen_den, sp_st_fac):
"""Generate the infiles needed to run the script that plots multiple 3D RV plots."""
from os.path import join, basename
from numpy import sqrt
LENSSUB_DIR = '/ccs/home/ajacobs/Projects/SubChandra/lensSubmit'
PLOT3DRVS_IN = join(LENSSUB_DIR, 'gen3DRVPlots.in')
PLTS_DIR = join(self._stage_dir, 'plots')
#Create the top-level infile
with open(PLOT3DRVS_IN, 'w') as prvs_in:
#Get a list of all valid pltfiles
pltfiles = self._getPltFiles()
#Write xmax and min_rv
prvs_in.write('#Maximum x (same as max y, z)\n')
prvs_in.write('xmax = {0}\n\n'.format(xmax))
prvs_in.write('#Minimum radial velocity to plot\n')
prvs_in.write('min_rv = {0}\n\n'.format(min_rv))
#Write sub-infiles
sub_infiles = []
i = 0
for pf in pltfiles:
#Build and store sub-infile name
infilename = join(LENSSUB_DIR, 'gen3DRVPlots-{0}.in'.format(i))
sub_infiles.append(infilename)
i += 1
#Store important radii
#cbot = s.derived_datalist[SCSlice.IRCONV]
#ctop = cbot + s.derived_datalist[SCSlice.ILCONV]
#H = s.derived_datalist[SCSlice.IPSCALE]
#Calculate cutoff radii
#sp_st_den = sp_cen_den*sp_st_fac
#r_anelastic = None
#r_sp_start = None
#for r, rho in zip(s.datalist[SCSlice.IRADIUS], s.datalist[SCSlice.IRHO]):
# #TODO add error checking
# if not r_anelastic and rho <= anelastic_cutoff:
# r_anelastic = r
# if r_sp_start:
# break
# if not r_sp_start and rho <= sp_st_den:
# r_sp_start = r
# if r_anelastic:
# break
#Build savefilename
bpf = basename(pf)
ts_idx = bpf.find('_plt') + 4
timestep = bpf[ts_idx:]
base = self._label + '_3DRV' + timestep + '.png'
savefile = join(PLTS_DIR, base)
#TODO: Only write infile if png doesn't exist?
#Write the sub-infile
pltfull = join(pf, 'Header')
self._write3DRVIn(infilename, pltfull, xmax, min_rv, savefile)
prvs_in.write('#Input files for each individual frame\n')
prvs_in.write('plt_count = {0}\n'.format(i))
for si in sub_infiles:
prvs_in.write('{0}\n'.format(si))
def _write3DRVIn(self, infilename, pltfile, xmax, min_rv, savefile):
"""Write infile for a single frame of 3D RV plot."""
from numpy import sqrt
with open(infilename, 'w') as prv_in:
#Write header
prv_in.write('#Inputs for the plotRV3D VisIt script\n\n')
#Write pltfile's location
prv_in.write('#Database/file to be read\n')
prv_in.write('pltfile = {0}\n\n'.format(pltfile))
#Maximum x
prv_in.write('#Maximum x (same as max y, z)\n')
prv_in.write('xmax = {0}\n\n'.format(xmax))
#Minimum radial velocity to plot
prv_in.write('#Minimum radial velocity to plot\n')
prv_in.write('min_rv = {0}\n\n'.format(min_rv))
#File to save to
prv_in.write('#File to save the plot to\n')
prv_in.write('savefile = {0}\n'.format(savefile))
class SCTempHist(object):
"""Temperature histograms for all refinement lenghtscales in a pltfile."""
#Constructor
def __init__(self, thistfile, parent):
"""self --> implicitly passed reference to this instance of SCSlice
slicefile --> a .temphist file generated by fsubchandra.f90
parent --> reference to this histogram's parent SCOutput"""
from os.path import basename
(self.ells, self.temps, self.counts, self.timestamp) = self._parseTHfile(thistfile)
self.sco_parent = parent
self.TMax = self._calcMax(self.temps, self.counts[1])
## Public Methods ##
## Private Methods ##
def _calcMax(self, bin_arr, counts_arr):
"""Calculate the largest non-zero bin for histogram data in bin and counts arrays."""
import numpy as np
vmax_arr = []
for b, c in zip(bin_arr, counts_arr):
vmax = -1.0
for val, num in zip(b,c):
if num > 0 and val > vmax:
vmax = val
vmax_arr.append(vmax)
return np.array(vmax_arr)
def _parseTHfile(self, thistfile):
"""Return (ells, temp array, counts array, and timestep) based on thistfile."""
import numpy as np
thf = open(thistfile)
tret = (None, None) #(time step, time in seconds)
#Parse header / global info
#TODO: I make strict assumptions about the structure of the header,
#might be nice to develop a more portable/generic algorithm
for line in thf:
if not line.strip():
continue #If blank line, just loop to next line
if not line.strip().startswith('#') :
break #Reached end of header
if line.count('time') == 1:
tokens = line.split()
time = float( tokens[len(tokens)-1] )
break
#Record time info
li = thistfile.index('_plt') + 4
ri = thistfile.index('.temphist')
step = int(thistfile[li:ri])
tret = (step, time)
#For each lengthscale, collect the counts for each temperature bin
#The format of the file after the header is
# <lengthscale of finest level>
# <temp bin 1> <counts>
# ...
# <lengthscale of next level down>
# <temp bin 1> <counts>
# ...
# ...
# <lengthscale of coarsest level>
# <temp bin 1> <counts>
# ...
ells = []
temps = [] #temps[i][j] --> temperature bin j for lengthscale i
counts = [] #counts[i][j] --> counts for temperature in bin j for lengthscale i
counts_eos = []
for line in thf:
if line.strip().startswith('#'): #Skip comments
continue
tokens = line.split()
if len(tokens) == 1:
#Only one entry means it's a lengthscale
ells.append(float(tokens[0]))
temps.append([])
counts.append([])
counts_eos.append([])
elif len(tokens) == 3:
#Two entires means a temperature bin and its counts,
#store them in the corresponding lengthscale's array
temps[len(temps)-1].append(float(tokens[0]))
counts[len(counts)-1].append(int(tokens[1]))
counts_eos[len(counts_eos)-1].append(int(tokens[2]))
else:
print('Unexpected line format in file!')
thf.close()
sys.exit(2)
thf.close()
ells = np.array(ells)
temps = np.array(temps)
countsret = [np.array(counts), np.array(counts_eos)]
return (ells, temps, countsret, tret)
class SCHotspots(object):
"""The hotspot data from a particular sub-Chandra simulation plotfile/timestamp."""
##Shared class data##
TEMP_COL = 0
RHO_COL = 1
X_COL, Y_COL, Z_COL = 2, 3, 4
LEV_COL = 5
##Constructor##
def __init__(self, hsfile, in_dict=None):
"""SCHotspots constructor.
self --> reference to this instance of SCHotspots (implicitly passed)
hsfile --> .hotspots file
in_dict --> the inputs dictionary for this simulation"""
self._hsfile = hsfile
self.timestamp = self._parseHSFile()
self._hsarray = None
self.in_dict = in_dict
##Public methods##
def plotHotspots(self, radii, rlim=None, templog=False, plot_top=False, reset=False):
"""Plot radius and temperature histograms for this timestep's top hotspots
as well as temperature contours."""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import cm, colors
from mpl_toolkits_ext.basemap import Basemap#, cm
TCRIT = 2.25e7
TMAX = 2.4e9
#When tweaking plots it's nice if I can store the data, but then when I load new
#data I need to reset. Here I delete all data arrays so they'll be rebuilt.
if(reset and hasattr(self, 'r')):
del self.r
del self.theta
del self.phi
del self.temp
del self.logtemp
del self.rho6
del self.temp_lons
del self.temp_lats
#Get data, and save data so we only go through all the arrays once
if not self._hsarray: #If no array yet, build it
self._buildHSArr()
if not hasattr(self, 'r'):
self.r = np.array([hs.loc[1][0] for hs in self._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(self, 'theta'):
self.theta = np.array([hs.loc[1][1] for hs in self._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(self, 'phi'):
self.phi = np.array([hs.loc[1][2] for hs in self._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(self, 'temp'):
self.temp = np.array([hs.temp for hs in self._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(self, 'logtemp'):
self.logtemp = np.log10(self.temp)
if not hasattr(self, 'rho6'):
self.rho6 = np.array([hs.rho/1.e6 for hs in self._hsarray if hs.temp > TCRIT and hs.temp < TMAX])
if not hasattr(self, 'temp_lons'):
self.temp_lons = np.array([deg for deg in np.degrees(self.phi)])
if not hasattr(self, 'temp_lats'):
self.temp_lats = np.array([-(deg-90.) for deg in np.degrees(self.theta)])
#Local aliases for data
r = self.r
#theta = self.theta
#phi = self.phi
temp = self.temp
logtemp = self.logtemp
rho6 = self.rho6
temp_lons = self.temp_lons
temp_lats = self.temp_lats
#Critical hotspots
#ctemp = np.array([hs.temp for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#ctheta = np.array([hs.loc[1][1] for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#cphi = np.array([hs.loc[1][2] for hs in self._hsarray if hs.rho/1.e6 > (1.68e-4*np.exp(20.0/(hs.temp/1.e8)))**(1.0/2.3)])
#ctemp_lons = np.array([deg for deg in np.degrees(cphi)])
#ctemp_lats = np.array([-(deg-90.) for deg in np.degrees(ctheta)])
crit_temp = 2.3e8
#ctemp = np.array([hs.temp for hs in self._hsarray if hs.temp > crit_temp])
#ctheta = np.array([hs.loc[1][1] for hs in self._hsarray if hs.temp > crit_temp])
#cphi = np.array([hs.loc[1][2] for hs in self._hsarray if hs.temp > crit_temp])
#ctemp_lons = np.array([deg for deg in np.degrees(cphi)])
#ctemp_lats = np.array([-(deg-90.) for deg in np.degrees(ctheta)])
#Get important radii of interest
rbot = radii[0]
H = radii[1]
iface = radii[2]
#Get min, max temp
min_temp = temp.min()
max_temp = temp.max()
hs_count = len(temp)
#Calculate temperature bins for color map
shsarr = sorted(self._hsarray, key=lambda hs: hs.temp)
stemp = sorted(temp)
tlevs = [temp.min()]
tllevs = [logtemp.min()]
count = 0
for t in stemp:
count += 1
if count > (1./9.)*hs_count:
tlevs.append(t)
tllevs.append(np.log10(t))
count = 0
#For hottest temps break into top 9% and top 1%
#if len(tlevs) == 9:
# if count > 0.09*hs_count:
# tlevs.append(hs.temp)
# tllevs.append(np.log10(hs.temp))
# count = 0
#else:
# if count > 0.1*hs_count:
# tlevs.append(hs.temp)
# tllevs.append(np.log10(hs.temp))
# count = 0
else:
tlevs.append(t)
tllevs.append(np.log10(t))
#Build colormap
#TODO: Get nice paper-worthy plot for 'ignition' section
#TODO: Figure out discrepancy between pltfile cells and valid cells
# Update: currently working with OLCF on this, seems to only happen with optimized Cray compiler
#rr = np.linspace(1.0, 0.7, 11)
#gb = np.linspace(1.0, 0.0, 11)
#temp_cmap = colors.ListedColormap([
# (rr[0], gb[0], gb[0]), #Coldest
# (rr[1], gb[1], gb[1]),
# (rr[2], gb[2], gb[2]),
# (rr[3], gb[3], gb[3]),
# (rr[4], gb[4], gb[4]),
# (rr[5], gb[5], gb[5]),
# (rr[6], gb[6], gb[6]),
# (rr[7], gb[7], gb[7]),
# (rr[8], gb[8], gb[8]),
# (rr[9], gb[9], gb[9]),
# (rr[10], gb[10], gb[10])]) #Hottest
# #(1, 1, 0)]) #Hottest
# RGB colors from 9-class OrRd at colorbrewer2.org
temp_cmap = colors.ListedColormap([
(255./255., 247./255., 236./255.), #Coldest
(254./255., 232./255., 200./255.),
(253./255., 212./255., 158./255.),
(253./255., 187./255., 132./255.),
(252./255., 141./255., 89./255.),
(239./255., 101./255., 72./255.),
(215./255., 48./255., 31./255.),
(179./255., 0./255., 0./255.),
(127./255., 0./255., 0./255.)]) #Hottest
tc_bounds = tlevs
tc_norm = colors.BoundaryNorm(tc_bounds, temp_cmap.N)
#Calculate critical density range based on eqn (8) of Woosley & Kasen 2011
t8 = max_temp/1.e8
rho_critl = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
t8 = min_temp/1.e8
rho_critr = (1.68e-4*np.exp(20.0/t8))**(1.0/2.3)
print('Min, Max temp of {0} hottest cells: {1}, {2}'.format(hs_count, min_temp, max_temp))
print('Critical density range: [{0}, {1}]'.format(rho_critl, rho_critr))
sys.stdout.flush()
if 'inline' in matplotlib.get_backend():
#Build plots
fig = plt.figure()
#subplot2grid call signature: (grid_row, grid_cols), (subplot_row, subplot_col), colspan=1, rowspan=1
ax_rad = plt.subplot2grid((3,3), (0,0))
ax_temp = plt.subplot2grid((3,3), (0,1))
ax_rho = plt.subplot2grid((3,3), (0,2))
ax_proj = plt.subplot2grid((3,3), (1,0), colspan=2, rowspan=2)
#Plot temperature histogram
ax_temp.hist(temp, bins=1000)
ax_temp.set_xlabel("temperature (K)")
#Build projection map for temperature theta, phi locations
map = Basemap(projection='nsper', lon_0=45, lat_0=45,
llcrnrlon=-180, llcrnrlat=-90, urcrnrlon=180, urcrnrlat=90, resolution=None, ax=ax_proj)
#map.drawmeridians(np.arange(0, 90, 15), color="0.65", latmax=90)
#map.drawparallels(np.arange(0, 90, 15), color="0.65", latmax=90) #, labels=[1,0,0,1])
#It's stupid that I have to do this, but below I "erase" extraneous latitude lines
#by writing over them with thick white lines.
#for lat in range(0,90,15):
# for long in range(15,180,15):
# #Erase extraneous latitude lines on the left
# left_long=-long
# map.drawgreatcircle(left_long+15, lat, left_long, lat, linewidth=5, color="w")
# #Erase extraneous latitude lines on the right
# right_long=long+90
# map.drawgreatcircle(right_long-15, lat, right_long, lat, linewidth=5, color="w")
##Same with extraneous longitude lines at the bottom
#map.drawgreatcircle(0, 0, 0, -25, linewidth=5, color="w")
#map.drawgreatcircle(15, 0, 15, -30, linewidth=5, color="w")
#map.drawgreatcircle(30, 0, 30, -35, linewidth=5, color="w")
#map.drawgreatcircle(45, 0, 45, -30, linewidth=5, color="w")
#map.drawgreatcircle(60, 0, 60, -35, linewidth=5, color="w")
#map.drawgreatcircle(75, 0, 75, -30, linewidth=5, color="w")
# draw the boundary of our domain -- we want great circles here
# note that we draw in 15 degree increments. Otherwise the lat/long grid
# doesn't line up with the boundary
#Left boundary
for lat in range(0,90,15):
map.drawgreatcircle(0, lat, 0, lat+15, linewidth=1, color="k")
#Right boundary
for lat in range(0,90,15):
map.drawgreatcircle(90, lat, 90, lat+15, linewidth=1, color="k")
#Bottom boundary
for lon in range(0,90,15):
map.drawgreatcircle(lon, 0, lon+15, 0, linewidth=1, color="k")
if templog:
clevs = np.linspace(logtemp.min(), logtemp.max(), 11)
#cs = map.contourf(temp_lons, temp_lats, logtemp, clevs, latlon=True, tri=True, cmap=cm.Reds)
cs = map.contourf(temp_lons, temp_lats, logtemp, tllevs, latlon=True, tri=True, cmap=cm.jet)
else:
#clevs = np.linspace(temp.min(), temp.max(), 11)
clevs = np.linspace(2.25e8, crit_temp, 11)
cs = map.contourf(temp_lons, temp_lats, temp, tlevs, latlon=True, tri=True, cmap=temp_cmap, norm=tc_norm)
#map.contourf(ctemp_lons, ctemp_lats, ctemp, clevs, latlon=True, tri=True, cmap=cm.Greens)
cbar = map.colorbar(cs, location='right', pad='5%', ticks=tlevs)
cbar.set_label('Kelvin') #cbar.set_label('temperature ($\times 10^8$ Kelvin)')
#Plot radius histogram with temperature color-coding
# color-coding is achieved by plotting several bars instead of using
# ax.hist(), and we use the projection map's colorbar
#ax_rad.hist(r, bins=1000)
dr = 1.e5 #use a dr of 1 km, which is roughly the radial resolution in these simulations
radii, counts, cols = self._binData(r, temp, dr, cbar)
for r, c, col in zip(radii, counts, cols):
ax_rad.bar(r, c, width=dr, color=col, edgecolor=col, align='center')
#ax_rad.bar(radii, counts, width=dr, color=(1.0, 1.0, 1.0), align='center')
ax_rad.set_xlabel("radius (cm)")
if rlim:
ax_rad.set_xlim(rlim[0], rlim[1])
# plot the radii (CO/He interface, start of convective region, top of convective region)
ax_rad.plot([iface, iface], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
ax_rad.plot([rbot, rbot], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
if plot_top:
ax_rad.plot([rbot + H, rbot + H], [0, ax_rad.get_ylim()[1]], color="k", linestyle='--')
#Annotate the interface line
ifrac = (iface - ax_rad.get_xlim()[0]) / (ax_rad.get_xlim()[1] - ax_rad.get_xlim()[0])
ax_rad.annotate('WD/He\ninterface',
xy=(ifrac,0.8),
xytext=(-60,-30),
xycoords='axes fraction', textcoords='offset points',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
#Annotate the convective base line
cbfrac = (rbot - ax_rad.get_xlim()[0]) / (ax_rad.get_xlim()[1] - ax_rad.get_xlim()[0])
ax_rad.annotate('Convective\nbase',
xy=(cbfrac,0.8),
xytext=(30,-30),
xycoords='axes fraction', textcoords='offset points',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
#Plot density histogram with color-coding
drho6 = 0.001
rho6_bins, rho_counts, rho_colors = self._binData(rho6, temp, drho6, cbar)
for r6, c, col in zip(rho6_bins, rho_counts, rho_colors):
ax_rho.bar(r6, c, width=drho6, color=col, edgecolor=col, align='center')
#ax_rho.hist(rho6, bins=1000)
ax_rho.set_xlabel(r"density ($\times 10^6$ g cm$^{-3}$)")
#ax_rho.fill_between([rho_critl, rho_critr], 0, 500, facecolor='0.9', edgecolor='1.0')
ax_rho.plot([rho_critr, rho_critr], [0, ax_rho.get_ylim()[1]], color="k", linestyle='--')
#Annotate the critical density line
rhofrac = (rho_critr - ax_rho.get_xlim()[0]) / (ax_rho.get_xlim()[1] - ax_rho.get_xlim()[0])
ax_rho.annotate(r'$\rho_{\mathrm{cr},\mathrm{WK}}$',
xy=(rhofrac,0.8), size=12.5,
xytext=(30,-30),
xycoords='axes fraction', textcoords='offset points',
arrowprops=dict(facecolor='black', arrowstyle='simple',
connectionstyle='arc3,rad=-0.2'))
#Set plot properties
fig.set_size_inches(15.0, 12.5)
#This fixes a problem with mpl's pstoeps converter when using ghostscript as distiller
#matplotlib.rc('ps', usedistiller='xpdf')
fig.savefig("test_ig.png", bbox_inches='tight')
else:
#TODO: Need to implement non-inline plotting
pass
##Private methods##
def _parseHSFile(self):
"""Parse the hotspots file, return (timestep, time)."""
#Get timestep
li = self._hsfile.index('_plt') + 4
ri = self._hsfile.index('.hotspots')
step = int(self._hsfile[li:ri])
#Get time
f = open(self._hsfile)
for line in f:
if line.strip().startswith('# time'):
tokens = line.partition('=')
time = float(tokens[2])
break
else:
assert False, "Invalid .hotspots file, couldn't find time in header."
f.close()
return (step, time)
def _buildHSArr(self):
"""Build array of hotspots on demand."""
print('Building hotspots array for step {0:5d}'.format(self.timestamp[0]))
sys.stdout.flush()
#Build hotspots array
self._hsarray = []
f = open(self._hsfile)
for i, line in enumerate(f):
#Skip comments and blanks
blank = not line.strip()
if line.strip().startswith('#') or blank:
continue
tokens = line.split()
temp = float(tokens[SCHotspots.TEMP_COL])
rho = float(tokens[SCHotspots.RHO_COL])
x, y, z = float(tokens[SCHotspots.X_COL]), float(tokens[SCHotspots.Y_COL]), float(tokens[SCHotspots.Z_COL])
lev = float(tokens[SCHotspots.LEV_COL])
self._hsarray.append(_Hotspot(temp, rho, x, y, z, lev, dx))
if (i % 100000 == 0):
print('Read 100K!, Total: {0:7d}'.format(i))
#break
nx = float(self.in_dict['n_cellx'])
xmax = float(self.in_dict['prob_hi_x'].replace('d', 'e'))
dx = xmax/(nx*2**(lev-1))
f.close()
def _buildIgArr(self, critT):
"""Build array of ignitors on demand."""
from datetime import datetime
print('Building ignitors for step {0:5d}'.format(self.timestamp[0]))
sys.stdout.flush()
#Pick out the igniting cells from the hotspots array
#(as determined by a critical temperature).
igset = []
for hs in self._hsarray:
if hs.temp > critT:
igset.append(hs)
if len(igset) > 5000:
break
print('igset size: ', len(igset), datetime.utcnow())
sys.stdout.flush()
#Build Ignitor arrays out of igniting hotspots
# Each array is a collection of hotspots near one another
# We sort igset by temperature first so that searches should happen
# radially outward from the center of unique igniting volumes
#igset_srt = sorted(igset, key=lambda ele: ele.temp, reverse=True)
self._igarrays = []
#Build the initial sets, which will allow for multiple arrays containing
#the same hotspot
print('build initial set ', datetime.utcnow())
sys.stdout.flush()
initial_sets = []
for hs in igset:
stored = False
sti = -1
for i in range(len(initial_sets)):
#If hs is near any of the hs's in arr, append it
if self._nearby(initial_sets[i], hs):
initial_sets[i].append(hs)
stored = True
sti = i
if not stored:
#If hs didn't find a home, made a new igarray
initial_sets.append([hs,])
#Go through the sets and flag for merging any that have a non-empty intersection.
print('flag for merging', datetime.utcnow())
sys.stdout.flush()
merge_map = dict([(i, i) for i in range(len(initial_sets))]) #Maps merged indices to the new index
#for hs in igset_srt:
for hs in igset:
stored = False
sti = -1
for i in range(len(initial_sets)):
#If hs is near any of the hs's in arr, append it
if self._nearby(initial_sets[i], hs):
if stored:
merge_map[i] = sti
else:
stored = True
sti = i
#Merge intersecting sets
print('merge', datetime.utcnow())
sys.stdout.flush()
for k in merge_map:
v = merge_map[k]
if (k != v):
initial_sets[v] = list(set(initial_sets[v] + initial_sets[k]))
#Create the final list of ignition arrays
#by only including merged lists
print('build final list', datetime.utcnow())
sys.stdout.flush()
self._igarrays = []
for k in merge_map:
v = merge_map[k]
if (k == v):
self._igarrays.append(initial_sets[k])
#Make array of _Ignitor objects
self._Ig_arr = [_Ignitor(arr) for arr in self._igarrays]
def _nearby(self, arr, hs):
"""Return true if hs is within 10dx of any hs in arr, false otherwise."""
from numpy import sqrt
if arr is None:
return False
for h in arr:
x1, y1, z1 = h.loc[0][0], h.loc[0][1], h.loc[0][2]
x2, y2, z2 = hs.loc[0][0], hs.loc[0][1], hs.loc[0][2]
dist = sqrt( (x1-x2)**2 + (y1-y2)**2 + (z1-z2)**2 )
if dist < 30.0e5: #30 km
return True
return False
def _binData(self, data, color_data, dx, cmap):
"""Bin data with bin size dx and return corresponding colors based on the max value in
color_data using the given colormap."""
import numpy as np
#Build data bins
dmin = data.min()
dmax = data.max()
bin_cnt = int(round((dmax-dmin)/dx))
data_bins = np.linspace(dmin + dx*0.5, dmax - dx*0.5, num=bin_cnt)
#Prepare arrays and variables for loop
counts = np.zeros(bin_cnt + 1, dtype = np.int32)
cols = []
sorted_data_col = sorted(zip(data, color_data))
i = 0
upper_bound = dmin + dx
avg_col = 0.0
max_col = 0.0
#Loop through parallel lists of data and color_data, sorted by data.
#Calculate the counts, the average color_data, and the maximum color_data for each bin.
#Convert color_data to color
for dat, col_dat in sorted_data_col:
if col_dat > max_col:
max_col = col_dat
if dat < upper_bound:
counts[i] += 1
avg_col += col_dat
else:
#Before incrementing, append avg_col's color
avg_col = avg_col / counts[i]
cols.append(cmap.to_rgba([avg_col,]))
#cols.append(cmap.to_rgba([max_col,]))
#cols.append((0.5,0.5,0.5,1))
#Increment to next bin, don't forget to count the data and color just found
#in this loop iteration
upper_bound += dx
i += 1
counts[i] += 1
avg_col = col_dat
max_col = 0.0
return (data_bins, counts, cols)
class _Hotspot(object):
"""Class representing a single hotspot"""
##Shared class data
##Constructor
def __init__(self, temp, rho, x, y, z, lev, dx):
"""_Hotspot constructor.
self --> reference to this instance of _Hotspot (implicitly passed)
temp --> temperature of hotspot [K]
x, y, z --> Cartesian location of hotspot [cm]
lev --> refinement level of hotspot's location
dx --> size of hotspot's cell"""
self.temp = temp
self.rho = rho
#Loc = ( (x, y, z) , (r, theta, phi) )
self.loc = ( (x, y, z), self._getSphTuple(x, y, z) )
self.lev = lev
self.dx = dx
##Public methods
##Private methods
def _getSphTuple(self, x, y, z):
"""Return tuple of (r, theta, phi[azimuth]) based on Cartesian x,y,z."""
import numpy as np
r = np.sqrt(x**2 + y**2 + z**2)
theta = np.arccos(z/r)
phi = np.arctan2(y, x)
return (r, theta, phi)
class _Ignitor(object):
"""Class representing a spatially coherent volume of initing fluid."""
##Shared class data
##Constructor
def __init__(self, arr):
"""_Ignitor constructor.
self --> reference to this instance of _Ignitor (implicitly passed)
arr --> array of _Hotspots in this _Ignitor volume"""
import numpy as np
self.com = _Ignitor._calcCom(arr)
self.rho_avg = _Ignitor._calcRavg(arr)
self.M = _Ignitor._calcMtot(arr)
x = self.com[0]
y = self.com[1]
z = self.com[2]
self.r = np.sqrt(x**2 + y**2 + z**2)
self.theta = np.arccos(z/self.r)
self.phi = np.arctan2(y, x)
##Public methods
##Private methods
@staticmethod
def _calcCom(arr):
"""Return the center of mass of an array of _Hotspot's."""
comx = 0.0
comy = 0.0
comz = 0.0
m_tot = 0.0
for hs in arr:
m = hs.dx**3 * hs.rho
m_tot += m
x, y, z = hs.loc[0][0], hs.loc[0][1], hs.loc[0][2]
comx += x * m
comy += y * m
comz += z * m
comx = comx/m_tot
comy = comy/m_tot
comz = comz/m_tot
return (comx, comy, comz)
@staticmethod
def _calcRavg(arr):
"""Return the average density (rho) of an array of _Hotspot's."""
import numpy as np
rho_tot = 0.0
for hs in arr:
rho_tot += hs.rho
rho_avg = rho_tot/len(arr)
return rho_avg
@staticmethod
def _calcMtot(arr):
"""Return the total mass of an array of _Hotspot's."""
m_tot = 0.0
for hs in arr:
m = hs.dx**3 * hs.rho
m_tot += m
return m_tot
class _Globals(object):
"""struct-like class containing a simulation's globals, e.g. T_peak, r(T_peak), ..."""
Tpeak = None #peak temperature
tploc = (None, None, None, None) #x, y, z, r
tpvel = (None, None, None, None) #vx, vy, vz, vr
epeak = None #peak enucdot
eploc = (None, None, None, None) #x, y, z, r
def __str__(self):
ret = 'Tpeak: ' + str(self.Tpeak) + '\n'
ret += ' loc (x,y,z,r): ' + str(self.tploc) + '\n'
ret += ' vel (vx,vy,vz,vr): ' + str(self.tpvel) + '\n'
ret += 'peak enucdot: ' + str(self.epeak) + '\n'
ret += ' loc (x,y,z,r): ' + str(self.eploc) + '\n'
return ret
class SCPltfile(object):
"""A class representing a pltfile from a sub-Chandra simulation."""
##Shared class data##
##Constructor##
def __init__(self, stage_dir, scratch_dir, label):
"""self --> implicitly passed reference to this instance of SCSimulation
stage_dir --> staging directory containing all simulations
scratch_dir --> scratch directory where the work is done but data's purged
label --> this simulation's label (e.g. 12050-107-175-3levs)"""
##Public methods##
def sample(self):
"""Check the scratch directory for any updated output. If found, copy to
the stage directory."""
#TODO: Implement this
pass
##Private methods##
def sampleprv(self):
"""Check the scratch directory for any updated output. If found, copy to
the stage directory."""
#TODO: Implement this
pass
#################
### Functions ###
#################
|
import random
import particle
def setup():
global Balls
Balls = []
for i in range (10):
Ball = particle.Particle()
Balls.append(Ball)
size(800,400)
background(0)
def draw():
background(0)
stroke(220,45,86)
global Balls
for i in range(len(Balls)):
Ball = Balls[i]
Ball.draw_it()
Ball.move()
for j in range (10):
line (Ball.pos_x, Ball.pos_y, Balls[j].pos_x, Balls[j].pos_y)
|
"""Plugwise Climate (current only Anna) component for Home Assistant."""
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.