def __init__(self, input_path, node_parser, edge_parser, attack_parser,
solution_parser, **kwargs):
self.path = input_path
self.load_nodes = node_parser
self.load_edges = edge_parser
if attack_parser:
self.load_attack = attack_parser
if solution_parser:
self.load_solution = solution_parser
self.nodes_file = kwargs.get(
'nodes_file', os.path.join(self.path, 'input', 'nodes.txt'))
self.edges_file = kwargs.get(
'edges_file', os.path.join(self.path, 'input', 'edges.txt'))
# initialize the color mapper
self.color_mapper = {
name: hex_code
for name, hex_code in cnames.items()
}
colors = kwargs.get('colors', None)
if colors:
colors = {k: self.color_mapper.get(k, colors[k]) for k in colors}
self.tvis = TVis(nodes=self.load_nodes(self.nodes_file),
edges=self.load_edges(self.edges_file),
colors=colors)
else:
self.tvis = TVis(
nodes=self.load_nodes(self.nodes_file),
edges=self.load_edges(self.edges_file),
)
self.attack_file = kwargs.get(
'attack_file', os.path.join(self.path, 'input', 'attack.txt'))
self.solution_file = kwargs.get(
'solution_file', os.path.join(self.path, 'solution.txt'))
# initialize the configurations
self.before_config = None
self.after_config = None
from django.db import models
from matplotlib.colors import cnames
# Create your models here.
COLOR_CHOICES = [(name, hex) for name, hex in cnames.items()]
class Tag(models.Model):
"""
主机标签
name 标签名
color 标签颜色
"""
id = models.AutoField(primary_key=True)
name = models.CharField(max_length=10, null=False)
color = models.CharField(choices=COLOR_CHOICES,
default='yellow',
max_length=100,
null=False)
created = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
class Meta:
db_table = 'tag'
def __str__(self):
return self.name
def get_colordict(filter_:str='dark') -> dict:
""" return dictionary of colornames by filter """
return dict((k, v) for k, v in cnames.items() if filter_ in k)
from scipy.cluster.hierarchy import linkage, leaves_list from scipy.spatial.distance import squareform from sklearn.manifold import TSNE, MDS EPSILON = 1e-8 # Define a list of random colors, excluding near-white colors. NEAR_WHITE_COLORS = ['silver', 'whitesmoke', 'floralwhite', 'aliceblue', \ 'lightgoldenrodyellow', 'lightgray', 'w', 'seashell', 'ivory', \ 'lemonchiffon','ghostwhite', 'white', 'beige', 'honeydew', 'azure', \ 'lavender', 'snow', 'linen', 'antiquewhite', 'papayawhip', 'oldlace', \ 'cornsilk', 'lightyellow', 'mintcream', 'lightcyan', 'lavenderblush', \ 'blanchedalmond', 'lightcoral'] COLOR_LIST = [] for name, hex in cnames.items(): if name not in NEAR_WHITE_COLORS: COLOR_LIST.append(name) COLOR_LIST = np.array(COLOR_LIST) np.random.seed(42) np.random.shuffle(COLOR_LIST) np.random.seed(None) def mmd_matrix_plot_DC(dc, condition_from_fn, mmd2_fn, condition_fn, \ parallel=False, load_data=False, cluster=False, alg='quadratic', max_n=None,\ sigma=None, cmap='Greys', colorbar=True, cax=None, ticks=[0.0,0.3], \ filename='mmd_matrix.pdf', ax=None, save_and_close=True): """ Plot a pairwise MMD matrix.
# pylint: disable=C0103
import gym
import numpy as np
import serial
import struct
from matplotlib import pyplot as plt
from matplotlib.widgets import Cursor
from matplotlib.colors import cnames
from scipy.integrate import ode
from time import time, sleep
from threading import Thread
from multiprocessing import Process, Pipe, Event
from kusanagi.utils import print_with_stamp, gTrig_np
color_generator = iter(cnames.items())
class Plant(gym.Env):
def __init__(self, dt=0.1, noise_dist=None,
angle_dims=[], name='Plant',
*args, **kwargs):
self.name = name
self.dt = dt
self.noise_dist = noise_dist
self.angle_dims = angle_dims
self.state = None
self.u = None
self.t = 0
self.done = False
self.renderer = None
import sys
import serial
import struct
from enum import Enum
import matplotlib
matplotlib.use('tkagg')
from matplotlib import pyplot as plt
from matplotlib.widgets import Cursor
from matplotlib.colors import cnames
from scipy.integrate import ode
from time import time, sleep
from threading import Thread, Lock
from multiprocessing import Process,Pipe,Event
color_generator = cnames.items()
def gTrig_np(x,angi):
if type(x) is list:
x = np.array(x)
if x.ndim == 1:
x = x[None,:]
D = x.shape[1]
Da = 2*len(angi)
n = x.shape[0]
xang = np.zeros((n,Da))
xi = x[:,angi]
xang[:,::2] = np.sin(xi)
xang[:,1::2] = np.cos(xi)
non_angle_dims = list(set(range(D)).difference(angi))
from itertools import cycle
from sklearn import datasets
from sklearn.datasets import make_classification
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score, accuracy_score
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from bokeh.plotting import output_file, figure, show, ColumnDataSource
from bokeh.models import HoverTool
from bokeh.layouts import row
import matplotlib.pyplot as plt
from matplotlib.colors import cnames
cnames = dict((k, v) for k, v in cnames.items() if 'dark' in k)
def PCA_2D_labeled(X, y, cnames:list, target_names:list):
"""
Get a quick 2D rescaled PCA of a labeled dataset
Args:
X (numpy.ndarray): data
y (numpy.ndarray): labels
cnames: a list of color names (str)
target_names: a list of target names (str)
Returns:
matplotlib plot object
"""
import itertools
import sys
from math import floor
from matplotlib.colors import cnames
import classifiers as cl
from IBL import *
import image
import plotter as pl
from spiral import *
import utils
# allcolors = [color for color in sorted(cnames)]
allcolors = [color for key, color in sorted(cnames.items())]
def plot(training_set, test_set, data, header, category):
categories = list(set([x[category] for x in training_set]))
pl.plot(
training_set, test_set, data, utils.without_column(header, category), {
categories[i]: allcolors[((i + 1) * 41) % len(allcolors)]
for i in range(len(categories))
})
def save_spirals(training_set,
output,
filename,
size,
show,
descriptor=None,