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,