import ast import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import matplotlib.patches as mpatches from ast import literal_eval from collections import OrderedDict from matplotlib.ticker import StrMethodFormatter import fig_config as CONFIG CONFIG.plot_setup() def set_axis_precision(axis='y'): if axis == 'y': plt.gca().yaxis.set_major_formatter(StrMethodFormatter('{x:,.3f}')) elif axis == 'x': plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.3f}')) def get_palette_colour(label): palette = CONFIG.base_palette(n=7) mapping = {
from ioutil import sample_paths import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler import matplotlib.patches as mpatches import seaborn as sns import matplotlib.pyplot as plt import fig_config as CONF CONF.plot_setup() # Ng: Number of graylevels. hassan_gl_transforms = { 'original_glcm_DifferenceEntropy': lambda Ng, feature: feature / np.log(Ng ** 2), 'original_glcm_JointEntropy': lambda Ng, feature: feature / np.log(Ng ** 2), 'original_glcm_SumEntropy': lambda Ng, feature: feature * Ng, 'original_glcm_Contrast': lambda Ng, feature: feature / (Ng ** 2), 'original_glcm_DifferenceVariance': lambda Ng, feature: feature / (Ng ** 2), 'original_glcm_SumAverage': lambda Ng, feature: feature / Ng, 'original_glcm_DifferenceAverage': lambda Ng, feature: feature / Ng, 'original_glrlm_GrayLevelNonUniformity': lambda Ng, feature: feature * Ng, 'original_glrlm_HighGrayLevelRunEmphasis': lambda Ng, feature: feature / (Ng ** 2), 'original_glrlm_ShortRunHighGrayLevelEmphasis': lambda Ng, feature: feature / (Ng ** 2), 'original_ngtdm_Contrast': lambda Ng, feature: feature / Ng,