Python plot_setup Examples

Example #1

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 = {

Example #2

File: feat_redundancy.py Project: gsel9/biorad

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,