Implemented function to compute asymptotic exponents

This PR implements functions to compute the asymptotic exponents in the form of plots and of a table of their expectation values and uncertainties at given x and Q. The definition of the asymptotic exponents is as in Eq. (4) of [arXiv:1604.00024]. Examples are provided; the functions are documented. New PDF bases have been defined to introduce PDF combinations that may be relevant for the computation of the asymptotic exponents.

validphys2/src/validphys/asy_exponents.py

 # -*- coding: utf-8 -*-
 """
-Tools for computing and plotting effective exponents.
+Tools for computing and plotting asymptotic exponents.
 """
-from __future__ import generator_stop
-
 import logging
 import numbers
-import random
 import warnings
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
 from reportengine import collect
-from reportengine.compat import yaml
 from reportengine.figure import figuregen
-from reportengine.floatformatting import format_number, significant_digits, format_error_value_columns
+from reportengine.floatformatting import format_number 
 from reportengine.table import table
 
 from validphys.checks import check_positive, check_pdf_normalize_to, make_argcheck, check_xlimits
-from validphys.core import PDF, FitSpec
+from validphys.core import PDF
 from validphys.pdfbases import check_basis, Basis
 from validphys.pdfplots import BandPDFPlotter, PDFPlotter
 
@@ -30,9 +25,6 @@ from findiff import FinDiff
 
 log = logging.getLogger(__name__)
 
-INTERNAL_LINESTYLE = ['-.', ':']
-INTERNAL_COLOR = plt.rcParams['axes.prop_cycle'].by_key()["color"]
-
 @check_positive('Q')
 @make_argcheck(check_basis)
 @check_xlimits