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E_7.py
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E_7.py
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# E_7
"""Extremal Values for Codimensions of Unstable Loci
Authors: Valdemar Tsanov, Yana Staneva
Date: Oct 1, 2021
Let X=G/B be the flag variety of a semisimple complex Lie group G, B being a fixed Borel subgroup.
We are interested in the description of the GIT-classes of ample line bundles on X w.r.t. a given reductive subgroup.
More specifically, we are interested in extremal values of the codimensions of unstable loci.
This script computes the extremal values for codimensions of unstable loci for one-paramater subgroups.
The one-parameter subgroups we have implemented here are:
- fundamental coweights
- the sum of the fundamental weights (or one half times the sum of the positive roots)
Type E_7
Python 3.9
"""
# Import packages, if unavailable, use command: pip3 install numpy
import numpy as np
import itertools
import pandas as pd
import tabulate
from tabulate import tabulate
# Import package for loop progress bar in console
import tqdm
from tqdm import tqdm
# Class to store computations
comp_list = []
class Computations:
def __init__(self, fund, h, length, weyl, scalar_product):
self.fund = fund
self.h = h
self.length = length
self.weyl = weyl
self.scalar_product = scalar_product
def __str__(self):
return "h: {}, fund: {}, length: {}, weyl: {}, scalar_product: {} ".format(self.h, self.fund, self.length, [int(s) for s in self.weyl if s.isdigit()], self.scalar_product)
def __getitem__(self, item):
return getattr(self, item)
# Function to store the fundamental weights in an nxn array
def fundamental(n):
fundamental = np.zeros(shape=(n-1,n))
fundamental[0] = np.array([0,0,0,0,0,0,-1,1])
fundamental[1] = np.array([1,1,1,1,1,1,-1,1])
fundamental[2] = np.array([-1,1,1,1,1,1,-3,3])
fundamental[3] = np.array([0,0,1,1,1,1,-2,2])
fundamental[4] = np.array([0,0,0,2,2,2,-3,3])
fundamental[5] = np.array([0,0,0,0,1,1,-1,1])
fundamental[6] = np.array([0,0,0,0,0,2,-1,1])
return fundamental
# Function to compute the vectors h
def h_vector(n):
funds = fundamental(n)
h = np.vstack([funds, [0,1,2,3,4,5,-17/2,-17/2]])
# print('h_array = %s' % h )
return h
# Function to represent the simple reflections in matrix form
def matrix_form(elt):
elts = list(elt)
reflections = elts[1::3]
matrixform = np.eye(n)
for elt in reflections:
a = int(elt)
mat = np.eye(n)
if a == 1:
mat = -1*np.ones((n,n))
mat[a-1, ::-1] = 1
mat[:, a-1] = 1
mat[n-1, :] = 1
mat[:, n-1] = 1
mat[a-1, n-1] = -1
mat[n-1, a-1] = -1
np.fill_diagonal(mat, 3)
matrixform = np.dot(0.25*matrixform, mat)
elif a == 2:
mat[a-1, a-2] = -1
mat[a-2, a-1] = -1
mat[a-2, a-2] = 0
mat[a-1, a-1] = 0
matrixform = np.dot(matrixform, mat)
elif a == 8:
mat[a-2, a-2] = 0
mat[a-3, a-2] = 1
mat[a-2, a-3] = 1
mat[a-3, a-3] = 0
matrixform = np.dot(matrixform, mat)
else:
mat[a-2, a-2] = 0
mat[a-3, a-2] = 1
mat[a-2, a-3] = 1
mat[a-3, a-3] = 0
matrixform = np.dot(matrixform, mat)
return matrixform
# Initialize two lists, one for the generating symbolic reflections (r_1,r_2,...,r_j,...,r_n),
# one for the tuples of reflections, i.e. w = r_1*r_2*...*r_j.
# To reduce complexity, comment in or out the lines which generate the n-tuples of reflections needed
generators = []
all_possible_roots = []
def generate_reflections(n):
for i in range(1, n+1):
generators.append("r"+str(i))
# all_possible_roots.extend(generators)
# for subset in itertools.product(generators, generators):
# for subset in itertools.product(generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators):
for subset in itertools.product(generators, generators, generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators, generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators, generators, generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators, generators, generators, generators, generators, generators):
# for subset in itertools.product(generators, generators, generators, generators, generators, generators, generators, generators, generators, generators):
all_possible_roots.append('*'.join(subset))
return(all_possible_roots)
# Function to print results in LaTeX table
def latex_with_lines(df, *args, **kwargs):
kwargs['column_format'] = '|'.join([''] + ['l'] * df.index.nlevels
+ ['r'] * df.shape[1] + [''])
kwargs['index'] = False
res = df.to_latex(*args, **kwargs)
return res.replace('\\\\\n', '\\\\ \\hline\n')
# # Main function to test all fundamental weights
if __name__ == "__main__":
n = 8
fund = fundamental(n)
generators = generate_reflections(n-1)
h_vectors = h_vector(n)
nonzero_reflections = []
nonzero_reflections_matrix_form = []
scalar = []
for i in tqdm(generators):
a = matrix_form(i)
nonzero_reflections.append(i)
nonzero_reflections_matrix_form.append(a)
for j in range(len(fund)):
weyl_acting_fund = np.dot(a, fund[j])
for h in fund:
scalar_prod = np.dot(weyl_acting_fund, h)
if scalar_prod<0:
scalar.append(scalar_prod)
temp_permutation = Computations(fund[j], np.ravel(h), i.count("r"), i, scalar_prod)
comp_list.append(temp_permutation)
# for item in comp_list:
# print(item)
df = pd.DataFrame({'h': x.h.astype(int), '\varpi': x.fund.astype(int), 'Length': x.length, 'Scalar': x.scalar_product, 'Weyl': [int(s) for s in x.weyl if s.isdigit()]} for x in comp_list)
df_to_print = latex_with_lines(df)
print(df_to_print)
pass
# # Main function to test specific fundamental weight
# if __name__ == "__main__":
# n = 8
# fund = np.array([0,0,0,0,0,2,-1,1])
# generators = generate_reflections(n-1)
# # h_vectors = h_vector(n)
# nonzero_reflections = []
# nonzero_reflections_matrix_form = []
# scalar = []
# for i in tqdm(generators):
# a = matrix_form(i)
# nonzero_reflections.append(i)
# nonzero_reflections_matrix_form.append(a)
# weyl_acting_fund = np.dot(a, fund)
# scalar_prod = np.dot(weyl_acting_fund, fund)
# if scalar_prod<0:
# scalar.append(scalar_prod)
# temp_permutation = Computations(fund, fund, i.count("r"), i, scalar_prod)
# comp_list.append(temp_permutation)
# # for item in comp_list:
# # print(item)
# df = pd.DataFrame({'h': x.fund.astype(int), '\varpi': x.fund.astype(int), 'Length': x.length, 'Scalar': x.scalar_product, 'Weyl': [int(s) for s in x.weyl if s.isdigit()]} for x in comp_list)
# df_to_print = latex_with_lines(df)
# print(df_to_print)
# pass