From bd39af109910f634772760c90117825e98a312b1 Mon Sep 17 00:00:00 2001 From: Maria Marchwicka Date: Thu, 19 Nov 2020 06:47:52 +0100 Subject: [PATCH] verbose option added --- cable_signature.sage | 142 +- main.ipynb | 47923 ++++++++++++++++++++++++++++++++++++++++- 2 files changed, 47484 insertions(+), 581 deletions(-) diff --git a/cable_signature.sage b/cable_signature.sage index d08020b..2b20190 100644 --- a/cable_signature.sage +++ b/cable_signature.sage @@ -37,8 +37,9 @@ PLOTS_DIR = "plots" class CableSummand(): - def __init__(self, knot_as_k_values): + def __init__(self, knot_as_k_values, verbose=False): + self.verbose = verbose self.knot_as_k_values = knot_as_k_values self.knot_description = self.get_summand_descrption(knot_as_k_values) self.signature_as_function_of_theta = \ @@ -198,19 +199,25 @@ class CableSummand(): for theta in range(range_limit): self.plot_summand_for_theta(theta) - def get_sigma_as_function_of_theta(self): + def get_sigma_as_function_of_theta(self, verbose=None): + default_verbose = verbose or self.verbose last_k = self.knot_as_k_values[-1] last_q = 2 * abs(last_k) + 1 ksi = 1/last_q - def sigma_as_function_of_theta(theta, print_results=False): + def sigma_as_function_of_theta(theta, verbose=None, details=None): + verbose = verbose or default_verbose + details = details or verbose # satellite part (Levine-Tristram signatures) satellite_part = 0 + for i, k in enumerate(self.knot_as_k_values[:-1][::-1]): # print("layer") layer_num = i + 1 sigma_q = self.get_untwisted_signature_function(k) + if details: + # print(sigma_q(ksi * theta * layer_num)) # print(sigma_q) sp = 2 * sigma_q(ksi * theta * layer_num) * sign(k) @@ -219,6 +226,11 @@ class CableSummand(): pp = (-last_q + 2 * theta - 2 * (theta^2/last_q)) * sign(last_k) else: pp = 0 + if verbose: + print(self.knot_description + ", theta = " + str(theta)) + print("pp = " + str(pp), end=', ') + print("satellite_part = " + str(satellite_part)) + return pp + satellite_part return sigma_as_function_of_theta @@ -229,8 +241,9 @@ class CableSummand(): class CableSum(): - def __init__(self, knot_sum): + def __init__(self, knot_sum, verbose=False): + self.verbose = verbose self.knot_sum_as_k_valus = knot_sum self.knot_description = self.get_knot_descrption(knot_sum) self.patt_k_list = [abs(i[-1]) for i in knot_sum] @@ -243,7 +256,7 @@ class CableSum(): raise ValueError(msg) self.q_order = LCM_list(self.patt_q_list) - self.knot_summands = [CableSummand(k) for k in knot_sum] + self.knot_summands = [CableSummand(k, verbose) for k in knot_sum] self.signature_as_function_of_theta = \ self.get_signature_as_function_of_theta() self.sigma_as_function_of_theta = \ @@ -286,20 +299,20 @@ class CableSum(): for i, knot in enumerate(self.knot_summands): knot.plot_summand_for_theta(thetas[i], save_path=save_path) - pp, sp, sf = self.signature_as_function_of_theta(*thetas) - title = self.knot_description + ", thetas = " + str(thetas) - if save_path is not None: - file_name = re.sub(r', ', '_', str(thetas)) - file_name = re.sub(r'[\[\]]', '', str(file_name)) - file_path = os.path.join(save_path, file_name) - sig.SignaturePloter.plot_sum_of_two(pp, sp, title=title, - save_path=file_path) - - if save_path is not None: - file_path = os.path.join(save_path, "all_" + file_name) - sf_list = [knot.signature_as_function_of_theta(thetas[i])[2] - for i, knot in enumerate(self.knot_summands)] - sig.SignaturePloter.plot_many(*sf_list, cols=2) + # pp, sp, sf = self.signature_as_function_of_theta(*thetas) + # title = self.knot_description + ", thetas = " + str(thetas) + # if save_path is not None: + # file_name = re.sub(r', ', '_', str(thetas)) + # file_name = re.sub(r'[\[\]]', '', str(file_name)) + # file_path = os.path.join(save_path, file_name) + # sig.SignaturePloter.plot_sum_of_two(pp, sp, title=title, + # save_path=file_path) + # + # if save_path is not None: + # file_path = os.path.join(save_path, "all_" + file_name) + # sf_list = [knot.signature_as_function_of_theta(thetas[i])[2] + # for i, knot in enumerate(self.knot_summands)] + # sig.SignaturePloter.plot_many(*sf_list, cols=2) # pp, sp, sf = knot.signature_as_function_of_theta(thetas[i]) # (pp + sp) = sp.plot # @@ -390,6 +403,10 @@ class CableSum(): signature_as_function_of_theta_docstring return signature_as_function_of_theta + def get_sign_ext_for_theta(self, thetas, limit): + _, _, sf = self.signature_as_function_of_theta(*thetas) + return sf.extremum(limit=limit)[1] + def is_metabolizer(self, theta): # Check if square alternating difference # divided by last q value is integer. @@ -399,7 +416,13 @@ class CableSum(): # old_sum += (el^2 / self.patt_q_list[idx] * (-1)^idx) return result.is_integer() - def is_signature_big_in_ranges(self, ranges_list): + def is_function_big_in_ranges(self, ranges_list, invariant=SIGMA, + verbose=None): + verbose = verbose or self.verbose + if invariant == SIGNATURE: + get_invariant = self.get_sign_ext_for_theta + else: + get_invariant = self.sigma_as_function_of_theta for thetas in it.product(*ranges_list): @@ -407,31 +430,34 @@ class CableSum(): if not self.is_metabolizer(thetas) or not any(thetas): continue - signature_is_small = True + function_is_small = True # Check if any element generated by thetas vector # has a large signature. for shift in range(1, self.q_order): shifted_thetas = [shift * th for th in thetas] - pp, sp, sf= self.signature_as_function_of_theta(*shifted_thetas) limit = 5 + np.count_nonzero(shifted_thetas) - ext = sf.extremum(limit=limit)[1] - extremum = abs(ext) + + # pp, sp, sf= self.signature_as_function_of_theta(*shifted_thetas) + inv_value = get_invariant(shifted_thetas, limit=limit) + # print(ext) + abs_value = abs(inv_value) if shift > 1: print(shifted_thetas, end=" ") - print(extremum) - if extremum > limit: - signature_is_small = False - break - elif shift == 1: + print(inv_value) + elif shift == 1 and verbose: print("*" * 10) print(shifted_thetas, end=" ") - print(ext) - if signature_is_small: + print(inv_value) + + if abs_value > limit: + function_is_small = False + break + if function_is_small: print("\n" * 10 + "!" * 1000) return False return True - def is_function_big_for_all_metabolizers(self, function_type=SIGMA): + def is_function_big_for_all_metabolizers(self, invariant=SIGMA): num_of_summands = len(self.knot_sum_as_k_valus) if num_of_summands % 4: f_name = self.is_signature_big_for_all_metabolizers.__name__ @@ -444,61 +470,19 @@ class CableSum(): ranges_list[shift : shift + 3] = \ [range(0, i + 1) for i in self.patt_k_list[shift: shift + 3]] ranges_list[shift + 3] = range(0, 2) - if function_type == SIGNATURE: - if not self.is_signature_big_in_ranges(ranges_list): - return False - else: - if not self.is_sigma_big_in_ranges(ranges_list): - return False + if not self.is_function_big_in_ranges(ranges_list, invariant): + return False print("\nOK") return True - def is_sigma_big_in_ranges(self, ranges_list): - - for thetas in it.product(*ranges_list): - - # Check only non-zero metabolizers. - if not self.is_metabolizer(thetas) or not any(thetas): - continue - - signature_is_small = True - # Check if any element generated by thetas vector - # has a large signature. - for shift in range(1, self.q_order): - shifted_thetas = [shift * th for th in thetas] - # pp, sp, sf= self.signature_as_function_of_theta(*shifted_thetas) - limit = 5 + np.count_nonzero(shifted_thetas) - ext = self.sigma_as_function_of_theta(shifted_thetas) - # print(ext) - extremum = abs(ext) - if shift > 1: - print(shifted_thetas, end=" ") - print(extremum) - if extremum > limit: - signature_is_small = False - break - elif shift == 1: - print("*" * 10) - print(shifted_thetas, end=" ") - print(ext) - if signature_is_small: - print("\n" * 10 + "!" * 1000) - return False - return True - - - - - - - class CableTemplate(): def __init__(self, knot_formula, q_vector=None, k_vector=None, - generate_q_vector=True, slice=True): + generate_q_vector=True, slice=True, verbose=False): + self.verbose = verbose self._knot_formula = knot_formula # q_i = 2 * k_i + 1 if k_vector is not None: @@ -537,7 +521,7 @@ class CableTemplate(): raise IndexError(msg) self.knot_sum_as_k_valus = eval(self.knot_formula) - self._cable = CableSum(self.knot_sum_as_k_valus) + self._cable = CableSum(self.knot_sum_as_k_valus, verbose=self.verbose) self._q_vector = [2 * k_val + 1 for k_val in k] @property diff --git a/main.ipynb b/main.ipynb index fa60834..9102acd 100644 --- a/main.ipynb +++ b/main.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The pycodestyle_magic extension is already loaded. To reload it, use:\n", + " %reload_ext pycodestyle_magic\n" + ] + } + ], "source": [ "%matplotlib inline\n", "%load_ext pycodestyle_magic\n", @@ -24,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -47,6 +56,481 @@ "# sig = import_sage('signature')" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Other cables" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[0, 0, 1, 1] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[0, 1, 1, 0] -4\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "[0, 2, 2, 0] -8\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[0, 2, 2, 0] -8\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[0, 2, 4, 1] -13\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[0, 3, 3, 0] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[0, 4, 4, 0] -20\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[0, 5, 2, 1] -13\n", + "T(2, 3; 2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[0, 5, 5, 0] -20\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 0, 0, 1] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[1, 1, 0, 0] -4\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "[2, 2, 0, 0] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 1, 1, 1] -16\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 2, 2, 1] -20\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[1, 2, 5, 0] -27\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 3, 3, 1] -24\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[1, 4, 2, 0] -27\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 4, 4, 1] -32\n", + "T(2, 3; 2, 5; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[1, 5, 5, 1] -32\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[2, 1, 3, 1] -20\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[2, 2, 0, 0] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[2, 2, 1, 1] -24\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[2, 3, 4, 0] -31\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[2, 4, 1, 0] -31\n", + "T(2, 3; 2, 5; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[2, 5, 0, 1] -17\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[3, 0, 5, 1] -31\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[3, 1, 2, 1] -16\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[3, 1, 5, 0] -23\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[3, 3, 0, 0] -12\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[3, 3, 1, 1] -24\n", + "T(2, 3; 2, 5; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[3, 5, 4, 0] -35\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[4, 2, 0, 1] -13\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 2\n", + "pp = -85/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[4, 3, 2, 0] -27\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 3\n", + "pp = 73/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[4, 3, 4, 1] -32\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[4, 4, 0, 0] -20\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[4, 4, 1, 1] -32\n", + "T(2, 3; 2, 5; 2, 11), theta = 4\n", + "pp = -65/11, satellite_part = -12\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[4, 5, 3, 0] -35\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[5, 0, 3, 1] -27\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = 0\n", + "T(2, 7; 2, 11), theta = 3\n", + "pp = -73/11, satellite_part = -8\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[5, 1, 3, 0] -19\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 2\n", + "pp = 85/11, satellite_part = -4\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[5, 2, 1, 0] -23\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 4\n", + "pp = 65/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -12\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[5, 4, 5, 1] -32\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "-T(2, 3; 2, 7; 2, 11), theta = 0\n", + "pp = 0, satellite_part = 0\n", + "**********\n", + "[5, 5, 0, 0] -16\n", + "T(2, 3; 2, 5; 2, 11), theta = 5\n", + "pp = -61/11, satellite_part = -8\n", + "-T(2, 5; 2, 11), theta = 5\n", + "pp = 61/11, satellite_part = -8\n", + "T(2, 7; 2, 11), theta = 1\n", + "pp = -101/11, satellite_part = -4\n", + "-T(2, 3; 2, 7; 2, 11), theta = 1\n", + "pp = 101/11, satellite_part = -8\n", + "**********\n", + "[5, 5, 1, 1] -28\n", + "\n", + "OK\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knot_formula = \"[[k[0], k[1], k[3]],\" + \\\n", + " \" [-k[1], -k[3]],\" + \\\n", + " \" [k[2], k[3]],\" + \\\n", + " \" [-k[0], -k[2], -k[3]]]\"\n", + "# q_vector = (3, 5, 7, 13)\n", + "q_vector = (3, 5, 7, 11)\n", + "\n", + "template = cs.CableTemplate(knot_formula, q_vector=q_vector, verbose=True)\n", + "cable = template.cable\n", + "# cable.plot_all_summands()\n", + "cable.is_function_big_for_all_metabolizers(invariant=cs.SIGMA)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -85,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -101,34 +585,8 @@ " 7, 17, 23, 43)\n", "cable_template.fill_q_vector(q_vector=q_vector)\n", "cable = cable_template.cable\n", - "print(cable.knot_description)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# cable.is_signature_big_for_all_metabolizers()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T(2, 5; 2, 17; 2, 41) # -T(2, 13; 2, 41) # T(2, 19; 2, 41) # -T(2, 5; 2, 19; 2, 41) # T(2, 7; 2, 13; 2, 43) # -T(2, 17; 2, 43) # T(2, 23; 2, 43) # -T(2, 7; 2, 23; 2, 43)\n" - ] - } - ], - "source": [ + "print(cable.knot_description)\n", + "\n", "q_vector_small = (3, 7, 13, 19,\\\n", " 5, 11, 17, 23)\n", "cable_template.fill_q_vector(q_vector=q_vector)\n", @@ -138,8 +596,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 5, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# cable.is_signature_big_for_all_metabolizers()" @@ -180,13 +640,6 @@ "# cable.is_signature_big_for_all_metabolizers()\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Other cables" - ] - }, { "cell_type": "code", "execution_count": 9, @@ -211,26 +664,6 @@ "# [-k[0], -k[1], -k[3]], [-k[2]]]\"" ] }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "knot_formula = \"[[k[0], k[1], k[3]],\" + \\\n", - " \" [-k[1], -k[3]],\" + \\\n", - " \" [k[2], k[3]],\" + \\\n", - " \" [-k[0], -k[2], -k[3]]]\"\n", - "# q_vector = (3, 5, 7, 13)\n", - "q_vector = (3, 5, 7, 11)\n", - "\n", - "template = cs.CableTemplate(knot_formula, q_vector=q_vector)\n", - "cable = template.cable\n", - "# cable.plot_all_summands()" - ] - }, { "cell_type": "code", "execution_count": 11, @@ -305,6 +738,30 @@ "execution_count": 12, "metadata": {}, "output_type": "execute_result" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n" + ] + }, + { + "data": { + "text/plain": [ + "-28" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -329,7 +786,11 @@ "print(slice_canidate.knot_description)\n", "\n", "slice_canidate.q_order\n", - "# slice_canidate.is_signature_big_for_all_metabolizers()" + "# slice_canidate.is_signature_big_for_all_metabolizers()\n", + "sigma = slice_canidate.get_sigma_as_function_of_theta()\n", + "# sigma((0, 6, 6, 0, 0,0,0,0))\n", + "# 13450/83\n", + "sigma((9, 9, 9, 9, 0,0,0,0))" ] }, { @@ -440,7 +901,7 @@ "sf = slice_canidate(4,4,4,4,0,0,0,0)\n", "sf = slice_canidate(4,1,1,4,0,0,0,0)\n", "slice_canidate.q_order\n", - "# slice_canidate.is_signature_big_for_all_metabolizers()" + "# slice_canidate.is_signature_big_for_all_metabolizers()\n" ] }, { @@ -460,7 +921,7 @@ "outputs": [ { "data": { - "image/png": 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Qmk7Zr3H2/E3gcoAkP8UwDIfXdMq1NQu8ufvppFcDT1bVY30+wUn5UlJVHU1yPbCP4U813FZVB5LcBMxV1SxwK8NLznmGVwo7pzfxZMbc758CLwT+vnuP/ZtVdfXUhp7QmHs+qYy5533AlUm+Anwf+KOq+p/pTT2ZMff8B8DfJHkbw5dU3rKOv8kjyccYvhS4sXvf5EbgRwCq6sMM30d5PTAPPA38Zu8zrON/fpKkVXCyvpQkSTpBhkGS1DAMkqSGYZAkNQyDJKlhGCRJDcMgSWoYBklS4/8ARBfoYLOq5tAAAAAASUVORK5CYII=\n", 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" ] @@ -496,267 +957,6 @@ "execution_count": 17, "metadata": {}, "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "**********\n", - "[0, 0, 1, 1, 0, 0, 0, 0] 2\n", - "[0, 0, 2, 2, 0, 0, 0, 0] 2\n", - "[0, 0, 3, 3, 0, 0, 0, 0] 2\n", - "[0, 0, 4, 4, 0, 0, 0, 0] 6\n", - "[0, 0, 5, 5, 0, 0, 0, 0] 6\n", - "[0, 0, 6, 6, 0, 0, 0, 0] 6\n", - "[0, 0, 7, 7, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 1, 1, 0, 0, 0, 0, 0] 2\n", - "[0, 2, 2, 0, 0, 0, 0, 0] 2\n", - "[0, 3, 3, 0, 0, 0, 0, 0] 2\n", - "[0, 4, 4, 0, 0, 0, 0, 0] 6\n", - "[0, 5, 5, 0, 0, 0, 0, 0] 6\n", - "[0, 6, 6, 0, 0, 0, 0, 0] 6\n", - "[0, 7, 7, 0, 0, 0, 0, 0] 6\n", - "[0, 8, 8, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 2, 2, 0, 0, 0, 0, 0] 2\n", - "[0, 4, 4, 0, 0, 0, 0, 0] 6\n", - "[0, 6, 6, 0, 0, 0, 0, 0] 6\n", - "[0, 8, 8, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 3, 3, 0, 0, 0, 0, 0] 2\n", - "[0, 6, 6, 0, 0, 0, 0, 0] 6\n", - "[0, 9, 9, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 4, 4, 0, 0, 0, 0, 0] -6\n", - "[0, 8, 8, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 5, 5, 0, 0, 0, 0, 0] -6\n", - "[0, 10, 10, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[0, 6, 6, 0, 0, 0, 0, 0] -6\n", - "[0, 12, 12, 0, 0, 0, 0, 0] 10\n", - "**********\n", - "[0, 7, 7, 0, 0, 0, 0, 0] -6\n", - "[0, 14, 14, 0, 0, 0, 0, 0] 10\n", - "**********\n", - "[1, 0, 0, 1, 0, 0, 0, 0] 4\n", - "[2, 0, 0, 2, 0, 0, 0, 0] 4\n", - "[3, 0, 0, 3, 0, 0, 0, 0] 8\n", - "**********\n", - "[1, 1, 0, 0, 0, 0, 0, 0] 4\n", - "[2, 2, 0, 0, 0, 0, 0, 0] 4\n", - "[3, 3, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[1, 1, 1, 1, 0, 0, 0, 0] 6\n", - "[2, 2, 2, 2, 0, 0, 0, 0] 6\n", - "[3, 3, 3, 3, 0, 0, 0, 0] 10\n", - "**********\n", - "[1, 2, 2, 1, 0, 0, 0, 0] 6\n", - "[2, 4, 4, 2, 0, 0, 0, 0] 10\n", - "**********\n", - "[1, 3, 3, 1, 0, 0, 0, 0] 6\n", - "[2, 6, 6, 2, 0, 0, 0, 0] 10\n", - "**********\n", - "[2, 2, 0, 0, 0, 0, 0, 0] 4\n", - "[4, 4, 0, 0, 0, 0, 0, 0] 4\n", - "[6, 6, 0, 0, 0, 0, 0, 0] 4\n", - "[8, 8, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[2, 2, 1, 1, 0, 0, 0, 0] 6\n", - "[4, 4, 2, 2, 0, 0, 0, 0] 6\n", - "[6, 6, 3, 3, 0, 0, 0, 0] 6\n", - "[8, 8, 4, 4, 0, 0, 0, 0] 6\n", - "[10, 10, 5, 5, 0, 0, 0, 0] 6\n", - "[12, 12, 6, 6, 0, 0, 0, 0] 8\n", - "[14, 14, 7, 7, 0, 0, 0, 0] 8\n", - "[16, 16, 8, 8, 0, 0, 0, 0] 6\n", - "[18, 18, 9, 9, 0, 0, 0, 0] 10\n", - "**********\n", - "[4, 4, 0, 0, 0, 0, 0, 0] 4\n", - "[8, 8, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[4, 4, 1, 1, 0, 0, 0, 0] 6\n", - "[8, 8, 2, 2, 0, 0, 0, 0] 8\n", - "[12, 12, 3, 3, 0, 0, 0, 0] 6\n", - "[16, 16, 4, 4, 0, 0, 0, 0] 8\n", - "[20, 20, 5, 5, 0, 0, 0, 0] 8\n", - "[24, 24, 6, 6, 0, 0, 0, 0] 10\n", - "**********\n", - "[5, 5, 0, 0, 0, 0, 0, 0] -4\n", - "[10, 10, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[5, 5, 1, 1, 0, 0, 0, 0] 4\n", - "[10, 10, 2, 2, 0, 0, 0, 0] 8\n", - "[15, 15, 3, 3, 0, 0, 0, 0] 10\n", - "**********\n", - "[6, 6, 0, 0, 0, 0, 0, 0] -4\n", - "[12, 12, 0, 0, 0, 0, 0, 0] 4\n", - "[18, 18, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[6, 6, 1, 1, 0, 0, 0, 0] -4\n", - "[12, 12, 2, 2, 0, 0, 0, 0] 6\n", - "[18, 18, 3, 3, 0, 0, 0, 0] 10\n", - "**********\n", - "[7, 7, 0, 0, 0, 0, 0, 0] -4\n", - "[14, 14, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[7, 7, 1, 1, 0, 0, 0, 0] -6\n", - "[14, 14, 2, 2, 0, 0, 0, 0] 10\n", - "**********\n", - "[8, 8, 1, 1, 0, 0, 0, 0] 8\n", - "[16, 16, 2, 2, 0, 0, 0, 0] 10\n", - "**********\n", - "[12, 12, 0, 0, 0, 0, 0, 0] -4\n", - "[24, 24, 0, 0, 0, 0, 0, 0] 8\n", - "**********\n", - "[12, 12, 1, 1, 0, 0, 0, 0] -6\n", - "[24, 24, 2, 2, 0, 0, 0, 0] 10\n", - "**********\n", - "[26, 29, 0, 1, 0, 0, 0, 0] -8\n", - "[52, 58, 0, 2, 0, 0, 0, 0] 10\n", - "\n", - "OK\n", - "**********\n", - "[0, 0, 0, 0, 0, 0, 1, 1] -4\n", - "[0, 0, 0, 0, 0, 0, 2, 2] 4\n", - "[0, 0, 0, 0, 0, 0, 3, 3] 4\n", - "[0, 0, 0, 0, 0, 0, 4, 4] 4\n", - "[0, 0, 0, 0, 0, 0, 5, 5] 4\n", - "[0, 0, 0, 0, 0, 0, 6, 6] 4\n", - "[0, 0, 0, 0, 0, 0, 7, 7] 4\n", - "[0, 0, 0, 0, 0, 0, 8, 8] 4\n", - "[0, 0, 0, 0, 0, 0, 9, 9] 4\n", - "[0, 0, 0, 0, 0, 0, 10, 10] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 1, 1, 0] -2\n", - "[0, 0, 0, 0, 0, 2, 2, 0] 2\n", - "[0, 0, 0, 0, 0, 3, 3, 0] 2\n", - "[0, 0, 0, 0, 0, 4, 4, 0] 6\n", - "[0, 0, 0, 0, 0, 5, 5, 0] 6\n", - "[0, 0, 0, 0, 0, 6, 6, 0] 6\n", - "[0, 0, 0, 0, 0, 7, 7, 0] 6\n", - "[0, 0, 0, 0, 0, 8, 8, 0] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 2, 2, 0] -2\n", - "[0, 0, 0, 0, 0, 4, 4, 0] 6\n", - "[0, 0, 0, 0, 0, 6, 6, 0] 6\n", - "[0, 0, 0, 0, 0, 8, 8, 0] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 3, 3, 0] -2\n", - "[0, 0, 0, 0, 0, 6, 6, 0] 6\n", - "[0, 0, 0, 0, 0, 9, 9, 0] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 4, 4, 0] 6\n", - "[0, 0, 0, 0, 0, 8, 8, 0] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 5, 5, 0] 6\n", - "[0, 0, 0, 0, 0, 10, 10, 0] 8\n", - "**********\n", - "[0, 0, 0, 0, 0, 6, 6, 0] 6\n", - "[0, 0, 0, 0, 0, 12, 12, 0] 10\n", - "**********\n", - "[0, 0, 0, 0, 0, 7, 7, 0] 6\n", - "[0, 0, 0, 0, 0, 14, 14, 0] 10\n", 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"slice_canidate.is_signature_big_for_all_metabolizers()" + "slice_canidate.q_order" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# slice_canidate.is_sigma_big_for_all_metabolizers()\n", + "# slice_canidate.is_signature_big_for_all_metabolizers()\n", + "# slice_canidate.is_function_big_for_all_metabolizers()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "**********\n", + "[0, 0, 1, 1, 0, 0, 0, 0] 0\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "not verbose\n", + "[0, 0, 2, 2, 0, 0, 0, 0] 0\n", + "not verbose\n", + "not verbose\n", + "not 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"slice_canidate.is_function_big_for_all_metabolizers(invariant=cs.SIGNATURE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sigma = slice_canidate.get_sigma_as_function_of_theta()\n", + "sigma((0, 6, 6, 0, 0,0,0,0))\n", + "# 13450/83\n", + "sigma((9, 0, 0, 9, 0,0,0,0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "_, _, sf = slice_canidate((1, 1, 0, 0, 0,0,0,0))\n", + "sf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cs = import_sage('cable_signature')\n", + "sg = import_sage('signature')\n", + "sg.SignaturePloter.plot(sf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "cs = import_sage('cable_signature')\n", + "sg = import_sage('signature')\n", + "\n", + "slice_canidate.plot_sum_for_theta_vector([0,4,0,4,0,0,0,0], save_to_dir=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sf.plot()\n" ] }, { @@ -798,183 +47890,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "_, _, sf = slice_canidate((1, 1, 0, 0, 0,0,0,0))" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "T_17_83_inv_T_11_83_T_83_inv_T_13_83_T_11_103_inv_T_103_T_13_103_inv_T_17_103\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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