From 68cf72305efa8096b02414ff3b937eb7d1fdee83 Mon Sep 17 00:00:00 2001
From: Maria Marchwicka <maria.marchwicka@amu.edu.pl>
Date: Thu, 19 Nov 2020 10:32:15 +0100
Subject: [PATCH] plots

---
 cable_signature.sage |   124 +-
 main.ipynb           | 48840 ++---------------------------------------
 2 files changed, 2392 insertions(+), 46572 deletions(-)

diff --git a/cable_signature.sage b/cable_signature.sage
index d4ba0ed..afb0a7f 100644
--- a/cable_signature.sage
+++ b/cable_signature.sage
@@ -44,8 +44,11 @@ class CableSummand():
         self.knot_description = self.get_summand_descrption(knot_as_k_values)
         self.signature_as_function_of_theta = \
                                 self.get_summand_signature_as_theta_function()
-        self.sigma_as_function_of_theta = \
-                                self.get_sigma_as_function_of_theta()
+        if verbose:
+            s = self.get_verbose_sigma_as_function_of_theta(verbose=True)
+        else:
+            s = self.get_sigma_as_function_of_theta()
+        self.sigma_as_function_of_theta = s
 
     @staticmethod
     def get_summand_descrption(knot_as_k_values):
@@ -192,14 +195,14 @@ class CableSummand():
             file_name = self.get_file_name_for_summand_plot(theta)
             save_path = os.path.join(save_path, file_name)
         sig.SignaturePloter.plot_sum_of_two(pp, sp, title=title,
-                                              save_path=save_path)
+                                            save_path=save_path)
 
     def plot_summand(self):
         range_limit = min(self.knot_as_k_values[-1] + 1, 3)
         for theta in range(range_limit):
             self.plot_summand_for_theta(theta)
 
-    def get_sigma_as_function_of_theta(self, verbose=None):
+    def get_verbose_sigma_as_function_of_theta(self, verbose=None):
 
         default_verbose = verbose or self.verbose
         last_k = self.knot_as_k_values[-1]
@@ -211,28 +214,63 @@ class CableSummand():
             details = details or verbose
             # satellite part (Levine-Tristram signatures)
             satellite_part = 0
+            if verbose:
+                print(3 * "\n" + 10 * "#" + " " + self.knot_description +
+                      " " + 10 * "#" + "\n")
 
+            for layer_num, k in enumerate(self.knot_as_k_values[::-1]):
+
+                sigma_q = self.get_untwisted_signature_function(k)
+                arg = ksi * theta * layer_num
+                sp = sigma_q(arg)
+                satellite_part += 2 * sp
+
+                if details and arg:
+                    label = "ksi * theta * layer_num = " + str(arg)
+                    title = self.knot_description + ", layer " + str(layer_num)
+                    title += ", theta = " + str(theta)
+                    sigma_q.plot(special_point=(mod_one(arg), sp),
+                                 special_label=label,
+                                 title=title,)
+
+            if theta:
+                pp = (-last_q + 2 * theta - 2 * (theta^2/last_q)) * sign(last_k)
+            else:
+                pp = 0
+            sigma = pp + satellite_part
+            if verbose and theta:
+                print(self.knot_description + ", theta = " + str(theta))
+                print("pp = " + str(pp), end=', ')
+                print("satellite_part = " + str(satellite_part) + "\n")
+            if verbose:
+                print("sigma({}) = {}".format(self.knot_description,
+                                              pp + satellite_part))
+
+            return pp, satellite_part, sigma
+        return sigma_as_function_of_theta
+
+
+
+
+    def get_sigma_as_function_of_theta(self):
+
+        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):
+
+            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:
-                    pass
-
-                # print(sigma_q(ksi * theta * layer_num))
-                # print(sigma_q)
-                sp = 2 * sigma_q(ksi * theta * layer_num) * sign(k)
+                sp = 2 * sigma_q(ksi * theta * layer_num)
                 satellite_part += sp
             if theta:
                 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 pp, satellite_part, pp + satellite_part
         return sigma_as_function_of_theta
 
 
@@ -297,8 +335,8 @@ class CableSum():
         else:
             save_path = None
 
-        for i, knot in enumerate(self.knot_summands):
-            knot.plot_summand_for_theta(thetas[i], save_path=save_path)
+        for theta, knot in zip(thetas, self.knot_summands):
+            knot.plot_summand_for_theta(thetas, save_path=save_path)
 
         # pp, sp, sf = self.signature_as_function_of_theta(*thetas)
         # title = self.knot_description + ", thetas = " + str(thetas)
@@ -358,14 +396,36 @@ class CableSum():
             description = description[:-2] + ") # "
         return description[:-3]
 
-    def get_sigma_as_function_of_theta(self):
-        def sigma_as_function_of_theta(*thetas, **kwargs):
+    def get_sigma_as_function_of_theta(self, verbose=None):
+        default_verbose = verbose or self.verbose
+        def sigma_as_function_of_theta(*thetas, verbose=None, **kwargs):
+            verbose = verbose or default_verbose
             thetas = self.parse_thetas(*thetas)
-            result = 0
-            for i, knot in enumerate(self.knot_summands):
-                sigma_of_th = knot.sigma_as_function_of_theta
-                result += sigma_of_th(thetas[i])
-            return result
+            sigma_list = []
+
+            for theta, knot in zip(thetas, self.knot_summands):
+                if theta:
+                    sigma_of_th = knot.sigma_as_function_of_theta
+                    sigma_list.append(sigma_of_th(theta))
+                else:
+                    sigma_list.append((0,0,0))
+            if verbose:
+                print(100 * "*")
+                print("Calculation summary for a cable sum:\n" +
+                      self.knot_description)
+                for i, knot in enumerate(self.knot_summands):
+                    if thetas[i]:
+                        print("{}. {}".format(i, knot.knot_description))
+                        print("Pattern part = {}".format(sigma_list[i][0]))
+                        # print("Pattern part = -{}".format(knot_sum.))
+
+                        print("Satellite part = {}".format(sigma_list[i][1]))
+                        print("Sigma = {}\n".format(sigma_list[i][2]))
+
+
+
+
+            return sum(r[2] for r in sigma_list)
         return sigma_as_function_of_theta
 
     def get_signature_as_function_of_theta(self, **key_args):
@@ -386,11 +446,17 @@ class CableSum():
             pattern_part = sig.SignatureFunction()
 
             # for each cable knot (summand) in cable sum apply theta
-            for i, knot in enumerate(self.knot_summands):
-                sfth = knot.signature_as_function_of_theta
-                pp, sp, _ = sfth(thetas[i])
+            # for i, knot in enumerate(self.knot_summands):
+            #     sfth = knot.signature_as_function_of_theta
+            #     pp, sp, _ = sfth(thetas[i])
+            #     pattern_part += pp
+            #     satellite_part += sp
+            for theta, knot in zip(thetas, self.knot_summands):
+                pp, sp, _ = knot.signature_as_function_of_theta(theta)
                 pattern_part += pp
                 satellite_part += sp
+
+
             sf = pattern_part + satellite_part
 
             if verbose:
diff --git a/main.ipynb b/main.ipynb
index 9102acd..8ff1da5 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The pycodestyle_magic extension is already loaded. To reload it, use:\n",
-      "  %reload_ext pycodestyle_magic\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib inline\n",
     "%load_ext pycodestyle_magic\n",
@@ -33,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -74,450 +65,833 @@
      "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"
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 1\n",
+      "pp = -101/11, satellite_part = -4\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -145/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 1\n",
+      "pp = 101/11, satellite_part = 8\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 189/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -101/11\n",
+      "Satellite part = -4\n",
+      "Sigma = -145/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 101/11\n",
+      "Satellite part = 8\n",
+      "Sigma = 189/11\n",
+      "\n",
+      "**********\n",
+      "[0, 0, 1, 1] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 2\n",
+      "pp = -85/11, satellite_part = -4\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -129/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 2\n",
+      "pp = 85/11, satellite_part = 8\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 173/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -85/11\n",
+      "Satellite part = -4\n",
+      "Sigma = -129/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 85/11\n",
+      "Satellite part = 8\n",
+      "Sigma = 173/11\n",
+      "\n",
+      "[0, 0, 2, 2] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 3\n",
+      "pp = -73/11, satellite_part = -8\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -161/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 3\n",
+      "pp = 73/11, satellite_part = 12\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 205/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -73/11\n",
+      "Satellite part = -8\n",
+      "Sigma = -161/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 73/11\n",
+      "Satellite part = 12\n",
+      "Sigma = 205/11\n",
+      "\n",
+      "[0, 0, 3, 3] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 4\n",
+      "pp = -65/11, satellite_part = -12\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -197/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 4\n",
+      "pp = 65/11, satellite_part = 16\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 241/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -65/11\n",
+      "Satellite part = -12\n",
+      "Sigma = -197/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 65/11\n",
+      "Satellite part = 16\n",
+      "Sigma = 241/11\n",
+      "\n",
+      "[0, 0, 4, 4] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 5\n",
+      "pp = -61/11, satellite_part = -12\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -193/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 5\n",
+      "pp = 61/11, satellite_part = 12\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 193/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -61/11\n",
+      "Satellite part = -12\n",
+      "Sigma = -193/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 61/11\n",
+      "Satellite part = 12\n",
+      "Sigma = 193/11\n",
+      "\n",
+      "[0, 0, 5, 5] 0\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 6\n",
+      "pp = -61/11, satellite_part = -12\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -193/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 6\n",
+      "pp = 61/11, satellite_part = 12\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 193/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -61/11\n",
+      "Satellite part = -12\n",
+      "Sigma = -193/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 61/11\n",
+      "Satellite part = 12\n",
+      "Sigma = 193/11\n",
+      "\n",
+      "[0, 0, 6, 6] 0\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 7\n",
+      "pp = -65/11, satellite_part = -12\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -197/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 7\n",
+      "pp = 65/11, satellite_part = 16\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 241/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -65/11\n",
+      "Satellite part = -12\n",
+      "Sigma = -197/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 65/11\n",
+      "Satellite part = 16\n",
+      "Sigma = 241/11\n",
+      "\n",
+      "[0, 0, 7, 7] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 8\n",
+      "pp = -73/11, satellite_part = -8\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -161/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 8\n",
+      "pp = 73/11, satellite_part = 12\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 205/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -73/11\n",
+      "Satellite part = -8\n",
+      "Sigma = -161/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 73/11\n",
+      "Satellite part = 12\n",
+      "Sigma = 205/11\n",
+      "\n",
+      "[0, 0, 8, 8] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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VLF++nMWLF7N06VLWrFmzJXAl0a9fP3bccUcAnnzySVatWtWtw9d6DoevmW13AwYM4NJLL+Xwww9n1qxZRYdva/r06cOIESMYMWIERx111Jb5DQ0NrFy5khUrVrBs2TIWLVrE8uXLaWhooF+/fowZM6bUXTErCw87m1lSEZH0/GtE8Pbbb/Pqq68yfvz4ba6+7k487Nxz+FuNzCyp1Bc+Nd1zPHHixG2Cd8WKFUyYMKHovo488sht5jUdwEyfPn2r54WuuuqqTm8T4O677+aZZ57p0DrKjM4/i6Gl5ZMk3duhTnswSddKWpw//rHZstMkTZP0d5L+LGmDpAubtblR0huSFhezPYevmVmR5s2bt8282bNnM3PmTNavX8+MGTOYPXv2Nm0Kw7czOhO+wM+Bo4CRkm6QtHdJRXSQpG5zWlPSPwCHANXAJ4ELJe1S0OQk4D7gbeDbwA9a6OYm4MRit+nwNTMDXnzxRQ4++GCeeOIJlixZwuGHH051dTUTJ05k+fLlAOy8887brDdlyhSGDx/OzJkzGTlyJFOmTNlq+SWXXEJDQwPV1dWcccYZQHbB2DnnnMP48eM54YQTaGhoALIPKDnxxBM59NBDOfroo7d8FWNtbW3T1y+Ok/QJSedIekLS05L+S9KOLezS+cDpZJ+7f2lEvNravks6PD+ie0rSPEn75/MfkVRd0O5RSQdJ2ik/0ns8X2dyvvxsSbWSHgb+0Mq2JkmaK+kOSUslzVY+HCJphaSh+XSNpLn59HRJN0v6k6SVkk6RNEPSIkn3SSr15u5xwCMRsTkiPgQWkgdpXls18NeIeCMingA2Ne8gIh4hC+fiNN0bl/Jx6KGHhplZpb300ksxfvz4WLp0aVRXV8eCBQsiIuKCCy6IX/ziFxERsWHDhli3bl1EROy0007b9DF79uyYMWNGXHzxxXHttdfG7Nmzt2lTuN5LL70Uffr0iaeeeioiIk499dS49dZbIyLi05/+dDz33HMRETF//vw49thjIyLirLPOijlz5gRQF9mw9u6R/z0FrgC+Fc3+zgL/AUwBLgP+E/h4s+WTgHvz6V2Aqnz6eOC/8umzgH/Pp/cr2P5VwJR8ejDwHLATcDawChjSvJ5m210LDCc7APwzcFS+bAUwNJ+uAebm09OBR4G+wEHAOuCkfNldwOdb2M5UYEELj5+00PYE4DFgR2Ao8CLwz/myQ4BbmrWfDlzYQj+jgcWt7Xvho9sMC5iZbQ/19fVMnjyZO++8k3HjxgFwxBFHcOWVV7Jq1SpOOeUU9t1331bXP/3005HE9OnTueiii1o859vcmDFjqK6uBuDQQw9lxYoVfPDBB8ybN49TTz11S7sNGza01sUESVeQBd/OwP0ttDkfGEUWqv/WTkm7AjdL2hcIspADmAP8i6SpZEfQN+XzTwA+V3DecwDQ9HV1D0ZEe0eAj0fEKgBJC8hC69F21vl9RGyStAjoQzYMDLAoX38rETETmNlOn01tH5B0GDAPqCd7Q/C3fPGJwO+L6acjPOxsZr3arrvuysiRI3n00Y/+9n/lK1+htraWgQMHcvLJJ/Pwww+3un7TBWRNF1wVc0FZ//79t0w3fVxmY2MjgwcPZsGCBVsezz77bGtd3ARcEBEHAt8nC7+t5AfaKyLipnYLgsuBP0bEBOCzTf1F9mU5DwKTgS8DTSe0BXwxIqrzx8iIaCr2wyK2V/iu4m98dNvrZj7Kpeb7tCGvqRHYFB+9y2mkhdtmJU2VtKCFx09aKigirsz35TP5/j2XLzoBeKCldUrh8DWzXq1fv37cdddd3HLLLdx2221Adv537NixfPvb32by5MksXLiwpG307duXTZu2OU24lV122YUxY8YwZ84cIDsl+PTTTwMwaNAg3n///cLmg4DX8nOdZ5RUXGZXoOmc8NnNll0P/AR4IiLeyefdD3yr4FztwWWoAbJh50Pz6S+W0lFEzCx4c1D4+HbztpL6SNo9n54ITAQekLQr2cjBW83XKZXD18x6vZ122ol7772XWbNmUVtby+23386ECROorq5m8eLFnHnmmSX1f+655zJx4sQtF1y1Zvbs2dxwww0cdNBBjB8/nnvuuQeA0047jZkzZ0J+wRXwL8BfyM5TLi2puMwM4GpJT9HsKDIingTeA/5vwezLyYamF0pakj8vh+8DP5ZUx0fDvin0Bf4k6RngOrLz2ZuBzwAPNTWSNEzSKuA7wPckrWq6KlrSL8mGq/fP53+9rQ36QzbMzLqJSnzIhqSPA3OBv8uHfHsNSdcD10fE/HL37SNfMzNrkaQzyY6wp/W24AWIiP+xPYIX/NnOZmbWioi4BbilM+tKOhC4tdnsDRHxyZIL6wEcvmZmVnYRsYjswymsBR52NjMzS8zha2ZmlpjD18zMLDGHr5mZWWIOXzMzs8QcvmZmZok5fM3MzBJz+JqZmSVWUvhKulzSwvxrmh7IPwPUzMzM2lDqke/MiJgYEdXAvcBlpZdkZmbWs5UUvhHxXsHTnYD0X5FkZmbWzZT82c6SrgTOBNYCx7bR7lzgXICRI0eWulkzM7Nuq93v85X0EDCshUXTIuKegnaXAgMi4l/b26i/z9fMrOMq8X2+tn20e+QbEccX2dds4HdAu+FrZmbWm5V6tfO+BU8nA0tLK8fMzKznK/Wc7zWS9gcagZXAeaWXZGZm1rOVFL4R8cVyFWJmZtZb+BOuzMzMEnP4mpmZJebwNTMzS8zha2ZmlpjD18zMLDGHr5mZWWIOXzMzs8QcvmZmZok5fM3MzBJz+JqZmSXm8DUzM0vM4WtmZpaYw9fMzCwxh6+ZmVliDl8zM7PEHL5mZmaJOXzNzMwSc/iamZkl5vA1MzNLzOFrZmaWmMPXzMwsMYevmZlZYg5fMzOzxBy+ZmZmiZUlfCX9s6SQNLQc/ZmZmfVkJYevpBHACcDLpZdjZmbW81WVoY9ZwEXAPWXoyxKbNKnSFWTmzq10BV2P/2/Meq6SjnwlTQZejYini2h7rqQ6SXX19fWlbNbMzKxbU0S03UB6CBjWwqJpwHeBEyJiraQVQE1EvNneRmtqaqKurq4T5ZqZ9V6SnoyImkrXYaVrd9g5Io5vab6kA4ExwNOSAIYDf5V0eES8XtYqzczMepBOn/ONiEXAnk3PO3Lka2Zm1pv5Pl8zM7PEynG1MwARMbpcfZmZmfVkPvI1MzNLzOFrZmaWmMPXzMwsMYevmZlZYg5fMzOzxBy+ZmZmiTl8zczMEnP4mpmZJebwNTMzS8zha2ZmlpjD18zMLDGHr5mZWWIOXzMzs8QcvmZmZok5fM3MzBJz+JqZmSXm8DUzM0vM4WtmZpaYw9fMzCwxh6+ZmVliDl8zM7PEHL5mZmaJOXzNzMwSc/iamZklVlL4Spou6VVJC/LHyeUqzMzMrKeqKkMfsyLiB2Xox8zMrFfwsLOZmVli5QjfCyQtlHSjpN1aayTpXEl1kurq6+vLsFkzM7PuSRHRdgPpIWBYC4umAfOBN4EALgc+FhFfa2+jNTU1UVdX1/Fqzcx6MUlPRkRNpeuw0rV7zjciji+mI0n/CdxbckVmZmY9XKlXO3+s4OkXgMWllWNmZtbzlXq18wxJ1WTDziuAb5RakJmZWU9XUvhGxFfLVYiZmVlv4VuNzMzMEnP4mpmZJebwNTMzS8zha2ZmlpjD18zMLDGHr5mZWWIOXzMzs8QcvmZmZok5fM3MzBJz+JqZmSXm8DUzM0vM4WtmZpaYw9fMzCwxh6+ZmVliDl8zM7PEHL5mZmaJOXzNzMwSc/iamZkl5vA1MzNLzOFrZmaWmMPXzMwsMYevmZlZYg5fMzOzxBy+ZmZmiZUcvpK+JWmppCWSZpSjKDMzs56sqpSVJR0LTAYOiogNkvYsT1lmZmY9V0nhC3wTuCYiNgBExBull2S90aRJla6g65k7t9IVmNn2Uuqw837A0ZL+Iun/STqstYaSzpVUJ6muvr6+xM2amZl1X+0e+Up6CBjWwqJp+fpDgE8BhwG3SxobEdG8cURcB1wHUFNTs81y6918lGdmvUm74RsRx7e2TNI3gTvzsH1cUiMwFPChrZmZWStKHXa+GzgWQNJ+QD/gzRL7NDMz69FKveDqRuBGSYuBjcBZLQ05m5mZ2UdKCt+I2AhMKVMtZmZmvYI/4crMzCwxh6+ZmVliDl8zM7PEHL5mZmaJqRIXJ0t6H1iWfMPlM5TufUtVd66/O9cOrr/Sunv9+0fEoEoXYaUr9VajzloWETUV2nbJJNW5/srozrWD66+0nlB/pWuw8vCws5mZWWIOXzMzs8QqFb7XVWi75eL6K6c71w6uv9Jcv3UJFbngyszMrDfzsLOZmVliDl8zM7PEkoSvpCGSHpS0PP93txbaVEv6s6QlkhZK+scUtbVF0omSlkl6XtIlLSzvL+nX+fK/SBpdgTJbVETt35H0TP5a/0HSqErU2Zr26i9o90VJIalL3T5STP2Svpz/HyyRdFvqGttSxM/PSEl/lPRU/jN0ciXqbImkGyW9kX/bWkvLJekn+b4tlHRI6hrbUkT9Z+R1L5I0T9JBqWu0MoiI7f4AZgCX5NOXANe20GY/YN98+uPAa8DgFPW1UnMf4AVgLNn3FD8NjGvW5nzg5/n0acCvK1VvJ2o/Ftgxn/5mV6m92PrzdoOAR4D5QE2l6+7g678v8BSwW/58z0rX3cH6rwO+mU+PA1ZUuu6C2v4eOARY3Mryk4HfAwI+Bfyl0jV3sP4jC35uTupq9ftR3CPVsPNk4OZ8+mbg880bRMRzEbE8n14NvAHskai+lhwOPB8RL0b21Ym/ItuPQoX7dQdwnCQlrLE17dYeEX+MiHX50/nA8MQ1tqWY1x7gcuBaYH3K4opQTP3nAD+NiHcAIuKNxDW2pZj6A9gln94VWJ2wvjZFxCPA2200mQzcEpn5wGBJH0tTXfvaqz8i5jX93ND1fnetSKnCd6+IeC2ffh3Yq63Gkg4ne8f9wvYurA17A68UPF+Vz2uxTURsBtYCuyeprm3F1F7o62RHAl1Fu/XnQ4UjIuK3KQsrUjGv/37AfpIekzRf0onJqmtfMfVPB6ZIWgX8DvhWmtLKoqO/H11ZV/vdtSKV7eMlJT0EDGth0bTCJxERklq9vyl/B3orcFZENJarPmuZpClADXBMpWsplqQdgB8BZ1e4lFJUkQ09TyI7cnlE0oER8W4li+qA04GbIuKHko4AbpU0wb+z6Ug6lix8j6p0LdZxZQvfiDi+tWWS1kj6WES8lodri0NsknYBfgtMy4eDKulVYETB8+H5vJbarJJURTb89laa8tpUTO1IOp7szdExEbEhUW3FaK/+QcAEYG4+yj8MqJX0uYjoCp99W8zrv4rsXN0m4CVJz5GF8RNpSmxTMfV/HTgRICL+LGkA2ZcWdKXh89YU9fvRlUmaCFwPnBQRXeFvjnVQqmHnWuCsfPos4J7mDST1A+4iOxdzR6K62vIEsK+kMXltp5HtR6HC/foS8HBEdIVPLWm3dkkHA/8H+FwXO98I7dQfEWsjYmhEjI6I0WTnvbpK8EJxPzt3kx31Imko2TD0iwlrbEsx9b8MHAcg6QBgAFCftMrOqwXOzK96/hSwtuC0WJcnaSRwJ/DViHiu0vVYJ6W4qovsPOgfgOXAQ8CQfH4NcH0+PQXYBCwoeFRX8mo0sqsinyM79zwtn/dvZH/oIfuDMwd4HngcGFvJejtY+0PAmoLXurbSNXek/mZt59KFrnYu8vUX2dD5M8Ai4LRK19zB+scBj5FdCb0AOKHSNRfU/kuyuyU2kY0wfB04Dziv4LX/ab5vi7rgz0579V8PvFPwu1tX6Zr96PjDHy9pZmaWmD/hyszMLDGHr5mZWWIOXzMzs8QcvmZmZok5fM3MzBJz+JqZmSXm8DUzM0vs/wMBJBkrK8zbygAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 9\n",
+      "pp = -85/11, satellite_part = -4\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -129/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 9\n",
+      "pp = 85/11, satellite_part = 8\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 173/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -85/11\n",
+      "Satellite part = -4\n",
+      "Sigma = -129/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 85/11\n",
+      "Satellite part = 8\n",
+      "Sigma = 173/11\n",
+      "\n",
+      "[0, 0, 9, 9] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## T(2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T(2, 7; 2, 11), theta = 10\n",
+      "pp = -101/11, satellite_part = -4\n",
+      "\n",
+      "sigma(T(2, 7; 2, 11)) = -145/11\n",
+      "\n",
+      "\n",
+      "\n",
+      "########## -T(2, 3; 2, 7; 2, 11) ##########\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-T(2, 3; 2, 7; 2, 11), theta = 10\n",
+      "pp = 101/11, satellite_part = 8\n",
+      "\n",
+      "sigma(-T(2, 3; 2, 7; 2, 11)) = 189/11\n",
+      "****************************************************************************************************\n",
+      "Calculation summary for a cable sum:\n",
+      "T(2, 3; 2, 5; 2, 11) # -T(2, 5; 2, 11) # T(2, 7; 2, 11) # -T(2, 3; 2, 7; 2, 11)\n",
+      "2. T(2, 7; 2, 11)\n",
+      "Pattern part = -101/11\n",
+      "Satellite part = -4\n",
+      "Sigma = -145/11\n",
+      "\n",
+      "3. -T(2, 3; 2, 7; 2, 11)\n",
+      "Pattern part = 101/11\n",
+      "Satellite part = 8\n",
+      "Sigma = 189/11\n",
+      "\n",
+      "[0, 0, 10, 10] 4\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "True"
+       "False"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "cs = import_sage('cable_signature')\n",
     "knot_formula = \"[[k[0], k[1], k[3]],\" + \\\n",
     "               \" [-k[1], -k[3]],\" + \\\n",
     "               \" [k[2], k[3]],\" + \\\n",
@@ -540,7 +914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,13 +943,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "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",
       "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"
      ]
     }
@@ -596,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -614,7 +989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +1057,7 @@
        "127349"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +1094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -735,31 +1110,17 @@
        "127349"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "not verbose\n",
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-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "-28"
+       "-4"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -807,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "scrolled": false
    },
@@ -851,7 +1212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
@@ -869,7 +1230,7 @@
        "40301"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -916,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -938,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -954,7 +1315,7 @@
        "8549"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -990,46824 +1351,2000 @@
   },
   {
    "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,
+   "execution_count": 17,
    "metadata": {
-    "scrolled": true
+    "collapsed": true
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
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-      "[1, 0, 0, 1, 0, 0, 0, 0] 0\n",
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+      "[0, 4, 4, 0, 0, 0, 0, 0] 4\n",
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+      "[0, 6, 6, 0, 0, 0, 0, 0] 4\n",
+      "[0, 7, 7, 0, 0, 0, 0, 0] 4\n",
+      "[0, 8, 8, 0, 0, 0, 0, 0] 4\n",
+      "[0, 9, 9, 0, 0, 0, 0, 0] 4\n",
+      "[0, 10, 10, 0, 0, 0, 0, 0] 4\n",
+      "[0, 11, 11, 0, 0, 0, 0, 0] 4\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] 8\n",
+      "[0, 4, 4, 0, 0, 0, 0, 0] 4\n",
+      "[0, 6, 6, 0, 0, 0, 0, 0] 4\n",
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+      "[0, 10, 10, 0, 0, 0, 0, 0] 4\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] 8\n",
+      "[0, 6, 6, 0, 0, 0, 0, 0] 4\n",
+      "[0, 9, 9, 0, 0, 0, 0, 0] 4\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] 8\n",
+      "[0, 8, 8, 0, 0, 0, 0, 0] 4\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] 8\n",
+      "[0, 10, 10, 0, 0, 0, 0, 0] 4\n",
+      "[0, 15, 15, 0, 0, 0, 0, 0] 8\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] 8\n",
+      "[0, 14, 14, 0, 0, 0, 0, 0] 8\n",
+      "[0, 16, 16, 0, 0, 0, 0, 0] 8\n",
+      "[0, 18, 18, 0, 0, 0, 0, 0] 8\n",
+      "[0, 20, 20, 0, 0, 0, 0, 0] 12\n",
+      "[0, 22, 22, 0, 0, 0, 0, 0] 12\n",
       "[2, 0, 0, 2, 0, 0, 0, 0] 0\n",
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+      "[7, 0, 0, 7, 0, 0, 0, 0] 0\n",
+      "[8, 0, 0, 8, 0, 0, 0, 0] -4\n",
+      "[9, 0, 0, 9, 0, 0, 0, 0] -4\n",
+      "[10, 0, 0, 10, 0, 0, 0, 0] 0\n",
+      "[11, 0, 0, 11, 0, 0, 0, 0] 0\n",
+      "[12, 0, 0, 12, 0, 0, 0, 0] 0\n",
+      "[13, 0, 0, 13, 0, 0, 0, 0] -4\n",
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+      "[17, 0, 0, 17, 0, 0, 0, 0] 0\n",
+      "[18, 0, 0, 18, 0, 0, 0, 0] -4\n",
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+      "[20, 0, 0, 20, 0, 0, 0, 0] -4\n",
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+      "[12, 12, 0, 0, 0, 0, 0, 0] 0\n",
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-      "[1, 9, 9, 1, 0, 0, 0, 0] -4\n",
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-      "[2, 34, 34, 2, 0, 0, 0, 0] -20\n",
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+      "[4, 4, 4, 4, 0, 0, 0, 0] 4\n",
+      "[5, 5, 5, 5, 0, 0, 0, 0] 4\n",
+      "[6, 6, 6, 6, 0, 0, 0, 0] 4\n",
+      "[7, 7, 7, 7, 0, 0, 0, 0] 4\n",
+      "[8, 8, 8, 8, 0, 0, 0, 0] 0\n",
+      "[9, 9, 9, 9, 0, 0, 0, 0] 0\n",
+      "[10, 10, 10, 10, 0, 0, 0, 0] 4\n",
+      "[11, 11, 11, 11, 0, 0, 0, 0] 4\n",
+      "[12, 12, 12, 12, 0, 0, 0, 0] 8\n",
+      "[13, 13, 13, 13, 0, 0, 0, 0] 4\n",
+      "[14, 14, 14, 14, 0, 0, 0, 0] 4\n",
+      "[15, 15, 15, 15, 0, 0, 0, 0] 4\n",
+      "[16, 16, 16, 16, 0, 0, 0, 0] 8\n",
+      "[17, 17, 17, 17, 0, 0, 0, 0] 8\n",
+      "[18, 18, 18, 18, 0, 0, 0, 0] 4\n",
+      "[19, 19, 19, 19, 0, 0, 0, 0] 8\n",
+      "[20, 20, 20, 20, 0, 0, 0, 0] 8\n",
+      "[21, 21, 21, 21, 0, 0, 0, 0] 8\n",
+      "[22, 22, 22, 22, 0, 0, 0, 0] 4\n",
+      "[23, 23, 23, 23, 0, 0, 0, 0] 8\n",
+      "[24, 24, 24, 24, 0, 0, 0, 0] 8\n",
+      "[25, 25, 25, 25, 0, 0, 0, 0] 8\n",
+      "[26, 26, 26, 26, 0, 0, 0, 0] 8\n",
+      "[27, 27, 27, 27, 0, 0, 0, 0] 8\n",
+      "[28, 28, 28, 28, 0, 0, 0, 0] 8\n",
+      "[29, 29, 29, 29, 0, 0, 0, 0] 12\n",
+      "[2, 4, 4, 2, 0, 0, 0, 0] 4\n",
+      "[3, 6, 6, 3, 0, 0, 0, 0] 0\n",
+      "[4, 8, 8, 4, 0, 0, 0, 0] 4\n",
+      "[5, 10, 10, 5, 0, 0, 0, 0] 4\n",
+      "[6, 12, 12, 6, 0, 0, 0, 0] 8\n",
+      "[7, 14, 14, 7, 0, 0, 0, 0] 8\n",
+      "[8, 16, 16, 8, 0, 0, 0, 0] 4\n",
+      "[9, 18, 18, 9, 0, 0, 0, 0] 4\n",
+      "[10, 20, 20, 10, 0, 0, 0, 0] 12\n",
+      "[2, 6, 6, 2, 0, 0, 0, 0] 4\n",
+      "[3, 9, 9, 3, 0, 0, 0, 0] 0\n",
+      "[4, 12, 12, 4, 0, 0, 0, 0] 8\n",
+      "[5, 15, 15, 5, 0, 0, 0, 0] 8\n",
+      "[6, 18, 18, 6, 0, 0, 0, 0] 8\n",
+      "[7, 21, 21, 7, 0, 0, 0, 0] 12\n",
+      "[2, 8, 8, 2, 0, 0, 0, 0] 4\n",
+      "[3, 12, 12, 3, 0, 0, 0, 0] 4\n",
+      "[4, 16, 16, 4, 0, 0, 0, 0] 8\n",
+      "[5, 20, 20, 5, 0, 0, 0, 0] 12\n",
+      "[2, 10, 10, 2, 0, 0, 0, 0] 4\n",
+      "[3, 15, 15, 3, 0, 0, 0, 0] 4\n",
+      "[4, 20, 20, 4, 0, 0, 0, 0] 12\n",
+      "[2, 12, 12, 2, 0, 0, 0, 0] 8\n",
+      "[3, 18, 18, 3, 0, 0, 0, 0] 4\n",
+      "[4, 24, 24, 4, 0, 0, 0, 0] 12\n",
+      "[2, 14, 14, 2, 0, 0, 0, 0] 8\n",
+      "[3, 21, 21, 3, 0, 0, 0, 0] 8\n",
+      "[4, 28, 28, 4, 0, 0, 0, 0] 16\n",
+      "[2, 16, 16, 2, 0, 0, 0, 0] 8\n",
+      "[3, 24, 24, 3, 0, 0, 0, 0] 8\n",
+      "[4, 32, 32, 4, 0, 0, 0, 0] 16\n",
+      "[2, 18, 18, 2, 0, 0, 0, 0] 8\n",
+      "[3, 27, 27, 3, 0, 0, 0, 0] 12\n",
+      "[2, 20, 20, 2, 0, 0, 0, 0] 12\n",
+      "[2, 22, 22, 2, 0, 0, 0, 0] 12\n",
+      "[2, 24, 24, 2, 0, 0, 0, 0] 12\n",
+      "[2, 26, 26, 2, 0, 0, 0, 0] 12\n",
+      "[2, 28, 28, 2, 0, 0, 0, 0] 16\n",
+      "[2, 30, 30, 2, 0, 0, 0, 0] 16\n",
+      "[2, 32, 32, 2, 0, 0, 0, 0] 16\n",
+      "[2, 34, 34, 2, 0, 0, 0, 0] 20\n",
+      "[2, 36, 36, 2, 0, 0, 0, 0] 20\n",
+      "[4, 4, 0, 0, 0, 0, 0, 0] 0\n",
+      "[6, 6, 0, 0, 0, 0, 0, 0] 0\n",
+      "[8, 8, 0, 0, 0, 0, 0, 0] -4\n",
+      "[10, 10, 0, 0, 0, 0, 0, 0] -4\n",
+      "[12, 12, 0, 0, 0, 0, 0, 0] 0\n",
+      "[14, 14, 0, 0, 0, 0, 0, 0] -4\n",
+      "[16, 16, 0, 0, 0, 0, 0, 0] -4\n",
+      "[18, 18, 0, 0, 0, 0, 0, 0] -8\n",
+      "[4, 4, 2, 2, 0, 0, 0, 0] 0\n",
+      "[6, 6, 3, 3, 0, 0, 0, 0] 0\n",
+      "[8, 8, 4, 4, 0, 0, 0, 0] 0\n",
+      "[10, 10, 5, 5, 0, 0, 0, 0] 0\n",
+      "[12, 12, 6, 6, 0, 0, 0, 0] 4\n",
+      "[14, 14, 7, 7, 0, 0, 0, 0] 0\n",
+      "[16, 16, 8, 8, 0, 0, 0, 0] 0\n",
+      "[18, 18, 9, 9, 0, 0, 0, 0] -4\n",
+      "[20, 20, 10, 10, 0, 0, 0, 0] 4\n",
+      "[22, 22, 11, 11, 0, 0, 0, 0] 0\n",
+      "[24, 24, 12, 12, 0, 0, 0, 0] 0\n",
+      "[26, 26, 13, 13, 0, 0, 0, 0] 0\n",
+      "[28, 28, 14, 14, 0, 0, 0, 0] 0\n",
+      "[30, 30, 15, 15, 0, 0, 0, 0] 0\n",
+      "[32, 32, 16, 16, 0, 0, 0, 0] 0\n",
+      "[34, 34, 17, 17, 0, 0, 0, 0] 4\n",
+      "[36, 36, 18, 18, 0, 0, 0, 0] 4\n",
+      "[38, 38, 19, 19, 0, 0, 0, 0] 0\n",
+      "[40, 40, 20, 20, 0, 0, 0, 0] 0\n",
+      "[42, 42, 21, 21, 0, 0, 0, 0] 0\n",
+      "[44, 44, 22, 22, 0, 0, 0, 0] 0\n",
+      "[46, 46, 23, 23, 0, 0, 0, 0] 4\n",
+      "[48, 48, 24, 24, 0, 0, 0, 0] 8\n",
+      "[50, 50, 25, 25, 0, 0, 0, 0] 4\n",
+      "[52, 52, 26, 26, 0, 0, 0, 0] 8\n",
+      "[54, 54, 27, 27, 0, 0, 0, 0] 8\n",
+      "[56, 56, 28, 28, 0, 0, 0, 0] 8\n",
+      "[58, 58, 29, 29, 0, 0, 0, 0] 12\n",
+      "[4, 48, 36, 2, 0, 0, 0, 0] 20\n",
+      "[4, 66, 32, 2, 0, 0, 0, 0] 20\n",
+      "[4, 74, 22, 2, 0, 0, 0, 0] 20\n",
+      "[4, 76, 28, 2, 0, 0, 0, 0] 28\n",
+      "[6, 6, 0, 0, 0, 0, 0, 0] 0\n",
+      "[9, 9, 0, 0, 0, 0, 0, 0] -4\n",
+      "[12, 12, 0, 0, 0, 0, 0, 0] 0\n",
+      "[15, 15, 0, 0, 0, 0, 0, 0] -4\n",
+      "[18, 18, 0, 0, 0, 0, 0, 0] -8\n",
+      "[6, 6, 2, 2, 0, 0, 0, 0] 0\n",
+      "[9, 9, 3, 3, 0, 0, 0, 0] -4\n",
+      "[12, 12, 4, 4, 0, 0, 0, 0] 4\n",
+      "[15, 15, 5, 5, 0, 0, 0, 0] 0\n",
+      "[18, 18, 6, 6, 0, 0, 0, 0] -4\n",
+      "[21, 21, 7, 7, 0, 0, 0, 0] 0\n",
+      "[24, 24, 8, 8, 0, 0, 0, 0] -4\n",
+      "[27, 27, 9, 9, 0, 0, 0, 0] -4\n",
+      "[30, 30, 10, 10, 0, 0, 0, 0] 0\n",
+      "[33, 33, 11, 11, 0, 0, 0, 0] -4\n",
+      "[36, 36, 12, 12, 0, 0, 0, 0] 0\n",
+      "[39, 39, 13, 13, 0, 0, 0, 0] -4\n",
+      "[42, 42, 14, 14, 0, 0, 0, 0] -4\n",
+      "[45, 45, 15, 15, 0, 0, 0, 0] -4\n",
+      "[48, 48, 16, 16, 0, 0, 0, 0] 4\n",
+      "[51, 51, 17, 17, 0, 0, 0, 0] 0\n",
+      "[54, 54, 18, 18, 0, 0, 0, 0] 4\n",
+      "[57, 57, 19, 19, 0, 0, 0, 0] 4\n",
+      "[60, 60, 20, 20, 0, 0, 0, 0] 4\n",
+      "[63, 63, 21, 21, 0, 0, 0, 0] 8\n",
+      "[66, 66, 22, 22, 0, 0, 0, 0] 8\n",
+      "[69, 69, 23, 23, 0, 0, 0, 0] 12\n",
+      "[6, 20, 6, 2, 0, 0, 0, 0] -4\n",
+      "[9, 30, 9, 3, 0, 0, 0, 0] -4\n",
+      "[12, 40, 12, 4, 0, 0, 0, 0] 8\n",
+      "[15, 50, 15, 5, 0, 0, 0, 0] 8\n",
+      "[18, 60, 18, 6, 0, 0, 0, 0] 12\n",
+      "[6, 22, 28, 2, 0, 0, 0, 0] 20\n",
+      "[6, 44, 24, 2, 0, 0, 0, 0] 16\n",
+      "[6, 56, 38, 2, 0, 0, 0, 0] 24\n",
+      "[6, 58, 26, 2, 0, 0, 0, 0] 16\n",
+      "[6, 62, 34, 2, 0, 0, 0, 0] 24\n",
+      "[8, 8, 0, 0, 0, 0, 0, 0] -4\n",
+      "[12, 12, 0, 0, 0, 0, 0, 0] 0\n",
+      "[16, 16, 0, 0, 0, 0, 0, 0] -4\n",
+      "[20, 20, 0, 0, 0, 0, 0, 0] -4\n",
+      "[24, 24, 0, 0, 0, 0, 0, 0] -8\n",
+      "[8, 8, 2, 2, 0, 0, 0, 0] -4\n",
+      "[12, 12, 3, 3, 0, 0, 0, 0] 0\n",
+      "[16, 16, 4, 4, 0, 0, 0, 0] 0\n",
+      "[20, 20, 5, 5, 0, 0, 0, 0] 0\n",
+      "[24, 24, 6, 6, 0, 0, 0, 0] -4\n",
+      "[28, 28, 7, 7, 0, 0, 0, 0] -4\n",
+      "[32, 32, 8, 8, 0, 0, 0, 0] -8\n",
+      "[36, 36, 9, 9, 0, 0, 0, 0] -4\n",
+      "[40, 40, 10, 10, 0, 0, 0, 0] -4\n",
+      "[44, 44, 11, 11, 0, 0, 0, 0] -4\n",
+      "[48, 48, 12, 12, 0, 0, 0, 0] 0\n",
+      "[52, 52, 13, 13, 0, 0, 0, 0] 0\n",
+      "[56, 56, 14, 14, 0, 0, 0, 0] 0\n",
+      "[60, 60, 15, 15, 0, 0, 0, 0] 0\n",
+      "[64, 64, 16, 16, 0, 0, 0, 0] 8\n",
+      "[68, 68, 17, 17, 0, 0, 0, 0] 8\n",
+      "[72, 72, 18, 18, 0, 0, 0, 0] 8\n",
+      "[76, 76, 19, 19, 0, 0, 0, 0] 12\n",
+      "[8, 16, 14, 2, 0, 0, 0, 0] 8\n",
+      "[12, 24, 21, 3, 0, 0, 0, 0] 16\n",
+      "[8, 34, 10, 2, 0, 0, 0, 0] 0\n",
+      "[12, 51, 15, 3, 0, 0, 0, 0] 8\n",
+      "[16, 68, 20, 4, 0, 0, 0, 0] 24\n",
+      "[8, 50, 38, 2, 0, 0, 0, 0] 20\n",
+      "[8, 72, 12, 2, 0, 0, 0, 0] 8\n",
+      "[12, 108, 18, 3, 0, 0, 0, 0] 112\n",
+      "[10, 10, 0, 0, 0, 0, 0, 0] -4\n",
+      "[15, 15, 0, 0, 0, 0, 0, 0] -4\n",
+      "[20, 20, 0, 0, 0, 0, 0, 0] -4\n",
+      "[25, 25, 0, 0, 0, 0, 0, 0] -8\n",
+      "[10, 10, 2, 2, 0, 0, 0, 0] -4\n",
+      "[15, 15, 3, 3, 0, 0, 0, 0] -4\n",
+      "[20, 20, 4, 4, 0, 0, 0, 0] 0\n",
+      "[25, 25, 5, 5, 0, 0, 0, 0] -4\n",
+      "[30, 30, 6, 6, 0, 0, 0, 0] -4\n",
+      "[35, 35, 7, 7, 0, 0, 0, 0] -4\n",
+      "[40, 40, 8, 8, 0, 0, 0, 0] -8\n",
+      "[45, 45, 9, 9, 0, 0, 0, 0] -8\n",
+      "[50, 50, 10, 10, 0, 0, 0, 0] -4\n",
+      "[55, 55, 11, 11, 0, 0, 0, 0] 0\n",
+      "[60, 60, 12, 12, 0, 0, 0, 0] 0\n",
+      "[65, 65, 13, 13, 0, 0, 0, 0] 0\n",
+      "[70, 70, 14, 14, 0, 0, 0, 0] 4\n",
+      "[75, 75, 15, 15, 0, 0, 0, 0] 4\n",
+      "[80, 80, 16, 16, 0, 0, 0, 0] 8\n",
+      "[85, 85, 17, 17, 0, 0, 0, 0] 12\n",
+      "[10, 14, 10, 2, 0, 0, 0, 0] 8\n",
+      "[15, 21, 15, 3, 0, 0, 0, 0] 12\n",
+      "[10, 34, 8, 2, 0, 0, 0, 0] 0\n",
+      "[15, 51, 12, 3, 0, 0, 0, 0] 4\n",
+      "[20, 68, 16, 4, 0, 0, 0, 0] 20\n",
+      "[10, 46, 32, 2, 0, 0, 0, 0] 24\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "[16, 36, 52, 2, 0, 0, 0, 0] -12\n",
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-      "**********\n",
-      "[8, 23, 36, 1, 0, 0, 0, 0] 0\n",
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-      "[16, 46, 72, 2, 0, 0, 0, 0] -32\n",
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-      "**********\n",
-      "[9, 1, 2, 1, 0, 0, 0, 0] 8\n",
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+      "[10, 74, 20, 2, 0, 0, 0, 0] 24\n",
+      "[10, 78, 26, 2, 0, 0, 0, 0] 36\n",
+      "[12, 12, 0, 0, 0, 0, 0, 0] 0\n",
+      "[18, 18, 0, 0, 0, 0, 0, 0] -8\n",
+      "[12, 12, 2, 2, 0, 0, 0, 0] 0\n",
+      "[18, 18, 3, 3, 0, 0, 0, 0] -8\n",
+      "[24, 24, 4, 4, 0, 0, 0, 0] -4\n",
+      "[30, 30, 5, 5, 0, 0, 0, 0] -4\n",
+      "[36, 36, 6, 6, 0, 0, 0, 0] -4\n",
+      "[42, 42, 7, 7, 0, 0, 0, 0] -8\n",
+      "[48, 48, 8, 8, 0, 0, 0, 0] -4\n",
+      "[54, 54, 9, 9, 0, 0, 0, 0] -4\n",
+      "[60, 60, 10, 10, 0, 0, 0, 0] 0\n",
+      "[66, 66, 11, 11, 0, 0, 0, 0] 4\n",
+      "[72, 72, 12, 12, 0, 0, 0, 0] 4\n",
+      "[78, 78, 13, 13, 0, 0, 0, 0] 8\n",
+      "[84, 84, 14, 14, 0, 0, 0, 0] 8\n",
+      "[90, 90, 15, 15, 0, 0, 0, 0] 8\n",
+      "[96, 96, 16, 16, 0, 0, 0, 0] 8\n",
+      "[102, 102, 17, 17, 0, 0, 0, 0] 8\n",
+      "[108, 108, 18, 18, 0, 0, 0, 0] 4\n",
+      "[114, 114, 19, 19, 0, 0, 0, 0] 4\n",
+      "[120, 120, 20, 20, 0, 0, 0, 0] 0\n",
+      "[126, 126, 21, 21, 0, 0, 0, 0] 0\n",
+      "[132, 132, 22, 22, 0, 0, 0, 0] 4\n",
+      "[138, 138, 23, 23, 0, 0, 0, 0] 8\n",
+      "[144, 144, 24, 24, 0, 0, 0, 0] 8\n",
+      "[150, 150, 25, 25, 0, 0, 0, 0] 12\n",
+      "[12, 54, 28, 2, 0, 0, 0, 0] 24\n",
+      "[12, 58, 30, 2, 0, 0, 0, 0] 24\n",
+      "[12, 72, 8, 2, 0, 0, 0, 0] 8\n",
+      "[18, 108, 12, 3, 0, 0, 0, 0] 104\n",
+      "[12, 76, 18, 2, 0, 0, 0, 0] 28\n",
+      "[14, 14, 0, 0, 0, 0, 0, 0] -4\n",
+      "[21, 21, 0, 0, 0, 0, 0, 0] -4\n",
+      "[28, 28, 0, 0, 0, 0, 0, 0] -8\n",
+      "[14, 14, 2, 2, 0, 0, 0, 0] -4\n",
+      "[21, 21, 3, 3, 0, 0, 0, 0] -4\n",
+      "[28, 28, 4, 4, 0, 0, 0, 0] -4\n",
+      "[35, 35, 5, 5, 0, 0, 0, 0] -4\n",
+      "[42, 42, 6, 6, 0, 0, 0, 0] -8\n",
+      "[49, 49, 7, 7, 0, 0, 0, 0] -4\n",
+      "[56, 56, 8, 8, 0, 0, 0, 0] -4\n",
+      "[63, 63, 9, 9, 0, 0, 0, 0] 0\n",
+      "[70, 70, 10, 10, 0, 0, 0, 0] 4\n",
+      "[77, 77, 11, 11, 0, 0, 0, 0] 8\n",
+      "[84, 84, 12, 12, 0, 0, 0, 0] 8\n",
+      "[91, 91, 13, 13, 0, 0, 0, 0] 4\n",
+      "[98, 98, 14, 14, 0, 0, 0, 0] 4\n",
+      "[105, 105, 15, 15, 0, 0, 0, 0] 0\n",
+      "[112, 112, 16, 16, 0, 0, 0, 0] 4\n",
+      "[119, 119, 17, 17, 0, 0, 0, 0] 4\n",
+      "[126, 126, 18, 18, 0, 0, 0, 0] 0\n",
+      "[133, 133, 19, 19, 0, 0, 0, 0] 0\n",
+      "[140, 140, 20, 20, 0, 0, 0, 0] 4\n",
+      "[147, 147, 21, 21, 0, 0, 0, 0] 8\n",
+      "[154, 154, 22, 22, 0, 0, 0, 0] 12\n",
+      "[14, 16, 8, 2, 0, 0, 0, 0] 4\n",
+      "[21, 24, 12, 3, 0, 0, 0, 0] 8\n",
+      "[28, 32, 16, 4, 0, 0, 0, 0] 12\n",
+      "[14, 38, 16, 2, 0, 0, 0, 0] 12\n",
+      "[14, 42, 24, 2, 0, 0, 0, 0] 20\n",
+      "[14, 48, 28, 2, 0, 0, 0, 0] 24\n",
+      "[14, 54, 20, 2, 0, 0, 0, 0] 16\n",
+      "[14, 72, 26, 2, 0, 0, 0, 0] 28\n",
+      "[16, 16, 0, 0, 0, 0, 0, 0] -4\n",
+      "[24, 24, 0, 0, 0, 0, 0, 0] -8\n",
+      "[16, 16, 2, 2, 0, 0, 0, 0] -4\n",
+      "[24, 24, 3, 3, 0, 0, 0, 0] -8\n",
+      "[32, 32, 4, 4, 0, 0, 0, 0] -8\n",
+      "[40, 40, 5, 5, 0, 0, 0, 0] -8\n",
+      "[48, 48, 6, 6, 0, 0, 0, 0] -4\n",
+      "[56, 56, 7, 7, 0, 0, 0, 0] -4\n",
+      "[64, 64, 8, 8, 0, 0, 0, 0] 0\n",
+      "[72, 72, 9, 9, 0, 0, 0, 0] 0\n",
+      "[80, 80, 10, 10, 0, 0, 0, 0] 4\n",
+      "[88, 88, 11, 11, 0, 0, 0, 0] 8\n",
+      "[96, 96, 12, 12, 0, 0, 0, 0] 4\n",
+      "[104, 104, 13, 13, 0, 0, 0, 0] 4\n",
+      "[112, 112, 14, 14, 0, 0, 0, 0] 0\n",
+      "[120, 120, 15, 15, 0, 0, 0, 0] -4\n",
+      "[128, 128, 16, 16, 0, 0, 0, 0] 0\n",
+      "[136, 136, 17, 17, 0, 0, 0, 0] 4\n",
+      "[144, 144, 18, 18, 0, 0, 0, 0] 4\n",
+      "[152, 152, 19, 19, 0, 0, 0, 0] 8\n",
+      "[160, 160, 20, 20, 0, 0, 0, 0] 12\n",
+      "[16, 24, 18, 2, 0, 0, 0, 0] 16\n",
+      "[16, 38, 14, 2, 0, 0, 0, 0] 12\n",
+      "[16, 50, 16, 2, 0, 0, 0, 0] 12\n",
+      "[16, 60, 32, 2, 0, 0, 0, 0] 28\n",
+      "[16, 66, 28, 2, 0, 0, 0, 0] 28\n",
+      "[16, 74, 24, 2, 0, 0, 0, 0] 32\n",
       "[18, 2, 4, 2, 0, 0, 0, 0] 12\n",
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-      "**********\n",
-      "[9, 12, 8, 1, 0, 0, 0, 0] -8\n",
-      "not verbose\n",
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-      "not verbose\n",
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-      "[18, 24, 16, 2, 0, 0, 0, 0] -12\n",
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-      "not verbose\n",
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-      "not verbose\n",
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+      "[18, 18, 0, 0, 0, 0, 0, 0] -8\n",
+      "[18, 18, 2, 2, 0, 0, 0, 0] -8\n",
+      "[27, 27, 3, 3, 0, 0, 0, 0] -8\n",
+      "[36, 36, 4, 4, 0, 0, 0, 0] -4\n",
+      "[45, 45, 5, 5, 0, 0, 0, 0] -8\n",
+      "[54, 54, 6, 6, 0, 0, 0, 0] -4\n",
+      "[63, 63, 7, 7, 0, 0, 0, 0] 0\n",
+      "[72, 72, 8, 8, 0, 0, 0, 0] 0\n",
+      "[81, 81, 9, 9, 0, 0, 0, 0] 4\n",
+      "[90, 90, 10, 10, 0, 0, 0, 0] 8\n",
+      "[99, 99, 11, 11, 0, 0, 0, 0] 4\n",
+      "[108, 108, 12, 12, 0, 0, 0, 0] 0\n",
+      "[117, 117, 13, 13, 0, 0, 0, 0] 0\n",
+      "[126, 126, 14, 14, 0, 0, 0, 0] -4\n",
+      "[135, 135, 15, 15, 0, 0, 0, 0] 0\n",
+      "[144, 144, 16, 16, 0, 0, 0, 0] 4\n",
+      "[153, 153, 17, 17, 0, 0, 0, 0] 8\n",
+      "[162, 162, 18, 18, 0, 0, 0, 0] 12\n",
+      "[18, 24, 16, 2, 0, 0, 0, 0] 12\n",
+      "[18, 48, 18, 2, 0, 0, 0, 0] 16\n",
+      "[18, 76, 12, 2, 0, 0, 0, 0] 20\n",
+      "[18, 78, 28, 2, 0, 0, 0, 0] 40\n",
+      "[20, 10, 6, 2, 0, 0, 0, 0] 8\n",
+      "[30, 15, 9, 3, 0, 0, 0, 0] 8\n",
+      "[40, 20, 12, 4, 0, 0, 0, 0] 8\n",
+      "[50, 25, 15, 5, 0, 0, 0, 0] 8\n",
+      "[60, 30, 18, 6, 0, 0, 0, 0] 12\n",
+      "[20, 20, 0, 0, 0, 0, 0, 0] -4\n",
+      "[30, 30, 0, 0, 0, 0, 0, 0] -8\n",
+      "[20, 20, 2, 2, 0, 0, 0, 0] -4\n",
+      "[30, 30, 3, 3, 0, 0, 0, 0] -8\n",
+      "[40, 40, 4, 4, 0, 0, 0, 0] -8\n",
+      "[50, 50, 5, 5, 0, 0, 0, 0] -8\n",
+      "[60, 60, 6, 6, 0, 0, 0, 0] -4\n",
+      "[70, 70, 7, 7, 0, 0, 0, 0] 0\n",
+      "[80, 80, 8, 8, 0, 0, 0, 0] 0\n",
+      "[90, 90, 9, 9, 0, 0, 0, 0] 4\n",
+      "[100, 100, 10, 10, 0, 0, 0, 0] 4\n",
+      "[110, 110, 11, 11, 0, 0, 0, 0] 0\n",
+      "[120, 120, 12, 12, 0, 0, 0, 0] -4\n",
+      "[130, 130, 13, 13, 0, 0, 0, 0] 0\n",
+      "[140, 140, 14, 14, 0, 0, 0, 0] 0\n",
+      "[150, 150, 15, 15, 0, 0, 0, 0] 4\n",
+      "[160, 160, 16, 16, 0, 0, 0, 0] 12\n",
+      "[20, 54, 14, 2, 0, 0, 0, 0] 12\n",
+      "[20, 68, 24, 2, 0, 0, 0, 0] 28\n",
+      "[20, 74, 10, 2, 0, 0, 0, 0] 16\n",
+      "[20, 76, 20, 2, 0, 0, 0, 0] 32\n",
+      "[22, 22, 0, 0, 0, 0, 0, 0] -8\n",
+      "[22, 22, 2, 2, 0, 0, 0, 0] -8\n",
+      "[33, 33, 3, 3, 0, 0, 0, 0] -12\n",
+      "[22, 26, 14, 2, 0, 0, 0, 0] 8\n",
+      "[33, 39, 21, 3, 0, 0, 0, 0] 16\n",
+      "[22, 36, 22, 2, 0, 0, 0, 0] 20\n",
+      "[22, 74, 4, 2, 0, 0, 0, 0] 4\n",
+      "[33, 111, 6, 3, 0, 0, 0, 0] 108\n",
+      "[24, 16, 4, 2, 0, 0, 0, 0] 0\n",
+      "[36, 24, 6, 3, 0, 0, 0, 0] -4\n",
+      "[48, 32, 8, 4, 0, 0, 0, 0] 0\n",
+      "[60, 40, 10, 5, 0, 0, 0, 0] 4\n",
+      "[72, 48, 12, 6, 0, 0, 0, 0] 4\n",
+      "[84, 56, 14, 7, 0, 0, 0, 0] -8\n",
+      "[96, 64, 16, 8, 0, 0, 0, 0] -44\n",
+      "[24, 24, 0, 0, 0, 0, 0, 0] -8\n",
+      "[24, 24, 2, 2, 0, 0, 0, 0] -8\n",
+      "[36, 36, 3, 3, 0, 0, 0, 0] -8\n",
+      "[48, 48, 4, 4, 0, 0, 0, 0] -4\n",
+      "[60, 60, 5, 5, 0, 0, 0, 0] -4\n",
+      "[72, 72, 6, 6, 0, 0, 0, 0] 0\n",
+      "[84, 84, 7, 7, 0, 0, 0, 0] 4\n",
+      "[96, 96, 8, 8, 0, 0, 0, 0] 0\n",
+      "[108, 108, 9, 9, 0, 0, 0, 0] -4\n",
+      "[120, 120, 10, 10, 0, 0, 0, 0] -4\n",
+      "[132, 132, 11, 11, 0, 0, 0, 0] 0\n",
+      "[144, 144, 12, 12, 0, 0, 0, 0] 0\n",
+      "[156, 156, 13, 13, 0, 0, 0, 0] 4\n",
+      "[168, 168, 14, 14, 0, 0, 0, 0] 8\n",
+      "[180, 180, 15, 15, 0, 0, 0, 0] 4\n",
+      "[192, 192, 16, 16, 0, 0, 0, 0] 4\n",
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-      "**********\n",
-      "[10, 33, 34, 1, 0, 0, 0, 0] -8\n",
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-      "[20, 66, 68, 2, 0, 0, 0, 0] 0\n",
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-      "[30, 99, 102, 3, 0, 0, 0, 0] -8\n",
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-      "[40, 132, 136, 4, 0, 0, 0, 0] -40\n",
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+      "[38, 38, 0, 0, 0, 0, 0, 0] -12\n",
+      "[38, 38, 2, 2, 0, 0, 0, 0] -12\n",
+      "[38, 42, 18, 2, 0, 0, 0, 0] 12\n",
+      "[38, 50, 8, 2, 0, 0, 0, 0] -4\n",
+      "[57, 75, 12, 3, 0, 0, 0, 0] 20\n",
+      "[38, 56, 6, 2, 0, 0, 0, 0] -4\n",
+      "[57, 84, 9, 3, 0, 0, 0, 0] 28\n",
+      "[38, 68, 14, 2, 0, 0, 0, 0] 12\n",
+      "[40, 40, 0, 0, 0, 0, 0, 0] -12\n",
+      "[40, 40, 2, 2, 0, 0, 0, 0] -12\n",
+      "[40, 54, 18, 2, 0, 0, 0, 0] 12\n",
+      "[40, 62, 16, 2, 0, 0, 0, 0] 12\n",
+      "[40, 76, 14, 2, 0, 0, 0, 0] 20\n",
+      "[42, 38, 4, 2, 0, 0, 0, 0] -8\n",
+      "[63, 57, 6, 3, 0, 0, 0, 0] -4\n",
+      "[84, 76, 8, 4, 0, 0, 0, 0] 0\n",
+      "[105, 95, 10, 5, 0, 0, 0, 0] -28\n",
+      "[42, 42, 0, 0, 0, 0, 0, 0] -12\n",
+      "[42, 42, 2, 2, 0, 0, 0, 0] -12\n",
+      "[42, 74, 8, 2, 0, 0, 0, 0] 8\n",
+      "[63, 111, 12, 3, 0, 0, 0, 0] 120\n",
+      "[42, 80, 18, 2, 0, 0, 0, 0] 28\n",
+      "[44, 44, 2, 2, 0, 0, 0, 0] -12\n",
+      "[44, 66, 10, 2, 0, 0, 0, 0] 4\n",
+      "[66, 99, 15, 3, 0, 0, 0, 0] 80\n",
+      "[44, 68, 6, 2, 0, 0, 0, 0] 0\n",
+      "[66, 102, 9, 3, 0, 0, 0, 0] 84\n",
+      "[44, 80, 22, 2, 0, 0, 0, 0] 32\n",
+      "[44, 82, 12, 2, 0, 0, 0, 0] 24\n",
+      "[46, 46, 2, 2, 0, 0, 0, 0] -12\n",
+      "[46, 58, 16, 2, 0, 0, 0, 0] 8\n",
+      "[69, 87, 24, 3, 0, 0, 0, 0] 52\n",
+      "[46, 64, 18, 2, 0, 0, 0, 0] 16\n",
+      "[48, 18, 4, 2, 0, 0, 0, 0] -4\n",
+      "[72, 27, 6, 3, 0, 0, 0, 0] -4\n",
+      "[96, 36, 8, 4, 0, 0, 0, 0] -52\n",
+      "[48, 26, 6, 2, 0, 0, 0, 0] -4\n",
+      "[72, 39, 9, 3, 0, 0, 0, 0] 0\n",
+      "[96, 52, 12, 4, 0, 0, 0, 0] -48\n",
+      "[48, 28, 12, 2, 0, 0, 0, 0] 8\n",
+      "[72, 42, 18, 3, 0, 0, 0, 0] 12\n",
+      "[48, 36, 18, 2, 0, 0, 0, 0] 16\n",
+      "[48, 48, 2, 2, 0, 0, 0, 0] -8\n",
+      "[72, 72, 3, 3, 0, 0, 0, 0] -4\n",
+      "[96, 96, 4, 4, 0, 0, 0, 0] 0\n",
+      "[120, 120, 5, 5, 0, 0, 0, 0] -8\n",
+      "[144, 144, 6, 6, 0, 0, 0, 0] -4\n",
+      "[168, 168, 7, 7, 0, 0, 0, 0] 4\n",
+      "[192, 192, 8, 8, 0, 0, 0, 0] -4\n",
+      "[216, 216, 9, 9, 0, 0, 0, 0] -8\n",
+      "[240, 240, 10, 10, 0, 0, 0, 0] 4\n",
+      "[264, 264, 11, 11, 0, 0, 0, 0] 4\n",
+      "[288, 288, 12, 12, 0, 0, 0, 0] -4\n",
+      "[312, 312, 13, 13, 0, 0, 0, 0] 4\n",
+      "[336, 336, 14, 14, 0, 0, 0, 0] 8\n",
+      "[360, 360, 15, 15, 0, 0, 0, 0] 0\n",
+      "[384, 384, 16, 16, 0, 0, 0, 0] 4\n",
+      "[408, 408, 17, 17, 0, 0, 0, 0] 12\n",
+      "[48, 58, 20, 2, 0, 0, 0, 0] 16\n",
+      "[48, 66, 8, 2, 0, 0, 0, 0] 4\n",
+      "[72, 99, 12, 3, 0, 0, 0, 0] 72\n",
+      "[50, 30, 8, 2, 0, 0, 0, 0] 0\n",
+      "[75, 45, 12, 3, 0, 0, 0, 0] 0\n",
+      "[100, 60, 16, 4, 0, 0, 0, 0] -52\n",
+      "[50, 40, 10, 2, 0, 0, 0, 0] 4\n",
+      "[75, 60, 15, 3, 0, 0, 0, 0] 4\n",
+      "[100, 80, 20, 4, 0, 0, 0, 0] -32\n",
+      "[50, 50, 2, 2, 0, 0, 0, 0] -12\n",
+      "[50, 74, 18, 2, 0, 0, 0, 0] 24\n",
+      "[52, 36, 16, 2, 0, 0, 0, 0] 16\n",
+      "[52, 52, 2, 2, 0, 0, 0, 0] -8\n",
+      "[78, 78, 3, 3, 0, 0, 0, 0] 0\n",
+      "[104, 104, 4, 4, 0, 0, 0, 0] 0\n",
+      "[130, 130, 5, 5, 0, 0, 0, 0] -4\n",
+      "[156, 156, 6, 6, 0, 0, 0, 0] 0\n",
+      "[182, 182, 7, 7, 0, 0, 0, 0] 0\n",
+      "[208, 208, 8, 8, 0, 0, 0, 0] -8\n",
+      "[234, 234, 9, 9, 0, 0, 0, 0] 0\n",
+      "[260, 260, 10, 10, 0, 0, 0, 0] 4\n",
+      "[286, 286, 11, 11, 0, 0, 0, 0] -4\n",
+      "[312, 312, 12, 12, 0, 0, 0, 0] 4\n",
+      "[338, 338, 13, 13, 0, 0, 0, 0] 8\n",
+      "[364, 364, 14, 14, 0, 0, 0, 0] -4\n",
+      "[390, 390, 15, 15, 0, 0, 0, 0] 0\n",
+      "[416, 416, 16, 16, 0, 0, 0, 0] 12\n",
+      "[52, 64, 20, 2, 0, 0, 0, 0] 24\n",
+      "[52, 78, 8, 2, 0, 0, 0, 0] 20\n",
+      "[54, 20, 12, 2, 0, 0, 0, 0] 12\n",
+      "[54, 28, 14, 2, 0, 0, 0, 0] 12\n",
+      "[54, 40, 4, 2, 0, 0, 0, 0] -4\n",
+      "[81, 60, 6, 3, 0, 0, 0, 0] -12\n",
+      "[54, 54, 2, 2, 0, 0, 0, 0] -8\n",
+      "[81, 81, 3, 3, 0, 0, 0, 0] 0\n",
+      "[108, 108, 4, 4, 0, 0, 0, 0] -4\n",
+      "[135, 135, 5, 5, 0, 0, 0, 0] -4\n",
+      "[162, 162, 6, 6, 0, 0, 0, 0] 4\n",
+      "[189, 189, 7, 7, 0, 0, 0, 0] -4\n",
+      "[216, 216, 8, 8, 0, 0, 0, 0] -8\n",
+      "[243, 243, 9, 9, 0, 0, 0, 0] 4\n",
+      "[270, 270, 10, 10, 0, 0, 0, 0] 4\n",
+      "[297, 297, 11, 11, 0, 0, 0, 0] 0\n",
+      "[324, 324, 12, 12, 0, 0, 0, 0] 4\n",
+      "[351, 351, 13, 13, 0, 0, 0, 0] 4\n",
+      "[378, 378, 14, 14, 0, 0, 0, 0] -4\n",
+      "[405, 405, 15, 15, 0, 0, 0, 0] 4\n",
+      "[432, 432, 16, 16, 0, 0, 0, 0] 8\n",
+      "[459, 459, 17, 17, 0, 0, 0, 0] 0\n",
+      "[486, 486, 18, 18, 0, 0, 0, 0] 12\n",
+      "[56, 24, 10, 2, 0, 0, 0, 0] 4\n",
+      "[84, 36, 15, 3, 0, 0, 0, 0] -4\n",
+      "[112, 48, 20, 4, 0, 0, 0, 0] -96\n",
+      "[56, 34, 4, 2, 0, 0, 0, 0] -4\n",
+      "[84, 51, 6, 3, 0, 0, 0, 0] -20\n",
+      "[56, 56, 2, 2, 0, 0, 0, 0] -8\n",
+      "[84, 84, 3, 3, 0, 0, 0, 0] 0\n",
+      "[112, 112, 4, 4, 0, 0, 0, 0] -4\n",
+      "[140, 140, 5, 5, 0, 0, 0, 0] -4\n",
+      "[168, 168, 6, 6, 0, 0, 0, 0] 4\n",
+      "[196, 196, 7, 7, 0, 0, 0, 0] -4\n",
+      "[224, 224, 8, 8, 0, 0, 0, 0] -4\n",
+      "[252, 252, 9, 9, 0, 0, 0, 0] 0\n",
+      "[280, 280, 10, 10, 0, 0, 0, 0] 0\n",
+      "[308, 308, 11, 11, 0, 0, 0, 0] 0\n",
+      "[336, 336, 12, 12, 0, 0, 0, 0] 8\n",
+      "[364, 364, 13, 13, 0, 0, 0, 0] -4\n",
+      "[392, 392, 14, 14, 0, 0, 0, 0] 0\n",
+      "[420, 420, 15, 15, 0, 0, 0, 0] 8\n",
+      "[448, 448, 16, 16, 0, 0, 0, 0] 0\n",
+      "[476, 476, 17, 17, 0, 0, 0, 0] 4\n",
+      "[504, 504, 18, 18, 0, 0, 0, 0] 12\n",
+      "[58, 30, 14, 2, 0, 0, 0, 0] 12\n",
+      "[58, 42, 8, 2, 0, 0, 0, 0] 4\n",
+      "[87, 63, 12, 3, 0, 0, 0, 0] -16\n",
+      "[58, 58, 2, 2, 0, 0, 0, 0] -8\n",
+      "[87, 87, 3, 3, 0, 0, 0, 0] 0\n",
+      "[116, 116, 4, 4, 0, 0, 0, 0] -8\n",
+      "[145, 145, 5, 5, 0, 0, 0, 0] 0\n",
+      "[174, 174, 6, 6, 0, 0, 0, 0] 0\n",
+      "[203, 203, 7, 7, 0, 0, 0, 0] -8\n",
+      "[232, 232, 8, 8, 0, 0, 0, 0] 0\n",
+      "[261, 261, 9, 9, 0, 0, 0, 0] 4\n",
+      "[290, 290, 10, 10, 0, 0, 0, 0] -4\n",
+      "[319, 319, 11, 11, 0, 0, 0, 0] 4\n",
+      "[348, 348, 12, 12, 0, 0, 0, 0] 4\n",
+      "[377, 377, 13, 13, 0, 0, 0, 0] -4\n",
+      "[406, 406, 14, 14, 0, 0, 0, 0] 4\n",
+      "[435, 435, 15, 15, 0, 0, 0, 0] 4\n",
+      "[464, 464, 16, 16, 0, 0, 0, 0] 4\n",
+      "[493, 493, 17, 17, 0, 0, 0, 0] 12\n",
+      "[58, 62, 22, 2, 0, 0, 0, 0] 24\n",
+      "[58, 78, 20, 2, 0, 0, 0, 0] 36\n",
+      "[60, 48, 6, 2, 0, 0, 0, 0] 0\n",
+      "[90, 72, 9, 3, 0, 0, 0, 0] -24\n",
+      "[60, 58, 10, 2, 0, 0, 0, 0] 4\n",
+      "[90, 87, 15, 3, 0, 0, 0, 0] 12\n",
+      "[60, 60, 2, 2, 0, 0, 0, 0] -8\n",
+      "[90, 90, 3, 3, 0, 0, 0, 0] 0\n",
+      "[120, 120, 4, 4, 0, 0, 0, 0] -8\n",
+      "[150, 150, 5, 5, 0, 0, 0, 0] 0\n",
+      "[180, 180, 6, 6, 0, 0, 0, 0] 0\n",
+      "[210, 210, 7, 7, 0, 0, 0, 0] -8\n",
+      "[240, 240, 8, 8, 0, 0, 0, 0] 0\n",
+      "[270, 270, 9, 9, 0, 0, 0, 0] 0\n",
+      "[300, 300, 10, 10, 0, 0, 0, 0] -4\n",
+      "[330, 330, 11, 11, 0, 0, 0, 0] 8\n",
+      "[360, 360, 12, 12, 0, 0, 0, 0] 0\n",
+      "[390, 390, 13, 13, 0, 0, 0, 0] 0\n",
+      "[420, 420, 14, 14, 0, 0, 0, 0] 8\n",
+      "[450, 450, 15, 15, 0, 0, 0, 0] 0\n",
+      "[480, 480, 16, 16, 0, 0, 0, 0] 4\n",
+      "[510, 510, 17, 17, 0, 0, 0, 0] 12\n",
+      "[62, 62, 2, 2, 0, 0, 0, 0] -4\n",
+      "[93, 93, 3, 3, 0, 0, 0, 0] -4\n",
+      "[124, 124, 4, 4, 0, 0, 0, 0] -8\n",
+      "[155, 155, 5, 5, 0, 0, 0, 0] 0\n",
+      "[186, 186, 6, 6, 0, 0, 0, 0] 0\n",
+      "[217, 217, 7, 7, 0, 0, 0, 0] -8\n",
+      "[248, 248, 8, 8, 0, 0, 0, 0] 4\n",
+      "[279, 279, 9, 9, 0, 0, 0, 0] -4\n",
+      "[310, 310, 10, 10, 0, 0, 0, 0] 0\n",
+      "[341, 341, 11, 11, 0, 0, 0, 0] 4\n",
+      "[372, 372, 12, 12, 0, 0, 0, 0] -4\n",
+      "[403, 403, 13, 13, 0, 0, 0, 0] 8\n",
+      "[434, 434, 14, 14, 0, 0, 0, 0] 4\n",
+      "[465, 465, 15, 15, 0, 0, 0, 0] -4\n",
+      "[496, 496, 16, 16, 0, 0, 0, 0] 12\n",
+      "[62, 64, 16, 2, 0, 0, 0, 0] 20\n",
+      "[62, 70, 8, 2, 0, 0, 0, 0] 12\n",
+      "[62, 72, 4, 2, 0, 0, 0, 0] 4\n",
+      "[93, 108, 6, 3, 0, 0, 0, 0] 52\n",
+      "[64, 12, 6, 2, 0, 0, 0, 0] 8\n",
+      "[96, 18, 9, 3, 0, 0, 0, 0] -52\n",
+      "[64, 46, 4, 2, 0, 0, 0, 0] -4\n",
+      "[96, 69, 6, 3, 0, 0, 0, 0] -52\n",
+      "[64, 50, 20, 2, 0, 0, 0, 0] 16\n",
+      "[64, 54, 22, 2, 0, 0, 0, 0] 20\n",
+      "[64, 70, 12, 2, 0, 0, 0, 0] 16\n",
+      "[66, 32, 18, 2, 0, 0, 0, 0] 16\n",
+      "[66, 42, 20, 2, 0, 0, 0, 0] 20\n",
+      "[66, 52, 26, 2, 0, 0, 0, 0] 24\n",
+      "[66, 60, 24, 2, 0, 0, 0, 0] 24\n",
+      "[68, 38, 12, 2, 0, 0, 0, 0] 8\n",
+      "[102, 57, 18, 3, 0, 0, 0, 0] -64\n",
+      "[68, 46, 22, 2, 0, 0, 0, 0] 20\n",
+      "[68, 66, 20, 2, 0, 0, 0, 0] 20\n",
+      "[68, 68, 2, 2, 0, 0, 0, 0] -4\n",
+      "[102, 102, 3, 3, 0, 0, 0, 0] -4\n",
+      "[136, 136, 4, 4, 0, 0, 0, 0] -4\n",
+      "[170, 170, 5, 5, 0, 0, 0, 0] 4\n",
+      "[204, 204, 6, 6, 0, 0, 0, 0] -8\n",
+      "[238, 238, 7, 7, 0, 0, 0, 0] 0\n",
+      "[272, 272, 8, 8, 0, 0, 0, 0] -4\n",
+      "[306, 306, 9, 9, 0, 0, 0, 0] -4\n",
+      "[340, 340, 10, 10, 0, 0, 0, 0] 4\n",
+      "[374, 374, 11, 11, 0, 0, 0, 0] -4\n",
+      "[408, 408, 12, 12, 0, 0, 0, 0] 8\n",
+      "[442, 442, 13, 13, 0, 0, 0, 0] 0\n",
+      "[476, 476, 14, 14, 0, 0, 0, 0] 0\n",
+      "[510, 510, 15, 15, 0, 0, 0, 0] 8\n",
+      "[544, 544, 16, 16, 0, 0, 0, 0] 0\n",
+      "[578, 578, 17, 17, 0, 0, 0, 0] 8\n",
+      "[612, 612, 18, 18, 0, 0, 0, 0] 4\n",
+      "[646, 646, 19, 19, 0, 0, 0, 0] 4\n",
+      "[680, 680, 20, 20, 0, 0, 0, 0] 8\n",
+      "[714, 714, 21, 21, 0, 0, 0, 0] 0\n",
+      "[748, 748, 22, 22, 0, 0, 0, 0] 12\n",
+      "[70, 48, 8, 2, 0, 0, 0, 0] 0\n",
+      "[105, 72, 12, 3, 0, 0, 0, 0] -76\n",
+      "[70, 64, 14, 2, 0, 0, 0, 0] 12\n",
+      "[70, 70, 2, 2, 0, 0, 0, 0] -4\n",
+      "[105, 105, 3, 3, 0, 0, 0, 0] -8\n",
+      "[140, 140, 4, 4, 0, 0, 0, 0] -4\n",
+      "[175, 175, 5, 5, 0, 0, 0, 0] 0\n",
+      "[210, 210, 6, 6, 0, 0, 0, 0] -8\n",
+      "[245, 245, 7, 7, 0, 0, 0, 0] 4\n",
+      "[280, 280, 8, 8, 0, 0, 0, 0] -4\n",
+      "[315, 315, 9, 9, 0, 0, 0, 0] 0\n",
+      "[350, 350, 10, 10, 0, 0, 0, 0] 0\n",
+      "[385, 385, 11, 11, 0, 0, 0, 0] 0\n",
+      "[420, 420, 12, 12, 0, 0, 0, 0] 8\n",
+      "[455, 455, 13, 13, 0, 0, 0, 0] -4\n",
+      "[490, 490, 14, 14, 0, 0, 0, 0] 4\n",
+      "[525, 525, 15, 15, 0, 0, 0, 0] 0\n",
+      "[560, 560, 16, 16, 0, 0, 0, 0] 8\n",
+      "[595, 595, 17, 17, 0, 0, 0, 0] 8\n",
+      "[630, 630, 18, 18, 0, 0, 0, 0] 4\n",
+      "[665, 665, 19, 19, 0, 0, 0, 0] 12\n",
+      "[72, 26, 12, 2, 0, 0, 0, 0] 4\n",
+      "[108, 39, 18, 3, 0, 0, 0, 0] -84\n",
+      "[72, 30, 6, 2, 0, 0, 0, 0] -4\n",
+      "[108, 45, 9, 3, 0, 0, 0, 0] -96\n",
+      "[72, 36, 10, 2, 0, 0, 0, 0] 4\n",
+      "[108, 54, 15, 3, 0, 0, 0, 0] -88\n",
+      "[72, 46, 16, 2, 0, 0, 0, 0] 12\n",
+      "[72, 62, 18, 2, 0, 0, 0, 0] 16\n",
+      "[72, 64, 24, 2, 0, 0, 0, 0] 24\n",
+      "[72, 72, 2, 2, 0, 0, 0, 0] -4\n",
+      "[108, 108, 3, 3, 0, 0, 0, 0] -8\n",
+      "[144, 144, 4, 4, 0, 0, 0, 0] -4\n",
+      "[180, 180, 5, 5, 0, 0, 0, 0] 0\n",
+      "[216, 216, 6, 6, 0, 0, 0, 0] -8\n",
+      "[252, 252, 7, 7, 0, 0, 0, 0] 0\n",
+      "[288, 288, 8, 8, 0, 0, 0, 0] -8\n",
+      "[324, 324, 9, 9, 0, 0, 0, 0] 0\n",
+      "[360, 360, 10, 10, 0, 0, 0, 0] 0\n",
+      "[396, 396, 11, 11, 0, 0, 0, 0] 4\n",
+      "[432, 432, 12, 12, 0, 0, 0, 0] 4\n",
+      "[468, 468, 13, 13, 0, 0, 0, 0] 0\n",
+      "[504, 504, 14, 14, 0, 0, 0, 0] 8\n",
+      "[540, 540, 15, 15, 0, 0, 0, 0] -4\n",
+      "[576, 576, 16, 16, 0, 0, 0, 0] 12\n",
+      "[74, 14, 6, 2, 0, 0, 0, 0] 0\n",
+      "[111, 21, 9, 3, 0, 0, 0, 0] -108\n",
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-      "[16, 18, 22, 1, 0, 0, 0, 0] 8\n",
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-      "[16, 28, 23, 1, 0, 0, 0, 0] -8\n",
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-      "[16, 38, 39, 1, 0, 0, 0, 0] -8\n",
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-      "[41, 20, 31, 1, 0, 0, 0, 0] 4\n",
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-      "[82, 40, 62, 2, 0, 0, 0, 0] -32\n",
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-      "[41, 23, 29, 1, 0, 0, 0, 0] 0\n",
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-      "[82, 46, 58, 2, 0, 0, 0, 0] -28\n",
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-      "[41, 31, 32, 1, 0, 0, 0, 0] -8\n",
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-      "[82, 62, 64, 2, 0, 0, 0, 0] -16\n",
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+      "[74, 82, 16, 2, 0, 0, 0, 0] 28\n",
+      "[76, 12, 4, 2, 0, 0, 0, 0] 0\n",
+      "[114, 18, 6, 3, 0, 0, 0, 0] -124\n",
+      "[76, 18, 14, 2, 0, 0, 0, 0] 8\n",
+      "[114, 27, 21, 3, 0, 0, 0, 0] -104\n",
+      "[76, 28, 18, 2, 0, 0, 0, 0] 12\n",
+      "[76, 40, 12, 2, 0, 0, 0, 0] 4\n",
+      "[114, 60, 18, 3, 0, 0, 0, 0] -108\n",
+      "[76, 60, 22, 2, 0, 0, 0, 0] 16\n",
+      "[76, 62, 8, 2, 0, 0, 0, 0] 0\n",
+      "[114, 93, 12, 3, 0, 0, 0, 0] -68\n",
+      "[76, 66, 24, 2, 0, 0, 0, 0] 20\n",
+      "[76, 74, 6, 2, 0, 0, 0, 0] 4\n",
+      "[114, 111, 9, 3, 0, 0, 0, 0] -8\n",
+      "[152, 148, 12, 4, 0, 0, 0, 0] -8\n",
+      "[190, 185, 15, 5, 0, 0, 0, 0] -24\n",
+      "[78, 28, 4, 2, 0, 0, 0, 0] -12\n",
+      "[78, 32, 16, 2, 0, 0, 0, 0] 4\n",
+      "[117, 48, 24, 3, 0, 0, 0, 0] -116\n",
+      "[78, 36, 14, 2, 0, 0, 0, 0] 4\n",
+      "[117, 54, 21, 3, 0, 0, 0, 0] -120\n",
+      "[78, 72, 10, 2, 0, 0, 0, 0] 4\n",
+      "[117, 108, 15, 3, 0, 0, 0, 0] -28\n",
+      "[80, 22, 8, 2, 0, 0, 0, 0] -8\n",
+      "[120, 33, 12, 3, 0, 0, 0, 0] -148\n",
+      "[80, 26, 16, 2, 0, 0, 0, 0] 0\n",
+      "[120, 39, 24, 3, 0, 0, 0, 0] -132\n",
+      "[80, 42, 4, 2, 0, 0, 0, 0] -16\n",
+      "[80, 46, 6, 2, 0, 0, 0, 0] -12\n",
+      "[82, 12, 8, 2, 0, 0, 0, 0] 0\n",
+      "[123, 18, 12, 3, 0, 0, 0, 0] -156\n",
+      "[82, 38, 6, 2, 0, 0, 0, 0] -12\n",
+      "[82, 44, 14, 2, 0, 0, 0, 0] 0\n",
+      "[123, 66, 21, 3, 0, 0, 0, 0] -144\n",
+      "[82, 56, 20, 2, 0, 0, 0, 0] 8\n",
+      "[123, 84, 30, 3, 0, 0, 0, 0] -116\n",
+      "[82, 80, 26, 2, 0, 0, 0, 0] 28\n",
       "\n",
       "OK\n",
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-      "[0, 0, 0, 0, 0, 0, 1, 1] 0\n",
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       "[0, 0, 0, 0, 0, 0, 2, 2] 0\n",
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       "[0, 0, 0, 0, 0, 0, 3, 3] 0\n",
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-      "[0, 0, 0, 0, 0, 0, 4, 4] -8\n",
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+      "[0, 0, 0, 0, 0, 0, 4, 4] 0\n",
+      "[0, 0, 0, 0, 0, 0, 5, 5] 0\n",
+      "[0, 0, 0, 0, 0, 0, 6, 6] 0\n",
+      "[0, 0, 0, 0, 0, 0, 7, 7] 0\n",
+      "[0, 0, 0, 0, 0, 0, 8, 8] 0\n",
+      "[0, 0, 0, 0, 0, 0, 9, 9] 0\n",
+      "[0, 0, 0, 0, 0, 0, 10, 10] 4\n",
+      "[0, 0, 0, 0, 0, 0, 11, 11] 4\n",
+      "[0, 0, 0, 0, 0, 0, 12, 12] 0\n",
+      "[0, 0, 0, 0, 0, 0, 13, 13] 0\n",
+      "[0, 0, 0, 0, 0, 0, 14, 14] 0\n",
+      "[0, 0, 0, 0, 0, 0, 15, 15] 0\n",
+      "[0, 0, 0, 0, 0, 0, 16, 16] 4\n",
+      "[0, 0, 0, 0, 0, 0, 17, 17] 4\n",
+      "[0, 0, 0, 0, 0, 0, 18, 18] 4\n",
+      "[0, 0, 0, 0, 0, 0, 19, 19] 4\n",
+      "[0, 0, 0, 0, 0, 0, 20, 20] 0\n",
+      "[0, 0, 0, 0, 0, 0, 21, 21] 0\n",
+      "[0, 0, 0, 0, 0, 0, 22, 22] 4\n",
+      "[0, 0, 0, 0, 0, 0, 23, 23] 4\n",
+      "[0, 0, 0, 0, 0, 0, 24, 24] 4\n",
+      "[0, 0, 0, 0, 0, 0, 25, 25] 4\n",
+      "[0, 0, 0, 0, 0, 0, 26, 26] 4\n",
+      "[0, 0, 0, 0, 0, 0, 27, 27] 4\n",
+      "[0, 0, 0, 0, 0, 0, 28, 28] 4\n",
+      "[0, 0, 0, 0, 0, 0, 29, 29] 4\n",
+      "[0, 0, 0, 0, 0, 0, 30, 30] 4\n",
+      "[0, 0, 0, 0, 0, 0, 31, 31] 4\n",
+      "[0, 0, 0, 0, 0, 0, 32, 32] 4\n",
+      "[0, 0, 0, 0, 0, 0, 33, 33] 4\n",
+      "[0, 0, 0, 0, 0, 0, 34, 34] 8\n",
       "[0, 0, 0, 0, 0, 2, 2, 0] 0\n",
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       "[0, 0, 0, 0, 0, 3, 3, 0] 0\n",
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       "[0, 0, 0, 0, 0, 4, 4, 0] -4\n",
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       "[0, 0, 0, 0, 0, 5, 5, 0] -4\n",
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       "[0, 0, 0, 0, 0, 6, 6, 0] -4\n",
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       "[0, 0, 0, 0, 0, 7, 7, 0] -4\n",
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       "[0, 0, 0, 0, 0, 8, 8, 0] -4\n",
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       "[0, 0, 0, 0, 0, 9, 9, 0] -4\n",
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       "[0, 0, 0, 0, 0, 11, 11, 0] -4\n",
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       "[0, 0, 0, 0, 0, 12, 12, 0] -8\n",
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-      "[0, 0, 0, 0, 0, 2, 2, 0] 0\n",
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       "[0, 0, 0, 0, 0, 4, 4, 0] -4\n",
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       "[0, 0, 0, 0, 0, 6, 6, 0] -4\n",
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       "[0, 0, 0, 0, 0, 8, 8, 0] -4\n",
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-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
       "[0, 0, 0, 0, 0, 10, 10, 0] -4\n",
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+      "[0, 0, 0, 0, 10, 10, 10, 10] 0\n",
+      "[0, 0, 0, 0, 11, 11, 11, 11] 0\n",
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+      "[0, 0, 0, 0, 24, 24, 24, 24] -8\n",
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+      "[0, 0, 0, 0, 33, 33, 33, 33] -12\n",
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      "output_type": "stream",
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-      "[0, 0, 0, 0, 22, 22, 1, 1] -8\n",
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       "[0, 0, 0, 0, 44, 44, 2, 2] -20\n",
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       "[0, 0, 0, 0, 44, 48, 28, 2] 0\n",
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       "[0, 0, 0, 0, 66, 72, 42, 3] 12\n",
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       "[0, 0, 0, 0, 44, 58, 14, 2] -8\n",
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       "[0, 0, 0, 0, 66, 87, 21, 3] 20\n",
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       "[0, 0, 0, 0, 44, 62, 26, 2] 4\n",
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       "[0, 0, 0, 0, 66, 93, 39, 3] 36\n",
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       "[0, 0, 0, 0, 44, 64, 102, 2] -20\n",
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       "[0, 0, 0, 0, 44, 66, 40, 2] 8\n",
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       "[0, 0, 0, 0, 66, 99, 60, 3] 48\n",
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       "[0, 0, 0, 0, 66, 102, 120, 3] -24\n",
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       "[0, 0, 0, 0, 44, 78, 54, 2] 16\n",
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       "[0, 0, 0, 0, 44, 100, 72, 2] 48\n",
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       "[0, 0, 0, 0, 44, 102, 52, 2] 52\n",
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-      "[0, 0, 0, 0, 23, 21, 4, 1] -4\n",
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       "[0, 0, 0, 0, 46, 42, 8, 2] -12\n",
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-      "[0, 0, 0, 0, 86, 94, 38, 2] 28\n"
+      "[0, 0, 0, 0, 86, 94, 38, 2] 28\n",
+      "[0, 0, 0, 0, 86, 98, 96, 2] 16\n",
+      "[0, 0, 0, 0, 86, 100, 62, 2] 40\n",
+      "[0, 0, 0, 0, 86, 102, 98, 2] 20\n",
+      "[0, 0, 0, 0, 88, 46, 12, 2] -24\n",
+      "[0, 0, 0, 0, 88, 66, 18, 2] -12\n",
+      "[0, 0, 0, 0, 88, 72, 88, 2] -12\n",
+      "[0, 0, 0, 0, 88, 74, 38, 2] 0\n",
+      "[0, 0, 0, 0, 132, 111, 57, 3] -48\n",
+      "[0, 0, 0, 0, 88, 82, 80, 2] 4\n",
+      "[0, 0, 0, 0, 132, 123, 120, 3] -92\n",
+      "[0, 0, 0, 0, 88, 86, 58, 2] 12\n",
+      "[0, 0, 0, 0, 88, 94, 90, 2] 12\n",
+      "[0, 0, 0, 0, 88, 96, 72, 2] 28\n",
+      "[0, 0, 0, 0, 88, 100, 32, 2] 36\n",
+      "[0, 0, 0, 0, 90, 34, 8, 2] -20\n",
+      "[0, 0, 0, 0, 90, 60, 6, 2] -28\n",
+      "[0, 0, 0, 0, 90, 74, 26, 2] 0\n",
+      "[0, 0, 0, 0, 135, 111, 39, 3] -56\n",
+      "[0, 0, 0, 0, 90, 76, 96, 2] -16\n",
+      "[0, 0, 0, 0, 90, 80, 40, 2] 8\n",
+      "[0, 0, 0, 0, 135, 120, 60, 3] -32\n",
+      "[0, 0, 0, 0, 90, 88, 36, 2] 16\n",
+      "[0, 0, 0, 0, 90, 92, 70, 2] 24\n",
+      "[0, 0, 0, 0, 90, 94, 88, 2] 16\n",
+      "[0, 0, 0, 0, 90, 98, 90, 2] 20\n",
+      "[0, 0, 0, 0, 92, 40, 12, 2] -24\n",
+      "[0, 0, 0, 0, 92, 76, 32, 2] 0\n",
+      "[0, 0, 0, 0, 138, 114, 48, 3] -64\n",
+      "[0, 0, 0, 0, 92, 82, 18, 2] 0\n",
+      "[0, 0, 0, 0, 138, 123, 27, 3] -40\n",
+      "[0, 0, 0, 0, 92, 84, 48, 2] 8\n",
+      "[0, 0, 0, 0, 138, 126, 72, 3] -32\n",
+      "[0, 0, 0, 0, 92, 90, 84, 2] 12\n",
+      "[0, 0, 0, 0, 92, 100, 60, 2] 36\n",
+      "[0, 0, 0, 0, 94, 48, 8, 2] -32\n",
+      "[0, 0, 0, 0, 94, 54, 26, 2] -16\n",
+      "[0, 0, 0, 0, 94, 56, 22, 2] -20\n",
+      "[0, 0, 0, 0, 94, 64, 38, 2] -12\n",
+      "[0, 0, 0, 0, 94, 66, 46, 2] -12\n",
+      "[0, 0, 0, 0, 94, 68, 18, 2] -16\n",
+      "[0, 0, 0, 0, 94, 72, 36, 2] -8\n",
+      "[0, 0, 0, 0, 141, 108, 54, 3] -88\n",
+      "[0, 0, 0, 0, 94, 76, 72, 2] -4\n",
+      "[0, 0, 0, 0, 141, 114, 108, 3] -116\n",
+      "[0, 0, 0, 0, 94, 78, 62, 2] 0\n",
+      "[0, 0, 0, 0, 141, 117, 93, 3] -80\n",
+      "[0, 0, 0, 0, 94, 82, 40, 2] 4\n",
+      "[0, 0, 0, 0, 141, 123, 60, 3] -48\n",
+      "[0, 0, 0, 0, 94, 90, 68, 2] 16\n",
+      "[0, 0, 0, 0, 96, 58, 34, 2] -16\n",
+      "[0, 0, 0, 0, 96, 62, 20, 2] -24\n",
+      "[0, 0, 0, 0, 96, 76, 90, 2] -20\n",
+      "[0, 0, 0, 0, 96, 80, 22, 2] -8\n",
+      "[0, 0, 0, 0, 144, 120, 33, 3] -68\n",
+      "[0, 0, 0, 0, 96, 84, 78, 2] 0\n",
+      "[0, 0, 0, 0, 144, 126, 117, 3] -120\n",
+      "[0, 0, 0, 0, 96, 86, 48, 2] 4\n",
+      "[0, 0, 0, 0, 144, 129, 72, 3] -44\n",
+      "[0, 0, 0, 0, 96, 92, 30, 2] 12\n",
+      "[0, 0, 0, 0, 96, 98, 86, 2] 16\n",
+      "[0, 0, 0, 0, 96, 102, 70, 2] 32\n",
+      "[0, 0, 0, 0, 98, 44, 24, 2] -24\n",
+      "[0, 0, 0, 0, 98, 64, 26, 2] -20\n",
+      "[0, 0, 0, 0, 98, 70, 48, 2] -16\n",
+      "[0, 0, 0, 0, 98, 72, 42, 2] -12\n",
+      "[0, 0, 0, 0, 98, 82, 76, 2] -4\n",
+      "[0, 0, 0, 0, 147, 123, 114, 3] -132\n",
+      "[0, 0, 0, 0, 98, 94, 36, 2] 12\n",
+      "[0, 0, 0, 0, 98, 96, 50, 2] 16\n",
+      "[0, 0, 0, 0, 98, 100, 20, 2] 16\n",
+      "[0, 0, 0, 0, 98, 102, 86, 2] 20\n",
+      "[0, 0, 0, 0, 100, 38, 10, 2] -32\n",
+      "[0, 0, 0, 0, 100, 40, 16, 2] -28\n",
+      "[0, 0, 0, 0, 100, 56, 12, 2] -36\n",
+      "[0, 0, 0, 0, 100, 58, 28, 2] -24\n",
+      "[0, 0, 0, 0, 100, 60, 14, 2] -32\n",
+      "[0, 0, 0, 0, 100, 78, 38, 2] -8\n",
+      "[0, 0, 0, 0, 150, 117, 57, 3] -104\n",
+      "[0, 0, 0, 0, 100, 80, 100, 2] -28\n",
+      "[0, 0, 0, 0, 100, 88, 80, 2] 0\n",
+      "[0, 0, 0, 0, 150, 132, 120, 3] -136\n",
+      "[0, 0, 0, 0, 100, 90, 24, 2] 4\n",
+      "[0, 0, 0, 0, 150, 135, 36, 3] -52\n",
+      "[0, 0, 0, 0, 100, 92, 42, 2] 12\n",
+      "[0, 0, 0, 0, 100, 94, 30, 2] 12\n",
+      "[0, 0, 0, 0, 102, 32, 10, 2] -32\n",
+      "[0, 0, 0, 0, 102, 42, 4, 2] -48\n",
+      "[0, 0, 0, 0, 102, 48, 12, 2] -40\n",
+      "[0, 0, 0, 0, 102, 62, 6, 2] -44\n",
+      "[0, 0, 0, 0, 102, 64, 44, 2] -24\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "[0, 0, 0, 0, 102, 80, 74, 2] -12\n",
+      "[0, 0, 0, 0, 102, 82, 54, 2] -8\n",
+      "[0, 0, 0, 0, 153, 123, 81, 3] -104\n",
+      "[0, 0, 0, 0, 102, 84, 40, 2] -4\n",
+      "[0, 0, 0, 0, 153, 126, 60, 3] -88\n",
+      "[0, 0, 0, 0, 102, 96, 84, 2] 8\n",
+      "[0, 0, 0, 0, 153, 144, 126, 3] -128\n",
+      "[0, 0, 0, 0, 102, 98, 50, 2] 16\n",
+      "\n",
+      "OK\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -47816,32 +3353,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[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",
+      "[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",
+      "[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",
+      "[0, 6, 6, 0, 0, 0, 0, 0] -6\n",
+      "[0, 9, 9, 0, 0, 0, 0, 0] -8\n",
+      "[0, 8, 8, 0, 0, 0, 0, 0] -8\n",
+      "[0, 10, 10, 0, 0, 0, 0, 0] -8\n",
+      "[0, 12, 12, 0, 0, 0, 0, 0] -10\n",
+      "[0, 14, 14, 0, 0, 0, 0, 0] -10\n",
+      "[2, 0, 0, 2, 0, 0, 0, 0] 4\n",
+      "[3, 0, 0, 3, 0, 0, 0, 0] 8\n",
+      "[2, 2, 0, 0, 0, 0, 0, 0] 4\n",
+      "[3, 3, 0, 0, 0, 0, 0, 0] 8\n",
+      "[2, 2, 2, 2, 0, 0, 0, 0] 6\n",
+      "[3, 3, 3, 3, 0, 0, 0, 0] 10\n",
+      "[2, 4, 4, 2, 0, 0, 0, 0] -10\n",
+      "[2, 6, 6, 2, 0, 0, 0, 0] -10\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",
+      "[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",
+      "[8, 8, 0, 0, 0, 0, 0, 0] 8\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",
+      "[10, 10, 0, 0, 0, 0, 0, 0] 8\n",
+      "[10, 10, 2, 2, 0, 0, 0, 0] 8\n",
+      "[15, 15, 3, 3, 0, 0, 0, 0] 10\n",
+      "[12, 12, 0, 0, 0, 0, 0, 0] -4\n",
+      "[18, 18, 0, 0, 0, 0, 0, 0] 8\n",
+      "[12, 12, 2, 2, 0, 0, 0, 0] -6\n",
+      "[18, 18, 3, 3, 0, 0, 0, 0] 10\n",
+      "[14, 14, 0, 0, 0, 0, 0, 0] 8\n",
+      "[14, 14, 2, 2, 0, 0, 0, 0] 10\n",
+      "[16, 16, 2, 2, 0, 0, 0, 0] 10\n",
+      "[24, 24, 0, 0, 0, 0, 0, 0] 8\n",
+      "[24, 24, 2, 2, 0, 0, 0, 0] 10\n",
+      "[52, 58, 0, 2, 0, 0, 0, 0] 10\n",
+      "\n",
+      "OK\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",
+      "[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",
+      "[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",
+      "[0, 0, 0, 0, 0, 6, 6, 0] 6\n",
+      "[0, 0, 0, 0, 0, 9, 9, 0] 8\n",
+      "[0, 0, 0, 0, 0, 8, 8, 0] 8\n",
+      "[0, 0, 0, 0, 0, 10, 10, 0] 8\n",
+      "[0, 0, 0, 0, 0, 12, 12, 0] 10\n",
+      "[0, 0, 0, 0, 0, 14, 14, 0] 10\n",
+      "[0, 0, 0, 0, 0, 46, 36, 2] 10\n",
+      "[0, 0, 0, 0, 2, 0, 0, 2] -4\n",
+      "[0, 0, 0, 0, 3, 0, 0, 3] -4\n",
+      "[0, 0, 0, 0, 4, 0, 0, 4] -8\n",
+      "[0, 0, 0, 0, 2, 2, 0, 0] -2\n",
+      "[0, 0, 0, 0, 3, 3, 0, 0] -2\n",
+      "[0, 0, 0, 0, 4, 4, 0, 0] -2\n",
+      "[0, 0, 0, 0, 5, 5, 0, 0] 6\n",
+      "[0, 0, 0, 0, 6, 6, 0, 0] 6\n",
+      "[0, 0, 0, 0, 7, 7, 0, 0] 6\n",
+      "[0, 0, 0, 0, 8, 8, 0, 0] 6\n",
+      "[0, 0, 0, 0, 9, 9, 0, 0] 6\n",
+      "[0, 0, 0, 0, 10, 10, 0, 0] 8\n",
+      "[0, 0, 0, 0, 2, 2, 2, 2] -6\n",
+      "[0, 0, 0, 0, 3, 3, 3, 3] -6\n",
+      "[0, 0, 0, 0, 4, 4, 4, 4] -6\n",
+      "[0, 0, 0, 0, 5, 5, 5, 5] 10\n",
+      "[0, 0, 0, 0, 2, 4, 4, 2] 10\n",
+      "[0, 0, 0, 0, 2, 6, 6, 2] 10\n",
+      "[0, 0, 0, 0, 2, 12, 12, 2] 10\n",
+      "[0, 0, 0, 0, 2, 14, 14, 2] 10\n",
+      "[0, 0, 0, 0, 2, 16, 16, 2] 12\n",
+      "[0, 0, 0, 0, 2, 30, 22, 0] 10\n",
+      "[0, 0, 0, 0, 4, 4, 0, 0] -2\n",
+      "[0, 0, 0, 0, 6, 6, 0, 0] 6\n",
+      "[0, 0, 0, 0, 8, 8, 0, 0] 6\n",
+      "[0, 0, 0, 0, 10, 10, 0, 0] 8\n",
+      "[0, 0, 0, 0, 4, 4, 2, 2] -6\n",
+      "[0, 0, 0, 0, 6, 6, 3, 3] 10\n",
+      "[0, 0, 0, 0, 6, 6, 0, 0] 6\n",
+      "[0, 0, 0, 0, 9, 9, 0, 0] 6\n",
+      "[0, 0, 0, 0, 12, 12, 0, 0] 8\n",
+      "[0, 0, 0, 0, 6, 6, 2, 2] 10\n",
+      "[0, 0, 0, 0, 8, 8, 0, 0] 6\n",
+      "[0, 0, 0, 0, 12, 12, 0, 0] 8\n",
+      "[0, 0, 0, 0, 8, 8, 2, 2] 10\n",
+      "[0, 0, 0, 0, 10, 10, 0, 0] 8\n",
+      "[0, 0, 0, 0, 12, 12, 0, 0] 8\n",
+      "[0, 0, 0, 0, 14, 14, 0, 0] 8\n",
+      "[0, 0, 0, 0, 16, 16, 0, 0] 10\n",
+      "[0, 0, 0, 0, 18, 18, 0, 0] 10\n",
+      "[0, 0, 0, 0, 22, 30, 2, 0] 10\n",
+      "\n",
+      "OK\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "slice_canidate.is_function_big_for_all_metabolizers(invariant=cs.SIGNATURE)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-4"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "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,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0: 0, 49/2822: -1, 1/34: 2, 61/1826: 1, 117/2822: -1, 1/22: -2, 105/1826: 1, 215/2822: -1, 3/34: 2, 283/2822: -1, 227/1826: 1, 381/2822: -1, 3/22: -2, 5/34: 2, 271/1826: 1, 449/2822: -1, 547/2822: -1, 7/34: 2, 393/1826: 1, 615/2822: -1, 5/22: -2, 437/1826: 1, 713/2822: -1, 9/34: 2, 781/2822: -1, 559/1826: 1, 879/2822: -1, 7/22: -2, 11/34: 2, 603/1826: 1, 947/2822: -1, 1045/2822: -1, 13/34: 2, 1113/2822: -1, 725/1826: 1, 9/22: -2, 769/1826: 1, 1211/2822: -1, 15/34: 2, 1279/2822: -1, 1543/2822: 1, 19/34: -2, 1611/2822: 1, 1057/1826: -1, 13/22: 2, 1101/1826: -1, 1709/2822: 1, 21/34: -2, 1777/2822: 1, 1875/2822: 1, 1223/1826: -1, 23/34: -2, 15/22: 2, 1943/2822: 1, 1267/1826: -1, 2041/2822: 1, 25/34: -2, 2109/2822: 1, 1389/1826: -1, 17/22: 2, 2207/2822: 1, 1433/1826: -1, 27/34: -2, 2275/2822: 1, 2373/2822: 1, 1555/1826: -1, 29/34: -2, 19/22: 2, 2441/2822: 1, 1599/1826: -1, 2539/2822: 1, 31/34: -2, 2607/2822: 1, 1721/1826: -1, 21/22: 2, 2705/2822: 1, 1765/1826: -1, 33/34: -2, 2773/2822: 1, 1: 0."
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "_, _, sf = slice_canidate((1, 1, 0, 0, 0,0,0,0))\n",
     "sf"
@@ -47849,9 +3565,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "cs = import_sage('cable_signature')\n",
     "sg = import_sage('signature')\n",
@@ -47860,11 +3589,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "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"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unable to convert [0, 4, 0, 4, 0, 0, 0, 0] to a rational",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-c4fd4acceb8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_sage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'signature'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mslice_canidate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_sum_for_theta_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mInteger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_to_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/Users/Kasia/signature_function/cable_signature.py\u001b[0m in \u001b[0;36mplot_sum_for_theta_vector\u001b[0;34m(self, thetas, save_to_dir)\u001b[0m\n\u001b[1;32m    342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthetas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknot_summands\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m             \u001b[0mknot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_summand_for_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthetas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msave_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m         \u001b[0;31m# pp, sp, sf = self.signature_as_function_of_theta(*thetas)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/Kasia/signature_function/cable_signature.py\u001b[0m in \u001b[0;36mplot_summand_for_theta\u001b[0;34m(self, theta, save_path)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot_summand_for_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m         \u001b[0mpp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature_as_function_of_theta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mtitle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknot_description\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\", theta = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msave_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Users/Kasia/signature_function/cable_signature.py\u001b[0m in \u001b[0;36mget_summand_signture_function\u001b[0;34m(theta)\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;31m# theta should not be larger than k for the pattern.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mtheta\u001b[0m \u001b[0;34m%=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_sage_const_2\u001b[0m  \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatt_k\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_sage_const_1\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m             \u001b[0mtheta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtheta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_sage_const_2\u001b[0m  \u001b[0;34m*\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpatt_k\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_sage_const_1\u001b[0m  \u001b[0;34m-\u001b[0m \u001b[0mtheta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Applications/SageMath-9.0.app/Contents/Resources/sage/local/lib/python3.7/site-packages/sage/rings/rational.pyx\u001b[0m in \u001b[0;36msage.rings.rational.Rational.__mod__ (build/cythonized/sage/rings/rational.c:23158)\u001b[0;34m()\u001b[0m\n\u001b[1;32m   2815\u001b[0m         \u001b[0mcdef\u001b[0m \u001b[0mRational\u001b[0m \u001b[0mrat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2816\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRational\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2817\u001b[0;31m             \u001b[0mrat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRational\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2818\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2819\u001b[0m             \u001b[0mrat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Applications/SageMath-9.0.app/Contents/Resources/sage/local/lib/python3.7/site-packages/sage/rings/rational.pyx\u001b[0m in \u001b[0;36msage.rings.rational.Rational.__init__ (build/cythonized/sage/rings/rational.c:6392)\u001b[0;34m()\u001b[0m\n\u001b[1;32m    527\u001b[0m         \"\"\"\n\u001b[1;32m    528\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 529\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Applications/SageMath-9.0.app/Contents/Resources/sage/local/lib/python3.7/site-packages/sage/rings/rational.pyx\u001b[0m in \u001b[0;36msage.rings.rational.Rational.__set_value (build/cythonized/sage/rings/rational.c:8515)\u001b[0;34m()\u001b[0m\n\u001b[1;32m    683\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"unable to convert {!r} to a rational\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0mcdef\u001b[0m \u001b[0mvoid\u001b[0m \u001b[0mset_from_mpq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRational\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmpq_t\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unable to convert [0, 4, 0, 4, 0, 0, 0, 0] to a rational"
+     ]
+    }
+   ],
    "source": [
     "cs = import_sage('cable_signature')\n",
     "sg = import_sage('signature')\n",