From 3e17919559814b077e02213f832fb6befb310f9a Mon Sep 17 00:00:00 2001
From: gheber <gheber@hdfgroup.org>
Date: Tue, 12 Apr 2016 18:54:05 -0500
Subject: [PATCH] Added Francis' changes.

---
 examples/09_The_Eilenberg-Zilber_module.ipynb | 204 +++++++++---------
 examples/10_Programming_the_Kan_theory.ipynb  |  51 ++---
 2 files changed, 129 insertions(+), 126 deletions(-)

diff --git a/examples/09_The_Eilenberg-Zilber_module.ipynb b/examples/09_The_Eilenberg-Zilber_module.ipynb
index d861832..d14b488 100644
--- a/examples/09_The_Eilenberg-Zilber_module.ipynb
+++ b/examples/09_The_Eilenberg-Zilber_module.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# The Eilenberg-Zilber module\n",
     "\n",
-    "The functions of this module are related to the chain complex of the product of two simplicial sets. They implement the Eilenberg-Mac Lane homomorphism (function `eml`) and the Alexander-Whitney homomorphism  (function `aw`).\n",
+    "The functions of this module are related to the chain complex of the product of two simplicial sets. They implement the Eilenberg-Mac Lane morphism (function `eml`) and the Alexander-Whitney morphism  (function `aw`).\n",
     "\n",
     "`eml` *ssx ssy* *[Function]*\n",
     "> build the Eilenberg-Mac Lane homomorphism $\\cal EML$, also called the *shuffle homomorphism*. (**Marvin Greenberg**, *Lectures on Algebraic Topology*, W.A. Benjamin Inc, 1967.) The arguments *ssx* and *ssy* represent some simplicial sets $X$ and $Y$. The homomorphism \n",
@@ -14,7 +14,7 @@
     "is defined by:\n",
     "$${\\cal EML}(\\alpha \\otimes \\beta)= \n",
     " \\sum{{\\varepsilon(\\varpi)}(\\eta_{j_q}\\ldots \\eta_{j_1}\\alpha,\\, \\eta_{i_p}\\ldots \\eta_{i_1}\\beta)},$$\n",
-    "where $X\\times Y$ is the simplicial cartesian product of the two simplicial sets $X$ and $Y$,  $\\alpha$ (resp. $\\beta$) is a geometric simplex of $X$ (resp. $Y$), $\\eta_k$ is the $k^{th}$ degeneracy operator, the sum is over all permutations $\\varpi= (i_1 \\ldots i_p j_1 \\ldots j_q)$ of $(0 \\ldots p+q-1)$ such that $$ i_1 < i_2 < \\cdots < i_p,\\quad j_1 < j_2 < \\cdots < j_q, \\quad p+q=n$$\n",
+    "where $X\\times Y$ is the simplicial cartesian product of the two simplicial sets $X$ and $Y$,  $\\alpha$ (resp. $\\beta$) is a geometric simplex of $X$ (resp. $Y$), $\\eta_k$ is the $k^{th}$ degeneracy operator, the sum is over all permutations $\\varpi = (i_1, \\ldots, i_p, j_1, \\ldots, j_q)$ of $(0, \\ldots, p+q-1)$ such that $$ i_1 < i_2 < \\cdots < i_p,\\quad j_1 < j_2 < \\cdots < j_q, \\quad p+q=n$$\n",
     "and $\\varepsilon(\\varpi)$ is the signature of the corresponding permutation. The sum at the right hand part determines a decomposition of the geometric product $\\alpha \\times \\beta$ into a combination of generators of the simplicial cartesian product (see the simplicial sets chapter). The combination takes in account the orientation of every simplex. These generators are non-degenerate simplices of the cartesian product but their projections are in general degenerate simplices of $X$ and $Y$ (see the examples).\n",
     " \n",
     "`aw` *ssx ssy* *[Function]*\n",
@@ -27,8 +27,8 @@
     "respectively by\n",
     "$$\\lambda_p(\\sigma)=\\partial_{p+1}\\ldots \\partial_n \\sigma$$\n",
     "and\n",
-    "$$\\rho_q(\\sigma)=\\partial_0\\ldots\\partial_{n-q} \\sigma.$$\n",
-    "Now, for the generator $(\\alpha,\\beta)$  of degree $n$ in ${\\cal C}_*(X \\times Y)$, the Alexander-Whitney homomorphism is defined  by:\n",
+    "$$\\rho_q(\\sigma)=\\partial_0\\ldots\\partial_{n-1} \\sigma.$$\n",
+    "Now, for the generator $(\\alpha,\\beta)$  of degree $n$ in ${\\cal C}_*(X \\times Y)$, the Alexander-Whitney morphism is defined  by:\n",
     "$${\\cal AW}(\\alpha, \\beta)=\\sum_{p=0}^n{\\lambda_p(\\alpha) \\otimes \\rho_{n-p}(\\beta)}.$$\n",
     "This sum can be interpreted as a decomposition, up to a homotopy, of the simplex $\\alpha \\times \\beta$\n",
     "($\\alpha$ **and** $\\beta$ can be degenerate even if $\\alpha \\times \\beta$ is not) in terms of (tensor) products of non-degenerate simplices.\n",
@@ -64,7 +64,7 @@
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",
       "text/plain": [
-       "#<PNG-BYTES {1005112A43}>"
+       "#<PNG-BYTES {1005277403}>"
       ]
      },
      "execution_count": 1,
@@ -245,7 +245,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "#<PNG-BYTES {1005947FD3}>"
+       "#<PNG-BYTES {1005AB7FD3}>"
       ]
      },
      "execution_count": 7,
@@ -476,7 +476,7 @@
    "metadata": {},
    "source": [
     "But the composition ${\\cal EML} \\circ {\\cal AW}$ is by no means the identity. This composition\n",
-    "is related to the identity via the homotopy morphism $\\Phi$. Let us consider the two following simple simplicial sets {\\tt delta-0-1} and {\\tt delta-2-3}, where the second is the simplicial set, model of the segment $[2,\\,3]$. To build `delta-2-3`, we have just changed the basis function, the base point and the comment in the system function `soft-delta`."
+    "is related to the identity via the homotopy morphism $\\Phi$. Let us consider the two following simple simplicial sets `delta-0-1` and `delta-2-3`, where the second is the simplicial set, model of the segment $[2,\\,3]$. To build `delta-2-3`, we have just changed the basis function, the base point and the comment in the system function `soft-delta`."
    ]
   },
   {
@@ -824,7 +824,7 @@
      "data": {
       "image/png": 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",
       "text/plain": [
-       "#<PNG-BYTES {1006396A23}>"
+       "#<PNG-BYTES {10064FEA23}>"
       ]
      },
      "execution_count": 25,
@@ -939,7 +939,7 @@
     }
    ],
    "source": [
-    "(def ez-rdc (ez (soft-delta-infinity)(soft-delta-infinity)))"
+    "(def ez-rdc (ez (soft-delta-infinity) (soft-delta-infinity)))"
    ]
   },
   {
@@ -1406,7 +1406,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Applying  successively the  function `chcm-homology` (which computes directly the homology groups without using a homotopy equivalence)  on these chain complexes, shows that  the homology groups are  effectively isomorphic, but the method  of the tensor  product is  much faster than the method of the simple cartesian product, the number of generators being much more smaller."
+    "Applying  successively the  function `chcm-homology` (which computes directly the homology groups without using a homotopy equivalence)  on these chain complexes, shows that  the homology groups are  effectively isomorphic, but the method  of the tensor  product is  much faster than the method of the simple cartesian product, the number of generators being much smaller."
    ]
   },
   {
@@ -1425,7 +1425,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 0) :\n",
       "<CrPr - * - *> \n",
       "End of computing.\n",
@@ -1435,7 +1435,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 1) :\n",
       "<CrPr - M1 0 *> \n",
       "End of computing.\n",
@@ -1454,7 +1454,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 1) :\n",
       "<CrPr - M1 0 *> \n",
       "End of computing.\n",
@@ -1464,7 +1464,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 2) :\n",
       "<CrPr - N2 1-0 *> \n",
       "End of computing.\n",
@@ -1483,7 +1483,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 2) :\n",
       "<CrPr - N2 1-0 *> \n",
       "End of computing.\n",
@@ -1493,43 +1493,43 @@
       "Rank of the source-module : 7.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/7 (dimension 3) :\n",
       "<CrPr 0 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/7 (dimension 3) :\n",
       "<CrPr 1 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/7 (dimension 3) :\n",
       "<CrPr 1-0 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/7 (dimension 3) :\n",
       "<CrPr 2 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/7 (dimension 3) :\n",
       "<CrPr 2-0 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/7 (dimension 3) :\n",
       "<CrPr 2-1 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/7 (dimension 3) :\n",
       "<CrPr 2-1-0 * - S3> \n",
       "End of computing.\n",
@@ -1546,43 +1546,43 @@
       "Rank of the source-module : 7.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/7 (dimension 3) :\n",
       "<CrPr 0 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/7 (dimension 3) :\n",
       "<CrPr 1 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/7 (dimension 3) :\n",
       "<CrPr 1-0 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/7 (dimension 3) :\n",
       "<CrPr 2 N2 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/7 (dimension 3) :\n",
       "<CrPr 2-0 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/7 (dimension 3) :\n",
       "<CrPr 2-1 M1 - S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/7 (dimension 3) :\n",
       "<CrPr 2-1-0 * - S3> \n",
       "End of computing.\n",
@@ -1592,97 +1592,97 @@
       "Rank of the source-module : 16.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/16 (dimension 4) :\n",
       "<CrPr 1-0 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/16 (dimension 4) :\n",
       "<CrPr 1-0 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/16 (dimension 4) :\n",
       "<CrPr 2-0 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/16 (dimension 4) :\n",
       "<CrPr 2-0 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/16 (dimension 4) :\n",
       "<CrPr 2-1 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/16 (dimension 4) :\n",
       "<CrPr 2-1 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/16 (dimension 4) :\n",
       "<CrPr 2-1-0 M1 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 8/16 (dimension 4) :\n",
       "<CrPr 3-0 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 9/16 (dimension 4) :\n",
       "<CrPr 3-0 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 10/16 (dimension 4) :\n",
       "<CrPr 3-1 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 11/16 (dimension 4) :\n",
       "<CrPr 3-1 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 12/16 (dimension 4) :\n",
       "<CrPr 3-1-0 M1 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 13/16 (dimension 4) :\n",
       "<CrPr 3-2 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 14/16 (dimension 4) :\n",
       "<CrPr 3-2 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 15/16 (dimension 4) :\n",
       "<CrPr 3-2-0 M1 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 16/16 (dimension 4) :\n",
       "<CrPr 3-2-1 M1 0 S3> \n",
       "End of computing.\n",
@@ -1701,97 +1701,97 @@
       "Rank of the source-module : 16.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/16 (dimension 4) :\n",
       "<CrPr 1-0 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/16 (dimension 4) :\n",
       "<CrPr 1-0 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/16 (dimension 4) :\n",
       "<CrPr 2-0 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/16 (dimension 4) :\n",
       "<CrPr 2-0 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/16 (dimension 4) :\n",
       "<CrPr 2-1 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/16 (dimension 4) :\n",
       "<CrPr 2-1 N2 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/16 (dimension 4) :\n",
       "<CrPr 2-1-0 M1 3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 8/16 (dimension 4) :\n",
       "<CrPr 3-0 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 9/16 (dimension 4) :\n",
       "<CrPr 3-0 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 10/16 (dimension 4) :\n",
       "<CrPr 3-1 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 11/16 (dimension 4) :\n",
       "<CrPr 3-1 N2 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 12/16 (dimension 4) :\n",
       "<CrPr 3-1-0 M1 2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 13/16 (dimension 4) :\n",
       "<CrPr 3-2 N2 0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 14/16 (dimension 4) :\n",
       "<CrPr 3-2 N2 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 15/16 (dimension 4) :\n",
       "<CrPr 3-2-0 M1 1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 16/16 (dimension 4) :\n",
       "<CrPr 3-2-1 M1 0 S3> \n",
       "End of computing.\n",
@@ -1801,61 +1801,61 @@
       "Rank of the source-module : 10.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/10 (dimension 5) :\n",
       "<CrPr 2-1-0 N2 4-3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/10 (dimension 5) :\n",
       "<CrPr 3-1-0 N2 4-2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/10 (dimension 5) :\n",
       "<CrPr 3-2-0 N2 4-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/10 (dimension 5) :\n",
       "<CrPr 3-2-1 N2 4-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/10 (dimension 5) :\n",
       "<CrPr 4-1-0 N2 3-2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/10 (dimension 5) :\n",
       "<CrPr 4-2-0 N2 3-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/10 (dimension 5) :\n",
       "<CrPr 4-2-1 N2 3-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 8/10 (dimension 5) :\n",
       "<CrPr 4-3-0 N2 2-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 9/10 (dimension 5) :\n",
       "<CrPr 4-3-1 N2 2-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 10/10 (dimension 5) :\n",
       "<CrPr 4-3-2 N2 1-0 S3> \n",
       "End of computing.\n",
@@ -1874,61 +1874,61 @@
       "Rank of the source-module : 10.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/10 (dimension 5) :\n",
       "<CrPr 2-1-0 N2 4-3 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 2/10 (dimension 5) :\n",
       "<CrPr 3-1-0 N2 4-2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 3/10 (dimension 5) :\n",
       "<CrPr 3-2-0 N2 4-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 4/10 (dimension 5) :\n",
       "<CrPr 3-2-1 N2 4-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 5/10 (dimension 5) :\n",
       "<CrPr 4-1-0 N2 3-2 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 6/10 (dimension 5) :\n",
       "<CrPr 4-2-0 N2 3-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 7/10 (dimension 5) :\n",
       "<CrPr 4-2-1 N2 3-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 8/10 (dimension 5) :\n",
       "<CrPr 4-3-0 N2 2-1 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 9/10 (dimension 5) :\n",
       "<CrPr 4-3-1 N2 2-0 S3> \n",
       "End of computing.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 10/10 (dimension 5) :\n",
       "<CrPr 4-3-2 N2 1-0 S3> \n",
       "End of computing.\n",
@@ -1950,7 +1950,12 @@
     {
      "data": {
       "text/plain": [
-       "NIL"
+       "\"Evaluation took:\n",
+       "  0.002 seconds of real time\n",
+       "  0.000000 seconds of total run time (0.000000 user, 0.000000 system)\n",
+       "  0.00% CPU\n",
+       "  ...\n",
+       "\""
       ]
      },
      "execution_count": 44,
@@ -1959,7 +1964,8 @@
     }
    ],
    "source": [
-    "(time (dotimes (i 6) (chcm-homology p2-X-s3 i)))"
+    "(with-output-to-string (*trace-output*)\n",
+    "    (time (dotimes (i 6) (chcm-homology p2-X-s3 i))))"
    ]
   },
   {
@@ -1978,7 +1984,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 0) :\n",
       "<TnPr 0 * 0 *> \n",
       "End of computing.\n",
@@ -1988,7 +1994,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 1) :\n",
       "<TnPr 1 M1 0 *> \n",
       "End of computing.\n",
@@ -2007,7 +2013,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 1) :\n",
       "<TnPr 1 M1 0 *> \n",
       "End of computing.\n",
@@ -2017,7 +2023,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 2) :\n",
       "<TnPr 2 N2 0 *> \n",
       "End of computing.\n",
@@ -2036,7 +2042,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 2) :\n",
       "<TnPr 2 N2 0 *> \n",
       "End of computing.\n",
@@ -2046,7 +2052,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 3) :\n",
       "<TnPr 0 * 3 S3> \n",
       "End of computing.\n",
@@ -2063,7 +2069,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 3) :\n",
       "<TnPr 0 * 3 S3> \n",
       "End of computing.\n",
@@ -2073,7 +2079,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 4) :\n",
       "<TnPr 1 M1 3 S3> \n",
       "End of computing.\n",
@@ -2092,7 +2098,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 4) :\n",
       "<TnPr 1 M1 3 S3> \n",
       "End of computing.\n",
@@ -2102,7 +2108,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 5) :\n",
       "<TnPr 2 N2 3 S3> \n",
       "End of computing.\n",
@@ -2121,7 +2127,7 @@
       "Rank of the source-module : 1.\n",
       "\n",
       "\n",
-      ";; Clock -> 2016-03-12, 0h 20m 33s.\n",
+      ";; Clock -> 2016-04-12, 23h 22m 48s.\n",
       "Computing the boundary of the generator 1/1 (dimension 5) :\n",
       "<TnPr 2 N2 3 S3> \n",
       "End of computing.\n",
@@ -2143,7 +2149,12 @@
     {
      "data": {
       "text/plain": [
-       "NIL"
+       "\"Evaluation took:\n",
+       "  0.000 seconds of real time\n",
+       "  0.000000 seconds of total run time (0.000000 user, 0.000000 system)\n",
+       "  100.00% CPU\n",
+       "  ...\n",
+       "\""
       ]
      },
      "execution_count": 45,
@@ -2152,7 +2163,8 @@
     }
    ],
    "source": [
-    "(time (dotimes (i 6) (chcm-homology p2-T-s3 i)))"
+    "(with-output-to-string (*trace-output*)\n",
+    "    (time (dotimes (i 6) (chcm-homology p2-T-s3 i))))"
    ]
   },
   {
diff --git a/examples/10_Programming_the_Kan_theory.ipynb b/examples/10_Programming_the_Kan_theory.ipynb
index 2fa0c53..37d8fbd 100644
--- a/examples/10_Programming_the_Kan_theory.ipynb
+++ b/examples/10_Programming_the_Kan_theory.ipynb
@@ -10,22 +10,24 @@
     "\n",
     "Let us recall some definitions in relation with the Kan simplicial sets.\n",
     "\n",
-    "**Definition 1.** A Kan \"hat\" of dimension $n$ and index $i$ is a collection of $n$ simplices of dimension  $(n-1)$, noted  $(\\sigma_0, \\ldots, \\sigma_{i-1}, \\sigma_{i+1}, \\ldots, \\sigma_n)$, such that the following conditions are fulfilled:\n",
+    "**Definition 1.** A Kan \"horn\" of dimension $n$ and index $i$ is a collection of $n$ simplices of dimension  $(n-1)$, noted  $(\\sigma_0, \\ldots, \\sigma_{i-1}, \\sigma_{i+1}, \\ldots, \\sigma_n)$, such that the following conditions are fulfilled:\n",
     "$$ \\partial_j\\sigma_k=\\partial_k\\sigma_{j+1},\\quad k \\not= i,\\quad j+1 \\not= i, \\quad 0 \\leq j \\leq n-1, \\quad 0 \\leq k \\leq n-1, \\quad k \\leq j. $$\n",
     "Note that there is no $\\sigma_i$ term in the list above; in a sense, the Kan process consists in constructing the missing $\\sigma_i$ to be considered as a \"composition\" of the given $\\sigma_j$'s.\n",
     "\n",
     "### Example\n",
     "\n",
-    "We are going to work with the simplices of $\\Delta^\\mathbb{N}$. The set $(\\sigma_1, \\sigma_2) = $ `((0 2), (0 1))` is a Kan hat of dimension 2 and index 0. In effect, $\\delta_1\\sigma_1=\\delta_1\\sigma_2 =$ `(0)`. Likewise, the two sets `((1 2), (0 1))` and `((1 2), (0 2))` are  Kan hats  of dimension 2 and of respective index 1 and 2. Of course, this definition applies equally if the simplices are degenerate.\n",
+    "We are going to work with the simplices of $\\Delta^\\mathbb{N}$. The set $(\\sigma_1, \\sigma_2) = $ `((0 2), (0 1))` is a Kan horn of dimension 2 and index 0. In effect, $\\partial_1\\sigma_1=\\partial_1\\sigma_2 =$ `(0)`. Likewise, the two sets `((1 2), (0 1))` and `((1 2), (0 2))` are  Kan horns  of dimension 2 and of respective index 1 and 2. Of course, this definition applies equally if the simplices are degenerate.\n",
     "\n",
-    "**Definition 2.** A *filling* of a Kan hat $(\\sigma_0, \\ldots, \\sigma_{i-1}, \\sigma_{i+1}, \\ldots, \\sigma_n)$, is an $n$-simplex $\\tilde{\\sigma}$, such that for every $j \\not= i$, $\\partial_j\\tilde{\\sigma}=\\sigma_j.$\n",
+    "**Definition 2.** A *filling* of a Kan horn $(\\sigma_0, \\ldots, \\sigma_{i-1}, \\sigma_{i+1}, \\ldots, \\sigma_n)$, is an $n$-simplex $\\tilde{\\sigma}$, such that for every $j \\not= i$, $\\partial_j\\tilde{\\sigma}=\\sigma_j.$\n",
     "\n",
     "### Example\n",
     "\n",
-    "A filling of `((0 2), (0 1))` is the 2-simplex `(0 1 2)`. A filling of the Kan hat `((1 1), (0 1))` is the degenerate 2-simplex `(0 1 1)`. A filling of the vertex `(0)` is the degenerate 1-simplex `(0 0)`.\n",
+    "A filling of `((0 2), (0 1))` is the 2-simplex `(0 1 2)`. A filling of the Kan horn `((1 1), (0 1))` is the degenerate 2-simplex `(0 1 1)`. A filling of the vertex `(0)` is the degenerate 1-simplex `(0 0)`.\n",
     "\n",
     "**Definition 3.** A simplicial set is a Kan simplicial set if for every hat there exists a filling.\n",
     "\n",
+    "In a *constructive* context, an *algorithm* must be provided able to *compute* a solution for the Kan problem. In the Kenzo program, a Kan simplicial-set has an extra slot `kfll` devoted to this problem.\n",
+    "\n",
     "### Representation of a Kan simplicial set\n",
     "\n",
     "A Kan simplicial set is implemented as an instance of the class `KAN`, subclass of the class `SIMPLICIAL-SET`.\n",
@@ -38,7 +40,7 @@
     "We recall that this new class inherits also from the class `CHAIN-COMPLEX`. It has one slot of its own:\n",
     "\n",
     "`kfll`\n",
-    "> an object of type `KFLL`, in fact a lisp function with 3 parameters describing a Kan hat: an index (an integer), a dimension (an integer) and the Kan hat (a list of abstract simplices). This function must return a filling of the Kan hat argument, i.e. an abstract simplex satisfying the theoritical definition.\n",
+    "> an object of type `KFLL`, in fact a lisp function with 3 parameters describing a Kan horn: an index (an integer), a dimension (an integer) and the Kan horn (a list of abstract simplices). This function must return a filling of the Kan horn argument, i.e. an abstract simplex satisfying the theoritical definition.\n",
     "\n",
     "A printing method has been associated to the class `KAN` and the external representation of  an instance is a string like `[K`*n* `Kan-Simplicial-Set]`, where *n* is the number plate of the Kenzo object.\n",
     "\n",
@@ -51,23 +53,20 @@
     "> Retrieve in the list `*Kan-list*` the Kan object instance whose the Kenzo identification  is n. If it does not exist, return `NIL`.\n",
     "\n",
     "`kfll &rest` *args* *[Macro]*\n",
-    "> With only one argument (a Kan instance) return the slot `kfll` of this instance. With 4 arguments\n",
-    "like `(kfll` *kan indx dmns hat*`)`, return a filling of the Kan hat *hat*, of dimension *dmns*\n",
-    "and of index *indx* by applying the filling function value of the slot `kfll` of the Kan instance *kan*.\n",
+    "> With only one argument (a Kan instance) return the slot `kfll` of this instance. With 4 arguments like `(kfll` *kan indx dmns horn*`)`, return a filling of the Kan horn *horn*, of dimension *dmns* and of index *indx* by applying the filling function value of the slot `kfll` of the Kan instance *kan*.\n",
     "\n",
     "`smst-kan` *smst kfll* *[Function]*\n",
     "> With the filling lisp function *kfll*, **transform** the simplicial set *smst* in an object of type `KAN` (in other word, *smst* is `modified`). This is the easiest way to build a Kan simplicial set.\n",
     "\n",
-    "`check-hat` *kan indx dmns hat* *[Function]*\n",
-    "> Useful verification function to check if the collection of simplices `hat` is a valid Kan hat of dimension *dmns* and of index *indx* in the Kan simplicial set *kan*. In fact this works equally if *kan* is a general simplicial set.\n",
+    "`check-horn` *kan indx dmns horn* *[Function]*\n",
+    "> Useful verification function to check if the collection of simplices `horn` is a valid Kan horn of dimension *dmns* and of index *indx* in the Kan simplicial set *kan*. In fact this works equally if *kan* is a general simplicial set.\n",
     "\n",
-    "`check-kan` *kan indx dmns hat* *[Function]*\n",
-    "> Useful verification function to check if the collection of simplices *hat* is a valid Kan hat of dimension *dmns* and of index *indx* in the Kan simplicial set *kan*. This verification function applies the filling function of the instance *kan* to the argument *hat* and perform the  verification of the faces relations upon the resulting $dmns$-simplex.\n",
+    "`check-kan` *kan indx dmns horn* *[Function]*\n",
+    "> Useful verification function to check if the collection of simplices *horn* is a valid Kan horn of dimension *dmns* and of index *indx* in the Kan simplicial set *kan*. This verification function applies the filling function of the instance *kan* to the argument *horn* and perform the  verification of the faces relations upon the resulting $dmns$-simplex.\n",
     "\n",
     "### Examples\n",
     "\n",
-    "Let us take again the small examples of the introduction. First we  define a function `dkfll`, \n",
-    "a filling function suitable for a  Kan hat in $\\Delta^\\mathbb{N}$. The user will note that in the abstract simplices the degeneracy operators and the geometric simplices are coded in binary."
+    "Let us take again the small examples of the introduction. First we  define a function `dkfll`, a filling function suitable for a  Kan horn in $\\Delta^\\mathbb{N}$. The user will note that in the abstract simplices the degeneracy operators and the geometric simplices are coded in binary."
    ]
   },
   {
@@ -77,14 +76,6 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "SIMPLE-STYLE-WARNING: \n",
-      "  #<SB-INT:SIMPLE-STYLE-WARNING \"undefined ~(~A~): ~S\" {1005215EB3}>\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -97,23 +88,23 @@
     }
    ],
    "source": [
-    "(defun dkfll (indx dmns hat)\n",
+    "(defun dkfll (indx dmns horn)\n",
     "     (cond ((= 1 dmns)\n",
-    "            (absm 1 (gmsm (first hat))))\n",
+    "            (absm 1 (gmsm (first horn))))\n",
     "           ((= 0 indx)\n",
-    "            (let ((del-1 (absm-int-ext (first hat)))\n",
-    "                  (del-2 (absm-int-ext (second hat))))\n",
+    "            (let ((del-1 (absm-int-ext (first horn)))\n",
+    "                  (del-2 (absm-int-ext (second horn))))\n",
     "              (absm-ext-int\n",
     "                 (cons (first del-1)\n",
     "                       (cons (second del-2) (rest del-1))))))\n",
     "           ((= 1 indx)\n",
-    "            (let ((del-0 (absm-int-ext (first hat)))\n",
-    "                  (del-2 (absm-int-ext (second hat))))\n",
+    "            (let ((del-0 (absm-int-ext (first horn)))\n",
+    "                  (del-2 (absm-int-ext (second horn))))\n",
     "              (absm-ext-int\n",
     "                 (cons (first del-2) del-0))))\n",
     "           (t\n",
-    "            (let ((del-0 (absm-int-ext (first hat)))\n",
-    "                  (del-1 (absm-int-ext (second hat))))\n",
+    "            (let ((del-0 (absm-int-ext (first horn)))\n",
+    "                  (del-1 (absm-int-ext (second horn))))\n",
     "              (absm-ext-int\n",
     "                 (cons (first del-1) del-0))))))"
    ]