diff --git a/HappyBirthdaySophie/compute_distance_field.ipynb b/HappyBirthdaySophie/compute_distance_field.ipynb
index 9666b15..a136361 100644
--- a/HappyBirthdaySophie/compute_distance_field.ipynb
+++ b/HappyBirthdaySophie/compute_distance_field.ipynb
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -150,7 +150,13 @@
       "2\n",
       "3\n",
       "4\n",
-      "5\n"
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "CPU times: user 1min 8s, sys: 3min 11s, total: 4min 20s\n",
+      "Wall time: 8min 35s\n"
      ]
     }
    ],
@@ -204,29 +210,693 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=RGBA size=500x500 at 0x7FAE4A5CF690>"
+       "<PIL.Image.Image image mode=RGBA size=500x500 at 0x7FAE4A5B8E50>"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "quantised = 255 - (dist / np.max(dist) * 255).astype(np.uint8)\n",
+    "#quantised = (quantised % 2) * 255\n",
     "im2 = Image.fromarray(quantised, mode = 'L')\n",
     "im2 = im2.convert(\"RGBA\")\n",
     "im2.save('distfield.png')\n",
     "im2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on ufunc object:\n",
+      "\n",
+      "log = class ufunc(builtins.object)\n",
+      " |  Functions that operate element by element on whole arrays.\n",
+      " |  \n",
+      " |  To see the documentation for a specific ufunc, use `info`.  For\n",
+      " |  example, ``np.info(np.sin)``.  Because ufuncs are written in C\n",
+      " |  (for speed) and linked into Python with NumPy's ufunc facility,\n",
+      " |  Python's help() function finds this page whenever help() is called\n",
+      " |  on a ufunc.\n",
+      " |  \n",
+      " |  A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.\n",
+      " |  \n",
+      " |  **Calling ufuncs:** ``op(*x[, out], where=True, **kwargs)``\n",
+      " |  \n",
+      " |  Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.\n",
+      " |  \n",
+      " |  The broadcasting rules are:\n",
+      " |  \n",
+      " |  * Dimensions of length 1 may be prepended to either array.\n",
+      " |  * Arrays may be repeated along dimensions of length 1.\n",
+      " |  \n",
+      " |  Parameters\n",
+      " |  ----------\n",
+      " |  *x : array_like\n",
+      " |      Input arrays.\n",
+      " |  out : ndarray, None, or tuple of ndarray and None, optional\n",
+      " |      Alternate array object(s) in which to put the result; if provided, it\n",
+      " |      must have a shape that the inputs broadcast to. A tuple of arrays\n",
+      " |      (possible only as a keyword argument) must have length equal to the\n",
+      " |      number of outputs; use None for uninitialized outputs to be\n",
+      " |      allocated by the ufunc.\n",
+      " |  where : array_like, optional\n",
+      " |      This condition is broadcast over the input. At locations where the\n",
+      " |      condition is True, the `out` array will be set to the ufunc result.\n",
+      " |      Elsewhere, the `out` array will retain its original value.\n",
+      " |      Note that if an uninitialized `out` array is created via the default\n",
+      " |      ``out=None``, locations within it where the condition is False will\n",
+      " |      remain uninitialized.\n",
+      " |  **kwargs\n",
+      " |      For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.\n",
+      " |  \n",
+      " |  Returns\n",
+      " |  -------\n",
+      " |  r : ndarray or tuple of ndarray\n",
+      " |      `r` will have the shape that the arrays in `x` broadcast to; if `out` is\n",
+      " |      provided, it will be returned. If not, `r` will be allocated and\n",
+      " |      may contain uninitialized values. If the function has more than one\n",
+      " |      output, then the result will be a tuple of arrays.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __call__(self, /, *args, **kwargs)\n",
+      " |      Call self as a function.\n",
+      " |  \n",
+      " |  __repr__(self, /)\n",
+      " |      Return repr(self).\n",
+      " |  \n",
+      " |  __str__(self, /)\n",
+      " |      Return str(self).\n",
+      " |  \n",
+      " |  accumulate(...)\n",
+      " |      accumulate(array, axis=0, dtype=None, out=None)\n",
+      " |      \n",
+      " |      Accumulate the result of applying the operator to all elements.\n",
+      " |      \n",
+      " |      For a one-dimensional array, accumulate produces results equivalent to::\n",
+      " |      \n",
+      " |        r = np.empty(len(A))\n",
+      " |        t = op.identity        # op = the ufunc being applied to A's  elements\n",
+      " |        for i in range(len(A)):\n",
+      " |            t = op(t, A[i])\n",
+      " |            r[i] = t\n",
+      " |        return r\n",
+      " |      \n",
+      " |      For example, add.accumulate() is equivalent to np.cumsum().\n",
+      " |      \n",
+      " |      For a multi-dimensional array, accumulate is applied along only one\n",
+      " |      axis (axis zero by default; see Examples below) so repeated use is\n",
+      " |      necessary if one wants to accumulate over multiple axes.\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      array : array_like\n",
+      " |          The array to act on.\n",
+      " |      axis : int, optional\n",
+      " |          The axis along which to apply the accumulation; default is zero.\n",
+      " |      dtype : data-type code, optional\n",
+      " |          The data-type used to represent the intermediate results. Defaults\n",
+      " |          to the data-type of the output array if such is provided, or the\n",
+      " |          the data-type of the input array if no output array is provided.\n",
+      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      " |          A location into which the result is stored. If not provided or None,\n",
+      " |          a freshly-allocated array is returned. For consistency with\n",
+      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      " |          1-element tuple.\n",
+      " |      \n",
+      " |          .. versionchanged:: 1.13.0\n",
+      " |             Tuples are allowed for keyword argument.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      r : ndarray\n",
+      " |          The accumulated values. If `out` was supplied, `r` is a reference to\n",
+      " |          `out`.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      1-D array examples:\n",
+      " |      \n",
+      " |      >>> np.add.accumulate([2, 3, 5])\n",
+      " |      array([ 2,  5, 10])\n",
+      " |      >>> np.multiply.accumulate([2, 3, 5])\n",
+      " |      array([ 2,  6, 30])\n",
+      " |      \n",
+      " |      2-D array examples:\n",
+      " |      \n",
+      " |      >>> I = np.eye(2)\n",
+      " |      >>> I\n",
+      " |      array([[1.,  0.],\n",
+      " |             [0.,  1.]])\n",
+      " |      \n",
+      " |      Accumulate along axis 0 (rows), down columns:\n",
+      " |      \n",
+      " |      >>> np.add.accumulate(I, 0)\n",
+      " |      array([[1.,  0.],\n",
+      " |             [1.,  1.]])\n",
+      " |      >>> np.add.accumulate(I) # no axis specified = axis zero\n",
+      " |      array([[1.,  0.],\n",
+      " |             [1.,  1.]])\n",
+      " |      \n",
+      " |      Accumulate along axis 1 (columns), through rows:\n",
+      " |      \n",
+      " |      >>> np.add.accumulate(I, 1)\n",
+      " |      array([[1.,  1.],\n",
+      " |             [0.,  1.]])\n",
+      " |  \n",
+      " |  at(...)\n",
+      " |      at(a, indices, b=None)\n",
+      " |      \n",
+      " |      Performs unbuffered in place operation on operand 'a' for elements\n",
+      " |      specified by 'indices'. For addition ufunc, this method is equivalent to\n",
+      " |      ``a[indices] += b``, except that results are accumulated for elements that\n",
+      " |      are indexed more than once. For example, ``a[[0,0]] += 1`` will only\n",
+      " |      increment the first element once because of buffering, whereas\n",
+      " |      ``add.at(a, [0,0], 1)`` will increment the first element twice.\n",
+      " |      \n",
+      " |      .. versionadded:: 1.8.0\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      a : array_like\n",
+      " |          The array to perform in place operation on.\n",
+      " |      indices : array_like or tuple\n",
+      " |          Array like index object or slice object for indexing into first\n",
+      " |          operand. If first operand has multiple dimensions, indices can be a\n",
+      " |          tuple of array like index objects or slice objects.\n",
+      " |      b : array_like\n",
+      " |          Second operand for ufuncs requiring two operands. Operand must be\n",
+      " |          broadcastable over first operand after indexing or slicing.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      Set items 0 and 1 to their negative values:\n",
+      " |      \n",
+      " |      >>> a = np.array([1, 2, 3, 4])\n",
+      " |      >>> np.negative.at(a, [0, 1])\n",
+      " |      >>> a\n",
+      " |      array([-1, -2,  3,  4])\n",
+      " |      \n",
+      " |      Increment items 0 and 1, and increment item 2 twice:\n",
+      " |      \n",
+      " |      >>> a = np.array([1, 2, 3, 4])\n",
+      " |      >>> np.add.at(a, [0, 1, 2, 2], 1)\n",
+      " |      >>> a\n",
+      " |      array([2, 3, 5, 4])\n",
+      " |      \n",
+      " |      Add items 0 and 1 in first array to second array,\n",
+      " |      and store results in first array:\n",
+      " |      \n",
+      " |      >>> a = np.array([1, 2, 3, 4])\n",
+      " |      >>> b = np.array([1, 2])\n",
+      " |      >>> np.add.at(a, [0, 1], b)\n",
+      " |      >>> a\n",
+      " |      array([2, 4, 3, 4])\n",
+      " |  \n",
+      " |  outer(...)\n",
+      " |      outer(A, B, **kwargs)\n",
+      " |      \n",
+      " |      Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.\n",
+      " |      \n",
+      " |      Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of\n",
+      " |      ``op.outer(A, B)`` is an array of dimension M + N such that:\n",
+      " |      \n",
+      " |      .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =\n",
+      " |         op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])\n",
+      " |      \n",
+      " |      For `A` and `B` one-dimensional, this is equivalent to::\n",
+      " |      \n",
+      " |        r = empty(len(A),len(B))\n",
+      " |        for i in range(len(A)):\n",
+      " |            for j in range(len(B)):\n",
+      " |                r[i,j] = op(A[i], B[j]) # op = ufunc in question\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      A : array_like\n",
+      " |          First array\n",
+      " |      B : array_like\n",
+      " |          Second array\n",
+      " |      kwargs : any\n",
+      " |          Arguments to pass on to the ufunc. Typically `dtype` or `out`.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      r : ndarray\n",
+      " |          Output array\n",
+      " |      \n",
+      " |      See Also\n",
+      " |      --------\n",
+      " |      numpy.outer : A less powerful version of ``np.multiply.outer``\n",
+      " |                    that `ravel`\\ s all inputs to 1D. This exists\n",
+      " |                    primarily for compatibility with old code.\n",
+      " |      \n",
+      " |      tensordot : ``np.tensordot(a, b, axes=((), ()))`` and\n",
+      " |                  ``np.multiply.outer(a, b)`` behave same for all\n",
+      " |                  dimensions of a and b.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.multiply.outer([1, 2, 3], [4, 5, 6])\n",
+      " |      array([[ 4,  5,  6],\n",
+      " |             [ 8, 10, 12],\n",
+      " |             [12, 15, 18]])\n",
+      " |      \n",
+      " |      A multi-dimensional example:\n",
+      " |      \n",
+      " |      >>> A = np.array([[1, 2, 3], [4, 5, 6]])\n",
+      " |      >>> A.shape\n",
+      " |      (2, 3)\n",
+      " |      >>> B = np.array([[1, 2, 3, 4]])\n",
+      " |      >>> B.shape\n",
+      " |      (1, 4)\n",
+      " |      >>> C = np.multiply.outer(A, B)\n",
+      " |      >>> C.shape; C\n",
+      " |      (2, 3, 1, 4)\n",
+      " |      array([[[[ 1,  2,  3,  4]],\n",
+      " |              [[ 2,  4,  6,  8]],\n",
+      " |              [[ 3,  6,  9, 12]]],\n",
+      " |             [[[ 4,  8, 12, 16]],\n",
+      " |              [[ 5, 10, 15, 20]],\n",
+      " |              [[ 6, 12, 18, 24]]]])\n",
+      " |  \n",
+      " |  reduce(...)\n",
+      " |      reduce(a, axis=0, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)\n",
+      " |      \n",
+      " |      Reduces `a`'s dimension by one, by applying ufunc along one axis.\n",
+      " |      \n",
+      " |      Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then\n",
+      " |      :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =\n",
+      " |      the result of iterating `j` over :math:`range(N_i)`, cumulatively applying\n",
+      " |      ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.\n",
+      " |      For a one-dimensional array, reduce produces results equivalent to:\n",
+      " |      ::\n",
+      " |      \n",
+      " |       r = op.identity # op = ufunc\n",
+      " |       for i in range(len(A)):\n",
+      " |         r = op(r, A[i])\n",
+      " |       return r\n",
+      " |      \n",
+      " |      For example, add.reduce() is equivalent to sum().\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      a : array_like\n",
+      " |          The array to act on.\n",
+      " |      axis : None or int or tuple of ints, optional\n",
+      " |          Axis or axes along which a reduction is performed.\n",
+      " |          The default (`axis` = 0) is perform a reduction over the first\n",
+      " |          dimension of the input array. `axis` may be negative, in\n",
+      " |          which case it counts from the last to the first axis.\n",
+      " |      \n",
+      " |          .. versionadded:: 1.7.0\n",
+      " |      \n",
+      " |          If this is None, a reduction is performed over all the axes.\n",
+      " |          If this is a tuple of ints, a reduction is performed on multiple\n",
+      " |          axes, instead of a single axis or all the axes as before.\n",
+      " |      \n",
+      " |          For operations which are either not commutative or not associative,\n",
+      " |          doing a reduction over multiple axes is not well-defined. The\n",
+      " |          ufuncs do not currently raise an exception in this case, but will\n",
+      " |          likely do so in the future.\n",
+      " |      dtype : data-type code, optional\n",
+      " |          The type used to represent the intermediate results. Defaults\n",
+      " |          to the data-type of the output array if this is provided, or\n",
+      " |          the data-type of the input array if no output array is provided.\n",
+      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      " |          A location into which the result is stored. If not provided or None,\n",
+      " |          a freshly-allocated array is returned. For consistency with\n",
+      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      " |          1-element tuple.\n",
+      " |      \n",
+      " |          .. versionchanged:: 1.13.0\n",
+      " |             Tuples are allowed for keyword argument.\n",
+      " |      keepdims : bool, optional\n",
+      " |          If this is set to True, the axes which are reduced are left\n",
+      " |          in the result as dimensions with size one. With this option,\n",
+      " |          the result will broadcast correctly against the original `arr`.\n",
+      " |      \n",
+      " |          .. versionadded:: 1.7.0\n",
+      " |      initial : scalar, optional\n",
+      " |          The value with which to start the reduction.\n",
+      " |          If the ufunc has no identity or the dtype is object, this defaults\n",
+      " |          to None - otherwise it defaults to ufunc.identity.\n",
+      " |          If ``None`` is given, the first element of the reduction is used,\n",
+      " |          and an error is thrown if the reduction is empty.\n",
+      " |      \n",
+      " |          .. versionadded:: 1.15.0\n",
+      " |      \n",
+      " |      where : array_like of bool, optional\n",
+      " |          A boolean array which is broadcasted to match the dimensions\n",
+      " |          of `a`, and selects elements to include in the reduction. Note\n",
+      " |          that for ufuncs like ``minimum`` that do not have an identity\n",
+      " |          defined, one has to pass in also ``initial``.\n",
+      " |      \n",
+      " |          .. versionadded:: 1.17.0\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      r : ndarray\n",
+      " |          The reduced array. If `out` was supplied, `r` is a reference to it.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.multiply.reduce([2,3,5])\n",
+      " |      30\n",
+      " |      \n",
+      " |      A multi-dimensional array example:\n",
+      " |      \n",
+      " |      >>> X = np.arange(8).reshape((2,2,2))\n",
+      " |      >>> X\n",
+      " |      array([[[0, 1],\n",
+      " |              [2, 3]],\n",
+      " |             [[4, 5],\n",
+      " |              [6, 7]]])\n",
+      " |      >>> np.add.reduce(X, 0)\n",
+      " |      array([[ 4,  6],\n",
+      " |             [ 8, 10]])\n",
+      " |      >>> np.add.reduce(X) # confirm: default axis value is 0\n",
+      " |      array([[ 4,  6],\n",
+      " |             [ 8, 10]])\n",
+      " |      >>> np.add.reduce(X, 1)\n",
+      " |      array([[ 2,  4],\n",
+      " |             [10, 12]])\n",
+      " |      >>> np.add.reduce(X, 2)\n",
+      " |      array([[ 1,  5],\n",
+      " |             [ 9, 13]])\n",
+      " |      \n",
+      " |      You can use the ``initial`` keyword argument to initialize the reduction\n",
+      " |      with a different value, and ``where`` to select specific elements to include:\n",
+      " |      \n",
+      " |      >>> np.add.reduce([10], initial=5)\n",
+      " |      15\n",
+      " |      >>> np.add.reduce(np.ones((2, 2, 2)), axis=(0, 2), initial=10)\n",
+      " |      array([14., 14.])\n",
+      " |      >>> a = np.array([10., np.nan, 10])\n",
+      " |      >>> np.add.reduce(a, where=~np.isnan(a))\n",
+      " |      20.0\n",
+      " |      \n",
+      " |      Allows reductions of empty arrays where they would normally fail, i.e.\n",
+      " |      for ufuncs without an identity.\n",
+      " |      \n",
+      " |      >>> np.minimum.reduce([], initial=np.inf)\n",
+      " |      inf\n",
+      " |      >>> np.minimum.reduce([[1., 2.], [3., 4.]], initial=10., where=[True, False])\n",
+      " |      array([ 1., 10.])\n",
+      " |      >>> np.minimum.reduce([])\n",
+      " |      Traceback (most recent call last):\n",
+      " |          ...\n",
+      " |      ValueError: zero-size array to reduction operation minimum which has no identity\n",
+      " |  \n",
+      " |  reduceat(...)\n",
+      " |      reduceat(a, indices, axis=0, dtype=None, out=None)\n",
+      " |      \n",
+      " |      Performs a (local) reduce with specified slices over a single axis.\n",
+      " |      \n",
+      " |      For i in ``range(len(indices))``, `reduceat` computes\n",
+      " |      ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th\n",
+      " |      generalized \"row\" parallel to `axis` in the final result (i.e., in a\n",
+      " |      2-D array, for example, if `axis = 0`, it becomes the i-th row, but if\n",
+      " |      `axis = 1`, it becomes the i-th column).  There are three exceptions to this:\n",
+      " |      \n",
+      " |      * when ``i = len(indices) - 1`` (so for the last index),\n",
+      " |        ``indices[i+1] = a.shape[axis]``.\n",
+      " |      * if ``indices[i] >= indices[i + 1]``, the i-th generalized \"row\" is\n",
+      " |        simply ``a[indices[i]]``.\n",
+      " |      * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.\n",
+      " |      \n",
+      " |      The shape of the output depends on the size of `indices`, and may be\n",
+      " |      larger than `a` (this happens if ``len(indices) > a.shape[axis]``).\n",
+      " |      \n",
+      " |      Parameters\n",
+      " |      ----------\n",
+      " |      a : array_like\n",
+      " |          The array to act on.\n",
+      " |      indices : array_like\n",
+      " |          Paired indices, comma separated (not colon), specifying slices to\n",
+      " |          reduce.\n",
+      " |      axis : int, optional\n",
+      " |          The axis along which to apply the reduceat.\n",
+      " |      dtype : data-type code, optional\n",
+      " |          The type used to represent the intermediate results. Defaults\n",
+      " |          to the data type of the output array if this is provided, or\n",
+      " |          the data type of the input array if no output array is provided.\n",
+      " |      out : ndarray, None, or tuple of ndarray and None, optional\n",
+      " |          A location into which the result is stored. If not provided or None,\n",
+      " |          a freshly-allocated array is returned. For consistency with\n",
+      " |          ``ufunc.__call__``, if given as a keyword, this may be wrapped in a\n",
+      " |          1-element tuple.\n",
+      " |      \n",
+      " |          .. versionchanged:: 1.13.0\n",
+      " |             Tuples are allowed for keyword argument.\n",
+      " |      \n",
+      " |      Returns\n",
+      " |      -------\n",
+      " |      r : ndarray\n",
+      " |          The reduced values. If `out` was supplied, `r` is a reference to\n",
+      " |          `out`.\n",
+      " |      \n",
+      " |      Notes\n",
+      " |      -----\n",
+      " |      A descriptive example:\n",
+      " |      \n",
+      " |      If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as\n",
+      " |      ``ufunc.reduceat(a, indices)[::2]`` where `indices` is\n",
+      " |      ``range(len(array) - 1)`` with a zero placed\n",
+      " |      in every other element:\n",
+      " |      ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.\n",
+      " |      \n",
+      " |      Don't be fooled by this attribute's name: `reduceat(a)` is not\n",
+      " |      necessarily smaller than `a`.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      To take the running sum of four successive values:\n",
+      " |      \n",
+      " |      >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]\n",
+      " |      array([ 6, 10, 14, 18])\n",
+      " |      \n",
+      " |      A 2-D example:\n",
+      " |      \n",
+      " |      >>> x = np.linspace(0, 15, 16).reshape(4,4)\n",
+      " |      >>> x\n",
+      " |      array([[ 0.,   1.,   2.,   3.],\n",
+      " |             [ 4.,   5.,   6.,   7.],\n",
+      " |             [ 8.,   9.,  10.,  11.],\n",
+      " |             [12.,  13.,  14.,  15.]])\n",
+      " |      \n",
+      " |      ::\n",
+      " |      \n",
+      " |       # reduce such that the result has the following five rows:\n",
+      " |       # [row1 + row2 + row3]\n",
+      " |       # [row4]\n",
+      " |       # [row2]\n",
+      " |       # [row3]\n",
+      " |       # [row1 + row2 + row3 + row4]\n",
+      " |      \n",
+      " |      >>> np.add.reduceat(x, [0, 3, 1, 2, 0])\n",
+      " |      array([[12.,  15.,  18.,  21.],\n",
+      " |             [12.,  13.,  14.,  15.],\n",
+      " |             [ 4.,   5.,   6.,   7.],\n",
+      " |             [ 8.,   9.,  10.,  11.],\n",
+      " |             [24.,  28.,  32.,  36.]])\n",
+      " |      \n",
+      " |      ::\n",
+      " |      \n",
+      " |       # reduce such that result has the following two columns:\n",
+      " |       # [col1 * col2 * col3, col4]\n",
+      " |      \n",
+      " |      >>> np.multiply.reduceat(x, [0, 3], 1)\n",
+      " |      array([[   0.,     3.],\n",
+      " |             [ 120.,     7.],\n",
+      " |             [ 720.,    11.],\n",
+      " |             [2184.,    15.]])\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  identity\n",
+      " |      The identity value.\n",
+      " |      \n",
+      " |      Data attribute containing the identity element for the ufunc, if it has one.\n",
+      " |      If it does not, the attribute value is None.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.identity\n",
+      " |      0\n",
+      " |      >>> np.multiply.identity\n",
+      " |      1\n",
+      " |      >>> np.power.identity\n",
+      " |      1\n",
+      " |      >>> print(np.exp.identity)\n",
+      " |      None\n",
+      " |  \n",
+      " |  nargs\n",
+      " |      The number of arguments.\n",
+      " |      \n",
+      " |      Data attribute containing the number of arguments the ufunc takes, including\n",
+      " |      optional ones.\n",
+      " |      \n",
+      " |      Notes\n",
+      " |      -----\n",
+      " |      Typically this value will be one more than what you might expect because all\n",
+      " |      ufuncs take  the optional \"out\" argument.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.nargs\n",
+      " |      3\n",
+      " |      >>> np.multiply.nargs\n",
+      " |      3\n",
+      " |      >>> np.power.nargs\n",
+      " |      3\n",
+      " |      >>> np.exp.nargs\n",
+      " |      2\n",
+      " |  \n",
+      " |  nin\n",
+      " |      The number of inputs.\n",
+      " |      \n",
+      " |      Data attribute containing the number of arguments the ufunc treats as input.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.nin\n",
+      " |      2\n",
+      " |      >>> np.multiply.nin\n",
+      " |      2\n",
+      " |      >>> np.power.nin\n",
+      " |      2\n",
+      " |      >>> np.exp.nin\n",
+      " |      1\n",
+      " |  \n",
+      " |  nout\n",
+      " |      The number of outputs.\n",
+      " |      \n",
+      " |      Data attribute containing the number of arguments the ufunc treats as output.\n",
+      " |      \n",
+      " |      Notes\n",
+      " |      -----\n",
+      " |      Since all ufuncs can take output arguments, this will always be (at least) 1.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.nout\n",
+      " |      1\n",
+      " |      >>> np.multiply.nout\n",
+      " |      1\n",
+      " |      >>> np.power.nout\n",
+      " |      1\n",
+      " |      >>> np.exp.nout\n",
+      " |      1\n",
+      " |  \n",
+      " |  ntypes\n",
+      " |      The number of types.\n",
+      " |      \n",
+      " |      The number of numerical NumPy types - of which there are 18 total - on which\n",
+      " |      the ufunc can operate.\n",
+      " |      \n",
+      " |      See Also\n",
+      " |      --------\n",
+      " |      numpy.ufunc.types\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.ntypes\n",
+      " |      18\n",
+      " |      >>> np.multiply.ntypes\n",
+      " |      18\n",
+      " |      >>> np.power.ntypes\n",
+      " |      17\n",
+      " |      >>> np.exp.ntypes\n",
+      " |      7\n",
+      " |      >>> np.remainder.ntypes\n",
+      " |      14\n",
+      " |  \n",
+      " |  signature\n",
+      " |      Definition of the core elements a generalized ufunc operates on.\n",
+      " |      \n",
+      " |      The signature determines how the dimensions of each input/output array\n",
+      " |      are split into core and loop dimensions:\n",
+      " |      \n",
+      " |      1. Each dimension in the signature is matched to a dimension of the\n",
+      " |         corresponding passed-in array, starting from the end of the shape tuple.\n",
+      " |      2. Core dimensions assigned to the same label in the signature must have\n",
+      " |         exactly matching sizes, no broadcasting is performed.\n",
+      " |      3. The core dimensions are removed from all inputs and the remaining\n",
+      " |         dimensions are broadcast together, defining the loop dimensions.\n",
+      " |      \n",
+      " |      Notes\n",
+      " |      -----\n",
+      " |      Generalized ufuncs are used internally in many linalg functions, and in\n",
+      " |      the testing suite; the examples below are taken from these.\n",
+      " |      For ufuncs that operate on scalars, the signature is None, which is\n",
+      " |      equivalent to '()' for every argument.\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.core.umath_tests.matrix_multiply.signature\n",
+      " |      '(m,n),(n,p)->(m,p)'\n",
+      " |      >>> np.linalg._umath_linalg.det.signature\n",
+      " |      '(m,m)->()'\n",
+      " |      >>> np.add.signature is None\n",
+      " |      True  # equivalent to '(),()->()'\n",
+      " |  \n",
+      " |  types\n",
+      " |      Returns a list with types grouped input->output.\n",
+      " |      \n",
+      " |      Data attribute listing the data-type \"Domain-Range\" groupings the ufunc can\n",
+      " |      deliver. The data-types are given using the character codes.\n",
+      " |      \n",
+      " |      See Also\n",
+      " |      --------\n",
+      " |      numpy.ufunc.ntypes\n",
+      " |      \n",
+      " |      Examples\n",
+      " |      --------\n",
+      " |      >>> np.add.types\n",
+      " |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
+      " |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
+      " |      'GG->G', 'OO->O']\n",
+      " |      \n",
+      " |      >>> np.multiply.types\n",
+      " |      ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n",
+      " |      'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n",
+      " |      'GG->G', 'OO->O']\n",
+      " |      \n",
+      " |      >>> np.power.types\n",
+      " |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
+      " |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',\n",
+      " |      'OO->O']\n",
+      " |      \n",
+      " |      >>> np.exp.types\n",
+      " |      ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']\n",
+      " |      \n",
+      " |      >>> np.remainder.types\n",
+      " |      ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n",
+      " |      'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/HappyBirthdaySophie/distfield.png b/HappyBirthdaySophie/distfield.png
index 27e1c52..c3535b5 100644
Binary files a/HappyBirthdaySophie/distfield.png and b/HappyBirthdaySophie/distfield.png differ
diff --git a/HappyBirthdaySophie/sketch.js b/HappyBirthdaySophie/sketch.js
index 570b40c..3f61a26 100644
--- a/HappyBirthdaySophie/sketch.js
+++ b/HappyBirthdaySophie/sketch.js
@@ -1,8 +1,12 @@
 
 let w = 200;
 let stepsize = 1;
-let Nwalkers = 100;
-let fr = 100;
+let Nwalkers = 300;
+let fr = 30;
+let beta = 0.1; //beta = 0 chooses infinite temperature, beta = inf forces the walkers to go only towards the gradient
+let betaslider;
+
+let radio;
 
 let cw = 500;
 let canvas, src, pg;
@@ -15,61 +19,15 @@ let transparent;
 function proposal(pos) {}
 
 let img;
+let distfield;
 function preload() {
   img = loadImage('birthday.png');
+  distfield = loadImage('distfield.png');
 }
 
-/*
-class Walker {
-  constructor() {
-    this.pos = createVector(width, height);
-  }
-  //draw the walker
-  draw(ctx) {
-    ctx.circle(this.pos.x, this.pos.y, 1);
-    ctx.line(this.newpos.x, this.newpos.y, this.pos.x, this.pos.y);
-  }
-  //calculate dBH
-  get_dBH(landscape, prop) {
-    let l = lightness(landscape.get(this.newpos.x, this.newpos.y)) - lightness(landscape.get(this.pos.x, this.pos.y));
-    
-    if (mouseIsPressed) {
-      let tomouse = createVector(this.pos.x - mouseX, this.pos.y - mouseY);
-      let mouse = p5.Vector.dot(tomouse, prop) / prop.mag() / tomouse.mag()
-      //console.log('l, mouse:', l, mouse);
-      return l + mouse;
-    }
-    
-    //console.log('l:', l);
-    return l;
-  }
-  
-  //move the walker
-  step(landscape) {
-    this.prop = p5.Vector.random2D();
-    this.newpos = p5.Vector.add(this.pos, this.prop);
-    this.prop.mult(stepsize);
-    let dBH = this.get_dBH(landscape, this.prop);
-    
-    let n = this.newpos;
-    let withinBounds = (0 <= n.x) && (n.x <= w) && (0 <= n.y) && (n.y < w);
-    
-    if(withinBounds && (dBH <= 0 || exp(-dBH) > random(1))) {
-      this.pos = n;
-    }
-    
-  }
-  //leave behing a trail in the landscape
-  leaveFootstep(landscape) {
-    landscape.loadPixels();
-    let o = landscape.get(this.pos.x, this.pos.y);
-    let n = color(hue(o), saturation(o), lightness(o) * 0.9);
-    landscape.set(this.pos.x, this.pos.y, n);
-    landscape.updatePixels();
-  }
-}
-
-*/
+let dist, showdist, showtarget, showpaths, showwalkers;
+let step;
+let newpos;
 
 function setup() {
   console.log('canvas has size: ', cw, cw);
@@ -78,31 +36,68 @@ function setup() {
   //pixelDensity(1);
   //let d = pixelDensity();
   frameRate(fr);
+    
+  betaslider = createSlider(0, 1, 0.5, 0.0001);
+  //betaslider.position(10, 10);
+  betaslider.style('width', '80px');
   
+  showdist = createCheckbox('Show distance function', false);
+  showtarget = createCheckbox('Show target image', false);
+  showpaths = createCheckbox('Show paths', true);
+  showwalkers = createCheckbox('Show walkers', true);
+    
   overlay = createGraphics(windowWidth, windowHeight);
   overlay.pixelDensity(1);
-  overlay.background(255);
+  overlay.background(color(0,0,0,0));
+    
+  dist = function(pos) {
+      return distfield.get(pos.x, pos.y)[0];
+  }
   
   colorMode(HSL);
-  transparent = color(1,1,1,0);
 
   walkers = [];
   for(let i = 0; i < Nwalkers; i += 1) {
     append(walkerpos, createVector(random(width), random(height)));
   }
+    
+  step = createVector(0,0);
 }
 
+let b;
 function draw() {    
   background(255);
-  image(img, 0, 0);
-  image(overlay, 0, 0)
-    
+  if(showdist.checked()) image(distfield, 0, 0); //the min distance to the nearest non white pixel in the target image
+  if(showtarget.checked()) image(img, 0, 0); //the target image
+  if(showpaths.checked()) {
+    //tint(255, 5e6 / frameCount / Nwalkers);
+    image(overlay, 0, 0);
+  }
 
+  //text(dist(createVector(mouseX, mouseY)), width/2, height/2);
+  //text(overlay.get(mouseX, mouseY), width/2, height/2);
+
+  beta = betaslider.value();
+  beta = beta / (1 - beta);
+    
+  overlay.loadPixels();  
   for(let i = 0; i < Nwalkers; i += 1) {
-    walkerpos[i].add(p5.Vector.random2D().mult(stepsize));
-    circle(walkerpos[i].x, walkerpos[i].y, 5);
-    overlay.set(walkerpos[i].x, walkerpos[i].y, transparent);
+    //let debug = Math.sqrt((mouseX - walkerpos[i].x)**2 + (mouseY - walkerpos[i].y)**2) < 10;
+    step.x = 2*stepsize*(random() - 0.5);
+    step.y = 2*stepsize*(random() - 0.5);
+    newpos = p5.Vector.add(walkerpos[i], step);
+    let df = dist(newpos) - dist(walkerpos[i]);
+    if(df > 0 | exp(beta * df) > random(1.0)) {
+        walkerpos[i].add(step);
+    }
+    if(showwalkers.checked()) circle(walkerpos[i].x, walkerpos[i].y, 3);  
+    
+    // loop over
+    index = 4 * (int(walkerpos[i].y) * overlay.width + int(walkerpos[i].x));
+    b = overlay.pixels[index+3] + 5
+    overlay.pixels[index+3] = b;
   }
   overlay.updatePixels();
 
+
 }
\ No newline at end of file