diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 590194ff3a806e2d0466c0fbeedf1153969a740d..37694e65c73e17994d01b5d8913ca65c50ea1bc0 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -3,8 +3,17 @@ image: gitlab.kwant-project.org:5005/qt/research-docker
 variables:
   GIT_SUBMODULE_STRATEGY: recursive
 
-test:
-  before_script:
-    - pip install pytest-randomly
+
+run tests:
   script:
-    - py.test codes/modules
+    - pip install pytest-cov pytest-randomly pytest-ruff
+    - py.test
+  coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
+  artifacts:
+    paths:
+      - htmlcov
+    reports:
+      junit: junit.xml
+      coverage_report:
+        coverage_format: cobertura
+        path: coverage.xml
diff --git a/codes/GroupMeetingCodeFile.ipynb b/codes/GroupMeetingCodeFile.ipynb
deleted file mode 100644
index 4cfb16633c117a7f342bd9f9ea0631727baddb4f..0000000000000000000000000000000000000000
--- a/codes/GroupMeetingCodeFile.ipynb
+++ /dev/null
@@ -1,680 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#imports\n",
-    "import numpy as np\n",
-    "from modules import networks as nws\n",
-    "import matplotlib.pyplot as plt\n",
-    "from kwant.rmt import circular\n",
-    "import scipy"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Scattering theory"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A few basics about scattering theory which are useful to keep in mind while coding/working with s-matrices/networks etc.\n",
-    "- S-matrices are a convenient way of packaging the information about reflection and transmission coefficients\n",
-    "- S-matrices relate incoming wavefunctions to outgoing wavefunctions in the following format\n",
-    "$$ \\Psi_{out} = S \\Psi_{in} $$\n",
-    "where\n",
-    "$$ S = \\begin{bmatrix}\n",
-    "r & t \\\\\n",
-    "t' & r'\n",
-    "\\end{bmatrix} $$\n",
-    "\n",
-    "An important thing to remember about scattering matrices is that **conservation of current/probability** means that the S-matrix always has to be **unitary**. Unitarity means that our S-matrix always has to be square and as a result of this, we always have to have an equal number of incoming and outgoing wavefunctions.\n",
-    "\n",
-    "A final important thing to consider is that scattering theory can be used whenever **interactions** are either **absent** or considered at a mean-field level. This is because scattering theory only makes sense at a single energy, while interactions can/will increase the energy in the system. \n",
-    "\n",
-    "Also, consider that you can have a scattering matrix to describe both the whole system: how one lead on the left scatters into a lead on the right of your system, or describing only a small part of your system: how two chiral modes scatter into each other. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Network models"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Network models are models where these s-matrices are considered together. One could think of it as the tight-binding equivalent but then in scattering theory. Network models describe how different s-matrices are connected together. Some nomenclature which is useful to know in order to talk about network models: \n",
-    "- links/modes/edges are the wavefunctions connecting different s-matrices\n",
-    "- nodes are the 'points' on which the s-matrices 'live' and decide how the incoming wavefunctions scatter to outcoming wavefunctions. \n",
-    "\n",
-    "![tiny_network.jpg](../codes/Images/tiny_network.png)\n",
-    "\n",
-    "The purpose of the coding module is to create a library which allows researchers to flexibly perform calculations using scattering theory and particularly, these network models. It describes these networks as a collection of links (described by 2+d signifiers) and a collection of s-matrices. The drawn network above would be written in code as: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tiny_network = np.array([\n",
-    "    [0, 1, 0, 0],\n",
-    "    [1, 2, 0, 0],\n",
-    "    [2, 0, 1, 0],\n",
-    "])\n",
-    "\n",
-    "tiny_network_smatrices = np.array([\n",
-    "    np.tile(circular(2, \"A\"), (4, 1, 1)),  #check if this is correct\n",
-    "])"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Why networks?\n",
-    "\n",
-    "Why o why should we actually care about networks? Because they're useful! Scattering theory is used in a lot of different physical systems and can be used as looking from a different view to a problem. A strength of networks is the flexibility of wavefunctions refering to whatever you want them to refer to, in whatever basis you want to put them in. A typical example of how a network can be useful in simulating physics is the Chalker-Coddington network, which has historically been used to study the Anderson transition between different quantum hall states. In this Chalker-Coddington network the links are chiral modes of equipotential lines where the direction is determined by magnetic fields. To understand how scattering matrices are related to Everything, (try) and take a look at Anton's note: https://hackmd.io/HsS_OXkUTYaOecbjHoHFVw"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Code examples"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setting up a network"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll here use the code to create the chalker-coddington network, define it and continue with it. \n",
-    "\n",
-    "![chalker_coddington_unit_cell.jpg](../codes/Images/chalker_coddington_unit_cell.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Defining a unit cell:\n",
-    "unit_cell  = np.asarray(\n",
-    "    [[0, 1, 0, 0], #psi0\n",
-    "     [1, 3, 0, 0], #psi1\n",
-    "     [3, 2, 0, 0], \n",
-    "     [2, 0, 0, 0],\n",
-    "     [2, 0, 0, 1], \n",
-    "     [1, 3, 0, -1], \n",
-    "     [3, 2, 1, 0], \n",
-    "     [0,1, -1, 0] \n",
-    "    ])\n",
-    "n_in_cell = 4\n",
-    "x = y = 2\n",
-    "dimensions = np.array([x,y])\n",
-    "network = nws.tile_links(n_in_cell, unit_cell, dimensions)\n",
-    "#print(network)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Plotting network\n",
-    "node_positions_unitcell = np.array([[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]) \n",
-    "lattice_vectors = np.array([1.5,1.5])\n",
-    "dimensions = np.array([x,y])\n",
-    "tiled_node_pos = nws.tile_nodes_pos(node_positions_unitcell, lattice_vectors, dimensions)\n",
-    "nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Giving it an s-matrix\n",
-    "\n",
-    "The only thing the set-up of the network is now missing in order to actually use it is: the s-matrix which goes in it. Here there is a little bit of subtlety, namely the s-matrix is defined in a certain basis of the wavefunctions. We have configured everything so that the s-matrices are in the basis of the **order** in which the network is defined. In the unit cell specification $\\psi_0$ is defined before $\\psi_7$ meaning that in the s-matrix of node 0 $\\psi_0$ comes before $\\psi_7$\n",
-    "\n",
-    "![filling_in_smatrix.jpg](../codes/Images/filling_in_smatrix.png)\n",
-    "\n",
-    "Often it is therefore easier to define your s-matrices at the same time as defining the order of your links. Below we use the same network, but in a different order, so that we can conveniently use the s-matrices in the format that we want. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "links_in_cell = np.array([\n",
-    "    [0, 1, 0, 0], \n",
-    "    [0, 1, -1, -1],\n",
-    "    [1, 0, 1, 0],\n",
-    "    [1, 0, 0,1]])\n",
-    "n_in_cell = 2\n",
-    "\n",
-    "node_positions_uc = np.array([[0, 0],   [0.5, 0.5]])\n",
-    "lattice_vectors = np.array([1,1])\n",
-    "dimensions = np.array([1,1])\n",
-    "tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)\n",
-    "network = nws.tile_links(n_in_cell, links_in_cell, dimensions)\n",
-    "nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network, scale=None)\n",
-    "tot = dimensions[0]*dimensions[1] #number of unit cells"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p = 0.3\n",
-    "\n",
-    "s_0 = np.array([\n",
-    "    [np.sqrt(1-p), np.sqrt(p)],\n",
-    "    [-np.sqrt(p), np.sqrt(1-p)]])\n",
-    "s_1 = np.array([\n",
-    "    [np.sqrt(p), -np.sqrt(1-p)],\n",
-    "    [np.sqrt(1-p), np.sqrt(p)]])\n",
-    "\n",
-    "all_smatrices = ([np.tile(s_0, (tot, 1, 1))] + [np.tile(s_1, (tot, 1, 1))])\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Now what? (Ho-Chalker time)\n",
-    "\n",
-    "We have a network fully defined by the links and the s-matrices. What can we actually do with it? One thing we might want to look at is its spectrum. To be specific, this is not the same energyspectrum which you get from a tight-binding model. Instead it should be considered as a spectrum of quasienergies. Its spectrum can be calculated using the Ho-Chalker operator $S$, which is a matrix containing all information of reflection and transmission amplitudes between the wavefunctions (links). The stationary states of the network model obey: \n",
-    "\n",
-    "$$ S \\Phi = e^{-i \\epsilon} \\Phi $$\n",
-    "\n",
-    "where then the eigenphases $\\epsilon$ are the quasi-energies making up the spectrum. Read https://arxiv.org/abs/2009.07877 for a better/more in-depth explanation. More practically, the network module has the capability of calculating this Ho-chalker operator at different k-points, giving the option of obtaining a spectrum. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sparse = False\n",
-    "k_range = np.linspace(-np.pi, np.pi, 100)\n",
-    "all_energies = []\n",
-    "energies_s = np.zeros(len(links_in_cell)*tot)\n",
-    "for kx in k_range: \n",
-    "    ho_chalker = nws.ho_chalker_operator(network, all_smatrices, sparse = sparse, k = np.array([kx,0]))\n",
-    "    new_energies = np.angle(np.linalg.eigvals(ho_chalker))\n",
-    "    energies_s = np.vstack((energies_s, new_energies)) \n",
-    "energies_s = np.delete(energies_s, 0, axis=0)\n",
-    "\n",
-    "for sc in range(len(energies_s[0])):\n",
-    "        plt.scatter(k_range, energies_s[:, sc], c='black')\n",
-    "        plt.xlabel('ky')\n",
-    "        plt.ylabel('energy')\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using leads\n",
-    "\n",
-    "We have kept the format of links going 'into' and 'out of' the unit cell even when tiling a unit cell into a big network. If nothing is done with this information, the network will automatically be fully periodic in all dimensions by construction. However, it is also possible to define **leads** going into and out of the system. When you have these leads in your system, you will likely want to know the scattering matrix only in terms of these leads. This can be calculated using the module as well by telling the solver which links become leads."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.84589566+0.j  0.53334842+0.j]\n",
-      " [-0.53334842+0.j  0.84589566+0.j]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sparse = True\n",
-    "ho_chalker = nws.ho_chalker_operator(network, all_smatrices, sparse = sparse) #ho chalker without k-dependence\n",
-    "\n",
-    "incoming_and_outgoing = np.nonzero(network[:,2]) #all outsticking links in x are turned into leads\n",
-    "\n",
-    "#solving the network for only the leads\n",
-    "scattering_equations = scipy.sparse.csr_array(ho_chalker).astype(complex)\n",
-    "s, out_indices, in_indices = (\n",
-    "    nws.solve_scattering_equations(scattering_equations, 2, incoming_and_outgoing, incoming_and_outgoing)\n",
-    ")\n",
-    "print(s) "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Extra's "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Cutting a network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Tiling a defined network\n",
-    "n_in_cell = 4\n",
-    "links_in_cell = np.asarray(\n",
-    "    [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]\n",
-    "     ])\n",
-    "x = y = 4\n",
-    "dimensions = np.array([x,y])\n",
-    "network = nws.tile_links(n_in_cell, links_in_cell, dimensions)\n",
-    "\n",
-    "#Plotting network\n",
-    "node_positions_uc = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])\n",
-    "lattice_vectors = np.array([1.2,1.2])\n",
-    "dimensions = np.array([x,y])\n",
-    "tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)\n",
-    "nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network, scale=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Determining which links are in the central region\n",
-    "ins = []\n",
-    "for net in network[:,0]:\n",
-    "    pos_x, pos_y = tiled_node_pos[int(net)]\n",
-    "    if pos_x > 1 and pos_x < 3.5 and pos_y > 1 and pos_y <3.5:\n",
-    "        ins.append(net)\n",
-    "outs = []\n",
-    "for net in network[:,1]:\n",
-    "    pos_x, pos_y = tiled_node_pos[int(net)]\n",
-    "    if pos_x > 1 and pos_x < 3.5 and pos_y > 1 and pos_y <3.5:\n",
-    "        outs.append(net)\n",
-    "\n",
-    "ins = np.unique(ins)\n",
-    "outs = np.unique(outs)\n",
-    "\n",
-    "in_leads, out_leads = nws.determine_leads_from_nodes(network, ins, outs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Creating ho-chalker which afterwards can be cut\n",
-    "tot = x*y\n",
-    "s_0 = np.array([[1,2], [3,4]]) #completely made-up s-matrices\n",
-    "s_1 = np.array([[5,6], [7,8]])\n",
-    "s_2 = np.array([[9,10], [11,12]])\n",
-    "s_3 = np.array([[13,14], [15,16]])\n",
-    "\n",
-    "user_s = ( [np.tile(s_0, (tot, 1, 1))] +\n",
-    "            [np.tile(s_1, (tot, 1, 1))] +\n",
-    "            [np.tile(s_2, (tot, 1, 1))] +\n",
-    "            [np.tile(s_3, (tot, 1, 1))] ) \n",
-    "\n",
-    "incoming_and_outgoing = np.nonzero(network[:,2]) \n",
-    "sparse = False\n",
-    "ho_chalker = nws.ho_chalker_operator(network, user_s, sparse = sparse)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Cutting timee\n",
-    "ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(ho_chalker, network, in_leads, out_leads)\n",
-    "\n",
-    "#Plotting the new network to see what it did\n",
-    "nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network_new, dont_plot_nodes = np.concatenate((ins, outs)).astype(int))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Relinking a network\n",
-    "\n",
-    "A big thing that is still missing in the module is boundaries. The module works fine for considering periodic networks or networks with leads all around, however making finite networks is relatively tricky. This is because in order to preserve probability, the s-matrices need to stay unitary. This means that whatever boundaries are imposed by the user, everything should remain unitary (equal number of ins and outs for each node). One potential way making boundaries can be achieved is by relinking two links with a dummy node in between. This capability has been added to the code. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Defining the unit cell\n",
-    "links_in_cell_ho = np.asarray([  \n",
-    "    [1, 0, 0, 0],\n",
-    "    [1, 2, 0, 0],\n",
-    "    [3, 2, 0, 0],\n",
-    "    [4, 5, 0, 0],\n",
-    "    [6, 5, 0, 0],\n",
-    "    [6, 7, 0, 0],\n",
-    "    [0, 4, 0, 0],\n",
-    "    [5, 1, 0, 0],\n",
-    "    [2, 6, 0, 0],\n",
-    "    [7, 3, 0, 0],\n",
-    "    [7, 1, 1, 0], \n",
-    "    [0, 6, -1, 0], \n",
-    "    [2, 4, 0, -1], \n",
-    "    [5, 3, 0, 1],\n",
-    "    [4, 7, -1, 1], \n",
-    "    [3, 0, 1, -1]])\n",
-    "\n",
-    "\n",
-    "# +\n",
-    "#Preparation of s-matrices\n",
-    "th3 = 0\n",
-    "th4 = 0\n",
-    "th1 = np.pi/2\n",
-    "th2 = np.pi/2\n",
-    "\n",
-    "s_0 = np.asarray([  # they might have to be transposed #have to be checked for unitarity\n",
-    "    [np.cos(th4), np.sin(th4)],\n",
-    "    [-np.sin(th4), np.cos(th4)]])\n",
-    "\n",
-    "\n",
-    "s_1 = np.asarray([\n",
-    "    [np.sin(th1), np.cos(th1)],\n",
-    "    [-np.cos(th1), np.sin(th1)]])\n",
-    "\n",
-    "\n",
-    "s_2 = np.asarray([\n",
-    "    [-np.cos(th2), np.sin(th2)],\n",
-    "    [np.sin(th2), np.cos(th2)]])\n",
-    "\n",
-    "\n",
-    "s_3 = np.asarray([\n",
-    "    [np.cos(th3), np.sin(th3)],\n",
-    "    [-np.sin(th3), np.cos(th3)]])\n",
-    "\n",
-    "\n",
-    "s_4 = np.asarray([\n",
-    "    [np.cos(th3), -np.sin(th3)],\n",
-    "    [np.sin(th3), np.cos(th3)]])\n",
-    "\n",
-    "\n",
-    "s_5 = np.asarray([\n",
-    "    [np.sin(th2), -np.cos(th2)],\n",
-    "    [np.cos(th2), np.sin(th2)]])\n",
-    "\n",
-    "\n",
-    "s_6 = np.asarray([\n",
-    "    [np.cos(th1), np.sin(th1)],\n",
-    "    [np.sin(th1), -np.cos(th1)]])\n",
-    "\n",
-    "\n",
-    "s_7 = np.asarray([\n",
-    "    [np.cos(th4), np.sin(th4)],\n",
-    "    [-np.sin(th4), np.cos(th4)]])\n",
-    "\n",
-    "\n",
-    "# -\n",
-    "\n",
-    "tot = 1\n",
-    "user_s = ( [np.tile(s_0, (tot, 1, 1))] +\n",
-    "            [np.tile(s_1, (tot, 1, 1))] +\n",
-    "            [np.tile(s_2, (tot, 1, 1))] +\n",
-    "            [np.tile(s_3, (tot, 1, 1))] +\n",
-    "            [np.tile(s_4, (tot, 1, 1))] +\n",
-    "            [np.tile(s_5, (tot, 1, 1))] +\n",
-    "            [np.tile(s_6, (tot, 1, 1))] +\n",
-    "            [np.tile(s_7, (tot, 1, 1))]\n",
-    "            ) \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unit_cell_pos = np.array([[0.01, 0.75], [0.25, 0.5],[0.5, 0.25], [0.75, 0.01],[0.25, 0.99],[0.5, 0.75], [0.75, 0.5], [0.99, 0.25]])\n",
-    "lattice_vectors = np.array([1,1])\n",
-    "dimensions = np.array([2,1])\n",
-    "tiled_node_pos = nws.tile_nodes_pos(unit_cell_pos, lattice_vectors, dimensions)\n",
-    "network_ho = nws.tile_links(len(unit_cell_pos), links_in_cell_ho, dimensions)\n",
-    "nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network_ho, scale=None)\n",
-    "\n",
-    "perms = np.unique(np.nonzero(network_ho[:,2:])[0])\n",
-    "\n",
-    "perms_in_order = np.array([22, 21, 26, 27, 24, 25, 31, 30, 29, 28])\n",
-    "\n",
-    "network_relinked = nws.relink(network_ho[:,:2], perms_in_order)\n",
-    "\n",
-    "#Plotting relinked network: \n",
-    "extra_node_pos = np.array([\n",
-    "    [-0.25, 0.5],\n",
-    "    [2.25, 0.5], \n",
-    "    [0.6, 1.1],\n",
-    "    [1.75, 1.1],\n",
-    "    [0.25, 0],\n",
-    "    [1.25, 0], \n",
-    "    [2.1, 0],\n",
-    "    [1.1, 0], \n",
-    "    [0.8, 1.1], \n",
-    "    [-0.2, 1.1]])\n",
-    "\n",
-    "relinked_node_pos = np.vstack((tiled_node_pos, extra_node_pos))\n",
-    "network_ho_relinked = np.hstack((network_relinked, np.zeros((len(network_relinked),2))))\n",
-    "nws.plot_network(relinked_node_pos, lattice_vectors*dimensions, network_ho_relinked, scale=None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "f71b5179dcdcc4002f8b13a5d48ec819f030b5e972dee19de9bf27fc79c87fc5"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/codes/GroupMeetingCodeFile.py b/codes/GroupMeetingCodeFile.py
new file mode 100644
index 0000000000000000000000000000000000000000..797c679d55158f19a6b5890b979820139683c2db
--- /dev/null
+++ b/codes/GroupMeetingCodeFile.py
@@ -0,0 +1,368 @@
+# %%
+#imports
+import numpy as np
+import matplotlib.pyplot as plt
+from kwant.rmt import circular
+import scipy
+
+from modules import networks as nws
+
+# %% [markdown]
+# # Introduction
+
+# %% [markdown]
+# ## Scattering theory
+
+# %% [markdown]
+# A few basics about scattering theory which are useful to keep in mind while coding/working with s-matrices/networks etc.
+# - S-matrices are a convenient way of packaging the information about reflection and transmission coefficients
+# - S-matrices relate incoming wavefunctions to outgoing wavefunctions in the following format
+# $$ \Psi_{out} = S \Psi_{in} $$
+# where
+# $$ S = \begin{bmatrix}
+# r & t \\
+# t' & r'
+# \end{bmatrix} $$
+# 
+# An important thing to remember about scattering matrices is that **conservation of current/probability** means that the S-matrix always has to be **unitary**. Unitarity means that our S-matrix always has to be square and as a result of this, we always have to have an equal number of incoming and outgoing wavefunctions.
+# 
+# A final important thing to consider is that scattering theory can be used whenever **interactions** are either **absent** or considered at a mean-field level. This is because scattering theory only makes sense at a single energy, while interactions can/will increase the energy in the system. 
+# 
+# Also, consider that you can have a scattering matrix to describe both the whole system: how one lead on the left scatters into a lead on the right of your system, or describing only a small part of your system: how two chiral modes scatter into each other. 
+
+# %% [markdown]
+# ## Network models
+
+# %% [markdown]
+# Network models are models where these s-matrices are considered together. One could think of it as the tight-binding equivalent but then in scattering theory. Network models describe how different s-matrices are connected together. Some nomenclature which is useful to know in order to talk about network models: 
+# - links/modes/edges are the wavefunctions connecting different s-matrices
+# - nodes are the 'points' on which the s-matrices 'live' and decide how the incoming wavefunctions scatter to outcoming wavefunctions. 
+# 
+# ![tiny_network.jpg](../codes/Images/tiny_network.png)
+# 
+# The purpose of the coding module is to create a library which allows researchers to flexibly perform calculations using scattering theory and particularly, these network models. It describes these networks as a collection of links (described by 2+d signifiers) and a collection of s-matrices. The drawn network above would be written in code as: 
+
+# %%
+tiny_network = np.array([
+    [0, 1, 0, 0],
+    [1, 2, 0, 0],
+    [2, 0, 1, 0],
+])
+
+tiny_network_smatrices = np.array([
+    np.tile(circular(2, "A"), (4, 1, 1)),  #check if this is correct
+])
+
+# %% [markdown]
+# ## Why networks?
+# 
+# Why o why should we actually care about networks? Because they're useful! Scattering theory is used in a lot of different physical systems and can be used as looking from a different view to a problem. A strength of networks is the flexibility of wavefunctions refering to whatever you want them to refer to, in whatever basis you want to put them in. A typical example of how a network can be useful in simulating physics is the Chalker-Coddington network, which has historically been used to study the Anderson transition between different quantum hall states. In this Chalker-Coddington network the links are chiral modes of equipotential lines where the direction is determined by magnetic fields. To understand how scattering matrices are related to Everything, (try) and take a look at Anton's note: https://hackmd.io/HsS_OXkUTYaOecbjHoHFVw
+
+# %% [markdown]
+# # Code examples
+
+# %% [markdown]
+# ### Setting up a network
+
+# %% [markdown]
+# We'll here use the code to create the chalker-coddington network, define it and continue with it. 
+# 
+# ![chalker_coddington_unit_cell.jpg](../codes/Images/chalker_coddington_unit_cell.jpg)
+
+# %%
+#Defining a unit cell:
+links_in_cell  = np.asarray(
+    [[0, 1, 0, 0], #psi0
+     [1, 3, 0, 0], #psi1
+     [3, 2, 0, 0], 
+     [2, 0, 0, 0],
+     [2, 0, 0, 1], 
+     [1, 3, 0, -1], 
+     [3, 2, 1, 0], 
+     [0,1, -1, 0] 
+    ])
+nodes_in_cell = 4
+x = y = 2
+size = np.array([x,y])
+network = nws.network(nodes_in_cell, links_in_cell, size)
+#print(network)
+
+
+# %%
+#Plotting network
+node_positions_unitcell = np.array([[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]) 
+lattice_vectors = np.array([[1.5,0], [0,1.5]])
+size = np.array([x,y])
+tiled_node_pos = nws.tile_nodes_pos(node_positions_unitcell, lattice_vectors, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network)
+
+# %% [markdown]
+# ### Giving it an s-matrix
+# 
+# The only thing the set-up of the network is now missing in order to actually use it is: the s-matrix which goes in it. Here there is a little bit of subtlety, namely the s-matrix is defined in a certain basis of the wavefunctions. We have configured everything so that the s-matrices are in the basis of the **order** in which the network is defined. In the unit cell specification $\psi_0$ is defined before $\psi_7$ meaning that in the s-matrix of node 0 $\psi_0$ comes before $\psi_7$
+# 
+# ![filling_in_smatrix.jpg](../codes/Images/filling_in_smatrix.png)
+# 
+# Often it is therefore easier to define your s-matrices at the same time as defining the order of your links. Below we use the same network, but in a different order, so that we can conveniently use the s-matrices in the format that we want. 
+
+# %%
+links_in_cell = np.array([
+    [0, 1, 0, 0], 
+    [0, 1, -1, -1],
+    [1, 0, 1, 0],
+    [1, 0, 0,1]])
+nodes_in_cell = 2
+
+node_positions_uc = np.array([[0, 0],   [0.5, 0.5]])
+lattice_vectors = np.array([[1,0], [0,1]])
+size = np.array([1,1])
+tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
+network = nws.network(nodes_in_cell, links_in_cell, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network, scale=None)
+tot = size[0]*size[1] #number of unit cells
+
+# %%
+p = 0.3
+
+s_0 = np.array([
+    [np.sqrt(1-p), np.sqrt(p)],
+    [-np.sqrt(p), np.sqrt(1-p)]])
+s_1 = np.array([
+    [np.sqrt(p), -np.sqrt(1-p)],
+    [np.sqrt(1-p), np.sqrt(p)]])
+
+all_smatrices = ([np.tile(s_0, (tot, 1, 1))] + [np.tile(s_1, (tot, 1, 1))])
+
+
+# %% [markdown]
+# ### Now what? (Ho-Chalker time)
+# 
+# We have a network fully defined by the links and the s-matrices. What can we actually do with it? One thing we might want to look at is its spectrum. To be specific, this is not the same energyspectrum which you get from a tight-binding model. Instead it should be considered as a spectrum of quasienergies. Its spectrum can be calculated using the Ho-Chalker operator $S$, which is a matrix containing all information of reflection and transmission amplitudes between the wavefunctions (links). The stationary states of the network model obey: 
+# 
+# $$ S \Phi = e^{-i \epsilon} \Phi $$
+# 
+# where then the eigenphases $\epsilon$ are the quasi-energies making up the spectrum. Read https://arxiv.org/abs/2009.07877 for a better/more in-depth explanation. More practically, the network module has the capability of calculating this Ho-chalker operator at different k-points, giving the option of obtaining a spectrum. 
+
+# %%
+sparse = False
+k_range = np.linspace(-np.pi, np.pi, 100)
+all_energies = []
+energies_s = np.zeros(len(links_in_cell)*tot)
+for kx in k_range: 
+    ho_chalker = nws.ho_chalker_operator(network, all_smatrices, sparse = sparse, k = np.array([kx,0]))
+    new_energies = np.angle(np.linalg.eigvals(ho_chalker))
+    energies_s = np.vstack((energies_s, new_energies)) 
+energies_s = np.delete(energies_s, 0, axis=0)
+
+for sc in range(len(energies_s[0])):
+        plt.scatter(k_range, energies_s[:, sc], c='black')
+        plt.xlabel('ky')
+        plt.ylabel('energy')
+
+
+# %% [markdown]
+# ### Using leads
+# 
+# We have kept the format of links going 'into' and 'out of' the unit cell even when tiling a unit cell into a big network. If nothing is done with this information, the network will automatically be fully periodic in all dimensions by construction. However, it is also possible to define **leads** going into and out of the system. When you have these leads in your system, you will likely want to know the scattering matrix only in terms of these leads. This can be calculated using the module as well by telling the solver which links become leads.
+
+# %%
+sparse = True
+ho_chalker = nws.ho_chalker_operator(network, all_smatrices, sparse = sparse) #ho chalker without k-dependence
+
+incoming_and_outgoing = np.nonzero(network[:,2]) #all outsticking links in x are turned into leads
+
+#solving the network for only the leads
+scattering_equations = scipy.sparse.csr_array(ho_chalker).astype(complex)
+s, out_indices, in_indices = (
+    nws.solve_scattering_equations(scattering_equations, 2, incoming_and_outgoing, incoming_and_outgoing)
+)
+print(s) 
+
+# %% [markdown]
+# # Extra's 
+
+# %% [markdown]
+# ### Cutting a network
+
+# %%
+#Tiling a defined network
+nodes_in_cell = 4
+links_in_cell = np.asarray(
+    [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]
+     ])
+x = y = 4
+size = np.array([x,y])
+network = nws.network(nodes_in_cell, links_in_cell, size)
+
+#Plotting network
+node_positions_uc = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])
+lattice_vectors = np.array([[1.2,0], [0,1.2]])
+size = np.array([x,y])
+tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network, scale=None)
+
+
+# %%
+#Determining which links are in the central region
+ins = []
+for net in network[:,0]:
+    pos_x, pos_y = tiled_node_pos[int(net)]
+    if pos_x > 1 and pos_x < 3.5 and pos_y > 1 and pos_y <3.5:
+        ins.append(net)
+outs = []
+for net in network[:,1]:
+    pos_x, pos_y = tiled_node_pos[int(net)]
+    if pos_x > 1 and pos_x < 3.5 and pos_y > 1 and pos_y <3.5:
+        outs.append(net)
+
+ins = np.unique(ins)
+outs = np.unique(outs)
+
+in_leads, out_leads = nws.determine_leads_from_nodes(network, ins, outs)
+
+# %%
+#Creating ho-chalker which afterwards can be cut
+tot = x*y
+s_0 = np.array([[1,2], [3,4]]) #completely made-up s-matrices
+s_1 = np.array([[5,6], [7,8]])
+s_2 = np.array([[9,10], [11,12]])
+s_3 = np.array([[13,14], [15,16]])
+
+user_s = ( [np.tile(s_0, (tot, 1, 1))] +
+            [np.tile(s_1, (tot, 1, 1))] +
+            [np.tile(s_2, (tot, 1, 1))] +
+            [np.tile(s_3, (tot, 1, 1))] ) 
+
+incoming_and_outgoing = np.nonzero(network[:,2]) 
+sparse = False
+ho_chalker = nws.ho_chalker_operator(network, user_s, sparse = sparse)
+
+# %%
+#Cutting timee
+ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(ho_chalker, network, in_leads, out_leads)
+
+#Plotting the new network to see what it did
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network_new, dont_plot_nodes = np.concatenate((ins, outs)).astype(int))
+
+# %% [markdown]
+# ### Relinking a network
+# 
+# A big thing that is still missing in the module is boundaries. The module works fine for considering periodic networks or networks with leads all around, however making finite networks is relatively tricky. This is because in order to preserve probability, the s-matrices need to stay unitary. This means that whatever boundaries are imposed by the user, everything should remain unitary (equal number of ins and outs for each node). One potential way making boundaries can be achieved is by relinking two links with a dummy node in between. This capability has been added to the code. 
+
+# %%
+#Defining the unit cell
+links_in_cell_ho = np.asarray([  
+    [1, 0, 0, 0],
+    [1, 2, 0, 0],
+    [3, 2, 0, 0],
+    [4, 5, 0, 0],
+    [6, 5, 0, 0],
+    [6, 7, 0, 0],
+    [0, 4, 0, 0],
+    [5, 1, 0, 0],
+    [2, 6, 0, 0],
+    [7, 3, 0, 0],
+    [7, 1, 1, 0], 
+    [0, 6, -1, 0], 
+    [2, 4, 0, -1], 
+    [5, 3, 0, 1],
+    [4, 7, -1, 1], 
+    [3, 0, 1, -1]])
+
+
+# +
+#Preparation of s-matrices
+th3 = 0
+th4 = 0
+th1 = np.pi/2
+th2 = np.pi/2
+
+s_0 = np.asarray([  # they might have to be transposed #have to be checked for unitarity
+    [np.cos(th4), np.sin(th4)],
+    [-np.sin(th4), np.cos(th4)]])
+
+
+s_1 = np.asarray([
+    [np.sin(th1), np.cos(th1)],
+    [-np.cos(th1), np.sin(th1)]])
+
+
+s_2 = np.asarray([
+    [-np.cos(th2), np.sin(th2)],
+    [np.sin(th2), np.cos(th2)]])
+
+
+s_3 = np.asarray([
+    [np.cos(th3), np.sin(th3)],
+    [-np.sin(th3), np.cos(th3)]])
+
+
+s_4 = np.asarray([
+    [np.cos(th3), -np.sin(th3)],
+    [np.sin(th3), np.cos(th3)]])
+
+
+s_5 = np.asarray([
+    [np.sin(th2), -np.cos(th2)],
+    [np.cos(th2), np.sin(th2)]])
+
+
+s_6 = np.asarray([
+    [np.cos(th1), np.sin(th1)],
+    [np.sin(th1), -np.cos(th1)]])
+
+
+s_7 = np.asarray([
+    [np.cos(th4), np.sin(th4)],
+    [-np.sin(th4), np.cos(th4)]])
+
+
+# -
+
+tot = 1
+user_s = ( [np.tile(s_0, (tot, 1, 1))] +
+            [np.tile(s_1, (tot, 1, 1))] +
+            [np.tile(s_2, (tot, 1, 1))] +
+            [np.tile(s_3, (tot, 1, 1))] +
+            [np.tile(s_4, (tot, 1, 1))] +
+            [np.tile(s_5, (tot, 1, 1))] +
+            [np.tile(s_6, (tot, 1, 1))] +
+            [np.tile(s_7, (tot, 1, 1))]
+            ) 
+
+
+# %%
+unit_cell_pos = np.array([[0.01, 0.75], [0.25, 0.5],[0.5, 0.25], [0.75, 0.01],[0.25, 0.99],[0.5, 0.75], [0.75, 0.5], [0.99, 0.25]])
+lattice_vectors = np.array([[1,0], [0,1]])
+size = np.array([2,1])
+tiled_node_pos = nws.tile_nodes_pos(unit_cell_pos, lattice_vectors, size)
+network_ho = nws.network(len(unit_cell_pos), links_in_cell_ho, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network_ho, scale=None)
+
+perms = np.unique(np.nonzero(network_ho[:,2:])[0])
+
+perms_in_order = np.array([22, 21, 26, 27, 24, 25, 31, 30, 29, 28])
+
+network_relinked = nws.relink(network_ho[:,:2], perms_in_order)
+
+#Plotting relinked network: 
+extra_node_pos = np.array([
+    [-0.25, 0.5],
+    [2.25, 0.5], 
+    [0.6, 1.1],
+    [1.75, 1.1],
+    [0.25, 0],
+    [1.25, 0], 
+    [2.1, 0],
+    [1.1, 0], 
+    [0.8, 1.1], 
+    [-0.2, 1.1]])
+
+relinked_node_pos = np.vstack((tiled_node_pos, extra_node_pos))
+network_ho_relinked = np.hstack((network_relinked, np.zeros((len(network_relinked),2))))
+nws.plot_network(relinked_node_pos, lattice_vectors*size, network_ho_relinked, scale=None)
+
+# %%
+
+
+
diff --git a/codes/Network_tutorial.ipynb b/codes/Network_tutorial.ipynb
deleted file mode 100644
index e755fbef9828fc7305f181182a300fba683ab11c..0000000000000000000000000000000000000000
--- a/codes/Network_tutorial.ipynb
+++ /dev/null
@@ -1,314 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ec889936-4d8a-47bb-bfa2-4e452c706d43",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import kwant\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from kwant.rmt import circular\n",
-    "from scipy import sparse\n",
-    "from modules import network_just_for_solver as ncp\n",
-    "np.set_printoptions(threshold=np.inf)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17b3bb42-e7c9-4b35-828e-d348e03595b3",
-   "metadata": {},
-   "source": [
-    "# Tutorial on Network Construction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ce29342f-0546-4bcf-ad95-9328136d6c66",
-   "metadata": {},
-   "source": [
-    "This tutorial is intended as a walkthrough of how to use the network_consturction module. It uses the Chalker-Coddington with 1 mode as an example. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c1e8166b-a861-41bb-a714-7bcaae471f79",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Defining a unit cell:\n",
-    "real_space_unit_cell = np.asarray([[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]) \n",
-    "links_in_unit_cell = links_in_cell = np.asarray(\n",
-    "    [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0],[2, 0, 0, 1], [1, 3, 0, -1], [3,2,1,0], [0,1, -1, 0] \n",
-    "    ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12dbce1e-e6a3-40b1-b6bd-2da315566876",
-   "metadata": {},
-   "source": [
-    "The picture attached show how the unit cell just defined looks. It has 4 nodes in the unit cell. The links, including the order in which they are defined are drawn in thick purple lines. Each link is specified by 4 integers. The first two integers specify from which node in the unit cell to which node in the unit cell it goes. The last two integers specify whether it makes the connection to outside its own unit cell, with the two numbers specifying the relative x and relative y respectively. "
-   ]
-  },
-  {
-   "attachments": {
-    "c812438f-f55c-4e9c-8221-4bed48a56cca.jpg": {
-     "image/jpeg": 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-   "cell_type": "markdown",
-   "id": "475cfc8b-4050-4ce5-ae67-871e38204e98",
-   "metadata": {},
-   "source": [
-    "![Planning-128.jpg](attachment:c812438f-f55c-4e9c-8221-4bed48a56cca.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "920f019f-0d0a-4e92-a587-f73672d301e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#To construct a finite network \n",
-    "x = 5\n",
-    "y = 6\n",
-    "n_nodes_in_unit_cell = 4\n",
-    "nodes_network = ncp.create_nodes_array(n_nodes_in_unit_cell,x,y)\n",
-    "nodes_network_real_space = ncp.to_real_space(nodes_network, real_space_unit_cell, x, y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0253aff-47d6-4648-9f59-d045467f2dfe",
-   "metadata": {},
-   "source": [
-    "To be clear, you don't neccesarily have to define a real space unit cell as it is not used anywhere essentially, it only really used if you want to plot the network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e968044e-3a14-4775-8bb0-ab09daac1ff1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f8dbe22b280>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Let's plot the nodes of this real space network \n",
-    "xcoords_network = nodes_network_real_space[:, :, :, 1].flatten()\n",
-    "ycoords_network = nodes_network_real_space[:, :, :, 2].flatten()\n",
-    "plt.scatter(xcoords_network, ycoords_network)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26c4872a-f82c-4ca5-b025-c6ef4d57ac08",
-   "metadata": {},
-   "source": [
-    "Let's now construct a periodic in y network with global incoming and outcoming links defined on the left and right x-boundary. By doing this we create a network which looks visually like the picture below. Before we can obtain a global S-matrix from this network the user needs to specify the s-matrix on each node. It is possible to have the code tell you the sizes which the small s-matrices should be for each node type.  "
-   ]
-  },
-  {
-   "attachments": {
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-     "image/jpeg": 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/CeI1J2WJotead/wdvzP5uq/RH/gnD438R6Z8YLzwNbzSyaJrWm3Fxc2xJMUVxa7WjnA6K2Mxkj7wYZzhce0eM/8AgmLps135/wAPvGk1rbsebbV7UXDqD6TwGLOPQxfjX1b+zP8Aso+Ff2dba81GO+fXPEmpxCC51KSIQJHAGDeTBFucohYAuSxLlQeAAK3zzijLcRl86VOXNKSslZ6PvrpodGf8V5XictqUqcuaUlZKz0ffVW03Pq6iiivyg/IQooooAKKKKACiiigD89Pjp/wT98DfEO/1Txd8P75/DOvXxe4a0ZRJpc1wxLElAPMg8xvvFCyjORH2P4x+OPA3in4ceJ73wf4zsJNN1WwfbLDJyCDyrowyro45VlJBHSv6p6+Sf2tP2adO+Pvg03WkxxW/jHRY3fSro4UTpyzWkzf885D9wn/VvzwpcN91w5xbVoVI4fGyvTel3vH/ADX9eR99wzxjWoVI4fHSvTel3vH59V3vsfz76Dr2seF9asvEXh+7lsNS06dLm1uYTteKWM5Vgfr1B4I4PFf0RfsuftFaR+0D4GW9lMVr4n0pUh1mxU4AkI+W4iB58mbBI/usChJwCf51dT03UNG1G60jVreS0vbKZ7e4t5lKSRSxsVdHU8hlYEEHvXe/CP4q+Kfgz450/wAd+E5dtzaNsnt2JEN3bOR5kEoHVHA+qsAwwyg19vxFkcMyw/ufGvhf6ejPvOJcgp5phvc/iL4X+j8n+G5/UTRXyDo/7dP7NWoaLY6pqPin+y7m6gSSaxmsryWa2kI+aNzDA6EqcjKkqeo4NbVt+2p+zDdsEi8c26k/89bK+iH5vbqK/IJZNj07OhP/AMBf+R+MyyTMYtp0J/8AgL/yO2+IH7OHwR+KWsr4h8c+E7TUdTAUNdK81rLIEGF81reSIy4AwN+7A46V6l4b8M+H/B+i2vhzwtp1vpWmWa7ILW1jEUSAnJwq9ySSSeSSSSSa870P9oL4G+IyqaP498PTSP8AdibUYIpT9I5HV/0r12KWOaNZoXWSN1DKynKsp5BBHBBFY4ieLUFRruVlsney9EzDEzxcYRo4hy5Vsney9Ex9FFFcZxBRRRQAUUUUAFFFFABRRRQB+E//AAUX8Ja3pHx0j8U3cbtpmv6XbGzmwfLD2i+VLED/AHlIVyPSQHvXwro+s6v4e1O31rQb2407ULR/Mt7q0laGeJ+m5HQhlODjg1/UJ8SPhj4J+LXhifwj4802PUdPlO9MkrLBKAQssMi4aORcnBB5BIOVJB/J34pf8E2fHOkTzX/wm1i31+xyWSx1FltL5B2USY+zyn1YmH/dr9V4b4pwbwsMJinyuKtrs169PO5+ucM8WYJ4SGDxb5ZRVtdmvXppvc8s8F/8FAfj14atRpviGTTfFtkV8t11a1xMYyMFfNt2i3EjgmRXJ718m+O9d8PeJvFF7r3hnQl8N2d6/m/2ZFObiCCRuXELFEKxFuVQ52DgHGAOr8XfAf4zeBXdfFXgzWbKOMkNP9kea2464niDxH8HNeTMpUlWBBBwQeoIr6zA4PAwk62ESV9+V6P5LQ+wwGCwEJOvg0lfflej+S0Eooor0z1D9pP+CY6EfDPxdL2bXY1/75toz/Wv0wr4u/YI8FWPhL9nrTdRtbyC9m8S3dxq9w8Db1idtsCwE/3o1hAcfwuWHOMn7QJABJOAOpr8F4jrRq5lWnHa9vu0/Q/nviWtGrmlecdua33afofJvx0+Mvifwd4og8PeFLmKDyrVZbpmiSU+ZKSVX5wcYQA/8Crd/Z/8aePfHcuran4n1D7RYWixwQxiCGIGdyWZtyIrfIoAwTj5+lfGnj7xCfFXjPWNf3bku7uQxH/pinyRD8I1UV+gHwI8O/8ACO/DTS1kXbPqIbUJeMZ+0YKf+QgleKeGew0UUUAFFFFABRRRQAUUUUAFfz7ftGfsqfFzwb8Ttcu9B8N6nr+hatqFxe6feaXayXoEVzI0gilWFWaOSPdsO4ANjK5Br+gmivbyTPK2WVZVKaTTVmme7kOfVsrqyqUkmpKzTPzc/YB/Z78bfDC317x94/sJNIvNbghsrHT7gbblLdGMkkkydYy7bAqNhhtJIGRX6R0UVx5nmFTHYiWJq7vscWaZlVx+Jliq277dLBXwt+3p8E1+Jfwofxno8G/XvBiyXqbBl59PIBuouOuxVEq9cbGA5avummSRxzRtDMqvG6lWVhlWU8EEHggioy/GzwmIhiKe8X/w6+aIy7HVMHiYYmlvF/f3XzWh/JdX6Mf8E7PjJ/wiPxDu/hXrE+3S/Fo8yy3n5YtUgU7QM8Dz4gUPcskYFfJ37Q/gTR/hp8avFvgrw/Os+nadfn7Ntz+6inRZlhJPVoRJ5ZPcrmvKtG1fUNA1ex13SZmt77TrmK7tpV6xzQOHRh7hgDX7pjMNSzLAOHSauvLqmfvuNw1LNMvdPpON15X1T+R/WFRWVoWpf2zoenaxs8v7daQXOz+75yB8fhmtWvwBpp2Z/O0k07MKKKKQgooooAKKKwfFPibRPBnhzUvFfiO5Wz0zSraS6upm6LHGMnA6lj0VRyxIA5NVGLk1GKu2VGLlJRirtnmnx3+OPhP4C+CJvFviRvPuZSYdN06Ngs17c4yEXOdqL1kfBCL2LFVb+d74rfFjxp8ZfF1z4x8bXhuLmYlYIFyLe0gzlYYEydiL+JY5ZiWJJ6b9oH44eIPj18QrvxfqxeCwizb6TYFsrZ2aklV44Mj/AHpG/iY8fKFA8j0TRNX8SataaDoFnNqGo38qwW1tboZJZZHOAqqOSa/aOG+H6eXUfa1v4jWr7Lsv1Z+4cMcOU8soe2rfxWtX2XZfq/0M1EeR1jjUu7kKqqMkk9AB3Jr9eP2MP2M9d8Oa1pnxk+KkTWF1aAz6PorriZJHUhbi6B+4yg5SL7wbBfaV2n179lT9ivRPhJFa+OviLFBqvjMgSQQ8SWulE8gR9pLgfxS9FPEfTe33Rq2uaLoNsbzXdQtdOtx1lu5kgjGP9pyo/WvmeJOLXW5sHgNno5d/JeXn16efy/E3GLr82Cy/4Xo5d/JeXn16eepRXzp4l/a1/Zy8KbxqXjvS53T+HTmfUST6f6Isoz9TXgniD/gpH8C9MLRaLp2v6y4+68dtFbwn/gU0yyD/AL918hQyPMK2tOjL7rfiz4vD5DmNfWnRl9zS+92P0Gor8n9T/wCCodihK6N8PJZR2e51ZYz+KJav/wChVyE//BT7xixP2XwNpkY7CS9mkP6IlejDg/Npa+yt84/5npw4LzeWvsrf9vR/zP2Por8Vb/8A4KbfFOWJl0zwpoFtIejTm6nA/BZYv518XfFD43/Ez4v6++v+NdannYkGC0hdorO2UdFhhDbU9zyzHliTzXoYPgXHVJWrtQX3/gn+p6WC4Ax9SVsRJQX3v7k/1P6e6/Cn/gqP44vdU+LPh3wEkh+waDowvTHnAN3qErhyR3xFDHjPTLY68+0/8E8fj14013xLqHwg8WahcatYLpz6hpc11I001q1u6I8IdiWMTrJuUEkIUwuAxrwX/gqH4UutK+Nuh+K9p+ya9oMcauehuLGaRZVH0jkiP/Aq9fhDKvqHEaw9dptRbi++n+Vz8p8UcrxGXYSeGk76x1XVPb8bH5pUUUV+5n8/BXuPwX+Injj4M+Iz448DX4sNTms5rMlo1lQwT4J3I4KsQyq6gggMoJB6V4dXofh/7Rrjw6dpsEtzelSBbwo0kjCNSzFVUEkBQWOOgBzXwvH9fGQy7lwy92TtN2vpb8F3f+ZwY7E4mgo1MK2pX3W69DvPGvxF8dfEfUv7W8da7fa3c5OxruZnSMHqI4+EjX/ZRVHtXGV638Mfgl8Rfit4rtPC3hvRrvM0yJc3csDpbWcRPzyzOQFUKMnGdzH5VBYgV+1vwv8A2H/gR8OBFd3ulnxVqkeCbrWts8Yb/YtgBABnpuV2H96vwWU1HQrIeD80z2cqydo31lO+r8t23/TZ+G/gr4VfEn4jTCLwN4a1PWRu2tLa2ztAh/25iBEn/AmFfX/gv/gnR8bdfEc/iu80rwxC2N8csxvLpc+iW4aI/wDf4V+4dtbW9nBHa2kSQQxKEjjjUIiKOgVRgAD0FTVi6z6H6nl3hLltJJ4ypKo/L3V+F3+J+cnhD/gmx8KtKCS+Mdf1fX5lxuSDy9Ptm9coBLL+Uor6h8J/svfs/wDgrY2h+B9KaWPG2a+iOoSgjuHujKyn6EV71RWbnJ7s+3wPC2UYP/d8PFPva7+93ZDb21vZwJbWkSQQxjakcahEUegA4Aqaivy8/bo/aq1DwvNN8F/hxetbajJEDruowNtkt45VytrEw5WR0IaRhyqkKDkttIxcnZG2e53hsowcsXiNlokt2+iX9aLU7/8AaV/bm8OfDCW78F/DNYNe8URFori6Y79P0+QcEMVI86ZehRSFU/ebIKV+NPjbx54w+I+vTeJ/G+q3Or6lP1muGyEXOQkaDCRoM8IgVR2FcjX1R+z7+yZ8RfjxOmpwJ/YfhdH2zaxdRkrJtOGW1jypncHIJBVFIILA8HqUYwR/OGZ53nHE+LVCCbT+GEdl5v8AWT/BHzLpumajrN/BpWkWs19e3TiOC3to2lmlduioiAsxPoBmv1G/Z0/4J+XlzLa+MPjsv2e3UrLB4difMkncfbJEPyL6xIdx/iZcFT+gPwa/Z1+F3wO08Q+DdMV9SdNlzq13ia+n9QZMARof7kYVPUE817nWU619In6bwx4X0MM44nNGpz/lXwr1/m/L1KtlZWem2cGnadBHa2ttGsMEEKCOOKNAFVEVQAqqBgADAFWqKKwP1pJJWRU1CwtdUsLjTb6MS213E8MyHoySAqw/EGvyg8ZeGrrwf4n1Hw3d5L2U7IrkY3xn5o3/AOBIQfxr9aq+Ov2pfB2U07xxaJyuLG8IHY5aFz9PmUn3UUDPYfgV4y/4S/wDafaJN99pf+g3OT8x8sDy3Pc7o8ZPdga9kr87v2c/GH/COeO00i5k22euoLVgTwLhcmFvqTlB/v1+iNABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH/9f9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiqt5f2OnxG4v7iK2iHV5nWNfzYgV51q3xn+GOjZF14gtZWH8NruuiT6ZhVx+ZFAHp9FfMWrftTeDLXcmkadf37jozhLeM/iWdv/Ha8x1b9qrxVcZXRtIsbJT0MzPcuPoQYlz9VNAH3XUU88FtE01zIkUa9Xdgqj6k8V+ZmrfHD4o6xuWbXZrdD0W0VLfH0aNQ/wCbGvN9Q1XVNWl87VLy4vJP79xK0rfmxJoA/T3Vviz8N9E3C/8AENkWXqkEn2lx7FYQ5z+FeY6t+1B4Bssppltf6i46Msawxn8ZGDD/AL4r4c0rwz4j1wgaLpd5fZ728Dyj8SqkCvTdJ/Z++KWq7WbS1sY2/ju5o48fVAWkH/fNAHpGrftW67LldD0O0tfRrqV7g/XCCLH5mvMtW+PnxS1bcv8Aa/2OM/wWkUcWPo+0yf8Aj1epaT+yjq8mG13Xra39VtIXnz7bnMWPrg16dpP7MXw9scNqMt/qTdxJMIoz9BEqsP8Avo0AfCeqeIde1tt+s6ld3xznNzO8v/oRNRadousaxJ5WkWFzfPnG22heU5+iA1+n2k/Cz4daJg6f4esQy9HliE7j6NLvYfnXdxRRQRrFCixoowqqAAB7AUAfmhpPwK+KOr7Wj0SS1jPV7uRIMfVWbf8Aktem6T+yp4lnw2t6zZWYPUW6PcsP++vKGfxNfc1FAHzPpP7Lnge0w+q31/qDjqoZIIz+CqW/8fr07Sfg98M9F2mz8PWkjL/Fcqbo59f3xfn6V6VRQBXtbS0sohBZQx28S9EiQIo/AACrFFFAHxl+0F+xT8P/AI662fF8N/P4a8RSokdzeW0Szw3QQBVaaBmTMiqAodXUlQA2cDHw/wCM/wDgmh8TtJV5/BHiTSvEEajPlXSSabcN7KuZ4v8AvqVa/a2ivoMDxRmOEgqdOd4ro0n/AMH8T6PAcWZnhIRpU6l4rZNJ/wDB/E/mX8bfs4/HL4eCSTxX4M1W3t4s77q3h+2WqgdzPbGWJfxYV4n04Nf1qV5L46+A/wAHfiV5j+NfCOl6jPLnddeQILs5/wCnmHy5v/H6+pwniA9sVS+cX+j/AMz6zB+Iz2xdH5xf6P8AzP5ha9B8H/Fj4m+AGU+C/FOraOinPk2l3LHA3+9Fu8th7MpFfrp41/4JqfCrWDJP4J17VfDkz52xThNRtV9AFbypvzmNfJnjL/gnH8cdB8ybwtd6R4mhGdiQzmzuWx6pcBYh/wB/jX0lDijKcXHknNK/SSt+en4n09DivJ8ZHknNK/SSt+en4md4L/4KI/Hzw2Y4fEZ0vxRbrgMb21FvcFR/dktTEoPu0b19i+A/+ClHwr1wx23j3RNS8MTNgNPCRqNovqSyLHOPoIW+tfkt41+DPxX+HRc+NvCeq6TChwbia2c2x+k6BoW/BzXmVFfhnKcZHnhBLzi7flp+AV+FsnxseeEEr9YO35afgf1AeHPjj8IPFuh3XiPw94v0i8sLGB7q7cXSI9tDGNzPNE5WSIAc/Oor5T8Sf8FH/gRo15JZ6NZa9rojYgXNtaxQ2747qbiaOXH1jFfhXRXm4fgPBQk3Vk5Lotvvtv8AgeZhvD7Awm3VnKS6Lb77b/gftN/w83+GOf8AkUdex/v23/xytG0/4KZfBuRgL7w34mhHrHFaS4/O6SvxIortfBWV/wAr+9na+Bspa+B/+BM/fDS/+Chv7OWoFRd3WsaZnr9q09mx9fs7TfpX0x8NvjH8Mvi9ZT3/AMOdfttZS1Ki4jQPFPDuztMkEypKgbBwSoBwcE4Nfy719l/sE/8ACTf8NJ6CfD4lNr9lv/7W2Z8v7D9nf/W4/h8/ytuf+Wm2vGzfgvB0cLUr0JNOKb1aa06bX/E8XOeBsFRwlTEUJtOKb1aa06bX19T+gmiiivzA/KQooooAKKKKACiiigAooooAKgS2topXuI4kSWXG91UBmx0yRyce9T0UBcKKK5Xxp438J/Dvw7c+K/GupwaTpVoMyXE7YGT0RFALO7dFRQWY9BVQhKclGCu2VCEpyUIK7Z1VFfHXgj9uv9n7xz4ni8K2uoX2l3F1Mtva3Gp2vkWtxI5woWRXfZuPAMojGeOtfYtdGKwOIw0lHEQcW+6OnF4DEYWShiYOLfdWCvMPir8Hfh98Z/DreG/HumJeRAMba5TEd3aSMPvwSgFkPTI5VsYZWHFen0VjSqzpTVSm7NdUYUa1SlNVKTaktmj+f74/fsS/Ez4PS3OueHYpfFXhZCzi9tIybq1j64uoFywCjrIm5MDLbM7a+LK/rUr4/wDjl+xb8JfjFFc6pY2ieF/EsgZ11PTowscsh73NuNqS5PVhskP9/tX6Lk/HVrUswX/by/Vf5fcfpeS8f2tSzFf9vL9V/l9x+JPwh+OXxH+COurrXgXU3gidwbvT5iZLG7UdpocgE44Drtdf4WFfvN+zx+0x4H/aD0JptJP9m+ILKNW1HRpnDSxZwDJE3HmwFuA4AIJAYKSM/gh8Xvg147+CXil/C3jmyMEjbntLuLL2t7CDjzIJMDcOmVOGUnDAGuV8D+NvEHw68WaZ408L3T2mo6VOlxE6sQGCn5o3x96ORcq6nhlJBr6LOchwubUfb0Wue2kl18n3X4r8D6TPOH8Hm9D6xRaU7XUls/XuvxX4H9G/x1/aF+HP7Pnhka/44uy1zcbl0/S7ba97eyL1EaEgBF/jkYhF4GdxVT+OPxD/AOCl/wAevEt9L/wg0em+D7DcfJWK3TULrb28yW6V4mP+7Cg9q+Kvid8TPF/xd8aaj478bXjXmpahITjJEVvCCfLghUk7IowcKv4nLEk8BXr8P8BYHB0lLGRVSo976xXklt83+B/EWdcZYzFVHHCycIdLbvzb/Rfifcfhr/gol+1FoN6lzqWvWPiCBSCbbUdNtkjYdxutEt5P/H6/VL9mP9t/wH+0BcR+E9Wtx4Z8Y7CyafLL5lvehBljaSkKWYAFjEwDgcqXCsR/OVV7TNT1DRdStdY0i5ls76xmjuLa4hYpJFNEwZHRhyGVgCCOhrtzngjLMbScaVNU59HFW+9LRr8Tlyvi3MMJUTqTc49VJ3+5vVH9g1Ffhh4o/wCCjnxn1fSNHs/CttYaHcwWFuuo3jQLdTXV6EAmkRJAYoomfJVNrMP73YeH+IP2wv2lPEqNHf8AjvULdG4xp6Q6eQPZrWOJvxzmvyajwHmEv4kox+bf5L9T+p8u4GzDFUYV21BSSdne6v3Vt/K5/Rpe39jpts95qNxFa28fLyzusca/VmIArw7xN+1H+z34RLrrPjzR2dM7o7KY6hIpHYpaCZgfbGa/nC1vxL4j8TXP2zxJqt7qtxyfNvriS5fn/akZjWJXtYfw+pL+PWb9Fb87/kfR4bw5pLXEVm/RW/F3/I/R39qL4n/scfF68l8QaKPEFv4oKlTqmk6dElvdkDCm6iuprdpCoGA67XxwSQAB+c0oiEjCBmeME7WZQrEdiVBYA+2T9ajor7XL8BHB0lRpybS2u7n3WW5dDBUVQpyk4rbmd7BRRRXeegFFWLS0utQu4bCxhkuLm5kWKGGJS8kkjkKqIqglmYkAADJNfpV4D/4JpeOtc0yz1Lxz4os/Dz3MSSyWVvatfXEO8A+XITJDGJB0baXUHoWrz8fmuEwUVLFT5b7dfwWp52Y5thMDFSxU+W+27b+Suz8zaK/cXw3/AME2Pgrpm2TxDq+va1KPvL50NrA3/AY4jIP+/te7eH/2Of2a/Dm1rPwNY3Lr1bUJJ77cfUrcSSJ+AXHtXzdfjvL4aU1KXyt+b/Q+Xr+IGWw0pqUvlb82j+cZEeRxHGpZmIAUDJJPYCvR9F+DXxd8Rqsmg+CfEN/G3SS30u5kj/77Ee0fnX9MXh/wN4K8JqE8LeH9L0ZQMAafZQ2ox/2yRa6mvHreIMtqNH73/kv1PGr+I8tqND73+iX6n85nhv8AYy/aT8TTpHb+C7qwjYgNNqUsNkiA9yssiyHHoqsfav2T/Zl/Zx0/4FfDS68JaxNDq+qa87Ta1Kin7PJuTyxbxhgC0SJkZYAsWY4AIUfUFFfOZvxTjMfT9jO0Y72X6s+YzrizGZlS9hUSjDsuvrds/l2/ad+AGu/s+fE2+8M3cMr6FeSSXOhX7AlLmyZsqpfp50OQko4OQGxtZSfnSv61PiX8LfAnxe8MTeEPiFpMOq6dKd6B8rLBKAQJYZVw8UgzwykZBIOQSD+WPjz/AIJVTtfSXPwy8bRraOxMdprlu3mRjsDc2+Q//fhfxr9P4e8Q8HVoRpZlLkqLS9m0/PTZ9+nn0P56zvgjFU6zqYFc0H06ry13XbqfjzXc/Dj4deLPiv4y07wL4KsmvdU1KUIigHy4kH35pWAOyKMfM7HoPU4Ffpf4U/4JUeLJb1G8ceOdOtbRWBdNKtpbmV17gNP5CoT6lWx6Gv0K8M/AjwR+zX8IvFa/BzS2GvJol9Ouo3GJ9QvbuC3d4BJJtAx5gGI0VYweQuSSezNvEHL6MOTAy9pUei0aivNt2/D8DLJuBcdiq8Y4pckL9Wr/ACte3q/xON8OfGP9mv8AZS8J6H8ENR8WQi88PW6292trbT3b/aXJkuJZvs8cixvJK7OYy25c4xgV9W+D/GnhXx/oNv4n8Gapbavpdzny7m2fcu5eqsOGR1/iVgGHcCv5V5557qeS6upHmmmdpJJJGLO7scszMckknkk8k19Afs8ftFeMP2ffFQ1TR2a+0S9ZV1XSJHIiuYxxvQ8iOdB9xwPZgVJFfIZnwR7SnKtSqOVZ6u9rSb39L+r/AFP6yxPhxRo4NQwU3zxWztZ26Lt5Xb/U/pPorgPhn8TfB3xb8I2njTwRereWF0MMpws1vMAN8MyZJSRM8joRgglSCe/r8xqU505OE1ZrdH5nVpTpzdOorNboKK/O/wCOf/BQTwt8MfF154J8GaCfFF7pcrW9/dvdfZbSKdDh4oyI5WlZDlXPyqGBALV7D+zb+1n4O/aIW70mCyk0LxHYRfaJ9NmlE6yQZCmWCUKm9VZgHBRWUsOCDmvUq5Dj6WG+tzptQ76flv8AgetW4fzClhvrlSk1Dvpt3tuvuPq+iiivIPGCiiigAooooAKKKKACiiigAooooAKKKKAPyk/4KMfA7w1Do1v8ctIKWWqtdQabqkKrhb4SKwim46TRhNrE/eTHIKjP5DV+9v8AwUQMY/ZzuN/U6zp+z65f+ma/BKv2jgvEVKuWr2jvZtL00P3DgfEVKuVr2jvytpeitoFFFe1fAqb4Oy+NIdF+NlhPJoOplYBqVrcyW8unSk4WVgp2vCc4kBUlRhh0Kt9PXq+ypupyt26Lf5H1WIreypupyt26Lf5Hitfr/wD8EzfHPirVLDxb4F1Gea70XR1s7ux8wlltJLlpVkiQnokmwOFHAKsQMsa7nUP+Ca/wT1Ly7zQfEXiC0hlCuB59rcxMjcgofs6nBHQlmr7B+DfwT8B/AvwufC3gW2kSOaTzru7uXEl1dzY2h5XCqOBwqqqqvOACST+dcR8UYDGYGVCkm5O26taz3/TQ/NeJuK8vxmAlh6Kbk7bq1rPf9ND1uiiivzU/LwooooAKKKKACiiigAooooAKKKKACud1jwh4S8QknX9E07Uiev2y0in/APRitXRUVUZyi7xdiozlF3i7H5T/ALdv7K+jReG4/i58LtFtdNOjReXren6dbpbxSWgJIu1iiVV3xEkSnGTGQxwIzn8g6/rPngguoJLW5jSaGZGjkjkUMjowwysDwQRwQeor+e39sD9nG6+BPjxr/RIHbwfr8jzaXKMsttJ96Szduxj6xk8tHjksr4/UOC8/dWP1DEP3l8LfVdvVdPL0P1fgfiJ1Y/2fiZe8vhb6rt6rp5eh237D37Sq/CPxafAXjC68vwj4jnXEsrYj06/bCpOSeFilACSnoMK+QFbP7O/FXxAPDnw71vVYnxI1q0EBU8+Zc4iQj1wX3fhX8t9fsB+wh+1XJq62nwO+Il3uu4k8vw7fzNzNGg4spGP8aKP3JP3lGzqEDRxlw653zDDLVfEv1/z+8z424bdRPMcMtV8S/X/P7+5jeHfDOp+IdasNJt7eb/TbmKDzBG21BIwUsTjACg5Jr9Z7a3htLeK1t1CRQosaKOiqowB+AFTUV+YH5UFFFFABRRRQAUV+N/7T37dPxI0/4hav4E+Ed3Fomm6BdS2E9/5EVxdXdzAxSYjzlkSOJXBVNq7mxu3YYKPFvB//AAUB/aK8N3SSa1qVj4ltQfmg1GyijO3vtktVgcH0LFgPQ9K+uocFZhVoKsuVXV7Nu/5W/E+yw/AuZVaCrrlV1ezbv+VvxP35or4D+FH/AAUL+EHjdodN8cxT+C9Skwu+5b7Tp7MfS5RQyep82NFH941926Zquma3YQ6ro15Bf2Vyu+G5tZVmhkU90dCVYe4NfP43LcVhJcmJg4/l8nsz5zHZXisHLkxNNx/L5PZl+iiiuE4AooooAKKKKAPw/wD23v2aviBpfxS1b4neF9Ju9a8PeInW7lksomuJLK52KsqTIgLKjMu9Xxt+bbnI58k/Z3/ZK+Inxe8W2Mmu6Pe6L4Stpkk1LUL2F7bzYUOWhtg4VpJJANu5QVTOWPQH+huvxV/4KAftda3c+Ib/AOA/w2v3stO0/Nv4ivrZyst1cEfPZo68rFEDtmwQXfKH5VIf9M4bznMsxjHLMNFJpfG/sxWl7dX211fzPosy8UK2U5VyVIpyS5Yvq+2ndLr+HR/enxE/bW/Zu+FNy+g33iNNSvrT901jocLXpi2cbDImLdWXGChkDA8ECvPNA/4KSfsz6zeraX11rWiozbfPv9OLRDPc/ZpLhgP+A1/PJRX11LwyytU+WpKbl3ul+Fv8z+eqnH+Yyqc0YxS7Wb/G/wDkf13+E/GXhTx5okPiPwZq9nrWmXHEd1ZTLNGWHVSVJ2sv8SnDA8ECulr+Vn4EfHzx5+z/AOMofFHg+6ZrV3RdS0uRz9k1CAHlJF5AcDOyQDch6ZBKn+kDQfj18N9d+DcPx0Goi18MNZfa55JRmS3dW8t7d0XJMyy/utq53PjbkEE/m3FHB+IyqrH2b56cnZO2t+zXft3Pv+GuIoZsvZKNqq+zvfzXz6Hs1FfkH45/4KcavLLPbfDbwdb28QJEN5rU7TOy9mNvB5YQ+3nOK+P/ABr+2D+0T46MkeoeMbzTrZ8gW+kBdORVP8O+AJKw/wB92ownBGY1datoLzd39yv+Z+s4PgPMq2tW0F5u7+5X/Q/od1/xV4X8KW323xRrFho9vgnzb+6itY8D/alZRX5b/t6ftH+APGXw7sfh58N/E9rrEt3qaT6qtizyRm2tlZkUyhfKZTMUbCsTlAelfk7fahf6pdPe6lczXdxIcvNPI0kjH3ZiSfxNVK+syrgqjhK8cRUqOUo67WV/xPsMo4Fo4OvDE1KjlKLutLK/4hXrHwn+Mvi34L6le674Ih0+PV7uEW8eoXdqt1Pax87xb7yY0MmQHJRiQoAIBYHyeivs61GFWDp1FdPofbVqNOtB06qvF7pnvvib9qX9obxbuGsePdYRX+8ljMNPQg9itoIVI9sV4fqGp6jq1y17qt1PeXD/AHpbiRpZD9WYkmqVFRRwtGirUYKPokiKGFoUVajBR9El+QUVteHrbQbzWrS28T31xpumSSKtzd2tsLyWFD1ZYWlhD47jeDjoCeD+x/gT/gnV8C9R0ew8Q3PifW/Edpf28V1bTW0kFpbTwzKHRwoieTDKQR+8Fefmud4XL0niL67WTf47fiedm2fYXLknib67WTf47fifitRX9D2lfsNfsxaUqk+EDeSL/wAtLvUL2Qn6qJ1j/wDHa7S3/ZU/Z0tVCxfD/RWA/wCekBlP5uWNfOT4+wK+GnJ/d/mfMz8RMAn7tOT+7/M/mtrV0XQtb8R6hHpPh7T7rVL6Y4jtrOF7iZz/ALKRhmP4Cv6VbT9nD4A2TiSD4eeGiw6eZplvL/6GjV6no3h7QPDtt9j8P6bZ6Zb8furOCO3j46fLGqj9K5K3iDSS/dUW35u36M5K3iNSS/c0W35tL8rnwF+wx+y54i+Ecd/8SPiHALPX9XtRZ2enkhpLOzZ1kdpiCVEsrInyg5RRgncxVfav2vf2eY/2iPhZLoemmOHxLo0hv9EmkO1WmC7ZLd27JOvyk9FcIx4Ug/VVFfE1M9xcsesxTtUTuuyt09PzPzDPq8s4lUljdedWa7Lpb0/PU/kD8QeH9c8Ka1eeHPEtjPpuqafK0F1aXKGOWKReoZT+YPQjkcGsev6nvjH+zZ8HvjtAv/CwdCjnv4k8uDVLVjbX8S9gJk5dRk4SQOgJztzXxDqv/BLT4T20st/H451uy06IGR/tSWrlI15JabZGoAHUlMV+w5d4lZbVpr60nCfVWuvlbX70fiuN4Cx9OpbDWnHprZ/NP9Gfh/X7if8ABPD9lnWPAVvN8bPiFZPZaxqlqbbRLCdds1rZy4MlxIp5SSYAKinDLHuz9/A9o/Z//ZV/ZN8F6mNY8C3lj4513TmDi9vNRttUktXB4ZYLfbBGwb7rmPep6Nnr90V8vxfx2sXRlgcFFxi/ib0bXZLon1vvsfScPcFVMDXWJx/xraPbzfn2+8KKKK/LT78KKKKACiiigDz74rePbL4X/DjxD4+vwrpotjLcRxscCWc/JBFnt5krKn41/MZretan4j1m+8Qa1O11qGpXMt3dTv8AekmmYu7H6sSa/ev9vHQPEmv/ALO2rJ4djknFje2l9fxRAsz2UDEudo5IjYpI3oqE9BX4CwQT3U8dtbRvLNM6xxxxqWd3Y4CqBySTwAOSa6qC0ufz/wCLmMrTx9HCNe5GN15ttp/dZL7+59u/sWfsz2fxt8S3XirxlE7+EfD8iJLCCV+33hAZbfcMERopDy4IOCqj7xI/duwsLHS7KDTdMt4rS0tY1hgggQRxRRoMKiIoAVVAwABgCvFP2afho3wm+CvhnwjdQC31EWovNSX+L7bdnzZVYjqY9wjz6IK92rGpK7P1Hgrh2llWWwjy/vZJOT63etvRbW+fUKKKKzPrwooooAK5nxl4btvF/hfUvDlzgLfQMiseQko+aN/+AuA34V01FAH4+yJfaLqbRvutr2wnKns0c0LfzVhX6r+BvE0PjHwlpfiOLGbyBWlVeizL8sq/8BcEfSvh39pDwl/wj/js6zbpttdcj+0DHQXCYWYfj8rn3evSv2V/FheLVPBdy/MZF/agn+E4SYD6HYQPcmgD7DooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//0P38ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKjmmit4nnndY4o1Lu7kKqqoySSeAAOSTXk3xa+Ofw1+Cmkf2p481VLeWRC1tp8OJb66I7RQg5IzwXYqgP3mFfi1+0N+2V8QPjcJ/DulhvDnhJyV/s+3kJnu1HQ3cwxvB6+WuIx3DEBq0hTcj5DiXjTL8ng41HzVekFv8+y9deyZ9M/tA/8ABQfWdN8UT+GPgeLGXT7BjHPrVzEbj7VKv3hbIWCCFTxvYNvPK4XBb3v9j79rXUvjxcaj4M8bWdta+JNNtvtsU9mrJBeWqusbkxszbJI2dM4bDBsgLtIP4S1+w/8AwT5/Z98Q+FBd/GjxbC9i2r2BstHs5QVla1ldJHuXU8qH8tREDyV3NjBUnacIqJ+YcI8UZ7mmexfO3Td3KP2Yx/S2lnu38z9QaKKK5T9+CiiigAooooAKKKKACiiigAoqvdXdpYwNdXs0dvCnLSSuERfqzEAV5Hr/AMe/hloJaP8AtT+0Zl/5Z2CGfP0k+WI/990AeyUV8b6z+1fyyeHtA4/hlvZ/5xxj/wBqV5dqn7R3xQ1DItru205T2tbZTx9ZvNP60AfozWbqOsaRpEfnatfW1kmM7riVIlx9XIr8uNT+JXj/AFhWj1DxBqMkb/ejW4eOM/VEKqfyrind5GLyMWY8ksck/jQB+merfHP4XaRuWTW47lx0S0R58/RkUp+bV5jq37Vfhi33LoujXt4w6G4dLZT/AN8+acfgK+GKKAPprVv2pPG13uTSbCwsEPQsrzyD8WZV/wDHK8x1b4x/E3Wdwu/EF3Grfw2pFqMen7kIfzrzOqF5qmmacN2oXkFqPWaVYx/48RQROpGC5puyNi6vLy+lNxfTyXEp6vK5dj+LEmt7SfBXi/Xdp0fRb67VujxW7sn4vjaPxNecWHx58O+Cp/tOleI4IJQc7reMXJJHuqSCuvh/4KIazo5CnOu4/wCe1kkKn8UeEj/vk1ShJ9DxMVxTk+H/AIuJgv8At5N/crs9l0n9nT4oantNxZW+nI38V3cJ091i8xh+Ir07Sf2ULg7X13xAif3o7S3L/k7sv/oFfJmqf8FOfEz2vl6L4GsYbgf8trq9kljP/bJEjI/7+GvEfEf/AAUB/aN1zcNP1DTNBVu2nafGxA9jdm4P45zVqjI+axfihkVH4Jyn/hi//buU/W/Sf2avhpp+03sd5qbDr9ouCi5+kIj4/E16bpPw78CaHtOl6DYQuvSTyEeQf8DcM361/OP4j+P3xs8W7l1/xxrtzG/3oVvpYYDn/plEyR/+O17R+yP8ePiF4L+MPhrw5/a15f6H4j1O20q8064meaHN7IsKzIrk7JI3YMWXBYAqcg1Tou17nl4PxYwdfFww/sJKMmlzNrS+my/zP6AgAoCqMAcACloorA/VwooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAEZVdSrAEEYIPIINfPXxB/ZU+AnxKEkuv+ErK3vJMk3umL9gudx/iZoNiyN/10VxX0NRW+HxNahLnoycX5Ox0YfFVqEuehNxfk7H82f7TXwH1H4BfEm58NqtxPoN4PtOi30+CZ7Y4yjsqqplhY7HAA7NtAYV871/Ud8UfhV4J+MXhSfwf46sFvLOX54pF+W4tZgCFmgkwSki568gjKsGUkH8CP2j/2ZfGH7PWvol8Tqfhu/kZdN1eNNquRz5My8+XOq84zhwCVJwwX9d4a4np42Cw9d2qr/wAm8159193l+ycL8VU8dCOGru1Vf+Tea8+6+7y+aKKK+v8A4c/snaj8bvh1/wAJr8Idetb7VbBvs+r+H9RxbXNvcAZUwzjMckcwG6MuIgPmUsShNfTYvG0cNFTrytHa/T59vV6H1OLx1HCwVTEStG9r9Pn29XoeAeAPE3gvw7qQfxx4PtvFunOw8yJ768sLhV/6ZS20qoD/AL8b/hX7b/sk+P8A9lXUtPk0f4K2MHhjWrtVa80u/Zv7Tl2ZPE00krXEa5JASRgoOSqZr8OfG3w+8bfDjWG0Hx1ot5ot8uSI7uMoJFHG6N+UkT/aRmX3rlLe5uLO4ju7SV4J4XWSOWNijo6nIZWGCCDyCORXk5vk1LM6Pu1Gr7WbcX8r2fyseRnGSUs1oe7VavtaTcX6xvZ/Kx/WdRX4qfs/f8FB/FPhJrbwx8Zlm8Q6ONsaavGAdStl6Ayg4Fyg7kkS9Tuc4Wv2F8IeMvC/j7QLXxT4N1O31bSrxd0VzbNuUkdVYcMjr0ZGAZTwQDX5Hm2R4rL52rx93o1s/wCuzPxvOMhxeWz5a8fd6SWz/rszpqK5vxX4x8KeBdHl8QeM9YstE02Lhrq/nS3i3HooZyMseyjJPYGvk/Uv+Cg37K2nXhs08VT3m07WlttMvGiB74ZoV3D3UEHtmubCZVjcUr4alKS8k3+R8zicxwuHdq9SMX5tI+1KK8Y+Gn7Q3wW+MD/Z/h54tsNUu9pb7EWa2vNo5JFtcLHMVHchCB617PXNiMNVoT9nXi4y7NWf3M3o16daPPSkpLundBRRRWJqFFFFABRRUFzcwWdtLd3TiOGBGkkduiogJYn2AGaASOF+J3xP8G/CHwjd+NPG96LOxthtRBhp7mYglIYEyC8j44HQDLMQoJH8937Qv7RHjH9oHxWdW1pmstFs2ZdK0iNy0NrGf4m6CSZx9+Qj2ACgCk/aN+PfiL4+ePrnX76WSLRLOSSHRdPJxHbWueGK9DNKAGkbqThR8qqB4AiPI6xxqWZiAqgZJJ6AD1r9l4Z4ZhgYLEV1eq//ACXyXn3fy9f23hbhaGXwWIrq9V/+S+S8+7+S87mmabqGs6ja6RpNvJd3t7MlvbQQqXkllkYKiKo5LMxAAr+qTwhZarpvhPRNO12Xz9StdOtILyXO7zLiOJVlbPfLgnPevzI/ZI+D/wALvgXaRfFT41+JdC0zxbcxE6fp2oX9vFJpUMgwWaN3DfapFOCAMxqSv3iwH1Z4g/bb/Zn8P7kk8YR38q9I9PtLm53fR0i8r83FfN8WYmrmVaNDB0nKML6pN3b7abK2/U+Y4vxVbM68cPgqUpRhfVJu7fbyVt+p9W0V+b3iP/gph8JrAMnhnw5rurSLnDXAgsoW+jeZM+PrGK8A8S/8FN/iJebl8JeENH0tTwDfTT37qPYxm1XP1BHtXhUOEs0q/wDLqy82l+tzwMPwdm1X/l1ZebS/W/4H7QUV/PJ4h/bm/aY1/ci+KV0uFv8Allp9lbQ4+khjeUf9914b4h+MPxY8WBl8S+Mtd1KN+sVzqNw8XPYRl9gHsBXs0OAMXL+LUivS7/yPboeHeMl/GqxXpd/5H9CHx1tvgB4t8Lz+E/jVq+i2tpnzI/tt/Da3VvMBgSQMziRZB0+UHcMqwKkg/iX+0Z4J/Zj8KeGYbL4F+LtT8U+IJ9SRbr7XzbW+niKUtskW1gjd2l8sZDN8oPAzmvlwkk5NFfZZHw7LLpJxryaTvbaLfpr+Z9HS4GisHPA1MVU5JqzUWo7797X62OQk0q8jXdtDAf3Tk1nV6BXFX5Q3kpj+7u7evf8AWv0HBYuVVuMkfzj4teGeW8OYajjcuqStOXK4yab2bumktFazvfdFSiirFnaXWoXcFhYwvcXNzIkMMMSl5JJJCFVVUclmJAAHJNeg3bVn4YlfRHoVppmr3GgxeJBYXI0x5mtReeS/2Y3EaqzxiXGwuqupK5yAw9a2dF8H+LfEkixeHdE1HVXbhVsrSW4Y/QRqxr+jD9l74Qt8Efgj4d8CXqr/AGokLXmqlSCDfXZ8yVcjhhFkRAjqqA19A1+IY7j6MK9SnQpXim0nzbq+jtb9T+18n8RcZHL6UcZRTq8q5ne2tu1vv13P5yPC/wCxv+0l4rKNaeCryxibGZNUeLT9oPcpcOkv4BCfavo3wv8A8EzPidf7JPFvinRtHRuStok1/Ko91ZbdM/RyPev2qorwcRxzmNTSnyx9Ff8AO/5GeJ4+zKppTUY+iu/xv+R+a/h3/gmZ8LbIK/ijxRrmquvUWqwWMbH3VkuHx9HB969t0n9if9l7wlbPqF54XS7W1jaWW51S+uJUVEG5mdTKsIAAySU6V9d1+ff/AAUN+Lt14F+Flp4B0eRor/xrJNBPIpwU062CG4XI6GVpEj90LiuLCZlmuY4mGG9vL3n0dtOu1tkcODzTNszxUML7eV5Po7adXpbZH5RftBfEDwj48+IF2/w60LT/AA94U00taaXb2FpHaGeNDg3M+xVZ5JjyN+SqbV6gk+GUV+hv7BP7OkPxI8VyfFHxdaiXw74ZuFWzgkXMd7qagOoIP3o7cFXYdGcoORuFfrWKxNDLMG6kvhivm3/m/wDgn7FisTQyvAupP4YL5t/5t/5n1D+xD+yZF4F061+L3xGsgfEt9GJNJsZ150y3kHErqelzKp6HmNDjhiwX9JqKK/DsyzKtjq7xFZ6v7kuyPwTNMzrY/ESxFd6v7kuyCiiiuA88KKKKACiiigAooooAKKKKAPxz/bI/Ytl0B7/4tfCGyMmlsXudY0WBctZk/M9xbKOsHUvGP9V1X93kJ+W9f1qdeDX5D/tofsZw6bDf/GH4R2QS2QPc63osC/LEOr3Vqg6IOssY4UZZflyF/TOFuK+blwWNeu0Zfo/0f3n6nwnxfzcuBxz12jL9H+j+8+GPgF8ffGHwB8YJ4h8PObnTrkpHqmlyORBewA9+uyVMkxyAZU8EFSyn9+LT42+D/EHwV1D40+F7pbnSrTSLvUdr4EkUtrEzvBKoJ2yqy7Svc4wSCCf5kq9N8JfFrxj4N8EeK/h9pV0f7F8X28UN5buSVjkiljk86MfwuyIYn7Mjc5Krj3s/4ZpY+Ua0NJpq/mr6380tvu9PoOIuFqWYyhXhpNNX8431v5pbfd6ec3NzcXtzLeXUjSzzu0skjHLO7klmJ9STk19sf8E9tJ1DUf2kNPvbNmWHS9L1G6uwOjQvH9nAPt5s0Z+or4gr9cv+CYfg/baeNvH8yf6yS00e2fHTyw09wM+++E/hXXxPiFQyus+6t9+h2cVYlUMqrPurffp+p+sVFFFfhR+ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAH5u/8FMfEK2Xwm8N+GlbEuqa8Lgj1isreUMP++5kNfibX67/APBT3w/4gurHwL4mghkl0awbUbW4kUEpBc3XkNHvPbzFiIU+qEdxn8tvAXgzWfiH4y0fwToELzX2sXcVrGEUtsDt88jY6JGuXYngKCTwK/aOEPZ0sojNv+Zvy1f6H7jwZ7Olk0KjenvN+Wr/AESO38efAvx18PvAnhL4j6xbb9D8X2aXVvcRAkQSSbmjhmz915IQsqHoykgZKtjxqv6kvFHwv8H+L/hxP8LNZsxJoMunx6ekQxuhSFAsLxnHyyRFVZDjhlFfzYfFb4b678JPH+seAPEK5utKnKJMBhLiBhuhmTr8skZDAds4PINVw1xGsxU6dTSa19Y30+7Zj4X4mWZqdOorTjd+sb6fds/kfqp/wT1/aFl8R6O/wQ8WXRfUNHhM+hSytlprFP8AWW2T1a3zuQcnyiQABHX6e1/Kp4I8Y658P/Fuk+NfDc3kalo91HdW7c7SUPKMBjKOuVcd1JHev6c/hr490b4oeBNE8e6A2bLWbRLhUzuaKT7ssTEcb4pAyN7qa+L40yZYbELFUl7k9/KX/B3+8+H45yRYXErF0l7k9/KXX79/vO4ooor4k+FCiiigAooooAKKKKACiiigAorj/Hfj3wl8NPDN34v8bajFpml2a5eWU5LMfuxxoMtJI2MKigsfSvw//aH/AG4PiD8Wbu40LwRPc+FfCisVWK3kMd/eL03XM0ZyqsP+WUZ2jOGL8GvcybIMTmM/3StFbye3/BZ72ScO4rM5/ulaK3k9v+C/L7z98KK/mX+Cfxr8d/CPx5pniHw9qN28Buo1vtP813gvoHYCSN4ydrMwJ2tjcrYIOa/porTP8gqZXUjGUuZS2e22+mvc04i4dqZVUhGU+ZSvZ2ttvpr3XUK4T4k/Dnwt8V/BuoeBvGNr9p07UExlcCWCVeY5omIO2SNuVOMdiCpIPd0V4VOpKnJTg7NbHgU6k6c1Ug7Napn8xnxx+DHib4F+PrzwV4jHmxr+/wBPvlUrFe2jEhJV64PBV1ydrgjJGCfJ7O8u9Pu4L+wme3ubaRJoZomKSRyRkMrqw5DKQCCOQa/ez9tn9m7Xfjr4U0rWPBPlyeI/DTTmG0kdYlvba5CeZErsQqyK0asm4hTlgSMg1+Vfg39jf9oXxd4ji0GXwle6LEZQlxf6on2e1gTPzPubmUDsIg5Pb1r9qybiPD4nBKriakVJfEm7fO3n/wAA/c8j4lw2KwKq4qpGM18SbS+dvP8A4B+6H7PvxAvfil8GPCfjvUwPt2pWAF2QNoe5t3aCZwBwA8kbMB2BxXsdfM2qfEH4KfsgfDHQPCPizX47KDS7FLeztgpm1C+ZMmSVLePc37yQszMcRqzYLCvknVf+CqnwzgvDHovgvXLy1BwJbia3tnI9fLVph/49X5pS4ex2YVJ1sBQbptuz2Vr6aux/Pmc59lmGxU1Koopt2XVK+mi20P1Por4c+FX/AAUG/Z++Jd/Dot/eXfhHUp2CRx64iRW0jnoFuY3kiX2MpjyeBX3ErK6h0IKsMgjkEGvLx+WYrBT9ni6bi/Nb+nf5FYPH4fFw58NNSXl/WgtFFFcJ1n8/f7Zv7Pnij4W/EzWfGcFpLc+FPEt/Nf2t9GpaO3nunMkltMR/q2V2by88OmCCSGC/H0OlancWE+qwWc8llasiT3KRM0MTyZ2K7gbVLYOASCccV/V9c21teQSWt5Ek8MqlXjkUOjKeoZTkEexqlbaHotlp7aRZ6fawWLhla1ihRIGD8MDGAFII68c1+gYHjypRw8aVWlzSVle9rr7nqfouB8QalHDxpVaXNJWV72uvuep/J/Xpnw4+MfxM+Et//aHw/wDEF5pJZg0tuj77WYj/AJ628gaJ+OMspI7EV+1vxe/YR+B/xCS41XRYD4K1Rgzm50wKLMn1ltGIiCjqfKMRPc1+Ovxm+CF98HtWaxfxN4d8SW5YhJdI1KCW4T0E1rv8+Nv+Asg/vGvscuz/AAGaR9jbV7xkv6T/AK0Ptcs4hy/No+xtq94yX9J/1ofo38F/+CkGjarLb6H8atMXSZnIQazpqvJaE9MzW5LSxj1aMyDJ+6or9OdI1jSvEGmW2taHeQahp95GJbe6tpFlhljboyOpIYH2NfyeV9P/AAG/ay+JfwA07UdF8OJaarpl9iWOy1PzZILa4zzLEI5I2XeOHUEBsA9Rz4Wd8E0qi9rl/uy/l6P07fl6HgZ7wJSqL2uXe7L+Xo/Tt+Xof0a0V+A2t/8ABQb9pLVSxsdT0zRg3QWOnQuB9PtX2g/nXlmtftZftHa/G8d94+1aNXyD9jdLE8+htkiI/CvDpcBY+Xxzivm3+n6ngUvD3MJfHOK+bf6fqf0S+KPGvg/wRYnU/GOt6folqASJb+5jt1bHZd7DcfYZJr4a+JP/AAUa+EfhWWSx8B6fe+MblCQZkJsLHI9JZUaVsH0h2ns1fiPqmrarrd4+o6zeXF/dy/fnupWmlb6u5LH8TWfX0OB4DwlP3sTNzfbZf5/ij6TAeH2EpPmxU3N9tl/n+KP2k+G//BSDwt4qvZ9I8ZeG38O3MkMrWNzFdfbLaW4VC0cUuYoni8xgFVhuG4jO0c1+FOqalfazqd3rGpytcXl9PJc3Er8tJNMxd2PuzEk1614K8I69488V6V4P8MW73Op6rcx29uiAnDMeXYj7qIMs7HhVBJ4FeeeMvCureBvFmseDddiMOoaLez2NwpBA8yByhIz1VsZU9wQRX2/DWVYHA16scMrSkk7Xvor66621Pwjx6yPDYCphJYTSMue8bt2a5bPXXXX7nY5qiiivsD+egr7L+DeoeL/F37NXxO+Guj+ddW+hX+k+KhbRZZvswMlvekKOqriCUjsI2avjSv2E/wCCU/g+8M3j3x3PGRZsllo8DkfLJLl5519PkXys/wC+K+Z4vxMMNlk8RJXcXFr1UkfZ+H2Mq4XP8PiKSvyttrukn/Xqfl3WzofhzxD4nvV03w1pd5q1433bext5LmU59EjVm/Sv6O779lb9nfUtZfXrvwFpDXcj+Y+2Jo4WYnJJgRhCcnr8nPevadF8P6D4asl0zw7ptppVmn3beygjt4h9EjVVH5V+bV/EGio/uaTb82kvwvf8D+wMR4jUVD9xRbfm0l+F7/gfz9eD/wBhz9pDxcEmPhpdEt3xibWLiO1I+sIL3A/GOvp3wp/wTC1uXZL448cWttjG+DSrN7jPsJp2hx9fKP0r9f6K+dxPG+ZVfgaj6L/O583iuO80q/w2oLyX+dz4V8K/8E8P2etA2PrMOreI5Bgt9vvTFGT7LaLbkD2LH3Jr6J0H9nz4G+GYBBo3gPw/EMY3yafDPKR7yzK8h/Fq9horwcRm+Nr/AMWrJ/N2+7Y+exOc46v/ABq0n83b7tj5+8Yfsr/s++NrWS21bwPpNq8gP+kaZAunTq3Zt9t5ZYj/AGsg9wRX5s/G7/gnV4u8Lx3GvfB29fxLp6ZdtLutsepRqOf3bDbFcYHYCN+yqxr9p6K68u4jx+DlenNtdnqv+B8jty3ibMMFK9Oo2uz1X/A+Vj+VTT/A/jLVfEq+DtP0PUJtdaXyf7OW2k+1LJnBDRFQy477gAO+K/pT+BfgjU/hx8IPCfgjWpBJqGk6ZDDdFW3KszZd0Vu6ozFFPcAGvWMDOaK68/4mqZnCFNw5Utd73f3I7OIeKama04UnTUVHXe939yCiiivmD5QKKKKACiiigAr8c/8AgpV8TfEf/CXaJ8J7K6lt9Fj0yPVryGNiq3VxPNLHGJAPvLEsOVB43MTjIBH7GV+TX/BSP4OeItUvNG+Mmh2sl5YWGn/2Vq3lKWNqkcsksEzAc+WxmdWbopC5+9X0/CEqMczh7bztfv0/4HmfVcGToxzWn7a3W1+/T/geZ+WnhDxd4i8CeI7DxZ4UvpdP1TTZlmgniYggqeVYdGRhwynKspIIINf0w/Bn4m6b8YPhpoXxC01RENUtgbiAHPkXURMc8XrhJFYKT1XB71/L1X6x/wDBM/4oCOfxL8INQmwJQNc0xWP8ShYbpBnuR5TBR6OfWvuONssVfB/Wor3ofl1+7f7z73jvKo18F9aiveh/6S9/u3+8/XGiiivyA/GAooooAKKKKAEIDAqwyDwQehFebaR8G/hNoGvnxTong7Q7HV9xcXlvp8EcyOerIyoCjHuVwT3NelUUXMauHpVGpVIptbXV7enYKKKKDYKKKKACiiigAooooA8L/aG8K/8ACR/Dy5vYE3XWiuL2PA5MSjbMPpsO4/7or4b+G3ihvB3jbSdeLFYYZwlx728vySfXCsSPcCv1SubeG7t5bS5QSQzI0ciHoyOMEH2INfkx4v8AD83hXxPqfh2fO6xuXiVj1aPOUb/gSEN+NAH62AhgGU5B5BHQilryj4J+KD4q+HOl3Ur77myQ2Fx3O+3wqk+7R7WPua9XoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD//R/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKpajqWnaPYz6pq11DZWdshknuLiRYoYkHVndyFUDuScV+dPxt/4KHeDvC/2jQvhBar4l1NcodTuA0emRN0yi/LLcEH02IeCHYcVUYt7HkZvn2Byul7XG1FHsur9Fu/6ufoD4p8XeGPBGiz+IvF+qWukabbjMlzdyCJAeyjPLMf4VXLMeACa/LP46f8FFZ5xceHfgXaGFDlG1+/iBc/7VtbPkL7PMCf+mYPNfnR8Rvit8QfizrJ13x/rVxqtwCfKSQ7be3Vv4YYVAjjX12qM9Tk8158iPI6xxqWZiAqgZJJ6AD1rohRS1Z+H8R+KGMxd6GWr2cO/wBt/wDyPy18zW1/xDrvirVrjXvEuoXOqajdtvnuruVppZD7sxJ46AdAOBxVnwt4T8S+Ntbt/DfhLTbnVtTujiK2tYzI59WOOFVerMxCqOSQK+1PgV+wX8RviR9n174gmTwf4ffDhJk/4mdyh5/dwMP3II/jlwRwQjCv2C+Fvwa+HXwb0X+xPAOkRWKuF+0XTfvLu6YfxTTN8788heEXPyqBxTlVS0RxcO+HWY5nJYjG3p03rd/FL0T79362Z8Tfs3fsEaT4NmtPGnxm8jV9ZiKy22jIRLY2jjkGdulxIv8AdH7pTn/WcEfpSAAMDgClormlJyd2fvuTZHg8qofV8FCy6vq33b6/p0CiiipPXCiiigAooooAKK8h8e/GvwZ4E8y0mn/tHU1yPsVqQzK3pK/3Y/cHLf7Jr4y8b/HTxz4zMlstydK098j7LZsU3Ke0kn339xkKf7tAH214x+MfgLwUZINR1AXV6mQbOzxNMCOzYIRD7Oymvl7xX+0/4r1Pfb+FrSHSITwJpMXFxj1G4CNc+m1setfNtpp93fFvs0eUjwZJHYRxRg95JHIRB7sQKuNr3wL8LjzfH/xCsWkXn+z/AA8ratO3qpuIVe2Rv+BsPemk3scWMzLCYRc2KqxgvNpfmP1vxJ4g8SXH2rXtRub+TOQZ5WcLn+6CcKPYACtjw98O/G/ira2haNd3MbdJtnlwn/trJtT/AMermJv21PgP4H4+G3w4vNZuk4W91yeKFtw/jUAXRH0UR59q8x8U/wDBRX48a1vi8Pw6N4diPCNbWhuZlHu1y8sZP0jA9qtUpM+Qx3iTkWH0jVc3/dTf4uy/E+3tE/Zb8Z3oWTW9QstMQ9VXdcyj6hdqfk9dbe/A74N+CIhcfELxjHZ7RuJu7y206Jh9JSW/Jq/GTxR+0R8cvGRceIfHGtTxyfeghu3tYGz6wwGOP/x2vHZZZZ5GmndpJHOWdiWZie5J5JrRUO7PksZ4w01phMM35ylb8En+Z+3msfFn9hbwarRSapBq9wv8NmL6+L49HT9x/wCPDNeXav8At1fs6aGDH4M+Gc2pPHwsl5b2dmGx3Dj7TJj3K59q/JCirVGJ8vi/FXOqulLlh6K7/wDJm/yP0W1z/gop4rkDJ4Q8A+G9JQ9PtSyXjKPrGbZc/wDAce1eL+IP21f2g9eDRx63aaXC3/LKw020jx9HeKSUfg9fOuheDfF/ilxH4Y0PUtXcnAWwtJrk59MRK1e3+H/2Qf2kfEm02XgbULdW/i1BobDA9SLmSNvwxn2p8sEeR/b3FGY/w6lWV/5E1/6SkeU678U/iT4mdm17xPq14G6xyXkvl/hGGCD8BXCMzOxdyWYnJJ5JNff2g/8ABOL46antfWL/AEDR0P3llupZ5R9FhhZD/wB9ivbfD/8AwTEs12yeKfHssn96HT9OEePpLLM+f+/Yo9pBFw4K4kxr5qlGT85NL/0p3PySor93PD//AATv/Z80jadUGta4w+8Ly+ESn8LWOAgf8Cz717h4f/Za/Z58M7f7M8BaM5T7rXsH29h75ujMc+9S60T28L4S5tU1rVIRXq2/wVvxP5vrHT7/AFO4W0022mu53+7FBG0jn6KoJNejD4I/F8Wq31x4N1u0tX+7Pd2MtrCw9RJMqKR+Nf0x6Voei6Fb/ZND0+106Af8srSFIE4/2UAFaZAIIIyDwQal1+yPpMJ4PUFricS3/hSX4tv8j+aCw+A/jm7wbr7JZDuJZt7D8Ig4/Wvpn9n/AMA6D8IvHlj498Twf8JFc6dlrSBG+zxW8zDb53IfzHQE7AdoB56gEfrz4l+Dnw78VB3v9Iht7h/+XizH2eXJ7nZhWP8AvK1fOPiv9lnVbUPc+DtSS9QZItrwCKXHoJFGxj9QgrN1ZM+tyvw7yXA1Y14QcpRd05O+veysvwPoPwj8bPh94wKQWuoCxvHwBa32IXJPZWJMbn0CsT7V6zX5H+IfCniPwpdfY/EWnT2Epzt81MI+O6OMq491JFdn4L+MfjrwQUgsL43dimB9jvMywgeicho/+AkD1BrM+5P09orwHwN+0P4M8U+XZay39h6g2BtuGBt3b/Zm4A+jhfQE176rK6h0IKsMgjkEGgBaKKKACiiigAoorhfEXxQ+G3hDUI9I8V+K9F0a+l27La/1C3tpmD/dOyR1bB7HGKDOrWp0o81SSS83Y7qio4ZoriJJ4HWSKRQ6OhDKysMggjggjkEVJQaBRRRQAUUUUAFFFFABRRRQAVw3xI+Hvhv4qeC9U8C+K4BPp+pwmMkAeZDIOY5oyfuyRthlPqMHIyD3NFXTqSpyU4OzWqLp1JU5qcHZrVM/lx+LHwx8R/B/x5qngLxPHi60+T91MoIjurd+Yp489UkXn1U5U/MCB1v7Pfxw134C/ES08X6ZvuNPlxbatYBsLd2bEFgM8CRPvRt2YYPylgf2u/aw/ZmsP2g/CcMmlPBY+LdHDNpl5LlY5Y25e2nZQT5bHlWwSj8jhmB/MnwJ/wAE+vjzrviqDTfGenweHNFSYfa9Re8trkmEH5vIjgkkdnYfd3hVz94jpX7BgeJMBjsA1jpJO1pJ9fNLrfy1TP2jAcT5fj8vax8kna0k+vml1v5apn7YeI/CngP4t+Eo7DxRplrr2ianBHcRJcx7hslQMkkbcPG+1sh1KsOxFfk58f8A/gnl4g8Ni58T/BKWXXNOXdJJolwQdQhXqfIfhbhR2U7ZMYA8wmv2O0zTrTSNNtNJsE8u1soI7eBOu2OJQij8FAFXq/NcszzFZfUvhpe72ez/AOD5o/L8qz7F5dUvhpe7/K9n8u/mj+TO6tbqxuZbK9hkt7iB2jlhlUpJG6HDKysAVYHgg8g17J8E/j78QvgN4gOs+DLsPaXBH27S7nc9neKP76Agq4H3ZFIZemSpKn9sv2kP2R/A3x6sZdXtlj0PxfEn+j6tFH8txtHyx3aLjzU7B/8AWJxglQUP4H+O/A3iX4beLdS8E+LrRrPVNLmMM0Z5Vh1WRGwN0cikMjfxKQa/V8qznB5xQdKUdbe9F/mu68+nkfr2U53gs6oSpTjrb3ov813XnuvLQzvjN8X/AIjfGXxbN4l+ImqPezZb7LbISlnaQseI7eHJEajABPLMRlmY815FWzrUsbzIiHJQHdj37VjV+i4GnCnh4QhFRSWy0R/DHHmX4fA8QYrCYWpzwjLRt3eybTfXlbcfkWrG+vdMvINR024ltLq2kWWGeB2jlikQ5VkdSGVgeQQQQa/eb9g/9rvUfjHZS/C74kXIm8W6Tb+fZ37YVtTs48BvMxgG4hyNxHMiHdjKuT+B9esfAnxpqvw8+MXg/wAX6MT9psNXtspkjzIZnEM0Zx2kidkP1rxuKcio5ngZwkvfSbi+qf8Ak9n/AMMcHDmbVcDjIOD92TSkujT/AFXQ/qL8RfET4f8AhC6isfFnibR9EuZl3RQ6jf29pI6+qrK6kj3ArzjW/wBqD9nrw+pbUPiBoUm3qLO7W+b/AL5tvNOfbFfzheKfEmveL/EWoeJvE93Le6rqNw891PMSXaRjyOegXoqjhQAAAABWBX5pQ8P6PKvbVXfrZL/gn9q0PDmhyr21Z362SX3Xufvnr3/BQv8AZx0fcNPvdW1zb0+wac6bvp9ra2/WvGdc/wCCn3hKDcPDfgbUb3+6b69is/zEcdz/ADr8cqK9WjwTlcPiTl6v/Kx69HgTKofFFy9X/lY/S/Wv+Cm/xNuNw8PeEtCsQen2x7m8I/74ktwT+FeOeJ/29P2i/E1ldaadU07Tba8ikgmjs9OhOY5FKsoM4mYZBIyDmvl3RfAvjbxJt/4R3w9quq7/ALv2KymuM/Ty0bNes6N+yl+0Zru37D4A1mLd0+2Qix/P7S0WPxxXWspyXC/FCCt3a/VnWsoyPC6yhBW/ma/9uZ8+UV13jnwF4v8Ahr4kufCPjjTJdJ1a0CtJbylW+VxlWR0ZkdGHRkYqeecg1yNfQwnGcVODun1Po6dSM4qcHdPZoKKK67wj4j0bw7qCXes+GdN8SwggmDUZb2JcextLm359N24eoNE5OKulf+vOwTk4xulfy/4exyNdDoPhHxX4qm+z+GNF1HV5c42WFrLctn0xErGv1r/ZS+L37MHxD8Q23gofC/RPCXieZGayd7eG/iumiUsyR3M0YmSTaCwRsggHDlsA/qHBBBbRLBbRpFEgwqIoVVHoAOBXw2a8ZVMHVdGWHal5tfpe/wB58Fm/G1XBVXQlhmpebX36XT+8/nJ8O/sdftKeJgr2fge+tUbq2oyQ2G0epW5kjf8AAKT7V7n4f/4Js/G/Utsmu6roGjxn7ytcTXMw/wCAxw+Wf+/lfuRRXzdfjvMJ/wANRj8r/m/0PmK/iBmU/wCGox+Tf5t/kflj4Z/4JheG4Cj+MfHN9eA8tFpllHaY9hJM9xn67B9K+iPDP7Bv7NfhwK1x4fudbmTpLqd9O+frHC0MR/FK+xqK8XEcSZnW+Os/lp+Vjw8RxPmlf467+Wn5WPx4/ak/4J7a093ceM/2eY1e2lBe78MtKI3jYD5nspJGAZW6mF2BU/cJBCL+TnifwL418FXj6f4v0HUtEuIyVaO/tJbZsj08xVyPQjg1/XVRX1OS+IuMwVJUa9NVEut7S+bs7/dc/OOIuGZZtVVariJ3X8zc0vRN6eidvI/k18DfBv4qfEu9jsfAnhXVdYeQgeZBbP8AZ0z3knYLDGv+07qPevVPFfw88W/sx+N7LTLq/hi8aWlpDezT2ZEg0x7lSY44ZGGDOsZDNKo+VmAjPy72/p2r8Y/+ClHwvvNL8a6L8WbOMtp+t2yaXeMBxHe2oZo9x/6aw8KP+mTe1fQZfx3VzTGLCVIKEJJ6btvs3ppa+lj6Lw14GyvD5vCeN/eSSfLde7zdHbXXe17626mR+x/+1t8U/wDhaujfD/x7rV14k0XxHcCxVr9zPc2tzID5UkczfvCrPhWRmKgHIwRz+2dfye6Pq+o6Bq1lrujzva3+nXEV1azpw0U0LB0ce6sARX9MnwK+LOk/Gv4ZaP4800ok1zEIdQtkOfst/EAJ4j3ADfMmeSjK3evmuOMojRnDFUIJRejt36fevyP0zj3Jo0KkMZQglF6O3fp96/I9eooor4A/Owr4U/bl/Z28S/Gzwlo+veBohda94Ye4IsSwRru1ughkWNmIXzUaJWVSRuBYA7toP3XRXZl+Oq4PERxNH4onbl2Pq4LERxNH4o/8N+R/NX4Q/Za+PfjLxFF4ctfBWsac7SBJrrVLOaxtLdc8vJNMirgDnC7mYfdDGv6EPhP8ONI+Evw80P4faJhoNItljkm27TcXDEvNMw7GSRmbHOM46CvRKK9bPOJMRmSjCaUYrWy79z2M+4nxGaKMKiUYrWy793+gUUUV86fNBRRRQAUUUUAFFFFABRRRQAUUUUAeO/Hv4sW3wT+FetfEKW3F5PYpHFZ2zEhZru4cRxKxHIQM258c7VOOcV+DOu/ta/tE6/rM2tT+ONTtGlcsLayk+zWkanoiwIAhUDj5gxI+8Scmv3g/aC+E6/Gv4Ta58P0nS1u7yOOaxnkzsju7ZxLFvwCdjFdjkAkKxIBOK/nC8c+A/Fvw28S3XhHxtps2l6pZnDwyjhlP3ZI2GVkjbHyupKnsa/S+BqGBq0ZqpFOpfrZ+7ptfzvf8eh+o8A4fAVaNRVIp1b9Un7tltfzvf8ehzuoXrajdy38qRxzTuZJVhRYo97ckqiAKgJ52qAq9FAGAKVFFfpCVlZH6clZWQV/Q3+w34RHhP9m7w0zpsuNba51efjqbmUrEfxgSOv55K/qA+B174ev/AIN+CZvCs63OlJoOnwW0i/3YIEiZWHZ1ZCrg8hgQea+E4/rSjhKdNbOX5L/g/gfn/iJWlHB06a2ctfkv+D+B6pRRRX5OfkAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBQ1PS9M1vT59J1m0gv7G6Qxz211Es0MqHqro4KsPYgiuM8H/Cb4Y/D+6mv/BPhbSdEurgFZZ7GzihlZCc7d6qG2Z5252+1ehUVpGtUjFwjJ2e66Gka1SMXCMmk91fRhX5s/wDBRT4KDxR4JtPi/olvu1PwwBb6lsHzS6ZK/DHHJ+zytn2SRyeFr9Jqz9W0rT9d0u80TV4EurHUIJbW5gkGUlhmUo6MPRlJBrsyvMJ4LFQxMOj1811X3HblOYzwOLhiYdHr5rqvuP5O6/Uv/gnH8bl0vWL/AOCGvT4t9VZ9R0RnPC3SL/pEAz/z1jUSKOACj93r4Z+Pvwk1H4JfFLWPAd5ve1gk+0abcOP+PiwmJMMmehYDKPjgOrDtXmXh7XtV8La7p/iXQ52tdR0u6ivLWZeqTQMHQ++CBx3r9tzDCUc0wDhF6SV0/PdP/M/dsxwdHNsvdOL0mrxfnun/AJ/cf1eUV598KviBpvxT+HegeP8ASioh1myjneNTu8mcfLPCT6xShkPuteg1+C1acqc3TmrNaM/nyrSlTm6c1Zp2fqgoooqDMKKKKACiiigArzD4t/F3wX8FfB9x4x8a3fkwR5S2to8Nc3k5GVhgQkbmPc8KoyzEAE0vxe+LPhT4LeB73xz4umK29tiO3t0I867uXB8uCIHq7YJJ6KoLHgE1/Ox8a/jZ40+OvjGbxZ4un2ou6OwsImP2axtyciOMHqTwXc/M55PGAPqOHOHJ5jU9pU0pLd9/Jfq+h9XwzwzUzOp7SppSW77+S/V9DY+PX7QXjf4/eKW1rxLKbbTLZmGmaTC5NtZxH8vMlYY3yEZY8AKoCjwkAkgAZJ4AFaeiaJq/iTVrTQdAs5tQ1G+lWG2tbdDJLLI3RVUck1+2n7K37EOj/DAWnj34oxQar4tG2a2suJbTS26gjqstwv8Af5RD9zJAc/p2Y5ngsnwyja38sVu/66t/iz9UzLNcFkuFUbWt8MVu/wCur/U8R/ZB/Yj1KS+0z4sfGG2ezhtZI7zSdClG2aWRCGjmu1PKIpwywn5mON+F+Vv16orF1rxJ4d8OQfavEOqWWlw4z5l7cR26YH+1Iyivx7NM0xGZV/a1fkl0Xkfi2a5ticzxHta3yS2S7I2qK+edf/aw/Zy8N7v7R8faRKUzkWEjagePT7Is2fwr558bf8FIfgxocDp4N07VfE92P9X+7Fhan/ekmzKPwhNGHyPMK7tToy+6y+96BhshzGu7UqMvusvvdkfdXi3xBH4V8M6n4jlj84afbPMI92zzGUfKm7BxubAzg4z0r8/fi9+3fqPw/wDCNxqdj4dtRqVzm309Jrl5V89gcO6hEJSMfMwyM8LkZzXyH8Sv+CgvxV8f6ZdeH7PRtG0fSropvjVZri4ZUdXCtK0iLjKjOI1zXxZ8W/iDqPj6fSp7y3W2WygdCkbEoZpGy7KDyAVCDBJPB5r67IuBsVPG0ljYr2d7vXtrb57aHBxpkma5Nk9TMOTay0d+W7td/f562OF8ZeM/FHxB8SXvi7xlqU+q6vqEhkuLm4bLMeyqOAiKOFRQFUAAAAYrmKKK/e4QjCKhBWS6H8vTnKUnKTu2Ffrz/wAE6v2pdYOtW/7P/jy9e7tLmJz4aup2LSW8kKl2six6xMilocn5GXYMhlC/kNXYfD3xDfeEvHnhzxTprFbrSNVsr2HacEvBMjgfQ4wR3FePxBlFLMcDPD1FrZtPs+j/AK6Hq5HmNXBYyFWk+qTXdPdf11P6n/Hnxc+GfwwgWfx94l0/RS6F44biYfaJFHUxwLulcf7qmvkHxZ/wUe+Bmil4fDdnrXiKQZ2SQ262lu31a4dJR/36Nfip4x8X+IPHvifUfF/im7e+1TVJ2nuJnJPLHhVBJ2ogwqKOFUADgVzNfmWB4DwkIp4mTlLrbRf5/if3LgPD7BwinipOUuttF/n+J+oPif8A4KdeNbreng3wXpmnDkK+pXM18312xC1APtk/jXzv4n/bl/aV8S7418TrpED5/daZaQQY+kpR5h/38r5Gq5YadqGqXK2emW013cP92KCNpZG+iqCTX0NDh7LKCvCjH56/nc+jw/DeV4dXhRj89fzudJ4m+IXj3xo5k8X+I9V1ok5/4mF7NcgfQSOwAHYADFcfXv3hf9ln9oXxgUOj+A9XRH+7JfwjToyD3D3ZhUj3BNeR+LPDV54P8Q3vhnUbi1ubzTpTBctZzCeFJk4dBIo2uUbKsVJXIOCa76GIwzl7GhJXXRW0+49LD4nCuTo0JRuuia0+S2OdopQCSABknoK/cH9lP9jPwT4d+Htp4j+L/hu01fxNrBF4bXUovOjsLdgPKgML5TzCPnk3LkFtn8PPHnOc0MtoqrW1vokt2cOd55QyyiqtbVvRJbv/AIY/D5VZiFUEknAA5JNdlpPw4+IevFRofhfWtRL/AHRaafcT5+myNs1/T9ofg7wj4YQR+GtD03SUUYC2NpFbAD6RotdHXxdXxCf/AC7offL/AIH6nw9XxHe1Kh98v0t+p/Oz4M/Yn/aO8ZujJ4WfRLZ8ZuNalSyCZ9YmJuPyiNfa3w4/4Jm6JZvFe/FXxRLqLLgtYaMnkQkjs1zKGkdT32xxn0Pp+qFFeHjONMxrrlg1BeS1+93/AAseDjeOczrpxg1BeS1+93/Cx5d8O/gp8KvhQhHw/wDDNjpEzxiJ7qNDJdyIP4XuJC8zAkZIL4zzXwh+3h+xzqHxO3/GH4W2f2jxPbQqmr6ZEP3mpwQrhJYR/FcxIApTrKgAX51Af9QKK8rLM8xmCxixtOV59b63XZ+R8HnODjmlKVLGNyv1bu0+92fx43NtcWdxLaXcTwTwO0csUilHR1OGVlOCCCMEHkGoa/qg+J37NPwO+MM7X3j7wlZX1+wAN/DvtL04GBunt2jkcL2DlgPSvxd/bN+Dfwm+CPj/AEnwV8N9MubeR9NGpXtxd3UlySZ5ZI44kDnChREWJwSdw5GDn9syXj7C5hNUFTkqlr9GtPO9/wAD4LKfCjH5hj44SjVioPeTvdJbu3V9lffqtz5Z+D/wV+IHxw8XW/hDwJpz3EzlWurqQFbWygJwZp5MYRB2HLMeFBbAr+mX4J/CPw78DvhvpPw68N5khsEL3N0yhZLu7l+aadxzgu3QZO1Aqg4UV/NR4P8AGPibwD4hs/FXhDUJ9M1SxkEkM8DFTwclWHR0boyMCrDggiv6c/hf40g+I3w68N+OoPLH9uaZa3siRNvSKaWMGWIHJ5jk3IR1BUg818j4j4zF1Y0k2lRvsv5vP5bfM/TKvhhS4XtXhV9o56czVrdbWu9+9+n393RRRX5SZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVk69omm+JdD1Dw7rEQnsNUtZrO6iPR4bhDG6/irEVrUU02ndDjJp3W5/LF8SvA2p/DTx9r3gPVwftOiX0tqXIx5sanMUoH92WMq6+zCtH4QfEXUPhP8SvD/xA07czaReJLNGpwZrZ8pcRf9tImZfYnNfof/wUp+EjW2p6H8ZtKg/dXiro+rlR0mjBe1lb/fQPGSeBsQdTX5UV+95VjIZlgI1J68ytJeez/rsf0LlONp5nl0ak9eZWkvPZ/wBdj+sTSdV0/XdKs9b0mZbmx1C3iuraZOVkhmUOjj2ZSCK0K/Oz/gnd8Yv+Ew+G918MNXn36p4QYG03n5pNMuGJTGeT5Em5D2VGjFfonX4lmeAng8VPDT6P710fzR+E5rl88Fi54af2X966P5oKKKK4DzwooooAKKKKACiiigAooooAKKKKACiiigAr4W/al8M/YvEmneKYExHqcBgmIH/La3xgn3aNgB/uV9014z8e/Df/AAkfw11Expun0zbqEXt5OfM/8hM/40AeDfsr+Jvs2tar4Tnb5L2FbuAHp5sPyuB7sjA/RK+36/KD4feIz4T8aaP4gLbY7W6TziP+eEnyS/8AjjNX6vAhgCDkHkEUALRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//S/fyiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiivFfjJ8f/hr8DNI/tDxtqIF5Mha00u2xLfXWOPkjyNqZ4LuVQHjdnAppX0Rz4rF0cNSdfESUYrdt2R7UTjk18R/HP9ub4X/Cv7RofhVl8X+I48oYLOQCxt3HH7+5GVJU9UjDtkYYp1r80vjv+2b8UfjM1xo1jM3hnwxJlP7NsZD5k8Z/5+pwFaXI6oAsfqpIzXyBW8KPWR+L8SeKzd6GTx/7fa/9Jj+r+49s+L37QnxT+N1+Z/G+rO1ij77fS7XMFhB6bYgTuYdnkLv23YrxOvb/AIP/ALPPxT+N98IfBWlN9gR9lxqt3mCwg9cykHew7pGHfvtxzX6+fBH9hb4V/C77PrXipF8YeIY8P517GBYwOOf3Nqdykg9HlLnIyoTpWkpxjofFZRwpnPEFX6zO/K95zv8Ah1fy080flz8E/wBkP4ufGowalZ2X9heHpCCdX1JWjjdD3t4uJJzjoVAjzwXFfsH8Ef2SfhL8Elh1HTrL+2vEKAFtY1FVkmR+5t4/uQDrgqC+Dgu1fT4AUBVGAOAB0Apa55VWz9v4d4CyzKrVOX2lX+aXT0Wy/F+YUUUVmfbBRRRQAUV5X4y+OPwg+H29PGHi/SdOnjzutmuUkuhjr/o8ZaY/gtfKvi//AIKMfA/Q98Phm01jxJMM7HhtxaWzfV7hllH/AH6NUoSeyPFx/EeV4K6xVeMX2vr9y1/A+/qK/GDxb/wUu+I+ob4vBnhfSdFjbID3skuoTKPUFTboD9UYV8x+LP2tv2ifGW9NS8bahaQvkeVphTTlCn+HNqsbkf7zGtFRl1Pjcd4q5NR0oKVR+Ssv/JrP8D+gfxv8RPBfw50o6x4z1e10u3wfLE8iq8pHaNSQWPrjgdSQK/N34s/tzaNq5m0vw5qT2On8qVsg0l1Ov+1MuEQH+6jexYivya1DUtQ1a6e+1S6mvLmTl5riRpZG+rMST+JqlVqgup8djvF/GTusJQjH/E3L8uX9T6i1b9omHLLomku5PSS7kC/mibs/9915jq3xl8e6rlUvUsY2/gtIwn/jzbnH/fVeXRxSTSLFCjO7kKqqMsxPQADkk17x4M/Zf+Pnjzy30DwVqawSYK3F9GNPgKn+JXujEGH+7u9qvkgj5WrxVxJmsvZ06k35QVv/AElX+88V1DV9V1ZlfVL24vGXO0zytKRnrjcTjNZ1fpb4N/4JpfELUdk3jnxRpmiRtgmKxik1CcD0bd9njB9wzj69K+qvB/8AwTv+Avh/ZL4ibVvE0wwWW7uvs0BI9EtRE4HsZGodWKOnB+HWf4x89Sny36zf6K8vwPwor07wp8Fvi344CP4T8H6zqUMmNtxFZSi356ZmZREPxav6LfCXwX+EvgXY3hLwho2myx423EVnGbjjpmZlaU/ixr02s3X7I+wwPg91xmJ+UV+rf6H4L+Fv+CfP7Q2v7H1a10vw7G3JOo3yyOB/u2i3HPsSPfFfRnhj/gmNarsl8Z+OZH/vwaZYhMfSaaRs/wDfoV+rlFQ60j67B+GWRUPjg5v+9J/lGyPijw1/wT//AGctB2NqGnalr7pj5tSv5FBPqVtRbqfoRivoDw38Cfgz4R2Hw74J0K0kT7swsIZJ+P8Apq6tIf8AvqvV6Khzb3Z9ThMgyzC/7vh4RfdRV/v3GJGkSLHEoRFGFVRgADsAKfRRUnrhRRRQAUUUUAFFFFABRRRQBS1DTtP1a0ex1S2hu7aQYeKdFkRvqrAg181+Nv2ZPDuqiS98HXB0m6OSLeXMtqx9AeXjz7bgOy19Q0UAflB4u8A+LPA919n8R2ElurNiOdfngk/3JF+UnHODhh3ArpfAXxi8ZeAnS3srj7bpqn5rG6JeID/pmfvRn/d4zyQa/S++sLLU7SSw1G3iuraZdskUyB0cehVgQa+VPiF+zLZXgk1PwBKLSbljp87Ewsf+mchyUPoGyvuooA9e+H3xj8I/EBVtrSX7DqeMtY3JAcnv5bcCQfT5sclRXrFfkNq2j614Y1NtP1e2m0++t2B2SAo6kHhlPccZDAkHqDX0d8NP2j9U0bytI8deZqNkMKl6vzXUQ/2/+eqj14f3bgUAfddFZmj6zpXiDT4tV0W6jvLScZSWJtyn1B7gjuDgg8EVp0AZOvXN/ZaFqN5pUX2i9gtJ5baLGfMmRGKLjvuYAV/LHrutav4j1m917X7qW91K/ne4uric7pJJXJLMxPfPbt0r+rKvxa/bO/ZL8UaJ42u/iR8MNEudU0HXHa5vrTToWmlsL1jmU+TGC/kyn5wyghGLA7V253oySdmflHirk2LxeFpYnD3lGm3zRXnb3reVrP19Tlf2Tf2yr74P+T4C+ITT6h4OkfFvOuZLjSmY8lF6vbk8tGOV5ZMnKt+2nh3xJoHi7RrXxF4Y1C31PTb1BJBdWsgkjdfYjuDwQeQeCAa/lWlilgleCdGjkjYo6OCrKynBBB5BB6ivV/hV8cvid8F9SN/4B1mWzilYNcWMv76yuMcfvIGypOON67XA6MKudK+qPiuEfEatlsFg8enOktn9qPlruvLp3tof020V+ZHwz/4KTeD9TjhsfiroNxot1wr32l5u7MnuzRMRNGPZTMfevunwL8aPhT8S0RvA/inTNVlcZFtHOEugP9q3k2TL+KCueUJLc/bsq4nyvMUvqlZNvpe0vudmenUUUVJ7wUUUUAFFch4q+IHgbwNB9p8ZeINM0SMjcDf3cVuWH+yrsCx9AASa+VvGP7f37PPhjfFpd/qHiWdMjZpdmwTd/wBdLkwIR7oW9s1Si3seXj87y/Bf73WjHybV/u3PtiivyB8W/wDBTbxFPvi8C+C7OzAyEn1a6kuifcxQiAL9PMavmDxb+2t+0f4u3xP4qfSLd8/udIgjs9ufSVVM/wD5Eq1Rk9z4zHeKWSULqi5VH5Ky++Vv1P6DNU1jSdDs31DWr220+1j+9PdSpDEv1dyqj86+bPGf7Z37OngsPHP4sh1e5TOINGR78tj0ljHkfnIK/n01vxH4h8TXZ1DxJql7qt0c5nvriS5lOf8AbkZm/WsatFQXVnxmP8X8VK6wdBR85Ny/BW/U/Wrxt/wU1t1Mlv8ADnwY79dl1rVwF+mba33Z/wC/4r5R8W/tzftH+Ki6ReIYtDt3zmHSLWKDH0lkEk4x/wBdK+SIIJrmZLe3jaWWQhURAWZmPQADkk1794P/AGVf2gvHASXRfBOpRQSYIn1BV06Iqf4gbpotw/3Qc9qvkgj5KrxRxJmsnClUnLygmv8A0lX+84mP41fGCPWY/EI8beIG1GJg63D6lcO+R2O6Qgr2KnKkcEYq/wDtQeJPEvxEvfBnxh16Ly5/FWgC2uZo12RTX2j3E1pOVA4G6NYZGA4UyYHAFfZPg3/gmr8S9Rnhm8ceItJ0a1LAyx2Xm31yF7jBSGIMegIdgOuD0r7b+NP7H/g34gfAHT/g94WC6bdeFozN4evJzuK3XJl89lGSl0WYylRwxDhTtC19FwrnNHLsyhWq/A7p+SfX5O3yPq+G+FeIHRrzxHNFWuk3q5X7XutLq7tv9382dFdd458B+L/hr4lu/CHjjS7jSNWsm2y29wuMrkgOjDKyRtjKupKsOQTXI1/R9OpGcVODuns0eDOEoScZqzQV7Z+zj4JvfiH8dfBHhWyjaQXGs2s9xtGdlpaOJ7hv+AwxsfrXj1jYXup3SWOnQSXNxJnZFEpdztBYnA7AAknsBk8V9Jfs/wDxm1/9njXrrxT4V0rSdR1W8gFq0+qRTSmG3LBnjh8qaLZ5hA3sdxIUAYG4N5uc4icMLUjQ1qNOy83pc+44F4HzHiHF/wCyQ/dwacpN2S8vNu3T1Z+zvxO/YJ+CXxI8SXfiuN9T8O31/K090mlSxLbSzOcvJ5U0UmxmPJ2FVJyduTmuW0j/AIJufAawcSajqHiPUyOqTXcEUZ/CG2R//H69W/Zn/ar8KftC6bNYtCui+KbBA93pbSbxJFwPPtmIUvHk4YEboyQDkFWb6tr+dcRmub4OTwlWpKLj0v8Aqf0Nic3znBSeDrVZRcdLX/Xt21PmHQP2NP2afDu1rXwRaXTr1e/muL3cfUrPK6fgFAr2jQ/hp8OfDG3/AIRvwtoulbPumy0+3tyPxjRTXbUV5NbH4mt/FqSfq2zx6+YYqt/GqSl6tsKKKK5DjPHvi/8AAj4afHHSE0vx7pgnltwwtL+3byb21LdfKlAPB6lGDITyVJAr83vGH/BMTxBFcSSeAPGdncwMSY4dYt5Ld0HYNLAJg/1Ea/Sv2Cor2cuz/HYKPJQqe72eq/Hb5Ht5bxFmGBjyYep7vZ6r8dvlY/BLVv8Agnh+0ZpysbO30bVCucC01AKW+n2hIR+eK+dvG/wC+M/w4V5vGXg/VbC3j+/dCA3Fqv1uIDJD/wCP1/TtRX0GH49xsX+9hGS+af6/kfR4bxCx0H++hGS+af6/kfz0fsYfCXxl47+NXhrxLpVlcRaJ4b1CLUr/AFIoy26C2O9YQ+MNJKwCbASdpLEYBNf0L0gAUYUYA7Clrwc+zueZ11VlHlSVkt/x0Pn+IM9nmmIVaUeVJWS3/HQKKKK8Q8EKKKKACiiigAry74z/AAv0j4yfDbWvh/q5WMajATbXBGTbXcfzwTDv8jgbgPvLlehr1GitKNWdKaqU3Zp3RrRrTpVI1abtJO69UfymeLfCuu+B/EupeEfE1s1nqmk3D2tzC38LoeoP8SsMMrDhlII4NfQn7K/7SGrfs/eNfNuvMu/CurskesWS8soHC3MIPAliyeOjrlTztZf06/bS/ZWT4xaG3j/wPbKPGekQYeFBj+1bWMZ8k+s8Y/1TfxD92eCpX8JJoZbeV4J0aOWNijo4KsrKcEEHkEHgg1+25bj8NnWBcai30kuz/rVP9UfuuWZhhc9wDhUW6tKPZ91+af6o/oDvv2+f2a7LXBo41u8uItwVr+Cwme0UnryVErAeqxsPTNfVnhbxZ4Z8baLB4i8I6pa6vptyMx3NpKssZI6qSD8rL0ZThlPBANfymV6n8KvjP8Rfgvro13wBq0lkzlftNo/7yzu1X+GeEna/GQG4dcnaynmvAx3AVF0v9km1Jfzap/clb8T57H+HlB0v9jm1Nfzap/ctPx9D+oGivhT4Fft4fDH4nRW+i+OZIvB3iJgEK3UmNOuX6ZhuGwEJP8Eu0jICs55r7ojkSVFliYOjgMrKcgg8ggjqDX5zjcvxGEqeyxEHF/1s9mfmeOy7E4Op7LEwcX+fo9mPooorjOIKKKKACiiigAooooAKKKKACiiigAooooAK8U+N3wD+H/x58Of2J4xtSl3bhjYanbgLeWbt1KMR8yMQN8bZVvZgGHtdFbUK9SjUVWlK0ls0bYfEVKFRVaMrSWzR/Ot8av2PfjD8G7ue5fTZfEXh9CTHq+lxNKgj7G4hXdJAQOu7KZ4DtXypX9alcVrfw1+HfiWSWbxF4X0fUpZgyvJd2EE0jBhg/O6Fuh65zX32C4/qRgo4qnzPunb8LH6JgfESpGCji6XM+6dvwt/XY/lgr9G/2Cf2kl8AeIh8IvGN1s8Pa/cBtNnlb5LHUZMLsJP3YrjgHssmDwGc14B+1R+z7qHwC+IkunWySS+GdXL3WiXTZbMORvt3bvLASFP95Sr8bsD5jBxyK+6r0cNm2Bte8Jq6fZ9/Vf8AAPvsRQwucYDlveE1dPs+/qn/AJH9alFfA37D/wC02vxZ8Lr8O/GN1u8X6Bbjy5pW+bUrGPCrLk8tNFkLL3YYfkltv3zX4dmGAq4OvLD1lqvx816n4LmOX1sFiJYastV+PmvUKKKK4jhCiiigAooooAKK+Qfit+278EfhL4luPCOpy6jrWqWTiO8i0eCOZLaT+JHklmhQuv8AEqlip+U4IIHvnwx+KXgn4weFYfGPgO/F9YSuYpAymOaCZAC0U0Z5R1BBx0IIZSVIJ7a2XYqlSVepTag9nbQ7q2WYujRjiKtNqD2bWh6FRRRXEcIUUUUAFFFFABRRRQB+c3/BRb4Q3Hi74d6d8S9EtGn1DwnK6XvlLlzplx99zjkiCUK3oqu7HABr8RK/rRkjjljaKVQ6OCrKwyGB4IIPUGvyA/bW8Qfs+/APUYtH+HngTQpfiJqcf2szvbiSz0mBydsxtCTbNO5BMSGPaoG9gRtVv0rgvO6slHLI03J62a6Ld3vsl3+Vu/2OWeIWGyPLpRx8W4x2tvr018/60PZf2TvH/gr9n39m/SpvjL4jsvDj61eXerafZXsv+mNYzbVjeO2QNMyOUMgKoRhwe9eqWn7fv7KN5eCzXxm0W47Vlm0vUEiJ/wB423yj3bAr+crXNd1rxNq1zrviG+uNS1G8cyXF1dSNLNI57szEk+g9BwKya+wqeHGDxFSdfFVJc8m37tklf1Tfz6n8/wCceJ2MxeMqYmlSjFSbdndv700f12eEfG3g/wAfaSmveCdasdc09ztFxYXCXEYbqVYoTtYd1bBHcV1FfyafCv4uePvgz4pg8XfD/VJdPvIiomiyWtruIHJiuIs7ZIz6HkHlSrAEf0s/s8fHLQP2gvhnY+PdGQWtyWNrqdju3NZ30QBkjz3Uhg8bd0YZAOQPzrirg2tlFq0Jc9J6X6p9n+j/ACPpOHeKaWZ3pTXLUXTo13X+R7lRTJJI4UaWVgiICWZjgADqST0FeP8Aij9oT4H+Dd48ReONDt5Y/vQR3kdxcDHrDCXk/wDHa+PpUKtV8tKLb8lc+xo4erVfLSi5PyVz2OivgTxh/wAFGPgPoIePw1Fq/iaYZ2NbWv2W3JH957oxyAfSJq+SfG3/AAUs+KOr+ZB4G8P6V4ehbIWW5L6jdL6EMfKhB9jE1e9hOE8zr7U+Vf3tPw3/AAPoMJwhmuI2pcq/vafhv+Byv/BQ34k6n4p+Nr+BPNZdK8IW0EMcIPyNd3kSXE0pHdtrpH7bOOpz8DV1Xjfxt4n+Ivii+8Z+Mrz+0NY1Jka5ufKjh3mNFjT5IlRBhEUcKOlcrX7FleDWEwlPD9YpXt36/ez9qyrBLB4OnhusUk7d+r+bufoZ+zl8ff2cf2dvB8OuDRdW8RfELUYnF9crDEkNohYhbeGWV1KIVCl2RHLseTgKB0fjH/gpl8RtS3w+CPC+laJG2QJb2SXUJwPVdv2eMH6owr80KK4Z8N4GrWeIrxc5P+Zt/K21jz6nDOAq15YnERc5P+Zt/K21l6H0L4v/AGrf2hvGxddY8canBC+QYdOddOj2/wB0i1EW4f7xOe+a8EvL291G4e81C4luZ5Dl5ZnMjsfdmJJrd8NeCfGPjO5+yeENC1LW5gcFNPtJbpgfcRq2Pxr6X8LfsLftJ+JwksnhuPRYHxiXVbuGDH1iRpJh+MddUq2X4FWbhD7l+B1yrZdgFytwp+Wi/A+QaK/Tzw//AMExPHlyFPinxppOn5+8LC2nvsfTzDa5r3Twz/wTP+FWnukvirxLrWssmCY7cQ2ML+zDbM+PpID715lfi/Kqf/Ly78k/8rfieXX4zyml/wAvOZ+Sf+VvxPxPqTxH4Y8QWnhq18TXFjLHpdxc/Z0uWXCmUoWUc9nVWKnodrY6V+u/xc+D/wAKfht4xg8PeBvD1pYw2VjCZHfdczvNIzuXaWYvJkqV4zgY4Ar1z4V/Bnwr8Y/hN4y8KeMIC9jrM0FtHMgHnWs9qhkjmhJzh42lB9CMq2VJB8eHiHRhiYOFJ8l9W97eSXXrv5H5tx1xU84ymvlmChy86td+TTtbW17WvfQ/noor6K+PX7MHxR/Z+1qa28UafJeaGZCtnrtpGzWNwhPy7mGfJlPeKQg5ztLLhj861+xYXF0cTSVbDyUovZo/jzE4arh6jpVouMl0YV7v+zN8N774rfHPwh4RtYmkgbUoby/YDiOxs2E1wzHoMohVc9XZR1IFeL2GmXeovtt1wgYK8rnbEhIJG5jwOASB1OOATX7s/wDBPzwv8APBmj3cHg/xbY+JvH+qQBtUkEU1pJFboQ32e0iuo4ZXgRsGSRV+dsFgoCqvgcV52svwE5QTc2rKyva/Vvol57n1PDHCeOzGaxSpS9hF+9Kz5dOl9r/kYfxh/wCCcNp4n8VXniX4X+ILbRLbUZmnl0q+gdoYJJCWfyJYiSIyT8sZT5egbGANPwJ/wTP8A6fbxz/ETxLqOsXY5eDTQllaj/ZJdZZXHuDGfYV+mtFfg/8ArTmnslRVV2Xkr/fa5/R/+tua+xVFVXZdbK/32ufNnhf9kH9nDwlsaw8D6fdyLjMmp+ZqJY+pW6eVB+Cge1e+6PoGheHrb7HoGm2mmQcfurOBIE46fLGqitaivHr4yvXd603L1bZ4uIxuIru9eo5erb/M/PH9uX9qBvhlobfC3wNd7PFWtQZvbmFvn02xkGOCPuzzjhO6Jl+CUNfhzXofxbv/ABJqnxQ8V3vjAyHWZNYvReiXO5JUlZSmD0VANqjoFAA4Fe4/sj/s4Xfx88debq6SReEdDaObVp1ypnYnKWkbDnfLj5yPuJk5DFM/s+WYXC5Nl3tJvpeT7vy/JI/cMrwmFyTLfaTfS8n3fl+SX+Z9D/sIfstHxRqFt8bPH9p/xJ7CXfoVnMvF5cxH/j6cHrDCw/dj+OQZ+6uG/Zmqen6fY6TYW2l6Zbx2tnZxJBbwQqEjiijAVERRwFVQAAOgq5X5LnObVcwxDr1Nui7L+tz8dzvOKuZYl16mi2S7L+twoooryTyAooooAKKKKACvy0/4KO/BPUdc07TPjXoMTTnRbcaZrEaDLLaGRngnAH8MckjLIeoDqeikj9S6p6jp9jq+n3OlanBHdWd5C9vcQSqGjlilUq6Mp4KspII7ivSynMZ4HFRxMOm67rqj08nzOeAxcMVDW267rqj+Tev05/4J2fHafQvE83wS8Q3BOm62ZLrRjI3EF+i7pIVz0WdFLAdPMXgZc18c/tHfCOX4J/FzWvBCBzpyuLzSpH5Mlhc5aLJP3jHzEx7ujGvI/D+uan4Y13TvEmizG31DSrqG8tZR1SaBw6N+DAV+147DUc0wDgtVNXT7PdP/AD+4/dMfhaGbZe4LVTV0+z3T/wA/uP6vaK4v4deNdO+I3gTQfHWlYFtrdhBeKmcmNpFBeMn+9G+5G9wa7SvwWpCUJOElZrQ/nupTlCbhNWa0YUV8n/tC/tefD/8AZ+vbfw/f2tzrviC5iFx/Z9myxiCFiQrzytkJvwdqhWYgZIAIJs/s8/tZ+AP2hJbvSNKt7jRdfsovtEum3jK5kgBCmSCVOJFUsAwKqwJHGOa73lGM+rfW/Zv2ff8ArW3nseg8mxv1X657N+z7/r3t57H1PRRRXmnmBRRRQAUUUUAFFFFABRRRQAUUUUAFFFZWua1pnhvRb/xDrU622n6ZbTXl1M33Y4YELux+igmmk27LccYtuy3Pjb9u/wCKngfwd8G9Q8DeILddT1fxbC0GnWIba0JiZWF455KrBIFK8ZdwFHG4r+BNetfHD4sax8aviXq/j3Vi6x3cpisbZjkWtjESIIR2yq8sRwzlm715LX7rw5lH9n4NUpP3nq/XsvT8T9+4Zyb+zsEqUn7z1fr2Xp+J1Hg7xr4s+H+uw+JvBWq3Oj6nbghLi1coxU4yjD7ro2BuRgVPcGv1R+Bf/BRu0u2t/D3x0s1tZDhF17T4iYiembm2XLJ7vDkZ/wCWajmvyGorpzPJcJj42xEdejW6+f6PQ6s1yPB5hDlxENejWjXz/R6H9Xug+IND8U6Tb694b1C21TTrtN8F1aSrNDIv+y6kg46H0PBrXr+Yf4T/ABy+JnwU1b+0/AOsSWkUjhrmwl/e2N1jtLATtJxxvXa4H3WFfs5+zr+234D+M8tv4X8TInhjxZLhI7aWTNnev0/0aVsYcnpE/wA3OFL8kfluc8I4rBJ1aXv0+63Xqv1X4H5NnfBuLwKdWl79Put16r9V+B9uUUUV8kfHhRRRQAUUUUAFFFFABRRRQAUUUUAFQ3FvDd28trcKHimRo3U9GVhgg/UGpqKAPyL8TaLN4b8RaloM+S9hdS2+T/EEYhW/4EMEfWv0q+EPiL/hJ/h1ouou26aO3FrPnr5lt+7JPuwUN+NfIv7TXh7+y/H0esRLiLWLVJSexmh/dOP++Qh/GvRv2UvEG+y1vwvK3MUkd9CvqJB5cn4Aqn50AfX1FFFABRRRQAU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rQpGiQn/WXFpNE6gDuRE0p+gNft/XxPG9eu8w9lUfupJxXrv+J8Lx5XryzH2NSXuJJxXqtfxQUUUV8afEhRRRQAUUUUAFFFeZfFL4jr8M9FtdYfTzqIuboW2wTeTtJRnznY+fu4xigD02ivnr4b/HuH4heJk8OLorWBeGSYTG6Ev+rwcbfKTrnrmvoWgAooooAKKKKACvwS/by+DmveB/jHqfjyGzkbw74sdLyG7RCYorwoBcQyMOFkZ1Mig43K3GSrY/e2uP8e+IfBHhfwpf6z8Rruws/D8Mf+mSalsNuVPRCrgh2Y8KgBZjwATxXucP5tVwGKVSnHmvo139PO57WRZ68pxDxNrxtZq9tN9+lj+YvwH4D8VfErxRY+DvBthJqGp38gRI0B2oufmkkbGEjQcs54Ar+oHwdoA8KeEdE8LLKZxo+m2mniU8GT7LEsW4/wC9tzX5Ya9/wUd+CPw6urnTfgl8OFu7dnxJdIkGhW8xHR1jiglkdfTzFjb2FJ4W/wCCrWhXF6kPjX4f3VjakjdcaZqKXkij/rjLDbg4/wCutfa8QZVnuawhOOFcYR1s3Hmd/K6fyseXxP4pZVmdWFFTUYxv3d2/O1v63P1zory74U/Gb4b/ABr8P/8ACR/DnWYdUt4yq3EPMdzayMOEnhcB4ycHBI2tglSRzXqNfmdahUozdKrFxkt09GjlpVYVYKpTaaezWwUUUVkaBRRRQAV4x8d/jZ4Z+A/gK58ZeIP39wx8jTbBWCy3t2wJWNTztQY3SPg7VB4JIB6P4rfEzw78IPAWq/EDxOXNlpkYIiiwZZ5pGCRQxg8bncgZPAGWPANfzsfHL47eNfj14ubxN4rlEVtBvj03TYmJtrGBiDtTONztgGSQ/M5A6KFVfqOGuHZ5jV9pU0pRevn5L9ex9Zwvw3PMqvtKmlKL1ffyX69kcz8T/ij4y+L/AIuu/Gfja9a7vbk7Y4xkQW0IJKQwISQkaZ4HJJyzEsST55Wlo+j6t4g1S10TQrOfUNQvZFht7a2jaWaWRuioigkk+1fsT+zR+wTo/hNLbx18cYoNS1ZAJrfQyVksrMjkNcn7s8g/ucxL338EfqeY5rg8roJT07RW79F28z9ZzPN8FlNBKen8sVu/RdvM+R/2af2KfGPxma28V+MPO8O+DWIdZmXbeagnXFsjD5YyP+Wzjb/cD84/cDwJ4A8IfDPw3beEvBGmQ6VplqPlihHzO5AzJI5y0kjY+Z2JY+tY+tfF/wCEfhUGLXvGXh/TDGMCKfUraJwB2EZkDHHoBXkOtftrfsy6GzRz+NYLmRei2Vpd3Qb6PFA0f/j1flmaY7NM2nf2cuTpFJtflq/P8j8lzbMM1zid/Zy5OkUm1+Wr8/uPqeivz+1j/gpD8BdP8xdNsfEWqOoOww2cMUbHtkzXCMAfXYT7V4Trf/BUHUXdk8N+AIIkB+WS+1JpSw9THHBHt+m8/WuehwtmlX4aLXrZfmzmocJ5tV+Gi162X5tH671+H3/BRn9pbVtc8WT/AAF8I3jwaJo3lnXXhbab2+YBxAxHWK3BXK9DLncPkWq+sf8ABSb44XzAaXpXh/TY+fuW880mD/tSTlcj/c/Cvzc8Z6rqviHxRqnifW5TcXutXtxqFxKcAvNcyNJIcAAD5mPTivveCeEqmHx31jHxXur3Ve+vf5Lb7+h8T4l5BnGW5Sqzham3aTTTsuifk3pfbo9zl6KKK/Yz+dzW0HXtZ8L6zZ+IvDt7Np2p6fMlxa3Vu5jlilQ5VlYdD/PoeK/cbwf/AMFBl1X4JnxddaALvxNpdqbfUV84Q2zXygKkiqqs3lykq5X5cZZQ3y5P4R19l/sZ/DfUfjL4n8V/DK0uobSC90Nr6aScthfs88UKsgVWy6tcAgHAOOTXxPHOU4bEYH61WWtNp+fLdcy+7X1R9xwBmaw2bU6FbWlNpSX6+vT5nHeLvjv8YPG+tz6/r/i7V3uZnLBILuW3ghBP3YoY2WONR2Cge/PNefap4j8Q65g61ql5qGOR9quJJv8A0NjX1J4w/Ya/aM8LatPYWXhz+37RGPk32mXELxSr2Ox3SVD6hkHPQkc1k6Z+xT+05qkgSLwTPAvd7m8s4APf95OCfwBPtXDRzHK4QUqdSCXTWKP7qo5llMIKVKpBLprFHyxRX6E+Hv8Agm38cdTKya7qeg6NGfvK9xLczD6LFCYz/wB/BXvPhz/gmD4fh2v4u8dXl3n70Wm2Mdrj2Eksk+frsH0rmr8VZXS3qp+ib/LQ5cRxdlNHesn6Jv8AJWPx9or9tPGv7K/7Hn7Png248bfEO01DVoLb5II77UJDcXlyQSkMUVsbZXdsHgjaACzYUEj8gfiB4s0/xj4ludW0bQrDw1pudlnpmnxhUggB+UPIRvmk7tI5yT0CrhR0ZXnVPMLyw8Jcq+07JX7LW7OnKc8pZjeWHhLkX2mklfstbs4mum8HeD/Efj/xNp/g/wAJWT6hq2qTCG2gTA3MeSWY4VUVQWZmICqCSQBVPw54c13xdrll4a8M2U2o6pqMqwW1rAu6SR27DsAByScBQCSQATX78/sofsr6P8AdAOsa0Ir/AMaapCFvrxfmjtYjhvstuT/CCBvfgyMP7oUDLP8APaWW0eZ6zey/V+RjxDxBRyyhzPWo/hX6vyX/AAD4Z8Lf8Ey/iXf7H8X+KtH0dG5K2cc2oSqPcMLZM/RyPevpDwr/AME1vg9peyXxTret67Kv3kR4rK3b6oiPKPwlr9F6K/LcRxbmlbT2ll5JL8d/xPyfE8Y5tW09ryrySX42v+J88+Fv2UP2dvCGw6T4E0qZ05Emoo2pPn1zdtNg/TGO2K9407S9N0i1Wy0m0gsrdPuw28axRj6KgAH5Veorw6+Lr1netNy9W3+Z4FfF167vWm5erb/MK8K/aK+BWgftCfDS98Caw4tLsMLvS7/buazvowQj4/iRgxSRe6McYbBHutFLC4mrh6sa9F2lF3TOHEUKdenKjVV4tWaP5NPin8I/H3wZ8UT+EviDpUunXkZYwykFra7iBwJbeXAWSM+o5HRgrAgebV/Xd4s8F+EfHmkvoXjTRrHXNPc5Ntf26XEYbsyhwdrDswwR2NfMdz+wR+yhdXhvX8DhGY7mji1PUY4if9xboAD2XAr9jy/xRwzpJY2lJS/u2af3tW/E/L8b4e11UbwlROP966a+5O/4H85Hhzw14g8X61a+HPC2nXOq6peuI7e0tI2lmkY+iqCcAck9AOTgV/Qx+xV+ykv7PXhW417xWIpvG2vxoL1oyJEsLYHctpG4yGO7DTMvyswAGVQM3wn+0V8ZLP4K+OdT+EX7NmmWPgXT9H2W2qanpcCLqV/c7Q7xtduGmEUJIXG7cXViTjArX/ZT/bO+Jtl8RtH8DfErVp/Eeh+IbyHT0mvSHurK5uXEcUizY3vGXIV1ckAHcpBBDPifF5lmuXXw0VCk1zNN+9Jb+i72u/XofqHDXg5i8FhVmtaSlO11HXRW383b+rn7bUUUV+KGwUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfK37Vlj5nhfRdSx/x737wZ9PPiZv8A2lXjv7Mt99k+JX2fP/H7p9xDj1KlJf8A2nX0h+0lZ/avhfcz4z9ju7Wb6Zbyv/alfIXwOvPsPxV8Pyk4DzSwn386F4x+rUAfpxRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//9H9/KKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCKaaK2hkuLhxHFEpd3Y4VVUZJJ7ACvyx+JfjObx34xv8AX2LfZ2bybRG/gto8hBjsW5Yj+8xr7J/aQ8a/8I94OXw7Zybb3XS0TYPK2qYMp/4HkJ7gt6V8b/DLwbJ478Z2Gg4P2Yt514w/htosF+exbhAf7zCgD7E/Zw8Cf8I74UbxPfR7b7XArpkcpaL/AKsf9tD859Rt9K+jqjiiigiSCBBHHGoRFUYCqowAB2AFSUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB80/tFfDUeJND/4S/SYs6lpMZ89VHM9oMlvq0XLD/Z3Dnivza8VfGXXPgzaHV/Cd0LfW9Qhms7ckBgqSLh5Cp4Plkqy5yN4UkEAiv26IDAqwyDwQa/nl/bT0iHw5+0Brvh2xVo9PsorVrSM/dRLmFbhgvsryMo9lA7VpSjeWp8Xx7ndXLMonVoaTk1FPte939ydvOx8s3Nzc3tzLeXkrz3E7tLLLIxd5HckszMcksxOSTyTX2z+x7+yrc/GvWx4x8YwyQeCtKmw45RtTuEwfs8Z4IiX/lq45/gU7iWT4w0caS2rWS689xHphuIheNaKr3C2+4eYYldlQyBM7QzAE4yQK/dvwV+2H+yT4U8Eado3h7XW0fT9Ltkt7fTG0688+NVHQ7IXRnY5LPvbcxJLZJNdFRtKyPxDgbLMtxWMdfNa0YwhryyaXM/n0XXvttc+0rKys9Ns4NO06CO2tbWNIYIIVCRxRxgKqIqgBVUAAAcAVar85vFH/BSj4TaaXi8LeH9a1qRejzCGxgb6MXlk/OMV89+I/wDgpj8SLwuvhXwno2lq3Cm9knvnUfVGtlz9Vx7VzqlJn7bivELIMN7vtuZrpFN/ja34n7O0V/Pdr/7cn7SuvbkTxQmmRN/yz0+ytosfSRo3lH/fdeK678afi94n3DX/ABrr98jdYpdSuDFz6R7wg/AVaoPqz5vE+L2XR/gUZy9bL9X+R/S5rXirwv4bTzfEWsWGloBnde3UVuMeuZGWvGte/au/Z08Obv7Q8e6TLt6ixkbUPy+yLNmv5vpJJJXaWVi7scszHJJ9STTKpUF3Pn8T4wYuX+74eK9W5flyn7u67/wUQ/Z70rcNOOt6yR0NnYCNT+NzLAQPw/CvHNc/4KdaFFuXw34Du7r+699qCW2PcpHDNn6bh9a/IWiqVKJ8/ifE/PqvwTjD0iv/AG65+jWt/wDBSv4uXe5NB8OaBpyHoZkubqRfofOiXP1Q14/rf7c/7S+s7lj8UR6bE3VLGwtY/wAneJ5B+DV8+6J8MviR4m2/8I54V1vVA3Q2enXE4P4pGRivX9E/Y8/aT1/abTwNfQK3e+lt7LA9xcSxt+GM0+WCOH+2uKcd/DnVlf8AlUl/6SkcFrnx7+NniPcNZ8deILhG6xDUZ44j/wBs0dU/SvLru8vL+Zrm+nkuJm+9JK5dz9SxJr7u0T/gnP8AHzU9ranc6DpCn7wuLySVx9BBDKpP/AhXsWh/8ExL99r+JfH0MX96Kx01pc/SSSePH/fBo54IqPB3EuNd6lGb/wATt/6U0flNRX7eaF/wTb+Cthtk1vWPEGqyDqvnwW8J/wCApAXH/fyvaNB/Yu/Zq8P7Wg8GQXki9Xv7m5u931SSUx/kopOtE9fDeFGc1NakoR9W3+Sf5n87ddPongjxn4mKjw5oGqaqW+79hs5rnP08tGr+mTQvhX8MvDG3/hHPCWh6YU6Naadbwt9dyRgk+5Oa70AAYHAFQ6/kfQYbwde+IxXyUf1b/Q/nB0L9kr9o7xFt+weA9Uh3f8/wj0/H1+1PFivVtK/4J8ftF6hLEl5Z6TpiuQGe51BHEYPUkQLKTj2zX70UVLrSPdw/hLlEP4tScn6pL8v1PyT+O/7AQsv2e9Jtfh7nV/GfhWS51C8ZE2PqyXQT7RFEnJ3RCJPIUkkhWXl3r8WpYpIZHhmRo5I2KujAhlYHBBB5BB6iv7Ea+Qfjl+xL8E/jlezeINQs5vD/AIiny0uq6QUie4f+9cQsrRSn1bash6b+lfo3B/HMcvpfUscm6fRrdX3TXbrpqvPp257wNCpGMstSi4pLl6NLRa9+99999/5qgCxCqMk8ACtA6VehN+z3wCM1+kvxw/4J5z/BLwBq/wATtP8AGh1620cwM1i2l/Z5THcTJBuMouZB8nmBj8nIB6V8E1+o4fiPD4yHtcDLmitG7Na9tbH2Ph54NYbNcDVxOcylCXM4xUXHSyT5npK++i0289P0l/4J3fss6xf+IrT4++OrJ7XStOVm8O286lXu7lwV+17T/wAsYlJ8skfPIQy8J837X18n/sTeNU8afs5eGHecz3eiLNo11uO4o1m5EKk/9ezREexr6wr8F4rzPEY3MqksRo4txS6JJ/0/mdOE4ep5KpZfB3cW7vu9r/5eQUUUV84dYUUUUAcV8SdM1rWvh14p0bw05i1e/wBF1C2sHDbSt1NbyJCQ3GMSEc9q/lqvbK8028n07UYJLW6tZHhngmUpJFJGSrI6tgqykEEHkGv6yq871z4RfCvxNra+JfEXhDQ9T1VCp+23enwTTkr93c7oWbbj5c5x2xX1fDfEccsU4ThzKVtt7o+u4Y4mjlSqQqQ5lKz03uv0Pz+/4J5/s9nQ9Kk+OPi2zKahqaNb6BFMuGhs24lugDyGn+5GeD5YJGVkr9RaaqqihEAVVAAAGAAOgAp1eLmuZVMdiZYmp12XZdEeHm2Z1cwxUsTV67Lsui/r1CiiivOPNCiiigAooooAKKKo6nqFvpOm3eq3e7yLKCS4k2jLbIlLNgdzgcU0m3ZDSbdkXq/Ln/gpb8MF1Dwz4f8Ai1YQ5uNJm/sjUGUcm1uSXgZj/djmDKPeavhn4hftj/Hnxv4sudf07xTqHh6x84tZabpk7W8EEIPyI+zHnNj7zSbtxzwFwo+s/hJ+1In7SHg7Uf2b/jUIo9Z8TWUlhpOuoipFPegb7YXESgKkyzKrIyAK7ALtVsFvv8Hw5j8qq08wdmo/Eluk9/WyP0XBcM5hlFWnmLaaj8SW6i9/Wy7H5SdORX9Ln7M/xMHxZ+CfhjxbNL5uofZRZakSct9ts/3UrN6GQqJQP7riv5tNX0nUNB1a90PVoGtr7T7iW1uYX4aOaFijofdWBBr9GP8AgnR8Z4PCvjHU/hPr1ysOneJAb3T2kbakeoW6fOuTgDz4F6n+KJQOTX0vGWXfW8B7anq4a/Lr/n8j6fjbLfreX+2p6yhr8uv+fyPtr9sD9qW5/Z80jTNI8LWlvfeJ9dWWSH7VloLS2iIUzOisrOzMdsa5AyGJPy7W+S/2fP8AgoB4/wBX+IOl+EvizHY32l65dxWUd9bQC2ns552CRMwU7Hi3kBwVDKDu3HG0/F/7Tvxbb40fGTXPF1vIX0uFxp+kg9BYWpKxsAenmsWlI7FyKufso/De6+J/x48L6LGjNZ6fdpq+oOBwlrYMsrBvQSOEiB9XFcuG4bwWHylyxcFzcrbfVO19PTY5MLwxgcNlDljILn5W5Pqna+na23mz+kmiiivyQ/HQooooAKKKKACiiigAooooAKKKKACiiigAr4r/AG8fil/wrz4F3ui2Fx5Oq+L5f7IgCtiQWrDdduB/d8oeUT2Mor7UJxya/nS/bA+NTfGn4w6headP5vh/Qd2l6QFOUeKJj5twOx8+TLA9fLCA9K+n4Syt4zHRlJe7DV/ovm/wTPq+D8peNzCMpL3Ie8/0Xzf4JnyxRRVu/sbzS76403UIXt7q0leCeGQbXjkjJVlYHoQQQRX7Zfoful1ex9Yfs3fte+OPgJMmhXStr3hCSQtLpcr7Xti5y0lpIc+WxJLMh+RznhWO8fuB8Jvjd8N/jXov9s+AdVju2jVTdWUuIr20J7TQk7l54DDKMR8rGv5hK3vDPinxH4M1q28ReFNSudJ1O0bdDdWkhikX1GQeVPRlOQw4II4r5TPOE8Njm6tP3Knfo/Vfr+Z8jn3B+GzButT9yp36P1X6/mf1bUV+bH7Jv7cF18T9csvhj8UoIYNfukKafqtuBHDfSRqWMc0X3YpmUEqyYRz8oVTgN+k9fk2Y5bXwNb2GIVn+DXdH49meV4jAVnQxKs/wa7oKKKK4DzwooooAKKKKACiiigAooooAKKKKACiiigCG5t4Lu3ltLlBJDMjRyI3RkcYYH2INfy6/FrwFefC/4leI/AN7uLaNfywRO3Bktyd8En/bSFkf8a/qQr8Pv+Ck/hFNH+Mej+K4ECx+INGQStj79zYyNGx/79NCPwr7ngPGOnjJYd7TX4r/AIFz73w+xrp46WGe01+K1/K58a/B/wAeXHwx+KHhnx5buyDSNRhmn29XtmOy4Tj+/Czr+Nf1ERyRzRrLEwdHUMrKcgg8gg+hFfyXV/Tj+z1r9x4n+BngTW7vJuJ9BsFmZuryRRLGz/8AAmUt+Nen4g4VWo4hb6p/mv1PV8RsIuWjiVvrF/mv1PY6KKK/Mz8tCvNPi38VfCvwY8DX3jzxfI4s7QrHFDCA09zcScRwxAkAu2CeSAFBY8A16XXxN+3p8L/E3xL+CiSeFIJb288OalHq0llCC0k9usUsUuxByzxiTeAOSoYAEkCu/K6FGti6dKu7RbSbPQyqhRr4ynRxDtBtJs8M8Kf8FN9A1HxNDY+LfBk2kaLPKEN9b3/2yW3UnAd4fIj3qOrbG3AZwGOAf080rVtL13TrfV9Fu4L+xu0EsFzbSLLDKjdGR1JVgfUGv5OyCODXqXw1+NXxR+EV79r+H/iG70tGffLahhLZzH/ppbyBomOONxXcOxFfpOa8DYerFSwL5Guju0/nuvxP0/N+AcPVipYB8kl0d2n89WvxP6gq+av2po93w+sH/uavCfwME4r5C+EX/BSjSL5oNJ+M+inTpDhTq+kK0tv/AL0tqxaVAOpMbSEnogFfU/xt8W+FPiD8G4fEvgzVrTWdNOoW7rcWkokUHa6lWxyjjdyrAMOhAr86zHJ8ZgZWxELLvuvvPzXMslxmAlbEwaXfdP5/0zw79nKTZ8U7Bf8Anpb3S/8AkJj/AEr9HK/NP4ATeT8WdDz0f7Uh/G2lx+uK/SyvMPKCiiigAoorL1DXNE0l449V1C1s2lOEW4mSIuT/AHQxGfwppN6Iai27I0ndI0aSRgiICzMxwAB1JPYCv5pf2v8A9pTWP2gPiPdLZXUieDtEnkt9Es1JEcioSrXjr3ln6gn7iEKP4i372/tJ61d6F+z78QtX01ytxF4b1ERSIeUMsDIHBHdd24H2r+Vqv1vwvyqlOVXH1FeUfdj5dW/XZfefmniDmNSCp4ODsnq/Psv68gooor9kPy09S+Dvxe8YfBHx3YePPBly0VzauFuLZmIgvbYkGS3mUfeRwPqrYZcMoI/qO+HXjrQ/ib4G0Tx94bcvp2uWcd3CCQWjLjDxvjjfE4ZHHZlNfyP1+h/wA/af+IHw1/Zp8U+EPDZhElhfP9ivZAzTWaamEVvJAYKpSTfIpII3tyDX5v4hZBHF0aeKpK1RSUb91J2V/R2+9n6R4b4utUzCOVp6VNvKX/BW/ofud4g+Jnw48J3LWfirxXomjXCgFotQ1G3tZADyCVlkU/pXnOoftTfs7aYSLn4g6G+P+fe5Fz/6JD5r+bC7u7q/upb2+mkuLi4dpJZpWLySO5yzMzElmJ5JJyTVevmqXh/h0v3lVv0SX+Z/VtLw5w6X7ys36JL/ADP6JL39uD9l+xyG8apMw/hg06/kz+K2239a4jVP+Ch/7OenqxtLjWdTI6C108qT9PtDwj88V+CVFdkOA8ujvKT+a/yO6Hh9lsfilN/Nfoj9Hv2p/wBtDwL8dfh1J4B8NeH9Xsz9ut72O8vngjAMG4FWiiaXIZWP8YwcHmvzhoor6bLstoYGj7DDq0b33ufU5bllDAUfYYZWje+9zqvCXjjxb4Dv5dV8Garc6NfTQtbtdWbeVcCJiCypKPnTOBnaRnvUOu+MvF/ilzL4n1zUtXdjktf3c1ySfrK7Vzddx8Pvhz4r+KHiGLwr4MgtrrU5xmKCe9trMyY6iP7TLEJGA52pubHOK6K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-    }
-   },
-   "cell_type": "markdown",
-   "id": "9f15c62d-fb27-4625-8ce2-0e2b171e9250",
-   "metadata": {},
-   "source": [
-    "![Planning-127.jpg](attachment:954da14d-81c4-4e4e-83f9-004d9099cc52.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ea898d7f-5c19-4cb2-80c8-4139a4696b2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[2. 2.]\n",
-      " [2. 2.]\n",
-      " [2. 2.]\n",
-      " [2. 2.]\n",
-      " [0. 0.]\n",
-      " [0. 0.]\n",
-      " [0. 0.]\n",
-      " [0. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "s_sizes = ncp.s_size_determination(links_in_unit_cell)\n",
-    "print(s_sizes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "56605a02-6d4c-4bd5-b71f-eea7c8e4ae62",
-   "metadata": {},
-   "source": [
-    "The format in which the s-matrices should be input is as follows. It is a list with first all s-matrices belonging to node 0, then all s-matrices belonging to node 1... etc. Once we have defined the unit cell, the links in the unit cell and the s-matrices in the unit cell we can set up the network. The system set up function returns the real space nodes, all links (including external links) and the correct order of all s-matrices. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bd2787ad-b623-44ee-8895-3e805c88f5ea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### Setting up Network from scratch example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "b788c91a-9254-4713-a9ca-3a978711a100",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Let's start from scratch and set up a unit cell: \n",
-    "\n",
-    "np.random.seed(23)\n",
-    "n = 4\n",
-    "links_in_cell = np.asarray(\n",
-    "    [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]\n",
-    "     ])\n",
-    "\n",
-    "x = 2\n",
-    "y = 1\n",
-    "tot = x*y\n",
-    "s_0 = np.array([[1,2], [3,4]]) #completely made-up s-matrices\n",
-    "s_1 = np.array([[5,6], [7,8]])\n",
-    "s_2 = np.array([[9,10], [11,12]])\n",
-    "s_3 = np.array([[13,14], [15,16]])\n",
-    "\n",
-    "\n",
-    "user_s_matrices = ( [(np.zeros((tot, 1, 1)) + s_0.reshape(1, s_0.shape[0], s_0.shape[1]))] + #The s-matrices have to put into a format\n",
-    "                    [(np.zeros((tot, 1, 1)) + s_1.reshape(1, s_1.shape[0], s_1.shape[1]))] + #where for each node in the unit cells, \n",
-    "                    [(np.zeros((tot, 1, 1)) + s_2.reshape(1, s_2.shape[0], s_2.shape[1]))] + #all s-matrices are 3d arrays, with each array\n",
-    "                    [(np.zeros((tot, 1, 1)) + s_3.reshape(1, s_3.shape[0], s_3.shape[1]))] ) #the scattering matrix for that node\n",
-    "\n",
-    "links, own_smatrices, incoming_and_outgoing = ncp.create_network_for_solver(n, links_in_cell, user_s_matrices,\n",
-    "                                                 x, y, periodicity_y=True, periodicity_x=True) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a29e129-5b22-46f4-b6e1-b2f4497f695d",
-   "metadata": {},
-   "source": [
-    "Currently periodicity on y is assigned and the outsticking links in x are used to determine the 'external links' through which you calculate the total scattering of the whole network "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3e3689b9-d255-426a-8074-a4fcdb131c65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sparsey=True #if you want to construct it in a fully sparse way\n",
-    "scattering_equations = ncp.to_smatrix_equations(links, own_smatrices, sparsey) \n",
-    "s, out_indices, in_indices = (\n",
-    "    ncp.solve_scattering_equations(scattering_equations, 2, incoming_and_outgoing, incoming_and_outgoing)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fab5a299-d8f7-4750-b6db-00ba3da7e56a",
-   "metadata": {},
-   "source": [
-    "Now if we want to get the conductance from all the global links which are incoming on the left side and outgoing on the right side. We can calculate this as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4826edb4-0696-478c-8ae1-b91450917f8a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.006561320119198331\n"
-     ]
-    }
-   ],
-   "source": [
-    "conductance = ncp.get_conductance(s)\n",
-    "print(conductance)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bb2cbbd-0d7b-4bd3-b777-da6369851a3e",
-   "metadata": {},
-   "source": [
-    "If you want the energy specttrum as well, let me know, cause that might take a day (as I haven't touched it in a while and am not sure how correct its current status is)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3e39e115-eae9-43ed-b4be-a547acbae873",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#to be continued"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/codes/examples.py b/codes/examples.py
index ba84cb0249ea50dbb9598f7d8cff4309e2da9e5a..fc3a0732e7bcb6e4f2d956ebb1dcb17aaf8a866f 100644
--- a/codes/examples.py
+++ b/codes/examples.py
@@ -11,20 +11,20 @@ from modules import networks as nws
 # ### Basic Example use of Module
 
 #Tiling a defined network
-n_in_cell = 4
+nodes_in_cell = 4
 links_in_cell = np.asarray(
     [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]
      ])
-x = y = 3
-dimensions = np.array([x,y])
-network = nws.tile_links(n_in_cell, links_in_cell, dimensions)
+x = y = 2
+size = (x,y)
+network = nws.network(nodes_in_cell, links_in_cell, size)
 
 #Plotting network
 node_positions_uc = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])
-lattice_vectors = np.array([1,1.5])
-dimensions = np.array([x,y])
-tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)
-nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network, scale=None)
+lattice_vectors = np.array([[1,0], [0,1.5]])
+size = (x,y)
+tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network, scale=None)
 
 # +
 #preparing s-matrices: 
@@ -55,7 +55,7 @@ s, out_indices, in_indices = (
 
 # +
 #relinking
-links = np.array([
+lattice_links = np.array([
     [0, 0], 
     [0, 0],
     [0, 0],
@@ -68,7 +68,7 @@ links = np.array([
 
 indices = np.array([3, 6, 8])
 indices_in_permuted_order = np.array([6, 8, 3])
-l = nws.relink(links, indices_in_permuted_order)
+l = nws.relink(lattice_links, indices_in_permuted_order)
 # +
 ##Missing: 
 #-check bulk ho-chalker  --> done
@@ -80,14 +80,14 @@ l = nws.relink(links, indices_in_permuted_order)
 
 # ### Replicating Zirnbauer Dirac point 4 in unit cell
 
-n_in_cell = 4
+nodes_in_cell = 4
 links_in_cell = np.asarray(
     [[1, 0, 0, 0], [3, 1, 0, 0], [2, 3, 0, 0], [0, 2, 0, 0], [0, 2, 0, -1], [3, 1, 0, 1], [2, 3, -1, 0], [1, 0, 1, 0]
      ])
 x = y = 1
 tot = x*y
-dimensions = np.array([x,y])
-network = nws.tile_links(n_in_cell, links_in_cell, dimensions)
+size = (x,y)
+network = nws.network(nodes_in_cell, links_in_cell, size)
 
 # +
 
@@ -126,24 +126,25 @@ for sc in range(len(energies_s[0])):
         plt.scatter(k_range, energies_s[:, sc], c='black')
         plt.xlabel('ky')
         plt.ylabel('energy')
-
+plt.show()
 # ### Example cutting of network
 
 #Tiling a defined network
-n_in_cell = 4
+nodes_in_cell = 4
 links_in_cell = np.asarray(
     [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]
      ])
 x = y = 4
-dimensions = np.array([x,y])
-network = nws.tile_links(n_in_cell, links_in_cell, dimensions)
+size = (x,y)
+network = nws.network(nodes_in_cell, links_in_cell, size)
 
 #Plotting network
 node_positions_uc = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])
-lattice_vectors = np.array([1.2,1.2])
-dimensions = np.array([x,y])
-tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)
-nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network, scale=None)
+lattice_vectors = np.array([[1.2,0], [0,1.2]])
+size = (x,y)
+tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network, scale=None)
+plt.show()
 
 #Getting inleads to remove the middle 4 unit cells
 ins = []
@@ -190,8 +191,8 @@ ho_chalker = nws.ho_chalker_operator(network, user_s, sparse = sparse)
 ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(ho_chalker, network, in_leads, out_leads)
 
 #Plotting the new network to see what it did
-nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network_new, scale=None)
-
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network_new, scale=None)
+plt.show()
 # ### Chalker Coddington 2 in unit cell 
 
 links_in_cell = np.array([
@@ -199,15 +200,15 @@ links_in_cell = np.array([
     [0, 1, -1, -1],
     [1, 0, 1, 0],
     [1, 0, 0,1]])
-n_in_cell=2
+nodes_in_cell=2
 
 node_positions_uc = np.array([[0, 0],   [0.5, 0.5]])
-lattice_vectors = np.array([1,1])
-dimensions = np.array([1,1])
-tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)
-network = nws.tile_links(n_in_cell, links_in_cell, dimensions)
-nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network, scale=None)
-
+lattice_vectors = np.array([[1,0], [0,1]])
+size = (1,1)
+tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
+network = nws.network(nodes_in_cell, links_in_cell, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network, scale=None)
+plt.show()
 # +
 p = 0.5
 
@@ -236,7 +237,7 @@ for sc in range(len(energies_s[0])):
         plt.scatter(k_range, energies_s[:, sc], c='black')
         plt.xlabel('ky')
         plt.ylabel('energy')
-
+plt.show()
 # ### Hoti paper bulk spectrum
 #Defining the unit cell
 links_in_cell_ho = np.asarray([  
@@ -326,16 +327,16 @@ for kx in k_range:
         all_e = np.append(all_e, new_energies)
 
 plt.scatter(np.arange(len(all_e)), np.sort(all_e))
-
+plt.show()
 # ### Showcasing obc's using relinking 
 
 unit_cell_pos = np.array([[0.01, 0.75], [0.25, 0.5],[0.5, 0.25], [0.75, 0.01],[0.25, 0.99],[0.5, 0.75], [0.75, 0.5], [0.99, 0.25]])
-lattice_vectors = np.array([1,1])
-dimensions = np.array([2,1])
-tiled_node_pos = nws.tile_nodes_pos(unit_cell_pos, lattice_vectors, dimensions)
-network_ho = nws.tile_links(len(unit_cell_pos), links_in_cell_ho, dimensions)
-nws.plot_network(tiled_node_pos, lattice_vectors*dimensions, network_ho, scale=None)
-
+lattice_vectors = np.array([[1,0], [0,1]])
+size = (2,1)
+tiled_node_pos = nws.tile_nodes_pos(unit_cell_pos, lattice_vectors, size)
+network_ho = nws.network(len(unit_cell_pos), links_in_cell_ho, size)
+nws.plot_network(tiled_node_pos, lattice_vectors*size, network_ho, scale=None)
+plt.show()
 perms = np.unique(np.nonzero(network_ho[:,2:])[0])
 
 perms_in_order = np.array([22, 21, 26, 27, 24, 25, 31, 30, 29, 28])
@@ -357,8 +358,8 @@ extra_node_pos = np.array([
 
 relinked_node_pos = np.vstack((tiled_node_pos, extra_node_pos))
 network_ho_relinked = np.hstack((network_relinked, np.zeros((len(network_relinked),2))))
-nws.plot_network(relinked_node_pos, lattice_vectors*dimensions, network_ho_relinked, scale=None)
-
+nws.plot_network(relinked_node_pos, lattice_vectors*size, network_ho_relinked, scale=None)
+plt.show()
 #Now getting the spectrum as a bonus
 tot = 2
 s_dummy = np.array([[1]])
@@ -386,5 +387,5 @@ for kx in k_range:
         all_e = np.append(all_e, new_energies)
 
 plt.scatter(np.arange(len(all_e)), np.sort(all_e))
-
+plt.show()
 
diff --git a/codes/modules/networks.py b/codes/modules/networks.py
index 89b69e3ff5888b15b4b42d43fdb8b101501dc9ab..6a91a8e8a03b479beb81da13b7b2b6f033649e60 100644
--- a/codes/modules/networks.py
+++ b/codes/modules/networks.py
@@ -1,81 +1,65 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.14.4
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-#Imports
-import kwant
 import numpy as np
-from scipy import sparse
-from kwant.linalg import mumps 
-from kwant.rmt import circular
+from kwant.linalg import mumps
 import scipy
-from scipy import linalg
+import scipy.linalg
 import matplotlib.pyplot as plt
-import numpy as np
-
 
-# ### Tiling
 
-def _create_nodes_array(nodes_in_cell, dimensions):
-    """Creates a finite array determined by x, y and number of nodes in the cell
+def _create_nodes_array(nodes_in_cell, size):
+    """
+    Returns an array with all the nodes in a lattice determined by its size
+    and number of nodes in a unit cell.
 
     Parameters
     ------------
     nodes_in_cell : int
-                   number of nodes in unit cell
-    dimensions : tuple
-                specifies the number of dimensions and how many times it has to be tiled in each of those dimensions
+        Number of nodes in unit cell.
+    size : tuple
+        Length of tuple specifies the number of dimensions, and contents
+        specify how many times the lattice is tiled in each of those dimensions.
 
     Returns
     --------
-    all_nodes : numpy.ndarray
-                of shape (num_in_cell, dim, dim+1) containing all information of all nodes
+    lattice_nodes : numpy.ndarray
+        Contains node numbers and unit cell coordinates in a numpy array of shape
+        (num_in_cell, dim, dim+1) where dim is the number of dimensions.
 
     """
-    dimensions = tuple(dimensions) #temporary as dimensions is going to tuple anyway
-    all_nodes = np.mgrid[[slice(i) for i in ((nodes_in_cell,) + tuple(dimensions))]]
-    all_nodes = all_nodes.transpose(tuple([i+1 for i in range(len(dimensions)+1)])+tuple([0])) 
-    #exchange first axis to the last in order to keep in same format as before (can be removed away again 
-    # but also has to be adjusted in quite a few other places in the code then)
-    all_nodes = np.ascontiguousarray(all_nodes) #only needed because of the transpose
-    return all_nodes
+    lattice_nodes = np.moveaxis(
+        np.mgrid[[slice(i) for i in ((nodes_in_cell,) + tuple(size))]], 0, len(size) + 1
+    )
+    return np.ascontiguousarray(lattice_nodes, dtype=int)
 
-# ### Plotting
 
-def tile_nodes_pos(node_positions, lattice_vectors, dimensions, non_orthogonal=False):
+def tile_nodes_pos(node_positions, lattice_vectors, size):
     """
     Parameters
     ----------
     node_positions: np.ndarray
-                    the coordinates in however many dimensions of the node positions
+                    the coordinates in however many size of the node positions
     lattice_vectors: np.array
-                    of length dimensions which say between unit cell how things are translated
-    dimensions: np.array
-                specifies the number of dimensions and how many times it has to be tiled in each of those dimensions
+                    of length size which say between unit cell how things are
+                    translated
+    size : tuple
+                specifies the number of dimensions and how many times it has to
+                be tiled in each of those dimensions
 
     Returns
     -------
     tile_nodes_reshaped: np.ndarray
-                        the nodes tiled according to however many dimensions, where all nodes are specified by position coordinates
+                        the nodes tiled according to however many dimensions,
+                        where all nodes are specified by position coordinates
     """
-    nodes_array = _create_nodes_array(len(node_positions), dimensions)
-    tile_cells_nodes_array = nodes_array[:,:,:,1:]
-    if non_orthogonal:
-        tile_cells_nodes_array = nodes_array[:, :, :, 1:] @ lattice_vectors
-    else:
-        tile_cells_nodes_array = nodes_array[:, :, :, 1:] * lattice_vectors
+    if len(lattice_vectors) != len(size):
+        raise ValueError("lattice_vectors and size should have the same length")
+    if node_positions.shape[1] != len(size):
+        raise ValueError("Position coordinates and size should have the same length")
+
+    lattice_nodes = _create_nodes_array(len(node_positions), size)
+    tile_cells_nodes_array = lattice_nodes[:, :, :, 1:] @ lattice_vectors
+
     node_positions_reshaped = node_positions.reshape(
-        (len(node_positions), 1, 1, len(dimensions))
+        (len(node_positions), 1, 1, len(size))
     )
 
     tile_nodes = tile_cells_nodes_array + node_positions_reshaped
@@ -84,17 +68,21 @@ def tile_nodes_pos(node_positions, lattice_vectors, dimensions, non_orthogonal=F
     return tile_nodes_reshaped
 
 
-# + tags=[]
-def plot_network(node_positions, lattice_vectors, network, scale=None, dont_plot_nodes=None, non_orthogonal=False):
+def plot_network(
+    node_positions, lattice_vectors, network, scale=None, dont_plot_nodes=None
+):
     """
     Parameters
     ----------
     node_positions: np.ndarray
-                    the coordinates in however many dimensions of the node positions
+                    the coordinates in however many dimensions of the node
+                    positions
     lattice_vectors: np.array
-                    of length dimensions which say between unit cell how things are translated
+                    of length dimensions which say between unit cell how things
+                    are translated
     network: np.ndarray
-            which nodes are connected and whether they are connected within or outside the unit cell
+            which nodes are connected and whether they are connected within or
+            outside the unit cell
 
     Notes
     -----
@@ -103,31 +91,32 @@ def plot_network(node_positions, lattice_vectors, network, scale=None, dont_plot
     """
 
     link_coords = np.array(
-        [node_positions[network[:, 0].astype(int)], node_positions[network[:, 1].astype(int)]]
+        [node_positions[network[:, 0]], node_positions[network[:, 1]]]
     )
-    if non_orthogonal:
-        link_coords1 = np.array(
-            [link_coords[0, :], link_coords[1, :] + (network[:, 2:].astype(int) @ lattice_vectors)]
-        )
-    else:
-        link_coords1 = np.array(
-            [link_coords[0, :], link_coords[1, :] + (network[:, 2:].astype(int) * lattice_vectors)]
-        )
+
+    link_coords1 = np.array(
+        [link_coords[0, :], link_coords[1, :] + (network[:, 2:] @ lattice_vectors)]
+    )
+
     link_coords2 = np.array(
         [link_coords[0, :], link_coords1[1, :] - link_coords1[0, :]]
     )
     scale = np.max(node_positions.flatten())
-    if dont_plot_nodes is not None: 
+    if dont_plot_nodes is not None:
         node_positions = np.delete(node_positions, dont_plot_nodes, axis=0)
 
-    plt.scatter(node_positions[:, 0], node_positions[:, 1], 200/scale, color="red", zorder=3)
+    plt.scatter(
+        node_positions[:, 0], node_positions[:, 1], 200 / scale, color="red", zorder=3
+    )
 
-    for i in range(link_coords2.shape[1]):  # anyone know a matplotlib arrow which is vectorized?
+    for i in range(
+        link_coords2.shape[1]
+    ):  # anyone know a matplotlib arrow which is vectorized?
         plt.plot(
             np.array([link_coords1[0, i, 0], link_coords1[1, i, 0]]),
             np.array([link_coords1[0, i, 1], link_coords1[1, i, 1]]),
             color="blue",
-            linewidth=4/scale,
+            linewidth=4 / scale,
             zorder=1,
         )
         plt.arrow(
@@ -136,188 +125,138 @@ def plot_network(node_positions, lattice_vectors, network, scale=None, dont_plot
             0.5 * link_coords2[1, i, 0],
             0.5 * link_coords2[1, i, 1],
             color="blue",
-            linewidth=0.3/scale,
-            head_width=.2/(np.sqrt(scale)+1),
+            linewidth=0.3 / scale,
+            head_width=0.2 / (np.sqrt(scale) + 1),
             zorder=2,
         )
 
-        # plt.annotate("", xy=(0.5, 0.5), xytext=(0.25, 0.25),
-        #     arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=-0.2"))
-        #maybe use annotate so it's not as ugly
-
     plt.show()
-    return 0
-
-def _links_array(link, nodes_array):
-    """Creates multi-dimensional array where the content of the position of source is replaced by the intended sink
 
-    Parameters
-    -----------
-    link : numpy.ndarray
-          list of form [starting_node, goal_node, relative_x, relative_y], iterating over all chosen links in unit cell
-    nodes_array : numpy.ndarray
-                which contains all the sites both specified in position and content
 
-    Returns
-    --------
-    links_array : np.ndarray
-                 where the content of a position in sites is replaced by the target node information
-    Notes
-    ------
-    Zero checks on validity are done so far
+def _links_to_compressed_format(lattice_links, lattice_nodes):
     """
-    links_array = np.copy(nodes_array)
-
-    addition = np.concatenate(([link[1] - link[0]], link[2:]))
-    links_array[link[0], : np.max(links_array.shape)] = (
-        nodes_array[link[0], : np.max(links_array.shape)] + addition
-    )
-
-    return links_array
-
-
-def _get_links(link, link_array, nodes_array):  # only keep track of new information
-    """Gives the specific links specified by deleting all cases where the
-    entry in link_array and node_array is the exact same. Also reshape slightly.
+    Brings the links from the format of [source_num_in_cell,
+    source_coordinate, sink_num_in_cell, sink_coordinate] to the format of
+    [source_node_number, sink_node_number], which we call the compressed format.
 
     Parameters
     ----------
-    links_array : np.ndarray
-                 where the content of a position in sites is replaced by the target node information, usually in real space
-    nodes_array : numpy.ndarray
-                  which contains all the sites both specified in position and content
+    all_links : np.ndarray
+        of shape [sth, 6] where sth is decided by the number of links and the
+        first 3 numbers in 6 refer to the specifications of the source node and
+        the last 3 numbers of 6 refer to the specifications of sink node.
+    lattice_nodes :  numpy.ndarray
+        which contains all the bulk sites both specified in position and content.
 
     Returns
-    -------
-    actual_links : np.ndarray
-                   of shape [sth, 6] where sth is decided by the number of links and the first 3 numbers in 6 refer to the
-                  specifications of the source node and the last 3 numbers of 6 refer to the specifications of sink node
+    --------
+    links_single_numbered : np.ndarray
+        of shape [sth, 2] containing all bulk links represented as between node numbers.
     """
-    dim = len(link) - 2
+    dim = (lattice_links.shape[1] - 2) // 2
+    if len(lattice_nodes.shape) - 2 != dim:
+        raise ValueError("Lattice dimensions should be consistent")
 
-    changed_link_array = link_array[link[0], : np.max(link_array.shape)]
-    changed_node_array = nodes_array[link[0], : np.max(nodes_array.shape)]
+    lattice_nodes = lattice_nodes.reshape([-1, dim + 1])
 
-    link_reshaped = changed_link_array.reshape([-1, dim + 1])
-    node_reshaped = changed_node_array.reshape([-1, dim + 1])
-
-    actual_links = np.hstack((node_reshaped, link_reshaped))
-
-    return actual_links
+    def create_dtype(n):
+        return [("f" + str(i), int) for i in range(n)]
 
+    nodes_as_recarray = lattice_nodes.view(np.dtype(create_dtype(dim + 1)))[:, 0]
+    node_indices = np.unique(nodes_as_recarray)
 
-def _make_periodic(link_array, dimensions):
-    """Ensures periodicity in all dimensions by applying a mod"""
-    dim = len(dimensions)
+    sources_as_recarray = np.copy(lattice_links[:, : dim + 1]).view(
+        create_dtype(dim + 1)
+    )[:, 0]
+    source_indices = np.searchsorted(node_indices, sources_as_recarray)
 
-    link_reshaped = link_array.reshape([-1, dim + 1])
-    link_reshaped_modded = np.mod(link_reshaped[:, 1:], dimensions)
-    link_reshaped = np.hstack(
-        (link_reshaped[:, 0].reshape([-1, 1]), link_reshaped_modded)
-    )
-    link_made_periodic = link_reshaped.reshape(link_array.shape)
+    targets_as_recarray = np.copy(lattice_links[:, (dim + 1) : ((dim + 1) * 2)]).view(
+        create_dtype(dim + 1)
+    )[:, 0]
+    target_indices = np.searchsorted(node_indices, targets_as_recarray)
 
-    return link_made_periodic
+    return np.column_stack((source_indices, target_indices))
 
 
-def _links_to_compressed_format(all_links, all_nodes, num_dim):
-    """Transforms the links written as between twice 3 specifiers for the nodes, to just 2 numbers.
+def network(nodes_in_cell, links_in_cell, size):
+    """
+    Returns the links in a lattice of a given size by tiling links_in_cell.
 
     Parameters
     ----------
-    all_links : np.ndarray
-                   of shape [sth, 6] where sth is decided by the number of links and the first 3 numbers in 6 refer to the
-                  specifications of the source node and the last 3 numbers of 6 refer to the specifications of sink node
-    all_nodes :  numpy.ndarray
-                  which contains all the bulk sites both specified in position and content
+    nodes_in_cell : int
+        Number of nodes in the unit cell.
+    links_in_cell : numpy.ndarray
+        List of form [source_node, target_node, relative_x, relative_y],
+        iterating over all chosen links in unit cell,
+    size : tuple
+        Length of tuple specifies the number of dimensions, and contents
+        specify how many times the lattice is tiled in each of those dimensions.
 
     Returns
-    --------
-    links_single_numbered : np.ndarray
-                            of shape [sth, 2] containing all bulk links represented as between node numbers
+    -------
+    all_links : np.ndarray
+        Links of network in compressed format
     """
-    links = all_links.astype(int)
+    assert isinstance(nodes_in_cell, int)
+    assert links_in_cell.dtype == int
 
-    all_nodes = all_nodes.reshape([-1, 3])
-    nodes = all_nodes.astype(int)
-    n_nodes = len(all_nodes)
+    if links_in_cell.shape[1] - 2 != len(size):
+        raise ValueError("Lattice dimensions should be consistent")
 
-    def create_dtype(n):
-        return [("f" + str(i), int) for i in range(n)]
+    lattice_nodes = _create_nodes_array(nodes_in_cell, size)
 
-    nodes_as_recarray1 = nodes.view(np.dtype(create_dtype(num_dim + 1)))[:, 0]
-    nodes_uniq = np.unique(nodes_as_recarray1)
+    lattice_links = []  # supercell
+    links_displacement = []  # supercell
 
-    outs_as_recarray = (
-        np.copy(all_links[:, : num_dim + 1]).astype(int)
-        .view(create_dtype(num_dim + 1))[:, 0]
-    )
-    outs_numbered = np.searchsorted(nodes_uniq, outs_as_recarray)
+    for link in links_in_cell:
+        source_nodes = lattice_nodes[link[0]].reshape([-1, len(size) + 1])
+        target_nodes = (
+            lattice_nodes[link[0]] + np.concatenate(([link[1] - link[0]], link[2:]))
+        ).reshape([-1, len(size) + 1])
 
-    ins_as_recarray = (
-        np.copy(links[:, (num_dim + 1) : ((num_dim + 1) * 2)]).astype(int)
-        .view(create_dtype(num_dim + 1))[:, 0]
-    )
-    ins_numbered = np.searchsorted(nodes_uniq, ins_as_recarray)
+        # save displacement
+        tiled_link = np.hstack((source_nodes, target_nodes))
+        links_displacement.append(tiled_link[:, -len(size) :] // size)
 
-    links_single_numbered = np.column_stack((outs_numbered, ins_numbered))
+        # make network periodic
+        target_nodes[:, 1:] = np.mod(target_nodes[:, 1:], size)
+        tiled_link = np.hstack((source_nodes, target_nodes))
+        lattice_links.append(tiled_link)  # uncompressed format
 
-    return links_single_numbered
+    lattice_links = np.vstack(lattice_links)
+    lattice_links = _links_to_compressed_format(lattice_links, lattice_nodes)
+    links_displacement = np.vstack(links_displacement)
 
+    return np.hstack((lattice_links, links_displacement))
 
-def tile_links(n_in_cell, links_in_cell, dimensions):
-    """ Create a network to be used in the network solver
+
+# ### Ho Chalker
+
+
+def ho_chalker_operator(links_in_cell, s_matrices, k=None, sparse=False):
+    """
+    Construct the ho-chalker operator in the basis of the network invariant (order
+    of links_in_cell) for a given network and its s-matrices.
 
     Parameters
     ----------
-    n_in_cell : int
-                Number of nodes in the unit cell
-    links_in_cell : numpy.ndarray
-                  list of form [starting_node, goal_node, relative_x, relative_y], iterating over all chosen links in unit cell,
-    dimensions : np.array
-                Numpy array containing the number of dimensions (length of array) and how much it should be tiled (specific numbers)
+    links_in_cell : np.ndarray
+                    list of form [starting_node, goal_node, relative_x, relative_y],
+                    iterating over all chosen links in unit cell,
+    s_matrices : list of np.ndarray
+                each list element is all the s matrices (in numpy array foramt)
+                for a given node type
+    k : np.ndarray
+        Optional k-point values where the ho-chalker operator should be evaluated
+    sparse : bool
+            If true, the operator will be returned as a sparse matrix
 
     Returns
     -------
-    all_links : np.ndarray
-                links of network in compressed format
+    A : np.ndarray or scipy.sparse.csr_matrix
+        The ho-chalker operator
     """
-    nodes_array = _create_nodes_array(n_in_cell, dimensions) 
-
-    all_links = np.zeros((1,(len(dimensions)*2+2)))
-    link_type = np.array([0])
-    all_across_cells = np.zeros(len(dimensions))
-
-    for num, link in enumerate(links_in_cell):
-        old_len = len(all_links)
-        link_array = _links_array(link, nodes_array)
-        
-        links_before_periodicity = _get_links(link, link_array, nodes_array)
-        
-        across_cells = links_before_periodicity[:,4:] // dimensions
-        all_across_cells = np.vstack((all_across_cells, across_cells))
- 
-        link_array = _make_periodic(link_array, dimensions)
-
-        all_links = np.vstack((all_links, _get_links(link,
-            link_array, nodes_array)))
-            
-        link_type = np.concatenate((link_type, np.repeat(num, len(all_links)-old_len)))
- 
-    all_links = np.delete(all_links, 0, axis=0)
-    all_across_cells = np.delete(all_across_cells, 0, axis=0)
-    link_type = np.delete(link_type, 0)
-
-    all_links = _links_to_compressed_format(all_links, nodes_array, len(dimensions))
-    all_links= np.hstack((all_links, all_across_cells))
- 
-    return all_links
-
-
-# ### Ho Chalker
-
-def ho_chalker_operator(links_in_cell, s_matrices, k = None, sparse = False): #k=none
-
     if sparse:
         blocks = [
             scipy.sparse.bsr_array(
@@ -327,72 +266,79 @@ def ho_chalker_operator(links_in_cell, s_matrices, k = None, sparse = False): #k
         ]
         s_blocks = scipy.sparse.csr_array(scipy.sparse.block_diag(blocks, "csr"))
     else:
-        s_blocks = linalg.block_diag(*(mat for block in s_matrices for mat in block))
+        s_blocks = scipy.linalg.block_diag(
+            *(mat for block in s_matrices for mat in block)
+        )
 
     def unpermute(indices):
-        return np.argsort(np.argsort(indices, kind='stable'), kind='stable')
+        return np.argsort(np.argsort(indices, kind="stable"), kind="stable")
 
     A = s_blocks[unpermute(links_in_cell[:, 0])][:, unpermute(links_in_cell[:, 1])]
 
-    if k is not None:
-        return A * np.exp(1j * k @ links_in_cell.T[2:])
-    else:
+    if k is None:
         return A
+    return A * np.exp(1j * k @ links_in_cell.T[2:])
 
 
-# ### Relink
-
-def relink(links, indices_to_permute, num_dummy_nodes=1):
+def relink(links, indices_to_permute):
     """
+    Add dummy nodes in between the links specified in indices_to_permute and
+    relink them together. Relink the specified links together in the order
+    specified by indices_to_permute.
+
     Parameter
     ---------
     links : np.ndarray
-            defines the network and includes all the links of the network in compressed format
+            defines the network and includes all the links of the network in
+            compressed format
     indices_to_permute : np.array
-                        which indices in links to relink together. The order/permutation of this argument determines which links get relinked together
-    num_dummy_nodes : int
-                      How many dummy nodes to place in between the relinking, sometimes you may want more than 1. The zero case still needs 
-                      quite some work. 
-
+                        which indices in links to relink together. The
+                        order/permutation of this argument determines which links
+                        get relinked together.
 
     Returns
     -------
     links : np.ndarray
-            network with extra dummy nodes added and the relinking done in between the links specified.
+            network with extra dummy nodes added and the relinking done in
+            between the links specified.
 
     """
 
     # creating extra dummy nodes
     last_largest_node = np.max(links[:, :2].flatten())
     extra_nodes_left_column = np.arange(
-        last_largest_node+1,
-        last_largest_node + 1 + len(indices_to_permute))
-    extra_nodes_right_column = extra_nodes_left_column[np.argsort(
-        indices_to_permute)]
-    extra_nodes = np.column_stack(
-        (extra_nodes_left_column, extra_nodes_right_column))
+        last_largest_node + 1, last_largest_node + 1 + len(indices_to_permute)
+    )
+    extra_nodes_right_column = extra_nodes_left_column[np.argsort(indices_to_permute)]
+    extra_nodes = np.column_stack((extra_nodes_left_column, extra_nodes_right_column))
 
     # inserting extra links between dummy nodes
-    links = np.insert(
-        links,
-        np.sort(indices_to_permute).flatten().astype(int),
-        extra_nodes,
-        axis=0)
+    links = np.insert(links, np.sort(indices_to_permute).flatten(), extra_nodes, axis=0)
 
     # determining correct permutation
-    newly_added_rows = np.nonzero(
-        links[:, 1] > int(last_largest_node))
+    newly_added_rows = np.nonzero(links[:, 1] > int(last_largest_node))
     # Need to do this because insertion added above
-    to_be_added = newly_added_rows + np.ones(len(newly_added_rows))
-    to_be_permuted = np.ravel(np.column_stack((np.asarray(newly_added_rows).reshape(
-        (-1, 1)), to_be_added.reshape((-1, 1)))))  # Still horribly non-readable
-    permutation = np.ravel(np.column_stack((to_be_added.reshape(
-        (-1, 1)), np.asarray(newly_added_rows).reshape((-1, 1)))))
+    to_be_added = newly_added_rows + np.ones(len(newly_added_rows), dtype=int)
+    to_be_permuted = np.ravel(
+        np.column_stack(
+            (
+                np.asarray(newly_added_rows).reshape((-1, 1)),
+                to_be_added.reshape((-1, 1)),
+            )
+        )
+    )  # Still horribly non-readable
+    permutation = np.ravel(
+        np.column_stack(
+            (
+                to_be_added.reshape((-1, 1)),
+                np.asarray(newly_added_rows).reshape((-1, 1)),
+            )
+        )
+    )
 
     # doing the permutation which links existing links to dummy links
     links_permuting = links[:, 0]
-    links_permuting[to_be_permuted.astype(
-        int)] = links_permuting[permutation.astype(int)]
+    links_permuting[to_be_permuted] = links_permuting[permutation]
 
     return links
 
@@ -400,6 +346,9 @@ def relink(links, indices_to_permute, num_dummy_nodes=1):
 # + tags=[]
 def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
     """
+    Determine all nodes which are reachable via an incoming lead numbers to the
+    outgoing lead numbers.
+
     Parameters
     ----------
     network : np.ndarray
@@ -412,12 +361,14 @@ def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
     Returns
     -------
     all_internal : np.array
-                    All node numbers which are reachable via an incoming lead to an outgoing lead
+                    All node numbers which are reachable via an incoming lead
+                    to an outgoing lead
 
     Note
     -----
     - Namings of variables everywhere in this function still need some changing/love.
-    - maybe do the post things, like getting in terms of links and deleting from ho-chalker in same function?
+    - maybe do the post things, like getting in terms of links and deleting from
+    ho-chalker in same function?
     """
 
     # Deleting around links which become leads
@@ -425,7 +376,9 @@ def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
     all_leadnums = np.hstack((in_lead_linknums, out_lead_linknums))
     in_leads = network[in_lead_linknums]
     out_leads = network[out_lead_linknums]
-    lead_nodes = np.hstack((in_leads[:, 0], out_leads[:, 1])) #the  nodes which will become non-existent
+    lead_nodes = np.hstack(
+        (in_leads[:, 0], out_leads[:, 1])
+    )  # the  nodes which will become non-existent
     linknums_w_leadnodes = np.hstack(
         (
             np.nonzero(np.isin(network[:, 0], lead_nodes)),
@@ -434,10 +387,12 @@ def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
     )
     nonlead_linknums_w_leadnodes = np.delete(
         linknums_w_leadnodes.flatten(),
-        np.nonzero(np.isin(linknums_w_leadnodes.flatten(), all_leadnums)), #np.isin is probably enough
+        np.nonzero(
+            np.isin(linknums_w_leadnodes.flatten(), all_leadnums)
+        ),  # np.isin is probably enough
     )
     network_shortened = np.delete(network, nonlead_linknums_w_leadnodes, axis=0)
-    
+
     # creating global source and global sink
     global_source_node = np.max(network_shortened.flatten()) + 1
     global_sink_node = global_source_node + 1
@@ -453,26 +408,26 @@ def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
 
     # doing the first order searches and taking intersection
     num = len(network_shortened)
-    max_node = np.max(network_shortened.flatten()).astype(int)+1
+    max_node = np.max(network_shortened.flatten()) + 1
     graph_in = scipy.sparse.coo_matrix(
         (
             np.ones(num),
             (
-                network_shortened[:, 0].astype(int),
-                network_shortened[:, 1].astype(int),
+                network_shortened[:, 0],
+                network_shortened[:, 1],
             ),
         ),
-        shape=(max_node,max_node),
+        shape=(max_node, max_node),
     )
     graph_out = scipy.sparse.coo_matrix(
         (
             np.ones(num),
             (
-                network_shortened[:, 1].astype(int),
-                network_shortened[:, 0].astype(int),
+                network_shortened[:, 1],
+                network_shortened[:, 0],
             ),
         ),
-        shape=(max_node,max_node),
+        shape=(max_node, max_node),
     )
     node_order_search_in = scipy.sparse.csgraph.depth_first_order(
         scipy.sparse.csr_matrix(graph_in), global_source_node, return_predecessors=False
@@ -488,12 +443,19 @@ def _determine_all_internal_nodes(network, in_lead_linknums, out_lead_linknums):
 # + tags=[]
 def cut_ho_chalker(ho_chalker, network, in_lead_links, out_lead_links):
     """
+    Remove links and nodes from a network, together with the corresponding links and
+    rows in the ho-chalker matrix to create a smaller network from a larger one. The
+    new smaller network is the one which is reachable from the in-leads to the
+    out-leads specified.
+
     Parameters
     ----------
     ho_chalker : np.ndarray
-                Ho-chalker operator (in non-sparse format) in the basis of the network invariant
+                Ho-chalker operator (in non-sparse format) in the basis of the
+                network invariant
     network : np.ndarray
-            All links in the network and whether they go in or out of the unit cell
+            All links in the network and whether they go in or out of the unit
+            cell
     in_lead_links : np.array
                     List of which the link numbers which get turned to in-leads
     out_lead_links : np.array
@@ -502,33 +464,34 @@ def cut_ho_chalker(ho_chalker, network, in_lead_links, out_lead_links):
     Returns
     -------
     ho_chalker_changed : np.ndarray
-                        Same Ho-chalker but with non-relevant all external links removed (both row and column)
+                        Same Ho-chalker but with non-relevant all external
+                        links removed (both row and column)
     network_changed : np.ndarray
                         Network with external links removed
     incoming_new : np.array
-                    Numbers denoting the in-links in the order of the changed ho-chalker/network
+                    Numbers denoting the in-links in the order of the changed
+                    ho-chalker/network
     outgoing_new : np.array
                     Numbers denoting out-leads in order of changed ho-chalker
 
     Notes
     -----
-    The graph search is done in terms of the network and not in terms of the ho-chalker. 
+    The graph search is done in terms of the network and not in terms of the
+    ho-chalker.
     """
     internal_nodes = _determine_all_internal_nodes(
         network, in_lead_links, out_lead_links
     )
 
     # don't allow connections between both two lead nodes
-    lead_nodes = np.hstack( 
+    lead_nodes = np.hstack(
         (network[in_lead_links][:, 0], network[out_lead_links][:, 1])
     )
     inlead_nodes = np.nonzero(np.isin(network[:, 0], lead_nodes))
     outlead_nodes = np.nonzero(np.isin(network[:, 1], lead_nodes))
     links_btwn_leadnodes = np.intersect1d(inlead_nodes, outlead_nodes)
 
-    noninternal_in_nodes = np.nonzero(
-        ~np.isin(network[:, 0], internal_nodes)
-    )  
+    noninternal_in_nodes = np.nonzero(~np.isin(network[:, 0], internal_nodes))
     noninternal_out_nodes = np.nonzero(~np.isin(network[:, 1], internal_nodes))
     noninternal_links = np.intersect1d(noninternal_in_nodes, noninternal_out_nodes)
 
@@ -537,7 +500,7 @@ def cut_ho_chalker(ho_chalker, network, in_lead_links, out_lead_links):
         np.delete(ho_chalker, links_to_delete, axis=0), links_to_delete, axis=1
     )
     cut_network = np.delete(network, links_to_delete, axis=0)
- 
+
     new_inlead_nodes = np.nonzero(np.isin(cut_network[:, 0], lead_nodes))
     new_outlead_nodes = np.nonzero(np.isin(cut_network[:, 1], lead_nodes))
     new_inleads = np.setdiff1d(new_inlead_nodes, new_outlead_nodes)
@@ -547,25 +510,57 @@ def cut_ho_chalker(ho_chalker, network, in_lead_links, out_lead_links):
 
 
 # + tags=[]
-def determine_leads_from_nodes(network, ins, outs):
-    
+def determine_leads_from_nodes(network, ins, outs):  # Not correctly functional
+    """
+    Determine the links which are in-leads and out-leads from
+    the nodes which are connected to them.
+
+    Parameters
+    ----------
+    network : np.ndarray
+            All links in the network and whether they go in or out of the unit
+            cell
+    ins : np.array
+          List of which nodes are  connected to in-leads
+    outs : np.array
+            List of which nodes are connected to out-leads
+
+    Returns
+    -------
+    in_leads : np.array
+                List of link numbers which are the in-leads
+    out_leads : np.array
+                List of link numbers which are the out-leads
+
+    Notes
+    -----
+    This function isn't doing what it should be doing yet (nor is it correct).
+    The idea is that it works as a convenience function for the user to determine
+    which links become in-leads and out-leads in order to cut the network.
+    (as i've noticed that as a user this is often more easily specified in
+    terms of nodes)
+    """
     act_ins = np.unique(ins)
     act_outs = np.unique(outs)
     # now to linkss
-    in_links = np.nonzero(np.isin(network[:,0], ins))
-    all_non_selec = np.nonzero(~np.isin(network[:,1], act_ins))
+    in_links = np.nonzero(np.isin(network[:, 0], ins))
+    all_non_selec = np.nonzero(~np.isin(network[:, 1], act_ins))
     in_leads = np.intersect1d(in_links, all_non_selec)
-    all_non_selec2 = np.nonzero(~np.isin(network[:,0], act_outs))
-    out_links = np.nonzero(np.isin(network[:,1], outs))
+    all_non_selec2 = np.nonzero(~np.isin(network[:, 0], act_outs))
+    out_links = np.nonzero(np.isin(network[:, 1], outs))
     out_leads = np.intersect1d(out_links, all_non_selec2)
-    
+
     return in_leads, out_leads
 
+
 # -
 
 # ### Solver
 
-def solve_scattering_equations(A, size_group_of_rhs_vectors, incoming_ind=None, outgoing_ind=None):
+
+def solve_scattering_equations(
+    A, size_group_of_rhs_vectors, incoming_ind=None, outgoing_ind=None
+):
     """Solve the scattering problem
 
     Parameters
@@ -574,9 +569,12 @@ def solve_scattering_equations(A, size_group_of_rhs_vectors, incoming_ind=None,
     A : csr_matrix
         The scattering equations :math:`Aψ=ψ`.
     size_group_of_rhs_vectors: int
-        how many vectors of the right-hand side of the equation to solve for at a time.
+        how many vectors of the right-hand side of the equation to solve for at
+        a time.
     incoming_ind : np.array
-                    indicies of the A array which correspond to the incoming links, if not specified, all rows/columns with only zeros are taken instead
+                    indicies of the A array which correspond to the incoming
+                    links, if not specified, all rows/columns with only zeros are
+                    taken instead
     outgoing_ind : np.array
                 indicies of the A array which correspond to the outgoing links
 
@@ -600,41 +598,41 @@ def solve_scattering_equations(A, size_group_of_rhs_vectors, incoming_ind=None,
         incoming = ~(A.getnnz(axis=1).astype(bool))
         outgoing = ~(A.getnnz(axis=0).astype(bool))
 
-    direct = incoming * outgoing
     internal = np.logical_and(~incoming, ~outgoing)
-    rhs = sparse.identity(np.sum(internal), format="csr") - \
-        A[internal][:, internal] #(1-S_bb)
-    lhs = A[internal][:, incoming] #S_bi
-    if rhs.shape == (1, 1): 
-    #we want: S_ii + S_ib(1-S_bb)^-1S_bi
-        #rhs -> (1-S_bb) 
-        #lhs -> S_bi
-        #other_lhs -> S_ib
+    rhs = (
+        scipy.sparse.identity(np.sum(internal), format="csr") - A[internal][:, internal]
+    )  # (1-S_bb)
+    lhs = A[internal][:, incoming]  # S_bi
+    if rhs.shape == (1, 1):
+        # we want: S_ii + S_ib(1-S_bb)^-1S_bi
+        # rhs -> (1-S_bb)
+        # lhs -> S_bi
+        # other_lhs -> S_ib
         other_lhs = A[outgoing][:, internal]
         s_ii = A[outgoing][:, incoming].toarray()
-        #rhs_inv = scipy.sparse.linalg.inv(rhs)
-        S = s_ii + other_lhs @ (sparse.csr_array(1/rhs.toarray()) @ lhs) 
+        # rhs_inv = scipy.sparse.linalg.inv(rhs)
+        S = s_ii + other_lhs @ (scipy.sparse.csr_array(1 / rhs.toarray()) @ lhs)
         return S, np.where(outgoing)[0], np.where(incoming)[0]
-    elif rhs.shape == (0, 0): 
-        #happens when everything is outgoing and incoming: S_bb.shape = (0,[0, 0)
-        #I guess if there are no internal links then scattering matrix is exact same
-        #as the ho-chalker matrix
+    elif rhs.shape == (0, 0):
+        # happens when everything is outgoing and incoming: S_bb.shape = (0,[0, 0)
+        # I guess if there are no internal links then scattering matrix is exact same
+        # as the ho-chalker matrix
         return A.toarray(), np.where(outgoing)[0], np.where(incoming)[0]
-    elif A[outgoing][:, incoming].shape == (0,0):
-        #only internal links, return empty arrays
+    elif A[outgoing][:, incoming].shape == (0, 0):
+        # only internal links, return empty arrays
         return np.empty([0, 0], complex), np.empty([0], int), np.empty([0], int)
-        
+
     context = mumps.MUMPSContext()
     context.factor(rhs)
     solve = context.solve
     sols = []
 
     for j in range(0, lhs.shape[1], size_group_of_rhs_vectors):
-        tmplhs = lhs[:, j:min(j + size_group_of_rhs_vectors, lhs.shape[1])]
+        tmplhs = lhs[:, j : min(j + size_group_of_rhs_vectors, lhs.shape[1])]
         tmplhs = tmplhs.toarray()
         tmplhs = np.asfortranarray(tmplhs)
         sol = solve(tmplhs)
-        sols.append(A[outgoing][:, internal] @ sol) #S_ib @ (1-S_bb)^-1 @ S_bi
+        sols.append(A[outgoing][:, internal] @ sol)  # S_ib @ (1-S_bb)^-1 @ S_bi
     solutions = np.concatenate(sols, axis=1)
 
     S = (
@@ -643,44 +641,3 @@ def solve_scattering_equations(A, size_group_of_rhs_vectors, incoming_ind=None,
     )
 
     return S, np.where(outgoing)[0], np.where(incoming)[0]
-
-if __name__=="__main__":
-    #print('allowed to do stuff only in here')
-    node_positions = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])
-    links_in_cell = np.asarray(
-    [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]
-     ])
-    lattice_vectors = np.array([1,1.5])
-    dimensions = np.array([4,4])
-    tiled_node_poss = tile_nodes_pos(node_positions, lattice_vectors, dimensions)
-    full_network = tile_links(4, links_in_cell, dimensions)
-    plot_network(tiled_node_poss, lattice_vectors*dimensions, full_network)
-
-# #%%post localization-in-obstructed-insulators
-if __name__=="__main__":
-    #print('allowed to do stuff only in here')
-    
-    node_positions = np.array([[0.25, 0.25],   [0.5, 0.5], [0.75, 0.25]])
-    links_in_cell = np.asarray(
-    [[0, 1, 0, 0], [1, 2, 0, 0], [2, 0, 0, 0], [2, 0, 1, 0], [1, 2, 0, 1]])
-    lattice_vectors = np.array([0.5,1])
-    dimensions = np.array([3,3])
-    tiled_node_poss = tile_nodes_pos(node_positions, lattice_vectors, dimensions)
-    full_network = tile_links(3, links_in_cell, dimensions)
-    plot_network(tiled_node_poss, lattice_vectors*dimensions, full_network)
-
-
-
-# ### TEST TO ADD
-# -test unitarity after cutting (only if it's a valid cutting)
-# -test with peridoidc comparison
-# -test composition thing
-# -test two disconnected networks which should return a 0 result
-#
-#
-#
-#
-#
-#
-
-
diff --git a/codes/modules/tests/test_network_construction.py b/codes/modules/tests/test_network_construction.py
index ef05bd47935350873015a4cbc759121b0139e42c..e7937e25567b2a6e0f026a98648cfb401fa3916f 100644
--- a/codes/modules/tests/test_network_construction.py
+++ b/codes/modules/tests/test_network_construction.py
@@ -1,266 +1,380 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.14.4
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-from codes.modules import networks as nws
 import numpy as np
 from kwant.rmt import circular
-from scipy import linalg
 from scipy import sparse
 import scipy
-import pytest
-
-def random_unitary_network(same_num_inoutleadsx=False):  
 
-    # np.random.seed(seed)
+from codes.modules import networks as nws
 
-    number_of_nodes = np.random.randint(2, 6)  # maybe too small max number
-    num_in_out_p_node = np.random.randint(1, 3, size=number_of_nodes)
 
-    xsize = np.random.randint(1, 4)
+def random_unitary_network(same_num_inoutleadsx=False, minx=1):
+    """
+    Creates a random network with unitary scattering matrices.
+
+    Parameters
+    ----------
+    same_num_inoutleadsx : bool
+        If True, the number of incoming and outgoing leads in the x direction
+        are equal to each other. Potentially desired for some tests
+    minx : int
+        The minimum number of cells in the x direction.
+
+    Returns
+    -------
+    links_in_cell : np.ndarray
+                  The links in the unit cell according to which the network is
+                    tiled.
+    network : np.ndarray
+                The network with the links in the unit cell.
+    smatrices : list
+                The scattering matrices of the nodes.
+    tot : int
+        The total number of cellsin the network.
+
+    """
+
+    nodes_in_cell = np.random.randint(2, 6)  # maybe too small max number
+    num_in_out_p_node = np.random.randint(1, 3, size=nodes_in_cell)
+
+    xsize = np.random.randint(minx, 4)
     ysize = np.random.randint(1, 4)
-    tot = xsize*ysize
+    tot = xsize * ysize
 
-    starting_nodes = np.repeat(np.arange(number_of_nodes), num_in_out_p_node)
+    starting_nodes = np.repeat(np.arange(nodes_in_cell), num_in_out_p_node)
     perm = np.random.permutation(np.arange(len(starting_nodes)))
     ending_nodes = starting_nodes[perm]
 
     start_and_end = np.column_stack((starting_nodes, ending_nodes))
     going_out_x = np.random.choice(
-        [-1, 0, 1], size=len(starting_nodes), p=[1/4, 1/2, 1/4])  # can still be adjusted
-    # the probabilities assigned here aren't very random
+        [-1, 0, 1], size=len(starting_nodes), p=[1 / 4, 1 / 2, 1 / 4]
+    )
+    # the probabilities can still be adjusted
     going_out_y = np.random.choice(
-        [-1, 0, 1], size=len(starting_nodes), p=[1/4, 1/2, 1/4])
+        [-1, 0, 1], size=len(starting_nodes), p=[1 / 4, 1 / 2, 1 / 4]
+    )
     # instead of using probabilities
     going_out_x = np.random.randint(-1, 2, size=len(starting_nodes))
 
-    if same_num_inoutleadsx: 
-        num_xleads = np.random.randint(0, int(len(starting_nodes)/2))
-        if num_xleads==0:
-            num_xleads=1
-        going_out_x = np.random.permutation(np.hstack((np.repeat(-1, num_xleads), np.repeat(1, num_xleads), 
-                                                       np.repeat(0, len(starting_nodes)- 2 * num_xleads))))
+    if same_num_inoutleadsx:
+        num_xleads = np.random.randint(0, int(len(starting_nodes) / 2))
+        if num_xleads == 0:
+            num_xleads = 1
+        going_out_x = np.random.permutation(
+            np.hstack(
+                (
+                    np.repeat(-1, num_xleads),
+                    np.repeat(1, num_xleads),
+                    np.repeat(0, len(starting_nodes) - 2 * num_xleads),
+                )
+            )
+        )
 
     links_in_cell = np.hstack(
-        (start_and_end, np.column_stack((going_out_x, going_out_y))))
-    smatrices = [np.tile(circular(n, "A"), (tot, 1, 1))
-                 for n in num_in_out_p_node]
-    network = nws.tile_links(
-        number_of_nodes, links_in_cell, np.array([xsize, ysize]))
+        (start_and_end, np.column_stack((going_out_x, going_out_y)))
+    )
+    smatrices = [np.tile(circular(n, "A"), (tot, 1, 1)) for n in num_in_out_p_node]
+    network = nws.network(nodes_in_cell, links_in_cell, (xsize, ysize))
 
-    return links_in_cell, network, smatrices, tot
+    return links_in_cell, network, smatrices, tot, (xsize, ysize)
 
 
 def test_unitarity_network_scattering_matrix():
-    
-    links_in_cell, network, smatrices, tot = random_unitary_network()
-    
-    incoming_and_outgoing = np.nonzero(network[:,2]) 
+    """
+    Create a random unitary network, create the ho-chalker operator,
+    solve for the total scattering matrix between the leads
+    and check if it is unitary.
+    """
+
+    _, network, smatrices, _, _ = random_unitary_network()
+
+    incoming_and_outgoing = np.nonzero(network[:, 2])
 
     ho_chalker = nws.ho_chalker_operator(network, smatrices)
     scattering_equations = sparse.csr_array(ho_chalker).astype(complex)
 
-    s, out_indices, in_indices = (
-    nws.solve_scattering_equations(scattering_equations, 1, incoming_and_outgoing, incoming_and_outgoing)
+    s, _, _ = nws.solve_scattering_equations(
+        scattering_equations, 1, incoming_and_outgoing, incoming_and_outgoing
     )
+    assert np.allclose(s @ np.conj(s).T, np.eye(s.shape[0]))  # np.allclose
 
-    assert np.allclose(s @ np.conj(s).T, np.eye(s.shape[0])) #np.allclose
 
 # + tags=[]
 
+
 def test_unitarity_cut_network():
-    #creating the uncut network
-    n_in_cell = 4
+    """
+    Create a ho-chalker network of 8 by 8, cut out all nodes in the region of
+    x>2.5 and x<6.5 and y>2.5 and y<6.5 and thus create extra leads for the
+    network. Solve the scattering equations between the leads and check if it
+    is unitary.
+    """
+    # creating the uncut network
+    nodes_in_cell = 4
     links_in_cell = np.asarray(
-        [[0, 1, 0, 0], [1, 3, 0, 0], [3, 2, 0, 0], [2, 0, 0, 0], [2, 0, 0, 1], [1, 3, 0, -1], [3, 2, 1, 0], [0, 1, -1, 0]
-         ])
+        [
+            [0, 1, 0, 0],
+            [1, 3, 0, 0],
+            [3, 2, 0, 0],
+            [2, 0, 0, 0],
+            [2, 0, 0, 1],
+            [1, 3, 0, -1],
+            [3, 2, 1, 0],
+            [0, 1, -1, 0],
+        ]
+    )
     x = y = 8
-    dimensions = np.array([x,y])
-    network = nws.tile_links(n_in_cell, links_in_cell, dimensions)
-
-    #Giving nodes real space postions to make the cutting easier
-    node_positions_uc = np.array([[0.25, 0.25],   [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]])
-    lattice_vectors = np.array([1.2,1.2])
-    dimensions = np.array([x,y])
-    tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, dimensions)
+    size = (x, y)
+    network = nws.network(nodes_in_cell, links_in_cell, size)
 
+    # Giving nodes real space postions to make the cutting easier
+    node_positions_uc = np.array(
+        [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]]
+    )
+    lattice_vectors = np.array([[1.2, 0], [0, 1.2]])
+    size = (x, y)
+    tiled_node_pos = nws.tile_nodes_pos(node_positions_uc, lattice_vectors, size)
 
-    #determining which nodes to delete
-    ins = [] #same cutting of network as in the examples file
-    for net in network[:,0]:
+    # determining which nodes to delete
+    ins = []  # same cutting of network as in the examples file
+    for net in network[:, 0]:
         pos_x, pos_y = tiled_node_pos[int(net)]
-        if pos_x > 2.5 and pos_x < 6.5 and pos_y > 2.5 and pos_y <6.5:
+        if pos_x > 2.5 and pos_x < 6.5 and pos_y > 2.5 and pos_y < 6.5:
             ins.append(net)
     outs = []
-    for net in network[:,1]:
+    for net in network[:, 1]:
         pos_x, pos_y = tiled_node_pos[int(net)]
-        if pos_x > 2.5 and pos_x < 6.5 and pos_y > 2.5 and pos_y <6.5:
+        if pos_x > 2.5 and pos_x < 6.5 and pos_y > 2.5 and pos_y < 6.5:
             outs.append(net)
 
-    tot = x*y
-    s_0 = circular(2, "A") #random unitary matrices
+    tot = x * y
+    s_0 = circular(2, "A")  # random unitary matrices
     s_1 = circular(2, "A")
     s_2 = circular(2, "A")
     s_3 = circular(2, "A")
 
-    user_s = ( [(np.zeros((tot, 1, 1)) + s_0.reshape(1, s_0.shape[0], s_0.shape[1]))] +
-                        [(np.zeros((tot, 1, 1)) + s_1.reshape(1, s_1.shape[0], s_1.shape[1]))] +
-                        [(np.zeros((tot, 1, 1)) + s_2.reshape(1, s_2.shape[0], s_2.shape[1]))] +
-                        [(np.zeros((tot, 1, 1)) + s_3.reshape(1, s_3.shape[0], s_3.shape[1]))] ) 
+    smatrices = (
+        [np.tile(s_0, (tot, 1, 1))]
+        + [np.tile(s_1, (tot, 1, 1))]
+        + [np.tile(s_2, (tot, 1, 1))]
+        + [np.tile(s_3, (tot, 1, 1))]
+    )
 
-    incoming_and_outgoing = np.nonzero(network[:,2]) 
     sparse = False
-    ho_chalker = nws.ho_chalker_operator(network, user_s, sparse = sparse)
+    ho_chalker = nws.ho_chalker_operator(network, smatrices, sparse=sparse)
 
     in_leads, out_leads = nws.determine_leads_from_nodes(network, ins, outs)
-    ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(ho_chalker, network, in_leads, out_leads)
-    
-    #to scattering equations to test unitartity
-    all_incoming_n = np.hstack((incoming_n, np.nonzero(network_new[:,2])[0])).flatten()
-    all_outgoing_n = np.hstack((outgoing_n, np.nonzero(network_new[:,2])[0] )).flatten()
+    ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(
+        ho_chalker, network, in_leads, out_leads
+    )
+
+    # to scattering equations to test unitartity
+    all_incoming_n = np.hstack((incoming_n, np.nonzero(network_new[:, 2])[0])).flatten()
+    all_outgoing_n = np.hstack((outgoing_n, np.nonzero(network_new[:, 2])[0])).flatten()
 
     scattering_equations = scipy.sparse.csr_array(ho_chalker_changed).astype(complex)
-    s, out_indices, in_indices = (
-    nws.solve_scattering_equations(scattering_equations, 1, all_incoming_n, all_outgoing_n)
+    s, _, _ = nws.solve_scattering_equations(
+        scattering_equations, 1, all_incoming_n, all_outgoing_n
     )
 
     assert np.allclose(s @ np.conj(s).T, np.eye(s.shape[0]))
 
 
-
 # + tags=[]
 def test_random_disconnected_networks():
-    
     np.random.seed(38)
-    _, network1, smatrices1, tot1 = random_unitary_network()
-    _, network2, smatrices2, tot2 = random_unitary_network()
+    _, network1, smatrices1, _, _ = random_unitary_network()
+    _, network2, smatrices2, _, _ = random_unitary_network()
 
-    highest_node = np.max(network1[:,:2].flatten())
+    highest_node = np.max(network1[:, :2].flatten())
 
-    network2[:,:2] = network2[:, :2]+highest_node+1
+    network2[:, :2] = network2[:, :2] + highest_node + 1
 
     both_networks = np.vstack((network1, network2))
-    random_inleads = np.unique(np.random.randint(0, len(network1)-1, size=int(len(network1)/5)))
-    random_outleads = np.unique(np.random.randint(len(network1), len(both_networks), size=int(len(network2)/5)))
+    random_inleads = np.unique(
+        np.random.randint(0, len(network1) - 1, size=int(len(network1) / 5))
+    )
+    random_outleads = np.unique(
+        np.random.randint(
+            len(network1), len(both_networks), size=int(len(network2) / 5)
+        )
+    )
 
-    all_smatrices = smatrices1+smatrices2 
-    ho_chalker = nws.ho_chalker_operator(both_networks, all_smatrices, sparse = False)
-    ho_chalker_changed, network_new, incoming_n, outgoing_n = nws.cut_ho_chalker(ho_chalker, both_networks, random_inleads, random_outleads)
+    all_smatrices = smatrices1 + smatrices2
+    ho_chalker = nws.ho_chalker_operator(both_networks, all_smatrices, sparse=False)
+    ho_chalker_changed, _, _, _ = nws.cut_ho_chalker(
+        ho_chalker, both_networks, random_inleads, random_outleads
+    )
 
-    assert ho_chalker_changed.shape[0]==0 and ho_chalker_changed.shape[1]==0
+    assert ho_chalker_changed.shape[0] == 0 and ho_chalker_changed.shape[1] == 0
 
 
 # -
 
+
 def test_1_by_1():
-    nws.solve_scattering_equations(sparse.coo_array(1 - np.eye(2)).astype(complex), 1, [0], [0])
+    """
+    Test that the solve_scattering_equations function works for a 1x1 network"""
+    nws.solve_scattering_equations(
+        sparse.coo_array(1 - np.eye(2)).astype(complex), 1, [0], [0]
+    )
 
 
-def test_sparse_and_dense_ho_chalker(): 
+def test_sparse_and_dense_ho_chalker():
+    """
+    Test that the sparse and dense ho chalker operators give the same result
+    given a random unitary network"""
+    _, network, smatrices, _, _ = random_unitary_network()
 
-    links_in_cell, network, smatrices, tot = random_unitary_network()
+    ho_chalker_sparse = nws.ho_chalker_operator(network, smatrices, sparse=True)
+    ho_chalker_dense = nws.ho_chalker_operator(network, smatrices, sparse=False)
+    ho_chalker_sparse_at_k = nws.ho_chalker_operator(
+        network, smatrices, sparse=True, k=np.array([0, 0])
+    )
+    ho_chalker_dense_at_k = nws.ho_chalker_operator(
+        network, smatrices, sparse=False, k=np.array([0, 0])
+    )
 
-    ho_chalker_sparse = nws.ho_chalker_operator(network, smatrices, sparse = True)
-    ho_chalker_dense = nws.ho_chalker_operator(network, smatrices, sparse = False)
-    
+    assert np.allclose(ho_chalker_sparse_at_k.toarray(), ho_chalker_dense_at_k)
     assert np.allclose(ho_chalker_sparse.toarray(), ho_chalker_dense)
+    assert np.allclose(ho_chalker_sparse_at_k.toarray(), ho_chalker_dense_at_k)
 
 
-def test_different_input_formats_to_hochalker(): 
-    
-    links_in_cell, network, smatrices, tot = random_unitary_network()
-    
-    smatrices_diffshaped = []
-    
-    for s in smatrices: #wasnt sure how to differently shape them so just in 1 block diag already
-        sblock = linalg.block_diag(*s)
-        smatrices_diffshaped.append([sblock])
+def test_specific_network_construction():
+    nodes_in_cell = 4
+    links_in_cell = np.asarray(
+        [
+            [0, 1, 0, 0],
+            [1, 3, 0, 0],
+            [3, 2, 0, 0],
+            [2, 0, 0, 0],
+            [2, 0, 0, 1],
+            [1, 3, 0, -1],
+            [3, 2, 1, 0],
+            [0, 1, -1, 0],
+        ]
+    )
+    x = y = 2
+    size = (x, y)
+    network = nws.network(nodes_in_cell, links_in_cell, size)
+    saved_network = np.array(
+        [
+            [0, 4, 0, 0],
+            [1, 5, 0, 0],
+            [2, 6, 0, 0],
+            [3, 7, 0, 0],
+            [4, 12, 0, 0],
+            [5, 13, 0, 0],
+            [6, 14, 0, 0],
+            [7, 15, 0, 0],
+            [12, 8, 0, 0],
+            [13, 9, 0, 0],
+            [14, 10, 0, 0],
+            [15, 11, 0, 0],
+            [8, 0, 0, 0],
+            [9, 1, 0, 0],
+            [10, 2, 0, 0],
+            [11, 3, 0, 0],
+            [8, 1, 0, 0],
+            [9, 0, 0, 1],
+            [10, 3, 0, 0],
+            [11, 2, 0, 1],
+            [4, 13, 0, -1],
+            [5, 12, 0, 0],
+            [6, 15, 0, -1],
+            [7, 14, 0, 0],
+            [12, 10, 0, 0],
+            [13, 11, 0, 0],
+            [14, 8, 1, 0],
+            [15, 9, 1, 0],
+            [0, 6, -1, 0],
+            [1, 7, -1, 0],
+            [2, 4, 0, 0],
+            [3, 5, 0, 0],
+        ]
+    )
 
-    ho_chalker_1 = nws.ho_chalker_operator(network, smatrices, sparse = False)
-    ho_chalker_2 = nws.ho_chalker_operator(network, smatrices_diffshaped, sparse = False)
-    
-    assert np.allclose(ho_chalker_1, ho_chalker_2)
+    assert np.allclose(network, saved_network)
 
 
-def test_network_invariant(): 
-    
-    links_in_cell, network, smatrices, tot_cells = random_unitary_network()
-    incoming_and_outgoing_b = np.nonzero(network[:,2]) 
+def test_network_invariant():
+    """
+    The network invariant is the order in which the user fills in the links in
+    the unit cell. The code should preserve this order throughout all the
+    functions. We test this by permuting the network, and the s-matrices away
+    from the original order, creating the new ho-chalker with this changed order
+    and then unpermuting the ho-chalker with the same permutation to compare
+    it to the very original ho-chalker."""
 
-    ho_chalker_before =  nws.ho_chalker_operator(network, smatrices, sparse = False)
-    
-    #permutation to the network 
+    links_in_cell, network, smatrices, tot_cells, _ = random_unitary_network()
+    ho_chalker_before = nws.ho_chalker_operator(network, smatrices, sparse=False)
+
+    # permutation to the network
     perm = np.random.permutation(np.arange(len(links_in_cell)))
     network_permed = network.reshape((len(links_in_cell), tot_cells, 4))
     network_permed = network_permed[perm]
     network_permed = network_permed.reshape((-1, 4))
-    
-    incoming_and_outgoing_a = np.nonzero(network_permed[:,2])
-    
+
     perm = np.argsort(perm)
-    
-    #permutation to the smatrices
-    links_and_perm = np.hstack((links_in_cell, perm.reshape((-1,1))))
+
+    # permutation to the smatrices
+    links_and_perm = np.hstack((links_in_cell, perm.reshape((-1, 1))))
 
     s_matrices_permed = []
-            
-    for node in range(len(smatrices)): 
-    
-        ll_to_change1 = links_and_perm[np.where(links_and_perm[:,0]==node)]
-        ll_to_change2 = links_and_perm[np.where(links_and_perm[:,1]==node)]
-     
-        permy1 = np.argsort(np.argsort(np.argsort(ll_to_change1[:, 4]))) #probably same as just one argsort but am too tired to think
+
+    for node in range(len(smatrices)):
+        ll_to_change1 = links_and_perm[np.where(links_and_perm[:, 0] == node)]
+        ll_to_change2 = links_and_perm[np.where(links_and_perm[:, 1] == node)]
+
+        # probably same as just one argsort?
+        permy1 = np.argsort(np.argsort(np.argsort(ll_to_change1[:, 4])))
         permy2 = np.argsort(np.argsort(np.argsort(ll_to_change2[:, 4])))
-    
+
         s_matrix_to_change = smatrices[node][0]
-        s_matrix_changed = s_matrix_to_change[permy1, :][:, permy2]        
-        s_matrix_changed_tiled = np.tile(s_matrix_changed, (tot_cells,1,1))
+        s_matrix_changed = s_matrix_to_change[permy1, :][:, permy2]
+        s_matrix_changed_tiled = np.tile(s_matrix_changed, (tot_cells, 1, 1))
         s_matrices_permed.append(s_matrix_changed_tiled)
-   
-    ho_chalker_after = nws.ho_chalker_operator(network_permed, s_matrices_permed, sparse=False)
-  
-    perm_ho = (np.repeat(perm, tot_cells)*tot_cells) + np.tile(np.arange(tot_cells), len(links_in_cell))
-    ho_chalker_after2 = ho_chalker_after[:,perm_ho][perm_ho,:]
-   
+
+    ho_chalker_after = nws.ho_chalker_operator(
+        network_permed, s_matrices_permed, sparse=False
+    )
+
+    perm_ho = (np.repeat(perm, tot_cells) * tot_cells) + np.tile(
+        np.arange(tot_cells), len(links_in_cell)
+    )
+    ho_chalker_after2 = ho_chalker_after[:, perm_ho][perm_ho, :]
+
     assert np.allclose(ho_chalker_before, ho_chalker_after2)
 
-@pytest.mark.xfail
-def test_composition_two_networks():
-    #create random network (with equal inleads to outleads and for ease only in x)
-    links_in_cell, network1, smatrices1, tot = random_unitary_network(same_num_inoutleadsx=True)
 
-    #duplicate the network 
-    highest_node = np.max(network1[:,:2].flatten())
-    network2 = network1.copy() 
-    network2[:,:2] = network2[:, :2]+highest_node+1
+# @pytest.mark.xfail
+# def test_composition_two_networks():
+#     """Don't look in here, it's a mess and not correct yet"""
+#     # create random network (with equal inleads to outleads and for ease only 
+#     # in x)
+#     _, network1, _, _, _ = random_unitary_network(same_num_inoutleadsx=True)
 
-    #define in and outleads of the networks in basis of the double network
-    inleads1 = np.nonzero(network1[:,2]<0)
-    outleads1 = np.nonzero(network1[:,2]>0)
-    inleads2 = np.nonzero(network2[:,2]<0) + len(network1) + 1
-    outleads2 = np.nonzero(network2[:,2]>0) + len(network1) + 1
+#     # duplicate the network
+#     highest_node = np.max(network1[:, :2].flatten())
+#     network2 = network1.copy()
+#     network2[:, :2] = network2[:, :2] + highest_node + 1
 
+#     # define in and outleads of the networks in basis of the double network
+#     inleads1 = np.nonzero(network1[:, 2] < 0)
+#     outleads1 = np.nonzero(network1[:, 2] > 0)
+#     inleads2 = np.nonzero(network2[:, 2] < 0) + len(network1) + 1
+#     outleads2 = np.nonzero(network2[:, 2] > 0) + len(network1) + 1
 
-    #double the network 
-    #unfortunately relinkin function introduces dummy nodes which will not give us the same result
-    #going to run into the same issue of not being able to guarantee the network invariant
-    #which will be important when calculating the total scattering
-    double_network = np.vstack((network1, network2))
+#     # double the network
+#     # unfortunately relinkin function introduces dummy nodes which will not 
+#     # give us the same result
+#     # going to run into the same issue of not being able to guarantee the 
+#     # network invariant
+#     # which will be important when calculating the total scattering
+#     double_network = np.vstack((network1, network2))
 
-    #calculate scattering matrix double network between inleads1 to outleads2
+#     # calculate scattering matrix double network between inleads1 to outleads2
 
-    #make the ho-chalker cut with input inleads1 to outleads1
+#     # make the ho-chalker cut with input inleads1 to outleads1
 
-    #calculate resulting cut scattering matrix
+#     # calculate resulting cut scattering matrix
 
-    #assert that the two scattering matrices are the same 
+#     # assert that the two scattering matrices are the same
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..f3346850e446f9c6018b08a18ce48e564489461b
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,7 @@
+[pytest]
+minversion = 7.0
+addopts = --cov-config=.coveragerc --verbose --junitxml=junit.xml
+    --cov=codes/modules --cov-report term --cov-report html --cov-report xml
+    --ruff
+testpaths = codes/modules
+required_plugins = pytest-randomly pytest-cov pytest-ruff