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cds-monte-carlo-methods/Exercise sheet 8/exercise_sheet_08.ipynb

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"# Exercise sheet\n",
"\n",
"Some general remarks about the exercises:\n",
"* For your convenience functions from the lecture are included below. Feel free to reuse them without copying to the exercise solution box.\n",
"* For each part of the exercise a solution box has been added, but you may insert additional boxes. Do not hesitate to add Markdown boxes for textual or LaTeX answers (via `Cell > Cell Type > Markdown`). But make sure to replace any part that says `YOUR CODE HERE` or `YOUR ANSWER HERE` and remove the `raise NotImplementedError()`.\n",
"* Please make your code readable by humans (and not just by the Python interpreter): choose informative function and variable names and use consistent formatting. Feel free to check the [PEP 8 Style Guide for Python](https://www.python.org/dev/peps/pep-0008/) for the widely adopted coding conventions or [this guide for explanation](https://realpython.com/python-pep8/).\n",
"* Make sure that the full notebook runs without errors before submitting your work. This you can do by selecting `Kernel > Restart & Run All` in the jupyter menu.\n",
"* For some exercises test cases have been provided in a separate cell in the form of `assert` statements. When run, a successful test will give no output, whereas a failed test will display an error message.\n",
"* Each sheet has 100 points worth of exercises. Note that only the grades of sheets number 2, 4, 6, 8 count towards the course examination. Submitting sheets 1, 3, 5, 7 & 9 is voluntary and their grades are just for feedback.\n",
"\n",
"Please fill in your name here:"
]
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"NAME = \"Kees van Kempen\"\n",
"NAMES_OF_COLLABORATORS = \"\""
]
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"---"
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"**Exercise sheet 8**\n",
"\n",
"Code from the lectures:"
]
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"import numpy as np\n",
"rng = np.random.default_rng() \n",
"import matplotlib.pylab as plt\n",
"%matplotlib inline\n",
"\n",
"def fan_triangulation(n):\n",
" '''Generates a fan-shaped triangulation of even size n.'''\n",
" return np.array([[(i-3)%(3*n),i+5,i+4,(i+6)%(3*n),i+2,i+1] \n",
" for i in range(0,3*n,6)],dtype=np.int32).flatten()\n",
"\n",
"def is_fpf_involution(adj):\n",
" '''Test whether adj defines a fixed-point free involution.'''\n",
" for x, a in enumerate(adj):\n",
" if a < 0 or a >= len(adj) or x == a or adj[a] != x:\n",
" return False\n",
" return True\n",
"\n",
"from collections import deque \n",
"\n",
"def triangle_neighbours(adj,i):\n",
" '''Return the indices of the three neighboring triangles.'''\n",
" return [j//3 for j in adj[3*i:3*i+3]]\n",
"\n",
"def connected_components(adj):\n",
" '''Calculate the number of connected components of the triangulation.'''\n",
" n = len(adj)//3 # the number of triangles\n",
" # array storing the component index of each triangle\n",
" component = np.full(n,-1,dtype=np.int32) \n",
" index = 0\n",
" for i in range(n):\n",
" if component[i] == -1: # new component found, let us explore it\n",
" component[i] = index\n",
" queue = deque([i]) # use an exploration queue for breadth-first search\n",
" while queue:\n",
" for nbr in triangle_neighbours(adj,queue.pop()):\n",
" # the neighboring triangle has not been explored yet\n",
" if component[nbr] == -1: \n",
" component[nbr] = index\n",
" queue.appendleft(nbr) # add it to the exploration queue\n",
" index += 1\n",
" return index\n",
"\n",
"def next_around_triangle(i):\n",
" '''Return the label of the side following side i in counter-clockwise direction.'''\n",
" return i - i%3 + (i+1)%3\n",
"\n",
"def prev_around_triangle(i):\n",
" '''Return the label of the side preceding side i in counter-clockwise direction.'''\n",
" return i - i%3 + (i-1)%3\n",
"\n",
"def vertex_list(adj):\n",
" '''\n",
" Return the number of vertices and an array `vertex` of the same size \n",
" as `adj`, such that `vertex[i]` is the index of the vertex at the \n",
" start (in ccw order) of the side labeled `i`.\n",
" '''\n",
" # a side i that have not been visited yet has vertex[i]==-1\n",
" vertex = np.full(len(adj),-1,dtype=np.int32) \n",
" vert_index = 0 \n",
" for i in range(len(adj)):\n",
" if vertex[i] == -1:\n",
" side = i\n",
" while vertex[side] == -1: # find all sides that share the same vertex\n",
" vertex[side] = vert_index\n",
" side = next_around_triangle(adj[side])\n",
" vert_index += 1\n",
" return vert_index, vertex\n",
"\n",
"def number_of_vertices(adj):\n",
" '''Calculate the number of vertices in the triangulation.'''\n",
" return vertex_list(adj)[0]\n",
"\n",
"def is_sphere_triangulation(adj):\n",
" '''Test whether adj defines a triangulation of the 2-sphere.'''\n",
" if not is_fpf_involution(adj) or connected_components(adj) != 1:\n",
" return False\n",
" num_vert = number_of_vertices(adj)\n",
" num_face = len(adj)//3\n",
" num_edge = len(adj)//2\n",
" # verify Euler's formula for the sphere\n",
" return num_vert - num_edge + num_face == 2\n",
"\n",
"def flip_edge(adj,i):\n",
" if adj[i] == next_around_triangle(i) or adj[i] == prev_around_triangle(i):\n",
" # flipping an edge that is adjacent to the same triangle on both sides makes no sense\n",
" return False\n",
" j = prev_around_triangle(i)\n",
" k = adj[i]\n",
" l = prev_around_triangle(k)\n",
" n = adj[l]\n",
" adj[i] = n # it is important that we first update\n",
" adj[n] = i # these adjacencies, before determining m,\n",
" m = adj[j] # to treat the case j == n appropriately\n",
" adj[k] = m\n",
" adj[m] = k\n",
" adj[j] = l\n",
" adj[l] = j\n",
" return True\n",
"\n",
"def random_flip(adj):\n",
" random_side = rng.integers(0,len(adj))\n",
" return flip_edge(adj,random_side)\n",
"\n",
"import networkx as nx\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
"\n",
"def triangulation_edges(triangulation,vertex):\n",
" '''Return a list of vertex-id pairs corresponding to the edges in the triangulation.'''\n",
" return [(vertex[i],vertex[j]) for i,j in enumerate(triangulation) if i < j]\n",
"\n",
"def triangulation_triangles(triangulation,vertex):\n",
" '''Return a list of vertex-id triples corresponding to the triangles in the triangulation.'''\n",
" return [vertex[i:i+3] for i in range(0,len(triangulation),3)]\n",
"\n",
"def plot_triangulation_3d(adj):\n",
" '''Display an attempt at embedding the triangulation in 3d.'''\n",
" num_vert, vertex = vertex_list(adj)\n",
" edges = triangulation_edges(adj,vertex)\n",
" triangles = triangulation_triangles(adj,vertex)\n",
" # use the networkX 3d graph layout algorithm to find positions for the vertices\n",
" pos = np.array(list(nx.spring_layout(nx.Graph(edges),dim=3).values()))\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, projection='3d')\n",
" tris = Poly3DCollection(pos[triangles])\n",
" tris.set_edgecolor('k')\n",
" ax.add_collection3d(tris)\n",
" ax.set_xlim3d(np.amin(pos[:,0]),np.amax(pos[:,0]))\n",
" ax.set_ylim3d(np.amin(pos[:,1]),np.amax(pos[:,1]))\n",
" ax.set_zlim3d(np.amin(pos[:,2]),np.amax(pos[:,2]))\n",
" plt.show()\n",
" \n",
"def vertex_neighbors_list(adj):\n",
" '''Return a list `neighbors` such that `neighbors[v]` is a list of neighbors of the vertex v.'''\n",
" num_vertices, vertex = vertex_list(adj)\n",
" neighbors = [[] for _ in range(num_vertices)]\n",
" for i,j in enumerate(adj):\n",
" neighbors[vertex[i]].append(vertex[j])\n",
" return neighbors\n",
"\n",
"def vertex_distance_profile(adj,max_distance=30):\n",
" '''Return array `profile` of size `max_distance` such that `profile[r]` is the number\n",
" of vertices that have distance r to a randomly chosen initial vertex.'''\n",
" profile = np.zeros((max_distance),dtype=np.int32)\n",
" neighbors = vertex_neighbors_list(adj)\n",
" num_vertices = len(neighbors)\n",
" start = rng.integers(num_vertices) # random starting vertex\n",
" distance = np.full(num_vertices,-1,dtype=np.int32) # array tracking the known distances (-1 is unknown)\n",
" queue = deque([start]) # use an exploration queue for the breadth-first search\n",
" distance[start] = 0\n",
" profile[0] = 1 # of course there is exactly 1 vertex at distance 0\n",
" while queue:\n",
" current = queue.pop()\n",
" d = distance[current] + 1 # every unexplored neighbour will have this distance\n",
" if d >= max_distance:\n",
" break\n",
" for nbr in neighbors[current]:\n",
" if distance[nbr] == -1: # this neighboring vertex has not been explored yet\n",
" distance[nbr] = d\n",
" profile[d] += 1\n",
" queue.appendleft(nbr) # add it to the exploration queue\n",
" return profile\n",
" \n",
"def perform_sweeps(adj,t):\n",
" '''Perform t sweeps of flip moves, where 1 sweep is N moves.'''\n",
" for _ in range(len(adj)*t//3):\n",
" random_flip(adj)\n",
"\n",
"def batch_estimate(data,observable,k):\n",
" '''Devide data into k batches and apply the function observable to each.\n",
" Returns the mean and standard error.'''\n",
" batches = np.reshape(data,(k,-1))\n",
" values = np.apply_along_axis(observable, 1, batches)\n",
" return np.mean(values), np.std(values)/np.sqrt(k-1)"
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"## Estimating Hausdorff dimensions in various 2D quantum gravity models \n",
"\n",
"**(100 Points)**\n",
"\n",
"In the lecture we considered the model of two-dimensional Dynamical Triangulations of the 2-sphere. The corresponding partition function is\n",
"$$ Z^{U}_{S^2,N} = \\sum_T 1, \\tag{1}$$\n",
"where the sum is over all triangulations of size $N$ with the topology of $S^2$, each of which is represented as an adjacency list $\\operatorname{adj}: \\{0,\\ldots,3N-1\\} \\to \\{0,\\ldots,3N-1\\}$. To emphasize that we are dealing with the **uniform** probability distribution on such triangulations, we have added the label $^U$. It is a lattice model of two-dimensional Euclidean quantum gravity with no coupled matter.\n",
"\n",
"One can also consider two-dimensional quantum gravity coupled to matter fields (e.g. a scalar field) supported on the geometry. Formally the corresponding path integral in the continuum reads\n",
"$$ Z = \\int [\\mathcal{D}g_{ab}]\\int [\\mathcal{D}\\phi] e^{-\\frac{1}{\\hbar}(S_E[g_{ab}] + S_m[\\phi,g_{ab}])} = \\int [\\mathcal{D}g_{ab}]e^{-\\frac{1}{\\hbar}S_E[g_{ab}]} Z^*_m[g_{ab}],$$\n",
"where $S_m[\\phi,g_{ab}]$ and $Z_m[g_{ab}]$ are the matter action and path integral of the field $\\phi$ on the geometry described by $g_{ab}$. The natural analogue in Dynamical Triangulations is\n",
"$$Z^*_{S^2,N} = \\sum_T Z^*_m[T],$$\n",
"where the sum is over the same triangulations as in (1) but now the summand $Z^*_m[T]$ is the lattice partition function of a matter system supported on the triangulation $T$, which generically depends in a non-trivial way on $T$. For instance, the matter system could be an Ising model in which the spin are supported on the triangles of $T$ and $Z^{\\text{Ising}}_m[T]$ would be the corresponding Ising partition function.\n",
"In other words, when Dynamical Triangulations are coupled to matter the uniform distribution $\\pi^U(T) = 1/Z^U_{S^2,N}$ is changed into a non-uniform distribution $\\pi^*(T) = Z^*_m[T] / Z^*_{S^2,N}$. This can have significant effect on the critical exponents of the random triangulation as $N\\to\\infty$, like the Hausdorff dimension. \n",
"\n",
"The goal of this exercise is to estimate the **Hausdorff dimension** of random triangulations in four different models and to conclude based on this that they belong to four different universality classes (i.e. that if they possess well-defined continuum limits that they are described by four different EQFTs): \n",
"* $Z^{U}_{S^2,N}$: the standard Dynamical Triangulations with **U**niform distribution (U)\n",
"* $Z^{W}_{S^2,N}$: triangulations coupled to a matter system called a Schnyder **W**ood (W)\n",
"* $Z^{S}_{S^2,N}$: triangulations coupled to a matter system called a **S**panning tree (S)\n",
"* $Z^{B}_{S^2,N}$: triangulations coupled to a matter system called a **B**ipolar orientation (B)\n",
"\n",
"What these matter systems precisely represent will not be important. We have provided for you a **black box generator** that samples from the corresponding four distributions $\\pi^U(T)$, $\\pi^W(T)$, $\\pi^S(T)$, $\\pi^B(T)$. It does so in an efficient manner (linear time in $N$) using direct Monte Carlo sampling algorithms and therefore returns independent samples with exactly the desired distribution $\\pi^*(T)$ (within numerical precision).\n",
"\n",
"The black box generator is provided by the executable program `generator` provided to you on the science server. It can be called directly from this notebook with the following function `generate_random_triangulation`, that takes the desired size $N$ and model (`'U'`,`'W'`, `'S'`, `'B'`) and returns a single random triangulation in the usual form of an adjacency list."
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"import subprocess\n",
"\n",
"def generate_random_triangulation(n,model):\n",
" '''\n",
" Returns a random triangulation generated by the program `generator` in the form \n",
" of an array of length 3n storing the adjacency information of the triangle sides.\n",
" Parameters:\n",
" n - number of triangles in the triangulation, must be positive and even\n",
" model - a one-letter string specifying the model from which the triangulation is sampled:\n",
" 'U': Uniform triangulations\n",
" 'W': Schnyder-Wood-decorated triangulations\n",
" 'S': Spanning-tree decorated triangulations\n",
" 'B': Bipolar-oriented triangulations\n",
" '''\n",
" program = \"/vol/cursus/NM042B/bin/generator\"\n",
" output = subprocess.check_output([program,\"-s{}\".format(n),\"-t{}\".format(model)]).decode('ascii').split('\\n')[:-1]\n",
" return np.array([int(num) for num in output],dtype=np.int32)\n",
"\n",
"adj = generate_random_triangulation(100,'B')\n",
"is_sphere_triangulation(adj)"
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"Recall that the **distance profile** $\\rho_T(r)$ of a triangulation is defined as \n",
"$$ \\rho_T(r) = \\frac{1}{V} \\sum_{x=0}^{V-1} \\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}},$$\n",
"where $V = (N+4)/2$ is the number of vertices and $d_T(x,y)$ is the graph distance between the vertices with label $x$ and $y$."
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"**(a)** Let $T$ be a random triangulation of size $N$ and $X$, $Y$ two independent numbers chosen uniformly from $0,\\ldots,V-1$, corresponding to two random vertices in $T$. Explain with a calculation that $\\frac{1}{V}\\mathbb{E}[ \\rho_T(r) ] = \\mathbb{P}(d_T(X,Y) = r)$ and that the expected distance between $X$ and $Y$ is related to the distance profile via\n",
"\n",
"$$\n",
"\\mathbb{E}[d_T(X,Y)] = \\frac{1}{V}\\sum_{r=0}^\\infty r\\, \\mathbb{E}[ \\rho_T(r) ]. \\tag{2}\n",
"$$\n",
"\n",
"**(20 pts)**"
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"**To proof**\n",
"\n",
"$\\frac{1}{V}\\mathbb{E}[ \\rho_T(r) ] = \\mathbb{P}(d_T(X,Y) = r)$\n",
"\n",
"**Proof**\n",
"\n",
"$$\n",
"\\frac{1}{V} \\mathbb{E}\\left[ \\rho_T(r)\\right]\n",
" = \\frac{1}{V} \\mathbb{E} \\left[\\frac{1}{V} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n",
" = \\frac{1}{V^2} \\mathbb{E} \\left[ \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n",
" = \\frac{1}{V^2} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{E} \\left[ \\mathbf{1}_{\\{d_T(x,y)=r\\}} \\right]\n",
"$$\n",
"\n",
"The order of summation is changed, as the sum of expectation values is equal to the expectation value of the sum.\n",
"The latter expectation value of the indicator function is exactly equal to the chance $\\mathbb{P}(d_T(x,y)=r)$ for given $x, y$.\n",
"For the uniformly distributed $X, Y$, we find $\\mathbb{P}(X = x) = \\frac{1}{V} = \\mathbb{P}(Y = y)$.\n",
"This allows us to write the right hand side as follows.\n",
"\n",
"$$\n",
"\\frac{1}{V} \\mathbb{E}\\left[ \\rho_T(r)\\right]\n",
" = \\frac{1}{V^2} \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{P}(d_T(x,y)=r)\n",
" = \\sum_{x=0}^{V-1}\\sum_{y=0}^{V-1} \\mathbb{P}(X = x) \\mathbb{P}(Y = y) \\mathbb{P}(d_T(x,y)=r)\n",
" = \\mathbb{P}(d_T(X,Y)=r),\n",
"$$\n",
"\n",
"which is what we sought.\n",
"\n",
"Using this result, it is just a matter of writing out the definition of an expectation value to get to the result.\n",
"\n",
"$$\n",
"\\mathbb{E}[d_T(X,Y)] = \\sum_{r=0}^\\infty r\\, \\mathbb{P}(d_T(X,Y) = r) = \\frac{1}{V}\\sum_{r=0}^\\infty r\\, \\mathbb{E}[ \\rho_T(r) ].\n",
"$$"
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"**(b)** We will work under the assumption that \n",
"\n",
"$$\n",
"\\mathbb{E}[\\rho_T(r)] \\approx V^{1-1/d_H} f(r V^{-1/d_H})\n",
"$$ \n",
"\n",
"for a positive real number $d_H$ called the **Hausdorff dimension** and a continuous function $f$ that are both independent of $N$ but do depend on the model. Show that \n",
"\n",
"$$\n",
"\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}, \\qquad c = \\int_0^\\infty \\mathrm{d}x\\,x\\,f(x). \\tag{3}\n",
"$$\n",
"\n",
"_Hint:_ Approximate the summation by an integral. **(15 pts)**"
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"**To proof**\n",
"\n",
"$$\n",
"\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}, \\qquad c = \\int_0^\\infty \\mathrm{d}x\\,x\\,f(x)\n",
"$$\n",
"\n",
"**Proof**\n",
"\n",
"$$\n",
"\\mathbb{E} \\left[ d_T(X,Y) \\right]\n",
" = \\frac{1}{V} \\sum_{r=0}^\\infty r\\, \\mathbb{E} \\left[ \\rho_T(r) \\right]\n",
" = \\frac{1}{V} \\sum_{r=0}^\\infty rV^{1-1/d_H}f(rV^{-1/d_H})\n",
" = \\frac{1}{V} \\sum_{r=0}^\\infty xV^{1/d_H} \\cdot V^{1-1/d_H}f(x)\n",
" = \\sum_{r=0}^\\infty xf(x),\n",
"$$\n",
"where the first equality sign is due to (2), the second due to the given assumption, the third using $x = rV^{-1/d_H}$.\n",
"\n",
"Now we approximate the summation by an integral.\n",
"\n",
"$$\n",
"\\sum_{r=0}^\\infty xf(x)\n",
" \\approx \\int_{r=0}^\\infty xf(x)dr\n",
" = V^{1/d_H} \\int_{x=0}^\\infty xf(x)dx\n",
" = cV^{1/d_H},\n",
"$$\n",
"using $\\frac{dr}{dx} = V^{1/d_H}$ for substitution.\n",
"This yields the desired approximation\n",
"$$\n",
" \\mathbb{E} \\left[ d_T(X,Y) \\right] \\approx cV^{1/d_H}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"id": "eba53e6d",
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "markdown",
"checksum": "ba14acd8cc24c2dfea35f3b8106cdfc8",
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"source": [
"**(c)** For each of the four models estimate $\\mathbb{E}[d_T(X,Y)]$ with errors for $N = 2^7, 2^8, \\ldots, 2^{12}$ using (2) and based on $100$ samples each. Store your data in the file `qgdimension.hdf5`. Make an estimate of $d_H$ (with errors) for each of the models by fitting the parameters $c$ and $d_H$ of the ansatz (3). For each model, plot the data together with the fit in a log-log plot. **(40 pts)**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ee683060",
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "c3664034dec3a350f7fe0533fe2454cb",
"grade": true,
"grade_id": "cell-01f5fde55f35f2dc",
"locked": false,
"points": 15,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [],
"source": [
"models = ['U','W','S','B']\n",
"sizes = [2**k for k in range(7,13)]\n",
"num_vertices = (np.array(sizes)+4)/2\n",
"measurements = 100\n",
"\n",
"# data gathering and storing in qgdimension.hdf5\n",
"import h5py\n",
"\n",
"max_distance = 30\n",
"def expected_distance(V, adj, max_distance=30):\n",
" '''\n",
" Calculates the expectation value of the distance profile given the amount\n",
" of vertices V, an array of adjacencies for a triangulation sample,\n",
" and max_distance as upper limit for the summation for the expectation value.\n",
" '''\n",
" return 1/V*vertex_distance_profile(adj,max_distance)@np.arange(max_distance)\n",
"\n",
"with h5py.File(\"qgdimension.hdf5\", \"a\") as f:\n",
" if not \"num-vertices\" in f:\n",
" f.create_dataset(\"num-vertices\",data=num_vertices)\n",
" \n",
" for model in models:\n",
" models_key = f\"expectation-graph-distance-{model}\"\n",
" if not models_key in f:\n",
" graph_distance_expectations = np.zeros((len(num_vertices), measurements))\n",
" for idx_N, N in enumerate(num_vertices):\n",
" V = (N + 4)/2\n",
" for idx_measurement in range(measurements):\n",
" adj = generate_random_triangulation(N, model)\n",
" expectation = expected_distance(V, adj, max_distance)\n",
" graph_distance_expectations[idx_N][idx_measurement] = expectation\n",
"\n",
" f.create_dataset(models_key,data=graph_distance_expectations)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "351f7a01",
"metadata": {
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"grade_id": "cell-9e8f666073e1e2df",
"locked": false,
"points": 25,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Fitting and plotting\n",
"from matplotlib import pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"\n",
"# Define a dictionary of model names for the plot titles.\n",
"model_names = {\"U\": \"Uniform triangulations\",\n",
" \"W\": \"Schnyder-Wood-decorated triangulations\",\n",
" \"S\": \"Spanning-tree decorated triangulations\",\n",
" \"B\": \"Bipolar-oriented triangulations\"}\n",
"\n",
"d_H_list = {}\n",
"\n",
"with h5py.File(\"qgdimension.hdf5\", \"r\") as f:\n",
" num_vertices = np.array(f[\"num-vertices\"])\n",
" expectations = {model: np.array(f[f\"expectation-graph-distance-{model}\"]) for model in models}\n",
" \n",
" fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
" axs = axs.ravel()\n",
" fig.suptitle(r\"Graph distance expectation Monte Carlo simulations and Hausdorff dimension $d_H$ fits using $\\mathbb{E}[d_T(X,Y)] \\approx c\\,V^{1/d_H}$ for different triangulation models\")\n",
" \n",
" for idx_model, model in enumerate(models):\n",
" # Calculate mean and standard deviation of the expectations.\n",
" # TODO: Look at whether I store the right data and do the right calculations.\n",
" mu = np.mean(expectations[model], 1)\n",
" sigma = np.std(expectations[model], 1)/np.sqrt(len(expectations[model]) - 1)\n",
"\n",
" fitfunc = lambda V, c, d_H: c*V**(1/d_H)\n",
" popt, pcov = curve_fit(fitfunc, num_vertices, mu, sigma=sigma)\n",
" d_H_list[model] = popt[1]\n",
" num_vertices_fit = np.linspace(np.min(num_vertices)/2, np.max(num_vertices)*2, 1000)\n",
"\n",
" ax = axs[idx_model]\n",
" ax.set_title(f\"{model_names[model]} ({model})\")\n",
" ax.errorbar(num_vertices, mu, sigma, label=\"Monte Carlo\",\n",
" fmt='.', markersize=10, capsize=4)\n",
" ax.plot(num_vertices_fit, fitfunc(num_vertices_fit, *popt),\n",
" label=r\"fit: $c = {:.2f}$, $d_H = {:.2f}$\".format(*popt))\n",
" ax.set_xlabel(r\"$V$\")\n",
" ax.set_ylabel(r\"$\\mathbb{E}[d_T(X,Y)]$\")\n",
" ax.set_yscale(\"log\")\n",
" ax.set_xscale(\"log\")\n",
" ax.grid(True, which=\"both\", ls=\"-\")\n",
" ax.legend()\n",
" \n",
" fig.tight_layout()\n",
" fig.show()"
]
},
{
"cell_type": "markdown",
"id": "b505b3cf",
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "markdown",
"checksum": "be7888d11d6b9ca0f2666739857578cb",
"grade": false,
"grade_id": "cell-032c7f8d6147d9f9",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"source": [
"**(d)** Produce a *collapse* plot for each of the four models as follows: plot \n",
"$$V^{1/d_H}\\,\\mathbb{E}[\\frac{1}{V}\\rho_T(r)] \\quad\\text{ as function of } x = r / V^{1/d_H},$$ \n",
"where for $d_H$ you take the estimate obtained in the previous exercise. Show errors in the mean distance profiles via shaded regions (just like in the lecture). Verify that the curves collapse reasonably well. **(25 pts)**"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "988bfe95",
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "7b7eceb7923231bc3710d4e3036265b6",
"grade": true,
"grade_id": "cell-faf328e7505cf6a2",
"locked": false,
"points": 25,
"schema_version": 3,
"solution": true,
"task": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"max_distance = 15\n",
"\n",
"for model in models:\n",
" d_H = d_H_list[model]\n",
" \n",
" with h5py.File(\"qgdimension.hdf5\", \"a\") as f:\n",
" mean_profiles_key = f\"mean-profiles-{model}\"\n",
" profiles_key = f\"profiles-{model}\"\n",
" \n",
" if not mean_profiles_key in f or not profiles_key in f:\n",
" # Recalculate the profiles as the data stored in the previous exercise is insufficient.\n",
" mean_profiles = []\n",
" for size in sizes:\n",
" profiles = []\n",
" for _ in range(measurements):\n",
" adj = generate_random_triangulation(size, model)\n",
" profiles.append(vertex_distance_profile(adj,max_distance))\n",
" mean_profiles.append([batch_estimate(data,np.mean,20) for data in np.transpose(profiles)])\n",
"\n",
" f.create_dataset(mean_profiles_key,data=mean_profiles)\n",
" f.create_dataset(profiles_key,data=profiles)\n",
" \n",
" else:\n",
" mean_profiles = np.array(f[mean_profiles_key])\n",
" profiles = np.array(f[profiles_key])\n",
"\n",
" # Plot the collapse plots.\n",
" for i, profile in enumerate(mean_profiles):\n",
" rvals = np.arange(len(profile))\n",
" plt.plot(rvals/num_vertices[i]**(1/d_H),\n",
" [y[0]*num_vertices[i]**(1/d_H - 1) for y in profile])\n",
" for i, profile in enumerate(mean_profiles):\n",
" plt.fill_between(np.arange(len(profile))/num_vertices[i]**(1/d_H),\n",
" [(y[0]-y[1])*num_vertices[i]**(1/d_H - 1) for y in profile],\n",
" [(y[0]+y[1])*num_vertices[i]**(1/d_H - 1) for y in profile],\n",
" alpha=0.2)\n",
" plt.legend(sizes, title=\"V\")\n",
" plt.xlabel(r\"$x = r/V^{1/d_H}$\")\n",
" plt.ylabel(r\"$V^{1/d_H}\\,\\mathbb{E}[\\frac{1}{V}\\rho_T(r)]$\")\n",
" plt.xlim(0,3)\n",
" plt.title(f\"Finite-size scaling with Hausdorff dimension \\n for {model_names[model]} ($d_H = {d_H:.2f}$)\")\n",
" plt.show()\n",
"\n"
]
},
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"source": [
"**(e) Bonus exercise:** Make more robust estimates of $d_H$ by optimizing the quality of the collapse. You could do this (for each model separately) by taking $\\hat{f}(r) = \\mathbb{E}[\\rho_T(r)] / V_0$, where the right-hand side is the mean distance profile for the largest system size with $V_0 = (2^{12} + 4)/2$ vertices. Then according to our assumption, for another size $V \\leq V_0$ we expect $\\mathbb{E}[\\rho_T(r)] / V \\approx k \\hat{f}(kr)$, where $k \\geq 1$ is a scale factor that should be $k\\approx (V_0/V)^{1/d_H}$. Making sure to interpolate the function $\\hat{f}(r)$ (using [`scipy.interpolate.interp1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html#scipy.interpolate.interp1d)), this scale factor can be determined by fitting the curve $k \\hat{f}(kr)$ to the data $\\mathbb{E}[\\rho_T(r)] / V$. Then $d_H$ can be estimated by fitting $k$ versus $V$. **(20 bonus points, but note that maximum grade is 10)**"
]
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"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
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"id": "c9e50c10",
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