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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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"source": [
"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": 4,
"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": 5,
"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": 6,
"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 864x576 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"max_distance = 15\n",
"\n",
"fig, axs = plt.subplots(2, 2, figsize=(12, 8))\n",
"axs = axs.ravel()\n",
"fig.suptitle(r\"Finite-size scaling with Hausdorff dimension $d_H$ for different triangulation models\")\n",
"\n",
"for idx_model, model in enumerate(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",
" ax = axs[idx_model]\n",
" ax.set_title(f\"{model_names[model]} ({model}) with $d_H = {d_H:.2f}$\")\n",
" for i, profile in enumerate(mean_profiles):\n",
" rvals = np.arange(len(profile))\n",
" ax.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",
" ax.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",
" ax.set_xlabel(r\"$x = r/V^{1/d_H}$\")\n",
" ax.set_ylabel(r\"$V^{1/d_H}\\,\\mathbb{E}[\\frac{1}{V}\\rho_T(r)]$\")\n",
" ax.set_xlim(0,3)\n",
" ax.grid(True, which=\"both\", ls=\"-\")\n",
" ax.legend(num_vertices.astype(int), title=\"V\")\n",
" \n",
"fig.tight_layout()\n",
"fig.show()\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "d8f25787",
"metadata": {
"deletable": false,
"editable": false,
"nbgrader": {
"cell_type": "markdown",
"checksum": "7f19410ed936f838773ee891b059d1a3",
"grade": false,
"grade_id": "cell-65ae9c46ece5b657",
"locked": true,
"schema_version": 3,
"solution": false,
"task": false
}
},
"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)**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ed4424ce",
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "199ffddc14c77d4174b92a61368cd5c9",
"grade": true,
"grade_id": "cell-e24b0602e4e8257d",
"locked": false,
"points": 20,
"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"
}
],
"source": [
"# I started an attempt, but did not finish it at all.\n",
"V_0 = np.max(num_vertices)\n",
"\n",
"with h5py.File(\"qgdimension.hdf5\", \"a\") as f:\n",
" for model in models:\n",
" mean_profiles_key = f\"mean-profiles-{model}\"\n",
" profiles_key = f\"profiles-{model}\"\n",
" \n",
" mean_profiles = np.array(f[mean_profiles_key])\n",
" f_hat = mean_profiles/V_0\n",
" profiles = np.array(f[profiles_key])\n",
" \n",
" for idx, profile in enumerate(mean_profiles):\n",
" rvals = np.arange(len(profile))\n",
" xdata = np.array([mean_profiles[i]/V_0 for i in range(len(mean_profiles))])\n",
" plt.plot(rvals/num_vertices[idx]**(1/d_H),\n",
" [y[0]*num_vertices[idx]**(1/d_H - 1) for y in f_hat[idx]])\n",
" \n",
" #xdata = np.array(profile/V_0)\n",
"\n",
" f_hat_mean = np.mean(f_hat[:,:,0],1)\n",
" f_hat_err = np.mean(f_hat[:,:,1],1)\n",
" #plt.errorbar(rvals, f_hat_mean[idx], yerr=f_hat_err)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9e50c10",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}