diff --git a/code/neumapper/plot_mapper_graph.m b/code/neumapper/plot_mapper_graph.m index 6803384..ba800dd 100644 --- a/code/neumapper/plot_mapper_graph.m +++ b/code/neumapper/plot_mapper_graph.m @@ -143,7 +143,7 @@ function plot_embedding_heatmap(filter_X, cmap_timing) smallBin_sizes = cellfun('size',smallBins,2); %max_size = max(smallBin_sizes); %node_sizes = 5+log(1+smallBin_sizes); -node_sizes = 1+log(1+smallBin_sizes); +node_sizes = 5+log(1+smallBin_sizes); %% Plot the graph and highlight nodes for ii = 1:nn diff --git a/data/timing.txt b/data/timing.txt new file mode 100644 index 0000000..cad1693 --- /dev/null +++ b/data/timing.txt @@ -0,0 +1,1018 @@ +Timing +1.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 +2.0000000e+00 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DyNeuGraph()
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" + ], + "text/plain": [ + "DyNeuGraph()" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "import networkx as nx\n", + "import scipy as sp\n", + "from sklearn.utils import Bunch\n", + "from scipy.io import loadmat\n", + "from dyneusr.core import DyNeuGraph \n", + "from collections import defaultdict\n", + "\n", + "\n", + "## Load the NeuMapper result\n", + "mat = loadmat('haxby_decoding_neumapper_14.mat')\n", + "res = mat['res'][0][0]\n", + "res = Bunch(**{k:res[i] for i,k in enumerate(res.dtype.names)})\n", + "res = res.get('res', res.get('var', res))\n", + "\n", + "# load one-hot encoding matrix of timing labels \n", + "timing_onehot = pd.read_csv('timing_onehot.tsv',header=None,delim_whitespace=True) \n", + "\n", + "\n", + "## Convert to KeplerMapper format\n", + "membership = res.clusterBins\n", + "adjacency = membership @ membership.T\n", + "np.fill_diagonal(adjacency, 0)\n", + "adjacency = (adjacency > 0).astype(int)\n", + "\n", + "# get node link data \n", + "G = nx.Graph(adjacency)\n", + "graph = nx.node_link_data(G)\n", + "\n", + "# update format of nodes e.g. {node: [row_i, ...]}\n", + "nodes = defaultdict(list) \n", + "for n, node in enumerate(membership):\n", + " nodes[n] = node.nonzero()[0].tolist()\n", + "\n", + "# update format of links e.g. {source: [target, ...]}\n", + "links = defaultdict(list) \n", + "for link in graph['links']:\n", + " u, v = link['source'], link['target']\n", + " if u != v:\n", + " links[u].append(v)\n", + "\n", + "# update graph data\n", + "graph['nodes'] = nodes\n", + "graph['links'] = links\n", + "\n", + "\n", + "## Visualize the shape graph using DyNeuSR's DyNeuGraph\n", + "dG = DyNeuGraph(G=graph, y=timing_onehot)\n", + "dG.visualize('haxby_decoding_neumapper_dyneusr_14.html')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": 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