From f5cb6f02dcec3acef274637b15984667fa20f5e7 Mon Sep 17 00:00:00 2001 From: Manish Saggar <32688917+manishsaggar1@users.noreply.github.com> Date: Fri, 8 Mar 2024 13:30:51 -0800 Subject: [PATCH] adding data --- examples/haxby_decoding/Untitled.ipynb | 260 ++++ .../haxby_decoding_neumapper_dyneusr.json | 1 + .../haxby_decoding_neumapper_dyneusr_14.json | 1 + .../haxby_decoding_neumapper_dyneusr_7.json | 1 + .../haxby_decoding/haxby_decoding_neumapper.m | 2 +- .../haxby_decoding_neumapper.png | Bin 193563 -> 153624 bytes .../haxby_decoding_neumapper_dyneusr.html | 478 ++----- .../haxby_decoding_neumapper_dyneusr_14.html | 1266 +++++++++++++++++ .../haxby_decoding_neumapper_dyneusr_7.html | 1266 +++++++++++++++++ 9 files changed, 2945 insertions(+), 330 deletions(-) create mode 100644 examples/haxby_decoding/Untitled.ipynb create mode 100644 examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr.json create mode 100644 examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr_14.json create mode 100644 examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr_7.json create mode 100644 examples/haxby_decoding/haxby_decoding_neumapper_dyneusr_14.html create mode 100644 examples/haxby_decoding/haxby_decoding_neumapper_dyneusr_7.html diff --git a/examples/haxby_decoding/Untitled.ipynb b/examples/haxby_decoding/Untitled.ipynb new file mode 100644 index 0000000..48d63f3 --- /dev/null +++ b/examples/haxby_decoding/Untitled.ipynb @@ -0,0 +1,260 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "55dcd7ea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/saggar/opt/anaconda3/lib/python3.9/site-packages/nilearn/input_data/__init__.py:23: FutureWarning: The import path 'nilearn.input_data' is deprecated in version 0.9. Importing from 'nilearn.input_data' will be possible at least until release 0.13.0. Please import from 'nilearn.maskers' instead.\n", + " warnings.warn(message, FutureWarning)\n", + "/Users/saggar/opt/anaconda3/lib/python3.9/site-packages/nilearn/image/resampling.py:493: UserWarning: The provided image has no sform in its header. Please check the provided file. Results may not be as expected.\n", + " warnings.warn(\n", + "/Users/saggar/opt/anaconda3/lib/python3.9/site-packages/joblib/memory.py:349: FutureWarning: The default strategy for standardize is currently 'zscore' which incorrectly uses population std to calculate sample zscores. The new strategy 'zscore_sample' corrects this behavior by using the sample std. In release 0.13, the default strategy will be replaced by the new strategy and the 'zscore' option will be removed. Please use 'zscore_sample' instead.\n", + " return self.func(*args, **kwargs)\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "import pandas as pd\n", + "from nilearn.datasets import fetch_haxby\n", + "from nilearn.input_data import NiftiMasker\n", + "\n", + "# Fetch dataset, extract time-series from ventral temporal (VT) mask\n", + "dataset = fetch_haxby(subjects=[2])\n", + "masker = NiftiMasker(\n", + " dataset.mask_vt[0], \n", + " standardize=True, detrend=True, smoothing_fwhm=4.0,\n", + " low_pass=0.09, high_pass=0.008, t_r=2.5,\n", + " memory=\"nilearn_cache\")\n", + "X = masker.fit_transform(dataset.func[0])\n", + "\n", + "# Encode labels as integers\n", + "df = pd.read_csv(dataset.session_target[0], sep=\" \")\n", + "target, labels = pd.factorize(df.labels.values)\n", + "timing = pd.DataFrame().assign(task=target, task_name=labels[target])\n", + "timing_onehot = pd.DataFrame({l:1*(target==i) for i,l in enumerate(labels)})\n", + "\n", + "# Save X and y\n", + "np.save('SBJ02_mask_vt.npy', X)\n", + "timing.to_csv('SBJ02_timing_labels.tsv', sep='\\t', index=0)\n", + "timing_onehot.to_csv('SBJ02_timing_onehot.tsv', sep='\\t', index=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "89ddf47e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + "5 0.0\n", + "6 0.0\n", + "7 0.0\n", + "8 0.0\n", + "9 0.0\n", + "Name: 0, dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "timing_onehot = pd.read_csv('timing_onehot.tsv',header=None,delim_whitespace=True) \n", + "timing_onehot[0][1:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "975e31be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "DyNeuGraph()" + ] + }, + "execution_count": 2, + "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.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('SBJ02_timing_onehot.tsv', sep='\\t') \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.html')" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr.json b/examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr.json new file mode 100644 index 0000000..24931b3 --- /dev/null +++ b/examples/haxby_decoding/graphs/haxby_decoding_neumapper_dyneusr.json @@ -0,0 +1 @@ +{"directed": false, "multigraph": false, "graph": {"labels": {"data_id": ["Group 1 (0.0, 362.75)", "Group 2 (362.75, 725.5)", "Group 3 (725.5, 1088.25)", "Group 4 (1088.25, 1451.0)"], "uniform": [], "multiclass": ["rest", "scissors", "face", "cat", "shoe", "house", "scrambledpix", "bottle", "chair"]}, "groups": {"rest": [0, 1], "scissors": [0, 1], "face": [0, 1], "cat": [0, 1], "shoe": [0, 1], "house": [0, 1], "scrambledpix": [0, 1], "bottle": [0, 1], "chair": [0, 1], "data_id": [0.0, 362.75, 725.5, 1088.25, 1451.0], "uniform": [0.0], "multiclass": [0, 1, 2, 3, 4, 5, 6, 7, 8]}, "color_by": "multiclass", "color": {"data_id": ["#3182bd", "#fd8d3c", "#a1d99b", "#dadaeb", "#d9d9d9"], "multiclass": ["#3182bd", "#9ecae1", "#fd8d3c", "#fdd0a2", "#a1d99b", "#756bb1", "#dadaeb", "#969696", "#d9d9d9"]}}, "nodes": [{"id": 0, "name": 0, "tooltip": "0 (size: 1)

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rest4
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rest0
scissors0
face0
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shoe0
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scrambledpix2
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face0
cat0
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house0
scrambledpix0
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shoe0
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cat0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest13
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cat0
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rest3
scissors0
face0
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shoe0
house0
scrambledpix0
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rest1
scissors0
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cat0
shoe0
house0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest1
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face0
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shoe0
house0
scrambledpix0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest5
scissors0
face0
cat0
shoe0
house0
scrambledpix0
bottle0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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face0
cat0
shoe0
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scrambledpix0
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rest5
scissors7
face0
cat1
shoe0
house0
scrambledpix4
bottle4
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rest0
scissors4
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shoe0
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scrambledpix9
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rest0
scissors2
face0
cat0
shoe0
house0
scrambledpix0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest0
scissors7
face0
cat0
shoe0
house0
scrambledpix0
bottle0
chair0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest0
scissors3
face0
cat0
shoe0
house0
scrambledpix0
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rest1
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rest17
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rest1
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cat0
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rest1
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rest1
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shoe0
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rest7
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shoe0
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rest9
scissors2
face2
cat1
shoe0
house1
scrambledpix0
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rest1
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shoe0
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rest2
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rest0
scissors0
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cat2
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rest4
scissors0
face1
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shoe0
house0
scrambledpix0
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rest2
scissors0
face0
cat0
shoe0
house0
scrambledpix0
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rest7
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rest9
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rest1
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cat0
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rest6
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face0
cat1
shoe3
house0
scrambledpix1
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rest12
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest26
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face1
cat0
shoe0
house0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest27
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shoe0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest27
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shoe0
house0
scrambledpix0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest27
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest3
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face0
cat0
shoe0
house0
scrambledpix0
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rest18
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face0
cat0
shoe0
house0
scrambledpix0
bottle0
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rest19
scissors0
face1
cat0
shoe0
house0
scrambledpix0
bottle0
chair0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest25
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face0
cat0
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house0
scrambledpix0
bottle0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest2
scissors0
face0
cat0
shoe0
house0
scrambledpix0
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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rest0
scissors1
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cat0
shoe0
house0
scrambledpix0
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rest1
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rest1
scissors2
face0
cat0
shoe2
house0
scrambledpix0
bottle4
chair0
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rest1
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face0
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shoe0
house0
scrambledpix0
bottle0
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rest2
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cat0
shoe0
house0
scrambledpix0
bottle0
chair0
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rest3
scissors0
face0
cat0
shoe0
house0
scrambledpix0
bottle0
chair0
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rest4
scissors0
face0
cat0
shoe0
house0
scrambledpix0
bottle0
chair0
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rest1
scissors1
face6
cat8
shoe0
house0
scrambledpix0
bottle0
chair0
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rest1
scissors0
face0
cat8
shoe0
house0
scrambledpix0
bottle0
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rest1
scissors2
face6
cat12
shoe0
house0
scrambledpix0
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