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0.3.0
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| # Changelog | ||
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| ## [0.3.0] - in development | ||
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| ### Added | ||
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| - Changelog added. (wiheto) | ||
| - Preliminary BIDS operations that generate edge df from BIDS layout `edges_from_bids()` (wiheto) | ||
| - Connectivity matrix as view option (wiheto) | ||
| - Rotation of connectivity matrix (wiheto) | ||
| - cm_order for connectivity matrix (wiheto) | ||
| - Dual colours for positive and negative edges (wiheto) | ||
| - Allow dual colors for positive and negative edges (wiheto) | ||
| - Some gallery examples (plot_dmn, connectivity matrix) (wiheto) | ||
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| ### Update | ||
| - Updated README with network neuroscience reference (wiheto) | ||
| - Removed matplotlib deprecated `matplotlib.cm.get_cmap` for `matplotlib.colormaps` (wiheto) | ||
| - Rasterization for glass brains (and all template/cms) for faster svg rendering (wiheto) | ||
| - Example data is now packaged in ./netplotbrain/example_data to be included in package (wiheto) | ||
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| ### Fixed | ||
| - Renaming "legend_tick_fontsize" and "legend_title_fontsize" to be more consistent (wiheto) | ||
| - Renaming "highlightlevel" to "highlight_level" to be more consistent (wiheto) | ||
| - Correcting some kwarg names in documentation (wiheto) | ||
| - A bug with spring layout and node_color when only subset of nodes plotted (wiheto) |
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| include examples/* | ||
| include netplotbrain/example_data/*.tsv | ||
| recursive-include netplotbrain/example_data/bids_test/ * | ||
| include netplotbrain/profiles/*.json | ||
| include netplotbrain/templatesettings/*.json |
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "cf30fa87", | ||
| "metadata": {}, | ||
| "source": [ | ||
| " \n", | ||
| "# Node properties: size\n", | ||
| "\n", | ||
| "Netplotbrain allows for specifying different figure properties by only specifyign the column name.\n", | ||
| "\n", | ||
| "Below we demonstrate how node_size can change based on different columns in the nodes dataframe.\n", | ||
| "\n", | ||
| "The key lines of code here are when:\n", | ||
| "\n", | ||
| "`node_size='centrality_measure1'`\n", | ||
| "\n", | ||
| "and\n", | ||
| "\n", | ||
| "`node_size='centrality_measure2'`\n", | ||
| "\n", | ||
| "These columns could be called anything such as \"module_degree_zscore\" or \"efficiency\"" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "jupytext": { | ||
| "cell_metadata_filter": "-all", | ||
| "main_language": "python", | ||
| "notebook_metadata_filter": "-all" | ||
| }, | ||
| "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 | ||
| } |
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| # | ||
| # # Highlight Parcels | ||
| # | ||
| # One possble use of netplotbrain is to highlight a seed region used. Here we will highlight two regions of interest. | ||
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| # + | ||
| # Load imports | ||
| import netplotbrain | ||
| import pandas as pd | ||
| import numpy as np | ||
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| # Get 100 node atlas from templateflow | ||
| node_dict = {'atlas': 'Schaefer2018', | ||
| 'desc': '100Parcels7Networks', | ||
| 'extension': '.nii.gz', | ||
| 'resolution': 1} | ||
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| # pyton index of atlas rois. | ||
| # this will start at 0 but atlas starts at 1. | ||
| # So below select node 15 and 66 in atlas. | ||
| seeds = [14, 65] | ||
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| # Create dataframe of 100 rois | ||
| nodeinfo = np.zeros(100) | ||
| nodeinfo[seeds] = 1 | ||
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| node_color = ['gray'] * 100 | ||
| node_color[seeds[0]] = 'cornflowerblue' | ||
| node_color[seeds[1]] = 'salmon' | ||
| nodes_df = pd.DataFrame({'seed': nodeinfo, 'color': node_color}) | ||
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| fig, _ = netplotbrain.plot(template = 'MNI152NLin2009cAsym', | ||
| nodes = node_dict, | ||
| nodes_df = nodes_df, | ||
| node_type = 'parcels', | ||
| view = 'preset-3', | ||
| node_color = 'color', | ||
| highlight_nodes = 'seed', | ||
| highlighlevel = 1, | ||
| node_alpha = 0.4, | ||
| save_name='/home/xthowi/scratch/sham.png', | ||
| figdpi=300) |
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| # | ||
| # # Visualizing a single network | ||
| # | ||
| # [Open interactive notebook in Binder](https://mybinder.org/v2/gh/wiheto/netplotbrain/main?filepath=docs/gallery/plot_connectivitymatrix.ipynb) | ||
| # | ||
| # ## Goal | ||
| # Here we are plot the nodes on the brain from the Schaefer atlas and plot the connectivity matrix next to it | ||
| # | ||
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| # + | ||
| # Import packages | ||
| import templateflow.api as tf | ||
| import netplotbrain | ||
| import pandas as pd | ||
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| # + | ||
| # Get template information | ||
| template = 'MNI152NLin2009cAsym' | ||
| atlas = 'Schaefer2018' | ||
| atlas_desc = '100Parcels7Networks' | ||
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| # + | ||
| # Get and load the tsv file (information about the atlas) | ||
| atlas_path = tf.get(atlas=atlas, | ||
| template=template, | ||
| desc=atlas_desc, | ||
| extension='.tsv') | ||
| atlas_df = pd.read_csv(atlas_path, sep='\t') | ||
| atlas_df.head() | ||
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| #+ | ||
| # Create a column called "network" by extracting the relevant information from the column "name" | ||
| atlas_df['network'] = list(map(lambda x: x.split('_')[2], atlas_df.name.values)) | ||
| atlas_df.head() | ||
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| # + | ||
| # Nodes in connectivity will automatically be organized by node_color, unless cm_order is specified. | ||
| netplotbrain.plot(nodes={'atlas': atlas, | ||
| 'desc': atlas_desc, | ||
| 'resolution': 1}, | ||
| view='LSc', | ||
| node_type='parcels', | ||
| nodes_df=atlas_df, | ||
| node_color='network', | ||
| node_cmap='Set2') |
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