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ledovsky/plotting_connectivity_2d.ipynb

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plotting_connectivity_2d.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting connectivity 2d\n",
"\n",
"Example of a MNE-based function which plots connectivity-like data in 2d"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import mne"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# no A1 and A2 here\n",
"channels_to_use = [\n",
" 't6',\n",
" 't4',\n",
" 'o1',\n",
" 'f8',\n",
" 'p4',\n",
" 'c4',\n",
" 't3',\n",
" 'f7',\n",
" 'f3',\n",
" 'o2',\n",
" 'f4',\n",
" 'c3',\n",
" 'p3',\n",
" 't5',\n",
" 'cz',\n",
" 'fp1',\n",
" 'fp2',\n",
" 'pz',\n",
" 'fz'\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating RawArray with float64 data, n_channels=94, n_times=1\n",
" Range : 0 ... 0 = 0.000 ... 0.000 secs\n",
"Ready.\n"
]
}
],
"source": [
"ten_twenty_montage = mne.channels.make_standard_montage('standard_1020')\n",
"\n",
"# create Info object to store info\n",
"info = mne.io.meas_info.create_info(ten_twenty_montage.ch_names, sfreq=256, ch_types=\"eeg\")\n",
"# using temporary RawArray to apply mongage to info\n",
"mne.io.RawArray(np.zeros((len(ten_twenty_montage.ch_names), 1)), info, copy=None).set_montage(ten_twenty_montage)\n",
"\n",
"# pick channels\n",
"channels_to_use_ = [ch for ch in info.ch_names if ch.lower() in channels_to_use]\n",
"info.pick_channels(channels_to_use_);"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def plot_topomap_connectivity_2d(info, con, picks=None, pairs=None, vmin=None, vmax=None, cm=None, show_values=False):\n",
" \"\"\"\n",
" Plots connectivity-like data in 2d\n",
" \n",
" Drawing every pair of channels will likely make a mess\n",
" There are two options to avoid it:\n",
" - provide picks\n",
" - provide specific pairs of channels to draw\n",
" \"\"\"\n",
" \n",
" # get positions\n",
" _, pos, _, _, _, _, _ = mne.viz.topomap._prepare_topomap_plot(info, 'eeg');\n",
" \n",
" ch_names_lower = [ch.lower() for ch in info.ch_names]\n",
" if picks:\n",
" picks_lower = [ch.lower() for ch in picks]\n",
" if pairs:\n",
" pairs_lower = [tuple(sorted([ch1.lower(), ch2.lower()])) for ch1, ch2 in pairs]\n",
" \n",
" rows = []\n",
" for idx1, ch1 in enumerate(ch_names_lower):\n",
" for idx2, ch2 in enumerate(ch_names_lower):\n",
" if ch1 >= ch2:\n",
" continue\n",
" if picks and (ch1 not in picks_lower or ch2 not in picks_lower):\n",
" continue\n",
" if pairs and (ch1, ch2) not in pairs_lower:\n",
" continue\n",
" rows.append((\n",
" pos[idx1],\n",
" pos[idx2],\n",
" con[idx1, idx2]\n",
" ))\n",
" if not len(rows):\n",
" raise ValueError('No pairs to plot')\n",
" \n",
" con_to_plot = np.array([row[2] for row in rows])\n",
" if vmin is None:\n",
" vmin = np.percentile(con_to_plot, 2)\n",
" if vmax is None:\n",
" vmax = np.percentile(con_to_plot, 98)\n",
" norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)\n",
" \n",
" if cm is None:\n",
" cm = sns.diverging_palette(240, 10, as_cmap=True)\n",
" \n",
" fig, ax = plt.subplots(figsize=[5, 5])\n",
" mne.viz.utils.plot_sensors(info, show_names=True, show=False, axes=ax);\n",
" for row in rows:\n",
" rgba_color = cm(norm(row[2]))\n",
" plt.plot([row[0][0], row[1][0]], [row[0][1], row[1][1]], color=rgba_color)\n",
" if show_values:\n",
" plt.text((row[0][0] + row[1][0]) / 2, \n",
" (row[0][1] + row[1][1]) / 2, \n",
" '{:.2f}'.format(row[2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lets test plotting on my data\n",
"\n",
"Lets visualize a difference of connectivity of patients and hc groups for different ages and connetivity metrics (coherence and envelopes correlation)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>N</th>\n",
" <th>N autism</th>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>7+</td>\n",
" <td>114</td>\n",
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"text/plain": [
" Age N N autism N hc\n",
"0 All 248 123 89\n",
"1 2-4 64 39 16\n",
"2 5-6 70 30 33\n",
"3 7+ 114 54 40\n",
"4 7-8 53 32 14\n",
"5 6-8 81 41 30\n",
"6 9+ 61 22 26"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path_df = pd.read_csv('../../preproc_data/autists/path_file.csv')\n",
"\n",
"intervals = [\n",
" ('All', 0, 100),\n",
" ('2-4', 2, 4),\n",
" ('5-6', 5, 6),\n",
" ('7+', 7, 100),\n",
" ('7-8', 7, 8),\n",
" ('6-8', 6, 8),\n",
" ('9+', 9, 100), \n",
"]\n",
"\n",
"rows = []\n",
"to_select = {}\n",
"for name, val_from, val_to in intervals:\n",
" cur_df = path_df[(path_df['age'] >= val_from) & (path_df['age'] <= val_to)]\n",
" to_select[name] = cur_df['fn']\n",
" rows.append({\n",
" 'Age': name,\n",
" 'N': len(cur_df),\n",
" 'N autism': len(cur_df[cur_df['target'] == 'asd']),\n",
" 'N hc': len(cur_df[cur_df['target'] == 'typical']),\n",
" })\n",
" \n",
"age_summary_df = pd.DataFrame(rows)\n",
"age_summary_df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# helper function to get con matrix from my csv file with computed features\n",
"def get_con_matrix(con_dict, ch_names):\n",
" ch_names = [ch.lower() for ch in ch_names]\n",
" n_ch = len(ch_names)\n",
" con = np.zeros((n_ch, n_ch))\n",
" for k, v in con_dict.items():\n",
" _, _, ch1, ch2 = k.split('_')\n",
" try:\n",
" ch1_idx = ch_names.index(ch1)\n",
" except ValueError:\n",
" continue\n",
" try: \n",
" ch2_idx = ch_names.index(ch2)\n",
" except ValueError:\n",
" continue\n",
" con[ch1_idx, ch2_idx] = v\n",
" con[ch2_idx, ch1_idx] = v\n",
" return con"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../own_data/200426_autists_features_v2_thr30/full/coh_alpha.csv').dropna()\n",
"\n",
"df = df[df['fn'].isin(to_select['2-4'])]\n",
"\n",
"coh_cols = [col for col in df if 'coh' in col]\n",
"\n",
"dict_diff = (\n",
" df[df['target'] == 'asd'][coh_cols].mean() -\n",
" df[df['target'] == 'typical'][coh_cols].mean()\n",
").to_dict()\n",
"\n",
"con_diff = get_con_matrix(dict_diff, info.ch_names)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
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"source": [
"plot_topomap_connectivity_2d(info, con_diff, picks=['fp1', 'fp2', 'f7', 'f3', 'fz', 'f4', 'f8'], vmin=-0.1, vmax=0.1)"
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{
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"source": [
"df = pd.read_csv('../own_data/200426_autists_features_v2_thr30/full/coh_alpha.csv').dropna()\n",
"\n",
"df = df[df['fn'].isin(to_select['5-6'])]\n",
"\n",
"coh_cols = [col for col in df if 'coh' in col]\n",
"\n",
"dict_diff = (\n",
" df[df['target'] == 'asd'][coh_cols].mean() -\n",
" df[df['target'] == 'typical'][coh_cols].mean()\n",
").to_dict()\n",
"\n",
"con_diff = get_con_matrix(dict_diff, info.ch_names)"
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{
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"source": [
"plot_topomap_connectivity_2d(info, con_diff, picks=['fp1', 'fp2', 'f7', 'f3', 'fz', 'f4', 'f8'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../own_data/200426_autists_features_v2_thr30/full/env_alpha.csv').dropna()\n",
"\n",
"df = df[df['fn'].isin(to_select['2-4'])]\n",
"\n",
"env_cols = [col for col in df if 'env' in col]\n",
"\n",
"dict_diff = (\n",
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" df[df['target'] == 'typical'][env_cols].mean()\n",
").to_dict()\n",
"\n",
"con_diff = get_con_matrix(dict_diff, info.ch_names)"
]
},
{
"cell_type": "code",
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"source": [
"plot_topomap_connectivity_2d(info, con_diff, picks=['fp1', 'fp2', 'f7', 'f3', 'fz', 'f4', 'f8'])"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../own_data/200426_autists_features_v2_thr30/full/env_alpha.csv').dropna()\n",
"\n",
"df = df[df['fn'].isin(to_select['5-6'])]\n",
"\n",
"env_cols = [col for col in df if 'env' in col]\n",
"\n",
"dict_diff = (\n",
" df[df['target'] == 'asd'][env_cols].mean() -\n",
" df[df['target'] == 'typical'][env_cols].mean()\n",
").to_dict()\n",
"\n",
"con_diff = get_con_matrix(dict_diff, info.ch_names)"
]
},
{
"cell_type": "code",
"execution_count": 16,
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"source": [
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{
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