Commit f661d7e2 authored by Hao Ting Wang's avatar Hao Ting Wang

quiet

parent d10e74da
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......@@ -4,17 +4,9 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock\n"
]
}
],
"outputs": [],
"source": [
"cd ~/TaskSCCA_craddock/"
"cd -q ~/TaskSCCA_craddock/"
]
},
{
......
......@@ -4,17 +4,9 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock\n"
]
}
],
"outputs": [],
"source": [
"cd ~/TaskSCCA_craddock/"
"cd -q ~/TaskSCCA_craddock/"
]
},
{
......@@ -73,21 +65,11 @@
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock/env/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
"For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" \"\"\"Entry point for launching an IPython kernel.\n",
"/home/h/hw1012/TaskSCCA_craddock/env/lib/python3.6/site-packages/scipy/stats/stats.py:2253: RuntimeWarning: invalid value encountered in true_divide\n",
" return (a - mns) / sstd\n"
]
}
],
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df_es = pd.read_pickle('./data/interim/CS_MWQ_prepro.pkl').convert_objects(convert_numeric=True)\n",
"df_es_mean = df_es.pivot_table(index=['RIDNO'],\n",
......
......@@ -4,17 +4,9 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock\n"
]
}
],
"outputs": [],
"source": [
"cd ~/TaskSCCA_craddock/"
"cd -q ~/TaskSCCA_craddock/"
]
},
{
......@@ -75,7 +67,8 @@
" label_names_yeo7 = label_names.iloc[:, [0, 1, -1]].reset_index()\n",
"\n",
" tmp = pd.DataFrame(mat, index=range(1, 101), columns=range(1, 101))\n",
" reorder = tmp.loc[label_names_yeo7.index.tolist(), label_names_yeo7.index.tolist()]\n",
" idx = label_names_yeo7.index.tolist()\n",
" reorder = tmp.reindex(index=idx, columns=idx)\n",
" ticks = get_ticks()\n",
" ticklabels = ['DMN', 'DAN', 'FPN', 'LIM', 'None', 'S-M', 'VAN', 'VIS']\n",
" return reorder.values, ticks, ticklabels"
......@@ -180,21 +173,9 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock/env/lib/python3.6/site-packages/ipykernel_launcher.py:31: FutureWarning: \n",
"Passing list-likes to .loc or [] with any missing label will raise\n",
"KeyError in the future, you can use .reindex() as an alternative.\n",
"\n",
"See the documentation here:\n",
"https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n"
]
},
{
"data": {
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\n",
......@@ -247,7 +228,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
......@@ -258,7 +239,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"metadata": {
"scrolled": false
},
......
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock\n"
]
}
],
"source": [
"cd ~/TaskSCCA_craddock/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/h/hw1012/TaskSCCA_craddock/env/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n",
"/home/h/hw1012/TaskSCCA_craddock/env/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
" return f(*args, **kwds)\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"from src.utils import save_pkl, load_pkl"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"def sort_by_yeo7(mat):\n",
" label_names = pd.read_csv('references/scorr05_2level_names_100.csv').set_index('ROI number')\n",
" label_names_yeo7 = label_names.sort_values('Yeo-Krienen 7 networks').iloc[:, 2]\n",
" tmp = pd.DataFrame(mat, index=range(1, 101), columns=range(1, 101))\n",
" reorder = tmp.loc[label_names_yeo7.index.tolist(), label_names_yeo7.index.tolist()]\n",
" return reorder.values"
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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