Skip to content

Instantly share code, notes, and snippets.

@chrisgorgo

chrisgorgo/Querying+MRIQC+Web+API.ipynb

Last active May 16, 2018 18:33
Show Gist options
  • Save chrisgorgo/eccdb8b98f8e74d24a3395a49fbadf03 to your computer and use it in GitHub Desktop.
Save chrisgorgo/eccdb8b98f8e74d24a3395a49fbadf03 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Querying+MRIQC+Web+API.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from json import load\n",
"import urllib.request, json \n",
"from pandas.io.json import json_normalize\n",
"import seaborn as sns\n",
"import pylab as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since version 0.9.6 [MRIQC](http://mriqc.org) (a quality control tool for MR images) submits anonymized quality metrics to a web API. This tutorial shows how to query the API to view distribution of crowdsourced quality metrics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Iterate thour the pages of JSON returned by the API. Note that we are restricting the query to specific software and version."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"url_root = 'https://mriqc.nimh.nih.gov/api/v1/bold'\n",
"software = 'mriqc'\n",
"version = '0.9.6'\n",
"\n",
"page = 1\n",
"dfs = []\n",
"while True:\n",
" page_url = url_root + \\\n",
" '?where={\"provenance.software\":\"%s\",\"provenance.version\":\"%s\"}'%(software,version) + \\\n",
" '&page=%d'%page\n",
" with urllib.request.urlopen(page_url) as url:\n",
" data = json.loads(url.read().decode())\n",
" dfs.append(json_normalize(data['_items']))\n",
" if 'next' not in data['_links'].keys():\n",
" break\n",
" else:\n",
" page += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Concatenate everything into one neat DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>aor</th>\n",
" <th>aqi</th>\n",
" <th>bids_meta.EchoTime</th>\n",
" <th>bids_meta.EffectiveEchoSpacing</th>\n",
" <th>bids_meta.FlipAngle</th>\n",
" <th>bids_meta.MagneticFieldStrength</th>\n",
" <th>bids_meta.RepetitionTime</th>\n",
" <th>dummy_trs</th>\n",
" <th>dvars_nstd</th>\n",
" <th>dvars_std</th>\n",
" <th>...</th>\n",
" <th>summary_bg_stdv</th>\n",
" <th>summary_fg_k</th>\n",
" <th>summary_fg_mad</th>\n",
" <th>summary_fg_mean</th>\n",
" <th>summary_fg_median</th>\n",
" <th>summary_fg_n</th>\n",
" <th>summary_fg_p05</th>\n",
" <th>summary_fg_p95</th>\n",
" <th>summary_fg_stdv</th>\n",
" <th>tsnr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>448.000000</td>\n",
" <td>3.100000e+02</td>\n",
" <td>448.000000</td>\n",
" <td>373.0</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>...</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" <td>816.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.003139</td>\n",
" <td>0.010722</td>\n",
" <td>0.028958</td>\n",
" <td>5.000080e-04</td>\n",
" <td>81.517857</td>\n",
" <td>3.0</td>\n",
" <td>2.036765</td>\n",
" <td>0.154412</td>\n",
" <td>25.953467</td>\n",
" <td>1.118805</td>\n",
" <td>...</td>\n",
" <td>63.878695</td>\n",
" <td>3.543160</td>\n",
" <td>131.324780</td>\n",
" <td>806.728076</td>\n",
" <td>811.267722</td>\n",
" <td>31808.042892</td>\n",
" <td>518.619718</td>\n",
" <td>1059.053452</td>\n",
" <td>171.267898</td>\n",
" <td>56.024184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.004615</td>\n",
" <td>0.006374</td>\n",
" <td>0.006619</td>\n",
" <td>1.628933e-18</td>\n",
" <td>5.597990</td>\n",
" <td>0.0</td>\n",
" <td>0.205439</td>\n",
" <td>0.418211</td>\n",
" <td>5.059479</td>\n",
" <td>0.108258</td>\n",
" <td>...</td>\n",
" <td>42.766615</td>\n",
" <td>1.178374</td>\n",
" <td>113.767484</td>\n",
" <td>406.774419</td>\n",
" <td>395.053324</td>\n",
" <td>7790.870244</td>\n",
" <td>235.797804</td>\n",
" <td>619.460646</td>\n",
" <td>120.334072</td>\n",
" <td>14.850467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000290</td>\n",
" <td>0.001024</td>\n",
" <td>0.020000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>70.000000</td>\n",
" <td>3.0</td>\n",
" <td>1.500000</td>\n",
" <td>0.000000</td>\n",
" <td>11.765937</td>\n",
" <td>0.878974</td>\n",
" <td>...</td>\n",
" <td>21.197292</td>\n",
" <td>-0.035018</td>\n",
" <td>55.146530</td>\n",
" <td>474.340027</td>\n",
" <td>474.438904</td>\n",
" <td>14779.000000</td>\n",
" <td>264.936682</td>\n",
" <td>615.543079</td>\n",
" <td>76.028267</td>\n",
" <td>26.553900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000633</td>\n",
" <td>0.006705</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>78.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>23.022825</td>\n",
" <td>1.059097</td>\n",
" <td>...</td>\n",
" <td>40.782249</td>\n",
" <td>2.795746</td>\n",
" <td>84.651619</td>\n",
" <td>602.982361</td>\n",
" <td>614.543335</td>\n",
" <td>26350.000000</td>\n",
" <td>406.639260</td>\n",
" <td>758.071356</td>\n",
" <td>117.175583</td>\n",
" <td>46.744843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.001370</td>\n",
" <td>0.009453</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>78.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>25.558709</td>\n",
" <td>1.109312</td>\n",
" <td>...</td>\n",
" <td>49.592535</td>\n",
" <td>3.435796</td>\n",
" <td>102.012768</td>\n",
" <td>697.461578</td>\n",
" <td>702.249237</td>\n",
" <td>32369.500000</td>\n",
" <td>453.458518</td>\n",
" <td>871.170877</td>\n",
" <td>145.896439</td>\n",
" <td>55.457203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.003058</td>\n",
" <td>0.012510</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>90.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>28.648504</td>\n",
" <td>1.152416</td>\n",
" <td>...</td>\n",
" <td>78.196987</td>\n",
" <td>4.196941</td>\n",
" <td>136.670071</td>\n",
" <td>873.652100</td>\n",
" <td>873.856018</td>\n",
" <td>36973.500000</td>\n",
" <td>568.478488</td>\n",
" <td>1144.218155</td>\n",
" <td>184.177444</td>\n",
" <td>62.599040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.035539</td>\n",
" <td>0.050159</td>\n",
" <td>0.050000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>90.000000</td>\n",
" <td>3.0</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>51.112139</td>\n",
" <td>1.765792</td>\n",
" <td>...</td>\n",
" <td>428.297760</td>\n",
" <td>8.460538</td>\n",
" <td>907.907837</td>\n",
" <td>4322.361816</td>\n",
" <td>4295.756836</td>\n",
" <td>51917.000000</td>\n",
" <td>2853.770264</td>\n",
" <td>5836.885254</td>\n",
" <td>950.784058</td>\n",
" <td>122.917000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" aor aqi bids_meta.EchoTime \\\n",
"count 816.000000 816.000000 448.000000 \n",
"mean 0.003139 0.010722 0.028958 \n",
"std 0.004615 0.006374 0.006619 \n",
"min 0.000290 0.001024 0.020000 \n",
"25% 0.000633 0.006705 0.028000 \n",
"50% 0.001370 0.009453 0.028000 \n",
"75% 0.003058 0.012510 0.028000 \n",
"max 0.035539 0.050159 0.050000 \n",
"\n",
" bids_meta.EffectiveEchoSpacing bids_meta.FlipAngle \\\n",
"count 3.100000e+02 448.000000 \n",
"mean 5.000080e-04 81.517857 \n",
"std 1.628933e-18 5.597990 \n",
"min 5.000080e-04 70.000000 \n",
"25% 5.000080e-04 78.000000 \n",
"50% 5.000080e-04 78.000000 \n",
"75% 5.000080e-04 90.000000 \n",
"max 5.000080e-04 90.000000 \n",
"\n",
" bids_meta.MagneticFieldStrength bids_meta.RepetitionTime dummy_trs \\\n",
"count 373.0 816.000000 816.000000 \n",
"mean 3.0 2.036765 0.154412 \n",
"std 0.0 0.205439 0.418211 \n",
"min 3.0 1.500000 0.000000 \n",
"25% 3.0 2.000000 0.000000 \n",
"50% 3.0 2.000000 0.000000 \n",
"75% 3.0 2.000000 0.000000 \n",
"max 3.0 3.000000 3.000000 \n",
"\n",
" dvars_nstd dvars_std ... summary_bg_stdv summary_fg_k \\\n",
"count 816.000000 816.000000 ... 816.000000 816.000000 \n",
"mean 25.953467 1.118805 ... 63.878695 3.543160 \n",
"std 5.059479 0.108258 ... 42.766615 1.178374 \n",
"min 11.765937 0.878974 ... 21.197292 -0.035018 \n",
"25% 23.022825 1.059097 ... 40.782249 2.795746 \n",
"50% 25.558709 1.109312 ... 49.592535 3.435796 \n",
"75% 28.648504 1.152416 ... 78.196987 4.196941 \n",
"max 51.112139 1.765792 ... 428.297760 8.460538 \n",
"\n",
" summary_fg_mad summary_fg_mean summary_fg_median summary_fg_n \\\n",
"count 816.000000 816.000000 816.000000 816.000000 \n",
"mean 131.324780 806.728076 811.267722 31808.042892 \n",
"std 113.767484 406.774419 395.053324 7790.870244 \n",
"min 55.146530 474.340027 474.438904 14779.000000 \n",
"25% 84.651619 602.982361 614.543335 26350.000000 \n",
"50% 102.012768 697.461578 702.249237 32369.500000 \n",
"75% 136.670071 873.652100 873.856018 36973.500000 \n",
"max 907.907837 4322.361816 4295.756836 51917.000000 \n",
"\n",
" summary_fg_p05 summary_fg_p95 summary_fg_stdv tsnr \n",
"count 816.000000 816.000000 816.000000 816.000000 \n",
"mean 518.619718 1059.053452 171.267898 56.024184 \n",
"std 235.797804 619.460646 120.334072 14.850467 \n",
"min 264.936682 615.543079 76.028267 26.553900 \n",
"25% 406.639260 758.071356 117.175583 46.744843 \n",
"50% 453.458518 871.170877 145.896439 55.457203 \n",
"75% 568.478488 1144.218155 184.177444 62.599040 \n",
"max 2853.770264 5836.885254 950.784058 122.917000 \n",
"\n",
"[8 rows x 50 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.concat(dfs, ignore_index=True)\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each set of metrics is accompanied by the MD5 hash of the contents of the image. This allows us to match the same image across different version of MRIQC or remove duplicate measures (since the same software and version was used the multiple measures of the same image should be identical)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>aor</th>\n",
" <th>aqi</th>\n",
" <th>bids_meta.EchoTime</th>\n",
" <th>bids_meta.EffectiveEchoSpacing</th>\n",
" <th>bids_meta.FlipAngle</th>\n",
" <th>bids_meta.MagneticFieldStrength</th>\n",
" <th>bids_meta.RepetitionTime</th>\n",
" <th>dummy_trs</th>\n",
" <th>dvars_nstd</th>\n",
" <th>dvars_std</th>\n",
" <th>...</th>\n",
" <th>summary_bg_stdv</th>\n",
" <th>summary_fg_k</th>\n",
" <th>summary_fg_mad</th>\n",
" <th>summary_fg_mean</th>\n",
" <th>summary_fg_median</th>\n",
" <th>summary_fg_n</th>\n",
" <th>summary_fg_p05</th>\n",
" <th>summary_fg_p95</th>\n",
" <th>summary_fg_stdv</th>\n",
" <th>tsnr</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>409.000000</td>\n",
" <td>3.100000e+02</td>\n",
" <td>409.000000</td>\n",
" <td>373.0</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>...</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" <td>670.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.003201</td>\n",
" <td>0.010402</td>\n",
" <td>0.027758</td>\n",
" <td>5.000080e-04</td>\n",
" <td>80.709046</td>\n",
" <td>3.0</td>\n",
" <td>2.032090</td>\n",
" <td>0.132836</td>\n",
" <td>25.870564</td>\n",
" <td>1.125548</td>\n",
" <td>...</td>\n",
" <td>69.393233</td>\n",
" <td>3.492196</td>\n",
" <td>135.492224</td>\n",
" <td>821.121824</td>\n",
" <td>823.952415</td>\n",
" <td>32656.280597</td>\n",
" <td>528.096383</td>\n",
" <td>1083.317220</td>\n",
" <td>176.118332</td>\n",
" <td>56.529504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.004599</td>\n",
" <td>0.005666</td>\n",
" <td>0.003743</td>\n",
" <td>1.628933e-18</td>\n",
" <td>5.176866</td>\n",
" <td>0.0</td>\n",
" <td>0.193543</td>\n",
" <td>0.403974</td>\n",
" <td>5.052656</td>\n",
" <td>0.113744</td>\n",
" <td>...</td>\n",
" <td>45.239326</td>\n",
" <td>1.153397</td>\n",
" <td>121.827120</td>\n",
" <td>421.480155</td>\n",
" <td>407.235969</td>\n",
" <td>7795.805781</td>\n",
" <td>245.710483</td>\n",
" <td>649.278647</td>\n",
" <td>127.217728</td>\n",
" <td>15.266086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000290</td>\n",
" <td>0.001024</td>\n",
" <td>0.020000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>70.000000</td>\n",
" <td>3.0</td>\n",
" <td>1.500000</td>\n",
" <td>0.000000</td>\n",
" <td>11.765937</td>\n",
" <td>0.878974</td>\n",
" <td>...</td>\n",
" <td>21.197292</td>\n",
" <td>-0.035018</td>\n",
" <td>55.146530</td>\n",
" <td>474.340027</td>\n",
" <td>474.438904</td>\n",
" <td>14779.000000</td>\n",
" <td>264.936682</td>\n",
" <td>615.543079</td>\n",
" <td>76.028267</td>\n",
" <td>26.553900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000703</td>\n",
" <td>0.006647</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>78.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>22.962936</td>\n",
" <td>1.060628</td>\n",
" <td>...</td>\n",
" <td>43.638181</td>\n",
" <td>2.796599</td>\n",
" <td>86.761835</td>\n",
" <td>618.982178</td>\n",
" <td>629.579895</td>\n",
" <td>26606.000000</td>\n",
" <td>410.264667</td>\n",
" <td>778.767811</td>\n",
" <td>120.855646</td>\n",
" <td>47.387969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.001506</td>\n",
" <td>0.009109</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>78.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>25.533349</td>\n",
" <td>1.110239</td>\n",
" <td>...</td>\n",
" <td>56.365828</td>\n",
" <td>3.402941</td>\n",
" <td>117.288349</td>\n",
" <td>771.926086</td>\n",
" <td>768.158356</td>\n",
" <td>33658.500000</td>\n",
" <td>479.818892</td>\n",
" <td>1024.082831</td>\n",
" <td>160.101219</td>\n",
" <td>55.521240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.003451</td>\n",
" <td>0.012315</td>\n",
" <td>0.028000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>78.000000</td>\n",
" <td>3.0</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>28.545927</td>\n",
" <td>1.157474</td>\n",
" <td>...</td>\n",
" <td>80.980783</td>\n",
" <td>4.149144</td>\n",
" <td>137.538956</td>\n",
" <td>876.432449</td>\n",
" <td>878.623596</td>\n",
" <td>37794.000000</td>\n",
" <td>571.656434</td>\n",
" <td>1146.996944</td>\n",
" <td>184.852634</td>\n",
" <td>62.862894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.035539</td>\n",
" <td>0.050159</td>\n",
" <td>0.050000</td>\n",
" <td>5.000080e-04</td>\n",
" <td>90.000000</td>\n",
" <td>3.0</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>51.112139</td>\n",
" <td>1.765792</td>\n",
" <td>...</td>\n",
" <td>428.297760</td>\n",
" <td>8.460538</td>\n",
" <td>907.907837</td>\n",
" <td>4322.361816</td>\n",
" <td>4295.756836</td>\n",
" <td>51917.000000</td>\n",
" <td>2853.770264</td>\n",
" <td>5836.885254</td>\n",
" <td>950.784058</td>\n",
" <td>122.917000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 50 columns</p>\n",
"</div>"
],
"text/plain": [
" aor aqi bids_meta.EchoTime \\\n",
"count 670.000000 670.000000 409.000000 \n",
"mean 0.003201 0.010402 0.027758 \n",
"std 0.004599 0.005666 0.003743 \n",
"min 0.000290 0.001024 0.020000 \n",
"25% 0.000703 0.006647 0.028000 \n",
"50% 0.001506 0.009109 0.028000 \n",
"75% 0.003451 0.012315 0.028000 \n",
"max 0.035539 0.050159 0.050000 \n",
"\n",
" bids_meta.EffectiveEchoSpacing bids_meta.FlipAngle \\\n",
"count 3.100000e+02 409.000000 \n",
"mean 5.000080e-04 80.709046 \n",
"std 1.628933e-18 5.176866 \n",
"min 5.000080e-04 70.000000 \n",
"25% 5.000080e-04 78.000000 \n",
"50% 5.000080e-04 78.000000 \n",
"75% 5.000080e-04 78.000000 \n",
"max 5.000080e-04 90.000000 \n",
"\n",
" bids_meta.MagneticFieldStrength bids_meta.RepetitionTime dummy_trs \\\n",
"count 373.0 670.000000 670.000000 \n",
"mean 3.0 2.032090 0.132836 \n",
"std 0.0 0.193543 0.403974 \n",
"min 3.0 1.500000 0.000000 \n",
"25% 3.0 2.000000 0.000000 \n",
"50% 3.0 2.000000 0.000000 \n",
"75% 3.0 2.000000 0.000000 \n",
"max 3.0 3.000000 3.000000 \n",
"\n",
" dvars_nstd dvars_std ... summary_bg_stdv summary_fg_k \\\n",
"count 670.000000 670.000000 ... 670.000000 670.000000 \n",
"mean 25.870564 1.125548 ... 69.393233 3.492196 \n",
"std 5.052656 0.113744 ... 45.239326 1.153397 \n",
"min 11.765937 0.878974 ... 21.197292 -0.035018 \n",
"25% 22.962936 1.060628 ... 43.638181 2.796599 \n",
"50% 25.533349 1.110239 ... 56.365828 3.402941 \n",
"75% 28.545927 1.157474 ... 80.980783 4.149144 \n",
"max 51.112139 1.765792 ... 428.297760 8.460538 \n",
"\n",
" summary_fg_mad summary_fg_mean summary_fg_median summary_fg_n \\\n",
"count 670.000000 670.000000 670.000000 670.000000 \n",
"mean 135.492224 821.121824 823.952415 32656.280597 \n",
"std 121.827120 421.480155 407.235969 7795.805781 \n",
"min 55.146530 474.340027 474.438904 14779.000000 \n",
"25% 86.761835 618.982178 629.579895 26606.000000 \n",
"50% 117.288349 771.926086 768.158356 33658.500000 \n",
"75% 137.538956 876.432449 878.623596 37794.000000 \n",
"max 907.907837 4322.361816 4295.756836 51917.000000 \n",
"\n",
" summary_fg_p05 summary_fg_p95 summary_fg_stdv tsnr \n",
"count 670.000000 670.000000 670.000000 670.000000 \n",
"mean 528.096383 1083.317220 176.118332 56.529504 \n",
"std 245.710483 649.278647 127.217728 15.266086 \n",
"min 264.936682 615.543079 76.028267 26.553900 \n",
"25% 410.264667 778.767811 120.855646 47.387969 \n",
"50% 479.818892 1024.082831 160.101219 55.521240 \n",
"75% 571.656434 1146.996944 184.852634 62.862894 \n",
"max 2853.770264 5836.885254 950.784058 122.917000 \n",
"\n",
"[8 rows x 50 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop_duplicates(subset=['provenance.md5sum'], inplace=True)\n",
"df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets plot the distribution of mean framewise displacement across those 670 images."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2c7d1eac828>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWkAAAEFCAYAAAAhTRZvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwv4/zQbHDYawQAgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c7d1edeb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df.fd_mean)\n",
"plt.xlabel(\"Mean Framewise Displacement [mm]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment