{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"import plotly.graph_objs as go\n",
"import plotly.express as px\n",
"from plotly import tools, subplots\n",
"import numpy as np\n",
"import pandas as pd\n",
"import ipywidgets as widgets\n",
"import math\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"init_notebook_mode(connected=True)\n",
"import scipy.io as sio\n",
"import random\n",
"random.seed(123)\n",
"from plotly.subplots import make_subplots\n",
"import os \n",
"\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"plotly_mimetype\"\n",
"data_path = os.path.join(\"..\",\"data\",\"venus-data\")\n",
"derivativesDir = os.path.join(data_path,\"qMRFlow\")\n",
"\n",
"sessions = ['vendor750rev','vendorPRIrev','vendorSKYrev',\n",
" 'rth750rev','rthPRIrev','rthSKYrev']\n",
"sides = [\"negative\",\"negative\",\"negative\",\"positive\",\"positive\",\"positive\"]\n",
"pointpos = [0,0,-0.14,0.14,0,0]\n",
"convert_tag = {'rth750':'G1VENUS','rthPRI':'S1VENUS','rthSKY':'S2VENUS',\n",
" 'vendor750':'G1NATIVE','vendorPRI':'S1NATIVE','vendorSKY':'S2NATIVE'}\n",
"colors = {'rth750':'rgba(255,8,30,1)','rthPRI':'rgba(255,117,0,1)','rthSKY':'rgba(255,220,0,1)',\n",
" 'vendor750':'rgba(0,75,255,1)','vendorPRI':'rgba(24,231,234,1)','vendorSKY':'rgba(24,234,141,1)'}\n",
"colors_line = {'rth750':'rgba(255,8,30,0.35)','rthPRI':'rgba(255,117,0,0.35)','rthSKY':'rgba(255,220,0,0.35)',\n",
" 'vendor750':'rgba(0,75,255,0.35)','vendorPRI':'rgba(24,231,234,0.35)','vendorSKY':'rgba(24,234,141,0.35)'}\n",
"\n",
"def plot_ridgelines(subject,sessions,metric,region,colors,sides,sampN,leg,lowlim,uplim):\n",
" df = pd.DataFrame()\n",
" traces = []\n",
" for jj in range(0,len(sessions),1):\n",
" avg_array = []\n",
" std_array = []\n",
" cur_region = derivativesDir + '/sub-' + subject + '/ses-' + sessions[jj] + '/stat/sub-' + subject + '_ses-' + sessions[jj] + '_desc-' + region + '_metrics.mat'\n",
" cur_contents = sio.loadmat(cur_region)\n",
" cur_contents = cur_contents[metric]\n",
" cur_contents = np.delete(cur_contents, np.where(cur_contents < lowlim))\n",
" cur_contents = np.delete(cur_contents, np.where(cur_contents > uplim))\n",
" cur_tag = sessions[jj][0:-3]\n",
" cur_contents = random.sample(list(cur_contents),sampN)\n",
" traces.append(go.Violin(\n",
" x=cur_contents,\n",
" name = convert_tag[cur_tag],\n",
" marker=dict(\n",
" opacity = 0.7, \n",
" outliercolor='rgba(255, 255, 255, 0.6)',\n",
" line=dict(\n",
" outliercolor='rgba(219, 64, 82, 0.6)',\n",
" outlierwidth=2)),\n",
" line_color= colors[cur_tag],\n",
" legendgroup= convert_tag[cur_tag],\n",
" showlegend=leg,\n",
" side=sides[jj],\n",
" pointpos = pointpos[jj]))\n",
" return traces\n",
"\n",
"def plot_mosaic(metric,axis_title,lowlim,uplim):\n",
" nSamp = 10000\n",
" traces1 = plot_ridgelines('invivo1',sessions,metric,'wm',colors,sides,nSamp,True,lowlim,uplim)\n",
" traces2 = plot_ridgelines('invivo1',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)\n",
" traces3 = plot_ridgelines('invivo2',sessions,metric,'wm',colors,sides,nSamp,False,lowlim,uplim)\n",
" traces4 = plot_ridgelines('invivo2',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)\n",
" traces5 = plot_ridgelines('invivo3',sessions,metric,'wm',colors,sides,nSamp,False,lowlim,uplim)\n",
" traces6 = plot_ridgelines('invivo3',sessions,metric,'gm',colors,sides,nSamp,False,lowlim,uplim)\n",
" fig = make_subplots(rows=3, cols=2,\n",
" subplot_titles=(\"P1 White Matter\", \"P1 Gray Matter\", \"P2 White Matter\", \"P2 Gray Matter\", \"P3 White Matter\", \"P3 Gray Matter\"),\n",
" vertical_spacing = 0.08,\n",
" shared_xaxes=True)\n",
" for trace in traces1:\n",
" fig.add_trace(trace,row=1, col=1)\n",
" for trace in traces2:\n",
" fig.add_trace(trace,row=1, col=2)\n",
" for trace in traces3:\n",
" fig.add_trace(trace,row=2, col=1)\n",
" for trace in traces4:\n",
" fig.add_trace(trace,row=2, col=2)\n",
" for trace in traces5:\n",
" fig.add_trace(trace,row=3, col=1)\n",
" for trace in traces6:\n",
" fig.add_trace(trace,row=3, col=2)\n",
" axis_template = dict(linecolor = 'white', \n",
" showticklabels = True,\n",
" tickfont=dict(color=\"white\",size=12), \n",
" gridcolor = 'rgba(255,255,255,0.2)')\n",
" fig.update_yaxes(axis_template,showgrid=False)\n",
" fig.update_traces(orientation='h')\n",
" fig.update_layout(paper_bgcolor = '#1c2a3d',plot_bgcolor = '#1c2a3d')\n",
" fig.update_traces(meanline=dict(visible=True,color=\"white\",width=3),\n",
" points= 'outliers', # show all points\n",
" jitter=0.1,\n",
" width=3) # add some jitter on points for better visibility\n",
" #scalemode='count')\n",
" fig.update_xaxes(axis_template)\n",
" fig.update_layout(width=800,height=950,title=f\"VENUS vs NATIVE in-vivo {metric} distributions for each participant (P)\", title_font_color=\"white\")\n",
" fig.update_annotations(font=dict(color=\"white\", family = \"Courier\"))\n",
" for i in range(1,7): \n",
" fig['layout']['xaxis{}'.format(i)]['title']= axis_title\n",
" fig.for_each_xaxis(lambda axis: axis.title.update(font=dict(color = 'white', size=12,family=\"Courier\"),standoff=0))\n",
" fig.update_layout(\n",
" legend=dict(\n",
" y = 0.5,\n",
" font=dict(\n",
" family=\"Courier\",\n",
" size=16,\n",
" color=\"white\")))\n",
" return fig\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"#| label: kdet1cell\n",
"\n",
"fig = plot_mosaic('T1','T1 (s)',0.5,3)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"#| label: kdemtrcell\n",
"\n",
"fig = plot_mosaic('MTR','MTR (%)',35,70)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"#| label: kdemtsatcell\n",
"\n",
"fig = plot_mosaic('MTsat','MTsat (a.u.)',0.5,5)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"#| label: shiftcell\n",
"\n",
"from scipy.stats import norm\n",
"# generate random numbers from N(0,1)\n",
"\n",
"fig = go.Figure()\n",
"\n",
"\n",
"data_normal = norm.rvs(size=10000,loc=0.5,scale=1)\n",
"data_normal2 = norm.rvs(size=10000,loc=0,scale=2)\n",
"\n",
"# Figure for ideal case \n",
"#data_normal = norm.rvs(size=10000,loc=0,scale=1)\n",
"#data_normal2 = norm.rvs(size=10000,loc=0,scale=1)\n",
"\n",
"fig.add_trace(go.Violin(x=data_normal,\n",
" opacity = 1, \n",
" line_color= \"crimson\",\n",
" side=\"positive\",\n",
" name = \"Y\",\n",
" meanline=dict(visible=True,color=\"crimson\",width=4),\n",
" width=2\n",
"))\n",
"fig.add_trace(go.Violin(x=data_normal2,\n",
" marker=dict(\n",
" opacity = 0.3, \n",
" outliercolor='rgba(255, 255, 255, 0.6)',\n",
" line=dict(\n",
" outliercolor='rgba(219, 64, 82, 0.6)',\n",
" outlierwidth=2)),\n",
" line_color= \"limegreen\",\n",
" side=\"positive\",\n",
" name = \"X\",\n",
" meanline=dict(visible=True,color=\"limegreen\",width=4),\n",
" width=2 \n",
"))\n",
"\n",
"\n",
"fig.update_traces(orientation='h')\n",
"fig.update_layout(paper_bgcolor = 'white',plot_bgcolor = 'white',height=600, width=800)\n",
"fig.update_traces(points=False)\n",
"qs = pd.qcut(data_normal2, 10, labels=False)\n",
"aa = []\n",
"for ii in range(10):\n",
" aa.append(np.median(data_normal2[qs==ii]))\n",
"fig.add_vrect(\n",
" x0=aa[9] + 0.8, x1=aa[9]+0.9,\n",
" fillcolor=\"limegreen\", opacity=0.7,\n",
" layer=\"above\", line_width=0,\n",
")\n",
"\n",
"for ii in range(0,9,1):\n",
" fig.add_vrect(\n",
" x0=aa[ii], x1=aa[ii] + 0.02,\n",
" fillcolor=\"limegreen\", opacity=0.3,\n",
" layer=\"above\", line_width=0)\n",
"fig.add_vrect(\n",
" x0=aa[0], x1=aa[0] + 0.1,\n",
" fillcolor=\"limegreen\", opacity=0.7,\n",
" layer=\"above\", line_width=0,\n",
")\n",
"qs = pd.qcut(data_normal, 10, labels=False)\n",
"aa = []\n",
"for ii in range(10):\n",
" aa.append(np.median(data_normal[qs==ii])) \n",
"fig.add_vrect(\n",
" x0=aa[9], x1=aa[9]+0.1,\n",
" fillcolor=\"crimson\", opacity=0.7,\n",
" layer=\"above\", line_width=0,\n",
")\n",
"fig.add_vrect(\n",
" x0=aa[0], x1=aa[0]+0.1,\n",
" fillcolor=\"crimson\", opacity=0.7,\n",
" layer=\"above\", line_width=0,\n",
")\n",
"for ii in range(0,9,1):\n",
" fig.add_vrect(\n",
" x0=aa[ii], x1=aa[ii] + 0.02,\n",
" fillcolor=\"crimson\", opacity=0.3,\n",
" layer=\"above\", line_width=0)\n",
" \n",
"fig.add_annotation(x=aa[0], y=0.8,\n",
" text=\"<-- D1\",\n",
" showarrow=False,\n",
" yshift=10)\n",
"fig.add_annotation(x=aa[-1], y=0.8,\n",
" text=\"D9-->\",\n",
" showarrow=False,\n",
" yshift=10)\n",
"\n",
"\n",
"\n",
"fig.show()"
]
}
],
"metadata": {
"celltoolbar": "Edit Metadata",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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