diff --git a/plot_annual_hydro_by_basins.ipynb b/plot_annual_hydro_by_basins.ipynb
index 82474be..c285053 100644
--- a/plot_annual_hydro_by_basins.ipynb
+++ b/plot_annual_hydro_by_basins.ipynb
@@ -2,18 +2,48 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# install dependencies\n",
+ "!pip install plotly --quiet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
+ "import os\n",
+ "from pathlib import Path\n",
+ "\n",
"import plotly.graph_objs as go\n",
"from plotly.subplots import make_subplots\n",
"import plotly.express as px"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Configure plotly's renderer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import plotly.io as pio\n",
+ "pio.renderers.default = \"notebook\""
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,19 +55,37 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "define data paths"
+ "Path to pre-mounted data"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
- "mpath = './'\n",
+ "# data_dir = Path(os.environ['DATA_DIR'])\n",
+ "mpath = '/data/'\n",
"dpath = mpath+'hydrosmade_v1/'"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Or if running locally and you've downloaded the data set the path:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# mpath = './'\n",
+ "# dpath = mpath+'hydrosmade_v1/'"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -47,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -62,7 +110,7 @@
" dtype='object')"
]
},
- "execution_count": 3,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -82,7 +130,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
@@ -92,7 +140,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -101,7 +149,7 @@
"3173"
]
},
- "execution_count": 5,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -134,16 +182,24 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 21,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_465/1223149010.py:2: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
+ " date = pd.date_range(''+str(syr)+'-1', periods=(2100-syr+1)*12, freq='M')\n"
+ ]
+ },
{
"data": {
"text/plain": [
"1812"
]
},
- "execution_count": 6,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -174,7 +230,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
@@ -203,73 +259,13 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "large_storage ssp126 canesm5\n",
- "large_storage ssp126 cnrm-cm6-1\n",
- "large_storage ssp126 cnrm-esm2-1\n",
- "large_storage ssp126 ec-earth3\n",
- "large_storage ssp126 gfdl-esm4\n",
- "large_storage ssp126 ipsl-cm6a-lr\n",
- "large_storage ssp126 miroc6\n",
- "large_storage ssp126 mpi-esm1-2-hr\n",
- "large_storage ssp126 mri-esm2-0\n",
- "large_storage ssp126 ukesm1-0-ll\n",
- "large_storage ssp370 canesm5\n",
- "large_storage ssp370 cnrm-cm6-1\n",
- "large_storage ssp370 cnrm-esm2-1\n",
- "large_storage ssp370 ec-earth3\n",
- "large_storage ssp370 gfdl-esm4\n",
- "large_storage ssp370 ipsl-cm6a-lr\n",
- "large_storage ssp370 miroc6\n",
- "large_storage ssp370 mpi-esm1-2-hr\n",
- "large_storage ssp370 mri-esm2-0\n",
- "large_storage ssp370 ukesm1-0-ll\n",
- "large_storage ssp585 canesm5\n",
- "large_storage ssp585 cnrm-cm6-1\n",
- "large_storage ssp585 cnrm-esm2-1\n",
- "large_storage ssp585 ec-earth3\n",
- "large_storage ssp585 gfdl-esm4\n",
- "large_storage ssp585 ipsl-cm6a-lr\n",
- "large_storage ssp585 miroc6\n",
- "large_storage ssp585 mpi-esm1-2-hr\n",
- "large_storage ssp585 mri-esm2-0\n",
- "large_storage ssp585 ukesm1-0-ll\n",
- "local_diversion ssp126 canesm5\n",
- "local_diversion ssp126 cnrm-cm6-1\n",
- "local_diversion ssp126 cnrm-esm2-1\n",
- "local_diversion ssp126 ec-earth3\n",
- "local_diversion ssp126 gfdl-esm4\n",
- "local_diversion ssp126 ipsl-cm6a-lr\n",
- "local_diversion ssp126 miroc6\n",
- "local_diversion ssp126 mpi-esm1-2-hr\n",
- "local_diversion ssp126 mri-esm2-0\n",
- "local_diversion ssp126 ukesm1-0-ll\n",
- "local_diversion ssp370 canesm5\n",
- "local_diversion ssp370 cnrm-cm6-1\n",
- "local_diversion ssp370 cnrm-esm2-1\n",
- "local_diversion ssp370 ec-earth3\n",
- "local_diversion ssp370 gfdl-esm4\n",
- "local_diversion ssp370 ipsl-cm6a-lr\n",
- "local_diversion ssp370 miroc6\n",
- "local_diversion ssp370 mpi-esm1-2-hr\n",
- "local_diversion ssp370 mri-esm2-0\n",
- "local_diversion ssp370 ukesm1-0-ll\n",
- "local_diversion ssp585 canesm5\n",
- "local_diversion ssp585 cnrm-cm6-1\n",
- "local_diversion ssp585 cnrm-esm2-1\n",
- "local_diversion ssp585 ec-earth3\n",
- "local_diversion ssp585 gfdl-esm4\n",
- "local_diversion ssp585 ipsl-cm6a-lr\n",
- "local_diversion ssp585 miroc6\n",
- "local_diversion ssp585 mpi-esm1-2-hr\n",
- "local_diversion ssp585 mri-esm2-0\n",
- "local_diversion ssp585 ukesm1-0-ll\n",
"large_diversion ssp126 canesm5\n",
"large_diversion ssp126 cnrm-cm6-1\n",
"large_diversion ssp126 cnrm-esm2-1\n",
@@ -300,6 +296,36 @@
"large_diversion ssp585 mpi-esm1-2-hr\n",
"large_diversion ssp585 mri-esm2-0\n",
"large_diversion ssp585 ukesm1-0-ll\n",
+ "local_diversion ssp126 canesm5\n",
+ "local_diversion ssp126 cnrm-cm6-1\n",
+ "local_diversion ssp126 cnrm-esm2-1\n",
+ "local_diversion ssp126 ec-earth3\n",
+ "local_diversion ssp126 gfdl-esm4\n",
+ "local_diversion ssp126 ipsl-cm6a-lr\n",
+ "local_diversion ssp126 miroc6\n",
+ "local_diversion ssp126 mpi-esm1-2-hr\n",
+ "local_diversion ssp126 mri-esm2-0\n",
+ "local_diversion ssp126 ukesm1-0-ll\n",
+ "local_diversion ssp370 canesm5\n",
+ "local_diversion ssp370 cnrm-cm6-1\n",
+ "local_diversion ssp370 cnrm-esm2-1\n",
+ "local_diversion ssp370 ec-earth3\n",
+ "local_diversion ssp370 gfdl-esm4\n",
+ "local_diversion ssp370 ipsl-cm6a-lr\n",
+ "local_diversion ssp370 miroc6\n",
+ "local_diversion ssp370 mpi-esm1-2-hr\n",
+ "local_diversion ssp370 mri-esm2-0\n",
+ "local_diversion ssp370 ukesm1-0-ll\n",
+ "local_diversion ssp585 canesm5\n",
+ "local_diversion ssp585 cnrm-cm6-1\n",
+ "local_diversion ssp585 cnrm-esm2-1\n",
+ "local_diversion ssp585 ec-earth3\n",
+ "local_diversion ssp585 gfdl-esm4\n",
+ "local_diversion ssp585 ipsl-cm6a-lr\n",
+ "local_diversion ssp585 miroc6\n",
+ "local_diversion ssp585 mpi-esm1-2-hr\n",
+ "local_diversion ssp585 mri-esm2-0\n",
+ "local_diversion ssp585 ukesm1-0-ll\n",
"local_storage ssp126 canesm5\n",
"local_storage ssp126 cnrm-cm6-1\n",
"local_storage ssp126 cnrm-esm2-1\n",
@@ -329,7 +355,37 @@
"local_storage ssp585 miroc6\n",
"local_storage ssp585 mpi-esm1-2-hr\n",
"local_storage ssp585 mri-esm2-0\n",
- "local_storage ssp585 ukesm1-0-ll\n"
+ "local_storage ssp585 ukesm1-0-ll\n",
+ "large_storage ssp126 canesm5\n",
+ "large_storage ssp126 cnrm-cm6-1\n",
+ "large_storage ssp126 cnrm-esm2-1\n",
+ "large_storage ssp126 ec-earth3\n",
+ "large_storage ssp126 gfdl-esm4\n",
+ "large_storage ssp126 ipsl-cm6a-lr\n",
+ "large_storage ssp126 miroc6\n",
+ "large_storage ssp126 mpi-esm1-2-hr\n",
+ "large_storage ssp126 mri-esm2-0\n",
+ "large_storage ssp126 ukesm1-0-ll\n",
+ "large_storage ssp370 canesm5\n",
+ "large_storage ssp370 cnrm-cm6-1\n",
+ "large_storage ssp370 cnrm-esm2-1\n",
+ "large_storage ssp370 ec-earth3\n",
+ "large_storage ssp370 gfdl-esm4\n",
+ "large_storage ssp370 ipsl-cm6a-lr\n",
+ "large_storage ssp370 miroc6\n",
+ "large_storage ssp370 mpi-esm1-2-hr\n",
+ "large_storage ssp370 mri-esm2-0\n",
+ "large_storage ssp370 ukesm1-0-ll\n",
+ "large_storage ssp585 canesm5\n",
+ "large_storage ssp585 cnrm-cm6-1\n",
+ "large_storage ssp585 cnrm-esm2-1\n",
+ "large_storage ssp585 ec-earth3\n",
+ "large_storage ssp585 gfdl-esm4\n",
+ "large_storage ssp585 ipsl-cm6a-lr\n",
+ "large_storage ssp585 miroc6\n",
+ "large_storage ssp585 mpi-esm1-2-hr\n",
+ "large_storage ssp585 mri-esm2-0\n",
+ "large_storage ssp585 ukesm1-0-ll\n"
]
}
],
@@ -367,7 +423,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -376,7 +432,7 @@
"120"
]
},
- "execution_count": 9,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -394,7 +450,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -403,7 +459,7 @@
"(60, 60)"
]
},
- "execution_count": 10,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -433,7 +489,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -442,7 +498,7 @@
"60"
]
},
- "execution_count": 11,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -471,7 +527,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -480,7 +536,7 @@
"60"
]
},
- "execution_count": 12,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -502,7 +558,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 41,
"metadata": {},
"outputs": [],
"source": []
@@ -523,7619 +579,3910 @@
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{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 42,
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",
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iv=F2(),qPt=Gl(),OPt=Cc().attributes,{hovertemplateAttrs:BPt,texttemplateAttrs:NPt,templatefallbackAttrs:rPe}=Ll(),Y2=Ao().extendFlat;iPe.exports={labels:iv.labels,label0:iv.label0,dlabel:iv.dlabel,values:iv.values,marker:{colors:iv.marker.colors,line:{color:Y2({},iv.marker.line.color,{dflt:null}),width:Y2({},iv.marker.line.width,{dflt:1}),editType:\"calc\"},pattern:iv.marker.pattern,editType:\"calc\"},text:iv.text,hovertext:iv.hovertext,scalegroup:Y2({},iv.scalegroup,{}),textinfo:Y2({},iv.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:NPt({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),texttemplatefallback:rPe({editType:\"plot\"}),hoverinfo:Y2({},qPt.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:BPt({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hovertemplatefallback:rPe(),textposition:Y2({},iv.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:iv.textfont,insidetextfont:iv.insidetextfont,title:{text:iv.title.text,font:iv.title.font,position:Y2({},iv.title.position,{values:[\"top 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c=a(\"text\"),f=a(\"texttemplate\");a(\"texttemplatefallback\");var h;if(f||(h=a(\"textinfo\",Array.isArray(c)?\"text+percent\":\"percent\")),a(\"hovertext\"),a(\"hovertemplate\"),a(\"hovertemplatefallback\"),f||h&&h!==\"none\"){var d=a(\"textposition\");HPt(t,r,i,a,d,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}else h===\"none\"&&a(\"textposition\",\"none\");GPt(r,i,a);var v=a(\"title.text\");v&&(a(\"title.position\"),aPe.coerceFont(a,\"title.font\",i.font)),a(\"aspectratio\"),a(\"baseratio\")}});var uPe=ye((egr,lPe)=>{\"use strict\";var XPt=Pr(),ZPt=_X();lPe.exports=function(t,r){function n(i,a){return XPt.coerce(t,r,ZPt,i,a)}n(\"hiddenlabels\"),n(\"funnelareacolorway\",r.colorway),n(\"extendfunnelareacolors\")}});var xX=ye((tgr,fPe)=>{\"use strict\";var cPe=kA();function YPt(e,t){return cPe.calc(e,t)}function KPt(e){cPe.crossTraceCalc(e,{type:\"funnelarea\"})}fPe.exports={calc:YPt,crossTraceCalc:KPt}});var gPe=ye((rgr,pPe)=>{\"use strict\";var K2=qa(),bX=So(),ax=Pr(),JPt=ax.strScale,hPe=ax.strTranslate,dPe=Zl(),$Pt=d2(),QPt=$Pt.toMoveInsideBar,vPe=bv(),eIt=vPe.recordMinTextSize,tIt=vPe.clearMinTextSize,rIt=g_(),XA=TD(),iIt=XA.attachFxHandlers,nIt=XA.determineInsideTextFont,aIt=XA.layoutAreas,oIt=XA.prerenderTitles,sIt=XA.positionTitleOutside,lIt=XA.formatSliceLabel;pPe.exports=function(t,r){var n=t._context.staticPlot,i=t._fullLayout;tIt(\"funnelarea\",i),oIt(r,t),aIt(r,i._size),ax.makeTraceGroups(i._funnelarealayer,r,\"trace\").each(function(a){var o=K2.select(this),s=a[0],l=s.trace;cIt(a),o.each(function(){var u=K2.select(this).selectAll(\"g.slice\").data(a);u.enter().append(\"g\").classed(\"slice\",!0),u.exit().remove(),u.each(function(f,h){if(f.hidden){K2.select(this).selectAll(\"path,g\").remove();return}f.pointNumber=f.i,f.curveNumber=l.index;var d=s.cx,v=s.cy,m=K2.select(this),b=m.selectAll(\"path.surface\").data([f]);b.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":n?\"none\":\"all\"}),m.call(iIt,t,a);var p=\"M\"+(d+f.TR[0])+\",\"+(v+f.TR[1])+wX(f.TR,f.BR)+wX(f.BR,f.BL)+wX(f.BL,f.TL)+\"Z\";b.attr(\"d\",p),lIt(t,f,s);var k=rIt.castOption(l.textposition,f.pts),M=m.selectAll(\"g.slicetext\").data(f.text&&k!==\"none\"?[0]:[]);M.enter().append(\"g\").classed(\"slicetext\",!0),M.exit().remove(),M.each(function(){var T=ax.ensureSingle(K2.select(this),\"text\",\"\",function(z){z.attr(\"data-notex\",1)}),L=ax.ensureUniformFontSize(t,nIt(l,f,i.font));T.text(f.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(bX.font,L).call(dPe.convertToTspans,t);var x=bX.bBox(T.node()),C,S,g,P=Math.min(f.BL[1],f.BR[1])+v,E=Math.max(f.TL[1],f.TR[1])+v;S=Math.max(f.TL[0],f.BL[0])+d,g=Math.min(f.TR[0],f.BR[0])+d,C=QPt(S,g,P,E,x,{isHorizontal:!0,constrained:!0,angle:0,anchor:\"middle\"}),C.fontSize=L.size,eIt(l.type,C,i),a[h].transform=C,ax.setTransormAndDisplay(T,C)})});var c=K2.select(this).selectAll(\"g.titletext\").data(l.title.text?[0]:[]);c.enter().append(\"g\").classed(\"titletext\",!0),c.exit().remove(),c.each(function(){var f=ax.ensureSingle(K2.select(this),\"text\",\"\",function(v){v.attr(\"data-notex\",1)}),h=l.title.text;l._meta&&(h=ax.templateString(h,l._meta)),f.text(h).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(bX.font,l.title.font).call(dPe.convertToTspans,t);var d=sIt(s,i._size);f.attr(\"transform\",hPe(d.x,d.y)+JPt(Math.min(1,d.scale))+hPe(d.tx,d.ty))})})})};function wX(e,t){var r=t[0]-e[0],n=t[1]-e[1];return\"l\"+r+\",\"+n}function uIt(e,t){return[.5*(e[0]+t[0]),.5*(e[1]+t[1])]}function cIt(e){if(!e.length)return;var t=e[0],r=t.trace,n=r.aspectratio,i=r.baseratio;i>.999&&(i=.999);var a=Math.pow(i,2),o=t.vTotal,s=o*a/(1-a),l=o,u=s/o;function c(){var q=Math.sqrt(u);return{x:q,y:-q}}function f(){var q=c();return[q.x,q.y]}var h,d=[];d.push(f());var v,m;for(v=e.length-1;v>-1;v--)if(m=e[v],!m.hidden){var b=m.v/l;u+=b,d.push(f())}var p=1/0,k=-1/0;for(v=0;vd[Pe][0]&&(Pe=me)}}var ce=M;ce[0]=ce[1]=ce[2]=0,ce[s.log2(Le^De)]=De&Le,ce[s.log2(De^Pe)]=De&Pe;var He=Pe^7;He===Z||He===we?(He=Le^7,ce[s.log2(Pe^He)]=He&Pe):ce[s.log2(Le^He)]=He&Le;for(var lt=T,mt=Z,H=0;H<3;++H)mt&1<
0){var H=this.triShader;H.bind(),H.uniforms=U,this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind()}},p.drawPick=function(C){C=C||{};for(var S=this.gl,g=C.model||m,P=C.view||m,E=C.projection||m,z=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],q=0;q<3;++q)z[0][q]=Math.max(z[0][q],this.clipBounds[0][q]),z[1][q]=Math.min(z[1][q],this.clipBounds[1][q]);this._model=[].slice.call(g),this._view=[].slice.call(P),this._projection=[].slice.call(E),this._resolution=[S.drawingBufferWidth,S.drawingBufferHeight];var U={model:g,view:P,projection:E,clipBounds:z,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},G=this.pickShader;G.bind(),G.uniforms=U,this.triangleCount>0&&(this.triangleVAO.bind(),S.drawArrays(S.TRIANGLES,0,this.triangleCount*3),this.triangleVAO.unbind())},p.pick=function(C){if(!C||C.id!==this.pickId)return null;var S=C.value[0]+256*C.value[1]+65536*C.value[2],g=this.cells[S],P=this.positions[g[1]].slice(0,3),E={position:P,dataCoordinate:P,index:Math.floor(g[1]/48)};return this.traceType===\"cone\"?E.index=Math.floor(g[1]/48):this.traceType===\"streamtube\"&&(E.intensity=this.intensity[g[1]],E.velocity=this.vectors[g[1]].slice(0,3),E.divergence=this.vectors[g[1]][3],E.index=S),E},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()};function T(C,S){var g=s(C,S.meshShader.vertex,S.meshShader.fragment,null,S.meshShader.attributes);return g.attributes.position.location=0,g.attributes.color.location=2,g.attributes.uv.location=3,g.attributes.vector.location=4,g}function L(C,S){var g=s(C,S.pickShader.vertex,S.pickShader.fragment,null,S.pickShader.attributes);return g.attributes.position.location=0,g.attributes.id.location=1,g.attributes.vector.location=4,g}function x(C,S,g){var P=g.shaders;arguments.length===1&&(S=C,C=S.gl);var E=T(C,P),z=L(C,P),q=c(C,d(new Uint8Array([255,255,255,255]),[1,1,4]));q.generateMipmap(),q.minFilter=C.LINEAR_MIPMAP_LINEAR,q.magFilter=C.LINEAR;var U=l(C),G=l(C),Z=l(C),j=l(C),N=l(C),H=u(C,[{buffer:U,type:C.FLOAT,size:4},{buffer:N,type:C.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:Z,type:C.FLOAT,size:4},{buffer:j,type:C.FLOAT,size:2},{buffer:G,type:C.FLOAT,size:4}]),re=new b(C,q,E,z,U,G,N,Z,j,H,g.traceType||\"cone\");return re.update(S),re}i.exports=x},614:function(i,a,o){var s=o(3236),l=s([`precision highp float;\n",
+ "\n",
+ "precision highp float;\n",
+ "#define GLSLIFY 1\n",
+ "\n",
+ "vec3 getOrthogonalVector(vec3 v) {\n",
+ " // Return up-vector for only-z vector.\n",
+ " // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+ " // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\n",
+ " // Assign z = 0, x = -b, y = a:\n",
+ " // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+ " if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+ " return normalize(vec3(-v.y, v.x, 0.0));\n",
+ " } else {\n",
+ " return normalize(vec3(0.0, v.z, -v.y));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Calculate the cone vertex and normal at the given index.\n",
+ "//\n",
+ "// The returned vertex is for a cone with its top at origin and height of 1.0,\n",
+ "// pointing in the direction of the vector attribute.\n",
+ "//\n",
+ "// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n",
+ "// These vertices are used to make up the triangles of the cone by the following:\n",
+ "// segment + 0 top vertex\n",
+ "// segment + 1 perimeter vertex a+1\n",
+ "// segment + 2 perimeter vertex a\n",
+ "// segment + 3 center base vertex\n",
+ "// segment + 4 perimeter vertex a\n",
+ "// segment + 5 perimeter vertex a+1\n",
+ "// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n",
+ "// To go from index to segment, floor(index / 6)\n",
+ "// To go from segment to angle, 2*pi * (segment/segmentCount)\n",
+ "// To go from index to segment index, index - (segment*6)\n",
+ "//\n",
+ "vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n",
+ "\n",
+ " const float segmentCount = 8.0;\n",
+ "\n",
+ " float index = rawIndex - floor(rawIndex /\n",
+ " (segmentCount * 6.0)) *\n",
+ " (segmentCount * 6.0);\n",
+ "\n",
+ " float segment = floor(0.001 + index/6.0);\n",
+ " float segmentIndex = index - (segment*6.0);\n",
+ "\n",
+ " normal = -normalize(d);\n",
+ "\n",
+ " if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n",
+ " return mix(vec3(0.0), -d, coneOffset);\n",
+ " }\n",
+ "\n",
+ " float nextAngle = (\n",
+ " (segmentIndex > 0.99 && segmentIndex < 1.01) ||\n",
+ " (segmentIndex > 4.99 && segmentIndex < 5.01)\n",
+ " ) ? 1.0 : 0.0;\n",
+ " float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n",
+ "\n",
+ " vec3 v1 = mix(d, vec3(0.0), coneOffset);\n",
+ " vec3 v2 = v1 - d;\n",
+ "\n",
+ " vec3 u = getOrthogonalVector(d);\n",
+ " vec3 v = normalize(cross(u, d));\n",
+ "\n",
+ " vec3 x = u * cos(angle) * length(d)*0.25;\n",
+ " vec3 y = v * sin(angle) * length(d)*0.25;\n",
+ " vec3 v3 = v2 + x + y;\n",
+ " if (segmentIndex < 3.0) {\n",
+ " vec3 tx = u * sin(angle);\n",
+ " vec3 ty = v * -cos(angle);\n",
+ " vec3 tangent = tx + ty;\n",
+ " normal = normalize(cross(v3 - v1, tangent));\n",
+ " }\n",
+ "\n",
+ " if (segmentIndex == 0.0) {\n",
+ " return mix(d, vec3(0.0), coneOffset);\n",
+ " }\n",
+ " return v3;\n",
+ "}\n",
+ "\n",
+ "attribute vec3 vector;\n",
+ "attribute vec4 color, position;\n",
+ "attribute vec2 uv;\n",
+ "\n",
+ "uniform float vectorScale, coneScale, coneOffset;\n",
+ "uniform mat4 model, view, projection, inverseModel;\n",
+ "uniform vec3 eyePosition, lightPosition;\n",
+ "\n",
+ "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+ "varying vec4 f_color;\n",
+ "varying vec2 f_uv;\n",
+ "\n",
+ "void main() {\n",
+ " // Scale the vector magnitude to stay constant with\n",
+ " // model & view changes.\n",
+ " vec3 normal;\n",
+ " vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n",
+ " vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+ "\n",
+ " //Lighting geometry parameters\n",
+ " vec4 cameraCoordinate = view * conePosition;\n",
+ " cameraCoordinate.xyz /= cameraCoordinate.w;\n",
+ " f_lightDirection = lightPosition - cameraCoordinate.xyz;\n",
+ " f_eyeDirection = eyePosition - cameraCoordinate.xyz;\n",
+ " f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n",
+ "\n",
+ " // vec4 m_position = model * vec4(conePosition, 1.0);\n",
+ " vec4 t_position = view * conePosition;\n",
+ " gl_Position = projection * t_position;\n",
+ "\n",
+ " f_color = color;\n",
+ " f_data = conePosition.xyz;\n",
+ " f_position = position.xyz;\n",
+ " f_uv = uv;\n",
+ "}\n",
+ "`]),u=s([`#extension GL_OES_standard_derivatives : enable\n",
+ "\n",
+ "precision highp float;\n",
+ "#define GLSLIFY 1\n",
+ "\n",
+ "float beckmannDistribution(float x, float roughness) {\n",
+ " float NdotH = max(x, 0.0001);\n",
+ " float cos2Alpha = NdotH * NdotH;\n",
+ " float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n",
+ " float roughness2 = roughness * roughness;\n",
+ " float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n",
+ " return exp(tan2Alpha / roughness2) / denom;\n",
+ "}\n",
+ "\n",
+ "float cookTorranceSpecular(\n",
+ " vec3 lightDirection,\n",
+ " vec3 viewDirection,\n",
+ " vec3 surfaceNormal,\n",
+ " float roughness,\n",
+ " float fresnel) {\n",
+ "\n",
+ " float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n",
+ " float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n",
+ "\n",
+ " //Half angle vector\n",
+ " vec3 H = normalize(lightDirection + viewDirection);\n",
+ "\n",
+ " //Geometric term\n",
+ " float NdotH = max(dot(surfaceNormal, H), 0.0);\n",
+ " float VdotH = max(dot(viewDirection, H), 0.000001);\n",
+ " float LdotH = max(dot(lightDirection, H), 0.000001);\n",
+ " float G1 = (2.0 * NdotH * VdotN) / VdotH;\n",
+ " float G2 = (2.0 * NdotH * LdotN) / LdotH;\n",
+ " float G = min(1.0, min(G1, G2));\n",
+ " \n",
+ " //Distribution term\n",
+ " float D = beckmannDistribution(NdotH, roughness);\n",
+ "\n",
+ " //Fresnel term\n",
+ " float F = pow(1.0 - VdotN, fresnel);\n",
+ "\n",
+ " //Multiply terms and done\n",
+ " return G * F * D / max(3.14159265 * VdotN, 0.000001);\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(float a, float b, float p) {\n",
+ " return ((p > max(a, b)) || \n",
+ " (p < min(a, b)));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+ " return (outOfRange(a.x, b.x, p.x) ||\n",
+ " outOfRange(a.y, b.y, p.y));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+ " return (outOfRange(a.x, b.x, p.x) ||\n",
+ " outOfRange(a.y, b.y, p.y) ||\n",
+ " outOfRange(a.z, b.z, p.z));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+ " return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+ "}\n",
+ "\n",
+ "uniform vec3 clipBounds[2];\n",
+ "uniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\n",
+ "uniform sampler2D texture;\n",
+ "\n",
+ "varying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\n",
+ "varying vec4 f_color;\n",
+ "varying vec2 f_uv;\n",
+ "\n",
+ "void main() {\n",
+ " if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+ " vec3 N = normalize(f_normal);\n",
+ " vec3 L = normalize(f_lightDirection);\n",
+ " vec3 V = normalize(f_eyeDirection);\n",
+ "\n",
+ " if(gl_FrontFacing) {\n",
+ " N = -N;\n",
+ " }\n",
+ "\n",
+ " float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n",
+ " float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n",
+ "\n",
+ " vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n",
+ " vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\n",
+ "\n",
+ " gl_FragColor = litColor * opacity;\n",
+ "}\n",
+ "`]),c=s([`precision highp float;\n",
+ "\n",
+ "precision highp float;\n",
+ "#define GLSLIFY 1\n",
+ "\n",
+ "vec3 getOrthogonalVector(vec3 v) {\n",
+ " // Return up-vector for only-z vector.\n",
+ " // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n",
+ " // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\n",
+ " // Assign z = 0, x = -b, y = a:\n",
+ " // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n",
+ " if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n",
+ " return normalize(vec3(-v.y, v.x, 0.0));\n",
+ " } else {\n",
+ " return normalize(vec3(0.0, v.z, -v.y));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Calculate the cone vertex and normal at the given index.\n",
+ "//\n",
+ "// The returned vertex is for a cone with its top at origin and height of 1.0,\n",
+ "// pointing in the direction of the vector attribute.\n",
+ "//\n",
+ "// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n",
+ "// These vertices are used to make up the triangles of the cone by the following:\n",
+ "// segment + 0 top vertex\n",
+ "// segment + 1 perimeter vertex a+1\n",
+ "// segment + 2 perimeter vertex a\n",
+ "// segment + 3 center base vertex\n",
+ "// segment + 4 perimeter vertex a\n",
+ "// segment + 5 perimeter vertex a+1\n",
+ "// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n",
+ "// To go from index to segment, floor(index / 6)\n",
+ "// To go from segment to angle, 2*pi * (segment/segmentCount)\n",
+ "// To go from index to segment index, index - (segment*6)\n",
+ "//\n",
+ "vec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n",
+ "\n",
+ " const float segmentCount = 8.0;\n",
+ "\n",
+ " float index = rawIndex - floor(rawIndex /\n",
+ " (segmentCount * 6.0)) *\n",
+ " (segmentCount * 6.0);\n",
+ "\n",
+ " float segment = floor(0.001 + index/6.0);\n",
+ " float segmentIndex = index - (segment*6.0);\n",
+ "\n",
+ " normal = -normalize(d);\n",
+ "\n",
+ " if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n",
+ " return mix(vec3(0.0), -d, coneOffset);\n",
+ " }\n",
+ "\n",
+ " float nextAngle = (\n",
+ " (segmentIndex > 0.99 && segmentIndex < 1.01) ||\n",
+ " (segmentIndex > 4.99 && segmentIndex < 5.01)\n",
+ " ) ? 1.0 : 0.0;\n",
+ " float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n",
+ "\n",
+ " vec3 v1 = mix(d, vec3(0.0), coneOffset);\n",
+ " vec3 v2 = v1 - d;\n",
+ "\n",
+ " vec3 u = getOrthogonalVector(d);\n",
+ " vec3 v = normalize(cross(u, d));\n",
+ "\n",
+ " vec3 x = u * cos(angle) * length(d)*0.25;\n",
+ " vec3 y = v * sin(angle) * length(d)*0.25;\n",
+ " vec3 v3 = v2 + x + y;\n",
+ " if (segmentIndex < 3.0) {\n",
+ " vec3 tx = u * sin(angle);\n",
+ " vec3 ty = v * -cos(angle);\n",
+ " vec3 tangent = tx + ty;\n",
+ " normal = normalize(cross(v3 - v1, tangent));\n",
+ " }\n",
+ "\n",
+ " if (segmentIndex == 0.0) {\n",
+ " return mix(d, vec3(0.0), coneOffset);\n",
+ " }\n",
+ " return v3;\n",
+ "}\n",
+ "\n",
+ "attribute vec4 vector;\n",
+ "attribute vec4 position;\n",
+ "attribute vec4 id;\n",
+ "\n",
+ "uniform mat4 model, view, projection;\n",
+ "uniform float vectorScale, coneScale, coneOffset;\n",
+ "\n",
+ "varying vec3 f_position;\n",
+ "varying vec4 f_id;\n",
+ "\n",
+ "void main() {\n",
+ " vec3 normal;\n",
+ " vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n",
+ " vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n",
+ " gl_Position = projection * (view * conePosition);\n",
+ " f_id = id;\n",
+ " f_position = position.xyz;\n",
+ "}\n",
+ "`]),f=s([`precision highp float;\n",
+ "#define GLSLIFY 1\n",
+ "\n",
+ "bool outOfRange(float a, float b, float p) {\n",
+ " return ((p > max(a, b)) || \n",
+ " (p < min(a, b)));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec2 a, vec2 b, vec2 p) {\n",
+ " return (outOfRange(a.x, b.x, p.x) ||\n",
+ " outOfRange(a.y, b.y, p.y));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec3 a, vec3 b, vec3 p) {\n",
+ " return (outOfRange(a.x, b.x, p.x) ||\n",
+ " outOfRange(a.y, b.y, p.y) ||\n",
+ " outOfRange(a.z, b.z, p.z));\n",
+ "}\n",
+ "\n",
+ "bool outOfRange(vec4 a, vec4 b, vec4 p) {\n",
+ " return outOfRange(a.xyz, b.xyz, p.xyz);\n",
+ "}\n",
+ "\n",
+ "uniform vec3 clipBounds[2];\n",
+ "uniform float pickId;\n",
+ "\n",
+ "varying vec3 f_position;\n",
+ "varying vec4 f_id;\n",
+ "\n",
+ "void main() {\n",
+ " if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n",
+ "\n",
+ " gl_FragColor = vec4(pickId, f_id.xyz);\n",
+ "}`]);a.meshShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},a.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},737:function(i){i.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},5171:function(i,a,o){var 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