burritojustice/.tangramplay

## .tangramplay
{"name":"denmark_ants.yaml","view":{"label":"Anholt, Denmark","lat":56.553427752820355,"lng":11.755371093750002,"zoom":7},"date":"2016-06-20T20:56:27.453Z"}

## scene.yaml
# Author @patriciogv - 2015
# modified by John Oram, 2016

import:
    - https://tangrams.github.io/blocks/space/constant.yaml
    - https://tangrams.github.io/blocks/space/tile.yaml
    - https://tangrams.github.io/blocks/functions/pulse.yaml
    - https://tangrams.github.io/blocks/patterns/stripes.yaml
    - https://tangrams.github.io/blocks/patterns/waves.yaml
    - https://tangrams.github.io/blocks/patterns/dots.yaml
    - https://tangrams.github.io/blocks/filter/grain.yaml
    - https://tangrams.github.io/blocks/shapes/circle.yaml
    - https://tangrams.github.io/blocks/patterns/zigzag.yaml
    - https://tangrams.github.io/blocks/lines/dash.yaml


sources:
    osm:
        type: TopoJSON
        url:  //vector.mapzen.com/osm/all/{z}/{x}/{y}.topojson
    scania:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/883/77/85688377.geojson
    halland:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/883/91/85688391.geojson
    blekinge:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/883/83/85688383.geojson

    bohulsan:
        type: GeoJSON
        url: https://gist.githubusercontent.com/burritojustice/9f2128acb3ced5545deea739298f386a/raw/623c04e11dec7026cf6293defc68e1c776530238/bohulsan.geojson

    nord_trøndelag:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/871/33/85687133.geojson

    sør_trøndelag:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/871/27/85687127.geojson

 #geojson for path and dates

    roskilde:
        type: GeoJSON
        url: https://gist.githubusercontent.com/burritojustice/6295c331c06eb0a5cd6bbf5f42ec8e61/raw/roskilde.geojson

    schleswig_holtein:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/825/17/85682517.geojson

    denmark:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/331/21/85633121.geojson

    sweden:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/337/89/85633789.geojson
    norway:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/333/41/85633341.geojson

    finland:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/331/43/85633143.geojson
    estonia:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/331/35/85633135.geojson

    ostvorpommern:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/111/102064111.geojson

    greifswald:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/107/102064107.geojson

    rügen:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/153/102064153.geojson

    nordvorpommern:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/133/102064133.geojson

    straisund:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/121/102064121.geojson
    greifswald:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/107/102064107.geojson

    ucker_randow:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/075/102064075.geojson

    kammin:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/080/153/102080153.geojson
    policki:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/080/119/102080119.geojson
    mawinouj:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/080/135/102080135.geojson
    goweniowski:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/080/123/102080123.geojson
    szczecin:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/080/073/102080073.geojson
    demmin:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/102/064/099/102064099.geojson
    bremen:
        type: GeoJSON
        url: https://gist.githubusercontent.com/burritojustice/daaf0a47f510044481edf3ed02c28980/raw/bremen_1657.geojson
    st_peterburg:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/880/69/85688069.geojson
    leningradskaja:
        type: GeoJSON
        url: https://whosonfirst.mapzen.com/data/856/878/61/85687861.geojson

scene:
    background:
        color: white
cameras:
    perspective:
        type: perspective
lights:
    directional1:
        type: directional
        direction: [.1, .5, -1]
        diffuse: .7
        ambient: .5

styles:
    functions-aastep:
        shaders:
            extensions: OES_standard_derivatives
            blocks:
                global: |
                    // AntiAliased Step function
                    //=============================
                    float aastep(float threshold, float value) {
                        #ifdef TANGRAM_FRAGMENT_SHADER
                            #ifdef TANGRAM_EXTENSION_OES_standard_derivatives
                                float afwidth = length(vec2(dFdx(value), dFdy(value))) * 0.70710678118654757;
                                return smoothstep(threshold-afwidth, threshold+afwidth, value);
                            #else
                                return step(threshold, value);
                            #endif
                        #else
                            return step(threshold, value);
                        #endif
                    }
    functions-pulse:
        shaders:
            blocks:
                global: |
                    float pulse (float x, float p, float w) {
                        x = abs(x - p);
                        if (x>w) return 0.0;
                        x /= w;
                        return 1.0 - x*x*(3.0-2.0*x);
                    }
    patterns-stripes:
        mix: functions-aastep
        shaders:
            defines:
                PI: 3.14159265358979323846
            blocks:
                global: |
                    // Return a distance function of stripes
                    float stripesDF (vec2 st) {
                        return abs(sin(st.y*PI));
                    }

                    // Adjustable width stripes
                    float stripes (vec2 st, float width) {
                        return aastep(width,stripesDF(st));
                    }

                    // Faster optimisation of diagonal stripes
                    float diagonalStripes (vec2 st) {
                        vec2 i_st = floor(st);
                        vec2 f_st = fract(st);
                        if (mod(i_st.y,2.) - mod(i_st.x,2.) == 0.) {
                            return 1.0 - aastep(f_st.x,f_st.y);
                        } else {
                            return aastep(f_st.x,f_st.y);
                        }
                    }
    pallete:
        shaders:
            blocks:
                global: |
                    // Common Values
                    //================================
                    float radio = 0.05;
                    vec2 size = vec2(1.,.6);
                    vec3 background = vec3(0.996,0.996,0.918);
                    vec3 midle = vec3(0.824, 0.812, 0.804);
                    vec3 foreground = vec3(0.275,0.298,0.251);
    border:
        base: lines
        mix: [functions-pulse, functions-aastep, pallete]
        texcoords: true
        shaders:
            blocks:
                color: |
                    vec2 st = v_texcoord.xy;
                    float pattern = aastep(.5,pulse(st.x,.7,.1)+pulse(st.x,.35,.25));
                    color.rgb = mix(background,color.rgb,pattern);
    b_border:
        base: lines
        mix: [functions-pulse, functions-aastep, pallete]
        texcoords: true
        shaders:
            blocks:
                color: |
                    vec2 st = v_texcoord.xy;
                    float pattern = aastep(.5,pulse(st.x,.5,.2));
                    color.rgb = mix(background,color.rgb,pattern);

    waves:
        base: polygons
        mix: [pallete, patterns-stripes]
        shaders:
            blocks:
                color: |
                    vec2 st = (gl_FragCoord.xy / u_resolution.xy);
                    float aspect = u_resolution.x/u_resolution.y;
                    // size.x *= aspect;

                    vec2 pos = st;
                    pos.x = pos.x*aspect;
                    pos.y += sin(pos.x*30.)*.01;
                    color.rgb = mix(color.rgb,background,stripes(pos*200.,.4));
    hatch:
        base: polygons
        mix: [functions-aastep, pallete]
        shaders:
            blocks:
                global: |
                    mat2 rotate2d(float angle){
                        return mat2(cos(angle),-sin(angle),
                                    sin(angle),cos(angle));
                    }
                    float stripes(vec2 st){
                        st = rotate2d(.72)*st;
                        st *= 92.;
                        return aastep(.3,abs(sin(st.y*3.14159265358)));
                    }
                color: |
                    vec2 st = (gl_FragCoord.xy / u_resolution.xy);
                    float aspect = u_resolution.x/u_resolution.y;
                    // size.x *= aspect;
                    color.rgb = mix(color.rgb,background,stripes(st*3.));
    ants:
        base: lines
        blend: overlay
        texcoords: true
        animated: true
        shaders:
            blocks:
                color: |
                    color.a = step(.5,fract(u_time-v_texcoord.y*.5));

    area:
        base: polygons
        mix: [patterns-stripes, space-constant]
        blend: inlay
        shaders:
            blocks:
                color: |
                    color = mix(color,vec4(0.),diagonalStripes( getConstantCoords()*33.))*.99;


layers:
    water:
        data: { source: osm }
        draw:
            waves:
                order: 10
                cap: round
                width: 8px
                color: [0.824, 0.812, 0.804]
                width: 5px

        lakes:
            filter: { kind: [lake, riverbank, dock, water, basin, reservoir, playa, canal, dam, ditch, drain, river, stream]}
            draw:
                lines:
                    order: 1
                    color: black
                    cap: round
                    width: .5px
    earth:
        data: { source:  osm}
        draw:
            border:
                order: 25
                color: black
                cap: round
                width: 6px

    landuse:
        data: { source: osm }
        draw:
            hatch:
                order: 2
                color: #464C40


    boundaries:
        data: { source: osm }
        # properties: { width: 3 }
        draw:
            lines:
                order: 5
                color: black
                width: .3px
            outlines:
                style: lines
                order: 4
                color: white
                width: 3px

    places:
        data: { source: osm }
        filter: { name: true }
        draw:
            text:
                font:
                    typeface: Italic 14px Times
                    fill: black
                    stroke: { color: white , width: 3 }
                    style: italic


    scania:
        data: { source:  scania}
        draw:
            area:
                color: [.99,.82,.09]
#                color: [.78,.05,.19]
                order: 5


    halland:
        data: { source: halland }
        draw:
            area:
                color: [.99,.82,.09]
                order: 5


    bleckinge:
        data: { source: blekinge }
        draw:
            area:
                color: [.99,.82,.09]
#                color: [.78,.05,.19]
                order: 5

    bohulsan:
        data: { source: bohulsan}
        draw:
            area:
                color: [.99,.82,.09]
                order: 5

    nord_trøndelag:
        data: { source: nord_trøndelag}
        draw:
            area:
                color: [.0,.36,.6]
                order: 5

    sør_trøndelag:
        data: { source: sør_trøndelag}
        draw:
            area:
                color: [.0,.36,.6]
                order: 5

    denmark:
        data: { source: denmark }
        draw:
            polygons:
                color: [.78,.05,.19]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px

    sweden:
        data: { source: sweden }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px

    finland:
        data: { source: finland }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px

    roskilde:
        data: { source: roskilde }
        filter: { display: false }
        draw:
            text:
                font:
                    typeface: Italic 24px Times
                    fill: red
                    stroke: { color: white, width: 5 }
                    transform: uppercase
            ants:

                color: [.0,.36,.6]
                order: 21
                width: 5px


    schleswig_holstein:
        data: { source: schleswig_holtein}
        draw:
            polygons:
                color: [.78,.05,.19]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px

    norway:
        data: { source: norway}
        draw:
            polygons:
                color: [.78,.05,.19]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px


    estonia:
        data: { source: estonia }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 26
            lines:
 #               color: grey
                order: 15
                width: 3px

    ostvorpommern:
        data: { source: ostvorpommern }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 4
            lines:
#                color: grey
                order: 15
                width: 3px

    rügen:
        data: { source: rügen }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 4
            lines:
#                color: grey
                order: 15
                width: 3px

    nordvorpommern:
        data: { source: nordvorpommern }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    straisund:
        data: { source: straisund }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px
    greifswald:
        data: { source: greifswald }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    ucker_randow:
        data: { source: ucker_randow }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    kammin:
        data: { source: kammin }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    policki:
        data: { source: policki }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    mawinouj:
        data: { source: mawinouj }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    goweniowski:
        data: { source: goweniowski }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px

    szczecin:
        data: { source: szczecin }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px
    demmin:
        data: { source: demmin }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
#                color: grey
                order: 15
                width: 3px
    bremen:
        data: { source: bremen }
        draw:
            area:
                color: [.0,.36,.6]
                order: 4
            lines:
                color: grey
                order: 15
                width: 3px
    st_petersburg:
        data: { source: st_peterburg }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
                color: grey
                order: 15
                width: 3px
    leningradskaja:
        data: { source: leningradskaja }
        draw:
            polygons:
                color: [.0,.36,.6]
                order: 3
            lines:
                color: grey
                order: 15
                width: 3px


## thumbnail.png
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
	# Author @patriciogv - 2015
	# modified by John Oram, 2016

	import:
	- https://tangrams.github.io/blocks/space/constant.yaml
	- https://tangrams.github.io/blocks/space/tile.yaml
	- https://tangrams.github.io/blocks/functions/pulse.yaml
	- https://tangrams.github.io/blocks/patterns/stripes.yaml
	- https://tangrams.github.io/blocks/patterns/waves.yaml
	- https://tangrams.github.io/blocks/patterns/dots.yaml
	- https://tangrams.github.io/blocks/filter/grain.yaml
	- https://tangrams.github.io/blocks/shapes/circle.yaml
	- https://tangrams.github.io/blocks/patterns/zigzag.yaml
	- https://tangrams.github.io/blocks/lines/dash.yaml





	sources:
	osm:
	type: TopoJSON
	url: //vector.mapzen.com/osm/all/{z}/{x}/{y}.topojson
	scania:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/883/77/85688377.geojson
	halland:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/883/91/85688391.geojson
	blekinge:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/883/83/85688383.geojson

	bohulsan:
	type: GeoJSON
	url: https://gist.githubusercontent.com/burritojustice/9f2128acb3ced5545deea739298f386a/raw/623c04e11dec7026cf6293defc68e1c776530238/bohulsan.geojson

	nord_trøndelag:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/871/33/85687133.geojson

	sør_trøndelag:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/871/27/85687127.geojson

	#geojson for path and dates

	roskilde:
	type: GeoJSON
	url: https://gist.githubusercontent.com/burritojustice/6295c331c06eb0a5cd6bbf5f42ec8e61/raw/roskilde.geojson

	schleswig_holtein:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/825/17/85682517.geojson

	denmark:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/331/21/85633121.geojson

	sweden:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/337/89/85633789.geojson
	norway:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/333/41/85633341.geojson

	finland:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/331/43/85633143.geojson
	estonia:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/331/35/85633135.geojson

	ostvorpommern:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/111/102064111.geojson

	greifswald:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/107/102064107.geojson

	rügen:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/153/102064153.geojson

	nordvorpommern:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/133/102064133.geojson

	straisund:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/121/102064121.geojson
	greifswald:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/107/102064107.geojson

	ucker_randow:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/075/102064075.geojson

	kammin:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/080/153/102080153.geojson
	policki:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/080/119/102080119.geojson
	mawinouj:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/080/135/102080135.geojson
	goweniowski:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/080/123/102080123.geojson
	szczecin:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/080/073/102080073.geojson
	demmin:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/102/064/099/102064099.geojson
	bremen:
	type: GeoJSON
	url: https://gist.githubusercontent.com/burritojustice/daaf0a47f510044481edf3ed02c28980/raw/bremen_1657.geojson
	st_peterburg:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/880/69/85688069.geojson
	leningradskaja:
	type: GeoJSON
	url: https://whosonfirst.mapzen.com/data/856/878/61/85687861.geojson

	scene:
	background:
	color: white
	cameras:
	perspective:
	type: perspective
	lights:
	directional1:
	type: directional
	direction: [.1, .5, -1]
	diffuse: .7
	ambient: .5

	styles:
	functions-aastep:
	shaders:
	extensions: OES_standard_derivatives
	blocks:
	global: \|
	// AntiAliased Step function
	//=============================
	float aastep(float threshold, float value) {
	#ifdef TANGRAM_FRAGMENT_SHADER
	#ifdef TANGRAM_EXTENSION_OES_standard_derivatives
	float afwidth = length(vec2(dFdx(value), dFdy(value))) * 0.70710678118654757;
	return smoothstep(threshold-afwidth, threshold+afwidth, value);
	#else
	return step(threshold, value);
	#endif
	#else
	return step(threshold, value);
	#endif
	}
	functions-pulse:
	shaders:
	blocks:
	global: \|
	float pulse (float x, float p, float w) {
	x = abs(x - p);
	if (x>w) return 0.0;
	x /= w;
	return 1.0 - xx(3.0-2.0*x);
	}
	patterns-stripes:
	mix: functions-aastep
	shaders:
	defines:
	PI: 3.14159265358979323846
	blocks:
	global: \|
	// Return a distance function of stripes
	float stripesDF (vec2 st) {
	return abs(sin(st.y*PI));
	}

	// Adjustable width stripes
	float stripes (vec2 st, float width) {
	return aastep(width,stripesDF(st));
	}

	// Faster optimisation of diagonal stripes
	float diagonalStripes (vec2 st) {
	vec2 i_st = floor(st);
	vec2 f_st = fract(st);
	if (mod(i_st.y,2.) - mod(i_st.x,2.) == 0.) {
	return 1.0 - aastep(f_st.x,f_st.y);
	} else {
	return aastep(f_st.x,f_st.y);
	}
	}
	pallete:
	shaders:
	blocks:
	global: \|
	// Common Values
	//================================
	float radio = 0.05;
	vec2 size = vec2(1.,.6);
	vec3 background = vec3(0.996,0.996,0.918);
	vec3 midle = vec3(0.824, 0.812, 0.804);
	vec3 foreground = vec3(0.275,0.298,0.251);
	border:
	base: lines
	mix: [functions-pulse, functions-aastep, pallete]
	texcoords: true
	shaders:
	blocks:
	color: \|
	vec2 st = v_texcoord.xy;
	float pattern = aastep(.5,pulse(st.x,.7,.1)+pulse(st.x,.35,.25));
	color.rgb = mix(background,color.rgb,pattern);
	b_border:
	base: lines
	mix: [functions-pulse, functions-aastep, pallete]
	texcoords: true
	shaders:
	blocks:
	color: \|
	vec2 st = v_texcoord.xy;
	float pattern = aastep(.5,pulse(st.x,.5,.2));
	color.rgb = mix(background,color.rgb,pattern);

	waves:
	base: polygons
	mix: [pallete, patterns-stripes]
	shaders:
	blocks:
	color: \|
	vec2 st = (gl_FragCoord.xy / u_resolution.xy);
	float aspect = u_resolution.x/u_resolution.y;
	// size.x *= aspect;

	vec2 pos = st;
	pos.x = pos.x*aspect;
	pos.y += sin(pos.x30.).01;
	color.rgb = mix(color.rgb,background,stripes(pos*200.,.4));
	hatch:
	base: polygons
	mix: [functions-aastep, pallete]
	shaders:
	blocks:
	global: \|
	mat2 rotate2d(float angle){
	return mat2(cos(angle),-sin(angle),
	sin(angle),cos(angle));
	}
	float stripes(vec2 st){
	st = rotate2d(.72)*st;
	st *= 92.;
	return aastep(.3,abs(sin(st.y*3.14159265358)));
	}
	color: \|
	vec2 st = (gl_FragCoord.xy / u_resolution.xy);
	float aspect = u_resolution.x/u_resolution.y;
	// size.x *= aspect;
	color.rgb = mix(color.rgb,background,stripes(st*3.));
	ants:
	base: lines
	blend: overlay
	texcoords: true
	animated: true
	shaders:
	blocks:
	color: \|
	color.a = step(.5,fract(u_time-v_texcoord.y*.5));

	area:
	base: polygons
	mix: [patterns-stripes, space-constant]
	blend: inlay
	shaders:
	blocks:
	color: \|
	color = mix(color,vec4(0.),diagonalStripes( getConstantCoords()33.)).99;


	layers:
	water:
	data: { source: osm }
	draw:
	waves:
	order: 10
	cap: round
	width: 8px
	color: [0.824, 0.812, 0.804]
	width: 5px

	lakes:
	filter: { kind: [lake, riverbank, dock, water, basin, reservoir, playa, canal, dam, ditch, drain, river, stream]}
	draw:
	lines:
	order: 1
	color: black
	cap: round
	width: .5px
	earth:
	data: { source: osm}
	draw:
	border:
	order: 25
	color: black
	cap: round
	width: 6px

	landuse:
	data: { source: osm }
	draw:
	hatch:
	order: 2
	color: #464C40


	boundaries:
	data: { source: osm }
	# properties: { width: 3 }
	draw:
	lines:
	order: 5
	color: black
	width: .3px
	outlines:
	style: lines
	order: 4
	color: white
	width: 3px

	places:
	data: { source: osm }
	filter: { name: true }
	draw:
	text:
	font:
	typeface: Italic 14px Times
	fill: black
	stroke: { color: white , width: 3 }
	style: italic


	scania:
	data: { source: scania}
	draw:
	area:
	color: [.99,.82,.09]
	# color: [.78,.05,.19]
	order: 5


	halland:
	data: { source: halland }
	draw:
	area:
	color: [.99,.82,.09]
	order: 5


	bleckinge:
	data: { source: blekinge }
	draw:
	area:
	color: [.99,.82,.09]
	# color: [.78,.05,.19]
	order: 5

	bohulsan:
	data: { source: bohulsan}
	draw:
	area:
	color: [.99,.82,.09]
	order: 5

	nord_trøndelag:
	data: { source: nord_trøndelag}
	draw:
	area:
	color: [.0,.36,.6]
	order: 5

	sør_trøndelag:
	data: { source: sør_trøndelag}
	draw:
	area:
	color: [.0,.36,.6]
	order: 5

	denmark:
	data: { source: denmark }
	draw:
	polygons:
	color: [.78,.05,.19]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px

	sweden:
	data: { source: sweden }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px

	finland:
	data: { source: finland }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px

	roskilde:
	data: { source: roskilde }
	filter: { display: false }
	draw:
	text:
	font:
	typeface: Italic 24px Times
	fill: red
	stroke: { color: white, width: 5 }
	transform: uppercase
	ants:

	color: [.0,.36,.6]
	order: 21
	width: 5px



	schleswig_holstein:
	data: { source: schleswig_holtein}
	draw:
	polygons:
	color: [.78,.05,.19]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px

	norway:
	data: { source: norway}
	draw:
	polygons:
	color: [.78,.05,.19]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px


	estonia:
	data: { source: estonia }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 26
	lines:
	# color: grey
	order: 15
	width: 3px

	ostvorpommern:
	data: { source: ostvorpommern }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 4
	lines:
	# color: grey
	order: 15
	width: 3px

	rügen:
	data: { source: rügen }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 4
	lines:
	# color: grey
	order: 15
	width: 3px

	nordvorpommern:
	data: { source: nordvorpommern }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	straisund:
	data: { source: straisund }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px
	greifswald:
	data: { source: greifswald }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	ucker_randow:
	data: { source: ucker_randow }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	kammin:
	data: { source: kammin }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	policki:
	data: { source: policki }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	mawinouj:
	data: { source: mawinouj }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	goweniowski:
	data: { source: goweniowski }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px

	szczecin:
	data: { source: szczecin }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px
	demmin:
	data: { source: demmin }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	# color: grey
	order: 15
	width: 3px
	bremen:
	data: { source: bremen }
	draw:
	area:
	color: [.0,.36,.6]
	order: 4
	lines:
	color: grey
	order: 15
	width: 3px
	st_petersburg:
	data: { source: st_peterburg }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	color: grey
	order: 15
	width: 3px
	leningradskaja:
	data: { source: leningradskaja }
	draw:
	polygons:
	color: [.0,.36,.6]
	order: 3
	lines:
	color: grey
	order: 15
	width: 3px