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7 months ago
286 kB
5
Indexable
Never
set cut_paste_input [stack 0]
version 13.1 v2
Read {
 inputs 0
 file_type exr
 file R:/work/CG/Ambition/1023_WINK_NY_24/shots/sh_0070/source/cc/sh_0070_cc/SH_0070_сс.########.exr
 format "3424 2202 0 0 3424 2202 1 "
 first 991
 last 1091
 origfirst 991
 origlast 1091
 origset true
 colorspace sRGB
 name Read6
 selected true
 xpos 2001
 ypos -188
}
Retime {
 input.first 991
 input.last 1091
 reverse true
 output.first 991
 output.last 1091
 time ""
 name Retime7
 selected true
 xpos 2001
 ypos -92
}
Retime {
 input.first 991
 input.last 1091
 output.first 993
 output.first_lock true
 output.last 1093
 time ""
 name Retime8
 selected true
 xpos 2001
 ypos -68
}
set N2016b800 [stack 0]
OFXcom.absoft.neatvideo4_v4 {
 DNP 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
 NFP ODAxAPotAAAAAAAAAAABAAAAAACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AAAAAAAAAAAAAIA/AACAPwAAgD/NzMw+AACAPwAAgD8BAAAAAADAPwAAwD8AAIA/AACAPwAAgD8AAAAAAAAAP5qZmT4AAAAAAAAAAAAAgD8AAAAAAAAAAAAAgD8AAIA/AACAPwAAgD8AAQAAAACAPwAAAAAAAAAAAACAPwAAAAAAAAAAAQEBAQEBAQEBAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAgAAAAAAgD8AAAAAAAAAAAAAwD8AAAAAAAAAAAAAAD8BAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQD8AAIA/AACAPwAAgD8BAAAAAAAAAAAAgD8AAIA/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADgwMQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==
 ParamsHash1 1450754537
 ParamsHash2 260
 ParamsHash3 0
 name "Reduce Noise v4_1"
 selected true
 xpos 2001
 ypos -9
}
set N20169c00 [stack 0]
NoOp {
 name NoOp3
 selected true
 xpos 1784
 ypos 470
}
push $N2016b800
NoOp {
 name NoOp4
 selected true
 xpos 2207
 ypos 470
}
push $N20169c00
Dot {
 name Dot1
 selected true
 xpos 2035
 ypos 54
}
set N2016a800 [stack 0]
Dot {
 name Dot2
 selected true
 xpos 1850
 ypos 54
}
Blur {
 size 4
 name Blur2
 selected true
 xpos 1816
 ypos 143
}
set N2016b400 [stack 0]
push $cut_paste_input
Roto {
 output alpha
 curves {{{v x3f99999a}
  {f 0}
  {n
   {layer Root
    {f 2097152}
    {t x44d60000 x4489a000}
    {a pt1x 0 pt1y 0 pt2x 0 pt2y 0 pt3x 0 pt3y 0 pt4x 0 pt4y 0 ptex00 0 ptex01 0 ptex02 0 ptex03 0 ptex10 0 ptex11 0 ptex12 0 ptex13 0 ptex20 0 ptex21 0 ptex22 0 ptex23 0 ptex30 0 ptex31 0 ptex32 0 ptex33 0 ptof1x 0 ptof1y 0 ptof2x 0 ptof2y 0 ptof3x 0 ptof3y 0 ptof4x 0 ptof4y 0 pterr 0 ptrefset 0 ptmot x40800000 ptref 0}
    {curvegroup BSpline10 512 bspline
     {{cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bf0e9e x443e261c}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44be3abe x443df4ad}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44be4505 x443c6022}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bf1505 x443ca483}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bf2505 x443d2ae9}}}
      {cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40a0c300 x40c02280}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0c85000 x40967380}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0cc4b00 xc090fc80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40334200 xc0ad9e80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40fa6200 x3e851800}}}}
     {tx x44816000 x44b665c2 x44436666}
     {a ft x40400000 osw x41200000 osf 0 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 tt x40c00000}}
    {curvegroup BSpline9 512 bspline
     {{cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44c25b4c x444b5f2f}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44c1876c x444b2dc0}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44c191b3 x44499935}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44c261b3 x4449dd96}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44c271b3 x444a63fc}}}
      {cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40a0c300 x40c02280}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0c85000 x40967380}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0cc4b00 xc090fc80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40334200 xc0ad9e80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40fa6200 x3e851800}}}}
     {tx x44816000 x44b665c2 x44436666}
     {a ft x40400000 osw x41200000 osf 0 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 tt x40c00000}}
    {curvegroup BSpline8 512 bspline
     {{cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bddd81 x44653cea}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bd09a1 x44650b7b}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bd13e8 x446376f0}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bde3e8 x4463bb51}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44bdf3e8 x446441b7}}}
      {cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40a0c300 x40c02280}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0c85000 x40967380}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     xc0cc4b00 xc090fc80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40334200 xc0ad9e80}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x40fa6200 x3e851800}}}}
     {tx x44816000 x44b665c2 x44436666}
     {a ft x40400000 osw x41200000 osf 0 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 tt x40c00000}}
    {curvegroup BSpline7 512 bspline
     {{cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44b42eb4 x446016b4}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44b35ad4 x445fe545}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44b3651b x445e50ba}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44b4351b x445e951b}
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
       {{x44816000 x40800000}}}     x44b4451b x445f1b81}}}
      {cc
       {f 8192}
       {px x44816000
        {{a t
       {{x44816000 1}}	 rp
       {{x44816000 1}}	 te
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 toolbox {selectAll {
  { selectAll str 1 ssx 1 ssy 1 sf 1 }
  { createBezier str 1 ssx 1 ssy 1 sf 1 sb 1 tt 4 }
  { createBezierCusped str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { createBSpline str 1 ssx 1 ssy 1 sf 1 sb 1 tt 6 }
  { createEllipse str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { createRectangle str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { createRectangleCusped str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { brush str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { eraser src 2 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { clone src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { reveal src 3 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { dodge src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { burn src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { blur src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { sharpen src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
  { smear src 1 str 1 ssx 1 ssy 1 sf 1 sb 1 }
} }
 toolbar_brush_hardness 0.200000003
 toolbar_source_transform_scale {1 1}
 toolbar_source_transform_center {1712 1101}
 colorOverlay {0 0 0 0}
 lifetime_type "all frames"
 lifetime_start 1035
 lifetime_end 1035
 motionblur_shutter_offset_type centred
 feather_type smooth
 source_black_outside true
 name Roto1
 selected true
 xpos 2106
 ypos 193
}
push $N2016b400
push $N2016a800
Merge2 {
 inputs 2
 operation from
 name Merge3
 selected true
 xpos 2001
 ypos 149
}
Grade {
 inputs 1+1
 white 0
 black_clamp false
 name Grade1
 selected true
 xpos 2001
 ypos 199
}
Merge2 {
 inputs 2
 operation plus
 name Merge4
 selected true
 xpos 2001
 ypos 292
}
Group {
 inputs 3
 name DasGrain1
 help "DasGrain makes regraining as simple as clicking a few buttons.\n\nFollow the steps in the Help tab and you'll have a perfect regrain in no time!"
 onCreate "import random\n\ntestimonials = \[\n    \"Such an elegant solution, love it!\",\n    \"Your gizmo is beyond expectation\",\n    \"Totally awesome!\",\n    \"DasGrain is officially the best thing ever\",\n    \"It's really working!\",\n    \"Das bringt Tr&auml;nen in meine Augen\",\n    \"DasGrain is the salvation we waited for\",\n    \"I save a lot of time, and definitely my nerves :)\",\n    \"It's alright\",\n    \"My new favourite node, thanks!<br>Having said that, ...\"\n    ]\n\nnode = nuke.thisNode()\nnode\['testimonial'].setValue('<br><br><br><i>&laquo;%s&raquo;</i><br>&mdash; anonymous<br><br>' % random.choice(testimonials))\nnode\['box'].setFlag(nuke.NO_ANIMATION)"
 knobChanged "n = nuke.thisNode()\nk = nuke.thisKnob()\n\nif k.name() == 'box':\n    this_frame = nuke.frame()\n    n\['sample_frame'].setValue(this_frame)\n\nif k.name() == 'scatter':\n    n\['divider04'].setVisible(k.value() == False)\n    n\['divider05'].setVisible(k.value() == True)"
 tile_color 0x7f7f7fff
 selected true
 xpos 2001
 ypos 464
 addUserKnob {20 DasGrain_tab l DasGrain}
 addUserKnob {41 output t "<strong>regrained comp</strong> it is what it sais\n<strong>plate grain</strong> plate minus degrained plate\n<strong>normalised grain</strong> check if the normalization worked. It should be as even as possible. This is what you want to output if you want to prerender a grain plate. Later you can plug it into the <i>external grain</i> input of another DasGrain\n<strong>adapted grain</strong> check if the adaptation worked. Output this if you want to further manipulate the grain (who knows what the sup is gonna come up with...). After, simply plus it to your comp (at that point the comp has to be in the <i>camera</i> colorspace, as set in the <i>Analyze</i> tab).\n<strong>grain QC</strong> check if voronoi seams are visible (&#8594; edgeblend), or the scattered grain looks different to the original plate grain (&#8594; maybe bad sample area or wrong luminance degrain amount)" T Output.output}
 addUserKnob {4 meta l "metadata from" t "Chances are you want to use the metadata from the plate, but who am I to assume :)" M {COMP PLATE}}
 addUserKnob {26 spacer01_1 l " " T " "}
 addUserKnob {20 GrainGroupBegin l "" +STARTLINE n -2}
 addUserKnob {20 Analyze_tab l Analyze}
 addUserKnob {26 text l <strong>Colorspace}
 addUserKnob {41 project_colorspace l project t "set this to the project color space" T OCIOColorSpace1.in_colorspace}
 addUserKnob {22 python_button l "What's this all about?" -STARTLINE T "nuke.message(\"Regraining in other color spaces than the camera native linear space can lead to unexpected behaviour.\\n\\nFor example converting Alexa plates to ACEScg might introduce negative values due to ACEScg's smaller gamut. In that case converting back to ARRI Linear ALEXA Wide Gamut will probably help.\\nJust set <i>project</i> to ACEScg and <i>camera</i> to ARRI Linear ALEXA Wide Gamut.\\n\\nThis might be transferable to other cameras, but I've only tested with Alexas.\\n---------\\nBypass by setting both knobs to the same value.\")"}
 addUserKnob {41 camera_colorspace l camera t "set this to the camera native linear space" T OCIOColorSpace1.out_colorspace}
 addUserKnob {26 text_2 l " " T " "}
 addUserKnob {26 level l "<strong>Degrain amount"}
 addUserKnob {78 luminance t "Leave this at 1 if you're working on a completely degrained plate.\n\nIn case you decided to leave some luminance grain in the degrained plate (use the DegrainHelper node for this!), set this to the same value as in the DegrainHelper in order to compensate.\n\nIf the luminance degrain amount was set to 0.8, this needs to be set to 0.8 as well.\n\nYou need to select a mask of all elements that cover the plate, otherwise the grain of whole comp will be too strong " n 1}
 luminance 1
 addUserKnob {26 divider01 l " "}
 addUserKnob {41 degrain_amount_mask l "degrain amount mask" t "Use this channel from the mask input to specify in what area of the comp the missing luminance grain needs to be compensated." T Multiply1.maskChannelMask}
 addUserKnob {41 invert_mask l invert -STARTLINE T Multiply1.invert_mask}
 addUserKnob {26 spacer02 l " " T " "}
 addUserKnob {26 divider02 l <strong>Analyze}
 addUserKnob {3 number_of_frames l "number of frames" t "Set the number of sample frames to be spread across the input range.\n\nMore frames lead to higher accuracy.\n\nIf there are particularly bright or dark frames, set them manually in the knob below to make sure they are part of the analysis.\n\nIf you want to set all sample frames manually, set this to 0 and add the frames in the knob below."}
 number_of_frames 10
 addUserKnob {1 additional_frames l "additional frames" t "Set additional frames like this:\n\n1001,1020,1053 (single frames)\n1020-1040 (frame ranges)\n1020-1040x4 (frame ranges with step)"}
 addUserKnob {3 sample_count l "sample count" t "The samples are spread across the sample range (which gets calculated automatically) based on the AlexaV3LogC curve. This results in more samples in the dark areas and less samples in the brights.\n\nMore samples lead to a more detailed response curve (while the accuracy is limited by the quality of the degrain)."}
 sample_count 20
 addUserKnob {22 analyze l Analyze t "this is where the magic happens" T "import base64\nthis = nuke.thisNode()\n\n\ndef _sample_count(this):\n    \"\"\"returns the sample count\"\"\"\n\n    sample_count = int(this\['sample_count'].value())\n\n    if sample_count <= 0:\n        raise RuntimeError('Enter a sample count greater than 0')\n\n    else:\n        return sample_count\n\n\ndef _generate_frame_list(this):\n    \"\"\"converts the frames submitted by the user into a list\"\"\"\n\n    frame_list = \[]\n    number_of_frames = int(this\['number_of_frames'].value())\n    additional_frames = this\['additional_frames'].value()\n\n    if number_of_frames < 1 and additional_frames is '':\n        raise RuntimeError('Either set the number of frames > 0\\nor define additional frames')\n\n    first_frame = max(this.input(1).firstFrame(), this.input(2).firstFrame())\n    last_frame = min(this.input(1).lastFrame(), this.input(2).lastFrame())\n\n    if number_of_frames > 0:\n        distance = (last_frame - first_frame) / (number_of_frames)\n        frame = first_frame + distance / 2\n\n        for x in range(number_of_frames):\n            int_frame = int(round(frame))\n            if int_frame not in frame_list:\n                frame_list.append(int_frame)\n\n            frame += distance\n\n    frange = nuke.FrameRanges(additional_frames.split(','))\n\n    for r in frange:\n        for f in r:\n            if f >= first_frame and f <= last_frame:\n                if f not in frame_list:\n                    frame_list.append(f)\n\n    frame_list.sort()\n\n    return frame_list\n\n\ndef _setup_for_multiframe(frame_list):\n    \"\"\" arranges all sample frames next to each other, starting at frame 0\n    and sets the frame number knob of the FrameBlend node\"\"\"\n\n    time_warp = nuke.toNode('TimeWarp1')\n    time_warp\['lookup'].clearAnimated()\n    time_warp\['lookup'].setAnimated()\n    anim_list = \[]\n\n    for n, frame in enumerate(frame_list):\n        anim_list.append(nuke.AnimationKey(n, frame))\n\n    anim = time_warp\['lookup'].animation(0)\n    anim.addKey(anim_list)\n\n    frame_blend = nuke.toNode('FrameBlend1')\n    frame_blend\['endframe'].setValue(len(frame_list)-1)\n\n\ndef _generate_sample_list(sample_count, sample_range, sample_radius):\n    \"\"\"generate a list of sample values spread equally between the\n    min and max values of the sample range\"\"\"\n\n    sample_list = \[]\n\n    for item in range(0, sample_count):\n        sample_list.append(float(item) / sample_count * (sample_range\[1] - sample_range\[0]) + sample_range\[0] + sample_radius)\n\n    return sample_list\n\n\ndef _get_sample_range(channel, channel_list, frame_list):\n    \"\"\" samples the minimum and maximum values of the given frame range and\n    sets the sample range to those values\"\"\"\n\n    curve_tool = nuke.toNode('CurveTool_Range')\n    min_knob = curve_tool\['minlumapixvalue']\n    max_knob = curve_tool\['maxlumapixvalue']\n\n    min_knob.setAnimated()\n    max_knob.setAnimated()\n\n    curve_tool\['channels'].setValue(channel)\n\n    nuke.execute(curve_tool, nuke.FrameRanges(frame_list))\n\n    index = channel_list.index(channel)\n    min_list = \[key.y for key in min_knob.animation(index).keys()]\n    max_list = \[key.y for key in max_knob.animation(index).keys()]\n\n    min_value = min(min_list)\n    max_value = max(max_list)\n\n    min_knob.clearAnimated()\n    max_knob.clearAnimated()\n    curve_tool\['minlumapixdata'].clearAnimated()\n    curve_tool\['maxlumapixdata'].clearAnimated()\n\n    return \[min_value, max_value]\n\n\ndef _sample_it(keyer, curve_tool, sample, sample_radius):\n    \"\"\"analyze the grain level per channel and sample value in the sample range\"\"\"\n\n    keyer\['temp_expr0'].setValue(str(sample - sample_radius))\n    keyer\['temp_expr1'].setValue(str(sample + sample_radius))\n\n    intensity_knob = curve_tool\['intensitydata']\n    intensity_knob.clearAnimated()\n    intensity_knob.setAnimated()\n\n    nuke.execute(curve_tool, nuke.frame(), nuke.frame())\n    sample_values = intensity_knob.value()\n    intensity_knob.clearAnimated()\n\n    return sample_values\n\n\ndef check_inputs(this):\n    if this.input(1) is None:\n        raise RuntimeError('no plate connected')\n\n    if this.input(2) is None:\n        raise RuntimeError('no degrained plate connected')\n\n    def format_tuple(node):\n        return node.format().width(), node.format().height(), node.format().pixelAspect()\n\n    if format_tuple(this.input(1)) != format_tuple(this.input(2)):\n        raise RuntimeError(\"Format missmatch: Make sure the formats of plate and degrained plate match.\")\n\n\ndef start(this):\n    \"\"\"let's do this!\"\"\"\n\n    check_inputs(this)\n\n    with this:\n        frame_list = _generate_frame_list(this)\n        _setup_for_multiframe(frame_list)\n        sample_count = _sample_count(this)\n\n        blank = base64.b64decode('cmVkIHtjdXJ2ZX0KZ3JlZW4ge2N1cnZlfQpibHVlIHtjdXJ2ZX0=').decode('ascii')\n\n        lut = nuke.toNode('Sampler1')\['lut']\n        lut.fromScript(blank)\n\n        channel_list = \['red', 'green', 'blue']\n\n        keyer = nuke.toNode('Expression2')\n        copy = nuke.toNode('Copy2')\n\n        curve_tool = nuke.toNode('CurveTool')\n        pixel = curve_tool\['ROI'].value()\[2] * curve_tool\['ROI'].value()\[3]\n\n        task = nuke.ProgressTask('Analysing...')\n        step = 100.0 / 3 / sample_count\n        progress = step\n\n        time_warp = nuke.toNode('TimeWarp1')\n        frame_blend = nuke.toNode('FrameBlend1')\n\n        time_warp\['disable'].setValue(False)\n        frame_blend\['disable'].setValue(False)\n\n        for channel in channel_list:\n            task.setMessage('\{\} range'.format(channel))\n\n            copy\['from0'].setValue('rgba.\{\}'.format(channel))\n\n            sample_range = _get_sample_range(channel, channel_list, frame_list)\n            sample_radius = (sample_range\[1] - sample_range\[0]) / sample_count / 2\n            sample_list = _generate_sample_list(sample_count, sample_range, sample_radius)\n\n            for sample in sample_list:\n                if task.isCancelled():\n                    return\n\n                task.setProgress(int(progress))\n\n                sample_values = _sample_it(keyer, curve_tool, sample, sample_radius)\n\n                task.setMessage('\{\} channel at \{\}'.format(channel, round(sample, 2)))\n\n                if sample_values\[3] * pixel >= 10:\n                    lut.setValueAt(sample_values\[0] / sample_values\[3], sample_values\[1] / sample_values\[3], channel_list.index(channel))\n\n                progress += step\n\n        time_warp\['lookup'].clearAnimated()\n        time_warp\['disable'].setValue(True)  # hopefully prevents slowing down the comp\n        frame_blend\['disable'].setValue(True)  # hopefully prevents slowing down the comp\n\n    del task\n\n\nstart(this)\n" +STARTLINE}
 addUserKnob {26 divider03 l " "}
 addUserKnob {41 analysis_mask l "analysis mask" t "Use this channel from the mask input to control what area of the plate will be analyzed.\n\nUsefull if the degrain is obviously bad in some areas." T ChannelMerge1.A}
 addUserKnob {6 invert_1 l invert -STARTLINE}
 addUserKnob {20 Adjust_tab l Adjust}
 addUserKnob {22 whatsthis l "What am I looking at?" T "nuke.message(\"After the analysis you'll see the sampled grain response curves here. On the x-axis is the brightness of the image and on the y-axis the grain intensity. Grain increases with brightness, so <strong>the slope of the curves should always be positive</strong> (they should always go up &#8599;).<br><br>The quality of the curves depends entirely on the quality of the degrain. If the curves look wrong (for example they go up and down), try to improve the degrain first. If they still look wrong and the resulting regrain doesn't work well enough, you can try to improve the curves here by deleting/correcting all points that don't follow an upwards trend.<br><br>You can also extend the curves (again: with an upwards trend) if the comp has values that don't exist in the plate.<br><br>Note: The curve is used for both the normalization as well as the adaptation of the grain, so it doesn't give direct control of the grain intensity.\")" +STARTLINE}
 addUserKnob {41 lut l "" +STARTLINE T Sampler1.lut}
 addUserKnob {20 Replace_tab l Replace}
 addUserKnob {6 external_grain l "use external grain" t "Use external grain from a second DasGrain, with the output set to 'normalised grain', to replace masked area.\nConnect it to the 'external grain' input of this DasGrain (it's a bit hidden on the left side of the node)." +STARTLINE}
 addUserKnob {26 divider04 l <strong>Scatter}
 addUserKnob {26 divider05 l <strong>Scatter +HIDDEN T "<span style=\"color:red\">Make sure you're sampling an area without any plate detail.</a>"}
 addUserKnob {6 scatter l activate t "Activates the scatter function. It generates a new grain based on the plate grain in the sample box using a Voronoi noise." +STARTLINE}
 addUserKnob {41 useGPUIfAvailable l "Use GPU if available" -STARTLINE T VoronoiScatter.useGPUIfAvailable}
 addUserKnob {15 box l "sample box" t "Define an area that is used as a source for the scatter function. The plate grain in this area should be as even as possible, without any visible detail."}
 box {100 100 500 300}
 addUserKnob {3 sample_frame l "sample frame" t "The frame at which the grain is being sampled. Is set automatically once the sample box is changed." +DISABLED}
 sample_frame 1
 addUserKnob {4 stereo l "stereo behaviour" t "randomize offset per view: same voronoy pattern for all views, but different offset\n\nrandomize pattern per view: different voronoy pattern for every view" M {none "randomize offset per view" "randomize pattern per view" ""}}
 addUserKnob {26 spacer06 l "" +STARTLINE T " "}
 addUserKnob {6 overlay l "overlay cell pattern" t "Overlay the cell pattern of the voronoy noise. Useful to check where the seams are and if distortion or blending is necessary." +STARTLINE}
 addUserKnob {7 cell_size l "cell size" t "Cell size of the scatter. Shoudn't be too small, as lower grain frequencies might break.\nCan't be too big either, to prevent it from breaking the border of the samplebox (will error if it does)." R 5 100}
 cell_size 40
 addUserKnob {26 spacer07 l "" +STARTLINE T " "}
 addUserKnob {20 concealer l "edge concealer" n 1}
 concealer 0
 addUserKnob {26 concealer_help l " " T "If you can see the voronoi pattern in the grain QC output,\nincrease the edge blend size."}
 addUserKnob {3 edge_blend_size l "edge blend size" t "Set the output to grain QC. If you see the cell seams, increase the edge blend size to conceal them.\n\nThis is a bit hacky and slow."}
 addUserKnob {26 tip l "" -STARTLINE T "sloooow - keep this below 3 if possible"}
 addUserKnob {26 distortion_help l " " T "\nDistortion might help as well, if somehow the straight\nseams are visible (you might want to toggle the overlay\nwhile adjusting)."}
 addUserKnob {7 amplitude R 0 50}
 addUserKnob {7 frequency R 0 50}
 frequency 15
 addUserKnob {20 endGroup n -1}
 addUserKnob {26 divider06 l "" +STARTLINE}
 addUserKnob {41 replace_mask l "replace mask" t "Use this channel from the mask input to specify where you want to use scattered grain instead of the adapted plate grain." -STARTLINE T Merge9.maskChannelMask}
 addUserKnob {41 invert_mask_1 l invert -STARTLINE T Merge9.invert_mask}
 addUserKnob {20 GrainGroupEnd l "" +STARTLINE n -3}
 addUserKnob {20 Help_tab l Help}
 addUserKnob {26 basic_setup l "" +STARTLINE T "<font size=\"5\">Basic setup</font>"}
 addUserKnob {26 ""}
 addUserKnob {26 explanation l "" +STARTLINE T "<strong>Bold</strong> steps are always necessary"}
 addUserKnob {26 steps l "" +STARTLINE T "<br><strong>1. This should be the only regrain node in your comp.<br>2. Connect <i>plate</i>, <i>degrained plate</i> and <i>comp</i>.<br>&nbsp;&nbsp;&nbsp;&nbsp;The comp should be done on the degrained plate!</strong><br>3. Set the <i>luminance degrain amount</i>.<br><strong>4. Press the <i>Analyze</i> button.</strong><br>5. Correct the response curves in the <i>Adjust</i> tab.<br>6. Move the <i>sample box</i> to an area without any plate detail and activate <i>scatter</i>.<br>7. If necessary, activate <i>edge blend</i> and/or <i>distortion</i> to conceal seams."}
 addUserKnob {26 in_depth l "" +STARTLINE T "<br>For an in depth explanation of the steps, read the tooltips and check out this video:<br><a href=\"https://vimeo.com/284820390/\"><span style=\"color:#C8C8C8;\">https://vimeo.com/284820390</a>"}
 addUserKnob {26 pushthebutton l "" +STARTLINE T "<br><br>If the result is not as expected and you don't know why, push this button:"}
 addUserKnob {22 troubleshoot l Troubleshoot t HEEEEEEELP T "import base64\n\nmessages = \[]\n\nthis = nuke.thisNode()\n\n#########################\n\nif this.input(0) is None or this.input(1) is None or this.input(2) is None:\n    messages.append(\"<font color='red'><strong>ERROR</strong></font> Plate, degrained plate and comp need to be connected to the appropriate inputs.\")\n\n#########################\n\nelse:\n\n    def format_to_tuple(g):\n        \"\"\"returns (1024, 786, 2.0)\n        \"\"\"\n        return (g.format().width(), g.format().height(), g.format().pixelAspect())\n\n    format_set = set(\[\n        format_to_tuple(this.input(0)),\n        format_to_tuple(this.input(1)),\n        format_to_tuple(this.input(2)),\n        ])\n    if len(format_set) != 1:\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> Format missmatch: Make sure formats of plate, degrained plate and comp match.\")\n\n    if (this.input(1).firstFrame() != this.input(2).firstFrame()) or (this.input(1).lastFrame() != this.input(2).lastFrame()):\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> The frame ranges of plate and degrained plate don't match. Double check that they belong together.\")\n\n#########################\n\nmessages.append(\"Double check that plate and degrained plate haven't been modified in any way (paint, despill, etc).\")\n\n#########################\n\nif this\['luminance'].getValue() == 1:\n    messages.append(\"Are you working on a completely degrained plate? If not, you might have to set the luminance degrain amount.\")\n\n#########################\n\nblank = base64.b64decode('cmVkIHtjdXJ2ZX0KZ3JlZW4ge2N1cnZlfQpibHVlIHtjdXJ2ZX0=').decode('ascii')\n\nwith this:\n    Sampler = nuke.toNode('Sampler1')     \n    if Sampler\['lut'].toScript() == blank:\n        messages.append(\"<font color='red'><strong>ERROR</strong></font> You haven't pressed the Analyze button yet!\")\n\n#########################\n\nclass BadThings(Exception): pass\n\ndef thingy():\n    with this:\n        Sampler = nuke.toNode('Sampler1')\n        list = this\['lut'].toScript().replace('\}','').split('\\n')\n        for item in list:\n            sample_value = 0\n            for value in item.split(' '):\n                try:\n                    value == float(value)\n                    if value < sample_value:\n                        raise BadThings(\"<font color='orange'><strong>WARNING</strong></font> Check and fix the response curves. Their slopes should always be positive (the curves should always go up &#8599;).\")\n                        \n                    else:\n                        sample_value = value\n                except ValueError:\n                    # Ignore non-numeric things like x-values of \"x5.46\" and channel names like \"red\{\" etc\n                    pass\ntry:\n    thingy()\nexcept BadThings as e:\n    messages.append(str(e))\n    \n#########################\n\nif this\['scatter'].value() == True:\n    if this\['box'].getValue() == \[100.0, 100.0, 500.0, 300.0]:\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> Scatter has been activated, but the sample box is still in its default position. Are you sure that's a good area to sample?\")\n\n#########################\n\nmessages.append(\"Did you copy/paste DasGrain from another script? Make sure to reanalyze and to reset the sample area if you are using scatter.\")\n\n#########################\n\nif len(messages) > 0:\n    nuke.message(\"<font size=\\\"5\\\">Things worth checking</font><br><br>\"\n                               \"%s<br><br><br>If any of this doesn't make sense to you, it might be worth checking out the video on vimeo.\" % (\n                                  \"<hr>\".join(\"%s: %s\" % (i+1, m) for i, m in enumerate(messages))))\n" +STARTLINE}
 addUserKnob {26 dont_despair l "" +STARTLINE T "<br>If it still doesn't work and you're about to flip the table, send me a <a href=\"mailto:holtzf+nuke@gmail.com?subject=Help with DasGrain v1.7.8\"><span style=\"color:#C8C8C8;\">mail</a>.<br>I'm happy to help! :)"}
 addUserKnob {20 Info_tab l Info}
 addUserKnob {26 dasname l "" +STARTLINE T "<font size='5'>DasGrain</font> v1.8<br>"}
 addUserKnob {26 text_1 l "" +STARTLINE T "DasGrain makes regraining as simple as clicking a few buttons.<br>Follow the steps in the <i>Help</i> tab and you'll have a perfect\nregrain<br>in no time!"}
 addUserKnob {26 ""}
 addUserKnob {26 info l "" +STARTLINE T "Last change: 2021-03-07\n\n"}
 addUserKnob {26 name_1 l "" +STARTLINE T "Fabian Holtz"}
 addUserKnob {26 mail l "" +STARTLINE T "<a href=\"mailto:holtzf+nuke@gmail.com?subject=Help with DasGrain v1.7.8\"><span style=\"color:#C8C8C8;\">holtzf+nuke@gmail.com</a>"}
 addUserKnob {26 testimonial l "" +STARTLINE T "<br><br><br><i>&laquo;DasGrain is officially the best thing ever&raquo;</i><br>&mdash; anonymous<br><br>"}
 addUserKnob {26 ""}
 addUserKnob {26 credit l "" +STARTLINE T "<br>VoronoiScatter based on <a href=\"http://www.nukepedia.com/blink/image/voronoi/\"><span style=\"color:#C8C8C8;\">Ivan Busquets' implementation</a> of<br> libNoise's\nVoronoi generator"}
 addUserKnob {26 thanks l "" +STARTLINE T "<br>Special thanks to Ben Dickson for bearing with my questions and<br>problems and RSP comp for the valuable feedback."}
}
 BackdropNode {
  inputs 0
  name BackdropNode1
  tile_color 0x7f7f7fff
  label "normalise grain"
  note_font_size 30
  xpos 170
  ypos 1662
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode11
  tile_color 0x7f7f7fff
  label "add grain"
  note_font_size 30
  xpos 830
  ypos 2766
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode13
  tile_color 0x7f7f7fff
  label scatter
  note_font_size 30
  xpos -50
  ypos 2022
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode14
  tile_color 0x7f7f7fff
  label "analyze grain"
  note_font_size 30
  xpos -159
  ypos 606
  bdwidth 319
  bdheight 877
 }
 BackdropNode {
  inputs 0
  name BackdropNode2
  tile_color 0x7f7f7fff
  label "grain response curve"
  note_font_size 30
  xpos 610
  ypos 2574
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode3
  tile_color 0x7f7f7fff
  label QC
  note_font_size 30
  xpos 1050
  ypos 3222
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode4
  tile_color 0x7f7f7fff
  label "grain response curve"
  note_font_size 30
  xpos 610
  ypos 1422
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode5
  tile_color 0x7f7f7fff
  label "adapt grain"
  note_font_size 30
  xpos 170
  ypos 2574
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode6
  tile_color 0x7f7f7fff
  label "sample range"
  note_font_size 30
  xpos -490
  ypos 606
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode7
  tile_color 0x7f7f7fff
  label "luminance level"
  note_font_size 30
  xpos 280
  ypos -282
  bdwidth 760
  bdheight 685
 }
 BackdropNode {
  inputs 0
  name BackdropNode8
  tile_color 0x7f7f7fff
  label "plate grain"
  note_font_size 30
  xpos 170
  ypos 606
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode9
  tile_color 0x7f7f7fff
  label replace
  note_font_size 30
  xpos 60
  ypos 2191
  bdwidth 540
  bdheight 226
 }
 Input {
  inputs 0
  name DEGRAINED_PLATE
  label "\[value number]"
  note_font_size 30
  xpos 730
  ypos -896
  number 2
 }
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.in_colorspace}}
  out_colorspace {{OCIOColorSpace1.out_colorspace}}
  name OCIOColorSpace2
  xpos 730
  ypos -490
 }
 Dot {
  name Dot9
  xpos 764
  ypos -390
 }
set Nfb88b800 [stack 0]
 Dot {
  name Dot28
  xpos 764
  ypos -198
 }
set Nfb88b400 [stack 0]
 Dot {
  name Dot32
  xpos 764
  ypos 234
 }
set Nfb88b000 [stack 0]
push $Nfb88b400
 Dot {
  name Dot27
  xpos 624
  ypos -198
 }
 Colorspace {
  colorspace_out YCbCr
  name Colorspace1
  xpos 590
  ypos -130
 }
 Dot {
  name Dot7
  xpos 624
  ypos -54
 }
set Nfb88a400 [stack 0]
 Input {
  inputs 0
  name PLATE
  label "\[value number]"
  note_font_size 30
  xpos 290
  ypos -892
  number 1
 }
 Dot {
  name Dot50
  xpos 324
  ypos -726
 }
set Nfb889c00 [stack 0]
 OCIOColorSpace {
  in_colorspace scene_linear
  out_colorspace scene_linear
  name OCIOColorSpace1
  xpos 290
  ypos -490
 }
 Dot {
  name Dot29
  xpos 324
  ypos -198
 }
set Nfb889000 [stack 0]
 Dot {
  name Dot6
  xpos 464
  ypos -198
 }
 Colorspace {
  colorspace_out YCbCr
  name Colorspace2
  xpos 430
  ypos -130
 }
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge4
  xpos 430
  ypos -58
 }
 Multiply {
  channels rgb
  value {{"1 / parent.luminance - 1"} 0 0 0}
  name Multiply6
  xpos 430
  ypos 14
 }
 Dot {
  name Dot31
  xpos 464
  ypos 90
 }
push $Nfb88a400
 Merge2 {
  inputs 2
  operation plus
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge5
  xpos 590
  ypos 86
 }
 Colorspace {
  colorspace_in YCbCr
  name Colorspace3
  xpos 590
  ypos 158
 }
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge6
  xpos 590
  ypos 230
 }
 Dot {
  name Dot35
  xpos 624
  ypos 306
 }
set Nfb83a800 [stack 0]
push $Nfb88b000
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge7
  xpos 730
  ypos 302
  disable {{"Multiply6.value.r == 0"}}
 }
 Dot {
  name Dot2
  xpos 764
  ypos 522
 }
set Nfb83a000 [stack 0]
 Dot {
  name Dot30
  xpos 764
  ypos 690
 }
set Nfb839c00 [stack 0]
 Dot {
  name Dot55
  xpos 764
  ypos 1170
 }
set Nfb839800 [stack 0]
 Input {
  inputs 0
  name mask
  label "\[value number]"
  note_font_size 30
  xpos 1170
  ypos -896
  number 3
 }
 Dot {
  name Dot39
  xpos 1204
  ypos 258
 }
set Nfb839000 [stack 0]
 Dot {
  name Dot26
  xpos 1204
  ypos 1074
 }
set Nfb838c00 [stack 0]
 Invert {
  name Invert2
  xpos 180
  ypos 1064
  disable {{!parent.invert_1}}
 }
push $Nfb839c00
push $Nfb889000
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge27
  xpos 290
  ypos 686
 }
 Dot {
  name Dot3
  xpos 324
  ypos 786
 }
set Ne2123c00 [stack 0]
 Dot {
  name Dot5
  xpos 104
  ypos 786
 }
set Ne2123800 [stack 0]
push $Ne2123800
 Copy {
  inputs 2
  from0 {{{parent.Copy2.from0}}}
  to0 rgba.red
  name Copy3
  xpos 70
  ypos 848
 }
 Expression {
  expr0 abs(r)
  channel1 {none none none rgba.alpha}
  expr1 "r == 0"
  channel2 none
  channel3 none
  name Expression4
  xpos 70
  ypos 926
 }
set Ne2123000 [stack 0]
push $Nfb83a000
 Colorspace {
  colorspace_out AlexaV3LogC
  name Colorspace5
  xpos 70
  ypos 518
 }
 Clamp {
  maximum_enable false
  name Clamp2
  xpos -40
  ypos 512
 }
 Dot {
  name Dot1
  xpos -116
  ypos 522
 }
set Ne2122400 [stack 0]
 Dot {
  name Dot48
  xpos -116
  ypos 786
 }
set Ne2122000 [stack 0]
push $Ne2122000
 Copy {
  inputs 2
  from0 rgba.blue
  to0 rgba.red
  name Copy2
  xpos -150
  ypos 848
 }
 Expression {
  temp_name0 min
  temp_expr0 0.5478476602584124
  temp_name1 max
  temp_expr1 0.5718689560890198
  channel0 {none none none rgba.alpha}
  expr0 "r >= min && r <= max"
  channel1 none
  channel2 none
  channel3 none
  name Expression2
  xpos -150
  ypos 926
 }
 Dot {
  name Dot4
  xpos -116
  ypos 1002
 }
 ChannelMerge {
  inputs 2
  operation stencil
  name ChannelMerge2
  xpos -40
  ypos 985
 }
push $Ne2123000
 Copy {
  inputs 2
  from0 rgba.alpha
  to0 rgba.alpha
  name Copy1
  xpos 70
  ypos 992
 }
 ChannelMerge {
  inputs 2
  A -rgba.green
  operation multiply
  name ChannelMerge1
  xpos 70
  ypos 1057
  disable {{!A}}
 }
 Copy {
  inputs 2
  from0 {{{parent.Copy2.from0}}}
  to0 rgba.green
  name Copy4
  xpos 70
  ypos 1160
 }
 Premult {
  channels {rgba.red rgba.green -rgba.blue none}
  name Premult1
  xpos 70
  ypos 1238
 }
 TimeWarp {
  lookup 1088
  time ""
  filter nearest
  name TimeWarp1
  xpos 70
  ypos 1286
  disable true
 }
 FrameBlend {
  channels {rgba.red rgba.green -rgba.blue rgba.alpha}
  startframe 0
  endframe 9
  userange true
  name FrameBlend1
  xpos 70
  ypos 1352
  disable true
 }
 CurveTool {
  avgframes 0
  channels {rgba.red rgba.green -rgba.blue rgba.alpha}
  ROI {0 0 {width} {height}}
  intensitydata {3.454302895e-07 7.802840197e-05 0 8.873093412e-05}
  name CurveTool
  xpos 70
  ypos 1424
 }
push $Ne2122400
 Dot {
  name Dot16
  xpos -336
  ypos 522
 }
 CurveTool {
  operation "Max Luma Pixel"
  channels {-rgba.red -rgba.green rgba.blue none}
  ROI {0 0 {width} {height}}
  maxlumapixdata {2089 1182}
  maxlumapixvalue {0 0 0.5716627836}
  minlumapixdata {1127 1304}
  minlumapixvalue {0 0 0.09195037186}
  name CurveTool_Range
  xpos -370
  ypos 680
 }
 Sampler {
  inputs 0
  lut {red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}}
  name Sampler1
  onCreate "n = nuke.thisNode()\nn\['sampler'].setEnabled(False)"
  knobChanged "n = nuke.thisNode()\nk = nuke.thisKnob()\np = nuke.thisParent()\n\nif k.name() == 'lut':\n    with p:\n        for c in \['ColorLookup1','ColorLookup2']:\n           nuke.toNode(c)\['lut'].fromScript(k.toScript())"
  xpos 840
  ypos 1502
 }
push $Nfb889c00
 Dot {
  name Dot51
  xpos 115
  ypos -726
 }
 Input {
  inputs 0
  name COMP
  label "\[value number]"
  note_font_size 30
  xpos 950
  ypos -896
 }
 Dot {
  name Dot49
  xpos 984
  ypos -605
 }
set Ne20b9800 [stack 0]
 Switch {
  inputs 2
  which {{parent.meta}}
  name Switch1
  xpos 81
  ypos -609
 }
 Dot {
  name Dot54
  xpos 115
  ypos -486
 }
 Dot {
  name Dot52
  xpos -685
  ypos -486
 }
 Dot {
  name Dot53
  xpos -685
  ypos 3762
 }
push $Nfb839000
 Dot {
  name Dot40
  xpos 874
  ypos 258
 }
push $Nfb83a800
 Dot {
  name Dot34
  xpos 624
  ypos 378
 }
 Multiply {
  inputs 1+1
  channels rgb
  value 0
  maskChannelMask -rgba.red
  name Multiply1
  xpos 840
  ypos 374
 }
push $Ne20b9800
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.in_colorspace}}
  out_colorspace {{OCIOColorSpace1.out_colorspace}}
  name OCIOColorSpace3
  xpos 950
  ypos -490
 }
 Dot {
  name Dot44
  xpos 984
  ypos -390
 }
set Ne2072c00 [stack 0]
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge8
  xpos 950
  ypos 374
  disable {{"Multiply6.value.r == 0"}}
 }
 Dot {
  name Dot18
  xpos 984
  ypos 2658
 }
set Ne2072400 [stack 0]
 ColorLookup {
  lut {master {}
    red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}
    alpha {}}
  name ColorLookup2
  xpos 730
  ypos 2654
 }
push $Nfb838c00
 Dot {
  name Dot38
  xpos 1204
  ypos 1842
 }
 Dot {
  name Dot37
  xpos 544
  ypos 1842
 }
 Dot {
  name Dot22
  xpos 544
  ypos 2271
 }
set Ne2071400 [stack 0]
 Dot {
  name Dot20
  xpos 544
  ypos 2391
 }
push $Ne2071400
 Dot {
  name Dot17
  xpos 434
  ypos 2271
 }
set Ne2070c00 [stack 0]
 Dot {
  name Dot13
  xpos 214
  ypos 2271
 }
 Input {
  inputs 0
  name external_grain
  label "\[value number]"
  note_font_size 30
  xpos -150
  ypos 1716
  number 4
 }
 Dot {
  name Dot21
  xpos -116
  ypos 1938
 }
push $Nfb839800
 ColorLookup {
  channels rgb
  lut {master {}
    red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}
    alpha {}}
  name ColorLookup1
  xpos 730
  ypos 1502
 }
 Dot {
  name Dot24
  xpos 764
  ypos 1746
 }
push $Ne2123c00
 Dot {
  name Dot33
  xpos 324
  ypos 1386
 }
 MergeExpression {
  inputs 2
  temp_name0 target
  temp_expr0 .01
  expr0 "Br * (target / Ar)"
  expr1 "Bg * (target / Ag)"
  expr2 "Bb * (target / Ab)"
  channel3 none
  name MergeExpression1
  xpos 290
  ypos 1742
 }
 Dot {
  name Dot15
  xpos 324
  ypos 1842
 }
set Ne241e800 [stack 0]
 Dot {
  name Dot25
  xpos 104
  ypos 1842
 }
 Switch {
  inputs 2
  which {{parent.external_grain}}
  name Switch2
  xpos 70
  ypos 1934
 }
 Group {
  name VoronoiScatter
  xpos 70
  ypos 2102
  disable {{!parent.scatter}}
  addUserKnob {20 User}
  addUserKnob {41 useGPUIfAvailable l "Use GPU if available" T VoroNoise.useGPUIfAvailable}
  addUserKnob {41 vectorize l "Vectorize on CPU" -STARTLINE T VoroNoise.vectorize}
  addUserKnob {15 box}
  box {{parent.box x1004 0 x1036 -75} {parent.box x1004 100 x1036 120} {parent.box x1004 496 x1036 325} {parent.box x1004 916 x1036 320}}
  addUserKnob {3 sample_frame l "sample frame"}
  sample_frame {{parent.sample_frame}}
  addUserKnob {7 cell_size l "cell size" R 0 100}
  cell_size {{parent.cell_size}}
  addUserKnob {6 overlay_pattern l "overlay pattern" -STARTLINE}
  overlay_pattern {{parent.overlay}}
  addUserKnob {3 edge_blend_size l "edge blend size"}
  edge_blend_size {{parent.edge_blend_size}}
  addUserKnob {7 amplitude R 0 100}
  amplitude {{parent.amplitude}}
  addUserKnob {7 frequency R 0 100}
  frequency {{parent.frequency}}
  addUserKnob {41 VoroNoise_Seed l Seed T VoroNoise.VoroNoise_Seed}
 }
  Input {
   inputs 0
   name Input1
   xpos 180
   ypos -879
  }
  Dot {
   name Dot14
   xpos 214
   ypos -750
  }
set Ne241d800 [stack 0]
  Dot {
   name Dot16
   xpos 434
   ypos -750
  }
  Remove {
   name Remove1
   xpos 400
   ypos -687
  }
  Dot {
   name Dot6
   xpos 434
   ypos -606
  }
set Ne241cc00 [stack 0]
  Dot {
   name Dot15
   xpos 654
   ypos -606
  }
set Ne241c800 [stack 0]
  Dot {
   name Dot7
   xpos 874
   ypos -606
  }
  Noise {
   output {rgba.red -rgba.green -rgba.blue none}
   replace true
   size {{parent.frequency} {"parent.frequency * pixel_aspect"}}
   zoffset {{"x + 1000"}}
   gamma 1
   name Noise1
   xpos 840
   ypos -514
  }
  Noise {
   output {-rgba.red rgba.green -rgba.blue none}
   replace true
   size {{parent.Noise1.size} {parent.Noise1.size}}
   zoffset {{x}}
   gamma 1
   name Noise2
   xpos 840
   ypos -466
  }
  Clamp {
   name Clamp1
   xpos 840
   ypos -424
  }
  Dot {
   name Dot11
   xpos 874
   ypos -366
  }
push $Ne241c800
  BlinkScript {
   ProgramGroup 1
   KernelDescription "2 \"VoroNoise\" iterate pixelWise c117be128a07c11b6d82fd34148d66b3bcac41976ec9c2082affe38e890c2c0f 2 \"src\" Read Point \"dst\" Write Point 6 \"Frequency\" Float 1 AABIQg== \"Seed\" Int 1 AAAAAA== \"aspect ratio\" Float 1 AACAPw== \"width\" Int 1 AAAAAA== \"height\" Int 1 AAAAAA== \"Randomness\" Float 1 AAAAPw== 6 \"frequency\" 1 1 \"seed\" 1 1 \"aspect_ratio\" 1 1 \"width\" 1 1 \"height\" 1 1 \"randomness\" 1 1 0"
   kernelSource "// Voronoi.blink\n// A test implementation of libNoise's Voronoi generator using Blink\n// Ivan Busquets - August 2013\n// Modified for DasGrain by Fabian Holtz - April 2019\n\n#define X_NOISE_GEN  1619\n#define Y_NOISE_GEN  31337\n#define Z_NOISE_GEN  6971\n#define SEED_NOISE_GEN  1013\n#define SQRT_3 1.73205081\n\ninline int IntValueNoise3D (int x, int y, int z, int seed)\n\{\n  // All constants are primes and must remain prime in order for this noise\n  // function to work correctly.\n  int n = (\n      X_NOISE_GEN    * x\n    + Y_NOISE_GEN    * y\n    + Z_NOISE_GEN    * z\n    + SEED_NOISE_GEN * seed)\n    & 0x7fffffff;\n  n = (n >> 13) ^ n;\n  return (n * (n * n * 60493 + 19990303) + 1376312589) & 0x7fffffff;\n\}\n\ninline float ValueNoise3D (int x, int y, int z, int seed)\n\{\n  return 1.0 - ((float)IntValueNoise3D (x, y, z, seed) / 1073741824.0);\n\}\n\nkernel VoroNoise : ImageComputationKernel<ePixelWise>\n\{\n  Image<eRead> src;\n  Image<eWrite, eAccessPoint> dst;\n\nparam:\n  float frequency;\n  int seed;\n  float aspect_ratio;\n  int width;\n  int height;\n  float randomness;\n\n\n  void define() \{\n    defineParam(frequency, \"Frequency\", 50.0f);\n    defineParam(aspect_ratio, \"aspect ratio\", 1.0f);\n    defineParam(seed, \"Seed\", 0);\n    defineParam(randomness, \"Randomness\", 0.5f);\n  \}\n\n\n\n\n  void process(int2 pos) \{\n   float x = pos.x * aspect_ratio * frequency / width;\n   float y = pos.y * frequency / width;\n   int xInt = (x > 0.0) ? x : x - 1;\n   int yInt = (y > 0.0) ? y : y - 1;\n\n\n    float minDist = 2147483647.0;\n    float xCandidate = 0;\n    float yCandidate = 0;\n\n    float dist;\n\nfor (int yCur = yInt - 2; yCur <= yInt + 2; yCur++) \{\n      for (int xCur = xInt - 2; xCur <= xInt + 2; xCur++) \{\n\n        // Calculate the position and distance to the seed point inside of\n        // this unit cube. Limited by the randomness value\n        float xPos = xCur + (ValueNoise3D (xCur, yCur, 0, seed    ) + 1 ) * randomness + (1-randomness) - 1;\n        float yPos = yCur + (ValueNoise3D (xCur, yCur, 0, seed + 1) + 1 ) * randomness + (1-randomness) - 1;\n\n        float xDist = xPos - x;\n        float yDist = yPos - y;\n\n        dist = pow(xDist, 2) + pow(yDist, 2);\n        if (dist < minDist) \{\n          // This seed point is closer to any others found so far, so record\n          // this seed point.\n          minDist = dist;\n          xCandidate = xPos;\n          yCandidate = yPos;\n\t\}\n    \}\n\}\n\n    SampleType(dst) sample(0.0f);\n\n    sample.x = xCandidate / aspect_ratio / frequency;\n    sample.y = yCandidate / height * width / frequency;\n    sample.z = 0;\n\n    dst() = sample;\n\}\n\};"
   rebuild ""
   VoroNoise_Frequency {{"width / parent.cell_size"}}
   VoroNoise_Seed {{"(x + (parent.parent.stereo == 2 ? \[lsearch \[value root.views] \[view]] / 2 : 0)) * 5"}}
   "VoroNoise_aspect ratio" {{pixel_aspect}}
   VoroNoise_width {{width}}
   VoroNoise_height {{height}}
   rebuild_finalise ""
   name VoroNoise
   xpos 620
   ypos -520
  }
  Copy {
   inputs 2
   from0 rgba.red
   to0 forward.u
   from1 rgba.green
   to1 forward.v
   name Copy1
   xpos 620
   ypos -382
   disable {{"parent.amplitude == 0"}}
  }
  IDistort {
   uv forward
   uv_offset 0.5
   uv_scale {{parent.amplitude} {"uv_scale.w * pixel_aspect"}}
   filter impulse
   name IDistort1
   xpos 620
   ypos -280
   disable {{"parent.amplitude == 0"}}
  }
  Dot {
   name Dot5
   xpos 654
   ypos -246
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur3
   xpos 620
   ypos -154
  }
  Transform {
   translate {{"floor((x * size) % 1 * (size)) - int(size / 2)"} {"floor(x % 1 * (size)) - int(size/2)"}}
   filter impulse
   black_outside false
   name Transform1
   xpos 620
   ypos -58
   disable {{"parent.edge_blend_size < 1"}}
   addUserKnob {20 User}
   addUserKnob {3 size}
   size {{"parent.edge_blend_size + 1"}}
  }
  Dot {
   name Dot9
   xpos 654
   ypos 42
  }
set Ne23dd400 [stack 0]
push $Ne241cc00
  Expression {
   expr0 "(x + .5) / width"
   expr1 "(y + .5) / height"
   expr2 0
   name STMapGenerator
   xpos 400
   ypos -514
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur2
   xpos 400
   ypos -154
  }
  Merge2 {
   inputs 2
   operation from
   Achannels {rgba.red rgba.green -rgba.blue none}
   Bchannels {rgba.red rgba.green -rgba.blue none}
   output {rgba.red rgba.green -rgba.blue none}
   name Merge2
   xpos 400
   ypos 38
  }
  Dot {
   name Dot10
   xpos 434
   ypos 210
  }
push $Ne23dd400
  Expression {
   temp_name0 view_index
   temp_expr0 "parent.parent.stereo == 1 ? \[lsearch \[value root.views] \[view]] / 2 : 0"
   expr0 "random((r + view_index) * 1000000, 0) * (maxx - minx) + minx"
   expr1 "random((g + view_index) * 1000000, 0) * (maxy - miny) + miny"
   channel2 none
   channel3 none
   name Expression3
   xpos 620
   ypos 110
   addUserKnob {20 User}
   addUserKnob {7 frequency R 0 100}
   frequency {{parent.parent.cell_size}}
   addUserKnob {7 multiplier R 0 3}
   multiplier 0.5
   addUserKnob {15 shrink}
   shrink {{"frequency * multiplier + ceil(parent.edge_blend_size / 2) + IDistort1.uv_scale.w / 2"} {"frequency * multiplier + ceil(parent.edge_blend_size / 2) + IDistort1.uv_scale.h / 2"} {"frequency * multiplier + floor(parent.edge_blend_size / 2) + IDistort1.uv_scale.w / 2"} {"frequency * multiplier + floor(parent.edge_blend_size / 2) + IDistort1.uv_scale.h / 2"}}
   addUserKnob {26 ""}
   addUserKnob {7 minx}
   minx {{"(parent.box.x + shrink.x + .5) / width"}}
   addUserKnob {7 maxx}
   maxx {{"(parent.box.r - shrink.r - .5) / width"}}
   addUserKnob {7 miny}
   miny {{"(parent.box.y + shrink.y + .5) / height"}}
   addUserKnob {7 maxy}
   maxy {{"(parent.box.t - shrink.t - .5) / height"}}
  }
  Merge2 {
   inputs 2
   operation plus
   Achannels {rgba.red rgba.green -rgba.blue none}
   Bchannels {rgba.red rgba.green -rgba.blue none}
   output {rgba.red rgba.green -rgba.blue none}
   name Merge3
   xpos 620
   ypos 206
  }
  Expression {
   expr0 "(r + (maxx - minx) - minx) % (maxx - minx) + minx"
   expr1 "(g + (maxy - miny) - miny) % (maxy - miny) + miny"
   channel2 none
   channel3 none
   name Expression7
   xpos 620
   ypos 278
   addUserKnob {20 User}
   addUserKnob {7 minx}
   minx {{"(parent.box.x + rint(x % 1 * parent.edge_blend_size) + .5) / width"}}
   addUserKnob {7 maxx}
   maxx {{"(parent.box.r + rint(x % 1 * parent.edge_blend_size) - .5) / width"}}
   addUserKnob {7 miny}
   miny {{"(parent.box.y + rint(x % 1 * parent.edge_blend_size) + .5) / height"}}
   addUserKnob {7 maxy}
   maxy {{"(parent.box.t + rint(x % 1 * parent.edge_blend_size) - .5) / height"}}
  }
  Dot {
   name Dot3
   xpos 654
   ypos 354
  }
set Ne2383000 [stack 0]
  Dot {
   name Dot13
   xpos 654
   ypos 546
  }
push $Ne2383000
  Dot {
   name Dot8
   xpos 874
   ypos 354
  }
  Blur {
   channels rgb
   size {{pixel_aspect} 1}
   name Blur1
   label "\[value size]"
   xpos 840
   ypos 440
  }
  Difference {
   inputs 2
   name Difference2
   xpos 840
   ypos 536
  }
  Expression {
   channel0 {none none none rgba.alpha}
   expr0 "a > 1e-9"
   channel1 none
   channel2 none
   channel3 none
   name Expression2
   xpos 840
   ypos 614
  }
  Shuffle {
   red alpha
   green alpha
   blue alpha
   name Shuffle1
   label "\[value in]:\[value out]"
   xpos 840
   ypos 680
  }
  Dot {
   name Dot4
   xpos 874
   ypos 762
  }
push $Ne2383000
push $Ne241d800
  FrameHold {
   firstFrame {{parent.sample_frame}}
   name FrameHold1
   xpos 180
   ypos -256
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur1
   xpos 180
   ypos -154
  }
  STMap {
   inputs 2
   channels rgb
   uv rgb
   filter impulse
   name STMap1
   xpos 180
   ypos 350
  }
set Ne2380c00 [stack 0]
  TimeBlur {
   divisions {{"max(Transform1.size == 1 ? 2 : pow2(Transform1.size), 1)"}}
   shutter 1
   shuttercustomoffset {{"1 / divisions / 2"}}
   name TimeBlur1
   xpos 180
   ypos 446
   disable {{"parent.edge_blend_size < 1"}}
  }
set Ne2380800 [stack 0]
push $Ne2380c00
  Dot {
   name Dot1
   xpos -6
   ypos 354
  }
  Difference {
   inputs 2
   name Difference1
   xpos -40
   ypos 440
  }
  Expression {
   channel0 {none none none rgba.alpha}
   expr0 "a > 1e-10"
   channel1 none
   channel2 none
   channel3 none
   name Expression1
   xpos -40
   ypos 494
  }
  Blur {
   channels alpha
   size {{parent.parent.edge_blend_size}}
   name Blur2
   xpos -40
   ypos 536
  }
  Grade {
   channels alpha
   blackpoint 0.5
   white_clamp true
   name Grade2
   xpos -40
   ypos 584
  }
  Dot {
   name Dot2
   xpos -6
   ypos 666
  }
push $Ne2380800
  Grade {
   inputs 1+1
   white 1.4
   black_clamp false
   name Grade1
   xpos 180
   ypos 662
   disable {{"parent.edge_blend_size < 1"}}
  }
  Merge2 {
   inputs 2
   Achannels rgb
   Bchannels rgb
   output rgb
   name Merge1
   xpos 180
   ypos 758
   disable {{!parent.overlay_pattern}}
  }
  Assert {
   expression {{"Expression3.maxx > Expression3.minx && Expression3.maxy > Expression3.miny"}}
   message "increase sample box size or decrease cell size"
   name error
   xpos 180
   ypos 854
  }
  Output {
   name Output1
   xpos 180
   ypos 950
  }
 end_group
 Multiply {
  inputs 1+1
  channels rgb
  value 1.8
  maskChannelMask {{{parent.Merge9.maskChannelMask}}}
  invert_mask {{!Merge9.invert_mask}}
  name Multiply7
  xpos 70
  ypos 2315
  disable {{"!maskChannelMask || !\[exists parent.input3.name]"}}
 }
 Dot {
  name Dot23
  xpos 104
  ypos 2391
 }
push $Ne2070c00
push $Ne241e800
 Multiply {
  inputs 1+1
  channels rgb
  value 1.8
  maskChannelMask {{{parent.Merge9.maskChannelMask}}}
  invert_mask {{parent.Merge9.invert_mask}}
  name Multiply2
  xpos 290
  ypos 2315
  disable {{"!maskChannelMask || (!parent.scatter && !parent.external_grain)"}}
 }
 Merge2 {
  inputs 2+1
  operation copy
  Achannels rgb
  Bchannels rgb
  output rgb
  maskChannelMask -rgba.alpha
  name Merge9
  xpos 290
  ypos 2387
  disable {{"!(parent.scatter || parent.external_grain)"}}
 }
 Dot {
  name Dot11
  xpos 324
  ypos 2490
 }
set Ne230c800 [stack 0]
 MergeExpression {
  inputs 2
  temp_name0 reverse
  temp_expr0 "1 / MergeExpression1.temp_expr0"
  expr0 "Br * Ar * reverse"
  expr1 "Bg * Ag * reverse"
  expr2 "Bb * Ab * reverse"
  name MergeExpression2
  xpos 290
  ypos 2654
 }
 Dot {
  name Dot8
  xpos 324
  ypos 2850
 }
push $Ne2072400
 Merge2 {
  inputs 2
  operation plus
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge3
  xpos 950
  ypos 2846
 }
 Dot {
  name Dot42
  xpos 984
  ypos 3018
 }
set Ne22ab400 [stack 0]
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.out_colorspace}}
  out_colorspace {{OCIOColorSpace1.in_colorspace}}
  name OCIOColorSpace4
  xpos 950
  ypos 3086
 }
 Dot {
  name Dot19
  xpos 984
  ypos 3162
 }
set Ne22aa800 [stack 0]
 Dot {
  name Dot41
  xpos 1204
  ypos 3162
 }
set Ne22aa400 [stack 0]
 Dot {
  name Dot36
  xpos 1314
  ypos 3162
 }
 Blur {
  channels rgb
  size 1
  name Blur1
  xpos 1280
  ypos 3254
 }
push $Ne22aa400
 Merge2 {
  inputs 2
  operation difference
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge10
  xpos 1170
  ypos 3254
 }
 Multiply {
  channels rgb
  value 50
  name Multiply3
  xpos 1170
  ypos 3302
 }
 Dot {
  name Dot43
  xpos 1204
  ypos 3402
 }
push $Ne2072c00
 Dot {
  name Dot45
  xpos 1424
  ypos -390
 }
push $Ne22ab400
 Merge2 {
  inputs 2
  operation from
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge11
  xpos 1390
  ypos 3014
 }
 Dot {
  name Dot46
  xpos 1424
  ypos 3522
 }
push $Ne230c800
 Dot {
  name Dot14
  xpos 104
  ypos 2490
 }
 Dot {
  name Dot12
  xpos 104
  ypos 3402
 }
push $Nfb88b800
 Dot {
  name Dot47
  xpos -556
  ypos -390
 }
push $Nfb889000
 Merge2 {
  inputs 2
  operation from
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge12
  xpos -590
  ypos -202
 }
 Dot {
  name Dot10
  xpos -556
  ypos 3522
 }
push $Ne22aa800
 Switch {
  inputs 5
  which {{output}}
  name Output
  xpos 950
  ypos 3656
  addUserKnob {20 User}
  addUserKnob {4 output M {"regrained comp" "plate grain" "normalised grain" "adapted grain" "grain QC"}}
 }
 CopyMetaData {
  inputs 2
  mergeMode "Meta only"
  name CopyMetaData1
  xpos 950
  ypos 3758
 }
 Output {
  name Output1
  xpos 950
  ypos 3854
 }
end_group
Read {
 inputs 0
 file_type exr
 file R:/work/CG/Ambition/1023_WINK_NY_24/shots/sh_0070/source/cc/sh_0070_cc/SH_0070_сс.########.exr
 format "3424 2202 0 0 3424 2202 1 "
 first 991
 last 1091
 origfirst 991
 origlast 1091
 origset true
 colorspace sRGB
 name Read7
 selected true
 xpos 2686
 ypos -134
}
Retime {
 input.first 991
 input.last 1091
 reverse true
 output.first 991
 output.last 1091
 time ""
 name Retime9
 selected true
 xpos 2686
 ypos -38
}
Retime {
 input.first 991
 input.last 1091
 output.first 993
 output.first_lock true
 output.last 1093
 time ""
 name Retime10
 selected true
 xpos 2686
 ypos -14
}
set N19020400 [stack 0]
OFXcom.absoft.neatvideo4_v4 {
 DNP 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 NFP ODAxAPotAAAAAAAAAAABAAAAAACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AAAAAAAAAAAAAIA/AACAPwAAgD/NzMw+AACAPwAAgD8BAAAAAADAPwAAwD8AAIA/AACAPwAAgD8AAAAAAAAAP5qZmT4AAAAAAAAAAAAAgD8AAAAAAAAAAAAAgD8AAIA/AACAPwAAgD8AAQAAAACAPwAAAAAAAAAAAACAPwAAAAAAAAAAAQEBAQEBAQEBAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAgAAAAAAgD8AAAAAAAAAAAAAwD8AAAAAAAAAAAAAAD8BAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQD8AAIA/AACAPwAAgD8BAAAAAAAAAAAAgD8AAIA/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADgwMQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA==
 ParamsHash1 1450754537
 ParamsHash2 260
 ParamsHash3 0
 name "Reduce Noise v4_2"
 selected true
 xpos 2686
 ypos 66
}
set N19a8d400 [stack 0]
NoOp {
 name NoOp2
 selected true
 xpos 2469
 ypos 470
}
push $N19020400
NoOp {
 name NoOp1
 selected true
 xpos 2892
 ypos 470
}
push $N19a8d400
Dot {
 name Dot3
 selected true
 xpos 2720
 ypos 129
}
set N19a8d000 [stack 0]
Dot {
 name Dot4
 selected true
 xpos 2535
 ypos 129
}
Blur {
 size 4
 name Blur3
 selected true
 xpos 2501
 ypos 218
}
set N19a8c800 [stack 0]
push $N19a8c800
push $N19a8d000
Merge2 {
 inputs 2
 operation from
 name Merge5
 selected true
 xpos 2686
 ypos 224
}
RotoPaint {
 curves {{{v x3f99999a}
  {f 0}
  {n
   {layer Root
    {f 2097152}
    {t x44d60000 x4489a000}
    {a pt1x 0 pt1y 0 pt2x 0 pt2y 0 pt3x 0 pt3y 0 pt4x 0 pt4y 0 ptex00 0 ptex01 0 ptex02 0 ptex03 0 ptex10 0 ptex11 0 ptex12 0 ptex13 0 ptex20 0 ptex21 0 ptex22 0 ptex23 0 ptex30 0 ptex31 0 ptex32 0 ptex33 0 ptof1x 0 ptof1y 0 ptof2x 0 ptof2y 0 ptof3x 0 ptof3y 0 ptof4x 0 ptof4y 0 pterr 0 ptrefset 0 ptmot x40800000 ptref 0}
    {cubiccurve Burn15 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44b86666 x4456cccd 1}
       {x44b84000 x4456cccd 1}
       {x44b80666 x4456c000 1}
       {x44b74000 x44566666 1}
       {x44b63333 x44563333 1}
       {x44b54ccd x44564000 1}
       {x44b4e666 x44568ccd 1}
       {x44b4c666 x44570000 1}
       {x44b4c000 x44574000 1}
       {x44b4d99a x44576666 1}
       {x44b54ccd x44574000 1}
       {x44b5e666 x4456cccd 1}
       {x44b6999a x44564ccd 1}
       {x44b73333 x4455cccd 1}
       {x44b6f333 x4455f333 1}
       {x44b6799a x44564000 1}
       {x44b50666 x44576666 1}
       {x44b4399a x4457c000 1}
       {x44b4799a x44577333 1}
       {x44b4e666 x44573333 1}
       {x44b5f99a x4456f333 1}}}
     {tx x44816000 x44b5fca7 x4456c925}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x42340000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn14 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44c14666 x4453c000 1}
       {x44c12666 x4453e666 1}
       {x44c0cccd x44544ccd 1}
       {x44c02666 x44550000 1}
       {x44c00666 x44553333 1}
       {x44bf599a x44560000 1}
       {x44bec666 x4456b333 1}
       {x44bee666 x4456199a 1}
       {x44bf3333 x44556666 1}
       {x44c0199a x44544ccd 1}
       {x44c0e000 x44538ccd 1}
       {x44c17333 x44530ccd 1}
       {x44c2199a x4452999a 1}
       {x44c27333 x44528ccd 1}
       {x44c2999a x44528ccd 1}
       {x44c2c000 x44528000 1}
       {x44c2d99a x4452b333 1}}}
     {tx x44816000 x44c0dee0 x44542061}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x42340000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn13 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44b99333 x4457599a 1}
       {x44b9499a x44574666 1}
       {x44b8f000 x44572ccd 1}
       {x44b81000 x4456c666 1}
       {x44b69000 x4455f333 1}
       {x44b5799a x44556ccd 1}
       {x44b3e333 x44551333 1}}}
     {tx x44816000 x44b765f1 x44566ea0}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x42340000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn12 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44bda000 x4464a000 1}
       {x44bd9000 x44646000 1}
       {x44bd8ccd x44640ccd 1}
       {x44bdb99a x4464199a 1}
       {x44bdd333 x4464a000 1}
       {x44bdd000 x4464e666 1}
       {x44bda666 x4464c666 1}
       {x44bd5000 x4464399a 1}
       {x44bd2ccd x4463eccd 1}}}
     {tx x44816000 x44bd94fc x44646667}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x41700000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn11 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44b33666 x445a8ccd 1}
       {x44b33333 x445a2ccd 1}
       {x44b3299a x4459cccd 1}
       {x44b3199a x44592000 1}
       {x44b2f000 x4459accd 1}
       {x44b2d333 x445a4000 1}
       {x44b2bccd x445b1333 1}
       {x44b2c333 x445a8000 1}
       {x44b2cccd x445a2000 1}
       {x44b2d666 x4459e000 1}
       {x44b2d666 x445a2000 1}
       {x44b2d666 x445a8000 1}
       {x44b2cccd x445b199a 1}
       {x44b2cccd x445aa000 1}
       {x44b2cccd x445a2000 1}
       {x44b2c99a x4459accd 1}
       {x44b2accd x4459f333 1}
       {x44b28666 x445a6666 1}
       {x44b21333 x445b4ccd 1}}}
     {tx x44816000 x44b2d3b5 x445a42b1}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x41700000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn10 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44b5d99a x4462b333 1}
       {x44b5a666 x44628000 1}
       {x44b55333 x44623333 1}
       {x44b50666 x4461c000 1}
       {x44b4f99a x44616666 1}
       {x44b53333 x4461a666 1}
       {x44b56666 x4461e666 1}
       {x44b5b99a x4462599a 1}
       {x44b5b333 x4462199a 1}
       {x44b5a000 x4461cccd 1}
       {x44b58000 x44617333 1}
       {x44b5accd x44618000 1}
       {x44b5f333 x4461a666 1}
       {x44b66666 x4461d99a 1}
       {x44b64000 x4461e666 1}
       {x44b5f99a x4461d99a 1}
       {x44b5199a x44618ccd 1}
       {x44b46666 x4460f333 1}
       {x44b44666 x4460d99a 1}}}
     {tx x44816000 x44b57943 x4461c8c2}
     {a ro 0 go 0 bo 0 ao 0 opc x3dcccccd bs x41700000 bm x40400000 bu 1 src 1 str 1 spx x44d60000 spy x4489a000 sb 1 ltn x44816000 ltm x44816000 ltt x40000000 tt x41b00000}}
    {cubiccurve Burn9 512 catmullrom
     {cc
      {f 2080}
      {px x44816000
       {x44b82000 x4451999a 1}
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 toolbox {burn {
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 toolbar_blending_mode color-burn
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 toolbar_brush_size 45
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 toolbar_lifetime_type single
 toolbar_lifetime_start 1035
 toolbar_lifetime_end 1035
 toolbar_paint_source fg
 toolbar_source_transform_scale {1 1}
 toolbar_source_transform_center {1712 1101}
 colorOverlay {0 0 0 0}
 opacity 0.1000000015
 paint_source foreground
 blending_mode color-burn
 lifetime_start 1035
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 motionblur_on true
 brush_size 45
 brush_spacing 0.05000000075
 brush_hardness 0.200000003
 source_black_outside true
 name RotoPaint2
 selected true
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Merge2 {
 inputs 2
 operation plus
 name Merge6
 selected true
 xpos 2686
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Group {
 inputs 3
 name DasGrain
 help "DasGrain makes regraining as simple as clicking a few buttons.\n\nFollow the steps in the Help tab and you'll have a perfect regrain in no time!"
 onCreate "import random\n\ntestimonials = \[\n    \"Such an elegant solution, love it!\",\n    \"Your gizmo is beyond expectation\",\n    \"Totally awesome!\",\n    \"DasGrain is officially the best thing ever\",\n    \"It's really working!\",\n    \"Das bringt Tr&auml;nen in meine Augen\",\n    \"DasGrain is the salvation we waited for\",\n    \"I save a lot of time, and definitely my nerves :)\",\n    \"It's alright\",\n    \"My new favourite node, thanks!<br>Having said that, ...\"\n    ]\n\nnode = nuke.thisNode()\nnode\['testimonial'].setValue('<br><br><br><i>&laquo;%s&raquo;</i><br>&mdash; anonymous<br><br>' % random.choice(testimonials))\nnode\['box'].setFlag(nuke.NO_ANIMATION)"
 knobChanged "n = nuke.thisNode()\nk = nuke.thisKnob()\n\nif k.name() == 'box':\n    this_frame = nuke.frame()\n    n\['sample_frame'].setValue(this_frame)\n\nif k.name() == 'scatter':\n    n\['divider04'].setVisible(k.value() == False)\n    n\['divider05'].setVisible(k.value() == True)"
 tile_color 0x7f7f7fff
 selected true
 xpos 2686
 ypos 464
 addUserKnob {20 DasGrain_tab l DasGrain}
 addUserKnob {41 output t "<strong>regrained comp</strong> it is what it sais\n<strong>plate grain</strong> plate minus degrained plate\n<strong>normalised grain</strong> check if the normalization worked. It should be as even as possible. This is what you want to output if you want to prerender a grain plate. Later you can plug it into the <i>external grain</i> input of another DasGrain\n<strong>adapted grain</strong> check if the adaptation worked. Output this if you want to further manipulate the grain (who knows what the sup is gonna come up with...). After, simply plus it to your comp (at that point the comp has to be in the <i>camera</i> colorspace, as set in the <i>Analyze</i> tab).\n<strong>grain QC</strong> check if voronoi seams are visible (&#8594; edgeblend), or the scattered grain looks different to the original plate grain (&#8594; maybe bad sample area or wrong luminance degrain amount)" T Output.output}
 addUserKnob {4 meta l "metadata from" t "Chances are you want to use the metadata from the plate, but who am I to assume :)" M {COMP PLATE}}
 addUserKnob {26 spacer01_1 l " " T " "}
 addUserKnob {20 GrainGroupBegin l "" +STARTLINE n -2}
 addUserKnob {20 Analyze_tab l Analyze}
 addUserKnob {26 text l <strong>Colorspace}
 addUserKnob {41 project_colorspace l project t "set this to the project color space" T OCIOColorSpace1.in_colorspace}
 addUserKnob {22 python_button l "What's this all about?" -STARTLINE T "nuke.message(\"Regraining in other color spaces than the camera native linear space can lead to unexpected behaviour.\\n\\nFor example converting Alexa plates to ACEScg might introduce negative values due to ACEScg's smaller gamut. In that case converting back to ARRI Linear ALEXA Wide Gamut will probably help.\\nJust set <i>project</i> to ACEScg and <i>camera</i> to ARRI Linear ALEXA Wide Gamut.\\n\\nThis might be transferable to other cameras, but I've only tested with Alexas.\\n---------\\nBypass by setting both knobs to the same value.\")"}
 addUserKnob {41 camera_colorspace l camera t "set this to the camera native linear space" T OCIOColorSpace1.out_colorspace}
 addUserKnob {26 text_2 l " " T " "}
 addUserKnob {26 level l "<strong>Degrain amount"}
 addUserKnob {78 luminance t "Leave this at 1 if you're working on a completely degrained plate.\n\nIn case you decided to leave some luminance grain in the degrained plate (use the DegrainHelper node for this!), set this to the same value as in the DegrainHelper in order to compensate.\n\nIf the luminance degrain amount was set to 0.8, this needs to be set to 0.8 as well.\n\nYou need to select a mask of all elements that cover the plate, otherwise the grain of whole comp will be too strong " n 1}
 luminance 1
 addUserKnob {26 divider01 l " "}
 addUserKnob {41 degrain_amount_mask l "degrain amount mask" t "Use this channel from the mask input to specify in what area of the comp the missing luminance grain needs to be compensated." T Multiply1.maskChannelMask}
 addUserKnob {41 invert_mask l invert -STARTLINE T Multiply1.invert_mask}
 addUserKnob {26 spacer02 l " " T " "}
 addUserKnob {26 divider02 l <strong>Analyze}
 addUserKnob {3 number_of_frames l "number of frames" t "Set the number of sample frames to be spread across the input range.\n\nMore frames lead to higher accuracy.\n\nIf there are particularly bright or dark frames, set them manually in the knob below to make sure they are part of the analysis.\n\nIf you want to set all sample frames manually, set this to 0 and add the frames in the knob below."}
 number_of_frames 10
 addUserKnob {1 additional_frames l "additional frames" t "Set additional frames like this:\n\n1001,1020,1053 (single frames)\n1020-1040 (frame ranges)\n1020-1040x4 (frame ranges with step)"}
 addUserKnob {3 sample_count l "sample count" t "The samples are spread across the sample range (which gets calculated automatically) based on the AlexaV3LogC curve. This results in more samples in the dark areas and less samples in the brights.\n\nMore samples lead to a more detailed response curve (while the accuracy is limited by the quality of the degrain)."}
 sample_count 20
 addUserKnob {22 analyze l Analyze t "this is where the magic happens" T "import base64\nthis = nuke.thisNode()\n\n\ndef _sample_count(this):\n    \"\"\"returns the sample count\"\"\"\n\n    sample_count = int(this\['sample_count'].value())\n\n    if sample_count <= 0:\n        raise RuntimeError('Enter a sample count greater than 0')\n\n    else:\n        return sample_count\n\n\ndef _generate_frame_list(this):\n    \"\"\"converts the frames submitted by the user into a list\"\"\"\n\n    frame_list = \[]\n    number_of_frames = int(this\['number_of_frames'].value())\n    additional_frames = this\['additional_frames'].value()\n\n    if number_of_frames < 1 and additional_frames is '':\n        raise RuntimeError('Either set the number of frames > 0\\nor define additional frames')\n\n    first_frame = max(this.input(1).firstFrame(), this.input(2).firstFrame())\n    last_frame = min(this.input(1).lastFrame(), this.input(2).lastFrame())\n\n    if number_of_frames > 0:\n        distance = (last_frame - first_frame) / (number_of_frames)\n        frame = first_frame + distance / 2\n\n        for x in range(number_of_frames):\n            int_frame = int(round(frame))\n            if int_frame not in frame_list:\n                frame_list.append(int_frame)\n\n            frame += distance\n\n    frange = nuke.FrameRanges(additional_frames.split(','))\n\n    for r in frange:\n        for f in r:\n            if f >= first_frame and f <= last_frame:\n                if f not in frame_list:\n                    frame_list.append(f)\n\n    frame_list.sort()\n\n    return frame_list\n\n\ndef _setup_for_multiframe(frame_list):\n    \"\"\" arranges all sample frames next to each other, starting at frame 0\n    and sets the frame number knob of the FrameBlend node\"\"\"\n\n    time_warp = nuke.toNode('TimeWarp1')\n    time_warp\['lookup'].clearAnimated()\n    time_warp\['lookup'].setAnimated()\n    anim_list = \[]\n\n    for n, frame in enumerate(frame_list):\n        anim_list.append(nuke.AnimationKey(n, frame))\n\n    anim = time_warp\['lookup'].animation(0)\n    anim.addKey(anim_list)\n\n    frame_blend = nuke.toNode('FrameBlend1')\n    frame_blend\['endframe'].setValue(len(frame_list)-1)\n\n\ndef _generate_sample_list(sample_count, sample_range, sample_radius):\n    \"\"\"generate a list of sample values spread equally between the\n    min and max values of the sample range\"\"\"\n\n    sample_list = \[]\n\n    for item in range(0, sample_count):\n        sample_list.append(float(item) / sample_count * (sample_range\[1] - sample_range\[0]) + sample_range\[0] + sample_radius)\n\n    return sample_list\n\n\ndef _get_sample_range(channel, channel_list, frame_list):\n    \"\"\" samples the minimum and maximum values of the given frame range and\n    sets the sample range to those values\"\"\"\n\n    curve_tool = nuke.toNode('CurveTool_Range')\n    min_knob = curve_tool\['minlumapixvalue']\n    max_knob = curve_tool\['maxlumapixvalue']\n\n    min_knob.setAnimated()\n    max_knob.setAnimated()\n\n    curve_tool\['channels'].setValue(channel)\n\n    nuke.execute(curve_tool, nuke.FrameRanges(frame_list))\n\n    index = channel_list.index(channel)\n    min_list = \[key.y for key in min_knob.animation(index).keys()]\n    max_list = \[key.y for key in max_knob.animation(index).keys()]\n\n    min_value = min(min_list)\n    max_value = max(max_list)\n\n    min_knob.clearAnimated()\n    max_knob.clearAnimated()\n    curve_tool\['minlumapixdata'].clearAnimated()\n    curve_tool\['maxlumapixdata'].clearAnimated()\n\n    return \[min_value, max_value]\n\n\ndef _sample_it(keyer, curve_tool, sample, sample_radius):\n    \"\"\"analyze the grain level per channel and sample value in the sample range\"\"\"\n\n    keyer\['temp_expr0'].setValue(str(sample - sample_radius))\n    keyer\['temp_expr1'].setValue(str(sample + sample_radius))\n\n    intensity_knob = curve_tool\['intensitydata']\n    intensity_knob.clearAnimated()\n    intensity_knob.setAnimated()\n\n    nuke.execute(curve_tool, nuke.frame(), nuke.frame())\n    sample_values = intensity_knob.value()\n    intensity_knob.clearAnimated()\n\n    return sample_values\n\n\ndef check_inputs(this):\n    if this.input(1) is None:\n        raise RuntimeError('no plate connected')\n\n    if this.input(2) is None:\n        raise RuntimeError('no degrained plate connected')\n\n    def format_tuple(node):\n        return node.format().width(), node.format().height(), node.format().pixelAspect()\n\n    if format_tuple(this.input(1)) != format_tuple(this.input(2)):\n        raise RuntimeError(\"Format missmatch: Make sure the formats of plate and degrained plate match.\")\n\n\ndef start(this):\n    \"\"\"let's do this!\"\"\"\n\n    check_inputs(this)\n\n    with this:\n        frame_list = _generate_frame_list(this)\n        _setup_for_multiframe(frame_list)\n        sample_count = _sample_count(this)\n\n        blank = base64.b64decode('cmVkIHtjdXJ2ZX0KZ3JlZW4ge2N1cnZlfQpibHVlIHtjdXJ2ZX0=').decode('ascii')\n\n        lut = nuke.toNode('Sampler1')\['lut']\n        lut.fromScript(blank)\n\n        channel_list = \['red', 'green', 'blue']\n\n        keyer = nuke.toNode('Expression2')\n        copy = nuke.toNode('Copy2')\n\n        curve_tool = nuke.toNode('CurveTool')\n        pixel = curve_tool\['ROI'].value()\[2] * curve_tool\['ROI'].value()\[3]\n\n        task = nuke.ProgressTask('Analysing...')\n        step = 100.0 / 3 / sample_count\n        progress = step\n\n        time_warp = nuke.toNode('TimeWarp1')\n        frame_blend = nuke.toNode('FrameBlend1')\n\n        time_warp\['disable'].setValue(False)\n        frame_blend\['disable'].setValue(False)\n\n        for channel in channel_list:\n            task.setMessage('\{\} range'.format(channel))\n\n            copy\['from0'].setValue('rgba.\{\}'.format(channel))\n\n            sample_range = _get_sample_range(channel, channel_list, frame_list)\n            sample_radius = (sample_range\[1] - sample_range\[0]) / sample_count / 2\n            sample_list = _generate_sample_list(sample_count, sample_range, sample_radius)\n\n            for sample in sample_list:\n                if task.isCancelled():\n                    return\n\n                task.setProgress(int(progress))\n\n                sample_values = _sample_it(keyer, curve_tool, sample, sample_radius)\n\n                task.setMessage('\{\} channel at \{\}'.format(channel, round(sample, 2)))\n\n                if sample_values\[3] * pixel >= 10:\n                    lut.setValueAt(sample_values\[0] / sample_values\[3], sample_values\[1] / sample_values\[3], channel_list.index(channel))\n\n                progress += step\n\n        time_warp\['lookup'].clearAnimated()\n        time_warp\['disable'].setValue(True)  # hopefully prevents slowing down the comp\n        frame_blend\['disable'].setValue(True)  # hopefully prevents slowing down the comp\n\n    del task\n\n\nstart(this)\n" +STARTLINE}
 addUserKnob {26 divider03 l " "}
 addUserKnob {41 analysis_mask l "analysis mask" t "Use this channel from the mask input to control what area of the plate will be analyzed.\n\nUsefull if the degrain is obviously bad in some areas." T ChannelMerge1.A}
 addUserKnob {6 invert_1 l invert -STARTLINE}
 addUserKnob {20 Adjust_tab l Adjust}
 addUserKnob {22 whatsthis l "What am I looking at?" T "nuke.message(\"After the analysis you'll see the sampled grain response curves here. On the x-axis is the brightness of the image and on the y-axis the grain intensity. Grain increases with brightness, so <strong>the slope of the curves should always be positive</strong> (they should always go up &#8599;).<br><br>The quality of the curves depends entirely on the quality of the degrain. If the curves look wrong (for example they go up and down), try to improve the degrain first. If they still look wrong and the resulting regrain doesn't work well enough, you can try to improve the curves here by deleting/correcting all points that don't follow an upwards trend.<br><br>You can also extend the curves (again: with an upwards trend) if the comp has values that don't exist in the plate.<br><br>Note: The curve is used for both the normalization as well as the adaptation of the grain, so it doesn't give direct control of the grain intensity.\")" +STARTLINE}
 addUserKnob {41 lut l "" +STARTLINE T Sampler1.lut}
 addUserKnob {20 Replace_tab l Replace}
 addUserKnob {6 external_grain l "use external grain" t "Use external grain from a second DasGrain, with the output set to 'normalised grain', to replace masked area.\nConnect it to the 'external grain' input of this DasGrain (it's a bit hidden on the left side of the node)." +STARTLINE}
 addUserKnob {26 divider04 l <strong>Scatter}
 addUserKnob {26 divider05 l <strong>Scatter +HIDDEN T "<span style=\"color:red\">Make sure you're sampling an area without any plate detail.</a>"}
 addUserKnob {6 scatter l activate t "Activates the scatter function. It generates a new grain based on the plate grain in the sample box using a Voronoi noise." +STARTLINE}
 addUserKnob {41 useGPUIfAvailable l "Use GPU if available" -STARTLINE T VoronoiScatter.useGPUIfAvailable}
 addUserKnob {15 box l "sample box" t "Define an area that is used as a source for the scatter function. The plate grain in this area should be as even as possible, without any visible detail."}
 box {100 100 500 300}
 addUserKnob {3 sample_frame l "sample frame" t "The frame at which the grain is being sampled. Is set automatically once the sample box is changed." +DISABLED}
 sample_frame 1
 addUserKnob {4 stereo l "stereo behaviour" t "randomize offset per view: same voronoy pattern for all views, but different offset\n\nrandomize pattern per view: different voronoy pattern for every view" M {none "randomize offset per view" "randomize pattern per view" ""}}
 addUserKnob {26 spacer06 l "" +STARTLINE T " "}
 addUserKnob {6 overlay l "overlay cell pattern" t "Overlay the cell pattern of the voronoy noise. Useful to check where the seams are and if distortion or blending is necessary." +STARTLINE}
 addUserKnob {7 cell_size l "cell size" t "Cell size of the scatter. Shoudn't be too small, as lower grain frequencies might break.\nCan't be too big either, to prevent it from breaking the border of the samplebox (will error if it does)." R 5 100}
 cell_size 40
 addUserKnob {26 spacer07 l "" +STARTLINE T " "}
 addUserKnob {20 concealer l "edge concealer" n 1}
 concealer 0
 addUserKnob {26 concealer_help l " " T "If you can see the voronoi pattern in the grain QC output,\nincrease the edge blend size."}
 addUserKnob {3 edge_blend_size l "edge blend size" t "Set the output to grain QC. If you see the cell seams, increase the edge blend size to conceal them.\n\nThis is a bit hacky and slow."}
 addUserKnob {26 tip l "" -STARTLINE T "sloooow - keep this below 3 if possible"}
 addUserKnob {26 distortion_help l " " T "\nDistortion might help as well, if somehow the straight\nseams are visible (you might want to toggle the overlay\nwhile adjusting)."}
 addUserKnob {7 amplitude R 0 50}
 addUserKnob {7 frequency R 0 50}
 frequency 15
 addUserKnob {20 endGroup n -1}
 addUserKnob {26 divider06 l "" +STARTLINE}
 addUserKnob {41 replace_mask l "replace mask" t "Use this channel from the mask input to specify where you want to use scattered grain instead of the adapted plate grain." -STARTLINE T Merge9.maskChannelMask}
 addUserKnob {41 invert_mask_1 l invert -STARTLINE T Merge9.invert_mask}
 addUserKnob {20 GrainGroupEnd l "" +STARTLINE n -3}
 addUserKnob {20 Help_tab l Help}
 addUserKnob {26 basic_setup l "" +STARTLINE T "<font size=\"5\">Basic setup</font>"}
 addUserKnob {26 ""}
 addUserKnob {26 explanation l "" +STARTLINE T "<strong>Bold</strong> steps are always necessary"}
 addUserKnob {26 steps l "" +STARTLINE T "<br><strong>1. This should be the only regrain node in your comp.<br>2. Connect <i>plate</i>, <i>degrained plate</i> and <i>comp</i>.<br>&nbsp;&nbsp;&nbsp;&nbsp;The comp should be done on the degrained plate!</strong><br>3. Set the <i>luminance degrain amount</i>.<br><strong>4. Press the <i>Analyze</i> button.</strong><br>5. Correct the response curves in the <i>Adjust</i> tab.<br>6. Move the <i>sample box</i> to an area without any plate detail and activate <i>scatter</i>.<br>7. If necessary, activate <i>edge blend</i> and/or <i>distortion</i> to conceal seams."}
 addUserKnob {26 in_depth l "" +STARTLINE T "<br>For an in depth explanation of the steps, read the tooltips and check out this video:<br><a href=\"https://vimeo.com/284820390/\"><span style=\"color:#C8C8C8;\">https://vimeo.com/284820390</a>"}
 addUserKnob {26 pushthebutton l "" +STARTLINE T "<br><br>If the result is not as expected and you don't know why, push this button:"}
 addUserKnob {22 troubleshoot l Troubleshoot t HEEEEEEELP T "import base64\n\nmessages = \[]\n\nthis = nuke.thisNode()\n\n#########################\n\nif this.input(0) is None or this.input(1) is None or this.input(2) is None:\n    messages.append(\"<font color='red'><strong>ERROR</strong></font> Plate, degrained plate and comp need to be connected to the appropriate inputs.\")\n\n#########################\n\nelse:\n\n    def format_to_tuple(g):\n        \"\"\"returns (1024, 786, 2.0)\n        \"\"\"\n        return (g.format().width(), g.format().height(), g.format().pixelAspect())\n\n    format_set = set(\[\n        format_to_tuple(this.input(0)),\n        format_to_tuple(this.input(1)),\n        format_to_tuple(this.input(2)),\n        ])\n    if len(format_set) != 1:\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> Format missmatch: Make sure formats of plate, degrained plate and comp match.\")\n\n    if (this.input(1).firstFrame() != this.input(2).firstFrame()) or (this.input(1).lastFrame() != this.input(2).lastFrame()):\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> The frame ranges of plate and degrained plate don't match. Double check that they belong together.\")\n\n#########################\n\nmessages.append(\"Double check that plate and degrained plate haven't been modified in any way (paint, despill, etc).\")\n\n#########################\n\nif this\['luminance'].getValue() == 1:\n    messages.append(\"Are you working on a completely degrained plate? If not, you might have to set the luminance degrain amount.\")\n\n#########################\n\nblank = base64.b64decode('cmVkIHtjdXJ2ZX0KZ3JlZW4ge2N1cnZlfQpibHVlIHtjdXJ2ZX0=').decode('ascii')\n\nwith this:\n    Sampler = nuke.toNode('Sampler1')     \n    if Sampler\['lut'].toScript() == blank:\n        messages.append(\"<font color='red'><strong>ERROR</strong></font> You haven't pressed the Analyze button yet!\")\n\n#########################\n\nclass BadThings(Exception): pass\n\ndef thingy():\n    with this:\n        Sampler = nuke.toNode('Sampler1')\n        list = this\['lut'].toScript().replace('\}','').split('\\n')\n        for item in list:\n            sample_value = 0\n            for value in item.split(' '):\n                try:\n                    value == float(value)\n                    if value < sample_value:\n                        raise BadThings(\"<font color='orange'><strong>WARNING</strong></font> Check and fix the response curves. Their slopes should always be positive (the curves should always go up &#8599;).\")\n                        \n                    else:\n                        sample_value = value\n                except ValueError:\n                    # Ignore non-numeric things like x-values of \"x5.46\" and channel names like \"red\{\" etc\n                    pass\ntry:\n    thingy()\nexcept BadThings as e:\n    messages.append(str(e))\n    \n#########################\n\nif this\['scatter'].value() == True:\n    if this\['box'].getValue() == \[100.0, 100.0, 500.0, 300.0]:\n        messages.append(\"<font color='orange'><strong>WARNING</strong></font> Scatter has been activated, but the sample box is still in its default position. Are you sure that's a good area to sample?\")\n\n#########################\n\nmessages.append(\"Did you copy/paste DasGrain from another script? Make sure to reanalyze and to reset the sample area if you are using scatter.\")\n\n#########################\n\nif len(messages) > 0:\n    nuke.message(\"<font size=\\\"5\\\">Things worth checking</font><br><br>\"\n                               \"%s<br><br><br>If any of this doesn't make sense to you, it might be worth checking out the video on vimeo.\" % (\n                                  \"<hr>\".join(\"%s: %s\" % (i+1, m) for i, m in enumerate(messages))))\n" +STARTLINE}
 addUserKnob {26 dont_despair l "" +STARTLINE T "<br>If it still doesn't work and you're about to flip the table, send me a <a href=\"mailto:holtzf+nuke@gmail.com?subject=Help with DasGrain v1.7.8\"><span style=\"color:#C8C8C8;\">mail</a>.<br>I'm happy to help! :)"}
 addUserKnob {20 Info_tab l Info}
 addUserKnob {26 dasname l "" +STARTLINE T "<font size='5'>DasGrain</font> v1.8<br>"}
 addUserKnob {26 text_1 l "" +STARTLINE T "DasGrain makes regraining as simple as clicking a few buttons.<br>Follow the steps in the <i>Help</i> tab and you'll have a perfect\nregrain<br>in no time!"}
 addUserKnob {26 ""}
 addUserKnob {26 info l "" +STARTLINE T "Last change: 2021-03-07\n\n"}
 addUserKnob {26 name_1 l "" +STARTLINE T "Fabian Holtz"}
 addUserKnob {26 mail l "" +STARTLINE T "<a href=\"mailto:holtzf+nuke@gmail.com?subject=Help with DasGrain v1.7.8\"><span style=\"color:#C8C8C8;\">holtzf+nuke@gmail.com</a>"}
 addUserKnob {26 testimonial l "" +STARTLINE T "<br><br><br><i>&laquo;Totally awesome!&raquo;</i><br>&mdash; anonymous<br><br>"}
 addUserKnob {26 ""}
 addUserKnob {26 credit l "" +STARTLINE T "<br>VoronoiScatter based on <a href=\"http://www.nukepedia.com/blink/image/voronoi/\"><span style=\"color:#C8C8C8;\">Ivan Busquets' implementation</a> of<br> libNoise's\nVoronoi generator"}
 addUserKnob {26 thanks l "" +STARTLINE T "<br>Special thanks to Ben Dickson for bearing with my questions and<br>problems and RSP comp for the valuable feedback."}
}
 BackdropNode {
  inputs 0
  name BackdropNode1
  tile_color 0x7f7f7fff
  label "normalise grain"
  note_font_size 30
  xpos 170
  ypos 1662
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode11
  tile_color 0x7f7f7fff
  label "add grain"
  note_font_size 30
  xpos 830
  ypos 2766
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode13
  tile_color 0x7f7f7fff
  label scatter
  note_font_size 30
  xpos -50
  ypos 2022
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode14
  tile_color 0x7f7f7fff
  label "analyze grain"
  note_font_size 30
  xpos -159
  ypos 606
  bdwidth 319
  bdheight 877
 }
 BackdropNode {
  inputs 0
  name BackdropNode2
  tile_color 0x7f7f7fff
  label "grain response curve"
  note_font_size 30
  xpos 610
  ypos 2574
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode3
  tile_color 0x7f7f7fff
  label QC
  note_font_size 30
  xpos 1050
  ypos 3222
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode4
  tile_color 0x7f7f7fff
  label "grain response curve"
  note_font_size 30
  xpos 610
  ypos 1422
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode5
  tile_color 0x7f7f7fff
  label "adapt grain"
  note_font_size 30
  xpos 170
  ypos 2574
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode6
  tile_color 0x7f7f7fff
  label "sample range"
  note_font_size 30
  xpos -490
  ypos 606
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode7
  tile_color 0x7f7f7fff
  label "luminance level"
  note_font_size 30
  xpos 280
  ypos -282
  bdwidth 760
  bdheight 685
 }
 BackdropNode {
  inputs 0
  name BackdropNode8
  tile_color 0x7f7f7fff
  label "plate grain"
  note_font_size 30
  xpos 170
  ypos 606
  bdwidth 320
  bdheight 110
 }
 BackdropNode {
  inputs 0
  name BackdropNode9
  tile_color 0x7f7f7fff
  label replace
  note_font_size 30
  xpos 60
  ypos 2191
  bdwidth 540
  bdheight 226
 }
 Input {
  inputs 0
  name DEGRAINED_PLATE
  label "\[value number]"
  note_font_size 30
  xpos 730
  ypos -896
  number 2
 }
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.in_colorspace}}
  out_colorspace {{OCIOColorSpace1.out_colorspace}}
  name OCIOColorSpace2
  xpos 730
  ypos -490
 }
 Dot {
  name Dot9
  xpos 764
  ypos -390
 }
set N2562b000 [stack 0]
 Dot {
  name Dot28
  xpos 764
  ypos -198
 }
set N2562ac00 [stack 0]
 Dot {
  name Dot32
  xpos 764
  ypos 234
 }
set N2562a800 [stack 0]
push $N2562ac00
 Dot {
  name Dot27
  xpos 624
  ypos -198
 }
 Colorspace {
  colorspace_out YCbCr
  name Colorspace1
  xpos 590
  ypos -130
 }
 Dot {
  name Dot7
  xpos 624
  ypos -54
 }
set N25629c00 [stack 0]
 Input {
  inputs 0
  name PLATE
  label "\[value number]"
  note_font_size 30
  xpos 290
  ypos -892
  number 1
 }
 Dot {
  name Dot50
  xpos 324
  ypos -726
 }
set N25629400 [stack 0]
 OCIOColorSpace {
  in_colorspace scene_linear
  out_colorspace scene_linear
  name OCIOColorSpace1
  xpos 290
  ypos -490
 }
 Dot {
  name Dot29
  xpos 324
  ypos -198
 }
set N25628800 [stack 0]
 Dot {
  name Dot6
  xpos 464
  ypos -198
 }
 Colorspace {
  colorspace_out YCbCr
  name Colorspace2
  xpos 430
  ypos -130
 }
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge4
  xpos 430
  ypos -58
 }
 Multiply {
  channels rgb
  value {{"1 / parent.luminance - 1"} 0 0 0}
  name Multiply6
  xpos 430
  ypos 14
 }
 Dot {
  name Dot31
  xpos 464
  ypos 90
 }
push $N25629c00
 Merge2 {
  inputs 2
  operation plus
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge5
  xpos 590
  ypos 86
 }
 Colorspace {
  colorspace_in YCbCr
  name Colorspace3
  xpos 590
  ypos 158
 }
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge6
  xpos 590
  ypos 230
 }
 Dot {
  name Dot35
  xpos 624
  ypos 306
 }
set N19c3e000 [stack 0]
push $N2562a800
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge7
  xpos 730
  ypos 302
  disable {{"Multiply6.value.r == 0"}}
 }
 Dot {
  name Dot2
  xpos 764
  ypos 522
 }
set N19c3d800 [stack 0]
 Dot {
  name Dot30
  xpos 764
  ypos 690
 }
set N19c3d400 [stack 0]
 Dot {
  name Dot55
  xpos 764
  ypos 1170
 }
set N19c3d000 [stack 0]
 Input {
  inputs 0
  name mask
  label "\[value number]"
  note_font_size 30
  xpos 1170
  ypos -896
  number 3
 }
 Dot {
  name Dot39
  xpos 1204
  ypos 258
 }
set N19c3c800 [stack 0]
 Dot {
  name Dot26
  xpos 1204
  ypos 1074
 }
set N19c3c400 [stack 0]
 Invert {
  name Invert2
  xpos 180
  ypos 1064
  disable {{!parent.invert_1}}
 }
push $N19c3d400
push $N25628800
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge27
  xpos 290
  ypos 686
 }
 Dot {
  name Dot3
  xpos 324
  ypos 786
 }
set N18fdf400 [stack 0]
 Dot {
  name Dot5
  xpos 104
  ypos 786
 }
set N18fdf000 [stack 0]
push $N18fdf000
 Copy {
  inputs 2
  from0 {{{parent.Copy2.from0}}}
  to0 rgba.red
  name Copy3
  xpos 70
  ypos 848
 }
 Expression {
  expr0 abs(r)
  channel1 {none none none rgba.alpha}
  expr1 "r == 0"
  channel2 none
  channel3 none
  name Expression4
  xpos 70
  ypos 926
 }
set N18fde800 [stack 0]
push $N19c3d800
 Colorspace {
  colorspace_out AlexaV3LogC
  name Colorspace5
  xpos 70
  ypos 518
 }
 Clamp {
  maximum_enable false
  name Clamp2
  xpos -40
  ypos 512
 }
 Dot {
  name Dot1
  xpos -116
  ypos 522
 }
set N18fddc00 [stack 0]
 Dot {
  name Dot48
  xpos -116
  ypos 786
 }
set N18fdd800 [stack 0]
push $N18fdd800
 Copy {
  inputs 2
  from0 rgba.blue
  to0 rgba.red
  name Copy2
  xpos -150
  ypos 848
 }
 Expression {
  temp_name0 min
  temp_expr0 0.5478476602584124
  temp_name1 max
  temp_expr1 0.5718689560890198
  channel0 {none none none rgba.alpha}
  expr0 "r >= min && r <= max"
  channel1 none
  channel2 none
  channel3 none
  name Expression2
  xpos -150
  ypos 926
 }
 Dot {
  name Dot4
  xpos -116
  ypos 1002
 }
 ChannelMerge {
  inputs 2
  operation stencil
  name ChannelMerge2
  xpos -40
  ypos 985
 }
push $N18fde800
 Copy {
  inputs 2
  from0 rgba.alpha
  to0 rgba.alpha
  name Copy1
  xpos 70
  ypos 992
 }
 ChannelMerge {
  inputs 2
  A -rgba.green
  operation multiply
  name ChannelMerge1
  xpos 70
  ypos 1057
  disable {{!A}}
 }
 Copy {
  inputs 2
  from0 {{{parent.Copy2.from0}}}
  to0 rgba.green
  name Copy4
  xpos 70
  ypos 1160
 }
 Premult {
  channels {rgba.red rgba.green -rgba.blue none}
  name Premult1
  xpos 70
  ypos 1238
 }
 TimeWarp {
  lookup 1088
  time ""
  filter nearest
  name TimeWarp1
  xpos 70
  ypos 1286
  disable true
 }
 FrameBlend {
  channels {rgba.red rgba.green -rgba.blue rgba.alpha}
  startframe 0
  endframe 9
  userange true
  name FrameBlend1
  xpos 70
  ypos 1352
  disable true
 }
 CurveTool {
  avgframes 0
  channels {rgba.red rgba.green -rgba.blue rgba.alpha}
  ROI {0 0 {width} {height}}
  intensitydata {3.454302895e-07 7.802840197e-05 0 8.873093412e-05}
  name CurveTool
  xpos 70
  ypos 1424
 }
push $N18fddc00
 Dot {
  name Dot16
  xpos -336
  ypos 522
 }
 CurveTool {
  operation "Max Luma Pixel"
  channels {-rgba.red -rgba.green rgba.blue}
  ROI {0 0 {width} {height}}
  maxlumapixdata {2089 1182}
  maxlumapixvalue {0 0 0.5716627836}
  minlumapixdata {1127 1304}
  minlumapixvalue {0 0 0.09195037186}
  name CurveTool_Range
  xpos -370
  ypos 680
 }
 Sampler {
  inputs 0
  lut {red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}}
  name Sampler1
  onCreate "n = nuke.thisNode()\nn\['sampler'].setEnabled(False)"
  knobChanged "n = nuke.thisNode()\nk = nuke.thisKnob()\np = nuke.thisParent()\n\nif k.name() == 'lut':\n    with p:\n        for c in \['ColorLookup1','ColorLookup2']:\n           nuke.toNode(c)\['lut'].fromScript(k.toScript())"
  xpos 840
  ypos 1502
 }
push $N25629400
 Dot {
  name Dot51
  xpos 115
  ypos -726
 }
 Input {
  inputs 0
  name COMP
  label "\[value number]"
  note_font_size 30
  xpos 950
  ypos -896
 }
 Dot {
  name Dot49
  xpos 984
  ypos -605
 }
set N1904d000 [stack 0]
 Switch {
  inputs 2
  which {{parent.meta}}
  name Switch1
  xpos 81
  ypos -609
 }
 Dot {
  name Dot54
  xpos 115
  ypos -486
 }
 Dot {
  name Dot52
  xpos -685
  ypos -486
 }
 Dot {
  name Dot53
  xpos -685
  ypos 3762
 }
push $N19c3c800
 Dot {
  name Dot40
  xpos 874
  ypos 258
 }
push $N19c3e000
 Dot {
  name Dot34
  xpos 624
  ypos 378
 }
 Multiply {
  inputs 1+1
  channels rgb
  value 0
  maskChannelMask -rgba.red
  name Multiply1
  xpos 840
  ypos 374
 }
push $N1904d000
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.in_colorspace}}
  out_colorspace {{OCIOColorSpace1.out_colorspace}}
  name OCIOColorSpace3
  xpos 950
  ypos -490
 }
 Dot {
  name Dot44
  xpos 984
  ypos -390
 }
set N24fde400 [stack 0]
 Merge2 {
  inputs 2
  operation from
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge8
  xpos 950
  ypos 374
  disable {{"Multiply6.value.r == 0"}}
 }
 Dot {
  name Dot18
  xpos 984
  ypos 2658
 }
set N24fddc00 [stack 0]
 ColorLookup {
  lut {master {}
    red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}
    alpha {}}
  name ColorLookup2
  xpos 730
  ypos 2654
 }
push $N19c3c400
 Dot {
  name Dot38
  xpos 1204
  ypos 1842
 }
 Dot {
  name Dot37
  xpos 544
  ypos 1842
 }
 Dot {
  name Dot22
  xpos 544
  ypos 2271
 }
set N24fdcc00 [stack 0]
 Dot {
  name Dot20
  xpos 544
  ypos 2391
 }
push $N24fdcc00
 Dot {
  name Dot17
  xpos 434
  ypos 2271
 }
set N24fdc400 [stack 0]
 Dot {
  name Dot13
  xpos 214
  ypos 2271
 }
 Input {
  inputs 0
  name external_grain
  label "\[value number]"
  note_font_size 30
  xpos -150
  ypos 1716
  number 4
 }
 Dot {
  name Dot21
  xpos -116
  ypos 1938
 }
push $N19c3d000
 ColorLookup {
  channels rgb
  lut {master {}
    red {curve x0.001361052622 0.0002230034669 x0.006430411246 0.0005951428306 x0.01105421502 0.0008994589171 x0.01628139243 0.001146730585 x0.02274016291 0.001403132278 x0.03080444597 0.00163270372 x0.04087587446 0.001859763983 x0.053586483 0.002055999969 x0.06944747269 0.002264037068 x0.08884223551 0.00243882259 x0.1131541282 0.002653067767 x0.1442549825 0.002822130602 x0.1814405024 0.003096113604 x0.2293340415 0.003327953235 x0.2919330597 0.003522355048 x0.3690226078 0.003658043992 x0.4605507255 0.003893747258 x0.5786529779 0.004396948744 x0.7082110643 0.004913048611 x0.9231189489 0.003771882119}
    green {curve x0.002225074451 0.0002086138276 x0.006718123332 0.0005114786423 x0.01113783848 0.0006940161211 x0.01635070331 0.0008797971662 x0.02278374881 0.001082866921 x0.03096000105 0.001309081842 x0.04078568891 0.001475058675 x0.05342977494 0.001624661542 x0.06917729229 0.001829751531 x0.08840883523 0.002091810703 x0.112894237 0.002562833656 x0.1437279135 0.003031363544 x0.1823064238 0.003319310345 x0.2292642146 0.003328314016 x0.2914674878 0.003209824447 x0.3613436222 0.003148078654 x0.4587926567 0.003378985091 x0.5774047375 0.003261888327 x0.7117970586 0.003246239536 x0.8756207824 0.003280454043}
    blue {curve x0.002008558717 0.0002586295553 x0.006193103269 0.0005384354734 x0.0109573612 0.0007919714349 x0.0161083322 0.0009666481834 x0.02255447395 0.001173497481 x0.03028626554 0.001366394549 x0.040295057 0.001563044506 x0.05251040682 0.001699286347 x0.06809803098 0.001865792829 x0.08791080117 0.002071608131 x0.1125093549 0.002332924835 x0.1419141144 0.002502789157 x0.1810215116 0.002746756186 x0.228581965 0.003003136212 x0.2877129018 0.003395638099 x0.3611852825 0.003619306395 x0.4465007782 0.003888860687 x0.5631905198 0.003624253537 x0.7170214057 0.003685941852 x0.8793821931 0.003893008599}
    alpha {}}
  name ColorLookup1
  xpos 730
  ypos 1502
 }
 Dot {
  name Dot24
  xpos 764
  ypos 1746
 }
push $N18fdf400
 Dot {
  name Dot33
  xpos 324
  ypos 1386
 }
 MergeExpression {
  inputs 2
  temp_name0 target
  temp_expr0 .01
  expr0 "Br * (target / Ar)"
  expr1 "Bg * (target / Ag)"
  expr2 "Bb * (target / Ab)"
  channel3 none
  name MergeExpression1
  xpos 290
  ypos 1742
 }
 Dot {
  name Dot15
  xpos 324
  ypos 1842
 }
set N24ae6000 [stack 0]
 Dot {
  name Dot25
  xpos 104
  ypos 1842
 }
 Switch {
  inputs 2
  which {{parent.external_grain}}
  name Switch2
  xpos 70
  ypos 1934
 }
 Group {
  name VoronoiScatter
  xpos 70
  ypos 2102
  disable {{!parent.scatter}}
  addUserKnob {20 User}
  addUserKnob {41 useGPUIfAvailable l "Use GPU if available" T VoroNoise.useGPUIfAvailable}
  addUserKnob {41 vectorize l "Vectorize on CPU" -STARTLINE T VoroNoise.vectorize}
  addUserKnob {15 box}
  box {{parent.box x1004 0 x1036 -75} {parent.box x1004 100 x1036 120} {parent.box x1004 496 x1036 325} {parent.box x1004 916 x1036 320}}
  addUserKnob {3 sample_frame l "sample frame"}
  sample_frame {{parent.sample_frame}}
  addUserKnob {7 cell_size l "cell size" R 0 100}
  cell_size {{parent.cell_size}}
  addUserKnob {6 overlay_pattern l "overlay pattern" -STARTLINE}
  overlay_pattern {{parent.overlay}}
  addUserKnob {3 edge_blend_size l "edge blend size"}
  edge_blend_size {{parent.edge_blend_size}}
  addUserKnob {7 amplitude R 0 100}
  amplitude {{parent.amplitude}}
  addUserKnob {7 frequency R 0 100}
  frequency {{parent.frequency}}
  addUserKnob {41 VoroNoise_Seed l Seed T VoroNoise.VoroNoise_Seed}
 }
  Input {
   inputs 0
   name Input1
   xpos 180
   ypos -879
  }
  Dot {
   name Dot14
   xpos 214
   ypos -750
  }
set N24ae5000 [stack 0]
  Dot {
   name Dot16
   xpos 434
   ypos -750
  }
  Remove {
   name Remove1
   xpos 400
   ypos -687
  }
  Dot {
   name Dot6
   xpos 434
   ypos -606
  }
set N24ae4400 [stack 0]
  Dot {
   name Dot15
   xpos 654
   ypos -606
  }
set N18dfbc00 [stack 0]
  Dot {
   name Dot7
   xpos 874
   ypos -606
  }
  Noise {
   output {rgba.red -rgba.green -rgba.blue none}
   replace true
   size {{parent.frequency} {"parent.frequency * pixel_aspect"}}
   zoffset {{"x + 1000"}}
   gamma 1
   name Noise1
   xpos 840
   ypos -514
  }
  Noise {
   output {-rgba.red rgba.green -rgba.blue none}
   replace true
   size {{parent.Noise1.size} {parent.Noise1.size}}
   zoffset {{x}}
   gamma 1
   name Noise2
   xpos 840
   ypos -466
  }
  Clamp {
   name Clamp1
   xpos 840
   ypos -424
  }
  Dot {
   name Dot11
   xpos 874
   ypos -366
  }
push $N18dfbc00
  BlinkScript {
   ProgramGroup 1
   KernelDescription "2 \"VoroNoise\" iterate pixelWise c117be128a07c11b6d82fd34148d66b3bcac41976ec9c2082affe38e890c2c0f 2 \"src\" Read Point \"dst\" Write Point 6 \"Frequency\" Float 1 AABIQg== \"Seed\" Int 1 AAAAAA== \"aspect ratio\" Float 1 AACAPw== \"width\" Int 1 AAAAAA== \"height\" Int 1 AAAAAA== \"Randomness\" Float 1 AAAAPw== 6 \"frequency\" 1 1 \"seed\" 1 1 \"aspect_ratio\" 1 1 \"width\" 1 1 \"height\" 1 1 \"randomness\" 1 1 0"
   kernelSource "// Voronoi.blink\n// A test implementation of libNoise's Voronoi generator using Blink\n// Ivan Busquets - August 2013\n// Modified for DasGrain by Fabian Holtz - April 2019\n\n#define X_NOISE_GEN  1619\n#define Y_NOISE_GEN  31337\n#define Z_NOISE_GEN  6971\n#define SEED_NOISE_GEN  1013\n#define SQRT_3 1.73205081\n\ninline int IntValueNoise3D (int x, int y, int z, int seed)\n\{\n  // All constants are primes and must remain prime in order for this noise\n  // function to work correctly.\n  int n = (\n      X_NOISE_GEN    * x\n    + Y_NOISE_GEN    * y\n    + Z_NOISE_GEN    * z\n    + SEED_NOISE_GEN * seed)\n    & 0x7fffffff;\n  n = (n >> 13) ^ n;\n  return (n * (n * n * 60493 + 19990303) + 1376312589) & 0x7fffffff;\n\}\n\ninline float ValueNoise3D (int x, int y, int z, int seed)\n\{\n  return 1.0 - ((float)IntValueNoise3D (x, y, z, seed) / 1073741824.0);\n\}\n\nkernel VoroNoise : ImageComputationKernel<ePixelWise>\n\{\n  Image<eRead> src;\n  Image<eWrite, eAccessPoint> dst;\n\nparam:\n  float frequency;\n  int seed;\n  float aspect_ratio;\n  int width;\n  int height;\n  float randomness;\n\n\n  void define() \{\n    defineParam(frequency, \"Frequency\", 50.0f);\n    defineParam(aspect_ratio, \"aspect ratio\", 1.0f);\n    defineParam(seed, \"Seed\", 0);\n    defineParam(randomness, \"Randomness\", 0.5f);\n  \}\n\n\n\n\n  void process(int2 pos) \{\n   float x = pos.x * aspect_ratio * frequency / width;\n   float y = pos.y * frequency / width;\n   int xInt = (x > 0.0) ? x : x - 1;\n   int yInt = (y > 0.0) ? y : y - 1;\n\n\n    float minDist = 2147483647.0;\n    float xCandidate = 0;\n    float yCandidate = 0;\n\n    float dist;\n\nfor (int yCur = yInt - 2; yCur <= yInt + 2; yCur++) \{\n      for (int xCur = xInt - 2; xCur <= xInt + 2; xCur++) \{\n\n        // Calculate the position and distance to the seed point inside of\n        // this unit cube. Limited by the randomness value\n        float xPos = xCur + (ValueNoise3D (xCur, yCur, 0, seed    ) + 1 ) * randomness + (1-randomness) - 1;\n        float yPos = yCur + (ValueNoise3D (xCur, yCur, 0, seed + 1) + 1 ) * randomness + (1-randomness) - 1;\n\n        float xDist = xPos - x;\n        float yDist = yPos - y;\n\n        dist = pow(xDist, 2) + pow(yDist, 2);\n        if (dist < minDist) \{\n          // This seed point is closer to any others found so far, so record\n          // this seed point.\n          minDist = dist;\n          xCandidate = xPos;\n          yCandidate = yPos;\n\t\}\n    \}\n\}\n\n    SampleType(dst) sample(0.0f);\n\n    sample.x = xCandidate / aspect_ratio / frequency;\n    sample.y = yCandidate / height * width / frequency;\n    sample.z = 0;\n\n    dst() = sample;\n\}\n\};"
   rebuild ""
   VoroNoise_Frequency {{"width / parent.cell_size"}}
   VoroNoise_Seed {{"(x + (parent.parent.stereo == 2 ? \[lsearch \[value root.views] \[view]] / 2 : 0)) * 5"}}
   "VoroNoise_aspect ratio" {{pixel_aspect}}
   VoroNoise_width {{width}}
   VoroNoise_height {{height}}
   rebuild_finalise ""
   name VoroNoise
   xpos 620
   ypos -520
  }
  Copy {
   inputs 2
   from0 rgba.red
   to0 forward.u
   from1 rgba.green
   to1 forward.v
   name Copy1
   xpos 620
   ypos -382
   disable {{"parent.amplitude == 0"}}
  }
  IDistort {
   uv forward
   uv_offset 0.5
   uv_scale {{parent.amplitude} {"uv_scale.w * pixel_aspect"}}
   filter impulse
   name IDistort1
   xpos 620
   ypos -280
   disable {{"parent.amplitude == 0"}}
  }
  Dot {
   name Dot5
   xpos 654
   ypos -246
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur3
   xpos 620
   ypos -154
  }
  Transform {
   translate {{"floor((x * size) % 1 * (size)) - int(size / 2)"} {"floor(x % 1 * (size)) - int(size/2)"}}
   filter impulse
   black_outside false
   name Transform1
   xpos 620
   ypos -58
   disable {{"parent.edge_blend_size < 1"}}
   addUserKnob {20 User}
   addUserKnob {3 size}
   size {{"parent.edge_blend_size + 1"}}
  }
  Dot {
   name Dot9
   xpos 654
   ypos 42
  }
set N18df8c00 [stack 0]
push $N24ae4400
  Expression {
   expr0 "(x + .5) / width"
   expr1 "(y + .5) / height"
   expr2 0
   name STMapGenerator
   xpos 400
   ypos -514
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur2
   xpos 400
   ypos -154
  }
  Merge2 {
   inputs 2
   operation from
   Achannels {rgba.red rgba.green -rgba.blue none}
   Bchannels {rgba.red rgba.green -rgba.blue none}
   output {rgba.red rgba.green -rgba.blue none}
   name Merge2
   xpos 400
   ypos 38
  }
  Dot {
   name Dot10
   xpos 434
   ypos 210
  }
push $N18df8c00
  Expression {
   temp_name0 view_index
   temp_expr0 "parent.parent.stereo == 1 ? \[lsearch \[value root.views] \[view]] / 2 : 0"
   expr0 "random((r + view_index) * 1000000, 0) * (maxx - minx) + minx"
   expr1 "random((g + view_index) * 1000000, 0) * (maxy - miny) + miny"
   channel2 none
   channel3 none
   name Expression3
   xpos 620
   ypos 110
   addUserKnob {20 User}
   addUserKnob {7 frequency R 0 100}
   frequency {{parent.parent.cell_size}}
   addUserKnob {7 multiplier R 0 3}
   multiplier 0.5
   addUserKnob {15 shrink}
   shrink {{"frequency * multiplier + ceil(parent.edge_blend_size / 2) + IDistort1.uv_scale.w / 2"} {"frequency * multiplier + ceil(parent.edge_blend_size / 2) + IDistort1.uv_scale.h / 2"} {"frequency * multiplier + floor(parent.edge_blend_size / 2) + IDistort1.uv_scale.w / 2"} {"frequency * multiplier + floor(parent.edge_blend_size / 2) + IDistort1.uv_scale.h / 2"}}
   addUserKnob {26 ""}
   addUserKnob {7 minx}
   minx {{"(parent.box.x + shrink.x + .5) / width"}}
   addUserKnob {7 maxx}
   maxx {{"(parent.box.r - shrink.r - .5) / width"}}
   addUserKnob {7 miny}
   miny {{"(parent.box.y + shrink.y + .5) / height"}}
   addUserKnob {7 maxy}
   maxy {{"(parent.box.t - shrink.t - .5) / height"}}
  }
  Merge2 {
   inputs 2
   operation plus
   Achannels {rgba.red rgba.green -rgba.blue none}
   Bchannels {rgba.red rgba.green -rgba.blue none}
   output {rgba.red rgba.green -rgba.blue none}
   name Merge3
   xpos 620
   ypos 206
  }
  Expression {
   expr0 "(r + (maxx - minx) - minx) % (maxx - minx) + minx"
   expr1 "(g + (maxy - miny) - miny) % (maxy - miny) + miny"
   channel2 none
   channel3 none
   name Expression7
   xpos 620
   ypos 278
   addUserKnob {20 User}
   addUserKnob {7 minx}
   minx {{"(parent.box.x + rint(x % 1 * parent.edge_blend_size) + .5) / width"}}
   addUserKnob {7 maxx}
   maxx {{"(parent.box.r + rint(x % 1 * parent.edge_blend_size) - .5) / width"}}
   addUserKnob {7 miny}
   miny {{"(parent.box.y + rint(x % 1 * parent.edge_blend_size) + .5) / height"}}
   addUserKnob {7 maxy}
   maxy {{"(parent.box.t + rint(x % 1 * parent.edge_blend_size) - .5) / height"}}
  }
  Dot {
   name Dot3
   xpos 654
   ypos 354
  }
set Nda36800 [stack 0]
  Dot {
   name Dot13
   xpos 654
   ypos 546
  }
push $Nda36800
  Dot {
   name Dot8
   xpos 874
   ypos 354
  }
  Blur {
   channels rgb
   size {{pixel_aspect} 1}
   name Blur1
   label "\[value size]"
   xpos 840
   ypos 440
  }
  Difference {
   inputs 2
   name Difference2
   xpos 840
   ypos 536
  }
  Expression {
   channel0 {none none none rgba.alpha}
   expr0 "a > 1e-9"
   channel1 none
   channel2 none
   channel3 none
   name Expression2
   xpos 840
   ypos 614
  }
  Shuffle {
   red alpha
   green alpha
   blue alpha
   name Shuffle1
   label "\[value in]:\[value out]"
   xpos 840
   ypos 680
  }
  Dot {
   name Dot4
   xpos 874
   ypos 762
  }
push $Nda36800
push $N24ae5000
  FrameHold {
   firstFrame {{parent.sample_frame}}
   name FrameHold1
   xpos 180
   ypos -256
  }
  NoTimeBlur {
   rounding floor
   name NoTimeBlur1
   xpos 180
   ypos -154
  }
  STMap {
   inputs 2
   channels rgb
   uv rgb
   filter impulse
   name STMap1
   xpos 180
   ypos 350
  }
set Nda34400 [stack 0]
  TimeBlur {
   divisions {{"max(Transform1.size == 1 ? 2 : pow2(Transform1.size), 1)"}}
   shutter 1
   shuttercustomoffset {{"1 / divisions / 2"}}
   name TimeBlur1
   xpos 180
   ypos 446
   disable {{"parent.edge_blend_size < 1"}}
  }
set Nd65fc00 [stack 0]
push $Nda34400
  Dot {
   name Dot1
   xpos -6
   ypos 354
  }
  Difference {
   inputs 2
   name Difference1
   xpos -40
   ypos 440
  }
  Expression {
   channel0 {none none none rgba.alpha}
   expr0 "a > 1e-10"
   channel1 none
   channel2 none
   channel3 none
   name Expression1
   xpos -40
   ypos 494
  }
  Blur {
   channels alpha
   size {{parent.parent.edge_blend_size}}
   name Blur2
   xpos -40
   ypos 536
  }
  Grade {
   channels alpha
   blackpoint 0.5
   white_clamp true
   name Grade2
   xpos -40
   ypos 584
  }
  Dot {
   name Dot2
   xpos -6
   ypos 666
  }
push $Nd65fc00
  Grade {
   inputs 1+1
   white 1.4
   black_clamp false
   name Grade1
   xpos 180
   ypos 662
   disable {{"parent.edge_blend_size < 1"}}
  }
  Merge2 {
   inputs 2
   Achannels rgb
   Bchannels rgb
   output rgb
   name Merge1
   xpos 180
   ypos 758
   disable {{!parent.overlay_pattern}}
  }
  Assert {
   expression {{"Expression3.maxx > Expression3.minx && Expression3.maxy > Expression3.miny"}}
   message "increase sample box size or decrease cell size"
   name error
   xpos 180
   ypos 854
  }
  Output {
   name Output1
   xpos 180
   ypos 950
  }
 end_group
 Multiply {
  inputs 1+1
  channels rgb
  value 1.8
  maskChannelMask {{{parent.Merge9.maskChannelMask}}}
  invert_mask {{!Merge9.invert_mask}}
  name Multiply7
  xpos 70
  ypos 2315
  disable {{"!maskChannelMask || !\[exists parent.input3.name]"}}
 }
 Dot {
  name Dot23
  xpos 104
  ypos 2391
 }
push $N24fdc400
push $N24ae6000
 Multiply {
  inputs 1+1
  channels rgb
  value 1.8
  maskChannelMask {{{parent.Merge9.maskChannelMask}}}
  invert_mask {{parent.Merge9.invert_mask}}
  name Multiply2
  xpos 290
  ypos 2315
  disable {{"!maskChannelMask || (!parent.scatter && !parent.external_grain)"}}
 }
 Merge2 {
  inputs 2+1
  operation copy
  Achannels rgb
  Bchannels rgb
  output rgb
  maskChannelMask -rgba.alpha
  name Merge9
  xpos 290
  ypos 2387
  disable {{"!(parent.scatter || parent.external_grain)"}}
 }
 Dot {
  name Dot11
  xpos 324
  ypos 2490
 }
set N9a13c00 [stack 0]
 MergeExpression {
  inputs 2
  temp_name0 reverse
  temp_expr0 "1 / MergeExpression1.temp_expr0"
  expr0 "Br * Ar * reverse"
  expr1 "Bg * Ag * reverse"
  expr2 "Bb * Ab * reverse"
  name MergeExpression2
  xpos 290
  ypos 2654
 }
 Dot {
  name Dot8
  xpos 324
  ypos 2850
 }
push $N24fddc00
 Merge2 {
  inputs 2
  operation plus
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge3
  xpos 950
  ypos 2846
 }
 Dot {
  name Dot42
  xpos 984
  ypos 3018
 }
set N9a12c00 [stack 0]
 OCIOColorSpace {
  in_colorspace {{OCIOColorSpace1.out_colorspace}}
  out_colorspace {{OCIOColorSpace1.in_colorspace}}
  name OCIOColorSpace4
  xpos 950
  ypos 3086
 }
 Dot {
  name Dot19
  xpos 984
  ypos 3162
 }
set N9a12000 [stack 0]
 Dot {
  name Dot41
  xpos 1204
  ypos 3162
 }
set N9a11c00 [stack 0]
 Dot {
  name Dot36
  xpos 1314
  ypos 3162
 }
 Blur {
  channels rgb
  size 1
  name Blur1
  xpos 1280
  ypos 3254
 }
push $N9a11c00
 Merge2 {
  inputs 2
  operation difference
  bbox B
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge10
  xpos 1170
  ypos 3254
 }
 Multiply {
  channels rgb
  value 50
  name Multiply3
  xpos 1170
  ypos 3302
 }
 Dot {
  name Dot43
  xpos 1204
  ypos 3402
 }
push $N24fde400
 Dot {
  name Dot45
  xpos 1424
  ypos -390
 }
push $N9a12c00
 Merge2 {
  inputs 2
  operation from
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge11
  xpos 1390
  ypos 3014
 }
 Dot {
  name Dot46
  xpos 1424
  ypos 3522
 }
push $N9a13c00
 Dot {
  name Dot14
  xpos 104
  ypos 2490
 }
 Dot {
  name Dot12
  xpos 104
  ypos 3402
 }
push $N2562b000
 Dot {
  name Dot47
  xpos -556
  ypos -390
 }
push $N25628800
 Merge2 {
  inputs 2
  operation from
  Achannels rgb
  Bchannels rgb
  output rgb
  name Merge12
  xpos -590
  ypos -202
 }
 Dot {
  name Dot10
  xpos -556
  ypos 3522
 }
push $N9a12000
 Switch {
  inputs 5
  which {{output}}
  name Output
  xpos 950
  ypos 3656
  addUserKnob {20 User}
  addUserKnob {4 output M {"regrained comp" "plate grain" "normalised grain" "adapted grain" "grain QC"}}
 }
 CopyMetaData {
  inputs 2
  mergeMode "Meta only"
  name CopyMetaData1
  xpos 950
  ypos 3758
 }
 Output {
  name Output1
  xpos 950
  ypos 3854
 }
end_group
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