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      "body": "Congratulations @alexandercore! You received a personal award!\n\n<table><tr><td>https://steemitimages.com/70x70/http://steemitboard.com/@alexandercore/birthday2.png</td><td>Happy Birthday! - You are on the Steem blockchain for 2 years!</td></tr></table>\n\n<sub>_You can view [your badges on your Steem Board](https://steemitboard.com/@alexandercore) and compare to others on the [Steem Ranking](https://steemitboard.com/ranking/index.php?name=alexandercore)_</sub>\n\n\n###### [Vote for @Steemitboard as a witness](https://v2.steemconnect.com/sign/account-witness-vote?witness=steemitboard&approve=1) to get one more award and increased upvotes!",
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      "parent_permlink": "python",
      "author": "alexandercore",
      "permlink": "python-blender-script-with-example-picture-of-its-result-and-textures-a-desktop-missing-extra-blender-files-of-the-mac-and-the",
      "title": "Python Blender Script with Example Picture of its result and textures, A Desktop, missing extra blender files of the Mac and the PC Towers! in this Script!!!",
      "body": "![schreibtisch7.jpg](https://cdn.steemitimages.com/DQmczWy6kW2jFKH3mk21GS7N3GMU7zFY9nDdrhHauLRUNxR/schreibtisch7.jpg)\nimport bpy\nimport math\nimport bmesh\ndef reset_blend():\n    #bpy.ops.wm.read_factory_settings()\n\n    for scene in bpy.data.scenes:\n        for obj in scene.objects:\n            scene.objects.unlink(obj)\n\n    # only worry about data in the startup scene\n    for bpy_data_iter in (\n            bpy.data.objects,\n            bpy.data.meshes,\n            bpy.data.lamps,\n            bpy.data.materials,\n            bpy.data.cameras,\n    ):\n        for id_data in bpy_data_iter:\n            bpy_data_iter.remove(id_data)\n\ndef joinmerge(obs):\n    ctx = bpy.context.copy()\n    # one of the objects to join\n    ctx['active_object'] = obs[0]\n    ctx['selected_objects'] = obs\n    ctx['selected_editable_bases'] = [scn.object_bases[ob.name] for ob in obs]\n    bpy.ops.object.join(ctx)\n\ndef createCube(x,y,z,xx,yy,zz):\n    bpy.ops.mesh.primitive_cube_add(location = (x, y, z))\n    #cube = bpy.context.scene.objects[-1]\n    bpy.ops.transform.resize(value=(xx, yy, zz))\n\ndef differ(from_,to):\n        mod_bool = from_.modifiers.new('my_bool_mod', 'BOOLEAN')\n        mod_bool.operation = 'DIFFERENCE'\n        mod_bool.object = to\n        bpy.context.scene.objects.active = from_\n        res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n\n\ndef curvyobj(coords,coords2):\n    curveData = bpy.data.curves.new('myCurve', type='SURFACE')\n    curveData2 = bpy.data.curves.new('myCurve', type='SURFACE')\n    curveData.dimensions = '3D'\n    curveData.resolution_u = len(coords)\n    curveData.resolution_v = len(coords)\n    curveData2.dimensions = '3D'\n    curveData2.resolution_u = len(coords2)\n    curveData2.resolution_v = len(coords2)\n    polyline = curveData.splines.new('POLY')\n    polyline.points.add(len(coords)-1)\n    polyline2 = curveData.splines.new('POLY')\n    polyline2.points.add(len(coords)-1)\n    for i, coord in enumerate(coords):\n        x,y,z = coord\n        polyline.points[i].co = (x, y, z, 1)\n    for i, coord in enumerate(coords2):\n        x,y,z = coord\n        polyline2.points[i].co = (x, y, z, 1)\n\n    # create Object\n    surface_object = bpy.data.objects.new('myCurve', curveData)\n    surface_object2 = bpy.data.objects.new('myCurve', curveData2)\n    #curveData.bevel_depth = 100\n\n    # attach to scene and validate context\n    scn = bpy.context.scene\n    scn.objects.link(surface_object)\n    scn.objects.link(surface_object2)\n    #scn.objects.active = curveOB\n    splines = surface_object.data.splines\n    for s in splines:\n        for p in s.points:\n            p.select = True\n    splines = surface_object2.data.splines\n    for s in splines:\n        for p in s.points:\n            p.select = True\n\n    bpy.context.scene.objects.active = surface_object\n    #bpy.ops.object.mode_set(mode = 'EDIT')\n    #bpy.ops.curve.make_segment()\n    #bpy.ops.object.mode_set(mode = 'OBJECT')\n\n\n    # mesh arrays\n    verts = []  # the vertex array\n    faces = []  # the face array\n\n    # mesh variables\n    numX = len(coords)  # number of quadrants in the x direction\n    numY = 2  # number of quadrants in the y direction\n\n    # wave variables\n    freq = 1  # the wave frequency\n    amp = 1  # the wave amplitude\n    scale = 1  #the scale of the mesh\n\n\n    #fill verts array\n    #coords3=coords[0],coords2[0]\n    #coords4=coords[1],coords2[1]\n    #coords5=coords[2],coords2[2]\n\n    matrix = []\n    for m,n in zip(coords,coords2):\n      matrix.append([m,n])\n\n    #for i in [coords3,coords4,coords5]:\n    for i in matrix:\n        for x,y,z in i:\n            vert = (x,y,z)\n            verts.append(vert)\n\n    #create mesh and object\n    mesh = bpy.data.meshes.new(\"wave\")\n    object = bpy.data.objects.new(\"wave\",mesh)\n\n    #set mesh location\n    object.location = bpy.context.scene.cursor_location\n    bpy.context.scene.objects.link(object)\n\n    #create mesh from python data\n    mesh.from_pydata(verts,[],faces)\n    mesh.update(calc_edges=True)\n\n    #fill faces array\n    count = 0\n    for i in range (0, numY *(numX-1)):\n        if count < numY-1:\n            A = i  # the first vertex\n            B = i+1  # the second vertex\n            C = (i+numY)+1 # the third vertex\n            D = (i+numY) # the fourth vertex\n\n            face = (A,B,C,D)\n            faces.append(face)\n            count = count + 1\n        else:\n            count = 0\n\n    #create mesh and object\n    mesh = bpy.data.meshes.new(\"wave\")\n    object = bpy.data.objects.new(\"wave\",mesh)\n\n    #set mesh location\n    object.location = bpy.context.scene.cursor_location\n    bpy.context.scene.objects.link(object)\n\n    #create mesh from python data\n    mesh.from_pydata(verts,[],faces)\n    mesh.update(calc_edges=True)\n\n    object.select = False\n#bpy.ops.wm.\n#bpy.ops.wm.open_mainfile(filepath=\"/home/alex/Downloads/Palm_Tree.blend\")\nreset_blend()\n#import bpy\n#def setState0():\n#    for ob in bpy.data.objects.values():ob.selected=False\n#    bpy.context.scene.objects.active = None\n#setState0()\n#original_type = bpy.context.area.type\n#bpy.context.area.type = \"VIEW_3D\"\n#bpy.context.mode='OBJECT'\n#bpy.ops.object.origin_set(type='GEOMETRY_ORIGIN')\n#bpy.context.area.type = original_type\n#bpy.ops.wm.read_homefile(use_empty=True)\n#bpy.ops.wm.read_factory_settings(use_empty=True)\n#original_type = bpy.context.area.type\n#bpy.context.\n#bpy.context.area.type = \"VIEW_3D\"\n#bpy.ops.object.origin_set(type='GEOMETRY_ORIGIN')\n#bpy.context.area.type = original_type\nbreite = 1.5\ntiefe = 1.5\n\nbpy.ops.mesh.primitive_cube_add(location = (0, 0, 0))\n#bpy.ops.mesh.primitive_cube_add()\n#cont = bpy.context.area.type\n#print(str(cont))\n#myobject = bpy.context.active_object\n#bpy.context.scene.objects.active = myobject\n#myobject.select = True\nscn = bpy.context.scene\ncube = scn.objects['Cube']\nbpy.context.scene.objects[0].select = True\nbpy.ops.transform.resize(value=(9/2*tiefe, 16/2*breite, 0.2))\nbpy.ops.mesh.primitive_cylinder_add(radius=3*(breite+tiefe)/math.pi,location=(5.5*tiefe,0,0))\nbpy.ops.transform.resize(value=(1*tiefe, 2*breite, 1))\ncylinder = scn.objects['Cylinder']\ncube.name = 'Tischplatte'\nbpy.context.scene.objects[0].select = False\n\nmod_bool = cube.modifiers.new('my_bool_mod', 'BOOLEAN')\nmod_bool.operation = 'DIFFERENCE'\nmod_bool.object = cylinder\nbpy.context.scene.objects.active = cube\nres = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\ncube.select = False\ncylinder.select = True\nbpy.ops.object.delete()\n\nbpy.ops.mesh.primitive_cube_add(location = (-(9/2-7/2)*tiefe, 0, -2))\ncube2 = scn.objects['Cube']\ncube2.name = 'Platte_2'\nbpy.ops.transform.resize(value=(7/2*tiefe, 16/2*breite, 0.2))\nbpy.ops.mesh.primitive_cylinder_add(radius=3*(breite+tiefe)/math.pi,location=(2.5*tiefe,0,-2))\nbpy.ops.transform.resize(value=(1*tiefe, 2*breite, 1))\ncylinder = scn.objects['Cylinder']\n\nmod_bool = cube2.modifiers.new('my_bool_mod', 'BOOLEAN')\nmod_bool.operation = 'DIFFERENCE'\nmod_bool.object = cylinder\nbpy.context.scene.objects.active = cube2\nres = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\ncube.select = False\ncube2.select = False\ncylinder.select = True\nbpy.ops.object.delete()\ncylinder.select = False\n\nbpy.ops.mesh.primitive_cube_add(location = (9/2*tiefe-0.5-tiefe*2, (16/2-0.5)*breite, -4.5+0.1))\nleg = scn.objects['Cube']\nleg.name = 'Tischbein1'\nleg.select = True\nbpy.ops.transform.resize(value=(0.5, 0.5, 4.5))\nleg.select = False\nbpy.ops.mesh.primitive_cube_add(location = (-9/2*tiefe+0.5, (-16/2+0.5)*breite,-4.5+0.1))\nleg = scn.objects['Cube']\nleg.name = 'Tischbein2'\nleg.select = True\nbpy.ops.transform.resize(value=(0.5, 0.5, 4.5))\nleg.select = False\nbpy.ops.mesh.primitive_cube_add(location = (9/2*tiefe-0.5-tiefe*2, (-16/2+0.5)*breite, -4.5+0.1))\nleg = scn.objects['Cube']\nleg.name = 'Tischbein3'\nleg.select = True\nbpy.ops.transform.resize(value=(0.5, 0.5, 4.5))\nleg.select = False\nbpy.ops.mesh.primitive_cube_add(location = (-9/2*tiefe+0.5, (16/2-0.5)*breite, -4.5+0.1))\nleg = scn.objects['Cube']\n\nleg.name = 'Tischbein4'\nleg.select = True\nbpy.ops.transform.resize(value=(0.5, 0.5, 4.5))\nleg.select = False\n\ncupboards = []\nfor a in [-1,1]:\n    coords=((-7/4+9/4)*tiefe,a*(16/2*breite-(1.8*breite)),-3.5)\n    bpy.ops.mesh.primitive_cube_add(location = coords)\n    cupboard = scn.objects['Cube']\n    cupboard.name = 'cupboard_'+str(int((a+1)/2+1))\n    cupboard.select = True\n    bpy.ops.transform.resize(value=(7/2*tiefe, 1.8*breite, 3.5))\n    cupboards.append(cupboard)\n    cupboard.select = False\n    mod_bool = scn.objects['Platte_2'].modifiers.new('my_bool_mod', 'BOOLEAN')\n    mod_bool.operation = 'DIFFERENCE'\n    mod_bool.object = cupboard\n    bpy.context.scene.objects.active = scn.objects['Platte_2']\n    res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n\nfor a in [-1,1]:\n    coords=((-7/4+9/4)*tiefe,a*(16/2*breite-(1.8*breite)),-3.5)\n    bpy.ops.mesh.primitive_cube_add(location = coords)\n    cupboardb = scn.objects['Cube']\n    cupboardb.name = 'cupboard_'+str(int((a+1)/2+1))+'_in'\n    cupboardb.select = True\n    bpy.ops.transform.resize(value=(7/2*1.4*tiefe, 1.8*breite-0.4, 3.5-0.4))\n    cupboardb.select = False\n    mod_bool = cupboards[int((a+1)/2)].modifiers.new('my_bool_mod', 'BOOLEAN')\n    mod_bool.operation = 'DIFFERENCE'\n    mod_bool.object = cupboardb\n    bpy.context.scene.objects.active = cupboards[int((a+1)/2)]\n    res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n    cupboard.select = False\n    cupboardb.select = True\n    bpy.ops.object.delete()\n\nfor a in [-1,1]:\n    for b in [[-4.5,1],[-1,0.4]]:\n        coords=((-7/4+9/4-2.5+b[1])*tiefe,a*(16/2-2-1.8)*breite,b[0])\n        bpy.ops.mesh.primitive_cube_add(location = coords)\n        cupboardb = scn.objects['Cube']\n        cupboardb.name = 'cupboardi_'+str(int((a+1)/2+1))+'_behind'\n        cupboardb.select = True\n        bpy.ops.transform.resize(value=(1.5*b[1]*tiefe, 1*breite, 1.5*b[1]))\n        cupboardb.select = False\n        mod_bool = cupboards[int((a+1)/2)].modifiers.new('my_bool_mod', 'BOOLEAN')\n        mod_bool.operation = 'DIFFERENCE'\n        mod_bool.object = cupboardb\n        bpy.context.scene.objects.active = cupboards[int((a+1)/2)]\n        res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n        cupboard.select = False\n        cupboardb.select = True\n        bpy.ops.object.delete()\n\n#bpy.ops.mesh.primitive_cube_add(location = (-7/4+9/4,-16/2+2,-3.5))\n#cupboard = scn.objects['Cube']\n#cupboard.name = 'cupboard_2'\n#cupboard.select = True\n#bpy.ops.transform.resize(value=(7/2, 1.8, 3.5))\n#cupboard.select = False\n#s/(breite+tiefe)/2/math.sqrt(math.pow(breite,2)+math.pow(tiefe,2))/g\n\ncolor = '663300'\n\nr = float.fromhex(color[0:2])/255.0\ng = float.fromhex(color[2:4])/255.0\nb = float.fromhex(color[4:6])/255.0\n\n\nfor l in bpy.data.lamps:\n    l.color = [r, g, b]\n    l.energy = 6\n\n\nbpy.ops.object.camera_add(view_align=True,\n                          enter_editmode=False,\n                          location=(30, 10, 7),\n                          rotation=(math.pi*1.4, -math.pi, math.pi*1.6))\ncam = scn.objects['Camera']\ncam.name = 'cam_1'\nbpy.data.cameras['Camera'].lens = 25\n\n#bpy.ops.object.lamp_add(location=(0,0,8))\n#for a in [10,-10]:\n#    for b in [8,-8]:\n#        bpy.ops.object.lamp_add(type='AREA',location=(a,b,8))\n#        lamp = scn.objects['Area']\n#        lamp.name = 'AREA-'+str(a)+'-'+str(b)\n#        lamp = bpy.data.lamps['Area']\n#        lamp.name = 'AREA-'+str(a)+'-'+str(b)\n#        lamp.energy = 1\n#        lamp.size = 10\n#        lamp.shadow_ray_samples_x = 5\n#        lamp.color=(lamp.color[0]-(a+10)/20/4,lamp.color[1]-(b+8)/16/4,lamp.color[2])\n#        lamp.use_nodes = True\n\n\nfor i in [1]:\n    if i == 1:\n        bpy.ops.mesh.primitive_cube_add(location = (10, 10, 10))\n    if i == 2:\n        bpy.ops.mesh.primitive_cube_add(location = (0, 0, -1))\n    lamp = scn.objects['Cube']\n    lamp.name = 'cycleslamp'+str(i)\n    lamp.select = True\n    bpy.ops.transform.resize(value=(3/i,3/i,3/i))\n    bpy.data.objects['cycleslamp'+str(i)].cycles_visibility.camera = False\n\n## Create a material.\n#mat = bpy.data.materials.new(name = 'my_material')\n\n## Set some properties of the material.\n#mat.diffuse_color = (1, 0., 0.)\n#mat.diffuse_shader = 'LAMBERT'\n#mat.diffuse_intensity = 1.0\n#mat.specular_color = (1., 1., 1.)\n#mat.specular_shader = 'COOKTORR'\n#mat.specular_intensity = 0.5\n#mat.alpha = 1\n#mat.ambient = 1\n\n#materialart =  'WOOD'\n\n#matname = \"mat\" + materialart\n#texname = \"tex\" + materialart\n\n# new material\n#textur = bpy.data.textures.new(texname, type=materialart)\n#textur.wood_type= 'RINGNOISE'\n#textur.saturation = 0.0\n#texture.active_texture = textur2\n#material = bpy.data.materials.new(matname)\n#holzmat = material\n#material.texture_slots.add()\n#material.active_texture = textur\n#tex = bpy.data.textures.new(\"SomeName\", type = 'IMAGE')\n#tex.image = bpy.data.images.load('/home/alex/workspace-noneclipse/blender/holz.jpg')\n#material.texture_slots.add()\n#material.active_texture = tex\n#material.diffuse_color = (1.0, .3, 0)\n#material.specular_color = (0.9, .4, 0.1)\n##material.line_color = (0.9, .4, 1)\n#material.mirror_color = (0.9, .4, 0.1)\n\n\n        #mesh.data.materials.append(material)\n\n\n\n#obj = cube.data\n#obj.materials.append(material)\n\n# new texture\n#bpy.data.textures[texname].specular_color=(0.9,0.3,0.0)\n#textur.specular_color=(0.9,0.3,0.0)\n\n# lits all properties and methods of a texture\n# print(dir(textur))\n\n# connect texture with material\n\n\nmesh = cube.data\n#mesh.materials.append(mat)\ncoords=(0,0,-8.8+30)\nbpy.ops.mesh.primitive_cube_add(location = coords)\ncupboardb = scn.objects['Cube']\ncupboardb.name = 'room'\ncupboardb.select = True\nbpy.ops.transform.resize(value=(40,50,30))\ncupboardb.select = False\n\n#materialart =  'MUSGRAVE'\n\n#matname = \"mat\" + materialart\n#texname = \"tex\" + materialart\n\n# new material\n#textur = bpy.data.textures.new(texname, type=materialart)\n#o = scn.objects['Material']\n#o.select = True\n#bpy.ops.transform.resize(value=(.2, .2, .2))\n#o.select = False\n#textur.noise_scale=0.1\n#textur.musgrave_type = 'HYBRID_MULTIFRACTAL'\n#textur.saturation = 0.0\n#texture.active_texture = textur2\n#material = bpy.data.materials.new(matname)\n#material.texture_slots.add()\n#material.active_texture = textur\n#material.resize(value=(0.2,0.2,0.2))\n#tex = bpy.data.textures.new(\"SomeName\", type = 'IMAGE')\n#tex.image = bpy.data.images.load('/home/alex/workspace-noneclipse/blender/wirr.jpg')\n#tex.im .noise_scale=0.01\n#tex.repeat_x=20\n#tex.repeat_y=20\n#material.texture_slots.add()\n#material.active_texture = tex\n#material.diffuse_color = (1.0, 1.0, 1.0)\n#material.raytrace_mirror.use = True\n#material.raytrace_mirror.reflect_factor = 0.4\n#material.raytrace_mirror.gloss_factor = 0.9\n#material.specular_color = (0.9, .4, 0.1)\n##material.line_color = (0.9, .4, 1)\n#material.mirror_color = (0.9, .4, 0.1)\n#scn.objects['room'].data.materials.append(material)\n#bpy.ops.wm.link(filepath=\"/home/alex/Downloads/Palm_Tree.blend\")\n#bpy.ops.wm.append(filepath=\"//Palm_Tree.blend\",directory=\"/home/alex/Downloads\",link=False)\n\n#for lamp in bpy.data.lamps:\n#    lamp.energy = 2\n\nfor a in [-1,1]:\n    for b in [-7.5,-1]:\n        bpy.ops.object.lamp_add(location=(3*tiefe,7*a*breite,b))\n        c = 0.0 if b == -7.5 else 0.2\n        bpy.data.lamps[-1].color = (1.0,c,0.0)\n        bpy.data.lamps[-1].distance = 1.0\n        bpy.data.lamps[-1].energy = 10\n        bpy.data.lamps[-1].shadow_method = 'RAY_SHADOW'\n        bpy.data.lamps[-1].shadow_ray_samples = 3\n\n\nfor lamp in bpy.data.lamps:\n    lamp.shadow_method = 'RAY_SHADOW'\n\n\n#Mesh\\\\['255_mesh_.001','255_mesh_']\n#Object\\\\['255_mesh_.001','255_mesh_']\n#Group\\\\255\n#Image\\\\['blatt.jpg','holz.jpg','Render Result']\n#Material\\\\['Material','Material.002','Material.001']\n#Scene\\\\Scene\n#Texture\\\\['Bild','bild','Blatt','Tex','Wood']\n\n#          ['blatt.jpg','holz.jpg','Render Result'],\n#          ['Material','Material.002','Material.001'],\n        #  ['Scene']\n #         ['Bild','bild','Blatt','Tex','Wood']]\n\n\ndef import_(blendfile,locat,resiz,tree = False):\n    objs=[]\n    for folder,things in zip(folders,inside):\n        section   = \"\\\\\"+folder+\"\\\\\"\n        for object in things:\n            filepath  = blendfile + section + object\n            directory = blendfile + section\n            filename  = object\n\n            len1_ = len(scn.objects)\n            bpy.ops.wm.append(\n                filepath=filepath,\n                filename=filename,\n                directory=directory)\n            len2_ = len(scn.objects)\n            if len1_ < len2_:\n                objs.append(scn.objects[-1])\n    for obj in scn.objects:\n        obj.select = False\n    if not resiz is None and not locat is None:\n        for obj in inside[0]:\n            obj = scn.objects[obj]\n            obj.select = True\n            bpy.ops.transform.resize(value=resiz)\n            obj.location = locat\n            a = str(obj.name)\n            if tree:\n                obj.name = 'tree 1 '+a\n                bpy.ops.object.duplicate_move_linked()\n                for obj2 in scn.objects:\n                    obj2.select = False\n    return objs\nfolders = ['Mesh','Object']\ninside = [['255_mesh_.001','255_mesh_'],\n          ['255_mesh_.001','255_mesh_']]\nimport_(\"/home/alex/workspace-noneclipse/blender/alxpalme.blend\",(-13,15,-8.8),(0.3, 0.3, 0.3),True)\nfolders = ['Mesh','Object','Material','Texture']\nobj_ = ['Box01','Box02','Box03','Box04','Box05','Box06','Box13.Box14.Box15']\nfor i in range(1,10):\n    obj_.append('Cylinder0'+str(i))\nfor i in range(10,13):\n    obj_.append('Cylinder'+str(i))\nstr_=\"\"\nfor i in range(14,19):\n    str_+=\"Cylinder\"+str(i)+\".\"\nstr_+=\"Cylinder\"\nobj_.append(str_)\nstr_=\"\"\nfor i in range(21,26):\n    str_+=\"Cylinder\"+str(i)+\".\"\nstr_+=\"Cylinder\"\nobj_.append(str_)\nfor i in range(28,36):\n    obj_.append('Cylinder'+str(i))\nobj_.append('Object01')\nobj_.append('Object02')\nobj_.append('Shape01')\nobj_.append('Sphere01')\nfor i in range(1,5):\n    obj_.append('Tube0'+str(i))\ninside = [obj_,obj_,['01 - Default','10 - Default','11 - Default','13 - Default','Material'],['Tex']]\n#print(str(obj_))\nobjs = import_(\"/home/alex/workspace-noneclipse/blender/pctower.blend\",(0,0,0),(1,1,1))\n#for obj in objs:\n#    print(obj.name)\njoinmerge(objs)\nscn.objects['Box01'].select = True\nbpy.ops.transform.resize(value=(0.85,0.85,0.85))\nscn.objects.active = scn.objects['Box01']\nbpy.context.object.rotation_euler = (0,0,math.pi)\nscn.objects['Box01'].location = (-0.3,3.5*breite,-7+0.4)\n#9/2*tiefe, 16/2*breite\nbpy.ops.object.duplicate_move_linked(OBJECT_OT_duplicate={\"linked\":True, \"mode\":'TRANSLATION'},TRANSFORM_OT_translate={\"value\":(0,-11.5*breite,0), \"constraint_axis\":(False, False, False),\n\"constraint_orientation\":'GLOBAL', \"mirror\":False, \"proportional\":'DISABLED', \"proportional_edit_falloff\":'SMOOTH', \"proportional_size\":1, \"snap\":False, \"snap_target\":'CLOSEST',\n\"snap_point\":(0, 0, 0), \"snap_align\":False, \"snap_normal\":(0, 0, 0), \"texture_space\":False, \"release_confirm\":False})\nscn.objects['Box01'].select = False\n\n\nfolders = ['Mesh','Object','Material','Texture','Image']\n#folders = ['Mesh','Object']\nobj_=['Cube','Keyboard']\nfor a in range(5,6):\n    obj_.append('Plane.00'+str(a))\nobj_.append('Plane')\nmati_=['Material']\nfor a in range(0,8):\n    mati_.append('Material.00'+str(a))\ntexti_=['Texture','Texture.001','Texture.002']\nimagi=['Bildschirmfoto 2011-1','Dots.png','keyboard.jpg.001','keyboard.jpg','Mac Desktop.png','Mac_back.png','Mac_front.png','Mac_side.png','mouse side.jpg','Render Result']\n#meshi=['Cube.001','Cube']\n#meshi=['Cube']\nmeshi=[]\nfor a in range(5,6):\n    meshi.append('Plane.00'+str(a))\nmeshi.append('Plane')\ninside = [meshi,obj_,mati_,texti_,imagi]\n#print(str(meshi))\n#inside = [meshi,obj_]\n#print(str(obj_))\nobjs = import_(\"/home/alex/workspace-noneclipse/blender/Mac_2.blend\",None,None)\nscn.objects['Cube'].name=\"Halterung\"\n\n\n\n\n#scn.objects['Plane.005']\n\n\n\nscn.objects['Plane'].select = False\nscn.objects['Keyboard'].select = True\n\nscn.objects.active = scn.objects['Keyboard']\nbpy.context.object.rotation_euler = (math.pi/12,0,math.pi/2)\nbpy.ops.transform.resize(value=(2,2,2))\nscn.objects['Keyboard'].location = (2,-3,0.45)\nscn.objects['Keyboard'].select = False\n\n\n\n\nfor o in [-1,-2]:\n    bpy.data.objects[o].select = True\n    bpy.data.objects[o].location = (-13,-15,-8.8)\n    bpy.data.objects[o].select = False\n\n\n#mist = bpy.data.worlds[\"World\"].mist_settings\n#mist.use_mist = True\n#mist.start = 25\n#mist.depth = 50\n#cube = scn.objects['Cube'].name = 'ExCube'\n\nbpy.ops.mesh.primitive_cube_add(location = (-(9/2/2*tiefe), 0, 2))\nscn = bpy.context.scene\ncube = scn.objects['Cube']\n#bpy.context.scene.objects[0].select = True\nbpy.ops.transform.resize(value=(9/2/2*tiefe, 16/2*breite, 0.2))\nbpy.ops.mesh.primitive_cylinder_add(radius=5*(breite+tiefe)/math.pi,location=(5.5*tiefe-(9/2/2*tiefe)*2,0,3))\nbpy.ops.transform.resize(value=(1*tiefe/1.5, 1.5*breite,4))\ncylinder = scn.objects['Cylinder']\ncube.name = 'Oberplatte'\n\noben=[cube]\nobenname=[cube.name]\n\nfor a in [-3,3]:\n    bpy.ops.mesh.primitive_cube_add(location = (-(9/2/2*tiefe), a*(16/2*breite/3-0.25), 1))\n    scn = bpy.context.scene\n    oben.append(scn.objects['Cube'])\n    #bpy.context.scene.objects[0].select = True\n    bpy.ops.transform.resize(value=(9/2/2*tiefe, 0.5, 1))\n    oben[-1].name = 'Oben seitlich '+str(a)\n    obenname.append(oben[-1].name)\n\n\nfor a in oben:\n    mod_bool = a.modifiers.new('my_bool_mod', 'BOOLEAN')\n    mod_bool.operation = 'DIFFERENCE'\n    mod_bool.object = cylinder\n    bpy.context.scene.objects.active = a\n    res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n    a.select = False\n    cylinder.select = True\nbpy.ops.object.delete()\n\n#flurbreite = 216\n#deckenwanddicke = 21\n#lukelaenger=1.5\n#treppenlaengenbereich = (165 + 6.65 + 20 - 20 + 63.5) * lukelaenger\n#lukenende = flurlaenge /2 - 63.5 - treppenlaengenbereich /2 + 40 - ( treppenlaengenbereich )\n#lukenmitte = -flurbreite + 59 + deckenwanddicke\n#coords = [(lukenmitte,lukenende,224),(lukenmitte,(lukenende + flurlaenge) / 2-110,-110), (lukenmitte,flurlaenge,-224)]\n#coords2 = [(lukenmitte+120,lukenende,224),(lukenmitte+120,(lukenende + flurlaenge) / 2-110,-110), (lukenmitte+120,flurlaenge,-224)]\n# rot,grün,blau = x,y,z\ncoords = []\ncoords2 = []\ncoords3 = []\ncoords4 = []\nome = math.pow(3,2)\nfor x2 in range(-int(1750/2),int(1750/2)):\n    x = x2 / 100\n    normalverteilung = 1 / (math.sqrt(2*math.pi*ome)) * math.exp(-((x)*(x))/(2*ome))\n    coords.append((normalverteilung*60-6.6,x,0-0.1-0.3))\n    coords2.append((normalverteilung*60-6.6,x,2-0.2+0.4))\n\nfor i,x2 in enumerate(range(-int(1750/2),int(1750/2))):\n    if x2 < 0:\n        coords3.append(coords2[i])\n    else:\n        coords4.append(coords2[int(len(coords2)*1.5)-i-1])\n\ncoords.append(coords[0])\ncoords2.append(coords2[0])\n\ncoords5=[]\ncoords6=[]\nfor x2 in range(-21,22):\n    x = x2 / 10\n    y = math.sin(-x)*(x*x)+0.5*x\n    coords5.append((x/4.3*6.8*tiefe-1.4,0-16/2*breite,y+2.7))\n    coords6.append((x/4.3*6.8*tiefe-1.4,0.4-16/2*breite,y+2.7))\n\nfor coordsx in [coords5,coords6]:\n    coordsx.append((coordsx[-1][0],coordsx[-1][1],0.2))\n    coordsx.append((coordsx[0][0],coordsx[0][1],0.2))\n    coordsx.append((coordsx[0][0],coordsx[0][1],coordsx[0][2]))\n\ncoords11=[]\ncoords12=[]\ncoords13=[]\ncoords14=[]\nfor x2 in range(-500,501):\n    x = x2 / 100\n    y = math.cos(x*2)+0.08*x*x-0.001*x*x*x*x+math.cos(x*8)/4\n    for i,coordsx in enumerate([coords11,coords12]):\n        coordsx.append((-9/2*tiefe+i*0.4,x/10*breite*16,y+5))\n    for i,coordsx in enumerate([coords13,coords14]):\n        coordsx.append((-9/2*tiefe+i*0.4,x/10*breite*16,0.2))\ncoords15=[coords11[0],coords13[0],coords13[-1],coords11[-1]]\ncoords16=[coords12[0],coords14[0],coords14[-1],coords12[-1]]\n\ncoords7=[]\ncoords8=[]\ncoords9=[]\ncoords10=[]\nflag = 0\nfor i,coordsx in enumerate([coords5,coords6]):\n    for coord in coordsx:\n        #for coordsx2,coordsx3 in zip([coords7,coords9],[coords8,coords10]):\n        for coordsx2,coordsx3 in zip([coords7,coords9],[coords8,coords10]):\n            #if coord[2]-2.7 > -0.62 and flag == 0: # wenn in der mitte der analysis funktion\n            if i < len(coordsx)-2: # wenn in der mitte der analysis funktion\n                #print(str(coord))\n                coordsx2.append(coord)\n                coordsx3.append(coordsx[-2])\n            else:\n                flag = 1\n\n\n#coords = [(1,1,0),(2,2,0),(5,3,0)]\n#coords2 = [(1,1,3),(2,2,3),(5,3,3)]\n#curveData = bpy.data.curves.new('myCurve', type='CURVE')\n\ncurvyobj(coords,coords2)\ncurvyobj(coords3,coords4)\ncurvyobj(coords5,coords6)\ncurvyobj(coords7,coords8)\ncurvyobj(coords9,coords10)\ncurvyobj(coords11,coords12)\ncurvyobj(coords11,coords13)\ncurvyobj(coords12,coords14)\ncurvyobj(coords15,coords16)\ntojoin = []\nfor i in range(5,10):\n    tojoin.append(scn.objects['wave.00'+str(i)])\n\njoinmerge([scn.objects['wave.001'],scn.objects['wave.003']])\njoinmerge(tojoin)\n\ntojoin=[]\nfor i in range(10,18):\n    tojoin.append(scn.objects['wave.0'+str(i)])\njoinmerge(tojoin)\n\nbpy.ops.object.select_all(action='DESELECT')\nscn.objects['wave.005'].select = True\nbpy.ops.object.duplicate_move(OBJECT_OT_duplicate={\"linked\":False, \"mode\":'TRANSLATION'},TRANSFORM_OT_translate={\"value\":(0, -0.4+16*breite, 0), \"constraint_axis\":(False, False, False),\n\"constraint_orientation\":'GLOBAL', \"mirror\":False, \"proportional\":'DISABLED', \"proportional_edit_falloff\":'SMOOTH', \"proportional_size\":1, \"snap\":False, \"snap_target\":'CLOSEST',\n\"snap_point\":(0, 0, 0), \"snap_align\":False, \"snap_normal\":(0, 0, 0), \"texture_space\":False, \"release_confirm\":False})\n\nbm = bmesh.new()\nmiddlehigh = scn.objects['wave.001'].data\n#bpy.context.scene.objects.active = middlehigh\n#bpy.ops.object.editmode_toggle()\nbm.from_mesh(middlehigh)\nbmesh.ops.remove_doubles(bm, verts=bm.verts, dist=0.0001)\nbm.to_mesh(middlehigh)\nmiddlehigh.update()\nbm.clear()\nbm.free()\n#bpy.ops.object.editmode_toggle()\n\ncylinders=[]\nbereich = [-5,-1.5,1.5,5]\nfor a in bereich:\n    if a in [-5,5]:\n        bpy.ops.object.lamp_add(location=(-3.6*tiefe,a*breite,0))\n    bpy.ops.object.lamp_add(location=(-3.6*tiefe,a*breite,2))\n    bpy.ops.mesh.primitive_cylinder_add(radius=3*(breite+tiefe)/math.pi,location=(-3.6*tiefe,a*breite,0))\n    bpy.ops.transform.resize(value=(0.2, 0.2, 10))\n    cylinder = scn.objects['Cylinder']\n    cylinder.name = 'hole_'+str(a)\n    cylinders.append(cylinder)\n    if a in [-5,5]:\n        differ(scn.objects['Tischplatte'],cylinder)\n    differ(scn.objects['Oberplatte'],cylinder)\n    differ(scn.objects['wave.001'],cylinder)\n    cylinder.select = True\n    scn.objects['Tischplatte'].select = False\n    scn.objects['Oberplatte'].select = False\n    scn.objects['wave.001'].select = False\n    bpy.ops.object.delete()\n\n#flurlaenge = 168+285+5\ndiffer(scn.objects['Tischplatte'],scn.objects['wave.001'])\n\nbpy.ops.mesh.primitive_cube_add(location = (9/2*tiefe+(9/6*tiefe), -16/2 * breite + (16/10*breite), -4.5+0.2))\ncube = scn.objects['Cube']\nbpy.context.scene.objects[0].select = True\nbpy.ops.transform.resize(value=(9/6*tiefe, 16/10*breite+0.1, 4.5+0.1))\nbpy.context.scene.objects[0].select = False\ndavor = scn.objects['Cube']\ndavor.name='davor'\ndavor.select = True\n#bpy.ops.object.editmode_toggle()\n\nme = bpy.context.object.data\n\nbm = bmesh.new()\nbm.from_mesh(me)\n\nEPSILON = 1.0e-5\nfor i,vert in enumerate(bm.verts):\n    #if -EPSILON <= vert.co.x <= EPSILON:\n    if i < 2:\n        vert.select = True\n        vert.co = vert.co[0]-tiefe,vert.co[1],vert.co[2]\n#for edge in bm.edges:\n#    if edge.verts[0].select and edge.verts[1].select:\n#        edge.select = True\n\n#bpy.ops.object.editmode_toggle()\n\nbm.to_mesh(me)\nbm.free()\n#bpy.ops.object.duplicate_move_linked(OBJECT_OT_duplicate={\"linked\":True, \"mode\":'TRANSLATION'},TRANSFORM_OT_translate=(0,2*(16/2 * breite - (16/10*breite)),0))\nbpy.ops.object.duplicate_move(OBJECT_OT_duplicate={\"linked\":False, \"mode\":'TRANSLATION'},TRANSFORM_OT_translate={\"value\":(0, 2*(16/2 * breite - (16/10*breite)), 0), \"constraint_axis\":(False, False, False),\n\"constraint_orientation\":'GLOBAL', \"mirror\":False, \"proportional\":'DISABLED', \"proportional_edit_falloff\":'SMOOTH', \"proportional_size\":1, \"snap\":False, \"snap_target\":'CLOSEST',\n\"snap_point\":(0, 0, 0), \"snap_align\":False, \"snap_normal\":(0, 0, 0), \"texture_space\":False, \"release_confirm\":False})\ndavor.select = False\nbpy.context.object.rotation_euler = (math.pi,0,0)\ndavor.name='davor'\n\n#bpy.ops.object.select_all(action='DESELECT')\nfor todiff in [scn.objects['davor'],scn.objects['davor.001']]:\n    for difffrom in [scn.objects['Tischplatte'],scn.objects['cupboard_1'],scn.objects['cupboard_2']]:\n        differ(difffrom,todiff)\n        ##bpy.ops.object.select_all(action='DESELECT')\n        #mod_bool = difffrom.modifiers.new('my_bool_mod', 'BOOLEAN')\n        #mod_bool.operation = 'DIFFERENCE'\n        #mod_bool.object = todiff\n        #bpy.context.scene.objects.active = difffrom\n        #res = bpy.ops.object.modifier_apply(apply_as='DATA',modifier = 'my_bool_mod')\n        ##bpy.ops.object.select_all(action='DESELECT')\n        ##break\n        #difffrom.select = False\n        #todiff.select = True\n        #bpy.ops.object.delete()\nbpy.ops.object.select_all(action='DESELECT')\n\n\nfor torsize in [scn.objects['davor'],scn.objects['davor.001']]:\n    torsize.select = True\n    bpy.ops.transform.resize(value=(1, 1/(16/10*breite+0.1), 1/(4.5+0.1)))\n    bpy.ops.transform.resize(value=(1, 16/10*breite, 3))\n    torsize.location.z -= 1.5\n    torsize.select = False\n#9/2*tiefe+(9/6*tiefe), -16/2 * breite + (16/10*breite), -4.5+0.2)\nfor a,obj in zip([-1,1],[scn.objects['davor'],scn.objects['davor.001']]):\n    createCube(9/2*tiefe+(9/6*tiefe), a*(16/2 * breite - (16/10*breite)),-4.5+0.2 - 1.5,9/6*tiefe*4, 16/10*breite-0.3, 3-0.3)\n    differ(obj,scn.objects['Cube'])\n    bpy.ops.object.select_all(action='DESELECT')\n    scn.objects['Cube'].select = True\n    bpy.ops.object.delete()\n\n#for mesh in bpy.context.scene.objects:\n#    if mesh.name in ['Tischplatte','Tischbein1','Tischbein2','Tischbein3','Tischbein4','Platte_2','cupboard_1','cupboard_2','Oberplatte'] or mesh.name in obenname:\n#        mesh.data.materials.append(holzmat)\n\nimage_path = '/home/alex/workspace-noneclipse/blender/holz2.jpg'\n\nmat = bpy.data.materials.new('holz')\nmat.use_nodes = True\nnt = mat.node_tree\nnodes = nt.nodes\nlinks = nt.links\nwhile(nodes): nodes.remove(nodes[0])\noutput  = nodes.new(\"ShaderNodeOutputMaterial\")\ndiffuse = nodes.new(\"ShaderNodeBsdfDiffuse\")\ntexture = nodes.new(\"ShaderNodeTexImage\")\ntexture.projection = 'BOX'\nuvmap   = nodes.new(\"ShaderNodeTexCoord\")\nbump   = nodes.new(\"ShaderNodeBump\")\ntexture.image = bpy.data.images.load(image_path)\nlinks.new( output.inputs['Surface'], diffuse.outputs['BSDF'])\nlinks.new(diffuse.inputs['Color'],   texture.outputs['Color'])\nlinks.new(texture.inputs['Vector'],    uvmap.outputs['Generated'])\nlinks.new(bump.inputs['Normal'],    texture.outputs['Color'])\nlinks.new(diffuse.inputs['Normal'], bump.outputs['Normal'])\n\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['wave.010','wave.005','wave.003','Cube.001','wave.001','davor','davor.001','tree 1 255_mesh_.000','Tischplatte','Tischbein1','Tischbein2','Tischbein3','Tischbein4','Platte_2','cupboard_1','cupboard_2','Oberplatte'] or mesh.name in obenname:\n        mesh.data.materials.clear()\n        mesh.data.materials.append(mat)\n#scn.render.engine =\njoinmerge1=[]\njoinmerge2=[]\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['wave.010','wave.005','wave.003','Cube.001','wave.001','Tischplatte','Tischbein1','Tischbein2','Tischbein3','Tischbein4','Platte_2','cupboard_1','cupboard_2','Oberplatte'] or mesh.name in obenname:\n        joinmerge1.append(mesh)\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['davor','davor.001']:\n        joinmerge2.append(mesh)\n#joinmerge(joinmerge1)\n#joinmerge(joinmerge2)\nbpy.context.scene.render.engine = 'CYCLES'\n\nfor i in [1]:\n    mat = bpy.data.materials.new('lampmat')\n    mat.use_nodes = True\n    nt = mat.node_tree\n    nodes = nt.nodes\n    links = nt.links\n    while(nodes): nodes.remove(nodes[0])\n    output  = nodes.new(\"ShaderNodeOutputMaterial\")\n    emission  = nodes.new(\"ShaderNodeEmission\")\n    emission.inputs[1].default_value = 20\n    links.new( output.inputs['Surface'], emission.outputs['Emission'])\n    scn.objects['cycleslamp'+str(i)].data.materials.append(mat)\n\nfor i in [1]:\n    mat = bpy.data.materials.new('lampmat2')\n    mat.use_nodes = True\n    nt = mat.node_tree\n    nodes = nt.nodes\n    links = nt.links\n    while(nodes): nodes.remove(nodes[0])\n    output  = nodes.new(\"ShaderNodeOutputMaterial\")\n    emission  = nodes.new(\"ShaderNodeEmission\")\n    emission.inputs[1].default_value = 5\n    links.new( output.inputs['Surface'], emission.outputs['Emission'])\n    image_path = '/home/alex/workspace-noneclipse/blender/linux_kde_plasma_desktop_by_vincecrue_d48eqs6-fullview.jpg'\n    texture = nodes.new(\"ShaderNodeTexImage\")\n    texture.projection = 'BOX'\n    texture.image = bpy.data.images.load(image_path)\n    texture.texture_mapping.rotation=(math.pi,0,math.pi/2)\n#bpy.context.object.rotation_euler = (-math.pi/2,0,math.pi/2)\n    links.new(emission.inputs['Color'],   texture.outputs['Color'])\n\n\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['Plane.005']:\n#        scn.objects['Plane.005'].select = True\n#        bpy.context.object.rotation_euler = (math.pi/2,0,-math.pi/2)\n        mesh.data.materials.clear()\n        mesh.data.materials.append(mat)\n#        bpy.context.object.rotation_euler = (-math.pi/2,0,math.pi/2)\n#        scn.objects['Plane.005'].select = False\n\n\n#scn.objects['Plane.005']\n\n#for mesh in bpy.context.scene.objects:\n#    if mesh.name in ['cycleslamp2','cyleslamp1']:\n#        mesh.data.materials.append(mat)\n   # scn.objects['cycleslamp'+str(i)].data.materials.append(mat)\n\nmat = bpy.data.materials.new('pflanze')\nimage_path = '/home/alex/workspace-noneclipse/blender/blatt.jpg'\nmat.use_nodes = True\nnt = mat.node_tree\nnodes = nt.nodes\nlinks = nt.links\nwhile(nodes): nodes.remove(nodes[0])\noutput  = nodes.new(\"ShaderNodeOutputMaterial\")\ndiffuse = nodes.new(\"ShaderNodeBsdfDiffuse\")\ntexture = nodes.new(\"ShaderNodeTexImage\")\ntexture.projection = 'BOX'\nuvmap   = nodes.new(\"ShaderNodeTexCoord\")\nbump   = nodes.new(\"ShaderNodeBump\")\ntexture.image = bpy.data.images.load(image_path)\nlinks.new( output.inputs['Surface'], diffuse.outputs['BSDF'])\nlinks.new(diffuse.inputs['Color'],   texture.outputs['Color'])\nlinks.new(texture.inputs['Vector'],    uvmap.outputs['Generated'])\nlinks.new(bump.inputs['Normal'],    texture.outputs['Color'])\nlinks.new(diffuse.inputs['Normal'], bump.outputs['Normal'])\n\n\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['tree 1 255_mesh_']:\n        mesh.data.materials.clear()\n        mesh.data.materials.append(mat)\nmatwood = mat\n\nmat = bpy.data.materials.new('Raumtex')\n#image_path = '/home/alex/Bilder/PS-Lemon-Stone-grey58b2fc1bca16c.jpg'\nmat.use_nodes = True\nnt = mat.node_tree\nnodes = nt.nodes\nlinks = nt.links\nwhile(nodes): nodes.remove(nodes[0])\noutput  = nodes.new(\"ShaderNodeOutputMaterial\")\ndiffuse = nodes.new(\"ShaderNodeBsdfDiffuse\")\ntexture = nodes.new(\"ShaderNodeTexBrick\")\ntexture.inputs[2].default_value = (0.7,0.7,0.7,1)\ntexture.inputs[3].default_value = (0.4,0.4,0.4,1)\ntexture.inputs[4].default_value = 13\nmix = nodes.new(\"ShaderNodeMixShader\")\nmix.inputs[0].default_value = 0.7\nglossy = nodes.new(\"ShaderNodeBsdfGlossy\")\nglossy.distribution = 'SHARP'\nglossy2 = nodes.new(\"ShaderNodeBsdfGlossy\")\nuvmap   = nodes.new(\"ShaderNodeTexCoord\")\nbump   = nodes.new(\"ShaderNodeBump\")\nbump.inputs[0].default_value = 100\nbump.inputs[1].default_value = 10\ntexture.inputs[4].default_value = 13\n#texture.image = bpy.data.images.load(image_path)\nlinks.new( output.inputs['Surface'], mix.outputs['Shader'])\nlinks.new(diffuse.inputs['Color'],   texture.outputs['Color'])\nlinks.new(glossy.inputs['Color'],   texture.outputs['Color'])\nlinks.new(texture.inputs['Vector'],    uvmap.outputs['Generated'])\nlinks.new(texture.inputs[1],    glossy2.outputs['BSDF'])\nlinks.new(bump.inputs['Height'],    texture.outputs['Color'])\nlinks.new(diffuse.inputs['Normal'], bump.outputs['Normal'])\nlinks.new(mix.inputs[1], diffuse.outputs['BSDF'])\nlinks.new(mix.inputs[2], glossy.outputs['BSDF'])\n\n\nfor mesh in bpy.context.scene.objects:\n    if mesh.name in ['room']:\n        mesh.data.materials.clear()\n        mesh.data.materials.append(mat)\n\nbpy.data.worlds[\"World\"].use_nodes = True\n#scn.render.layers[0].cycles.use_mist = True\n#bpy.context.scene.render.layers[0].layers[5] = True\n#scn.cycles.layers[5] = True\n#render_layers[0] = True\n#mist = bpy.data.worlds[\"World\"].mist_settings\n##mist.use_mist = True\n#mist.start = 25\n#mist.depth = 50\n\n#scn.objects['Plane'].select = True\n#bpy.ops.object.mode_set(mode = 'EDIT')\n#bm = bmesh.from_edit_mesh(cn.objects['Plane'].data)\n#bm.select_mode = {'FACE'}\n\n\njoinmerge([scn.objects['Plane'],scn.objects['Plane.005'],scn.objects['Halterung']])\nscn.objects.active = scn.objects['Plane']\nbpy.context.object.rotation_euler = (-math.pi/2,0,math.pi/2)\nscn.objects['Plane'].select = True\nbpy.ops.transform.resize(value=(2,2,2))\nscn.objects['Plane'].location = (-4,0,5.1)\n\n\nbpy.ops.object.select_all(action='DESELECT')\nscn.objects['myCurve'].select = True\nfor i in range(1,9):\n    scn.objects['myCurve.00'+str(i)].select = True\nfor i in range(10,18):\n    scn.objects['myCurve.0'+str(i)].select = True\nbpy.ops.object.delete()\nbpy.ops.object.select_all(action='DESELECT')\n![holz2.jpg](https://cdn.steemitimages.com/DQmWCdt75kehM34J9Y8ByFVnaQFGz9r6kcaPU9ZZfKTnAB8/holz2.jpg)![blatt.jpg](https://cdn.steemitimages.com/DQmUScoHG7qDGQ2DWmPdgZDHn8yYk4Fpt1srbioqzLk1aFS/blatt.jpg)![linux_kde_plasma_desktop_by_vincecrue_d48eqs6-fullview.jpg](https://cdn.steemitimages.com/DQmTdDP22qMGxvZsWmMGhJ9hZ5ueNsdDxjuadsCHvLFJu5s/linux_kde_plasma_desktop_by_vincecrue_d48eqs6-fullview.jpg)",
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      "author": "partiko",
      "permlink": "partiko-re-alexandercore-3152996893-20190226t000051292z",
      "title": "",
      "body": "Hello @alexandercore! This is a friendly reminder that you have 3000 Partiko Points unclaimed in your Partiko account!\n\nPartiko is a fast and beautiful mobile app for Steem, and it’s the most popular Steem mobile app out there! Download Partiko using the link below and login using SteemConnect to claim your 3000 Partiko points! You can easily convert them into Steem token!\n\nhttps://partiko.app/referral/partiko",
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      "title": "jounk33.sql.xz.gpg.aa.base64",
      "body": 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      "json_metadata": "{\"tags\":[\"gentoo\",\"use\",\"flags\"],\"app\":\"steemit/0.1\",\"format\":\"markdown\"}"
    }
  ]
}

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  "trx_in_block": 33,
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  "virtual_op": 0,
  "timestamp": "2019-01-22T16:46:18",
  "op": [
    "comment",
    {
      "parent_author": "",
      "parent_permlink": "gentoo",
      "author": "alexandercore",
      "permlink": "my-gentoo-make-conf",
      "title": "my gentoo make.conf",
      "body": "# These settings were set by the catalyst build script that automatically\n# built this stage.\n# Please consult /usr/share/portage/config/make.conf.example for a more\n# detailed example.\nCFLAGS=\"-march=broadwell -O3 -pipe\"\n\n# NOTE: This stage was built with the bindist Use flag enabled\nPORTDIR=\"/usr/portage\"\nDISTDIR=\"/usr/portage/distfiles\"\nPKGDIR=\"/usr/portage/packages\"\n\n# This sets the language of build output to English.\n# Please keep this setting intact when reporting bugs.\nLC_MESSAGES=C\n#MAKEOPTS=\"-j5\"\n#MAKEOPTS=\"-j2\"\n#MAKEOPTS=\"-j23 -l4\"\n#MAKEOPTS=\"-j17 -l5\"\nMAKEOPTS=\"-j7 -l1\"\n#MAKEOPTS=\"-j12 -l1\"\nFEATURES=\"distcc\"\nUSE=\"lm_sensors ibus openh264 x265 libcaca http2 vpx elogind -consolekit lzma xz brotli lz4 -ghostscript fftw openexr expat tiff dri3 iptables imagemagick gpg threads png jpeg2k screen thumbnail -postscript aqua svg image samba theora x264 x265 cups postscript perl equalizer bluetooth -systemd wayland pcre32 gnome xkb icu dbus jpeg X gtk gtk3 qt4 qt5 kde dvd alsa cdr wifi vnc bash-completion alsa pulseaudio handbook bzip2 egl introspection nls opengl orc vnc egl -gles2 pam extras themes pcre16 unicode policykit wireless apng mmx sse sse2 sse3 mmxext gles sound video xml kdenlive melt sdl ffmpeg svc widgets sdl mysql qml python tools flac mp3 mpeg speex vorbis cpu_flags_x86_avx2 cpu_flags_x86_avx\" \nGRUB_PLATFORMS=\"efi-64\"\nL10N=\"de de-DE\"\nLINGUAS=\"de en\"\nPORTAGE_BZIP2_COMMAND=\"/bin/bzip2\"\n#VIDEO_CARDS=\"nvidia\"\n#CPU_FLAGS_X86=\"mmx mmxext sse sse2 sse3 ssse3 sse4_1 avx2 sse4_2 avx avx512f f16c\"\nCPU_FLAGS_X86=\"aes avx avx2 f16c fma3 mmx mmxext pclmul popcnt sse sse2 sse3 sse4_1 sse4_2 ssse3\"\n#pentium eins könnte noch dazu: CPU_FLAGS_X86=\"aes mmx mmxext pclmul sse sse2 sse3 sse4_1 sse4_2 ssse3\"\nVIDEO_CARDS=\"nvidia intel i915 i965 vesa fbdev\"\nINPUT_DEVICES=\"keyboard mouse evdev synaptics\"\nACCEPT_LICENSE=\"*\"\nPYTHON_TARGETS=\"python3_6 python2_7 python3_7\"\nPYTHON_SINGLE_TARGET=\"python3_6\"\nGPSD_PROTOCOLS=\"fury geostar nmea0183 nmea2000 passthrough gpsd_protocols_aivdm aivdm ashtech earthmate evermore fv18 garmin garmintxt gpsclock isync itrax mtk3301 navcom ntrip oceanserver oncore rtcm104v2 rtcm104v3 sirf skytraq superstar2 tnt tripmate tsip ublox\"\nCONFIG_PROTECT=\"/usr/share/sddm/scripts/Xsetup\"\nAPACHE2_MPMS=\"worker\"\nAPACHE2_MODULES=\"access_compat* actions alias auth_basic authn_alias authn_anon authn_core authn_dbm authn_file authz_core authz_dbm authz_groupfile authz_host authz_owner authz_user autoindex cache cgi cgid dav dav_fs dav_lock deflate dir env expires ext_filter file_cache filter headers include info log_config logio mime mime_magic negotiation rewrite setenvif socache_shmcb speling status unique_id unixd userdir usertrack brotli http2 asis cache_disk vhost_alias\"\nQEMU_SOFTMMU_TARGETS=\"x86_64 i386\"\nQEMU_USER_TARGETS=\"x86_64 i386\"\nXTABLES_ADDONS=\"account chaos condition delude dhcpmac fuzzy geoip iface ipmark ipp2p ipv4options length2 logmark lscan psd quota2 sysrq tarpit\"\nMICROCODE_SIGNATURES=\"-S\"\nRUBY_TARGETS=\"ruby24\"",
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"app-accessibility/espeak\napp-admin/eclean-kernel\napp-admin/entr\napp-admin/keepass\napp-admin/sysklogd\napp-antivirus/clamav\napp-arch/lbzip2\napp-arch/lz4\napp-arch/lzop\napp-arch/p7zip\napp-arch/plzip\napp-arch/rar\napp-arch/unrar\napp-doc/zsh-lovers\napp-editors/nano\napp-editors/vim\napp-emulation/q4wine\napp-emulation/qemu\napp-emulation/virtualbox\napp-emulation/virtualbox-additions\napp-emulation/virtualbox-extpack-oracle\napp-emulation/virtualbox-modules\napp-emulation/winetricks\napp-eselect/eselect-repository\napp-misc/byobu\napp-misc/livecd-tools\napp-misc/mc\napp-misc/ranger\napp-misc/screen\napp-misc/task\napp-misc/xmind\napp-office/lyx\napp-office/texstudio\napp-portage/cpuid2cpuflags\napp-portage/eix\napp-portage/fquery\napp-portage/gentoolkit\napp-portage/layman\napp-portage/mirrorselect\napp-shells/fish\napp-shells/gentoo-zsh-completions\napp-shells/zsh\napp-text/djview\napp-text/djvu\napp-text/wgetpaste\napp-vim/vim-addon-mw-utils\napp-vim/vim-misc\napp-vim/vim-spell-de\napp-vim/vim-tmux\napp-vim/vimoutliner\napp-vim/vimpython\ncross-i686-pc-linux-gnu/binutils\ncross-i686-pc-linux-gnu/gcc\ncross-i686-pc-linux-gnu/gdb\ncross-i686-pc-linux-gnu/glibc\ncross-i686-pc-linux-gnu/linux-headers\ndev-lang/go\ndev-lang/python\ndev-libs/openssl\ndev-libs/wayland\ndev-perl/Crypt-Simple\ndev-python/PyQt4\ndev-python/PyQt5\ndev-python/cycler\ndev-python/lmdb\ndev-python/matplotlib\ndev-python/pip\ndev-python/pyopenssl\ndev-python/virtualenv\ndev-qt/qtcore\ndev-qt/qtgui\ndev-qt/qtnetwork\ndev-util/cloc\ndev-util/eclipse-sdk-bin\ndev-util/meld\ndev-util/nvidia-cuda-toolkit\ngames-util/steam-launcher\nkde-apps/dolphin\nkde-apps/dolphin-plugins\nkde-apps/ffmpegthumbs\nkde-apps/gwenview\nkde-apps/k3b\nkde-apps/kate\nkde-apps/kblocks\nkde-apps/kdeadmin-meta\nkde-apps/kdenlive\nkde-apps/kmines\nkde-apps/konsole\nkde-apps/korganizer\nkde-apps/ksudoku\nkde-apps/kwalletmanager\nkde-apps/kwrite\nkde-apps/marble\nkde-apps/okular\nkde-frameworks/bluez-qt\nkde-frameworks/kinit\nkde-frameworks/kwallet\nkde-frameworks/networkmanager-qt\nkde-misc/kdeconnect\nkde-plasma/breeze-plymouth\nkde-plasma/kdeplasma-addons\nkde-plasma/kscreen\nkde-plasma/kscreenlocker\nkde-plasma/plasma-desktop\nkde-plasma/plasma-meta\nkde-plasma/plasma-nm\nkde-plasma/plymouth-kcm\nkde-plasma/powerdevil\nkde-plasma/sddm-kcm\nmail-client/thunderbird\nmedia-fonts/alegreya-sans\nmedia-fonts/clearsans\nmedia-fonts/fantasque-sans-mono\nmedia-fonts/fira-sans\nmedia-fonts/font-adobe-75dpi\nmedia-fonts/font-adobe-utopia-type1\nmedia-fonts/open-sans\nmedia-fonts/oxygen-fonts\nmedia-fonts/ubuntu-font-family\nmedia-gfx/blender\nmedia-gfx/gnome-screenshot\nmedia-gfx/grub-splashes\nmedia-gfx/inkscape\nmedia-gfx/scrot\nmedia-gfx/xsane\nmedia-libs/harfbuzz\nmedia-libs/libaacs\nmedia-libs/mesa\nmedia-libs/openimageio\nmedia-plugins/fil-plugins\nmedia-plugins/frei0r-plugins\nmedia-plugins/gst-plugins-bluez\nmedia-plugins/rev-plugins\nmedia-sound/alsamixer-app\nmedia-sound/mumble\nmedia-sound/pulseaudio\nmedia-sound/smixer\nmedia-sound/spotify\nmedia-video/kmplayer\nmedia-video/mplayer\nmedia-video/mpv\nmedia-video/smplayer\nmedia-video/vlc\nnet-analyzer/net-snmp\nnet-analyzer/nmap\nnet-analyzer/traceroute\nnet-dns/avahi\nnet-firewall/iptables\nnet-firewall/xtables-addons\nnet-fs/samba\nnet-ftp/filezilla\nnet-im/pidgin\nnet-im/signal-desktop-bin\nnet-im/skypeforlinux\nnet-irc/hexchat\nnet-misc/dhcpcd\nnet-misc/electrum\nnet-misc/netifrc\nnet-misc/networkmanager\nnet-misc/nextcloud-client\nnet-misc/ntp\nnet-misc/openssh\nnet-misc/unison\nnet-p2p/amule\nnet-p2p/ktorrent\nnet-vpn/openvpn\nnet-vpn/tor\nnet-wireless/bluez\nnet-wireless/bluez-hcidump\nnet-wireless/bluez-tools\nnet-wireless/irda-utils\nnet-wireless/iw\nnet-wireless/wireless-tools\nnet-wireless/wpa_supplicant\nsys-apps/dbus\nsys-apps/dstat\nsys-apps/fakechroot\nsys-apps/iucode_tool\nsys-apps/keyutils\nsys-apps/lshw\nsys-apps/mlocate\nsys-apps/pciutils\nsys-apps/smartmontools\nsys-apps/usbutils\nsys-apps/watchdog\nsys-apps/yarn\nsys-block/partimage\nsys-boot/grub\nsys-boot/grub:2\nsys-boot/plymouth\nsys-boot/plymouth-openrc-plugin\nsys-devel/binutils\nsys-devel/distcc\nsys-devel/gcc\nsys-firmware/bluez-firmware\nsys-firmware/intel-microcode\nsys-fs/btrfs-progs\nsys-fs/ddrescue\nsys-fs/dosfstools\nsys-fs/ecryptfs-utils\nsys-fs/encfs\nsys-fs/fuse-exfat\nsys-fs/libfat\nsys-fs/lvm2\nsys-fs/ncdu\nsys-fs/ntfs3g\nsys-fs/unionfs-fuse\nsys-kernel/genkernel\nsys-kernel/gentoo-sources\nsys-kernel/gentoo-sources:4.14.60\nsys-kernel/gentoo-sources:4.18.11\nsys-kernel/gentoo-sources:4.18.19\nsys-kernel/linux-firmware\nsys-libs/glibc\nsys-libs/gpm\nsys-libs/ncurses\nsys-power/acpi\nsys-power/cpupower\nsys-power/ncpufreqd\nsys-power/powertop\nsys-process/cronie\nsys-process/iotop\nsys-process/lsof\nvirtual/jre\nvirtual/wine\nwww-client/chromium\nwww-client/firefox\nwww-client/lynx\nwww-client/vivaldi\nwww-client/w3m\nwww-plugins/adobe-flash\nwww-plugins/freshplayerplugin\nwww-servers/apache\nx11-apps/xauth\nx11-apps/xclock\nx11-apps/xdm\nx11-apps/xkill\nx11-apps/xrandr\nx11-base/xorg-drivers\nx11-base/xorg-server\nx11-drivers/nvidia-drivers\nx11-drivers/xf86-video-intel\nx11-libs/cairo\nx11-misc/dmenu\nx11-misc/i3status\nx11-misc/j4-dmenu-desktop\nx11-misc/read-edid\nx11-misc/sddm\nx11-misc/vym\nx11-misc/xkbd\nx11-misc/xvkbd\nx11-plugins/enigmail\nx11-plugins/pidgin-opensteamworks\nx11-plugins/pidgin-otr\nx11-terms/guake\nx11-terms/terminator\nx11-terms/xterm\nx11-themes/gentoo-artwork\nx11-wm/i3\nx11-wm/twm\n\n# 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