diff --git a/examples/Drawings/gear_rack.FCStd b/examples/Drawings/gear_rack.FCStd
index f50f991..0e37759 100644
Binary files a/examples/Drawings/gear_rack.FCStd and b/examples/Drawings/gear_rack.FCStd differ
diff --git a/examples/worm_cutting_tool/cutting_tool_worm_assembly.ipynb b/examples/worm_cutting_tool/cutting_tool_worm_assembly.ipynb
index de7bb0c..c67ffb8 100644
--- a/examples/worm_cutting_tool/cutting_tool_worm_assembly.ipynb
+++ b/examples/worm_cutting_tool/cutting_tool_worm_assembly.ipynb
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 27,
    "id": "cfd8026b-5a84-4882-a1de-63580776a579",
    "metadata": {},
    "outputs": [],
@@ -163,37 +163,37 @@
     "\n",
     "from pygears.transformation import symbolic_transformation, numeric_transformation\n",
     "\n",
-    "t, x, z, m = sym.symbols([\"t\", \"x\", \"z\", \"m\"], real=True)\n",
-    "s, alpha, n_t, y = sym.symbols([\"s\", \"alpha\", \"n_t\", \"y\"], real=True, positiv=True)"
+    "t, x, z, m, r_w = sym.symbols([\"t\", \"x\", \"z\", \"m\", \"r_w\"], real=True)\n",
+    "s, alpha, n_t, y, phi = sym.symbols([\"s\", \"alpha\", \"n_t\", \"y\", \"phi\"], real=True, positiv=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "65bb90d7-0f5b-410e-9a3a-0f9953a4d846",
+   "execution_count": 9,
+   "id": "618dd2ae-e8e5-430d-8c8c-8983358334b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(2 \\pi t \\right)} & \\sin{\\left(2 \\pi t \\right)} & 0 & 0\\\\- \\sin{\\left(2 \\pi t \\right)} & \\cos{\\left(2 \\pi t \\right)} & 0 & 0\\\\0 & 0 & 1 & \\pi m n_{t} t\\\\0 & 0 & 0 & 1.0\\end{matrix}\\right]$"
+       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & r_{w}\\\\0 & 1 & 0 & 0\\\\0 & 0 & 1 & 0\\\\0.0 & 0.0 & 0.0 & 1.0\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "Matrix([\n",
-       "[ cos(2*pi*t), sin(2*pi*t), 0,          0],\n",
-       "[-sin(2*pi*t), cos(2*pi*t), 0,          0],\n",
-       "[           0,           0, 1, pi*m*n_t*t],\n",
-       "[           0,           0, 0,        1.0]])"
+       "[  1,   0,   0, r_w],\n",
+       "[  0,   1,   0,   0],\n",
+       "[  0,   0,   1,   0],\n",
+       "[0.0, 0.0, 0.0, 1.0]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "T_spiral = symbolic_transformation(t * 2 *  sym.pi, np.array([0, 0, 1]), np.array([0, 0, m * sym.pi * n_t * t ]))\n",
-    "T_spiral"
+    "p0 = symbolic_transformation(0,np.array([0, 0, 1]), [r_w, 0, 0, 1])\n",
+    "p0"
    ]
   },
   {
@@ -205,14 +205,14 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}0\\\\s \\cos{\\left(\\alpha \\right)}\\\\s \\sin{\\left(\\alpha \\right)}\\\\1\\end{matrix}\\right]$"
+       "$\\displaystyle \\left[\\begin{matrix}r_{w} + s \\cos{\\left(\\alpha \\right)}\\\\0\\\\- s \\sin{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "Matrix([\n",
-       "[           0],\n",
-       "[s*cos(alpha)],\n",
-       "[s*sin(alpha)],\n",
-       "[           1]])"
+       "[r_w + s*cos(alpha)],\n",
+       "[                 0],\n",
+       "[     -s*sin(alpha)],\n",
+       "[               1.0]])"
       ]
      },
      "execution_count": 11,
@@ -221,30 +221,59 @@
     }
    ],
    "source": [
-    "l = sym.Matrix([0, s * sym.cos(alpha), s * sym.sin(alpha), 1])\n",
+    "l = p0 @ sym.Matrix([s * sym.cos(alpha), 0, -s * sym.sin(alpha), 1])\n",
     "l"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
+   "id": "65bb90d7-0f5b-410e-9a3a-0f9953a4d846",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\left[\\begin{matrix}\\cos{\\left(\\phi \\right)} & \\sin{\\left(\\phi \\right)} & 0 & 0\\\\- \\sin{\\left(\\phi \\right)} & \\cos{\\left(\\phi \\right)} & 0 & 0\\\\0 & 0 & 1 & m n_{t} \\phi\\\\0.0 & 0.0 & 0.0 & 1.0\\end{matrix}\\right]$"
+      ],
+      "text/plain": [
+       "Matrix([\n",
+       "[ cos(phi), sin(phi),   0,         0],\n",
+       "[-sin(phi), cos(phi),   0,         0],\n",
+       "[        0,        0,   1, m*n_t*phi],\n",
+       "[      0.0,      0.0, 0.0,       1.0]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T_spiral = symbolic_transformation(phi, np.array([0, 0, 1]), np.array([0, 0, m * phi * n_t]))\n",
+    "T_spiral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "id": "da3c8575-99ad-4258-8734-c165ea65b014",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}s \\sin{\\left(2 \\pi t \\right)} \\cos{\\left(\\alpha \\right)}\\\\s \\cos{\\left(\\alpha \\right)} \\cos{\\left(2 \\pi t \\right)}\\\\\\pi m n_{t} t + s \\sin{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
+       "$\\displaystyle \\left[\\begin{matrix}\\left(r_{w} + s \\cos{\\left(\\alpha \\right)}\\right) \\cos{\\left(\\phi \\right)}\\\\- \\left(r_{w} + s \\cos{\\left(\\alpha \\right)}\\right) \\sin{\\left(\\phi \\right)}\\\\1.0 m n_{t} \\phi - s \\sin{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "Matrix([\n",
-       "[ s*sin(2*pi*t)*cos(alpha)],\n",
-       "[ s*cos(alpha)*cos(2*pi*t)],\n",
-       "[pi*m*n_t*t + s*sin(alpha)],\n",
-       "[                      1.0]])"
+       "[ (r_w + s*cos(alpha))*cos(phi)],\n",
+       "[-(r_w + s*cos(alpha))*sin(phi)],\n",
+       "[  1.0*m*n_t*phi - s*sin(alpha)],\n",
+       "[                           1.0]])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,103 +285,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "id": "e47eb83b-6e89-4246-a82a-bd5629aedc2a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\frac{\\operatorname{asin}{\\left(\\frac{x}{s \\cos{\\left(\\alpha \\right)}} \\right)}}{2 \\pi}$"
+       "$\\displaystyle \\operatorname{acos}{\\left(\\frac{x}{r_{w} + s \\cos{\\left(\\alpha \\right)}} \\right)}$"
       ],
       "text/plain": [
-       "asin(x/(s*cos(alpha)))/(2*pi)"
+       "acos(x/(r_w + s*cos(alpha)))"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "x_cross_section = sym.simplify(sym.solve(spiral[0] - x, t)[1])\n",
-    "x_cross_section  # parameter s"
+    "x_cross_section = sym.simplify(sym.solve(spiral[0] - x, phi)[1])\n",
+    "x_cross_section"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "id": "c2954b39-eea0-4e27-987f-07a5d0dedaad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}x\\\\s \\sqrt{1 - \\frac{x^{2}}{s^{2} \\cos^{2}{\\left(\\alpha \\right)}}} \\cos{\\left(\\alpha \\right)}\\\\\\frac{m n_{t} \\operatorname{asin}{\\left(\\frac{x}{s \\cos{\\left(\\alpha \\right)}} \\right)}}{2} + s \\sin{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
+       "$\\displaystyle \\left[\\begin{matrix}x\\\\- \\sqrt{- \\frac{x^{2} - \\left(r_{w} + s \\cos{\\left(\\alpha \\right)}\\right)^{2}}{\\left(r_{w} + s \\cos{\\left(\\alpha \\right)}\\right)^{2}}} \\left(r_{w} + s \\cos{\\left(\\alpha \\right)}\\right)\\\\1.0 m n_{t} \\operatorname{acos}{\\left(\\frac{x}{r_{w} + s \\cos{\\left(\\alpha \\right)}} \\right)} - s \\sin{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "Matrix([\n",
-       "[                                               x],\n",
-       "[s*sqrt(1 - x**2/(s**2*cos(alpha)**2))*cos(alpha)],\n",
-       "[   m*n_t*asin(x/(s*cos(alpha)))/2 + s*sin(alpha)],\n",
-       "[                                             1.0]])"
+       "[                                                                                    x],\n",
+       "[-sqrt(-(x**2 - (r_w + s*cos(alpha))**2)/(r_w + s*cos(alpha))**2)*(r_w + s*cos(alpha))],\n",
+       "[                                1.0*m*n_t*acos(x/(r_w + s*cos(alpha))) - s*sin(alpha)],\n",
+       "[                                                                                  1.0]])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "spiral_x = sym.simplify(spiral.subs({t: x_cross_section}))\n",
+    "spiral_x = sym.simplify(spiral.subs({phi: x_cross_section}))\n",
     "spiral_x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 29,
    "id": "268c6302-4e7d-45e0-be28-1b64cf8b766e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\frac{\\sqrt{x^{2} + y^{2}}}{\\cos{\\left(\\alpha \\right)}}$"
+       "$\\displaystyle \\frac{- r_{w} + \\sqrt{x^{2} + y^{2}}}{\\cos{\\left(\\alpha \\right)}}$"
       ],
       "text/plain": [
-       "sqrt(x**2 + y**2)/cos(alpha)"
+       "(-r_w + sqrt(x**2 + y**2))/cos(alpha)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "y_cross_section = sym.simplify(sym.solve(spiral_x[1]- y, s)[0])\n",
+    "y_cross_section\n",
+    "y_cross_section = (sym.sqrt(x**2 + y**2) - r_w) / sym.cos(alpha)\n",
     "y_cross_section"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 30,
    "id": "7284bd6a-b47b-483c-8e9f-79fcaecce5e3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left[\\begin{matrix}x\\\\y\\\\\\frac{m n_{t} \\operatorname{asin}{\\left(\\frac{x}{r{\\left(x,y \\right)}} \\right)}}{2} + r{\\left(x,y \\right)} \\tan{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
+       "$\\displaystyle \\left[\\begin{matrix}x\\\\- y\\\\1.0 m n_{t} \\operatorname{acos}{\\left(\\frac{x}{r{\\left(x,y \\right)}} \\right)} + 1.0 r_{w} \\tan{\\left(\\alpha \\right)} - 1.0 r{\\left(x,y \\right)} \\tan{\\left(\\alpha \\right)}\\\\1.0\\end{matrix}\\right]$"
       ],
       "text/plain": [
        "Matrix([\n",
-       "[                                           x],\n",
-       "[                                           y],\n",
-       "[m*n_t*asin(x/r(x, y))/2 + r(x, y)*tan(alpha)],\n",
-       "[                                         1.0]])"
+       "[                                                                      x],\n",
+       "[                                                                     -y],\n",
+       "[1.0*m*n_t*acos(x/r(x, y)) + 1.0*r_w*tan(alpha) - 1.0*r(x, y)*tan(alpha)],\n",
+       "[                                                                    1.0]])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -366,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 31,
    "id": "e7b9f7cc-59f0-4dc6-922c-c94a753c0fd5",
    "metadata": {},
    "outputs": [
@@ -379,7 +410,7 @@
        "z(x, y)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -391,7 +422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 32,
    "id": "080e1845-8ff4-493e-aa28-5b677bfa3cb4",
    "metadata": {},
    "outputs": [
@@ -404,7 +435,7 @@
        "(y - y_p + z(x, y)*Derivative(z(x, y), y))/sqrt((y - y_p)**2 + z(x, y)**2)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -458,9 +489,9 @@
     "def compute_involute(module=1, teeth=15, height=5, worm_pitch_diameter=10, num_threads=1, alpha=np.deg2rad(20)):\n",
     "    x = 0.\n",
     "    r_w = module * teeth / 2\n",
-    "    y_p = worm_pitch_diameter / 2\n",
+    "    x_p = worm_pitch_diameter / 2\n",
     "    r_thales = r_w / 2.\n",
-    "    y_thales = y_p + r_thales\n",
+    "    x_thales = y_p + r_thales\n",
     "    \n",
     "    def length(y):\n",
     "        return (x**2 + y**2)**(0.5)\n",
@@ -470,7 +501,7 @@
     "    \n",
     "    def z(y, t):\n",
     "        r = length(y)\n",
-    "        return - module * num_threads * np.arcsin(x / r) / 2 + r * np.tan(alpha) + t\n",
+    "        return - module * num_threads * np.arcsin(x / r) / 2 + (r-r_w) * np.tan(alpha) + t\n",
     "    \n",
     "    def dz_dy(y):\n",
     "        r = length(y)\n",
@@ -609,10 +640,131 @@
     "part.show(part.makeLoft(curves_head))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "73769524-0356-4364-acbc-c4a28d26fc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t_start_1: -2.7474774194546225\n",
+      "t_start_0: 2.7474774194546225\n",
+      "t0: 0.7015390936864874, x0: 3.1224989948933315, min: 1.8775010319993481\n",
+      "t1: 0.7015390936864874, x1: 5.454356051426789, min: 0.47093369124192\n",
+      "t0: 0.5482381702072173, x0: 3.7996710369252895, min: 1.2005967588403554\n",
+      "t1: 0.5482381702072173, x1: 5.868347291705316, min: 0.8705739256491289\n",
+      "t0: -2.7474774194546225, x0: 4.0, min: 3.2681962153219666\n",
+      "t1: -2.7474774194546225, x1: 6.0, min: 3.2681962153219666\n",
+      "t0: -2.7218186874199746, x0: 3.7996710378205463, min: 3.1634473327759642\n",
+      "t1: -2.7218186874199746, x1: 5.868347293420843, min: 0.86835219843846\n",
+      "t0: 0.024534606456801573, x0: 3.1224989985781515, min: 1.877501936426569\n",
+      "t1: 0.024534606456801573, x1: 5.454356049869874, min: 0.45435622754073013\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy as sp\n",
+    "import numpy as np\n",
+    "from freecad import part\n",
+    "from freecad import app\n",
+    "from pygears.transformation import numeric_transformation\n",
+    "\n",
+    "debug = False\n",
+    "def compute_involute(module=1, teeth=15, height=5, worm_pitch_diameter=10, num_threads=1, alpha=np.deg2rad(20)):\n",
+    "    y = 0.\n",
+    "    xw = worm_pitch_diameter / 2\n",
+    "    \n",
+    "    def length(x, y):\n",
+    "        return (x**2 + y**2)**(0.5)\n",
+    "    \n",
+    "    def z(x, y, t):\n",
+    "        r = length(x, y)\n",
+    "        return module * num_threads * np.arcsin(y / r) / 2 + (r - xw) * np.tan(alpha) + t\n",
+    "\n",
+    "    def distance_pw(x, y, t):\n",
+    "        return np.sqrt((xw - x) ** 2 + z(x, y, t) ** 2)\n",
+    "\n",
+    "    def min_root(x, y, t):\n",
+    "        r0 = xw -  module # * (1 + clearence)\n",
+    "        x_t = sp.optimize.minimize(lambda x: distance_pw(x, y, t), (t)).x[0]\n",
+    "        return distance_pw(x, y, t) + np.abs(r0**2 - x**2 - y**2)\n",
+    "\n",
+    "    def min_head(x, y, t):\n",
+    "        r1 = xw +  module # * (1 + clearence)\n",
+    "        return distance_pw(x, y, t) + np.abs(r1**2 - x**2 - y**2)\n",
+    "        \n",
+    "    xyz = []        \n",
+    "    r0 = xw -  module # * (1 + clearence)\n",
+    "    r1 = xw +  module # * (1 + clearence)\n",
+    "    t_start_0 = (r0 - xw) / np.tan(alpha)\n",
+    "    t_start_1 = (r1 - xw) / np.tan(alpha)\n",
+    "\n",
+    "    \n",
+    "    for y in np.linspace(-height / 2, height / 2, 20):\n",
+    "        for t in np.linspace(t_start_0, t_start_1, 20):\n",
+    "            x_t = sp.optimize.minimize(lambda x: distance_pw(x, y, t), xw).x[0]\n",
+    "            z_t = z(x_t, y, t)\n",
+    "            point = App.Vector(x_t, y, z_t)\n",
+    "            part.show(part.Point(point).toShape())\n",
+    "    \n",
+    "    \n",
+    "compute_involute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "26079daf-9e99-4365-89f4-c560e2aac9bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "expected ':' (2870794076.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[20], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    def debug_surface()\u001b[0m\n\u001b[0m                       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected ':'\n"
+     ]
+    }
+   ],
+   "source": [
+    "    def debug_surface()\n",
+    "        x_i = np.linspace(r_w / 10., r_w * 3. / 2., 10)\n",
+    "        y_i = np.linspace(-r_w, r_w, 10)\n",
+    "    \n",
+    "    \n",
+    "        # Gitterpunkte berechnen\n",
+    "        xv, yv = np.meshgrid(x_i, y_i)\n",
+    "        zv = z(xv, yv)\n",
+    "        \n",
+    "        # Punkte für die B-Spline-Fläche erzeugen\n",
+    "        points = []\n",
+    "        for i in range(xv.shape[0]):\n",
+    "            row = []\n",
+    "            for j in range(xv.shape[1]):\n",
+    "                point = FreeCAD.Vector(xv[i, j], yv[i, j], zv[i, j])\n",
+    "                # part.show(part.Point(point).toShape())\n",
+    "                row.append(point)\n",
+    "            points.append(row)\n",
+    "        \n",
+    "        # B-Spline-Fläche erstellen\n",
+    "        bspline_surface = Part.BSplineSurface()\n",
+    "        bspline_surface.approximate(points)\n",
+    "        \n",
+    "        # Fläche in FreeCAD-Dokument einfügen\n",
+    "        doc = FreeCAD.ActiveDocument if FreeCAD.ActiveDocument else FreeCAD.newDocument()\n",
+    "        bspline_shape = Part.Shape(bspline_surface)\n",
+    "        part_object = doc.addObject(\"Part::Feature\", \"BSplineSurface\")\n",
+    "        part_object.Shape = bspline_shape\n",
+    "        doc.recompute()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cfcc64d4-3756-431d-a78b-a81d30c5f6db",
+   "id": "e0bfeffb-7261-4976-8eb3-805af31d4bfd",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/examples/worm_cutting_tool/worm_1.ipynb b/examples/worm_cutting_tool/worm_1.ipynb
new file mode 100644
index 0000000..9efb0d2
--- /dev/null
+++ b/examples/worm_cutting_tool/worm_1.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "633e2090-6b89-48a0-a725-f3fe5dded070",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sympy as sym\n",
+    "import numpy as np\n",
+    "import scipy as sp\n",
+    "\n",
+    "from pygears.transformation import symbolic_transformation, numeric_transformation\n",
+    "\n",
+    "t, x, m, xw = sym.symbols([\"t\", \"x\", \"m\", \"xw\"])\n",
+    "s, alpha, n_t, y, phi = sym.symbols([\"s\", \"alpha\", \"n_t\", \"y\", \"phi\"])\n",
+    "r = sym.Function(\"r\")(x, y)\n",
+    "z = sym.Function(\"z\")(x, y, t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2849ad6-1521-4a8b-9238-10db3978d14a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sqrt{\\left(x - xw\\right)^{2} + z^{2}{\\left(x,y,t \\right)}}$"
+      ],
+      "text/plain": [
+       "sqrt((x - xw)**2 + z(x, y, t)**2)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dp = sym.sqrt( (x - xw)**2 + z**2)\n",
+    "dp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5a954527-68c8-4d9c-9a4a-149d044dbd9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x - xw + z{\\left(x,y,t \\right)} \\frac{\\partial}{\\partial x} z{\\left(x,y,t \\right)}}{\\sqrt{\\left(x - xw\\right)^{2} + z^{2}{\\left(x,y,t \\right)}}}$"
+      ],
+      "text/plain": [
+       "(x - xw + z(x, y, t)*Derivative(z(x, y, t), x))/sqrt((x - xw)**2 + z(x, y, t)**2)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sym.simplify(sym.diff(dp, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "967cad10-d3d6-4803-bc7f-9c4a3ea15611",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x - xw + z(x,y,t) * dz_dx(x,y,t) ) / np.sqrt((x - xw)**2 + z(x,y,t)**2 )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d1c2c2ea-02b0-4c20-bfc4-e07c99a07dcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{m n_{t} \\operatorname{asin}{\\left(\\frac{y}{r{\\left(x,y \\right)}} \\right)}}{2} + t + \\left(- xw + r{\\left(x,y \\right)}\\right) \\tan{\\left(\\alpha \\right)}$"
+      ],
+      "text/plain": [
+       "m*n_t*asin(y/r(x, y))/2 + t + (-xw + r(x, y))*tan(alpha)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "z = m * n_t * sym.asin(y / r) / 2 + (r - xw) * sym.tan(alpha) + t\n",
+    "z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b27511ac-5d0a-4a18-848b-7f026d5e4268",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle - \\frac{m n_{t} y \\frac{\\partial}{\\partial x} r{\\left(x,y \\right)}}{2 \\sqrt{- \\frac{y^{2}}{r^{2}{\\left(x,y \\right)}} + 1} r^{2}{\\left(x,y \\right)}} + \\tan{\\left(\\alpha \\right)} \\frac{\\partial}{\\partial x} r{\\left(x,y \\right)}$"
+      ],
+      "text/plain": [
+       "-m*n_t*y*Derivative(r(x, y), x)/(2*sqrt(-y**2/r(x, y)**2 + 1)*r(x, y)**2) + tan(alpha)*Derivative(r(x, y), x)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sym.simplify(sym.diff(z, x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de2be944-6b75-41d5-86ba-fd896fe7ffda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.tan(alpha)*dr_dx(x,y) - module * n_t * y * d_r(x, y) / 2 / np.sqrt(1 - y**2/r(x, y)**2) / r(x, y)**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "614329e0-8a9a-4ba3-83bd-6f36a6ebb8ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\sqrt{x^{2} + y^{2}}$"
+      ],
+      "text/plain": [
+       "sqrt(x**2 + y**2)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = sym.sqrt(x**2 + y**2)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "654802b1-ef8f-4a55-b434-fa96c4daabba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{x}{\\sqrt{x^{2} + y^{2}}}$"
+      ],
+      "text/plain": [
+       "x/sqrt(x**2 + y**2)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sym.diff(r, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f07e684-1a54-4cfb-bc8c-6d73964a743a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x / r(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "05428eb2-d03e-41e8-9ffd-5263b1db6344",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t_start_1: -3.111447653720825\n",
+      "t_start_0: 3.111447653720825\n",
+      "t0: 5.8844353430527, min: 0.19329497493516876\n",
+      "t1: -1.3918753246864404, min: 0.19329497493516876\n",
+      "t0_start: -3.111447653720825, min: -0.20976372522071376\n",
+      "t1_start: 3.111447653720825, min: -0.3288153318829363\n",
+      "t0: 4.763708750550963, min: 0.2463933630820882\n",
+      "t1: -1.9272734926716688, min: 0.2463933630820882\n",
+      "t0_start: -3.111447653720825, min: -0.14675485280160713\n",
+      "t1_start: 3.111447653720825, min: -0.2126219350562448\n",
+      "t0: 4.012667297079241, min: 0.30223171174663616\n",
+      "t1: -2.348311308798585, min: 0.30223171174663616\n",
+      "t0_start: -3.111447653720825, min: -0.09533233882593803\n",
+      "t1_start: 3.111447653720825, min: -0.12058229145232521\n",
+      "t0: 3.5159311350817872, min: 0.3521073426668734\n",
+      "t1: -2.6728907365690646, min: 0.3521073426668734\n",
+      "t0_start: -3.111447653720825, min: -0.05493410831717574\n",
+      "t1_start: 3.111447653720825, min: -0.05451021796109734\n",
+      "t0: 3.220308014046925, min: 0.3861092685562969\n",
+      "t1: -2.9117371528892018, min: 0.3861092685562969\n",
+      "t0_start: -3.111447653720825, min: -0.024994493001116624\n",
+      "t1_start: 3.111447653720825, min: -0.014409133892437162\n",
+      "t0: 3.1060728037339382, min: 0.3960421566528499\n",
+      "t1: -3.070223189410352, min: 0.3960421566528499\n",
+      "t0_start: -3.111447653720825, min: -0.0051418470624212086\n",
+      "t1_start: 3.111447653720825, min: 0.000683749829790753\n",
+      "t0: 3.178050212054612, min: 0.37819253041835493\n",
+      "t1: -3.149166976494136, min: 0.37819253041835493\n",
+      "t0_start: -3.111447653720825, min: 0.004679032665391107\n",
+      "t1_start: 3.111447653720825, min: -0.007975192061447627\n",
+      "t0: 3.4669537229544676, min: 0.33507654125843167\n",
+      "t1: -3.1448811322457937, min: 0.33507654125843167\n",
+      "t0_start: -3.111447653720825, min: 0.004116345465010848\n",
+      "t1_start: 3.111447653720825, min: -0.03918284373884396\n",
+      "t0: 4.04142240794985, min: 0.2754383781803408\n",
+      "t1: -3.048491342959033, min: 0.2754383781803408\n",
+      "t0_start: -3.111447653720825, min: -0.007673356822998837\n",
+      "t1_start: 3.111447653720825, min: -0.09185855015081275\n",
+      "t0: 5.040851531314434, min: 0.21150842804581535\n",
+      "t1: -2.8443012072201954, min: 0.21150842804581535\n",
+      "t0_start: -3.111447653720825, min: -0.032110254104131264\n",
+      "t1_start: 3.111447653720825, min: -0.16493694228836014\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy as sp\n",
+    "import numpy as np\n",
+    "from freecad import part\n",
+    "from freecad import app\n",
+    "from pygears.transformation import numeric_transformation\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "debug = False\n",
+    "def compute_involute(module=1, teeth=15, height=5, worm_pitch_diameter=10, num_threads=1, alpha=np.deg2rad(20)):\n",
+    "    y = 0.\n",
+    "    xw = worm_pitch_diameter / 2\n",
+    "    \n",
+    "    def r(x, y):\n",
+    "        return (x**2 + y**2)**(0.5)\n",
+    "\n",
+    "    def dr_dx(x, y):\n",
+    "        return x / r(x, y)\n",
+    "    \n",
+    "    def z(x, y, t):\n",
+    "        r_xy = r(x, y)\n",
+    "        return module * num_threads * np.arcsin(y / r_xy) / 2 + (r_xy - xw) * np.tan(alpha) + t\n",
+    "\n",
+    "    def dz_dx(x, y, t):\n",
+    "        r_xy = r(x, y)\n",
+    "        return np.tan(alpha) * dr_dx(x,y) - module * num_threads * y * dr_dx(x, y) / 2 / np.sqrt(1 - y**2/r_xy**2) / r_xy**2\n",
+    "\n",
+    "    def distance_pw(x, y, t):\n",
+    "        return np.sqrt((xw - x) ** 2 + z(x, y, t) ** 2)\n",
+    "\n",
+    "    def d_distance_pw_dx(x, y, t):\n",
+    "        return (x - xw + z(x,y,t) * dz_dx(x,y,t)) / np.sqrt((x-xw) ** 2 + z(x,y,t) ** 2)\n",
+    "\n",
+    "    def min_root(y, t):\n",
+    "        r0 = xw +  module # * (1 + clearence)\n",
+    "        x0 = np.sqrt(r0**2 - y**2)\n",
+    "        return d_distance_pw_dx(x0, y, t)\n",
+    "        \n",
+    "    def min_head(y, t):\n",
+    "        r1 = xw -  module # * (1 + clearence)\n",
+    "        x1 = np.sqrt(r1**2 - y**2)\n",
+    "        return d_distance_pw_dx(x1, y, t)\n",
+    "        \n",
+    "    xyz = []        \n",
+    "    r0 = xw -  module # * (1 + clearence)\n",
+    "    r1 = xw +  module # * (1 + clearence)\n",
+    "    t_start_0 = (r0 - xw) * (np.tan(alpha) + 1. / np.tan(alpha))\n",
+    "    t_start_1 = (r1 - xw) * (np.tan(alpha) + 1. / np.tan(alpha))\n",
+    "    print(f\"t_start_1: {t_start_0}\")\n",
+    "    print(f\"t_start_0: {t_start_1}\")\n",
+    "\n",
+    "    for y in np.linspace(- height / 2, height / 3, 20):\n",
+    "        t_i = np.linspace(t_start_0 * 2, t_start_1 * 2, 100)\n",
+    "        plt.plot(t_i, [min_root(y, t) for t in t_i])\n",
+    "        plt.plot(t_i, [min_head(y, t) for t in t_i])\n",
+    "        plt.grid()\n",
+    "        t0 = sp.optimize.root(lambda t: min_head(y, t)**2, t_start_1).x[0]\n",
+    "        t1 = sp.optimize.root(lambda t: min_root(y, t)**2, t_start_0).x[0]\n",
+    "        print(f\"t0: {t0}, min: {min_root(y, t0)**2}\")\n",
+    "        print(f\"t1: {t1}, min: {min_root(y, t0)**2}\")        \n",
+    "        print(f\"t0_start: {t_start_0}, min: {min_root(y, t_start_0)}\")\n",
+    "        print(f\"t1_start: {t_start_1}, min: {min_head(y, t_start_1)}\")\n",
+    "        for t in np.linspace(t0, t1, 20):\n",
+    "            x_t = sp.optimize.minimize(lambda x: distance_pw(x, y, t), xw).x[0]\n",
+    "            z_t = z(x_t, y, t)\n",
+    "            point = App.Vector(x_t, y, z_t)\n",
+    "            part.show(part.Point(point).toShape())\n",
+    "\n",
+    "compute_involute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08796217-5c32-4970-9c2e-486855bfe01a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pixi.lock b/pixi.lock
index d17207b..5f27acf 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -27,6 +27,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.6-hef167b5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda
@@ -40,7 +42,9 @@ environments:
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   timestamp: 1719266616208
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+  - libbrotlienc 1.1.0 h00291cd_2
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+  license: MIT
+  license_family: MIT
+  purls: []
+  size: 16643
+  timestamp: 1725267837325
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+  license: MIT
+  license_family: MIT
+  purls: []
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+  timestamp: 1725268026061
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+  md5: d22534a9be5771fc58eb7564947f669d
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+  platform: win
+  license: MIT
+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py312h2ec8cdc_2.conda
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   md5: 83b4ad1e6dc14df5891f3fcfdeb44351
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-  platform: win
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   - python_abi 3.12.* *_cp312
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-  platform: linux
   license: MIT
   license_family: MIT
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   - python_abi 3.12.* *_cp312
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-  platform: linux
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-  platform: osx
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   - python >=3.12,<3.13.0a0 *_cpython
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-  platform: osx
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-  platform: win
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-  platform: linux
   license: BSD-3-Clause
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   size: 12103
   timestamp: 1733503053903
+- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py312h68727a3_0.conda
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+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  arch: x86_64
+  platform: linux
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/contourpy?source=hash-mapping
+  size: 276332
+  timestamp: 1731428454756
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.3.1-py312h451a7dd_0.conda
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+  - python >=3.12,<3.13.0a0 *_cpython
+  - python_abi 3.12.* *_cp312
+  arch: aarch64
+  platform: linux
+  license: BSD-3-Clause
+  license_family: BSD
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.3.1-py312hc47a885_0.conda
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+  license: BSD-3-Clause
+  license_family: BSD
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+  - python_abi 3.12.* *_cp312
+  arch: arm64
+  platform: osx
+  license: BSD-3-Clause
+  license_family: BSD
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+  timestamp: 1731428781337
+- conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.1-py312hd5eb7cc_0.conda
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+  - vc14_runtime >=14.29.30139
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+  license: BSD-3-Clause
+  license_family: BSD
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+- conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
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   license_family: MIT
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+  license_family: MIT
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+  license: MIT
+  license_family: MIT
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+  - vc >=14.2,<15
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+  license: MIT
+  license_family: MIT
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   license: Apache-2.0
   license_family: APACHE
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@@ -4468,8 +4611,6 @@ packages:
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@@ -4485,8 +4626,6 @@ packages:
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@@ -4502,8 +4641,6 @@ packages:
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@@ -4518,8 +4655,6 @@ packages:
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@@ -4535,8 +4670,6 @@ packages:
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@@ -4552,8 +4685,6 @@ packages:
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@@ -4571,8 +4702,6 @@ packages:
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@@ -4585,8 +4714,6 @@ packages:
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@@ -4599,8 +4726,6 @@ packages:
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@@ -4613,8 +4738,6 @@ packages:
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@@ -4627,8 +4750,6 @@ packages:
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@@ -4641,8 +4762,6 @@ packages:
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@@ -4657,8 +4776,6 @@ packages:
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@@ -4673,8 +4790,6 @@ packages:
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-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
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@@ -4689,8 +4804,6 @@ packages:
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-  platform: linux
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@@ -4775,8 +4876,6 @@ packages:
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@@ -4792,8 +4891,6 @@ packages:
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   - python_abi 3.12.* *_cp312
-  arch: x86_64
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   license: LGPL-3.0-or-later
   license_family: LGPL
   purls:
@@ -4811,8 +4908,6 @@ packages:
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   - python >=3.12,<3.13.0a0 *_cpython
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-  arch: aarch64
-  platform: linux
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   purls:
@@ -4829,8 +4924,6 @@ packages:
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   purls:
@@ -4848,8 +4941,6 @@ packages:
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   - python >=3.12,<3.13.0a0 *_cpython
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-  arch: arm64
-  platform: osx
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@@ -4879,8 +4970,6 @@ packages:
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@@ -4909,8 +4998,6 @@ packages:
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@@ -4933,8 +5020,6 @@ packages:
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@@ -4957,8 +5042,6 @@ packages:
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@@ -4981,8 +5064,6 @@ packages:
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@@ -5008,8 +5087,6 @@ packages:
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@@ -5046,8 +5119,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: EPL-1.0
   license_family: Other
   purls: []
@@ -5097,8 +5166,6 @@ packages:
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-  arch: aarch64
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   purls: []
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-  arch: x86_64
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@@ -5147,8 +5212,6 @@ packages:
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-  arch: arm64
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@@ -5170,8 +5233,6 @@ packages:
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-  arch: x86_64
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@@ -5195,8 +5256,6 @@ packages:
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@@ -5218,8 +5277,6 @@ packages:
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@@ -5235,8 +5292,6 @@ packages:
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@@ -5452,8 +5485,6 @@ packages:
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-  platform: linux
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-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -7846,8 +7909,6 @@ packages:
   - vc14_runtime >=14.29.30139
   constrains:
   - expat 2.6.4.*
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -7858,8 +7919,6 @@ packages:
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   - libgcc-ng >=9.4.0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -7870,8 +7929,6 @@ packages:
   md5: dddd85f4d52121fab0a8b099c5e06501
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   - libgcc-ng >=9.4.0
-  arch: aarch64
-  platform: linux
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   license_family: MIT
   purls: []
@@ -7880,8 +7937,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2
   sha256: 7a2d27a936ceee6942ea4d397f9c7d136f12549d86f7617e8b6bad51e01a941f
   md5: ccb34fb14960ad8b125962d3d79b31a9
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -7890,8 +7945,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.2-h3422bc3_5.tar.bz2
   sha256: 41b3d13efb775e340e4dba549ab5c029611ea6918703096b2eaa9c015c0750ca
   md5: 086914b672be056eb70fd4285b6783b6
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -7903,8 +7956,6 @@ packages:
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   - vs2015_runtime >=14.16.27012
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -7919,8 +7970,6 @@ packages:
   constrains:
   - libgomp 14.2.0 h77fa898_1
   - libgcc-ng ==14.2.0=*_1
-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -7934,8 +7983,6 @@ packages:
   constrains:
   - libgcc-ng ==14.2.0=*_1
   - libgomp 14.2.0 he277a41_1
-  arch: aarch64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -7946,8 +7993,6 @@ packages:
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-  arch: x86_64
-  platform: linux
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@@ -7958,8 +8003,6 @@ packages:
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-  arch: aarch64
-  platform: linux
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   license_family: GPL
   purls: []
@@ -7981,8 +8024,6 @@ packages:
   - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
-  arch: x86_64
-  platform: linux
   license: GD
   license_family: BSD
   purls: []
@@ -8003,8 +8044,6 @@ packages:
   - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
-  arch: aarch64
-  platform: linux
   license: GD
   license_family: BSD
   purls: []
@@ -8026,8 +8065,6 @@ packages:
   - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
-  arch: x86_64
-  platform: osx
   license: GD
   license_family: BSD
   purls: []
@@ -8049,8 +8086,6 @@ packages:
   - libtiff >=4.6.0,<4.8.0a0
   - libwebp-base >=1.4.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
-  arch: arm64
-  platform: osx
   license: GD
   license_family: BSD
   purls: []
@@ -8074,8 +8109,6 @@ packages:
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   - xorg-libxpm >=3.5.17,<4.0a0
-  arch: x86_64
-  platform: win
   license: GD
   license_family: BSD
   purls: []
@@ -8088,8 +8121,6 @@ packages:
   - libgfortran5 14.2.0 hd5240d6_1
   constrains:
   - libgfortran-ng ==14.2.0=*_1
-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8102,8 +8133,6 @@ packages:
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   constrains:
   - libgfortran-ng ==14.2.0=*_1
-  arch: aarch64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8114,8 +8143,6 @@ packages:
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   depends:
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-  arch: x86_64
-  platform: osx
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   license_family: GPL
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@@ -8126,8 +8153,6 @@ packages:
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-  arch: arm64
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   license_family: GPL
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@@ -8140,8 +8165,6 @@ packages:
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   constrains:
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-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8154,8 +8177,6 @@ packages:
   - libgcc >=14.2.0
   constrains:
   - libgfortran 14.2.0
-  arch: aarch64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8168,8 +8189,6 @@ packages:
   - llvm-openmp >=8.0.0
   constrains:
   - libgfortran 5.0.0 13_2_0_*_3
-  arch: x86_64
-  platform: osx
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8182,8 +8201,6 @@ packages:
   - llvm-openmp >=8.0.0
   constrains:
   - libgfortran 5.0.0 13_2_0_*_3
-  arch: arm64
-  platform: osx
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8196,8 +8213,6 @@ packages:
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   - libglvnd 1.7.0 ha4b6fd6_2
   - libglx 1.7.0 ha4b6fd6_2
-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
   size: 134712
@@ -8208,8 +8223,6 @@ packages:
   depends:
   - libglvnd 1.7.0 hd24410f_2
   - libglx 1.7.0 hd24410f_2
-  arch: aarch64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
   size: 145442
@@ -8226,8 +8239,6 @@ packages:
   - pcre2 >=10.44,<10.45.0a0
   constrains:
   - glib 2.82.2 *_0
-  arch: x86_64
-  platform: linux
   license: LGPL-2.1-or-later
   purls: []
   size: 3931898
@@ -8243,8 +8254,6 @@ packages:
   - pcre2 >=10.44,<10.45.0a0
   constrains:
   - glib 2.82.2 *_0
-  arch: aarch64
-  platform: linux
   license: LGPL-2.1-or-later
   purls: []
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@@ -8261,8 +8270,6 @@ packages:
   - pcre2 >=10.44,<10.45.0a0
   constrains:
   - glib 2.82.2 *_0
-  arch: x86_64
-  platform: osx
   license: LGPL-2.1-or-later
   purls: []
   size: 3692367
@@ -8279,8 +8286,6 @@ packages:
   - pcre2 >=10.44,<10.45.0a0
   constrains:
   - glib 2.82.2 *_0
-  arch: arm64
-  platform: osx
   license: LGPL-2.1-or-later
   purls: []
   size: 3635416
@@ -8299,8 +8304,6 @@ packages:
   - vc14_runtime >=14.29.30139
   constrains:
   - glib 2.82.2 *_0
-  arch: x86_64
-  platform: win
   license: LGPL-2.1-or-later
   purls: []
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@@ -8320,8 +8323,6 @@ packages:
   - xorg-libxdamage >=1.1.6,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxxf86vm >=1.1.5,<2.0a0
-  arch: x86_64
-  platform: linux
   license: SGI-2
   purls: []
   size: 325361
@@ -8342,8 +8343,6 @@ packages:
   - xorg-libxdmcp >=1.1.5,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxxf86vm >=1.1.5,<2.0a0
-  arch: aarch64
-  platform: linux
   license: SGI-2
   purls: []
   size: 317433
@@ -8353,8 +8352,6 @@ packages:
   md5: 434ca7e50e40f4918ab701e3facd59a0
   depends:
   - __glibc >=2.17,<3.0.a0
-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
   size: 132463
@@ -8362,8 +8359,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libglvnd-1.7.0-hd24410f_2.conda
   sha256: 57ec3898a923d4bcc064669e90e8abfc4d1d945a13639470ba5f3748bd3090da
   md5: 9e115653741810778c9a915a2f8439e7
-  arch: aarch64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
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@@ -8375,8 +8370,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libglvnd 1.7.0 ha4b6fd6_2
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
   size: 75504
@@ -8387,8 +8380,6 @@ packages:
   depends:
   - libglvnd 1.7.0 hd24410f_2
   - xorg-libx11 >=1.8.9,<2.0a0
-  arch: aarch64
-  platform: linux
   license: LicenseRef-libglvnd
   purls: []
   size: 77736
@@ -8398,8 +8389,6 @@ packages:
   md5: cc3573974587f12dda90d96e3e55a702
   depends:
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-  arch: x86_64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8408,8 +8397,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libgomp-14.2.0-he277a41_1.conda
   sha256: 5aa53874a5e57a00f2e0c2e2910684eb674429cd5fcb803619b226a73e89aedf
   md5: 376f0e73abbda6d23c0cb749adc195ef
-  arch: aarch64
-  platform: linux
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
   purls: []
@@ -8423,8 +8410,6 @@ packages:
   - libgcc >=13
   - libstdcxx >=13
   - libxml2 >=2.13.4,<3.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8437,8 +8422,6 @@ packages:
   - libgcc >=13
   - libstdcxx >=13
   - libxml2 >=2.13.4,<3.0a0
-  arch: aarch64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8451,8 +8434,6 @@ packages:
   - __osx >=10.13
   - libcxx >=18
   - libxml2 >=2.13.4,<3.0a0
-  arch: x86_64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8465,8 +8446,6 @@ packages:
   - __osx >=11.0
   - libcxx >=18
   - libxml2 >=2.13.4,<3.0a0
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8481,8 +8460,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8493,8 +8470,6 @@ packages:
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-  platform: linux
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@@ -8504,8 +8479,6 @@ packages:
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-  platform: linux
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@@ -8513,8 +8486,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libiconv-1.17-hd75f5a5_2.conda
   sha256: 23d4923baeca359423a7347c2ed7aaf48c68603df0cf8b87cc94a10b0d4e9a23
   md5: 6c3628d047e151efba7cf08c5e54d1ca
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-  platform: osx
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@@ -8522,8 +8493,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libiconv-1.17-h0d3ecfb_2.conda
   sha256: bc7de5097b97bcafcf7deaaed505f7ce02f648aac8eccc0d5a47cc599a1d0304
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-  arch: arm64
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@@ -8535,8 +8504,6 @@ packages:
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-  arch: x86_64
-  platform: win
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   purls: []
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@@ -8547,8 +8514,6 @@ packages:
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-  arch: x86_64
-  platform: osx
   license: LGPL-2.1-or-later
   purls: []
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@@ -8559,8 +8524,6 @@ packages:
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-  arch: arm64
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@@ -8570,8 +8533,6 @@ packages:
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-  arch: x86_64
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@@ -8583,8 +8544,6 @@ packages:
   - libgcc-ng >=12
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-  arch: x86_64
-  platform: linux
   license: IJG AND BSD-3-Clause AND Zlib
   purls: []
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@@ -8596,8 +8555,6 @@ packages:
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-  arch: aarch64
-  platform: linux
   license: IJG AND BSD-3-Clause AND Zlib
   purls: []
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@@ -8607,8 +8564,6 @@ packages:
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-  arch: x86_64
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   purls: []
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@@ -8618,8 +8573,6 @@ packages:
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-  arch: arm64
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   purls: []
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@@ -8633,8 +8586,6 @@ packages:
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-  arch: x86_64
-  platform: win
   license: IJG AND BSD-3-Clause AND Zlib
   purls: []
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@@ -8649,8 +8600,6 @@ packages:
   - libcblas 3.9.0 26_linux64_openblas
   - liblapacke 3.9.0 26_linux64_openblas
   - blas * openblas
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8666,8 +8615,6 @@ packages:
   - blas * openblas
   - liblapacke 3.9.0 26_linuxaarch64_openblas
   - libcblas 3.9.0 26_linuxaarch64_openblas
-  arch: aarch64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8683,8 +8630,6 @@ packages:
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   - blas * openblas
   - liblapacke 3.9.0 26_osx64_openblas
-  arch: x86_64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8700,8 +8645,6 @@ packages:
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   - libcblas 3.9.0 26_osxarm64_openblas
   - blas * openblas
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8717,8 +8660,6 @@ packages:
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   - blas * mkl
   - libcblas 3.9.0 8_mkl
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -8732,8 +8673,6 @@ packages:
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   - zstd >=1.5.5,<1.6.0a0
-  arch: x86_64
-  platform: osx
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8748,8 +8687,6 @@ packages:
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-  arch: arm64
-  platform: osx
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8765,8 +8702,6 @@ packages:
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-  arch: x86_64
-  platform: linux
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8781,8 +8716,6 @@ packages:
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-  arch: aarch64
-  platform: linux
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8797,8 +8730,6 @@ packages:
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-  arch: x86_64
-  platform: osx
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8813,8 +8744,6 @@ packages:
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-  arch: arm64
-  platform: osx
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
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@@ -8826,8 +8755,6 @@ packages:
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   - libgcc >=13
-  arch: x86_64
-  platform: linux
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   purls: []
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@@ -8837,8 +8764,6 @@ packages:
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@@ -8848,8 +8773,6 @@ packages:
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@@ -8859,8 +8782,6 @@ packages:
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   size: 56283
   timestamp: 1729066082188
+- conda: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
+  sha256: f86fb22b58e93d04b6f25e0d811b56797689d598788b59dcb47f59045b568306
+  md5: 2ba8498c1018c1e9c61eb99b973dfe19
+  depends:
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+  license: Apache-2.0
+  license_family: Apache
+  purls:
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+  size: 12452
+  timestamp: 1600387789153
 - conda: https://conda.anaconda.org/conda-forge/linux-64/mysql-common-9.0.1-h266115a_3.conda
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   timestamp: 1733327448200
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+  - python_abi 3.12.* *_cp312
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+  platform: linux
+  license: HPND
+  purls:
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+  - python >=3.12,<3.13.0a0 *_cpython
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+  license: HPND
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+  - python_abi 3.12.* *_cp312
+  - tk >=8.6.13,<8.7.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
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+  license: HPND
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+- conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.0-pyhd8ed1ab_2.conda
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@@ -17220,8 +16644,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17233,8 +16655,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17246,8 +16666,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libxcb >=1.16,<2.0.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17262,8 +16680,6 @@ packages:
   - libgcc >=13
   - libnsl >=2.0.1,<2.1.0a0
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   license_family: Apache
   purls: []
@@ -17277,8 +16693,6 @@ packages:
   - libgcc >=13
   - libnsl >=2.0.1,<2.1.0a0
   - libstdcxx >=13
-  arch: aarch64
-  platform: linux
   license: Apache-2.0
   license_family: Apache
   purls: []
@@ -17291,8 +16705,6 @@ packages:
   - __osx >=10.13
   - icu >=75.1,<76.0a0
   - libcxx >=17
-  arch: x86_64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls: []
@@ -17305,8 +16717,6 @@ packages:
   - __osx >=11.0
   - icu >=75.1,<76.0a0
   - libcxx >=17
-  arch: arm64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls: []
@@ -17319,8 +16729,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: Apache-2.0
   license_family: Apache
   purls: []
@@ -17333,8 +16741,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17346,8 +16752,6 @@ packages:
   depends:
   - libgcc >=13
   - xorg-libx11 >=1.8.9,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17359,8 +16763,6 @@ packages:
   depends:
   - m2w64-gcc-libs
   - xorg-xextproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17371,8 +16773,6 @@ packages:
   md5: 8d11c1dac4756ca57e78c1bfe173bba4
   depends:
   - m2w64-gcc-libs
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17384,8 +16784,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17396,8 +16794,6 @@ packages:
   md5: c8d8ec3e00cd0fd8a231789b91a7c5b7
   depends:
   - libgcc >=13
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17408,8 +16804,6 @@ packages:
   md5: d894608e2c18127545d67a096f1b4bab
   depends:
   - __osx >=10.13
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17420,8 +16814,6 @@ packages:
   md5: daf3b34253eea046c9ab94e0c3b2f83d
   depends:
   - __osx >=11.0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17434,8 +16826,6 @@ packages:
   - m2w64-gcc-libs
   - m2w64-gcc-libs-core
   - xorg-libx11 >=1.8.4,<2.0a0
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17449,8 +16839,6 @@ packages:
   - libgcc >=13
   - libuuid >=2.38.1,<3.0a0
   - xorg-libice >=1.1.2,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17463,8 +16851,6 @@ packages:
   - libgcc >=13
   - libuuid >=2.38.1,<3.0a0
   - xorg-libice >=1.1.1,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17476,8 +16862,6 @@ packages:
   depends:
   - __osx >=10.13
   - xorg-libice >=1.1.2,<2.0a0
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17489,8 +16873,6 @@ packages:
   depends:
   - __osx >=11.0
   - xorg-libice >=1.1.2,<2.0a0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17503,8 +16885,6 @@ packages:
   - m2w64-gcc-libs
   - m2w64-gcc-libs-core
   - xorg-libice >=1.1.1,<2.0a0
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17517,8 +16897,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - libxcb >=1.17.0,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17530,8 +16908,6 @@ packages:
   depends:
   - libgcc >=13
   - libxcb >=1.17.0,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17543,8 +16919,6 @@ packages:
   depends:
   - __osx >=10.13
   - libxcb >=1.17.0,<2.0a0
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17556,8 +16930,6 @@ packages:
   depends:
   - __osx >=11.0
   - libxcb >=1.17.0,<2.0a0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17573,8 +16945,6 @@ packages:
   - xorg-kbproto
   - xorg-xextproto >=7.3.0,<8.0a0
   - xorg-xproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17586,8 +16956,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17598,8 +16966,6 @@ packages:
   md5: d5397424399a66d33c80b1f2345a36a6
   depends:
   - libgcc >=13
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17610,8 +16976,6 @@ packages:
   md5: 4cf40e60b444d56512a64f39d12c20bd
   depends:
   - __osx >=10.13
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17622,8 +16986,6 @@ packages:
   md5: 50901e0764b7701d8ed7343496f4f301
   depends:
   - __osx >=11.0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17635,8 +16997,6 @@ packages:
   depends:
   - m2w64-gcc-libs
   - m2w64-gcc-libs-core
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17650,8 +17010,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17664,8 +17022,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17680,8 +17036,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17695,8 +17049,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17711,8 +17063,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17726,8 +17076,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17739,8 +17087,6 @@ packages:
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17751,8 +17097,6 @@ packages:
   md5: 25a5a7b797fe6e084e04ffe2db02fc62
   depends:
   - libgcc >=13
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17763,8 +17107,6 @@ packages:
   md5: 9f438e1b6f4e73fd9e6d78bfe7c36743
   depends:
   - __osx >=10.13
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17775,8 +17117,6 @@ packages:
   md5: 77c447f48cab5d3a15ac224edb86a968
   depends:
   - __osx >=11.0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17787,8 +17127,6 @@ packages:
   md5: 46878ebb6b9cbd8afcf8088d7ef00ece
   depends:
   - m2w64-gcc-libs
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17801,8 +17139,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17814,8 +17150,6 @@ packages:
   depends:
   - libgcc >=13
   - xorg-libx11 >=1.8.9,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17827,8 +17161,6 @@ packages:
   depends:
   - __osx >=10.13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17840,8 +17172,6 @@ packages:
   depends:
   - __osx >=11.0
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17854,8 +17184,6 @@ packages:
   - m2w64-gcc-libs
   - xorg-libx11 >=1.7.2,<2.0a0
   - xorg-xextproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17868,8 +17196,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17881,8 +17207,6 @@ packages:
   depends:
   - libgcc >=13
   - xorg-libx11 >=1.8.9,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17894,8 +17218,6 @@ packages:
   depends:
   - __osx >=10.13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17907,8 +17229,6 @@ packages:
   depends:
   - __osx >=11.0
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -17921,8 +17241,6 @@ packages:
   - m2w64-gcc-libs
   - xorg-fixesproto
   - xorg-libx11 >=1.7.0,<2.0a0
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -17937,8 +17255,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17952,8 +17268,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxfixes >=6.0.1,<7.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17968,8 +17282,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxt >=1.3.0,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -17983,8 +17295,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxt >=1.3.0,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18001,8 +17311,6 @@ packages:
   - xorg-libxt >=1.3.0,<2.0a0
   - xorg-xextproto >=7.3.0,<8.0a0
   - xorg-xproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18017,8 +17325,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18032,8 +17338,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxrender >=0.9.11,<0.10.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18046,8 +17350,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18059,8 +17361,6 @@ packages:
   depends:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18072,8 +17372,6 @@ packages:
   depends:
   - __osx >=10.13
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18085,8 +17383,6 @@ packages:
   depends:
   - __osx >=11.0
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18100,8 +17396,6 @@ packages:
   - m2w64-gcc-libs-core
   - xorg-libx11 >=1.8.6,<2.0a0
   - xorg-renderproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18116,8 +17410,6 @@ packages:
   - xorg-libice >=1.1.1,<2.0a0
   - xorg-libsm >=1.2.4,<2.0a0
   - xorg-libx11 >=1.8.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18131,8 +17423,6 @@ packages:
   - xorg-libice >=1.1.1,<2.0a0
   - xorg-libsm >=1.2.4,<2.0a0
   - xorg-libx11 >=1.8.9,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18149,8 +17439,6 @@ packages:
   - xorg-libsm >=1.2.4,<2.0a0
   - xorg-libx11 >=1.8.6,<2.0a0
   - xorg-xproto
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18165,8 +17453,6 @@ packages:
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxi >=1.7.10,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18180,8 +17466,6 @@ packages:
   - xorg-libx11 >=1.8.9,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
   - xorg-libxi >=1.7.10,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18195,8 +17479,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18209,8 +17491,6 @@ packages:
   - libgcc >=13
   - xorg-libx11 >=1.8.10,<2.0a0
   - xorg-libxext >=1.3.6,<2.0a0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18221,8 +17501,6 @@ packages:
   md5: d5dc44d9c5a98b56111d9492333b71e6
   depends:
   - m2w64-gcc-libs
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18233,8 +17511,6 @@ packages:
   md5: 6e6c2639620e436bddb7c040cd4f3adb
   depends:
   - m2w64-gcc-libs
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18245,8 +17521,6 @@ packages:
   md5: 88f3c65d2ad13826a9e0b162063be023
   depends:
   - m2w64-gcc-libs
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18257,8 +17531,6 @@ packages:
   md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae
   depends:
   - libgcc-ng >=9.4.0
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18269,8 +17541,6 @@ packages:
   md5: b853307650cb226731f653aa623936a4
   depends:
   - libgcc-ng >=9.4.0
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18279,8 +17549,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-64/yaml-0.2.5-h0d85af4_2.tar.bz2
   sha256: 5301417e2c8dea45b401ffee8df3957d2447d4ce80c83c5ff151fc6bfe1c4148
   md5: d7e08fcf8259d742156188e8762b4d20
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18289,8 +17557,6 @@ packages:
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/yaml-0.2.5-h3422bc3_2.tar.bz2
   sha256: 93181a04ba8cfecfdfb162fc958436d868cc37db504c58078eab4c1a3e57fbb7
   md5: 4bb3f014845110883a3c5ee811fd84b4
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18302,8 +17568,6 @@ packages:
   depends:
   - vc >=14.1,<15.0a0
   - vs2015_runtime >=14.16.27012
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18315,8 +17579,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libstdcxx-ng >=12
-  arch: x86_64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18328,8 +17590,6 @@ packages:
   depends:
   - libgcc-ng >=12
   - libstdcxx-ng >=12
-  arch: aarch64
-  platform: linux
   license: MIT
   license_family: MIT
   purls: []
@@ -18340,8 +17600,6 @@ packages:
   md5: 1bb3addc859ed1338370da6e2996ef47
   depends:
   - libcxx >=15.0.7
-  arch: x86_64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18352,8 +17610,6 @@ packages:
   md5: e783a232972a5c7dca549111e63a78b2
   depends:
   - libcxx >=15.0.7
-  arch: arm64
-  platform: osx
   license: MIT
   license_family: MIT
   purls: []
@@ -18366,8 +17622,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MIT
   license_family: MIT
   purls: []
@@ -18384,8 +17638,6 @@ packages:
   - propcache >=0.2.1
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  arch: x86_64
-  platform: linux
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -18403,8 +17655,6 @@ packages:
   - python >=3.12,<3.13.0a0
   - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  arch: aarch64
-  platform: linux
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -18421,8 +17671,6 @@ packages:
   - propcache >=0.2.1
   - python >=3.12,<3.13.0a0
   - python_abi 3.12.* *_cp312
-  arch: x86_64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -18440,8 +17688,6 @@ packages:
   - python >=3.12,<3.13.0a0
   - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
-  arch: arm64
-  platform: osx
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -18460,8 +17706,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: Apache-2.0
   license_family: Apache
   purls:
@@ -18477,8 +17721,6 @@ packages:
   - libgcc >=13
   - libsodium >=1.0.20,<1.0.21.0a0
   - libstdcxx >=13
-  arch: x86_64
-  platform: linux
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -18492,8 +17734,6 @@ packages:
   - libgcc >=13
   - libsodium >=1.0.20,<1.0.21.0a0
   - libstdcxx >=13
-  arch: aarch64
-  platform: linux
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -18507,8 +17747,6 @@ packages:
   - krb5 >=1.21.3,<1.22.0a0
   - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
-  arch: x86_64
-  platform: osx
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -18522,8 +17760,6 @@ packages:
   - krb5 >=1.21.3,<1.22.0a0
   - libcxx >=18
   - libsodium >=1.0.20,<1.0.21.0a0
-  arch: arm64
-  platform: osx
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -18538,8 +17774,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
@@ -18563,8 +17797,6 @@ packages:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
   - libzlib 1.3.1 hb9d3cd8_2
-  arch: x86_64
-  platform: linux
   license: Zlib
   license_family: Other
   purls: []
@@ -18576,8 +17808,6 @@ packages:
   depends:
   - libgcc >=13
   - libzlib 1.3.1 h86ecc28_2
-  arch: aarch64
-  platform: linux
   license: Zlib
   license_family: Other
   purls: []
@@ -18589,8 +17819,6 @@ packages:
   depends:
   - __osx >=10.13
   - libzlib 1.3.1 hd23fc13_2
-  arch: x86_64
-  platform: osx
   license: Zlib
   license_family: Other
   purls: []
@@ -18602,8 +17830,6 @@ packages:
   depends:
   - __osx >=11.0
   - libzlib 1.3.1 h8359307_2
-  arch: arm64
-  platform: osx
   license: Zlib
   license_family: Other
   purls: []
@@ -18617,8 +17843,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: Zlib
   license_family: Other
   purls: []
@@ -18635,8 +17859,6 @@ packages:
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -18654,8 +17876,6 @@ packages:
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: aarch64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -18672,8 +17892,6 @@ packages:
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -18691,8 +17909,6 @@ packages:
   - python_abi 3.12.* *_cp312
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -18711,8 +17927,6 @@ packages:
   - vc14_runtime >=14.29.30139
   - zstd >=1.5.6,<1.5.7.0a0
   - zstd >=1.5.6,<1.6.0a0
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls:
@@ -18726,8 +17940,6 @@ packages:
   - libgcc-ng >=12
   - libstdcxx-ng >=12
   - libzlib >=1.2.13,<2.0.0a0
-  arch: x86_64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -18740,8 +17952,6 @@ packages:
   - libgcc-ng >=12
   - libstdcxx-ng >=12
   - libzlib >=1.2.13,<2.0.0a0
-  arch: aarch64
-  platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -18753,8 +17963,6 @@ packages:
   depends:
   - __osx >=10.9
   - libzlib >=1.2.13,<2.0.0a0
-  arch: x86_64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -18766,8 +17974,6 @@ packages:
   depends:
   - __osx >=11.0
   - libzlib >=1.2.13,<2.0.0a0
-  arch: arm64
-  platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
@@ -18781,8 +17987,6 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  arch: x86_64
-  platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
diff --git a/pixi.toml b/pixi.toml
index 43b4906..092fe0b 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -20,3 +20,4 @@ sympy = ">=1.13.3,<2"
 jupyter = ">=1.1.1,<2"
 freecad = ">=1.0.0,<2"
 pylint = ">=3.3.2,<4"
+matplotlib = ">=3.10.0,<4"