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diff --git a/out/bspline0.dat b/out/bspline0.dat
new file mode 100644
index 0000000..774fa6d
--- /dev/null
+++ b/out/bspline0.dat
@@ -0,0 +1,100 @@
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diff --git a/out/bspline1.dat b/out/bspline1.dat
new file mode 100644
index 0000000..4c3a161
--- /dev/null
+++ b/out/bspline1.dat
@@ -0,0 +1,100 @@
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new file mode 100644
index 0000000..4f87871
--- /dev/null
+++ b/out/bspline2.dat
@@ -0,0 +1,100 @@
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new file mode 100644
index 0000000..bbc211f
--- /dev/null
+++ b/out/bspline9.dat
@@ -0,0 +1,100 @@
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new file mode 100644
index 0000000..355f8fa
--- /dev/null
+++ b/out/bsplines.dat
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diff --git a/out/grid.bin b/out/grid.bin
index a6ba9ad..e0ae447 100644
Binary files a/out/grid.bin and b/out/grid.bin differ
diff --git a/out/grid.dat b/out/grid.dat
index 8a3f38d..61c3b79 100644
--- a/out/grid.dat
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- 9.710000e+00
- 9.720000e+00
- 9.730000e+00
- 9.740000e+00
- 9.750000e+00
- 9.760000e+00
- 9.770000e+00
- 9.780000e+00
- 9.790000e+00
  9.800000e+00
- 9.810000e+00
- 9.820000e+00
- 9.830000e+00
- 9.840000e+00
- 9.850000e+00
- 9.860000e+00
- 9.870000e+00
- 9.880000e+00
- 9.890000e+00
- 9.900000e+00
- 9.910000e+00
- 9.920000e+00
- 9.930000e+00
- 9.940000e+00
- 9.950000e+00
- 9.960000e+00
- 9.970000e+00
- 9.980000e+00
  1.000000e+01
diff --git a/out/knotpoints.dat b/out/knotpoints.dat
index 038ede1..3a60048 100644
--- a/out/knotpoints.dat
+++ b/out/knotpoints.dat
@@ -2,33 +2,12 @@
  0.000000e+00
  0.000000e+00
  0.000000e+00
- 3.571429e-01
- 7.142857e-01
- 1.071429e+00
  1.428571e+00
- 1.785714e+00
- 2.142857e+00
- 2.500000e+00
  2.857143e+00
- 3.214286e+00
- 3.571429e+00
- 3.928571e+00
  4.285714e+00
- 4.642857e+00
- 5.000000e+00
- 5.357143e+00
  5.714286e+00
- 6.071429e+00
- 6.428571e+00
- 6.785714e+00
  7.142857e+00
- 7.500000e+00
- 7.857143e+00
- 8.214286e+00
  8.571429e+00
- 8.928571e+00
- 9.285714e+00
- 9.642857e+00
  1.000000e+01
  1.000000e+01
  1.000000e+01
diff --git a/py_notebook/.ipynb_checkpoints/EigsolGPU_test-checkpoint.ipynb b/py_notebook/.ipynb_checkpoints/EigsolGPU_test-checkpoint.ipynb
index d42c265..e4946d7 100644
--- a/py_notebook/.ipynb_checkpoints/EigsolGPU_test-checkpoint.ipynb
+++ b/py_notebook/.ipynb_checkpoints/EigsolGPU_test-checkpoint.ipynb
@@ -2,9 +2,84 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "1d08e1d3-0f62-467e-8217-9eaf458afeb4",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1bf472eaf00>]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "x = np.linspace(0, 2*2.0*np.pi, 1000)\n",
+    "y = np.sin(x)\n",
+    "\n",
+    "\n",
+    "grid_bin = np.fromfile(\"../out/grid.bin\", dtype=np.float64)\n",
+    "plt.plot(grid_bin, np.zeros(len(grid_bin)))\n",
+    "grid = np.loadtxt(\"../out/grid.dat\")\n",
+    "plt.plot(grid, np.zeros(len(grid)), linestyle='--')\n",
+    "knots = np.loadtxt(\"../out/knotpoints.dat\")\n",
+    "plt.plot(knots, np.zeros(len(knots)), marker='o')\n",
+    "bspline0 = np.loadtxt(\"../out/bspline0.dat\")\n",
+    "plt.plot(grid, bspline0)\n",
+    "bspline1 = np.loadtxt(\"../out/bspline1.dat\")\n",
+    "plt.plot(grid, bspline1)\n",
+    "bspline2 = np.loadtxt(\"../out/bspline2.dat\")\n",
+    "plt.plot(grid, bspline2)\n",
+    "bspline2 = np.loadtxt(\"../out/bspline3.dat\")\n",
+    "plt.plot(grid, bspline3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cda5f82-13ac-4b95-aed4-f999a8dbd185",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce5642b3-6387-45f7-aaaf-dfb9085b3d49",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4866585-9cb8-4eb8-a8c6-0489f831c55d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59ee4447-5513-48f4-88fc-a20b8430e15c",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/py_notebook/EigsolGPU_test.ipynb b/py_notebook/EigsolGPU_test.ipynb
index 8cdea9c..1692066 100644
--- a/py_notebook/EigsolGPU_test.ipynb
+++ b/py_notebook/EigsolGPU_test.ipynb
@@ -2,23 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "id": "1d08e1d3-0f62-467e-8217-9eaf458afeb4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ab329b59a0>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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7HJYGByUl3xlxQg0AAF5EGEmTo+8nk2wY0eCgrHA4FWUBAJB1CCNpYvVFuhlGMCgjEBjXaxwdYhiqAQB4FWEkTZLd8EySDL9fRnFx5PV6e1NSFwAA2YYwkiZWf3IraRxMYgUAeB1hJE2cYZrkwwhbwgMAvI0wkibJ7jHioDMCAPA6wkiapCqM+EsIIwAAbyOMpEkqJrBKdEYAAN5HGEmTZO/Y6yCMAAC8jjCSJqmeM2ISRgAAHkUYSZPhMMIwDQAAx0MYSZPhOSMM0wAAcDyEkTQZnjOSqs4IN8sDAHgTYSRNUr7PCNvBAwA8ijCSJmx6BgBAfAgjaeKEBz/bwQMAcFyEkTSJzRlJ1aZn/cwZAQB4E2EkTRimAQAgPoSRNEnZvWnojAAAPI4wkgZ2OCx7cFBS8mHEiA7zWP39sm076doAAMg2hJE0OHrr9mTnjMQmwFqW7MOHk3otAACyEWEkDezokIpRWCijoCCp1zKKi2P/zVANAMCLCCNpYKZovogkGT5frLvCJFYAgBcRRtIgVZNXHSzvBQB4GWEkDZz7yKQsjNAZAQB4GGEkDax+5469yU1edbDXCADAywgjaZC2zgjDNAAADxpXGNmwYYOqqqpUVFSk2tpa7dixI67zHnroIRmGocsuu2w8l80ZaZszQmcEAOBBCYeRzZs3q6mpSWvXrtWuXbu0cOFCLVmyRAcOHDjuee+++67+7d/+TZ/5zGfGXWyuYAIrAADxSziMrFu3TitWrFBDQ4Pmz5+vlpYWlZSUaNOmTWOeY5qmrrjiCn3ve9/TKaecklTBuSAWRlI2Z4QJrAAA70oojITDYe3cuVP19fXDL+Dzqb6+Xtu3bx/zvO9///sqLy/Xv/zLv8R1nYGBAXV3d4945JLYHXtTNmeEYRoAgHclFEY6OztlmqZCodCI50OhkNrb20c955lnntGPf/xjbdy4Me7rNDc3q6ysLPaorKxMpEzXpX6YhgmsAADvSutqmp6eHn3961/Xxo0bNX369LjPW716tbq6umKPtra2NFaZeqkfpqEzAgDwrkAiB0+fPl1+v18dHR0jnu/o6FBFRcUxx//lL3/Ru+++q0svvTT2nGVZkQsHAtqzZ49OPfXUY84LBoMKBoOJlJZVUj5MwwRWAICHJdQZKSws1KJFi9Ta2hp7zrIstba2qq6u7pjj582bp5dfflm7d++OPf7u7/5On/vc57R79+6cG36J1/AwTYo6I+zACgDwsIQ6I5LU1NSk5cuXq6amRosXL9b69evV19enhoYGSdKyZcs0a9YsNTc3q6ioSAsWLBhx/uTJkyXpmOe9JNYZKUltZ8QkjAAAPCjhMLJ06VIdPHhQa9asUXt7u6qrq7Vly5bYpNa9e/fK58vvjV3T1RmxGaYBAHhQwmFEkhobG9XY2Djq97Zt23bcc++7777xXDKn0BkBACB++d3CSJN0TWC1++iMAAC8hzCSYvbQkOwjRySlfpjGZJgGAOBBhJEUsw4fjv13qjsjGhyUFQ6n5DUBAMgWhJEUiy2/DQRkFBSk5DWP3jyN5b0AAK8hjKTY0fNFDMNIyWsagYCM6CZwFvNGAAAeQxhJsVRvBe8Y3oWVzggAwFsIIynmdC5SHkbYhRUA4FGEkRRzOhepmrzqGL5ZHsM0AABvIYykWNo6IwzTAAA8ijCSYsNbwae4MxIbpqEzAgDwFsJIig1vBU9nBACAeBBGUmx4aW+6JrDSGQEAeAthJMWGl/amawIrnREAgLcQRlIsbZ2R2DANnREAgLcQRlIsbZ0R9hkBAHgUYSTF6IwAAJAYwkiK0RkBACAxhJEUS/vSXsIIAMBjCCMplrZNzximAQB4FGEkxdLWGWGYBgDgUYSRFKMzAgBAYggjKWTbdhpX09AZAQB4E2EkhexwWDJNSenrjNhHjsiOXgMAAC8gjKTQ0V0LX3FxSl/76HDDUA0AwEsIIynkhASjuFiG35/S1zYKCqRAIHIdhmoAAB5CGEmh4Q3PUjtfRJIMw2ASKwDAkwgjKWT1OZNXUztfxMHyXgCAFxFGUiidnRHp6BU1dEYAAN5BGEmhdG145hgepqEzAgDwDsJICqVrwzMHwzQAAC8ijKRQxjojDNMAADyEMJJCdEYAAEgcYSSFMjdnhM4IAMA7CCMpNHxfmvR0RvyxYRo6IwAA7yCMpFC6l/YaDNMAADyIMJJCGeuMMEwDAPAQwkgKpX/TM4ZpAADeQxhJoXR3RmKraeiMAAA8hDCSQnRGAABI3LjCyIYNG1RVVaWioiLV1tZqx44dYx7785//XDU1NZo8ebImTJig6upq3X///eMuOJsNd0bSFEaYwAoA8KCEw8jmzZvV1NSktWvXateuXVq4cKGWLFmiAwcOjHr81KlTdd1112n79u3605/+pIaGBjU0NOi3v/1t0sVnm7RvesYEVgCAByUcRtatW6cVK1aooaFB8+fPV0tLi0pKSrRp06ZRj7/wwgv1D//wDzrzzDN16qmnatWqVTr77LP1zDPPJF18tsncdvB0RgAA3pFQGAmHw9q5c6fq6+uHX8DnU319vbZv337C823bVmtrq/bs2aPPfvazYx43MDCg7u7uEY9sZ5um7MOHJWVmAqtt22m5BgAAmZZQGOns7JRpmgqFQiOeD4VCam9vH/O8rq4uTZw4UYWFhbrkkkt011136aKLLhrz+ObmZpWVlcUelZWViZTpCisaRKT0d0Zk27HgAwBArsvIaprS0lLt3r1bL7zwgm688UY1NTVp27ZtYx6/evVqdXV1xR5tbW2ZKDMpsTvp+v0ygsG0XMMoLpYMI3o9hmoAAN4QSOTg6dOny+/3q6OjY8TzHR0dqqioGPM8n8+n0047TZJUXV2t119/Xc3NzbrwwgtHPT4YDCqYpg/0dDl6Wa8RDQypZhiGfCUlsvr6mMQKAPCMhDojhYWFWrRokVpbW2PPWZal1tZW1dXVxf06lmVpYGAgkUtnvXRveOZgEisAwGsS6oxIUlNTk5YvX66amhotXrxY69evV19fnxoaGiRJy5Yt06xZs9Tc3CwpMv+jpqZGp556qgYGBvTEE0/o/vvv1z333JPan8Rl6d7wzMEurAAAr0k4jCxdulQHDx7UmjVr1N7erurqam3ZsiU2qXXv3r3y+YYbLn19ffrWt76lffv2qbi4WPPmzdNPf/pTLV26NHU/RRaw+tO7x4iDzggAwGsSDiOS1NjYqMbGxlG/9/GJqTfccINuuOGG8VwmpzgTWDPWGSGMAAA8gnvTpEisM5LuMMIurAAAjyGMpEisM8IwDQAACSGMpEjGOiNMYAUAeAxhJEXojAAAMD6EkRTJdGfEJIwAADyCMJIimd70zGaYBgDgEYSRFMnY0t5oGKEzAgDwCsJIimRu07NI2KEzAgDwCsJIigxPYKUzAgBAIggjKZLxe9MQRgAAHkEYSZFYGJkwMa3XYQdWAIDXEEZSJBZGJmZqnxHCCADAGwgjKWDbtqzeXkkZmMDKDqwAAI8hjKSA3d8v2bYkyT8xM8M0GhyUFQ6n9VoAAGQCYSQFzN7oZFKfT0ZxcVqvdfQEWSaxAgC8gDCSAsPzRSbKMIy0Xsvw+2UUFUWvy1ANACD3EUZSwOrLzHwRBzfLAwB4CWEkBZzJq/40r6RxDE9iJYwAAHIfYSQFMrXHiIPlvQAALyGMpICZoWW9DoZpAABeQhhJAat3eAJrJrDXCADASwgjKZCp3VcddEYAAF5CGEmB4QmsdEYAAEgUYSQFWNoLAMD4EUZSIPOraUpGXBcAgFxGGEkBM+MTWKOdEYZpAAAeQBhJgUzdsddBZwQA4CWEkRTI+GoaOiMAAA8hjKRAxlfTREOPc10AAHIZYSQFTGc1TYbCiBN6rN6ejFwPAIB0IoykgHOPmIzNGZlYKml44iwAALmMMJIk2zRlR+duZKwzUhrtjPTQGQEA5D7CSJKOXtGSsc5IaWns2rZlZeSaAACkC2EkSc4kUqOgQL7CwoxcM9aBsW2W9wIAch5hJEnDy3ozM0QjSb5gUEZBQeT6DNUAAHIcYSRJZm9mV9I4nKEak+W9AIAcRxhJkuVsBZ+h+SIOnzOJlTACAMhxhJEkZXr3VYc/uryXYRoAQK4jjCTJim545s/QHXsdzrCQ2UNnBACQ28YVRjZs2KCqqioVFRWptrZWO3bsGPPYjRs36jOf+YymTJmiKVOmqL6+/rjH5xrLtTkj7MIKAPCGhMPI5s2b1dTUpLVr12rXrl1auHChlixZogMHDox6/LZt2/S1r31NTz31lLZv367KykpdfPHFev/995MuPhuYfe7MGYkN0zBnBACQ4xIOI+vWrdOKFSvU0NCg+fPnq6WlRSUlJdq0adOox//sZz/Tt771LVVXV2vevHn6z//8T1mWpdbW1qSLzwaxCaxuraZhmAYAkOMSCiPhcFg7d+5UfX398Av4fKqvr9f27dvjeo3+/n4NDg5q6tSpiVWapYaHaTK8msa5cy8TWAEAOS6QyMGdnZ0yTVOhUGjE86FQSG+88UZcr3HNNddo5syZIwLNxw0MDGhgYCD2dXd3dyJlZpQTRvwuDdOYzBkBAOS4jK6mufnmm/XQQw/p0UcfVVFR0ZjHNTc3q6ysLPaorKzMYJWJccKAr3RSRq8bm8DKMA0AIMclFEamT58uv9+vjo6OEc93dHSooqLiuOfefvvtuvnmm/Xkk0/q7LPPPu6xq1evVldXV+zR1taWSJkZZXVHwoh/UmlGr+svZQIrAMAbEgojhYWFWrRo0YjJp85k1Lq6ujHPu/XWW/WDH/xAW7ZsUU1NzQmvEwwGNWnSpBGPbGX2uNQZmch28AAAb0hozogkNTU1afny5aqpqdHixYu1fv169fX1qaGhQZK0bNkyzZo1S83NzZKkW265RWvWrNEDDzygqqoqtbe3S5ImTpyoiRlegZIOVnQ+i78005ueMYEVAOANCYeRpUuX6uDBg1qzZo3a29tVXV2tLVu2xCa17t27Vz7fcMPlnnvuUTgc1j/+4z+OeJ21a9fqu9/9bnLVZ4HYjfIy3L2JDdMQRgAAOc6wbdt2u4gT6e7uVllZmbq6urJqyMYOh/XG2QslSZ94/jn5y8oydu3Bjg69dcGFkt+vea+8LMMwMnZtAADiEe/nN/emScLR8zUyvemZ37meaco+ciSj1wYAIJUII0lwhkh8EybI8Pszem2jpESKDoeZDNUAAHIYYSQJZnRZb6bni0iSYRixbgzLewEAuYwwkgSrx1lJk9k9RhzOUA2TWAEAuYwwkoRYZ8SlMMLN8gAAXkAYSYIV3Qrerc5IbEt4hmkAADmMMJKE4Tkjbg3TOFvCM0wDAMhdhJEkmM6ckYkudUaic0YYpgEA5DLCSBKcO+a61RkZvnMvnREAQO4ijCRheDWNO7vC+mM3yyOMAAByF2EkCcOrady54Z9/cmT7eaur25XrAwCQCoSRJDjDI36X7pfjbLZmdnW5cn0AAFKBMJIEZxt2t/YZcW7MRxgBAOQywkgSTLd3YC2bHKmjm2EaAEDuIowkwXJ5B1Z/GcM0AIDcRxgZJ9s0ZfX1SXJvzsjRwzS2bbtSAwAAySKMjJPZ3S1FA4DbYUSDg7IPH3alBgAAkkUYGSfz0CFJkV1QjYICV2owioul6LUZqgEA5CrCyDg5YcQ/ebJrNRiGwYoaAEDOI4yMk/PhHxsqcYk/ttcIK2oAALmJMDJO2dAZkY6exHrI1ToAABgvwsg4WdnSGWGYBgCQ4wgj4zSUNZ2RyDCNxcZnAIAcRRgZp2wZpvE5nZFDdEYAALmJMDJOsWGayQzTAACQDMLIOGVLZ8Q/KRpGGKYBAOQowsg4OcMirk9gncxqGgBAbiOMjFPWdEYYpgEA5DjCyDjFNj1zO4xENz2zmMAKAMhRhJFxsMPh4Tv2uj5MM1nScKcGAIBcQxgZh9iQiGHI59Idex3+qVMlSVZ/v6yBAVdrAQBgPAgj4xAbopk0SYbP3bfQV1o6fOfeDz90tRYAAMaDMDIO2TJ5VYrcuTcwZYokaegDwggAIPcQRsYhWyavOpyhGvMjwggAIPcQRsZhKDocki1hJDA10hlhmAYAkIsII+NgRodD/NOnuVxJhH9KpDMy9OFHLlcCAEDiCCPjMPTBB5KkwLTpLlcSERumoTMCAMhBhJFxMGNhZKrLlUQ4dQx9+IHLlQAAkDjCyDg4nRH/1OwapjEZpgEA5CDCyDiY0Q5EIFvmjDCBFQCQw8YVRjZs2KCqqioVFRWptrZWO3bsGPPYV199VV/5yldUVVUlwzC0fv368daaNZz9PLKlMxKIzhkZ+ojOCAAg9yQcRjZv3qympiatXbtWu3bt0sKFC7VkyRIdOHBg1OP7+/t1yimn6Oabb1ZFRUXSBbvNHhqSGf3Qz57OCBNYAQC5K+Ewsm7dOq1YsUINDQ2aP3++WlpaVFJSok2bNo16/LnnnqvbbrtNl19+uYLBYNIFu808dEiybcnny6J9RqL3p+ntlRUOu1wNAACJSSiMhMNh7dy5U/X19cMv4POpvr5e27dvT1lRAwMD6u7uHvHIFrHJq1OmyPD7Xa4mwjdpkhQISKI7AgDIPQmFkc7OTpmmqVAoNOL5UCik9vb2lBXV3NyssrKy2KOysjJlr52s2LLeqdmxrFeK3J/GP2WyJMIIACD3ZOVqmtWrV6urqyv2aGtrc7ukmNjk1WnZMV/EEYhOpnU6NwAA5IpAIgdPnz5dfr9fHR0dI57v6OhI6eTUYDCYtfNLhj7olCQFsi2MlJdrYM8eDR046HYpAAAkJKHOSGFhoRYtWqTW1tbYc5ZlqbW1VXV1dSkvLhvF7kuTJbuvOgLlJ0mShg6OvqoJAIBslVBnRJKampq0fPly1dTUaPHixVq/fr36+vrU0NAgSVq2bJlmzZql5uZmSZFJr6+99lrsv99//33t3r1bEydO1GmnnZbCHyUzsu2+NI5AebkkaWiMJdYAAGSrhMPI0qVLdfDgQa1Zs0bt7e2qrq7Wli1bYpNa9+7dK59vuOGyf/9+nXPOObGvb7/9dt1+++264IILtG3btuR/ggwbHqbJrs5IQTSMDBJGAAA5JuEwIkmNjY1qbGwc9XsfDxhVVVWybXs8l8lKQx2RD/vAx1YUuW24M8KcEQBAbsnK1TTZbCi6hDnrwshJ0TkjdEYAADmGMJIAa2AgsgOrpIJsCyNOZ6SzU7ZluVwNAADxI4wkYCi6pNkoLo7seppFAtOmSYYhHXXvHAAAcgFhJAGD0SGagvJyGYbhcjUjGQUFsY3YGKoBAOQSwkgCYpNXs/Tuw7G9RggjAIAcQhhJwFCHM3m13OVKRldwEst7AQC5hzCSgMFoZyTbJq862PgMAJCLCCMJGF7Wm63DNNEw0kEYAQDkDsJIAgYPRFbTZO0wzcwZkqTB/ftdrgQAgPgRRhIw1B4JIwVZOoG1YNYsSdLg+++7XAkAAPEjjMTJHhrS0MHIVuuB8uycM1Iwe7akSBhh4zMAQK4gjMRpqKNDsiypoECB6dPcLmdUBaGQ5PPJDoc11NnpdjkAAMSFMBKn8L7I0EfBzBky/H6XqxmdUVCgQEWka8NQDQAgVxBG4jS4b58kqXB2pcuVHF/hrOhQzT7CCAAgNxBG4hTe1yZpeF5GtmISKwAg1xBG4uR0Ggpmz3K5kuMbDiP7XK4EAID4EEbiFBumqczuYZqjV9QAAJALCCNxig3TzMr2YZqZkqQwYQQAkCMII3Ewe/tkHowslS2ck92dEadzM7j/r7IHB12uBgCAEyOMxCH87ruSJP+0afKXlblbzAkEQiEZJSXS4KDCbcwbAQBkP8JIHMLvvC1JCs6d63IlJ2b4fLE6B/7ylsvVAABwYoSROITfeUeSVJgDYUSSgqedKkkK/+UvLlcCAMCJEUbiMPB2NIyccorLlcSn8NTTJEkDf3nb5UoAADgxwkgcwm9HPtQL51a5W0icgqdGQhPDNACAXEAYOQE7HNZAdJgmeNrpLlcTn+CpzjDN27JN0+VqAAA4PsLICQy8/bY0OChfaWlsD49sVzB7tozCQtkDAxrcv9/tcgAAOC7CyAkceeMNSVLRGWfIMAyXq4mPEQiosKpKkjTwZ4ZqAADZjTByAgNv7JEkBefNc7mSxBSdGan3yKuvulwJAADHRxg5gVhnZN4ZLleSmKIFZ0mSDr/yssuVAABwfISR47BNU0f+9CdJUtFZZ7tcTWKKz1ogSTryyquybdvlagAAGBth5DgG/vxnWf398k2cGNtILFcE582TAgGZH3ygob/+1e1yAAAYE2HkOA6/+KIkqfjss2X4/S5XkxhfUZGKzogMLfXv3OlyNQAAjI0wchz9O3dJkoqrq90tZJxKamslSX3PP+9yJQAAjI0wMgbbstT37LOSpJJP17pczfhMqF0sSep/fofLlQAAMDbCyBiOvPqazA8/lG/CBJXkaGekeFGN5PdrsK1N4b173S4HAIBREUbG0PuHpyVJJXWfllFY6HI14+OfOEEl554rSerZ+juXqwEAYHSEkVHYtq3ux5+QJJV+7nMuV5Oc0osvkiT1PPmky5UAADA6wsgojrz6msJvvy0jGFTpkiVul5OU0vp6yTB0+KWXNPD2O26XAwDAMQJuF+CW3iO9+s6z39G+nn2aXTpbN513kyYWTZQkffSzn0mSSj//P+SfOHHU803L1K4Du3Sw/6BOKjlJnyr/lPy++Jf/Jnt+vK9RUF6uiRdeqN6nntJHDz6oiuu+kzU/A+fnxu8Q53N+us7Phhry/fzjfRZmkmGPY3vODRs26LbbblN7e7sWLlyou+66S4sXLx7z+IcffljXX3+93n33XZ1++um65ZZb9MUvfjHu63V3d6usrExdXV2aNGlSouUe42u//ppe+eCVY55fMG2BflJ9u/7yhS9IQ0Oq2vyQihcuPOa43733O92842Z19HfEnguVhHTt4mtVf3L9Ca+f7PmJvkbvM/9Xbd/8poziYp265TcqCIVc/xk4P7d+hzif81N9fjbUkO/nH++z8MEvPXjC8+MR7+d3wmFk8+bNWrZsmVpaWlRbW6v169fr4Ycf1p49e1ReXn7M8c8++6w++9nPqrm5WV/60pf0wAMP6JZbbtGuXbu0YMGClP4w8RjrzZck2bZuerREp+3p0YTz6jRn06ZjDvnde79T07Ym2Rr5thmK3NF33YXrjvtLkOz543kN27b13v93hQ6/+KJKv/AFvXHVF9X09P9y7Wfg/Nz7HeJ8zk/l+dlQQ76ff9zPQqUukKQtjNTW1urcc8/V3XffLUmyLEuVlZX613/9V1177bXHHL906VL19fXp17/+dey5T3/606qurlZLS0tKf5gT6T3Sq7rNdaN+z2/aWv47S1/YZUuFhZp6/0YF5s6VAkWxY8yBbl3+5D/r4JHOMa9RXlyuBy/53/IPHjnme6ZtxnH+SXrwovvlN6JtNsOQCkqGXyPco8t/e0V8NQyFJduSJA2+vkcfffNbkmnpyfMn6Md/c0S2z4ivBkkqnDD8M/z6yzp45OCJr2/4paEjkmUm9x44CkpkytLlj39FBw8fOP75X3xEfl9B9KJhyRxM/vqSTH+BLv/NV098/Yvul79gguS0S81ByQzHef1yPXjR/z/69W1Tl2/9ug4ePt77f1T9/kLJH30frCFpaMCFGgoidUiSbckM98Z3/S8+HPkdHu36if4Z+gJSIBg53xrS5Y9/Ob6/Q4ZfCvclf31JMnxSQXHk/Hh+hy/5+fD5g/1S9J/qpH6HDUNmIBjf9S+6X/7gUf/eDh6O/Pkl+XdIksyCovhrKCyN/DsoSUMDkjWU/N/jRH+HC4ojf35S5O/x0JH4foe/8KD8ljX69ROtPxCM/B5Lkev/5qvx/Q7bduR9O0rvUK++9Pg/jHmuY/vS7UkP2aQljITDYZWUlOiRRx7RZZddFnt++fLlOnTokB577LFjzpkzZ46ampp01VVXxZ5bu3atfvGLX+ill14a9ToDAwMaGBh+87q7u1VZWZl0GPn277+tp9qein39ty9YmvmBrYlHpE+8b+uk7sjzGy7x6emzvTe39+Kdlr75ZOQvRvtkac9sQ0cKJdMnJZRIAQCe88S5Ph2cPPx/Uj9X+Tnd+T/uTOo14w0jCU1g7ezslGmaCoVCI54PhUJ64403Rj2nvb191OPb29vHvE5zc7O+973vJVJaXPb17Bvx9fmvWfrE/uGvu0qkH1/s03Nnei+ISNKTi3w6XCg1/M5SxSGp4hARBAAQ8ex86eDk4a8//pmZTlm5mmb16tVqamqKfe10RpI1u3S2/nzoz7Gvnz7Lpz/NtdVXZKhjsvSnuYbCBYbOD9Xpxk9/P9JiP2qY5sX9z+rq//tvJ7zODy+4W+dMPuOY51/sfCm+88+/XedMj06c/dgwTUI1TDkzNkwTO//8l3TlGf9L1W/bmvGhVGDa8h/bRdRFs+sVKj5qDlC01d/R366te397wutfNGeJQiUVkSGao2roOHxAW/edeAO2Y64frSHu61derNCEGZEvbFOKtkqTur6kjiOd2tp24j1bLppdr9CEmcPtZduSLDP56yd6vs8/3F52rQafFGuV2+ro3R/f+ZUXK1Q0PfnrS5H3IDpk1tHXrq1tCfwOR4f4krq+FPld8AUS/zskRYbYok3spP78DCPyOxzP9WfXKzRx1vAT0RqS/f2RpI6BD8ZZQ+Tfk4z/DvsDkpy/y2bkdyiu3+GLFCo6KfnrSyP+Lnf07dfWtq0nPn/Oksj5Rw2VS9K2/X/Qvr5jg8aHHxuRmV06+4TXSJWsHKb5uEzMGTnaWONkpmVqyf9eogP9B46ZNCRFJg6FSkLa8pUtoy6tSvb8bKiB83P7/GyogfPz+/xsqCHfz0/2szAR8X5+JzQeUVhYqEWLFqm1tTX2nGVZam1tVV3d6D9YXV3diOMlaevWrWMen04TiyZqwbTjr+BZMG3BmG++3+fXtYsjk3SdGcsO5+trFl8z5l/AZM/Phho4P7fPz4YaOD+/z8+GGvL9/GQ/C9Mh4ckRTU1N2rhxo37yk5/o9ddf15VXXqm+vj41NDRIkpYtW6bVq1fHjl+1apW2bNmiO+64Q2+88Ya++93v6o9//KMaGxtT91Mk4MEvPTjmH0I8S5nqT67XugvXqbxkZOsvVBKKazlbsudnQw2cn9vnZ0MNnJ/f52dDDfl+frKfhak2rk3P7r777timZ9XV1brzzjtVW1srSbrwwgtVVVWl++67L3b8ww8/rP/4j/+IbXp26623urrpmZT8rnNu75qXDTVwfm6fnw01cH5+n58NNeT7+enegTVt+4y4IR1hBAAApFda5owAAACkGmEEAAC4ijACAABcRRgBAACuIowAAABXEUYAAICrCCMAAMBVhBEAAOAqwggAAHBVwO0C4uFsEtvd3e1yJQAAIF7O5/aJNnvPiTDS09MjSaqsrHS5EgAAkKienh6VlZWN+f2cuDeNZVnav3+/SktLZRjGiU+IU3d3tyorK9XW1sY9b9KI9zlzeK8zg/c5M3ifMyOd77Nt2+rp6dHMmTPl8409MyQnOiM+n0+zZ89O2+tPmjSJX/QM4H3OHN7rzOB9zgze58xI1/t8vI6IgwmsAADAVYQRAADgqrwOI8FgUGvXrlUwGHS7FE/jfc4c3uvM4H3ODN7nzMiG9zknJrACAADvyuvOCAAAcB9hBAAAuIowAgAAXEUYAQAArsrrMLJhwwZVVVWpqKhItbW12rFjh9sleUpzc7POPfdclZaWqry8XJdddpn27Nnjdlmed/PNN8swDF111VVul+I577//vv75n/9Z06ZNU3Fxsc466yz98Y9/dLsszzFNU9dff73mzp2r4uJinXrqqfrBD35wwvub4Pj+8Ic/6NJLL9XMmTNlGIZ+8YtfjPi+bdtas2aNZsyYoeLiYtXX1+vPf/5zRmrL2zCyefNmNTU1ae3atdq1a5cWLlyoJUuW6MCBA26X5hlPP/20Vq5cqeeee05bt27V4OCgLr74YvX19bldmme98MIL+tGPfqSzzz7b7VI856OPPtL555+vgoIC/eY3v9Frr72mO+64Q1OmTHG7NM+55ZZbdM899+juu+/W66+/rltuuUW33nqr7rrrLrdLy2l9fX1auHChNmzYMOr3b731Vt15551qaWnR888/rwkTJmjJkiU6cuRI+ouz89TixYvtlStXxr42TdOeOXOm3dzc7GJV3nbgwAFbkv3000+7XYon9fT02Keffrq9detW+4ILLrBXrVrldkmecs0119h/8zd/43YZeeGSSy6xv/GNb4x47stf/rJ9xRVXuFSR90iyH3300djXlmXZFRUV9m233RZ77tChQ3YwGLQffPDBtNeTl52RcDisnTt3qr6+Pvacz+dTfX29tm/f7mJl3tbV1SVJmjp1qsuVeNPKlSt1ySWXjPi9Rur88pe/VE1Njf7pn/5J5eXlOuecc7Rx40a3y/Kk8847T62trXrzzTclSS+99JKeeeYZ/e3f/q3LlXnXO++8o/b29hH/fpSVlam2tjYjn4s5caO8VOvs7JRpmgqFQiOeD4VCeuONN1yqytssy9JVV12l888/XwsWLHC7HM956KGHtGvXLr3wwgtul+JZb7/9tu655x41NTXpO9/5jl544QV9+9vfVmFhoZYvX+52eZ5y7bXXqru7W/PmzZPf75dpmrrxxht1xRVXuF2aZ7W3t0vSqJ+LzvfSKS/DCDJv5cqVeuWVV/TMM8+4XYrntLW1adWqVdq6dauKiorcLsezLMtSTU2NbrrpJknSOeeco1deeUUtLS2EkRT77//+b/3sZz/TAw88oE9+8pPavXu3rrrqKs2cOZP32qPycphm+vTp8vv96ujoGPF8R0eHKioqXKrKuxobG/XrX/9aTz31lGbPnu12OZ6zc+dOHThwQJ/61KcUCAQUCAT09NNP684771QgEJBpmm6X6AkzZszQ/PnzRzx35plnau/evS5V5F3//u//rmuvvVaXX365zjrrLH3961/X1VdfrebmZrdL8yzns8+tz8W8DCOFhYVatGiRWltbY89ZlqXW1lbV1dW5WJm32LatxsZGPfroo/r973+vuXPnul2SJ33+85/Xyy+/rN27d8ceNTU1uuKKK7R79275/X63S/SE888//5il6W+++aZOPvlklyryrv7+fvl8Iz+e/H6/LMtyqSLvmzt3rioqKkZ8LnZ3d+v555/PyOdi3g7TNDU1afny5aqpqdHixYu1fv169fX1qaGhwe3SPGPlypV64IEH9Nhjj6m0tDQ27lhWVqbi4mKXq/OO0tLSY+bhTJgwQdOmTWN+TgpdffXVOu+883TTTTfpq1/9qnbs2KF7771X9957r9ulec6ll16qG2+8UXPmzNEnP/lJvfjii1q3bp2+8Y1vuF1aTuvt7dVbb70V+/qdd97R7t27NXXqVM2ZM0dXXXWVbrjhBp1++umaO3eurr/+es2cOVOXXXZZ+otL+3qdLHbXXXfZc+bMsQsLC+3Fixfbzz33nNsleYqkUR//9V//5XZpnsfS3vT41a9+ZS9YsMAOBoP2vHnz7Hvvvdftkjypu7vbXrVqlT1nzhy7qKjIPuWUU+zrrrvOHhgYcLu0nPbUU0+N+m/y8uXLbduOLO+9/vrr7VAoZAeDQfvzn/+8vWfPnozUZtg2W9oBAAD35OWcEQAAkD0IIwAAwFWEEQAA4CrCCAAAcBVhBAAAuIowAgAAXEUYAQAAriKMAAAAVxFGAACAqwgjAADAVYQRAADgKsIIAABw1f8DG70qd78mvu8AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -40,9 +30,38 @@
     "grid = np.loadtxt(\"../out/grid.dat\")\n",
     "plt.plot(grid, np.zeros(len(grid)), linestyle='--')\n",
     "knots = np.loadtxt(\"../out/knotpoints.dat\")\n",
-    "plt.plot(knots, np.zeros(len(knots)), marker='o')\n",
-    "bspline3 = np.loadtxt(\"../out/Bspline3.dat\")\n",
-    "plt.plot(grid, bspline3)\n"
+    "order_k = 4\n",
+    "knots_y = np.zeros(len(knots))\n",
+    "y_shift = order_k-1\n",
+    "for i in range(order_k-1):\n",
+    "    knots_y[i] = y_shift\n",
+    "    y_shift = y_shift - 1\n",
+    "\n",
+    "y_shift = order_k-1\n",
+    "for i in range(order_k-1):\n",
+    "    knots_y[len(knots)-1-i] = y_shift\n",
+    "    y_shift = y_shift - 1\n",
+    "    \n",
+    "plt.plot(knots, knots_y, marker='o')\n",
+    "\n",
+    "\n",
+    "#bspline0 = np.loadtxt(\"../out/bspline0.dat\")\n",
+    "#plt.plot(grid, bspline0)\n",
+    "#bspline1 = np.loadtxt(\"../out/bspline1.dat\")\n",
+    "#plt.plot(grid, bspline1)\n",
+    "#bspline2 = np.loadtxt(\"../out/bspline2.dat\")\n",
+    "#plt.plot(grid, bspline2)\n",
+    "#bspline3 = np.loadtxt(\"../out/bspline3.dat\")\n",
+    "#plt.plot(grid, bspline3)\n",
+    "\n",
+    "bsplines = np.loadtxt(\"../out/bsplines.dat\")\n",
+    "bspl_shape = bsplines.shape\n",
+    "num_rows = bspl_shape[0]\n",
+    "num_cols = bspl_shape[1]\n",
+    "for i in range(num_cols):\n",
+    "    bspl = bsplines[:, i]\n",
+    "    plt.plot(grid, bspl)\n",
+    "\n"
    ]
   },
   {
@@ -68,6 +87,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59ee4447-5513-48f4-88fc-a20b8430e15c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/base/base_memory.c b/src/base/base_memory.c
index e92d4a1..1482517 100644
--- a/src/base/base_memory.c
+++ b/src/base/base_memory.c
@@ -58,9 +58,6 @@ m_make_arena() {
   return result;
 }
 
-// NOTE(anton): rjf calls this "arena push no zero", as opposed to pushing a zeroed array.
-// Not sure why we would make this differentiation, maybe not important.
-
 root_function void*
 m_arena_push(Arena *arena, U64 size) {
   void *result = 0;
diff --git a/src/base/base_memory.h b/src/base/base_memory.h
index 907a14c..b3be5ab 100644
--- a/src/base/base_memory.h
+++ b/src/base/base_memory.h
@@ -47,9 +47,8 @@ root_function void m_arena_align(Arena *arena, U64 pow2_alignment);
 root_function void* m_arena_push_zero(Arena *arena, U64 size);
 root_function void m_arena_clear(Arena *arena);
 
-#define PushArray(arena, type, count) (type *)m_arena_push((arena), sizeof(type)*(count))
-#define PushArrayZero(arena, type, count) (type *)m_arena_push_zero((arena), sizeof(type)*(count))
-
+#define PushArrayNoZero(arena, type, count) (type *)m_arena_push((arena), sizeof(type)*(count))
+#define PushArray(arena, type, count) (type *)m_arena_push_zero((arena), sizeof(type)*(count))
 //~ temp arena
 
 root_function ArenaTemp m_arena_temp_begin(Arena *arena);
diff --git a/src/base/base_strings.c b/src/base/base_strings.c
index 5a8ea3b..da79cad 100644
--- a/src/base/base_strings.c
+++ b/src/base/base_strings.c
@@ -87,7 +87,7 @@ str8_list_push_node_front(String8List *list, String8Node *n) {
 // Wrapper that pushes the memory for a node onto the arena, and then puts the node in the linked list (in the back).
 root_function void
 str8_list_push(Arena *arena, String8List *list, String8 string) {
-  String8Node *n = PushArrayZero(arena, String8Node, 1);
+  String8Node *n = PushArray(arena, String8Node, 1);
   n->string = string;
   str8_list_push_node(list, n);
 }
@@ -95,7 +95,7 @@ str8_list_push(Arena *arena, String8List *list, String8 string) {
 // Wrapper that pushes the memory for a node onto the arena, and then puts the node in the linked list (in the front).
 root_function void
 str8_list_push_front(Arena *arena, String8List *list, String8 string) {
-  String8Node *n = PushArrayZero(arena, String8Node, 1);
+  String8Node *n = PushArray(arena, String8Node, 1);
   n->string = string;
   str8_list_push_node_front(list, n);
 }
diff --git a/src/main.c b/src/main.c
index d515771..fd59aeb 100644
--- a/src/main.c
+++ b/src/main.c
@@ -19,6 +19,7 @@
 #define grid_file_path_bin "D:\\dev\\eigsol_gpu\\out\\grid.bin"
 #define grid_file_path "D:\\dev\\eigsol_gpu\\out\\grid.dat"
 #define knotpoints_file_path "D:\\dev\\eigsol_gpu\\out\\knotpoints.dat"
+#define bspline_array_file_path "D:\\dev\\eigsol_gpu\\out\\bsplines.dat"
 
 global U32 enable_logging = 1;
 
@@ -49,11 +50,13 @@ struct BSplineCtx
   U32 num_knotpoints;
   U32 num_bsplines;
   U32 num_phys_points;
+  F64 *bsplines;
 };
 
 
 
 //~ Globals
+global Grid g_grid = {0};
 global BSplineCtx g_bspline_ctx = {0};
 
 //~ Functions
@@ -94,39 +97,57 @@ write_array_binary_F64(String8 path_to_file, F64 *values, U32 array_size)
 }
 
 function void
+write_string_list_to_file(Arena *arena, String8 path, String8List *list)
+{
+
+  OS_Handle file_handle = OS_file_open(OS_AccessFlag_Write | OS_AccessFlag_CreateNew, 
+      path);
+  OS_file_write(arena, file_handle, 0, *list, 0);
+
+  U32 debug = 1;
+  if(debug)
+  {
+    String8List log_list = {0};
+    str8_list_push(arena, &log_list, str8_lit("Wrote array to"));
+    str8_list_push(arena, &log_list, path);
+    StringJoin join = {0};
+    join.sep = str8_lit(" ");
+    join.post = str8_lit("\n");
+    String8 log_msg = str8_list_join(arena, log_list, &join);
+    OutputDebugString(log_msg.str);
+  }
+  OS_file_close(file_handle);
+}
+
+
+  function void
 write_array_F64(String8 path_to_file, F64 *values, U32 array_size, char* fmt) 
 {
-  OS_Handle file_handle = OS_file_open(OS_AccessFlag_Write | OS_AccessFlag_CreateNew, 
-      path_to_file);
-  {
     ArenaTemp scratch = scratch_get(0, 0);
     String8List list = {0};
     for(U32 i = 0; i < array_size; i++) 
     {
       str8_list_pushf(scratch.arena, &list, fmt, values[i]);
     }
-    OS_file_write(scratch.arena, file_handle, 0, list, 0);
-    
-    String8List log_list = {0};
-    str8_list_push(scratch.arena, &log_list, str8_lit("Wrote array to"));
-    str8_list_push(scratch.arena, &log_list, path_to_file);
-    StringJoin join = {0};
-    join.sep = str8_lit(" ");
-    join.post = str8_lit("\n");
-    String8 log_msg = str8_list_join(scratch.arena, log_list, &join);
-    OutputDebugString(log_msg.str);
-  }
-  OS_file_close(file_handle);
-  
+    write_string_list_to_file(scratch.arena, path_to_file, &list);
 }
 
-function F64 bspline_recursion(F64 x, U32 k, U32 i)
+  function F64 
+bspline_recursion(F64 x, U32 k, U32 i)
 {
   F64 *t = g_bspline_ctx.knotpoints;
 
   if(k == 1)
   {
-    if(i < g_bspline_ctx.num_bsplines && x >= t[i] && x < t[i+1])
+    if(i == g_bspline_ctx.num_bsplines-1 && x == g_grid.end) 
+    {
+      // TODO(anton):
+      // This is like a hack to get the last bspline to be 1 at the last point.
+      // I dont get how the Cox-de Boor recursion formula can force the last bspline 
+      // to unity at the last point, actually. I have to check this.
+      return 1.0;
+    }
+    else if(i < g_bspline_ctx.num_bsplines && (x >= t[i] && x < t[i+1]))
     {
       return 1.0;
     } else {
@@ -135,8 +156,15 @@ function F64 bspline_recursion(F64 x, U32 k, U32 i)
   } 
   else 
   {
-    F64 term1 = (x - t[i])/(t[i+k-1]-t[i])*bspline_recursion(x, k-1, i);
-    F64 term2 = (t[i+k]-x)/(t[i+k]-t[i+1])*bspline_recursion(x, k-1, i+1);
+    F64 recursion1 = bspline_recursion(x, k-1, i);
+    F64 term1_enum = (x - t[i]);
+    F64 term1_denom = (t[i+k-1] - t[i]);
+    F64 term1 = recursion1 > 0.0 ? (term1_enum/term1_denom)*recursion1 : 0.0;
+
+    F64 recursion2 = bspline_recursion(x, k-1, i+1);
+    F64 term2_enum = (t[i+k] - x);
+    F64 term2_denom = (t[i+k] - t[i+1]);
+    F64 term2 = recursion2 > 0.0 ? (term2_enum/term2_denom)*recursion2 : 0.0;
 
     return term1 + term2;
   }
@@ -145,11 +173,148 @@ function F64 bspline_recursion(F64 x, U32 k, U32 i)
 }
 
 
-function F64 get_bspline_F64(F64 x_coord, U32 index)
+  function F64 
+get_bspline_F64(F64 x_coord, U32 index)
 {
   U32 k = g_bspline_ctx.order;
   F64 out = bspline_recursion(x_coord, k, index);
   return out;
+}
+
+  function void
+set_up_grid(Arena *arena)
+{
+  g_grid.start = 0.0;
+  g_grid.end = 10.0;
+  g_grid.num_steps = 100;
+
+  g_grid.points = PushArray(arena, F64, g_grid.num_steps);
+  F64 step_size = (g_grid.end-g_grid.start)/(F64)g_grid.num_steps;
+  g_grid.points[0] = g_grid.start;
+  g_grid.points[g_grid.num_steps-1] = g_grid.end;
+  for(U32 i = 1; i < g_grid.num_steps-1; i++)
+  {
+    g_grid.points[i] = g_grid.points[i-1] + step_size;
+  }
+
+}
+
+
+  function void
+set_up_bspline_context(Arena* arena)
+{
+  // Create knotpoint sequence.
+  U32 k = 4;
+  U32 N = 14;
+  g_bspline_ctx.order = k; 
+  g_bspline_ctx.num_knotpoints = N; 
+  g_bspline_ctx.num_bsplines = N-k;
+  g_bspline_ctx.num_phys_points = N-(2*k)+2; // Remove k points at each end, and then add back the first and last points.
+  g_bspline_ctx.arena = arena;
+  g_bspline_ctx.knotpoints = PushArray(arena, F64, g_bspline_ctx.num_knotpoints);
+  // Set up physical points;
+  F64 delta = (g_grid.end-g_grid.start)/(g_bspline_ctx.num_phys_points-1);
+  // Set ghost points including first physical
+  U32 phys_point_last_index = g_bspline_ctx.num_phys_points + k-1;
+  for(U32 i = 0; i < k; i++) 
+  {
+    g_bspline_ctx.knotpoints[i] = g_grid.start;
+  }
+  for(U32 i = k; i < phys_point_last_index; i++) 
+  {
+    g_bspline_ctx.knotpoints[i] = g_bspline_ctx.knotpoints[i-1] + delta;
+  }
+  // Set the last points
+  F64 last_physical = g_grid.end;
+  for(U32 i = phys_point_last_index; i < g_bspline_ctx.num_knotpoints; i++)
+  {
+    g_bspline_ctx.knotpoints[i] = last_physical;
+  }
+}
+
+
+  function void
+write_bsplines_to_matrix_F64(Arena *arena)
+{
+
+  U64 num_bsplines = g_bspline_ctx.num_bsplines;
+  U64 k = g_bspline_ctx.order;
+  F64 *t = g_bspline_ctx.knotpoints;
+  U32 grid_size = g_grid.num_steps;
+
+  // For sanity check we make the first 4 bsplines by hand.
+  {
+    F64 *bspl0 = PushArray(arena, F64, grid_size);
+    F64 *bspl1 = PushArray(arena, F64, grid_size);
+    F64 *bspl2 = PushArray(arena, F64, grid_size);
+    F64 *bspl3 = PushArray(arena, F64, grid_size);
+    F64 *bspl9 = PushArray(arena, F64, grid_size);
+    for(U32 i = 0; i < grid_size; i++)
+    {
+      F64 x = g_grid.points[i];
+      bspl0[i] = get_bspline_F64(x, 0);
+      bspl1[i] = get_bspline_F64(x, 1);
+      bspl2[i] = get_bspline_F64(x, 2);
+      bspl3[i] = get_bspline_F64(x, 3);
+      bspl9[i] = get_bspline_F64(x, 9);
+    }
+
+    F64 test = get_bspline_F64(g_grid.points[grid_size-1], 9);
+
+    write_array_F64(str8_lit("D:\\dev\\eigsol_gpu\\out\\bspline0.dat"), bspl0, grid_size, "%13.6e\n");
+    write_array_F64(str8_lit("D:\\dev\\eigsol_gpu\\out\\bspline1.dat"), bspl1, grid_size, "%13.6e\n");
+    write_array_F64(str8_lit("D:\\dev\\eigsol_gpu\\out\\bspline2.dat"), bspl2, grid_size, "%13.6e\n");
+    write_array_F64(str8_lit("D:\\dev\\eigsol_gpu\\out\\bspline3.dat"), bspl3, grid_size, "%13.6e\n");
+    write_array_F64(str8_lit("D:\\dev\\eigsol_gpu\\out\\bspline9.dat"), bspl9, grid_size, "%13.6e\n");
+  }
+
+  g_bspline_ctx.bsplines = PushArray(arena, F64, grid_size*num_bsplines);
+  for(U32 i = 0; i < g_grid.num_steps; i++)
+  {
+    for(U32 j = 0; j < num_bsplines; j++)
+    {
+      U32 index = g_grid.num_steps * i + j;
+      g_bspline_ctx.bsplines[index] = get_bspline_F64(g_grid.points[i], j);
+    }
+  }
+
+  ArenaTemp scratch = scratch_get(0, 0);
+  String8 bspline_filename = str8_lit("D:\\dev\\eigsol_gpu\\out\\Bsplines.dat");
+
+  // First line is just the bspline indices.
+  String8List first_line_list = {0};
+  StringJoin join = {0};
+  join.sep = str8_lit("\t\t");
+  for(U32 i = 0; i < num_bsplines; i++) 
+  {
+    str8_list_pushf(scratch.arena, &first_line_list, "%i", i);
+  }
+  String8 first_line = str8_list_join(scratch.arena, first_line_list, &join);
+
+  String8List bspline_array_list = {0};
+  for(U32 i = 0; i < g_grid.num_steps; i++)
+  {
+    String8List row = {0};
+    for(U32 j = 0; j < num_bsplines; j++)
+    {
+      U32 index = g_grid.num_steps * i + j;
+      F64 bspline_value = g_bspline_ctx.bsplines[index];
+      str8_list_pushf(scratch.arena, &row, "%13.6e", bspline_value);
+    }
+    StringJoin bspl_join = {0};
+    bspl_join.sep = str8_lit("   ");
+    bspl_join.post = str8_lit("\n");
+    String8 row_joined = str8_list_join(scratch.arena, row, &bspl_join);
+    str8_list_push(scratch.arena, &bspline_array_list, row_joined);
+  }
+
+
+  write_string_list_to_file(scratch.arena, str8_lit(bspline_array_file_path), &bspline_array_list);
+
+
+
+
+
 }
 
 
@@ -161,69 +326,17 @@ void EntryPoint()
 
   Arena *arena = m_make_arena();
 
-  Grid grid = {0};
-  grid.start = 0.0;
-  grid.end = 10.0;
-  grid.num_steps = 1000;
+  set_up_grid(arena);
 
-  grid.points = PushArrayZero(arena, F64, grid.num_steps);
-  F64 step_size = (grid.end-grid.start)/(F64)grid.num_steps;
-  grid.points[0] = grid.start;
-  grid.points[grid.num_steps-1] = grid.end;
-  for(U32 i = 1; i < grid.num_steps-1; i++)
-  {
-    grid.points[i] = grid.points[i-1] + step_size;
-  }
+  write_array_binary_F64(str8_lit(grid_file_path_bin), g_grid.points, g_grid.num_steps);
+  write_array_F64(str8_lit(grid_file_path), g_grid.points, g_grid.num_steps, "%13.6e\n");
 
-  write_array_binary_F64(str8_lit(grid_file_path_bin), grid.points, grid.num_steps);
-  write_array_F64(str8_lit(grid_file_path), grid.points, grid.num_steps, "%13.6e\n");
+  set_up_bspline_context(arena);
 
-  // Create knotpoint sequence.
-  U32 k = 4;
-  U32 N = 35;
-  g_bspline_ctx.order = k; 
-  g_bspline_ctx.num_knotpoints = N; 
-  g_bspline_ctx.num_bsplines = N-k;
-  g_bspline_ctx.num_phys_points = N-(2*k)+2; // Remove k points at each end, and then add back the first and last points.
-  g_bspline_ctx.arena = arena;
-  g_bspline_ctx.knotpoints = (F64*)PushArrayZero(arena, F64, g_bspline_ctx.num_knotpoints);
-  // Set up physical points;
-  F64 delta = (grid.end-grid.start)/(g_bspline_ctx.num_phys_points-1);
-  // Set ghost points including first physical
-  U32 phys_point_index = g_bspline_ctx.num_phys_points + k-1;
-  for(U32 i = 0; i < k; i++) 
-  {
-    g_bspline_ctx.knotpoints[i] = grid.start;
-  }
-  for(U32 i = k; i < phys_point_index; i++) 
-  {
-     g_bspline_ctx.knotpoints[i] = g_bspline_ctx.knotpoints[i-1] + delta;
-  }
-  // Set the last points
-  F64 last_physical = grid.end;
-  for(U32 i = phys_point_index; i < g_bspline_ctx.num_knotpoints; i++)
-  {
-     g_bspline_ctx.knotpoints[i] = last_physical;
-  }
 
   write_array_F64(str8_lit(knotpoints_file_path), g_bspline_ctx.knotpoints, g_bspline_ctx.num_knotpoints, "%13.6e\n");
 
-
-  String8 Bspline3_fn = str8_lit("D:\\dev\\eigsol_gpu\\out\\Bspline3.dat");
-  F64 *Bspline3 = (F64 *)PushArrayZero(arena, F64, grid.num_steps);
-  for(U32 i = 0; i < grid.num_steps; i++)
-  {
-    Bspline3[i] = get_bspline_F64(grid.points[i], 3);
-  }
-
-  write_array_F64(Bspline3_fn, Bspline3, grid.num_steps, "%13.6e\n");
-
-
-
-
-
-
-
+  write_bsplines_to_matrix_F64(arena);
 
 
 }
diff --git a/src/os/win32/os_core_win32.c b/src/os/win32/os_core_win32.c
index 070e8bd..f295ceb 100644
--- a/src/os/win32/os_core_win32.c
+++ b/src/os/win32/os_core_win32.c
@@ -289,7 +289,7 @@ OS_to_string_list_F64(Arena *arena, F64 *values, U32 values_size, String8 format
   {
     String8 value_string = {0};
     value_string.size = character_length;
-    value_string.str = PushArrayZero(arena, U8, character_length);
+    value_string.str = PushArray(arena, U8, character_length);
     StringCchPrintfA(value_string.str, character_length, "%e\n", values[i]);
     str8_list_push(arena, &out, value_string);
   }
diff --git a/timeBuild.ctm b/timeBuild.ctm
index 99b2bed..9288a67 100644
Binary files a/timeBuild.ctm and b/timeBuild.ctm differ